Distinct Body Fat Distribution and Its Association with Metabolic Syndrome in Tibetan Population

Distinct Body Fat Distribution and Its Association with Metabolic Syndrome in Tibetan Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct Body Fat Distribution and Its Association with Metabolic Syndrome in Tibetan Population Lin YUAN, Haijing Wang, Qingxia Huang, Tiemei Li, Bin Zhang, Huiru Tang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7335258/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted 16 You are reading this latest preprint version Abstract Objective: Although ethnic/racial differences in body fat distribution have been documented, the specific pattern in Tibetans and its implications for metabolic health in this high-altitude population remain unclear. Methods: A total of 1480 participants from the Tibetan cohort and the NHANES were included. Principal component analysis and Mantel tests were employed to identify Tibetan-specific body fat indicators. Linear models assessed associations with metabolic syndrome (MetS), and mediation analyses evaluated the indirect effects of serum lipoproteins. Results: Tibetans showed distinct trunk and total fat mass compared to other ethnic/racial groups. Trunk fat percentage was identified as a risk factor for MetS (OR = 1.59, 95% CI: 1.27~1.91). The triglycerides to total lipids ratio in low density lipoprotein 3 (L3TGP) and triglycerides to high density lipoprotein cholesterol ratio (TGHCR) exhibited significant mediating effect between trunk fat percentage and MetS (L3TGP:β = 1.7 x 10 -4 g, 95% CI: 4 x 10 -5 ~3.6 x 10 -4 ;TGHCR: β = 1.8 x 10 -4 g, 95% CI: 4 x 10 -5 ~4.6 x 10 -4 ). Conclusions: This study revealed novel evidence for distinct fat distribution in Tibetans, linked to elevated MetS risk. L3TGp and TGHCR were identified as key lipoprotein mediators, supporting the need for environmental- and ethnicity-specific indicators in metabolic risk assessment. Body fat composition Metabolome Lipoproteins Metabolic syndrome Mediation analysis Figures Figure 1 Figure 2 Figure 3 Introduction Although body mass index (BMI) has long been regarded as a core indicator for assessing obesity and related health risks, emerging insights from in-depth obesity research and biological perspectives reveal inherent limitations in using BMI as a criterion for metabolic healthy [ 1 ]. It fails to precisely reflect the specific distribution and content of body fat, nor can it effectively determine whether excess fat has already posed health risks [ 2 ]. Studies have demonstrated that individuals with identical BMI values may exhibit markedly distinct metabolic risk due to heterogeneous fat distribution patterns (e.g., visceral fat accumulation versus subcutaneous fat deposition) [ 3 ]. Furthermore, the distribution of adipose tissue has been confirmed to significantly correlate with metabolic dysfunction, and this association may demonstrate ethnic-specific variations influenced by genetic factors and environmental adaptations [ 4 ]. Tibetans have undergone long-term high-altitude adaptation, which may have shaped unique body composition characteristics. These distinct features provide a natural model to study how environmental pressures shape metabolic physiology and influence metabolic disease risk in ways that differ across populations and their health implications should be further explored. Adipose tissue is an important part of body composition, also as a dynamic metabolic organ, not only participates in energy storage but also extensively regulates lipoprotein metabolism through the secretion of bioactive substances such as adiponectin and inflammatory cytokines [ 5 ]. Adipose tissue distribution exhibits a well-established association with dyslipidemia, and previous studies demonstrate that android obesity serves as an independent risk factor for lipid metabolism disorders, with high density lipoprotein cholesterol (HDL-C) levels being inversely influenced by total adiposity [ 6 ]. The association between adipose tissue distribution and lipoprotein levels exhibits heterogeneity across different populations. In males, adipose tissue distribution patterns correlate significantly with serum lipids and lipoprotein subfractions, such as the waist-to-hip ratio and waist circumference, and this effect is independent of age [ 7 ]. Among early postmenopausal women, adipose distribution parameters, specifically abdominal fat percentage or waist-to-hip ratio, constitute stronger predictors of atherogenic lipoprotein and apolipoprotein profiles than either body weight or BMI [ 8 ]. Previous study identified the association between lipoprotein profile and distinct obesity phenotypes in Tibetans [ 9 ]. However, the relationship between lipoproteins and adipose tissue distribution remains unknown. The distribution of adipose tissue is an important determinant of metabolic risk. Metabolic syndrome (MetS) is a complex metabolic disorder, arises from complex interactions among genetic, environmental, and lifestyle factors [ 10 ]. Critically, MetS substantially increases the risk of a range of non-communicable diseases, including cardiovascular disease, non-alcoholic fatty liver disease, chronic kidney disease, and certain cancers [ 11 ]. Globally, the prevalence of MetS is estimated at approximately 25%. In China, around 454 million adults are impacted by MetS, with a national prevalence rate of 33.9% [ 12 ], while notable ethnic disparities exist. Our previous study revealed that urbanized and semi-urbanized Tibetan populations exhibit MetS prevalence rates of 30.1% in males and 32.1% in females, approaching the national average [ 13 ]. However, the specific body composition characteristics and underlying pathophysiological mechanisms contributing to MetS in Tibetan populations remain poorly understood. Here, this study aims to analyze heterogeneity in body fat distribution between Tibetans and various racial/ethnic groups from the National Health and Nutrition Examination Survey (NHANES) database, further investigating whether such heterogeneity directly or indirectly contributes to elevated MetS risk. By identifying population-specific adiposity profiles and uncovering the mediating roles of key serum lipoproteins, this study provides mechanistic insights into ethnic differences in metabolic health and highlights the need for precision approaches to risk assessment and prevention in diverse populations. Methods Study design and participants This study utilized a cross-sectional design, with data from an independently established High-Altitude Multi-Ethnic Cohort, and integrated participants from the NHANES database. Specifically, it included participants from the NHANES 2017–2018 survey cycle and the 2022 cross-sectional data from the High-Altitude Multi-Ethnic Cohort, with age and gender matched between groups. The High-Altitude Multi-Ethnic Cohort was established in Golmud City, Haixi Prefecture, Qinghai Province, China. The main participants in this cohort are Tibetans, and the baseline surveys commenced in 2018, with supplementary recruitment of new participants conducted between December 2021 and May 2022 [ 9 ]. Both databases employed the same technical standard of dual-energy X-ray absorptiometry (DXA) for body composition assessment. A total of 10,865 participants were initially considered. Exclusion criteria were applied as follows ( Fig. 1 ) : (1) Individuals with missing body composition data were removed from both databases (NHANES: n = 4,961; High-Altitude Multi-Ethnic Cohort: n = 706; total excluded = 5,667); (2) Individuals of Han Chinese or unidentified ethnicity were excluded from the High-Altitude Multi-Ethnic Cohort (n = 7). Then, 4,293 participants remained in the NHANES cohort and 898 in the High-Altitude Multi-Ethnic Cohort. Subsequently, 1:1 matching between the two databases was performed based on age and sex. Exact matching was used for gender, while age was matched using a caliper of ± 1 year. This step excluded 3,713 individuals. Finally, 1,480 individuals were included for following analysis, comprising 740 Tibetan participants from the High-Altitude Multi-Ethnic Cohort and 740 matched participants from the NHANES. Individuals from the NHANES represented diverse racial and ethnic groups, including Hispanic (Mexican American and Other Hispanic), White, Black, Asian, and other racial backgrounds. Body composition measurements DXA is recognized as a gold-standard method for assessing body composition [ 14 ]. Scans were performed with Hologic Apex software (version 4.0) following the manufacturer's protocols. All measurements and quality control procedures were performed as previous reported: (1) Subject preparation and positioning: Participants wore lightweight clothing with metal objects removed; subject repositioning was required after each scan. (2) Device calibration: Daily calibration of the equipment was performed. (3) Technician certification: All four operators completed unified training certified by the International Society for Clinical Densitometry (ISCD), covering the official ISCD technologist practical manual and the manufacturer-provided test protocols and operational guidelines. (4) Precision verification: Measurement precision was verified through triplicate scans (with subjects leaving the scanning table and being repositioned between each scan) of the lumbar spine and hip regions in fifteen participants; only a single scan per region was acquired during formal measurements [ 15 ]. For this study, nine fat mass indices were included in the analysis. Covariates In High-Altitude Multi-Ethnic cohort, physical examinations were conducted by trained physicians, including standardized measurements of height, weight, and waist circumference, taken while participants wore lightweight clothing. BMI was calculated as weight (kg) divided by height squared (m²). Covariates related to general demographics (age, sex, marital), socioeconomic status (education, insurance, household income), and lifestyle behaviors (smoking, drinking, physical activity) were collected through face-to-face interviews conducted by certified investigators using structured questionnaires. Serum metabolome quantification The lipoprotein subfractions in serum were performed on a 600 MHz AVANCE III NMR spectrometer equipped with a BBI probe (Bruker Biospin GmbH, Germany), following a previously established protocol [ 16 – 18 ]. After data cleaning and imputation of missing values, this study included a total of 333 quantifiable metabolomic parameters, comprising 129 lipoprotein subfractions, 165 lipoprotein-to-fatty acid ratio parameters, and 39 low-molecular-weight metabolites [ 9 ]. The detailed lipoprotein profile covered very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), along with their 15 subfractions (VLDL1–5, LDL1–6, HDL1–4). These lipoproteins and their subfractions were quantified for compositional parameters, including: apolipoproteins (Apo-A1, Apo-A2, Apo-B); total cholesterol (CH), free cholesterol (FC), cholesterol ester (CE); phospholipids (PL), triglycerides (TG). Additionally, functional parameters derived from the aforementioned quantitative data were also calculated, such