The Relationship Between the Polymorphisms of RANK Gene and Type 2 Diabetes Mellitus in Postmenopausal Women

Abstract

Background

Studies have indicated an association between the receptor activator of nuclear factor-κB (RANK) gene and the pathogenesis of type 2 diabetes mellitus (T2DM). This study aimed to investigate the relationship between the polymorphisms at the RANK gene loci (rs3018362 and rs78326403) and T2DM in postmenopausal women from Xinjiang, China.

Methods

This study enrolled 200 postmenopausal Han Chinese women who attended the First Affiliated Hospital of Shihezi University from October 2024 to March 2025. The research subjects were divided into two groups based on their medical history and results of the oral glucose tolerance test (OGTT): normal glucose tolerance group (NGT group, n = 95) and type 2 diabetes mellitus group (T2DM group, n = 105). Baseline data and biochemical indicators of the research subjects were collected and recorded. The polymorphisms of RANK gene loci were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

Results

1) After correction for multiple testing using the Benjamini-Hochberg method, the levels of FPG, HbA₁c, HDL, and TyG index were significantly higher in the T2DM group compared with the NGT group. 2) At the rs3018362 locus, there were statistically significant differences in the genotypic and allelic frequencies between the two groups. 3) Following Benjamini-Hochberg multiple testing correction, at the rs78326403 locus, compared with the major homozygous genotype (AA), carriers of the minor allele (TT + AT) in the NGT group had significantly lower levels of TG, LDL, TG/HDL ratio, and TyG index. 4) Binary logistic regression analysis revealed that HbA₁c and the rs3018362 genotype were independently and statistically associated with T2DM.

Conclusion

The polymorphism at the rs3018362 locus of the RANK gene may be related to T2DM in postmenopausal women from Xinjiang, China. Additionally, the polymorphism at the rs78326403 locus may be involved in glucose and lipid metabolism.

Plain Language Summary

Why was this study done? Type 2 diabetes mellitus (T2DM) is one of the major public health issues worldwide, and its pathogenesis remains unclear. For women after menopause, hormonal changes may increase their risk of developing T2DM. Recent studies have suggested that the OPG-RANKL-RANK signaling pathway exerts a potential impact on T2DM. However, how exactly this pathway works in T2DM is still unclear. Therefore, this study aims to investigate the relationship between the polymorphisms at the RANK gene loci (rs3018362 and rs78326403) and T2DM in postmenopausal women. What did the researchers do? This study enrolled 200 postmenopausal Han Chinese women who were categorized into two groups. Baseline data of the participants were collected, relevant biochemical indicators were measured, the polymorphisms of RANK gene loci were determined, and subsequent statistical analysis were conducted. What did the researchers find? Our findings revealed that the polymorphism at the rs3018362 locus of the RANK gene may be associated with T2DM in postmenopausal women from Xinjiang, China. What do the findings mean? From a genetic perspective, this study investigated the correlations between the RANK gene loci polymorphisms and T2DM, as well as glycemic and lipid metabolic indicators, in postmenopausal women. The results lay a preliminary foundation for future basic and clinical research on the RANK gene.

Keywords

gene polymorphism RANK type 2 diabetes mellitus postmenopausal women

Introduction

The prevalence of diabetes mellitus (DM) has been on the rise year by year,¹ and it has now become one of the most prevalent chronic metabolic diseases worldwide.¹ The International Diabetes Federation (IDF) estimates that approximately 536.6 million adults are currently living with diabetes worldwide, a figure projected to reach 783.2 million by 2045.² Type 2 diabetes mellitus (T2DM) accounts for more than 90% of these cases.^3,4 T2DM is currently one of the most common chronic diseases in China with the highest number of complications.⁵ Characterized by hyperglycemia and impaired insulin secretion, it is associated with complications affecting multiple systems, such as neuropathy, retinopathy, and cardiovascular disease.⁶ As such, T2DM has become a major public health concern at present.⁶ A review has indicated that T2DM is recognized as one of the chronic diseases associated with menopause.⁷ In postmenopausal women, obesity and declining estrogen levels are associated with adverse metabolic alterations, which may contribute to insulin resistance and an elevated risk of T2DM.⁸ The pathogenesis of T2DM is complex, and its underlying mechanisms remain unclear to date, requiring further investigation.

