Polygenic risk scores for cluster of newly diagnosed type 2 diabetes: genetic insights and clinical implications

Polygenic risk scores for cluster of newly diagnosed type 2 diabetes: genetic insights and clinical implications | 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 Polygenic risk scores for cluster of newly diagnosed type 2 diabetes: genetic insights and clinical implications Watip Tangjittipokin, Sarocha Suthon, Nipaporn Teerawattanapong, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6268252/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Type 2 diabetes (T2D) is a heterogeneous disease that can be distinguished at newly diagnosis by variations in clinical presentation, disease trajectory, therapeutic response, and risk of complications. Its global prevalence is increasing, particularly among Asian populations such as Thais, due to unique genetic and environmental factors. Thai patients with newly diagnosed T2D have been classified into four clusters based on standard clinical parameters. However, the polygenic basis underlying these distinct phenotypes remains unclear. In this study, we investigated the association between polygenic risk score (PRS) models and T2D in 680 Thai participants. Of these, 487 were T2D patients in four clusters, and 193 were nondiabetic controls. Genotyping was performed, and we calculated PRS models using data from the PGScatalog. Five PRS models significantly differentiated T2D from controls, with PGS000804 displaying the strongest predictive power. Two PRS models (PGS000804 and PGS003402) showed an inverse correlation with age at diagnosis. Moreover, eight genetic loci (rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, rs3132469) were significantly associated with both T2D and age at diagnosis. Among these loci, the alternative allele of rs2216063 (G/A), rs9358356 (T/C), and rs9472138 (C/T) conferred a lower T2D risk and were positively associated with older age at diagnosis. Individuals with the GTC/GTC genotype at these three loci developed diabetes approximately 10 years earlier than those with other genotypes. Our findings underscore the utility of PRS models in refining T2D subtypes and promoting precision medicine in the Thai population. Cluster Polygenic risk score SNP Thais Type 2 diabetes Figures Figure 1 Figure 2 Figure 3 Introduction Type 2 diabetes (T2D) is a complex, heterogeneous metabolic disorder characterized by chronic hyperglycemia arising from defects in insulin secretion, insulin action, or both (American Diabetes 2009 ). It represents a major global health challenge due to its substantial morbidity and mortality, largely driven by associated cardiovascular diseases, kidney failure, and other severe complications (DeFronzo et al. 2015 ). The prevalence of T2D is increasing worldwide, with a pronounced impact on Asian populations, including Thailand, where distinct genetic and environmental factors influence the disease course (Zheng et al. 2018 ). Evidence of T2D heterogeneity can be observed early in its clinical course among both Caucasian and Asian populations (Ahlqvist et al. 2018 ; Mansour Aly et al. 2021 ; Wang et al. 2021 ). Recently, we identified four distinct clusters of newly diagnosed Thai T2D patients using routine clinical parameters, revealing variability in clinical manifestations, disease progression, treatment responses, and complications (Preechasuk et al. 2022 ). Because T2D is polygenic, involving numerous genetic loci (Udler 2019 ), it is plausible that these loci influence disease risk differently across various T2D subgroups. In this context, the polygenic risk score (PRS) consolidates the effects of multiple single nucleotide polymorphisms (SNPs) and other genetic variants into a single measure of genetic predisposition. This unified metric provides critical insights into the genetic architecture of T2D and supports personalized treatment decisions based on individual genomic profiles (Imamura and Maeda 2024 ). Given the increasing prevalence of T2D and its diverse genetic and environmental determinants, this study aims to identify PRS models associated with each cluster of newly diagnosed T2D in the Thai population. By integrating genetic data with clinical variables, the study strives to advance precision medicine and mitigate complications in this high-risk demographic. Material and methods Participants A total of 680 participants were recruited for this study. Informed consent was obtained from all individual participants included in the study. The nondiabetic control group comprised 193 individuals older than 30 years, with no family history of diabetes, fasting plasma glucose < 100 mg/dL, and glycated hemoglobin < 5.7%. The remaining 487 participants with T2D were diagnosed according to the American Diabetes Association 2024 criteria (American Diabetes Association Professional Practice 2024). They were categorized into four clusters, as previously described (Preechasuk et al. 2022 ), namely, mild age-related diabetes (MARD, n = 220), mild obesity-related diabetes (MOD, n = 119), metabolic syndrome diabetes (n = 55), and severe insulin-deficient diabetes (SIDD, n = 93). DNA isolation and genotyping Genomic DNA was isolated using the FlexiGene DNA Kit (Qiagen GmbH, Hilden, Germany). An Axiom PangenomiX Array Kit with the Axiom 2.0 Assay (Life Technologies, Foster City, CA, USA) was used to detect genetic variants across the genome. Genotype data were merged, and quality control procedures excluded positions with a missing rate ≥ 5% or a minor allele frequency (MAF) ≤ 0.05. PRS database and calculation PRSs were obtained from the PGScatalog using the Mondo Disease Ontology (MONDO) ID “[MONDO:0005148] type 2 diabetes mellitus.” This identifier is linked to 92 PRS models. Each individual’s genetic score (ii) was calculated by summing the products of the position coefficients and the genotype dosage for each variant (0/0 = 0, 0/1 = 1, 1/1 = 2), as shown in Eq. 1. $$\:{PRS}_{i}={\sum\:}_{j}^{M}{\widehat{\beta\:}}_{j}\times\:{dosage}_{ij}$$ Equation 1 Calculation of the genetic score for all MM positions. Each term is the product of the position coefficient and the genotype dosage. Statistical analysis All statistical analyses were conducted using R statistical software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria). Logistic regression was employed to develop predictive models, and model performance was assessed using McFadden’s R-squared. Multiple comparisons were corrected with the Benjamini‒Hochberg method (false discovery rate, FDR), using an FDR threshold of < 0.05. Linear regression was then applied to examine how well these models predicted the age at diagnosis. In addition, both logistic and linear regression analyses were performed to evaluate the associations between each genetic variant and two outcomes: the presence of T2D and the age at diagnosis. Results PRS associated with Thai T2D patients We first examined which PRS models from the database were suitable for Thai newly diagnosed T2D patients. Multiple logistic regression analyses revealed that five PRS models significantly distinguished T2D patients from nondiabetic individuals in the Thai population (Table 1 ). Among these models, PGS000804 had the highest McFadden R-squared value and showed significant differences between the patient and control groups