Association of Calpain-10 Gene Polymorphisms with Type 2 Diabetes Mellitus: A Case-Control Study from a Tertiary Care Hospital in Pakistan

Association of Calpain-10 Gene Polymorphisms with Type 2 Diabetes Mellitus: A Case-Control Study from a Tertiary Care Hospital in Pakistan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Calpain-10 Gene Polymorphisms with Type 2 Diabetes Mellitus: A Case-Control Study from a Tertiary Care Hospital in Pakistan Humaira Farooqi, Nakhshab Choudhry, Muhammad Nabeel Saddique, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6171604/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 May, 2025 Read the published version in BMC Medical Genomics → Version 1 posted 12 You are reading this latest preprint version Abstract Introduction: Type 2 diabetes mellitus (T2DM) is a major public health challenge, with rising prevalence in low- and middle-income countries such as Pakistan. Genetic susceptibility plays a critical role in its pathogenesis. Calpain-10 (CAPN-10), a gene implicated in insulin secretion and glucose homeostasis, has been studied for its potential involvement in T2DM. This study aimed to evaluate the association of CAPN-10 polymorphisms—SNP44 (rs2975760) and SNP43 (rs3792267)—with T2DM in a Pakistani cohort. Methods: This case-control study included 164 T2DM patients and 164 healthy controls (mean age ± SD: 57.2 ± 8.2 vs. 53.9 ± 6.3 years; age range: 41–82 years). The male-to-female ratio was 41.4% to 58.6% in cases and 37.2% to 62.8% in controls. Participants were enrolled using non-probability convenience sampling. Genomic DNA was extracted from whole blood, and genotyping of CAPN-10 SNPs (rs3792267 and rs2975760) was performed using PCR-RFLP. Genotype distributions were assessed for Hardy-Weinberg equilibrium. Associations with T2DM were evaluated using odds ratios (ORs) and 95% confidence intervals (CIs) via logistic regression. Chi-square tests were used for categorical comparisons, with p < 0.05 considered statistically significant. Analyses were conducted using SPSS version 26. Results: For SNP44, no significant association with T2DM was observed under dominant, heterozygous, or recessive models after Bonferroni correction (adjusted p > 0.05). Similarly, SNP43 showed no statistically significant association with T2DM in either dominant or recessive models (adjusted p > 0.05), although the AA genotype appeared more frequently among T2DM cases. These findings suggest no significant role of CAPN-10 polymorphisms in T2DM susceptibility in this population. Conclusion : CAPN-10 polymorphisms SNP44 and SNP43 showed no significant association with T2DM in this population, suggesting limited predictive value for disease susceptibility. Type 2 Diabetes Mellitus CAPN-10 gene polymorphisms Single nucleotide polymorphisms (SNPs) Genetic susceptibility Pakistani population Figures Figure 1 1 Introduction Type 2 diabetes mellitus (T2DM) is the most prevalent form of diabetes, accounting for approximately 90% of all cases in developed countries. It primarily affects individuals aged 45 years and older. This chronic metabolic disorder is characterized by insulin resistance in peripheral tissues and impaired insulin secretion by pancreatic β-cells, resulting in disrupted glucose homeostasis [ 1 ]. To date, over 36 genes have been associated with T2DM, among which the Calpain-10 (CAPN-10) gene has emerged as a notable candidate. CAPN-10, a member of the calpain protease family located on chromosome 2q37.3, plays a pivotal role in glucose metabolism, insulin-stimulated glucose uptake, and glycogen synthesis. Given its biological relevance, CAPN-10 is considered a potential susceptibility gene for T2DM [ 2 ]. CAPN-10 is a gene associated with type 2 diabetes mellitus (T2DM) and is predominantly expressed in insulin-sensitive tissues such as adipose tissue, skeletal muscle, and the liver. Its role in T2DM is linked to its modulation of key components within the insulin signaling pathway, thereby affecting both glucose metabolism and insulin sensitivity. The broader calpain protease family, to which CAPN-10 belongs, has been implicated in the molecular pathogenesis of T2DM and represents a potential target for therapeutic intervention. The functional relevance of CAPN-10 highlights the complex interplay between genetic factors and insulin signaling, contributing to a deeper understanding of glucose homeostasis in T2DM [ 3 , 4 ] Recent genome-wide association studies (GWAS) have identified over 1,200 genetic loci associated with the pathogenesis of T2DM, underscoring its multifactorial and polygenic nature. In a landmark study, Suzuki et al. (2024) reported 1,289 single nucleotide polymorphisms (SNPs) linked to T2DM risk, highlighting the complexity of its genetic architecture.[ 5 ] Among these, polymorphisms in the CAPN-10 gene—particularly SNP44 (rs2975760) and SNP43 (rs3792267)—have been associated with altered gene expression and function in the context of T2DM. These variants contribute to inter-individual disease susceptibility and have shown associations in diverse populations, including Mexican Americans and British/Irish cohorts [ 6 ]. Differences in allelic frequencies between diabetic and non-diabetic individuals further support the genetic contribution of CAPN-10 to disease risk. Despite the high burden and increasing prevalence of T2DM in Pakistan, research on CAPN-10 polymorphisms in this population remains limited. This gap presents a critical opportunity to explore population-specific genetic associations. Investigating the frequency and potential role of CAPN-10 variants in the Pakistani population may provide novel insights into T2DM pathophysiology and support the development of targeted prevention and management strategies.[ 7 ] This study aims to address the existing research gap by investigating the prevalence of CAPN-10 gene polymorphisms—rs2975760 (SNP44) and rs3792267 (SNP43)—in Pakistani patients with T2DM. The objective is to evaluate potential associations between these variants and T2DM susceptibility, thereby contributing to the broader understanding of the disease’s genetic etiology. The findings may yield novel insights into the molecular mechanisms underlying T2DM and support the development of genetically informed therapeutic strategies. Given the rising burden of T2DM in Pakistan, this research underscores the significance of CAPN-10 in glucose metabolism and insulin signaling, with implications for advancing personalized medicine in the prevention and management of T2DM. 2 Material and methods This case-control study was reported according to Strengthening the reporting of observational studies in epidemiology (STROBE) checklist for case-control study. 2.1 Study Design and Setting This case-control study was conducted in Pathology Department, King Edward Medical University, Lahore and Advance Research Center for Biomedical Sciences (ARCBS) . 2.2 Sample size A total of 328 participants were included in the study, comprising 164 healthy controls and 164 T2DM cases. The sample size was estimated using a 95% confidence level and a 10% absolute precision, with the expected genotype frequencies of 23% for the 2AA + GA genotypes in T2DM cases and 64% in the control group. This calculation ensured adequate statistical power to detect significant differences between the two groups. Participants were recruited through a non-probability convenient sampling method, with informed consent obtained from all individuals prior to inclusion in the study. 2.3 Ethical Approval This study as approved by the Institutional Review Board (IRB) of King Edward Medical University, Lahore vide letter no. 482/RC/KEMU. Written informed consent was obtained from all participants prior to sample collection, and the study was conducted in accordance with the Declaration of Helsinki. 2.4 Participants selection criteria 2.4.1 Inclusion criteria Participants included in this study were adults aged 18 years or older, both male and female, who had been previously diagnosed with Type 2 Diabetes Mellitus (T2DM) by a healthcare professional. The diagnosis of T2DM was confirmed based on the American Diabetes Association (ADA) criteria [8], which include fasting plasma glucose ≥126 mg/dL, 2-hour plasma glucose ≥200 mg/dL during an oral glucose tolerance test, or HbA1c ≥6.5%. Controls were recruited from among attendants of diabetic patients at Mayo Hospital. Only individuals who were willing to participate and provided informed consent were included in the study. 2.4.2 Exclusion criteria T2DM patients with any of the following conditions were excluded from the study: 1) Participants with additional metabolic conditions, such as obesity, hyperlipidemia, or metabolic syndrome, which could confound the results of the genetic analysis, were excluded; 2) Patients with chronic liver diseases, including cirrhosis, liver cancer, or viral hepatitis, were excluded to avoid potential interference in glucose metabolism and insulin signaling, which could alter the genetic associations being studied; 3) Individuals diagnosed with CHF were excluded due to the impact of cardiovascular disease on metabolic function, which could affect insulin resistance and glucose regulation; 4) Participants with autoimmune conditions such as rheumatoid arthritis, lupus, or multiple sclerosis were excluded, as these diseases can influence glucose metabolism and insulin sensitivity; 5) Patients with current or previous infections, such as hepatitis C virus (HCV), human immunodeficiency virus (HIV), or any active bacterial or viral infections, were excluded due to their potential impact on insulin resistance and inflammatory markers; 6) Any participant with a history of cancer was excluded to ensure that genetic factors linked to cancer would not interfere with the study of T2DM-specific genetic polymorphisms; 7) To avoid genetic bias, controls were screened to ensure non-relatedness to T2DM cases, based on self-report and verbal family pedigree inquiries. First- and second-degree relatives of cases were excluded. 2.5 Data Collection Informed written consent was obtained from all participants prior to sample collection. The consent process ensured that participants were fully informed regarding the study’s objectives, procedures, and any potential risks. Confidentiality of all personal and medical information was strictly maintained, and data were used exclusively for research purposes. Diagnosed T2DM cases were recruited from the diabetic clinic at Mayo Hospital, Lahore. All cases had been previously diagnosed by a specialist physician according to the American Diabetes Association (ADA) criteria. Healthy control participants were recruited from the attendants of patients, provided they had normal HbA1c levels and no history of metabolic disorders, including T2DM. Both cases and controls underwent identical consent and sampling procedures. Peripheral blood samples were collected from all participants for subsequent genetic analysis. 