Prenatal exposure to maternal hyperglycemia is associated with an increased risk of later adverse metabolic health. Changes in the regulation of peroxisome proliferator–activated receptor-γ coactivator-1α (PPARGC1A) in skeletal muscle and subcutaneous adipose tissue (SAT) is suggested to play a role in the developmental programming of dysmetabolism based on studies of human subjects exposed to an abnormal intrauterine environment (e.g., individuals with a low birth weight). We studied 206 adult offspring of women with gestational diabetes mellitus (O-GDM) or type 1 diabetes (O-T1D) and of women from the background population (O-BP) using a clinical examination, oral glucose tolerance test, and gene expression and DNA methylation of PPARGC1A in skeletal muscle and SAT. Plasma glucose was significantly higher for both O-GDM and O-T1D compared with O-BP (P < 0.05). PPARGC1A gene expression in muscle was lower in O-GDM compared with O-BP (P = 0.0003), whereas no differences were found between O-T1D and O-BP in either tissue. PPARGC1A DNA methylation percentages in muscle and SAT were similar among all groups. Decreased PPARGC1A gene expression in muscle has previously been associated with abnormal insulin function and may thus contribute to the increased risk of metabolic disease in O-GDM. The unaltered PPARGC1A gene expression in muscle of O-T1D suggests that factors other than intrauterine hyperglycemia may contribute to the decreased PPARGC1A expression in O-GDM.

Diabetes during pregnancy is associated with increased risk of obesity, metabolic syndrome, and type 2 diabetes (T2D) in offspring, as shown in several epidemiological and observational studies and further supported in animal models (17). However, the molecular mechanisms linking fetal exposure to intrauterine hyperglycemia and later dysmetabolic traits remain unknown. Epigenetics defines the concept of heritable changes in gene function that occur without accompanying changes in nucleotide sequence and may represent a key mechanism behind intrauterine programming of the later phenotype (812).

Peroxisome proliferator–activated receptor-γ coactivator-1α (gene: PPARGC1A; protein: PGC-1α) is a potent, multifunctional transcriptional coactivator that regulates genes involved in pathways controlling glucose homeostasis (13). PPARGC1A influences key cellular functions such as mitochondrial biogenesis, microvascular flow, and oxidative phosphorylation, and may play a role in the pathogenesis of T2D by affecting main traits such as insulin function (1316). There are data to support this in both skeletal muscle (1620) and in subcutaneous adipose tissue (SAT) (2123). Furthermore, increased PPARGC1A methylation in skeletal muscle and SAT was found in human subjects exposed to an unfavorable intrauterine environment, that is, people with a low birth weight and increased risk of T2D (18,21), suggesting that the epigenetic profile of this gene may be sensitive to adverse influences during fetal life. Differences in DNA methylation in offspring exposed to intrauterine hyperglycemia compared with controls have previously been shown in both animal studies and in human studies of placenta, cord blood, and peripheral blood cells from newborns or young children of women with gestational diabetes mellitus (GDM) (2431). However, human studies of adult subjects using major target tissues of glucose metabolism and insulin action are lacking. Thus the aim of this study was to evaluate gene expression and DNA methylation of PPARGC1A in skeletal muscle and SAT in a cohort of adult offspring exposed to intrauterine hyperglycemia caused by diet-treated maternal GDM and type 1 diabetes (T1D).

Study Design

This study is a second follow-up of a cohort of adult offspring of women with either GDM (O-GDM) or T1D in pregnancy (O-T1D) and a control group of offspring representing the background population (O-BP). Offspring from all singleton pregnancies complicated by either diet-treated GDM or T1D during the period 1978–1985 in a university obstetrics department (Rigshospitalet, Copenhagen, Denmark) and an equal number of control subjects were invited for the first follow-up examination in 2005–2008. The control subjects were randomly selected from among the larger population of healthy women giving birth during the same period at the same hospital. Of 1,066 eligible offspring, 597 accepted the first follow-up invitation (56%) (1,2,32,33) (Fig. 1). This second follow-up was designed in 2012, and all offspring participating in the first follow-up were invited again, giving 456 eligible offspring (Fig. 1). It was possible to trace the adult offspring through the Danish Civil Registration System (34). Baseline information regarding the health status of the mother during pregnancy and of the offspring during the perinatal period was accessible from the original maternal medical records.

Figure 1

Study design: Flowchart of subjects participating in the study including both the first and second rounds of follow-up. Reasons for offspring lost to follow-up at first examination have been previously published (2). Offspring were lost to follow-up between the first and second rounds of follow-up examination because they declined the possibility of future invitations at the first follow-up (7.6% [19 of 250]), did not respond to letters or several phone calls (37.6% [94 of 250]), refused to participate for individual/private reasons (35.2% [88 of 250]), were working or traveling in foreign countries (0.8% [2 of 250]), were pregnant (female offspring only; 5.6% [14 of 250]), or developed diseases warranting exclusion (5.2% [13 of 250]; this includes 5 offspring from the O-T1D group who developed T1D [2.0%]), immigrated (4.8% [12 of 250]), or other reasons (2.8% [7 of 250]). O-NoGDM, offspring of mothers with risk factors for GDM but who had a normal OGTT during pregnancy.

Figure 1

Study design: Flowchart of subjects participating in the study including both the first and second rounds of follow-up. Reasons for offspring lost to follow-up at first examination have been previously published (2). Offspring were lost to follow-up between the first and second rounds of follow-up examination because they declined the possibility of future invitations at the first follow-up (7.6% [19 of 250]), did not respond to letters or several phone calls (37.6% [94 of 250]), refused to participate for individual/private reasons (35.2% [88 of 250]), were working or traveling in foreign countries (0.8% [2 of 250]), were pregnant (female offspring only; 5.6% [14 of 250]), or developed diseases warranting exclusion (5.2% [13 of 250]; this includes 5 offspring from the O-T1D group who developed T1D [2.0%]), immigrated (4.8% [12 of 250]), or other reasons (2.8% [7 of 250]). O-NoGDM, offspring of mothers with risk factors for GDM but who had a normal OGTT during pregnancy.

The study was performed in accordance with the Declaration of Helsinki and approved by the regional ethical committee. All participants provided written consent before inclusion.

Offspring Exclusion Criteria

Offspring diagnosed with T1D, maturity-onset diabetes of the young, or other severe chronic diseases or who were pregnant were excluded from participation.

Maternal Selection Criteria and Diabetes During Pregnancy in 1978–1985

Screening for GDM in Denmark has traditionally been based on risk factors (3537). During the period 1978–1985, the following risk factors were used: ≥20% overweight before pregnancy, family history of diabetes, previous GDM, previous delivery of a child weighing ≥4,500 g, and/or glucosuria (35,38). Women with one or more risk indicators and two consecutive fasting capillary blood glucose (BG) measurements ≥4.1 mmol were offered a 3-h, 50-g oral glucose tolerance test (OGTT). The OGTT was defined as abnormal if more than two of seven values during the test exceeded the mean +3 SDs for a reference group of normal-weight, nonpregnant women without a family history of diabetes (37,39). Only offspring of mothers with diet-treated GDM were included, whereas mothers with insulin-treated GDM were excluded from participation to minimize the risk of misclassification (e.g., undiagnosed T2D, early stage T1D, and maturity-onset diabetes of the young).

As previously published (1,2,32,33), women with T1D were selected according to the following criteria: onset of diabetes at age ≤40 years, a classical history of hyperglycemic symptoms before disease diagnosis, and insulin treatment started ≤6 months after diagnosis. Routine care in Denmark in 1978–1985 (baseline) for pregnant women with T1D included hospitalization for 3 days in both the first and third trimesters, among other things, for the purpose of measuring seven-point BG profiles, as self-monitored BG and HbA1c were not used routinely at that time. Average maternal BG in the first and third trimesters was calculated from the seven-point profiles over these 3 days.