in the 1st, 3rd, 9th, and 10th deciles. Individuals with PRS values in the 1st and 3rd deciles had 0.41 (0.24, 0.7) and 0.55 (0.32, 0.95) times the odds of being diabetic compared with the control group. Meanwhile, those in the 9th and 10th deciles had increased odds of T2D by 2.48 (1.19, 5.79) and 2.8 (1.3, 6.92), respectively (Supplementary Fig. S1 A–C and Supplementary Table S1 ). Table 1 Polygenic risk scores associated with type 2 diabetes in the Thai population, including model estimates and performance metrics PGSid Estimate SE Z-score McFadden R 2 AUC p value FDR Population PGS000804 1.17 0.23 5.02 0.0851 0.6245 < 0.0001 < 0.0001 Multiethnic [1] PGS003091 16.43 3.5 4.7 0.0808 0.6189 < 0.0001 0.0002 US and UK Caucasian [2] PGS000125 0.08 0.02 3.77 0.0699 0.5916 0.00016 0.0101 US Hispanic/Latino [3] PGS000855 0.29 0.08 3.77 0.0699 0.5774 0.00016 0.01 Swedish [4] PGS003402 0.16 0.05 3.62 0.0687 0.5941 0.00029 0.0175 South Asian [5] Abbreviations AUC, area under the curve; FDR, false discovery rate; SE, standard error Because the timing of T2D diagnosis can affect disease progression, treatment outcomes, and the risk of complications (Nanayakkara et al. 2021 ), we further investigated associations between these PRS models and the age at diagnosis (Table 2 ). Two models—PGS000804 and PGS003402—were significantly inversely correlated with age at diabetes diagnosis in the overall group of T2D patients. When analyzed by subgroup, the SIDD cluster showed a significant inverse correlation between PGS000125 and PGS000804 and age at diagnosis. In addition, PGS000804 demonstrated a trend toward an inverse correlation with age at diagnosis in the MARD cluster. Table 2 Correlation between genetic scores and age at diabetes diagnosis across type 2 diabetes subtypes in the Thai population PGSid All types MARD SIDD MSD MOD r p value r p value r p value r p value r p value PGS000125 -0.049 0.292 0.021 0.765 -0.214 0.044 -0.072 0.609 -0.021 0.824 PGS000804 -0.160 < 0.001 -0.130 0.060 -0.244 0.021 -0.212 0.127 -0.009 0.927 PGS000855 -0.027 0.557 -0.096 0.166 0.117 0.273 -0.197 0.157 0.130 0.161 PGS003091 -0.025 0.594 0.061 0.379 0.048 0.657 -0.197 0.158 -0.087 0.347 PGS003402 -0.130 0.005 -0.080 0.249 -0.070 0.512 -0.137 0.330 -0.017 0.858 Abbreviations MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; MSD, metabolic syndrome diabetes; r, Pearson correlation coefficient; SIDD, severe insulin-deficient diabetes Risk loci and SNPs associated with T2D and age at diagnosis To determine which genomic positions were associated with T2D and age at diagnosis, we performed logistic regression for each position included in PGS000804 and PGS003402 (total of 746 positions) against diabetes status. We then applied linear regression to assess relationships with age at diagnosis (Supplementary Fig. S2A). This analysis identified eight positions—rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, and rs3132469—that were significantly associated with both T2D and age at diagnosis (FDR < 0.05; Supplementary Fig. S2B). These eight SNPs showed an inverse relationship with T2D (Table 3 ). While the other SNPs were negatively correlated with age at diabetes diagnosis, the SNPs rs2216063 (G/A), rs9358356 (T/C), and rs9472138 (C/T) were positively correlated (Table 3 and Supplementary Table S2). This indicated that the alternative allele has a protective effect, which is uncommonly found in Thai T2D population and is associated with an older age at T2D diagnosis. The effect of each SNP allele on age at diagnosis is shown in Fig. 1 A–C. Table 3 Risk loci associated with type 2 diabetes and age at diagnosis, including odds ratios, beta estimates, and p values. Position ID SNP Mapped gene(s) Type 2 diabetes Age at diagnosis Direction Odds ratio p value FDR Beta estimation p value FDR T2D AaD 16_54353172_G/A rs2216063 IRX3-LINC02140 0.4 (0.37,0.44) 7.02E-27 5.72E-24 3.73 (3.18, 4.28) 4.38E-11 3.55E-08 - ++ 6_20667151_T/C rs9358356 CDKAL1 0.62 (0.56, 0.68) 4.14E-07 3.29E-04 3.46 (2.65, 4.27) 2.21E-05 0.017 - + 6_43844025_C/T rs9472138 VEGFA-LINC02537 0.2 (0.17, 0.23) 5.78E-24 4.69E-21 6.38 (4.61, 8.15) 0.0003 0.27 -- +++ 9_95033139_G/A rs6479591 AOPEP 0.34 (0.32, 0.37) 4.53E-39 3.69E-36 -6.97 (-7.57, -6.37) 1.43E-27 1.17E-24 - -- 9_122927415_C/T rs10818763 ZBTB26 0.41 (0.38, 0.44) 1.91E-26 1.55E-23 -6.21 (-6.84, -5.58) 8.84E-21 7.20E-18 - -- 12_65858514_A/G rs189339 HMGA2-AS1, HMGA2 0.42 (0.38, 0.45) 2.64E-26 2.15E-23 -5.87 (-6.47, -5.27) 1.58E-20 1.29E-17 - -- 8_8863963_G/A rs4382480 MFHAS1 0.48 (0.44, 0.54) 6.48E-13 5.26E-10 -4.18 (-4.8, -3.56) 3.97E-11 3.22E-08 -- - 6_31488790_A/G rs3132469 MICB-DT 0.17 (0.13, 0.23) 8.53E-09 6.88E-06 -4.48 (-5.1, -3.86) 2.07E-12 1.68E-09 --- -- Abbreviations AaD, age at diagnosis; FDR, false discovery rate; ID, identifier; T2D, type 2 diabetes Moreover, Pearson’s correlation analysis indicated a significant relationship between the combined alternate allele dosages at these three loci (wild type = 0, heterozygous = 1, homozygous = 2) and age at diagnosis. Notably, we found the combination for only 4 out of the 6 possible forms in our population ( p < 0.0001, r = 0.31, 95% CI = 0.23‒0.39; Fig. 1 D). SNP allele frequencies across T2D clusters We analyzed allele frequencies across the T2D clusters. The nondiabetic control group shared similar frequencies with the MARD and MOD clusters for rs2216063(G/A) and rs9358356(T/C). In contrast, rs9472138(C/T) showed a significant difference between the control group and the MARD and MOD clusters (Fig. 2 ). Both the MOD and SIDD clusters had significantly different allele frequencies from the control group at all three SNPs. Notably, the SIDD cluster lacked alternate variants for these SNPs (Table 4 ). This finding suggests a unique genetic profile for the SIDD cluster. Table 4 Allele frequencies of key single nucleotide polymorphisms in different Thai type 2 diabetes clusters and controls Control (N = 178) Type 2 diabetes (N = 471) MARD (N = 211) MSD (N = 53) MOD (N = 188) SIDD (N = 89) SNP A1 A2 A1 A2 AF A1 A2 AF A1 A2 AF A1 A2 AF A1 A2 AF χ 2 p value rs2216063 G A 79 277 0.222 117 305 0.277 31 75 0.292 172 64 0.729 178 0 1 327.4 < 0.001 rs9358356 T C 243 113 0.683 280 142 0.664 71 35 0.67 209 27 0.886 178 0 1 107.5 < 0.001 rs9472138 C T 292 64 0.82 388 34 0.919 106 0 1 236 0 1 178 0 1 43.5 < 0.001 Abbreviations MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; MSD, metabolic syndrome diabetes; SIDD, severe insulin-deficient diabetes; SNP, single nucleotide polymorphism We also examined the frequency of individuals homozygous for the GTC/GTC genotype at rs2216063(G/A), rs9358356(T/C), and rs9472138(C/T) in each cluster (Supplementary Table S3). Survival analysis indicated that patients carrying GTC/GTC at these loci developed diabetes 10 years earlier than those with other genotypes, based on the 50% survival rate (Fig. 3 ). Discussion This study identified five PRS models that effectively distinguished newly diagnosed T2D patients from nondiabetic individuals in a Thai population. Among them, two PRS (PGS000804 and PGS003402) and eight SNPs (rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, and rs3132469) were significantly associated with age at T2D diagnosis. PRS for T2D can enhance prediction before clinical risk factors appear, facilitating early lifestyle interventions (Mars et