Association between PPAR-γ (rs1801282) polymorphism and susceptibility to obesity and type 2 diabetes mellitus: a meta-analysis with trial sequential analysis

Association between PPAR-γ (rs1801282) polymorphism and susceptibility to obesity and type 2 diabetes mellitus: a meta-analysis with trial sequential analysis | 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 Systematic Review Association between PPAR-γ (rs1801282) polymorphism and susceptibility to obesity and type 2 diabetes mellitus: a meta-analysis with trial sequential analysis Jing Wang, Cheng Shi, Ziyan Fang, Zhaobin Jiang, Jingjing Zuo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9008547/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background peroxisome proliferator-activated receptor-γ (PPAR-γ) plays a pivotal role in lipid homeostasis and insulin signaling; however, the association of its PPAR-γ rs1801282 polymorphism with obesity and type 2 diabetes mellitus (T2DM) remains controversial. Methods We searched PubMed, Web of Science, CNKI and WanFang up to September 2025. Meta-analysis was conducted across five genetic models, with Odds Ratio (OR) and 95% Confidence Interval (95% CI) assessing rs1801282 association with obesity/T2DM susceptibility. Sensitivity analysis and trial sequential analysis (TSA) were performed to verify the reliability of our findings.This meta-analysis was registered in PROSPERO (CRD420261297714). Results A total of 24 studies involving 6739 cases and 7337 controls were included in this meta-analysis. PPAR-γ (rs1801282) C allele was significantly associated with increased overall obesity risk (OR = 2.339, 95% CI: 1.418–3.864, P = 0.001) and it was strongly correlated with obesity in Caucasian populations whereas in Asian populations it tended to elevate obesity risk but was based on only one study. For T2DM, no significant association was observed between the rs1801282 polymorphism and overall disease risk (OR = 0.975, 95% CI: 0.827–1.148, P = 0.759), though a marginally significant association was detected in the Asian subgroup under the dominant model (OR = 0.740, 95% CI: 0.551–0.994, P = 0.046). No significant associations were found in other ethnic groups or genetic models (all P > 0.05). Trial sequential analysis (TSA) confirmed the reliability of the conclusions regarding the association between rs1801282 and obesity/T2DM in Asian populations. Conclusions In conclusion, the PPAR-γ rs1801282 C allele is significantly associated with increased obesity risk and shows a marginally significant association with T2DM in Asians under the dominant model, but no consistent link with overall T2DM risk. PPAR-γ polymorphism Obesity T2DM Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Obesity and type 2 diabetes mellitus (T2DM) are two interconnected chronic metabolic disorders that have emerged as a major global public health crisis 1 . Obesity is defined as a body mass index (BMI) ≥ 30 kg/m², characterized by excessive adipose tissue accumulation that disrupts metabolic homeostasis and triggers systemic inflammation 2 . T2DM, a progressive disease, is primarily marked by insulin resistance and impaired insulin secretion, with obesity being its most prominent modifiable risk factor approximately 80–90% of T2DM patients are overweight or obese 3 . Notably, the global prevalence of obesity has nearly tripled since 1975, and over 537 million adults worldwide currently live with T2DM, a figure projected to rise to 783 million by 2045 1 . These diseases not only reduce patients’ quality of life and increase the risk of comorbidities such as cardiovascular disease, chronic kidney disease, and certain cancers but also impose a heavy economic burden on healthcare systems. In 2021, the global direct medical costs associated with T2DM and its complications exceeded $ 966 billion, and the combined annual global economic cost of obesity and T2DM is estimated to surpass $ 4 trillion 3 , highlighting the urgent need for effective prevention and intervention strategies. Peroxisome proliferator-activated receptor gamma (PPAR-γ), a key regulator of adipocyte differentiation, insulin sensitivity, and lipid metabolism, has been extensively implicated in the pathogenesis of both obesity and T2DM 4 , 5 . Its pivotal role lies in orchestrating adipocyte maturation, enhancing peripheral insulin responsiveness, and modulating lipid storage and utilization—dysregulation of PPAR-γ signaling directly contributes to adipose tissue dysfunction, insulin resistance, and subsequent metabolic dysregulation, the core pathological features of obesity and T2DM.Among its genetic variants, the single nucleotide polymorphism (SNP) rs1801282 has gained significant attention due to its potential role in modifying disease susceptibility 6 studies have shown that this polymorphism affects PPAR-γ transcriptional activity and is associated with altered risks of obesity, insulin resistance, and T2DM across diverse populations 7 . PPAR-γ is a key nuclear receptor in metabolic regulation, located on human chromosome 3p25, and is involved in adipocyte differentiation, glucose metabolism, and insulin sensitivity modulation 8 . As a central regulator of metabolic pathways, activating PPAR-γ promotes adipogenesis, enhances insulin-mediated glucose uptake, and reduces systemic inflammation, playing a vital role in maintaining energy balance and glucose homeostasis 9 , 10 . The PPAR-γ gene contains multiple functional polymorphisms, among which the PPAR-γ rs1801282 variant is the most extensively studied.This single nucleotide polymorphism (SNP) results from a cytosine-to-guanine substitution (C > G), leading to the replacement of proline with alanine (Ala) at position 12 of the PPAR-γ protein 11 , 12 . In vitro and animal studies have demonstrated that the Ala allele alters PPAR-γ transcriptional activity, potentially affecting adipocyte function and insulin sensitivity 13 . These biological findings have spurred numerous epidemiological studies investigating the association between this polymorphism and the risk of obesity and T2DM. However, significant inconsistencies and controversies exist across studies and populations 14 . Regarding obesity, some studies have reported that the Ala allele is associated with an increased risk of obesity in Asian populations 11 , but no such association has been found in Caucasian 15 or African 16 populations. Conversely, a few studies have even suggested a protective effect of the Ala allele against obesity in European populations 17 .For T2DM, similar contradictions persist: multiple studies in Chinese 7 and Japanese 18 populations have indicated that the Ala allele increases T2DM susceptibility, while studies in Western populations 19 and some Middle Eastern cohorts 20 have failed to replicate this finding. Reza 21 observed that the Ala allele elevates T2DM risk in Northern Chinese Han populations with metabolic syndrome 22 , and Tziastoudi 23 linked the C/G and G/G genotypes to gestational diabetes mellitus (GDM) in Filipino women. In contrast, Scott 7 , 19 found no association between rs1801282 and T2DM in American adults. Additionally, a meta-analysis conducted a decade ago 24 reported a weak association between the Ala allele and T2DM risk, but it was limited by a small number of included studies and lacked stratification by ethnicity or adjustment for potential confounders such as BMI. These inconsistencies may stem from several limitations of individual studies, including small sample sizes, ethnic heterogeneity, differences in genotyping methods, variations in environmental exposures, inadequate adjustment for confounding variables, and potential publication bias 25 . Furthermore, few studies have simultaneously analyzed the association of this polymorphism with both obesity and T2DM, and understudied ethnic groups such as Mongolians and Southeast Asians remain underrepresented in existing research 18 , 26 . Previous meta-analyses have mostly explored its relationship with a single disease, failing to comprehensively integrate data from both obesity and T2DM or validate result robustness using Trial Sequential Analysis (TSA)a method critical for reducing the risk of type I errors in meta-analyses 27 , 28 . Given the high global burden of obesity and T2DM, resolving these discrepancies and clarifying the role of the PPAR-γ rs1801282 polymorphism in disease susceptibility is imperative. The present meta-analysis aims to: (1) systematically evaluate the association between this polymorphism and the risk of obesity and T2DM in the overall population; (2) explore potential ethnic differences in this association through subgroup analyses stratified by ethnicity and continent; (3) assess the combined effect of the polymorphism on obesity-related T2DM; and (4) integrate TSA to minimize random errors and enhance result reliability. By achieving these objectives, this study seeks to provide valuable insights for personalized risk assessment, early intervention, and the development of targeted therapies for obesity and T2DM. Materials and methods Search strategy This systematic review and meta-analysis was prospectively registered in the International Prospective Register of Systematic Reviews, registration number CRD420261297714. A systematic search of PubMed, Web of Science, Embase, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Data was conducted, covering the period from the inception of each database to September 2025. The search strategy utilized a combination of Medical Subject Headings and relevant keywords: (“PPAR-γ” OR “peroxisome proliferator-activated receptor-γ” OR “rs1801282” ) AND (“Polymorphism, Genetic” OR “Genetic Variation”) AND (“Obesity” OR “Diabetes Mellitus, Type 2”). No restrictions on language or publication date were applied. Additionally, the reference lists of all retrieved articles and relevant reviews were manually screened to identify any studies not captured in the initial database search. Inclusion and exclusion criteria Eligible studies met the following criteria: (1) case-control or cohort design investigating the association between the PPAR-γ polymorphism and the risk of obesity and/or type 2 diabetes; (2) provision of sufficient genotype or allele frequency data to calculate odds ratios and 95% confidence intervals ; (3) genotype distribution in the control group consistent with Hardy-Weinberg equilibrium (HWE) ( P > 0.05) 29 ; (4) original, full-text articles. Exclusion criteria were: (1) reviews, meta-analyses, animal studies, or conference abstracts; (2) studies lacking a defined control group or with overlapping data; (3) studies not focusing on obesity or type 2 diabetes risk; (4) insufficient data for analysis. Data extraction and quality assessment Two investigators independently performed the study screening, data extraction, and quality assessment. Any discrepancies were resolved through discussion or, if necessary, consultation with a third reviewer. A standardized form was used to extract the following data: first author, publication year, country, ethnicity, specific disease outcome (obesity or T2DM), diagnostic criteria, numbers of cases and controls, genotype frequencies, and genotyping method. The methodological quality of each included study was appraised using the Newcastle-Ottawa Scale (NOS) 30 . A score of 7 or above (out of 9) was considered indicative of high methodological quality. Statistical analyses The association between the PPAR-γ rs1801282 polymorphism and susceptibility to obesity and T2DM was evaluated under five genetic models. Specifically, pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the following comparisons: the allele model (C vs. G), the homozygous model (CC vs. GG), the heterozygous model (GC vs. GG), the dominant model (CC + GC vs. GG), and the recessive model (CC vs. GC + GG).Pooled ORs with 95% CIs were calculated. Heterogeneity across studies was assessed using Cochran’s Q test and the I² statistic. A fixed-effects model was employed for low heterogeneity ( P > 0.10 and I² ≤ 50%),When multiple comparisons was performed, P-value was set to 0.05/n, where n represents the number of comparisons. otherwise, a random-effects model was used. Pre-specified subgroup analyses were performed by ethnicity and disease type. Sensitivity analyses were conducted by sequentially excluding individual studies to evaluate result robustness. Potential publication bias was examined using Begg’s funnel plot and Egger’s regression test. STATA software (STATA 11.0, StataCorp, College Station, TX, USA) was used for the entire statistical process required for the meta-analysis. TSA To account for the risk of random error in repeated meta-analyses and to assess the reliability of conclusions, TSA was performed for the primary allelic model using TSA software (v0.9.5.10 Beta, Copenhagen Trial Unit). The required information size was calculated assuming a two-sided alpha of 5%, a beta of 20% (80% power), and a relative risk reduction of 20% based on control group event rates. Sequential monitoring boundaries were established. Firm evidence for or against an association is suggested if the cumulative Z-curve crosses the monitoring boundary or the RIS before the RIS is reached; otherwise, evidence is considered inconclusive 31 . Results Characteristics of eligible studies The study flow diagram is presented in Fig. 1 . A total of 97 records were initially identified via database searching, with no additional records obtained from other sources. After duplicate removal, 