Twenty-Year Temporal Trends in Sarcopenic Obesity and Mortality Risk: The Evolving Landscape of Body Composition in US Adults

Twenty-Year Temporal Trends in Sarcopenic Obesity and Mortality Risk: The Evolving Landscape of Body Composition in US Adults | 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 Article Twenty-Year Temporal Trends in Sarcopenic Obesity and Mortality Risk: The Evolving Landscape of Body Composition in US Adults Liwang Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7423254/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Body composition changes and sarcopenic obesity represent emerging health concerns with mortality implications. We analyzed 26,829 adults from NHANES 1999–2018 to examine temporal trends in sarcopenic obesity prevalence and mortality associations. Sarcopenic obesity was defined using fat-to-fat-free mass ratio (general) and trunk fat-to-appendicular muscle mass ratio (abdominal). Age- and sex-adjusted analyses revealed overall declining prevalence of general sarcopenic obesity (6.2% to 4.9%) and abdominal sarcopenic obesity (6.2% to 3.6%) from 1999–2018. However, stratified analyses showed increasing prevalence among Asian populations and older adults (≥ 60 years) for abdominal sarcopenic obesity. Multivariable Cox regression demonstrated that general sarcopenic obesity was associated with elevated all-cause mortality [HR: 1.29 (95% CI: 1.13–1.47)] and cardiovascular mortality [HR: 1.57 (95% CI: 1.25–1.97)], while abdominal sarcopenic obesity showed stronger associations with cardiovascular [HR: 1.83 (95% CI: 1.47–2.29)] and cancer mortality [HR: 1.68 (95% CI: 1.27–2.22)]. These findings highlight demographic-specific trends in sarcopenic obesity prevalence and underscore the independent prognostic value of body composition phenotypes for mortality risk assessment. Health sciences/Health care/Public health/Epidemiology Health sciences/Health care/Disease prevention/Preventive medicine Body composition sarcopenic obesity mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Obesity persists as a critical global public health challenge, with the prevalence of adult obesity—defined by body mass index (BMI)—rising markedly over the past decade [ 1 , 2 ] . Yet, growing evidence indicates that BMI alone is an insufficient metric, failing to capture nuances in body fat distribution and muscle composition, which can lead to the misclassification of obesity phenotypes and suboptimal metabolic disease risk assessment [ 3 , 4 ] . In recognition of these limitations, the European Association for the Study of Obesity (EASO) has introduced a revised diagnostic framework emphasizing body composition as a key determinant of obesity [ 5 ] . Concurrently, the European Society for Clinical Nutrition and Metabolism (ESPEN) and EASO have highlighted the clinical significance of the concurrent decline in muscle mass and increase in adiposity, termed sarcopenic obesity [ 6 ] . Although prior cross-sectional studies estimate a global sarcopenic obesity prevalence of 11% (95% CI: 10–13%) [ 7 ] , the lack of longitudinal data and standardized diagnostic criteria has obscured long-term trends and hindered epidemiological insights. Establishing unified diagnostic standards is thus imperative to elucidate the temporal dynamics of body composition changes and sarcopenic obesity prevalence, offering a clearer perspective on its epidemiological trajectory and progression. The association between obesity (defined by BMI ≥ 30 kg/m²) and elevated mortality risk is well-documented across epidemiological studies. Large-scale cohort analyses and meta-analyses consistently report increased cardiovascular and all-cause mortality in this population, with Zembic et al.'s multi-cohort European study reporting a hazard ratio (HR) of 1.30 (95% CI: 1.19–1.60) for obese individuals [ 8 ] . Emerging evidence highlights sarcopenic obesity—a phenotype characterized by concurrent low muscle mass and high adiposity—as a clinically significant risk factor. Observational studies link this condition to higher mortality and morbidity, including Mirzai et al.'s findings on cardiovascular disease risk in older adults [ 9 ] , and Kim et al.'s NHANES-based analysis associating it with all-cause and cardiovascular mortality [ 10 ] . However, research on sarcopenic obesity remains limited by methodological challenges. Most studies rely on cross-sectional designs, which hinder causal inference, and focus on non-representative or clinical populations [ 11 , 12 ] ,. Additionally, heterogeneous diagnostic criteria and cut-off values impede comparability across studies [ 13 ] . Large-scale longitudinal studies investigating the relationship between sarcopenic obesity and mortality remain scarce. Despite growing recognition of its clinical significance, the lack of standardized diagnostic criteria and robust prospective cohorts has hindered accurate assessment of sarcopenic obesity's disease burden and prognostic value [ 14 ] . Comprehensive, well-designed cohort studies with extended follow-up periods are urgently needed to clarify the mortality risks associated with this condition. This study utilized eight cross-sectional datasets from NHANES 1999–2018 to analyze temporal trends in body composition indices and sarcopenic obesity over nearly two decades. By linking with National Death Index data, a cohort study was constructed examine the impact of body composition-defined sarcopenic obesity on all-cause and cause-specific mortality. The findings will provide important epidemiological evidence for risk stratification and preventive interventions for sarcopenic obesity. 