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Gestational diabetes

The two case studies presented here provide an overview of gestational diabetes (GDM). The scenarios cover the screening, identification and management of GDM, as well as the steps that should be taken to screen for, and ideally prevent, development of type 2 diabetes in the long term post-pregnancy. By actively engaging with the case studies, readers will feel more confident and empowered to manage GDM effectively in the future.

Useful resources

At a glance factsheet: Diabetes before, during and after pregnancy. Diabetes & Primary Care 23 : 73–4

Bellamy L, Casas JP, Hingorani AD, Williams D (2009) Type 2 diabetes mellitus after gestational diabetes: A systematic review and meta-analysis. Lancet 373 : 1773–9

Catalano PM (2014) Trying to understand gestational diabetes. Diabet Med 31 : 273–81

ElSayed NA, Aleppo G, Aroda VR et al; American Diabetes Association (2023a) 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes–2023. Diabetes Care 46 (Suppl 1): S19–40

ElSayed NA, Aleppo G, Aroda VR et al; American Diabetes Association (2023b) 15. Management of Diabetes in Pregnancy: Standards of Care in Diabetes–2023. Diabetes Care 46 (Suppl 1): S254–66

Horvath K, Koch K, Jeitler K et al (2010) Effects of treatment in women with gestational diabetes mellitus: Systematic review and meta-analysis. BMJ 340 : c1395

Iftakhar R (2012) Benefit of metformin in reducing weight gain and insulin requirements in pregnancies complicated by gestational diabetes. Diabesity in Practice 3 : 108–13

Knowler WC, Barrett-Connor E, Fowler SE et al; Diabetes Prevention Program Research Group (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346 : 393–403

Lindström J, Louheranta A, Mannelin M et al; Finnish Diabetes Prevention Study Group (2003) The Finnish Diabetes Prevention Study (DPS): Lifestyle intervention and 3-year results on diet and physical activity. Diabetes Care 26 : 3230–6

McGovern A, Butler L, Munro M, de Lusignan S (2014) Gestational diabetes mellitus follow-up in primary care: A missed opportunity. Diabetes & Primary Care 16 : 60

Meltzer SJ (2010) Treatment of gestational diabetes. BMJ 340 : c1708

NICE (2017) Type 2 diabetes: prevention in people at high risk [PH38]. Available at: https://www.nice.org.uk/guidance/ph38

NICE (2020) Diabetes in pregnancy: management from preconception to the postnatal period [NG3]. Available at: https://www.nice.org.uk/guidance/ng3

NICE (2022) Type 2 diabetes in adults: management [NG28]. Available at: https://www.nice.org.uk/guidance/ng28

Noctor E, Dunne F (2017) A practical guide to pregnancy complicated by diabetes. Diabetes & Primary Care 19 : 35–4

Rowan JA, Hague WM, Gao W et al; MiG Trial Investigators (2008) Metformin versus insulin for the treatment of gestational diabetes. N Engl J Med 358 : 2003–15

Silverman BL, Rizzo TA, Cho NH, Metzger BE (1998) Long-term effects of the intrauterine environment. The Northwestern University Diabetes in Pregnancy Center. Diabetes Care 21 (Suppl 2): B142–9

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1 . Question

Section 1 – holly.

Holly is a 31-year-old lady who is now 26 weeks into her first pregnancy. She sees you with a 3-day history of dysuria and frequency of micturition. There is no history of abdominal pain or fever.

A urine dipstick reveals a positive test for nitrites and the presence of white cells. It also shows glycosuria ++.

What is your assessment of Holly’s situation?

This response will be awarded full points automatically, but it can be reviewed and adjusted after submission.

2 . Question

Holly appears to have a lower urinary tract infection (UTI). The glycosuria suggests she may have gestational diabetes (GDM), although it is possible she may have entered the pregnancy with undiagnosed type 2 diabetes.

What factors would you look for in Holly’s history that could suggest she is at high risk of gestational diabetes?

3 . Question

Risk factors for developing GDM include (NICE, 2020):

• BMI >30 kg/m 2 .
• Previous GDM.
• Previous macrosomic baby weighing 4.5 kg or more.
• Family history of diabetes (first-degree relative).
• Ethnic background with high prevalence of diabetes (South or East Asian, African–Caribbean, Middle Eastern).

  Further considerations regarding the likelihood of GDM include advanced maternal age (>35 years), a history of polycystic ovarian syndrome (a condition of increased insulin resistance) and previous unexplained fetal death.

Holly had a pre-pregnancy BMI of 29.4 kg/m 2 but no family history of type 2 diabetes.

What action would you take?

4 . Question

In addition to treating the UTI, Holly needs to be investigated for possible GDM.

Holly is prescribed a course of cephalexin (a safe and effective antibiotic in pregnancy) and encouraged to drink plenty of fluids. A mid-stream urine sample is sent off for laboratory analysis. A fingerprick glucose reading demonstrates a glucose level of 11.7 mmol/L. A blood sample is sent off for HbA 1c assessment (HbA 1c will not accurately reflect recent onset of diabetes in a pregnancy but could point toward the likelihood of pre-existing undiagnosed type 2 diabetes when entering the pregnancy).

NICE (2020) recommends formal testing for GDM if pregnant women have:

• Glycosuria of ++ or above on one occasion.
• Glycosuria of + or above on more than one occasion.

  In Holly’s case, local maternity services are contacted and an oral glucose tolerance test (OGTT) is arranged to ascertain the diagnosis of GDM. In the meantime, the importance of controlling blood glucose levels in pregnancy is explained to her and she is advised on eating a healthy diet and taking exercise to help achieve this.

Can you explain why gestational diabetes arises?

5 . Question

GDM is defined as diabetes diagnosed during the second and third trimester of pregnancy that was not clearly overt diabetes prior to gestation (ElSayed et al, 2023). As pregnancy progresses, insulin resistance rises and this is normally countered by increasing insulin production. However, women with GDM inherently have a greater degree of insulin resistance compared to those without GDM, and this coupled with reduced beta-cell capacity to produce the required insulin response leads to maternal hyperglycaemia (Catalano, 2014).

GDM is a more common cause of diabetes in pregnancy than pre-existing diabetes, accounting for nearly 90% of cases, and the prevalence is increasing in line with the demographic rise in body mass index (Noctor and Dunne, 2017).

Why is it important to identify gestational diabetes?

6 . Question

Maternal hyperglycaemia from poorly controlled GDM leads to fetal macrosomia. The consequence at delivery is an increased risk of obstructed delivery from shoulder dystocia, clavicular fracture and brachial plexus injury (Melzer, 2010).

Fetal hyperglycaemia is associated with a rise in congenital abnormalities. The incidence of neonatal respiratory distress, meconium aspiration and jaundice are all raised following GDM, and the carry-over of fetal hyperinsulinaemia post-delivery can lead to neonatal hypoglycaemia. All of the above are common reasons for admission to the neonatal intensive care unit (Noctor and Dunne, 2017).

For the mother with GDM, the incidence of pre-eclampsia, polyhydramnios, pre- and post-partum haemorrhage, and infection are all increased. Failure to progress with labour and delivery by caesarean section are more frequent with pregnancy complicated by GDM (Noctor and Dunne, 2017).

In the longer term, there is an association between offspring exposed to in utero GDM and the later occurrence of obesity and glucose intolerance (Silverman et al, 1998). Women who have had GDM are at increased risk of developing type 2 diabetes (Bellamy et al, 2009).

It is important, therefore, that primary and community care practitioners can appropriately advise women at high risk of GDM, understand the management of GDM and, as indicated later in this case study, assume responsibility for post-pregnancy follow-up of GDM (McGovern et al, 2014).

What treatments for diabetes are considered safe and effective in gestational diabetes?

7 . Question

Whilst the joint diabetes and antenatal clinic will assume responsibility for the majority of treatment decisions, primary healthcare workers need to be aware of GDM management. Effective treatment of GDM has been demonstrated to reduce fetal macrosomia, fetal and maternal birth trauma, and perinatal death (Horvath et al, 2010).

Lifestyle change constitutes the first-line treatment of GDM. NICE advises that a trial of diet and exercise should be offered to women with GDM without complications who have a fasting plasma glucose (FPG) level below 7 mmol/L at diagnosis (NICE, 2020). If dietary change and physical exercise prove ineffective in achieving blood glucose targets within 1–2 weeks, medical treatment should be commenced. In the absence of contraindications, this would usually be metformin.

For those women with an FPG of 7 mmol/L or above at diagnosis, and for women with an FPG of 6.0–6.9 mmol/L who have a complication such as fetal macrosomia or hydramnios, immediate medical treatment with insulin (with or without metformin), alongside lifestyle measures, is recommended (NICE, 2020).

Metformin, although not licensed for use in pregnancy, is considered safe and has gained ground as an option for treating GDM when lifestyle measures are insufficient. The risk of perinatal complications appears to be no higher than in insulin-treated patients (Rowan et al, 2008) and there is the advantage of less weight gain, less monitoring and reduced risk of hypoglycaemia in comparison to insulin (Iftakhar, 2012). Should gastrointestinal side effects prove troublesome with metformin, the modified-release preparation may be tried. However, if glycaemic control is inadequate then insulin should be promptly initiated.

Insulin therapy has traditionally been regarded as the first-line medical treatment in GDM and remains the preferred treatment of the American Diabetes Association (ElSayed et al, 2022b). Often a basal–bolus insulin regimen will be required, and insulin doses can be adjusted on a frequent basis, noting that requirements are likely to rise as pregnancy progresses, reflecting increasing insulin resistance. The rapid-acting insulin analogues (e.g. insulin aspart and insulin lispro) offer advantages in improved glycaemic control and reduced hypoglycaemia compared with human soluble insulins (NICE, 2020).

• Mark as read

8 . Question

Holly’s OGTT revealed an FPG of 6.7 mmol/L and a 2-hour plasma glucose of 9.4 mmol/L, confirming the diagnosis of GDM (thresholds for diagnosis: either FPG ≥5.6 mmol/L or 2-hour plasma glucose ≥7.8 mmol/L), and she was reviewed in the joint diabetes and antenatal clinic.

The need to carefully control blood glucose levels was explained to Holly and she was offered lifestyle advice, instructed on glucose monitoring, referred to a dietitian and subsequently commenced on metformin.

What frequency of glucose monitoring should be advised for Holly, who is using metformin for her gestational diabetes?

9 . Question

Holly, as a person using oral medication to control GDM, should be encouraged to take fasting and 1-hour post-meal readings to guide treatment. This monitoring pattern would also be appropriate for those on dietary control alone and if insulin is administered as a basal injection alone.

10 . Question

Section 10 – nadia.

Nadia is a 34-year-old lady of Indian ethnic origin who is now 24 weeks into her second pregnancy, her last pregnancy being 7 years ago. Nadia’s BMI is 32.4 kg/m 2 and her father has type 2 diabetes. GDM was not, however, diagnosed during her first pregnancy and her first baby was born at term weighing 3.8 kg.

