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Association of GCKR and GLIS3 gene polymorphisms with gestational diabetes mellitus: A case-control study
For correspondence: Dr Ramakrishnan Veerabathiran, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Chennai 603 103, Tamil Nadu, India e-mail: rkgenes@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Muruganantham JK, KandasamyV, VeerabathiranR. Association of GCKR and GLIS3 gene polymorphisms with gestational diabetes mellitus: A case-control study. Indian J Med Res. 2026;163:273-81. doi: 10.25259/IJMR_3385_2025
Abstract
Background and objectives
Gestational diabetes mellitus (GDM) increases the chances of negative consequences for both the mother and the foetus. It shares genetic and physiological characteristics with type 2 diabetes mellitus (T2DM), particularly insulin resistance and impaired insulin secretion. While gene variants involved in glucose metabolism, such as those in glucokinase receptor (GCKR) and GLI similar 3 (GLIS3), have been linked to diabetes risk, their association with GDM in South Indian populations remains underexplored.
Methods
This study comprised 195 patients with GDM and 195 normoglycemic pregnant women of South Indian ethnicity. GDM diagnosis was recognised using an oral glucose tolerance test. Genotyping of GCKR (rs780094) and GLIS3 (rs701847, rs7020673, rs10814916) were performed using Tetra-ARMS PCR and validated through Sanger sequencing. Associations between genotypes and the risk of GDM were assessed using logistic regression.
Results
Women with GDM exhibited significantly higher age, body mass index, blood pressure, and adverse metabolic profiles. There was a strong genotype-specific correlation between GDM and the GCKR rs780094 CT genotype. When dominant models and the AG genotype were used, rs701847 exhibited the strongest correlation with GLIS3. rs10814916 was linked through the AC genotype, whereas rs7020673 only demonstrated a connection under the recessive model. In women with GDM, HOMA-IR was significantly higher (P<0.001).
Interpretation and conclusion
This study highlights significant associations between GCKR and GLIS3 polymorphisms and the risk of GDM in South Indian women, supporting the role of ethnicity-specific genetic screening in predicting GDM risk.
Keywords
Case-control
GCKR
Gestational diabetes mellitus
GLIS03
Polymorphism
Glucose intolerance that arises during pregnancy is known as gestational diabetes mellitus (GDM).1 Several genetic variants have been identified as potentially associated with an increased likelihood of developing GDM; most of these polymorphisms overlap with those correlated with a heightened risk of type 2 diabetes mellitus (T2DM).2,3 In a follow up study, Huopio and associates genotyped 407 healthy pregnant controls and 533 GDM women for 69 single-nucleotide polymorphisms (SNPs) to investigate the connection between specific genetic variations related to an amplified risk of T2DM and GDM.4 Genetic variations impacting insulin secretion, glucose metabolism, and pancreatic β-cell function are critical to the development of GDM.5 Genes related to β-cell development and glucose regulation are key targets for genetic research in GDM, with GCKR and GLIS3 standing out for their roles in insulin control and glucose homeostasis.
Glucokinase (GCK), also known as HK-D, HK-IV, or ATP: D-hexose 6-phosphotransferase, is a crucial regulating enzyme in glucose metabolism. The glucokinase receptor (GCKR) gene has 19 exons and is located on chromosome 2p23.3.6 In the glycolysis pathway, GCK is responsible for catalysing the phosphorylation of glucose. As such, it is essential for preserving blood glucose homeostasis. The liver’s overexpression of GCKR increases GCK activity, which lowers blood glucose levels while raising lipid levels.7
The GLIS3 (GLI-similar 3) protein is a Krüppel-like zinc finger transcription factor that belongs to the GLIS subfamily and can either activate or repress genes.GLIS3 polymorphisms have been linked to type 1, 2, and GDM in humans, according to genome-wide association studies (GWAS).8 While these SNPs may not represent the only functional variants within these genes, they serve as informative markers for assessing genetic susceptibility in the studied population. Genetic polymorphisms affecting glucose metabolism and pancreatic β-cell function can significantly influence pregnancy, particularly in GDM. GCKR (rs780094) and GLIS3 (rs701847, rs7020673, rs10814916) polymorphisms have not been previously studied in the South Indian population. Our study aims to investigate the correlation between these specific SNPs and the risk of developing GDM in these women.