2. Methods 2.1. Study Participants NHANES is dedicated to assessing the health and nutritional status of the general population in the United States. Employing a stratified, multi-stage probability sampling method, it selects a series of nationally-representative samples across cross-sections. Since 1999–2000, this survey has been conducted in two-year cycles. For the current analysis, data from eight out of ten cycles, spanning from 1999–2000 to 2017–2018 were utilized, excluding the cycles of 2007–2008 and 2009–2010 due to the absence of body composition data during those cycles. The study was approved by the National Center for Health Statistics Institutional Review Board, and all participants signed informed consent. Mortality status of the NHANES participants was ascertained by probabilistic matching with the National Death Index through December 31, 2019 [ 15 ] . In this study, we analyzed individuals aged ≥ 20 years (n = 55,081). After excluding those with missing body composition data (n = 28,205) or missing person-years (n = 47), the final analytical cohort comprised 26,829 participants ( Fig S1 ). 2.2. Body composition Whole-body DXA scanning was performed to test body composition indicators in this study using Hologic fan beam densitometers (Hologic, Bedford, Massachusetts, USA) by trained technicians according to the International Society of Clinical Densitometry’s (ISCD) standard operating procedure [ 16 , 17 ] . Data from DXA included values for fat mass (g), and fat-free mass (bone mineral content and lean mass) (g) of the whole body and different parts of the body. Appendicular muscle mass was calculated as fat-free mass of the limbs minus the bone mineral content of the limbs, fat-to-fat-free mass ratio was calculated as whole-fat mass divided by the fat-free mass ratio, and trunk fat-to-appendicular muscle mass ratio was calculated as trunk fat mass divided by the appendicular muscle mass. 2.3. Definition of sarcopenic obesity Based on DXA data, the study employed two distinct methods to define sarcopenic obesity: general sarcopenic obesity and abdominal sarcopenic obesity. The difference between these two methods lies in the specific metrics used for definition, but both approaches classify individuals as having sarcopenic obesity based on predetermined cut-off values for the respective metrics. These cut-off values were defined by age-standardized reference curves derived from DXA data of 13,236 participants in the NHANES 1999–2004 study. General sarcopenic obesity was defined using the fat-to-fat-free mass ratio model, based on the ratio of total fat mass to total fat-free mass [ 18 ] . Cutoff points of the reference curve for this method are delineated as follows: ratios falling below the 15th percentile, between the 15th and 85th percentiles, between the 85th and 95th percentiles, or exceeding the 95th percentile. Groups with fat-to-fat-free mass ratio surpassing the 95th percentile are deemed to belong to the sarcopenic obesity category [ 19 ] . Abdominal myogenic obesity is defined by the ratio of trunk fat mass to appendicular muscle mass, using the trunk fat to limb muscle mass ratio curve [ 18 ] . 2.4. Covariates Body measurements were collected during a mobile physical examination. Interviews were conducted using standardized questionnaires to obtain detailed information on covariates including race/ethnicity, educational level, race and ethnicity, family income-to-poverty ratio, home ownership, and type of health insurance. Serum samples were collected during the physical examination. Serum total cholesterol (TC) was measured enzymatically, and high-density lipoprotein cholesterol (HDL-C) by direct immunoassay. 2.5. Ascertainment of death Data on the primary cause of death was used for case definition, according to International Classification of Diseases 10th Revision (ICD-10) codes. The primary outcomes for this study were mortality from (1) all causes, (2) cardiovascular diseases: codes I00–I09 (Acute rheumatic fever and chronic rheumatic heart diseases), I11 (hypertensive heart disease), I13 (hypertensive heart and renal disease), I20–I25 (ischemic heart disease), I26–I51 (other heart diseases), and I60–I69 (cerebrovascular diseases), and (3) cancer (codes C00–C97). 