37 records were retained for title/abstract screening, among which 13 were excluded for irrelevance.The remaining 24 records underwent full-text eligibility assessment; articles were excluded for overlapping reasons. Ultimately, 24 studies were included in both the qualitative synthesis and quantitative synthesis (meta-analysis).The characteristics of these 24 studies are summarized in Table 1 , covering regions, ethnicities, and T2DM as the target disease. Genotyping methods and key parameters are detailed in Table 1 . Association betweenrs1801282 and T2DM Susceptibility Eighteen studies involving 5118 cases and 5116 controls explored the correlation between PPAR-γ rs1801282 polymorphism and T2DM susceptibility. In the overall population, no significant association was found under any genetic model (all P > 0.05; Table 2 ). The forest plot of the pooled ORs for the allele model (C vs. G) is presented, showing an overall OR of 0.97 (95% CI = 0.83–1.15, P = 0.759; Fig. 2 A) with high heterogeneity (I² = 61.1%, P < 0.001). Subgroup analyses by ethnicity revealed that the rs1801282 C allele neither exerted a protective effect nor increased the risk of T2DM in Caucasians (allele model: OR = 1.116, 95% CI = 0.965–1.289, P = 0.139) nor in Asians (allele model: OR = 0.806, 95% CI = 0.605–1.073, P = 0.139; Table 2 ). For the heterozygous model (GC vs. GG), no significant association was observed in the overall population (OR = 1.07, 95% CI = 0.74–1.56, P = 0.714; Fig. 2 B) with moderate heterogeneity (I² = 25.9%, P = 0.163), and consistent non-significant results were found in the Caucasian (OR = 1.198, P = 0.532) and Asian (OR = 0.81, P = 0.18) subgroups. Similarly, the homozygous model (CC vs. GG) showed no significant associations in the overall population (OR = 1.03, 95% CI = 0.65–1.63, P = 0.888; Fig. 2 C) or either ethnic subgroup (Caucasians: OR = 1.28, P = 0.366; Asians: OR = 0.771, P = 0.476; Table 2 ). The dominant model (CC + GC vs. GG) also yielded non-significant results across all populations (overall OR = 1.03, 95% CI = 0.70–1.52, P = 0.871; Fig. 2 D) with low to moderate heterogeneity (I² = 32.9%, P = 0.099). The recessive model (CC vs. GC + GG) showed no significant association in the overall population (OR = 0.96, 95% CI = 0.80–1.16, P = 0.675; Figure S1 A ) with moderate heterogeneity (I² = 58.20%, P = 0.001; Table 2 ).Regarding heterogeneity, moderate to high heterogeneity was observed in the allele model (C vs. G: I² = 61.10%, P < 0.001) and recessive model (CC vs. GC + GG: I² = 58.20%, P = 0.001) in the overall population, while low heterogeneity was detected in other genetic models (I² 0.05; Table 2 ). Subgroup analysis by ethnicity reduced heterogeneity in Caucasian populations ( allele model: I² = 19.10%, P = 0.268; recessive model: I² = 3.90%, P = 0.404), but high heterogeneity persisted in Asian populations for the allele model (I² = 67.30%, P = 0.003) and recessive model (I² = 58.20%, P = 0.002; Table 2 ). The results of Begg’s test and Egger’s test for T2DM are summarized in Table 2 . Association between rs1801282 and Obesity Susceptibility Six studies with a total sample size of 1621 cases and 2221 controls were included to analyze the association between PPAR-γ rs1801282 polymorphism and obesity risk. In the overall population, the rs1801282 C allele was significantly associated with an increased risk of obesity (allele model: OR = 2.339, 95% CI = 1.418–3.864, P = 0.001; Fig. 3 A; Table 3 ) with no heterogeneity detected (I² = 0.00%, P < 0.001). Subgroup analyses by ethnicity indicated that the rs1801282 C allele was significantly more prevalent in Caucasian obesity cases than in healthy controls (allele model: OR = 1.675, 95% CI = 1.444–1.942, P < 0.001; Table 3 ). In the Asian population, the C allele showed a strong tendency toward increased obesity risk (allele model: OR = 15.973, 95% CI = 7.022–36.331, P < 0.001; Fig. 3 A), however, the statistical power was limited as only one study was included ( Table 3 ). For the heterozygous model (GC vs. GG), no significant association was found in the overall population (OR = 1.12, 95% CI = 0.54–2.31, P = 0.765; Fig. 3 B) or Caucasian subgroup (OR = 1.056, P = 0.896; Table 3 ). The homozygous model (CC vs. GG) also showed no significant associations across populations (overall OR = 1.13, 95% CI = 0.45–2.83, P = 0.799; Fig. 3 C) with moderate heterogeneity in the overall population (I² = 52.5%, P = 0.061). Similarly, the dominant model (CC + GC vs. GG) yielded non-significant results (overall OR = 1.18, 95% CI = 0.53–2.60, P = 0.689; Fig. 3 D) and low to moderate heterogeneity (I² = 38.2%, P = 0.151; Table 3 ).The recessive model (CC vs. GC + GG) showed no significant association in the overall population (OR = 0.292, 95% CI = 0.04–2.11, P = 0.222; Figure S1 B ) with extremely high heterogeneity (I² = 97.80%, P < 0.001; Table 3 ) Regarding heterogeneity, significant high heterogeneity was only detected in the recessive model (CC vs. GC + GG: I² = 97.80% in the overall population, I² = 97.40% in Caucasians, both P < 0.001; Table 3 ). The results of Begg’s test and Egger’s test for obesity are shown in Table 3 . Heterogeneity, Sensitivity Analysis, and Publication Bias Assessment Sensitivity analysis was performed by sequentially omitting each included study to verify the robustness of the meta-analysis results (Fig. 4 C-D). The pooled OR estimates for all genetic models showed no significant fluctuations after excluding any single study, and the 95% CIs remained consistent with the overall results. These findings indicate that individual studies did not unduly influence the pooled results, and the outcomes of this meta-analysis are stable and reliable. Publication bias was evaluated using funnel plots and statistical tests (Begg’s test and Egger’s test). The funnel plots for the primary allele model (C vs. G) of T2DM and obesity showed no obvious asymmetry (Fig. 4 A-B), suggesting no significant publication bias. Egger’s test further confirmed this result for T2DM ( P begg = 0.218, P egger = 0.297; Table 2 ) and obesity ( P begg = 0.133, P egger = 0.09; Table 3 ) in the overall population. For the Asian subgroup of obesity, the small number of included studies (n = 1) limited the reliability of publication bias assessment, but no obvious bias was observed in visual inspection of the funnel plot. TSA We performed TSA on the genetic association between the polymorphism and T2DM/obesity risk. The cumulative Z-curve crossed the conventional test boundary and the trial sequential monitoring boundary, yet did not reach the required information size (RIS = 14741 for T2DM and RIS = 9104 for obesity), suggesting that the current evidence is sufficient to support a preliminary conclusion, but additional studies are still needed to confirm the stability of the effect. With respect to the obesity subgroup analysis, the cumulative Z-curve similarly crossed the conventional test boundary but failed to reach the required information size, indicating that further well-powered studies are warranted to validate the findings. (Fig. 5 A-B) Discussion This meta-analysis systematically evaluated the association between PPAR-γ rs1801282 polymorphism and susceptibility to T2DM and obesity, incorporating 24 eligible studies involving over 14,000 participants. The results revealed distinct association patterns between the polymorphism and the two metabolic diseases, with ethnicity-specific differences further highlighting the complexity of genetic susceptibility in metabolic disorders. For T2DM, no significant association was observed between rs1801282 polymorphism and disease risk in the overall population or in Caucasian/Asian subgroups across all genetic models. This finding is consistent with several previous studies: for example, Malecki 32 reported no correlation between rs1801282 and T2DM in a Polish Caucasian population, while Nagaraja 33 similarly found no significant association in Indians. PPARG, as a key regulator of adipocyte differentiation and insulin sensitivity, is theoretically involved in T2DM pathogenesis 2 , 21 , 34 ; however, the lack of consistent association in this meta-analysis may be attributed to multiple factors. First, moderate to high heterogeneity was detected in the allele and recessive models of the overall T2DM population (I²> 50%), which was partially mitigated by ethnicity stratification—suggesting that genetic background, environmental factors, or gene-environment interactions may mask potential associations. Second, the PPAR-γ polymorphism rs1801282 may exert a weak effect on PPAR-γ function, and its impact on T2DM risk could be overridden by other functional variants in the PPAR-γ gene or linked loci, leading to inconsistent results across individual studies 29 , 35 .A recent functional study by Bulzico 36 demonstrated that the PPAR-γ variant reduces PPAR-γ transcriptional activity but has minimal direct effects on insulin signaling, supporting the hypothesis of weak functional impact. Additionally, He 37 found that rs1801282 interacts with obesity to influence T2DM risk, indicating that the polymorphism may only affect disease susceptibility in the context of specific metabolic phenotypes. In contrast, the rs1801282 C allele was significantly associated with increased obesity risk in the overall population and Caucasian subgroup. The allele model showed no heterogeneity (I² = 0.00%) in these populations, indicating robust consistency of the association 38 , 39 . This aligns with the biological function of PPAR-γ: the Pro12Ala variant rs1801282 has been shown to reduce PPAR-γ transcriptional activity, impair adipocyte differentiation, and promote visceral fat accumulation key pathological features of obesity 40 . In Caucasians, the C allele was more prevalent in obese cases, supporting the hypothesis that this allele may enhance obesity susceptibility by compromising PPAR-γ mediated lipid metabolism 41 . For the Asian subgroup, the C allele also showed a strong trend toward increased obesity risk (OR = 15.973), but the result was based on only one study, limiting statistical power and generalizability. Future studies with larger Asian samples are needed to validate this potential association. Notably, high heterogeneity was observed in the recessive model of obesity (I² > 97% in overall and Caucasian populations), which may be due to differences in study design, sample size, or unmeasured confounding factors across included studies 42 . Sensitivity analysis confirmed that the overall results were stable and not affected by individual studies or HWE-violating data, while funnel plots and statistical tests ruled out significant publication bias—strengthening the reliability of the meta-analysis findings. This study has several limitations. First, although ethnicity stratification reduced heterogeneity, potential sources such as age, gender, and lifestyle factors were not adjusted for due to limited data availability in individual studies 43 . Second, the Asian subgroup for obesity included only one study, which may lead to biased estimates of the association 44 , . Third, the meta-analysis focused solely on the rs1801282 polymorphism, and interactions with other PPAR-γ variants or genes were not explored. Finally, most included studies were observational case-control designs, which cannot establish causal relationships between the polymorphism and disease susceptibility. In conclusion, PPAR-γ rs1801282 polymorphism is significantly associated with obesity susceptibility, particularly in Caucasians, while no consistent association with T2DM was found. These findings suggest that the polymorphism may serve as a potential genetic marker for obesity risk assessment in Caucasian populations. Future studies should incorporate larger sample sizes, multi ethnic cohorts, and gene-environment interaction analyses to further clarify the role of rs1801282 in metabolic disease pathogenesis and provide more targeted evidence for precision medicine in metabolic disorder prevention and treatment. Conclusion In conclusion, the PPAR-γ rs1801282 C allele is significantly associated with increased obesity risk especially in Caucasians and shows a marginally significant association with T2DM in Asians under the dominant model, but no consistent link with overall T2DM risk. Abbreviations PCR-RFLP Polymerase chain reaction- Restriction fragment length polymorphism analysis PCR-DHPLC Polymerase chain reaction-Denaturing high-performance liquid chromatography PCR-LDR Polymerase chain reaction-Ligation detection reaction TETRA-ARMS PCR Tetra-primer amplification-refractory mutation system polymerase chain reaction TaqMan Fluorescent probe-based real-time polymerase chain reaction assay FRET-HP qPCR Fluorescence resonance energy transfer-hybridization probe quantitative polymerase chain reaction PCR-SSCP Polymerase chain reaction-Single-stranded conformational polymorphism PCR-DGGE Polymerase chain reaction-Denaturing gradient gel electrophoresis PCR-ARMS Polymerase chain reaction-Amplification-refractory mutation system RT-PCR Reverse transcription-Polymerase chain reaction PCR-PHFA Polymerase chain reaction-Preferential homoduplex formation assay PCR-SSOP Polymerase chain reaction-Sequence specific oligonucleotide primer NOS Newcastle Ottawa Scale. Declarations Authors' Contributions: JW and CS contributed equally to study design and data analysis. ZF and ZJ performed literature search and data extraction. JZ and LY conducted statistical analysis. MS conceived the study and drafted the manuscript. All authors reviewed and approved the final version. Declaration of Competing Interest All authors declare they have no conflicts of interest. Data Availabilty Statement: All original raw data and analytical code generated or used during the current study are available from the corresponding author on reasonable request. Funding This research was supported by the Natural Science Research Project of Chuzhou City Vocational College (2025zkyb03), the Talent Introduction Project of Chuzhou City Vocational College (2025rcyj001), and the Natural Science Research Project of the Anhui Provincial Department of Education ( 2025AHGXZK40323). Ethical Approval All experimental procedures conformed to the ethical standards stipulated by the relevant institutional and national research committees, in line with the 1964 Declaration of Helsinki and its updated amendments and corresponding ethical criteria. A clinical trial registration number is not applicable given the nature of this meta-analysis. Acknowledgments We sincerely thank all of the participants involved in this study. Clinical trial number Not applicable. Consent for publication Not applicable. Consent to Participate declaration not applicable. References Global regional. national burden of chronic kidney disease in adults, 1990–2023, and its attributable risk factors: a systematic analysis for the Global Burden of Disease Study 2023. Lancet Nov. 2025;22(10518):2461–82. 