Existing studies have indicated that genetic factors play a crucial role in the development and progression of T2DM.⁹ In recent years, genome-wide association studies (GWAS) and other genetic investigations have identified numerous genes associated with susceptibility to T2DM.¹⁰ A study conducted on Han Chinese women in southern China demonstrated that polymorphisms in the osteoprotegerin (OPG), receptor activator of nuclear factor-κB (RANK), and receptor activator of nuclear factor-κB ligand (RANKL) genes are correlated with T2DM.¹¹ Initial studies on the signaling pathway composed of OPG, RANK, and RANKL have shown its association with osteoporosis and other metabolic bone diseases.¹² However, recent studies have indicated that the OPG-RANKL-RANK signaling pathway also exerts a potential impact on the pathogenesis of metabolic diseases, including obesity, T2DM, and non-alcoholic fatty liver disease,¹³ with its specific underlying mechanisms remaining to be further elucidated. To date, no studies have investigated the association between polymorphisms at the rs3018362 and rs78326403 loci of the RANK gene and T2DM, nor have the population distribution characteristics of these polymorphisms been documented in postmenopausal women from Xinjiang, China. Differences in genetic population distribution represent a key factor influencing the outcomes of gene-disease association studies, and clarifying the distribution profiles of gene loci in specific populations constitutes the fundamental basis and prerequisite for conducting such research. Therefore, this study enrolled postmenopausal Han Chinese women from the Shihezi region of Xinjiang as participants to explore the association between the polymorphisms at the RANK gene loci (rs3018362 and rs78326403) and T2DM.

Materials and Methods

Research Subjects

This study was a case-control study. Postmenopausal Han Chinese women who attended the First Affiliated Hospital of Shihezi University were consecutively recruited for this study from October 2024 to March 2025. The sample size was determined based on similar genetic association studies previously conducted by our research group,¹⁴ with a total of 200 postmenopausal Han Chinese women ultimately enrolled. The diagnosis of normal glucose tolerance and type 2 diabetes mellitus was established in accordance with the 1999 World Health Organization (WHO) diagnostic criteria.¹⁵ Patients with T2DM were defined as having fasting plasma glucose ≥ 7.0 mmol/L and/or 2-hour post-load plasma glucose ≥ 11.1 mmol/L during the oral glucose tolerance test (OGTT).¹⁵ Participants with normal glucose tolerance had fasting plasma glucose < 6.1 mmol/L and 2-hour post-load plasma glucose < 7.8 mmol/L¹⁵. Combined with the participants’ relevant medical histories and OGTT results, all enrolled subjects were divided into two groups: 95 cases in the normal glucose tolerance group (NGT group) and 105 cases in the type 2 diabetes mellitus group (T2DM group).

Inclusion criteria: (1) The participants were women with natural menopause. (2) The participants were patients with T2DM. (3) The participants were individuals who were able to cooperate and communicate effectively. Exclusion criteria: (1) Participants with menopause caused by non-natural causes were excluded. (2) Participants with type 1 diabetes mellitus or special types of diabetes mellitus (including gestational diabetes and monogenic diabetes) were excluded, the diagnosis of these special types was confirmed by reviewing participants’ medical histories and relevant laboratory or genetic test results. (3) Participants with a history of taking medications that affect blood glucose metabolism (e.g., glucocorticoids, thiazide diuretics, immunosuppressants, and certain psychiatric medications) were excluded. (4) Participants with comorbid severe diseases affecting the heart, brain, liver, or kidneys (e.g., acute myocardial infarction, acute heart failure, acute cerebral infarction, intracerebral hemorrhage, liver failure, and end-stage renal disease) were excluded. (5) Participants with a history of malignant tumors were excluded. (6) Participants with severe infections were excluded. (7) Participants who were unable to cooperate with the study were excluded, including those with severe cognitive impairment, mental disorders, or those who refused to complete the required study procedures.

Methods

Clinical Data

Baseline data of the participants, including age (years), menopausal years (years), height (cm), weight (kg), waist circumference (cm), and hip circumference (cm), were collected and recorded. Body mass index (BMI, kg/m²) and waist-to-hip ratio (WHR) were then calculated based on these measurements.