al. 2020 ). Here, we noted that PGS000804, PGS003091, PGS000125, PGS000855, and PGS003402 from published databases (Lamri et al. 2022 ; Ma et al. 2022 ; Mansour Aly et al. 2021 ; Polfus et al. 2021 ; Qi et al. 2017 ) could separate T2D patients from healthy controls in the Thai population. The most robust PRS model, PGS000804, was developed using multiethnic populations. This finding underscores the influence of population-specific factors, such as allele frequencies and linkage disequilibrium patterns, on genetic risk (Mercader et al. 2021 ). It also highlights the need for studies involving Southeast Asian populations to refine PRS models for this ethnicity. Age at diagnosis is crucial in T2D because onset timing can affect disease progression, treatment outcomes, and complication risk (Nanayakkara et al. 2021 ). Of the five PRS models, PGS000804 and PGS003402 showed a significant inverse correlation with age at diagnosis across all T2D subtypes. Eight genetic loci—rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, and rs3132469—were associated with both T2D and age at diagnosis, enhancing our understanding of T2D’s genetic architecture. Interestingly, rs2216063(G/A), rs9358356(T/C), and rs9472138(C/T) showed an inverse relationship with T2D but a positive correlation with age at diagnosis. The wild-type allele of these SNPs was common prevalent in Thai T2D and was associated with an increased risk of being diagnosed with T2D at a young age. Pearson’s correlation analysis and survival analysis confirmed that the wild-type alleles at these positions predisposed individuals to earlier disease onset, suggesting that the alternative alleles may confer protection. Furthermore, these SNPs highlight critical genetic determinants of metabolic dysfunction and vascular pathology in diabetes. SNP rs2216063 is mapped to the IRX3-LINC02140 region, where iroquois homeobox 3 ( IRX3 ) gene regulates the expression of FTO alpha-ketoglutarate dependent dioxygenase ( FTO ) gene (Ragvin et al. 2010 ; Smemo et al. 2014 ). This regulation influences metabolic pathways linked to T2D and obesity (Bjune et al. 2020 ). SNP rs9358356 located in CDKAL1 threonylcarbamoyladenosine tRNA methylthiotransferase ( CDKAL1 ) gene. Dysfunction in CDKAL1 impairs insulin secretion, affects adipocyte differentiation, and contributes to the development of microvascular complications in diabetes (Ghosh et al. 2022 ). Meanwhile, SNP rs9472138 in the VEGFA-LINC02537 region, where the vascular endothelial growth factor A (VEGFA) is a key regulator of vascular function, playing a central role in diabetic complications such as retinopathy, neuropathy, and nephropathy (Aiello and Wong 2000 ). Nevertheless, the specific biological roles of these SNPs in influencing age at onset require further exploration. When these PRS models were analyzed within specific T2D clusters, the inverse association with age at diagnosis was evident only in the SIDD cluster. Further analysis showed a significant relationship between earlier T2D onset and the presence of first-degree relatives with diabetes among SIDD patients (unpublished data). Additionally, the SIDD cluster completely lacked alternate variants at rs2216063(G/A), rs9358356(T/C), and rs9472138(C/T), with patients exhibiting homozygous GTC/GTC genotypes at these loci. This pattern is consistent with the early disease onset in this cluster (Preechasuk et al. 2022 ) and underscores the strong genetic influence observed in SIDD (Udler 2019 ). Notably, the MOD and MARD clusters showed a significant difference in the allele frequency of SNP rs9472138 ( VEGFA-LINC02537 ) compared to the control group. MOD patients had a high BMI, lower hemoglobin A1C (HbA1c) levels, and a younger age at diagnosis. Meanwhile, MARD patients were older and had relatively lower HbA1c levels at diagnosis (Preechasuk et al. 2022 ). HbA1c levels have been correlated with VEGF levels in the blood (Jiang et al. 2023 ; Zehetner et al. 2013 ). It is possible that rs9472138 influences HbA1c levels; however, the exact mechanism by which this SNP is associated with HbA1c requires further investigation. Our study has several limitations. First, because the PRS models were primarily derived from non-Asian populations, relevant Thai-specific variants may have been missed. Second, the limited sample size prevented us from defining separate PRS for each T2D cluster. Third, using age at diagnosis as a cluster criterion could have influenced the statistical evaluation of PRS effects on age at onset. Lastly, we did not collect sufficient data on clinical parameters and complications to investigate associations between PRS and complication risks. Nonetheless, our findings highlight a PRS model and variants that associate with both T2D and age at diagnosis, which could improve the identification of T2D clusters in the Thai population. In conclusion, this study advances our understanding of the genetic factors underlying newly diagnosed T2D in a Thai cohort. The distinct genetic architecture observed across T2D clusters supports the potential for personalized medicine in diabetes management and underscores the value of PRS for early diagnosis and cluster differentiation. Future research should validate these findings in larger cohorts, explore associations with diabetic complications, and investigate the functional roles of these variants to enable more targeted therapeutic interventions. Abbreviations HbA1c Hemoglobin A1C MARD Mild age-related diabetes MOD Mild obesity-related diabetes MSD Metabolic syndrome diabetes PRS Polygenic risk score SIDD Severe insulin-deficient diabetes SNP Single nucleotide polymorphism T2D Type 2 diabetes Declarations Acknowledgements The authors are grateful to the Siriraj Diabetes Center for assistance for the type 2 diabetes data. Funding This research project was supported by Mahidol University (Fundamental Fund: Fiscal Year 2023 and National Science Research and Innovation Fund; grant number FF-028/2566). This project was partially supported by the Research Excellence Development Program, Faculty of Medicine Siriraj Hospital, Mahidol University. Authors’ contributions W.T. and N.P. were responsible for conceptualization and funding acquisition. T.N. and V.L. were responsible for project administration. N.T. (Nipaporn Teerawattanapong) performed DNA extraction. N.S. performed DNA sequencing. V.N. and R.W. performed analysis. S.T., L.P., N.T. (Nuntakorn Thongtang), and N.P. were responsible for data curation. S.S. and N.T. (Nipaporn Teerawattanapong) performed writing–original draft preparation. All authors read and approved the final manuscript. Ethical declarations Ethical approval The study was approved by the Siriraj Institutional Review Board, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand (approval number Si 826/2022). Consent to participate Informed consent was obtained from all individual participants included in the study. Competing interests All authors declare no financial and non-financial competing interests. Data availability The datasets supporting the current study have not been deposited in a public repository but are available from the corresponding author on request. 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Curr Diab Rep 19:55. 