Interactions between genetic, metabolic, and environmental factors lead to gestational diabetes mellitus (GDM). We aimed to examine interactive effects of cyclin-dependent kinase 5 regulatory subunit-associated protein1-like 1(CDKAL1) rs7747752 polymorphism with low serum levels of L-carnitine, choline, and betaine for GDM.
A nested case-control study of 207 GDM women and their one-to-one, age-matched controls was organized from a prospective cohort of pregnant women in Tianjin, China. Conditional logistic regressions were used to test associations between CDKAL1 rs7747752 and serum levels of L-carnitine, choline, and betaine, and the risk of GDM. Additive interactions were performed to examine interactive effects of rs7747752 and low serum levels of L-carnitine, choline, and betaine on the risk of GDM.
The CDKAL1 rs7747752 G > C was associated with GDM in additive, dominant, and recessive model (P <0.05). The rs7747752 CC genotype enhanced the OR of L-carnitine ≤ vs. > 150 nmol/mL for GDM from 6.14 (2.61–14.4) to 19.6 (5.65–68.1) and the OR of choline ≤ vs. > 110 nmol/mL from 2.37 (1.07–5.28) to 12.1 (3.22–45.6), with significant additive interactions. Similarly, CG genotype also enhanced the OR of L-carnitine ≤ vs. > 150 nmol/mL for GDM from 4.70 (2.01–11.0) to 11.4 (3.98–32.9), with a significant additive interaction. However, the additive interaction between rs7747752 and betaine ≤ 200 nmol/mL on the risk of GDM was not significant.
The CC or CG genotype carriers in rs7747752 of CDKAL1 who have a low serum level of L-carnitine or choline are at a particular high risk of GDM. Randomized controlled trials are warranted to test the effect of supplement of L-carnitine or choline on the risk of GDM in the high-risk group.
With increasing prevalence globally, gestational diabetes mellitus (GDM) has become one of the most common metabolic disorders in pregnancy . GDM leads to adverse short- and long-term consequences for mothers and their offspring, including type 2 diabetes mellitus (T2DM) and cardiovascular diseases in the mothers, as well as macrosomia and childhood obesity in their offspring [2,3,4]. On the other hand, intensive management of GDM during pregnancy did not have a detectable effect on postpartum diabetes and prediabetes in the mothers  and childhood obesity in their offspring [6, 7]. Our meta-analysis showed that lifestyle intervention within 15 weeks of gestation only achieved a 20% reduction in the risk of GDM . Indeed, how to prevent GDM remains one of the priorities in diabetes research.
GDM is a complex disease caused by interactions between genetic, metabolic, and environmental factors . Previous studies have identified some genetic high susceptibilities to GDM [10, 11]. The cyclin-dependent kinase 5 regulatory subunit-associated protein1-like 1 (CDKAL1) gene is located at the short arm of human chromosome 6 and encodes a 65 kDa protein. CDKAL1, as a mechanism-related protein for diabetes , is related to the defects of proinsulin conversion and insulin response under glucose stimulation [13, 14]. Several studies have examined associations between CDKAL1 and GDM, but their findings are inconsistent. In European populations, CDKAL1 polymorphisms, i.e., rs7754840 and rs10946398, were not associated with the risk of GDM [15, 16]. On the other hand, CDKAL1 polymorphisms were strongly associated with risk of GDM in Chinese and other Asian populations [17, 18]. Single nucleotide polymorphism (SNP) rs7747752 is in the intron of CDKAL1. Wang et al  validated the association between rs7747752 and the risk of GDM in Chinese pregnant women.
Choline, betaine, and L-carnitine, as important metabolic and nutritional factors, are abundant in a wide variety of foods and play a critical role in several physiological processes, such as neurotransmitter synthesis, cell-membrane signaling, and lipid transport . Several studies have attempted to address associations between abnormal levels of these compounds and the risk of diabetes but their findings are inconsistent. Both dietary and serum choline are reported to be positively associated with the risk of T2DM, but betaine is not [20, 21]. Small randomized controlled trials showed that high intake of dietary choline and betaine  and oral supplement of carnitine  improved insulin resistance in the general population. In this connection, our group observed that low serum levels of L-carnitine, choline, and betaine in early pregnancy were independently associated with markedly increased risk of GDM . The interaction between genetic predisposition and metabolic factors plays a critical role in the development of GDM . Indeed, the CDKAL1 gene has a strong association with GDM. Importantly, CDKAL1 genetic variants are predictive of GDM and related glycemic traits , suggesting that it may have a synergistic effect with other risk factors. In this regard, our group reported that CDKAL1 genetic variant had a significant synergistic effect with palmitic acids on the risk of GDM . In addition, animal studies showed that maternal betaine supplementation enhanced lipid metabolism and improved insulin resistance in mice fed a high-fat diet [28, 29]. Additionally, carnitine was able to improve hyperlipidemia, insulin-dependent diabetes mellitus, insulin resistance, and obesity . Given the important role of CDKAL1 in proinsulin conversion and insulin resistance, it is of interest to explore whether CKDAL1 genetic variants and low serum levels of L-carnitine, choline, and betaine have synergistic effects on the risk of GDM in Chinese pregnant women.