Methods
This case-control study was undertaken by the department of Obstetrics and Gynecology and Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Chennai, Tamil Nadu, India. Approval for ethical considerations was granted by the Institutional Human Ethics Committee (IHEC) of the academy. All participants provided written informed consent before their involvement in the study. The research was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.
Study population and ethical considerations
Between February 2024 and April 2025, this case-control study was integrated into a prospective, hospital-based cohort of expectant mothers who were tested for GDM. From the cohort, 195 women with a diagnosis of GDM and 195 normoglycemic controls were chosen at random. All the participants were of ethnic South Indian origin, as determined by self-reported parental ancestry from southern Indian States. Eligibility criteria included singleton pregnancy and maternal age between 18 and 40 yr. Women with existing diabetes, medical conditions affecting carbohydrate metabolism, or fasting glucose levels exceeding 7.0 mmol/L (126 mg/dL) at the first prenatal visit were excluded from the study. None of the women in the GDM group had impaired glucose tolerance or diabetes before becoming pregnant, and all were newly diagnosed during pregnancy.
GDM screening and biochemical evaluation
A two-hour oral glucose tolerance test (OGTT) was conducted to diagnose GDM cases using 75 g of glucose for participants between 24 and 28 wk of gestation, according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Following an 8–12 h fast, plasma glucose was measured at 0-h (fasting), 1-h, and 2-h post-glucose load. The glucose oxidase method was employed to determine plasma glucose concentrations in fresh samples. In this study, pregnant women with normal glucose tolerance (NGT) served as the control group, whereas the cases were identified as those who fulfilled the diagnostic standards for GDM. A fasting blood glucose (FBG) of 5.1 mmol/L (92 mg/dL) or more, a 1-hour postprandial glucose (PPG) of 10.0 mmol/L (180 mg/dL) or higher, and/or a 2-h PPG of 8.5 mmol/L (153 mg/dL) or more are the requirements for these tests.9 HbA1c was used in this study as a supplementary marker of overall glycaemic trend rather than a diagnostic criterion. The calculation of the homeostatic model assessment insulin resistance (HOMA-IR) involves the formula: [fasting serum insulin (FSI) (mIU/L) × FBG (mg/dL)]/405]. This formula is used to indicate the presence of insulin resistance.
Data acquisition and anthropometric assessment
To maintain accuracy and consistency, socio-demographic details were collected from all participants using a standardised questionnaire. Anthropometric, clinical, and demographic data were gathered from medical reports and follow-up visits. The collected data included measurements such as height, weight, systolic and diastolic blood pressure (SBP and DBP), as well as pre-pregnancy weight. To determine body mass index (BMI), the formula weight (in kilograms) divided by height squared (in meters) was utilised. Age, domicile (urban/rural), education level, occupation, dietary habits (vegetarian/non-vegetarian), current diet and drug intake, and family history of GDM, T2DM, or impaired glucose tolerance (IGT), was also recorded.
Power analysis in IBM SPSS Statistics version 21.0 (SPSS Inc., USA) was used to estimate the sample size for this case-control genetic association study. Based on prior studies on potential genes linked to GDM, the minimum necessary sample size was estimated to be approximately 180 individuals per group. Power assumptions were based on previously published methodological studies in biomedical research.10
Collection of blood samples and genetic analysis
During the OGTT, blood samples were obtained from pregnant women for genotyping and serum for biochemical analysis. These samples were stored at –80°C until they could be examined further. Biochemical parameters were measured using standard laboratory procedures on an automated analyser, employing appropriate reagents, calibrators, and controls to ensure accuracy and reliability.
DNA was retrieved from whole blood using a refined salting-out procedure, also known as Miller’s method.11 The integrity and evaluation of DNA sample concentrations was conducted using UV–visible spectroscopy, 1% AGE, and measurements of the absorbance ratio at 260/280 nm, utilising a nanodrop spectrophotometer (Thermo Scientific, Thermo Fisher Scientific Inc., Waltham, MA, USA). Only samples exhibiting a ratio between 1.8 and 2.0 were deemed suitable for analysis and subsequently normalised to a concentration of 20 ng/µL for further testing. Following quality assessment, samples were kept at –20°C until use. Genotyping of the GCKR and GLIS3 genes was conducted using the ARMS-PCR procedure. Primer details used in this procedure are provided in Supplementary Table.