10.1007/s11892-019-1169-7 Wang W, Pei X, Zhang L, Chen Z, Lin D, Duan X, Fan J, Pan Q, Guo L (2021) Application of new international classification of adult-onset diabetes in Chinese inpatients with diabetes mellitus. Diabetes Metab Res Rev 37:e3427. 10.1002/dmrr.3427 Zehetner C, Kirchmair R, Kralinger M, Kieselbach G (2013) Correlation of vascular endothelial growth factor plasma levels and glycemic control in patients with diabetic retinopathy. Acta Ophthalmol 91:e470–e473. 10.1111/aos.12081 Zheng Y, Ley SH, Hu FB (2018) Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 14:88–98. 10.1038/nrendo.2017.151 Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Sarocha","middleName":"","lastName":"Suthon","suffix":""},{"id":432634779,"identity":"11134d06-f6da-4337-97f4-6906a9cd69c8","order_by":2,"name":"Nipaporn Teerawattanapong","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Nipaporn","middleName":"","lastName":"Teerawattanapong","suffix":""},{"id":432634780,"identity":"b1b51b40-babe-4660-8a80-f49a83176e3c","order_by":3,"name":"Tassanee Narkdontri","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Tassanee","middleName":"","lastName":"Narkdontri","suffix":""},{"id":432634781,"identity":"95c30edf-660b-4ba4-bbfe-5a0ecd1981c8","order_by":4,"name":"Vorthunju Nakhonsri","email":"","orcid":"","institution":"National Biobank of Thailand, National Science and Technology Development 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University","correspondingAuthor":false,"prefix":"","firstName":"Nuntakorn","middleName":"","lastName":"Thongtang","suffix":""},{"id":432634788,"identity":"9c3bde99-795f-48b5-9d49-6d4cb0d15733","order_by":11,"name":"Nattachet Plengvidhya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACZgSr8QGQ5OEjWgsPA2OzAYhmI9o2oJY2CRCDoBb5dt5jj3kY7sjbSze2VX7NsZNhY2B++OgGHi0Gh/nSjXkYnhn2yBxsuy27LRnoMDZj4xx8Wph5zCRnMBxm7JFIbLstuY0ZqIWHTRqfFvlmiBZ7kJZiyW31hLUwHOYxk/jAcDgRpIXx47bDhLUYHOYxN/hgcDi5587BZmnGbcd52JgJ+EW+/4zZg4SKw7bts5sPfvy5rdqen7354WO8DgNHBCgOgZHCzAPiM+NXDtXCANHC+IOw6lEwCkbBKBiBAABuCD+/gMF5+AAAAABJRU5ErkJggg==","orcid":"","institution":"Mahidol University","correspondingAuthor":true,"prefix":"","firstName":"Nattachet","middleName":"","lastName":"Plengvidhya","suffix":""}],"badges":[],"createdAt":"2025-03-20 09:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6268252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6268252/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79259985,"identity":"d42f5dc0-0063-4922-a5e1-d8607197cb6f","added_by":"auto","created_at":"2025-03-26 09:23:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62761,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between genotype and age at type 2 diabetes diagnosis for (A) rs9358356, (B) rs2216063 and (C) rs9472138. (D) The combined alternate allele dosages at these three loci and age at diagnosis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6268252/v1/c204dc9cd8edcecce526ffc4.png"},{"id":79259986,"identity":"ee004298-b3a4-42a4-b917-f4187a2f6f21","added_by":"auto","created_at":"2025-03-26 09:23:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73810,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot comparing the odds ratios of individual single nucleotide polymorphisms across different type 2 diabetes clusters versus controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; MSD, metabolic syndrome diabetes; OR, odds ratio; SIDD, severe insulin-deficient diabetes; SNP, single nucleotide polymorphism\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6268252/v1/009c575f99f477adac75983c.png"},{"id":79259988,"identity":"c40a3641-0401-4916-96be-4d429df2891b","added_by":"auto","created_at":"2025-03-26 09:23:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":704683,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of diabetes incidence in patients homozygous for the GTC/GTC genotype at rs2216063, rs9358356, and rs9472138 (red), compared to those with other genotypes (blue).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6268252/v1/88336d3d42e737abf98a03fc.png"},{"id":79261080,"identity":"ec6172c4-7096-4566-80ca-a1a2feff4be3","added_by":"auto","created_at":"2025-03-26 09:31:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1837459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6268252/v1/911a8c57-0ce3-4f56-96b2-a65774c1a2b3.pdf"},{"id":79259987,"identity":"1f4664ca-b0b6-461e-b90e-6195c52b8560","added_by":"auto","created_at":"2025-03-26 09:23:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":776301,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6268252/v1/22b86dd279c1b1d0759de9b2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Polygenic risk scores for cluster of newly diagnosed type 2 diabetes: genetic insights and clinical implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes (T2D) is a complex, heterogeneous metabolic disorder characterized by chronic hyperglycemia arising from defects in insulin secretion, insulin action, or both (American Diabetes \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). It represents a major global health challenge due to its substantial morbidity and mortality, largely driven by associated cardiovascular diseases, kidney failure, and other severe complications (DeFronzo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The prevalence of T2D is increasing worldwide, with a pronounced impact on Asian populations, including Thailand, where distinct genetic and environmental factors influence the disease course (Zheng et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvidence of T2D heterogeneity can be observed early in its clinical course among both Caucasian and Asian populations (Ahlqvist et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mansour Aly et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, we identified four distinct clusters of newly diagnosed Thai T2D patients using routine clinical parameters, revealing variability in clinical manifestations, disease progression, treatment responses, and complications (Preechasuk et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Because T2D is polygenic, involving numerous genetic loci (Udler \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), it is plausible that these loci influence disease risk differently across various T2D subgroups.\u003c/p\u003e \u003cp\u003eIn this context, the polygenic risk score (PRS) consolidates the effects of multiple single nucleotide polymorphisms (SNPs) and other genetic variants into a single measure of genetic predisposition. This unified metric provides critical insights into the genetic architecture of T2D and supports personalized treatment decisions based on individual genomic profiles (Imamura and Maeda \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the increasing prevalence of T2D and its diverse genetic and environmental determinants, this study aims to identify PRS models associated with each cluster of newly diagnosed T2D in the Thai population. By integrating genetic data with clinical variables, the study strives to advance precision medicine and mitigate complications in this high-risk demographic.