quantitative research examples for students in the philippines

250 Grade 12 Quantitative Research Topics for Senior High School Students in the Philippines

Greetings, dear senior high school students in the Philippines! If you’re on the hunt for that ideal quantitative research topic for your Grade 12 project, you’ve struck gold! You’re in for a treat because we’ve got your back. Within the pages of this blog, we’ve meticulously assembled an extensive catalog of 250 intriguing quantitative research themes for your exploration.

We completely grasp that the process of selecting the right topic might feel a tad overwhelming. To alleviate those concerns, we’ve crafted this resource to simplify your quest. We’re about to embark on a journey of discovery together, one that will empower you to make a well-informed choice for your research project. So, without further ado, let’s plunge headfirst into this wealth of research possibilities!

What is Quantitative Research?

Quantitative research is a type of research that deals with numbers and data. It involves collecting and analyzing numerical information to draw conclusions or make predictions. It’s all about using statistics and mathematical methods to answer research questions. Now, let’s explore some exciting quantitative research topics suitable for Grade 12 students in the Philippines.

Unlock educational insights at newedutopics.com . Explore topics, study tips, and more! Get started on your learning journey today.

• How Social Media Affects Academic Performance
• Factors Influencing Students’ Choice of College Courses
• The Relationship Between Study Habits and Grades
• The Effect of Parental Involvement on Students’ Achievements
• Bullying in High Schools: Prevalence and Effects
• How Does Nutrition Affect Student Concentration and Learning?
• Examining the Relationship Between Exercise and Academic Performance
• The Influence of Gender on Math and Science Performance
• Investigating the Factors Leading to School Dropouts
• The Effect of Peer Pressure on Decision-Making Among Teens
• Exploring the Connection Between Socioeconomic Status and Academic Achievement
• Assessing the Impact of Technology Use in Education
• The Correlation Between Sleep Patterns and Academic Performance
• Analyzing the Impact of Classroom Size on Student Engagement
• The Role of Extracurricular Activities in Character Development
• Investigating the Use of Alternative Learning Modalities During the Pandemic
• The Effectiveness of Online Learning Platforms
• The Influence of Parental Expectations on Career Choices
• The Relationship Between Music and Concentration While Studying
• Examining the Link Between Personality Traits and Academic Success

Now that we’ve given you a taste of the topics, let’s break them down into different categories:

Education and Academic Performance:

• The Impact of Teacher-Student Relationships on Learning
• Exploring the Benefits of Homework in Learning
• Analyzing the Effectiveness of Different Teaching Methods
• Investigating the Use of Technology in Teaching
• The Role of Educational Field Trips in Learning
• The Relationship Between Reading Habits and Academic Success
• Assessing the Impact of Standardized Testing on Students
• The Effect of School Uniforms on Student Behavior
• Analyzing the Benefits of Bilingual Education
• How Classroom Design Influences Student Engagement

Health and Wellness:

• Analyzing the Connection Between Fast Food Consumption and Health Outcomes
• Exploring How Physical Activity Impacts Mental Health
• Investigating the Prevalence of Stress Among Senior High School Students
• The Effect of Smoking on Academic Performance
• The Relationship Between Nutrition and Physical Fitness
• Analyzing the Impact of Vaccination Programs on Public Health
• Understanding the Importance of Sleep in Mental and Emotional Well-being
• Investigating the Use of Herbal Remedies in Health Management
• The Effect of Screen Time on Eye Health
• Examining the Connection Between Drug Abuse and Academic Performance

Social Issues:

• Exploring the Factors Leading to Teenage Pregnancy
• Analyzing the Impact of Social Media on Body Image
• Investigating the Causes of Youth Involvement in Juvenile Delinquency
• The Effect of Cyberbullying on Mental Health
• The Relationship Between Gender Equality and Education
• Assessing the Impact of Poverty on Student Achievement
• The Influence of Religion on Moral Values
• Analyzing the Role of Filipino Culture in Shaping Values
• The Effect of Political Instability on Education
• Investigating the Impact of Mental Health Awareness Campaigns

Technology and Innovation:

• The Role of Artificial Intelligence in Education
• Examining the Impact of E-Learning Platforms on Student Performance
• Exploring the Application of Virtual Reality in Education
• The Effect of Smartphone Use on Classroom Distractions
• The Relationship Between Coding Skills and Future Employment
• Assessing the Benefits of Gamification in Education
• The Influence of Online Gaming on Academic Performance
• Analyzing the Role of 3D Printing in Education
• Investigating the Use of Drones in Environmental Research
• Analyzing How Social Networking Sites Affect Socialization

Environmental Concerns:

• Assessing the Effects of Climate Change Awareness on Conservation Efforts
• Investigating the Impact of Pollution on Local Ecosystems
• Exploring the Link Between Waste Management Practices and Environmental Sustainability
• Analyzing the Benefits of Renewable Energy Sources
• The Effect of Deforestation on Biodiversity
• Exploring Sustainable Agriculture Practices
• The Role of Ecotourism in Conservation
• Investigating the Impact of Plastic Waste on Marine Life
• Analyzing Water Quality in Local Rivers and Lakes
• Assessing the Importance of Coral Reef Conservation

Economic Issues:

• The Influence of Economic Status on Educational Opportunities
• Examining the Impact of Inflation on Student Expenses
• Investigating the Role of Microfinance in Poverty Alleviation
• Analyzing the Effects of Unemployment on Youth
• The Relationship Between Entrepreneurship Education and Business Success
• The Effect of Taxation on Small Businesses
• Assessing the Impact of Tourism on Local Economies
• The Role of Online Marketplaces in Small Business Growth
• Investigating the Benefits of Financial Literacy Programs
• Analyzing the Impact of Foreign Investments on the Philippine Economy

Cultural and Historical Topics:

• Exploring the Influence of Spanish Colonization on Filipino Culture
• Analyzing the Role of Filipino Heroes in Nation-Building
• Investigating the Impact of K-Pop on Filipino Youth Culture
• The Relationship Between Traditional and Modern Filipino Values
• Assessing the Importance of Philippine Indigenous Languages
• The Effect of Colonial Mentality on Identity
• The Role of Filipino Cuisine in Tourism
• Investigating the Influence of Filipino Art on National Identity
• Analyzing the Significance of Historical Landmarks
• Examining the Role of Traditional Filipino Clothing in Society

Government and Politics:

• The Influence of Social Media on Political Participation
• Investigating Voter Education and Awareness Campaigns
• Analyzing the Impact of Political Dynasties on Local Governance
• Assessing the Effectiveness of Disaster Response Programs
• The Relationship Between Corruption and Public Services
• The Role of Youth in Nation-Building
• Investigating the Impact of Martial Law on Philippine Society
• Analyzing the Role of Social Movements in Policy Change
• Assessing the Importance of Good Governance in National Development
• The Effect of Federalism on Local Autonomy

Science and Technology:

• Exploring Advances in Biotechnology and Genetic Engineering
• Analyzing the Impact of Space Exploration on Scientific Discovery
• Investigating the Use of Nanotechnology in Medicine
• The Relationship Between STEM Education and Innovation
• The Effect of Pollution on Biodiversity
• Assessing the Benefits of Solar Energy in the Philippines
• The Role of Robotics in Industry Automation
• Investigating the Potential of Hydrogen Fuel Cells
• Analyzing the Use of 5G Technology in Communication
• The Impact of Artificial Intelligence in Healthcare

Healthcare and Medicine:

• The Influence of Traditional Medicine Practices on Health
• Investigating the Impact of Mental Health Stigma
• Analyzing the Use of Telemedicine in Remote Areas
• The Relationship Between Diet and Chronic Diseases
• Assessing the Effectiveness of Healthcare Access Programs
• The Role of Nurses in Public Health
• Investigating the Benefits of Medical Missions
• Analyzing the Impact of Healthcare Quality on Patient Outcomes
• Assessing the Importance of Health Education
• The Effect of Access to Clean Water on Public Health

Business and Finance:

• Exploring the Impact of E-Commerce on Local Businesses
• Analyzing the Role of Digital Payment Systems
• Investigating Consumer Behavior in Online Shopping
• The Relationship Between Customer Loyalty and Business Success
• Assessing the Effectiveness of Marketing Strategies
• The Influence of Branding on Consumer Preferences
• The Role of Supply Chain Management in Business Efficiency
• Investigating the Impact of Globalization on Small Enterprises
• Analyzing the Benefits of Employee Training Programs
• Assessing the Importance of Ethical Business Practices

Social Media and Technology:

• The Effect of Social Media Influencers on Consumer Behavior
• Investigating the Impact of Online Dating Apps on Relationships
• Analyzing the Use of Social Media for Activism
• The Relationship Between Internet Addiction and Mental Health
• The Influence of Online Filters on Self-Image
• Assessing the Benefits of Digital Detox Programs
• The Role of Virtual Reality in Online Gaming
• Investigating the Impact of Artificial Intelligence in Personalized Marketing
• Analyzing the Use of Augmented Reality in Education
• The Effect of Cybersecurity Measures on Online Privacy

Family and Relationships:

• Exploring the Impact of Divorce on Children’s Well-being
• Analyzing the Role of Sibling Relationships in Character Development
• Investigating the Effect of Parental Divorce on Academic Performance
• The Relationship Between Parenting Styles and Child Behavior
• The Influence of Extended Family Support on Parenthood
• Assessing the Benefits of Pre-marital Counseling
• The Role of Grandparents in Child Rearing
• Investigating the Impact of Long-distance Relationships on Couples
• Analyzing the Use of Technology in Maintaining Family Ties
• The Effect of Cultural Differences on Intercultural Marriages

Arts and Culture:

• The Influence of Philippine Folk Dances on National Identity
• Investigating the Role of Art in Social Commentary
• Analyzing the Impact of Cultural Festivals on Tourism
• The Relationship Between Music and Emotions
• The Effect of Theater and Drama on Empathy
• Assessing the Benefits of Art Therapy
• The Role of Literature in Shaping Society
• Investigating the Impact of Film on Social Awareness
• Analyzing the Use of Social Media in Promoting Local Artists
• The Influence of Indigenous Art Forms on Modern Filipino Art

Sports and Recreation:

• Exploring the Effect of Sports Participation on Character Development
• Analyzing the Role of Sports in Building Discipline
• Investigating the Impact of Sports Injuries on Athletes’ Careers
• The Relationship Between Physical Fitness and Academic Performance
• The Influence of Team Sports on Social Skills
• Assessing the Benefits of Recreational Activities in Stress Reduction
• The Role of Esports in Philippine Sports Culture
• Investigating the Impact of Sports Sponsorship on Athlete Development
• Analyzing the Use of Sports Analytics in Decision-making
• The Effect of Gender Stereotypes in Sports

Travel and Tourism:

• The Influence of Travel Experience on Cultural Awareness
• Investigating the Impact of Sustainable Tourism Practices
• Analyzing the Role of Social Media in Travel Planning
• The Relationship Between Travel and Stress Reduction
• The Effect of Tourism on Local Communities
• Assessing the Benefits of Ecotourism in Conservation
• The Role of Historical Sites in Tourism Promotion
• Investigating the Impact of Travel Bans on Tourism
• Analyzing the Use of Technology in Travel Booking
• The Impact of COVID-19 on the Travel and Tourism Industry

Technology and Education:

• Exploring the Role of Virtual Reality in Science Education
• Analyzing the Impact of Flipped Classrooms on Learning
• Investigating the Use of Artificial Intelligence in Personalized Education
• The Relationship Between Gamification and Student Engagement
• The Effect of Online Learning on Academic Achievement
• Assessing the Benefits of Blended Learning Approaches
• The Role of Educational Apps in Language Learning
• Investigating the Impact of Robotics in STEM Education
• Analyzing the Use of Educational Videos in Teaching
• The Influence of Social Media in Collaborative Learning

Environmental Sustainability:

• The Influence of Eco-friendly Practices on Business Success
• Investigating the Impact of Plastic Pollution on Marine Life
• Analyzing the Role of Renewable Energy in Reducing Carbon Footprint
• The Relationship Between Urbanization and Environmental Degradation
• The Effect of Deforestation on Climate Change
• Assessing the Benefits of Sustainable Agriculture
• The Role of Green Building Practices in Energy Efficiency
• Investigating the Impact of Conservation Education on Environmental Awareness
• Analyzing the Use of Electric Vehicles in Reducing Air Pollution
• The Impact of Waste Reduction Campaigns on Environmental Sustainability

Economic Development:

• Investigating the Contribution of Small and Medium Enterprises to Economic Growth
• Assessing How Foreign Direct Investment Influences Local Economies
• Investigating the Use of Microfinance in Poverty Alleviation
• The Relationship Between Economic Policies and Income Inequality
• The Effect of Tourism on Local Economic Development
• Assessing the Benefits of Export-Oriented Industries
• The Role of Infrastructure Development in Economic Growth
• Investigating the Impact of Technological Innovation on Economic Competitiveness
• Analyzing the Use of Public-Private Partnerships in Infrastructure Projects
• The Influence of Economic Literacy on Financial Decision-making

Health and Nutrition:

• The Effect of Food Advertising on Children’s Eating Habits
• Investigating the Impact of Fast Food Consumption on Health
• Analyzing the Role of Nutrition Education in Promoting Healthy Eating
• The Relationship Between Diet and Cardiovascular Health
• The Influence of Food Labels on Consumer Choices
• Assessing the Benefits of Organic Food Consumption
• The Role of Physical Activity in Preventing Lifestyle Diseases
• Investigating the Impact of Nutritional Supplements on Health
• Analyzing the Use of Plant-Based Diets in Health Improvement
• The Impact of Sleep Quality on Mental and Physical Health

Education and Technology:

• Exploring the Use of Augmented Reality in History Education
• Analyzing the Impact of Online Learning on Teacher-Student Interaction
• Investigating the Role of Educational Apps in Language Learning
• Understanding How Digital Literacy Relates to Academic Performance
• The Effect of Virtual Laboratories in Science Education
• Assessing the Benefits of Distance Learning for Students with Disabilities
• The Role of Gamification in Enhancing Math Skills
• Investigating the Impact of Technology Integration in Special Education
• Analyzing the Use of Artificial Intelligence in Personalized Learning
• The Influence of Social Media on Student Engagement

Social Issues and Awareness:

• The Effect of Social Media on Youth Political Engagement
• Investigating the Impact of Online Activism on Social Change
• Analyzing the Role of Media in Shaping Public Opinion
• The Relationship Between Gender Stereotypes and Career Choices
• The Influence of Cultural Sensitivity on Social Harmony
• Assessing the Benefits of Multicultural Education
• The Role of Youth in Promoting Environmental Awareness
• Investigating the Impact of Mental Health Advocacy
• Analyzing the Use of Arts and Culture in Promoting Social Values
• The Impact of Volunteerism on Community Development

Globalization and Culture:

• Exploring the Influence of Globalization on Traditional Filipino Culture
• Analyzing the Impact of International Trade on Philippine Economy
• Investigating the Role of Filipino Diaspora in Cultural Exchange
• The Relationship Between Globalization and Cultural Homogenization
• The Effect of Westernization on Filipino Identity
• Assessing the Benefits of Cultural Exchange Programs
• The Role of Social Media in Global Cultural Awareness
• Investigating the Impact of Global Brands on Local Culture
• Analyzing the Use of Technology in Promoting Filipino Culture Worldwide
• The Influence of International Travel on Cultural Perspective

Phew! That’s quite a list of quantitative research topics for Grade 12 students in the Philippines. Remember, the key to a successful research project is to choose a topic that genuinely interests you. When you’re passionate about your research, the journey becomes more enjoyable, and your findings are likely to be more valuable.

Take your time to explore these topics, do some preliminary research, and consult with your teachers and mentors to ensure that your chosen topic is feasible and relevant. Good luck with your Grade 12 research project, and may you discover valuable insights that contribute to the betterment of the Philippines and beyond!

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150+ Quantitative Research Topics For HumSS Students In 2023

Are you a student in HumSS (Humanities and Social Sciences) wondering what that means? HumSS is about understanding how people behave, how societies work, and what makes cultures unique. But why should you care about finding the right research topic in HumSS? Well, it’s important because it helps us figure out and deal with the complex issues in our world today.

In this blog, we are going to talk about HumSS research topics, specifically Quantitative Research Topics For HumSS Students in 2023. We’ll help you choose a topic that you find interesting and that fits your academic goals. Whether you study sociology, psychology, or another HumSS subject, we’ve got you covered.

So, stick with us to explore 150+ Quantitative Research Topics For HumSS Students. Let’s start this learning journey together!

What is HumSS?

Table of Contents

HumSS stands for “Humanities and Social Sciences.” It is a way to group together different subjects that focus on people, society, and the world we live in. In HumSS, we study things like history, language, culture, and how people interact with each other and their environment.

In HumSS, you learn about the past and present of human societies, their beliefs, and how they shape the world. It helps us understand our own actions and the world around us better, making us more informed and responsible members of society. So, HumSS is all about exploring the fascinating aspects of being human and the world we share with others.

Why Are Humss Research Topics Important?

HumSS research topics are important because they help us understand people and society better. When we study these topics, like history or how people think and behave, we can learn from the past and make better choices in the present. It helps us solve problems, like how to create a fairer society or how to preserve our culture. HumSS research topics are like a guide that helps us make the world a better place by learning about ourselves and others.

• Understanding Society: They allow us to comprehend human societies’ complexities, values, and norms.
• Problem Solving: HumSS research helps us tackle societal issues like poverty, inequality, and discrimination.
• Cultural Preservation: It aids in preserving and celebrating diverse cultures, languages, and traditions.
• Historical Lessons: Research in HumSS enables us to learn from history, avoid past mistakes and make informed decisions.
• Personal Growth: These topics contribute to personal development by fostering critical thinking and empathy, making us more responsible global citizens.