How would you assess Nadia’s risk of acquiring gestational diabetes?

11 . Question

Nadia’s ethnic origin, obesity and family history of type 2 diabetes place her at high risk of GDM.

These risk factors were identified by the midwives at booking, and Nadia is scheduled for an OGTT at 28 weeks’ gestation.

There are conflicting recommendations over screening arrangements and diagnostic criteria for GDM. NICE (2020) recommends an OGTT between 24 and 28 weeks’ gestation for those at high risk of GDM (or as soon as possible after booking if previous GDM), with the following diagnostic criteria:

• Fasting plasma glucose of 5.6 mmol/L or more.
• 2-hour plasma glucose of 7.8 mmol/L or more.

  The American Diabetes Association now recommends screening for diabetes (with an HbA 1c or fasting plasma glucose test) in those women with risk factors who are planning a pregnancy, so that those found to have diabetes can optimise their glucose control ahead of pregnancy (ElSayed et al, 2022a).

12 . Question

A diagnosis of GDM is made from Nadia’s OGTT (FPG 8.1 mmol/L; 2-hour plasma glucose 12.7 mmol/L). Nadia is accordingly referred to the joint diabetes and antenatal clinic.

Nadia is quickly established on a basal–bolus insulin regimen of Lantus and NovoRapid in the diabetes/antenatal clinic, provided with a meter to self-monitor her capillary glucose and set targets for glycaemic control. NovoRapid is licensed for use in pregnancy and the SmPC of Lantus advises that Lantus may be considered during pregnancy if clinically needed.

What frequency of glucose monitoring might you expect Nadia to undertake?

13 . Question

Intensive glucose monitoring is advised during a pregnancy affected by diabetes, and you should be prepared to issue more glucose test strips. For Nadia, on a basal–bolus insulin regimen, NICE (2020) recommends capillary blood glucose monitoring prior to meals (including fasting), 1-hour post-meals and before bedtime.

NICE (2020) recommends setting the same capillary plasma glucose target levels for women with GDM as for those with pre-existing diabetes. Thus, ideal glucose targets would be the following:

• Fasting: 5.3 mmol/L.
• 1 hour after meals: 7.8 mmol/L; or 2 hours after meals: 6.4 mmol/L.

  In practice, targets will need to be individualised, recognising the need to avoid problematic hypoglycaemia. In the case of women using insulin, such as Nadia, capillary glucose levels should be kept above 4 mmol/L. Women at risk of hypoglycaemia should be advised to carry a fast-acting form of glucose at all times.

It should be mentioned that continuous subcutaneous insulin infusion (an insulin pump) is an alternative to a basal–bolus insulin regimen for pregnant women who do not achieve adequate glucose control without troublesome hypoglycaemia. Real-time or intermittently scanned (flash) glucose monitoring is an option where there is severe hypoglycaemia (especially if there is hypoglycaemia unawareness) or where unstable glucose readings are problematic (NICE, 2020).

What role does primary care have in managing women with gestational diabetes?

14 . Question

Whilst management of GDM will primarily occur in the diabetes/antenatal clinic, women with GDM may, in addition to the usual pregnancy-related health issues, need support from practice nurses and GPs in blood glucose monitoring and interpretation, and reassurance that maintaining good glycaemic control will improve outcomes for their babies and themselves. Advice regarding medication use and side-effects, and in particular the recognition and treatment of hypoglycaemia for those using insulin, may be necessary.

Nadia is frequently reviewed in the diabetes/antenatal clinic and achieves satisfactory glycaemic control with her insulin regimen. She delivers a healthy girl at 39 weeks’ gestation without significant problems. Nadia’s insulin is stopped after delivery and glucose levels are checked and seen to be running below 10 mmol/L. It is also important to check baby’s glucose levels, as there is a risk of neonatal hypoglycaemia.

How should Nadia’s glucose control be assessed in the post-partum period?

15 . Question

Whilst diabetes medications used for GDM are usually stopped at delivery in the expectation that glucose levels will fall to pre-pregnancy levels, there is the possibility that hyperglycaemia will persist (and hence the need for glucose checks immediately following delivery).

Nadia should have a formal test for hyperglycaemia within 3 months of delivery, and there needs to be clarity as to who assumes responsibility for this.

NICE (2020) advises that women with GDM (whose blood glucose levels return to normal after delivery) should be offered a fasting plasma glucose (FPG) test at 6–13 weeks postpartum to exclude diabetes (pragmatically, this could be at the 6-week postnatal review); otherwise, beyond 13 weeks postpartum, an HbA 1c test can be offered.

• If FPG is ≥7.0 mmol/L or HbA 1c is ≥48 mmol/mol, a confirmatory test for type 2 diabetes should be carried out and type 2 diabetes pathways (NICE, 2022) should then be followed.
• If FPG is 6.0–6.9 mmol/L or HbA 1c is 39–47 mmol/mol, there is a high risk of developing type 2 diabetes. Lifestyle advice and an offer to refer to the NHS Diabetes Prevention Programme should follow.
• Note these are different HbA 1c risk thresholds than for “prediabetes” in the NICE (2017) PH38 advice, because they refer to a different population.

Nadia has an FPG test at 6 weeks post-delivery, which reveals an FPG <6.0 mmol/L, suggesting that she does not have prediabetes or type 2 diabetes.

What about Nadia’s future risk of developing type 2 diabetes?

16 . Question

Nadia’s GDM is a marker for insulin resistance and places her at increased risk of developing prediabetes and type 2 diabetes in the future compared to women without GDM (relative risk 7.4; Bellamy et al, 2009). Nadia should be alerted to the likelihood of recurrence of GDM in future pregnancies and of the possibility of type 2 diabetes in the future.

Nadia sees you at the clinic following her normal FPG test and asks about her GDM and its future implications.

What advice would you offer Nadia for the future, and what arrangements would you set in place for future screening of diabetes?

17 . Question

It is helpful to think that GDM confers “prediabetic status”. There is good evidence that lifestyle adjustment can prevent or delay the onset of diabetes (Knowler et al, 2002; Lindström et al, 2003). Encourage Nadia to be careful with her diet, take regular exercise and aim for weight loss – similar principles as for those with type 2 diabetes – as detailed in NICE public health guidance on prevention of type 2 diabetes (NICE, 2017). She is offered referral to the Healthier You NHS Diabetes Prevention Programme.

Nadia should be placed on annual recall, checking HbA 1c , lipids, renal function, weight and blood pressure.

With her history of GDM, in any future pregnancy Nadia should be offered early self-monitoring of blood glucose or an OGTT as soon as possible after booking (NICE, 2020).

In reality, there is a disappointingly low rate of both short-term and longer-term review of GDM, with rates of both around 20% in one study (McGovern et al, 2014). Possible reasons for this poor follow-up include lack of awareness amongst women with GDM, poor communication between secondary and primary care, a lack of consensus over responsibility for post-natal tests and missed opportunities in primary care for the annual review.

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Early onset gestational diabetes mellitus: A case report and importance of early screening

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Case Studies

Case 9: gestational diabetes.

A 28-year-old G 4 P 2 presents to your office for a routine prenatal visit at 24 weeks’ gestation. Her physical examination is unremarkable and fetal wellbeing is reassuring. You recommend testing for gestational diabetes mellitus (GDM).

1. What is GDM?

Show Answer

Correct answer: GDM refers to any form of glucose intolerance with the onset of pregnancy or first recognized during pregnancy, and complicates approximately 5% of all pregnancies. It likely includes some women who have undiagnosed pregestational diabetes.

2. Should everyone be screened for GDM? If so, at what gestational age should they be screened?

Correct answer: Patients with GDM are typically asymptomatic. There is a small cohort of pregnant women in whom routine screening for GDM is not cost-effective. These are women under age 25 who have normal body mass index (BMI 2 ), no first-degree relatives with diabetes, no risk factors (such as a history of GDM, insulin resistance/PCOS [polycystic ovarian syndrome], a prior macrosomic infant, a prior unexplained late fetal demise, and women with persistent glycosuria), and who are not members of ethnic or racial groups with a high prevalence of diabetes (such as Hispanic, Native American, Asian, or African–American). As such patients are rare, most experts and organizations recommend screening for GDM in all pregnant women. The ideal time to screen for GDM is 24–28 weeks of gestation. For women at high risk of developing GDM (listed above), early screening for GDM is recommended at the first prenatal visit. If the early screen is negative, it should be repeated at 24–28 weeks.

3. Her 1-hour GLT is 182 mg/dL. Does she have GDM?

Correct answer: The most common screening test for GDM is the glucose load test (GLT) – also known as the glucose challenge test (GCT) – which is a non-fasting 50-g oral glucose challenge followed by a venous plasma glucose measurement at 1 hour. Most authorities consider the GLT to be positive if the 1-hour glucose measurement is >140 mg/dL. Use of a lower cut-off (such as >130 mg/dL) will increase the detection rate of women with GDM, but will result in a substantial increase in the false-positive rate.

There is no GLT cut-off that should be regarded as diagnostic of GDM . A definitive diagnosis of GDM requires a 3-hour glucose tolerance test (GTT). In pregnancy, the GTT involves 3 days of carbohydrate loading followed by a 100-g oral glucose challenge after an overnight fast. Venous plasma glucose is measured fasting and at 1 hour, 2 hours, and 3 hours. Although there is agreement that two or more abnormal values are required to confirm the diagnosis, there is little consensus about the glucose values that define the upper range of normal in pregnancy (see below). Most institutions use the National Diabetes Data Group (NDDG) or Carpenter and Coustan cut-offs. Measurement of glycated hemoglobin (HbA1c) levels is not useful in making the diagnosis of GDM, although it may be useful in the diagnosis of pregestational diabetes.

Plasma glucose values (mg/dL) (mmol/L) *

Sacks et al.

Carpenter and Coustan

* Values in parentheses are mmol/L.

4. All four values of her 3-hour GTT are elevated and her fasting glucose level is 127 mg/dL. How would you manage her GDM? How long would you allow her to try dietary restriction before adding a hypoglycemic agent?

Correct answer: GDM poses little risk to the mother. Such women are not at risk of diabetic ketoacidosis (DKA), which is primarily a disease of absolute insulin deficiency. However, GDM has been associated with an increase in infant birth trauma and perinatal morbidity and mortality. The risk to the fetus/infant is directly related to its size. Fetal macrosomia is defined as an estimated fetal weight (not birthweight) of ≥4,500 g. It is a single cut-off that is unrelated to gestational age, the sex of the baby, or the presence or absence of diabetes, or to the actual birthweight.

The goal of antepartum treatment of GDM is to prevent fetal macrosomia and its resultant complications by maintaining maternal blood glucose at desirable levels throughout gestation, defined as a fasting glucose level 95 mg/dL, treatment can be started immediately because “you can’t diet more than fasting.”