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 680 participants were recruited for this study. Informed consent was obtained from all individual participants included in the study. The nondiabetic control group comprised 193 individuals older than 30 years, with no family history of diabetes, fasting plasma glucose\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL, and glycated hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;5.7%. The remaining 487 participants with T2D were diagnosed according to the American Diabetes Association 2024 criteria (American Diabetes Association Professional Practice 2024). They were categorized into four clusters, as previously described (Preechasuk et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), namely, mild age-related diabetes (MARD, n\u0026thinsp;=\u0026thinsp;220), mild obesity-related diabetes (MOD, n\u0026thinsp;=\u0026thinsp;119), metabolic syndrome diabetes (n\u0026thinsp;=\u0026thinsp;55), and severe insulin-deficient diabetes (SIDD, n\u0026thinsp;=\u0026thinsp;93).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA isolation and genotyping\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was isolated using the FlexiGene DNA Kit (Qiagen GmbH, Hilden, Germany). An Axiom PangenomiX Array Kit with the Axiom 2.0 Assay (Life Technologies, Foster City, CA, USA) was used to detect genetic variants across the genome. Genotype data were merged, and quality control procedures excluded positions with a missing rate\u0026thinsp;\u003cb\u003e\u0026ge;\u003c/b\u003e\u0026thinsp;5% or a minor allele frequency (MAF)\u0026thinsp;\u003cb\u003e\u0026le;\u003c/b\u003e\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003ePRS database and calculation\u003c/h3\u003e\n\u003cp\u003ePRSs were obtained from the PGScatalog using the Mondo Disease Ontology (MONDO) ID \u0026ldquo;[MONDO:0005148] type 2 diabetes mellitus.\u0026rdquo; This identifier is linked to 92 PRS models. Each individual\u0026rsquo;s genetic score (ii) was calculated by summing the products of the position coefficients and the genotype dosage for each variant (0/0\u0026thinsp;=\u0026thinsp;0, 0/1\u0026thinsp;=\u0026thinsp;1, 1/1\u0026thinsp;=\u0026thinsp;2), as shown in Eq.\u0026nbsp;1.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{PRS}_{i}={\\sum\\:}_{j}^{M}{\\widehat{\\beta\\:}}_{j}\\times\\:{dosage}_{ij}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEquation 1\u003c/strong\u003e \u003cp\u003eCalculation of the genetic score for all MM positions. Each term is the product of the position coefficient and the genotype dosage.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R statistical software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria). Logistic regression was employed to develop predictive models, and model performance was assessed using McFadden\u0026rsquo;s R-squared. Multiple comparisons were corrected with the Benjamini‒Hochberg method (false discovery rate, FDR), using an FDR threshold of \u0026lt;\u0026thinsp;0.05. Linear regression was then applied to examine how well these models predicted the age at diagnosis. In addition, both logistic and linear regression analyses were performed to evaluate the associations between each genetic variant and two outcomes: the presence of T2D and the age at diagnosis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePRS associated with Thai T2D patients\u003c/h2\u003e \u003cp\u003eWe first examined which PRS models from the database were suitable for Thai newly diagnosed T2D patients. Multiple logistic regression analyses revealed that five PRS models significantly distinguished T2D patients from nondiabetic individuals in the Thai population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these models, PGS000804 had the highest McFadden R-squared value and showed significant differences between the patient and control groups in the 1st, 3rd, 9th, and 10th deciles. Individuals with PRS values in the 1st and 3rd deciles had 0.41 (0.24, 0.7) and 0.55 (0.32, 0.95) times the odds of being diabetic compared with the control group. Meanwhile, those in the 9th and 10th deciles had increased odds of T2D by 2.48 (1.19, 5.79) and 2.8 (1.3, 6.92), respectively (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u0026ndash;C and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePolygenic risk scores associated with type 2 diabetes in the Thai population, including model estimates and performance metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGSid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMcFadden R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS000804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMultiethnic [1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS003091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUS and UK Caucasian [2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS000125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUS Hispanic/Latino [3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS000855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSwedish [4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS003402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSouth Asian [5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e AUC, area under the curve; FDR, false discovery rate; SE, standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBecause the timing of T2D diagnosis can affect disease progression, treatment outcomes, and the risk of complications (Nanayakkara et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we further investigated associations between these PRS models and the age at diagnosis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Two models\u0026mdash;PGS000804 and PGS003402\u0026mdash;were significantly inversely correlated with age at diabetes diagnosis in the overall group of T2D patients. When analyzed by subgroup, the SIDD cluster showed a significant inverse correlation between PGS000125 and PGS000804 and age at diagnosis. In addition, PGS000804 demonstrated a trend toward an inverse correlation with age at diagnosis in the MARD cluster.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between genetic scores and age at diabetes diagnosis across type 2 diabetes subtypes in the Thai population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePGSid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMARD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSIDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMOD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS000125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.214\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePGS000804\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.160\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.244\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS000855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGS003091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePGS003402\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.130\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; MSD, metabolic syndrome diabetes; r, Pearson correlation coefficient; SIDD, severe insulin-deficient diabetes\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk loci and SNPs associated with T2D and age at diagnosis\u003c/h3\u003e\n\u003cp\u003eTo determine which genomic positions were associated with T2D and age at diagnosis, we performed logistic regression for each position included in PGS000804 and PGS003402 (total of 746 positions) against diabetes status. We then applied linear regression to assess relationships with age at diagnosis (Supplementary Fig. S2A). This analysis identified eight positions\u0026mdash;rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, and rs3132469\u0026mdash;that were significantly associated with both T2D and age at diagnosis (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Fig. S2B).