2.6. Statistical analysis All statistical analyses accounted for NHANES complex survey design factors, including sample weights, stratification, and clustering, following NHANES guidelines. Age- and sex-adjusted means or proportions of sarcopenic obesity were calculated for each of the 8 two-year cycles from 1999–2000 to 2017–2018, excluding the 2007–2008 and 2009–2010 cycles. Adjustments for age and sex used direct standardization to the 2000 US Census population, with categories for men and women aged 20–39, 40–59, and 60 or older. Linear regression assessed age- and sex-adjusted means, while logistic regression evaluated proportions, testing linear and nonlinear trends and group differences. Nonlinearity was tested by adding a quadratic term to the regression models, and homogeneity of secular trends among subgroups was tested using an interaction term for time and subgroup. Restricted cubic splines with four knots at the 25th, 50th, 75th, and 95th centiles were used to flexibly model the association between the mean body composition indices across all cycles and mortality. Kaplan-Meier survival curves were generated for all-cause and cause-specific mortality, stratified by sarcopenic obesity status. Multivariable Cox regression examined the associations of sarcopenic obesity with mortality, adjusting for known or suspected confounders, including age, sex, height, race/ethnicity, education level, family income-to-poverty ratio, home ownership, health insurance type, HDL-C, and TC. Fat mass, fat-free mass, and muscle mass were further adjusted for each other in additional models. Stratified analyses by sex, age, and race assessed potential modifiers of the association between sarcopenic obesity and all-cause mortality. All statistical tests were two-sided, with P < 0.05 considered significant. Analyses were conducted using R 4.3.4 software ( http://www.R-project.org/ ). Due to the potential for type I errors from multiple comparisons, results from secondary analyses and outcomes should be considered exploratory. 3. Results 3.1. Characteristics of participants The mean age ranged from 38.7 to 51.4 years, with the proportion of males varying from 49.6% to 53.3% (Table 1 ). The percentage of individuals with a high school education or less decreased from 39.4% in 1999–2000 to 16.6% in 2017–2018, while those with college degrees or higher increased from 15.6% to 28.0%. Home ownership ranged from 50.3% to 68.2%, and the proportion without medical insurance varied from 19.0% to 31.0%. Table 1 Characteristics of study participants in the National Health and Nutrition Examination Surveys (NHANES), 1999–2018 a Characteristics 1999–2000 2001–2002 2003–2004 2005–2006 2011–2012 2013–2014 2015–2016 2017–2018 Total No. of participants 4120 4514 4440 3582 2606 2983 2618 1996 Men, No. (%) 2044(49.6) 2268(50.2) 2238(50.4) 1832(51.1) 1388(53.3) 1486(49.8) 1307(49.9) 978(49.8) Age, mean (SD), y 51.0(18.5) 50.0(18.5) 51.4(19.3) 43.3(14.2) 38.7(11.7) 39.2(11.3) 39.0(11.4) 39.4(11.6) Age group, y b 20–39 1311(31.8) 1493(33.1) 1418(31.9) 1502(41.9) 1371(52.6) 1478(49.6) 1351(51.6) 999(50.8) 40–59 1248(30.3) 1499(33.2) 1316(29.6) 1433(40) 1235(47.4) 1505(50.5) 1267(48.4) 967(49.2) 60 or more 1561(37.9) 1522(33.7) 1706(38.4) 647(18.1) NA NA NA NA Race and ethnicity, No. (%) c 3994 4375 4249 3426 2516 2867 2523 1850 Non-Hispanic White 1834(45.9) 2359(53.9) 2366(55.7) 1626(47.5) 934(37.1) 1210(42.2) 752(29.8) 580(31.4) Non-Hispanic Black 800(20.0) 879(20.1) 876(20.6) 902(26.3) 664(26.4) 536(18.7) 539(21.4) 376(20.3) Hispanic 1360(34.1) 1137(26) 1007(23.7) 898(26.2) 517(20.5) 727(25.4) 853(33.8) 492(26.6) Non-Hispanic Asian NA NA NA NA 401(15.9) 394(13.7) 379(15.0) 402(21.7) Education, No. (%) 4108 4509 4432 3580 2606 2982 2618 1965 Less than high school 1618(39.4) 1378(30.6) 1309(29.5) 904(25.3) 467(17.9) 555(18.6) 534(20.4) 326(16.6) High school graduate and some college 1850(45.0) 2230(49.5) 2334(52.7) 1923(53.7) 1396(53.6) 1622(54.4) 1390(53.1) 1088(55.4) College graduate 640(15.6) 901(20.0) 789(17.8) 753(21.0) 743(28.5) 805(27.0) 694(26.5) 551(28.0) Family income-to-poverty ratio, No. (%) 3534 4203 4185 3443 2446 2764 2401 1755 ≤ 100% 725(20.5) 712(16.9) 769(18.4) 599(17.4) 642(26.3) 641(23.2) 508(21.2) 341(19.4) > 100%-299% 1505(42.6) 1729(41.1) 1844(44.1) 1296(37.6) 895(36.6) 1011(36.6) 1062(44.2) 732(41.7) 300%-499% 712(20.2) 907(21.6) 886(21.2) 826(24) 483(19.8) 567(20.5) 426(17.7) 363(20.7) ≥ 500% 592(16.8) 855(20.3) 686(16.4) 722(21) 426(17.4) 545(19.7) 405(16.9) 319(18.2) Home ownership, No. (%) 4061 4455 4401 3552 2595 2944 2544 1892 Owned home 2613(64.3) 3038(68.2) 2874(65.3) 2292(64.5) 1305(50.3) 1597(54.2) 1354(53.2) 999(52.8) Rented home or other arrangement 1362(35.7) 1327(31.8) 1411(34.7) 1176(35.5) 1199(49.7) 1268(45.8) 1147(46.8) 847(47.2) Covered by health insurance, No. (%) d 4068 4460 4401 3582 2606 2983 2618 1966 Yes 3202(78.7) 3603(80.8) 