We organized an age-matched case-control study from a large population-based cohort of pregnant women in Tianjin, China. This analysis aimed to explore additive interactions between rs7747752 and low serum levels of L-carnitine, choline, and betaine for the risk of GDM.
Materials and methods
Research design and population
The design, cohort, and methods of this study were described in detail before . In brief, a total of 22,302 pregnant women were recruited into a prospective cohort at their first antenatal care visit through a universal screening and management system for GDM from October 2010 to August 2012. Upon enrollment, they were followed up longitudinally from the first prenatal care till the postpartum period. Ethics of the study protocol was approved by the Ethics Committee of Tianjin Women and Children’s Health Center (TWCHC). Written informed consent was obtained from participants before data collection.
Among the recruited participants, a two-step screening procedure was used to identify GDM. First, a 1-h 50-g glucose challenge test (GCT) in a non-fasting state was performed on pregnant women at 24–28 weeks of gestation at a primary hospital. Women with GCT ≥ 7.8 mmol/L were referred to the GDM clinic in TWCHC for a 2-h 75-g oral glucose tolerance test (OGTT) in the morning after fasting for at least 8 hours. GDM was diagnosed using the International Association of Diabetes and Pregnancy Study Group’s criteria, i.e., a fasting plasma glucose (PG) ≥ 5.1 mmol/L, a 1-h PG ≥ 10.0 mmol/L, or a 2-h PG ≥ 8.5 mmol/L .
From July 2011 to June 2012, 2991 pregnant women donated their fasting blood samples overnight at the primary care hospitals. Of them, we excluded 227 women who lacked GCT results or lacked OGTT results when their GCT was ≥ 7.8 mmol/L. Among the remaining 2764 women, a total of 243 GDM women were treated as the cases, and 243 non-GDM women were selected as the controls of the nested case-control study matched by maternal age (±1 year) . After excluding 16 women with low ability of deoxyribonucleic acid (DNA) extraction, the blood samples of 470 women underwent whole-genome sequencing. After further excluding 23 women who lacked high-quality DNA data  and 33 women who did not have an age-matched case or control, 414 subjects (207 GDM women and 207 non-GDM women) were included in this study. The flowchart of the study participants was available elsewhere .
Data collection procedures
Data were collected at the first antenatal care visit and at the GCT/OGTT time. The detailed methods of data collection were previously published . Briefly, the data were collected from pregnant women by a series of self-administered questionnaires or retrieved from the database of Maternal and Child Health Information System, including age, ethnicity, education attainment, parity, family history of diabetes in first-degree relatives, and smoking and drinking habits before and during pregnancy. Anthropometric and clinical measurements were performed to collect body weight, height, and systolic/diastolic blood pressure (SBP/DBP). Body mass index (BMI) was estimated as body weight in kilograms divided by the square of body height in meters. The difference in body weight (weight gain) between the first antenatal care visit and GCT was also determined.
Measurement of serum levels of L-carnitine/choline/betaine
Liquid chromatography-tandem mass spectrometry (LC–MS/MS) was used to assay the serum concentrations of L-carnitine, choline, and betaine. Details of measurement of serum levels of L-carnitine, choline, and betaine were available elsewhere .
DNA samples were genotyped by the Illumina Infinium® Global Screening Array. Genotyping data from specific candidate SNP (rs7747752) were extracted from the genome-wide genotyping. The genotype data were imputed using minimac 3 with the 1000 Genomes Project phase 3 V.5 as a reference panel. The overall genotype call rate was 99.4%.
All statistical analyses were performed using the Statistical Analysis System (SAS) release 9.4 (SAS Institute, Cary, NC). Quantitative data were compared between the GDM group and the non-GDM group with the paired Student’s t test or Wilcoxon signed-rank test. The categorical data were compared with McNemar test or Fisher’s exact test. In this analysis, a P value < 0.05 was considered to be statistically significant.
