Abstract
Purpose
Gestational diabetes mellitus (GDM) is a growing public health problem worldwide and its etiology remains unclear. The pathophysiology of GDM is similar to that of type 2 diabetes (T2DM) and insulin resistance (IR) is the main reason for the development of GDM. Carnitine palmitoyltransferase 1A (CPT1A) is a candidate gene for metabolic disorders; however, the association of the CPT1A gene and GDM has not yet been studied. We aimed to explore whether single-nucleotide polymorphisms (SNPs) of the CPT1A gene could influence the risk of GDM.
Methods
We examined 18 single-nucleotide polymorphisms (SNPs) in the CPT1A gene and the risk of GDM in a nested case-control study of 334 GDM patients and 334 controls. The controls who had no GDM were randomly selected through matching to cases by age and residence.
Results
After adjusting the family history of diabetes, pre-pregnancy body mass index, and multiple comparison correction, the CPT1A rs2846194 and rs2602814 were associated with reduced GDM risk while rs59506005 was associated with elevated GDM risk. Moreover, the GGAC haplotype in the CPT1A gene (rs17399246 rs1016873 rs11228450 rs10896396) was associated with a reduced risk of GDM.
Conclusion
Our study provides evidence for an association between genetic polymorphisms in the CPT1A and the risk of GDM.
Keywords: Gestational diabetes mellitus, Carnitine palmitoyltransferase 1A (CPT1A), Haplotype, Single-nucleotide polymorphism
Introduction
Gestational diabetes mellitus (GDM) refers to different degrees of glucose tolerance abnormalities that occur or are first discovered during pregnancy, and it is one of the most common pregnancy complications. GDM affects approximately 1.8-25.1% of pregnant women worldwide [1, 2], and in recent years, with the adoption of the new IADPASG diagnostic criteria, the prevalence of GDM has been increasing and even doubled [3, 4]. Studies have also shown that in the past 20 years, the prevalence of GDM in different populations has increased by 10-100% [5, 6]. GDM is one of the main causes of maternal and neonatal morbidity and mortality. Women with GDM are associated with a higher incidence of depressive disorder, obesity, type 2 diabetes (T2D), and cardiovascular disease. Furthermore, offspring from women with GDM have a higher likelihood of hypoglycemia, obesity, and other metabolic syndromes [7–11]. GDM seriously endangers the health of maternal and neonatal and is an important public health problem worldwide.
The pathogenesis of GDM is still unclear, but the current research believes that it is a complex disease that combines genetic and environmental factors. Some risk factors for GDM have been found, mainly including high maternal age, obesity, history of maternal GDM, family history of diabetes, history of maternal low birth weight, twin and multiple pregnancies, lack of proper physical activity, and genetic susceptibility sensibility [12, 13]. It is generally believed that the pathogenesis of GDM is similar to type 2 diabetes (T2DM) [14], and insulin resistance (IR) plays an important role in the development of GDM [15].
CPT1A (carnitine palmitoyltransferase 1A) is a subtype of carnitine palmitate hydrazine transferase (CPT). Human CPT1A is located in the 11q13.1~q13.5 region and is mainly expressed in liver, kidney, and lung tissues. The activity of CPT1A in the liver is the strongest [16] and it is also expressed in sugar-sensitive cell types such as pancreatic β cells and hypothalamic neurons [17]. It has been reported that fat accumulation in the liver is related to insulin resistance and type 2 diabetes [18–23] and CPT1A can regulate the oxidation process of fatty acids in the body. By increasing the expression of CPT1A, the oxidation rate of fatty acids can be accelerated and insulin resistance can be improved to a certain extent. The accumulation of fatty acids in cells can lead to the formation of insulin resistance, which can lead to type 2 diabetes and hyperinsulinemia [24, 25].
CPT1A is a candidate gene for metabolic disorders. One study from Japan had investigated the associations between CPT1A and T2DM [26]. However, as far as we are aware, no genetic association studies have focused on the relation of CPT1A to GDM.