Mothers of O-BP were from the local community and were routinely referred for antenatal care and delivery in the same hospital. Because GDM screening was based on risk factors during the index period, BG was not measured in mothers representing the background population.

Clinical Examination of Offspring at Follow-up

Tissue Samples

After an overnight fast, biopsies were obtained through a small skin incision first from the abdominal SAT and then the vastus lateralis muscle under local anesthesia using a Bergström needle. The tissue was immediately frozen in liquid nitrogen and stored at −80°C until analyzed.

OGTT and Clinical Examination

After tissue sampling and while still fasting, the participants underwent a 75-g glucose 2-h OGTT with venous sampling at 30 and 120 min. Weight, height, and waist and hip circumferences were measured in duplicate. Body composition was assessed by a DEXA whole-body scanner (Lunar Prodigy Advance; GE Medical Systems, Fairfield, CT). After 10 min of rest, blood pressure was measured in the left upper arm in the supine position. Mean systolic and diastolic blood pressures were calculated after three repetitive measurements.

From around 1 April until 24 June 2013, some participants (a maximum of 32) accidentally received 76.4 g glucose during their OGTT, instead of the 75 g specified by the protocol; this equates to a glucose amount 1.8% in excess of the protocol. We performed independent samples t tests to compare mean glucose values for all participants examined during this period and those examined before and after this period, both in total and divided into offspring groups. The results showed no significant differences in glucose values within the period compared with outside the period.

Venous Blood Samples

Plasma lipid profile (HDL, LDL, total cholesterol, triglycerides), hs-CRP, and HbA1c were measured in a fasted state. Glucose, insulin, and C-peptide were measured during OGTT (fasting, 30 and 120 min). All samples were analyzed using standard laboratory methods on a Cobas 8000 (Roche Diagnostics International Ltd, Rotkreuz, Switzerland), except HbA1c, which was measured on an Afinion system (Axis-Shield PoC AS, Oslo, Norway).

Gene Expression

Total RNA was extracted using a miRNeasy Mini kit (Qiagen). cDNA was synthesized with a QuantiTect reverse transcription kit (Qiagen). mRNA expression of PPARGC1A and the reference gene cyclophilin A (PPIA) was measured by quantitative real-time PCR (q-PCR) using a LightCycler 480 Real-Time PCR system (Roche Applied Science) and predesigned assays for PPARGC1A (Hs00174877_m1*, labeled with FAM) and PPIA (43263116E, labeled with VIC) (both Life Technologies).

DNA Methylation

Genomic DNA was extracted from muscle biopsies using a DNeasy Blood & Tissue kit, and from SAT biopsies using a QIAamp DNA Mini kit (Qiagen). Bisulfite conversion was performed using 400 ng DNA (SAT) and 500 ng DNA (muscle) using an Epitect Bisulfite kit (Qiagen).

To study PPARGC1A DNA methylation in skeletal muscle tissue and SAT, we selected the CpG sites −841, −816, and −783 bp upstream transcription start sites based on the literature in the field focusing on studies performed in metabolically important tissues. We considered CpG sites included in our own previous studies of adults with a low birth weight (18,21) as being the most important in this selection, since these are suggested to be affected by an adverse fetal environment. Furthermore, the methylation percentage of these sites has been shown to be negatively associated with gene expression (16,18,40). Methylation percentage was measured using pyrosequencing (PyroMark Q96ID; Qiagen) with PyroMark Gold Q96 reagents. PCR and sequencing primers were designed using PyroMark Assay Design 2.0 (Supplementary Table 1), and pyrosequencing data were analyzed with the PyroMark Q96 software (version 2.5.8; Qiagen).

Outcome Variables

Gene expression and DNA methylation of PPARGC1A in skeletal muscle and SAT were primary outcomes. Secondary outcomes were clinical characteristics: glucose tolerance status (evaluated according to World Health Organization criteria [41]), insulin resistance represented by HOMA-IR (HOMA of insulin resistance [HOMA-IR] = G0 (mmol/L) × I0 (pmol/L)/135) (42,43), BMI (kilograms per square meter), and total body fat percentage (total BF%) measured by the DEXA scanner.

Exposure Variables

Diet-treated maternal GDM and T1D were the primary exposure variables and used as surrogate measures of different intrauterine exposures. Maternal BG during pregnancy (fasting and BG at 120 min during the OGTT in mothers with GDM and mean BG in the first and third trimesters in mothers with T1D), as well as maternal BMI before pregnancy, were used in correlation analyses.

Analyses and Statistics

Normally distributed continuous data are presented as the mean (±SD), whereas nonparametric data are given as the median (25th–75th percentiles) or the geometric mean and 95% confidence intervals. Differences between groups (O-GDM vs. O-BP and O-T1D vs. O-BP) were analyzed with the independent Student t test, Wilcoxon rank score test, Mann-Whitney U test, or χ2 test when appropriate. Correlation analyses were performed using the Pearson correlation test for parametric data and the Spearman rank correlation test for nonparametric data. All tests were two-tailed at a significance level of 0.05. Data were processed using SPSS version 18 (SPSS Inc., Chicago, IL) and SAS 9.3 (SAS Institute Inc., Cary, NC).

Characteristics of the Study Population

In total, 206 offspring aged 25–36 years were included at the second follow-up (O-GDM: n = 82; O-T1D: n = 67; and O-BP: n = 57) (Fig. 1).

Baseline Data

Baseline data for mothers and offspring are given in Table 1. Significant differences between the mothers of O-GDM and O-BP were found for the following covariates: age, mean BMI before pregnancy, and the percentage of overweight mothers. No differences in maternal data were found between mothers with T1D and mothers from the background population. Offspring of women with both types of diabetes were born at an earlier gestational age, and in O-T1D, a significantly larger proportion of babies were large for their gestational age.

Table 1

Baseline data of mothers and offspring (1978–1985) (N = 206)

O-GDM (n = 82)O-T1D(n = 67)O-BP (n = 57)P value*
O-GDM vs. O-BPO-T1D vs. O-BP
Maternal data (1978–1985)      
 Age at delivery (years) 30.39 (5.16) 26.40 (4.67) 26.79 (4.61) <0.0001 0.645 
 Nordic Caucasian ethnicity (yes vs. no) 94% (77/82) 97% (65/67) 93% (53/57) 0.828 0.297 
 Pregestational BMI (kg/m224.28 (5.60) 21.74 (1.93) 21.20 (3.47) <0.0001 0.301 
 Pregestational overweight (BMI ≥25 kg/m234% (28/82) 8% (5/64) 12% (7/57) 0.003 0.412 
 Family history of diabetes (yes vs. no) 26% (21/82) 25% (17/67) 16% (9/57) 0.166 0.191 
 Fasting blood glucose before OGTT (mmol/L) 5.2 (0.6) NA NA   
 Blood glucose during OGTT (at 120 min) (mmol/L) 7.9 (1.8) NA NA   
 Mean blood glucose in the first trimester NA 9.0 (3.0) NA   
 Mean blood glucose in the third trimester NA 6.7 (1.6) NA   
Offspring birth data (1978–1985)      
 Birth weight (g) 3,398 (560) 3,322 (719) 3,484 (431) 0.331 0.125 
 Gestational age (days) 274 (269–277) 261 (257–263) 282 (276–287) <0.0001 <0.0001 
 Small for gestational age (yes vs. no) 10% (8/82) 5% (3/67) 16% (9/57) 0.286 0.034 
 Large for gestational age (yes vs. no) 17% (14/82) 42% (28/67) 12% (7/57) 0.438 <0.0001 
O-GDM (n = 82)O-T1D(n = 67)O-BP (n = 57)P value*
O-GDM vs. O-BPO-T1D vs. O-BP
Maternal data (1978–1985)      
 Age at delivery (years) 30.39 (5.16) 26.40 (4.67) 26.79 (4.61) <0.0001 0.645 
 Nordic Caucasian ethnicity (yes vs. no) 94% (77/82) 97% (65/67) 93% (53/57) 0.828 0.297 
 Pregestational BMI (kg/m224.28 (5.60) 21.74 (1.93) 21.20 (3.47) <0.0001 0.301 
 Pregestational overweight (BMI ≥25 kg/m234% (28/82) 8% (5/64) 12% (7/57) 0.003 0.412 
 Family history of diabetes (yes vs. no) 26% (21/82) 25% (17/67) 16% (9/57) 0.166 0.191 
 Fasting blood glucose before OGTT (mmol/L) 5.2 (0.6) NA NA   
 Blood glucose during OGTT (at 120 min) (mmol/L) 7.9 (1.8) NA NA   
 Mean blood glucose in the first trimester NA 9.0 (3.0) NA   
 Mean blood glucose in the third trimester NA 6.7 (1.6) NA   
Offspring birth data (1978–1985)      
 Birth weight (g) 3,398 (560) 3,322 (719) 3,484 (431) 0.331 0.125 
 Gestational age (days) 274 (269–277) 261 (257–263) 282 (276–287) <0.0001 <0.0001 
 Small for gestational age (yes vs. no) 10% (8/82) 5% (3/67) 16% (9/57) 0.286 0.034 
 Large for gestational age (yes vs. no) 17% (14/82) 42% (28/67) 12% (7/57) 0.438 <0.0001 