Genotyping
Genotyping of GCKR (rs780094) and GLIS3 (rs701847, rs7020673, rs10814916) polymorphisms was performed using the Tetra-ARMS PCR method. Amplification was performed using a Nexus Gradient PCR instrument, with additional verification conducted using an Applied Biosystems PCR system. Each reaction contained 10 picomoles of each primer (outer forward and reverse, inner forward and reverse), EmeraldAmp GT PCR Master Mix (2X premix, Takara Bio Inc.), genomic DNA, and the final volume was adjusted using molecular-grade water.
PCR cycling conditions were standardised across all SNPs, with slight variation in annealing temperatures depending on the SNP. SNP-specific annealing temperatures (described below) for 45 sec, the general conditions included initial denaturation at 95°C for 5 min, 35 cycles of denaturation at 95°C for 45 sec, an extension at 72°C for 45 sec, a final extension at 72°C for 5 min, and a final hold at 4°C.
GCKR (rs780094): 63.5°C, GLIS3 (rs701847: 65.5°C, rs7020673: 61.5°C, rs10814916: 62.5°C). The amplified outcomes were analysed by AGE to confirm the specificity and quality of genotyping.
DNA sequencing
Sanger sequencing was performed using a standard protocol that involves multiple steps to ensure accurate and high-quality results. Genomic DNA was extracted and amplified using PCR, followed by PCR product purification (clean-up). Quality control was performed at both the amplification and purification stages using AGE. The sequencing reaction was set up by adding the purified PCR product (DNA template), specific primers, and a sequencing master mix. Sequencing PCR was conducted to enable DNA synthesis, with chain termination achieved through the incorporation of dideoxynucleotides (ddNTPs). The resulting DNA fragments were separated by size using capillary electrophoresis, and the sequencing data were analysed through the interpretation of chromatograms. ABI 3730XL genetic analyser (Applied Biosystems) was used for the sequencing process, with PCR carried out using Applied Biosystems thermal cyclers, and sequencing reagents supplied by NimaGen’s BigDye Terminator (BDT) chemistry.
Statistical analysis
Statistical analyses for this study were carried out using IBM SPSS Statistics version 21.0 (SPSS Inc., USA). Continuous variables were summarised as mean±standard deviation (SD), while categorical variables were presented as percentages, odds ratios (ORs), and 95% confidence intervals (CIs). Genotypic data were expressed as allele frequencies and genotype distributions in percentages. Comparisons involving categorical variables were assessed using the Chi-square test or Fisher’s exact test, as appropriate. When the data fit a normal distribution, the Student’s t-test was applied to continuous variables. If necessary, log-transformed values were applied.
Results
Case-control study
Our study included a total of 390 pregnant women, with 195 diagnosed with GDM and 195 who were normoglycemic controls. The clinical, biochemical, and demographic features of the study groups are compared in Table I.