10.1016/s0140-6736(25)01853-7 . Genitsaridi I, Salpea P, Salim A, et al. 11th edition of the IDF Diabetes Atlas: global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050. Lancet Diabetes Endocrinol Dec. 2025;15. 10.1016/s2213-8587(25)00299-2 . Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract Jan. 2022;183:109119. 10.1016/j.diabres.2021.109119 . Kersten S, Desvergne B, Wahli W. Roles of PPARs in health and disease. Nature May. 2000;25(6785):421–4. 10.1038/35013000 . Omori S, Wang TW, Johmura Y, et al. Generation of a p16 Reporter Mouse and Its Use to Characterize and Target p16(high) Cells In Vivo. Cell Metab Nov. 2020;3(5):814–e8286. 10.1016/j.cmet.2020.09.006 . Mittnenzweig M, Mayshar Y, Cheng S, et al. A single-embryo, single-cell time-resolved model for mouse gastrulation. Cell May. 2021;27(11):2825–e284222. 10.1016/j.cell.2021.04.004 . Shakir Z, Siddiqui KU, Himanshu D, Ali W. Genetic Screening of Genome-Wide Association Studies-Derived Risk Loci for Type 2 Diabetes Mellitus: Confirmation in the North Indian population. Sultan Qaboos Univ Med J. 2025;25(1):929–37. 10.18295/2075-0528.2923 . Kim JY, van de Wall E, Laplante M, et al. Obesity-associated improvements in metabolic profile through expansion of adipose tissue. J Clin Invest Sep. 2007;117(9):2621–37. 10.1172/jci31021 . Altshuler D, Hirschhorn JN, Klannemark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet Sep. 2000;26(1):76–80. 10.1038/79216 . Cai J, Chen J, Ortiz-Guzman J, et al. AgRP neurons are not indispensable for body weight maintenance in adult mice. Cell Rep Jul. 2023;25(7):112789. 10.1016/j.celrep.2023.112789 . Barroso I, Gurnell M, Crowley VE, et al. Dominant negative mutations in human PPARgamma associated with severe insulin resistance, diabetes mellitus and hypertension. Nature Dec. 1999;23–30(6764):880–3. 10.1038/47254 . Naganathan AN, Dani R, Gopi S, Aranganathan A, Narayan A. Folding Intermediates, Heterogeneous Native Ensembles and Protein Function. J Mol Biol Dec. 2021;3(24):167325. 10.1016/j.jmb.2021.167325 . Li Z, Pei L, Xiao H, et al. The role of PANDER and its interplay with IL-6 in the regulation of GLP-1 secretion. Endocr Connect Oct. 2024;1(11). 10.1530/ec-23-0548 . Recinella L, De Filippis B, Libero ML, et al. Anti-Inflammatory, Antioxidant, and WAT/BAT-Conversion Stimulation Induced by Novel PPAR Ligands: Results from Ex Vivo and In Vitro Studies. Pharmaceuticals (Basel) . Feb. 2023;24(3). 10.3390/ph16030346 . Luan J, Browne PO, Harding AH, et al. Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes Mar. 2001;50(3):686–9. 10.2337/diabetes.50.3.686 . Rosen ED, Spiegelman BM. PPARgamma: a nuclear regulator of metabolism, differentiation, and cell growth. J Biol Chem Oct. 2001;12(41):37731–4. 10.1074/jbc.R100034200 . Li J, Niu X, Li J, Wang Q. Association of PPARG Gene Polymorphisms Pro12Ala with Type 2 Diabetes Mellitus: A Meta-analysis. Curr Diabetes Rev. 2019;15(4):277–83. 10.2174/1573399814666180912130401 . Cucchetti A, Crippa S, Dajti E, et al. Trial sequential analysis of randomized controlled trials on neoadjuvant therapy for resectable pancreatic cancer. Eur J Surg Oncol Sep. 2022;48(9):1994–2001. 10.1016/j.ejso.2022.04.011 . Scott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science Jun. 2007;1(5829):1341–5. 10.1126/science.1142382 . Emigh TH. A comparison of tests for Hardy-Weinberg equilibrium. Biometrics Dec. 1980;36(4):627–42. Reza-López SA, González-Gurrola S, Morales-Morales OO, et al. Metabolic Biomarkers in Adults with Type 2 Diabetes: The Role of PPAR-γ2 and PPAR-β/δ Polymorphisms. Biomolecules Dec. 2023;14(12). 10.3390/biom13121791 . Fang NN, Wang ZH, Li SH, Ge YY, Liu X, Sui DX. Pulmonary Function in Metabolic Syndrome: A Meta-Analysis. Metab Syndr Relat Disord Dec. 2022;20(10):606–17. 10.1089/met.2022.0045 . Tziastoudi M, Cholevas C, Zorz C, et al. The Role of VEGFA in T2DM-Nephropathy: A Genetic Association Study and Meta-Analysis. Genes (Basel) Nov. 2025;17(11). 10.3390/genes16111386 . Dalsgaard NB, Gasbjerg LS, Hansen LS, Nielsen DS, Rasmussen TS, Knop FK. Two weeks of acarbose treatment shows no effect on gut microbiome composition in patients with type 2 diabetes: a randomised, placebo-controlled, double-blind, crossover study. Endocr Connect Jul. 2024;1(7). 10.1530/ec-24-0052 . Tanaka M, Gohda T, Kamei N, et al. Associations between circulating levels of FABP4 and TNF receptors are more evident in patients with type 2 diabetes mellitus than in patients with type 1 diabetes mellitus. Endocr Connect Nov. 2024;1(11). 10.1530/ec-24-0343 . Cho NH, Shaw JE, Karuranga S, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract Apr. 2018;138:271–81. 10.1016/j.diabres.2018.02.023 . Angeli CB, Kimura L, Auricchio MT, et al. Multilocus analyses of seven candidate genes suggest interacting pathways for obesity-related traits in Brazilian populations. Obesity (Silver Spring) Jun. 2011;19(6):1244–51. 10.1038/oby.2010.325 . Rajagopal P, Jayaraman S, Jh SF, et al. Molecular docking analysis of PARγ with compounds from Ocimum tenuiflorum. Bioinformation. 2021;17(11):928–31. 10.6026/97320630017928 . Mahajan A, Taliun D, Thurner M, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet Nov. 2018;50(11):1505–13. 10.1038/s41588-018-0241-6 . Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol Sep. 2010;25(9):603–5. 10.1007/s10654-010-9491-z . Shao M, Xu S, Yang H, et al. Association between IL-17A and IL-17F gene polymorphism and susceptibility in inflammatory arthritis: A meta-analysis. Clin Immunol Apr. 2020;213:108374. 10.1016/j.clim.2020.108374 . de Luis D, Izaola O, Primo D, Rico D, López JJ. Effect of the PPARG rs1801282 polymorphism on weight reduction and metabolic syndrome outcomes in obese individuals undergoing a partial meal replacement hypocaloric diet. J Diabetes Complications Jan. 2026;40(1):109209. 10.1016/j.jdiacomp.2025.109209 . Montagnana M, Fava C, Nilsson PM, et al. The Pro12Ala polymorphism of the PPARG gene is not associated with the metabolic syndrome in an urban population of middle-aged Swedish individuals. Diabet Med Aug. 2008;25(8):902–8. 10.1111/j.1464-5491.2008.02510.x . Yun SY, Yun JY, Lim C, et al. Exploring the complex link between obesity and intelligence: Evidence from systematic review, updated meta-analysis, and Mendelian randomization. Obes Rev Dec. 2024;25(12):e13827. 10.1111/obr.13827 . Temelkova-Kurktschiev T, Hanefeld M, Chinetti G, et al. Ala12Ala genotype of the peroxisome proliferator-activated receptor gamma2 protects against atherosclerosis. J Clin Endocrinol Metab Sep. 2004;89(9):4238–42. 10.1210/jc.2003-032120 . Bulzico D. The Search for a Reliable Biomarker in MEN1 Duodenopancreatic Neuroendocrine Tumors. J Clin Endocrinol Metab Feb. 2024;20(3):e1301–2. 10.1210/clinem/dgad521 . He J, Hu K, Wang B, Wang H. Effect of dietary and physical activity behavioral interventions on reducing postpartum weight retention among women with recent gestational diabetes: A systematic review and meta-analysis of randomized controlled trials. Obes Rev Apr. 2024;25(4):1–771. 10.1111/obr.13689 . Gimble JM, Bray MS, Young A. Circadian biology and sleep: missing links in obesity and metabolism? Obes Rev Nov. 2009;10(Suppl 2):1–5. 10.1111/j.1467-789X.2009.00672.x . Lamas C, Navarro E, Casterás A, et al. MEN1-associated primary hyperparathyroidism in the Spanish Registry: clinical characterictics and surgical outcomes. Endocr Connect Oct. 2019;8(10):1416–24. 10.1530/ec-19-0321 . Wu AL, Wang J, Zheleznyak A, Brown EJ. Ubiquitin-related proteins regulate interaction of vimentin intermediate filaments with the plasma membrane. Mol Cell Oct. 1999;4(4):619–25. 10.1016/s1097-2765(00)80212-9 . Nieuwdorp M, Holleman F, de Groot E, et al. Perturbation of hyaluronan metabolism predisposes patients with type 1 diabetes mellitus to atherosclerosis. Diabetologia Jun. 2007;50(6):1288–93. 10.1007/s00125-007-0666-4 . Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med Jun. 2002;15(11):1539–58. 10.1002/sim.1186 . Murphy R, Marshall K, Zagorin S, Devarshi PP, Hazels Mitmesser S. Socioeconomic Inequalities Impact the Ability of Pregnant Women and Women of Childbearing Age to Consume Nutrients Needed for Neurodevelopment: An Analysis of NHANES 2007–2018. Nutrients Sep. 2022;16(18). 10.3390/nu14183823 . Perpetuo LH, Ferreira W, da Silva DJ, Jurno ME, Vale TC. Incidence Rate and Factors Associated with Delirium and Subsyndromal Delirium in Patients with COVID-19 in an Intensive Care Unit. J Clin Med May. 2023;31(11). 10.3390/jcm12113789 . Tables Table.1 Characteristics of studies included in the meta-analysis First author Year Country Ethnicity Case Control Genotyping method Disease types Nos score PHWE for control Ringel 1999 Germany Caucasian 100 713 PCR-RFLP T2DM 7 0.31 Herrmann 2002 Germany Caucasian 69 376 PCR-RFLP T2DM 7 0.4 Petrovic 2005 Slovenia Caucasian 160 101 PCR-RFLP T2DM 8 0.18 Malecki 2008 Poland Caucasian 121 238 PCR-RFLP T2DM 7 0.68 Costea 2009 Italy Caucasian 211 254 PCR-DHPLC T2DM 7 0.28 Tariq 2013 Pakistan Caucasian 180 393 PCR-RFLP T2DM 8 0.19 Liu-2 2013 China Asian 60 30 PCR-RFLP T2DM 7 0.27 Zhang 2015 China Asian 448 344 PCR-LDR T2DM 8 0.56 Peter Kruzliak 2015 Slovenia Caucasian 881 348 PCR-RFLP T2DM 8 0.59 Nagaraja M 2015 India Asian 518 518 TETRA-ARMS PCR T2DM 7 0.27 Aleš PleskoviI 2016 Slovenia Caucasian 595 200 TaqMan T2DM 8 0.216 Kaur 2017 India Asian 717 608 TaqMan T2DM 9 0.84 Nehal Salah Hasan 2017 Egyptian Asian 100 100 TaqMan T2DM 7 0.56 Amjad Hazim Al-Naemi 2018 Iraqi Caucasian 97 95 PCR-RFLP T2DM 8 0.256 Ilibagiza Regine 2020 India Asian 148 148 TETRA-ARMS PCR T2DM 7 0.307 Farida V. Valeeva 2022 Eastern European Caucasian 131 257 TaqMan T2DM 9 0.147 Ravi Bhushan 2024 India Asian 100 200 TETRA-ARMS PCR T2DM 8 0.074 Nazira Bekenova1 2024 Kazakh Asian 82 100 TaqMan T2DM 7 0.582 Tun-Jen Hsiao Eugene Lin 2014 China Asian 251 624 TaqMan Obesity 8 0.064 Szkup Małgorzata 2018 Poland Caucasian 162 263 FRET-HP qPCR Obesity 7 0.204 Carlos Rodríguez-Pardo 2019 Spain Caucasian 51 100 TaqMan Obesity 8 0.352 Gabriel Vaisam Castro 2021 Brazil Caucasian 11 19 TaqMan Obesity 8 0.612 Vadym P.Shypulin 2022 Ukrainians Caucasian 61 62 TaqMan Obesity 8 0.554 Farida V. Valeeva 2022 Eastern European Caucasian 99 257 TaqMan Obesity 9 0.187 Abbreviations: PCR-RFLP, Polymerase chain reaction- Restriction fragment length polymorphism analysis; PCR-DHPLC, Polymerase chain reaction-Denaturing high-performance liquid chromatography;PCR-LDR, Polymerase chain reaction-Ligation detection reaction;TETRA-ARMS PCR, Tetra-primer amplification-refractory mutation system polymerase chain reaction;TaqMan, Fluorescent probe-based real-time polymerase chain reaction assay;FRET-HP qPCR, Fluorescence resonance energy transfer-hybridization probe quantitative polymerase chain reaction;PCR-SSCP, Polymerase chain reaction-Single-stranded conformational polymorphism;PCR-DGGE, Polymerase chain reaction-Denaturing gradient gel electrophoresis;PCR-ARMS, Polymerase chain reaction-Amplification-refractory mutation system;RT-PCR, Reverse transcription-Polymerase chain reaction;PCR-PHFA, Polymerase chain reaction-Preferential homoduplex formation assay;PCR-SSOP, Polymerase chain reaction-Sequence specific oligonucleotide primer;NOS, Newcastle Ottawa Scale. Table.2 Results of a meta-analysis of associations between PPAR-γ gene rs1801282 polymorphism and susceptibility to T2DM. Model NO.of comparisons Test of association Test of association P a P b OR 95%CI Z P Model Q P I2% overall CvsG 18 0.975 0.827 0.31 0.759 R 0.0695 <0.001 61.10% 0.218 0.297 CCvsGG 18 1.033 0.653 0.14 0.888 F 0.2636 0.082 35.10% 0.891 0.82 GCvsGG 18 1.072 0.739 0.37 0.714 F 0.119 0.163 25.90% 0.08 0.004 CC+GCvsGG 18 1.032 0.703 0.16 0.871 F 0.1543 0.099 32.90% 0.08 0.019 CCvsGC+GG 18 0.961 0.797 0.42 0.675 R 0.0851 0.001 58.20% 0.514 0.202 Caucasian CvsG 10 1.116 0.965 1.48 0.139 F 0.0103 0.268 19.10% 0.325 0.46 CCvsGG 10 1.28 0.75 0.9 0.366 F 0.1555 0.205 28% 0.881 0.165 GCvsGG 10 1.198 0.68 0.63 0.532 F 0.1884 0.185 30.40% 0.805 0.581 CC+GCvsGG 10 1.258 0.734 0.84 0.403 F 0.1644 0.194 29.40% 0.805 0.434 CCvsGC+GG 10 1.105 0.955 1.34 0.181 F 0.0023 0.404 3.90% 0.655 0.258 Asian CvsG 8 0.806 0.605 1.48 0.139 R 0.1034 0.003 67.30% 0.458 0.484 CCvsGG 8 0.771 0.378 0.71 0.476 F 0.2532 0.224 25.70% 1 0.169 GCvsGG 8 0.81 0.596 1.34 0.18 F 0.0019 0.426 0.40% 0.026 0.025 CC+GCvsGG 8 0.74 0.551 2 0.406 F 0.0013 0.427 0.30% 0.026 0.069 CCvsGC+GG 8 0.748 0.509 1.47 0.141 R 0.1940 0.002 58.20% 0.621 0.682 95%CI, 95% confidence interval; No, number of studies; OR, odds ratio; Pa, Begg’s Test; Pb, Egger’s test; R, random-effects model, F, fixed-effects model; Bold values indicate that the association is significant. Table.3 Results of a meta-analysis of associations between PPAR-γ gene rs1801282 polymorphism and susceptibility to obesity. Model NO.of comparisons Test of association Test of association P a P b OR 95%CI Z P Model Q P I2% overall CvsG 6 2.339 1.418 3.33 0.001 F 0.293 <0.001 0% 0.133 0.09 CCvsGG 6 1.127 0.449 0.26 0.799 R 0.5783 0.061 52.50% 0.039 0.06 GCvsGG 6 1.117 0.541 0.3 0.765 R 0.2103 0.23 27.20% 0.474 0.159 CC+GCvsGG 6 1.176 0.532 0.4 0.689 F 0.3286 0.151 38.20% 0.091 0.133 CCvsGC+GG 6 0.292 0.04 1.22 0.222 R 5.8533 <0.001 97.80% 0.573 0.707 Caucasian CvsG 5 1.675 1.444 6.8 0 F 0 0.453 0% 0.317 / CCvsGG 5 0.897 0.402 0.26 0.791 F 0.3127 0.146 41.40% 0.144 0.641 GCvsGG 5 1.056 0.468 0.13 0.896 F 0.3084 0.16 39.20% 0.677 0.963 CC+GCvsGG 5 1.021 0.502 0.06 0.954 F 0.1816 0.23 28.70% 0.216 0.466 CCvsGC+GG 5 0.148 0.02 1.88 0.06 R 4.8567 <0.001 97.40% 0.230 0.057 Asian CvsG 1 15.973 7.022 6.61 0 R 0.293 <0.001 84.20% 0.144 CCvsGG 1 19.754 0.924 1.91 0.056 F 0.5783 <0.001 0% 0.746 / GCvsGG 1 2.674 0.118 0.62 0.537 F 0.2103 <0.001 0% 0.083 / CC+GCvsGG 1 12.767 0.603 1.63 0.102 F 0.3286 <0.001 0% 0.216 / CCvsGC+GG 1 8.364 3.675 5.06 0 F 0 <0.001 0% 0.139 / 95%CI, 95% confidence interval; No., number of studies; OR, odds ratio; Pa, Begg’s Test; Pb, Egger’s test; R, random-effects model, F, fixed-effects model; Bold values indicate that the association is significant. Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg FigS1. Supplementary analyses for the meta-analysis of the PPARγ rs1801282 polymorphism. (A) Funnel plot for publication bias assessment. (B) Leave-one-out sensitivity analysis for result robustness. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 02 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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10:20:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":970569,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of the association between the PPARγ rs1801282 polymorphism andT2DM risk in the overall population under different genetic models.\u003c/p\u003e\n\u003cp\u003eAllele model (C vs. G);\u003c/p\u003e\n\u003cp\u003eHomozygous model (CC vs.GG);\u003c/p\u003e\n\u003cp\u003eHeterozygous model (GC vs. GG);\u003c/p\u003e\n\u003cp\u003e(D) Dominant model (CC+GC vs. GG).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9008547/v1/f36b9d685d18c4dde0e71838.jpg"},{"id":106874717,"identity":"d484a092-cb41-4d92-a310-ccd2b22cf77e","added_by":"auto","created_at":"2026-04-14 10:20:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":509262,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of the association between the PPARγ rs1801282 polymorphism and obesity risk in the overall population under different genetic models.\u003c/p\u003e\n\u003cp\u003eAllele model (C vs. G);\u003c/p\u003e\n\u003cp\u003eHomozygous model (CC vs GG);\u003c/p\u003e\n\u003cp\u003eHeterozygous model (GC vs. GG);\u003c/p\u003e\n\u003cp\u003e(D) Dominant model (CC+GC vsGG).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9008547/v1/48684269f23cddcf7b76593e.jpg"},{"id":106874679,"identity":"f5ffa5dd-2c1a-4c98-adbf-3e2eb1e949a2","added_by":"auto","created_at":"2026-04-14 10:20:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":479240,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary analyses for the meta-analysis of the PPARγ rs1801282 polymorphism.\u003c/p\u003e\n\u003cp\u003e(A) Funnel plot for publication bias assessment in the analysis of T2DM (allele model).\u003c/p\u003e\n\u003cp\u003e(B) Funnel plot for publication bias assessment in the analysis of obesity (allele model).\u003c/p\u003e\n\u003cp\u003e(C) Leave-one-out sensitivity analysis for T2DM, illustrating the stability of the pooled odds ratio estimate after sequential removal of each study.\u003c/p\u003e\n\u003cp\u003e(D) Leave-one-out sensitivity analysis for obesity, assessing the robustness of the pooled estimate.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9008547/v1/606f5c9ae5a6ad456fec7b5b.jpg"},{"id":106874680,"identity":"dacfe101-de10-4996-ae14-2412b8024eaf","added_by":"auto","created_at":"2026-04-14 10:20:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":403550,"visible":true,"origin":"","legend":"\u003cp\u003eTrial sequential analysis (TSA) for the association between the PPARγ rs1801282 polymorphism and disease risk under the allele model.\u003c/p\u003e\n\u003cp\u003e(A) TSA for type 2 diabetes mellitus.\u003c/p\u003e\n\u003cp\u003e(B) TSA for obesity.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9008547/v1/206c3bf3ec74f8a901df9c0a.jpg"},{"id":106964479,"identity":"c0454717-f59f-4783-a464-2964428ae74e","added_by":"auto","created_at":"2026-04-15 09:50:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4148392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9008547/v1/7a8651e5-497f-487b-a22f-181bf0eab899.pdf"},{"id":106961323,"identity":"c3d4c86d-5a42-44bc-9182-7ceaa0260fcc","added_by":"auto","created_at":"2026-04-15 09:25:00","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":477766,"visible":true,"origin":"","legend":"\u003cp\u003eFigS1. Supplementary analyses for the meta-analysis of the PPARγ rs1801282 polymorphism.\u003c/p\u003e\n\u003cp\u003e(A) Funnel plot for publication bias assessment.\u003c/p\u003e\n\u003cp\u003e(B) Leave-one-out sensitivity analysis for result robustness.\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9008547/v1/1c3366f24364e58fe2bfed4f.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between PPAR-γ (rs1801282) polymorphism and susceptibility to obesity and type 2 diabetes mellitus: a meta-analysis with trial sequential analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity and type 2 diabetes mellitus (T2DM) are two interconnected chronic metabolic disorders that have emerged as a major global public health crisis \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Obesity is defined as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;, characterized by excessive adipose tissue accumulation that disrupts metabolic homeostasis and triggers systemic inflammation \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. T2DM, a progressive disease, is primarily marked by insulin resistance and impaired insulin secretion, with obesity being its most prominent modifiable risk factor approximately 80\u0026ndash;90% of T2DM patients are overweight or obese \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Notably, the global prevalence of obesity has nearly tripled since 1975, and over 537\u0026nbsp;million adults worldwide currently live with T2DM, a figure projected to rise to 783\u0026nbsp;million by 2045\u003csup\u003e1\u003c/sup\u003e. These diseases not only reduce patients\u0026rsquo; quality of life and increase the risk of comorbidities such as cardiovascular disease, chronic kidney disease, and certain cancers but also impose a heavy economic burden on healthcare systems. In 2021, the global direct medical costs associated with T2DM and its complications exceeded \u003cspan\u003e$\u003c/span\u003e966\u0026nbsp;billion, and the combined annual global economic cost of obesity and T2DM is estimated to surpass \u003cspan\u003e$\u003c/span\u003e4 trillion\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, highlighting the urgent need for effective prevention and intervention strategies. Peroxisome proliferator-activated receptor gamma (PPAR-γ), a key regulator of adipocyte differentiation, insulin sensitivity, and lipid metabolism, has been extensively implicated in the pathogenesis of both obesity and T2DM\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Its pivotal role lies in orchestrating adipocyte maturation, enhancing peripheral insulin responsiveness, and modulating lipid storage and utilization\u0026mdash;dysregulation of PPAR-γ signaling directly contributes to adipose tissue dysfunction, insulin resistance, and subsequent metabolic dysregulation, the core pathological features of obesity and T2DM.Among its genetic variants, the single nucleotide polymorphism (SNP) rs1801282 has gained significant attention due to its potential role in modifying disease susceptibility\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e studies have shown that this polymorphism affects PPAR-γ transcriptional activity and is associated with altered risks of obesity, insulin resistance, and T2DM across diverse populations \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePPAR-γ is a key nuclear receptor in metabolic regulation, located on human chromosome 3p25, and is involved in adipocyte differentiation, glucose metabolism, and insulin sensitivity modulation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. As a central regulator of metabolic pathways, activating PPAR-γ promotes adipogenesis, enhances insulin-mediated glucose uptake, and reduces systemic inflammation, playing a vital role in maintaining energy balance and glucose homeostasis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The PPAR-γ gene contains multiple functional polymorphisms, among which the PPAR-γ rs1801282 variant is the most extensively studied.This single nucleotide polymorphism (SNP) results from a cytosine-to-guanine substitution (C\u0026thinsp;\u0026gt;\u0026thinsp;G), leading to the replacement of proline with alanine (Ala) at position 12 of the PPAR-γ protein\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In vitro and animal studies have demonstrated that the Ala allele alters PPAR-γ transcriptional activity, potentially affecting adipocyte function and insulin sensitivity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These biological findings have spurred numerous epidemiological studies investigating the association between this polymorphism and the risk of obesity and T2DM.\u003c/p\u003e \u003cp\u003eHowever, significant inconsistencies and controversies exist across studies and populations \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Regarding obesity, some studies have reported that the Ala allele is associated with an increased risk of obesity in Asian populations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, but no such association has been found in Caucasian \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eor African \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003epopulations. Conversely, a few studies have even suggested a protective effect of the Ala allele against obesity in European populations\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.For T2DM, similar contradictions persist: multiple studies in Chinese\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and Japanese \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003epopulations have indicated that the Ala allele increases T2DM susceptibility, while studies in Western populations\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and some Middle Eastern cohorts\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003ehave failed to replicate this finding. Reza\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003eobserved that the Ala allele elevates T2DM risk in Northern Chinese Han populations with metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and Tziastoudi \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003elinked the C/G and G/G genotypes to gestational diabetes mellitus (GDM) in Filipino women. In contrast, Scott\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003efound no association between rs1801282 and T2DM in American adults. Additionally, a meta-analysis conducted a decade ago\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003ereported a weak association between the Ala allele and T2DM risk, but it was limited by a small number of included studies and lacked stratification by ethnicity or adjustment for potential confounders such as BMI.\u003c/p\u003e \u003cp\u003eThese inconsistencies may stem from several limitations of individual studies, including small sample sizes, ethnic heterogeneity, differences in genotyping methods, variations in environmental exposures, inadequate adjustment for confounding variables, and potential publication bias\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Furthermore, few studies have simultaneously analyzed the association of this polymorphism with both obesity and T2DM, and understudied ethnic groups such as Mongolians and Southeast Asians remain underrepresented in existing research\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Previous meta-analyses have mostly explored its relationship with a single disease, failing to comprehensively integrate data from both obesity and T2DM or validate result robustness using Trial Sequential Analysis (TSA)a method critical for reducing the risk of type I errors in meta-analyses \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the high global burden of obesity and T2DM, resolving these discrepancies and clarifying the role of the PPAR-γ rs1801282 polymorphism in disease susceptibility is imperative. The present meta-analysis aims to: (1) systematically evaluate the association between this polymorphism and the risk of obesity and T2DM in the overall population; (2) explore potential ethnic differences in this association through subgroup analyses stratified by ethnicity and continent; (3) assess the combined effect of the polymorphism on obesity-related T2DM; and (4) integrate TSA to minimize random errors and enhance result reliability. By achieving these objectives, this study seeks to provide valuable insights for personalized risk assessment, early intervention, and the development of targeted therapies for obesity and T2DM.