This table presents historical data concerning maternal characteristics and birth data of the offspring, who participated in the second follow-up in 2012–2013. Data are mean (SD), median (25th–75th percentiles), or percentage (number) unless otherwise indicated.

NA, not available.

*Analysis of differences between the two groups were performed by independent samples t test, Mann-Whitney U test, or χ2 test, respectively. P values <0.05 are bold.

Clinical Data: Adult Offspring

O-GDM demonstrated higher plasma glucose concentrations during the OGTT and a higher diastolic blood pressure compared with O-BP. A trend toward increased HbA1c was seen in O-GDM compared with O-BP. O-T1D had higher plasma glucose concentrations after 120 min compared with O-BP (Table 2).

Table 2

Clinical characteristics of the adult offspring at follow-up (2012–2013) (N = 206)

Offspring data (2012–1013)O-GDM (n = 82)O-T1D (n = 67)O-BP (n = 57)P value*
O-GDM vs. O-BPO-T1D vs. O-BP
Age (years) 30.2 (2.1) 30.8 (2.4) 30.8 (2.4) 0.183 0.879 
Male sex 52% (43/82) 46% (31/67) 46% (26/57) 0.429 0.942 
Height (m) 1.76 (0.10) 1.74 (0.10) 1.74 (0.10) 0.481 0.676 
Weight (kg) 77.76 (17.4) 78.3 (17.9) 75.27 (16.5) 0.398 0.331 
Abnormal glucose tolerance (IFG, IGT, or T2D) 13% (11/82) 13% (9/67) 5% (3/57) 0.116 0.125 
T2D      
 Diagnosed at follow-up examination 2% (2/82) 1.5% (1/67) 0% (0/57) 0.235 0.35 
 Previously known 1% (1/82) 1.5% (1/67) 0% (0/57) 0.403 0.35 
Prediabetes (IFG, IGT, or both) 10% (8/82) 10% (7/67) 5% (3/57) 0.334 0.291 
IFG 1% (1/82) 0% (0/67) 0% (0/57) 0.403 NA 
IGT 7% (6/82) 10% (7/67) 5% (3/57) 0.628 0.291 
Both IFG and IGT 1% (1/82) 0% (0/67) 0% (0/57) 0.403 NA 
Fasting plasma glucose during OGTT (mmol/L) 4.95 (0.64) 4.91 (0.39) 4.85 (0.33) 0.245 0.381 
Plasma glucose during OGTT (30 min) (mmol/L) 8.15 (1.71) 7.80 (1.65) 7.34 (1.61) 0.006 0.125 
2-h plasma glucose (mmol/L) 5.99 (1.81) 6.27 (1.69) 5.34 (1.23) 0.016 0.001 
HbA1c IFCC (mmol/mol) 35.05 (3.56) 34.49 (3.31) 34.04 (2.78) 0.076 0.415 
Total tissue fat (%) 29.8% (0.09) 31.4% (0.1) 28.7% (0.08) 0.428 0.093 
BMI (kg/m225.2 (5.09) 26.0 (5.90) 24.6 (3.93) 0.493 0.113 
Obese (BMI ≥30 kg/m215% (12/82) 16% (11/67) 7% (4/57) 0.166 0.11 
Waist-to-hip ratio 0.82 (0.08) 0.81 (0.08) 0.81 (0.07) 0.266 0.821 
Mean systolic blood pressure (mmHg) 116.8 (9.1) 116.7 (8.8) 115.8 (11.9) 0.606 0.626 
Mean diastolic blood pressure (mmHg) 73.5 (7.4) 70.8 (9.0) 70.5 (7.3) 0.023 0.859 
HOMA-IR 1.77 (1.56–2.02) 1.95 (1.71–2.22) 1.72 (1.47–2.02) 0.784 0.222 
Triglycerides 0.89 (0.81–0.98) 0.84 (0.76–0.94) 1.00 (0.76–1.31) 0.391 0.233 
LDL cholesterol (mmol/L) 2.72 (2.57–2.87) 2.64 (2.48–2.81) 2.79 (2.60–3.00) 0.54 0.233 
HDL cholesterol (mmol/L) 1.33 (1.26–1.41) 1.44 (1.36–1.52) 1.36 (1.26–1.48) 0.605 0.241 
Total cholesterol (mmol/L) 4.68 (4.51–4.84) 4.70 (4.53–4.89) 4.78 (4.56–5.02) 0.447 0.592 
hs-CRP (mg/L) 1.02 (0.81–1.28) 1.17 (0.90–1.52) 0.87 (0.66–1.14) 0.369 0.124 
Offspring data (2012–1013)O-GDM (n = 82)O-T1D (n = 67)O-BP (n = 57)P value*
O-GDM vs. O-BPO-T1D vs. O-BP
Age (years) 30.2 (2.1) 30.8 (2.4) 30.8 (2.4) 0.183 0.879 
Male sex 52% (43/82) 46% (31/67) 46% (26/57) 0.429 0.942 
Height (m) 1.76 (0.10) 1.74 (0.10) 1.74 (0.10) 0.481 0.676 
Weight (kg) 77.76 (17.4) 78.3 (17.9) 75.27 (16.5) 0.398 0.331 
Abnormal glucose tolerance (IFG, IGT, or T2D) 13% (11/82) 13% (9/67) 5% (3/57) 0.116 0.125 
T2D      
 Diagnosed at follow-up examination 2% (2/82) 1.5% (1/67) 0% (0/57) 0.235 0.35 
 Previously known 1% (1/82) 1.5% (1/67) 0% (0/57) 0.403 0.35 
Prediabetes (IFG, IGT, or both) 10% (8/82) 10% (7/67) 5% (3/57) 0.334 0.291 
IFG 1% (1/82) 0% (0/67) 0% (0/57) 0.403 NA 
IGT 7% (6/82) 10% (7/67) 5% (3/57) 0.628 0.291 
Both IFG and IGT 1% (1/82) 0% (0/67) 0% (0/57) 0.403 NA 
Fasting plasma glucose during OGTT (mmol/L) 4.95 (0.64) 4.91 (0.39) 4.85 (0.33) 0.245 0.381 
Plasma glucose during OGTT (30 min) (mmol/L) 8.15 (1.71) 7.80 (1.65) 7.34 (1.61) 0.006 0.125 
2-h plasma glucose (mmol/L) 5.99 (1.81) 6.27 (1.69) 5.34 (1.23) 0.016 0.001 
HbA1c IFCC (mmol/mol) 35.05 (3.56) 34.49 (3.31) 34.04 (2.78) 0.076 0.415 
Total tissue fat (%) 29.8% (0.09) 31.4% (0.1) 28.7% (0.08) 0.428 0.093 
BMI (kg/m225.2 (5.09) 26.0 (5.90) 24.6 (3.93) 0.493 0.113 
Obese (BMI ≥30 kg/m215% (12/82) 16% (11/67) 7% (4/57) 0.166 0.11 
Waist-to-hip ratio 0.82 (0.08) 0.81 (0.08) 0.81 (0.07) 0.266 0.821 
Mean systolic blood pressure (mmHg) 116.8 (9.1) 116.7 (8.8) 115.8 (11.9) 0.606 0.626 
Mean diastolic blood pressure (mmHg) 73.5 (7.4) 70.8 (9.0) 70.5 (7.3) 0.023 0.859 
HOMA-IR 1.77 (1.56–2.02) 1.95 (1.71–2.22) 1.72 (1.47–2.02) 0.784 0.222 
Triglycerides 0.89 (0.81–0.98) 0.84 (0.76–0.94) 1.00 (0.76–1.31) 0.391 0.233 
LDL cholesterol (mmol/L) 2.72 (2.57–2.87) 2.64 (2.48–2.81) 2.79 (2.60–3.00) 0.54 0.233 
HDL cholesterol (mmol/L) 1.33 (1.26–1.41) 1.44 (1.36–1.52) 1.36 (1.26–1.48) 0.605 0.241 
Total cholesterol (mmol/L) 4.68 (4.51–4.84) 4.70 (4.53–4.89) 4.78 (4.56–5.02) 0.447 0.592 
hs-CRP (mg/L) 1.02 (0.81–1.28) 1.17 (0.90–1.52) 0.87 (0.66–1.14) 0.369 0.124 