Insulin (which has to be given several times a day by injection) remains the “gold standard” for the medical management of GDM. The use of oral hypoglycemic agents has traditionally been avoided in pregnancy because of concerns over fetal teratogenesis and prolonged neonatal hypoglycemia. However, recent studies suggest that second-generation hypoglycemic agents (glyburide, glipizide) do not cross the placenta, are safe in pregnancy, and can achieve adequate glycemic control in 85% of pregnancies complicated by GDM.

5. The estimated fetal weight at 38 weeks’ gestation is 4,600 g (10 lb 2 oz). She has had six prior uncomplicated vaginal deliveries. How would you counsel her about delivery?

Correct answer: As noted above, the complications of GDM are related primarily to fetal macrosomia, including an increased risk of cesarean section delivery, operative vaginal delivery, and birth injury to both the mother (vaginal, perineal, and rectal trauma) and fetus (including orthopedic and neurologic injury). Shoulder dystocia with resultant brachial plexus injury (Erb’s palsy) is a serious consequence of fetal macrosomia, and further increased in the setting of GDM because the macrosomia of diabetes is associated with increased diameters in the upper thorax of the fetus.

The use of elective cesarean section delivery to reduce the risk of maternal and fetal birth injury in the setting of fetal macrosomia remains controversial. According to current ACOG guidelines, an elective cesarean section delivery at or after 39 weeks’ gestation should be recommended for all non-diabetic women who have a fetus with an estimated fetal weight (EFW) ≥5,000 g (or ≥4,500 g in a diabetic individual) to minimize the risk of birth trauma. Furthermore, it is recommended that a discussion be held about the safest route of delivery with non-diabetic women who have a fetus with an EFW ≥4,500 g (or ≥4,000 g in a diabetic individual) and that this discussion be documented in the medical record.

6. After extensive counseling, the couple decline elective cesarean section delivery. She is now 38 weeks’ gestation. How should she be managed at this point in time?

Correct answer: If the patient declines elective cesarean section delivery, spontaneous labor should be awaited. Induction of labor for so-called “impending macrosomia” does not decrease the risk of cesarean section delivery or intrapartum complications, and is therefore not routinely recommended. If she is still undelivered at 41 weeks’ gestation, she should be counseled again about induction of labor and/or elective cesarean section.

During labor, maternal glucose levels should be maintained at 100–120 mg/dL to minimize the risk of intrapartum fetal hypoxic–ischemic injury. Continuous fetal monitoring is recommended throughout labor and the progress of labor should be carefully charted. Internal monitors such as an intrauterine pressure catheter (IUPC) and/or fetal scalp electrode can be used, if indicated. Neonatal blood glucose levels should be measured within 1 hour of birth and early feeding encouraged.

Delivery of the fetus and placenta effectively removes the source of the anti-insulin (counter-regulatory) hormones that cause GDM. As such, no further management is required in the immediate postpartum period. A 2-hour non-pregnant GTT should be performed at 6–8 weeks postpartum in all women with GDM to exclude pre-gestational diabetes.

See Chapter 45.

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Clinical pearls, case study: a 36-year-old woman with type 2 diabetes and pregnancy.

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Diane M. Karl; Case Study: A 36-Year-Old Woman With Type 2 Diabetes and Pregnancy. Clin Diabetes 1 January 2001; 19 (1): 24–25. https://doi.org/10.2337/diaclin.19.1.24

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C.M. is a 36-year-old Spanish-speaking Mexican-American woman with a 3-year history of type 2 diabetes. She was seen in her primary physician’s office because of a missed menstrual period; a pregnancy test was positive.

Her past obstetrical history included five vaginal deliveries and six miscarriages. All of her previous pregnancies occurred before the diagnosis of diabetes. Her previous medical care was in Mexico. She was never told of any glucose problem during her pregnancies, and she does not know the birth weights of her children. At the time of referral, she was 8 weeks pregnant and taking glyburide 10 mg twice daily. She was checking her blood glucose once daily in the morning with typical readings between 180 and 220 mg/dl on a plasma-referenced meter. Family history was positive for diabetes in her mother.

Her height was 62 inches, and her weight was 198 lb. Other than mild acanthosis nigricans and obesity, her physical examination was normal. She had no retinopathy and no evidence of neuropathy. Her glycosylated hemoglobin (HbA 1c ) level was 10.5% (normal <6.0%), and an office capillary blood glucose 4 h after lunch was 201 mg/dl.

She was started on insulin immediately and her glyburide was discontinued. She began monitoring her glucose before and after each meal, making daily adjustments in insulin. She received nutrition education with an appropriate calorie intake plus an emphasis on frequent smaller meals and limited carbohydrate intake. Within 1 week, her plasma glucose values were in the target range for pregnancy, but in the following week she had a spontaneous miscarriage. After her miscarriage, she discontinued insulin on her own and resumed taking glyburide 10 mg twice daily.

1.  Is there a relationship between C.M.’s diabetes and her adverse obstetrical history?

2.  What should have been done before her recent pregnancy to increase the odds of a favorable outcome?

3.  What considerations affect the choice of therapy for her diabetes now?

In the past, most diabetic women who conceived had type 1 diabetes. Today, however, we see an increasing number of women who have preconception type 2 diabetes. One reason is the tendency for many women to delay pregnancy until a later age. Another important factor, however, is the increasing number of children and young adults, especially in minority groups, who are developing type 2 diabetes. 1  

The presence of diabetes in a woman of childbearing years is a special challenge. Blood glucose control during the first 2 months of pregnancy is critical to normal organ development. Commonly, however, women do not seek medical attention until after this period of early fetal development. Many women do not yet realize they are pregnant during this important period, especially if the pregnancy is not planned, which is the situation in well over half of all pregnancies. For this reason, preconception counseling must be an important aspect of management in all diabetic women of childbearing years, regardless of whether there is an expressed desire to conceive. 2 , 3  

Even though C.M.’s diabetes was diagnosed 3 years ago, the fact that she is already poorly controlled on maximal sulfonylurea treatment suggests a longer duration of diabetes. This supports the possibility that her poor obstetrical history may have been related to undiagnosed (and, therefore, uncontrolled) diabetes. Certainly during her most recent pregnancy, C.M. was poorly controlled during the critical period of organ development, possibly leading to an anomaly incompatible with fetal viability.

Comprehensive preconception counseling is now indicated for C.M. Oral diabetic medications have not been adequately studied for safety during pregnancy. Therefore, a woman who is taking oral medication and who wishes to conceive should be switched to insulin, and control should be established before she becomes pregnant. If C.M. plans another pregnancy or if she is not actively using birth control, she needs to resume insulin treatment.

Even patients whose diabetes is well controlled with diet and exercise are almost certain to require insulin during the later stages of gestation, when insulin resistance increases markedly. Preparing patients for this likelihood and teaching insulin administration as part of preconception counseling is advisable. Before pregnancy occurs is the ideal time to address any patient fears and misconceptions about insulin treatment.

For a woman of childbearing age who does not wish to become pregnant, choice of therapy can be important. Insulin resistance, almost universally present in type 2 diabetes, may be associated with decreased fertility. This is most clearly evident in polycystic ovary syndrome. 4 Oral diabetic medications that reduce insulin resistance, such as metformin and thiazolidinediones, 5 may also restore fertility. Thus, a previously infertile patient with type 2 diabetes may become unexpectedly pregnant after starting an insulin-sensitizing medication unless she is counseled regarding the need for birth control.

1.  Preconception counseling is important for all women with diabetes, type 1 or type 2, who are in their childbearing years, since many pregnancies are not planned and poor glucose control early in pregnancy is associated with a higher incidence of major congenital defects.

2.  Especially in minority populations, increasing numbers of women with type 2 diabetes who are treated with oral medications may be in their childbearing years. There are not adequate safety data to recommend the use of oral diabetic medications during pregnancy.

3.  Oral diabetic medications that reduce insulin resistance may increase fertility in women previously unable to conceive.

Diane M. Karl, MD, is medical director of diabetes services at Adventist Health and an assistant professor of clinical medicine at Oregon Health Sciences University in Portland, Ore.

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• Published: 28 August 2024

Exposure to gestational diabetes mellitus in utero impacts hippocampal functional connectivity in response to food cues in children

• Sixiu Zhao 1 , 2 ,
• Lorenzo Semeia   ORCID: orcid.org/0000-0002-2079-7147 1 , 2 ,
• Ralf Veit   ORCID: orcid.org/0000-0001-9860-642X 1 , 2 ,
• Shan Luo 3 , 4 , 5 , 6 ,
• Brendan C. Angelo   ORCID: orcid.org/0000-0003-3558-2840 3 , 4 ,
• Ting Chow 7 ,
• Andreas L. Birkenfeld   ORCID: orcid.org/0000-0003-1407-9023 1 , 2 , 8 ,
• Hubert Preissl   ORCID: orcid.org/0000-0002-8859-4661 1 , 2 , 8 , 9 ,
• Anny H. Xiang 7   na1 ,
• Kathleen A. Page 3 , 4 , 10   na1 &
• Stephanie Kullmann   ORCID: orcid.org/0000-0001-9951-923X 1 , 2 , 8   na1  

International Journal of Obesity ( 2024 ) Cite this article

Metrics details

• Risk factors

Intrauterine exposure to gestational diabetes mellitus (GDM) increases the risk of obesity in the offspring, but little is known about the underlying neural mechanisms. The hippocampus is crucial for food intake regulation and is vulnerable to the effects of obesity. The purpose of the study was to investigate whether GDM exposure affects hippocampal functional connectivity during exposure to food cues using functional magnetic resonance imaging (fMRI).

Participants were 90 children age 7–11 years (53 females) who underwent an fMRI-based visual food cue task in the fasted state. Hippocampal functional connectivity (FC) was examined using generalized psychophysiological interaction in response to food versus non-food cues. Hippocampal FC was compared between children with and without GDM exposure, while controlling for possible confounding effects of age, sex and waist-to-hip ratio. In addition, the influence of childhood and maternal obesity were investigated using multiple regression models.

While viewing high caloric food cues compared to non-food cure, children with GDM exposure exhibited higher hippocampal FC to the insula and striatum (i.e., putamen, pallidum and nucleus accumbens) compared to unexposed children. With increasing BMI, children with GDM exposure had lower hippocampal FC to the somatosensory cortex (i.e., postcentral gyrus).

Conclusions

Intrauterine exposure to GDM was associated with higher food-cue induced hippocampal FC especially to reward processing regions. Future studies with longitudinal measurements are needed to clarify whether altered hippocampal FC may raise the risk of the development of metabolic diseases later in life.

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Introduction.