Due to the similarities of pathogenesis between T2DM and GDM and to further elucidate the role for the CPT1A gene polymorphism in GDM risk, we used a candidate gene approach to investigate 18 single-nucleotide polymorphisms (SNPs) in the CPT1A gene and the risk of GDM in a case-control study of 334 GDM and 334 controls.
Materials and methods
Study population
The subjects were recruited from a birth cohort at the First Affiliated Hospital of Shanxi Medical University in Taiyuan, China, between March 1, 2012 and July 30, 2014. The inclusion criteria of the study subjects were pregnant women with 18 years or older, gestational age at delivery were 20 weeks or more, and no serious mental or organic diseases. This study was reviewed and approved by the Shanxi Medical University School of Medicine Committee, and written informed consent was obtained from all study participants. A standardized and structured questionnaire is used by trained investigators to collect information on demographic factors and reproductive and medical history. Information on birth outcomes and pregnancy complications was abstracted from medical records.
Women with GDM were diagnosed using a 75-g oral glucose tolerance test (OGTT) during weeks 24-28 of pregnancy. According to the 2015 International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria, subjects were diagnosed as having GDM who met at least one of the following: (1) fasting plasma glucose ≥ 5.1 mmol/L, (2) the 1-h plasma glucose ≥ 10.0 mmol/L, and (3) the 2-h plasma glucose ≥ 8.5 mmol/L [27]. A total of 334 pregnant women were diagnosed with GDM and 334 pregnant women without GDM were randomly selected through matching to cases by age and residence. Finally, 334 cases and 334 controls were included in the analysis.
Genotyping
All selected subjects voluntarily donated blood with informed consent and stored it in the −80°C refrigerator. DNA was extracted, isolated, and purified from whole blood samples according to a standard phenol-chloroform extraction method. Genotyping was conducted using an Illumina Goldengate Platform. Each 96-well panel selects 5% of the samples for repeated sample testing as quality control. The complete detection rate of all SNPs exceeds 99%. SNPs were selected based on the following criteria: (a) minor allele frequency (MAF) > 0.05 and (b) SNPs with a P-value of HWE >0.05. Hardy–Weinberg equilibrium (HWE) for each SNP was examined by the chi-square test. Two SNPs were excluded from the final analysis due to violation of HWE (P < 0.05; as shown in Table 3). A total of 16 SNPs from the CPT1A gene were included in the study.
Table 3.
Genetic balance test of CPT1A gene polymorphism
| Gene | Chromosome | SNP | BP | Major allele | Minor allele | HWE P-value |
MAF |
|---|---|---|---|---|---|---|---|
| CPT1A | 11 | rs2846194 | 68755043 | G | A | 0.107 | 0.27 |
| CPT1A | 11 | rs2846207 | 68757913 | A | C | 1 | 0.29 |
| CPT1A | 11 | rs2602820 | 68758840 | C | A | 0.592 | 0.28 |
| CPT1A | 11 | rs2602814 | 68761422 | C | A | 0.374 | 0.48 |
| CPT1A | 11 | rs2846199a | 68761525 | G | A | 0.026 | 0.13 |
| CPT1A | 11 | rs1249582 | 68765895 | A | G | 0.741 | 0.49 |
| CPT1A | 11 | rs12803642 | 68771755 | C | A | 0.614 | 0.21 |
| CPT1A | 11 | rs59506005 | 68774585 | C | G | 0.181 | 0.26 |
| CPT1A | 11 | rs2513102 | 68779633 | G | A | 0.064 | 0.12 |
| CPT1A | 11 | rs2846196 | 68782223 | A | G | 0.317 | 0.15 |
| CPT1A | 11 | rs4930257a | 68792499 | G | A | 0.001 | 0.01 |
| CPT1A | 11 | rs11228444 | 68793206 | A | G | 0.531 | 0.32 |
| CPT1A | 11 | rs17399246 | 68798901 | G | A | 0.332 | 0.14 |
| CPT1A | 11 | rs1016873 | 68799623 | G | A | 0.441 | 0.48 |