Fasting elbow venous blood samples (with a fasting duration of ≥ 8 hours) were collected from the participants in the morning. Biochemical indicators were measured using a Roche automatic biochemical analyzer, including fasting plasma glucose (FPG, mmol/L), triglycerides (TG, mmol/L), low-density lipoprotein (LDL, mmol/L), high-density lipoprotein (HDL, mmol/L), serum calcium (Ca, mmol/L), serum phosphorus (P, mmol/L), alkaline phosphatase (ALP, U/L), alanine transaminase (ALT, U/L), aspartate transaminase (AST, U/L), albumin (ALB, g/L), serum uric acid (SUA, mmol/L), and serum creatinine (Scr, μmol/L). Glycosylated hemoglobin (HbA₁c, %) was determined by high-performance liquid chromatography (HPLC). Additionally, the TG/HDL ratio and Triglyceride-Glucose (TyG) index [calculated as Ln (TG (mg/dL) × FPG (mg/dL)/2)] were computed.

Genetic Polymorphism Detection

Elbow venous blood samples were collected from the participants, and genomic DNA was extracted using a whole-blood genomic DNA extraction kit (Tiangen Biochemical Technology Co. , Ltd, China). Primers for the target single nucleotide polymorphism (SNP) loci were designed based on the NCBI database and the Assay Designer software package (SEQUENOM, Inc. , USA). Following PCR amplification, the amplicons were purified using resin, and the purified products were spotted onto Spectro-CHIP bioarrays for subsequent matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis (SEQUENOM, Inc. , USA). Genotyping was performed on the generated data using Typer 4.0 software to obtain the polymorphism results of the RANK gene loci. To ensure the accuracy of genotyping, 20% of the samples were randomly selected for duplicate genotyping, which included the entire process from PCR amplification to mass spectrometry detection, and the consistency rate of genotyping results was greater than 98%.

Statistical Analysis

Statistical analysis of the data was performed using SPSS 27.0 software. The normality of all data was tested via the Shapiro-Wilk test, and the homogeneity of variances for the quantitative variables across groups was assessed using Levene’s test. Measurement data with a normal distribution were presented as mean±standard deviation ( $\bar{x} \pm s$ ), and independent samples t-test was used for comparisons between groups. When comparing biochemical indicators between genotypes of different gene loci, the Benjamini-Hochberg method was applied to correct for the false discovery rate (FDR) in multiple comparisons. Categorical data were expressed as n (%), and chi-square test was employed for comparisons. Binary logistic regression analysis with the forward stepwise likelihood ratio (LR) method was applied to identify factors associated with T2DM in postmenopausal women. Binary logistic regression was used to assess the association between RANK gene polymorphisms and T2DM risk. Since our primary goal was to examine the direction and relative magnitude of the genetic association (rather than absolute T2DM risk), logistic regression was appropriate. Although OR may overestimate RR for common outcomes (>50% prevalence), it sufficiently reflects the association’s presence and direction, consistent with our study’s core aim. P < 0.05 was considered statistically significant.

Population stratification was not a concern in our study, as all participants were Han Chinese permanent residents of the Shihezi region recruited from a single center, with a homogeneous genetic background. Thus, genetic principal components (GPCs) were not included in the analysis, given their primary role in correcting for population stratification in genetically diverse cohorts.

Results

Comparison of Baseline Data and Biochemical Indicators Between Groups

The baseline data of the T2DM group, including age, menopausal years, BMI, and WHR, showed no statistically significant differences compared with the NGT group, indicating that the baseline data were well-balanced. Compared with the NGT group, the T2DM group had higher levels of FPG, HbA₁c, HDL, TyG index, and Scr, while lower levels of ALT and SUA. No statistically significant differences were found in the remaining indicators between the two groups (Table 1).

Table 1.

Comparison of Baseline Data and Biochemical Indicators Between Groups( $\bar{x} \pm s$ )