2.6 Quantitative Variables The primary outcome of the study was the presence of Type 2 Diabetes Mellitus (T2DM), confirmed through clinical diagnosis by a specialist physician based on the American Diabetes Association (ADA) diagnostic criteria, which include fasting blood glucose levels ≥126 mg/dL, HbA1c levels ≥6.5%, or a 2-hour plasma glucose level ≥200 mg/dL during an oral glucose tolerance test (OGTT). Secondary outcomes included the frequencies of CAPN-10 gene polymorphisms (rs2975760 and rs3792267) among T2DM cases and healthy controls. The exposures analyzed were the CAPN-10 gene polymorphisms, assessed through genotyping techniques. Predictors included the genetic variants rs2975760 and rs3792267, along with demographic factors such as age and gender. Potential confounders were identified as Body Mass Index (BMI), HbA1c levels, fasting blood glucose levels, and histories of metabolic disorders, infectious diseases, or other chronic conditions. Age and gender were also considered potential effect modifiers, as they could influence the association between CAPN-10 polymorphisms and T2DM. All variables were clearly defined to ensure standardization of data collection and analytical procedures. 2.7 Data Measurement Genomic DNA was isolated from peripheral blood samples to genotype the CAPN-10 polymorphisms. Approximately 5.0 mL of blood was collected in EDTA-coated tubes, and DNA extraction was performed using a commercially available kit following the manufacturer’s protocol. PCR amplification was then conducted using sequence-specific primers targeting the regions flanking SNP43 (rs3792267) and SNP44 (rs2975760). Amplification was carried out in a thermal cycler (T100, Bio-Rad, USA) under standardized cycling conditions: initial denaturation at 95°C, primer annealing at 60°C, and extension at 72°C. The expected amplicon sizes were 245 bp for SNP43 and 166 bp for SNP44. Post-amplification, enzymatic digestion was performed to identify genotypes using restriction fragment length polymorphism (RFLP) analysis. For SNP43, the NdeI enzyme was used, while BstUI was used for SNP44. The resulting restriction fragments were indicative of specific genotypes based on known cleavage patterns. (Table 1) Digested products were resolved using gel electrophoresis. Initial PCR products were confirmed using 2.5% agarose gel electrophoresis with DNA gel stain, while post-digestion fragments were analyzed via 10% polyacrylamide gel electrophoresis (PAGE). Gel imaging was performed under UV illumination using a gel documentation system (SYNGENE Gel Doc System, UK), and genotypes were inferred based on the fragment size patterns. This standardized approach ensured the comparability of measurements across both T2DM cases and control groups. While PCR-RFLP is a well-established and cost-effective genotyping method suitable for resource-limited settings, it has lower specificity and sensitivity compared to techniques such as real-time PCR, SNP arrays, or next-generation sequencing. These limitations are acknowledged and should be considered when interpreting the results. Schematic representation of the four-step process used to genotype SNP43 and SNP44: (1) DNA extraction from whole blood, (2) PCR amplification of target regions, (3) enzymatic digestion with NdeI (SNP43) and BstUI (SNP44), and (4) gel electrophoresis for genotype identification based on fragment size. Table 1: Primer Sequences and Restriction Enzymes Used for the Analysis of CAPN-10 Gene Polymorphisms SNP Primer Sequence 5’ à 3’ PCR Product Restriction Enzyme Restriction Product (bp) SNP-43 (rs3792267) GàA F: gct ggc tgg tga cat cag tgc 245bp NdeI GG: 245 GA: 245, 223, 31 AA: 223, 31 R: acc aag tca agg ctt agc ctc acc ttc ata SNP-44 (rs2975760) TàC F: gca ggg cgc tca cgc ttg ccg 166 bp BstUl TT: 166 TC: 166, 145, 21 CC: 145, 21 R: gca tgg ccc cct ctc tga ttc SNP: Single Nucleotide Polymorphism; PCR: Polymerase Chain Reaction; bp: Base Pair; NdeI: A restriction enzyme used for digesting DNA at specific sites; BstUI: A restriction enzyme used for DNA fragment analysis 2.8 Statistical Analysis Descriptive statistics were used to summarize demographic and clinical characteristics. Continuous variables (e.g., age, BMI, HbA1c, fasting glucose) were expressed as mean ± standard deviation (SD), and categorical variables (e.g., sex, genotype) were presented as frequencies and percentages. The association between CAPN-10 gene polymorphisms—SNP44 (rs2975760) and SNP43 (rs3792267)—and T2DM was evaluated using chi-square tests under various genetic models: dominant (e.g., TC+CC vs. TT), recessive (CC vs. TC+TT), and additive. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Genotypic distributions were assessed for Hardy-Weinberg equilibrium (HWE) in the control group using the chi-square goodness-of-fit test; a p-value > 0.05 indicated conformity with HWE. Bonferroni correction was applied to account for multiple comparisons, and adjusted significance thresholds were reported accordingly. Subgroup analyses were performed to assess interactions between CAPN-10 variants and demographic or clinical variables, including age, sex, and BMI. Potential confounders were controlled using multivariate logistic regression models adjusting for age, gender, BMI, and relevant clinical parameters. Missing data were addressed using multiple imputation to enhance statistical robustness. Participants with incomplete key variables were excluded from subgroup analyses but retained in primary analyses if core data were available. Cases and controls were matched on age and gender to reduce confounding bias.Sensitivity analyses were conducted by excluding outliers and re-evaluating models with and without imputed data to validate the robustness of findings. All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). 3 Results This study assessed the association between two key polymorphisms, SNP44 (rs2975760) and SNP43 (rs3792267) , of the CAPN-10 gene with Type 2 Diabetes Mellitus (T2DM) in a cohort of Pakistani participants. The sample included 164 T2DM patients and 164 healthy controls. The results are presented below in terms of demographic, clinical characteristics, and genotypic frequencies. 3.1 Demographics and Clinical Characteristics The mean age of T2DM cases was 57.19 ± 8.22 years, significantly higher than the controls (53.93 ± 6.32 years, p = 0.0003). BMI was also significantly higher in cases (27.5 ± 4.1 kg/m²) compared to controls (25.8 ± 3.9 kg/m², p =0.0306). T2DM cases had significantly elevated HbA1c levels (183.95 ± 51.54) compared to controls (175.06 ± 33.05, p <0.0001). Similarly, fasting blood glucose levels were higher in cases (88.72 ± 23.1 mg/dL) compared to controls (82.61 ± 20.6 mg/dL, p = 0.01). Gender distribution did not differ significantly between groups, with males comprising 41.4% of cases and 37.2% of controls ( p = 0.3461). (Table 2) Table 2. The demographic and clinical characteristics of the T2DM cases and healthy controls Characteristics T2DM cases ( n = 165) Healthy controls ( n = 164) p value Age (years) (Mean ± SD) 41–82 (57.19 ± 8.22) 41–60 (53.93 ± 6.32) p = 0.0003 BMI (kg/m 2 ) (Mean ± SD) 27.5 ± 4.1 25.8 ± 3.9 p = 0.0306 HBA1c (Mean ± SD) 183.95 ± 51.54 175.06 ± 33.05 p < 0.0001 Fasting blood glucose level (mg/dl) (Mean ± SD) 88.72 ± 23.1 82.61 ± 20.6 p = 0.01 Males/females (%) 68 (41.4%) / 96 (58.6%) 61 (37.2%) / 103 (62.8%) p = 0.3461 T2DM: Type 2 diabetes mellitus; SD: Standard deviation; BMI: Body-mass index 3.2 Genotypic Distribution SNP44 (rs2975760) The association between SNP44 (rs2975760) and T2DM was evaluated under multiple genetic models, including dominant (TC + CC vs. TT), heterozygous (TC vs. TT + CC), and recessive (CC vs. TC + TT) contrasts. Genotype distributions among controls were in Hardy-Weinberg equilibrium (p = 0.216), indicating appropriate population structure for analysis. The odds ratio (OR) for the dominant model (TC + CC vs. TT) was 1.07 (95% CI: 0.86–1.37), with an unadjusted p-value of 0.752 and a Bonferroni-adjusted p-value of 1.000. For the heterozygous model (TC vs. TT + CC), the OR was 1.10 (95% CI: 0.63–1.43), with an adjusted p-value of 1.000. Under the recessive model (CC vs. TC + TT), the OR was 1.43 (95% CI: 0.70–3.03), with an unadjusted p-value of 0.063 and a Bonferroni-adjusted p-value of 0.379. Although some comparisons indicated elevated odds ratios, none of the associations were statistically significant following correction for multiple testing. These findings suggest that SNP44 (rs2975760) is not significantly associated with T2DM susceptibility in this population. (Table 3) Table 3: Genotypic Distribution of SNP44 (rs2975760) Comparison OR 95% CI Unadjusted p-value Bonferroni-adjusted p-value TC + CC vs. TT 1.07 0.86–1.37 0.752 1.000 TC vs. TT + CC 1.10 0.63–1.43 0.176 1.000 CC vs. TC + TT 1.43 0.70–3.03 0.063 0.379 3.3 Genotypic variations of SNP43 (rs3792267) For SNP43 (rs3792267), genotype distributions among controls also conformed to Hardy-Weinberg equilibrium (p = 0.174). Association analyses were conducted using dominant (GA + AA vs. GG) and recessive (AA vs. GA + GG) models. Under the dominant model, the OR for T2DM was 1.54 (95% CI: 0.77–1.58), with an unadjusted p-value of 0.740 and a Bonferroni-adjusted p-value of 1.000. The recessive model yielded an OR of 2.05 (95% CI: 1.14–3.68), with an unadjusted p-value of 0.403 and an adjusted p-value of 1.000. (Table 4) Despite the elevated point estimate for the AA genotype in the recessive model, no statistically significant associations were observed after correction for multiple comparisons. Thus, these results do not support a significant role of SNP43 (rs3792267) in T2DM predisposition within this cohort. The complete genotypic and allelic distributions for both SNPs are presented in Table 5. Table 4: Genotypic variations of SNP43 (rs3792267) Comparison OR 95% CI Unadjusted p-value Bonferroni-adjusted p-value GA + AA vs. GG 1.54 0.77–1.58 0.740 1.000 AA vs. GG + GA 2.05 1.14–3.68 0.403 1.000 Table 5. Genotypic Distribution and Allelic Frequencies (Final sample: 164 T2DM cases, 164 healthy controls) Genotype T2DM Cases (%) Controls (%) SNP44 (rs2975760) TT 48 29.3 59 36.0 TC 82 50.0 66 40.2 CC 34 20.7 39 23.8 SNP43 (rs3792267) GG 32 19.5 49 29.9 GA 81 49.4 85 51.8 AA 51 31.1 30 18.3 4 Discussion This study examined the association between SNP44 (rs2975760) and SNP43 (rs3792267) polymorphisms of the CAPN-10 gene and type 2 diabetes mellitus (T2DM) in a Pakistani population. Significant differences in age, BMI, HbA1c, and fasting blood glucose levels between T2DM cases and healthy controls were observed, consistent with established metabolic markers of the disease and supporting the validity of the sample. Gender distribution did not differ significantly between groups, indicating a balanced representation of male and female participants. Although the TC and