Conditional logistic regression was performed to obtain the odds ratios (ORs) and 95% confidence intervals (CIs) of CDKAL1 rs7747752 genetic variant and serum levels of L-carnitine, choline, and betaine for the risk of GDM. In our previous analyses, we reported that betaine ≤ 200 nmol/mL and choline ≤ 110 and > 270 nmol/mL vs. > 110 to ≤ 270 nmol/mL were independently associated with the increased risk of GDM [24, 35]. In this analysis, we replotted the OR curve of L-carnitine for GDM (Additional file 1: Fig. S1) and refined the selection of the cutoff point of L-carnitine for GDM at ≤ 150 nmol/mL. We tested additive interactions between the rs7747752 genotypes and low serum levels of L-carnitine, choline, and betaine on the risk of GDM. Three measures, i.e., relative excess risk due to the interaction (RERI), attributable proportion due to the interaction (AP), and synergy index (SI), were used to judge the statistical significance of additive interactions . The additive interaction was considered to be significant if any of the following items was statistically significant: RERI > 0, AP > 0, or SI > 1. To control the confounding effects of traditional GDM risk factors, we adjusted for traditional risk factors that included pre-pregnancy BMI, family history of diabetes in first-degree relatives, SBP, current smoker before pregnancy, gestational weeks at GCT, and weight gain to GCT in multivariable model 1, and further adjusted for choline at ≤ 110 vs. > 110 nmol/mL and > 270 vs. ≤ 270 nmol/mL, betaine ≤ 200 nmol/mL, and L-carnitine ≤ 150 nmol/mL (except for testing of its own significance) in multivariable model 2. In addition, we further adjusted for rs7747752 CC genotype (CC vs. CG/GG) in analyses of the additive interaction between rs7747752 genotype (CG vs. GG) and low serum levels of metabolisms for the risk of GDM and further adjusted for rs7747752 CG genotype (CG vs. CC/GG) in analyses of the additive interaction between rs7747752 genotype (CC vs. GG) and low serum levels of metabolisms for the risk of GDM.
Characteristics of the participants
The mean age of the participants was 29.2 (±3.0) years, and the mean gestational age was 10.1 (±2.1) weeks at their first antenatal care visit. There were no significant differences between the GDM group and the non-GDM group in terms of height, ethnicity, education attainment, parity, current smoker and alcohol drinker before pregnancy, gestational weeks at GCT, and weight gain from registration to GCT. However, women with GDM had higher values of body weight, BMI, and SBP/DBP at registration than women without GDM. In the GDM group, the proportion of women with a family history of diabetes in first-degree relatives was also higher than that in the non-GDM group. Compared with the non-GDM group, serum levels of choline, L-carnitine, and betaine were lower in the GDM group. The frequencies of the CG and CC genotypes of rs7747752 were found to be significantly higher in women with GDM than in non-GDM women (Table 1).
Associations of the CDKAL1 rs7747752 and serum levels of L-carnitine, choline, and betaine with the risk of GDM
Serum levels of L-carnitine were negatively associated with the risk of GDM in a non-linear manner (Additional file 1: Fig. S1). L-carnitine ≤ 150 nmol/mL was associated with a markedly increased risk of GDM (OR: 5.73, 95% CI: 2.96–11.1) in multivariable model 2. Similarly, choline ≤ 110 and > 270 nmol/mL vs. > 110 to ≤ 270 nmol/mL (OR: 2.82, 95% CI: 1.44–5.53; OR: 4.21, 95% CI: 1.42–12.5) were all associated with elevated risks of GDM in multivariable model 2. Betaine ≤200 nmol/mL was also associated with an increased risk of GDM (OR: 6.02, 95% CI: 2.87–12.6) in multivariable model 1.
In our cohort, the frequency of C allele for rs7747752 genetic variant was 49.89%. The CDKAL1 rs7747752 C allele was significantly higher in pregnant women with GDM than controls in univariable analysis (OR: 1.48, 95% CI: 1.12–1.96) and after further adjustment for traditional risk factors (OR: 1.74, 95% CI: 1.25–2.42). The CC genotype of rs7747752 was associated with an increased risk of GDM in univariable analysis (OR: 2.19, 95% CI: 1.24–3.85) and after adjustment for traditional risk factors (OR: 2.99, 95% CI: 1.54–5.81). The CG genotype of rs7747752 was also associated with an elevated risk of GDM after adjustment for traditional risk factors (OR: 2.06, 95% CI: 1.20–3.53). Likewise, as compared with GG genotype, CC/CG genotype carriers presented with higher GDM susceptibility in univariable analysis (OR: 1.68, 95% CI: 1.08–2.62) and after adjustment for traditional risk factors (OR: 2.28, 95% CI: 1.35–3.83). As compared with CG/GG genotype, the homozygous CC genotype conferred a 1.63-fold risk of GDM in univariable analysis (OR: 1.63, 95% CI: 1.04–2.57) and a 1.79-fold risk of GDM after adjustment for traditional risk factors (OR: 1.79, 95% CI: 1.06–3.00) (Table 2).