Variables	NGT group (n = 95)	T2DM group (n = 105)	t	P
Age (years)	61.77±8.40	64.01±8.95	-1.821	0.070
Menopausal years (years)	13.16±8.18	14.98±8.97	-1.497	0.136
BMI (kg/m²)	25.28±3.47	25.65±3.92	-0.699	0.485
WHR	0.90±0.10	0.91±0.07	-0.761	0.447
FPG (mmol/L)	5.10±0.67	8.85±2.67	-13.911	<0.001**
HbA₁c (%)	5.82±0.54	8.55±1.66	-15.954	<0.001**
TG (mmol/L)	1.51±1.06	1.97±2.21	-1.924	0.056
LDL (mmol/L)	2.58±0.86	2.40±1.14	1.289	0.199
HDL (mmol/L)	1.41±0.52	1.72±0.86	-3.070	0.002**
TG/HDL ratio	1.28±1.26	1.37±1.32	-0.494	0.622
TyG index	8.56±0.57	9.16±0.90	-5.781	<0.001**
Ca (mmol/L)	2.27±0.09	2.28±0.19	-0.536	0.593
P (mmol/L)	1.15±0.20	1.14±0.18	0.604	0.546
ALP (U/L)	77.09±29.82	74.40±17.54	0.769	0.443
ALT (U/L)	28.19±20.67	22.69±14.81	2.145	0.033*
AST (U/L)	22.88±12.87	21.41±15.08	0.738	0.462
ALB (g/L)	40.57±4.01	40.79±5.01	-0.339	0.735
SUA (mmol/L)	282.36±66.11	262.23±72.97	2.037	0.043*
Scr (μmol/L)	69.52±45.09	89.35±71.50	-2.369	0.019*

Notes. Compared with the NGT group, *P < 0.05, **P < 0.01. BMI: body mass index; WHR: waist-hip ratio; FPG: fasting plasma glucose; HbA₁c: glycosylated hemoglobin; TG: triglyceride; LDL: low density lipoprotein; HDL: high density lipoprotein; TyG index: Triglyceride-glucose index; Ca: calcium; P: phosphorus; ALP: alkaline phosphatase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALB: albumin; SUA: serum uric acid; Scr: serum creatinine.

Comparison of Genotypic and Allelic Frequencies at Different Loci Between Groups

The genotype distributions of the RANK gene loci rs3018362 and rs78326403 all conformed to Hardy-Weinberg equilibrium (P > 0.05), indicating that the study participants were representative of the target population. At the rs3018362 locus, there were statistically significant differences in the genotypic and allelic frequencies between the two groups (P < 0.01) (Table 2). At the rs78326403 locus, no statistically significant differences in genotypic and allelic frequencies were observed between the groups (P > 0.05).

Table 2.

Comparison of Genotypic and Allelic Frequencies at rs3018362 Locus [n (%)]

Groups	Genotype			P	Allele frequency		P
Groups	AA	GG	AG	P	A	G	P
NGT Group	32(33.7)	23(24.2)	40(42.1)		104(54.7)	86(45.3)
T2DM Group	55(52.4)	11(10.5)	39(37.1)	0.007**	149(71.0)	61(29.0)	<0.001**

Notes. Compared with the NGT group, *P < 0.05, **P < 0.01.

Linkage Disequilibrium (LD) Analysis

Linkage disequilibrium (LD) analysis was performed using SHEsis software to evaluate the genetic correlation between the RANK gene SNPs. The results showed that the D′ value was 0.77 and the r² value was 0.04, indicating weak LD between the two loci. These two SNPs could be regarded as relatively independent genetic markers.

Comparison of Biochemical Indicators Across Genotypes of Different Loci in Different Groups

Due to the small sample size of homozygotes for the minor allele at certain RANK gene loci, minor allele homozygotes and heterozygotes were combined into a single group of minor allele carriers for comparison with the major homozygous genotype.

At the rs3018362 locus, compared with the major homozygous genotype (AA), carriers of the minor allele (GG + AG) in the NGT group hadlower levels of HDL and P, while minor allele carriers in the T2DM group showed lower levels of HbA₁c and ALB (Table 3). To account for the risk of Type I error associated with multiple comparisons, the Benjamini-Hochberg method was applied to perform false discovery rate (FDR) correction. After adjustment, the FDR-corrected P-values for all four aforementioned biochemical parameters were 0.315, which did not reach statistical significance.

Table 3.

Comparison of Biochemical Indicators Across Genotypes of RANK Gene Loci in Different Groups( $\bar{x} \pm s$ )