Gestational diabetes mellitus (GDM) is traditionally defined as glucose intolerance with first-time diagnosis during pregnancy [ 1 ]. It develops in approximately 10% of pregnancies, making it one of the prevalent complications during gestation [ 2 ]. Intrauterine exposure to GDM increases the risk of developing obesity in offspring [ 2 ]. It is not yet clear which factors might drive these conditions later in life, but early neurodevelopmental processes appear sensitive to intrauterine hyperglycemia, hyperinsulinemia and neuroinflammation caused by maternal overnutrition, including hyperglycemia [ 3 , 4 ]. Furthermore, intrauterine exposure to GDM may lead to increased food intake, which is regulated by multiple brain regions, as the hypothalamus, striatum, insula, hippocampus etc. [ 5 , 6 ]. Significantly, functional imaging data demonstrated that food cue reactivity in these brain regions can predict weight gain including in children [ 7 , 8 ].

Children exposed to GDM display higher food cue reactivity in the orbitofrontal cortex [ 9 ], fail to inhibit hypothalamic activity after glucose ingestion [ 10 ] and exhibit hypothalamic inflammation [ 11 ]. Moreover, data from animals and humans suggests the development of the hippocampus is sensitive to adverse in utero environmental exposures (e.g., GDM) [ 4 , 12 , 13 , 14 , 15 ]. In animals, intrauterine exposure to diabetes caused decreased neuronal density and reduced synaptic integrity in the hippocampus [ 4 , 12 , 13 ]. GDM exposure in utero and maternal obesity also associated with reduced thickness and volume in the hippocampus in children [ 14 , 15 ].

The hippocampus is known for its major role in learning and memory and is believed to influence food intake by integrating learned experiences with interoceptive signals (for review, see [ 16 ]). Animal models and behavioral studies in humans suggest that even a brief exposure to a diet rich in dietary fat and sugar can impair hippocampal-dependent learning and memory [ 17 , 18 ]. Behavioral data in healthy humans showed that influencing meal memory can reduce or enhance later food intake [ 19 , 20 ]. Furthermore, amnesic patients fail to interpret interoceptive signals related to hunger and satiety [ 21 ]. Using fMRI, the hippocampus has been shown to be responsive to the ingestion of sugar, visual food cues, and postprandial hormones in healthy adults [ 16 , 22 , 23 ]. Hence, hippocampal dysfunctions may impair the ability to retrieve memories of meals, detect interoceptive signals, which may lead to overeating (for reviews, see [ 24 ]).

However, there is currently no available research on the hippocampus functional network in response to visual food cues in children with GDM exposure, who exhibit higher risk of developing obesity [ 2 ]. Thus, the current study investigates the relation between GDM exposure and functional connectivity (FC) of the hippocampus in children.

We examined task-based FC of the bilateral hippocampus in children with and without GDM exposure using generalized psychophysiological interaction (gPPI) in response to visual food cues (food minus non-food) in the BrainChild Cohort [ 9 , 25 ]. Prior studies [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ] indicate higher food-cue-induced neural reactivity of reward regions and alterations in hippocampal FC in both children and adults with obesity. Hence, we hypothesized that hippocampal FC is higher to reward-related regions during food cue presentation in children with GDM exposure when compared to children without exposure. In addition, we explored the relationship between adiposity measures of children and mothers and hippocampal FC. Given prior evidence suggesting that GDM has distinct effects on the left and right hippocampus in children [ 14 ], we conducted separate exploratory analyses on the FC of the left and right hippocampus.

Participants

Participants included 112 children from the larger BrainChild study assessing the impact of exposure to GDM in utero on neural and endocrine systems underlying risk for obesity and diabetes [ 10 ]. The BrainChild study included typically developing children aged 7–11 years recruited from Kaiser Permanente Southern California (KPSC) [ 9 , 25 ]. Inclusion criteria included KPSC’s electronic medical records, which documented maternal GDM or normal glucose tolerance during pregnancy, uncomplicated singleton birth, and children with no history of medical/psychiatric disorders or taking medicines affecting metabolism. Twenty-two participants were excluded due to excessive movement, image artifacts, or the presence of brain lesions. The final analyses included a total of 90 participants. Based on the sample size of N  = 90 and the detected effect size of 0.8 (primary analysis: GDM versus Non-GDM), we achieved a statistical power of 0.96 at an alpha level of 0.05.

Ethics approval and consent to participate

The institutional review board at both KPSC (# 10282) and University of Southern California (USC) (# HS-14-00034) approved this study. This study was in accordance with the Declaration of Helsinki. Parents and children were provided with written informed consent and informed child assent prior to the study.

Maternal GDM exposure

GDM during pregnancy was determined based on one of the following laboratory plasma glucose values during pregnancy: (1) plasma glucose values ≥ 200 mg/dL from a 50 g 1-hr glucose challenge test, (2) at least two plasma glucose values meeting or exceeding the following values on either the 75 g 2-hrs or 100 g 3-hrs oral glucose tolerance test: fasting, 95 mg/dL; 1 h, 180 mg/dL; 2 h, 155 mg/dL; and 3 h, 140 mg/dL [ 33 ].

Study procedures

The data for this study were collected over two visits conducted after a 12-h overnight fast. The first visit consisted of metabolic phenotyping, including assessments of anthropometric measures. The second visit was a neuroimaging visit, including functional magnetic resonance imaging (fMRI) measurement during a food cue task after the overnight fast.

First visit: anthropometric measurement

During the first visit, anthropometric data, including height, weight, waist and hip circumferences of both the mother and child, tanner stage of child were collected at the Clinical Research Unit of the USC Diabetes and Obesity Research Institute as previously reported [ 10 ]. Specific to children, BMI z -scores (BMI-z) were calculated using the Center for Disease Control (CDC) guidelines [ 34 ].

Second visit: MRI measurement

After the overnight fast, fMRI measurements of the children were performed at the USC Dana and David Dornsife Neuroimaging Center. Children first underwent training on a mock scanner, after which they were imaged in a 3 T MRI scanner. All children were scanned between 8 and 10 am following 12-h of overnight fasting. They completed a visual food cue task in the scanner (For more details, see [ 25 ]). Briefly, children were presented high-calorie food (e.g., ice cream) and non-food (e.g., pencils) pictures and instructed to watch the pictures attentively. The stimuli were selected based on pilot studies of children’s ratings of familiarity and appeal of the food and non-food items. And, the food and non-food tems were also selected to include similar characteristics such as contrast, salience, color, shape and complexity. A total of 12 blocks of stimuli were included, comprising an equal distribution of 50% food images and 50% non-food images. Each block included three images and each image was displayed for 4 s with 1 s consistent inter-stimulus interval between pictures. The sequence of the blocks was randomized. The food cue task lasted 196 s in total. The task was designed to be particularly efficient for differential effects (food versus non-food) with a short stimulus onset asynchrony and not for common task effects or task effects versus implicit baseline.

Image acquisition and preprocessing

The imaging was conducted on a Siemens MAGNETOM Prismafit 3 T MRI scanner with a 20-channel head coil. Functional images were obtained using a 2D single-shot gradient echo planar imaging sequence with the following parameters: repetition time (TR) = 2000 ms; echo time = 25 ms; flip angle = 85°; voxel resolution 3.4 × 3.4 × 4 mm 3 ; 32 axial slices. A high-resolution structural image was also acquired at 1 × 1 × 1 mm 3 resolution. For more details, see publication [ 25 ].

The preprocessing of the fMRI data was performed using SPM12 ( http://www.fil.ion.ucl.ac.uk/spm ). Slice timing and realignment were performed for each fMRI time series. Movement criteria was movement > 2° or 2 mm in any direction, or mean framewise displacement of more than 0.3 mm. The resulting mean functional image and the structural image was coregistered. Unified segmentation was performed to the anatomical image and normalization parameters were estimated. Then, these parameters were applied to the functional images and normalized into Montreal Neurological Institute (MNI) space, using the same method applied in our previous paper by Luo et al. [ 25 ] and in other studies [ 35 , 36 ] with children within the same age range. The data were then smoothed with an 8 mm field-width half-maximum (FWHM) Gaussian kernel. Physiological noise signals in the white matter and cerebrospinal fluid were extracted using Principal Component Analysis (PCA) using the PhysIO toolbox [ 37 ].

Region of interest (ROI) definition

To specifically investigate the effect of GDM on the hippocampus FC, we used an anatomical ROI-based approach. Left, right and bilateral ROIs of the hippocampus were created using the AAL atlas 3 (AAL3, https://www.oxcns.org ) (Fig. 1 ).

Hippo, Hippocampus; L, left; R, right.

Generalized psychophysiological interaction (first level analysis)

For each participant, the brain response to high-calorie food and non-food images was convolved with a canonical hemodynamic response function, and then added to the General Linear Model (GLM). The six motion parameters, and three components each of the white matter and cerebrospinal fluid signals extracted by PCA were also included in the GLM as confounds. High-pass filtering was applied using bandwidth = 0.0078 (1/128) Hz.

Task-based FC between anatomical seed region of the hippocampus (i.e., bilateral hippocampus) and all other brain voxels was assessed using a generalized psychophysiological interaction (gPPI) approach ( https://www.nitrc.org/projects/gppi version 13.1). In an exploratory analysis, FC was assessed for the right and left hippocampus separately in the same way.

First, the time series from the seed region were extracted. Second, the PPI interaction terms were generated for food and non-food stimuli according to the time series. Finally, FC of the seed region was computed for food and non-food stimuli for each participant.

Statistical analyses

Hippocampal functional connectivity in response to food minus non-food cues.

To evaluate intrauterine exposure to GDM on food-cue induced hippocampal FC, the gPPI contrast maps of food minus non-food were entered into a second-level two-sample t-test model with the GDM exposure (GDM vs. Non-GDM) as grouping factor. Age and sex were included in the model as covariates due to their potential effects on hippocampal structure and function [ 14 , 38 ]. Waist-to-hip ratio (WHR) rather than BMI has been reported to be positively correlated with hippocampus activity in response to food cues [ 39 ] and we recently reported higher WHR in children with GDM exposure [ 9 ]. Therefore, WHR was adjusted for the possible impact of adiposity.

The statistical parametric maps were thresholded using an uncorrected threshold of p  < 0.001 and a cluster-level family-wise error (FWE) corrected threshold of p  < 0.05. In addition, small volume correction (SVC) was performed for the insula and striatum (caudate, putamen, nucleus accumbens, pallidum), based on their activation in response to food reward processing and influenced by obesity in children and adolescents [ 40 , 41 ]. The striatal mask and the insular mask were generated based on AAL3 ( https://www.oxcns.org ) and the wfu pick atlas ( https://www.nitrc.org/projects/wfu_pickatlas/ ). Multiple comparison was implemented for two masks using corrected threshold p  < 0.025.

Correlation between task-based hippocampal functional connectivity and obesity measures of children and mothers

To explore the effect of children’s obesity and maternal adiposity on bilateral hippocampal FC in children, a second-level multiple regression model was created using SPM 12 at the whole-brain level. This analysis was performed separately for children with and without GDM exposure. These models included the gPPI food minus non-food contrast as intercept, with WHR, BMI z -score, maternal current BMI or maternal prepregnancy BMI as the regressors of interest, adjusted for age and sex. An uncorrected threshold of p  < 0.001 and a cluster-level FWE corrected threshold of p  < 0.05 were used. The correlations were assessed for the right and left hippocampus separately in the same way.