How To Choose A Humss Research Topic

Here are some points that must be kept in mind before choosing the research topic for HumSS:

1. Pick What You Like

Choose a research topic that you find interesting. When you enjoy it, you’ll be more motivated to study and learn about it.

2. Think About Real Problems

Select a topic that relates to problems in the world, like fairness or the environment. Your research can help find solutions to these issues.

3. Check for Books and Information

Make sure there are enough books and information available for your topic. You need resources to help with your research.

4. Make Sure It’s Doable

Consider if you have enough time and skills to study your topic well. Don’t pick something too hard or complicated.

5. Ask for Help

See if you can get help from teachers or experts. They can guide you and make your research better.

Here are some points on 150+ Quantitative Research Topics For HumSS Students In 2023: 

HUMSS Research Topics in Philosophy and Religion

The HumSS strand, which encompasses Philosophy and Religion, allows students to delve into the complexities of belief systems, ethics, and the nature of existence. Below are research topics in this field:

• Examining the ethical aspects of artificial intelligence and robotics.
• Analyzing the role of religion in shaping social and cultural norms in the Philippines.
• Investigating the philosophy of environmental ethics and its relevance in sustainable development.
• Exploring the concept of free will in the context of determinism.
• Analyzing the ethical considerations of genetic engineering and cloning in the Philippines.
• Evaluating the intersection of philosophy and mental health in the Filipino context.
• Investigating the philosophical foundations of human rights and their application in the country.
• Exploring the ethical dilemmas of capital punishment in the Philippines.
• Examining the philosophy of education and its impact on pedagogical approaches.
•  Analyzing the role of religious pluralism and tolerance in Philippine society.

HUMSS Research Topics in Literature and Language

Studying Literature and Language within the HumSS strand provides students with a deeper understanding of human expression, communication, and culture. Here are research topics in this field:

•  Analyzing the themes of identity and belonging in contemporary Filipino literature.
•  Examining the impact of colonialism on the evolution of Philippine literature and language.
•  Investigating the use of language in social media and its effects on communication.
•  Exploring the role of folklore and oral traditions in Filipino literature.
•  The ethical consequences of artificial intelligence and automation are being investigated.
•  Evaluating the influence of English as a global language on Philippine languages.
•  Investigating the use of code-switching and its sociolinguistic implications in the Philippines.
•  Examining how mental health issues are portrayed in Filipino literature and media.
•  Exploring the role of translation in bridging cultural and linguistic gaps.
•  Analyzing the impact of language policies on minority languages in the country.

Quantitative Research Topics For HumSS Students In The Philippines

Quantitative Research Topics For HumSS Students involve using numerical data and statistical methods to analyze and draw conclusions about social phenomena in the Philippines.

•  Analyzing the relationship between income levels and access to quality education.
•  Examining the impact of inflation on consumer purchasing power in the Philippines.
•  Investigating factors contributing to youth unemployment rates.
•  Investigating the connection between economic expansion and environmental damage.
•  Assessing the effectiveness of government welfare programs in poverty reduction.
•  Exploring financial literacy levels among Filipinos.
•  Analyzing the economic consequences of the COVID-19 pandemic.
•  The role of FDI in the Philippine economy is being investigated.
•  Studying economic challenges faced by small and medium-sized enterprises (SMEs).
•  Analyzing the economic implications of infrastructure development programs.

Social Justice And Equity Research Topics For HumSS Students

Social justice and equity research topics in the HumSS field revolve around issues of fairness, justice, and equality in society.

•  Examining the impact of gender-based violence on access to justice.
•  Analyzing the role of social media in advocating for social justice causes.
•  Investigating the effects of government’s “war on drugs” on human rights.
•  Exploring the intersection of poverty, gender, and healthcare access.
•  Assessing the experiences of indigenous communities in pursuing justice and land rights.
•  Analyzing the effectiveness of inclusive education in promoting equity.
•  Investigating challenges faced by LGBTQ+ individuals in accessing legal rights.
•  Examining responses to juvenile offenders in the criminal justice system.
•  Analyzing discrimination’s impact on employment opportunities for people with disabilities.
•  Evaluating the effectiveness of affirmative action policies.

Cultural Studies Research Topics For HumSS Students

Cultural studies research topics in HumSS examine culture, identity, and society.

•  Analyzing the influence of K-pop culture on Filipino youth.
•  Exploring the preservation of indigenous cultures in modern Filipino society.
•  Studying the impact of Filipino cinema on cultural identity.
•  Investigating the influence of social media on cultural globalization.
•  Analyzing the cultural significance of Filipino cuisine.
•  Investigating how gender and sexuality are portrayed in Filipino media.
•  Studying the influence of colonial history on contemporary Filipino culture.
•  Investigating the significance of traditional festivals and rituals.
•  Analyzing the portrayal of mental health in Filipino literature and art.
•  Exploring the cultural implications of migration and diaspora.
• Epidemiology Research Topics
• Neuroscience Research Topics

Environmental Ethics Research Topics For HumSS Students

Environmental ethics research topics in HumSS delve into the moral and ethical considerations of environmental and sustainability.

•  Analyzing the ethics of mining practices in the Philippines.
•  Investigating the moral responsibilities of corporations in environmental conservation.
•  Examining the ethical implications of plastic pollution in Philippine waters.
•  Exploring the ethics of ecotourism and its impact on ecosystems.
•  Assessing the ethical aspects of climate change adaptation and mitigation.
•  Investigating the moral responsibility of individuals in sustainable living.
•  Analyzing the ethics of wildlife conservation and protection.
•  Exploring cultural and ethical dimensions of sustainable fishing practices.
•  Examining the ethical dilemmas of land-use conflicts and deforestation.
•  Assessing the ethics of water resource management.

Global Politics And International Relations Research Topics For HumSS Students

Global politics and international relations research topics in HumSS focus on issues related to international diplomacy, governance, and global affairs.

•  Analyzing the Philippines’ role in the South China Sea dispute.
•  Investigating the impact of globalization on Philippine sovereignty.
•  Examining the country’s involvement in regional organizations like ASEAN.
•  Exploring the Philippines’ response to global humanitarian crises.
•  Assessing the ethics of international aid and development projects.
•  Analyzing the country’s foreign policy and alliances.
•  Investigating the challenges of diplomacy in the digital age.
•  Exploring the role of non-governmental organizations in shaping policy.
•  Analyzing the influence of international organizations like the United Nations.
•  Investigating the Philippines’ stance on global issues such as climate change.

Psychology And Mental Health Research Topics For HumSS Students

Psychology and mental health research topics in HumSS involve the study of human behavior, mental health, and well-being.

•  Analyzing the impact of social media on the mental health of Filipino adolescents.
•  Investigating the stigma surrounding mental health in the Philippines.
•  Examining the effects of government policies on mental health support.
•  Exploring the psychological effects of disasters and trauma.
•  Assessing the relationship between personality traits and academic performance.
•  Investigating cultural factors affecting help-seeking behavior.
•  Analyzing the mental health challenges faced by healthcare workers during the pandemic.
•  Exploring the experiences of Filipino overseas workers and their mental well-being.
•  Studying the impact of online gaming addiction on Filipino youth.
•  Evaluating the success of school-based mental health programs.

Education And Pedagogy Research Topics For HumSS Students

Education and pedagogy research topics in HumSS encompass the study of teaching, learning, and educational systems.

•  Assessing the effectiveness of online learning during the COVID-19 pandemic.
•  Investigating the role of technology in enhancing classroom engagement.
•  Examining inclusive education practices for students with disabilities.
•  Analyzing the effects of teacher training on student outcomes.
•  Exploring alternative education models like homeschooling.
•  Studying parental involvement’s impact on student achievement.
•  Investigating sex education programs’ effectiveness in schools.
•  Exploring the role of arts education in fostering creativity.
•  Analyzing the challenges of implementing K-12 education reform.
•  Assessing standardized testing’s benefits and drawbacks in education.

History And Historical Perspectives Research Topics For HumSS Students

History and historical perspectives research topics in HumSS delve into the study of past events and their significance.

•  Reinterpreting indigenous peoples’ roles in Philippine history.
•  Analyzing the impact of Spanish colonization on Filipino culture.
•  Investigating the historical roots of political dynasties.
•  Examining the contributions of Filipino women in the fight for independence.
•  Exploring the role of propaganda and media in key historical events.
•  Assessing the legacy of martial law under Ferdinand Marcos.
•  Investigating indigenous resistance and revolts in history.
•  Studying the evolution of Philippine democracy and political institutions.
•  Analyzing the role of Filipino migrants in global history.
• Exploring cultural and historical significance through ancient artifacts.

Economics And Economic Policy Research Topics For HumSS Students

Economics and economic policy research topics in HumSS focus on economic systems, policies, and their impact on society.

• Analyzing the economic impact of natural disasters.
• Investigating microfinance’s role in poverty alleviation.
• Examining the informal economy and labor rights.
• Exploring the effects of trade policies on local industries.
• Assessing the relationship between education and income inequality.
• Analyzing the economic consequences of informal settler issues.
• Investigating agricultural modernization challenges.
• Exploring the role of foreign aid in development.
• Analyzing the economic effects of healthcare disparities.
• Investigating renewable energy adoption’s economic benefits.

Philosophy And Ethics Research Topics For HumSS Students

Philosophy and ethics research topics in HumSS involve exploring questions of morality, ethics, and philosophy.

• Examining the ethics of truth-telling in medical practice.
• Analyzing the philosophical foundations of human rights.
• Investigating ethics in artificial intelligence and automation.
• Exploring ethical dilemmas of genetic engineering and cloning.
• Assessing moral considerations in end-of-life care decisions.
• Investigating ethics in environmental conservation and sustainability.
• Analyzing the ethics of capital punishment.
• Exploring the moral responsibility of corporations in social issues.
• Assessing the ethics of data privacy and surveillance.
• Investigating ethical considerations in public health.

Healthcare And Public Health Research Topics For HumSS Students

Healthcare and public health research topics in HumSS involve studying health-related issues, healthcare systems, and public health policies.

• Analyzing the effectiveness of the Philippine healthcare system in addressing public health crises.
• Investigating healthcare disparities and their impact on marginalized communities.
• Examining factors contributing to vaccine hesitancy in the country.
• Exploring the role of traditional medicine and alternative healthcare practices in Filipino culture.
• Analyzing the mental health challenges faced by healthcare workers during the COVID-19 pandemic.
• Assessing the accessibility and affordability of healthcare services in rural areas.
• Investigating the ethical considerations of organ transplantation and donation.
• Examining the effectiveness of health education programs in preventing diseases.
• Analyzing public perceptions of the pharmaceutical industry and drug pricing.
• Investigating the social determinants of health and their impact on population health outcomes.

Exploring HumSS Research Topics in Gender Studies

Gender studies research topics in HumSS focus on issues related to gender identity, roles, and equality in society.

• Analyzing the representation of gender in Philippine media and popular culture.
• Investigating the experiences of transgender individuals in the Philippines.
• Examining the impact of religion on gender norms in Filipino society.
• Exploring the role of gender-based violence prevention programs.
• Assessing the impact of gender stereotypes on career choices and opportunities.
• Analyzing the portrayal of women in political leadership roles.
• Investigating the role of masculinity and its effects on men’s mental health.
• Exploring the experiences of LGBTQ+ youth in Philippine schools.
• Studying the intersectionality of gender, class, and race in the Philippines.
• Evaluating the effectiveness of gender mainstreaming policies in government agencies.

HumSS Research Topics in Global Governance

Research topics in global governance within HumSS focus on international diplomacy, governance structures, and global challenges.

• Analyzing the role of the Philippines in regional security alliances like the ASEAN Regional Forum.
• Investigating the country’s involvement in international peacekeeping missions.
• Examining the country’s stance on global human rights issues.
• Evaluating the effectiveness of international organizations in addressing global challenges.
• Exploring the Philippines’ participation in global climate change negotiations.
• Analyzing the country’s compliance with international treaties and agreements.
• Investigating the role of Filipino diaspora communities in global governance issues.
• Assessing the impact of globalization on Philippine sovereignty and governance.
• Analyzing the country’s foreign policy responses to global health crises.
• Exploring ethical dilemmas in international humanitarian intervention.
• Investigating the diplomatic and economic implications of the Philippines’ bilateral relations with neighboring countries in Southeast Asia.

After exploring 150+ Quantitative Research Topics For HumSS Students, now we will discuss tips for writing a HumSS research paper

Tips for Writing a HumSS Research Paper

Here are some tips for writing a HumSS Research Paper: 

#Tip 1: Choose a Clear Topic

Start your HumSS research paper by picking a topic that’s not too big. Instead of something huge like “History,” go for a smaller idea like “The Life of Ancient Egyptians.” This helps you focus and find the right information.

#Tip 2: Plan Your Paper

Before you write, make a plan. Think about what you’ll say in the beginning, middle, and end of your paper. It’s like making a roadmap for your writing journey. Planning helps you stay on track.

#Tip 3: Use Good Sources

Use trustworthy sources for your paper, like books, experts’ articles, or reliable websites. Avoid sources that might not have the right information. Trustworthy sources make your paper stronger.

#Tip 4: Say Thanks to Your Sources

When you use information from other places, it’s important to give credit. This is called citing your sources. Follow the rules for citing, like APA , MLA, or Chicago, so you don’t copy someone else’s work and show where you found your facts.

#Tip 5: Make Your Paper Better

After you finish writing, go back and fix any mistakes. Check for spelling or grammar error and make your sentences smoother. A well-edited paper is easier for others to read and makes your ideas shine.

Understanding HumSS (Humanities and Social Sciences) is the first step in your journey to exploring the world of quantitative research topics for HumSS students. These topics are crucial because they help us unravel the complexities of human behavior, society, and culture. 

In addition, we have discussed selecting the right HumSS research topic that aligns with your interests and academic goals. With 150+ quantitative research ideas for HumSS students in 2023, you have a wide array of options to choose from. Plus, we’ve shared valuable tips for writing a successful HumSS research paper. So, dive into the world of HumSS research and uncover the insights that await you!

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How Quantitative Research Can Help Senior High School Students

Quantitative research is a scientific approach to research that employs numerical methods to analyze data. This type of research is used to answer questions about the world around us by measuring and quantifying the characteristics of a given population or phenomenon.

Quantitative research has many benefits, including its objectivity, precision, and ability to generate statistically significant results. It also allows researchers to control for confounding variables and test hypotheses rigorously. Using quantitative research, scientists can gain a deeper understanding of how the world works and make more accurate predictions about future events.

Table of Contents

What Are the Benefits of Using Quantitative Research?

There are several benefits to using quantitative research. First, it is a very efficient way to gather data. Using numerical methods allows researchers to quickly and accurately analyze a large amount of data. This can be especially helpful when identifying patterns or relationships in the data.

Second, quantitative research is highly reliable and objective. Using measurable data, researchers can produce results free from bias or personal opinion. This makes the findings from quantitative research more trustworthy and reliable.

Lastly, quantitative research is versatile and can be used in various fields. It is often used in the social sciences but has also been applied in business, marketing, education, and many other areas.

Steps Involved in Conducting Quantitative Research 

When conducting quantitative research, several steps need to be followed in order to ensure accuracy and validity:

• A hypothesis must be developed. This is a statement of prediction that can be tested through data analysis.
• The population or phenomenon of interest must be identified and defined. The time frame, geographical area, and demographic information must be specified.
• The data must be collected. This can be done through surveys, interviews, observations, or record reviews. The data must then be analyzed to determine whether the hypothesis is supported.
• Conclusions must be drawn and recommendations made based on the study’s findings.

Types of Questions That Can Be Answered Through Quantitative Research

• What is the distribution of a given attribute or variable in a population? This type of question can be answered through a census or survey.
• What is the average (mean) value of a given attribute or variable in a population? This type of question can be answered through a survey.
• What is the median value of a given attribute or variable in a population? This type of question can be answered through a survey.
• What is the mode value of a given attribute or variable in a population? This type of question can be answered through a survey.
• What are the minimum and maximum values of a given attribute or variable in a population? This type of question can be answered through data mining or statistical analysis.
• How does the distribution of a given attribute or variable change as we move from one population to another? This type of question can be answered through statistical analysis.

The Advantages and Disadvantages of Using Quantitative Research

There are many benefits to using quantitative research in order to study a population or phenomenon. For one, quantitative data can be collected relatively quickly and cheaply compared to other data types. Additionally, quantitative data is often easier to analyze than other types of data, making it possible to conclude a population or phenomenon accurately. Finally, quantitative research can study large populations or phenomena, making it possible to generalize findings to a broad audience.

Despite its many advantages, quantitative research is not without its limitations. One such limitation is that quantitative data does not always provide insight into the motivations or behaviors of individuals. 

For one, collecting data representative of your study population can be difficult. This is because people often do not answer surveys truthfully, or they may not answer them at all.

Another disadvantage of quantitative research is that it can be hard to study complex phenomena with this approach. This is because you are limited to numerical data, which cannot always capture the richness and complexity of human behavior.

Additionally, quantitative data can be difficult to interpret, and findings from quantitative research may be difficult to replicate. Finally, quantitative research relies heavily on statistical methods, which can sometimes be complex and challenging to understand.

Overall, quantitative research is a powerful tool that can be used to study a wide variety of populations and phenomena. When used correctly, quantitative research can provide otherwise unavailable insights. However, it is vital to keep in mind the limitations of this approach in order to avoid making faulty conclusions.

Senior high school students can benefit from using quantitative research to develop their skills in data analysis, critical thinking, and problem-solving.

Quantitative research involves the collection and analysis of data in order to conclude it. This type of research can be used to study various topics, including senior high school students’ academic performance.

Data analysis is a crucial component of quantitative research. Senior high school students can learn how to identify patterns and relationships by analyzing data. Additionally, they can develop their critical thinking skills by considering different interpretations of the data.

Another essential skill that senior high school students can develop is problem-solving through quantitative research. Students often have to find creative solutions to problems when working with data. By learning how to problem solve effectively, students will be better prepared to handle challenges in their future studies and careers.