This table presents clinical characteristics of the adult offspring from the second round of follow-up (2012–2013) and includes offspring with previous known or screen-detected treatment-naive T2D but without previous known T1D.

Data are mean (SD), median (25th–75th percentiles), or percentage (number) unless otherwise indicated.

IFG, impaired fasting glucose; IGT, impaired glucose tolerance.

*Analysis of differences (means, medians, or proportions) between either O-GDM vs. O-BP or O-T1D vs. O-BP were performed by an independent samples t test, Mann-Whitney U test, or χ2 test, respectively. P values <0.05 are bold.

†Based on 2-h, 75-g OGTT and evaluated according to World Health Organization criteria of 1999 (44).

‡Data are given as geometric mean and confidence interval.

No significant differences were found between either of the two groups exposed to intrauterine hyperglycemia (O-GDM and O-T1D) and O-BP with regard to anthropometric measurements including BMI, number of subjects diagnosed with prediabetes or T2D, lipid profile (triglycerides, HDL, LDL, total cholesterol) and hs-CRP, and total BF% (Table 2). Parameters of insulin function and different measurements of body composition did neither differ between the exposed groups and the control group (Supplementary Table 2).

Reasons for subjects being lost to follow-up in the second examination are given in Fig. 1, and differences in the basic characteristics between participants and nonparticipants are given in Supplementary Table 3.

PPARGC1A Gene Expression

In skeletal muscle, PPARGC1A gene expression was approximately 40% lower in O-GDM compared with O-BP, whereas O-T1D showed expression levels similar to those of O-BP (Fig. 2A). In SAT, no differences in PPARGC1A gene expression were found between O-GDM and O-BP or between O-T1D and O-BP (Fig. 2B).

Figure 2

The level of PPARGC1A gene expression in skeletal muscle (A) and SAT (B). PPARGC1A mRNA levels were calculated relative (Rel.) to the amount of mRNA of the endogenous control gene PPIA. Data are mean ± SEM (muscle: n = 64 [O-GDM], n = 54 [O-T1D], n = 34 [O-BP]; SAT: n = 59 [O-GDM], n = 61 [O-T1D], n = 42 [O-BP]).

Figure 2

The level of PPARGC1A gene expression in skeletal muscle (A) and SAT (B). PPARGC1A mRNA levels were calculated relative (Rel.) to the amount of mRNA of the endogenous control gene PPIA. Data are mean ± SEM (muscle: n = 64 [O-GDM], n = 54 [O-T1D], n = 34 [O-BP]; SAT: n = 59 [O-GDM], n = 61 [O-T1D], n = 42 [O-BP]).

DNA Methylation

No differences of mean or site-specific PPARGC1A DNA methylation percentage were found either in skeletal muscle or in SAT between the groups exposed to intrauterine hyperglycemia and O-BP (Fig. 3).

Figure 3

The level of DNA methylation in skeletal muscle (A) and SAT (B) at specific CpG sites and average methylation percentages. Data are mean ± SEM (muscle: n = 61 [O-GDM], n = 62 [O-T1D], n = 41 [O-BP]; SAT: n = 51 [O-GDM], n = 49 [O-T1D], n = 36 [O-BP]).

Figure 3

The level of DNA methylation in skeletal muscle (A) and SAT (B) at specific CpG sites and average methylation percentages. Data are mean ± SEM (muscle: n = 61 [O-GDM], n = 62 [O-T1D], n = 41 [O-BP]; SAT: n = 51 [O-GDM], n = 49 [O-T1D], n = 36 [O-BP]).

Associations Between PPARGC1A Gene Expression and DNA Methylation and Clinical Variables Related to Pregnancy or the Adult Offspring

Skeletal Muscle

No association between PPARGC1A expression and DNA methylation percentage was found for any of the offspring groups. PPARGC1A muscle gene expression was not associated with maternal BG values during pregnancy for women with GDM or T1D, nor was it associated with pregestational maternal BMI.

Significant negative associations were found between PPARGC1A gene expression and HOMA-IR in O-GDM and between PPARGC1A gene expression and total BF% in O-BP. No associations were found in O-T1D.

No associations were found between DNA methylation percentage in the adult offspring and the maternal pregnancy variables, nor between DNA methylation percentage and the metabolic adult offspring variables, apart from an inverse association between DNA methylation and total BF% in O-T1D.

Subcutaneous Adipose Tissue

A significant negative association was found between PPARGC1A gene expression and DNA methylation in O-GDM (Fig. 4A and Table 3). Significant negative associations were found between PPARGC1A gene expression and adult total BF% in all offspring groups. Negative associations were found between PPARGC1A gene expression and HOMA-IR in all offspring groups, though they were nonsignificant in O-GDM (Fig. 4B–D and Table 3), but in O-GDM a significant association was found between PPARGC1A gene expression and adult offspring BMI.

Figure 4

Associations between PPARGC1A gene expression and average DNA methylation in SAT in O-GDM (A) and PPARGC1A gene expression in SAT and HOMA-IR in O-GDM (B), O-T1D (C), and O-BP (D). PPARGC1A mRNA levels were calculated relative to the amount of mRNA of the endogenous control gene PPIA (unadjusted Pearson or Spearman correlation). avr, average.

Figure 4

Associations between PPARGC1A gene expression and average DNA methylation in SAT in O-GDM (A) and PPARGC1A gene expression in SAT and HOMA-IR in O-GDM (B), O-T1D (C), and O-BP (D). PPARGC1A mRNA levels were calculated relative to the amount of mRNA of the endogenous control gene PPIA (unadjusted Pearson or Spearman correlation). avr, average.