CC genotypes of SNP44, and the GA and AA genotypes of SNP43, were more frequent among T2DM cases, no statistically significant associations were found following Bonferroni correction. These findings suggest that, in this population, SNP44 and SNP43 are not independently associated with increased T2DM risk, despite prior reports of CAPN-10 involvement in glucose metabolism and insulin signaling [9]. These findings underscore the importance of population-specific genetic research. Although CAPN-10 polymorphisms have been linked to T2DM in certain populations, the current study suggests that SNP44 and SNP43 may not significantly contribute to T2DM susceptibility in the Pakistani population [10]. It is plausible that gene–environment interactions—such as region-specific lifestyle, dietary patterns, and environmental exposures—modulate the phenotypic expression of these variants, thereby diminishing their detectable effect in this cohort. Previous studies investigating the association of CAPN-10 SNPs with T2DM across different populations have reported inconsistent results, reflecting the complex nature of genetic predisposition to the disease. While associations between SNP44 and SNP43 and T2DM have been observed in Hispanic and select Asian populations, studies in European cohorts have failed to demonstrate significant associations [11]. Early studies conducted in European populations provided initial evidence that certain CAPN-10 polymorphisms may influence susceptibility to T2DM. For example, the T allele of SNP44 (rs2975760) was associated with an increased risk of T2DM in cohorts of Spanish and Finnish ancestry. However, subsequent investigations in other European populations produced inconsistent findings, with several studies failing to replicate this association [12]. These discrepancies highlight the complexity of genetic contributions to T2DM and underscore the need for further investigation across diverse ethnic groups [10] Beyond Europe, CAPN-10 polymorphisms have also been examined in Asian populations. While some studies have reported associations between SNP44 and increased T2DM risk in Chinese, Japanese, and Korean cohorts, others have not found significant associations, reflecting inter-population variability. Similar inconsistency has been observed in studies of SNP43 (rs3792267), where associations with T2DM risk have been identified in certain Asian subgroups, but not in others[12]. These mixed results reinforce the importance of conducting population-specific research to better understand the genetic architecture of T2DM across different ethnic backgrounds. Research in Hispanic/Latino populations has also yielded inconsistent results regarding the role of CAPN-10 polymorphisms in T2DM. While some studies have reported associations between SNP44 and T2DM risk in Mexican American and Puerto Rican populations, others have found no significant correlations. Similarly, investigations of SNP43 in these cohorts have demonstrated both positive and negative findings [13,14] Studies conducted in African and Middle Eastern populations have been limited and inconclusive. While some African cohorts have shown significant associations between CAPN-10 variants and T2DM, other studies have failed to replicate these findings [15] Variation in allelic frequencies and genotype–phenotype associations of CAPN-10 polymorphisms across populations is particularly noteworthy, as it may provide insight into population-specific genetic risk profiles for T2DM. Allelic frequencies represent the distribution of genetic variants within a population and are shaped by evolutionary, demographic, and genetic factors[16]. Differences in these frequencies may reflect historical patterns of migration and admixture. Furthermore, genotype associations can identify specific alleles that may confer increased or decreased risk of T2DM [17]. The identification of genetic variants associated with T2DM susceptibility offers significant potential for the advancement of personalized medicine. Incorporating genetic markers into clinical risk stratification could enable earlier identification of high-risk individuals and support targeted preventive strategies. Moreover, pharmacogenomic insights derived from genotype data may inform more precise therapeutic decisions, optimizing treatment efficacy and minimizing adverse effects [18,19]. From a public health perspective, characterizing the distribution and impact of CAPN-10 variants in diverse populations is critical to reducing the global burden of T2DM. Such knowledge can inform the development of population-specific screening and prevention programs, particularly in high-risk regions [20]. The integration of genetic data into clinical care may enhance early diagnosis, improve disease management, and ultimately contribute to better health outcomes at the population level [21]. Despite notable progress, significant gaps remain in the genetic understanding of T2DM. Genetic heterogeneity across populations and limited knowledge of gene–environment interactions hinder the identification of universally applicable markers and individualized prevention strategies [22]. While research has largely focused on common variants with modest effects, rare variants with potentially greater impact remain underexplored due to technical and financial constraints.[10,23,24]. The CAPN10 gene encodes calpain-10, a calcium-dependent cysteine protease involved in various cellular processes, including insulin secretion and action. Polymorphisms such as SNP-43 (rs3792267) and SNP-19 (rs3842570) have been studied for their potential functional impact. For instance, SNP-19, a 32-base pair insertion/deletion in intron 6, may influence gene expression or alternative splicing, thereby affecting insulin sensitivity [25]. Associations between these polymorphisms and T2DM have been reported in diverse populations. In a Bangladeshi cohort, the 2R/3R genotype of SNP-19 was significantly associated with increased T2DM risk [25]. Similarly, in a Tunisian Arab population, the 2R allele of SNP-19 and the 111 haplotypes (comprising SNP-43, SNP-19, and SNP-63) were linked to higher T2DM susceptibility [26]. However, these associations have not been consistently replicated across all ethnic groups, reflecting the influence of genetic heterogeneity and underscoring the importance of population-specific research to elucidate the role of CAPN10 polymorphisms in T2DM pathogenesis.[27] CAPN-10 has emerged as a biologically plausible candidate gene, particularly in Mexican American populations where certain polymorphisms have been linked to T2DM risk [28] However, data on CAPN-10 variants in the Pakistani population are scarce, despite the country's escalating T2DM burden [29]. Addressing this gap may enhance our understanding of T2DM pathogenesis and support tailored interventions. Moreover, such research has global implications, contributing to the broader effort to map genetic diversity and advance precision medicine in diabetes care[24,30,31]. Population-specific differences in the association between CAPN10 polymorphisms and T2DM have been observed globally, with varying results reported in Hispanic, Asian, African, and European cohorts (Table 6) Table 6: Summary of CAPN10 SNP Associations with Type 2 Diabetes Mellitus (T2DM) Across Populations Population SNPs Studied Association with T2DM Pakistani (current study) SNP44, SNP43 No significant association [9] Mexican American SNP44, SNP43 Mixed results (positive in some studies) [13] Puerto Rican SNP44, SNP43 Mixed results [14] Spanish SNP44 Positive (initial studies) [12] Finnish SNP44 Positive (initial studies) [12] Chinese SNP44 Mixed results [12] Japanese SNP44 Mixed results [12] Korean SNP44 Mixed results [12] European (general) SNP44, SNP43 No consistent association [12] Bangladeshi SNP19 Positive (2R/3R genotype) [25] Tunisian Arab SNP19, SNP43, SNP63 Positive (2R allele, 111 haplotype) [26] African CAPN10 (varied) Mixed/limited evidence [15] Middle Eastern CAPN10 (varied) Limited/inconclusive [15] Study Limitations This study has several limitations. First, the sample size of 328 participants may have limited the statistical power to detect modest genetic associations, potentially contributing to the non-significant findings. Second, the study was conducted in a single Pakistani population, which may limit the generalizability of results to other ethnic groups. Third, key environmental and lifestyle factors—such as diet, physical activity, socioeconomic status, and degree of urbanization—were not assessed, despite their known influence on T2DM risk. Another limitation is the absence of a formal power analysis at the design stage. While the sample size of 328 participants offers a reasonable basis for preliminary analysis, it may be underpowered to detect small to moderate genetic effects typically observed in complex diseases such as T2DM. Lastly, the analysis was restricted to two polymorphisms (SNP44 and SNP43) within the CAPN-10 gene, excluding other potentially relevant genetic variants. Future research should incorporate larger, multi-ethnic cohorts and a broader panel of genetic markers to comprehensively elucidate the genetic architecture of T2DM. Conclusion No statistically significant association was observed between CAPN-10 SNP44 or SNP43 polymorphisms and T2DM in this Pakistani cohort. While minor genotype distribution trends were noted, these findings were not supported after correction for multiple testing. Future research incorporating larger, multi-ethnic samples and genome-wide analyses is warranted to elucidate the genetic basis of T2DM more comprehensively. Declarations Credit Author Statement: H.F: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Supervision, Writing - Original Draft, Writing - Review & Editing; M.N.S : Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review & Editing; N.C.: Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review & Editing; S.Q: Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review & Editing; R.D : Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review & Editing; S.K: Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review & Editing; H.J: Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review & Editing; A.J.G: Writing - Original Draft, Writing - Review & Editing; U.A.N: Writing - Original Draft, Writing - Review & Editing Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Use of generative AI and AI-assisted technologies ChatGPT-4o (OpenAI) was used solely for improving the grammar, readability, and clarity of the manuscript. No AI tools were used for data analysis, interpretation, or generation of scientific content. All intellectual content and scientific conclusions were formulated by the authors. Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of King Edward Medical University, Lahore (Approval No. 482/RC/KEMU). Written informed consent was obtained from all participants prior to enrollment. Participants were informed of the study's objectives, procedures, potential risks, and their right to withdraw at any time without consequences. Consent for publication All participants provided written informed consent for the publication of anonymized data collected during the study. No personally identifiable information is presented in this manuscript. Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request, in compliance with institutional and ethical guidelines to protect participant confidentiality. Acknowledgements The authors acknowledge the support of the Advance Research Center for Biomedical Sciences, King Edward Medical University, Lahore, Pakistan, for providing laboratory facilities and assistance with data collection and analysis. References Nowakowska M, Zghebi SS, Ashcroft DM, Buchan I, Chew-Graham C, Holt T, et al. The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort. BMC Med. 2019;17:145. https://doi.org/10.1186/s12916-019-1373-y . Raciti GA, Longo M, Parrillo L, Ciccarelli M, Mirra P, Ungaro P, et al. Understanding type 2 diabetes: from genetics to epigenetics. Acta Diabetol. 2015;52:821–7. https://doi.org/10.1007/s00592-015-0741-0 . Singh SP, Raza ST, Mahdic F. Role of Capn-10, Tcf7l2, Pparg and Kcnj11 Gene in Type 2 Diabetes Mellitus (t2dm): Era’s Journal of Medical Research. Eras J Med Res. 2018;5:166–9. https://doi.org/10.24041/ejmr2018.86 . Tumminia A, Vinciguerra F, Parisi M, Frittitta L. Type 2 Diabetes Mellitus and Alzheimer’s Disease: Role of Insulin Signalling and Therapeutic Implications. Int J Mol Sci. 2018;19:3306. https://doi.org/10.3390/ijms19113306 . Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024;627:347–57. https://doi.org/10.1038/s41586-024-07019-6 . Khan KM, Chakraborty R, Bundschuh J, Bhattacharya P, Parvez F. Health effects of arsenic exposure in Latin America: An overview of the past eight years of research. Sci Total Environ. 2020;710:136071. https://doi.org/10.1016/j.scitotenv.2019.136071 . Consortium GWA-PA, Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC), Lagou V, Jiang L, Ulrich A, Zudina L, et al. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification. Nat Genet. 2023;55:1448–61. https://doi.org/10.1038/s41588-023-01462-3 . American Diabetes Association Professional Practice Committee, ElSayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, et al. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care. 2024;47:S20–42. https://doi.org/10.2337/dc24-S002 . Zaharna MM, Abed AA, Sharif FA. Calpain-10 Gene Polymorphism in Type 2 Diabetes Mellitus Patients in the Gaza Strip. Med Princ Pract. 2010;19:457–62. https://doi.org/10.1159/000320304 . Mir S. Calpain 10 gene SNP-44 T > C polymorphism in type 2 diabetes mellitus of Bangladeshi origin. Thesis. BRAC University, 2010. Song Y, Niu T, Manson JE, Kwiatkowski DJ, Liu S. Are Variants in the CAPN10 Gene Related to Risk of Type 2 Diabetes? A Quantitative Assessment of Population and Family-Based Association Studies. Am J Hum Genet. 2004;74:208–22. El-Far SW, Kassem HSh, Embaby AM, Saad AA, Mowafy N, Haroun M. Association of CAPN10 haplotype combinations with type 2 diabetes mellitus and metabolic syndrome among Egyptians: pilot study—genotyping of three CAPN10 variants. Egypt J Med Hum Genet. 2022;23:26. https://doi.org/10.1186/s43042-022-00212-0 . Islam H, Masud J, Islam YN. An update on Polycystic Ovary Syndrome (PCOS): diagnosis, risks, etiology, and treatment options revisited. Thesis. Brac University, 2021. Gamboa-Meléndez MA, Huerta-Chagoya A, Moreno-Macías H, Vázquez-Cárdenas P, Ordóñez-Sánchez ML, Rodríguez-Guillén R, et al. Contribution of Common Genetic Variation to the Risk of Type 2 Diabetes in the Mexican Mestizo Population. Diabetes. 2012;61:3314–21. https://doi.org/10.2337/db11-0550 . Mishra KC, Banerjee P. Meta-Analysis of Association of SNP 19 In-del Polymorphism at the CAPN 10 Gene with Type2 Diabetes Mellitus. UTTAR PRADESH J Zool. 2024;45:8–19. https://doi.org/10.56557/upjoz/2024/v45i94017 . Han LY, Wu QH, Jiao ML, Hao YH, Liang LB, Gao LJ, et al. Associations between single-nucleotide polymorphisms (+ 45T > G, + 276G > T, – 11377C > G, – 11391G > A) of adiponectin gene and type 2 diabetes mellitus: a systematic review and meta-analysis. Diabetologia. 2011;54:2303–14. https://doi.org/10.1007/s00125-011-2202-9 . Yako YY, Guewo-Fokeng M, Balti EV, Bouatia-Naji N, Matsha TE, Sobngwi E, et al. Genetic risk of type 2 diabetes in populations of the African continent: A systematic review and meta-analyses. Diabetes Res Clin Pract. 2016;114:136–50. https://doi.org/10.1016/j.diabres.2016.01.003 . Bao W, Hu FB, Rong S, Rong Y, Bowers K, Schisterman EF, et al. Predicting Risk of Type 2 Diabetes Mellitus with Genetic Risk Models on the Basis of Established Genome-wide Association Markers: A Systematic Review. Am J Epidemiol. 2013;178:1197–207. https://doi.org/10.1093/aje/kwt123 . Läll K, Mägi R, Morris A, Metspalu A, Fischer K. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med. 2017;19:322–9. https://doi.org/10.1038/gim.2016.103 . Gloyn AL, Drucker DJ. Precision medicine in the management of type 2 diabetes. Lancet Diabetes Endocrinol. 2018;6:891–900. https://doi.org/10.1016/S2213-8587(18)30052-4 . Ramos-Lopez O. Genotype-based precision nutrition strategies for the prediction and clinical management of type 2 diabetes mellitus. World J Diabetes. 2024;15:142–53. https://doi.org/10.4239/wjd.v15.i2.142 . Fitipaldi H, McCarthy MI, Florez JC, Franks PW. A Global Overview of Precision Medicine in Type 2 Diabetes. Diabetes. 2018;67:1911–22. https://doi.org/10.2337/dbi17-0045 . IJERPH | Free Full-Text. | Transcriptional Profiling and Biological Pathway(s) Analysis of Type 2 Diabetes Mellitus in a Pakistani Population n.d. https://www.mdpi.com/1660-4601/17/16/5866 (accessed April 19, 2024). Chowdhury R, Venkat Narayan M, Zabetian K, Raj A, Tabassum S. Genetic Studies of Type 2 Diabetes in South Asians: A Systematic Overview. Curr Diabetes Rev. 2014;10:258–74. Sarkar P, Chatterjee D, Bandyopadhyay AR. Association of CAPN10 (SNP-19) genetic polymorphism and obesity with T2DM: a study on Bengali Hindu caste population. Int J Diabetes Dev Ctries. 2021;41:37–42. https://doi.org/10.1007/s13410-020-00861-0 . Mtiraoui N, Turki A, Nemr R, Echtay A, Izzidi I, Al-Zaben GS, et al. Contribution of common variants of ENPP1, IGF2BP2, KCNJ11, MLXIPL, PPARγ, SLC30A8 and TCF7L2 to the risk of type 2 diabetes in Lebanese and Tunisian Arabs. Diabetes Metab. 2012;38:444–9. https://doi.org/10.1016/j.diabet.2012.05.002 . Horikoshi M, Mӓgi R, Van De Bunt M, Surakka I, Sarin A-P, Mahajan A, et al. Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation. PLOS Genet. 2015;11:e1005230. https://doi.org/10.1371/journal.pgen.1005230 . Meza-Espinoza JP, Zavala-Rubio JDD, Leal-Ugarte E, Picos-Cárdenas VJ, Contreras-Gutiérrez JA, Madueña-Molina J, et al. Lack of association of the calpain-10 Indel-19 variant with chronic diseases in a Mexican population. BMC Res Notes. 2025;18:135. https://doi.org/10.1186/s13104-025-07184-5 . Rare variant association. studies: considerations, challenges and opportunities | Genome Medicine n.d. https://link.springer.com/article/10.1186/s13073-015-0138-2 (accessed April 19, 2024). Bellary S. Enhanced care to people of South Asian Ethnicity-the United Kingdom Asian Diabetes Study (UKADS). h_md. University of Birmingham; 2010. Islam M, Jafar TH, Wood AR, De Silva NMG, Caulfield M, Chaturvedi N, et al. Multiple genetic variants explain measurable variance in type 2 diabetes-related traits in Pakistanis. Diabetologia. 2012;55:2193–204. https://doi.org/10.1007/s00125-012-2560-y . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 May, 2025 Read the published version in BMC Medical Genomics → Version 1 posted Editorial decision: Revision requested 30 Apr, 2025 Editor assigned by journal 30 Apr, 2025 Reviews received at journal 27 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers invited by journal 14 Apr, 2025 Submission checks completed at journal 13 Apr, 2025 First submitted to journal 13 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6171604","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442710448,"identity":"0bd64922-35c6-4f0e-9460-f906b8ccdc68","order_by":0,"name":"Humaira Farooqi","email":"","orcid":"","institution":"King Edward Medical University","correspondingAuthor":false,"prefix":"","firstName":"Humaira","middleName":"","lastName":"Farooqi","suffix":""},{"id":442710449,"identity":"e8fe30f1-fad3-4afd-aa38-c7a61242f839","order_by":1,"name":"Nakhshab Choudhry","email":"","orcid":"","institution":"King Edward Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nakhshab","middleName":"","lastName":"Choudhry","suffix":""},{"id":442710450,"identity":"f5aee76b-54ff-4b15-b8ec-b524de9619e1","order_by":2,"name":"Muhammad Nabeel Saddique","email":"","orcid":"","institution":"King Edward Medical University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Nabeel","lastName":"Saddique","suffix":""},{"id":442710451,"identity":"006540d4-68c2-4474-bfc2-97674931634c","order_by":3,"name":"Samina Qamar","email":"","orcid":"","institution":"King Edward Medical University","correspondingAuthor":false,"prefix":"","firstName":"Samina","middleName":"","lastName":"Qamar","suffix":""},{"id":442710452,"identity":"0214ec95-b88d-4011-b84c-49950fdc0ef9","order_by":4,"name":"Rehma Dar","email":"","orcid":"","institution":"Services Institute of Medical 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University","correspondingAuthor":true,"prefix":"","firstName":"Ursula","middleName":"Abu","lastName":"Nahla","suffix":""}],"badges":[],"createdAt":"2025-03-06 14:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6171604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6171604/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12920-025-02152-1","type":"published","date":"2025-05-19T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80815551,"identity":"696538fb-ac19-414a-ad4c-06cf372f238f","added_by":"auto","created_at":"2025-04-17 11:00:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2542064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSteps of PCR and genotyping for CAPN-10 polymorphisms (\u003c/strong\u003eSchematic representation of the four-step process used to genotype SNP43 and SNP44: (1) DNA extraction from whole blood, (2) PCR amplification of target regions, (3) enzymatic digestion with NdeI (SNP43) and BstUI (SNP44), and (4) gel electrophoresis for genotype identification based on fragment size\u003cstrong\u003e.)