In addition to the skills that senior high school students can develop through quantitative research, this type of research can also help them better understand complex concepts and theories. When working with data, students can see how different factors interact. This can give them a deeper understanding of the concepts they are studying.

Quantitative research can help students understand how the world works and make better decisions. It can also help them assess the effectiveness of interventions.

Overall, quantitative research is a powerful tool that can be used to answer questions about the world around us. It has many advantages that make it an essential part of scientific inquiry.

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How to Cite this Article

Llego, M. A. (2022, August 25). How Quantitative Research Can Help Senior High School Students. TeacherPH. Retrieved August 25, 2022 from, https://www.teacherph.com/quantitative-research-senior-high-school-students/

Mark Anthony Llego

Mark Anthony Llego, a visionary from the Philippines, founded TeacherPH in October 2014 with a mission to transform the educational landscape. His platform has empowered thousands of Filipino teachers, providing them with crucial resources and a space for meaningful idea exchange, ultimately enhancing their instructional and supervisory capabilities. TeacherPH's influence extends far beyond its origins. Mark's insightful articles on education have garnered international attention, featuring on respected U.S. educational websites. Moreover, his work has become a valuable reference for researchers, contributing to the academic discourse on education.

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Quantitative Research Topics For STEM Students In The Philippines

– "genetic diversity and conservation of endangered species in the philippines" – "microbial ecology in philippine coral reefs: a study on resilience", 2. environmental science, – "impact of urbanization on biodiversity in metro manila" – "water quality assessment of major rivers in the philippines", 3. computer science, – "developing efficient algorithms for large-scale data analysis in healthcare" – "cybersecurity threats and countermeasures in philippine businesses", – "exploring the feasibility of renewable energy sources in the philippines" – "quantum computing: future prospects and challenges in the philippine context", 5. mathematics, – "statistical modeling of economic trends in the philippines" – "optimizing resource allocation in public health using mathematical models", 6. chemistry, – "analysis of air quality and its impact on human health in urban areas" – "green chemistry: sustainable practices in philippine industries", 7. engineering, – "infrastructure resilience to natural disasters: a case study in the philippines" – "developing sustainable and cost-effective building materials", explore more topics.

Philippine E-Journals

Home ⇛ psychology and education: a multidisciplinary journal ⇛ vol. 6 no. 8 (2023), a quantitative study: impact of public teacher qualifications and teaching experience on students' academic performance and satisfaction.

Nikki Numeron | Myra Arado | Flordelyn Perez

Discipline: Education

Student feedback is a crucial indicator of how well teachers and the educational system are performing. Maintaining high standards of quality and assessing teacher effectiveness as well as student academic performance depend on students' satisfaction. The goal of the study was to investigate the impact of public school teacher qualifications and teaching experience on students’ academic performance and satisfaction. The study was conducted at the selected elementary schools in Agusan del Sur, Philippines. It has been randomly picked based on the criteria and guidelines portrayed by the researchers. The study has utilized a quantitative research approach with a descriptive research design. The study has used a descriptive survey questionnaire. The participants in the study were 50 randomly selected public elementary teachers and another 40 randomly selected samples of students from the said schools. The reliability tests were just performed to measure the internal consistency and construct validity of the scales of the study. Furthermore, the study used a linear regression to greatly attain the main objectives of the study as well as a multiple moderation regression, known as the MMR test, to attest to the relationships between the given objectives. Based on the findings, the linear regression analysis was applied to investigate the relationship between teachers’ experience, qualifications, and the level of students’ satisfaction. It was stated on the table that the R2 value of public teacher experience was 0.512 and their qualifications were 0.611, whereas the students’ satisfaction score of 0.877 had significantly increased based on their perceived interest in the teaching methods of the teachers in the actual setting. Model 1 findings: effective knowledge sharing has been extensively researched and found to have significant effects on student satisfaction and academic performance (R2 = 0.678; p 0.000; beta coefficient = 0.694, respectively). Also, (Model 2: R2 = 0.522; p 0.000; beta coefficient = 0.759), which is significantly correlated to the level of student satisfaction Model 3: R2 = 0.711, p<0.000, and beta coefficient = 0.473, respectively, with a 95% confidence level based on the outcome. The findings show that teacher qualification predicts student satisfaction more accurately than teacher experience. The association between teachers' experience and qualifications and student satisfaction was somewhat mediated by teacher methods, skills, and knowledge-sharing efficiency.

References:

• Aguinis, H. (2004). Regression analysis for categorical moderators: Guilford Press. Aldridge, S., & Rowley, J. (1998). Measuring customer satisfaction in higher education. Quality Assurance in Education, 6(4), 197-204.
• Aslam, U., Rehman, M., Imram, M. I., & Muqadas, F. (2016). The Impact of Teacher Qualifications and Experience on Student Satisfaction: A Mediating and Moderating Research Model. Pakistan Journal of Commerce and Social Sciences 2016, Vol. 10 (3), 505- 524 Pak J Commer Soc Sci.
• Butron, P. V. V. (2021). Responsiveness, Emotions, and Tasks of Teachers in the New Normal of Education in the Philippines. Journal homepage: www. ijrpr. com ISSN, 2582, 7421.
• Chapman, D. W., Al-Barwani, T., Al Mawali, F., & Green, E. (2012). Ambivalent journey: Teacher career paths in Oman. International Review of Education, 58(3), 387- 403.
• Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approach: Sage publications.
• Douglas, J. A., Douglas, A., McClelland, R. J., & Davies, J. (2015). Understanding student satisfaction and dissatisfaction: an interpretive study in the UK higher education context. Studies in Higher Education, 40(2), 329-349.
• Johnson, S. M., Kraft, M. A., & Papay, J. P. (2012). How context matters in high-need schools: The effects of teachers’ working conditions on their professional satisfaction and their students’ achievement. Teachers College Record, 114(10), 1-39.
• Lenton, P. (2015). Determining student satisfaction: An economic analysis of the National Student Survey. Economics of Education Review, 47, 118-127.
• Mendoza, E. M., Cimagala, L. C. P., Villagonzalo, A. P., Guillarte, M. C., Saro, J. M. (2022). Coping Mechanisms and Teachers' Innovative Practices in Distance Learning: Challenges and Difficulties for the Modular Teaching and Learning Approach. Psychology and Education: A Multidisciplinary Journal.
• Michaelowa, K. (2002). Teacher job satisfaction, student achievement, and the cost of primary education in Francophone SubSaharan Africa: HWWA Discussion Paper.
• Sekaran, U. (2014). Research methods for business: A skill building approach. John Wiley & Sons.
• Sagales, J., Gonzaga, E., Gonzaga, D. & Miranda, M. (2021). Coping mechanisms of public teachers during the pandemic: an evaluative review. International Journal of Current Research, 12,(9), 13836-13839
• Saro, J., Cuasito, R., Doliguez, Z., Maglinte, F., Pableo, R., (2022). Teaching Competencies and Coping Mechanisms among the Selected Public Primary and Secondary Schools in Agusan del Sur Division: Teachers in the New Normal Education. Psychology and Education: A Multidisciplinary Journal, 3(10), 969-974.
• Skaalvik, E. M., & Skaalvik, S. (2012). Teacher job satisfaction and motivation to leave the teaching profession: Relations with school context, feeling of belonging, and emotional exhaustion. Teaching and Teacher Education, 27(6), 1029-1038.
• Veldman, I., Van Tartwijk, J., Brekelmans, M., & Wubbels, T. (2013). Job satisfaction and teacher–student relationships across the teaching career: Four case studies. Teaching and Teacher Education, 32, 55-65.

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A Pre-Experimental Research on the Implementation of Selected Classroom Assessment Techniques for Music, Arts, Physical Education, and Health

International Journal of Multidisciplinary: Applied Business and Education Research, volume 2, issue 2, p. 99 - 107

9 Pages Posted: 22 Mar 2021 Last revised: 26 Jul 2021

Almighty Cortezo Tabuena

Pamantasan ng Lungsod ng Valenzuela; University of Perpetual Help System DALTA; Philippine Normal University; International Journal of Academic and Practical Research; National Research Council of the Philippines

Date Written: February 11, 2021

The primary objective of this study is to examine and identify the classroom assessment techniques (CATs) that might provide and help teachers through assessment and evaluation processes in Music, Arts, Physical Education, and Health (MAPEH) using pre-experimental research through one-group pretest-posttest design. The study was conducted at Jose Abad Santos High School in Manila, Philippines during the third grading period of the researcher’s practicum. The sample of the study consists of 20 students selected through the purposive sampling technique. Quantitative and qualitative data analyses were employed. The instruments used in the study were a teacher-made quiz, CATs sample assessment as the treatment, and the perception survey questionnaire (PSQ) regarding the CATs. The result revealed that there is a significant difference between the pre-evaluation and post-evaluation after the CATs implementation. In the post-evaluation, the mean score was significantly higher than the pre-evaluation, which indicates that the CATs improved the performance of students in MAPEH. On the other hand, as revealed in the PSQ, the students regarded the CATs positively. It shows, in this case, that CATs are a great alternative to traditional assessments. Other CATs to be developed and implemented for better learning and effective teaching are recommended.

Keywords: Assessment, Classroom Assessment Techniques, MAPEH, Evaluation, Perception

JEL Classification: 129

Suggested Citation: Suggested Citation

Almighty Tabuena (Contact Author)

Pamantasan ng lungsod ng valenzuela ( email ).

Tongco Street Brgy. Maysan Valenzuela City Philippines

University of Perpetual Help System DALTA ( email )

Alabang- Zapote Road Pamplona 3 Las Piñas City Philippines

Philippine Normal University ( email )

Taft Avenue Manila Philippines

International Journal of Academic and Practical Research ( email )

Manila Philippines

National Research Council of the Philippines ( email )

Taguig Philippines

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Risk and Influencing Factors for School Absenteeism among Students on the Autism Spectrum—A Systematic Review

• Review Paper
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• Published: 05 September 2024

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• Isabella Sasso   ORCID: orcid.org/0009-0007-0726-0939 1 &
• Teresa Sansour 1  

School plays an important role in the development of a child. The impact of school absenteeism extends beyond academic achievement, affecting one's ability to participate in life successfully. In particular, children with difficulties in communication and interaction are at risk of developing school absences. This systematic review therefore focused on school absenteeism among children on the autism spectrum and examined the risk and influencing factors contributing to school absences. Eighteen studies were included, thirteen of which used a quantitative design, two of which were mixed-method studies, and three of which had a qualitative design. Different studies had varying definitions of school absenteeism and employed diverse study designs, prompting the need for a narrative synthesis. We evaluated the data regarding the factors of individual, parental, and school based on the KiTes bioecological systems framework for school attendance and absence by Melvin et al. (2019). We identified the majority of risks and influences in relation to the school factor and identified interacting factors contributing to school absenteeism in all factors. We recognised research gaps and provided guidance for further research.

Avoid common mistakes on your manuscript.

Introduction

When children and youth attend school, they have access to education, a right proclaimed in Article 28 of the United Nations Convention on the Rights of the Child (The United Nations, 1989 ). School education fundamentally contributes to the cognitive, social, and emotional development of children and young people while simultaneously fulfilling essential social tasks (Pellegrini, 2007 ). Accordingly, absences from school have enormous consequences not only for educational success but also for emotional and social development and successful participation in life. School absenteeism increases the risk of all forms of mental illness (Lenzen et al., 2013 ; Melvin et al., 2019 ). Absences can cause distress in families (Gallé-Tessonneau & Heyne, 2020 ) and challenge professionals and resources (Wilson et al., 2008 ; Finning et al., 2019 ).

Defining School Absenteeism

In international research, scholars utilise a range of terms and criteria to assess school absenteeism. Consequently, it's imperative to interpret and compare research findings within this context. For the scope of this review, we employ the term 'school absenteeism' as an overarching concept. Absenteeism, broadly defined, refers to a student's absence from school for any reason, encompassing various forms of non-attendance (Kearney, 2016 ).

A distinction exists between problematic and non-problematic absences. Non-problematic absences may result from factors such as illness, bereavement, or other causes. However, even initially non-problematic or technically excused absences can transition into problematic ones if more than 10% of lessons are missed (Heyne et al., 2019 ; Lenzen et al., 2013 ), or if the child's development is compromised by the absence, leading to decreased grades or challenges in reintegrating into the academic environment (Kearney, 2016 ). Hence, Kearney ( 2003 ) defines non-problematic absenteeism as short- or long-term absences mutually agreed upon by parents and the school, with the possibility of compensatory measures. Additional terms for distinguishing between problematic and non-problematic absences include unexcused/excused, unauthorised/authorised (Gentle-Genitty et al., 2015 ), and illegitimate/legitimate (Kearney, 2003 ).

Heyne et al. ( 2019 ) differentiates four types of problematic absenteeism:

School refusal is defined as non-attendance at school due to emotional stress related to school attendance, where the parents are informed of absences and make reasonable efforts to ensure the child's attendance at school.

School withdrawal is defined as non-attendance with the knowledge of the parents or withholding by the parents.

Truancy includes absence without permission from the school and the parents. In addition, there are efforts to hide truancy from parents.

In the case of school exclusion , the school initiates the absence, for example, as a disciplinary action.

Kearney et al. ( 2019 ) intend to categorise heterogeneous concepts and provide general descriptions of common terms. School refusal must be distinguished from school refusal behaviour . While school refusal involves absence from school, school refusal behaviour is a broader term for various behaviour patterns based on the goal of avoiding school, whether anxiety-related or not (Kearney, 2016 ). School avoidance refers to an absence based on anxiety related to school. Most of these terms refer to an absence initiated by the individual, while school exclusion is initiated by the school, and school withdrawal is parent-initiated (Kearney et al., 2019 ).

Risk and Influencing Factors for School Absenteeism

To identify factors increasing the likelihood of experiencing school absenteeism, various system levels must be considered (Kearney, 2008 ). Melvin et al. ( 2019 ) propose a multilevel approach that applies Bronfenbrenner’s bio-ecological model to the factors associated with school absenteeism.

At the micro- and meso-system levels, factors such as the individual, parental/family, and school levels have been demonstrated to be associated with school attendance. Knowledge of these factors and their interactions can contribute to an understanding of school absenteeism (Melvin et al., 2019 ) (Fig.  1 ).

The KiTeS bioecological systems framework for school attendance and absence (Melvin et al., 2019 )

To categorise different types of absenteeism according to their initiation and relationship to the individual, the school, and the parental level, we constructed Fig.  2 . The categorisation is based on comprehensive research regarding different types of school absenteeism and their relation (Heyne et al., 2019 ; Kearney, 2008 ; Reissner et al., 2019 ; Tonge & Silverman, 2019 ).

Categorisation of absenteeism types based on individual, school, and parental levels

Defining Autism

The World Health Organisation (WHO, 2023 ) categorises “Autism Spectrum Disorder” (ASD) as a neurodevelopmental disorder characterised by “persistent deficits in the ability to initiate and to sustain reciprocal social interaction and social communication”. Another criterion implies “a range of restricted, repetitive, and inflexible patterns of behaviour, interests or activities that are clearly atypical or excessive for the individual’s age and sociocultural context”. The onset is typically in early childhood, but symptoms may manifest when social demands increase. Since there is a surge in social demands at school age, coping with developmental tasks becomes even more difficult, and special support is often needed (Kamp-Becker & Bölte, 2021 ). Overall, transitions from different developmental and life phases are important, as these are associated with a rise in vulnerability. As the term ‘spectrum’ suggests, symptoms and therefore needs for support vary among autistic individuals (Kamp-Becker & Bölte, 2021 ). Since autism is a lifelong condition, most individuals need support and services throughout their lifetime. Most services (such as therapy and social skills training) are used in early and middle childhood (Song et al., 2022 ). The US Center for Disease Control and Prevention (CDC, 2023 ) estimated that one in 36 children is diagnosed with ASD. ASD was 3,8 times more prevalent among boys than among girls (approximately 4% of boys and 1% of girls). In total, 37.9% of the autistic individuals had an intellectual disability (IQ < 70), 23.5% had an IQ between 71 and 85, and 38.6% had an IQ > 85. However, it should be noted that the prevalence varies between countries and studies. Autism is also often associated with psychiatric conditions. It is estimated that 70–72% of autistic youth have at least one psychiatric condition. Some of the most common disorders are anxiety, depression and attention deficit hyperactivity disorder (ADHD; Rosen et al., 2018 ).

School Absenteeism among Autistic Individuals

According to the Department for Education ( 2019 ) autistic students in England explore higher rates of school absences than non-autistic students with or without special educational needs. When educational needs align with challenges in social skills and communication, absenteeism rates tend to increase. On the other hand, schools comprise a variety of communication and interaction situations (Ashburner et al., 2010 ). Autistic students often find social interactions stressful, thereby facing an increased risk of limited participation and social exclusion (Roberts & Simpson, 2016 ). These circumstances elevate the risk of psychiatric conditions (Hebron & Humphrey, 2014 ). These co-occurring psychiatric conditions heighten the risk of school absenteeism (Finning et al., 2019 ).

To date no systematic review has investigated the risk factors of school absenteeism among autistic students. The aim of this study is to systematically review studies regarding the risk and influence factors for school absenteeism in autistic students. In particular, individual, educational, and parental factors of the micro- and mesosystems are considered. Another aim is to identify research gaps for further investigations.

The primary research questions for this systematic review include:

What types of school absenteeism have been identified in prior studies for autistic students?

What individual, school, and parental factors contribute to school absenteeism among autistic students?

Review Methods

The protocol for this systematic review has been registered online at PROSPERO, an international register for systematic reviews (Registration number: CRD42022343467). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards has been followed for all stages of this systematic review (Page et al., 2021 ). The following electronic databases covering all relevant disciplines have been searched for journal articles: ERIC (Ped), Web of Science and Scopus (Psych), and PubMed (Med) on 26th June 2022. Prior to this search, a preliminary search was performed and an updated search was carried out on 30th November 2023. The Cochrane and PROSPERO databases have been searched to confirm that there was no other existing or registered systematic review about the current topic. The search strategy included terms regarding autism and school absenteeism (see Table  1 ).