Associations between combinations of rs7747752 genotypes (CC vs. CG vs. GG) and low serum levels of L-carnitine, choline, and betaine for the risk of GDM
Among the combinations of CDKAL1 rs7747752 genotypes and low serum levels of L-carnitine, L-carnitine ≤ 150 nmol/mL combined with CG genotype was associated with an increased risk of GDM after adjustment for traditional GDM risk factors as well as low & high choline and low betaine (OR: 9.90, 95% CI: 3.12–31.4). L-carnitine ≤ 150 nmol/mL combined with CC genotype was also associated with an increased risk of GDM (OR: 14.6, 95% CI: 3.84–55.9). Among the combinations of rs7747752 genotypes and choline ≤ 110 nmol/mL, choline ≤ 110 nmol/mL and CG genotype was associated with an elevated risk of GDM after adjustment for traditional GDM risk factors as well as low L-carnitine and low betaine (OR: 4.30, 95% CI: 1.49–12.4). Choline ≤ 110 nmol/mL and CC genotype were associated with an increased risk of GDM (OR: 12.4, 95% CI: 3.10–49.2). Similarly, the combination of betaine ≤ 200 nmol/mL and CC genotype was also associated with an increased risk of GDM after adjustment for traditional GDM risk factors as well as low & high choline and low L-carnitine (OR: 9.18, 95% CI: 1.54–54.7) (Table 3).
Additive interactions between rs7747752 genotypes (CC/CG vs. GG) and low serum levels of L-carnitine, choline, and betaine for the risk of GDM
The CG genotype of rs7747752 greatly enhanced the OR (95% CI) of L-carnitine ≤ vs. > 150 nmol/mL for GDM from 6.23 (2.89–13.5) to 16.1 (6.26–41.3) in multivariable model 1 and from 4.70 (2.01–11.0) to 11.4 (3.98–32.9) in multivariable model 2. The AP was 0.60 (0.25–0.94) and 0.56 (0.15–0.97), respectively (Table 4). However, the additive interactions between rs7747752 CG and low choline and between rs7747752 CG and low betaine for the risk of GDM were nonsignificant (Tables 4 and 5).
The CC genotype of rs7747752 markedly enhanced the OR (95% CI) of L-carnitine ≤ vs. > 150 nmol/mL for GDM from 6.14 (2.61–14.4) to 19.6 (5.65–68.1) and also enhanced the OR of choline ≤ vs. > 110 nmol/mL for GDM from 2.37 (1.07–5.28) to 12.1 (3.22–45.6) in multivariable model 2. Their APs were, respectively, 0.56 (0.06–1.06) and 0.66 (0.20–1.11), being statistically significant (Table 4). However, we failed to detect a significant additive interaction between rs7747752 CC and low serum levels of betaine for the risk of GDM (Table 5).
We verified that CDKAL1 rs7747752 genetic variant was associated with an elevated risk of GDM in Chinese pregnant women. Interestingly, we, for the first time, found that the CDKAL1 rs7747752 CC/CG genotype markedly amplified the GDM-promoting effects of low serum levels of L-carnitine and choline, leading to a large increase in the risk of GDM in Chinese women. Unfortunately, we did not find that CDKAL1 rs7747752 had a significant synergistic effect with betaine on the risk of GDM.