| CPT1A | 11 | rs11228450 | 68804451 | G | A | 0.870 | 0.21 |
| CPT1A | 11 | rs10896396 | 68804806 | C | A | 0.741 | 0.49 |
| CPT1A | 11 | rs4930642 | 68816370 | G | A | 0.874 | 0.24 |
| CPT1A | 11 | rs3750965 | 68840160 | A | G | 0.161 | 0.25 |
CPT1A carnitine palmitoyl transferase 1A, SNP single-nucleotide polymorphism, BP base pair, HWE Hardy–Weinberg equilibrium, MAF minor allele frequency
aDoes not meet Hardy-Weinberg equilibrium and is eliminated
Statistical analysis
All the statistical analyses were performed with the SPSS 26.0 and R3.6.2 software. Demographic factors and medical history were compared between cases and controls by using the χ2 test and Student’s t-test for categorical variables and continuous variables, respectively. At the gene level, the minimum P-value (min P) tests were conducted to evaluate the relationship between gene and GDM using the “min P test” R statistical package. The genotype distribution was assessed by the Hardy-Weinberg equilibrium test. The odds ratio (OR) and their 95% confidence interval (95% CI) were estimated by logistic regression as a measure of the associations between genotypes and GDM. The common homozygous genotypes served as references. A linear trend test was conducted by assigning the ordinal value 1, 2, and 3 to the homozygous common, heterozygous, and homozygous rare alleles, respectively. Haplotype block structures were evaluated with Haploview version 4.2 using the method of Four Gamete Rule. Multiple comparisons were adjusted using the false discovery rate (FDR) method at a significant level of 0.2 [28].
Results
Demographic characteristics
As shown in Table 1, there were significant differences in BMI and family history of diabetes between the cases and control group. More pregnant women were overweight in the case group than in the controls (P < 0.001). The case group contains more pregnant women with a family history of diabetes compared with controls (P = 0.006). No significant differences in age, parity, family history of high blood pressure, and weight gain during pregnancy were observed between cases and controls.
Table 1.
Distribution of selected characteristics of the study population
| Gestational diabetes (N = 334) Number (%) |
Controls (N = 334) Number (%) |
P-value | |
|---|---|---|---|
| Age (years) | 0.938 | ||
| <30 | 165 (49.40%) | 163 (48.80%) | |
| ≥30 | 169 (50.60%) | 171 (51.20%) | |
| Body mass index (kg/m2) | <0.001* | ||
| <18.5 | 34 (10.18%) | 56 (16.77%) | |
| 18.5~ | 203 (60.78%) | 223 (66.77%) | |
| ≥24 | 97 (29.04%) | 55 (16.46%) | |
| Parity | 0.536 | ||
| Nulliparous | 165 (49.40%) | 174 (52.10%) | |
| Parous | 169 (50.60%) | 160 (47.90%) | |
| Family history of diabetes | 0.006* | ||
| No | 290 (86.83%) | 312 (93.41%) | |
| Yes | 44 (13.17%) | 22 (6.59%) | |
| Family history of high blood pressure | 0.753 | ||
| No | 282 (84.43%) | 278 (83.23%) | |
| Yes | 52 (15.57%) | 55 (16.47%) | |
| Gestational week | 0.027* | ||
| ‾x ± s | 38.38 ± 1.78 | 38.68 ± 1.71 | |
| Weight gain during pregnancy | 0.333 | ||
| ‾x ± s | 16.17 ± 6.11 | 16.59 ± 5.19 |
Chi-squared test was used for qualitative characteristics
*P < 0.05
Gene and single-nucleotide polymorphism analysis
As shown in Table 2, no significant association was found between the CPT1A gene and the risk of GDM. The Hardy-Weinberg equilibrium test was performed on the 18 SNPs of the CPT1A gene included in the analysis. Two SNPs, rs2846199 and rs4930257, were excluded from the final analysis due to violation of HWE (P < 0.05). Finally, a total of 16 SNPs were used for subsequent correlation analysis (Table 3).
Table 2.