| Variables | GDM (195) | Control (195) | P value |
|---|---|---|---|
| Age (yr) | 27.1 ± 3.9 | 26.1 ± 3.5 | 0.008 |
| Body mass index (BMI) at gestation (kg/m2) | 28.6 ± 6.7 | 26.1 ± 5.8 | <0.001 |
| Pre-pregnancy BMI (Kg/m2) | 27.4 ± 6.8 | 23.1 ± 5.6 | <0.001 |
| Systolic BP (mm Hg) | 122.4 ± 6.4 | 106.3 ± 11.0 | <0.001 |
| Diastolic BP (mm Hg) | 79.1 ± 5.6 | 70.5 ± 6.7 | <0.001 |
| Urban domicile | 158 (81%) | 125 (64%) | <0.001 |
| Education upto graduation | 152 (77.9%) | 146 (74.8%) | 0.30 |
| Occupation: Employed | 64 (32.8%) | 99 (50.7%) | <0.001 |
| Food habits vegetarian | 62 (31.7%) | 17 (8.7%) | <0.001 |
| Report diet modification in pregnancy | 81 (41.5%) | 127 (65.1%) | <0.0001 |
| Drug intake- Yes | 179 (91.7) | 109 (55.8%) | <0.001 |
| Family history of diabetes-Yes | 115 (58.97%) | 29 (14.8%) | <0.001 |
| FBS (mg/dL) | 101.8 ± 7.3 | 83.7 ± 5.7 | <0.001 |
| PPBS (mg/dL)1 h post glucose | 183.6 ± 10.8 | 141.1 ± 11.5 | <0.001 |
| 2 h Post glucose | 162.2 ± 9.9 | 117.5 ± 9.9 | <0.001 |
| HbA1C (%) | 6.0 ± 0.2 | 5.2 ± 0.3 | <0.001 |
| HOMA-IR | 4.5 ± 0.9 | 1.7 ± 0.4 | <0.001 |
| Fasting serum insulin (mIU/L) | 18.0 ± 3.7 | 8.3 ± 2.2 | < 0.001 |
| Serum C-Peptide (ng/mL) | 4.1 ± 0.9 | 2.0 ± 0.6 | <0.001 |
| Serum cholesterol (mg/dL) | 204.9 ± 13.9 | 191.1 ± 11.3 | <0.001 |
| Serum triglycerides (mg/dL) | 174.9 ± 14.7 | 142.4 ± 13.2 | <0.001 |
| Serum high-density lipoprotein (mg/dL) | 48.9 ± 4.2 | 57.2 ± 4.5 | <0.001 |
| Serum low-density lipoprotein (mg/dL) | 129.4 ± 11.6 | 106.1 ± 10.1 | <0.001 |
| Serum very low-density lipoprotein (mg/dL) | 34.6 ± 3.0 | 26.1 ± 3.7 | <0.001 |
| C-reactive protien (mg/dL) | 6.6 ± 1.9 | 1.64 ± 0.6 | <0.001 |
| Hemoglobin (g/dL) | 11.5 ± 0.5 | 12.2 ± 0.7 | <0.001 |
P values were calculated using the independent student’s t-test to compare cases and controls. Data are presented as mean ± standard deviation (SD). BP, blood pressure; FBS, fasting blood Sugar; HOMA-IR, homeostatic model assessment for insulin resistance
Genotype
The genotyping results are presented in Table II. Substantial changes in genotype and allele dispersals were detected between GDM cases and controls for the GCKR (rs780094) and GLIS3 (rs701847, rs7020673, rs10814916) polymorphisms. Figures 1-4 display the results of all the SNPs obtained from Sanger sequencing.
| Gene (SNP) | Genotype/Allele | GDM, n (%) | Control, n (%) | P value | OR 95% CI | Model |
|---|---|---|---|---|---|---|
| GCKR (rs780094) | CC | 105 (53.84) | 106 (54.35) | Reference | Genotype | |
| CT | 23 (11.79) | 08 (4.10) | 0.013 | 2.90 (1.24-6.78) | ||
| TT | 67 (34.3) | 81 (41.5) | 0.401 | 0.83 (0.54-1.27) | ||
| C | 233 (59.74) | 220 (56.41) | Reference | Allele | ||
| T | 157 (40.25) | 170 (43.58) | 0.34 | 0.87 (0.65-1.15) | ||
| (CT + TT vs. CC) | 90/105 | 89/106 | 0.91 | 1.02 (0.68-1.52) | Dominant | |
| (CT + CC vs. CT) | 128/67 | 114/81 | 0.14 | 1.35 (0.90-2.04) | Recessive | |
| (CC + TT vs. CT) | 172/23 | 187/08 | 0.007 | 0.31 (0.13-0.73) | Over dominant | |
| GLIS3 (rs701847) | GG | 77 (39.48) | 113 (57.94) | Reference | Genotype | |
| GA | 45 (23.07) | 13 (6.66) | 0.0001 | 5.07 (2.56-10.04) | ||
| AA | 73 (37.43) | 69 (35.38) | 0.049 | 1.55 (1.00-2.40) | ||