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6171604/v1/b46f59e1aa9c635f3e986f44.jpeg"},{"id":83460030,"identity":"42fb23f4-dcbc-4201-9016-af505efbfef2","added_by":"auto","created_at":"2025-05-26 16:09:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3773882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6171604/v1/3f4856a7-a899-445b-9c58-c7ffae017bb4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Calpain-10 Gene Polymorphisms with Type 2 Diabetes Mellitus: A Case-Control Study from a Tertiary Care Hospital in Pakistan","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is the most prevalent form of diabetes, accounting for approximately 90% of all cases in developed countries. It primarily affects individuals aged 45 years and older. This chronic metabolic disorder is characterized by insulin resistance in peripheral tissues and impaired insulin secretion by pancreatic β-cells, resulting in disrupted glucose homeostasis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To date, over 36 genes have been associated with T2DM, among which the Calpain-10 (CAPN-10) gene has emerged as a notable candidate. CAPN-10, a member of the calpain protease family located on chromosome 2q37.3, plays a pivotal role in glucose metabolism, insulin-stimulated glucose uptake, and glycogen synthesis. Given its biological relevance, CAPN-10 is considered a potential susceptibility gene for T2DM [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCAPN-10 is a gene associated with type 2 diabetes mellitus (T2DM) and is predominantly expressed in insulin-sensitive tissues such as adipose tissue, skeletal muscle, and the liver. Its role in T2DM is linked to its modulation of key components within the insulin signaling pathway, thereby affecting both glucose metabolism and insulin sensitivity. The broader calpain protease family, to which CAPN-10 belongs, has been implicated in the molecular pathogenesis of T2DM and represents a potential target for therapeutic intervention. The functional relevance of CAPN-10 highlights the complex interplay between genetic factors and insulin signaling, contributing to a deeper understanding of glucose homeostasis in T2DM [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eRecent genome-wide association studies (GWAS) have identified over 1,200 genetic loci associated with the pathogenesis of T2DM, underscoring its multifactorial and polygenic nature. In a landmark study, Suzuki et al. (2024) reported 1,289 single nucleotide polymorphisms (SNPs) linked to T2DM risk, highlighting the complexity of its genetic architecture.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Among these, polymorphisms in the CAPN-10 gene\u0026mdash;particularly SNP44 (rs2975760) and SNP43 (rs3792267)\u0026mdash;have been associated with altered gene expression and function in the context of T2DM. These variants contribute to inter-individual disease susceptibility and have shown associations in diverse populations, including Mexican Americans and British/Irish cohorts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Differences in allelic frequencies between diabetic and non-diabetic individuals further support the genetic contribution of CAPN-10 to disease risk. Despite the high burden and increasing prevalence of T2DM in Pakistan, research on CAPN-10 polymorphisms in this population remains limited. This gap presents a critical opportunity to explore population-specific genetic associations. Investigating the frequency and potential role of CAPN-10 variants in the Pakistani population may provide novel insights into T2DM pathophysiology and support the development of targeted prevention and management strategies.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study aims to address the existing research gap by investigating the prevalence of CAPN-10 gene polymorphisms\u0026mdash;rs2975760 (SNP44) and rs3792267 (SNP43)\u0026mdash;in Pakistani patients with T2DM. The objective is to evaluate potential associations between these variants and T2DM susceptibility, thereby contributing to the broader understanding of the disease\u0026rsquo;s genetic etiology. The findings may yield novel insights into the molecular mechanisms underlying T2DM and support the development of genetically informed therapeutic strategies. Given the rising burden of T2DM in Pakistan, this research underscores the significance of CAPN-10 in glucose metabolism and insulin signaling, with implications for advancing personalized medicine in the prevention and management of T2DM.\u003c/p\u003e"},{"header":"2 Material and methods","content":"\u003cp\u003eThis case-control study was reported according to Strengthening the reporting of observational studies in epidemiology (STROBE) checklist for case-control study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis case-control study was conducted in Pathology Department, King Edward Medical University, Lahore and Advance Research Center for Biomedical Sciences (ARCBS)\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 328 participants were included in the study, comprising 164 healthy controls and 164 T2DM cases. The sample size was estimated using a 95% confidence level and a 10% absolute precision, with the expected genotype frequencies of 23% for the 2AA + GA genotypes in T2DM cases and 64% in the control group. This calculation ensured adequate statistical power to detect significant differences between the two groups. Participants were recruited through a non-probability convenient sampling method, with informed consent obtained from all individuals prior to inclusion in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study as approved by the Institutional Review Board (IRB) of King Edward Medical University, Lahore vide letter no. 482/RC/KEMU. Written informed consent was obtained from all participants prior to sample collection, and the study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Participants selection criteria\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e2.4.1 Inclusion criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants included in this study were adults aged 18 years or older, both male and female, who had been previously diagnosed with Type 2 Diabetes Mellitus (T2DM) by a healthcare professional. The diagnosis of T2DM was confirmed based on the American Diabetes Association (ADA) criteria [8], which include fasting plasma glucose \u0026ge;126 mg/dL, 2-hour plasma glucose \u0026ge;200 mg/dL during an oral glucose tolerance test, or HbA1c \u0026ge;6.5%. Controls were recruited from among attendants of diabetic patients at Mayo Hospital. Only individuals who were willing to participate and provided informed consent were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.2 Exclusion criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT2DM patients with any of the following conditions were excluded from the study: 1) Participants with additional metabolic conditions, such as obesity, hyperlipidemia, or metabolic syndrome, which could confound the results of the genetic analysis, were excluded; 2) Patients with chronic liver diseases, including cirrhosis, liver cancer, or viral hepatitis, were excluded to avoid potential interference in glucose metabolism and insulin signaling, which could alter the genetic associations being studied; 3) Individuals diagnosed with CHF were excluded due to the impact of cardiovascular disease on metabolic function, which could affect insulin resistance and glucose regulation; 4) Participants with autoimmune conditions such as rheumatoid arthritis, lupus, or multiple sclerosis were excluded, as these diseases can influence glucose metabolism and insulin sensitivity; 5) Patients with current or previous infections, such as hepatitis C virus (HCV), human immunodeficiency virus (HIV), or any active bacterial or viral infections, were excluded due to their potential impact on insulin resistance and inflammatory markers; 6) \u0026nbsp; Any participant with a history of cancer was excluded to ensure that genetic factors linked to cancer would not interfere with the study of T2DM-specific genetic polymorphisms; 7) To avoid genetic bias, controls were screened to ensure non-relatedness to T2DM cases, based on self-report and verbal family pedigree inquiries. First- and second-degree relatives of cases were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from all participants prior to sample collection. The consent process ensured that participants were fully informed regarding the study\u0026rsquo;s objectives, procedures, and any potential risks. Confidentiality of all personal and medical information was strictly maintained, and data were used exclusively for research purposes.\u003c/p\u003e\n\u003cp\u003eDiagnosed T2DM cases were recruited from the diabetic clinic at Mayo Hospital, Lahore. All cases had been previously diagnosed by a specialist physician according to the American Diabetes Association (ADA) criteria. Healthy control participants were recruited from the attendants of patients, provided they had normal HbA1c levels and no history of metabolic disorders, including T2DM. Both cases and controls underwent identical consent and sampling procedures. Peripheral blood samples were collected from all participants for subsequent genetic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Quantitative Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome of the study was the presence of Type 2 Diabetes Mellitus (T2DM), confirmed through clinical diagnosis by a specialist physician based on the American Diabetes Association (ADA) diagnostic criteria, which include fasting blood glucose levels \u0026ge;126 mg/dL, HbA1c levels \u0026ge;6.5%, or a 2-hour plasma glucose level \u0026ge;200 mg/dL during an oral glucose tolerance test (OGTT). Secondary outcomes included the frequencies of CAPN-10 gene polymorphisms (rs2975760 and rs3792267) among T2DM cases and healthy controls. The exposures analyzed were the CAPN-10 gene polymorphisms, assessed through genotyping techniques. Predictors included the genetic variants rs2975760 and rs3792267, along with demographic factors such as age and gender. Potential confounders were identified as Body Mass Index (BMI), HbA1c levels, fasting blood glucose levels, and histories of metabolic disorders, infectious diseases, or other chronic conditions. Age and gender were also considered potential effect modifiers, as they could influence the association between CAPN-10 polymorphisms and T2DM. All variables were clearly defined to ensure standardization of data collection and analytical procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Data Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was isolated from peripheral blood samples to genotype the CAPN-10 polymorphisms. Approximately 5.0 mL of blood was collected in EDTA-coated tubes, and DNA extraction was performed using a commercially available kit following the manufacturer\u0026rsquo;s protocol. PCR amplification was then conducted using sequence-specific primers targeting the regions flanking SNP43 (rs3792267) and SNP44 (rs2975760). Amplification was carried out in a thermal cycler (T100, Bio-Rad, USA) under standardized cycling conditions: initial denaturation at 95\u0026deg;C, primer annealing at 60\u0026deg;C, and extension at 72\u0026deg;C. The expected amplicon sizes were 245 bp for SNP43 and 166 bp for SNP44. Post-amplification, enzymatic digestion was performed to identify genotypes using restriction fragment length polymorphism (RFLP) analysis. For SNP43, the \u003cstrong\u003eNdeI\u003c/strong\u003e enzyme was used, while \u003cstrong\u003eBstUI\u003c/strong\u003e was used for SNP44. The resulting restriction fragments were indicative of specific genotypes based on known cleavage patterns. \u003cstrong\u003e(Table 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigested products were resolved using gel electrophoresis. Initial PCR products were confirmed using 2.5% agarose gel electrophoresis with DNA gel stain, while post-digestion fragments were analyzed via 10% polyacrylamide gel electrophoresis (PAGE). Gel imaging was performed under UV illumination using a gel documentation system (SYNGENE Gel Doc System, UK), and genotypes were inferred based on the fragment size patterns. This standardized approach ensured the comparability of measurements across both T2DM cases and control groups. While PCR-RFLP is a well-established and cost-effective genotyping method suitable for resource-limited settings, it has lower specificity and sensitivity compared to techniques such as real-time PCR, SNP arrays, or next-generation sequencing. These limitations are acknowledged and should be considered when interpreting the results.