3532(80.3) 2614(73) 1797(69) 2145(71.9) 1971(75.3) 1556(79.2) No 858(21.1) 846(19) 867(19.7) 964(26.9) 808(31) 835(28) 643(24.6) 403(20.5) Refused/Missing 8(0.2) 11(0.2) 2(0.1) 4(0.1) 1(0) 3(0.1) 4(0.2) 7(0.4) a The study included 8 out of 10 rounds of data from 1999 to 2018 for analysis, and no body composition data from two rounds of surveys from 2007–2008 and 2009–2010. b Body composition information was not collected from individuals aged 60 and above starting in NHANES 2011–2012. c NHANES collected self-reported race data in 6 categories (American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, or other) and data on Hispanic origin. Non-Hispanic Asian was listed as a race and ethnicity group starting in NHANES 2011–2012. American Indian or Alaska Native and Native Hawaiian or Pacific Islander were not listed as separate race and ethnicity groups in the data sets. d Answered “Yes” to “Are you covered by health insurance or some other kind of health care plan?” 3.2. Trends of body composition from 1999–2000 to 2017–2018 From 1999–2000 to 2017–2018, age- and sex-adjusted mean fat mass decreased from 28.1 kg (95% CI, 27.3–28.9) to 26.5 kg (95% CI, 25.5–27.5), while fat-free mass increased from 52.7 kg (95% CI, 52.1–53.2) to 54.2 kg (95% CI, 53.5–54.9) (Fig. 1 ). Appendicular muscle mass rose from 22.1 kg (95% CI, 21.9–22.4) to 22.8 kg (95% CI, 22.5–23.2), and trunk fat mass declined from 14.2 kg (95% CI, 13.7–14.6) to 12.8 kg (95% CI, 12.2–13.3). Correspondingly, the fat-to-fat-free mass ratio decreased from 0.55 (95% CI, 0.54–0.56) to 0.50 (95% CI, 0.49–0.52), and the trunk fat-to-appendicular muscle mass ratio declined from 0.66 (95% CI, 0.64–0.68) to 0.58 (95% CI, 0.56–0.60) (all P < 0.001 for linear trend). These trends were consistent across non-Hispanic White and Black populations (all P < 0.05 for subgroup homogeneity) ( Fig S2 ), while Hispanics showed divergent patterns and non-Hispanic Asians exhibited lower absolute values but similar fat-to-muscle ratios. Age-stratified analysis revealed declining fat and muscle mass in participants under 60 years, whereas those over 60 years demonstrated increases in both parameters ( Fig S3 ). 3.3. Prevalence of sarcopenic obesity from 1999–2000 to 2017–2018 General sarcopenic obesity decreased from 6.2% (95% CI, 5.4%–7.1%) to 4.9% (95% CI, 3.6%–11.9%), and abdominal sarcopenic obesity from 6.2% (95% CI, 4.8%–7.9%) to 3.6% (95% CI, 2.6%–4.8%) (all P < 0.001 for trend) (Fig. 2 ). Sex-stratified analysis revealed contrasting patterns, with prevalence increasing among males but decreasing more substantially among females for general sarcopenic obesity, though both sexes demonstrated overall downward trends for both definitions. Racial disparities were evident, with significant decreases among non-Hispanic White and Black individuals but increases among Asians for both definitions. The prevalence of abdominal sarcopenic obesity increased among older adults (≥ 60 years) (Detailed data in Table S1 ). 3.4. Association of body composition with all-cause and cause-specific mortality During median 11.3-year follow-up (IQR: 5.6–16.8 years), 4117 deaths occurred, including 1284 cardiovascular and 927 cancer deaths. Fat mass and trunk fat mass demonstrated reverse J-shaped associations with all-cause mortality, with lowest risk at fat-free mass of 55–80 kg and increased risk at both extremes. Appendicular muscle mass below 22 kg correlated positively with mortality, while higher values showed protective effects. Fat-to-muscle ratios exhibited U-shaped patterns with elevated mortality risk at both extremes (Fig. 3 ). For cause-specific mortality, fat mass and trunk fat mass were positively correlated with cardiovascular and cancer mortality, with similar associations for the fat-to-muscle ratios. Higher muscle mass was associated with a lower risk of cancer mortality. For cardiovascular mortality, both excessive and insufficient fat-free mass or appendicular skeletal muscle mass increased risk ( Fig S4-S5 ). 3.5. Association of sarcopenic obesity with all-cause and cause-specific mortality outcomes Compared to non-sarcopenic obesity, general sarcopenic obesity conferred increased risks of all-cause mortality (HR: 1.29; 95% CI: 1.13–1.47) and cardiovascular mortality (HR: 1.57; 95% CI: 1.25–1.97). Abdominal sarcopenic obesity demonstrated even greater mortality risks, with cardiovascular mortality HR of 1.83 (95% CI: 1.47–2.29) and cancer mortality HR of 1.68 (95% CI: 1.27–2.22) ( Table 2 ) . In subgroup analyses, general sarcopenic obesity was not significantly associated with all-cause mortality in females, individuals aged < 40 years and Non-Hispanic Blacks compared to non-sarcopenic obesity. In contrast, abdominal sarcopenic obesity was associated with increased all-cause mortality risk in women (HR: 1.55; 95% CI: 1.30–1.85), individuals aged < 40 years (HR: 1.76; 95% CI: 1.08–2.86) and Non-Hispanic Blacks (HR: 3.41; 95% CI: 