Table 3

Correlations between DNA methylation, gene expression of PPARGC1A, and clinical variables in the two groups of offspring exposed to intrauterine hyperglycemia (O-GDM and O-T1D) and the control group (O-BP)

PPARGC1A gene expression
Mean PPARGC1A DNA methylation
O-GDMO-T1DO-BPO-GDMO-T1DO-BP
Skeletal muscle       
 Maternal data (1978–1985)       
  Fasting BG (mmol/L) 0.16 (0.21) NA NA −0.12 (0.39) NA NA 
  2-h BG during OGTT (mmol/L) 0.12 (0.34) NA NA −0.03 (0.84) NA NA 
  Mean BG in first trimester NA 0.26 (0.07) NA NA −0.28 (0.05) NA 
  Mean BG in third trimester NA 0.02 (0.83) NA NA −0.19 (0.17) NA 
  Pregestational BMI (kg/m2−0.07 (0.60) −0.04 (0.80) −0.22 (0.21) 0.13 (0.36) −0.05 (0.74) 0.13 (0.45) 
 Adult offspring data (2012–2013)       
  Mean DNA methylation 0.24 (0.13) −0.15 (0.29) −0.02 (0.92) — — — 
  Gene expression — — — 0.24 (0.13) −0.15 (0.29) −0.02 (0.92) 
  BMI (kg/m20.24 (0.13) −0.12 (0.39) −0.28 (0.10) 0.08 (0.59) −0.02 (0.89) 0.29 (0.07) 
  Total tissue fat (%) −0.02 (0.90) −0.08 (0.57) −0.48 (0.004) 0.11 (0.93) −0.15 (0.28) −0.18 (0.27) 
  HOMA-IR −0.30 (0.03) 0.08 (0.59) −0.20 (0.30) 0.14 (0.35) −0.08 (0.56) 0.24 (0.19) 
SAT   
 Maternal data (1978–1985)       
  Fasting BG (mmol/L) −0.12 (0.39) NA NA 0.03 (0.85) NA NA 
  2-h BG during OGTT (mmol/L) −0.03 (0.84) NA NA −0.13 (0.35) NA NA 
  Mean BG in first trimester NA 0.20 (0.12) NA NA 0.14 (0.38) NA 
  Mean BG in third trimester NA 0.10 (0.45) NA NA 0.001 (0.99) NA 
  Pregestational BMI (kg/m2−0.05 (0.72) 0.20 (0.13) −0.17 (0.29) 0.26 (0.07) 0.09 (0.58) 0.37 (0.03) 
 Adult offspring data (2012–2013)       
  Mean DNA methylation −0.45 (0.01) −0.15 (0.37) −0.11 (0.57) — — — 
  Gene expression — — — −0.45 (0.01) −0.15 (0.37) −0.11 (0.57) 
  BMI (kg/m2−0.37 (0.003) −0.19 (0.14) −0.11 (0.57) 0.29 (0.04) 0.45 (0.002) 0.20 (0.23) 
  Total tissue fat (%) −0.29 (0.02) −0.26 (0.04) −0.56 (0.0001) 0.12 (0.41) 0.12 (0.41) 0.12 (0.54) 
  HOMA-IR −0.23 (0.09) −0.34 (0.009) −0.47 (0.004) 0.38 (0.01) 0.10 (0.26) 0.11 (0.54) 
PPARGC1A gene expression
Mean PPARGC1A DNA methylation
O-GDMO-T1DO-BPO-GDMO-T1DO-BP
Skeletal muscle       
 Maternal data (1978–1985)       
  Fasting BG (mmol/L) 0.16 (0.21) NA NA −0.12 (0.39) NA NA 
  2-h BG during OGTT (mmol/L) 0.12 (0.34) NA NA −0.03 (0.84) NA NA 
  Mean BG in first trimester NA 0.26 (0.07) NA NA −0.28 (0.05) NA 
  Mean BG in third trimester NA 0.02 (0.83) NA NA −0.19 (0.17) NA 
  Pregestational BMI (kg/m2−0.07 (0.60) −0.04 (0.80) −0.22 (0.21) 0.13 (0.36) −0.05 (0.74) 0.13 (0.45) 
 Adult offspring data (2012–2013)       
  Mean DNA methylation 0.24 (0.13) −0.15 (0.29) −0.02 (0.92) — — — 
  Gene expression — — — 0.24 (0.13) −0.15 (0.29) −0.02 (0.92) 
  BMI (kg/m20.24 (0.13) −0.12 (0.39) −0.28 (0.10) 0.08 (0.59) −0.02 (0.89) 0.29 (0.07) 
  Total tissue fat (%) −0.02 (0.90) −0.08 (0.57) −0.48 (0.004) 0.11 (0.93) −0.15 (0.28) −0.18 (0.27) 
  HOMA-IR −0.30 (0.03) 0.08 (0.59) −0.20 (0.30) 0.14 (0.35) −0.08 (0.56) 0.24 (0.19) 
SAT   
 Maternal data (1978–1985)       
  Fasting BG (mmol/L) −0.12 (0.39) NA NA 0.03 (0.85) NA NA 
  2-h BG during OGTT (mmol/L) −0.03 (0.84) NA NA −0.13 (0.35) NA NA 
  Mean BG in first trimester NA 0.20 (0.12) NA NA 0.14 (0.38) NA 
  Mean BG in third trimester NA 0.10 (0.45) NA NA 0.001 (0.99) NA 
  Pregestational BMI (kg/m2−0.05 (0.72) 0.20 (0.13) −0.17 (0.29) 0.26 (0.07) 0.09 (0.58) 0.37 (0.03) 
 Adult offspring data (2012–2013)       
  Mean DNA methylation −0.45 (0.01) −0.15 (0.37) −0.11 (0.57) — — — 
  Gene expression — — — −0.45 (0.01) −0.15 (0.37) −0.11 (0.57) 
  BMI (kg/m2−0.37 (0.003) −0.19 (0.14) −0.11 (0.57) 0.29 (0.04) 0.45 (0.002) 0.20 (0.23) 
  Total tissue fat (%) −0.29 (0.02) −0.26 (0.04) −0.56 (0.0001) 0.12 (0.41) 0.12 (0.41) 0.12 (0.54) 
  HOMA-IR −0.23 (0.09) −0.34 (0.009) −0.47 (0.004) 0.38 (0.01) 0.10 (0.26) 0.11 (0.54) 

Data are shown as the R coefficient (P value). P values <0.05 are bold.

BG, blood glucose; NA, not available.

Mean PPARGC1A DNA methylation was not associated with maternal BG values during pregnancy for women with GDM or with T1D, but a positive significant association was found with maternal BMI before gestation in O-BP. Mean PPARGC1A DNA methylation was significantly associated with the following variables in the adult offspring: BMI (both O-GDM and O-T1D) and HOMA-IR (O-GDM).

Principal Findings

We found decreased expression of the PPARGC1A gene in skeletal muscle in a unique cohort of adult offspring of women with GDM; furthermore, we found that muscle PPARGC1A expression correlated negatively with in vivo insulin action in the O-GDM group. The decreased PPARGC1A gene expression was not explained by increased DNA methylation of the PPARGC1A promotor region, and the unaltered muscle PPARGC1A gene expression in O-T1D indicated that factors other than intrauterine hyperglycemia may explain the finding in the O-GDM group. Although PPARGC1A gene expression in SAT correlated negatively with insulin resistance in all groups, gene expression and DNA methylation of PPARGC1A in SAT did not differ between offspring of women with diabetes and controls.

Adult offspring exposed to maternal diabetes during pregnancy presented higher plasma concentrations of glucose during OGTT compared with controls, and O-GDM had higher diastolic blood pressure. The clinical results are in accordance with previous findings of dysmetabolism in the same cohort (1,2).