Demographics

The demographics of the 90 participants included in this study are shown in Table 1 (ages 7–11 years, 53 females, 50 GDM exposed), and 89% of children were in Tanner Stage 1. There were no significant differences in children’s age, sex, BMI z -score, or maternal current BMI or maternal prepregnancy BMI among GDM exposed vs. unexposed groups ( p  > 0.05, Table 1 ). There was a trend towards a higher WHR for children exposed to GDM than unexposed (t [88] = 1.97, p  = 0.052, Table 1 ).

We observed higher FC in children with GDM exposure compared to children without GDM exposure between the bilateral hippocampus and the left insula ( p FWE  = 0.037) and left putamen, which extended to the left pallidum ( p FWE  = 0.019, SVC) (Table 2 , Fig. 2 ).

In an exploratory analysis, FC was assessed for the right and left hippocampus separately. In children with GDM exposure compared to children without exposure, we observed higher FC between the left hippocampus and the right putamen ( p FWE  = 0.007), left putamen ( p FWE  = 0.017, SVC), right insula ( p FWE  = 0.017), left insula ( p FWE  = 0.011, SVC), and left nucleus accumbens (NAcc, p FWE  = 0.013, SVC) (Table 2 , Fig. 2 ). The cluster of the right putamen extended to the right insula. The cluster of the left putamen extended to the left pallidum. No group differences were found for the right hippocampus.

a Children with GDM exposure showed higher FC between bilateral hippocampus and left insula, left putamen/pallidum. b Children with GDM exposure showed higher FC between left hippocampus and the bilateral putamen, insula, and left NAcc. The cluster of the right putamen extended to the right insula. The cluster of left putamen extended to the left pallidum. Color map corresponds to T values ( p  < 0.001 uncorrected for display) overlaid on the normalized average T1 weighted image of the children. Hippo hippocampus, FC functional connectivity, GDM gestational diabetes mellitus, NAcc nucleus accumbens, L left, R right.

Association between task-based hippocampal functional connectivity and obesity measures of children and mothers

No significant correlation was observed between the FC of the bilateral hippocampus and WHR, BMI z -score, maternal current or maternal prepregnancy BMI in both the GDM and Non-GDM groups (all p FWE-corrected  > 0.05).

Further analysis of the FC of the left or right hippocampus separately revealed significant correlations. In the GDM group, there was a negative correlation between BMI z -score and the FC of the left hippocampus and the right postcentral gyrus (peak-voxel (MNI) x: 57, y: −34, z: 26); r  = −0.607; p FWE-corrected  < 0.001) (Fig. 3 ). In the Non-GDM group, a positive correlation was found between the maternal current BMI and the FC of the left hippocampus to the right superior frontal gyrus (peak-voxel (MNI) x: 18, y: 59, z: −1); r  = 0.574; p FWE-corrected  = 0.001).

a Children with GDM exposure showed lower FC between the left hippocampus and the right postcentral gyrus with higher BMI z -score. Color map corresponds to T values (Multiple regression analysis with BMI z -score; p  < 0.001 uncorrected for display) overlaid on the normalized average T1 weighted image of the children. b Negative correlation between BMI z -score and the extracted cluster of the FC of the left hippocampus and the right postcentral gyrus in the GDM group. Error bars indicate 95% confidence interval. Hippo hippocampus, FC functional connectivity, GDM gestational diabetes mellitus, L left, R right.

The current study investigated the relationship between intrauterine GDM exposure and food cue induced hippocampal functional connectivity in children aged 7–11 years in the fasted state. Consistent with our hypothesis, children with GDM exposure compared to unexposed showed higher hippocampal FC to reward processing regions (i.e., putamen, pallidum, NAcc and insular cortex) and lower hippocampal FC to the somatosensory cortex with increasing BMI.

We observed higher functional coupling between hippocampus and striatal regions and insula in children with intrauterine GDM exposure compared to children without exposure, primarily driven by the left hippocampus. A previous structural MRI report found reduced left hippocampal thickness in children with GDM exposure compared to unexposed children [ 14 ]. Therefore, GDM may affect both the structure and function of the hippocampus.

Hippocampal neurons interact with other neurons in the mesolimbic system receiving dopamine projections to communicate rewarding properties of environmental stimuli [ 16 , 42 ]. As potent rewards, palatable foods can trigger associations with reward and motivational behaviors that potentially could lead to overeating and, eventually, weight gain [ 42 ]. These food cues tend to evoke heightened memories and mental simulations of consumption in children [ 43 ]. Moreover, a meta-analysis indicated that the hippocampus-striatum connection may play a role in craving and the formation of habits associated with obesity [ 44 ]. Concomitantly, higher activation in the striatum and insula in response to food images were observed in children and adolescents with obesity compared to their healthy-weight peers [ 40 , 41 , 45 ]. In the resting state, higher striatal and insular network FC was also linked to eating in the absence of hunger, food craving, disinhibited eating, weight gain and obesity in both children and adults [ 46 , 47 , 48 , 49 ]. In the current study, no significant influence of WHR or BMI was identified on these hippocampal connections in children. However, BMI negatively correlated with the left hippocampus to the somatosensory cortex FC in children exposed to GDM, aligning with resting-state studies in children with obesity [ 50 ]. The oral somatosensory cortex is known to sense fat and food texture [ 51 ] and children and adolescents with obesity show greater activation in the somatosensory cortex to food [ 8 , 52 ]. The higher preference for high-fat foods in children is a predictor of future weight gain [ 53 ]. Nonetheless, it is yet unknown whether altered hippocampal to somatosensory connectivity patterns in children with GDM exposure predict the development of obesity later in life.

Our study points to a distinct effect of intrauterine GDM exposure on the hippocampal network primarily to reward processing regions, rather than obesity itself at this young age. These results align with animal studies [ 4 , 12 , 13 ] and provide evidence to support the hypothesis that prenatal exposure to diabetes might result in changes in brain pathways. These changes, in turn, may contribute to the increased risk of weight gain and obesity in affected children at a later age. Interestingly, previous studies suggest that hyperactivity in the brain’s reward system might be a susceptibility factor for weight gain [ 8 , 54 ]. Similarly, our previous study showed that children exposed to GDM had higher daily energy intake [ 9 ]. Moreover, parental obesity has been related to greater striatum and insula activation in response to food rewards and higher ad libitum intake even in adolescents of healthy-weight [ 8 , 55 ]. In the current study, we also found higher food-cue induced hippocampal FC to the frontal cortex in children of mothers with higher BMI. Although this connection in relation to maternal obesity has not yet been fully investigated, higher FC between temporal and frontal regions has been reported in adolescent obesity [ 56 ]. Future studies with longitudinal measurements are necessary to evaluate whether hippocampal changes in FC result in weight gain and raise the risk of developing obesity later in life.

Our study includes some limitations. Given the limited size of our sample, each subgroup, based on GDM exposure, included a relatively small number of subjects. In addition, food intake and behavioral assessments were not assessed, and future studies are necessary to provide a more detailed understanding how the observed functional alterations in the hippocampus are related to cognitive and metabolic processes. Moreover, longitudinal data are needed to examine the association between functional alterations in the hippocampus and future weight gain in children.

Our study suggests that intrauterine exposure to GDM alters hippocampal food cue processing network in children. During palatable food picture presentation, children with GDM exposure exhibited higher hippocampal connectivity specifically to reward processing regions and lower hippocampal connectivity, with increasing BMI, to the somatosensory cortex. These alterations may be associated with a potential risk for future weight gain. Longitudinal research is required to determine if altered hippocampal functional connectivity during exposure to food cues leads to future weight gain and a higher likelihood of metabolic disorders, including obesity.

Data availability

Data is available upon reasonable request from KAP.

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Acknowledgements

The authors would like to thank the volunteers who participated in this study.

This work was supported by an American Diabetes Association Pathway Accelerator Award (#1-14-ACE-36) (principal investigator: KAP) and in part by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, grants R03DK103083 (principal investigator: KAP), R01DK116858 (principal investigator: KAP, AHX), and K01DK115638 (principal investigator: SL). A Research Electronic Data Capture, database was used for this study, which is supported by the Southern California Clinical and Translational Science Institute (SC CTSI) through U.S. Department of Health and Human Services (DHHS) grant UL1-TR-001855. SXZ is funded by China Scholarship Council (CSC). ALB, HP, RV and SK were partially funded by a grant (01GI0925) from the Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). Open Access funding enabled and organized by Projekt DEAL.

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These authors contributed equally: Anny H. Xiang, Kathleen A. Page, Stephanie Kullmann.

Authors and Affiliations

Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany

Sixiu Zhao, Lorenzo Semeia, Ralf Veit, Andreas L. Birkenfeld, Hubert Preissl & Stephanie Kullmann

German Center for Diabetes Research (DZD), Tübingen, Germany

Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Shan Luo, Brendan C. Angelo & Kathleen A. Page

Diabetes and Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Department of Psychology, University of Southern California, Los Angeles, CA, USA

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA

Ting Chow & Anny H. Xiang

Department of Internal Medicine, Division of Diabetology, Endocrinology and Nephrology, Eberhard Karls University Tübingen, Tübingen, Germany

Andreas L. Birkenfeld, Hubert Preissl & Stephanie Kullmann

Department of Pharmacy and Biochemistry, Eberhard Karls University Tübingen, Tübingen, Germany

Hubert Preissl

Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA

Kathleen A. Page

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Contributions

SXZ and SK conceptualized and conducted the analysis, drafted the manuscript; RV and LS supported the analysis and discussed the results; HP, AHX, KAP and SK provided critical review and revisions to the manuscript; AHX and KAP conceptualized the original study, have full access to all data in the study and take responsibility for the integrity of the data; SL, BCA, and TC managed and coordinated the study execution; ALB, HP, SK supervised the work. All authors discussed the results and implications, reviewed and edited the manuscript and approved its final version. KAP, AHX and SL provided funding for this study.

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Correspondence to Stephanie Kullmann .

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Zhao, S., Semeia, L., Veit, R. et al. Exposure to gestational diabetes mellitus in utero impacts hippocampal functional connectivity in response to food cues in children. Int J Obes (2024). https://doi.org/10.1038/s41366-024-01608-1

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Received : 13 February 2024

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Published : 28 August 2024

DOI : https://doi.org/10.1038/s41366-024-01608-1

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Gestational diabetes mellitus and development of intergenerational overall and subtypes of cardiovascular diseases: a systematic review and meta-analysis

Affiliations.