There is a lack of studies that addressed associations of serum L-carnitine, choline, and betaine with the risk of GDM. However, several studies have attempted to address associations between L-carnitine, choline, and betaine and diabetes, but their findings are inconsistent and inconclusive. A cross-sectional study of 7074 Norwegian men and women observed that choline was positively while betaine was negatively associated with key components of metabolic syndrome . A case-control study of 427 pairs of individuals with and without incident diabetes nested in the Diabetes Prevention Program (DPP) found that low plasma betaine at baseline was associated with an increased risk of incident diabetes. It is also noted that the increase in betaine levels at 2 years of follow-up was associated with a lower risk of incident diabetes while higher dietary intakes of L-carnitine, choline, and betaine were associated with a decreased risk of T2DM . A cross-sectional study of 2394 adults in Canada showed that dietary choline and betaine intakes were inversely correlated with the levels of fasting glucose and insulin resistance . However, the Atherosclerosis Risk in Communities (ARIC) study (n = 13,440) found that dietary intake of choline was associated with the risk of T2DM in women, but betaine was not . Stronger evidence came from a meta-analysis of 5 randomized controlled trials showing that L-carnitine treatment was effective in reducing insulin resistance over a 12-month period . In our case-control study, low serum levels of L-carnitine, choline, and betaine in early pregnancy were also associated with markedly elevated risks of GDM . In this analysis, we further found that low serum levels of L-carnitine and choline had synergistic effects with the CDKAL1 rs7747752 CC/CG genotype towards the increasing risk of GDM. This observation provides a plausible explanation for some inconsistent findings regarding associations of L-carnitine and choline with the risk of T2DM, i.e., the effects of the supplement of L-carnitine and choline being particularly large among CDKAL1 rs7747752 CC/CG genotype carriers who also had a low serum level of L-carnitine or choline.
To our best knowledge, we are the first to report the additive interactions between CDKAL1 genetic variants and low serum levels of L-carnitine and choline for GDM. CDKAL1 is identified so far as being significantly associated with T2DM . The CDKAL1 genetic variants could predict the development of diabetes in individuals with impaired insulin secretion, which indicates that there may be potential synergistic and interactive effects among different risk factors [13, 41]. In a recent study, our group found that CDKAL1 genetic variant had a significant synergistic effect with serum palmitate acids, leading to an increased risk of GDM . The interaction between the two risk factors may suggest that impaired beta cells cannot produce enough insulin to cope with increased insulin resistance as caused by high palmitate acids, thereby triggering a high risk of GDM . Intriguingly, we further detected significant additive interactions between CDKAL1 rs7747752 CC/CG genotype and low serum levels of L-carnitine and choline for the risk of GDM. Women who had both CDKAL1 rs7747752 CC/CG genotype and low serum levels of L-carnitine and choline were at a particularly high risk of GDM.
The molecular mechanism of additive interactions between low serum levels of L-carnitine and choline and CDKAL1 rs7747752 for the risk of GDM remains unclear. However, it is biologically plausible that L-carnitine and choline play a critical role in the link between CDKAL1 genetic variants and GDM. Studies suggest that CDKAL1 polymorphisms may modulate insulin secretion  or be related to insulin resistance [43, 44]. Notably, the Cardiovascular Health Study found that high plasma concentrations of choline were associated with decreased fasting glucose and insulin levels and increased insulin sensitivity . Furthermore, Zhang et al. observed that a decrease in serum choline was associated with an increase in insulin resistance . Thus, it is likely that the copresence of low serum levels of choline and CDKAL1 rs7747752 genetic variant led to a markedly increased risk of GDM via increasing insulin resistance or decreasing insulin sensitivity. A primary physiological role of L-carnitine is to transfer long-chain fatty acids into the mitochondrial matrix and to increase the efflux of acyl groups out of the mitochondria . Accumulation of intracellular lipid derivatives within mitochondria plays an important role in the development of insulin resistance . Several lines of evidence suggest a role of L-carnitine in regulating insulin resistance and glucose metabolism [49,50,51]. Hence, their interactions for GDM may suggest that GDM develops when impaired beta cells could not produce enough insulin in response to increased insulin resistance as manifested by low L-carnitine. It is also possible that the copresence of both risk factors further contributed to increased insulin resistance, as compared with any one risk factor alone, thereby triggering a high GDM risk status. Hereby, our findings seem to support that low serum levels of L-carnitine and choline among CC or CG genotype carriers at CDKAL1 rs7747752 play a critical role in the etiology of GDM.
Our findings have potential implications for prevention of GDM. GDM is prevalent in the world, and an increasing number of women are being affected by the disease. What is worse, women with GDM are at a much higher risk of diabetes in later life and their offspring are also at a high risk of obesity in childhood. It is critical to reduce the burden of GDM and its short- and long-term complications. In this connection, our study suggests that pregnant women should be screened for copresence of CC/CG genotypes of rs7747752 in CDKAL1 and low serum levels of L-carnitine or choline. Specific interventions, such as increased dietary intakes or supplements of L-carnitine or choline, may be used to prevent GDM in CC/CG genotype carriers who had low serum L-carnitine or choline in early pregnancy. Indeed, randomized controlled trials are warranted to test the efficacy of supplements of L-carnitine or choline among pregnant women who have both risk factors in early pregnancy.