2.15–5.41). For cardiovascular mortality, general sarcopenic obesity showed no significant association in women, individuals aged < 40 years and Non-Hispanic Blacks, while abdominal sarcopenic obesity was linked to increased risk in all subgroups. For cancer mortality, general sarcopenic obesity was not associated with any subgroup, whereas abdominal sarcopenic obesity increased risk in women, individuals aged ≥ 60 years, and both Non-Hispanic Whites and Non-Hispanic Blacks ( Tables S2-S3 ). Adjusted Kaplan-Meier curves demonstrated significantly reduced survival for both sarcopenic obesity definitions across all mortality outcomes compared to non-sarcopenic obesity (Fig. 4 ). Table 2 Hazard ratios (95% CI) for all-cause and cause-specific mortality in sarcopenic obesity Parameters No. of deaths/total Mortality rate (%) a Model 1 b Model 2 b HR (95%CI) P value HR (95%CI) P value All-cause mortality Fat mass-Fat-free mass ratio c Non-SO 4102/25456 14.3 Ref Ref SO 283/1373 17.1 1.28(1.13,1.44) < 0.001 1.29(1.13,1.47) < 0.001 Trunk fat mass-Appendicular muscle mass ratio c Non-SO 4060/25500 14.1 Ref Ref SO 325/1329 20.8 1.51(1.35,1.70) < 0.001 1.54(1.36,1.75) < 0.001 Cardiovascular mortality Fat mass-Fat-free mass ratio c Non-SO 1277/22632 4.9 Ref Ref SO 104/1194 7.0 1.56(1.28,1.91) < 0.001 1.57(1.25,1.97) < 0.001 Trunk fat mass-Appendicular muscle mass ratio c Non-SO 1273/22714 4.9 Ref Ref SO 108/1112 7.9 1.85(1.52,2.25) < 0.001 1.83(1.47,2.29) < 0.001 Cancer mortality Fat mass-Fat-free mass ratio c Non-SO 926/22281 3.6 Ref Ref SO 52/1142 3.6 1.16(0.88,1.53) 0.303 1.14(0.83,1.56) 0.411 Trunk fat mass-Appendicular muscle mass ratio c Non-SO 909/22350 3.5 Ref Ref SO 69/1073 5.2 1.69(1.32,2.16) < 0.001 1.68(1.27,2.22) < 0.001 a Mortality rates are presented as per 1000 person-years. b Model 1: adjusted for age; Model 2: adjusted for age, sex, height, race/ethnicity, education level, race and ethnicity, education, family income-to-poverty ratio, home ownership, type of health insurance, high-density lipoprotein cholesterol, and total cholesterol. c According to the gender and age curves of fat-lean ratio, the group with a fat mass to free fat mass ratio above the 95th centile is considered as the sarcopenic obesity group. The definition of sarcopenic obesity was based on the ratio between truncal fat mass and appendicular muscle mass were similar to the fat-lean ratio approach. SO: sarcopenic obesity 4. Discussion This study provides the most recent national trend estimates of body composition and sarcopenic obesity from 1999–2000 to 2017–2018, highlighting significant disparities among racial, ethnic, and age groups. Notably, higher total and trunk fat mass were strongly associated with increased risks of all-cause and cardiovascular mortality, whereas greater appendicular muscle mass exhibited protective effects against all-cause and cancer mortality. Furthermore, sarcopenic obesity was independently linked to elevated all-cause and cause-specific mortality risks, with abdominal sarcopenic obesity demonstrating particularly pronounced associations. Consistent with previous research among U.S. adults from 2011–2018 [ 12 ] , we observed decreased mean fat mass alongside increased fat-free mass and appendicular muscle mass. Furthermore, our analysis revealed significant racial, ethnic, and age disparities. Non-Hispanic Black individuals demonstrated the highest absolute fat mass, fat-free mass, and appendicular muscle mass, yet maintained favorable fat-to-muscle ratios, particularly for trunk fat-to-appendicular muscle mass, with declining trends over time. Conversely, Hispanic and non-Hispanic Asian populations exhibited lower absolute values but higher fat-to-muscle ratios with increasing trends over time, indicating greater susceptibility to sarcopenic obesity. This pattern may explain the increased metabolic risks—including insulin resistance, dyslipidemia, and elevated cholesterol—observed at lower BMI thresholds in Hispanic and Asian populations compared to other groups [ 20 , 21 ] . Notably, non-Hispanic Asians were the only group demonstrating increases across all body composition parameters, potentially reflecting escalating obesity-related health burdens in this population. Age-stratified analysis revealed contrasting trends: adults aged 20–59 years showed decreases in both fat and muscle mass, while those ≥ 60 years exhibited increases in both parameters, with disproportionate fat mass accumulation driving elevated trunk fat-to-appendicular muscle mass ratios. Our sarcopenic obesity prevalence estimates, using two distinct diagnostic ratios, align with recent North American meta-analyses reporting < 8% prevalence [ 22 ] , though published estimates vary widely (3.2%-26.3%) [ 19 , 23 – 25 ] , likely reflecting diagnostic heterogeneity and population differences Previous studies investigating the association between body composition and mortality have employed