PPARGC1A Gene Expression in Muscle

Since the initial discoveries from the first generation of expression array studies of reduced PPARGC1A expression in muscle from patients with T2D (16,20), several studies have supported decreased PPARGC1A gene expression and increased PPARGC1A DNA promotor methylation percentages in skeletal muscle to be implicated in the development of T2D and prediabetic conditions (15,16,18). We found significantly decreased gene expression of PPARGC1A in skeletal muscle from dysmetabolic O-GDM with a known, markedly increased risk of developing T2D. Furthermore, our finding of an inverse association between HOMA-IR and muscle PPARGC1A expression among O-GDM is in accordance with a putative role of impaired PPARGC1A expression in the development of insulin resistance in these individuals. Among more central genes regulated by PPARGC1A in skeletal muscle are genes involved in oxidative phosphorylation (OXPHOS genes) (20,44). Thus, a consequence of decreased PPARGC1A expression in skeletal muscle could be decreased mitochondrial biogenesis and lower expression of OXPHOS genes, resulting in a decreased capacity for oxidative phosphorylation and decreased muscle fat oxidation, increasing the risk of developing T2D.

The severity of intrauterine hyperglycemia was most likely different in O-GDM versus O-T1D, as only women with mild GDM (treated with diet) were included in the study, in contrast to women with overt T1D. This assumption is further supported by the markedly higher rate of infants among O-T1D who were large for their gestational age. This stronger exposure was not reflected in the results, however, as a significant difference in PPARGC1A expression in skeletal muscle was found in O-GDM only, and not in O-T1D, leading us to propose that other mechanisms than solely intrauterine exposure to hyperglycemia may be involved in the decreased expression of PPARGC1A in muscle. Such factors could include genetics as well as nongenetic factors such as birth weight, physical activity, diet, and socioeconomic factors (45). Other mechanistic implications behind the increased risk of dysmetabolism in O-T1D could be abnormalities in adipokine or incretin function, or even periconceptional effects since hyperglycemia was present also at conception in this offspring group.

PPARGC1A Gene Expression in SAT

Others have reported altered PPARGC1A gene expression in SAT among insulin-resistant subjects, suggesting that PPARGC1A in adipose tissue could also be of importance in the development of insulin resistance and T2D (22). Similar levels of PPARGC1A expression in SAT were found between the exposed groups and O-BP. Therefore it does not seem likely that the increased risk of developing dysmetabolism (2) in offspring exposed to intrauterine hyperglycemia is mediated by pathways involving PPARGC1A expression in SAT.

Despite similar PPARGC1A expression levels in SAT among the three groups with different dysmetabolic traits, including insulin resistance (33), we found negative associations between PPARGC1A expression in SAT and HOMA-IR for all three offspring groups. This suggests that PPARGC1A may, to some extent, be involved in the development of insulin resistance in SAT, most likely mediated by obesity. In support, others reported decreased PPARGC1A expression with increasing body and visceral fat mass and impaired glucose metabolism (23). In SAT, we found a positive association between PPARGC1A DNA methylation percentage and maternal BMI before pregnancy among O-GDM and O-BP, which supports the importance of maternal overweight.

DNA Methylation

No differences in the DNA methylation percentage of the PPARGC1A promoter region were found in either muscle or SAT between the exposed offspring and O-BP. This is in contrast to the study by Gemma et al. (46), who showed that maternal BMI was positively associated with umbilical cord PPARGC1A DNA methylation percentage of CpG sites (−615, −519, −513). It is generally understood that increased DNA methylation in the promoter region of a gene prevents the binding of transcription factors and thereby interferes with transcription, resulting in decreased expression. Barrès et al. (15) found inverse correlations between DNA methylation versus mRNA expression level of PPARGC1A in patients with T2D. They proposed that PPARGC1A hypermethylation occurs as a consequence of a deleterious metabolic milieu rather than inherited factors. Methylation percentage at the PPARGC1A promoter has also been shown increased in pancreatic islets from patients with T2D at CpG sites within the same region examined in this study (40).

However, we did not observe correlations between PPARGC1A methylation and gene expression in muscle, and it is unlikely that the observed methylation percentage alone may explain the quantitatively significant 40% decreased muscle PPARGC1A gene expression in O-GDM. This suggests that methylation at the sites investigated may not play any major role in the development of metabolic disease among offspring of women with diabetes. Other epigenetic mechanisms could be involved, including nuclear regulative factors, histone modifications, and methylation at other CpG or non-CpG sites in the PPARGC1A promoter. Furthermore, in a previous study of relatives of patients with T2D we observed positive associations between PPARGC1A promoter DNA methylation and whole-body insulin sensitivity, showing that this mechanism was unlikely to play a role in the development of insulin resistance in first-degree relatives (19). It is possible that individuals from diverse diabetes risk groups—that is, subjects with a low birth weight or those with different degrees of disease development—may exhibit dissimilar associations between PPARGC1A methylation and expression. In this study we did find a positive association between PPARGC1A promoter methylation and HOMA-IR in only the O-GDM subgroup. However, given that this was not found in all three subgroups, we cannot exclude this as an incidental finding.

We did not observe differences at mean DNA methylation percentages between exposed offspring and controls, but we found a positive correlation between DNA methylation in SAT and BMI in both exposed groups, which was not present in O-BP. This is in accordance with other studies reporting associations between BMI and genome-wide DNA methylation in both blood and adipose tissue (21,47). However, we cannot exclude this finding as incidental because of the many explorative subgroup analyses.

Impact of Intrauterine Exposure to Maternal Hyperglycemia

Possible pathogenesis in offspring exposed to intrauterine hyperglycemia could involve hyperplasia and hypertrophy of fetal β-cells, resulting in hyperinsulinemia and increased growth of adipose tissues, muscle, and liver, generating, for example, macrosomia and neuroendocrine malprogramming of hypothalamic regions followed by short- and long-term health implications (48).

Mechanisms other than epigenetic alterations founded during fetal life may influence the regulation of PPARGC1A expression. Women with GDM have an increased risk for T2D later in life, and a high association with common genetic variants of T2D has been shown (49). A proportion of these variants are transmitted to their offspring, contributing to the prediabetic abnormalities. A shared postnatal environment is often used as another argument for shared traits related to T2D between family members, and we acknowledge that socioeconomic factors most likely play a role, too.

Before the blastocyst forms, genome-wide DNA methylation drastically changes from a hypermethylated state to almost 0% methylation. Thereafter, DNA methylation increases in a tissue-specific manner during the remainder of the pregnancy (50). These highly dynamic changes could be affected by an adverse hyperglycemic intrauterine environment, generating oxidative stress, which in turn may contribute to DNA damage and thereby affect epigenetic patterns in the fetal cells. However, the molecular mechanisms possibly driving this are still unknown. It is possible that metabolically important genes other than PPARGC1A may be epigenetically affected through fetal exposure to maternal hyperglycemia, as previously reported at birth (27,29,31). Future exploratory genome-wide methylation studies could be of great importance.

Strengths and Weaknesses of the Study Design

This is the first human study to evaluate the detailed effect of exposure to intrauterine hyperglycemia on the levels of gene expression and DNA methylation of PPARGC1A in two primary metabolic important tissues. Exposure status to intrauterine hyperglycemia is documented by detailed objective information from maternal hospital files. Although BG in healthy women without risk factors for GDM was not measured, it is likely that the vast majority of mothers of O-BP had normal glucose tolerance since the prevalence of GDM previously was only 1–2% (39). Thus this limitation pushes toward an underestimation of our findings.

Unfortunately, HbA1c was not introduced to clinical practice during the baseline period (1978–1985). Therefore we used less accurate surrogate measures of glycemia during pregnancy (diagnostic OGTT and in-hospital, 3-day, seven-point profiles). These measures are not suitable to compare levels of glycemia between pregnancies of women with T1D and GDM, but they enable us, to some extent, to evaluate the effect of different glycemic levels within the two types of pregnancy and to evaluate the association between maternal glucose measures and offspring outcomes.