This study had several limitations. First, our findings were obtained from a nested case-control study of Chinese pregnant women. Further cohort studies are needed to replicate these important findings in other populations of pregnant women. Second, we used a two-step GDM screening procedure to identify GDM and some GDM cases might have been missed. Third, diagnostic methods of GDM differ from country to country and even from medical organization to medication organization in a single country. These differences might also affect the implacability of our study findings. Fourth, dietary intakes of L-carnitine, choline, and betaine may affect its serum level . However, information on dietary intakes was not collected due to the busy clinical setting. Interactions between high intakes of L-carnitine and choline and CDKAL1 genetic variants for the risk of GDM need to be further confirmed.
We found that the CDKAL1 rs7747752 genetic variant interacted with serum L-carnitine ≤ 150 nmol/mL and choline ≤ 110 nmol/mL, leading to a markedly increased risk of GDM. Identification of interactions of rs7747752 CC/CG genotype with L-carnitine and choline metabolites is an important step towards effectively preventing GDM. Replication studies are needed to confirm our findings in other populations of pregnant women, and further mechanistic studies are also warranted to understand the molecular mechanism of the interactions between CDKAL1 rs7747752 genetic variant and low levels of L-carnitine and choline for the risk of GDM.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF Diabetes Atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group's criteria. Diabetes Res Clin Pract. 2022;183:109050.
Metzger BE, Coustan DR, Trimble ER. Hyperglycemia and adverse pregnancy outcomes. Clin Chem. 2019;65:937–8.
Song C, Lyu Y, Li C, Liu P, Li J, Ma RC, et al. Long-term risk of diabetes in women at varying durations after gestational diabetes: a systematic review and meta-analysis with more than 2 million women. Obes Rev. 2018;19:421–9.
Gao M, Cao S, Li N, Liu J, Lyu Y, Li J, et al. Risks of overweight in the offspring of women with gestational diabetes at different developmental stages: a meta-analysis with more than half a million offspring. Obes Rev. 2022;23:e13395.
Li N, Yang Y, Cui D, Li C, Ma RCW, Li J, et al. Effects of lifestyle intervention on long-term risk of diabetes in women with prior gestational diabetes: a systematic review and meta-analysis of randomized controlled trials. Obes Rev. 2021;22:e13122.
Gillman MW, Oakey H, Baghurst PA, Volkmer RE, Robinson JS, Crowther CA. Effect of treatment of gestational diabetes mellitus on obesity in the next generation. Diabetes Care. 2010;33:964–8.
Li J, Liu J, Zhang C, Liu G, Leng J, Wang L, et al. Effects of lifestyle intervention of maternal gestational diabetes mellitus on offspring growth pattern before two years of age. Diabetes Care. 2021;44:e42–e4.
Song C, Li J, Leng J, Ma RC, Yang X. Lifestyle intervention can reduce the risk of gestational diabetes: a meta-analysis of randomized controlled trials. Obes Rev. 2016;17:960–9.
Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331–6.
Wang K, Chen Q, Feng Y, Yang H, Wu W, Zhang P, et al. Single nucleotide polymorphisms in CDKAL1 gene are associated with risk of gestational diabetes mellitus in Chinese population. J Diabetes Res. 2019;2019:3618103.
Ding M, Chavarro J, Olsen S, Lin Y, Ley SH, Bao W, et al. Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations. Diabetologia. 2018;61:1758–68.
Guo F, Long W, Zhou W, Zhang B, Liu J, Yu B. FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and the risk of gestational diabetes mellitus: a meta-analysis. Arch Gynecol Obstet. 2018;298:705–15.
Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39:770–5.
Pascoe L, Tura A, Patel SK, Ibrahim IM, Ferrannini E, Zeggini E, et al. Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are associated with decreased pancreatic beta-cell function. Diabetes. 2007;56:3101–4.
Popova PV, Klyushina AA, Vasilyeva LB, Tkachuk AS, Vasukova EA, Anopova AD, et al. Association of common genetic risk variants with gestational diabetes mellitus and their role in GDM prediction. Front Endocrinol (Lausanne). 2021;12:628582.
Tarnowski M, Malinowski D, Pawlak K, Dziedziejko V, Safranow K, Pawlik A. GCK, GCKR, FADS1, DGKB/TMEM195 and CDKAL1 gene polymorphisms in women with gestational diabetes. Can J Diabetes. 2017;41:372–9.