Variables	rs3018362 (NGT group)		rs3018362 (T2DM group)		rs78326403 (NGT group)		rs78326403 (T2DM group)
Variables	AA	GG+AG	AA	GG+AG	AA	TT+AT	AA	TT+AT
FPG (mmol/L)	4.92±0.63	5.18±0.68	9.26±2.65	8.39±2.64	5.09±0.67	5.12±0.70	9.06±2.79	8.30±2.29
HbA₁c (%)	5.68±0.52	5.90±0.55	8.86±1.90	8.21±1.27	5.85±0.48	5.72±0.78	8.53±1.67	8.61±1.66
TG (mmol/L)	1.56±1.30	1.48±0.94	1.71±1.24	2.26±2.91	1.62±1.14	1.02±0.29**	1.99±2.48	1.93±1.27
LDL (mmol/L)	2.44±0.85	2.65±0.86	2.24±1.14	2.57±1.12	2.70±0.82	2.07±0.87**	2.45±1.15	2.26±1.12
HDL (mmol/L)	1.58±0.64	1.33±0.43	1.80±0.95	1.63±0.75	1.37±0.50	1.60±0.57	1.70±0.87	1.77±0.86
TG/HDL ratio	1.28±1.66	1.27±1.00	1.22±1.05	1.53±1.56	1.41±1.36	0.70±0.27**	1.39±1.46	1.30±0.89
TyG index	8.49±0.66	8.59±0.52	9.19±0.81	9.14±0.99	8.62±0.59	8.28±0.34**	9.14±0.96	9.24±0.70
Ca (mmol/L)	2.27±0.12	2.27±0.07	2.26±0.22	2.30±0.15	2.27±0.08	2.27±0.11	2.30±0.18	2.23±0.21
P (mmol/L)	1.21±0.23	1.12±0.17	1.13±0.19	1.14±0.16	1.15±0.20	1.15±0.17	1.14±0.19	1.13±0.14
ALP (U/L)	82.25±41.64	74.48±21.45	72.95±18.26	76.00±16.76	78.27±31.63	72.06±20.26	74.71±16.72	73.59±19.83
ALT (U/L)	26.72±18.15	28.94±21.94	23.44±14.07	21.86±15.69	28.64±21.14	26.28±18.96	21.62±14.26	25.48±16.10
AST (U/L)	24.22±15.90	22.20±11.11	22.47±17.69	20.24±11.61	23.72±13.91	19.28±5.85	22.17±17.07	19.41±7.63
ALB (g/L)	40.86±4.24	40.43±3.92	41.81±5.29	39.67±4.47	40.44±3.64	41.12±5.44	41.04±5.13	40.14±4.70
SUA (mmol/L)	284.49±67.72	281.28±65.80	272.91±70.31	250.48±74.74	289.46±65.96	251.99±59.23	260.05±78.89	267.94±55.34
Scr (μmol/L)	71.88±51.10	68.32±42.10	89.11±72.15	89.62±71.52	70.47±45.98	65.47±42.03	82.83±62.11	106.46±90.75

Notes. Compared with the carriers of the minor allele, *P < 0.05, **P < 0.01 after FDR correction for multiple testing. FPG: fasting plasma glucose; HbA₁C: glycosylated hemoglobin; TG: triglyceride; LDL: low density lipoprotein; HDL: high density lipoprotein; TyG index: Triglyceride-glucose index; Ca: calcium; P: phosphorus; ALP: alkaline phosphatase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALB: albumin; SUA: serum uric acid; Scr: serum creatinine.

At the rs78326403 locus, compared with the major homozygous genotype (AA), carriers of the minor allele (TT + AT) in the NGT group had lower levels of TG, LDL, TG/HDL ratio, TyG index, and SUA (Table 3). After correcting for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR), the FDR-adjusted P-values for TG, LDL, TG/HDL ratio, and TyG index were all 0.008, which remained statistically significant. In contrast, the FDR-adjusted P-value for SUA was 0.225, which did not reach statistical significance. No statistically significant differences in any biochemical indicators were observed between the major homozygous genotype (AA) and minor allele carriers (TT + AT) in the T2DM group (Table 3).

Binary Logistic Regression Analysis for Variables Associated With T2DM

Binary logistic regression analysis with the forward stepwise likelihood ratio (LR) method was performed, with the presence or absence of T2DM as the dependent variable, and baseline data, relevant biochemical indicators, and genotypes as the independent variables. Prior to model fitting, age and menopausal years were not included simultaneously due to severe multicollinearity between them, and menopausal years was ultimately retained for subsequent analyses. The variance inflation factor (VIF) values of all other independent variables were less than 5, indicating the absence of significant multicollinearity.