The original study design included all offspring born to women with diabetes during pregnancy in a large hospital setting, and the unique civil registration system in Denmark made it possible to trace the offspring without involving the mother, minimizing the risk of selection bias. However, we are aware that a selection process has taken place between the two rounds of follow-up, as some subjects already diagnosed with prediabetes, T2D, or metabolic syndrome in the first follow-up declined to participate again—in other words, the healthiest subjects agreed to participate in the second follow-up study (Supplementary Table 3). We did not have access to solid data on offspring socioeconomic status and lifestyle. Adjustment for sex and birth weight in our previous studies of the cohort did not change estimates for offspring risk of dysmetabolism (1,2,32,33), and these covariates were therefore not included in this study. Our study supports the need for a continued struggle to optimize conditions for pregnant women with hyperglycemia with respect to glycemic control, diagnosis of GDM, maternal weight control, medical treatment, nutrition, and lifestyle, among other factors.

Conclusion

Adult offspring exposed to intrauterine hyperglycemia exhibit mildly elevated glucose concentrations, and adult offspring of women with GDM have reduced gene expression of PPARGC1A in skeletal muscle. This was not the case for offspring of women with T1D, indicating that intrauterine hyperglycemia does not seem to be involved in altered gene expression and DNA methylation of PPARGC1A. Further studies of the role of nonglycemic factors, including obesity, hypertension, and lifestyle, during pregnancy are needed to understand the developmental programming of T2D mediated by altered PPARGC1A expression in skeletal muscle.

Acknowledgments. The authors thank Anne Cathrine Thuesen, MD, and research nurse Malan Egholm (Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark) for their skillful assistance during data collection. The authors also thank Dr. Richard Saffery, of the Cancer & Disease Epigenetics group, Murdoch Childrens Research Institute, Parkville, Australia, for providing laboratory facilities for real-time qPCR and pyrosequencing analyses. Furthermore, the authors acknowledge all participants in the study.

Funding. Funding for this study was provided by the Danish Council for Independent Research, the Novo Nordisk Foundation, the Danish Diabetes Academy, the Augustinus Foundation, the Danish Diabetes Association, the A.P. Møller Foundation for the Advancement of Medical Science, the European Foundation for the Study of Diabetes (EFSD), and Rigshospitalet.

Duality of Interest. A.A.V. is a shareholder of Novo Nordisk A/S. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. L.K. wrote the manuscript, analyzed data, interpreted the result of the experiments, collected in vivo data and muscle and adipose tissue biopsies, designed the setup for data collection, and wrote the protocol. L.H. wrote the manuscript, analyzed data, interpreted the result of the experiments, processed biopsies, and performed gene expression and DNA methylation analyses. A.H.-O. collected in vivo data and muscle and adipose tissue biopsies. T.D.C. collected baseline data and wrote the protocol. N.S.H. and C.B. collected in vivo data and designed the setup for data collection. L.B.-J. collected in vivo data. E.R.M. wrote the protocol. A.A.V. and P.D. interpreted the result of the experiments, designed the setup for data collection, and wrote the protocol. All authors critically revised the manuscript. P.D. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Data from this study were presented as an abstract at the 75th Scientific Sessions of the American Diabetes Association, Boston, MA, 5–9 June 2015; as an oral presentation at the European Association for the Study of Diabetes (EASD) 51st annual meeting, Stockholm, Sweden, 14–18 September 2015; and as an oral presentation at the Diabetes in Pregnancy Study Group (DPSG) 47th annual meeting, Malaga, Spain, 1–4 October 2015.