Fang X, Jin L, Tang M, Lu W, Lai S, Zhang R, et al. Common single-nucleotide polymorphisms combined with a genetic risk score provide new insights regarding the etiology of gestational diabetes mellitus. Diabet Med. 2022;39:e14885.
Amin USM, Parvez N, Rahman TA, Hasan MR, Das KC, Jahan S, et al. CDKAL1 gene rs7756992 A/G and rs7754840 G/C polymorphisms are associated with gestational diabetes mellitus in a sample of Bangladeshi population: implication for future T2DM prophylaxis. Diabetol Metab Syndr. 2022;14:18.
Wallace TC, Fulgoni VL. Assessment of total choline intakes in the United States. J Am Coll Nutr. 2016;35:108–12.
Dibaba DT, Johnson KC, Kucharska-Newton AM, Meyer K, Zeisel SH, Bidulescu A. The association of dietary choline and betaine with the risk of type 2 diabetes: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Care. 2020;43:2840–6.
Chen L, Chen YM, Wang LJ, Wei J, Tan YZ, Zhou JY, et al. Higher homocysteine and lower betaine increase the risk of microangiopathy in patients with diabetes mellitus carrying the GG genotype of PEMT G774C. Diabetes Metab Res Rev. 2013;29:607–17.
Gao X, Wang Y, Sun G. High dietary choline and betaine intake is associated with low insulin resistance in the Newfoundland population. Nutrition. 2017;33:28–34.
Malaguarnera M, Gargante MP, Russo C, Antic T, Vacante M, Malaguarnera M, et al. L-carnitine supplementation to diet: a new tool in treatment of nonalcoholic steatohepatitis--a randomized and controlled clinical trial. Am J Gastroenterol. 2010;105:1338–45.
Huo X, Li J, Cao YF, Li SN, Shao P, Leng J, et al. Trimethylamine N-oxide metabolites in early pregnancy and risk of gestational diabetes: a nested case-control study. J Clin Endocrinol Metab. 2019;104:5529–39.
Mo M, Shao B, Xin X, Luo W, Si S, Jiang W, et al. The association of gene variants in the vitamin D metabolic pathway and its interaction with vitamin D on gestational diabetes mellitus: a prospective cohort study. Nutrients. 2021;13:4220.
Powe CE, Kwak SH. Genetic studies of gestational diabetes and glucose metabolism in pregnancy. Curr Diab Rep. 2020;20:69.
Wang H, Yang W, Liu J, Leng J, Li W, Yu Z, et al. Serum concentrations of SFAs and CDKAL1 single-nucleotide polymorphism rs7747752 are related to an increased risk of gestational diabetes mellitus. Am J Clin Nutr. 2021;114:1698–707.
Du J, Shen L, Tan Z, Zhang P, Zhao X, Xu Y, et al. Betaine supplementation enhances lipid metabolism and improves insulin resistance in mice fed a high-fat diet. Nutrients. 2018;10:131.
Joselit Y, Nanobashvili K, Jack-Roberts C, Greenwald E, Malysheva OV, Caudill MA, et al. Maternal betaine supplementation affects fetal growth and lipid metabolism of high-fat fed mice in a temporal-specific manner. Nutr Diabetes. 2018;8:41.
Wang ZY, Liu YY, Liu GH, Lu HB, Mao CY. l-Carnitine and heart disease. Life Sci. 2018;194:88–97.
Leng J, Shao P, Zhang C, Tian H, Zhang F, Zhang S, et al. Prevalence of gestational diabetes mellitus and its risk factors in Chinese pregnant women: a prospective population-based study in Tianjin, China. PLoS One. 2015;10:e0121029.
Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676–82.
Menard GN, Bryant FM, Kelly AA, Craddock CP, Lavagi I, Hassani-Pak K, et al. Natural variation in acyl editing is a determinant of seed storage oil composition. Sci Rep. 2018;8:17346.
Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. Data quality control in genetic case-control association studies. Nat Protoc. 2010;5:1564–73.
Liu J, Li J, Li S, Leng J, Li W, Yang W, et al. Circulating lysophosphatidylcholines in early pregnancy and risk of gestational diabetes in Chinese women. J Clin Endocrinol Metab. 2020;105:e982–e93.
Andersson T, Alfredsson L, Kallberg H, Zdravkovic S, Ahlbom A. Calculating measures of biological interaction. Eur J Epidemiol. 2005;20:575–9.
Konstantinova SV, Tell GS, Vollset SE, Nygård O, Bleie Ø, Ueland PM. Divergent associations of plasma choline and betaine with components of metabolic syndrome in middle age and elderly men and women. J Nutr. 2008;138:914–20.