diverse measurement approaches, including both direct and indirect methods [ 26 – 36 ] . However, only a limited number of these studies utilized dual-energy X-ray absorptiometry (DXA) for body composition assessment [ 27 – 31 , 33 ] , and the majority focused on specific populations [ 27 – 32 , 36 ] , resulting in inconsistent findings regarding these relationships. Our findings demonstrated a reverse J-shaped association between fat mass and all-cause mortality, with elevated risks at both extremes, while appendicular muscle mass showed inverse associations with mortality risk. These results align with previous body composition-mortality analyses from 1988–2014 [ 36 ] . The increased mortality risk associated with low fat mass may reflect loss of metabolically beneficial adipose depots, including gluteofemoral fat and brown adipose tissue, which confer protective lipid and glucose profiles and reduce cardiovascular-metabolic risk [ 37 , 38 ] . Trunk fat mass demonstrated stronger associations with all-cause and cause-specific mortality than total fat mass, consistent with NHANES 1999–2006 findings [ 34 ] , suggesting central adiposity as a primary mortality determinant. Our cardiovascular mortality findings regarding fat-free mass align with male cohort studies [ 26 ] , though all-cause and cancer mortality associations differed. Higher fat-free mass and appendicular muscle mass were consistently protective against all-cause and cancer mortality. Discrepancies with previous studies may reflect methodological differences, including sex-specific variations and reference point selection. Unlike prospective cohorts using 25th percentile lean mass references—potentially obscuring protective effects—our 50th percentile reference and separate appendicular muscle mass analysis revealed stronger protective associations. Recent evidence supports our finding linking reduced muscle mass to elevated cancer-specific mortality [ 32 ] . Thus, our study highlights the importance of maintaining higher appendicular muscle mass in cancer patients and moderate levels in those with cardiovascular disease. Our analysis revealed a significant elevation in all-cause and cause-specific mortality among individuals with sarcopenic obesity. These findings align with recent meta-analyses, which further demonstrate that sarcopenic obesity—unlike obesity alone—is associated with heightened risks of all-cause and cardiovascular mortality compared to metabolically healthy individuals [ 22 , 39 , 40 ] . Our findings corroborate recent large-scale studies underscoring the prognostic relevance of sarcopenic obesity. In a NHANES-based analysis, Kim et al. observed markedly elevated all-cause mortality (HR: 1.57; 95% CI: 1.41–1.75) and cardiovascular mortality (HR: 1.63; 95% CI: 1.34–1.98) among individuals with low muscle mass and obesity, aligning closely with our results for general sarcopenic obesity [ 10 ] . Further supporting our conclusions, Benz et al. reported that sarcopenic obesity with altered body composition components was linked to a substantially higher 10-year mortality risk (HR: 1.94–2.84; 95% CI: 1.60–4.11), reinforcing the consistency of mortality risk across varying definitions of the condition [ 41 ] . Subgroup analyses yielded critical clinical insights. Notably, abdominal sarcopenic obesity was associated with greater mortality risks than general sarcopenic obesity—a finding consistent with growing evidence emphasizing the prognostic significance of fat distribution. For instance, Lee et al. demonstrated that body composition-defined sarcopenic obesity exhibited stronger mortality associations in younger adults and women, paralleling our results where abdominal sarcopenic obesity remained significant in women and individuals aged < 40 years, whereas general sarcopenic obesity did not [ 42 ] . This pattern suggests that central fat accumulation coupled with muscle loss may constitute a more pathogenic phenotype, particularly among younger populations in whom traditional cardiovascular risk factors are less prevalent. The stronger associations observed for abdominal sarcopenic obesity are consistent with the established pathophysiology of visceral adiposity and deteriorating muscle quality. Emerging mechanistic evidence indicates that visceral fat deposition drives chronic inflammation and insulin resistance, potentially exacerbating muscle protein catabolism and functional decline [ 9 , 14 ] . This biological plausibility reinforces our clinical observations, positioning abdominal sarcopenic obesity as a high-risk phenotype that merits early and targeted intervention alongside rigorous monitoring. This study offers several key strengths. First, we employed a large, nationally representative sample with a broad age distribution, bolstering statistical power and enhancing the generalizability of our findings. Second, comprehensive stratified analyses—categorized by race/ethnicity, age, and