as the cholesterol-to-triglyceride ratio (CHTGR) and lactate-to-pyruvate ratio (LactPyR). Cardiometabolic outcomes Metabolic syndrome was defined in this study as the presence of at least three of the following criteria: (a) central obesity: waist circumference ≥ 90 cm for males and ≥ 80 cm for females; (b) systolic blood pressure (BP) ≥ 130 mmHg or diastolic BP ≥ 85 mmHg or on antihypertensive medication; (c) fasting plasma glucose (FBG) ≥ 5.6 mmol/L or on medication for high blood glucose; (d) HDL-cholesterol < 1.03 mmol/L for males and < 1.30 mmol/L for females or on medication for reduced HDL-cholesterol; (e) TG ≥ 1.7 mmol/L or on medication for elevated TG [ 13 ]. Statistical analysis Continuous variables were described using mean ± standard deviation (SD) for normally distributed data and compared via Student’s t-test or analysis of variance (ANOVA), while non-normally distributed data were reported as median (interquartile range) and analyzed using Wilcoxon rank-sum or Kruskal-Wallis tests. Categorical variables were summarized as frequencies (proportions) and assessed with chi-square tests. Principal component analysis (PCA) was employed to explore the distribution patterns of body fat-related variables across ethnic/racial groups following dimensionality reduction, selecting adiposity indicators that were significantly represented in the top 10 loading variables of both PC1 and PC2. Ethnic characteristics were analyzed using Mantel tests and Spearman correlations to dual-validate the robustness of the PCA-selected indicators. Additionally, linear regression models adjusted for age, sex, marital status, education level, health insurance coverage, household income, smoking status, alcohol consumption, and physical activity were used to assess differences in two key adiposity indices between Tibetans and other ethnic/racial groups. To eliminate confounding by body weight, subsequent analyses utilized the ratio of key adiposity indices to body weight. The trunk fat percentage and total fat percentage were defined as trunk fat mass/weight and total fat mass/weight, respectively. Given the high dimensionality and strong collinearity of serum lipoprotein data, we integrated linear regression, logistic regression, and LASSO regression (via the R glmnet package) for variable selection. First, variables significant in both linear regression (key adiposity percentage vs. lipoprotein) and logistic regression (lipoprotein vs. MetS) were retained. Second, mediation analysis was conducted using the R mediation package, with bootstrap resampling (1,000 iterations) to estimate confidence intervals for mediation effects, thereby elucidating the intermediary roles of serum lipoproteins. Statistical significance was defined as a two-tailed p < 0.05, and all analyses were performed in R version 4.4.1. Results 1. Identification of key body fat indicators among ethnic/racial groups To minimizing potential confounding effects from demographic disparities, age and sex matching was performed between Tibetan participants and other ethnic/racial groups from the NHANES database. The median age across all groups ranged from 42 to 46 years, with no significant differences in age and sex. While, significant difference was observed in marital status and BMI ( Table 1 ) . Table 1 Demographic characteristics among Tibetan and other ethnical/racial groups. Mexican American Non-Hispanic (Asian) Non-Hispanic (Black) Non-Hispanic (Other) Non-Hispanic (White) Other Hispanic Tibetan P N = 98 N = 151 N = 132 N = 48 N = 231 N = 80 N = 740 Age 42.0 (33.0–51.0) 46.0 (37.0–52.0) 45.0 (36.0–52.0) 45.5 (37.0-52.5) 43.0 (33.0–51.0) 45.5 (37.0-52.5) 44.0 (35.0–52.0) 0.12 Sex (%) 0.16 Men 44 (44.9%) 62 (41.1%) 66 (50.0%) 27 (56.3%) 96 (41.6%) 27 (33.8%) 322 (43.5%) Women 54 (55.1%) 89 (58.9%) 66 (50.0%) 21 (43.8%) 135 (58.4%) 53 (66.3%) 418 (56.5%) Marital (%) < 0.001 Unmarried 17 (17.7%) 11 (7.3%) 46 (35.4%) 11 (22.9%) 44 (19.5%) 9 (11.4%) 82 (11.2%) Marital 63 (65.6%) 126 (83.4%) 61 (46.9%) 25 (52.1%) 144 (63.7%) 53 (67.1%) 620 (84.7%) Others 16(16.7%) 14(9.3%) 23(17.7%) 12 (25.0%) 38(16.8%) 17(21.5%) 30(4.1%) BMI (kg/m 2 ) 28.5(26.0-32.5) 25.5(22.8–28.6) 28.1(24.6–36.2) 28.4(24.3–32.6) 27.4(23.8–32.9) 28.2(25.0-31.6) 26.5 (23.0-29.7) < 0.001 Furthermore, principal component analysis (PCA) was performed on nine body fat mass indicators common to both databases following normalization. The PCA plot demonstrated that the first principal component (PC1) explained 80.07% of the total variance, revealing distinct separation from non-Hispanic White and Black participants ( Fig. 2 A ) . Significant differences in both PC1 and PC2 scores were also observed among ethnic/racial groups, collectively demonstrating ethnic divergence in body fat distribution. Specifically, as evidenced in box plots, the Tibetans exhibits significant differences from most Non-Hispanic groups (such as Non-Hispanic Asian, Non-Hispanic Black, and Non-Hispanic White) on PC1. On PC2, significant differences are observed between the Tibetan population and the aforementioned groups except for Non-Hispanic Other (Figure S1 ) . Analysis of PC1 loading scores identified total fat mass and trunk fat mass as the top two contributors, underscoring their dominant influence on this principal component ( Fig. 2 B ) . To validate their discriminative capacity, mantel tests confirmed statistically significant differences in total fat mass and trunk fat mass across ethnic/racial groups, further supporting their role as dominant indicators of ethnic/racial-specific body fat distribution ( Fig. 2 C, Table S1 ) . 2. Differences in key body fat indicators among ethnic/racial groups To further control the potential confounding effects of BMI and marital status, linear regression models were applied. Using Tibetans as the reference group, Mexican Americans exhibited a higher trunk fat mass 0.312 g (β = 0.312, 95% CI: 0.103 ~ 0.521) and total fat mass 0.279 g (β = 0.279, 95% CI: 0.071 ~ 0.487). A similar trend was observed among White and Black individuals. Conversely, Non-Hispanic Asians exhibited significantly lower trunk fat mass (β = -0.221, 95% CI: -0.392~ -0.049) and total fat mass (β = -0.177, 95% CI: -0.347~ -0.007) compared to Tibetans ( Table 2 ) . The results demonstrated significant differences of trunk and total fat between Tibetans and other major ethnic groups, including Mexican Americans, non-Hispanic Asians, non-Hispanic Blacks, and non-Hispanic Whites. Table 2 The Trunk/Total Fat mass (g) distribution among Tibetan and other ethnical/racial groups. Trunk fat(g) Total fat(g) β(95%CI) P β(95%CI) P Tibetan Reference Reference Mexican American Model1 0.275 (0.067, 0.484) 0.010 0.246 (0.038, 0.453) 0.021 Model2 0.312 (0.103, 0.521) 0.004 0.279 (0.071, 0.487) 0.009 Non-Hispanic (Asian) Model1 -0.199 (-0.373, -0.026) 0.024 -0.156 (-0.329, 0.016) 0.076 Model2 -0.221 (-0.392, -0.049) 0.012 -0.177 (-0.347, -0.007) 0.041 Non-Hispanic (Black) Model1 0.172 (-0.011, 0.356) 0.066 0.393 (0.211, 0.575) < 0.001 Model2 0.3 (0.112, 0.488) 0.002 0.523 (0.336, 0.71) < 0.001 Non-Hispanic (Other) Model1 0.153 (-0.136, 0.442) 0.300 0.198 (-0.09, 0.485) 0.178 Model2 0.219 (-0.07, 0.508) 0.138 0.268 (-0.019, 0.555) 0.067 Non-Hispanic (White) Model1 0.299 (0.152, 0.445) < 0.001 0.383 (0.237, 0.528) < 0.001 Model2 0.342 (0.194, 0.491) < 0.001 0.423 (0.275, 0.571) < 0.001 Other Hispanic Model1 0.025 (-0.204, 0.253) 0.832 0.078 (-0.149, 0.306) 0.500 Model2 0.018 (-0.212, 0.248) 0.876 0.068 (-0.16, 0.297) 0.558 Tibetans as a reference group. Model 1 was unadjusted model. Model2 was adjusted for marital status and BMI. 3. Associations of key body fat percentage with MetS and its components in the Tibetan population The observed disparities in body fat distribution between Tibetans and other ethnic/racial groups underscore the importance of trunk fat mass and total fat mass as key indicators, highlighting their potential health implications and prompting further scientific investigation. Among these 740 participants, 162 (21.9%) was identified as MetS, and significant differences were observed in sex, age, marital status, income level, smoking behavior, and BMI. Specifically, compared to healthy participants, the MetS group exhibited significantly higher median for trunk fat and total fat distribution, as well as a higher BMI (Table S2) . To address potential confounding effects of body weight on key adiposity indicators, trunk fat percentage and total fat percentage were utilized in subsequent analyses. In the fully adjusted model (adjusted for age, sex, marital status, education, insurance, household income, smoking, drinking, physical activity, and BMI), each one unit increase in trunk fat percentage was associated with significantly higher risks of MetS (OR = 1.59, 95% CI: 1.27 ~ 1.91), hypertension (OR = 1.53, 95% CI: 1.21 ~ 1.85), hypercholesterolemia (OR = 1.61, 95% CI: 1.22 ~ 2.00), and central obesity (OR = 2.28, 95% CI: 1.83 ~ 2.73). In contrast, total fat percentage showed no significant associations with these outcomes except for central obesity (OR = 2.16, 95% CI: 1.62 ~ 2.70). After BMI adjustment, trunk fat percentage shows broader metabolic implications than total fat percentage ( Table 3 ) . These findings underscore the pivotal role of trunk fat percentage in the development of metabolic disorders. Table 3 Associations between Trunk/Total Fat mass percentage and Metabolic Syndrome in Tibetan. Trunk fat/Weight Total fat/Weight OR(95%CI) P-value OR(95%CI) P-value Metabolic syndrome Model1 1.83 (1.63, 2.03) < 0.001 1.25 (1.07, 1.43) 0.018 Model2 2.34 (2.1, 2.58) < 0.001 2.22 (1.93, 2.51) < 0.001 Model3 2.42 (2.16, 2.68) < 0.001 2.25 (1.95, 2.55) < 0.001 Model4 1.59 (1.27, 1.91) 0.004 1.11 (0.72, 1.5) 0.605 Hypertension Model1 1.79 (1.62, 1.96) < 0.001 1.41 (1.25, 1.57) < 0.001 Model2 1.79 (1.59, 1.99) < 0.001 1.93 (1.68, 2.18) < 0.001 Model3 1.84 (1.63, 2.05) < 0.001 1.98 (1.72, 2.24) < 0.001 Model4 1.53 (1.21, 1.85) 0.011 1.26 (0.85, 1.67) 0.268 Diabetes Model1 1.51 (1.25, 1.77) 0.002 1.15 (0.9, 1.4) 0.266 Model2 1.46 (1.16, 1.76) 0.014 1.27 (0.9, 1.64) 0.198 Model3 1.49 (1.17, 1.81) 0.015 1.3 (0.91, 1.69) 0.180 Model4 1.04 (0.63, 1.45) 0.859 0.67 (0.14, 1.2) 0.131 Hypercholesterolemia Model1 1.36 (1.11, 1.61) 0.015 1 (0.76, 1.24) 0.990 Model2 1.63 (1.34, 1.92) 0.001 1.6 (1.24, 1.96) 0.010 Model3 1.72 (1.41, 2.03) < 0.001 1.69 (1.32, 2.06) 0.006 Model4 1.61 (1.22, 2) 0.017 1.43 (0.95, 1.91) 0.143 Hypertriglyceridemia Model1 1.34 (1.07, 1.61) 0.037 0.86 (0.6, 1.12) 0.242 Model2 1.89 (1.56, 2.22) < 0.001 1.63 (1.23, 2.03) 0.016 Model3 2 (1.65, 2.35) < 0.001 1.74 (1.32, 2.16) 0.009 Model4 1.33 (0.88, 1.78) 0.219 0.81 (0.24, 1.38) 0.485 Central Obesity Model1 6.46 (6.18, 6.74) < 0.001 3.59 (3.38, 3.8) < 0.001 Model2 8.15 (7.82, 8.48) < 0.001 12.73 (12.32, 13.14) < 0.001 Model3 8.68 (8.33, 9.03) < 0.001 14.03 (13.59, 14.47) < 0.001 Model4 2.28 (1.83, 2.73) < 0.001 2.16 (1.62, 2.7) 0.005 Model 1 was unadjusted model. Model 2 was adjusted for age and sex. Model 3 further adjusted for marital, education, insurance, household income, smoking, drinking and physical activity. Model 4 further adjusted for BMI. 4. Mediating role of lipoproteins in