Walford GA, Ma Y, Clish C, Florez JC, Wang TJ, Gerszten RE. Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes. 2016;65:1424–33.
Xu Y, Jiang W, Chen G, Zhu W, Ding W, Ge Z, et al. L-carnitine treatment of insulin resistance: a systematic review and meta-analysis. Adv Clin Exp Med. 2017;26:333–8.
Krentz NAJ, Gloyn AL. Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics. Nat Rev Endocrinol. 2020;16:202–12.
Malik VS, Li Y, Tobias DK, Pan A, Hu FB. Dietary protein intake and risk of type 2 diabetes in US men and women. Am J Epidemiol. 2016;183:715–28.
Ruchat SM, Elks CE, Loos RJ, Vohl MC, Weisnagel SJ, Rankinen T, et al. Association between insulin secretion, insulin sensitivity and type 2 diabetes susceptibility variants identified in genome-wide association studies. Acta Diabetol. 2009;46:217–26.
Rask-Andersen M, Philippot G, Moschonis G, Dedoussis G, Manios Y, Marcus C, et al. CDKAL1-related single nucleotide polymorphisms are associated with insulin resistance in a cross-sectional cohort of Greek children. PLoS One. 2014;9:e93193.
Liang J, Pei Y, Liu X, Qiu Q, Sun Y, Zhu Y, et al. The CDKAL1 gene is associated with impaired insulin secretion and glucose-related traits: the Cardiometabolic Risk in Chinese (CRC) study. Clin Endocrinol (Oxf). 2015;83:651–5.
Lemaitre RN, Jensen PN, Wang Z, Fretts AM, McKnight B, Nemet I, et al. Association of trimethylamine N-oxide and related metabolites in plasma and incident type 2 diabetes: the cardiovascular health study. JAMA Netw Open. 2021;4:e2122844.
Zhang X, Wang Y, Hao F, Zhou X, Han X, Tang H, et al. Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J Proteome Res. 2009;8:5188–95.
Bene J, Hadzsiev K, Melegh B. Role of carnitine and its derivatives in the development and management of type 2 diabetes. Nutr Diabetes. 2018;8:8.
Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M, Ilkayeva O, et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 2008;7:45–56.
Bruls YM, de Ligt M, Lindeboom L, Phielix E, Havekes B, Schaart G, et al. Carnitine supplementation improves metabolic flexibility and skeletal muscle acetylcarnitine formation in volunteers with impaired glucose tolerance: a randomised controlled trial. EBioMedicine. 2019;49:318–30.
Sangouni AA, Pakravanfar F, Ghadiri-Anari A, Nadjarzadeh A, Fallahzadeh H, Hosseinzadeh M. The effect of L-carnitine supplementation on insulin resistance, sex hormone-binding globulin and lipid profile in overweight/obese women with polycystic ovary syndrome: a randomized clinical trial. Eur J Nutr. 2022;61:1199–207.
Kranenburg LC, Westermann CM, de Sain-van der Velden MG, de Graaf-Roelfsema E, Buyse J, Janssens GP, et al. The effect of long-term oral L-carnitine administration on insulin sensitivity, glucose disposal, plasma concentrations of leptin and acylcarnitines, and urinary acylcarnitine excretion in warmblood horses. Vet Q. 2014;34:85–91.
Hirsch MJ, Growdon JH, Wurtman RJ. Relations between dietary choline or lecithin intake, serum choline levels, and various metabolic indices. Metabolism. 1978;27:953–60.
The authors thank all the health professionals of Tianjin Antenatal Network for their involvement and contribution to the study. X.Y. was the guarantor of this manuscript.
This work was supported by the National Natural Science Foundation of China (Grant No: 81870549), the Engaged Talents of Guangdong Medical University in 2017 (Grant No: 2XB17028), the Sailing Plan of Guangdong Province (Grant No: 4YF16001G), and the National Key Research and Development Program of China (Grant No: 2019YFA0802300).
Ethics approval and consent to participate
Ethics approval was obtained from the Ethics Committee for Clinical Research of Tianjin Women and Children’s Health Centre on 1 December 2009 (ref. no. 2009-02). The patients/participants provided their written informed consent to participate in this study.
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The authors declare no competing interests.
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Wang, H., Li, J., Liu, J. et al. Interactions of CDKAL1 rs7747752 polymorphism and serum levels of L-carnitine and choline are related to increased risk of gestational diabetes mellitus. Genes Nutr 17, 14 (2022). https://doi.org/10.1186/s12263-022-00716-9
- Gestational diabetes mellitus