Inclusion Criteria

The studies included in this review have been selected based on the following predetermined inclusion criteria: (a) they focused on school-aged individuals with a formal diagnosis of autism; (b) they focused on individual, family or school factors having an influence on any form of school absenteeism; and (c) they were published in German or English. No restrictions were applied regarding the publication period of the included articles.

Exclusion Criteria

Studies were excluded from the review based on one or more of the following criteria: (a) they were published in languages other than English or German; (b) they were not empirical studies; (c) they focused on non-autism samples or mixed etiology groups and the data for autistic individuals were not reported separately; and (d) they did not scrutinise school absenteeism and influencing factors. This review did not include grey literature, but the search was not restricted to peer-reviewed articles.

Study Selection

Electronic searches identified 322 records. Following the removal of duplicates, each reviewer independently assessed 148 articles based on the title and abstract; each reviewer was blinded to the other’s ratings. Disagreements were solved by discussion.

After screening the full texts, 18 studies that met the inclusion criteria were identified and included in the current review.

Quality Assessment

The quality of each included study was assessed by both reviewers independently by using the Mixed Method Appraisal Tool (MMAT) described by Hong et al. ( 2018 ). Disagreements were resolved by both authors discussing the information presented. The MMAT is an appraisal tool for systematic reviews that include quantitative, qualitative and mixed methods studies. Hong et al. ( 2018 ) developed the tool based on a literature review of critical appraisal tools. By using this tool, the study quality was categorised as good, moderate or low. Sixteen studies were rated as ‘good’, and two studies were rated as ‘moderate’. One of these studies has limitations in its quality since the actual research question was not answered (Ochi et al., 2020 ). However, the authors identified these limitations. The other study did not formulate any explicit research questions (Kurita, 1991 ). No studies were excluded due to low quality.

Data Extraction

The first author (I.S.) extracted the data according to predefined criteria. The second author (T.S.) controlled the integrity and verified the accuracy of all the extracted data.

Data were extracted and coded for each study that met the inclusion criteria. The following descriptive data were extracted: study details, information about the sample, the definition of school absenteeism, the criteria for school absenteeism (e.g., 10% absence of school days), the data collection tool for school absenteeism, the risk and influencing factors, the absence rate, the intervention, and the effect of the intervention.

A narrative synthesis was provided due to the heterogeneity of the studies, especially regarding the terminology and measurement of school absenteeism as well as the criteria of different forms of school absenteeism.

Figure  3 shows the evaluation and screening process used to select the 18 studies included in this systematic review. Table 1 provides the details for each included study.

PRISMA 2020 flow diagram

Three studies were from the same research group (Bitsika et al., 2020 , 2021 , 2022 ). All three address the topic of bullying. Bitsika et al. ( 2022 ) studied a subsample of a larger cohort from Bitsika and Sharpley ( 2016 ). Bitsika et al. ( 2021 ) used a sample from Bitsika et al. ( 2020 ). Each article has a different research question.

Munkhaugen et al. ( 2019 ) based their research on a subsample from the first study (Munkhaugen et al., 2017 ).

Study Characteristics

All studies except Kurita ( 1991 ) are very recent (2017–2023). These studies were conducted in Australia (Adams, 2021 ; Bitsika et al., 2020 , 2021 , 2022 ), the United Kingdom (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ; Totsika et al., 2020 ; Truman et al., 2021 ), Sweden (Anderson, 2020 ), Japan (Kurita, 1991 ; Ochi et al., 2020 ), Norway (Munkhaugen et al., 2017 , 2019 ), the United States (Mattson et al., 2022 ; McClemont et al., 2021 ) and Denmark (Lassen et al., 2022 ).

Three of the 18 studies used qualitative designs, two used mixed method designs, and 13 used quantitative designs. Eleven studies employed cross-sectional designs, while there was also an evaluative case study that aimed to identify the impact of an intervention, a retrospective chart review study, a longitudinal study based on retrospective school datasets, a brief report of an observational study, a qualitative study with a multi-informant approach, a qualitative study that is based on case reports and a qualitative study consisting of an interpretative phenomenological approach.

The sample size ranged from N = 1799 in a quantitative cross-sectional study to N = 3 in qualitative case reports. The total sample included 3304 autistic students. The ages ranged between 3 and 21 years. Two studies examined absenteeism in preschoolers with autism. The sample was predominantly male except for the only qualitative study that explicitly focused on girls with autism (O’Hagan et al., 2022 ). Fourteen studies based their results on parental reports, and four studies considered additional school staff or other professionals involved. One qualitative study collected data by interviewing the autistic young people and one used a multi-informant approach by interviewing parents, professionals and school staff. Two studies focused on data bases: one used clinical data (Ochi et al., 2020 ) and the other used school datasets (Mattson et al., 2022 ). All studies collected data regarding mainstream schools. Six studies also collected data in a special school setting. The detailed information for each included study is summarised in Table  1 .

Types of School Absenteeism

In the included studies, school refusal was the most commonly used term for absenteeism. In total, 11 studies referred to this term (Adams, 2021 ; Bitsika et al., 2020 , 2021 , 2022 ; Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ; Ochi et al., 2020 ; Preece & Howley, 2018 ; Totsika et al., 2020 ). The studies are based on the definition in which school refusal occurs due to emotional distress with knowledge of the parents (Heyne et al., 2019 ; Kearney, 2008 ). Totsika et al. ( 2020 ) referred to school withdrawal, truancy, school exclusion, and nonproblematic absence, as these are all categories included in the data collection tool they used (the School Non-Attendance Checklist [SNACK] by Heyne et al., 2019 ). Adams ( 2021 ) also used the SNACK and referred to the types defined by Heyne et al. ( 2019 ) but also described the difference between emerging and established school refusal . Furthermore, the author investigated full- and half-day absences. Bitsika et al., ( 2020 , 2021 , 2022 ) also followed the definition of school refusal established by Heyne et al. ( 2019 ). They argued that school refusal is often associated with absence from school, but it is not necessarily defined by absence; therefore, they used the term emerging school refusal (Bitsika et al., 2020 ).

Munkhaugen et al., ( 2017 , 2019 ) chose school refusal behaviour as the object of research. They referred to Kearney ( 2008 ) who defined school refusal behaviour as ‘child-motivated refusal to attend school and/or difficulties remaining in class’ (Munkhaugen et al., 2017 ).

Kurita ( 1991 ) operationalised school refusal according to Berg et al.’s ( 1969 ) definition as absence from school due to reluctance to attend with the knowledge of the parents, while no antisocial disorders occur with this absence.

O’Hagan et al. ( 2022 ) used the phrase ‘emotionally based’ school avoidance and referred to Munkhaugen et al. ( 2017 ). Hence, it can be assumed that O’Hagan et al. ( 2022 ) used school avoidance as a synonym for school refusal behaviour . Gray et al. ( 2023 ) also used the term school avoidance as a synonym for school refusal .

Truman et al. ( 2021 ) did not directly focus on school absenteeism. They evaluate school experiences in the context of extreme demand avoidance behaviour. One aspect relating to this group of autistic children is school exclusion due to challenging behaviour. They included both formal and informal exclusions. In contrast, Gray et al. ( 2023 ) and Martin-Denham ( 2022 ) had an explicit focus on school exclusion. Martin-Denham ( 2022 ) referred to the Education Act and the European Court, which stated that a decision to exclude has to be lawful, rational, proportionate and fair. A differentiation was made between a fixed period exclusion, where a student was excluded from school for a set period, and a permanent exclusion, when a student did not return to school. Gray et al. ( 2023 ) also referred to this differentiation between fixed-term and permanent exclusion.

Two other included studies addressed the differentiation between unexcused and excused absences (Mattson et al., 2022 ), school absences and nonproblematic absences, respectively (Anderson, 2020 ) (Table  2 ).

Criteria and Frequency of School Absenteeism

Criteria for school absenteeism.

The included studies used different criteria to operationalise absenteeism. Two studies used the criterion of 10% absence from school days (O’Hagan et al., 2022 ; Totsika et al., 2020 ). Adams ( 2021 ) also used this criterion but to discuss ‘persistent’ absence. Munkhaugen et al., ( 2017 , 2019 ) relied on the criteria described by Kearney and Silverman ( 1996 ), who differentiated between ‘self-corrective’ for < 2 weeks, ‘acute’ absence for 2–52 weeks, and ‘chronic’ absence for > 53 weeks. However, they did not provide information about how often the behaviour occurred during the period. Ochi et al. ( 2020 ) used more than 30 days per year as a criterion. Gray et al. ( 2023 ) utilised a broad definition of school exclusion “to ensure it captured the full range of experiences of autistic pupils who had persistent, problematic attendance and experience of leaving a mainstream setting due to unmet needs”. Martin-Denham ( 2022 ) refers to school exclusion as a legal term. Five studies did not explicitly determine a criterion for absence in terms of a number. Rather, they explained it with descriptions such as ‘prolonged’ (Preece & Howley, 2018 ). For the remaining five studies, it was not necessary to determine the criterion thematically or because of the study design.

Frequency of Absences

Despite the different study designs and terms, the results regarding the frequencies of absences are considered in the following.

Adams ( 2021 ) reported the highest rate of absenteeism: 72.6% of autistic children had shown ‘persistent absence’, defined as a 10% absence within the 20-day survey period. The average absenteeism rate in the study was 6.3 full days and 3.8 half days. In addition, 5.7% of the autistic students were absent for 4 weeks; all of them (partly among other reasons) did so due to school refusal.

Totsika et al. ( 2020 ) reported that 43% of autistic children showed persistent absence during a 23-day period. The average absence rate was 5 days. The median number of days missed was 2. Moreover, 64% of the autistic children missed at least 1 day, and 7% did not attend school on any of the 23 days. Similarly, Munkhaugen et al. ( 2017 ) reported that 42.6% of autistic students exhibited school refusal behaviour. Bitsika et al. ( 2020 ) reported that 56.1% of autistic boys who reported being bullied experienced emerging school refusal, but as seen in the definition stated above, this is not a clear indication of actual absence from school.

Kurita ( 1991 ) reported the lowest frequencies: 23.7% of autistic students experienced school refusal (as defined above). In addition, 28.1% were reported to have shown an unwillingness to go to school that did not result in absenteeism. According to the data provided by parents of autistic children, 35% indicated that their child had already refused to go to school (McClemont et al., 2021 ). Regarding school exclusion, Truman et al. ( 2021 ) reported that 50% of autistic children were informally excluded from school.

Anderson ( 2020 ) reported the frequency of absences between different school types. The rates of absences for reasons other than illness (unexcused absence) did not significantly differ between primary (51.3%) and secondary (57.6%) schools. In primary schools for students with learning disabilities absences due to illness (excused absences) were the main cause (83.8%) among autistic students. The rate of absenteeism for reasons other than illness (unexcused absence) increased in secondary schools for students with learning disabilities (36.3%). In elementary schools, the median percentage of school day absences was 9.1% in the study by Mattson et al. ( 2022 ). On 39.1% of all missed school days analysed, students had excused absences, while 60.9% of absences were unexcused. Lassen et al. ( 2022 ) reported more absences among autistic children than among the control group.

Data Collection Tool for School Absenteeism

As stated above, two studies used the SNACK conducted by Heyne et al. ( 2019 ). Adams ( 2021 ) modified the SNACK by also asking about half-day absences.

The majority of studies used nonvalidated scales. Six studies used self-constructed questionnaires (Anderson, 2020 ; Bitsika et al., 2020 , 2021 , 2022 ; Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ; Truman et al., 2021 ). Lassen et al. ( 2022 ) asked about frequency via a 5-point Likert scale with descriptive ratings (never, rarely, sometimes, often, very often).

Qualitative studies (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ) as well as a mixed-method study (Preece & Howley, 2018 ) have used interviews for data collection.

Two studies used existing datasets. One of them used clinical data (Ochi et al., 2020 ), whereas the other used school datasets (Mattson et al., 2022 ).

Risk and Influencing Factors

According to the Kids and Teens at School Framework (KiTeS) by Melvin et al. ( 2019 ), the extracted risk and influencing factors for school absenteeism among autistic students were divided into individual, school and parental factors.

Individual Factors

Age, gender, diagnosis, intellectual level and psychiatric conditions were identified as factors at the individual level.

Mattson et al. ( 2022 ) reported that age was weakly and negatively correlated with the median percentage of days absent. They demonstrated that younger participants exhibited more frequent absences on average than older students. Another study reported that the mean age at the onset of school refusal was 12.6 ± 2.2 years in autistic students, which was significantly younger than in those without autism (13.8 ± 2.1 years; Ochi et al., 2020 ). In contrast, Totsika et al. ( 2020 ) reported slightly increased rates of not attending school with increasing age. Among children who missed any school days, refusal was more likely among older children.

Anderson ( 2020 ) revealed a gender difference in the disadvantage of girls on the autism spectrum. They exhibited higher rates of absence (54.6%) for reasons other than illness than autistic boys (43.9%). Compared with boys, girls exhibited significantly more short absences for reasons other than illness. For continuous periods of absence longer than four weeks, there was no significant difference between boys and girls.

In the study of O’Hagan et al. ( 2022 ), two mothers of autistic children with school avoidance indicated that feeling different from others without an explanation of a diagnosis led to low confidence and self-esteem. Families and professionals, in the study of Preece and Howley ( 2018 ), identified a late diagnosis as contributing to non-attendance since the special needs of autistic students were therefore not recognised and addressed. The findings of Martin-Denham ( 2022 ) indicate “barriers to gaining prompt assessment and identification of special educational needs and disability (SEND)”. However, students who already have a diagnosis may struggle with feeling different (Martin-Denham, 2022 ) and having a “desire to fit in” (Gray et al., 2023 ).

Intellectual Level

While most of the related studies have focused on autistic students without intellectual disabilities, Kurita ( 1991 ) found that autistic students who experienced school refusal tended to be more intelligent than those who did not. The intellectual level was significantly greater for autistic children who refused school than for those who did not.

Psychiatric Conditions

Truman et al. ( 2021 ) focused on a group of autistic children with extreme demand avoidance behaviour. These children showed more specific behavioural difficulties. They were able to mask their difficulties at school and then experienced a meltdown after. Fifty percent of parents informally excluded their children from school so that they could be home-educated, reducing their anxiety and stress.

On the other hand, the parents in the study of Gray et al. ( 2023 ) reported that they did not notice the anxiety of their children because they could not communicate their feelings. Anxiety was also demonstrated by aggression, which in turn led to school exclusions and, in some cases, led to symptoms of depression, including self-harm and suicide attempts.

Adams ( 2021 ) reported a 3% increased risk for half-day absences when the child experienced anxiety.

Munkhaugen et al. ( 2019 ) showed that autistic students with school refusal behaviour were more socially impaired than those without such behaviour. Nonetheless, low social motivation had the strongest association with school refusal behaviour. Parents commented that negative thoughts about relationships with peers and teachers, as well as about school subjects, were frequent reasons for their children’s school refusal behaviour. Lassen et al. ( 2022 ) reported that school absence is accompanied by internalising symptoms such as anxiety. This association was even stronger than that with autistic or externalising symptoms and was not unique to the autism group.

School Factors

School factors had the greatest influence on school absenteeism. Five studies focused their research on bullying. Other factors related to the school setting are the school type, the school environment and negative experiences.

The significance of school factors can even be seen in the oldest study. Two-thirds of parents of autistic children who refused school indicated that school refusal behaviour was a precipitating factor. The majority were school-related, with “teasing by schoolmates” being the most common factor (Kurita, 1991 ).

McClemont et al. ( 2021 ) reported that autistic children with ADHD were more likely to refuse school due to bullying (68%) than autistic children without ADHD (28%) or no diagnosis (18%). In this study, an autism diagnosis or another diagnosis did not impact the frequency of school refusal due to bullying compared to children with no diagnosis. In contrast, Ochi et al. ( 2020 ) reported that bullying was significantly associated with school refusal in autistic boys and girls. In the sample of Bitsika et al. ( 2020 ), which consisted only of autistic boys, “being bullied explained more of the variance in emerging school refusal than did age, ASD-related difficulties (judged by their mothers), and self-reported anxiety and depression”. Eighty-five percent of the surveyed boys reported that they had been bullied at school, and 56% of them asked their parents if they could stay at home as a result of the bullying. Bitsika et al. ( 2021 ) identified in another sample from a previous study (Bitsika & Sharpley, 2016 ) the most common bullying experiences: being called mean names or being sworn at (experienced by 75.9% of the sample); being joked about or laughed at (67.2%); being hit, pushed, or kicked (63.8%); having had something taken from them (55.1%); being “ganged up on” (56.9%); and having been reported to teachers when they had not done things that were reportable (51.3%). A participant of Gray et al. ( 2023 ) talked about being bullied because he “didn’t know what they were going on about”.

McClemont et al. ( 2021 ) described another aspect of bullying: autistic youth with a behaviour support plan (BSP) were more likely to refuse school due to bullying than were those without. Anderson ( 2020 ) cited bullying as a factor that had a limited influence. The remaining factors outlined below exerted a more pronounced influence on absences.

School Type

Totsika et al. ( 2020 ) highlighted the significance of school type. The risk for persistent non-attendance increased by 104% when the autistic child attended a mainstream school, by 100% for total days absent, and by 79% for total number of days missed. Additionally, school exclusions were slightly more frequent in mainstream schools. Anderson ( 2020 ) also revealed a significant difference between school type and absence from school. School absence due to illness was the main cause of absence in primary schools for students with learning disabilities (83.8%), but the rate of absenteeism for reasons other than illness increased when students attended secondary schools for students with learning disabilities (36.3%). The results indicate that the rate of school absenteeism among autistic students in primary school is relatively high and increases when pupils start secondary school. Gray et al. ( 2023 ) revealed that the amount of support in schools varied “depending on knowledge, willingness to accommodate needs and carrying out advice and implementing statutory guidelines”.