| G | 199 (51.02) | 239 (61.28) | Reference | Allele | ||
| A | 191 (48.97) | 151 (38.71) | 0.004 | 1.51 (1.14-2.01) | ||
| (GA + AA vs. GG) | 118/77 | 82/113 | 0.0003 | 2.11 (1.40-3.16) | Dominant | |
| (GA + GG vs. AA) | 112/73 | 125/69 | 0.434 | 0.84 (0.55-1.28) | Recessive | |
| (GG + AA vs. GA) | 150/45 | 182/13 | 0.0001 | 0.23 (0.12-0.45) | Over dominant | |
| GLIS3 (rs7020673) | CC | 119 (61.02) | 125 (64.1) | Reference | Genotype | |
| CG | 35 (17.94) | 44 (22.56) | 0.489 | 0.83 (0.50-1.39) | ||
| GG | 41 (21.02) | 26 (13.33) | 0.073 | 1.65 (0.95-2.87) | ||
| C | 273 (70) | 294 (75.38) | Reference | Allele | ||
| G | 117 (30) | 96 (24.35) | 0.091 | 1.31 (0.95-1.80) | ||
| (CG + GG vs. CC) | 76/119 | 70/125 | 0.53 | 1.14 (0.75-1.71) | Dominant | |
| (CC + CG vs. GG) | 154/41 | 169/26 | 0.04 | 0.57 (0.33-0.98) | Recessive | |
| (GG + CC vs. CG) | 160/35 | 151/44 | 0.25 | 1.33 (0.81-2.18) | Over dominant | |
| GLIS3 (rs10814916) | AA | 75 (61.02) | 110 (64.1) | Reference | Genotype | |
| AC | 120 (17.94) | 85 (22.56) | 0.0004 | 2.07 (1.38-3.10) | ||
| A | 270 (69.23) | 305 (78.2) | Reference | Allele | ||
| C | 120 (30.76) | 85 (21.79) | 0.004 | 1.59(1.15–2.20) | ||
| (AC + CC vs. AA) | 120/75 | 85/110 | 0.0004 | 2.07 (1.38-3.10) | Dominant | |
| Gene (SNP) | Genotype/Allele | GDM n (%) | Control n (%) | P value | OR 95% CI | Model |
| GCKR (rs780094) | CC | 105 (53.84) | 106 (54.35) | Reference | Genotype | |
| CT | 23 (11.79) | 08 (4.10) | 0.013 | 2.90 (1.24-6.78) | ||
| TT | 67 (34.3) | 81 (41.5) | 0.401 | 0.83 (0.54-1.27) | ||
| C | 233 (59.74) | 220 (56.41) | Reference | Allele | ||
| T | 157 (40.25) | 170 (43.58) | 0.34 | 0.87 (0.65-1.15) | ||
| (CT + TT vs. CC) | 90/105 | 89/106 | 0.91 | 1.02 (0.68-1.52) | Dominant | |
| (CT + CC vs. CT) | 128/67 | 114/81 | 0.14 | 1.35 (0.90-2.04) | Recessive | |
| (CC + TT vs. CT) | 172/23 | 187/08 | 0.007 | 0.31 (0.13-0.73) | Over dominant | |
| GLIS3 (rs701847) | GG | 77 (39.48) | 113 (57.94) | Reference | Genotype | |
| GA | 45 (23.07) | 13 (6.66) | 0.0001 | 5.07 (2.56-10.04) | ||
| AA | 73 (37.43) | 69 (35.38) | 0.049 | 1.55 (1.00-2.40) | ||
| G | 199 (51.02) | 239 (61.28) | Reference | Allele | ||
| A | 191 (48.97) | 151 (38.71) | 0.004 | 1.51 (1.14-2.01) | ||
| (GA + AA vs. GG) | 118/77 | 82/113 | 0.0003 | 2.11 (1.40-3.16) | Dominant | |
| (GA + GG vs. AA) | 112/73 | 125/69 | 0.434 | 0.84 (0.55-1.28) | Recessive | |
| (GG + AA vs. GA) | 150/45 | 182/13 | 0.0001 | 0.23 (0.12-0.45) | Over dominant | |
| GLIS3 (rs7020673) | CC | 119 (61.02) | 125 (64.1) | Reference | Genotype | |
| CG | 35 (17.94) | 44 (22.56) | 0.489 | 0.83 (0.50-1.39) | ||
| GG | 41 (21.02) | 26 (13.33) | 0.073 | 1.65 (0.95-2.87) | ||
| C | 273 (70) | 294 (75.38) | Reference | Allele | ||
| G | 117 (30) | 96 (24.35) | 0.091 | 1.31 (0.95-1.80) | ||
| (CG + GG vs. CC) | 76/119 | 70/125 | 0.53 | 1.14 (0.75-1.71) | Dominant | |
| (CC + CG vs. GG) | 154/41 | 169/26 | 0.04 | 0.57 (0.33-0.98) | Recessive | |
| (GG + CC vs. CG) | 160/35 | 151/44 | 0.25 | 1.33 (0.81-2.18) | Over dominant | |
| GLIS3 (rs10814916) | AA | 75 (61.02) | 110 (64.1) | Reference | Genotype | |