sex—uncovered distinct variations in body composition and sarcopenic obesity patterns across demographic subgroups. Third, our methodological approach addresses critical gaps in the literature by examining two-decade temporal trends in body composition and elucidating the mortality implications of sarcopenic obesity. Nevertheless, certain limitations should be noted. The lack of DXA data for the 2007–2008 and 2009–2010 survey cycles restricted comparative analyses across these periods. Furthermore, the absence of body composition measurements for adults aged ≥ 60 years during 2011–2018 precluded precise estimation of sarcopenic obesity prevalence in older populations. These data constraints necessitate cautious interpretation of temporal trends and prevalence estimates, particularly among elderly participants. Overall, this study reveals favorable 20-year trends in body composition, marked by a decline in sarcopenic obesity prevalence. Across racial/ethnic and age subgroups, health improvements were most pronounced in non-Hispanic White and Black populations, whereas Hispanic, non-Hispanic Asian, and older adults (≥ 60 years) exhibited concerning rises in fat-to-muscle ratios. Importantly, we identified a robust association between sarcopenic obesity and mortality, highlighting the critical role of maintaining a lower trunk fat-to-appendicular muscle mass ratio for reducing mortality risk. References Magliano DJ, Chen L, Carstensen B et al (2022) Trends in all-cause mortality among people with diagnosed diabetes in high-income settings: a multicountry analysis of aggregate data[J]. Lancet Diabetes Endocrinol 10(2):112–119 Dai H, Alsalhe TA, Chalghaf N et al (2020) The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: An analysis of the Global Burden of Disease Study[J]. PLoS Med 17(7):e1003198 Garrow JS (1988) Three limitations of body mass index[J]. Am J Clin Nutr 47(3):553 Palumbo AM, Jacob CM, Khademioore S et al (2025) Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis[J]. Obes Rev 26(6):e13894 Busetto L, Dicker D, Frühbeck G et al (2024) A new framework for the diagnosis, staging and management of obesity in adults[J]. Nat Med Donini LM, Busetto L, Bischoff SC et al (2022) Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement[J]. Clin Nutr 41(4):990–1000 Gao Q, Mei F, Shang Y et al (2021) Global prevalence of sarcopenic obesity in older adults: A systematic review and meta-analysis[J]. Clin Nutr 40(7):4633–4641 Zembic A, Eckel N, Stefan N et al (2021) An Empirically Derived Definition of Metabolically Healthy Obesity Based on Risk of Cardiovascular and Total Mortality[J]. JAMA Netw Open 4(5):e218505 Mirzai S, Carbone S, Batsis JA et al (2024) Sarcopenic Obesity and Cardiovascular Disease: An Overlooked but High-Risk Syndrome[J]. Curr Obes Rep 13(3):532–544 Kim D, Lee J, Park R et al (2024) Association of low muscle mass and obesity with increased all-cause and cardiovascular disease mortality in US adults[J]. J Cachexia Sarcopenia Muscle 15(1):240–254 Donini LM, Busetto L, Bauer JM et al (2020) Critical appraisal of definitions and diagnostic criteria for sarcopenic obesity based on a systematic review[J]. Clin Nutr 39(8):2368–2388 Liu B, Du Y, Wu Y et al (2021) Trends in obesity and adiposity measures by race or ethnicity among adults in the United States 2011-18: population based study[J]. BMJ 372:n365 Guan Z, Stephan BCM, Donini LM et al (2024) Exploring the association between sarcopenic obesity and cardiovascular risk: a summary of findings from longitudinal studies and potential mechanisms[J]. Proc Nutr Soc, : 1–9 Wei S, Nguyen TT, Zhang Y et al (2023) Sarcopenic obesity: epidemiology, pathophysiology, cardiovascular disease, mortality, and management[J]. Front Endocrinol (Lausanne) 14:1185221 National Center for Health Statistics (2019) [J] 2019 Dong H, Yan Y, Liu J et al (2021) Reference centiles for evaluating total body fat development and fat distribution by dual-energy x-ray absorptiometry among children and adolescents aged 3–18 years[J]. Clin Nutr 40(3):1289–1295 Lewiecki EM, Binkley N, Morgan SL et al (2016) Best Practices for Dual-Energy X-ray Absorptiometry Measurement and Reporting: International Society for Clinical Densitometry Guidance[J]. J Clin Densitom 19(2):127–140 Siervo M, Prado CM, Mire E et al (2015) Body composition indices of a load-capacity model: gender- and BMI-specific reference curves[J]. Public Health Nutr 18(7):1245–1254 Van Aller C, Lara J, Stephan BCM et al (2019) Sarcopenic obesity and overall mortality: Results from the application of novel models of body composition phenotypes to the National Health and Nutrition Examination Survey 1999–2004[J]. Clin Nutr 38(1):264–270 Zheng W, Mclerran DF, Rolland B et al (2011) Association between body-mass index and risk of death in more than 1 million Asians[J]. N Engl J Med 364(8):719–729 Wulan SN, Westerterp KR, Plasqui G (2010) Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians[J]. Maturitas 