\u003c/p\u003e \u003cp\u003eThese eight SNPs showed an inverse relationship with T2D (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While the other SNPs were negatively correlated with age at diabetes diagnosis, the SNPs rs2216063 (G/A), rs9358356 (T/C), and rs9472138 (C/T) were positively correlated (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table S2). This indicated that the alternative allele has a protective effect, which is uncommonly found in Thai T2D population and is associated with an older age at T2D diagnosis. The effect of each SNP allele on age at diagnosis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk loci associated with type 2 diabetes and age at diagnosis, including odds ratios, beta estimates, and \u003cem\u003ep\u003c/em\u003e values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePosition ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMapped gene(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eType 2 diabetes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAge at diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eDirection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBeta estimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eT2D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAaD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16_54353172_G/A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2216063\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIRX3-LINC02140\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003cp\u003e(0.37,0.44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.02E-27\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.72E-24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003cp\u003e(3.18, 4.28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.38E-11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.55E-08\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6_20667151_T/C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ers9358356\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCDKAL1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.56, 0.68)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.14E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.29E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.46\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(2.65, 4.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.21E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6_43844025_C/T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ers9472138\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eVEGFA-LINC02537\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.17, 0.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.78E-24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.69E-21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.38\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(4.61, 8.15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e+++\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9_95033139_G/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6479591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAOPEP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003cp\u003e(0.32, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.53E-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.69E-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.97\u003c/p\u003e \u003cp\u003e(-7.57, -6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.17E-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9_122927415_C/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers10818763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZBTB26\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003cp\u003e(0.38, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91E-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55E-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.21\u003c/p\u003e \u003cp\u003e(-6.84, -5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.84E-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.20E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12_65858514_A/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers189339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHMGA2-AS1, HMGA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003cp\u003e(0.38, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64E-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15E-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.87\u003c/p\u003e \u003cp\u003e(-6.47, -5.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58E-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.29E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8_8863963_G/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers4382480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMFHAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003cp\u003e(0.44, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.48E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.26E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.18\u003c/p\u003e \u003cp\u003e(-4.8, -3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.97E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.22E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6_31488790_A/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers3132469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMICB-DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003cp\u003e(0.13, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.53E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.88E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.48\u003c/p\u003e \u003cp\u003e(-5.1, -3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.07E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.68E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e AaD, age at diagnosis; FDR, false discovery rate; ID, identifier; T2D, type 2 diabetes\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, Pearson\u0026rsquo;s correlation analysis indicated a significant relationship between the combined alternate allele dosages at these three loci (wild type\u0026thinsp;=\u0026thinsp;0, heterozygous\u0026thinsp;=\u0026thinsp;1, homozygous\u0026thinsp;=\u0026thinsp;2) and age at diagnosis. Notably, we found the combination for only 4 out of the 6 possible forms in our population (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, r\u0026thinsp;=\u0026thinsp;0.31, 95% CI\u0026thinsp;=\u0026thinsp;0.23‒0.39; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n\u003ch3\u003eSNP allele frequencies across T2D clusters\u003c/h3\u003e\n\u003cp\u003eWe analyzed allele frequencies across the T2D clusters. The nondiabetic control group shared similar frequencies with the MARD and MOD clusters for rs2216063(G/A) and rs9358356(T/C). In contrast, rs9472138(C/T) showed a significant difference between the control group and the MARD and MOD clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both the MOD and SIDD clusters had significantly different allele frequencies from the control group at all three SNPs. Notably, the SIDD cluster lacked alternate variants for these SNPs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding suggests a unique genetic profile for the SIDD cluster.