1.
Clausen
TD
,
Mathiesen
ER
,
Hansen
T
, et al
.
High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia
.
Diabetes Care
2008
;
31
:
340
346
[PubMed]
2.
Clausen
TD
,
Mathiesen
ER
,
Hansen
T
, et al
.
Overweight and the metabolic syndrome in adult offspring of women with diet-treated gestational diabetes mellitus or type 1 diabetes
.
J Clin Endocrinol Metab
2009
;
94
:
2464
2470
[PubMed]
3.
Dabelea
D
,
Hanson
RL
,
Lindsay
RS
, et al
.
Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships
.
Diabetes
2000
;
49
:
2208
2211
[PubMed]
4.
Lawlor
DA
,
Fraser
A
,
Lindsay
RS
, et al
.
Association of existing diabetes, gestational diabetes and glycosuria in pregnancy with macrosomia and offspring body mass index, waist and fat mass in later childhood: findings from a prospective pregnancy cohort
.
Diabetologia
2010
;
53
:
89
97
[PubMed]
5.
Patel
S
,
Fraser
A
,
Davey Smith
G
, et al
.
Associations of gestational diabetes, existing diabetes, and glycosuria with offspring obesity and cardiometabolic outcomes
.
Diabetes Care
2012
;
35
:
63
71
[PubMed]
6.
Sobngwi
E
,
Boudou
P
,
Mauvais-Jarvis
F
, et al
.
Effect of a diabetic environment in utero on predisposition to type 2 diabetes
.
Lancet
2003
;
361
:
1861
1865
[PubMed]
7.
Boloker
J
,
Gertz
SJ
,
Simmons
RA
.
Gestational diabetes leads to the development of diabetes in adulthood in the rat
.
Diabetes
2002
;
51
:
1499
1506
[PubMed]
8.
Bird
A
.
Perceptions of epigenetics
.
Nature
2007
;
447
:
396
398
[PubMed]
9.
Fernández-Morera
JL
,
Rodríguez-Rodero
S
,
Menéndez-Torre
E
,
Fraga
MF
.
The possible role of epigenetics in gestational diabetes: cause, consequence, or both
.
Obstet Gynecol Int
2010
;
2010
:
605163
[PubMed]
10.
Hochberg
Z
,
Feil
R
,
Constancia
M
, et al
.
Child health, developmental plasticity, and epigenetic programming
.
Endocr Rev
2011
;
32
:
159
224
[PubMed]
11.
Pinney
SE
,
Simmons
RA
.
Metabolic programming, epigenetics, and gestational diabetes mellitus
.
Curr Diab Rep
2012
;
12
:
67
74
[PubMed]
12.
Ruchat
SM
,
Hivert
MF
,
Bouchard
L
.
Epigenetic programming of obesity and diabetes by in utero exposure to gestational diabetes mellitus
.
Nutr Rev
2013
;
71
(
Suppl. 1
):
S88
S94
[PubMed]
13.
Soyal
S
,
Krempler
F
,
Oberkofler
H
,
Patsch
W
.
PGC-1alpha: a potent transcriptional cofactor involved in the pathogenesis of type 2 diabetes
.
Diabetologia
2006
;
49
:
1477
1488
[PubMed]
14.
Handschin
C
,
Spiegelman
BM
.
Peroxisome proliferator–activated receptor gamma coactivator 1 coactivators, energy homeostasis, and metabolism
.
Endocr Rev
2006
;
27
:
728
735
[PubMed]
15.
Barrès
R
,
Osler
ME
,
Yan
J
, et al
.
Non-CpG methylation of the PGC-1alpha promoter through DNMT3B controls mitochondrial density
.
Cell Metab
2009
;
10
:
189
198
[PubMed]
16.
Patti
ME
,
Butte
AJ
,
Crunkhorn
S
, et al
.
Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: potential role of PGC1 and NRF1
.
Proc Natl Acad Sci U S A
2003
;
100
:
8466
8471
[PubMed]
17.
Barres
R
,
Zierath
JR
.
DNA methylation in metabolic disorders
.
Am J Clin Nutr
2011
;
93
:
897S
900
[PubMed]
18.
Brøns
C
,
Jacobsen
S
,
Nilsson
E
, et al
.
Deoxyribonucleic acid methylation and gene expression of PPARGC1A in human muscle is influenced by high-fat overfeeding in a birth-weight-dependent manner
.
J Clin Endocrinol Metab
2010
;
95
:
3048
3056
[PubMed]
19.
Gillberg
L
,
Jacobsen
SC
,
Ribel-Madsen
R
, et al
.
Does DNA methylation of PPARGC1A influence insulin action in first degree relatives of patients with type 2 diabetes? [Published correction appears in PLoS One 2013;8(6).]
PLoS One
2013
;
8
:
e58384
[PubMed]
20.
Mootha
VK
,
Lindgren
CM
,
Eriksson
KF
, et al
.
PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes
.
Nat Genet
2003
;
34
:
267
273
[PubMed]
21.
Gillberg
L
,
Jacobsen
SC
,
Rönn
T
,
Brøns
C
,
Vaag
A
.
PPARGC1A DNA methylation in subcutaneous adipose tissue in low birth weight subjects–impact of 5 days of high-fat overfeeding
.
Metabolism
2014
;
63
:
263
271
[PubMed]
22.
Hammarstedt
A
,
Jansson
PA
,
Wesslau
C
,
Yang
X
,
Smith
U
.
Reduced expression of PGC-1 and insulin-signaling molecules in adipose tissue is associated with insulin resistance
.
Biochem Biophys Res Commun
2003
;
301
:
578
582
[PubMed]
23.
Ruschke
K
,
Fishbein
L
,
Dietrich
A
, et al
.
Gene expression of PPARgamma and PGC-1alpha in human omental and subcutaneous adipose tissues is related to insulin resistance markers and mediates beneficial effects of physical training
.
Eur J Endocrinol
2010
;
162
:
515
523
[PubMed]
24.
West
NA
,
Kechris
K
,
Dabelea
D
.
Exposure to maternal diabetes in utero and DNA methylation patterns in the offspring
.
Immunometabolism
2013
;
1
:
1
9
[PubMed]
25.
Houde
AA
,
Guay
SP
,
Desgagné
V
, et al
.
Adaptations of placental and cord blood ABCA1 DNA methylation profile to maternal metabolic status
.
Epigenetics
2013
;
8
:
1289
1302
[PubMed]
26.
Houde
AA
,
St-Pierre
J
,
Hivert
MF
, et al
.
Placental lipoprotein lipase DNA methylation levels are associated with gestational diabetes mellitus and maternal and cord blood lipid profiles
.
J Dev Orig Health Dis
2014
;
5
:
132
141
[PubMed]
27.
Bouchard
L
,
Hivert
MF
,
Guay
SP
,
St-Pierre
J
,
Perron
P
,
Brisson
D
.
Placental adiponectin gene DNA methylation levels are associated with mothers’ blood glucose concentration
.
Diabetes
2012
;
61
:
1272
1280
[PubMed]
28.
El Hajj
N
,
Pliushch
G
,
Schneider
E
, et al
.
Metabolic programming of MEST DNA methylation by intrauterine exposure to gestational diabetes mellitus
.
Diabetes
2013
;
62
:
1320
1328
[PubMed]
29.
Ruchat
SM
,
Houde
AA
,
Voisin
G
, et al
.
Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases
.
Epigenetics
2013
;
8
:
935
943
[PubMed]
30.
Gill-Randall
R
,
Adams
D
,
Ollerton
RL
,
Lewis
M
,
Alcolado
JC
.
Type 2 diabetes mellitus–genes or intrauterine environment? An embryo transfer paradigm in rats
.
Diabetologia
2004
;
47
:
1354
1359
[PubMed]
31.
Quilter
CR
,
Cooper
WN
,
Cliffe
KM
, et al
.
Impact on offspring methylation patterns of maternal gestational diabetes mellitus and intrauterine growth restraint suggest common genes and pathways linked to subsequent type 2 diabetes risk
.
FASEB J
2014
;
28
:
4868
4879
[PubMed]
32.
Kelstrup
L
,
Clausen
TD
,
Mathiesen
ER
,
Hansen
T
,
Damm
P
.
Low-grade inflammation in young adults exposed to intrauterine hyperglycemia
.
Diabetes Res Clin Pract
2012
;
97
:
322
330
[PubMed]
33.
Kelstrup
L
,
Damm
P
,
Mathiesen
ER
, et al
.
Insulin resistance and impaired pancreatic β-cell function in adult offspring of women with diabetes in pregnancy
.
J Clin Endocrinol Metab
2013
;
98
:
3793
3801
[PubMed]
34.
Schmidt
M
,
Pedersen
L
,
Sørensen
HT
.
The Danish Civil Registration System as a tool in epidemiology
.
Eur J Epidemiol
2014
;
29
:
541
549
[PubMed]
35.
Guttorm
E
.
Practical screening for diabetes mellitus in pregnant women
.
Acta Endocrinol Suppl (Copenh)
1974
;
182
:
11
24
[PubMed]
36.
Jensen
DM
,
Mølsted-Pedersen
L
,
Beck-Nielsen
H
,
Westergaard
JG
,
Ovesen
P
,
Damm
P
.
Screening for gestational diabetes mellitus by a model based on risk indicators: a prospective study
.
Am J Obstet Gynecol
2003
;
189
:
1383
1388
[PubMed]
37.
Kühl
C
.
Glucose metabolism during and after pregnancy in normal and gestational diabetic women. 1. Influence of normal pregnancy on serum glucose and insulin concentration during basal fasting conditions and after a challenge with glucose
.
Acta Endocrinol (Copenh)
1975
;
79
:
709
719
[PubMed]
38.
Damm
P
,
Kühl
C
,
Bertelsen
A
,
Mølsted-Pedersen
L
.
Predictive factors for the development of diabetes in women with previous gestational diabetes mellitus
.
Am J Obstet Gynecol
1992
;
167
:
607
616
[PubMed]
39.
Damm
P
.
Gestational diabetes mellitus and subsequent development of overt diabetes mellitus
.
Dan Med Bull
1998
;
45
:
495
509
[PubMed]
40.
Ling
C
,
Del Guerra
S
,
Lupi
R
, et al
.
Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion
.
Diabetologia
2008
;
51
:
615
622
[PubMed]
41.
Alberti
KG
,
Zimmet
PZ
.
Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation
.
Diabet Med
1998
;
15
:
539
553
[PubMed]
42.
Matthews
DR
,
Hosker
JP
,
Rudenski
AS
,
Naylor
BA
,
Treacher
DF
,
Turner
RC
.
Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man
.
Diabetologia
1985
;
28
:
412
419
[PubMed]
43.
Vølund
A
.
Conversion of insulin units to SI units
.
Am J Clin Nutr
1993
;
58
:
714
715
[PubMed]
44.
Scarpulla
RC
.
Nuclear control of respiratory chain expression by nuclear respiratory factors and PGC-1-related coactivator
.
Ann N Y Acad Sci
2008
;
1147
:
321
334
[PubMed]
45.
Ling
C
,
Poulsen
P
,
Carlsson
E
, et al
.
Multiple environmental and genetic factors influence skeletal muscle PGC-1alpha and PGC-1beta gene expression in twins
.
J Clin Invest
2004
;
114
:
1518
1526
[PubMed]
46.
Gemma
C
,
Sookoian
S
,
Alvariñas
J
, et al
.
Maternal pregestational BMI is associated with methylation of the PPARGC1A promoter in newborns
.
Obesity (Silver Spring)
2009
;
17
:
1032
1039
[PubMed]
47.
Dick
KJ
,
Nelson
CP
,
Tsaprouni
L
, et al
.
DNA methylation and body-mass index: a genome-wide analysis
.
Lancet
2014
;
383
:
1990
1998
[PubMed]
48.
Plagemann
A
.
“Fetal programming” and “functional teratogenesis”: on epigenetic mechanisms and prevention of perinatally acquired lasting health risks
.
J Perinat Med
2004
;
32
:
297
305
[PubMed]
49.
Lauenborg
J
,
Grarup
N
,
Damm
P
, et al
.
Common type 2 diabetes risk gene variants associate with gestational diabetes
.
J Clin Endocrinol Metab
2009
;
94
:
145
150
[PubMed]
50.
Slieker
RC
,
Roost
MS
,
van Iperen
L
, et al
.
DNA methylation landscapes of human fetal development
.
PLoS Genet
2015
;
11
:
e1005583
[PubMed]
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