the link between body fat percentage and MetS To further investigate whether lipoproteins mediate the effects of trunk fat percentage on MetS, 333 quantifiable parameters such as lipoprotein subfractions and small molecular weight metabolites were included. The top three lipoprotein contributors were LDL (31.23%), VLDL (21.62%), and HDL (21.02%) subfractions and their components (Fig. 3 A). Firstly, 263 metabolites significantly associated with trunk fat percentage were screened using linear regression (FDR-adjusted P < 0.05; Table S3) , and 275 metabolites significantly associated with MetS were screened using logistic regression (FDR-adjusted P < 0.05; Table S4) . Then, 235 metabolites were found to be associated with both the trunk fat percentage and MetS. Finally, LASSO regression was performed on these 235 metabolites and MetS, identifying two metabolites significantly associated with MetS: triglyceride to HDL-cholesterol ratio (TGHCR) and the proportion of triglyceride to total lipid in LDL3 (L3TGp) (P < 0.05; Fig. 3 B, Table S5) . Subsequently, the mediating effects of these two metabolic markers were further investigated. Mediation analyses showed the effect of the trunk fat percentage on MetS through metabolites, TGHCR and L3TGp, respectively. The trunk fat percentage demonstrated clear associations with MetS, with both significant mediation effects (path ab) and direct effects (path c'). Specifically, both L3TGp (β = 1.7 x 10 − 4 g, 95% CI: 4 x 10 − 5 ~3.6 x 10 − 4 ) and TGHCR (β = 1.8 x 10 − 4 g, 95% CI: 4 x 10 − 5 ~4.6 x 10 − 4 ) demonstrated statistically significant mediating effects after adjustment for potential confounders ( Fig. 3 C and 3 D ) . These findings suggest that the mediating effects of specific metabolites modulate the association between trunk fat and MetS. Discussion This study demonstrates distinct patterns of fat distribution across racial/ethnic groups, with Tibetan populations exhibiting significant different trunk fat and total fat mass distributions compared to major racial/ethnic groups represented in the NHANES database. Furthermore, the trunk fat percentage emerged as independent risk factors for MetS within the Tibetan population. A key mechanistic insight was the identification of two specific serum lipoproteins, L3TGp and TGHCR, as significant mediators in the association between trunk fat percentage and MetS. Together, these results highlight the need for ethnicity-tailored obesity metrics and support the integration of lipoprotein profiling into metabolic risk assessments. Our findings replicate previously reported racial/ethnic differences in body fat distribution [ 19 , 20 ], demonstrating that Tibetan populations exhibit a distinct adiposity pattern characterized by significantly lower trunk and total fat mass compared to Mexican-American, Black, and White individuals, yet higher levels than those observed in Asian participants within the NHANES database. Previous studies have shown that, at same levels of BMI and waist circumference, White individuals tend to have a higher visceral fat area compared to Black and Hispanic populations [ 21 ]. In studies involving children, total fat mass and trunk fat mass were found to be higher in females than in males [ 22 ]. In adults, Indians showed greater total fat mass index and greater trunk fat percentage than Creoles [ 23 ]. The higher total fat and trunk fat mass observed in Tibetans compared to Asian population might be related to the adaptation to hypoxic stress, and the specific fat distribution may favor subcutaneous storage and improved metabolic efficiency [ 15 ]. Long-term exposure to a hypobaric hypoxic environment at high altitudes can lead to alterations in body fat deposition sites and distribution patterns [ 24 ]. The high-altitude environment, characterized by hypobaric hypoxia, induces metabolic dysregulation, elevating oxidative stress and increasing energy demands for thermogenesis and oxygen transport [ 25 ]. In this context, subcutaneous fat may maintain oxygen supply through enhanced angiogenesis, thus reducing hypoxic damage, while its enhanced glycolytic capacity may improve hypoxia tolerance [ 26 ]. These mechanisms are consistent with our findings, suggesting an optimization of fat distribution driven by natural selection pressures to enhance the adaptation in the high-altitude environment. The contribution of fat mass in specific regions varies in its implication for healthy. In adolescents, trunk fat has been associated with increased clustered cardiometabolic risk [ 27 ]. Among adults, trunk fat is positively correlated with elevated blood pressure, and total fat mass/height 2 was linked to a higher risk of MetS, particularly when exceeding 26.9 kg/m 2 [ 28 ]. Furthermore, higher percentage of total and trunk fat mass have been significantly associated with increased cardiovascular diseases mortality in NHANES based study [ 29 ]. While, trunk fat, particularly visceral fat, is more predictive of cardiometabolic risk than total fat mass [ 30 ]. Excess trunk fat, particularly visceral adipose tissue deposition, may leads to adipocyte hypertrophy, excessive extracellular matrix accumulation causing tissue fibrosis, infiltration of pro-inflammatory immune cells, and reduced protective adipokine secretion [ 31 ]. Notably, only trunk fat mass percentage was found to be significantly associated with MetS in the Tibetan population, reinforcing its role as a greater predictive power for adverse metabolic outcomes. Trunk fat accumulation is independently associated with elevated TG, reduced HDL, and atherogenic dyslipidemia (increased TC/HDL ratio), which is consistent with the diagnostic criteria for metabolic syndrome. [ 32 ]. These observations provide a compelling rationale to explore the underlying mechanisms. Here, both L3TGp and TGHCR were identified as significant mediators in the Tibetan population. The TGHCR (also known as TG/HDL-C) has emerged as a promising novel risk marker for predicting MetS [ 33 – 35 ]. These results highlight the critical role of relative triglyceride enrichment in the development of MetS and further support TGHCR as a robust predictor of MetS risk in this specific population [ 36 , 37 ], suggesting its broader utility in metabolic risk assessment. L3TGp, defined as the proportion of triglyceride content relative to total lipid content within LDL3 particles, was found to be significantly higher in older than in young adults [ 38 ]. LDL3 (medium-sized low-density lipoprotein particles), a subclass of LDL, has a density range of 1.034–1.037 kg/L [ 39 ]. Elevated levels of LDL3 (medium-sized low-density lipoprotein particles) are closely linked to increased risks of coronary heart disease, coronary atherosclerosis, and stroke [ 40 , 41 ]. Excess visceral adipose tissue may accelerate lipolysis, leading to the release of large amounts of free fatty acids that are transported directly to the liver via the portal vein [ 42 ]. This process may contribute to increased hepatic synthesis and secretion of very-low-density lipoprotein, which in turn elevate circulating levels of L3TGp [ 43 ]. However, the underlying biological mechanisms require further investigation. Here, we first examined differences in fat distribution between Tibetans, an ethnic group adapted to high-altitude environments, and other ethnically/racially groups. Furthermore, the core mediating role of specific serum lipoproteins in the association between body fat and MetS was identified in Tibetans. These findings underscore the importance of population-specific assessment tools in obesity research. It not only highlights unique fat distribution patterns in high-altitude Tibetans but also uncovers the lipoprotein-mediated metabolic pathways linking adiposity to disease. However, the cross-sectional design of this study limits the ability to draw causal inferences between fat distribution, lipoprotein profiles, and MetS. Furthermore, confounding was minimized through 1:1 age- and sex-matching and statistical adjustment for key covariates, unmeasured factors may still influence the observed associations. Additionally, the generalizability of the findings is limited due to the unique environmental and genetic background of the Tibetan population. Conclusion This study provides novel evidence that Tibetan populations exhibit distinct patterns of fat distribution compared to major racial/ ethnic groups. The trunk fat was identified as independent risk factor for MetS, with specific serum lipoproteins, L3TGp and TGHCR, acting as key mediators. These findings underscore the critical need to move toward precision metabolic risk assessment that reflects the unique physiological adaptations and environmental exposures of diverse populations. To this end, incorporating ethnicity-specific indicator may be essential to achieving equitable and effective strategies for the identification and prevention of obesity related metabolic disorders. Abbreviations BMI, body mass index; HDL-C, high density lipoprotein cholesterol; MetS, Metabolic syndrome; NHANES, National Health and Nutrition Examination Survey; DXA, dual-energy X-ray absorptiometry; ISCD, International Society for Clinical Densitometry; VLDL, very low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein; CH, total cholesterol; FC, free cholesterol; CE, cholesterol ester; PL, phospholipids; TG, triglycerides; CHTGR, cholesterol-to-triglyceride ratio; LactPyR, lactate-to-pyruvate ratio; SBP, systolic blood pressure; FBG, fasting plasma glucose; SD, standard deviation; ANOVA, analysis of variance; PCA, principal component analysis; CI, Confidence Interval; TGHCR, triglyceride to HDL-cholesterol ratio; L3TGp, proportion of triglyceride to total lipid in LDL3. Declarations Data Availability Some or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. Funding: We acknowledge financial supports from Natural Science Foundation of Qinghai Province (2024-ZJ-980 to H.W.), National Natural Science Foundation of China (U24A20774 to Y.W., 72364032 to W.P., 31821002 to H.T.), National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0508500 and 2023ZD050850# to Y.W.), National Key R&D Program of China (2022YFC3400700 and 2022YFA0806400 to H.T.). Authors’ contributions: W.P. and H.W. conceptualized the idea, L.Y., drafted the initial manuscript, Q.H. and H.T. performed the metabolomics examination and provided biochemical interpretation of the metabolites, B.Z. and T.L. conducted the statistical analysis and produced figures and tables. H.W., L.Y., B.Z., W.P., Y.W., and H.T. reviewed/edited the manuscript. All authors have intellectual input to this manuscript. All authors have read and agreed to the published version of the manuscript. Acknowledgments: We thank all the local medical workers who assisted this study. They are from the disease prevention and control center of Golmud, Tanggula Mountain Town health center and Golmud Children’s Hospital. Conflict of interest: The authors declare no conflict of interest. References Prillaman M. Why BMI is flawed - and how to redefine obesity. Nature. 