| AC | 120 (17.94) | 85 (22.56) | 0.0004 | 2.07 (1.38-3.10) | ||
| A | 270 (69.23) | 305 (78.2) | Reference | Allele | ||
| C | 120 (30.76) | 85 (21.79) | 0.004 | 1.59(1.15–2.20) | ||
| (AC + CC vs. AA) | 120/75 | 85/110 | 0.0004 | 2.07 (1.38-3.10) | Dominant | |
The ‘Reference’ category represents the wild-type genotype or major allele used as the baseline for comparison. Odds ratios (OR) and P values were determined using the chi-square (χ2) test. The result is significant at P < 0.05. (Note: For rs701847 genotype AA, the result is considered borderline as the 95% CI includes 1.00). SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; n, number of patients; GDM, gestational diabetes mellitus
- Detection of GCKR (rs780094) homozygous and heterozygous using Sanger sequencing.
- Detection of GLIS3 (rs701847) homozygous and heterozygous using Sanger sequencing.
- Detection of GLIS3 (rs7020673) homozygous and heterozygous using Sanger sequencing.
- Detection of GLIS3 (rs10814916) homozygous and heterozygous using Sanger sequencing.
GCKR (rs780094) polymorphisms and GDM
The distribution of genotypes and alleles for GCKR rs780094 revealed notable differences between GDM patients and controls. The CC genotype was used as the reference because it is more frequent in this population. In the codominant model, the CT genotype was significantly more frequent in people with GDM compared to healthy controls, whereas the TT genotype did not show a significant association. There is no substantial difference between the two alleles T and C. Both the dominant and recessive models showed no significant association with GDM; however, the over-dominant model revealed a significant association.
GLIS3 (rs701847) polymorphisms and GDM
Strong links were found between GLIS3 rs701847 genotypes and GDM. For these polymorphisms, the GG genotype was considered the reference because it was more predominant in controls than in cases. In the codominant model, the GA genotype showed a higher association with GDM. At the same time, the AA genotype demonstrated a borderline association. Allele frequency analysis revealed a higher frequency of the A allele in cases compared to controls. The dominant model (AG+AA vs. GG) demonstrated a significant link with GDM, while the recessive model did not show significance. A notable association was found using the over-dominant model.
GLIS3 (rs7020673) polymorphisms and GDM
For GLIS3 rs7020673, the CC genotype was used as the reference, while GG was considerably more common in cases than in controls, indicating borderline association with GDM. The analysis of allele frequency did not show any significant relationship between both C and G alleles. A significant association was observed under the recessive model, but the dominant model did not reveal any substantial association.
GLIS3 (rs10814916) polymorphisms and GDM
For GLIS3 rs10814916, the AA genotype was used as the reference. The AC genotype was significantly associated with GDM in the codominant model. The C allele showed a higher frequency among cases than the controls. The CC genotype was not observed in either group; therefore, recessive model analysis was not applicable.