In a study by Martin-Denham ( 2022 ), caregivers noted a lack of knowledge, skills, understanding, and funding in mainstream secondary schools. All participants noted that barriers to mainstream education occurred because the school staff was not adequately trained in supporting children with SEND. Similar factors regarding the school staff were mentioned by participants in the study by Gray et al. ( 2023 ): lack of understanding of autism, negative attitudes and problematic responses and interactions. Additional factors included a lack of flexibility regarding rules and homework on the one hand and unstructured times on the other hand. Not knowing the needs and not understanding the reactions of the autistic children led to school exclusions or exclusions from school events.

Anderson ( 2020 ) asserted that a lack of autism competence among school staff was the most common reason for children’s school absence.

School Environment

The second most common reason in the study by Anderson ( 2020 ) was the lack of adaptation of the school environment (24.3%), followed by a lack of support in learning (23.5%) and social situations (23.8%). Factors identified by Preece and Howley ( 2018 ) regarding the school environment include a lack of understanding and appropriate support, the size of the school, and the number of students because of sensory issues such as noise. Gray et al. ( 2023 ) also listed the sensory issues of participants. The number of people, large classrooms with bright light and unstructured times led to feelings of overwhelm or sensory overload. Contrary to reports recommending the use of safe spaces to support emotional regulation, Martin-Denham ( 2022 ) noted that schools were unable to implement them due to a lack of space. A different perspective regarding the learning environment was found in the study by Gray et al. ( 2023 ). Many young people described meltdowns when doing homework because of their need for “straight separation between school and home”.

Negative Experiences

The factors described above led in the study by Gray et al. ( 2023 ) to a feeling of being treated unfairly, “which made me just feel stressed and I just refused to engage [in school]”. The results of a parent and teacher questionnaire survey of autistic students by Munkhaugen et al. ( 2017 ) suggested avoiding specific subjects, conflicts with peers or teachers, and insufficient information concerning the subjects or activities in school as possible reasons for school refusal behaviour. In the study of Truman et al. ( 2021 ), parents described negative school experiences to be at least partly caused by a lack of understanding of autism. Some of these parents considered the reason for the misunderstanding in their child’s ability to mask their difficulties. The parents also indicated that ‘masking’ may be the reason why their children’s special needs were not adequately addressed. Others reported that home-education can reduce the anxiety of their autistic children: “All the stress of having to deal with the situations gone. Can now concentrate on learning and living” (Truman et al., 2021 ). A mother in the study by Martin-Denham ( 2022 ) described that anxiety due to a focus on negative aspects in school led to a desire to die: “You can see the anxiety, and when your son says he wants to die that is hard to listen to. So, every day he would come home with this planner and […] there would be no positives, […]. So, he felt down all the time”.

Parental Factors

Parental factors that exert an influence are parental unemployment and illness. In addition, demographic characteristics are closely linked to parental factors, such as living in a two-parent household or having educational qualifications. In most studies, demographic characteristics had no influence on school absenteeism (Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ). However, Totsika et al. ( 2020 ) reported an association between school exclusions and not living in a two-parent household: the risk increased by 37% to 75%.

Parental Unemployment

The risk of non-attendance increased by 52% to 78% if parents were unemployed. Fifty-two percent had persistent absence, 57% had total days missed, and 78% had days absent (Totsika et al., 2020 ). Adams ( 2021 ) even reported an increase of 85% when parents reported not having paid employment. On the other hand, parents in two studies (Gray et al., 2023 ; Martin-Denham, 2022 ) reported “having to give up” their jobs as a result of school absenteeism.

Illness of family members

In the study by Munkhaugen et al. ( 2017 ), illness of other family members was the only sociodemographic factor that showed a significant association with school refusal behaviour in autistic students. Similarly, Adams ( 2021 ) reported that the risk of school refusal increased by 20% as parental depression scores increased.

Supporting Factors

Mainly, four included studies (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ) identified several aspects to support re-engagement. The most mentioned aspects were the quality of interactions between teachers and autistic students, as well as between parents and teachers. The development of a flexible learning approach was identified as supporting, as well as incorporating the voice of the young person into their support plan and the opportunity to ask questions (Martin-Denham, 2022 ; O’Hagan et al., 2022 ). Autistic students valued a flexible and structured approach in support, as well as the opportunity to control their own learning and feel respected and listened to (Gray et al., 2023 ). Additionally, regarding the overall school environment, smaller group sizes and structures in the classroom and learning were mentioned, as well as being part of the school community and relationships with peers (Gray et al., 2023 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ). The consistent and effective collaboration and communication between parents and teachers were affirmed by both parents and teachers (Gray et al., 2023 ; Martin-Denham, 2022 ). Parents also claim that an earlier diagnosis contributes to school engagement (Martin-Denham, 2022 ; O’Hagan et al., 2022 ).

The risk and influencing factors of school absenteeism among students on the autism spectrum were systematically reviewed across 18 studies. Nine studies solely included parents as participants, one study additionally included teachers, and one other study additionally included autistic children. Three studies based their data on the children, parents and professional staff. Two studies used existing records.

The most common term for school absenteeism was school refusal, while studies have used different criteria for determining absences. Based on the frequencies of school absenteeism shown among different study designs, it is clear that this is a serious phenomenon that occurs among autistic students internationally. Several identified factors also showed similarities and complementarities within the included studies.

The school level predominantly exhibited the most significant factors influencing absenteeism. Bullying was the most frequently studied influence and the factor with the greatest impact.

Other factors at the school level that significantly influenced absenteeism included school type, school environment, and negative experiences. In five studies, bullying was found to be a risk factor for school absenteeism (McClemont et al., 2021 ; Ochi et al., 2020 ; Bitsika et al., 2020 , 2021 , 2022 ). All studies revealed significant associations between bullying and absenteeism. Being bullied can also lead to anxiety and depression up to suicidal attempts or ideation (Martin-Denham, 2022 ). Autistic students with school refusal had higher scores for major depression, general anxiety, and separation anxiety, as well as significantly greater levels of somatic symptoms and sleeping difficulties (Bitsika et al., 2022 ). Conversely, individual factors, including externalizing symptoms and reduced social motivation, also increase the risk for bullying (Karande, 2018 ). However, other studies that examined bullying, among other factors, found that these factors had greater influences on absences (Anderson, 2020 ; Gray et al., 2023 ). The influence of school type was mostly related to a lack of knowledge of school staff in mainstream schools (Anderson, 2020 ; Gray et al., 2023 ; Martin-Denham, 2022 ). This factor was accompanied by a lack of adapting to the school environment due to a lack of resources or support (Anderson, 2020 ; Gray et al., 2023 ; Martin-Denham, 2022 ; Preece & Howley, 2018 ). A lack of support and understanding in schools increases the risk of anxiety and depression (Martin-Denham, 2022 ). The results may suggest that these conditions are contributing factors because individual needs are not addressed. Therefore, it is necessary to explore in more detail what leads to bullying and which interactional processes take place. Similarly, more research focusing on interactive processes in schools is needed.

At the individual level, age, gender, diagnosis, intellectual level and psychiatric conditions were identified. Three studies reported on the influence of age (Mattson et al., 2022 ; Ochi et al., 2020 ; Totsika et al., 2020 ). Autistic students were younger at the onset of school absences, and the frequency of absences increased with age. These results are consistent with the increasing social demands at school age (Kamp-Becker & Bölte, 2021 ). However, there is a need for further investigation of the influence of age. Mediating variables associated with age must also be considered. Anderson ( 2020 ) showed that girls had greater rates of short absences than boys. There was no other statement regarding gender since most participants were boys. Recent surveys continue to show that autism is four times more prevalent in boys than in girls (CDC, 2020 ). Nevertheless, there is clearly a lack of research regarding autism in female students. Three studies showed that feeling different from others contributed to school absences (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ). This shows the need for education on neurodiversity in schools (Honeybourne, 2018 ). Since Kurita ( 1991 ) reported that autistic students who experienced school refusal tended to be more intelligent, studies have focused on autistic students without intellectual disability. Nevertheless, more current research on the school attendance of autistic children with intellectual disabilities would be desirable. Five studies acknowledged the contribution of psychiatric conditions to school absences (Adams, 2021 ; Gray et al., 2023 ; Lassen et al., 2022 ; Munkhaugen et al., 2019 ; Truman et al., 2021 ). All five studies found anxiety to be a contributing factor. Lassen et al. ( 2022 ) even found a stronger association with internalising symptoms such as anxiety than with externalising or autistic symptoms. As anxiety is one of the most common psychiatric conditions in autistic individuals, it can influence school outcomes (for more information, see the review of Adams et al., 2019 ). The influence of other psychiatric disorders should be investigated in more detail in further research.

The parental factors that influence school absenteeism are parental unemployment and illness.

Two studies (Adams, 2021 ; Totsika et al., 2020 ) revealed an increased risk when parents were unemployed.

On the other hand, parents reported having to quit their jobs due to the school absenteeism of their children (Gray et al., 2023 ; Martin-Denham, 2022 ). Due to the effects on the socioeconomic status of a family, a further link between these two aspects should be explored in further research.

The effect of the illness of a family member was also reported by two studies. Munkhaugen et al. ( 2017 ) found this to be the only sociodemographic factor with a significant association. However, Adams ( 2021 ) reported an increased risk when parents had high depression scores. Mental health issues in parents due to stress and guilt were also shown to result from school absenteeism in two studies (Gray et al., 2023 ; Martin-Denham, 2022 ). Parents mentioned that they also need support in regard to school absenteeism in their autistic children (Martin-Denham, 2022 ). Families benefit from organisations that support the family as well as the school (Martin-Denham, 2022 ). In particular, children with special needs are dependent on the support of their parents or other caregivers (Romero & Lee, 2007 ), emphasising the need to support them in dealing with their children and school. Research that involves further system levels is needed. Support for parents can also be provided through interaction between parents and schools. Successful collaboration between parents and schools has a positive impact on the school experience of autistic students (Lilley, 2019 ) (Fig.  4 ).

Interactions between factors

Missing school is claimed to be going hand in hand with missing important developmental steps for life in society (Pellegrini, 2007 ). However, as seen in the results, for children on the autism spectrum, there are also risks for development and mental health in schools, which need to be fixed to enable the development of autistic children in schools. Parents affirmed home-education as a possibility for their autistic children to “concentrate on learning and living” (Truman et al., 2021 ). The advantages and disadvantages of home-education could be further investigated, as school is an important area in the lives of children and adolescents. The results show that the interaction between parental and individual factors is necessary but has not been adequately investigated. None of the included studies investigated parental withholding. Nevertheless, this sensitive and complex phenomenon should be examined in future research.

The results show that the school situation of autistic children should be investigated further. There is a lack of longitudinal studies regarding the education and school situation of autistic students, as well as a lack of validated scales for data collection. Nevertheless, the actuality of the included studies indicates that there is a growing research base on this topic.

Limitations

The results of this systematic review must be classified within its limitations. Given the overall scarcity of research in this area, a research question was developed that yielded the broadest possible results while allowing us to draw consistent conclusions. Therefore, studies with different terms of school absenteeism and different study designs were included. In addition, school absenteeism must be viewed in the context of school and health care systems in each country. This diversity makes comparability difficult and was carried out by the researchers on the basis of a narrative synthesis. The synthesis might reflect the researcher’s interpretation of the data. Given the heterogeneity, it is possible that other studies included risk and influencing factors that were not identified. The specific influences of the COVID-19 pandemic were not considered. Due to limited resources, this review was conducted by only two researchers, which may have introduced limitations in the search strategy. Finally, only studies published in English or German were considered.

This systematic review provides a comprehensive summary of mainly recently published studies on the factors influencing school absenteeism among autistic students. Eighteen studies were included, each with a different research focus and study design. Taken together, the results provide a picture of the different influences at the individual, school and parental levels. Future research should incorporate other system levels as well as self-reports of autistic students and validated scales to draw conclusions for the inclusion of neurodivergent students.

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Sasso, I., Sansour, T. Risk and Influencing Factors for School Absenteeism among Students on the Autism Spectrum—A Systematic Review. Rev J Autism Dev Disord (2024). https://doi.org/10.1007/s40489-024-00474-x

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• Published: 06 September 2024

Glutathione peroxidase 3 is a potential biomarker for konzo

• Matthew S. Bramble   ORCID: orcid.org/0000-0003-1216-7134 1 , 2   na1 ,
• Victor Fourcassié 3   na1 ,
• Neerja Vashist 4 ,
• Florence Roux-Dalvai   ORCID: orcid.org/0000-0002-9961-8964 3 ,
• Yun Zhou   ORCID: orcid.org/0000-0002-6865-9480 1 ,
• Guy Bumoko 5 ,
• Michel Lupamba Kasendue 6 ,
• D’Andre Spencer 1 ,
• Hilaire Musasa Hanshi-Hatuhu 5 , 6 ,
• Vincent Kambale-Mastaki 6 ,
• Rafael Vincent M. Manalo   ORCID: orcid.org/0000-0001-5763-1637 7 ,
• Aliyah Mohammed 1 ,
• David R. McIlwain 8 ,
• Gary Cunningham 1 ,
• Marshall Summar 1 ,
• Michael J. Boivin   ORCID: orcid.org/0000-0002-0097-1777 9 ,
• Ljubica Caldovic   ORCID: orcid.org/0000-0002-9140-5585 1 , 2 ,
• Eric Vilain 10 ,
• Dieudonne Mumba-Ngoyi   ORCID: orcid.org/0000-0002-5886-2004 6 ,
• Desire Tshala-Katumbay   ORCID: orcid.org/0000-0003-0096-9471 6 , 11 &
• Arnaud Droit   ORCID: orcid.org/0000-0001-7922-790X 3  

Nature Communications volume  15 , Article number:  7811 ( 2024 ) Cite this article

Metrics details

• Diseases of the nervous system
• Motor neuron disease
• Neurotoxicity syndromes
• Predictive markers

Konzo is a neglected paralytic neurological disease associated with food (cassava) poisoning that affects the world’s poorest children and women of childbearing ages across regions of sub-Saharan Africa. Despite understanding the dietary factors that lead to konzo, the molecular markers and mechanisms that trigger this disease remain unknown. To identify potential protein biomarkers associated with a disease status, plasma was collected from two independent Congolese cohorts, a discovery cohort (n = 60) and validation cohort (n = 204), sampled 10 years apart and subjected to multiple high-throughput assays. We identified that Glutathione Peroxidase 3 (GPx3), a critical plasma-based antioxidant enzyme, was the sole protein examined that was both significantly and differentially abundant between affected and non-affected participants in both cohorts, with large reductions observed in those affected with konzo. Our findings raise the notion that reductions in key antioxidant mechanisms may be the biological risk factor for the development of konzo, particularly those mediated through pathways involving the glutathione peroxidase family.

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Introduction.

Konzo is a neglected and irreversible paralytic neurological condition that predominately affects the legs of children and women of childbearing age in sub-Saharan Africa. The occurrence of konzo outbreaks has been consistently associated with undernourished populations that have a chronic dietary reliance on cyanogenic cassava as their main source of food 1 . However, the biological factors that cause some individuals to develop the disease remain elusive. Konzo is characterized by a sudden onset of a non-progressive spastic paraparesis of varying severity, evolving as a mild (stage 1), moderate (stage 2), or severe (stage 3) form of neurodisability 1 . In addition to the physical manifestations, visual deficits and speech disturbances are commonly reported 2 . Recent studies have also reported cognition deficits which, however, were also documented in children unaffected with konzo within the same study population 3 . While epidemiological evidence indicates that this motor system disease is associated with poisoning from cyanogenic glucosides found in improperly processed cassava, not all individuals who rely on a cyanogenic diet are affected during outbreaks of konzo, suggesting additional and yet unknown factors required for onset of disease 4 , 5 .

The primary cyanogenic glucoside found in cassava, linamarin, liberates glucose and hydrogen cyanide (HCN) following hydrolysis in the gastrointestinal (GI) tract, presumably by the gut flora which harbors the necessary β-glucosidase enzyme 6 . Thus, reliance on a monotonous diet rich in cyanogenic glucosides effectively exposes populations to constant doses of cyanide (CN) to degrees that can greatly vary depending on environmental stressors such as drought 7 . While the molecular consequences of cyanide exposure and its role in arresting the electron transport chain during aerobic respiration have been extensively studied, the effects of long-term and chronic exposure to sub-lethal toxicity as observed in those residing in konzo-prone regions are less understood 8 . Because of the lethal potential of cyanide toxicity, most organisms have developed effective detoxification mechanisms that classically require sulfur donors, primarily sulfur-containing amino acids, to convert CN into thiocyanate (SCN), a putatively less toxic molecule 9 . However, the prevailing biological hypothesis for konzo development is that famine-stricken populations rely on diets that are deficient in the key sulfur amino acids methionine and cysteine, likely decreasing their ability to fully detoxify cyanide 10 , 11 . In addition, due to the inability to effectively detoxify CN, a deficiency of such amino acids has also been postulated to reduce a key antioxidant, glutathione, thereby hindering responses to the cyanide exposure-related oxidative stress, however, direct evidence of this mechanism has been scarce 12 , 13 .

Many studies have clearly demonstrated an increased incidence of oxidative damage either from reactive oxygen species (ROS) or peroxides in other neurological diseases including Parkinson’s 14 , Huntington’s 15 , Alzheimer’s 16 , and Amyotrophic Lateral Sclerosis (ALS) 17 . Since cyanide is a key trigger of konzo development, oxidative damage has also been suggested as a key mechanism of neuronal dysfunction in the pathogenesis of this disease 18 , 19 . This theory has been augmented by several studies that demonstrated increased markers of oxidative stress (by-products of non-enzymatic lipid peroxidation) in children suffering from konzo as well as documentation of deficiencies in micronutrients which are required to maintain effective antioxidant response in konzo-prone populations 18 , 19 .