Univariate binary logistic regression analysis was first conducted for preliminary screening. Indicators with a P < 0.20 in the univariate analysis were selected as candidate variables for the subsequent multivariate model, which included menopausal years, HbA₁c, TG, TyG index, ALT, SUA, Scr, and the rs3018362 and rs78326403 genotypes. Collinearity validation was reexamined for these candidate variables, and the results demonstrated that the VIF values of all indicators were less than 5, meeting the criteria for model fitting. Subsequently, all candidate variables were entered into a single final multivariate binary logistic regression model using the forward stepwise method. After adjusting for confounding factors, HbA₁c and the rs3018362 genotype were identified to have an independent statistical association with T2DM. Notably, carriers of the minor allele (GG + AG) of rs3018362 served as an independent protective factor against T2DM compared with the major homozygous genotype (AA) (Table 4).

Table 4.

Binary Logistic Regression Analysis for Variables Associated With T2DM in Postmenopausal Women

Variables	β	SE	Waldχ2	OR(95%CI)	P
HbA₁c (%)	6.902	1.399	24.322	993.853(63.991,15435.581)	<0.001**
rs3018362	-1.764	0.899	3.850	0.171(0.029,0.998)	0.050

Notes. *P < 0.05, **P < 0.01. TyG index: Triglyceride-glucose index; SUA: serum uric acid.

Discussion

With the intensification of population aging in China, the prevalence of T2DM is rising rapidly. According to epidemiological data projections, T2DM will lead to a continuous increase in global healthcare expenditures. Therefore, great importance should be attached to the prevention and mitigation of the development and progression of T2DM. Currently, genetic research on T2DM has become a major research focus in this field. Several in vitro and in vivo studies have confirmed the expression of OPG, RANKL, and RANK in pancreatic β-cells.^16,17 Additionally, some studies have proposed that the OPG-RANKL-RANK signaling pathway may serve as a potential mechanism regulating glucose and insulin metabolism.^18,19 Currently, OPG has been proposed as a potentially useful biomarker for predicting blood glucose control levels in patients with T2DM.²⁰ However, there remains no definitive conclusion from studies on the specific association between RANKL/RANK and T2DM.

Our study results demonstrated that the T2DM group had higher HDL levels but lower ALT and SUA levels compared with the NGT group, which appears inconsistent with the typical metabolic alterations observed in T2DM. These between-group differences are presented only for cohort characterization and baseline balance assessment, without statistical adjustment or causal interpretation. The discrepant distribution may reflect inherent regional and population heterogeneity. Due to the unique lifestyle and dietary patterns in the Xinjiang population, the participants exhibited a distinct baseline metabolic spectrum relative to general populations.

In this study, three genotypes (AA, AG, GG) were identified at the rs3018362 locus, with respective frequencies of 43.50%, 39.50%, and 17.00%. The minor allele frequency (MAF) at this locus was 36.75%. The MAF at this locus in our study differed substantially from previous reports: 45.00% for the G allele in Han Chinese women reported by Shang et al²¹ and 27.00% in Chinese women reported by Liu et al²² A more pronounced discrepancy was observed in the Mexican population,²³ where the A and G allele frequencies were nearly equal. These differences may be attributed to ethnic and geographical variations. Statistical analysis revealed a significant difference in the genotype distribution of rs3018362 between the NGT and T2DM groups. Binary logistic regression analysis further confirmed that the genotype at the rs3018362 locus was independently and statistically associated with T2DM in postmenopausal women. These findings suggest that the polymorphism at the rs3018362 locus of the RANK gene may be associated with the development of T2DM in the study population. We speculate on the underlying mechanism as follows: the polymorphism at rs3018362 may regulate the transcription and expression of the RANK gene, thereby acting on the OPG-RANKL-RANK pathway. This pathway is involved in regulating insulin secretion by pancreatic β-cells, mediating insulin resistance in peripheral tissues, and influencing signaling pathways related to glucose metabolism, ultimately contributing to the pathogenesis of T2DM.¹³