Discussion
In this study, we analysed the association of the selected polymorphisms in the GCKR and GLIS3 genes with GDM in a South Indian population. This analysis showed that certain genotypes were distributed differently between cases and controls, indicating a possible genetic association with GDM susceptibility. The study shows statistical relationships, not causation, but the risk and protective effects of certain alleles relate to the biological activities of the examined genes. Both GLIS3 and GCKR play significant roles in glucose metabolism and insulin regulation, two processes critical to the pathophysiology of GDM.
The case-control approach allows for the identification of genetic correlations but does not allow for causal inference. Even though the individuals were generally categorised as South Indian, genetic variation within this community cannot be totally ruled out, and modest population stratification may influence the observed relationships. This study lacked functional tests to elucidate the molecular processes behind the observed relationships. Finally, only certain polymorphisms in GCKR and GLIS3 were examined; additional variants or gene-gene interactions associated with GDM susceptibility were not evaluated.
Our findings suggest a genotype-specific connection between GCKR rs780094 and GDM, with the heterozygous (CT) genotype exhibiting a high correlation, despite non-significant allelic, dominant, or recessive models. A 2024 meta-analysis revealed that the C allele was the primary risk factor in populations in Brazil (2017) and Malaysia (2018). However, a Chinese cohort study carried out in 2022 found no significant link.12-15 These variations show how ethnic background and genetic architecture influence GDM susceptibility, underscoring the necessity of population-specific research like this one among South Indians.
The GLIS3 variation with the strongest correlation to GDM was rs701847. Significant associations were found under the dominant and over-dominant models, and the AG genotype was substantially more common among the cases. Additionally, the allele frequencies between cases and controls were noted. Regarding the other two GLIS3 polymorphisms, rs10814916 was mostly linked to GDM through the AC (heterozygous) genotype, whereas rs7020673 only demonstrated indications of linkage under the recessive model. Further model-based research was hindered by the lack of the CC genotype for rs10814916. In contrast, a 2022 study conducted in the Egyptian population on T2DM reported that the GC genotype was significantly more prevalent in the control group than among patients. These differing findings highlight potential ethnic or disease-specific variations in the genetic role of GLIS3 rs7020673, suggesting that its impact may vary between GDM and T2DM and across populations.16
These findings are consistent with a 2019 Danish study that identified rs10814916 as a variant associated with GDM, supporting the role of GLIS3 in GDM pathogenesis.17 The association between the GLIS3 polymorphism and GDM risk in our South Indian cohort aligns with recent findings in other Asian groups. Notably, a 2024 study in a Chinese population similarly identified GLIS3 rs10814916 as a significant risk factor (P=0.028, OR=1.172).18 A study identified several SNPs associated with an increased risk of GDM in the European cohorts. These findings suggest that maternal genotyping may help identify women who are at risk for impaired gestational glucose tolerance.19 The GLIS3 locus’s involvement in GDM risk is supported by recent studies conducted in other Asian populations. For example, in 2025, there was a significant correlation between other GLIS3 variants, like rs7034200, and Chinese women’s susceptibility to GDM.20 Although the specific risk alleles may vary by ethnicity, the consistent correlation between multiple GLIS3 variants and GDM across South Indian and East Asian cohorts highlights this gene as a conserved regulator of beta-cell function and gestational glucose homeostasis.
A 2017 study observed a higher prevalence of the C allele among women with GDM; however, this association did not reach statistical significance.21 Although our research shows a strong statistical correlation between the risk of GDM and GLIS3 and GCKR polymorphisms, it is crucial to remember that these findings do not offer concrete mechanistic evidence. The observed correlations may indicate the involvement of these variants in glucose homeostasis pathways because this is a case-control association study; however, functional studies (like gene expression analysis or protein activity assays) are necessary to clarify the precise molecular mechanisms by which these SNPs affect the pathophysiology of GDM.
This case–control study found strong associations between GCKR and GLIS3 gene polymorphisms and GDM susceptibility in South Indian women. These findings enhance understanding of GDM’s genetic factors and suggest potential for personalised screening in high-risk groups.
Author contributions
JM: Experimental work, data interpretation, manuscript writing; RV: Designed the study, manuscript writing; VK: Provided clinical support, assisted with sample collection, and acquired patient data. All authors have read and approved the final printed version of the manuscript.
Financial support and sponsorship
None.
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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