Using multiple high-throughput proteomics assays in two independent cohorts of konzo-affected children compared to unrelated individuals (Discovery) or their unaffected sibling (Validation) respectively, we demonstrate that levels of the critical plasma-based antioxidant enzyme Glutathione Peroxidase 3 (GPx3) are significantly reduced in those affected. This finding is of particular interest, as genomic and proteomic differences in GPx3 have recently been identified in large cohorts of individuals affected with a similar motor neuron disease, ALS 20 , 21 . Additionally, we performed quantitative amino acid profiling of individuals from konzo-prone regions which suggests a deficiency in cystine, the precursor for the antioxidant glutathione, which is required by GPx3 to effectively remove ROS from circulation 22 . Collectively, these data have identified a biological difference between children who are discordant for konzo and emphasize the role of oxidative stress in neglected motor system diseases, effectively opening new venues for research into potential disease mitigation strategies.

Key proteomic differences between children discordant for konzo in pilot study

To determine if protein levels differed between children affected with konzo and unrelated/unaffected individuals, we carried out liquid chromatography-tandem mass spectrometry analyses (LC-MS/MS) of plasma samples collected from 60 individuals (31 affected, 29 unaffected) from the Kahemba region of the DRC in 2011 (Discovery Cohort). In an untargeted analysis, we identified a high degree of homogeneity across all sample groups at the global scale (Fig.  1A ) However, when assessing individual protein differences where a specific protein signal had to be present in at least 75% of the participants, we showed that 28 proteins were significantly increased, and 5 proteins significantly reduced in children affected with konzo versus unrelated/unaffected individuals (q < 0.05, Welch t-test) (Supplementary Fig.  1A, B , Supplementary Data  1 ). Of the 3 proteins that had a significant differential abundance and a log2 transformed fold change (log(FC)) greater than 0.5, Glutathione Peroxidase 3 (GPx3) displayed the largest signal reduction in affected children (q < 0.01 Welch t-test, log(FC) = −0.88) (Fig.  1B ). Using the same criteria, we found that the variable region of an Immunoglobulin heavy chain (IGHV3OR16-12) displayed the highest signal increase in affected children; however, the functionality of the protein is not well established (Fig.  1B ).

Blood plasma proteome from 29 children affected with konzo (orange) and 31 unaffected (blue) were analyzed by untargeted and targeted LC-MS/MS analyses. A Principal component analysis from the untargeted analysis with ellipses drawn at the 95% confidence interval level for each group. B Untargeted analysis representation of log2 of protein intensity ratio versus -log10 of two-sided Welch t-test p -value. Significant proteins (Benjamini–Hochberg adjusted p -value and log2(fold-change) thresholds) are highlighted in blue when they were less expressed in konzo or in red when overexpressed in affected children. C Left: Untargeted analysis sparse partial least square discriminant analysis (sPLS-DA). Right: Top loadings of PC1 and PC2 are highlighted in bar plots where the weight of each variable contribution is represented along the x-axis. D Targeted analysis boxplots represented using log2 transformed intensities of some of the regulated proteins selected based on the results from the untargeted analysis sPLS-DA component 1 ( n  = 29 konzo individuals, n  = 31 unaffected individuals). Two-sided paired Welch t-test p -values are represented above the plots. For each box, data is illustrated as follows: an inner line to represent the median, lower edge and upper edge for the 25 th percentile and 75 th percentile and whiskers extends from the hinges to either the highest or lowest value within a maximum of 1.5 times the interquartile range (IQR) from the hinge. Data points beyond the IQR are considered as outliers and represented as dots. Within the box plots, orange represents measures obtained from children affected with konzo and blue denotes measures obtained from their siblings who are unaffected with konzo.

To further expand potential protein targets of interest in this cohort, we performed a supervised analysis (Sparse PLS Discriminant Analysis (sPLS-DA)) to determine if factors other than GPx3 may be relevant to the biology of konzo. The approach showed that groups with and without konzo had sample distributions with minimal overlap in their confidence ellipses as well as high predictive value regarding group prediction (Fig.  1C , Supplementary Fig.  1B ), albeit with small contributing percentages along the primary and secondary component axis (Fig.  1C ). In addition to GPx3, we found other proteins that are largely involved in inflammation, immune response and metabolic disease such as CD44, Afamin (AFM), Alpha-1-acid glycoprotein 1 (ORM1) and Kininogen-1 (KNG1) were the most influential contributors to the differences between children affected with konzo and those unaffected, identifying additional targets that may warrant investigation for pathogenetic relevance to konzo (Fig.  1C ). Considering the important role of GPx3 as the only known plasma-based antioxidant enzyme of the Glutathione Peroxidase family, and the relevance of this pathway for neurodegeneration, we next sought to confirm these finding using a targeted LC-MS/MS acquisition. We also targeted peptides of the 38 other proteins that displayed a statistically significant difference, along with those identified through the sPLS-DA analysis that served as important contributors to distinguish groups. Using this targeted acquisition, we found that of the 39 proteins targeted, 25 displayed significant differences in abundance (Fig.  1D , Supplementary Fig.  1C , Supplementary Data  1 ) with GPx3 still displaying a significant reduction in protein signal (Welch t-test q = 0.01, log(FC) = −0.35) in the konzo-affected children, adding additional layers of confidence to the observations of the untargeted analysis (Fig.  1D ).

GPX3 is significantly reduced in affected siblings who are discordant for konzo disease

To validate our findings from our pilot/discovery study we conducted a new LC-MS/MS untargeted analysis on a larger cohort ( n  = 204) of siblings discordant for konzo (Validation Cohort). Using a more sensitive mass spectrometer than for the previous study, operating in Data Independent Acquisition (DIA) for high throughput deep proteome coverage, we found that GPx3 was the only protein that showed a large signal reduction in children affected with konzo (Welch t-test q < 0.05, log(FC) > 0.5) (Fig.  2A , Supplementary Data  1 ). When comparing GPx3 signal intensities between affected children versus their unaffected siblings, we found that while individuals with stage 1 of the disease had a significant reduction of the GPx3 signal intensity when compared to their unaffected siblings (Welch t-test q < 0.05), this reduction was the most modest of the stage comparisons (log(FC) = −0.30) (Fig.  2A ). We also found that children with stage 2 or 3 of the disease had more dramatic reductions of GPx3 signal relative to their unaffected siblings (Welch t-test q < 0.05, log(FC) = −0.88 and −0.66, respectively) (Fig.  2A , Supplementary Data  1 ).

Plasma from unaffected children (blue) and their siblings with different stages of konzo disease (orange). A Untargeted analysis protein volcano plot for all stages combined or for each stage independently. Statistical significance of Glutathione Peroxidase 3 (GPx3) regulation was observed in all comparisons (two-sided paired Welch Benjamini–Hochberg adjusted p -value < 0.05) and a log2Fold change greater than 0.5 was observed for all groups except for Stage 1 comparisons. 204 samples were analyzed with 63 sibling pairs for stage 1, 11 pairs for stage 2 and 28 pairs for stage 3. B Left: Stage 3 sPLS-DA for the untargeted analysis. Right: Top protein contributors on component 1. C StringDB GPx3 interaction network of high confidence experimental interactors identified in the untargeted analysis. D Targeted analysis boxplot representation of log2 transformed protein intensities from the 3 monitored proteins (Glutathione Peroxidase 3 and SelenoProtein1) for each stage (K1/U1 for stage 1 with 62 sibling pairs, K2/U2 for stage 2 with 11 pairs and K3/U3 for stage 3 with 27 pairs). Two-sided paired Welch t-test p -values are represented above the plots. For each box, data is illustrated as follows: an inner line to represent the median, lower edge and upper edge for the 25th percentile and 75th percentile and whiskers extends from the hinges to either the highest or lowest value within a maximum of 1.5 times the interquartile range (IQR) from the hinge. Data points beyond the IQR are considered as outliers and represented as dots. Within the box plots, orange represents measures obtained from children affected with konzo and blue denotes measures obtained from their siblings who are unaffected with konzo.

Like the approach used for the discovery cohort, we next searched for additional proteins that may be indicative of differences in the same cohort using (s)PLS-DA analysis. We found that GPx3 remains one of the most important proteins to discriminate children living with konzo from those not affected, using this supervised model for group clustering, regardless of disease stage severity (Fig.  2B , Supplementary Fig.  2A ). When comparing children with stage 1 of konzo with their unaffected siblings, this supervised method identified Ceruloplasmin (CP) and Protein C (PROC) as important classifiers along the component 1 axis (Supplementary Fig.  2A ). When comparing stage 2 siblings, it was identified that Ceruloplasmin (CP), Butyrylcholinesterase (BCHE) and Complement component 7 (C7) were the main additional proteins that aided in differentiating between those affected from those unaffected (Supplementary Fig.  2A ). Lastly, when comparing children with stage 3 konzo with their unaffected siblings, we found that in addition to GPx3, Zinc-alpha-2-glycoprotein (AZGP1) and Carboxypeptidase B2 (CPB2) were major contributors to group segregation, with CP protein also being identified as being a potential contributor (Fig.  2B ).

Among the stage 3 comparisons for the sPLS-DA component 1 proteins, we also observed that one of the top-ranking proteins was Selenoprotein P (SELENOP), one of the few GPx3 interactors detected experimentally (Fig.  2B ). Considering that SELENOP is also involved in the defense against oxidative damage and had a q-value that was second (Welch t-test q = 0.08, log(FC) = −0.66) to GPx3 in this group comparisons using an untargeted approach, we sought to monitor peptides of both of these proteins by targeted LC-MS/MS (Supplementary Fig.  2B , Supplementary Data  1 ). We found that regardless of disease stage, GPx3 was significantly reduced in affected children (Welch t-test p  < 0.05) (Fig.  2D ), validating the findings obtained using the untargeted LC-MS/MS acquisition. We determined through this target approach that SELENOP was also significantly reduced in children with all three stages of konzo (Welch t-test p  < 0.05) (Fig.  2D ) and displayed a strong positive correlation with GPx3 signal intensity (Pearson correlation coefficient (r) = 0.9, p  = 2.2 × 10 −16 ) (Supplementary Data  1 , Supplementary Fig.  2C ).

Secondary analysis using an ELISA based approach confirms GPx3 is substantially reduced in children affected with konzo

To validate the findings obtained using proteomic-based applications, we sought to determine if the reduced abundance of GPx3 protein in konzo affected children as compared to their unaffected siblings could also be detected using an orthogonal ELISA-based approach. When assessing a large subset of konzo affected children ( n  = 87) regardless of stage versus their unaffected siblings ( n  = 87), we detected a significant reduction of GPx3 in those affected ( p  < 0.0001, Paired t-test) with non-overlapping 95% confident intervals (95% CI Konzo [1.45 µg/ml, 1.78 µg/ml], 95% CI Unaffected [2.04 µg/ml, 2.35 µg/ml]) (Fig.  3a , Supplementary Data  1 ). These findings appear to be a result of disease and not other variables such as age or biological sex, as no significant difference in biological sex distribution within the cohort was found (Student’s t Test, p  = 0.98) (Supplementary Data  1 ). When GPx3 was correlated to age, we also found a poor correlation exists between these two variables (Pearson correlation coefficient (r) = 0.17, p  = 0.02), indicating that age is not strongly influencing the differences of GPx3 abundance in this sibling cohort (Supplementary Fig.  3D ). When separating by disease severity, we found that children with stage 1 of the disease had a significant reduction in GPx3 as compared to their unaffected siblings ( p  < 0.001, Paired t-test), with very minimal overlap in the 95% confidence intervals (95% CI Stage 1 [1.78 µg/ml, 2.19 µg/ml], 95% CI Unaffected [2.17 µg/ml, 2.62 µg/ml]) (Fig.  3b ). For those with stage 2 of konzo, the difference between siblings was again significant ( p  < 0.01, Paired t-test), with minimal overlapping 95% confidence intervals of [0.87 µg/ml, 1.52 µg/ml] for those affected with konzo as compared to [1.52 µg/ml, 2.10 µg/ml] for their unaffected siblings (Fig.  3c ). While measuring GPx3 levels in children with the most severe form of konzo, stage 3, we again observed a significant difference in protein levels ( p  < 0.0001, Paired t-test), however these differences were the most dramatic between the sibling comparisons. We find that those affected with stage 3 have the lowest overall levels of GPx3 and display non-overlapping 95% confidence intervals as compared to their unaffected siblings (95% CI Stage 3 [0.93 µg/ml, 1.36 µg/ml], 95% CI Unaffected [1.78 µg/ml, 2.23 µg/ml]) (Fig.  3d ). Given the smaller sample size of children with Stage 2, we next combined the most severe stages (2 and 3) and assessed differences in GPx3 concentration, to increase statistical confidence. Our findings, mirror the differences of Stage 2 or Stage 3 siblings when independently measured and show dramatic and statistically significant reductions of GPx3 in the most severely affect children ( p  = 2.47 × 10 −8 , Paired t-test) again with non-overlapping 95% confidence intervals (95% CI Unaffected Stage 2/3 [1.7 ug/ml, 2.1 ug/ml], 95% CI Konzo Stage 2/3 [0.98 ug/ml, 1.3 ug/ml]) (Fig.  3e ).

A general distribution visualization and box plot representations of Glutathione Peroxidase 3 (GPx3) plasma concentration (ng/ml) as measured by human GPx3 ELISA for a Unaffected controls (Unaff Sib) by all konzo (Konzo Sib) stages combined (88 Unaffected, 88 Affected), b Sibling comparisons for Stage 1 konzo (48 Unaffected, 48 Affected), c Sibling comparisons for stage 2 konzo (11 Unaffected and 11 Affected), d Sibling comparisons for Stage 3 konzo (28 Unaffected, 28 Affected) and e Sibling comparisons for Stage 2 and 3 combined (39 Unaffected and 39 Affected). 95% confidence intervals are highlights in dashed lines with corresponding colors to distinguish the unaffected group or those affected with konzo. Statistical differences were calculated using a two-sided paired-t test of the average GPx3 concentration (ng/ml) of the duplicate runs for each sample for Stage 1, 2, and 3 and a T-test of the mean of each sample for unaffected by affected comparisons and for the comparison assessing stages 2 and 3 combined ( A and e respectively). For each box, data is illustrated as follows: an inner line to represent the median, lower edge and upper edge for the 25th percentile and 75th percentile and whiskers extends from the hinges to either the highest or lowest value within a maximum of 1.5 times the interquartile range (IQR) from the hinge. Data points beyond the IQR are considered as outliers. The diamond within the box plots represents the data mean for that measure and data point measures can be found in the Data Source File.

When comparing the GPx3 intensity values obtained through mass spectrometry to the quantified values of GPx3 as measured by ELISA for the same individuals, we observe a moderate to strong correlation (Pearson correlation coefficient (r) = 0.64, p  < 2.2 × 10 −16 ), indicating that both approaches yield similar conclusions of differential protein abundance (Supplementary Fig.  3A ). We next sought to determine how well the ratio of GPx3 abundance/signal between matched discordant siblings (unaffected/affected sibling) correlated between both approaches. We find that a moderate correlation exists between ELISA ratios and Mass Spectrometry intensity ratios for sibling pair differences (Pearson correlation coefficient (r) = 0.55, p  < 4.9 × 10 −8 ) (Supplementary Fig.  3B ). However, if 2 sibling pairs that display differences in the same direction just to larger degrees depending on method used are removed from analysis, the correlation of ratios between methods substantially increases (Pearson correlation coefficient (r) = 0.75, p  < 2.7 × 10 −16 ) (Supplementary Fig.  3C ), indicating a good agreement between mass spectrometry and ELISA measures, particularly regarding sibling pair differences.

Because a risk factor for severe konzo disease could cluster in families, we asked whether GPx3 levels might vary between families with different stages of konzo disease, even for unaffected siblings. When comparing unaffected children with a stage 1 sibling versus unaffected children with a stage 2,3, or combined (2 and 3) siblings, we observe a significant difference for all three ( p  < 0.05, t-test), but for each comparison of unaffected children there are larger overlaps in the 95% confidence intervals (Supplementary Figs.  4 A, 5 A, 4 B, 5B and 4 D, 5D respectively). However, when assessing GPx3 protein levels of unaffected children with a stage 2 sibling compared to unaffected children with a stage 3 sibling, we find no significant difference ( p  = 0.3, t-test) (Supplementary Figs.  4 C, 5C ). Thus, GPx3 levels trend lower in families affected by more severe konzo disease, even for unaffected siblings.

When measuring the GPx3 levels for children with stage 1 of konzo compared to stage 2, we found a significant decrease in protein abundance for those affected with the moderate stage of disease as compared to the less severe stage ( p  < 0.01), with stage 1 having a higher lower limit of the 95% confidence interval (1.78 µg/ml) than the upper 95% CI (1.52 µg/ml) of stage 2 (Supplementary Figs.  4e , 5e ). The most dramatic difference is observed when measuring protein levels between stage 1 and stage 3 konzo, where the most significant differences are observed with the lower limit of the 95% CI for unaffected siblings being 1.78 µg/ml being as compared to the upper limit of 1.36 µg/ml for the 95% CI for those with stage 3 (Supplementary Fig.  4F , 5F ). Like what we observed for the unaffected children, we found that overall GPx3 levels between stage 2 and stage 3 are not significantly different ( p  = 0.79) (Supplementary Figs.  4G , 5G ). Overall, these findings indicate that reductions in GPx3 abundance trends with disease severity with stage 1 having the highest mean levels of GPx3 (1.99 µg/ml ± 0.73 µg/ml) and the highest K1/U1 ratio (0.86), followed by the stage 2 with a mean of 1.2 µg/ml ± 0.54 µg/ml and average K2/U2 ratio of 0.67, with the most severe form of konzo, stage 3, displaying the largest reduction in average protein abundance (mean: 1.15 µg/ml ± 0.58 µg/ml) and the lowest K3/U3 ratio of 0.59 (Supplementary Data  1 ). When combining the most severe forms of konzo (Stages 2/3) as compared to the milder form (Stage 1) we again see a strong statistical difference ( p  < 0.0001) with non-overlapping 95% confidence intervals (Stage 1 95% CI [1.78ug/ml, 2.1ug/ml], Stages 2/3 95% CI [0.98 ug/ml, 1.3 ug/ml) (Supplementary Figs.  4 H, 5H ).