• 1 Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
• 2 Statistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
• 3 Department of O&G, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
• 4 Global Centre for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
• 5 School of Medicine, NUS Bia-Echo Asia Centre for Reproductive Longevity and Equality (ACRLE), Yong Loo Lin, National University of Singapore, Singapore, Singapore.
• 6 Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore.
• 7 Duke-NUS Medical School, Singapore, Singapore.
• 8 Department of O&G, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. [email protected].
• 9 Global Centre for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. [email protected].
• 10 School of Medicine, NUS Bia-Echo Asia Centre for Reproductive Longevity and Equality (ACRLE), Yong Loo Lin, National University of Singapore, Singapore, Singapore. [email protected].
• 11 Yong Loo Lin School of Medicine, Global Centre for Asian Women's Health, National University of Singapore, Singapore. 12 Science Drive 2, Level 16, Singapore, 117549, Singapore. [email protected].
• PMID: 39198842
• PMCID: PMC11360578
• DOI: 10.1186/s12933-024-02416-7

Objective: We aimed to summarize the association between gestational diabetes mellitus (GDM) and its intergenerational cardiovascular diseases (CVDs) impacts in both mothers and offspring post-delivery in existing literature.

Methods: PubMed, Embase, Web of Science, and Scopus were utilized for searching publications between January 1980 and June 2024, with data extraction and meta-analysis continuing until 31 July 2024. Based on a predefined PROSPERO protocol, studies published as full-length, English-language journal articles that reported the presence of GDM during pregnancy and its association with any CVD development post-delivery were selected. All studies were evaluated using the Newcastle-Ottawa Scale. Maximally adjusted risk estimates were pooled using random-effects meta-analysis to assess the risk ratio (RR) of GDM, and overall and subtypes of CVDs in both mothers and offspring post-delivery.

Results: The meta-analysis was based on 38 studies with a total of 77,678,684 participants. The results showed a 46% increased risk (RR 1.46, 95% CI 1.34-1.59) for mothers and a 23% increased risk (1.23, 1.05-1.45) for offspring of developing overall CVDs after delivery, following a GDM-complicated pregnancy. Our subgroup analysis revealed that mothers with a history of GDM faced various risks (20% to 2-fold) of developing different subtypes of CVDs, including cerebrovascular disease, coronary artery disease, heart failure, and venous thromboembolism.

Conclusions: These findings underscore the heightened risk of developing various CVDs for mothers and offspring affected by GDM, emphasizing the importance of preventive measures even right after birth to mitigate the burden of CVDs in these populations.

Keywords: Cardiovascular diseases; Gestational diabetes mellitus; Intergenerational; Meta-analysis; Mothers; Offspring; Pregnancy; Systematic review.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

Meta-analysis Results. Evidence of risk…

Meta-analysis Results. Evidence of risk ratio (RR) and 95% confidence interval (CI) of…

Subgroup analyses stratified by subtypes…

Subgroup analyses stratified by subtypes of CVDs. Evidence of risk ratio (RR) and…

• American Diabetes A. 2. Classification and diagnosis of diabetes: standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S14–31. 10.2337/dc20-S002 - DOI - PubMed
• McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers. 2019;5(1):47. 10.1038/s41572-019-0098-8 - DOI - PubMed
• Parikh NI, Gonzalez JM, Anderson CAM, Judd SE, Rexrode KM, Hlatky MA, Gunderson EP, Stuart JJ, Vaidya D, American Heart Association Council on E. Adverse pregnancy outcomes and Cardiovascular Disease Risk: Unique Opportunities for Cardiovascular Disease Prevention in Women: A Scientific Statement from the American Heart Association. Circulation. 2021;143(18):e902–16. 10.1161/CIR.0000000000000961 - DOI - PubMed
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• Perng W, Hockett CW, Sauder KA, Dabelea D. In utero exposure to gestational diabetes mellitus and cardiovascular risk factors in youth: a longitudinal analysis in the EPOCH cohort. Pediatr Obes. 2020;15(5):e12611. 10.1111/ijpo.12611 - DOI - PMC - PubMed

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Deep Learning Model for Gestational Diabetes Prediction Based on Imbalanced Data and Feature Selection Optimization

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• Heba Askr 7 &
• Aboul Ella Hassanien 8 , 9  

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Gestational diabetes mellitus (GDM) is a form of elevated blood sugar which appears in pregnancy. It can happen at any point during pregnancy as well as create problems both for the mother and the child, both before and after birth. Giving GDM patients an early and accurate diagnosis is essential for effective treatment along with disease control as well as achieving the third sustainable development goal. Undiagnosed diabetes can lead to several dangerous conditions such as heart attack and kidney disease. This necessitates the need for learning model improvement in GDM detection and evaluation. Digital health has gained significant traction in recent years with the aim of enhancing care for diabetic pregnant women. This technology has generated an enormous amount of data that could be used to improve the management of this chronic disease. Benefiting from this, artificial intelligence (AI) methods, particularly deep learning (DL) which is a newly developed branch of machine learning (ML), are commonly used and showing good outcomes. In this paper, a Multilayer Perceptron (MLP) model is proposed to determine whether a woman has GDM. Pregnant women with and without diabetes are represented in the dataset under consideration. Imbalanced data of 1012 patients with six major features and a target column with result diabetic or non-diabetic have been analyzed and preprocessed. Feature selection optimization is developed to enhance the performance of the proposed model. The results show that the proposed model significantly improved the prediction accuracy over other related works with promising prediction accuracy which reaches almost to 98%.

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Faculty of Computers and Artificial Intelligence, University of Sadat City, Menoufia, Egypt

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College of Business Administration (CBA), Kuwait University, Kuwait, Kuwait

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Correspondence to Heba Askr .

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Computer and Information Engineering, Harbin University Of Commerce, Harbin, Heilongjiang, China

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School of Computer and Information Engg, Harbin University of Commerce, Harbin, China

Zhijie Zhao

Harbin University of Commerce, HARBIN, China

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Askr, H., Hassanien, A.E. (2024). Deep Learning Model for Gestational Diabetes Prediction Based on Imbalanced Data and Feature Selection Optimization. In: Hassanien, A.E., Zheng, D., Zhao, Z., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2023. Smart Innovation, Systems and Technologies, vol 394. Springer, Singapore. https://doi.org/10.1007/978-981-97-3980-6_54

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Case 6–2020: A 34-Year-Old Woman with Hyperglycemia

Presentation of case.

Dr. Max C. Petersen (Medicine): A 34-year-old woman was evaluated in the diabetes clinic of this hospital for hyperglycemia.

Eleven years before this presentation, the blood glucose level was 126 mg per deciliter (7.0 mmol per liter) on routine laboratory evaluation, which was performed as part of an annual well visit. The patient could not recall whether she had been fasting at the time the test had been performed. One year later, the fasting blood glucose level was 112 mg per deciliter (6.2 mmol per liter; reference range, <100 mg per deciliter [<5.6 mmol per liter]).

Nine years before this presentation, a randomly obtained blood glucose level was 217 mg per deciliter (12.0 mmol per liter), and the patient reported polyuria. At that time, the glycated hemoglobin level was 5.8% (reference range, 4.3 to 5.6); the hemoglobin level was normal. One year later, the glycated hemoglobin level was 5.9%. The height was 165.1 cm, the weight 72.6 kg, and the body-mass index (BMI; the weight in kilograms divided by the square of the height in meters) 26.6. The patient received a diagnosis of prediabetes and was referred to a nutritionist. She made changes to her diet and lost 4.5 kg of body weight over a 6-month period; the glycated hemoglobin level was 5.5%.

Six years before this presentation, the patient became pregnant with her first child. Her prepregnancy BMI was 24.5. At 26 weeks of gestation, the result of a 1-hour oral glucose challenge test (i.e., the blood glucose level obtained 1 hour after the oral administration of a 50-g glucose load in the nonfasting state) was 186 mg per deciliter (10.3 mmol per liter; reference range, <140 mg per deciliter [<7.8 mmol per liter]). She declined a 3-hour oral glucose tolerance test; a presumptive diagnosis of gestational diabetes was made. She was asked to follow a meal plan for gestational diabetes and was treated with insulin during the pregnancy. Serial ultrasound examinations for fetal growth and monitoring were performed. At 34 weeks of gestation, the fetal abdominal circumference was in the 76th percentile for gestational age. Polyhydramnios developed at 37 weeks of gestation. The child was born at 39 weeks 3 days of gestation, weighed 3.9 kg at birth, and had hypoglycemia after birth, which subsequently resolved. Six weeks post partum, the patient’s fasting blood glucose level was 120 mg per deciliter (6.7 mmol per liter), and the result of a 2-hour oral glucose tolerance test (i.e., the blood glucose level obtained 2 hours after the oral administration of a 75-g glucose load in the fasting state) was 131 mg per deciliter (7.3 mmol per liter; reference range, <140 mg per deciliter). Three months post partum, the glycated hemoglobin level was 6.1%. Lifestyle modification for diabetes prevention was recommended.

Four and a half years before this presentation, the patient became pregnant with her second child. Her prepregnancy BMI was 25.1. At 5 weeks of gestation, she had an elevated blood glucose level. Insulin therapy was started at 6 weeks of gestation, and episodes of hypoglycemia occurred during the pregnancy. Serial ultrasound examinations for fetal growth and monitoring were performed. At 28 weeks of gestation, the fetal abdominal circumference was in the 35th percentile for gestational age, and the amniotic fluid level was normal. Labor was induced at 38 weeks of gestation; the child weighed 2.6 kg at birth. Neonatal blood glucose levels were reported as stable after birth. Six weeks post partum, the patient’s fasting blood glucose level was 133 mg per deciliter (7.4 mmol per liter), and the result of a 2-hour oral glucose tolerance test was 236 mg per deciliter (13.1 mmol per liter). The patient received a diagnosis of type 2 diabetes mellitus; lifestyle modification was recommended. Three months post partum, the glycated hemoglobin level was 5.9% and the BMI was 30.0. Over the next 2 years, she followed a low-carbohydrate diet and regular exercise plan and self-monitored the blood glucose level.

Two years before this presentation, the patient became pregnant with her third child. Blood glucose levels were again elevated, and insulin therapy was started early in gestation. She had episodes of hypoglycemia that led to adjustment of her insulin regimen. The child was born at 38 weeks 5 days of gestation, weighed 3.0 kg at birth, and had hypoglycemia that resolved 48 hours after birth. After the birth of her third child, the patient started to receive metformin, which had no effect on the glycated hemoglobin level, despite adjustment of the therapy to the maximal dose.

One year before this presentation, the patient became pregnant with her fourth child. Insulin therapy was again started early in gestation. The patient reported that episodes of hypoglycemia occurred. Polyhydramnios developed. The child was born at 38 weeks 6 days of gestation and weighed 3.5 kg. The patient sought care at the diabetes clinic of this hospital for clarification of her diagnosis.

The patient reported following a low-carbohydrate diet and exercising 5 days per week. There was no fatigue, change in appetite, change in vision, chest pain, shortness of breath, polydipsia, or polyuria. There was no history of anemia, pancreatitis, hirsutism, proximal muscle weakness, easy bruising, headache, sweating, tachycardia, gallstones, or diarrhea. Her menstrual periods were normal. She had not noticed any changes in her facial features or the size of her hands or feet.