CPT1A gene polymorphism and risk of GDM
| Gene | SNP Number | SNP database ID | min P | |||
|---|---|---|---|---|---|---|
| CPT1A | 16 |
rs2846194 rs1249582 rs2846196 rs11228450 |
rs2846207 rs12803642 rs11228444 rs10896396 |
rs2602820 rs59506005 rs17399246 rs4930642 |
rs2602814 rs2513102 rs1016873 rs3750965 |
0.124 |
CPT1A carnitine palmitoyltransferase 1A, SNP single-nucleotide polymorphism
All associations between the 16 SNPs and GDM risk are displayed in Table 4. We found that 3 SNPs of CPT1A (rs2846194, rs2602814, rs59506005) were associated with the risk of GDM after adjustment for the multiple comparisons using the FDR method. Reduced risk of GDM was observed in women who carried the CPT1A rs2846194 AA genotype (OR = 0.43, 95% CI = 0.22~0.83) compared with the GG genotype and the rs2602814 AA genotype (OR = 0.52, 95% CI = 0.33~0.81) compared with the CC genotype. Elevated risk of GDM was observed among women who carried the CPT1A rs59506005 CG genotype (OR = 1.78, 95% CI = 1.28~2.48) and the rs59506005 CG or GG genotype (OR = 1.68, 95% CI = 1.22~2.30) compared with the CC genotype.
Table 4.
Association analysis of CPT1A gene polymorphism and GDM risk
| SNPs | Genotypes | Cases N = 334 | Controls N = 334 | ORa | 95% CI | P | qb |
|---|---|---|---|---|---|---|---|
| rs2846194 | |||||||
| GG | 168 | 182 | 1 | ||||
| GA | 137 | 136 | 0.88 | 0.64-1.22 | 0.457 | 0.975 | |
| AA | 29 | 15 | 0.43 | 0.22-0.83 | 0.014 | 0.149 | |
| Trend | 0.037 | 0.296 | |||||
| GA or AA | 166 | 151 | 0.82 | 0.60-1.12 | 0.217 | 0.731 | |
| rs2846207 | |||||||
| AA | 168 | 161 | 1 | ||||
| AC | 140 | 138 | 1.04 | 0.75-1.44 | 0.828 | 1.060 | |
| CC | 26 | 30 | 1.30 | 0.73-2.35 | 0.380 | 0.973 | |
| Trend | 0.466 | 0.904 | |||||
| AC or CC | 166 | 169 | 1.09 | 0.80-1.49 | 0.583 | 0.933 | |
| rs2602820 | |||||||
| CC | 175 | 169 | 1 | ||||
| CA | 138 | 132 | 1.02 | 0.73-1.41 | 0.918 | 1.013 | |
| AA | 20 | 30 | 1.66 | 0.90-3.12 | 0.110 | 0.704 | |
| Trend | 0.255 | 0.777 | |||||
| CA or AA | 158 | 162 | 1.11 | 0.81-1.52 | 0.508 | 0.929 | |
| rs2602814 | |||||||
| CC | 73 | 98 | 1 | ||||
| CA | 176 | 174 | 0.71 | 0.48-1.03 | 0.072 | 0.512 | |
| AA | 85 | 62 | 0.52 | 0.33-0.81 | 0.004 | 0.085 | |
| Trend | 0.004 | 0.064 | |||||
| CA or AA | 261 | 236 | 0.66 | 0.46-0.94 | 0.024 | 0.219 | |
| rs1249582 | |||||||
| AA | 87 | 87 | 1 | ||||
| AG | 162 | 163 | 1.04 | 0.71-1.51 | 0.851 | 1.028 | |
| GG | 84 | 81 | 0.99 | 0.64-1.52 | 0.946 | 0.993 | |
| Trend | 0.950 | 0.981 | |||||
| AG or GG | 246 | 244 | 1.03 | 0.72-1.47 | 0.864 | 1.024 | |
| rs12803642 | |||||||
| CC | 205 | 211 | 1 | ||||
| CA | 111 | 105 | 0.90 | 0.64-1.25 | 0.524 | 0.932 | |
| AA | 16 | 15 | 0.95 | 0.45-2.02 | 0.903 | 1.014 | |
| Trend | 0.600 | 0.914 | |||||
| CA or AA | 127 | 120 | 0.90 | 0.65-1.24 | 0.530 | 0.917 | |
| rs59506005 | |||||||
| CC | 203 | 163 | 1 | ||||
| CG | 108 | 148 | 1.78 | 1.28-2.48 | 0.000645 | 0.041 | |
| GG | 22 | 23 | 1.41 | 0.75-2.66 | 0.289 | 0.841 | |