Amino acid profiling siblings discordant for konzo identifies deficiencies in key sulfur amino acids and suggests mitochondrial distress

To determine if siblings discordant for konzo had major differences in amino acid profiles which may contribute to changes in GPx3 abundance, we used an HPLC-based approach to quantify plasma amino acids in samples from study participants. Regardless of disease status, the plasma levels of the 9 essential amino acids are suggestive of a protein deficiency (Fig.  4 ). Particularly, we found that mean plasma levels of Valine (U:140.9 µmol/L, K:127.8 µmol/L), Lysine (U:109.9 µmol/L, K:95.7 µmol/L), Threonine (U:67.5 µmol/L, K:57.9 µmol/L) and Tryptophan (U:2.9 µmol/L, K:2.3 µmol/L), fell below the expected ranges for healthy adolescents (UCSF Clinical Core Accepted Reference Ranges) for both those affected with konzo and their unaffected siblings. Average plasma levels of other essential amino acids such as Leucine (U:82.3 µmol/L, K:75.5 µmol/L), Isoleucine (U:43.0 µmol/L, K:37.1 µmol/L) and Histidine (U:75.8 µmol/L, K:74.9 µmol/L) were also considered at the very low limits for healthy adolescents, except for Phenylalanine (U:37.6 µmol/L, K:43.3 µmol/L) and Methionine (U:18.7 µmol/L, K:16.9 µmol/L), both of which fall within normal expected values, on average, regardless of disease status (Fig.  4 , Supplementary Data  1 ).

Boxplot representations of unaffected ( n  = 102) and konzo affected ( n  = 102) children’s amino acids (µmol/L) that were most relevant to the study. Panel a displays Valine levels, b Lysine levels, c Threonine levels, d Tryptophan levels, e Leucine levels, f Isoleucine levels, g Histidine levels, h Phenylalanine levels, i Methionine levels, j Cystine levels and k Alanine levels. Black dots represent samples that fell within the normal range of the particular AA based on UCSF clinical core standards and red dots, represents samples that fell outside of the accepted normal values. Blue shaded box plots represent measures obtained from unaffected siblings while orange shaded box plots represents measures obtained from children affected with konzo. Statistical differences were based on a two-sided t-test outcomes of amino acid means for each sample and compared unaffected groups to affected individuals. For each box, data is illustrated as follows: an inner line to represent the median, lower edge and upper edge for the 25th percentile and 75th percentile and whiskers extends from the hinges to either the highest or lowest value within a maximum of 1.5 times the interquartile range (IQR) from the hinge. Data points beyond the IQR are considered as outliers. Data presented in Fig.  4 can be found in the Data Source File.

When assessing non-essential amino acid profiles, we found that on average, the vast majority fell within healthy reference ranges for adolescents (Supplementary Fig.  6 , Supplementary Data  1 ). However, we found that both Alanine and Cystine substantially fell outside of healthy reference ranges, with Cystine being dramatically reduced in both affected and unaffected siblings with average plasma concentrations of 3.0 µmol/L and 3.3 µmol/L respectively, while Alanine was found to be elevated regardless of disease status (U:595.5 µmol/L, K:589.0 µmol/L). When determining the Alanine to Lysine ratio as an indirect measure of mitochondrial distress we found that unaffected children had an average ratio of 5.8 and those with konzo have an average ratio of 6.9, both of which are substantially higher than the expected ratio of less than 3 for healthy adolescents (Fig.  4 ). Collectively, regardless of disease status, this cohort of children from a konzo-affected region show signs of protein malnourishment, harbor measures of mitochondrial distress, and deficiencies in a key sulfur-containing amino acid (Cystine).

Although konzo was first described in 1938, the biological mechanisms that contribute to the development of this disease have remained elusive 23 . Studies have continually demonstrated a strong link between a dietary reliance on cyanogenic cassava coupled with general malnutrition, however, these risk factors are present in many regions of sub-Saharan Africa where konzo does not occur 5 , 24 , 25 . In konzo-prone regions, particularly the Democratic Republic of the Congo, this disease appears to affect children and women of childbearing ages at a much higher frequency than adults 1 . While the exact location of the neurological injury has yet to be identified since MRI (0.5 T) assessments conducted in 1993 on 2 konzo-affected individuals reported no pathological findings, the phenotypic presentation of various severities (Stages 1–3) of hyperreflexia and non-spastic paraparesis suggests damage of motor neurons and corresponding brain regions; conclusions supported by cranial magnetic stimulation procedures 26 , 27 , 28 . Investigations have sought to identify biological differences between affected and non-affected children residing in prone regions, however, the vast majority of said studies have found that little differences exist between groups regardless of disease status. When searching for potential biomarkers of disease status, it was determined that individuals with konzo did have a higher incidence of albumin carbamoylation, a post-translational modification likely induced by increased exposure to a toxic byproduct of linamarin degradation, cyanate 29 . While markers of cyanide and cyanate exposure can serve as reliable indicators of acute toxin exposure, there remains a lack of mechanistic understanding of physiological differences that contribute to of disease susceptibility.

To expand the search for biological influences of disease, we sought to investigate if major protein differences were present in children affected with konzo as compared to unaffected individuals. In an initial pilot study using high throughput proteomics applications on plasma samples collected from 29 affected children and 31 unrelated and unaffected children in 2011, we identified several proteins of interest that showed statistical differences between the groups. However, using a more stringent inclusion criteria, we identified one well-characterized protein of interest that was both significantly altered in abundance and displayed a large reduction in signal for those affected with konzo, Glutathione Peroxidase 3 (GPx3). While this protein was an interesting target warranting further investigation, given the smaller sample size and duration post sample collection, we sought to determine if these findings were replicable in a larger and more recent study cohort. Accordingly, in 2021 we acquired plasma from one hundred children affected with all three stages of konzo along with their discordant sex-matched siblings and utilized the most up-to-date mass spectrometry analysis to assess potential protein differences. Interestingly, GPx3 was identified as the sole protein with the most significant reduction in signal for children affected with konzo, a finding that was further validated using ELISA as a secondary method of confirmation.

Glutathione peroxidase 3 belongs to the conserved family of Glutathione Peroxidases and is the only member that is plasma-based, with the remainder of the 7 known GPx enzymes being localized intracellularly 30 . In humans, this enzyme family serves as one of the critical antioxidant mechanisms, readily scavenging reactive oxides and peroxides that are naturally or exogenously produced, protecting cells and structures from oxidative damage 31 . GPx3 is also one of the 25 known selenoproteins in humans, which harbor a unique selenocysteine found in the active site that utilizes glutathione as the primary reducing agent to remove oxidative compounds 31 , 32 . Therefore, the functionality of this class of enzymes along with other selenium dependent enzymes could be influenced by dietary selenium and adequate levels of glutathione production. In fact, a prior study demonstrated that children residing in konzo-prone regions of the DRC had a collective micronutrient deficiency that trended with disease severity, but such deficiencies are commonplace in populations across sub-Saharan Africa including those with low incidence of konzo 18 . While our study design did not allow for a direct selenium measure to be accurately determined, we sought to utilize a targeted acquisition to determine if other selenium-containing proteins were also reduced in abundance as observed for GPx3. When focusing on Selenoprotein P (SELENOP), a primary selenium transport protein we found that individuals affected with konzo as compared to their unaffected sibling yielded no significant difference in protein signal in an untargeted analysis. However, when SELENOP was directly targeted, we found that children with all stages of the disease did show a statistically significant reduction in signal as compared to their unaffected sibling. Interestingly, these two plasma-based selenoproteins exhibited a strong positive correlation in regards to protein signal acquired via mass spectrometry, findings that are similar to studies assessing the same measures in both Chinese 33 and Swedish cohorts 34 , where a strong correlation of SELENOP and GPx3 existed. Collectively, this raises the notion that the observed differences here are perhaps related to long-term selenium intake, a micronutrient that has been speculated to play a role in konzo pathogenesis, due to its essential requirement for maintaining antioxidant machinery 18 . However, since these trends are observed in the sibling study cohort who share a household, overall lifestyle, and likely comparable access to dietary selenium this finding warrants a deeper investigation into the actual causes of these protein differences between siblings discordant for konzo. Particularly, since at time of collection, this cohort appear to harbor overall similar levels of plasma-based amino acids, regardless of disease status or disease severity, indicating minimal differences in nutrition status. An important notion regardless of these and others findings is that all food-stricken populations including those in konzo-prone regions, are likely to have insufficient micronutrient levels that would certainly affect their inherent antioxidant capacities, putting large percentages of children living in developing countries at heightened risk for oxidative damage 35 , 36 .

The notion that oxidative damage likely influences konzo susceptibility has been long theorized since this disease is associated with the consumption of cyanogenic compounds that are well known to increase radicals either through direct inhibition of mitochondrial respiration or through toxicity of the downstream metabolites of cyanide 12 , 18 . Despite such a likely connection, only one study to date has assessed differences in markers of oxidative damage in children affected with konzo, where increased levels of 8,12-iso-iPF2α-VI isoprostane in affected children was associated with deficits in neurocognition, but not deficits in motor impairment 3 . Despite scarce data in relation to oxidative damage in children suffering from konzo, the role of oxidative damage in many other neurologic diseases has been well established 37 . Particularly, it was recently demonstrated in a large multiethnic study that the GPx3 loci was strongly associated with a similar motor neuron disease, ALS, perhaps identifying a risk locus for neuronal disease susceptibility 20 , 38 . Other groups found similar outcomes that linked the GPx3 locus to ALS but were also able to demonstrate that lower GPx3 levels strongly correlated with more advanced disease, in addition to showing significant motor deficits in a knockdown zebra fish model 21 . Interestingly, in our sibling cohort we also observe that children with the most severe form of konzo (stage 3), have on average the lowest GPx3 levels as well as the largest difference when compared to unaffected siblings. Other groups have also raised the possibility that genetic variants of GPx3 can alter gene expression and have associated certain variants with numerous diseases, including but not limited to gastric cancer 39 , schizophrenia 40 and arterial ischemic stroke 41 . This raises the notion that perhaps within konzo-prone regions of sub-Saharan Africa there may be genetic variants of GPx3, or genes related to associated pathways that circulate which could alter gene expression and subsequently cause a reduction of protein abundances in these populations. Given the decrease in GPx3 that we observe in konzo affected children, a deeper investigation of possible genetic predispositions is thus warranted, as this has also been a long-standing theory within the research community.

Collectively, these findings represent the first biomarker/molecular difference that has been found to be different between children affected with konzo as compared to unaffected individuals and may serve as a biomarker for not only disease, but to identify children that harbor increased risk for disease susceptibility and severity. Considering the notion has been raised that sub-clinical presentation of konzo exists, these measures may allow for the identification of individuals who show similar reduced antioxidant capacity as those with konzo, but who do not show a visible physical disability, enabling the overdue refinement of the clinical features associated with this disease 3 , 42 . As this is the first study to assess such measures, the predictive value of these markers will become even more solidified as future studies are completed and similar protein concentrations can be compared on new and even larger populations from the DRC, as well as konzo affected communities in other countries of sub-Saharan Africa. However, many lines of evidence indicate neuronal sensitivity to oxidative damage; thus it seems highly likely that these processes occur in konzo, a motor neuron disease triggered in part by the consumption of toxic compounds that are well established to increase oxidative stress 43 . Given that we have established selenoproteins such as GPx3 and SELENOP to be substantially reduced in those affected with disease using multiple applications and cohorts, perhaps a reduction in the ability to effectively manage oxidative damage is a leading factor for disease susceptibility and severity, when all other risk factors have been met, which are typically present even in those without konzo. Findings from the discovery cohort also identified numerous proteins that are associated with acute inflammation, redox balance and immune responses such as ORM1, ORM2 and Afamin (AFM) that were elevated in konzo affected children, perhaps identify additional future targets of investigation 44 , 45 , 46 . Considering an inability to manage food-induced oxidative stress would damage tissue and cells, an elevation of immune response that altered inflammation markers also seems highly likely 47 . While these specific proteins were not significantly elevated in the sibling validation cohort, computational predictions based on (s)PLS-DA analysis did identify proteins associated with immunity and inflammatory responses such as Zinc-alpha-2-glycoprotein (AZGP1) and Carboxypeptidase B2 (CPB2) as being important contributors to group separation 48 , 49 , 50 . So, additional investigation is necessary to link these potential associations to konzo pathogenesis, particularly in relation to time of disease onset, as such markers may only be present during early phases of konzo onset when inflammation or neural damage is likely highest.

In addition to a reduction of GPx3 levels, we also observed in our sibling cohort a large deficiency in cystine, the limiting factor to produce glutathione, which is required as a reducing agent for GPx3 and the entire glutathione peroxidase family 30 , 51 . While a sulfur containing amino acid deficiency in konzo-prone regions has been speculated for several decades 11 , this is the first quantitative evidence that demonstrates a cystine deficiency within these populations, regardless of disease status. Given a reduction in both GPx3 and a likely reduction of glutathione production, as has been theorized by others in the field of konzo 13 , the role of oxidative damage and mitigation as a contributing factor of disease susceptibility or severity is only further bolstered by the findings of this study. Theoretically, deficiencies in GPx3 and cystine reduce the ability of the body to counter oxidative stress by virtue of the glutathione pathway and associated enzymes, which may lead to increased ROS in the circulation. Given that cyanide exposure from linamarin is strongly associated with the development of konzo, an increase in plasma oxidative stress may also affect cyanide action or metabolism, potentially amplifying its capacity to induce mitochondrial stress or giving rise to its oxidized form cyanate, which is a strong neurotoxin in various experimental models that can further decrease glutathione levels in the CNS 52 , 53 . Therefore, based on these and other findings, it should become a priority to begin offering nutritional intervention with a focus on cystine/sulfur containing amino acids and micronutrient supplementation to konzo-prone communities. While changing food related behaviors has always proven difficult, particularly in impoverished communities, a precision nutritional approach focused on increasing the inherent ability to mitigate oxidative stress may be a practical solution—as the global reliance on cassava is poised to only increase in the coming decades, particularly in light of climate change.

While this finding is of great interest to the field of konzo as well as other neurodegenerative diseases, we cannot determine if the reduction in GPx3 is a result of disease or an inherent contributing factor for disease that is triggered by a micronutrient deficiency or underlying genetic variability. Furthermore, the increased reduction in the abundance of GPx3 in unaffected siblings of children with stage 3 as compared to children with a stage 1 sibling with the disease suggests that GPx3 levels may serve as an indicator for individuals within one household most at risk for developing the severe form of disease. As such, it is apparent that additional experimentation focusing on genomics, environmental/nutritional components and their crosstalk is warranted to determine the causal factor(s) that reduce both cystine and selenoprotein levels in children affected with konzo to unravel the biological mechanism behind this disease.

Ethics statement

Prior to any specimen collection, community consent was first obtained from village leaders. Informed and written consent was then obtained from the Chef de zone/Médecin de zone, who represents the interests of the ministry of health and individuals in the study population. Upon approval and consent by the representatives, verbal and/or written consent was obtained from the parent and/or guardian of the children who participated in the study. Verbal consent was obtained when there were limitations with literacy and the individual expressed a general disinclination to signing written documents that cannot be read and fully comprehended by them. Participants were compensated for their travel expenses to the study site and small monetary and food donations were also provided to the families that participated in this research. The study posed no harm to participants, and participants could chose to not donate samples. The study was approved by the IRB review board at the Oregon Health & Science University (OSHU) (IRB FWA00000161) and by the Ministry of Health of the Democratic Republic of the Congo (DRC).

Inclusion and ethics statement

The study presented here, has been approved and annually reviewed by the ethics board of the Ministry of Health of the Democratic Republic of the Congo (DRC) and the IRB board of the Oregon Health and Sciences University. Additionally, this research was also invited and approved from the regional administration of the Kahemba health zone, where the individual’s assessed in this study reside. As the research presented in this manuscript is highly relevant to the local communities of the DRC, input from the local physicians, scientists and students who are familiar with konzo have been instrumental to the successful outcome of this study. Experts on Konzo who are based at the University of Kinshasa and Institute National de Research Biomedical (INRB) helped design and execute the research activities presented in this manuscript, in collaboration with the large group of co-authors who aided in research activities related to this work. Projects related to unraveling the biological mechanisms of konzo susceptibility have been ongoing and led in the DRC by Dr. Desire Tshala-Katumbay for over 20 years and as such, capacity building and long-term solutions have been at the forefront of all research related activities in the DRC. In addition, any biological material that were collected from DRC-based individuals remains bio banked at the INRB in Kinshasa and managed under the discretion of that local institute, to insure research related to konzo is held at the highest standard when foreign counterparts are involved. The research teams who are authors on this manuscript have been working closely together for over 7 years, and research roles, responsibility and authorship topics are clearly discussed early in research planning, and we strive to be as inclusive as possible, to attribute credit where it is due. As this team is very familiar with conducting research in limited-resource settings that deal with vulnerable populations, safety to both the participants and researchers were and continue to also be a top priority. As such, we strive to prevent any stigmatization or discrimination of individuals affected with konzo during our research studies. As the community of Kahemba is very familiar with the ongoing research efforts to prevent konzo, these studies may even help de-stigmatize individuals with konzo through determining the biology behind this disease all while attempting to draw global attention to this neglected disease. Considering Konzo is an Afro-centric disease that affects very isolated communities, global collaborations are necessary to utilize the most advanced scientific technologies to conduct high-throughput measures as presented in this work. As such, the majority of citations referenced in this manuscript originate from local researchers in the DRC and other sub-Saharan African countries as this is where the current expertise related to konzo is most prevalent. Collectively, this study and others that are conducted with this global team of experts focused on unraveling the biological mechanisms of konzo operates at the highest standards of inclusion, ethical considerations, participant safety and authorship, with the goal of bringing global attention to konzo and other neglected diseases that burden the world’s most impoverished populations.