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAllele frequencies of key single nucleotide polymorphisms in different Thai type 2 diabetes clusters and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c6\" namest=\"c4\" rowspan=\"2\"\u003e \u003cp\u003eControl (N\u0026thinsp;=\u0026thinsp;178)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c18\" namest=\"c7\"\u003e \u003cp\u003eType 2 diabetes (N\u0026thinsp;=\u0026thinsp;471)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMARD (N\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eMSD (N\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003eMOD (N\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c18\" namest=\"c16\"\u003e \u003cp\u003eSIDD (N\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2216063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e327.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9358356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e107.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9472138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e106\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e236\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"20\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; MSD, metabolic syndrome diabetes; SIDD, severe insulin-deficient diabetes; SNP, single nucleotide polymorphism\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also examined the frequency of individuals homozygous for the GTC/GTC genotype at rs2216063(G/A), rs9358356(T/C), and rs9472138(C/T) in each cluster (Supplementary Table S3). Survival analysis indicated that patients carrying GTC/GTC at these loci developed diabetes 10 years earlier than those with other genotypes, based on the 50% survival rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified five PRS models that effectively distinguished newly diagnosed T2D patients from nondiabetic individuals in a Thai population. Among them, two PRS (PGS000804 and PGS003402) and eight SNPs (rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, and rs3132469) were significantly associated with age at T2D diagnosis.\u003c/p\u003e \u003cp\u003ePRS for T2D can enhance prediction before clinical risk factors appear, facilitating early lifestyle interventions (Mars et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Here, we noted that PGS000804, PGS003091, PGS000125, PGS000855, and PGS003402 from published databases (Lamri et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mansour Aly et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Polfus et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) could separate T2D patients from healthy controls in the Thai population. The most robust PRS model, PGS000804, was developed using multiethnic populations. This finding underscores the influence of population-specific factors, such as allele frequencies and linkage disequilibrium patterns, on genetic risk (Mercader et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It also highlights the need for studies involving Southeast Asian populations to refine PRS models for this ethnicity.\u003c/p\u003e \u003cp\u003eAge at diagnosis is crucial in T2D because onset timing can affect disease progression, treatment outcomes, and complication risk (Nanayakkara et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Of the five PRS models, PGS000804 and PGS003402 showed a significant inverse correlation with age at diagnosis across all T2D subtypes. Eight genetic loci\u0026mdash;rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, and rs3132469\u0026mdash;were associated with both T2D and age at diagnosis, enhancing our understanding of T2D\u0026rsquo;s genetic architecture.\u003c/p\u003e \u003cp\u003eInterestingly, rs2216063(G/A), rs9358356(T/C), and rs9472138(C/T) showed an inverse relationship with T2D but a positive correlation with age at diagnosis. The wild-type allele of these SNPs was common prevalent in Thai T2D and was associated with an increased risk of being diagnosed with T2D at a young age. Pearson\u0026rsquo;s correlation analysis and survival analysis confirmed that the wild-type alleles at these positions predisposed individuals to earlier disease onset, suggesting that the alternative alleles may confer protection.\u003c/p\u003e \u003cp\u003eFurthermore, these SNPs highlight critical genetic determinants of metabolic dysfunction and vascular pathology in diabetes. SNP rs2216063 is mapped to the \u003cem\u003eIRX3-LINC02140\u003c/em\u003e region, where iroquois homeobox 3 (\u003cem\u003eIRX3\u003c/em\u003e) gene regulates the expression of FTO alpha-ketoglutarate dependent dioxygenase (\u003cem\u003eFTO\u003c/em\u003e) gene (Ragvin et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Smemo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This regulation influences metabolic pathways linked to T2D and obesity (Bjune et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SNP rs9358356 located in CDKAL1 threonylcarbamoyladenosine tRNA methylthiotransferase (\u003cem\u003eCDKAL1\u003c/em\u003e) gene. Dysfunction in CDKAL1 impairs insulin secretion, affects adipocyte differentiation, and contributes to the development of microvascular complications in diabetes (Ghosh et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, SNP rs9472138 in the \u003cem\u003eVEGFA-LINC02537\u003c/em\u003e region, where the vascular endothelial growth factor A (VEGFA) is a key regulator of vascular function, playing a central role in diabetic complications such as retinopathy, neuropathy, and nephropathy (Aiello and Wong \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Nevertheless, the specific biological roles of these SNPs in influencing age at onset require further exploration.\u003c/p\u003e \u003cp\u003eWhen these PRS models were analyzed within specific T2D clusters, the inverse association with age at diagnosis was evident only in the SIDD cluster. Further analysis showed a significant relationship between earlier T2D onset and the presence of first-degree relatives with diabetes among SIDD patients (unpublished data). Additionally, the SIDD cluster completely lacked alternate variants at rs2216063(G/A), rs9358356(T/C), and rs9472138(C/T), with patients exhibiting homozygous GTC/GTC genotypes at these loci. This pattern is consistent with the early disease onset in this cluster (Preechasuk et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and underscores the strong genetic influence observed in SIDD (Udler \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, the MOD and MARD clusters showed a significant difference in the allele frequency of SNP rs9472138 (\u003cem\u003eVEGFA-LINC02537\u003c/em\u003e) compared to the control group. MOD patients had a high BMI, lower hemoglobin A1C (HbA1c) levels, and a younger age at diagnosis. Meanwhile, MARD patients were older and had relatively lower HbA1c levels at diagnosis (Preechasuk et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). HbA1c levels have been correlated with VEGF levels in the blood (Jiang et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zehetner et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is possible that rs9472138 influences HbA1c levels; however, the exact mechanism by which this SNP is associated with HbA1c requires further investigation.