The patient had a history of acne and low-back pain. Her only medication was metformin. She had no known medication allergies. She lived with her husband and four children in a suburban community in New England and worked as an administrator. She did not smoke tobacco or use illicit drugs, and she rarely drank alcohol. She identified as non-Hispanic white. Both of her grandmothers had type 2 diabetes mellitus. Her father had hypertension, was overweight, and had received a diagnosis of type 2 diabetes at 50 years of age. Her mother was not overweight and had received a diagnosis of type 2 diabetes at 48 years of age. The patient had two sisters, neither of whom had a history of diabetes or gestational diabetes. There was no family history of hemochromatosis.

On examination, the patient appeared well. The blood pressure was 126/76 mm Hg, and the heart rate 76 beats per minute. The BMI was 25.4. The physical examination was normal. The glycated hemoglobin level was 6.2%.

A diagnostic test was performed.

DIFFERENTIAL DIAGNOSIS

Dr. Miriam S. Udler: I am aware of the diagnosis in this case and participated in the care of this patient. This healthy 34-year-old woman, who had a BMI just above the upper limit of the normal range, presented with a history of hyperglycemia of varying degrees since 24 years of age. When she was not pregnant, she was treated with lifestyle measures as well as metformin therapy for a short period, and she maintained a well-controlled blood glucose level. In thinking about this case, it is helpful to characterize the extent of the hyperglycemia and then to consider its possible causes.

CHARACTERIZING HYPERGLYCEMIA

This patient’s hyperglycemia reached a threshold that was diagnostic of diabetes 1 on two occasions: when she was 25 years of age, she had a randomly obtained blood glucose level of 217 mg per deciliter with polyuria (with diabetes defined as a level of ≥200 mg per deciliter [≥11.1 mmol per liter] with symptoms), and when she was 30 years of age, she had on the same encounter a fasting blood glucose level of 133 mg per deciliter (with diabetes defined as a level of ≥126 mg per deciliter) and a result on a 2-hour oral glucose tolerance test of 236 mg per deciliter (with diabetes defined as a level of ≥200 mg per deciliter). On both of these occasions, her glycated hemoglobin level was in the prediabetes range (defined as 5.7 to 6.4%). In establishing the diagnosis of diabetes, the various blood glucose studies and glycated hemoglobin testing may provide discordant information because the tests have different sensitivities for this diagnosis, with glycated hemoglobin testing being the least sensitive. 2 Also, there are situations in which the glycated hemoglobin level can be inaccurate; for example, the patient may have recently received a blood transfusion or may have a condition that alters the life span of red cells, such as anemia, hemoglobinopathy, or pregnancy. 3 These conditions were not present in this patient at the time that the glycated hemoglobin measurements were obtained. In addition, since the glycated hemoglobin level reflects the average glucose level typically over a 3-month period, discordance with timed blood glucose measurements can occur if there has been a recent change in glycemic control. This patient had long-standing mild hyperglycemia but met criteria for diabetes on the basis of the blood glucose levels noted.

Type 1 and Type 2 Diabetes

Now that we have characterized the patient’s hyperglycemia as meeting criteria for diabetes, it is important to consider the possible types. More than 90% of adults with diabetes have type 2 diabetes, which is due to progressive loss of insulin secretion by beta cells that frequently occurs in the context of insulin resistance. This patient had received a diagnosis of type 2 diabetes; however, some patients with diabetes may be given a diagnosis of type 2 diabetes on the basis of not having features of type 1 diabetes, which is characterized by autoimmune destruction of the pancreatic beta cells that leads to rapid development of insulin dependence, with ketoacidosis often present at diagnosis.

Type 1 diabetes accounts for approximately 6% of all cases of diabetes in adults (≥18 years of age) in the United States, 4 and 80% of these cases are diagnosed before the patient is 20 years of age. 5 Since this patient’s diabetes was essentially nonprogressive over a period of at least 9 years, she most likely does not have type 1 diabetes. It is therefore not surprising that she had received a diagnosis of type 2 diabetes, but there are several other types of diabetes to consider, particularly since some features of her case do not fit with a typical case of type 2 diabetes, such as her age at diagnosis, the presence of hyperglycemia despite a nearly normal BMI, and the mild and nonprogressive nature of her disease over the course of many years.

Less Common Types of Diabetes

Latent autoimmune diabetes in adults (LADA) is a mild form of autoimmune diabetes that should be considered in this patient. However, there is controversy as to whether LADA truly represents an entity that is distinct from type 1 diabetes. 6 Both patients with type 1 diabetes and patients with LADA commonly have elevated levels of diabetes-associated autoantibodies; however, LADA has been defined by an older age at onset (typically >25 years) and slower progression to insulin dependence (over a period of >6 months). 7 This patient had not been tested for diabetes-associated autoantibodies. I ordered these tests to help evaluate for LADA, but this was not my leading diagnosis because of her young age at diagnosis and nonprogressive clinical course over a period of at least 9 years.

If the patient’s diabetes had been confined to pregnancy, we might consider gestational diabetes, but she had hyperglycemia outside of pregnancy. Several medications can cause hyperglycemia, including glucocorticoids, atypical antipsychotic agents, cancer immunotherapies, and some antiretroviral therapies and immunosuppressive agents used in transplantation. 8 However, this patient was not receiving any of these medications. Another cause of diabetes to consider is destruction of the pancreas due to, for example, cystic fibrosis, a tumor, or pancreatitis, but none of these were present. Secondary endocrine disorders — including excess cortisol production, excess growth hormone production, and pheochromocytoma — were considered to be unlikely in this patient on the basis of the history, review of symptoms, and physical examination.

Monogenic Diabetes

A final category to consider is monogenic diabetes, which is caused by alteration of a single gene. Types of monogenic diabetes include maturity-onset diabetes of the young (MODY), neonatal diabetes, and syndromic forms of diabetes. Monogenic diabetes accounts for 1 to 6% of cases of diabetes in children 9 and approximately 0.4% of cases in adults. 10 Neonatal diabetes is diagnosed typically within the first 6 months of life; syndromic forms of monogenic diabetes have other abnormal features, including particular organ dysfunction. Neither condition is applicable to this patient.

MODY is an autosomal dominant condition characterized by primary pancreatic beta-cell dysfunction that causes mild diabetes that is diagnosed during adolescence or early adulthood. As early as 1964, the nomenclature “maturity-onset diabetes of the young” was used to describe cases that resembled adult-onset type 2 diabetes in terms of the slow progression to insulin use (as compared with the rapid progression in type 1 diabetes) but occurred in relatively young patients. 11 Several genes cause distinct forms of MODY that have specific disease features that inform treatment, and thus MODY is a clinically important diagnosis. Most forms of MODY cause isolated abnormal glucose levels (in contrast to syndromic monogenic diabetes), a manifestation that has contributed to its frequent misdiagnosis as type 1 or type 2 diabetes. 12

Genetic Basis of MODY

Although at least 13 genes have been associated with MODY, 3 genes — GCK , which encodes glucokinase, and HNF1A and HNF4A , which encode hepatocyte nuclear factors 1A and 4A, respectively — account for most cases. MODY associated with GCK (known as GCK-MODY) is characterized by mild, nonprogressive hyperglycemia that is present since birth, whereas the forms of MODY associated with HNF1A and HNF4A (known as HNF1A-MODY and HNF4A-MODY, respectively) are characterized by the development of diabetes, typically in the early teen years or young adulthood, that is initially mild and then progresses such that affected patients may receive insulin before diagnosis.

In patients with GCK-MODY, genetic variants reduce the function of glucokinase, the enzyme in pancreatic beta cells that functions as a glucose sensor and controls the rate of entry of glucose into the glycolytic pathway. As a result, reduced sensitivity to glucose-induced insulin secretion causes asymptomatic mild fasting hyperglycemia, with an upward shift in the normal range of the fasting blood glucose level to 100 to 145 mg per deciliter (5.6 to 8.0 mmol per liter), and also causes an upward shift in postprandial blood glucose levels, but with tight regulation maintained ( Fig. 1 ). 13 This mild hyperglycemia is not thought to confer a predisposition to complications of diabetes, 14 is largely unaltered by treatment, 15 and does not necessitate treatment outside of pregnancy.

Key features suggesting maturity-onset diabetes of the young (MODY) in this patient were an age of less than 35 years at the diagnosis of diabetes, a strong family history of diabetes with an autosomal dominant pattern of inheritance, and hyperglycemia despite a close-to-normal body-mass index. None of these features is an absolute criterion. MODY is caused by single gene–mediated disruption of pancreatic beta-cell function. In MODY associated with the GCK gene (known as GCK-MODY), disrupted glucokinase function causes a mild upward shift in glucose levels through-out the day and does not necessitate treatment. 13 In the pedigree, circles represent female family members, squares male family members, blue family members affected by diabetes, and green unaffected family members. The arrow indicates the patient.

In contrast to GCK-MODY, the disorders HNF1A-MODY and HNF4A-MODY result in progressive hyperglycemia that eventually leads to treatment. 16 Initially, there may be a normal fasting glucose level and large spikes in postprandial glucose levels (to >80 mg per deciliter [>4.4 mmol per liter]). 17 Patients can often be treated with oral agents and discontinue insulin therapy started before the diagnosis of MODY. 18 Of note, patients with HNF1A-MODY or HNF4A-MODY are typically sensitive to treatment with sulfonylureas 19 but may also respond to glucagon-like peptide-1 receptor agonists. 20

This patient had received a diagnosis of diabetes before 35 years of age, had a family history of diabetes involving multiple generations, and was not obese. These features are suggestive of MODY but do not represent absolute criteria for the condition ( Fig. 1 ). 1 Negative testing for diabetes-associated autoantibodies would further increase the likelihood of MODY. There are methods to calculate a patient’s risk of having MODY associated with GCK , HNF1A , or HNF4A . 21 , 22 Using an online calculator ( www.diabetesgenes.org/mody-probability-calculator ), we estimate that the probability of this patient having MODY is at least 75.5%. Genetic testing would be needed to confirm this diagnosis, and in patients at an increased risk for MODY, multigene panel testing has been shown to be cost-effective. 23 , 24

DR. MIRIAM S. UDLER’S DIAGNOSIS

Maturity-onset diabetes of the young, most likely due to a GCK variant.