Three genotypes (AA, AT, TT) were identified at the rs78326403 locus, with corresponding frequencies of 76.50%, 22.50%, and 1.00%, respectively. The MAF at this locus was 12.25%. This MAF differed significantly from the 7.90% reported in a study of Spanish women.²⁴ Currently, research on the rs78326403 locus is limited, and no relevant reports have been published in China, which constitutes a novel aspect of the present study. Statistical analysis showed that in the NGT group, AA genotype carriers had significantly higher TG/HDL ratio and TyG index. No significant differences in any biochemical indicators were observed between carriers of AA genotype and those of the AT+TT genotypes in the T2DM group. A recent review concluded that the TyG index is significantly associated with T2DM and is regarded as a promising biomarker for screening insulin resistance and metabolic disorder-related diseases.²⁵ Additionally, a cross-sectional study has indicated that indicators such as the TyG index and TG/HDL ratio can be used as predictive markers for evaluating blood glucose control in patients with T2DM.²⁶ Combined with the results of this study, we speculate that the polymorphism at the rs78326403 locus may be involved in the regulation of glucose metabolism, and the AA genotype may be associated with glucose metabolism abnormalities. Furthermore, in the NGT group, AA genotype carriers also had elevated TG and LDL levels, suggesting that the polymorphism at this locus may also exert an effect on lipid metabolism. We propose the underlying mechanism as follows: the polymorphism at the rs78326403 locus may regulate the expression level of the RANK gene. As a core component of the OPG-RANKL-RANK pathway, RANK may participate in the regulation of glucose and lipid metabolism by mediating insulin resistance and activating hepatic lipid metabolism-related signaling pathways,²⁷ thereby potentially influencing the risk of developing glucose and lipid metabolic disorders.

This study investigated the association between RANK gene locus polymorphisms and T2DM in postmenopausal Han Chinese women from Xinjiang, China, filling the gap in relevant research for this specific population. Several limitations of this study should be acknowledged. First, the sample size was determined based on our research group’s prior related studies without conducting a dedicated power analysis. Furthermore, this was a single-center study with a relatively small sample size, including only postmenopausal Han Chinese women from Shihezi, Xinjiang, China. Future studies should expand the sample size and adopt a multi-center design across more regions to further clarify the molecular mechanisms underlying the role of RANK gene polymorphisms in T2DM development and progression. In addition, potential confounding variables need to be incorporated in future studies, including ethnicity, dietary habits, lifestyle factors, medication use, and the duration and severity of T2DM.

Conclusion

In conclusion, this study of postmenopausal Han Chinese women from Xinjiang, China, suggests that the polymorphism at the rs3018362 locus of the RANK gene may be associated with type 2 diabetes mellitus. We also investigated the relationship between gene locus polymorphisms and glycemic and lipid metabolic indicators. Our findings indicate that polymorphism at the rs78326403 locus of the RANK gene may be involved in glucose and lipid metabolism, which provides preliminary population-based evidence for the involvement of gene polymorphisms in the regulation of glucose and lipid metabolism. Despite certain limitations in this study, it still offers preliminary population-based evidence for subsequent relevant research. Given the complex pathogenesis of T2DM, further in-depth research is needed to explore the interactive effects between genetic and environmental factors, which will provide more evidence for better prevention and treatment of T2DM in the future.

Footnotes

Acknowledgements

Jun Li is the corresponding author and first author, and Chuanbing Sun and Siyuan Li are the co-first authors. Thanks to all authors for their participation and support.

ORCID iDs

Chuanbing Sun

Ya Li

Ethical Considerations

This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Shihezi University (ethics number: KJ2024-337-01, approved on September 19, 2024).

Consent to Participate

All participants signed the written informed consent. This study strictly followed the Declaration of Helsinki.

Consent for Publication

Written informed consent for the publication of the data included in this study was obtained from all participants.

Author Contributions

Jun Li is the corresponding author and first author, and Chuanbing Sun and Siyuan Li are the co-first authors. Jun Li designed this study, directed the entire project, and secured the funding. Chuanbing Sun obtained informed consent from the study participants, conducted the experiments, and collated and analyzed the data. Siyuan Li participated in the experimental design, oversaw the project, and secured the funding. Jun Li, Chuanbing Sun and Siyuan Li all wrote the main manuscript text and tables. Xueyuan An and Ya Li performed the experiments and collated the data. Yujuan Shen and Hangning Tian collected the samples, and collated and analyzed the data. Jun Li was responsible for the final revision of the manuscript and ensured the accuracy of all content. All authors have read and approved the final version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the funding of Xinjiang Production and Construction Corps Guided Science and Technology Plan Project (2022ZD044 and 2024ZD041); National Science and Technology Major Program (2024ZD0532303); 2024 Talent Development Fund of the Corps Clinical Medical Research Center for Metabolic Diseases (CZ001237); 2025 Talent Development Fund of the Tianshan Mountain Yingcai (the third batch) Medicine and Health Leading Talent (TSYC202401A061); Science and Technology Tackling Program in Key Areas (2026YD027).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets used and analysed during the study are available from the corresponding author on reasonable request.*

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