2023;622(7982):232–3. Rubino F, Cummings DE, Eckel RH, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025;13(3):221–62. Coral DE, Smit F, Farzaneh A, et al. Subclassification of obesity for precision prediction of cardiometabolic diseases. Nat Med. 2025;31(2):534–43. Reinisch I, Ghosh A, Noé F, et al. Unveiling adipose populations linked to metabolic health in obesity. 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Hypoxia promotes white adipose tissues browning in rats under simulated environment at altitude of 5000 m. Biochem Biophys Res Commun. 2023;666:146–53. Pena E, El Alam S, Siques P, Brito J. Oxidative Stress and Diseases Associated with High-Altitude Exposure. Antioxid (Basel). 2022;11(2):267. Trayhurn P. Hypoxia and adipose tissue function and dysfunction in obesity. Physiol Rev. 2013;93(1):1–21. Yang Q, Ma P, Zhang H, et al. Body fat distribution in trunk and legs are associated with cardiometabolic risk clustering among Chinese adolescents aged 10–18 years old. J Pediatr Endocrinol Metab. 2021;34(6):721–6. Zhao S, Tang J, Zhao Y, et al. The impact of body composition and fat distribution on blood pressure in young and middle-aged adults. Front Nutr. 2022;9:979042. Srikanthan P, Horwich TB, Tseng CH. Relation of Muscle Mass and Fat Mass to Cardiovascular Disease Mortality. Am J Cardiol. 2016;117(8):1355–60. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881–7. Maniyadath B, Zhang Q, Gupta RK, Mandrup S. Adipose tissue at single-cell resolution. Cell Metab. 2023;35(3):386–413. Niederauer CM, Binkley TL, Specker BL. Effect of truncal adiposity on plasma lipid and lipoprotein concentrations. J Nutr Health Aging. 2006;10(2):154–60. Kosmas CE, Rodriguez Polanco S, Bousvarou MD et al. The Triglyceride/High-Density Lipoprotein Cholesterol (TG/HDL-C) Ratio as a Risk Marker for Metabolic Syndrome and Cardiovascular Disease. Diagnostics (Basel). 2023;13(5). Lee J, Ah Lee Y, Yong Lee S, Ho Shin C, Hyun Kim J. Comparison of Lipid-Derived Markers for Metabolic Syndrome in Youth: Triglyceride/HDL Cholesterol Ratio, Triglyceride-Glucose Index, and non-HDL Cholesterol. Tohoku J Exp Med. 2022;256(1):53–62. Grugni G, Lupi F, Bonetti M et al. Assessing Metabolic Syndrome Risk in Children and Adolescents with Prader-Willi Syndrome: A Comparison of Index Performance. J Clin Med. 2025;14(13). Che B, Zhong C, Zhang R, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22(1):34. Mederos-Torres CV, Díaz-Burke Y, Muñoz-Almaguer ML, et al. Triglyceride/high-density cholesterol ratio as a predictor of cardiometabolic risk in young population. J Med Life. 2024;17(7):722–7. Moosavi D, Vuckovic I, Kunz HE, Lanza IR. A Randomized Trial of ω-3 Fatty Acid Supplementation and Circulating Lipoprotein Subclasses in Healthy Older Adults. J Nutr. 2022;152(7):1675–89. Lin C, Xia M, Dai Y, et al. Cross-ancestry analyses of Chinese and European populations reveal insights into the genetic architecture and disease implication of metabolites. Cell Genom. 2025;5(4):100810. Mei Z, Xu L, Huang Q, et al. Metabonomic Biomarkers of Plaque Burden and Instability in Patients With Coronary Atherosclerotic Disease After Moderate Lipid-Lowering Therapy. J Am Heart Assoc. 2024;13(24):e036906. Mochel JP, Ward JL, Blondel T, et al. Preclinical modeling of metabolic syndrome to study the pleiotropic effects of novel antidiabetic therapy independent of obesity. Sci Rep. 2024;14(1):20665. Lafontan M, Girard J. Impact of visceral adipose tissue on liver metabolism. Part I: heterogeneity of adipose tissue and functional properties of visceral adipose tissue. Diabetes Metab. 2008;34(4 Pt 1):317–27. Chaudhary R, Mathew D, Bliden K, et al. Low-density lipoprotein 4: a novel predictor of coronary artery disease severity. Curr Med Res Opin. 2017;33(11):1979–84. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1YUANWANGandHUANG.xlsx Supplementary Information The online version contains supplementary material available at Additional file 1. Cite Share Download PDF Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 29 Aug, 2025 Reviews received at journal 29 Aug, 2025 Reviews received at journal 28 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 09 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7335258","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503207615,"identity":"e46d87e4-219d-4c96-8496-9ee819775ad2","order_by":0,"name":"Lin YUAN","email":"","orcid":"","institution":"Qinghai University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"YUAN","suffix":""},{"id":503207616,"identity":"194ad7c8-d19c-4c03-9fbf-13e592c88b4e","order_by":1,"name":"Haijing Wang","email":"","orcid":"","institution":"Qinghai University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Haijing","middleName":"","lastName":"Wang","suffix":""},{"id":503207617,"identity":"b5d8665c-f33b-45c8-860d-26537e9596ca","order_by":2,"name":"Qingxia Huang","email":"","orcid":"","institution":"Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Qingxia","middleName":"","lastName":"Huang","suffix":""},{"id":503207618,"identity":"d43241be-ebc8-4200-ad0b-44fd8e29311c","order_by":3,"name":"Tiemei Li","email":"","orcid":"","institution":"Qinghai University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tiemei","middleName":"","lastName":"Li","suffix":""},{"id":503207619,"identity":"5bc6d2b3-45b7-40f0-b76d-95ac3b22db72","order_by":4,"name":"Bin Zhang","email":"","orcid":"","institution":"Qinghai Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Zhang","suffix":""},{"id":503207620,"identity":"0f7ff2fa-1386-4fcc-982b-25acbea96d6d","order_by":5,"name":"Huiru Tang","email":"","orcid":"","institution":"Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Huiru","middleName":"","lastName":"Tang","suffix":""},{"id":503207621,"identity":"f598a875-57f3-41b1-bc37-8c012d284338","order_by":6,"name":"Youfa Wang","email":"","orcid":"","institution":"Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Youfa","middleName":"","lastName":"Wang","suffix":""},{"id":503207622,"identity":"fcb2dd44-6a9d-4966-85eb-e44af38f0e6a","order_by":7,"name":"Wen Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDADfmbGBoMPNiAmY+MBorRItjc3FM5IA2tpIE6LwZnjDZ950iAcvFoMjvceYC6ouGPXcCOxcbNNgk3i2vbDQFtqbKJxG34ugXnGmWfJjTMSm41zEtKMzc4kArUcS8ttwKHF7EaOATNv2+FkZonENuPcH4flzA4AtTA2HMat5f4boJZ/h5PZJBLbf1skHOYxO/+QgJYbPEAtDYfteHgONhgzJABtuUHAFvszOQaHZxw7nCDB3thg2APyyw2gLQl4/CLZfsbwcUHNYXv7w+wPDH4AQ2zb+fSHDz7U2ODUAgKHgTgRVUECHuUgwAxyIAE1o2AUjIJRMJIBABNTZ5NSKSxAAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai University Medical College","correspondingAuthor":true,"prefix":"","firstName":"Wen","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-08-09 17:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7335258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7335258/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-025-02808-y","type":"published","date":"2025-11-22T15:58:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89972273,"identity":"44e896f3-058b-4013-a296-83c9742d0d7a","added_by":"auto","created_at":"2025-08-27 05:39:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":822550,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant data screening.\u003c/p\u003e","description":"","filename":"Figure1YUANWANGandHUANGBodyfatcompositionandMetS.png","url":"https://assets-eu.researchsquare.com/files/rs-7335258/v1/06c51387f5b5d9a61414d266.png"},{"id":89973047,"identity":"2b26b04e-8503-4ee8-9c37-c33a75f0e821","added_by":"auto","created_at":"2025-08-27 05:47:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2152249,"visible":true,"origin":"","legend":"\u003cp\u003eKey body fat indicators were identified across ethnic/racial groups using multimodal approaches.(A) Principal component analysis (PCA) visualizes body fat indicators, with distinct colors representing different ethnic/racial groups. (B) Loading values of variables in Principal Component 1 (PC1) are ranked to highlight their contributions. (C) Mantel test for nine body fat indicators among different ethnic/racial groups.\u003c/p\u003e","description":"","filename":"Figure2YUANWANGandHUANGBodyfatcompositionandMetS.png","url":"https://assets-eu.researchsquare.com/files/rs-7335258/v1/42845c3be212e1dc214e5b24.png"},{"id":89973052,"identity":"e03c6ea7-3ac7-42f2-908a-7fa0ecc09176","added_by":"auto","created_at":"2025-08-27 05:47:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1217683,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of lipoproteins and its mediation effect. (A) Distribution of 333 lipid metabolism categories. (B) Results of LASSO screening for serum lipoproteins. (C) Mediation effect of L3TGp in the association between trunk fat percentage and metabolic syndrome risk. (D) Mediation effect of TGHCR in the association between trunk fat percentage and metabolic syndrome risk. ACME denotes the average causal mediation effect, and ADE represents the average direct effect. Model included adjustments for age, sex, marital, education, insurance, household income, smoking, drinking, physical activity.\u003c/p\u003e","description":"","filename":"Figure3YUANWANGandHUANGBodyfatcompositionandMetS.png","url":"https://assets-eu.researchsquare.com/files/rs-7335258/v1/8174829266b8786ea87d3033.png"},{"id":96650223,"identity":"f4dcb158-7a94-4977-98d2-f8fae0dd78f8","added_by":"auto","created_at":"2025-11-24 16:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5283471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7335258/v1/d2cab90c-f162-485d-86a2-d87b6cef9200.pdf"},{"id":89974959,"identity":"16d46524-8c83-4849-8c75-6ea157450fa8","added_by":"auto","created_at":"2025-08-27 05:55:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":187296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at \u003cstrong\u003eAdditional file 1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Additionalfile1YUANWANGandHUANG.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7335258/v1/8ce5f87f64be0a9f0bfe6b5a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct Body Fat Distribution and Its Association with Metabolic Syndrome in Tibetan Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlthough body mass index (BMI) has long been regarded as a core indicator for assessing obesity and related health risks, emerging insights from in-depth obesity research and biological perspectives reveal inherent limitations in using BMI as a criterion for metabolic healthy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It fails to precisely reflect the specific distribution and content of body fat, nor can it effectively determine whether excess fat has already posed health risks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Studies have demonstrated that individuals with identical BMI values may exhibit markedly distinct metabolic risk due to heterogeneous fat distribution patterns (e.g., visceral fat accumulation versus subcutaneous fat deposition) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, the distribution of adipose tissue has been confirmed to significantly correlate with metabolic dysfunction, and this association may demonstrate ethnic-specific variations influenced by genetic factors and environmental adaptations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Tibetans have undergone long-term high-altitude adaptation, which may have shaped unique body composition characteristics. These distinct features provide a natural model to study how environmental pressures shape metabolic physiology and influence metabolic disease risk in ways that differ across populations and their health implications should be further explored.