\u003c/p\u003e\n\u003cp\u003eSchematic representation of the four-step process used to genotype SNP43 and SNP44: (1) DNA extraction from whole blood, (2) PCR amplification of target regions, (3) enzymatic digestion with NdeI (SNP43) and BstUI (SNP44), and (4) gel electrophoresis for genotype identification based on fragment size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Primer Sequences and Restriction Enzymes Used for the Analysis of CAPN-10 Gene Polymorphisms\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026rsquo;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;3\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCR Product\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRestriction Enzyme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRestriction Product\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSNP-43 (rs3792267)\u003c/p\u003e\n \u003cp\u003eG\u0026agrave;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e gct ggc tgg tga cat cag tgc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e245bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNdeI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG:\u003c/strong\u003e 245\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGA:\u003c/strong\u003e 245, 223, 31\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAA:\u003c/strong\u003e 223, 31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR:\u003c/strong\u003e acc aag tca agg ctt agc ctc acc ttc ata\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSNP-44 (rs2975760)\u003c/p\u003e\n \u003cp\u003eT\u0026agrave;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e gca ggg cgc tca cgc ttg ccg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e166 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003eBstUl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTT:\u003c/strong\u003e 166\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTC:\u003c/strong\u003e 166, 145, 21\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCC:\u003c/strong\u003e145, 21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR:\u003c/strong\u003e gca tgg ccc cct ctc tga ttc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNP: Single Nucleotide Polymorphism; PCR: Polymerase Chain Reaction; bp: Base Pair; NdeI: A restriction enzyme used for digesting DNA at specific sites; BstUI: A restriction enzyme used for DNA fragment analysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize demographic and clinical characteristics. Continuous variables (e.g., age, BMI, HbA1c, fasting glucose) were expressed as mean \u0026plusmn; standard deviation (SD), and categorical variables (e.g., sex, genotype) were presented as frequencies and percentages. The association between CAPN-10 gene polymorphisms\u0026mdash;SNP44 (rs2975760) and SNP43 (rs3792267)\u0026mdash;and T2DM was evaluated using chi-square tests under various genetic models: dominant (e.g., TC+CC vs. TT), recessive (CC vs. TC+TT), and additive. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Genotypic distributions were assessed for Hardy-Weinberg equilibrium (HWE) in the control group using the chi-square goodness-of-fit test; a p-value \u0026gt; 0.05 indicated conformity with HWE. Bonferroni correction was applied to account for multiple comparisons, and adjusted significance thresholds were reported accordingly. Subgroup analyses were performed to assess interactions between CAPN-10 variants and demographic or clinical variables, including age, sex, and BMI. Potential confounders were controlled using multivariate logistic regression models adjusting for age, gender, BMI, and relevant clinical parameters.\u003c/p\u003e\n\u003cp\u003eMissing data were addressed using multiple imputation to enhance statistical robustness. Participants with incomplete key variables were excluded from subgroup analyses but retained in primary analyses if core data were available. Cases and controls were matched on age and gender to reduce confounding bias.Sensitivity analyses were conducted by excluding outliers and re-evaluating models with and without imputed data to validate the robustness of findings. All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eThis study assessed the association between two key polymorphisms,\u0026nbsp;\u003cstrong\u003eSNP44 (rs2975760)\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eSNP43 (rs3792267)\u003c/strong\u003e, of the\u0026nbsp;\u003cstrong\u003eCAPN-10 gene\u003c/strong\u003e with\u0026nbsp;\u003cstrong\u003eType 2 Diabetes Mellitus (T2DM)\u003c/strong\u003e in a cohort of Pakistani participants. The sample included 164 T2DM patients and 164 healthy controls. The results are presented below in terms of demographic, clinical characteristics, and genotypic frequencies.\u003c/p\u003e\n\u003ch4\u003e3.1 Demographics and Clinical Characteristics\u003c/h4\u003e\n\u003cp\u003eThe mean age of T2DM cases was 57.19 ± 8.22 years, significantly higher than the controls (53.93 ± 6.32 years, \u003cem\u003ep\u003c/em\u003e = 0.0003). BMI was also significantly higher in cases (27.5 ± 4.1 kg/m²) compared to controls (25.8 ± 3.9 kg/m², \u003cem\u003ep\u003c/em\u003e=0.0306). T2DM cases had significantly elevated HbA1c levels (183.95 ± 51.54) compared to controls (175.06 ± 33.05, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001). Similarly, fasting blood glucose levels were higher in cases (88.72 ± 23.1 mg/dL) compared to controls (82.61 ± 20.6 mg/dL, \u003cem\u003ep\u003c/em\u003e = 0.01). Gender distribution did not differ significantly between groups, with males comprising 41.4% of cases and 37.2% of controls (\u003cem\u003ep\u003c/em\u003e = 0.3461). \u003cstrong\u003e(Table 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The demographic and clinical characteristics of the T2DM cases and healthy controls\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT2DM cases (\u003cem\u003en\u003c/em\u003e = 165)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy controls (\u003cem\u003en\u003c/em\u003e = 164)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41–82 (57.19\u0026nbsp;±\u0026nbsp;8.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41–60 (53.93\u0026nbsp;±\u0026nbsp;6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e(Mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.5\u0026nbsp;±\u0026nbsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.8\u0026nbsp;±\u0026nbsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.0306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBA1c\u003c/p\u003e\n \u003cp\u003e(Mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183.95\u0026nbsp;±\u0026nbsp;51.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e175.06\u0026nbsp;±\u0026nbsp;33.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting blood glucose level (mg/dl)\u003c/p\u003e\n \u003cp\u003e(Mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88.72\u0026nbsp;±\u0026nbsp;23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.61\u0026nbsp;±\u0026nbsp;20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMales/females (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68 (41.4%) / 96 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61 (37.2%) / 103 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.3461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eT2DM: Type 2 diabetes mellitus; SD: Standard deviation; BMI: Body-mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Genotypic Distribution\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SNP44 (rs2975760)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between SNP44 (rs2975760) and T2DM was evaluated under multiple genetic models, including dominant (TC + CC vs. TT), heterozygous (TC vs. TT + CC), and recessive (CC vs. TC + TT) contrasts. Genotype distributions among controls were in Hardy-Weinberg equilibrium (p = 0.216), indicating appropriate population structure for analysis. The odds ratio (OR) for the dominant model (TC + CC vs. TT) was 1.07 (95% CI: 0.86–1.37), with an unadjusted p-value of 0.752 and a Bonferroni-adjusted p-value of 1.000. For the heterozygous model (TC vs. TT + CC), the OR was 1.10 (95% CI: 0.63–1.43), with an adjusted p-value of 1.000. Under the recessive model (CC vs. TC + TT), the OR was 1.43 (95% CI: 0.70–3.03), with an unadjusted p-value of 0.063 and a Bonferroni-adjusted p-value of 0.379. Although some comparisons indicated elevated odds ratios, none of the associations were statistically significant following correction for multiple testing. These findings suggest that SNP44 (rs2975760) is not significantly associated with T2DM susceptibility in this population.\u0026nbsp;\u003cstrong\u003e(Table 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Genotypic Distribution of SNP44 (rs2975760)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBonferroni-adjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC + CC vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.86–1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC vs. TT + CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63–1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCC vs. TC + TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70–3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch1\u003e3.3 Genotypic variations of SNP43 (rs3792267)\u003c/h1\u003e\n\u003cp\u003eFor SNP43 (rs3792267), genotype distributions among controls also conformed to Hardy-Weinberg equilibrium (p = 0.174). Association analyses were conducted using dominant (GA + AA vs. GG) and recessive (AA vs. GA + GG) models. Under the dominant model, the OR for T2DM was 1.54 (95% CI: 0.77–1.58), with an unadjusted p-value of 0.740 and a Bonferroni-adjusted p-value of 1.000. The recessive model yielded an OR of 2.05 (95% CI: 1.14–3.68), with an unadjusted p-value of 0.403 and an adjusted p-value of 1.000. \u003cstrong\u003e(Table 4)\u003c/strong\u003e Despite the elevated point estimate for the AA genotype in the recessive model, no statistically significant associations were observed after correction for multiple comparisons. Thus, these results do not support a significant role of SNP43 (rs3792267) in T2DM predisposition within this cohort. The complete genotypic and allelic distributions for both SNPs are presented in \u003cstrong\u003eTable 5.