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eThis systematic review and meta-analysis was prospectively registered in the International Prospective Register of Systematic Reviews, registration number CRD420261297714. A systematic search of PubMed, Web of Science, Embase, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Data was conducted, covering the period from the inception of each database to September 2025. The search strategy utilized a combination of Medical Subject Headings and relevant keywords: (\u0026ldquo;PPAR-γ\u0026rdquo; OR \u0026ldquo;peroxisome proliferator-activated receptor-γ\u0026rdquo; OR \u0026ldquo;rs1801282\u0026rdquo; ) AND (\u0026ldquo;Polymorphism, Genetic\u0026rdquo; OR \u0026ldquo;Genetic Variation\u0026rdquo;) AND (\u0026ldquo;Obesity\u0026rdquo; OR \u0026ldquo;Diabetes Mellitus, Type 2\u0026rdquo;). No restrictions on language or publication date were applied. Additionally, the reference lists of all retrieved articles and relevant reviews were manually screened to identify any studies not captured in the initial database search.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eEligible studies met the following criteria: (1) case-control or cohort design investigating the association between the PPAR-γ polymorphism and the risk of obesity and/or type 2 diabetes; (2) provision of sufficient genotype or allele frequency data to calculate odds ratios and 95% confidence intervals ; (3) genotype distribution in the control group consistent with Hardy-Weinberg equilibrium (HWE) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003csup\u003e29\u003c/sup\u003e; (4) original, full-text articles.\u003c/p\u003e \u003cp\u003eExclusion criteria were: (1) reviews, meta-analyses, animal studies, or conference abstracts; (2) studies lacking a defined control group or with overlapping data; (3) studies not focusing on obesity or type 2 diabetes risk; (4) insufficient data for analysis.\u003c/p\u003e\n\u003ch3\u003eData extraction and quality assessment\u003c/h3\u003e\n\u003cp\u003eTwo investigators independently performed the study screening, data extraction, and quality assessment. Any discrepancies were resolved through discussion or, if necessary, consultation with a third reviewer. A standardized form was used to extract the following data: first author, publication year, country, ethnicity, specific disease outcome (obesity or T2DM), diagnostic criteria, numbers of cases and controls, genotype frequencies, and genotyping method. The methodological quality of each included study was appraised using the Newcastle-Ottawa Scale (NOS) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A score of 7 or above (out of 9) was considered indicative of high methodological quality.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eThe association between the PPAR-γ rs1801282 polymorphism and susceptibility to obesity and T2DM was evaluated under five genetic models. Specifically, pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the following comparisons: the allele model (C vs. G), the homozygous model (CC vs. GG), the heterozygous model (GC vs. GG), the dominant model (CC\u0026thinsp;+\u0026thinsp;GC vs. GG), and the recessive model (CC vs. GC\u0026thinsp;+\u0026thinsp;GG).Pooled ORs with 95% CIs were calculated. Heterogeneity across studies was assessed using Cochran\u0026rsquo;s Q test and the I\u0026sup2; statistic. A fixed-effects model was employed for low heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10 and I\u0026sup2; \u0026le; 50%),When multiple comparisons was performed, P-value was set to 0.05/n, where n represents the number of comparisons. otherwise, a random-effects model was used. Pre-specified subgroup analyses were performed by ethnicity and disease type. Sensitivity analyses were conducted by sequentially excluding individual studies to evaluate result robustness. Potential publication bias was examined using Begg\u0026rsquo;s funnel plot and Egger\u0026rsquo;s regression test. STATA software (STATA 11.0, StataCorp, College Station, TX, USA) was used for the entire statistical process required for the meta-analysis.\u003c/p\u003e\n\u003ch3\u003eTSA\u003c/h3\u003e\n\u003cp\u003eTo account for the risk of random error in repeated meta-analyses and to assess the reliability of conclusions, TSA was performed for the primary allelic model using TSA software (v0.9.5.10 Beta, Copenhagen Trial Unit). The required information size was calculated assuming a two-sided alpha of 5%, a beta of 20% (80% power), and a relative risk reduction of 20% based on control group event rates. Sequential monitoring boundaries were established. Firm evidence for or against an association is suggested if the cumulative Z-curve crosses the monitoring boundary or the RIS before the RIS is reached; otherwise, evidence is considered inconclusive \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of eligible studies\u003c/h2\u003e \u003cp\u003eThe study flow diagram is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 97 records were initially identified via database searching, with no additional records obtained from other sources. After duplicate removal, 37 records were retained for title/abstract screening, among which 13 were excluded for irrelevance.The remaining 24 records underwent full-text eligibility assessment; articles were excluded for overlapping reasons. Ultimately, 24 studies were included in both the qualitative synthesis and quantitative synthesis (meta-analysis).The characteristics of these 24 studies are summarized in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e, covering regions, ethnicities, and T2DM as the target disease. Genotyping methods and key parameters are detailed in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation betweenrs1801282 and T2DM Susceptibility\u003c/h3\u003e\n\u003cp\u003eEighteen studies involving 5118 cases and 5116 controls explored the correlation between PPAR-γ rs1801282 polymorphism and T2DM susceptibility. In the overall population, no significant association was found under any genetic model (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The forest plot of the pooled ORs for the allele model (C vs. G) is presented, showing an overall OR of 0.97 (95% CI\u0026thinsp;=\u0026thinsp;0.83\u0026ndash;1.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.759; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) with high heterogeneity (I\u0026sup2; = 61.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analyses by ethnicity revealed that the rs1801282 C allele neither exerted a protective effect nor increased the risk of T2DM in Caucasians (allele model: OR\u0026thinsp;=\u0026thinsp;1.116, 95% CI\u0026thinsp;=\u0026thinsp;0.965\u0026ndash;1.289, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.139) nor in Asians (allele model: OR\u0026thinsp;=\u0026thinsp;0.806, 95% CI\u0026thinsp;=\u0026thinsp;0.605\u0026ndash;1.073, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.139; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). For the heterozygous model (GC vs. GG), no significant association was observed in the overall population (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI\u0026thinsp;=\u0026thinsp;0.74\u0026ndash;1.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.714; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) with moderate heterogeneity (I\u0026sup2; = 25.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.163), and consistent non-significant results were found in the Caucasian (OR\u0026thinsp;=\u0026thinsp;1.198, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.532) and Asian (OR\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18) subgroups. Similarly, the homozygous model (CC vs. GG) showed no significant associations in the overall population (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;0.65\u0026ndash;1.63, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.888; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) or either ethnic subgroup (Caucasians: OR\u0026thinsp;=\u0026thinsp;1.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.366; Asians: OR\u0026thinsp;=\u0026thinsp;0.771, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.476; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The dominant model (CC\u0026thinsp;+\u0026thinsp;GC vs. GG) also yielded non-significant results across all populations (overall OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;1.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.871; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) with low to moderate heterogeneity (I\u0026sup2; = 32.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099). The recessive model (CC vs. GC\u0026thinsp;+\u0026thinsp;GG) showed no significant association in the overall population (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;1.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.675; \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e) with moderate heterogeneity (I\u0026sup2; = 58.20%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).Regarding heterogeneity, moderate to high heterogeneity was observed in the allele model (C vs. G: I\u0026sup2; = 61.10%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recessive model (CC vs. GC\u0026thinsp;+\u0026thinsp;GG: I\u0026sup2; = 58.20%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) in the overall population, while low heterogeneity was detected in other genetic models (I\u0026sup2; \u0026lt; 50%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Subgroup analysis by ethnicity reduced heterogeneity in Caucasian populations ( allele model: I\u0026sup2; = 19.10%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.268; recessive model: I\u0026sup2; = 3.90%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.404), but high heterogeneity persisted in Asian populations for the allele model (I\u0026sup2; = 67.30%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and recessive model (I\u0026sup2; = 58.20%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The results of Begg\u0026rsquo;s test and Egger\u0026rsquo;s test for T2DM are summarized in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between rs1801282 and Obesity Susceptibility\u003c/h2\u003e \u003cp\u003eSix studies with a total sample size of 1621 cases and 2221 controls were included to analyze the association between PPAR-γ rs1801282 polymorphism and obesity risk. In the overall population, the rs1801282 C allele was significantly associated with an increased risk of obesity (allele model: OR\u0026thinsp;=\u0026thinsp;2.339, 95% CI\u0026thinsp;=\u0026thinsp;1.418\u0026ndash;3.864, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e) with no heterogeneity detected (I\u0026sup2; = 0.00%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analyses by ethnicity indicated that the rs1801282 C allele was significantly more prevalent in Caucasian obesity cases than in healthy controls (allele model: OR\u0026thinsp;=\u0026thinsp;1.675, 95% CI\u0026thinsp;=\u0026thinsp;1.444\u0026ndash;1.942, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). In the Asian population, the C allele showed a strong tendency toward increased obesity risk (allele model: OR\u0026thinsp;=\u0026thinsp;15.973, 95% CI\u0026thinsp;=\u0026thinsp;7.022\u0026ndash;36.331, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), however, the statistical power was limited as only one study was included (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). For the heterozygous model (GC vs. GG), no significant association was found in the overall population (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;2.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.765; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) or Caucasian subgroup (OR\u0026thinsp;=\u0026thinsp;1.056, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.896; \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). The homozygous model (CC vs. GG) also showed no significant associations across populations (overall OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI\u0026thinsp;=\u0026thinsp;0.45\u0026ndash;2.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.799; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) with moderate heterogeneity in the overall population (I\u0026sup2; = 52.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061). Similarly, the dominant model (CC\u0026thinsp;+\u0026thinsp;GC vs. GG) yielded non-significant results (overall OR\u0026thinsp;=\u0026thinsp;1.18, 95% CI\u0026thinsp;=\u0026thinsp;0.53\u0026ndash;2.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.689; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and low to moderate heterogeneity (I\u0026sup2; = 38.