\u003c/p\u003e\u003cp\u003eAdipose tissue is an important part of body composition, also as a dynamic metabolic organ, not only participates in energy storage but also extensively regulates lipoprotein metabolism through the secretion of bioactive substances such as adiponectin and inflammatory cytokines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Adipose tissue distribution exhibits a well-established association with dyslipidemia, and previous studies demonstrate that android obesity serves as an independent risk factor for lipid metabolism disorders, with high density lipoprotein cholesterol (HDL-C) levels being inversely influenced by total adiposity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The association between adipose tissue distribution and lipoprotein levels exhibits heterogeneity across different populations. In males, adipose tissue distribution patterns correlate significantly with serum lipids and lipoprotein subfractions, such as the waist-to-hip ratio and waist circumference, and this effect is independent of age [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among early postmenopausal women, adipose distribution parameters, specifically abdominal fat percentage or waist-to-hip ratio, constitute stronger predictors of atherogenic lipoprotein and apolipoprotein profiles than either body weight or BMI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous study identified the association between lipoprotein profile and distinct obesity phenotypes in Tibetans [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the relationship between lipoproteins and adipose tissue distribution remains unknown.\u003c/p\u003e\u003cp\u003eThe distribution of adipose tissue is an important determinant of metabolic risk. Metabolic syndrome (MetS) is a complex metabolic disorder, arises from complex interactions among genetic, environmental, and lifestyle factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Critically, MetS substantially increases the risk of a range of non-communicable diseases, including cardiovascular disease, non-alcoholic fatty liver disease, chronic kidney disease, and certain cancers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Globally, the prevalence of MetS is estimated at approximately 25%. In China, around 454\u0026nbsp;million adults are impacted by MetS, with a national prevalence rate of 33.9% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while notable ethnic disparities exist. Our previous study revealed that urbanized and semi-urbanized Tibetan populations exhibit MetS prevalence rates of 30.1% in males and 32.1% in females, approaching the national average [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the specific body composition characteristics and underlying pathophysiological mechanisms contributing to MetS in Tibetan populations remain poorly understood.\u003c/p\u003e\u003cp\u003eHere, this study aims to analyze heterogeneity in body fat distribution between Tibetans and various racial/ethnic groups from the National Health and Nutrition Examination Survey (NHANES) database, further investigating whether such heterogeneity directly or indirectly contributes to elevated MetS risk. By identifying population-specific adiposity profiles and uncovering the mediating roles of key serum lipoproteins, this study provides mechanistic insights into ethnic differences in metabolic health and highlights the need for precision approaches to risk assessment and prevention in diverse populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and participants\u003c/h2\u003e\u003cp\u003eThis study utilized a cross-sectional design, with data from an independently established High-Altitude Multi-Ethnic Cohort, and integrated participants from the NHANES database. Specifically, it included participants from the NHANES 2017\u0026ndash;2018 survey cycle and the 2022 cross-sectional data from the High-Altitude Multi-Ethnic Cohort, with age and gender matched between groups. The High-Altitude Multi-Ethnic Cohort was established in Golmud City, Haixi Prefecture, Qinghai Province, China. The main participants in this cohort are Tibetans, and the baseline surveys commenced in 2018, with supplementary recruitment of new participants conducted between December 2021 and May 2022 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Both databases employed the same technical standard of dual-energy X-ray absorptiometry (DXA) for body composition assessment.\u003c/p\u003e\u003cp\u003eA total of 10,865 participants were initially considered. Exclusion criteria were applied as follows \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e: (1) Individuals with missing body composition data were removed from both databases (NHANES: n\u0026thinsp;=\u0026thinsp;4,961; High-Altitude Multi-Ethnic Cohort: n\u0026thinsp;=\u0026thinsp;706; total excluded\u0026thinsp;=\u0026thinsp;5,667); (2) Individuals of Han Chinese or unidentified ethnicity were excluded from the High-Altitude Multi-Ethnic Cohort (n\u0026thinsp;=\u0026thinsp;7). Then, 4,293 participants remained in the NHANES cohort and 898 in the High-Altitude Multi-Ethnic Cohort. Subsequently, 1:1 matching between the two databases was performed based on age and sex. Exact matching was used for gender, while age was matched using a caliper of \u0026plusmn;\u0026thinsp;1 year. This step excluded 3,713 individuals. Finally, 1,480 individuals were included for following analysis, comprising 740 Tibetan participants from the High-Altitude Multi-Ethnic Cohort and 740 matched participants from the NHANES. Individuals from the NHANES represented diverse racial and ethnic groups, including Hispanic (Mexican American and Other Hispanic), White, Black, Asian, and other racial backgrounds.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBody composition measurements\u003c/h3\u003e\n\u003cp\u003eDXA is recognized as a gold-standard method for assessing body composition [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Scans were performed with Hologic Apex software (version 4.0) following the manufacturer's protocols. All measurements and quality control procedures were performed as previous reported: (1) Subject preparation and positioning: Participants wore lightweight clothing with metal objects removed; subject repositioning was required after each scan. (2) Device calibration: Daily calibration of the equipment was performed. (3) Technician certification: All four operators completed unified training certified by the International Society for Clinical Densitometry (ISCD), covering the official ISCD technologist practical manual and the manufacturer-provided test protocols and operational guidelines. (4) Precision verification: Measurement precision was verified through triplicate scans (with subjects leaving the scanning table and being repositioned between each scan) of the lumbar spine and hip regions in fifteen participants; only a single scan per region was acquired during formal measurements [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For this study, nine fat mass indices were included in the analysis.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIn High-Altitude Multi-Ethnic cohort, physical examinations were conducted by trained physicians, including standardized measurements of height, weight, and waist circumference, taken while participants wore lightweight clothing. BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;). Covariates related to general demographics (age, sex, marital), socioeconomic status (education, insurance, household income), and lifestyle behaviors (smoking, drinking, physical activity) were collected through face-to-face interviews conducted by certified investigators using structured questionnaires.\u003c/p\u003e\n\u003ch3\u003eSerum metabolome quantification\u003c/h3\u003e\n\u003cp\u003eThe lipoprotein subfractions in serum were performed on a 600 MHz AVANCE III NMR spectrometer equipped with a BBI probe (Bruker Biospin GmbH, Germany), following a previously established protocol [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. After data cleaning and imputation of missing values, this study included a total of 333 quantifiable metabolomic parameters, comprising 129 lipoprotein subfractions, 165 lipoprotein-to-fatty acid ratio parameters, and 39 low-molecular-weight metabolites [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The detailed lipoprotein profile covered very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), along with their 15 subfractions (VLDL1\u0026ndash;5, LDL1\u0026ndash;6, HDL1\u0026ndash;4). These lipoproteins and their subfractions were quantified for compositional parameters, including: apolipoproteins (Apo-A1, Apo-A2, Apo-B); total cholesterol (CH), free cholesterol (FC), cholesterol ester (CE); phospholipids (PL), triglycerides (TG). Additionally, functional parameters derived from the aforementioned quantitative data were also calculated, such as the cholesterol-to-triglyceride ratio (CHTGR) and lactate-to-pyruvate ratio (LactPyR).\u003c/p\u003e\n\u003ch3\u003eCardiometabolic outcomes\u003c/h3\u003e\n\u003cp\u003eMetabolic syndrome was defined in this study as the presence of at least three of the following criteria: (a) central obesity: waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for males and \u0026ge;\u0026thinsp;80 cm for females; (b) systolic blood pressure (BP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or on antihypertensive medication; (c) fasting plasma glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L or on medication for high blood glucose; (d) HDL-cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L for males and \u0026lt;\u0026thinsp;1.30 mmol/L for females or on medication for reduced HDL-cholesterol; (e) TG\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L or on medication for elevated TG [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were described using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed data and compared via Student\u0026rsquo;s t-test or analysis of variance (ANOVA), while non-normally distributed data were reported as median (interquartile range) and analyzed using Wilcoxon rank-sum or Kruskal-Wallis tests. Categorical variables were summarized as frequencies (proportions) and assessed with chi-square tests.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) was employed to explore the distribution patterns of body fat-related variables across ethnic/racial groups following dimensionality reduction, selecting adiposity indicators that were significantly represented in the top 10 loading variables of both PC1 and PC2. Ethnic characteristics were analyzed using Mantel tests and Spearman correlations to dual-validate the robustness of the PCA-selected indicators.