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Genotypic variations of SNP43 (rs3792267)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBonferroni-adjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGA + AA vs. GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77–1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. GG + GA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14–3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Genotypic Distribution and Allelic Frequencies (Final sample: 164 T2DM cases, 164 healthy controls)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT2DM Cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP44 (rs2975760)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP43 (rs3792267)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study examined the association between SNP44 (rs2975760) and SNP43 (rs3792267) polymorphisms of the \u003cem\u003eCAPN-10\u003c/em\u003e gene and type 2 diabetes mellitus (T2DM) in a Pakistani population. Significant differences in age, BMI, HbA1c, and fasting blood glucose levels between T2DM cases and healthy controls were observed, consistent with established metabolic markers of the disease and supporting the validity of the sample. Gender distribution did not differ significantly between groups, indicating a balanced representation of male and female participants. Although the TC and CC genotypes of SNP44, and the GA and AA genotypes of SNP43, were more frequent among T2DM cases, no statistically significant associations were found following Bonferroni correction. These findings suggest that, in this population, SNP44 and SNP43 are not independently associated with increased T2DM risk, despite prior reports of CAPN-10 involvement in glucose metabolism and insulin signaling\u0026nbsp;[9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings underscore the importance of population-specific genetic research. Although \u003cem\u003eCAPN-10\u003c/em\u003e polymorphisms have been linked to T2DM in certain populations, the current study suggests that SNP44 and SNP43 may not significantly contribute to T2DM susceptibility in the Pakistani population [10]. It is plausible that gene–environment interactions—such as region-specific lifestyle, dietary patterns, and environmental exposures—modulate the phenotypic expression of these variants, thereby diminishing their detectable effect in this cohort.\u003c/p\u003e\n\u003cp\u003ePrevious studies investigating the association of \u003cem\u003eCAPN-10\u003c/em\u003e SNPs with T2DM across different populations have reported inconsistent results, reflecting the complex nature of genetic predisposition to the disease. While associations between SNP44 and SNP43 and T2DM have been observed in Hispanic and select Asian populations, studies in European cohorts have failed to demonstrate significant associations [11].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEarly studies conducted in European populations provided initial evidence that certain \u003cem\u003eCAPN-10\u003c/em\u003e polymorphisms may influence susceptibility to T2DM. For example, the T allele of SNP44 (rs2975760) was associated with an increased risk of T2DM in cohorts of Spanish and Finnish ancestry. However, subsequent investigations in other European populations produced inconsistent findings, with several studies failing to replicate this association [12]. These discrepancies highlight the complexity of genetic contributions to T2DM and underscore the need for further investigation across diverse ethnic groups [10]\u003c/p\u003e\n\u003cp\u003eBeyond Europe, \u003cem\u003eCAPN-10\u003c/em\u003e polymorphisms have also been examined in Asian populations. While some studies have reported associations between SNP44 and increased T2DM risk in Chinese, Japanese, and Korean cohorts, others have not found significant associations, reflecting inter-population variability. Similar inconsistency has been observed in studies of SNP43 (rs3792267), where associations with T2DM risk have been identified in certain Asian subgroups, but not in others[12]. These mixed results reinforce the importance of conducting population-specific research to better understand the genetic architecture of T2DM across different ethnic backgrounds.\u003c/p\u003e\n\u003cp\u003eResearch in Hispanic/Latino populations has also yielded inconsistent results regarding the role of \u003cem\u003eCAPN-10\u003c/em\u003e polymorphisms in T2DM. While some studies have reported associations between SNP44 and T2DM risk in Mexican American and Puerto Rican populations, others have found no significant correlations. Similarly, investigations of SNP43 in these cohorts have demonstrated both positive and negative findings [13,14] Studies conducted in African and Middle Eastern populations have been limited and inconclusive. While some African cohorts have shown significant associations between \u003cem\u003eCAPN-10\u003c/em\u003e variants and T2DM, other studies have failed to replicate these findings [15]\u003c/p\u003e\n\u003cp\u003eVariation in allelic frequencies and genotype–phenotype associations of \u003cem\u003eCAPN-10\u003c/em\u003e polymorphisms across populations is particularly noteworthy, as it may provide insight into population-specific genetic risk profiles for T2DM. Allelic frequencies represent the distribution of genetic variants within a population and are shaped by evolutionary, demographic, and genetic factors[16]. Differences in these frequencies may reflect historical patterns of migration and admixture. Furthermore, genotype associations can identify specific alleles that may confer increased or decreased risk of T2DM [17].\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe identification of genetic variants associated with T2DM susceptibility offers significant potential for the advancement of personalized medicine. Incorporating genetic markers into clinical risk stratification could enable earlier identification of high-risk individuals and support targeted preventive strategies. Moreover, pharmacogenomic insights derived from genotype data may inform more precise therapeutic decisions, optimizing treatment efficacy and minimizing adverse effects [18,19].\u003c/p\u003e\n\u003cp\u003eFrom a public health perspective, characterizing the distribution and impact of \u003cem\u003eCAPN-10\u003c/em\u003e variants in diverse populations is critical to reducing the global burden of T2DM. Such knowledge can inform the development of population-specific screening and prevention programs, particularly in high-risk regions [20]. The integration of genetic data into clinical care may enhance early diagnosis, improve disease management, and ultimately contribute to better health outcomes at the population level [21].\u003c/p\u003e\n\u003cp\u003eDespite notable progress, significant gaps remain in the genetic understanding of T2DM. Genetic heterogeneity across populations and limited knowledge of gene–environment interactions hinder the identification of universally applicable markers and individualized prevention strategies [22]. While research has largely focused on common variants with modest effects, rare variants with potentially greater impact remain underexplored due to technical and financial constraints.[10,23,24].\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eCAPN10\u003c/em\u003e gene encodes calpain-10, a calcium-dependent cysteine protease involved in various cellular processes, including insulin secretion and action. Polymorphisms such as SNP-43 (rs3792267) and SNP-19 (rs3842570) have been studied for their potential functional impact. For instance, SNP-19, a 32-base pair insertion/deletion in intron 6, may influence gene expression or alternative splicing, thereby affecting insulin sensitivity [25]. Associations between these polymorphisms and T2DM have been reported in diverse populations. In a Bangladeshi cohort, the 2R/3R genotype of SNP-19 was significantly associated with increased T2DM risk [25]. Similarly, in a Tunisian Arab population, the 2R allele of SNP-19 and the 111 haplotypes (comprising SNP-43, SNP-19, and SNP-63) were linked to higher T2DM susceptibility [26]. However, these associations have not been consistently replicated across all ethnic groups, reflecting the influence of genetic heterogeneity and underscoring the importance of population-specific research to elucidate the role of \u003cem\u003eCAPN10\u003c/em\u003e polymorphisms in T2DM pathogenesis.[27]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCAPN-10\u003c/em\u003e has emerged as a biologically plausible candidate gene, particularly in Mexican American populations where certain polymorphisms have been linked to T2DM risk [28] However, data on \u003cem\u003eCAPN-10\u003c/em\u003e variants in the Pakistani population are scarce, despite the country's escalating T2DM burden [29]. Addressing this gap may enhance our understanding of T2DM pathogenesis and support tailored interventions. Moreover, such research has global implications, contributing to the broader effort to map genetic diversity and advance precision medicine in diabetes care[24,30,31]. Population-specific differences in the association between \u003cem\u003eCAPN10\u003c/em\u003e polymorphisms and T2DM have been observed globally, with varying results reported in Hispanic, Asian, African, and European cohorts\u003cstrong\u003e\u0026nbsp;(Table 6)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Summary of \u003cem\u003eCAPN10\u003c/em\u003e SNP Associations with Type 2 Diabetes Mellitus (T2DM) Across Populations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs Studied\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation with T2DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePakistani (current study)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44, SNP43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo significant association [9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44, SNP43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed results (positive in some studies) [13]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePuerto Rican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44, SNP43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed results [14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSpanish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive (initial studies) [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinnish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive (initial studies) [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed results [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJapanese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed results [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKorean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed results [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEuropean (general)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP44, SNP43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo consistent association [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBangladeshi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive (2R/3R genotype) [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTunisian Arab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP19, SNP43, SNP63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive (2R allele, 111 haplotype) [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAPN10 (varied)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed/limited evidence [15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAPN10 (varied)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited/inconclusive [15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp id=\"_Toc188123696\"\u003e\u003cstrong\u003eStudy Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the sample size of 328 participants may have limited the statistical power to detect modest genetic associations, potentially contributing to the non-significant findings. Second, the study was conducted in a single Pakistani population, which may limit the generalizability of results to other ethnic groups. Third, key environmental and lifestyle factors—such as diet, physical activity, socioeconomic status, and degree of urbanization—were not assessed, despite their known influence on T2DM risk. Another limitation is the absence of a formal power analysis at the design stage. While the sample size of 328 participants offers a reasonable basis for preliminary analysis, it may be underpowered to detect small to moderate genetic effects typically observed in complex diseases such as T2DM. \u0026nbsp;Lastly, the analysis was restricted to two polymorphisms (SNP44 and SNP43) within the \u003cem\u003eCAPN-10\u003c/em\u003e gene, excluding other potentially relevant genetic variants. Future research should incorporate larger, multi-ethnic cohorts and a broader panel of genetic markers to comprehensively elucidate the genetic architecture of T2DM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNo statistically significant association was observed between \u003cem\u003eCAPN-10\u003c/em\u003e SNP44 or SNP43 polymorphisms and T2DM in this Pakistani cohort. While minor genotype distribution trends were noted, these findings were not supported after correction for multiple testing. Future research incorporating larger, multi-ethnic samples and genome-wide analyses is warranted to elucidate the genetic basis of T2DM more comprehensively.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit Author Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH.F:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Supervision, Writing - Original Draft, Writing - Review \u0026amp; Editing;\u003cstrong\u003e\u0026nbsp;M.N.S\u003c/strong\u003e: Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing; \u003cstrong\u003eN.C.:\u003c/strong\u003e Methodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing; \u003cstrong\u003eS.Q:\u0026nbsp;\u003c/strong\u003eMethodology, Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing;\u003cstrong\u003e\u0026nbsp;R.D\u003c/strong\u003e: Software, Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing; \u003cstrong\u003eS.K:\u0026nbsp;\u003c/strong\u003e Validation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing; \u003cstrong\u003eH.J:\u0026nbsp;\u003c/strong\u003eValidation, Formal analysis, Investigations, Data curation, Visualization, Writing - Original Draft, Writing - Review \u0026amp; Editing; \u003cstrong\u003eA.J.G:\u0026nbsp;\u003c/strong\u003eWriting - Original Draft, Writing - Review \u0026amp; Editing; \u003cstrong\u003eU.A.N:\u0026nbsp;\u003c/strong\u003eWriting - Original Draft, Writing - Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of generative AI and AI-assisted technologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChatGPT-4o (OpenAI) was used solely for improving the grammar, readability, and clarity of the manuscript. No AI tools were used for data analysis, interpretation, or generation of scientific content. All intellectual content and scientific conclusions were formulated by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of King Edward Medical University, Lahore (Approval No. 482/RC/KEMU). Written informed consent was obtained from all participants prior to enrollment. Participants were informed of the study's objectives, procedures, potential risks, and their right to withdraw at any time without consequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent for the publication of anonymized data collected during the study. No personally identifiable information is presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request, in compliance with institutional and ethical guidelines to protect participant confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the support of the Advance Research Center for Biomedical Sciences, King Edward Medical University, Lahore, Pakistan, for providing laboratory facilities and assistance with data collection and analysis.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNowakowska M, Zghebi SS, Ashcroft DM, Buchan I, Chew-Graham C, Holt T, et al. 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University of Birmingham; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam M, Jafar TH, Wood AR, De Silva NMG, Caulfield M, Chaturvedi N, et al. Multiple genetic variants explain measurable variance in type 2 diabetes-related traits in Pakistanis. Diabetologia. 2012;55:2193\u0026ndash;204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-012-2560-y\u003c/span\u003e\u003cspan address=\"10.1007/s00125-012-2560-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, CAPN-10 gene polymorphisms, Single nucleotide polymorphisms (SNPs), Genetic susceptibility, Pakistani population","lastPublishedDoi":"10.21203/rs.3.rs-6171604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6171604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Type 2 diabetes mellitus (T2DM) is a major public health challenge, with rising prevalence in low- and middle-income countries such as Pakistan. Genetic susceptibility plays a critical role in its pathogenesis. Calpain-10 (CAPN-10), a gene implicated in insulin secretion and glucose homeostasis, has been studied for its potential involvement in T2DM. This study aimed to evaluate the association of CAPN-10 polymorphisms—SNP44 (rs2975760) and SNP43 (rs3792267)—with T2DM in a Pakistani cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis case-control study included 164 T2DM patients and 164 healthy controls (mean age ± SD: 57.2 ± 8.2 vs. 53.9 ± 6.3 years; age range: 41–82 years). The male-to-female ratio was 41.4% to 58.6% in cases and 37.2% to 62.8% in controls. Participants were enrolled using non-probability convenience sampling. Genomic DNA was extracted from whole blood, and genotyping of CAPN-10 SNPs (rs3792267 and rs2975760) was performed using PCR-RFLP. Genotype distributions were assessed for Hardy-Weinberg equilibrium. Associations with T2DM were evaluated using odds ratios (ORs) and 95% confidence intervals (CIs) via logistic regression. Chi-square tests were used for categorical comparisons, with p \u0026lt; 0.05 considered statistically significant. Analyses were conducted using SPSS version 26.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFor SNP44, no significant association with T2DM was observed under dominant, heterozygous, or recessive models after Bonferroni correction (adjusted p \u0026gt; 0.05). Similarly, SNP43 showed no statistically significant association with T2DM in either dominant or recessive models (adjusted p \u0026gt; 0.05), although the AA genotype appeared more frequently among T2DM cases. These findings suggest no significant role of CAPN-10 polymorphisms in T2DM susceptibility in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: CAPN-10 polymorphisms SNP44 and SNP43 showed no significant association with T2DM in this population, suggesting limited predictive value for disease susceptibility.\u003c/p\u003e","manuscriptTitle":"Association of Calpain-10 Gene Polymorphisms with Type 2 Diabetes Mellitus: A Case-Control Study from a Tertiary Care Hospital in Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 11:00:51","doi":"10.21203/rs.3.rs-6171604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-30T05:18:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T04:43:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T13:44:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T08:04:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T21:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329097485850584131313767442477795663067","date":"2025-04-16T12:14:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195486283782640907165663311415930384358","date":"2025-04-16T11:44:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98467128658324968970167598731676605994","date":"2025-04-16T11:44:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123516632665517212644054543613014245101","date":"2025-04-14T12:26:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-14T11:52:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-14T01:18:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2025-04-13T11:58:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d60d786-1c24-44ff-a608-3df553badc45","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:02:01+00:00","versionOfRecord":{"articleIdentity":"rs-6171604","link":"https://doi.org/10.1186/s12920-025-02152-1","journal":{"identity":"bmc-medical-genomics","isVorOnly":false,"title":"BMC Medical Genomics"},"publishedOn":"2025-05-19 15:57:05","publishedOnDateReadable":"May 19th, 2025"},"versionCreatedAt":"2025-04-17 11:00:51","video":"","vorDoi":"10.1186/s12920-025-02152-1","vorDoiUrl":"https://doi.org/10.1186/s12920-025-02152-1","workflowStages":[]},"version":"v1","identity":"rs-6171604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6171604","identity":"rs-6171604","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}