Sample collection

In October of 2011, our research group comprised of Democratic Republic of the Congo (DRC)-based physicians and experts on konzo along with research scientists collected 60 plasma samples from children affected with konzo and those who were unrelated and unaffected in Kahemba, DRC. In April of 2021, the DRC-based Konzo research group traveled to Kahemba DRC and collected plasma samples from 102 pairs of sex-matched sibling pairs who were discordant for konzo disease. Prior to collection, the Ministry of Health for the DRC and the institutional review board at the Oregon Health and Sciences University provided ethical approval for this study. All participants and parents were consented prior to collection in either French or the appropriate language for the region of collection (Kikongo). Roughly 3 ml of blood was collected from each participant in EDTA-coated tubes and spun at 3000 RPM for 10 min to separate plasma from red blood cells. Plasma was then collected from all participants and then transferred to cryovials and stored in liquid nitrogen within 1 h of sample collection. Upon arrival to the laboratory in Kinshasa, plasma samples were transferred to long-term storage at −80 °C and stored until proteomics applications were ready to be conducted. During sample collection an assessment as to whether an individual was affected with konzo was conducted following the WHO’s 3 main criteria for diagnosis including evidence of (1) a visible symmetric spastic abnormality of gait while walking or running, (2) a history of onset of less than 1 week followed by a nonprogressive course in a formerly healthy person, and (3) bilaterally exaggerated knee or ankle jerks without signs of disease of the spine.

Population characteristics

For this study we utilized data from two independent study cohorts that were consented and sampled 10 years apart. In 2011, plasma samples from the discovery cohort were collected from 60 unrelated individuals (31 unaffected, 29 konzo affected) in the Kahemba region of the Democratic Republic of the Congo, which is approximately 850 km southeast of the capital of Kinshasa. The average age of the unaffected children was 9.5 ± 2.6 years, with samples taken from 17 Males and 14 Females. The average age of those affected with konzo was 9.4 ± 2.6 years, with samples taken from 14 Males and 15 Females. Within the affected group 17 were diagnosed with Stage 1 Konzo, 7 with Stage 2 konzo and 5 with Stage 3 konzo, however proteomic analysis on the discovery cohort were not segregated based on stage of the disease. In 2021, plasma samples from the validation cohort were collected from 102 sibling pairs (102 unaffected and 102 konzo affected, 98 sex-matched, 4 pairs who are discordant for sex) in the Kahemba region of the Democratic Republic of the Congo. The average age of unaffected siblings in this cohort was 8.7 ± 2.6 years with samples taken from 56 unaffected Male siblings and 46 from unaffected Female siblings. The average age of konzo affected children was 9.25 ± 2.2 years with samples taken from 59 konzo affected Male siblings and 43 konzo affected Female Siblings. Within the affected sibling group, 63 were affected with stage 1 konzo, 11 with stage 2 konzo and 28 with stage 3 konzo. Recruitment was based on prior study populations that were familiar with ongoing research related to konzo, therefore the stage groups were generated based simply on those households who chose to participate in this study, which harbored a higher a frequency of children affected with Stage 1 and 3 than Stage 2 konzo.

Plasma protein extraction and digestion

For the discovery cohort (2011), two separate preparations were made with either 1 or 2 µL of plasma added to new tubes containing 24–23 µL of SDC buffer consisting of sodium deoxycholate 1% (DOC), tris(2-carboxyethyl) phosphine 10 mM (TCEP), chloroacetamide 40 mM and Tris 100 mM pH 8.5. Samples were heated at 95 °C for 10 min then cooled off at room temperature. For the untargeted experiment, proteins were digested with 0.7 µg of trypsin (sequencing grade, Promega, Madison, WI) and 0.7 µg of Lys-C (New England Biolabs) for 1 h at 37 °C. For the targeted analysis, proteins were digested with 0.4 µg of trypsin and 0.4 µgLys-C for overnight incubation at 37 °C (aprox. 18 h). After acidification with trifluoroacetic acid 1% (TFA) and centrifugation at 16,000 ×  g for 5 min, resulting peptides were purified on StageTips according to Rappsilber et al. 54 using C18 Empore solid phase (CDS) and vacuum dried. Samples were resuspended in 2% acetonitrile (ACN), 0.05%TFA and peptide concentrations were estimated with 205 nm absorbance readings (Nanodrop, Thermo Fischer). Prior to targeted analysis injections, 1uL of 1X standard indexed retention time peptides (Biognosys AG) was added to each sample to control for the instrumental variability.

For the validation cohort (2021), a similar protocol than above was used in 96 well plates with the following modifications: Digestion was made with 0.25 µg of trypsin only, acidification was performed with formic acid 1% (FA). Plates were then centrifuged at 800 g for 30 min, and the supernatant transferred into new plates. After vacuum evaporation, samples were resuspended in FA 0.1% and peptide concentration was estimated as described above. For the targeted analysis 8 fmol of Cytochrome C digest was added to the samples before injection in LC-MS/MS (Thermo Scientific).

LC-MS/MS acquisitions

For the discovery cohort, untargeted analysis samples were analyzed by nanoLC/MSMS using a Dionex UltiMate 3000 nanoRSLC liquid chromatography system (Thermo Fisher Scientific) interfaced to an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific, San Jose, CA,USA) equipped with a nanoelectrospray ion source. 1 µg of peptides were separated on a 50 cm length × 75 µm internal diameter (ID) C18 Pepmap Acclaim column (Thermo Fisher) using a linear 90-minute acetonitrile gradient. Mass spectra were acquired using a Data Dependent Acquisition mode (DDA) with full scan spectra done in the Orbitrap while fragments ions were generated by Higher energy Collision-induced Dissociation (HCD) and measured in the linear ion trap.

Targeted analysis was carried out on Dionex UltiMate 3000 nanoRSLC coupled to a TSQ Altis mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) equipped with a nanoelectrospray ion source. 1 µg of peptides were separated on a 15 cm length × 75 µm ID C18 separation column (Pepmap Acclaim column, ThermoFisher) using a linear 45-minute acetonitrile gradient. 556 parent/fragment transitions corresponding to 95 peptides of 38 proteins were monitored by Selected Reaction Monitoring method (SRM) in a scheduled manner (Supplementary Data  1 ) using isolation windows of 0.7 m/z in Q1(parent) and 1.2 m/z in Q3 (fragment).

The validation cohort was analyzed on an Evosep One (Evosep Inc., Odense, Denmark) liquid chromatography system coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). 500 ng of peptides loaded on Evotip pure devices (Evosep) according to manufacturer protocol were separated using the pre-programmed 60 samples per day gradient on an 8 cm length ×100 µm ID capillary column (Evosep). The untargeted analysis was carried with a data independent acquisition (DIA) mode in which the precursors were fragmented using HCD on a 350–875 m/z precursor mass range using 35 DIA windows of 15 m/z with a 0.1 m/z overlap. A gas phase fractionation (GPF) library was also acquired by injecting a pool of all samples 8 times in order to cover a 350–910 m/z precursor mass range using 35 DIA windows of 2 m/z with a 0.1 m/z overlap. Targeted analysis was performed using the same chromatographic conditions but with the instrument operating in a parallel reaction monitoring (PRM) method in which targeted HCD MS2 scan were acquired on 12 precursor ions, corresponding to 3 proteins, using an isolation windows of 0.7 m/z (Supplementary Data  1 ).

Protein identification and data analysis

Mass spectra for the discovery cohort DDA analysis were searched against a Uniprot Homo sapiens sequence database (UniProt Reference Proteome – Proteome ID UP000005640– 93634 entries – 2018.04) using the search engine Andromeda integrated into the MaxQuant software (version 1.6.3.4) 55 . Trypsin was set as the digestion enzyme. For protein validation, a false discovery rate (FDR) of 1% was allowed at peptide and protein levels based on a target/decoy search. For data processing, the intensity column from the proteinGroups text file were imported in R software. A protein was considered for quantification if it presented at least 2 identified peptides and if there was an intensity value in at least 75% of the replicates in one of the two groups. Mediane-based normalization was performed using protein intensities in each sample and missing values were imputed from the 1 st percentile intensity value for each sample independently. A list of candidate biomarkers was determined from the proteins that presented a Welch t-test q- value less than 0.05 (after Benjamini–Hochberg correction for multiple testing) and from the top contributors in component 1 and 2 of a sparse partial least square discriminant analysis (sPLS-Da) computed with the Mixomics R package 56 .

The data acquired for the targeted analysis of the discovery cohort was imported into the Skyline software 57 . Peaks were manually validated based on signal intensity, transition chromatogram superposition and retention time. The total area of the 98 peptides were imported into R software for data processing, t -test were done at both the peptide and protein levels. Peptides and proteins were considered regulated between Konzo and unaffected groups if they presented a Welch t -test q-value less than 0.05.

For the DIA analysis of the validation cohort, spectra were analyzed with DIA-NN (version 1.8). First a GPF library was generated using GPF acquisition files and an in silico digested Homo sapiens sequence database (UniProt Reference Proteome – Proteome ID UP000005640– 79052 entries –2022.04) to perform a library-free search. Trypsin/P was set as the enzyme parameter, a maximum of 2 missed cleavage was allowed, carbamidomethylation was set as a fixed modification and methionine N-terminal excision and oxidation were set as variable modifications with a maximum of 2 allowed per peptide. Only 2+ to 4+ precursors were considered along a 350–875 m/z mass range and fragments on a 200–2000 m/z range. Quantification of the sample was done using the same parameters as the GPF library except that the match between run option was enabled. DIANN main report file was processed into R using the diann package and the MaxLFQ normalization algorithm while applying a 1% q-value filter on both precursors and protein groups. Filtering of quantifiable proteins and missing values imputation were done as in the discovery untargeted analysis. Candidate proteins were selected based on a paired Welch’s t-test and z-score thresholds.

Analysis of the PRM data was done in Skyline software and chromatographic peaks were manually validated in a similar fashion as the SRM data which led to keep 4 GPX3 peptides and 2 SEPP1 peptides for quantification. Peptide total peak area from the 6 best fragment ion intensities were exported from Skyline into R software. For each peptide a paired Welch t-test p -value threshold of 0.05 was used to evaluate the significance of abundance variation

Following plasma thawing, a large subset of samples collected in 2021 from the discordant sibling cohort ( n  = 174) were vortexed briefly and diluted 1:100 in ELISA sample buffer (NOVUS Biologicals). Methodology was followed as outlined in the manufacture’s protocol for the Human GPX3 ELISA Detection Kit (NOVUS Biologicals). All samples were run in duplicate and the absorbance at 450 nm was averaged prior to statistical analysis. The significance of differences in protein abundance was determined with a Paired T-test for sibling cohort comparisons and a T-test for comparisons of unrelated groups such as Stage 1 versus Stage 3.

HPLC amino acid quantification

The analysis was conducted using our previously established chromatographic conditions (5) on an Agilent 1260 Infinity II LC System (5). Briefly, a C18 column with dimensions of 3.0 × 100 mm, and a particle size of 2.7 µm was employed. The injection volume was 20 µL, and detection was executed at a wavelength of 338 nm. The binary mobile phase composition and gradient elution program are detailed in the referenced publication. Sample preparation was done by using of an economical 3 K centrifuge filter instead of an acid or alcohol precipitation. This filtration step removes the larger proteins like acid precipitation, which can clog the analytical column. The filters have an added advantage of not diluting the sample, which improves the detection limits of the assay and allows less sample to be used. Peak quantification for corresponding amino acids followed the procedure as directly outlined in the referenced protocol.

Statistics and reproducibility

This study is highly synergistic with the goals of the long-term projects and funding secured by Drs. Tshala, Boivin and Bramble for investigating the molecular mechanisms associated with cassava induced neurotoxicity in the Kahmeba region of the DRC. As such, individuals from the Kahemba region were recruited based on prior diagnosis of konzo by expert phenotyping of this disease, who were participants in the long-term clinical trials aimed at altering cassava processing to reduce konzo. For the 2011 discovery cohort, these samples were obtained during the initial start of the long-term clinical trials conducted by Drs. Tshala and Boivin and individuals recruited for the 2021 validation sibling cohort were also familiar and participants in long-term studies conducted in Kahemba by Drs. Tshala, Boivin and Bramble. Unaffected individuals from both cohorts obtained from Kahemba were not considered to have konzo at the time of sampling following the 3 main criteria for diagnosis outlined by the World Health Organization. Prior to study design that involved proteomic analysis, samples sizes were not considered prior to sampling. The 2011 discovery cohort was sampled years prior to initial proteomics experimentation, however the sample size ( n  = 60) was sufficient to detect moderate to large differences between groups. However, for the 2021 validation cohort expanded the sample size for more statistical confidence while attempting to control for unknown variables. As such, we recruited 102 sex-matched sibling pairs who were discordant for konzo from the Kahemba region of DRC. This number was more than three times the size of the discovery cohort, while enabling paired statistical analysis due to the cohort being siblings. Details regarding sex and age and cohort size of the recruited participants can be found in the population characteristics section of this manuscript. For this study, all participants that samples were taken from were included. This focus of this study was to determine if children affected with konzo had different plasma-based protein differences that may shed light on the mechanisms of the development of the disease. As such, randomization or blinding was not possible in this study. All statistical tests associated with the outcomes presented in this work are detailed in their corresponding methods sections, presented figures and the results portion of this manuscript.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The mass spectrometry data generated in this study have been deposited in the Proteomexchange database under accession code PXD049973 and are freely accessible. The processed/cleaned mass spectrometry data associated with this manuscript are available in Supplementary Data  1 . The additional ELISA and HPLC data generated in this study are also provided in the Supplementary Information/Source Data file.  Source data are provided with this paper.

Code availability

Proteomics data were processed using open-source software: MaxQuant v 1.6 3.4 for DDA experiment ( https://maxquant.org/ ), DIA-NN v1.8 for DIA experiment ( https://github.com/vdemichev/DiaNN ), Skyline v.23.1.0.380 ( https://skyline.ms/project/home/software/Skyline/begin.view ) for SRM and PRM experiments. Statistics and visualization of proteomics data were done using R software v.4.1.1 ( https://www.r-project.org/ ) including the R packages Mixomics ( http://mixomics.org/ ) and diann ( https://github.com/vdemichev/diann-rpackage ).

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Acknowledgements

We would like to thank all the Congolese participants of this study for kindly donating specimens for proteomics analysis. We would also like to thank the funding sources for this project, with support from NIH grant NIEHS/FIC R01ES019841 (D.T.K.), and support by the Fogarty International Center of the National Institutes of Health (NIH) under Award Number 5K01TW011772 (M.S.B.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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These authors contributed equally: Matthew S. Bramble, Victor Fourcassié.

Authors and Affiliations

Center for Genetic Medicine Research, Children’s Research Institute, Children’s National Hospital, Washington, DC, USA

Matthew S. Bramble, Yun Zhou, D’Andre Spencer, Aliyah Mohammed, Gary Cunningham, Marshall Summar & Ljubica Caldovic

Department of Genomics and Precision Medicine, The George Washington University of Medicine and Health Sciences, Washington, DC, USA

Matthew S. Bramble & Ljubica Caldovic

Computational Biology Laboratory and The Proteomics Platform, CHU de Québec - Université Laval Research Center, Québec City, QC, Canada

Victor Fourcassié, Florence Roux-Dalvai & Arnaud Droit

Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

Neerja Vashist

Department of Neurology, Kinshasa University, Kinshasa, Democratic Republic of the Congo

Guy Bumoko & Hilaire Musasa Hanshi-Hatuhu

Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo

Michel Lupamba Kasendue, Hilaire Musasa Hanshi-Hatuhu, Vincent Kambale-Mastaki, Dieudonne Mumba-Ngoyi & Desire Tshala-Katumbay

Biological Models Laboratory, Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines, Manila, Ermita, Manila, Philippines

Rafael Vincent M. Manalo

Department of Microbiology and Immunology, University of Nevada, Reno School of Medicine, Reno, NV, USA

David R. McIlwain

Departments of Psychiatry and Neurology & Ophthalmology, Michigan State University, East Lansing, MI, USA

Michael J. Boivin

Institute for Clinical and Translational Science, University of California, Irvine, CA, USA

Eric Vilain

Department of Neurology, Oregon Health & Science University, Portland, OR, USA

Desire Tshala-Katumbay

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Contributions

M.S.B., N.V., D.T.K. and A.D. designed and carried out the experiments related to this manuscript and prepared the manuscript for this study. V.F. and F.R-D. performed all proteomic analysis and interpretation and aided in manuscript preparation. G.B., M.S.B., M.L.K., N.V., H.H.-H. and V.K.M. participated in sample collection and preparation for this study. Y.Z., A.M., G.C. and M.S. designed and carried out the experiments related to amino acid quantifications. R.M. and D.S. participated in manuscript and figure preparation and ELISA validation of GPX3. M.B., E.V., D.M.N., L.C., D.R.M., A.D. and D.T.K. provided the senior oversight of this work and aided in manuscript preparation, data finalization and interpretation.

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Correspondence to Matthew S. Bramble , Desire Tshala-Katumbay or Arnaud Droit .

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Bramble, M.S., Fourcassié, V., Vashist, N. et al. Glutathione peroxidase 3 is a potential biomarker for konzo. Nat Commun 15 , 7811 (2024). https://doi.org/10.1038/s41467-024-52136-5

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Received : 08 February 2024

Accepted : 27 August 2024

Published : 06 September 2024

DOI : https://doi.org/10.1038/s41467-024-52136-5

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