\u003c/p\u003e\u003cp\u003eAdditionally, linear regression models adjusted for age, sex, marital status, education level, health insurance coverage, household income, smoking status, alcohol consumption, and physical activity were used to assess differences in two key adiposity indices between Tibetans and other ethnic/racial groups. To eliminate confounding by body weight, subsequent analyses utilized the ratio of key adiposity indices to body weight. The trunk fat percentage and total fat percentage were defined as trunk fat mass/weight and total fat mass/weight, respectively.\u003c/p\u003e\u003cp\u003eGiven the high dimensionality and strong collinearity of serum lipoprotein data, we integrated linear regression, logistic regression, and LASSO regression (via the R glmnet package) for variable selection. First, variables significant in both linear regression (key adiposity percentage vs. lipoprotein) and logistic regression (lipoprotein vs. MetS) were retained. Second, mediation analysis was conducted using the R mediation package, with bootstrap resampling (1,000 iterations) to estimate confidence intervals for mediation effects, thereby elucidating the intermediary roles of serum lipoproteins.\u003c/p\u003e\u003cp\u003eStatistical significance was defined as a two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all analyses were performed in R version 4.4.1.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e1. Identification of key body fat indicators among ethnic/racial groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo minimizing potential confounding effects from demographic disparities, age and sex matching was performed between Tibetan participants and other ethnic/racial groups from the NHANES database. The median age across all groups ranged from 42 to 46 years, with no significant differences in age and sex. While, significant difference was observed in marital status and BMI \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics among Tibetan and other ethnical/racial groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003cp\u003e(Asian)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003cp\u003e(Black)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003cp\u003e(Other)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003cp\u003e(White)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTibetan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN = 98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN = 151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN = 132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN = 48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN = 231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN = 80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN = 740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.0 (33.0–51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.0 (37.0–52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.0 (36.0–52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.5 (37.0-52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43.0 (33.0–51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.5 (37.0-52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e44.0 (35.0–52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (41.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (56.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96 (41.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27 (33.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e322 (43.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (55.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135 (58.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e418 (56.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e82 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (65.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (83.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (52.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e144 (63.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53 (67.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e620 (84.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23(17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38(16.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17(21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30(4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e \u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.5(26.0-32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.5(22.8–28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.1(24.6–36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.4(24.3–32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.4(23.8–32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.2(25.0-31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.5 (23.0-29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, principal component analysis (PCA) was performed on nine body fat mass indicators common to both databases following normalization. The PCA plot demonstrated that the first principal component (PC1) explained 80.07% of the total variance, revealing distinct separation from non-Hispanic White and Black participants \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Significant differences in both PC1 and PC2 scores were also observed among ethnic/racial groups, collectively demonstrating ethnic divergence in body fat distribution. Specifically, as evidenced in box plots, the Tibetans exhibits significant differences from most Non-Hispanic groups (such as Non-Hispanic Asian, Non-Hispanic Black, and Non-Hispanic White) on PC1. On PC2, significant differences are observed between the Tibetan population and the aforementioned groups except for Non-Hispanic Other \u003cb\u003e(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. Analysis of PC1 loading scores identified total fat mass and trunk fat mass as the top two contributors, underscoring their dominant influence on this principal component \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. To validate their discriminative capacity, mantel tests confirmed statistically significant differences in total fat mass and trunk fat mass across ethnic/racial groups, further supporting their role as dominant indicators of ethnic/racial-specific body fat distribution \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Differences in key body fat indicators among ethnic/racial groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further control the potential confounding effects of BMI and marital status, linear regression models were applied. Using Tibetans as the reference group, Mexican Americans exhibited a higher trunk fat mass 0.312 g (β = 0.312, 95% CI: 0.103 ~ 0.521) and total fat mass 0.279 g (β = 0.279, 95% CI: 0.071 ~ 0.487). A similar trend was observed among White and Black individuals. Conversely, Non-Hispanic Asians exhibited significantly lower trunk fat mass (β = -0.221, 95% CI: -0.392~ -0.049) and total fat mass (β = -0.177, 95% CI: -0.347~ -0.007) compared to Tibetans \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The results demonstrated significant differences of trunk and total fat between Tibetans and other major ethnic groups, including Mexican Americans, non-Hispanic Asians, non-Hispanic Blacks, and non-Hispanic Whites.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Trunk/Total Fat mass (g) distribution among Tibetan and other ethnical/racial groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTrunk fat(g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eTotal fat(g)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTibetan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMexican American\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.275 (0.067, 0.484)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.246 (0.038, 0.453)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.312 (0.103, 0.521)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.279 (0.071, 0.487)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Hispanic (Asian)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.199 (-0.373, -0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.156 (-0.329, 0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.221 (-0.392, -0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.177 (-0.347, -0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Hispanic (Black)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.172 (-0.011, 0.356)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.393 (0.211, 0.575)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3 (0.112, 0.488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.523 (0.336, 0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Hispanic (Other)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.153 (-0.136, 0.442)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.198 (-0.09, 0.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.219 (-0.07, 0.508)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.268 (-0.019, 0.555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Hispanic (White)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.299 (0.152, 0.445)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.383 (0.237, 0.528)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.342 (0.194, 0.491)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.423 (0.275, 0.571)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther Hispanic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.025 (-0.204, 0.253)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.078 (-0.149, 0.306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.018 (-0.212, 0.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068 (-0.16, 0.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTibetans as a reference group. Model 1 was unadjusted model. Model2 was adjusted for marital status and BMI.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. Associations of key body fat percentage with MetS and its components in the Tibetan population\u003c/b\u003eThe observed disparities in body fat distribution between Tibetans and other ethnic/racial groups underscore the importance of trunk fat mass and total fat mass as key indicators, highlighting their potential health implications and prompting further scientific investigation. Among these 740 participants, 162 (21.9%) was identified as MetS, and significant differences were observed in sex, age, marital status, income level, smoking behavior, and BMI. Specifically, compared to healthy participants, the MetS group exhibited significantly higher median for trunk fat and total fat distribution, as well as a higher BMI \u003cb\u003e(Table S2)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo address potential confounding effects of body weight on key adiposity indicators, trunk fat percentage and total fat percentage were utilized in subsequent analyses. In the fully adjusted model (adjusted for age, sex, marital status, education, insurance, household income, smoking, drinking, physical activity, and BMI), each one unit increase in trunk fat percentage was associated with significantly higher risks of MetS (OR = 1.59, 95% CI: 1.27 ~ 1.91), hypertension (OR = 1.53, 95% CI: 1.21 ~ 1.85), hypercholesterolemia (OR = 1.61, 95% CI: 1.22 ~ 2.00), and central obesity (OR = 2.28, 95% CI: 1.83 ~ 2.73). In contrast, total fat percentage showed no significant associations with these outcomes except for central obesity (OR = 2.16, 95% CI: 1.62 ~ 2.70). After BMI adjustment, trunk fat percentage shows broader metabolic implications than total fat percentage \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These findings underscore the pivotal role of trunk fat percentage in the development of metabolic disorders.