| Trend | 0.005 | 0.061 | |||||
| CG or GG | 130 | 171 | 1.68 | 1.22-2.30 | 0.001 | 0.043 | |
| rs2513102 | |||||||
| GG | 250 | 264 | 1 | ||||
| GA | 78 | 61 | 0.76 | 0.52-1.12 | 0.165 | 0.754 | |
| AA | 2 | 8 | 3.32 | 0.81-22.29 | 0.135 | 0.785 | |
| Trend | 0.665 | 0.925 | |||||
| GA or AA | 80 | 69 | 0.84 | 0.58-1.22 | 0.354 | 0.944 | |
| rs2846196 | |||||||
| AA | 245 | 229 | 1 | ||||
| AG | 79 | 96 | 1.28 | 0.90-1.82 | 0.179 | 0.716 | |
| GG | 8 | 6 | 0.85 | 0.27-2.56 | 0.779 | 1.017 | |
| Trend | 0.330 | 0.918 | |||||
| AG or GG | 87 | 105 | 1.27 | 0.90-1.79 | 0.181 | 0.681 | |
| rs11228444 | |||||||
| AA | 156 | 147 | 1 | ||||
| AG | 140 | 153 | 1.13 | 0.81-1.57 | 0.462 | 0.924 | |
| GG | 34 | 32 | 0.97 | 0.56-1.67 | 0.919 | 0.997 | |
| Trend | 0.764 | 1.019 | |||||
| AG or GG | 174 | 185 | 1.09 | 0.80-1.49 | 0.591 | 0.923 | |
| rs17399246 | |||||||
| GG | 240 | 251 | 1 | ||||
| GA | 86 | 72 | 0.78 | 0.54-1.13 | 0.185 | 0.658 | |
| AA | 5 | 8 | 1.39 | 0.45-4.72 | 0.568 | 0.932 | |
| Trend | 0.419 | 0.925 | |||||
| GA or AA | 91 | 80 | 0.81 | 0.57-1.16 | 0.250 | 0.800 | |
| rs1016873 | |||||||
| GG | 92 | 90 | 1 | ||||
| GA | 164 | 158 | 1.06 | 0.73-1.54 | 0.749 | 1.020 | |
| AA | 73 | 82 | 1.20 | 0.78-1.87 | 0.407 | 0.965 | |
| Trend | 0.412 | 0.942 | |||||
| GA or AA | 237 | 240 | 1.10 | 0.77-1.56 | 0.603 | 0.897 | |
| rs11228450 | |||||||
| GG | 213 | 204 | 1 | ||||
| GA | 100 | 112 | 1.16 | 0.83-1.62 | 0.399 | 0.982 | |
| AA | 19 | 14 | 0.77 | 0.36-1.59 | 0.477 | 0.898 | |
| Trend | 0.891 | 1.018 | |||||
| GA or AA | 119 | 126 | 1.1 | 0.80-1.53 | 0.547 | 0.921 | |
| rs10896396 | |||||||
| CC | 86 | 87 | 1 | ||||
| CA | 169 | 163 | 1.02 | 0.70-1.48 | 0.933 | 0.995 | |
| AA | 77 | 82 | 1.11 | 0.71-1.72 | 0.652 | 0.948 | |
| Trend | 0.656 | 0.933 | |||||
| CA or AA | 246 | 245 | 1.04 | 0.73-1.48 | 0.840 | 1.034 | |
| rs4930642 | |||||||
| GG | 177 | 182 | 1 | ||||
| GA | 113 | 116 | 1.02 | 0.73-1.44 | 0.888 | 1.033 | |
| AA | 15 | 16 | 0.92 | 0.43-1.96 | 0.828 | 1.039 | |
| Trend | 0.973 | 0.988 | |||||
| GA or AA | 128 | 132 | 0.99 | 0.72-1.38 | 0.976 | 0.976 | |
| rs3750965 | |||||||
| AA | 179 | 201 | 1 | ||||
| AG | 127 | 107 | 0.78 | 0.56-1.09 | 0.144 | 0.709 | |
| GG | 25 | 22 | 0.79 | 0.42-1.47 | 0.461 | 0.952 | |
| Trend | 0.165 | 0.704 | |||||
| AG or GG | 152 | 129 | 0.79 | 0.57-1.08 | 0.140 | 0.747 | |
aOR was adjusted for pre-pregnancy body mass index and the family history of diabetes
bAfter adjusted for the multiple comparisons
Haplotype analysis
As shown in Fig. 1, three blocks were defined in 16 SNPs of the CPT1A gene, including block 1 (rs2846194, rs2846207, and rs2602820), block 2 (rs1249582, rs12803642, rs59506005, rs2513102, and rs2846196), and block3 (rs17399246, rs1016873, rs11228450, and rs10896396). Compared with the most common haplotype GAGA, the GGAC haplotype in block 3 (OR = 0.75, 95% CI = 0.57~0.99, P = 0.049) was associated with reduced risk of GDM (Table 5).