DIAGNOSTIC TESTING

Dr. Christina A. Austin-Tse: A diagnostic sequencing test of five genes associated with MODY was performed. One clinically significant variant was identified in the GCK gene (NM_000162.3): a c.787T→C transition resulting in the p.Ser263Pro missense change. Review of the literature and variant databases revealed that this variant had been previously identified in at least three patients with early-onset diabetes and had segregated with disease in at least three affected members of two families (GeneDx: personal communication). 25 , 26 Furthermore, the variant was rare in large population databases (occurring in 1 out of 128,844 European chromosomes in gnomAD 27 ), a feature consistent with a disease-causing role. Although the serine residue at position 263 was not highly conserved, multiple in vitro functional studies have shown that the p.Ser263Pro variant negatively affects the stability of the glucokinase enzyme. 26 , 28 – 30 As a result, this variant met criteria to be classified as “likely pathogenic.” 31 As mentioned previously, a diagnosis of GCK-MODY is consistent with this patient’s clinical features. On subsequent testing of additional family members, the same “likely pathogenic” variant was identified in the patient’s father and second child, both of whom had documented hyperglycemia.

DISCUSSION OF MANAGEMENT

Dr. Udler: In this patient, the diagnosis of GCK-MODY means that it is normal for her blood glucose level to be mildly elevated. She can stop taking metformin because discontinuation is not expected to substantially alter her glycated hemoglobin level 15 , 32 and because she is not at risk for complications of diabetes. 14 However, she should continue to maintain a healthy lifestyle. Although patients with GCK-MODY are not typically treated for hyperglycemia outside of pregnancy, they may need to be treated during pregnancy.

It is possible for a patient to have type 1 or type 2 diabetes in addition to MODY, so this patient should be screened for diabetes according to recommendations for the general population (e.g., in the event that she has a risk factor for diabetes, such as obesity). 1 Since the mild hyperglycemia associated with GCK-MODY is asymptomatic (and probably unrelated to the polyuria that this patient had described in the past), the development of symptoms of hyperglycemia, such as polyuria, polydipsia, or blurry vision, should prompt additional evaluation. In patients with GCK-MODY, the glycated hemoglobin level is typically below 7.5%, 33 so a value rising above that threshold or a sudden large increase in the glycated hemoglobin level could indicate concomitant diabetes from another cause, which would need to be evaluated and treated.

This patient’s family members are at risk for having the same GCK variant, with a 50% chance of offspring inheriting a variant from an affected parent. Since the hyperglycemia associated with GCK-MODY is present from birth, it is necessary to perform genetic testing only in family members with demonstrated hyperglycemia. I offered site-specific genetic testing to the patient’s parents and second child.

Dr. Meridale V. Baggett (Medicine): Dr. Powe, would you tell us how you would treat this patient during pregnancy?

Dr. Camille E. Powe: During the patient’s first pregnancy, routine screening led to a presumptive diagnosis of gestational diabetes, the most common cause of hyperglycemia in pregnancy. Hyperglycemia in pregnancy is associated with adverse pregnancy outcomes, 34 and treatment lowers the risk of such outcomes. 35 , 36 Two of the most common complications — fetal overgrowth (which can lead to birth injuries, shoulder dystocia, and an increased risk of cesarean delivery) and neonatal hypoglycemia — are thought to be the result of fetal hyperinsulinemia. 37 Maternal glucose is freely transported across the placenta, and excess glucose augments insulin secretion from the fetal pancreas. In fetal life, insulin is a potent growth factor, and neonates who have hyperinsulinemia in utero often continue to secrete excess insulin in the first few days of life. In the treatment of pregnant women with diabetes, we strive for strict blood sugar control (fasting blood glucose level, <95 mg per deciliter [<5.3 mmol per liter]; 2-hour postprandial blood glucose level, <120 mg per deciliter) to decrease the risk of these and other hyperglycemia-associated adverse pregnancy outcomes. 38 – 40

In the third trimester of the patient’s first pregnancy, obstetrical ultrasound examination revealed a fetal abdominal circumference in the 76th percentile for gestational age and polyhydramnios, signs of fetal exposure to maternal hyperglycemia. 40 – 42 Case series involving families with GCK-MODY have shown that the effect of maternal hyperglycemia on the fetus depends on whether the fetus inherits the pathogenic GCK variant. 43 – 48 Fetuses that do not inherit the maternal variant have overgrowth, presumably due to fetal hyperinsulinemia ( Fig. 2A ). In contrast, fetuses that inherit the variant do not have overgrowth and are born at a weight that is near the average for gestational age, despite maternal hyperglycemia, presumably because the variant results in decreased insulin secretion ( Fig. 2B ). Fetuses that inherit GCK-MODY from their fathers and have euglycemic mothers appear to be undergrown, most likely because their insulin secretion is lower than normal when they and their mothers are euglycemic ( Fig. 2D ). Because fetal overgrowth and polyhydramnios occurred during this patient’s first pregnancy and neonatal hypoglycemia developed after the birth, the patient’s first child is probably not affected by GCK-MODY.

Pathogenic variants that lead to GCK-MODY, when carried by a fetus, change the usual relationship of maternal hyperglycemia to fetal hyperinsulinemia and fetal overgrowth. GCK-MODY–affected fetuses have lower insulin secretion than unaffected fetuses in response to the same maternal blood glucose level. In a hyperglycemic mother carrying a fetus who is unaffected by GCK-MODY, excessive fetal growth is usually apparent (Panel A). Studies involving GCK-MODY–affected hyperglycemic mothers have shown that fetal growth is normal despite maternal hyperglycemia when a fetus has the maternal GCK variant (Panel B). The goal of treatment of maternal hyperglycemia when a fetus is unaffected by GCK-MODY is to establish euglycemia to normalize fetal insulin levels and growth (Panel C); whether this can be accomplished in the case of maternal GCK-MODY is controversial, given the genetically determined elevated maternal glycemic set point. In the context of maternal euglycemia, GCK-MODY–affected fetuses may be at risk for fetal growth restriction (Panel D).

In accordance with standard care for pregnant women with diabetes who do not meet glycemic targets after dietary modification, 38 , 39 the patient was treated with insulin during her pregnancies. In her second pregnancy, treatment was begun early, after hyperglycemia was detected in the first trimester. Because she had not yet received the diagnosis of GCK-MODY during any of her pregnancies, no consideration of this condition was given during her obstetrical treatment. Whether treatment affects the risk of hyperglycemia-associated adverse pregnancy outcomes in pregnant women with known GCK-MODY is controversial, with several case series showing that the birth weight percentile in unaffected neonates remains consistent regardless of whether the mother is treated with insulin. 44 , 45 Evidence suggests that it may be difficult to overcome a genetically determined glycemic set point in patients with GCK-MODY with the use of pharmacotherapy, 15 , 32 and affected patients may have symptoms of hypoglycemia when the blood glucose level is normal because of an enhanced counterregulatory response. 49 , 50 Still, to the extent that it is possible, it would be desirable to safely lower the blood glucose level in a woman with GCK-MODY who is pregnant with an unaffected fetus in order to decrease the risk of fetal overgrowth and other consequences of mildly elevated glucose levels ( Fig. 2C ). 46 , 47 , 51 In contrast, there is evidence that lowering the blood glucose level in a pregnant woman with GCK-MODY could lead to fetal growth restriction if the fetus is affected ( Fig. 2D ). 45 , 52 During this patient’s second pregnancy, she was treated with insulin beginning in the first trimester, and her daughter’s birth weight was near the 16th percentile for gestational age; this outcome is consistent with the daughter’s ultimate diagnosis of GCK-MODY.

Expert opinion suggests that, in pregnant women with GCK-MODY, insulin therapy should be deferred until fetal growth is assessed by means of ultrasound examination beginning in the late second trimester. If there is evidence of fetal overgrowth, the fetus is presumed to be unaffected by GCK-MODY and insulin therapy is initiated. 53 After I have counseled women with GCK-MODY on the potential risks and benefits of insulin treatment during pregnancy, I have sometimes used a strategy of treating hyperglycemia from early in pregnancy using modified glycemic targets that are less stringent than the targets typically used during pregnancy. This strategy attempts to balance the risk of growth restriction in an affected fetus (as well as maternal hypoglycemia) with the potential benefit of glucose-lowering therapy for an unaffected fetus.

Dr. Udler: The patient stopped taking metformin, and subsequent glycated hemoglobin levels remained unchanged, at 6.2%. Her father and 5-year-old daughter (second child) both tested positive for the same GCK variant. Her father had a BMI of 36 and a glycated hemoglobin level of 7.8%, so I counseled him that he most likely had type 2 diabetes in addition to GCK-MODY. He is currently being treated with metformin and lifestyle measures. The patient’s daughter now has a clear diagnosis to explain her hyperglycemia, which will help in preventing misdiagnosis of type 1 diabetes, given her young age, and will be important for the management of any future pregnancies. She will not need any medical follow-up for GCK-MODY until she is considering pregnancy.

FINAL DIAGNOSIS

Maturity-onset diabetes of the young due to a GCK variant.

Acknowledgments

We thank Dr. Andrew Hattersley and Dr. Sarah Bernstein for helpful comments on an earlier draft of the manuscript.

This case was presented at the Medical Case Conference.

No potential conflict of interest relevant to this article was reported.

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org .

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Characteristics, physiopathology and management of dyslipidemias in pregnancy: a narrative review.

1. Introduction

2. lipid physiology in pregnancy, 3. hyperlipidemia and possible adverse maternal and neonatal outcomes, lipid metabolism and pregestational conditions.

4. Dietary and Lifestyle Approaches in Pregnancy

4.1. macronutrients in pregnancy, 4.2. micronutrients: minerals and vitamins, 5. nutritional strategies for managing dyslipidemias in pregnancy, 6. pharmacological approach, 6.1. familial hypercholesterolemia: treatment in pregnancy, 6.1.1. bile acid sequestrants, 6.1.2. fibrates, nicotinic acid, ezetimibe, 6.1.3. statins, 6.1.4. new therapies, 6.2. lipoprotein apheresis for managing dyslipidemia in pregnancy, 6.3. severe hypertriglyceridemia: treatment in pregnancy, 6.3.1. statins, 6.3.2. fibrates, 6.3.3. omega-3, 6.3.4. nicotinic acid (niacin), 6.3.5. bile acid sequestrants, 6.3.6. new therapies, 6.3.7. plasmapheresis, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Formisano, E.; Proietti, E.; Perrone, G.; Demarco, V.; Galoppi, P.; Stefanutti, C.; Pisciotta, L. Characteristics, Physiopathology and Management of Dyslipidemias in Pregnancy: A Narrative Review. Nutrients 2024 , 16 , 2927. https://doi.org/10.3390/nu16172927

Formisano E, Proietti E, Perrone G, Demarco V, Galoppi P, Stefanutti C, Pisciotta L. Characteristics, Physiopathology and Management of Dyslipidemias in Pregnancy: A Narrative Review. Nutrients . 2024; 16(17):2927. https://doi.org/10.3390/nu16172927

Formisano, Elena, Elisa Proietti, Giuseppina Perrone, Valentina Demarco, Paola Galoppi, Claudia Stefanutti, and Livia Pisciotta. 2024. "Characteristics, Physiopathology and Management of Dyslipidemias in Pregnancy: A Narrative Review" Nutrients 16, no. 17: 2927. https://doi.org/10.3390/nu16172927

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