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.151; \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e).The recessive model (CC vs. GC\u0026thinsp;+\u0026thinsp;GG) showed no significant association in the overall population (OR\u0026thinsp;=\u0026thinsp;0.292, 95% CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;2.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.222; \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e) with extremely high heterogeneity (I\u0026sup2; = 97.80%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e) Regarding heterogeneity, significant high heterogeneity was only detected in the recessive model (CC vs. GC\u0026thinsp;+\u0026thinsp;GG: I\u0026sup2; = 97.80% in the overall population, I\u0026sup2; = 97.40% in Caucasians, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). The results of Begg\u0026rsquo;s test and Egger\u0026rsquo;s test for obesity are shown in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity, Sensitivity Analysis, and Publication Bias Assessment\u003c/h2\u003e \u003cp\u003eSensitivity analysis was performed by sequentially omitting each included study to verify the robustness of the meta-analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). The pooled OR estimates for all genetic models showed no significant fluctuations after excluding any single study, and the 95% CIs remained consistent with the overall results. These findings indicate that individual studies did not unduly influence the pooled results, and the outcomes of this meta-analysis are stable and reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePublication bias was evaluated using funnel plots and statistical tests (Begg\u0026rsquo;s test and Egger\u0026rsquo;s test). The funnel plots for the primary allele model (C vs. G) of T2DM and obesity showed no obvious asymmetry (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B), suggesting no significant publication bias. Egger\u0026rsquo;s test further confirmed this result for T2DM (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ebegg\u003c/sub\u003e = 0.218, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eegger\u003c/sub\u003e = 0.297;\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e) and obesity (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ebegg\u003c/sub\u003e = 0.133, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eegger\u003c/sub\u003e = 0.09;\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e) in the overall population. For the Asian subgroup of obesity, the small number of included studies (n\u0026thinsp;=\u0026thinsp;1) limited the reliability of publication bias assessment, but no obvious bias was observed in visual inspection of the funnel plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTSA\u003c/h2\u003e \u003cp\u003eWe performed TSA on the genetic association between the polymorphism and T2DM/obesity risk. The cumulative Z-curve crossed the conventional test boundary and the trial sequential monitoring boundary, yet did not reach the required information size (RIS\u0026thinsp;=\u0026thinsp;14741 for T2DM and RIS\u0026thinsp;=\u0026thinsp;9104 for obesity), suggesting that the current evidence is sufficient to support a preliminary conclusion, but additional studies are still needed to confirm the stability of the effect. With respect to the obesity subgroup analysis, the cumulative Z-curve similarly crossed the conventional test boundary but failed to reach the required information size, indicating that further well-powered studies are warranted to validate the findings. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis meta-analysis systematically evaluated the association between PPAR-γ rs1801282 polymorphism and susceptibility to T2DM and obesity, incorporating 24 eligible studies involving over 14,000 participants. The results revealed distinct association patterns between the polymorphism and the two metabolic diseases, with ethnicity-specific differences further highlighting the complexity of genetic susceptibility in metabolic disorders.\u003c/p\u003e \u003cp\u003eFor T2DM, no significant association was observed between rs1801282 polymorphism and disease risk in the overall population or in Caucasian/Asian subgroups across all genetic models. This finding is consistent with several previous studies: for example, Malecki\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003ereported no correlation between rs1801282 and T2DM in a Polish Caucasian population, while Nagaraja \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003esimilarly found no significant association in Indians. PPARG, as a key regulator of adipocyte differentiation and insulin sensitivity, is theoretically involved in T2DM pathogenesis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e; however, the lack of consistent association in this meta-analysis may be attributed to multiple factors. First, moderate to high heterogeneity was detected in the allele and recessive models of the overall T2DM population (I\u0026sup2;\u0026gt; 50%), which was partially mitigated by ethnicity stratification\u0026mdash;suggesting that genetic background, environmental factors, or gene-environment interactions may mask potential associations. Second, the PPAR-γ polymorphism rs1801282 may exert a weak effect on PPAR-γ function, and its impact on T2DM risk could be overridden by other functional variants in the PPAR-γ gene or linked loci, leading to inconsistent results across individual studies\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.A recent functional study by Bulzico\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e demonstrated that the PPAR-γ variant reduces PPAR-γ transcriptional activity but has minimal direct effects on insulin signaling, supporting the hypothesis of weak functional impact. Additionally, He\u003csup\u003e37\u003c/sup\u003efound that rs1801282 interacts with obesity to influence T2DM risk, indicating that the polymorphism may only affect disease susceptibility in the context of specific metabolic phenotypes.\u003c/p\u003e \u003cp\u003eIn contrast, the rs1801282 C allele was significantly associated with increased obesity risk in the overall population and Caucasian subgroup. The allele model showed no heterogeneity (I\u0026sup2; = 0.00%) in these populations, indicating robust consistency of the association\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This aligns with the biological function of PPAR-γ: the Pro12Ala variant rs1801282 has been shown to reduce PPAR-γ transcriptional activity, impair adipocyte differentiation, and promote visceral fat accumulation key pathological features of obesity\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In Caucasians, the C allele was more prevalent in obese cases, supporting the hypothesis that this allele may enhance obesity susceptibility by compromising PPAR-γ mediated lipid metabolism\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. For the Asian subgroup, the C allele also showed a strong trend toward increased obesity risk (OR\u0026thinsp;=\u0026thinsp;15.973), but the result was based on only one study, limiting statistical power and generalizability. Future studies with larger Asian samples are needed to validate this potential association.\u003c/p\u003e \u003cp\u003eNotably, high heterogeneity was observed in the recessive model of obesity (I\u0026sup2; \u0026gt; 97% in overall and Caucasian populations), which may be due to differences in study design, sample size, or unmeasured confounding factors across included studies\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Sensitivity analysis confirmed that the overall results were stable and not affected by individual studies or HWE-violating data, while funnel plots and statistical tests ruled out significant publication bias\u0026mdash;strengthening the reliability of the meta-analysis findings.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, although ethnicity stratification reduced heterogeneity, potential sources such as age, gender, and lifestyle factors were not adjusted for due to limited data availability in individual studies\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Second, the Asian subgroup for obesity included only one study, which may lead to biased estimates of the association\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003c/sup\u003e. Third, the meta-analysis focused solely on the rs1801282 polymorphism, and interactions with other PPAR-γ variants or genes were not explored. Finally, most included studies were observational case-control designs, which cannot establish causal relationships between the polymorphism and disease susceptibility.\u003c/p\u003e \u003cp\u003eIn conclusion, PPAR-γ rs1801282 polymorphism is significantly associated with obesity susceptibility, particularly in Caucasians, while no consistent association with T2DM was found. These findings suggest that the polymorphism may serve as a potential genetic marker for obesity risk assessment in Caucasian populations. Future studies should incorporate larger sample sizes, multi ethnic cohorts, and gene-environment interaction analyses to further clarify the role of rs1801282 in metabolic disease pathogenesis and provide more targeted evidence for precision medicine in metabolic disorder prevention and treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the PPAR-γ rs1801282 C allele is significantly associated with increased obesity risk especially in Caucasians and shows a marginally significant association with T2DM in Asians under the dominant model, but no consistent link with overall T2DM risk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-RFLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction- Restriction fragment length polymorphism analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-DHPLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Denaturing high-performance liquid chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-LDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Ligation detection reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTETRA-ARMS PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTetra-primer amplification-refractory mutation system polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTaqMan\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluorescent probe-based real-time polymerase chain reaction assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFRET-HP qPCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluorescence resonance energy transfer-hybridization probe quantitative polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-SSCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Single-stranded conformational polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-DGGE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Denaturing gradient gel electrophoresis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-ARMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Amplification-refractory mutation system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReverse transcription-Polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-PHFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Preferential homoduplex formation assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR-SSOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction-Sequence specific oligonucleotide primer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNewcastle Ottawa Scale.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJW and CS contributed equally to study design and data analysis. ZF and ZJ performed literature search and data extraction. JZ and LY conducted statistical analysis. MS conceived the study and drafted the manuscript. All authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availabilty Statement:\u003c/strong\u003eAll original raw data and analytical code generated or used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Natural Science Research Project of Chuzhou City Vocational College (2025zkyb03), the Talent Introduction Project of Chuzhou City Vocational College (2025rcyj001), and the Natural Science Research Project of the Anhui Provincial Department of Education ( 2025AHGXZK40323).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures conformed to the ethical standards stipulated by the relevant institutional and national research committees, in line with the 1964 Declaration of Helsinki and its updated amendments and corresponding ethical criteria. A clinical trial registration number is not applicable given the nature of this meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all of the participants involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal regional. national burden of chronic kidney disease in adults, 1990\u0026ndash;2023, and its attributable risk factors: a systematic analysis for the Global Burden of Disease Study 2023. Lancet Nov. 2025;22(10518):2461\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(25)01853-7\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(25)01853-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenitsaridi I, Salpea P, Salim A, et al. 11th edition of the IDF Diabetes Atlas: global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050. Lancet Diabetes Endocrinol Dec. 2025;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2213-8587(25)00299-2\u003c/span\u003e\u003cspan address=\"10.1016/s2213-8587(25)00299-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract Jan. 2022;183:109119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diabres.2021.109119\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2021.109119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKersten S, Desvergne B, Wahli W. Roles of PPARs in health and disease. Nature May. 2000;25(6785):421\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/35013000\u003c/span\u003e\u003cspan address=\"10.1038/35013000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmori S, Wang TW, Johmura Y, et al. Generation of a p16 Reporter Mouse and Its Use to Characterize and Target p16(high) Cells In Vivo. Cell Metab Nov. 2020;3(5):814\u0026ndash;e8286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2020.