Fig. 1.
Linkage disequilibrium (LD) plot of 16 single-nucleotide polymorphisms (SNPs) in carnitine palmitoyltransferase 1A (CPT1A) gene. The r2 values were shown in the middle of the squares. The triangular block circled the LD plot
Table 5.
Association of haplotypes in the CPT1A gene with gestational diabetes mellitus
| SNP combinations | Frequency (%) | ORa | 95% CI | P |
|---|---|---|---|---|
| rs2846194 rs2846207 rs2602820 | ||||
| GAC | 0.438 | 1 | ||
| AAC | 0.268 | 0.87 | 0.67-1.15 | 0.330 |
| GCA | 0.278 | 0.92 | 0.70-1.20 | 0.531 |
| GCC | 0.013 | 0.58 | 0.23-1.45 | 0.245 |
| Rare (<0.01) | 0.0026 | 0.50 | 0.14-1.87 | 0.305 |
| rs1249582 rs12803642 rs59506005 rs2513102 rs2846196 | ||||
| ACGGA | 0.250 | 1 | ||
| ACCGA | 0.248 | 1.02 | 0.75-1.40 | 0.888 |
| GACGA | 0.200 | 1.14 | 0.83-1.57 | 0.401 |
| GCCAA | 0.113 | 0.98 | 0.67-1.43 | 0.905 |
| GCCGA | 0.023 | 0.83 | 0.40-1.75 | 0.627 |
| GCCGG | 0.145 | 1.03 | 0.72-1.47 | 0.874 |
| Rare (<0.01) | 0.021 | 0.77 | 0.37-1.60 | 0.486 |
| rs17399246 rs1016873 rs11228450 rs10896396 | ||||
| GAGA | 0.469 | 1 | ||
| AGGC | 0.133 | 0.99 | 0.71-1.38 | 0.959 |
| GGAC | 0.204 | 0.75 | 0.57-0.99 | 0.049* |
| GGGA | 0.016 | 0.67 | 0.29-1.54 | 0.347 |
| GGGC | 0.162 | 0.81 | 0.59-1.12 | 0.205 |
| Rare (<0.01) | 0.016 | 0.82 | 0.37-1.82 | 0.618 |
aOR was adjusted for pre-pregnancy body mass index and the family history of diabetes
∗Indicate that the association was statistically significant after adjusting for multiple comparisons
Discussion
This study found that genetic polymorphisms in the CPT1A gene (rs2846194, rs2602814, rs59506005) were associated with GDM risk. Women who carried rs2846194 AA genotype and rs2602814 AA genotype reduced GDM risk while those who carried rs59506005 GG genotype and CG or GG genotype increased GDM risk. Besides, we also found the GGAC haplotype in block 3 (rs17399246, rs1016873, rs11228450, and rs10896396) was associated with decreased GDM risk.