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, because the PRS models were primarily derived from non-Asian populations, relevant Thai-specific variants may have been missed. Second, the limited sample size prevented us from defining separate PRS for each T2D cluster. Third, using age at diagnosis as a cluster criterion could have influenced the statistical evaluation of PRS effects on age at onset. Lastly, we did not collect sufficient data on clinical parameters and complications to investigate associations between PRS and complication risks. Nonetheless, our findings highlight a PRS model and variants that associate with both T2D and age at diagnosis, which could improve the identification of T2D clusters in the Thai population.\u003c/p\u003e \u003cp\u003eIn conclusion, this study advances our understanding of the genetic factors underlying newly diagnosed T2D in a Thai cohort. The distinct genetic architecture observed across T2D clusters supports the potential for personalized medicine in diabetes management and underscores the value of PRS for early diagnosis and cluster differentiation. Future research should validate these findings in larger cohorts, explore associations with diabetic complications, and investigate the functional roles of these variants to enable more targeted therapeutic interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHbA1c\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hemoglobin A1C\u003c/p\u003e\n\u003cp\u003eMARD\u0026nbsp;Mild age-related diabetes\u003cbr\u003eMOD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mild obesity-related diabetes\u003cbr\u003eMSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Metabolic syndrome diabetes\u003cbr\u003ePRS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Polygenic risk score\u003cbr\u003eSIDD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Severe insulin-deficient diabetes\u003cbr\u003eSNP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single nucleotide polymorphism\u003cbr\u003eT2D \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Type 2 diabetes\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eThe authors are grateful to the Siriraj Diabetes Center for assistance for the type 2 diabetes data.\u003c/p\u003e\n\u003ch3\u003eFunding \u003c/h3\u003e\n\u003cp\u003eThis research project was supported by Mahidol University (Fundamental Fund: Fiscal Year 2023 and National Science Research and Innovation Fund; grant number FF-028/2566). This project was partially supported by the Research Excellence Development Program, Faculty of Medicine Siriraj Hospital, Mahidol University.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eW.T. and N.P. were responsible for conceptualization and funding acquisition. T.N. and V.L. were responsible for project administration. N.T. (Nipaporn Teerawattanapong) performed DNA extraction. N.S. performed DNA sequencing. V.N. and R.W. performed analysis. S.T., L.P., N.T. (Nuntakorn Thongtang), and N.P. were responsible for data curation. S.S. and N.T. (Nipaporn Teerawattanapong) performed writing\u0026ndash;original draft preparation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003eEthical declarations\u003c/h3\u003e\n\u003ch3\u003eEthical approval\u003c/h3\u003e\n\u003cp\u003eThe study was approved by the Siriraj Institutional Review Board, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand (approval number Si 826/2022).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eConsent to participate\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eInformed consent was obtained from all individual participants included in the study.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eAll authors declare no financial and non-financial competing interests.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets supporting the current study have not been deposited in a public repository but are available from the corresponding author on request.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhlqvist E, Storm P, Karajamaki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spegel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark A, Lahti K, Forsen T, Tuomi T, Rosengren AH, Groop L (2018) Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. 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Nat Rev Endocrinol 14:88\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrendo.2017.151\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2017.151\" 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":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cluster, Polygenic risk score, SNP, Thais, Type 2 diabetes","lastPublishedDoi":"10.21203/rs.3.rs-6268252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6268252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes (T2D) is a heterogeneous disease that can be distinguished at newly diagnosis by variations in clinical presentation, disease trajectory, therapeutic response, and risk of complications. Its global prevalence is increasing, particularly among Asian populations such as Thais, due to unique genetic and environmental factors. Thai patients with newly diagnosed T2D have been classified into four clusters based on standard clinical parameters. However, the polygenic basis underlying these distinct phenotypes remains unclear. In this study, we investigated the association between polygenic risk score (PRS) models and T2D in 680 Thai participants. Of these, 487 were T2D patients in four clusters, and 193 were nondiabetic controls. Genotyping was performed, and we calculated PRS models using data from the PGScatalog. Five PRS models significantly differentiated T2D from controls, with PGS000804 displaying the strongest predictive power. Two PRS models (PGS000804 and PGS003402) showed an inverse correlation with age at diagnosis. Moreover, eight genetic loci (rs2216063, rs9358356, rs9472138, rs6479591, rs4382480, rs189339, rs10818763, rs3132469) were significantly associated with both T2D and age at diagnosis. Among these loci, the alternative allele of rs2216063 (G/A), rs9358356 (T/C), and rs9472138 (C/T) conferred a lower T2D risk and were positively associated with older age at diagnosis. Individuals with the GTC/GTC genotype at these three loci developed diabetes approximately 10 years earlier than those with other genotypes. Our findings underscore the utility of PRS models in refining T2D subtypes and promoting precision medicine in the Thai population.\u003c/p\u003e","manuscriptTitle":"Polygenic risk scores for cluster of newly diagnosed type 2 diabetes: genetic insights and clinical implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:23:00","doi":"10.21203/rs.3.rs-6268252/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0ba575e-be38-4823-9579-7f7c88c83fb9","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-26T09:23:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 09:23:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6268252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6268252","identity":"rs-6268252","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}