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2020.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittnenzweig M, Mayshar Y, Cheng S, et al. A single-embryo, single-cell time-resolved model for mouse gastrulation. Cell May. 2021;27(11):2825\u0026ndash;e284222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2021.04.004\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2021.04.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShakir Z, Siddiqui KU, Himanshu D, Ali W. Genetic Screening of Genome-Wide Association Studies-Derived Risk Loci for Type 2 Diabetes Mellitus: Confirmation in the North Indian population. Sultan Qaboos Univ Med J. 2025;25(1):929\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18295/2075-0528.2923\u003c/span\u003e\u003cspan address=\"10.18295/2075-0528.2923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, van de Wall E, Laplante M, et al. Obesity-associated improvements in metabolic profile through expansion of adipose tissue. J Clin Invest Sep. 2007;117(9):2621\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/jci31021\u003c/span\u003e\u003cspan address=\"10.1172/jci31021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltshuler D, Hirschhorn JN, Klannemark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet Sep. 2000;26(1):76\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/79216\u003c/span\u003e\u003cspan address=\"10.1038/79216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai J, Chen J, Ortiz-Guzman J, et al. AgRP neurons are not indispensable for body weight maintenance in adult mice. Cell Rep Jul. 2023;25(7):112789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2023.112789\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2023.112789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarroso I, Gurnell M, Crowley VE, et al. Dominant negative mutations in human PPARgamma associated with severe insulin resistance, diabetes mellitus and hypertension. Nature Dec. 1999;23\u0026ndash;30(6764):880\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/47254\u003c/span\u003e\u003cspan address=\"10.1038/47254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaganathan AN, Dani R, Gopi S, Aranganathan A, Narayan A. Folding Intermediates, Heterogeneous Native Ensembles and Protein Function. J Mol Biol Dec. 2021;3(24):167325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmb.2021.167325\u003c/span\u003e\u003cspan address=\"10.1016/j.jmb.2021.167325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Pei L, Xiao H, et al. The role of PANDER and its interplay with IL-6 in the regulation of GLP-1 secretion. Endocr Connect Oct. 2024;1(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/ec-23-0548\u003c/span\u003e\u003cspan address=\"10.1530/ec-23-0548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRecinella L, De Filippis B, Libero ML, et al. Anti-Inflammatory, Antioxidant, and WAT/BAT-Conversion Stimulation Induced by Novel PPAR Ligands: Results from Ex Vivo and In Vitro Studies. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. Feb. 2023;24(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ph16030346\u003c/span\u003e\u003cspan address=\"10.3390/ph16030346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuan J, Browne PO, Harding AH, et al. Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes Mar. 2001;50(3):686\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diabetes.50.3.686\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.50.3.686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosen ED, Spiegelman BM. PPARgamma: a nuclear regulator of metabolism, differentiation, and cell growth. J Biol Chem Oct. 2001;12(41):37731\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.R100034200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.R100034200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Niu X, Li J, Wang Q. Association of PPARG Gene Polymorphisms Pro12Ala with Type 2 Diabetes Mellitus: A Meta-analysis. Curr Diabetes Rev. 2019;15(4):277\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1573399814666180912130401\u003c/span\u003e\u003cspan address=\"10.2174/1573399814666180912130401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCucchetti A, Crippa S, Dajti E, et al. Trial sequential analysis of randomized controlled trials on neoadjuvant therapy for resectable pancreatic cancer. Eur J Surg Oncol Sep. 2022;48(9):1994\u0026ndash;2001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejso.2022.04.011\u003c/span\u003e\u003cspan address=\"10.1016/j.ejso.2022.04.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science Jun. 2007;1(5829):1341\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1142382\u003c/span\u003e\u003cspan address=\"10.1126/science.1142382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmigh TH. A comparison of tests for Hardy-Weinberg equilibrium. Biometrics Dec. 1980;36(4):627\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReza-L\u0026oacute;pez SA, Gonz\u0026aacute;lez-Gurrola S, Morales-Morales OO, et al. Metabolic Biomarkers in Adults with Type 2 Diabetes: The Role of PPAR-γ2 and PPAR-β/δ Polymorphisms. Biomolecules Dec. 2023;14(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biom13121791\u003c/span\u003e\u003cspan address=\"10.3390/biom13121791\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang NN, Wang ZH, Li SH, Ge YY, Liu X, Sui DX. Pulmonary Function in Metabolic Syndrome: A Meta-Analysis. Metab Syndr Relat Disord Dec. 2022;20(10):606\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/met.2022.0045\u003c/span\u003e\u003cspan address=\"10.1089/met.2022.0045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTziastoudi M, Cholevas C, Zorz C, et al. The Role of VEGFA in T2DM-Nephropathy: A Genetic Association Study and Meta-Analysis. Genes (Basel) Nov. 2025;17(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes16111386\u003c/span\u003e\u003cspan address=\"10.3390/genes16111386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalsgaard NB, Gasbjerg LS, Hansen LS, Nielsen DS, Rasmussen TS, Knop FK. Two weeks of acarbose treatment shows no effect on gut microbiome composition in patients with type 2 diabetes: a randomised, placebo-controlled, double-blind, crossover study. Endocr Connect Jul. 2024;1(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/ec-24-0052\u003c/span\u003e\u003cspan address=\"10.1530/ec-24-0052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka M, Gohda T, Kamei N, et al. Associations between circulating levels of FABP4 and TNF receptors are more evident in patients with type 2 diabetes mellitus than in patients with type 1 diabetes mellitus. Endocr Connect Nov. 2024;1(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/ec-24-0343\u003c/span\u003e\u003cspan address=\"10.1530/ec-24-0343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho NH, Shaw JE, Karuranga S, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract Apr. 2018;138:271\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diabres.2018.02.023\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2018.02.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngeli CB, Kimura L, Auricchio MT, et al. Multilocus analyses of seven candidate genes suggest interacting pathways for obesity-related traits in Brazilian populations. Obesity (Silver Spring) Jun. 2011;19(6):1244\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/oby.2010.325\u003c/span\u003e\u003cspan address=\"10.1038/oby.2010.325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajagopal P, Jayaraman S, Jh SF, et al. Molecular docking analysis of PARγ with compounds from Ocimum tenuiflorum. Bioinformation. 2021;17(11):928\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6026/97320630017928\u003c/span\u003e\u003cspan address=\"10.6026/97320630017928\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan A, Taliun D, Thurner M, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet Nov. 2018;50(11):1505\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0241-6\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0241-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol Sep. 2010;25(9):603\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-010-9491-z\u003c/span\u003e\u003cspan address=\"10.1007/s10654-010-9491-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao M, Xu S, Yang H, et al. Association between IL-17A and IL-17F gene polymorphism and susceptibility in inflammatory arthritis: A meta-analysis. Clin Immunol Apr. 2020;213:108374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clim.2020.108374\u003c/span\u003e\u003cspan address=\"10.1016/j.clim.2020.108374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Luis D, Izaola O, Primo D, Rico D, L\u0026oacute;pez JJ. Effect of the PPARG rs1801282 polymorphism on weight reduction and metabolic syndrome outcomes in obese individuals undergoing a partial meal replacement hypocaloric diet. J Diabetes Complications Jan. 2026;40(1):109209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jdiacomp.2025.109209\u003c/span\u003e\u003cspan address=\"10.1016/j.jdiacomp.2025.109209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontagnana M, Fava C, Nilsson PM, et al. The Pro12Ala polymorphism of the PPARG gene is not associated with the metabolic syndrome in an urban population of middle-aged Swedish individuals. Diabet Med Aug. 2008;25(8):902\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1464-5491.2008.02510.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1464-5491.2008.02510.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun SY, Yun JY, Lim C, et al. Exploring the complex link between obesity and intelligence: Evidence from systematic review, updated meta-analysis, and Mendelian randomization. Obes Rev Dec. 2024;25(12):e13827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/obr.13827\u003c/span\u003e\u003cspan address=\"10.1111/obr.13827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTemelkova-Kurktschiev T, Hanefeld M, Chinetti G, et al. Ala12Ala genotype of the peroxisome proliferator-activated receptor gamma2 protects against atherosclerosis. J Clin Endocrinol Metab Sep. 2004;89(9):4238\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2003-032120\u003c/span\u003e\u003cspan address=\"10.1210/jc.2003-032120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulzico D. The Search for a Reliable Biomarker in MEN1 Duodenopancreatic Neuroendocrine Tumors. J Clin Endocrinol Metab Feb. 2024;20(3):e1301\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/clinem/dgad521\u003c/span\u003e\u003cspan address=\"10.1210/clinem/dgad521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe J, Hu K, Wang B, Wang H. Effect of dietary and physical activity behavioral interventions on reducing postpartum weight retention among women with recent gestational diabetes: A systematic review and meta-analysis of randomized controlled trials. Obes Rev Apr. 2024;25(4):1\u0026ndash;771. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/obr.13689\u003c/span\u003e\u003cspan address=\"10.1111/obr.13689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGimble JM, Bray MS, Young A. Circadian biology and sleep: missing links in obesity and metabolism? Obes Rev Nov. 2009;10(Suppl 2):1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1467-789X.2009.00672.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-789X.2009.00672.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamas C, Navarro E, Caster\u0026aacute;s A, et al. MEN1-associated primary hyperparathyroidism in the Spanish Registry: clinical characterictics and surgical outcomes. Endocr Connect Oct. 2019;8(10):1416\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/ec-19-0321\u003c/span\u003e\u003cspan address=\"10.1530/ec-19-0321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu AL, Wang J, Zheleznyak A, Brown EJ. Ubiquitin-related proteins regulate interaction of vimentin intermediate filaments with the plasma membrane. Mol Cell Oct. 1999;4(4):619\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1097-2765(00)80212-9\u003c/span\u003e\u003cspan address=\"10.1016/s1097-2765(00)80212-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNieuwdorp M, Holleman F, de Groot E, et al. Perturbation of hyaluronan metabolism predisposes patients with type 1 diabetes mellitus to atherosclerosis. Diabetologia Jun. 2007;50(6):1288\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-007-0666-4\u003c/span\u003e\u003cspan address=\"10.1007/s00125-007-0666-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med Jun. 2002;15(11):1539\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sim.1186\u003c/span\u003e\u003cspan address=\"10.1002/sim.1186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy R, Marshall K, Zagorin S, Devarshi PP, Hazels Mitmesser S. Socioeconomic Inequalities Impact the Ability of Pregnant Women and Women of Childbearing Age to Consume Nutrients Needed for Neurodevelopment: An Analysis of NHANES 2007\u0026ndash;2018. Nutrients Sep. 2022;16(18). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14183823\u003c/span\u003e\u003cspan address=\"10.3390/nu14183823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerpetuo LH, Ferreira W, da Silva DJ, Jurno ME, Vale TC. Incidence Rate and Factors Associated with Delirium and Subsyndromal Delirium in Patients with COVID-19 in an Intensive Care Unit. J Clin Med May. 2023;31(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm12113789\u003c/span\u003e\u003cspan address=\"10.3390/jcm12113789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable.1\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eCharacteristics of studies included in the meta-analysis\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"953\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 181px;\"\u003e\n \u003cp\u003eFirst author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 124px;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 147px;\"\u003e\n \u003cp\u003eGenotyping method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eDisease types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNos score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePHWE\u0026nbsp;for control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRingel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHerrmann\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePetrovic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMalecki\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCostea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-DHPLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTariq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLiu-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eZhang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-LDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePeter Kruzliak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNagaraja M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTETRA-ARMS PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAle\u0026scaron; PleskoviI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eKaur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNehal Salah Hasan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEgyptian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAmjad Hazim Al-Naemi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIraqi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePCR-RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIlibagiza Regine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTETRA-ARMS PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFarida V. Valeeva\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEastern European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRavi Bhushan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTETRA-ARMS PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNazira Bekenova1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eKazakh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTun-Jen Hsiao Eugene Lin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSzkup Małgorzata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFRET-HP qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCarlos Rodr\u0026iacute;guez-Pardo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGabriel Vaisam Castro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVadym P.Shypulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUkrainians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFarida V. Valeeva\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEastern European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Abbreviations: PCR-RFLP, Polymerase chain reaction- Restriction fragment length polymorphism analysis; PCR-DHPLC, Polymerase chain reaction-Denaturing high-performance liquid chromatography;PCR-LDR, Polymerase chain reaction-Ligation detection reaction;TETRA-ARMS PCR, Tetra-primer amplification-refractory mutation system polymerase chain reaction;TaqMan, Fluorescent probe-based real-time polymerase chain reaction assay;FRET-HP qPCR, Fluorescence resonance energy transfer-hybridization probe quantitative polymerase chain reaction;PCR-SSCP, Polymerase chain reaction-Single-stranded conformational polymorphism;PCR-DGGE, Polymerase chain reaction-Denaturing gradient gel electrophoresis;PCR-ARMS, Polymerase chain reaction-Amplification-refractory mutation system;RT-PCR, Reverse transcription-Polymerase chain reaction;PCR-PHFA, Polymerase chain reaction-Preferential homoduplex formation assay;PCR-SSOP, Polymerase chain reaction-Sequence specific oligonucleotide primer;NOS, Newcastle Ottawa Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.2\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eResults of a meta-analysis of associations between PPAR-\u0026gamma; gene rs1801282 polymorphism and susceptibility to T2DM.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"970\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" style=\"width: 219px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO.of comparisons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"9\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of association\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 195px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of association\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003eoverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCvsG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e61.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.2636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e35.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e25.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCC+GCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e32.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCCvsGC+GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e58.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCvsG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e19.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e30.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCC+GCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e29.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCCvsGC+GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCvsG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e67.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.2532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e25.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCC+GCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCCvsGC+GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e58.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e95%CI, 95% confidence interval; No, number of studies; OR, odds ratio; Pa, Begg\u0026rsquo;s Test; Pb, Egger\u0026rsquo;s test; R, random-effects model, F, fixed-effects model; Bold values indicate that the association is significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.3\u0026nbsp;\u003c/strong\u003eResults of a meta-analysis of associations between PPAR-\u0026gamma; gene rs1801282 polymorphism and susceptibility to obesity.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"966\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO.of comparisons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 277px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of association\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of association\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eoverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCvsG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.5783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e52.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.2103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e27.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCC+GCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.3286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e38.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCCvsGC+GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.8533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e97.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCvsG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.3127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e41.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.3084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e39.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCC+GCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.1816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e28.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCCvsGC+GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.8567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e97.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.230\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCvsG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e15.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e84.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e19.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.5783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.2103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCC+GCvsGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.3286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 122px;\"\u003e\n \u003cp\u003eCCvsGC+GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e8.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 55px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e95%CI, 95% confidence interval; No., number of studies; OR, odds ratio; Pa, Begg\u0026rsquo;s Test; Pb, Egger\u0026rsquo;s test; R, random-effects model, F, fixed-effects model; Bold values indicate that the association is significant.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PPAR-γ, polymorphism, Obesity, T2DM, Meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-9008547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9008547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eperoxisome proliferator-activated receptor-γ (PPAR-γ) plays a pivotal role in lipid homeostasis and insulin signaling; however, the association of its PPAR-γ rs1801282 polymorphism with obesity and type 2 diabetes mellitus (T2DM) remains controversial.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe searched PubMed, Web of Science, CNKI and WanFang up to September 2025. Meta-analysis was conducted across five genetic models, with Odds Ratio (OR) and 95% Confidence Interval (95% CI) assessing rs1801282 association with obesity/T2DM susceptibility. Sensitivity analysis and trial sequential analysis (TSA) were performed to verify the reliability of our findings.This meta-analysis was registered in PROSPERO (CRD420261297714).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 24 studies involving 6739 cases and 7337 controls were included in this meta-analysis. PPAR-γ (rs1801282) C allele was significantly associated with increased overall obesity risk (OR\u0026thinsp;=\u0026thinsp;2.339, 95% CI: 1.418\u0026ndash;3.864, P\u0026thinsp;=\u0026thinsp;0.001) and it was strongly correlated with obesity in Caucasian populations whereas in Asian populations it tended to elevate obesity risk but was based on only one study. For T2DM, no significant association was observed between the rs1801282 polymorphism and overall disease risk (OR\u0026thinsp;=\u0026thinsp;0.975, 95% CI: 0.827\u0026ndash;1.148, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.759), though a marginally significant association was detected in the Asian subgroup under the dominant model (OR\u0026thinsp;=\u0026thinsp;0.740, 95% CI: 0.551\u0026ndash;0.994, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). No significant associations were found in other ethnic groups or genetic models (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Trial sequential analysis (TSA) confirmed the reliability of the conclusions regarding the association between rs1801282 and obesity/T2DM in Asian populations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn conclusion, the PPAR-γ rs1801282 C allele is significantly associated with increased obesity risk and shows a marginally significant association with T2DM in Asians under the dominant model, but no consistent link with overall T2DM risk.\u003c/p\u003e","manuscriptTitle":"Association between PPAR-γ (rs1801282) polymorphism and susceptibility to obesity and type 2 diabetes mellitus: a meta-analysis with trial sequential analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 10:20:21","doi":"10.21203/rs.3.rs-9008547/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-07T11:30:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T07:51:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T13:30:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T13:29:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-03-02T09:48:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68573af8-ff9f-4e89-8f90-544020a456cc","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T10:20:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 10:20:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9008547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9008547","identity":"rs-9008547","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}