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between Trunk/Total Fat mass percentage and Metabolic Syndrome in Tibetan.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTrunk fat/Weight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eTotal fat/Weight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic syndrome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83 (1.63, 2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.25 (1.07, 1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.34 (2.1, 2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22 (1.93, 2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.42 (2.16, 2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.25 (1.95, 2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.59 (1.27, 1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.72, 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.79 (1.62, 1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41 (1.25, 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.79 (1.59, 1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.93 (1.68, 2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.84 (1.63, 2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.98 (1.72, 2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.53 (1.21, 1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26 (0.85, 1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51 (1.25, 1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.9, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.46 (1.16, 1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27 (0.9, 1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.49 (1.17, 1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.3 (0.91, 1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (0.63, 1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67 (0.14, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.36 (1.11, 1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (0.76, 1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.63 (1.34, 1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.6 (1.24, 1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.72 (1.41, 2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.69 (1.32, 2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.61 (1.22, 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.43 (0.95, 1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertriglyceridemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.34 (1.07, 1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.6, 1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.89 (1.56, 2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.63 (1.23, 2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (1.65, 2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.74 (1.32, 2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.33 (0.88, 1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.81 (0.24, 1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCentral Obesity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.46 (6.18, 6.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.59 (3.38, 3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.15 (7.82, 8.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.73 (12.32, 13.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.68 (8.33, 9.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.03 (13.59, 14.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.28 (1.83, 2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.16 (1.62, 2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModel 1 was unadjusted model. Model 2 was adjusted for age and sex. Model 3 further adjusted for marital, education, insurance, household income, smoking, drinking and physical activity. Model 4 further adjusted for BMI.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4. Mediating role of lipoproteins in the link between body fat percentage and MetS\u003c/b\u003eTo further investigate whether lipoproteins mediate the effects of trunk fat percentage on MetS, 333 quantifiable parameters such as lipoprotein subfractions and small molecular weight metabolites were included. The top three lipoprotein contributors were LDL (31.23%), VLDL (21.62%), and HDL (21.02%) subfractions and their components (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Firstly, 263 metabolites significantly associated with trunk fat percentage were screened using linear regression (FDR-adjusted P \u0026lt; 0.05; \u003cb\u003eTable S3)\u003c/b\u003e, and 275 metabolites significantly associated with MetS were screened using logistic regression (FDR-adjusted P \u0026lt; 0.05; \u003cb\u003eTable S4)\u003c/b\u003e. Then, 235 metabolites were found to be associated with both the trunk fat percentage and MetS. Finally, LASSO regression was performed on these 235 metabolites and MetS, identifying two metabolites significantly associated with MetS: triglyceride to HDL-cholesterol ratio (TGHCR) and the proportion of triglyceride to total lipid in LDL3 (L3TGp) (P \u0026lt; 0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cb\u003eTable S5)\u003c/b\u003e. Subsequently, the mediating effects of these two metabolic markers were further investigated.\u003c/p\u003e\u003cp\u003eMediation analyses showed the effect of the trunk fat percentage on MetS through metabolites, TGHCR and L3TGp, respectively. The trunk fat percentage demonstrated clear associations with MetS, with both significant mediation effects (path ab) and direct effects (path c'). Specifically, both L3TGp (β = 1.7 x 10\u003csup\u003e− 4\u003c/sup\u003e g, 95% CI: 4 x 10\u003csup\u003e− 5\u003c/sup\u003e~3.6 x 10\u003csup\u003e− 4\u003c/sup\u003e) and TGHCR (β = 1.8 x 10\u003csup\u003e− 4\u003c/sup\u003e g, 95% CI: 4 x 10\u003csup\u003e− 5\u003c/sup\u003e~4.6 x 10\u003csup\u003e− 4\u003c/sup\u003e) demonstrated statistically significant mediating effects after adjustment for potential confounders\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. These findings suggest that the mediating effects of specific metabolites modulate the association between trunk fat and MetS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates distinct patterns of fat distribution across racial/ethnic groups, with Tibetan populations exhibiting significant different trunk fat and total fat mass distributions compared to major racial/ethnic groups represented in the NHANES database. Furthermore, the trunk fat percentage emerged as independent risk factors for MetS within the Tibetan population. A key mechanistic insight was the identification of two specific serum lipoproteins, L3TGp and TGHCR, as significant mediators in the association between trunk fat percentage and MetS. Together, these results highlight the need for ethnicity-tailored obesity metrics and support the integration of lipoprotein profiling into metabolic risk assessments.\u003c/p\u003e\u003cp\u003eOur findings replicate previously reported racial/ethnic differences in body fat distribution [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], demonstrating that Tibetan populations exhibit a distinct adiposity pattern characterized by significantly lower trunk and total fat mass compared to Mexican-American, Black, and White individuals, yet higher levels than those observed in Asian participants within the NHANES database. Previous studies have shown that, at same levels of BMI and waist circumference, White individuals tend to have a higher visceral fat area compared to Black and Hispanic populations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In studies involving children, total fat mass and trunk fat mass were found to be higher in females than in males [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In adults, Indians showed greater total fat mass index and greater trunk fat percentage than Creoles [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The higher total fat and trunk fat mass observed in Tibetans compared to Asian population might be related to the adaptation to hypoxic stress, and the specific fat distribution may favor subcutaneous storage and improved metabolic efficiency [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Long-term exposure to a hypobaric hypoxic environment at high altitudes can lead to alterations in body fat deposition sites and distribution patterns [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The high-altitude environment, characterized by hypobaric hypoxia, induces metabolic dysregulation, elevating oxidative stress and increasing energy demands for thermogenesis and oxygen transport [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this context, subcutaneous fat may maintain oxygen supply through enhanced angiogenesis, thus reducing hypoxic damage, while its enhanced glycolytic capacity may improve hypoxia tolerance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These mechanisms are consistent with our findings, suggesting an optimization of fat distribution driven by natural selection pressures to enhance the adaptation in the high-altitude environment.\u003c/p\u003e\u003cp\u003eThe contribution of fat mass in specific regions varies in its implication for healthy. In adolescents, trunk fat has been associated with increased clustered cardiometabolic risk [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Among adults, trunk fat is positively correlated with elevated blood pressure, and total fat mass/height\u003csup\u003e2\u003c/sup\u003e was linked to a higher risk of MetS, particularly when exceeding 26.9 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, higher percentage of total and trunk fat mass have been significantly associated with increased cardiovascular diseases mortality in NHANES based study [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While, trunk fat, particularly visceral fat, is more predictive of cardiometabolic risk than total fat mass [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Excess trunk fat, particularly visceral adipose tissue deposition, may leads to adipocyte hypertrophy, excessive extracellular matrix accumulation causing tissue fibrosis, infiltration of pro-inflammatory immune cells, and reduced protective adipokine secretion [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, only trunk fat mass percentage was found to be significantly associated with MetS in the Tibetan population, reinforcing its role as a greater predictive power for adverse metabolic outcomes.\u003c/p\u003e\u003cp\u003eTrunk fat accumulation is independently associated with elevated TG, reduced HDL, and atherogenic dyslipidemia (increased TC/HDL ratio), which is consistent with the diagnostic criteria for metabolic syndrome. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These observations provide a compelling rationale to explore the underlying mechanisms. Here, both L3TGp and TGHCR were identified as significant mediators in the Tibetan population. The TGHCR (also known as TG/HDL-C) has emerged as a promising novel risk marker for predicting MetS [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These results highlight the critical role of relative triglyceride enrichment in the development of MetS and further support TGHCR as a robust predictor of MetS risk in this specific population [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], suggesting its broader utility in metabolic risk assessment.\u003c/p\u003e\u003cp\u003eL3TGp, defined as the proportion of triglyceride content relative to total lipid content within LDL3 particles, was found to be significantly higher in older than in young adults [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. LDL3 (medium-sized low-density lipoprotein particles), a subclass of LDL, has a density range of 1.034–1.037 kg/L [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Elevated levels of LDL3 (medium-sized low-density lipoprotein particles) are closely linked to increased risks of coronary heart disease, coronary atherosclerosis, and stroke [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Excess visceral adipose tissue may accelerate lipolysis, leading to the release of large amounts of free fatty acids that are transported directly to the liver via the portal vein [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This process may contribute to increased hepatic synthesis and secretion of very-low-density lipoprotein, which in turn elevate circulating levels of L3TGp [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the underlying biological mechanisms require further investigation.\u003c/p\u003e\u003cp\u003eHere, we first examined differences in fat distribution between Tibetans, an ethnic group adapted to high-altitude environments, and other ethnically/racially groups. Furthermore, the core mediating role of specific serum lipoproteins in the association between body fat and MetS was identified in Tibetans. These findings underscore the importance of population-specific assessment tools in obesity research. It not only highlights unique fat distribution patterns in high-altitude Tibetans but also uncovers the lipoprotein-mediated metabolic pathways linking adiposity to disease. However, the cross-sectional design of this study limits the ability to draw causal inferences between fat distribution, lipoprotein profiles, and MetS. Furthermore, confounding was minimized through 1:1 age- and sex-matching and statistical adjustment for key covariates, unmeasured factors may still influence the observed associations. Additionally, the generalizability of the findings is limited due to the unique environmental and genetic background of the Tibetan population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides novel evidence that Tibetan populations exhibit distinct patterns of fat distribution compared to major racial/ ethnic groups. The trunk fat was identified as independent risk factor for MetS, with specific serum lipoproteins, L3TGp and TGHCR, acting as key mediators. These findings underscore the critical need to move toward precision metabolic risk assessment that reflects the unique physiological adaptations and environmental exposures of diverse populations. To this end, incorporating ethnicity-specific indicator may be essential to achieving equitable and effective strategies for the identification and prevention of obesity related metabolic disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, body mass index; HDL-C, high density lipoprotein cholesterol; MetS, Metabolic syndrome; NHANES, National Health and Nutrition Examination Survey; DXA, dual-energy X-ray absorptiometry; ISCD, International Society for Clinical Densitometry; VLDL, very low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein; CH, total cholesterol; FC, free cholesterol; CE, cholesterol ester; PL, phospholipids; TG, triglycerides; CHTGR, cholesterol-to-triglyceride ratio; LactPyR, lactate-to-pyruvate ratio; SBP, systolic blood pressure; FBG, fasting plasma glucose; SD, standard deviation; ANOVA, analysis of variance; PCA, principal component analysis; CI, Confidence Interval; TGHCR, triglyceride to HDL-cholesterol ratio; L3TGp, proportion of triglyceride to total lipid in LDL3.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eWe acknowledge financial supports from Natural Science Foundation of Qinghai Province (2024-ZJ-980 to H.W.), National Natural Science Foundation of China (U24A20774 to Y.W., 72364032 to W.P., 31821002 to H.T.), National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0508500 and 2023ZD050850# to Y.W.), National Key R\u0026amp;D Program of China (2022YFC3400700 and 2022YFA0806400 to H.T.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e W.P. and H.W. conceptualized the idea, L.Y., drafted the initial manuscript, Q.H. and H.T. performed the metabolomics examination and provided biochemical interpretation of the metabolites, B.Z. and T.L. conducted the statistical analysis and produced figures and tables. H.W., L.Y., B.Z., W.P., Y.W., and H.T. reviewed/edited the manuscript. All authors have intellectual input to this manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank all the local medical workers who assisted this study. They are from the disease prevention and control center of Golmud, Tanggula Mountain Town health center and Golmud Children\u0026rsquo;s Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrillaman M. Why BMI is flawed - and how to redefine obesity. Nature. 2023;622(7982):232\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRubino F, Cummings DE, Eckel RH, et al. 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Cell Genom. 2025;5(4):100810.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMei Z, Xu L, Huang Q, et al. Metabonomic Biomarkers of Plaque Burden and Instability in Patients With Coronary Atherosclerotic Disease After Moderate Lipid-Lowering Therapy. J Am Heart Assoc. 2024;13(24):e036906.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMochel JP, Ward JL, Blondel T, et al. Preclinical modeling of metabolic syndrome to study the pleiotropic effects of novel antidiabetic therapy independent of obesity. Sci Rep. 2024;14(1):20665.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLafontan M, Girard J. Impact of visceral adipose tissue on liver metabolism. Part I: heterogeneity of adipose tissue and functional properties of visceral adipose tissue. Diabetes Metab. 2008;34(4 Pt 1):317\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaudhary R, Mathew D, Bliden K, et al. Low-density lipoprotein 4: a novel predictor of coronary artery disease severity. Curr Med Res Opin. 2017;33(11):1979\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body fat composition, Metabolome, Lipoproteins, Metabolic syndrome, Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-7335258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7335258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Although ethnic/racial differences in body fat distribution have been documented, the specific pattern in Tibetans and its implications for metabolic health in this high-altitude population remain unclear.\u003cbr\u003e\n\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 1480 participants from the Tibetan cohort and the NHANES were included. Principal component analysis and Mantel tests were employed to identify Tibetan-specific body fat indicators. Linear models assessed associations with metabolic syndrome (MetS), and mediation analyses evaluated the indirect effects of serum lipoproteins.\u003cbr\u003e\n\u003cstrong\u003eResults:\u003c/strong\u003e Tibetans showed distinct trunk and total fat mass compared to other ethnic/racial groups. Trunk fat percentage was identified as a risk factor for MetS (OR = 1.59, 95% CI: 1.27~1.91). The triglycerides to total lipids ratio in low density lipoprotein 3 (L3TGP) and triglycerides to high density lipoprotein cholesterol ratio (TGHCR) exhibited significant mediating effect between trunk fat percentage and MetS (L3TGP:β = 1.7 x 10\u003csup\u003e-4\u003c/sup\u003eg, 95% CI: 4 x 10\u003csup\u003e-5\u003c/sup\u003e~3.6 x 10\u003csup\u003e-4\u003c/sup\u003e ;TGHCR: β = 1.8 x 10\u003csup\u003e-4\u003c/sup\u003eg, 95% CI: 4 x 10\u003csup\u003e-5\u003c/sup\u003e~4.6 x 10\u003csup\u003e-4\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study revealed novel evidence for distinct fat distribution in Tibetans, linked to elevated MetS risk. L3TGp and TGHCR were identified as key lipoprotein mediators, supporting the need for environmental- and ethnicity-specific indicators in metabolic risk assessment.\u003c/p\u003e","manuscriptTitle":"Distinct Body Fat Distribution and Its Association with Metabolic Syndrome in Tibetan Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:39:41","doi":"10.21203/rs.3.rs-7335258/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-29T13:00:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T10:01:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T23:29:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T22:12:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183906451596091108116709773811727304884","date":"2025-08-20T15:03:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44352546610233987503353644357448674452","date":"2025-08-18T09:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337545454941378643976965651638097974691","date":"2025-08-17T17:01:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249075960822644780643589091296614552682","date":"2025-08-17T13:07:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248091229689310500721346502776353949476","date":"2025-08-17T02:37:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78698799440317706716355648367041822919","date":"2025-08-16T23:35:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123516632665517212644054543613014245101","date":"2025-08-15T22:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93323737371942520327745930099108511195","date":"2025-08-15T14:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-15T12:49:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T21:40:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T03:07:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2025-08-09T17:12:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59120245-c648-4c1f-b69c-abcb52ae0e10","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:03:59+00:00","versionOfRecord":{"articleIdentity":"rs-7335258","link":"https://doi.org/10.1186/s12944-025-02808-y","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2025-11-22 15:58:40","publishedOnDateReadable":"November 22nd, 2025"},"versionCreatedAt":"2025-08-27 05:39:41","video":"","vorDoi":"10.1186/s12944-025-02808-y","vorDoiUrl":"https://doi.org/10.1186/s12944-025-02808-y","workflowStages":[]},"version":"v1","identity":"rs-7335258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7335258","identity":"rs-7335258","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}