Although no genetic association studies have focused on the relation of CPT1A to GDM, as mentioned earlier, it is generally believed that the pathogenesis of GDM is similar to type 2 diabetes (T2DM) [14]. An earlier study including 324 type 2 diabetic patients and 300 nondiabetic individuals from Japanese examined the association between CPT1A and T2DM [26], and they identified and analyzed SNPs at the CPT1A and found no association of SNPs or haplotypes with T2DM. This conclusion that is inconsistent with our current study may be due to the different populations of the selected research objects. Another study from Greenland suggested CPT1A (rs80356779) is associated with reduced insulin resistance [29]. And as far as we know, insulin resistance (IR) plays an important role in the development of GDM [15], so our conclusion that the CPT1A gene is related to the risk of GDM is worthy of attention.
Most of the existing studies focus on the association between CPT1A and lipid metabolism or insulin resistance (IR). The accumulation of fatty acids in cells can lead to the formation of insulin resistance and lead to type 2 diabetes and hyperinsulinemia [24, 25]. Increased expression of CPT1A in mouse liver cells can reduce the accumulation and secretion of triglycerides by 35% and 60%, respectively; in vivo experiments in obese mice and lean mice also show that increased expression of CPT1A can significantly reduce triglycerides in the liver content of ester [30]. Bruce et al. [31] found that enhancing the activity of CPT1A or increasing the expression of CPT1A can promote the β oxidation of fatty acids, thereby indirectly enhancing the activity of insulin, thereby improving the IR effect induced by a high-fat diet. Besides, in pancreatic β-cells, when both glucose and fatty acid content are increased, the first glucose oxidation pathway can promote the increase of malonyl-CoA synthesis and strengthen the inhibition of CPT1A activity, resulting in hindered fatty acid oxidation and accumulation in the cell. Damage to the function of pancreatic β-cells leads to the occurrence of type 2 diabetes [32]. However, increasing the expression of CPT1A in pancreatic β cells can reduce the secretion of basal insulin and enhance the insulin secretion induced by glucose, which can ultimately improve the IR effect while also inhibiting further apoptosis of pancreatic β cells [33]. Insulin can reduce blood sugar by regulating the expression of CPT1A and enzyme activity to inhibit fatty acid oxidation and then inhibit gluconeogenesis. This is also one of the mechanisms by which insulin is used to treat diabetes. The above suggests that CPT1A is likely to become a potential target for diabetes drug treatment.
Our study has both strengths and limitations. First, the diagnosis of GDM was obtained by investigating medical records and combined with the national GDM diagnosis guidelines to reduce information bias. Second, we used standardized questionnaires to obtain relevant information and control potential confounding factors. While our study had shown the CPT1A gene was associated with the risk of GDM, further studies are warranted to research and verify its biological functions in GDM.
To the best of our knowledge, our study provided the first evidence that genetic polymorphisms in the CPT1A gene were associated with the GDM risk. These findings provide insight into the pathogenesis of human gestational diabetes and have significance for future prediction, prevention, and treatment of GDM.
Acknowledgements
The authors express their appreciation to the participants in the Taiyuan Birth Cohort Study for their enthusiastic support.
Author contribution
All authors contributed to the study’s conception and design. Material preparation was performed by Feifei Yang, Tianbi Han, Wenqiong Du, and Feng Zhao. Data collection and analysis were performed by Qingwen Ren, Mengzhu Guo, Jinbo Li, and Wangjun Li. Yongliang Feng was responsible for grammar editing. Suping Wang, Yawei Zhang, and Weiwei Wu were in charge of study supervision. The first draft of the manuscript was written by Qingwen Ren and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
This study was funded by the National Natural Science Foundation of China (81703314) and the Scientific and Technological Innovation Project of Higher Education Institutions in Shanxi Province (2019L0439).
Data Availability
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Code availability
Custom code.
Declarations
Ethics approval
All study procedures were approved by the Human Investigation Committee at the Shanxi Medical University.
Consent to participate
Informed consent to inclusion in the study was obtained from all individual participants included in the study.
Consent for publication
Patients signed informed consent regarding publishing their data and photographs.
Conflict of interest
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Yawei Zhang, Email: Zhangya69@foxmail.com.
Weiwei Wu, Email: wuweiwei2008@sina.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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