65(4):315–319 Liu C, Wong PY, Chung YL et al (2023) Deciphering the obesity paradox in the elderly: A systematic review and meta-analysis of sarcopenic obesity[J]. Obes Rev 24(2):e13534 Cruz-Jentoft AJ, Bahat G, Bauer J et al (2019) Sarcopenia: revised European consensus on definition and diagnosis[J]. Age Ageing 48(4):601 Batsis JA, Mackenzie TA, Jones JD et al (2016) Sarcopenia, sarcopenic obesity and inflammation: Results from the 1999–2004 National Health and Nutrition Examination Survey[J]. Clin Nutr 35(6):1472–1483 Murdock DJ, Wu N, Grimsby JS et al (2022) The prevalence of low muscle mass associated with obesity in the USA[J]. Skelet Muscle 12(1):26 Lee DH, Keum N, Hu FB et al (2018) Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study[J]. BMJ 362:k2575 Cesari M, Pahor M, Lauretani F et al (2009) Skeletal muscle and mortality results from the InCHIANTI Study[J]. J Gerontol Biol Sci Med Sci 64(3):377–384 Newman AB, Kupelian V, Visser M et al (2006) Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort[J]. J Gerontol Biol Sci Med Sci 61(1):72–77 Toss F, Wiklund P, Nordström P et al (2012) Body composition and mortality risk in later life[J]. Age Ageing 41(5):677–681 Rolland Y, Gallini A, Cristini C et al (2014) Body-composition predictors of mortality in women aged ≥ 75 y: data from a large population-based cohort study with a 17-y follow-up[J]. Am J Clin Nutr 100(5):1352–1360 Wijnhoven HA, Snijder MB, Van Schueren B-D (2012) Region-specific fat mass and muscle mass and mortality in community-dwelling older men and women[J]. Gerontology 58(1):32–40 Spahillari A, Mukamal KJ, Defilippi C et al (2016) The association of lean and fat mass with all-cause mortality in older adults: The Cardiovascular Health Study[J]. Nutr Metab Cardiovasc Dis 26(11):1039–1047 Padwal R, Leslie WD, Lix LM et al (2016) Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study[J]. Ann Intern Med 164(8):532–541 Zong G, Zhang Z, Yang Q et al (2016) Total and regional adiposity measured by dual-energy X-ray absorptiometry and mortality in NHANES 1999–2006[J]. Obes (Silver Spring) 24(11):2414–2421 Bigaard J, Frederiksen K, Tjønneland A et al (2004) Body fat and fat-free mass and all-cause mortality[J]. Obes Res 12(7):1042–1049 Liu M, Zhang Z, Zhou C et al (2022) Predicted fat mass and lean mass in relation to all-cause and cause-specific mortality[J]. J Cachexia Sarcopenia Muscle 13(2):1064–1075 Becher T, Palanisamy S, Kramer DJ et al (2021) Brown adipose tissue is associated with cardiometabolic health[J]. Nat Med 27(1):58–65 Raajendiran A, Ooi G, Bayliss J et al (2019) Identification of Metabolically Distinct Adipocyte Progenitor Cells in Human Adipose Tissues[J]. Cell Rep 27(5):1528–1540e7 Eitmann S, Matrai P, Hegyi P et al (2024) Obesity paradox in older sarcopenic adults - a delay in aging: A systematic review and meta-analysis[J]. Ageing Res Rev 93:102164 Zhang X, Xie X, Dou Q et al (2019) Association of sarcopenic obesity with the risk of all-cause mortality among adults over a broad range of different settings: a updated meta-analysis[J]. BMC Geriatr 19(1):183 Benz E, Pinel A, Guillet C et al (2024) Sarcopenia and Sarcopenic Obesity and Mortality Among Older People[J]. JAMA Netw Open 7(3):e243604 Lee H, Chung HS, Kim YJ et al (2023) Association between body composition and the risk of mortality in the obese population in the United States[J]. Front Endocrinol (Lausanne) 14:1257902 Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarymaterials.docx Dataset 1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7423254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519580152,"identity":"02789e94-2f86-41cf-acd0-a651399a9c70","order_by":0,"name":"Liwang Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACPmYYi70ByjhAQAsbXAsPTClBLXCWRAKxWth5DD8X/KqV55d8/PDTzTYGOb4bCYyfC/A6jMdYembfccOZs9OMpXPbGIwlbyQwS8/Aq4V3gzRvzzHGDbdz2JiBWhI33EgAmoNfy+bfQC32G26eAWupJ0bLNmmeHzVAw3nAWhIMCGvh/2bN23AgeWYP0C855yQMZ5552CyNTws//7Hk2zx/6mz72Q8//JxTZiPPdzz54Gd8WsCAse0wjCkB4jYQ0gAEf+qIUDQKRsEoGAUjFgAAGGdF6n+80XgAAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liwang","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-08-21 07:15:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7423254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7423254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92189558,"identity":"86896830-f367-4a03-a78d-b943d522e6ec","added_by":"auto","created_at":"2025-09-25 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1","display":"","copyAsset":false,"role":"figure","size":201020,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in body composition indices in U.S adults\u003c/p\u003e\n\u003cp\u003e(A) mean fat mass (P = .004 overall, and P = .012 for males for linear trend); (B) mean fat-free mass (P \u0026lt; .001 overall, P = .008 for males, and P \u0026lt; .001 females for linear trend); (C) mean fat mass to fat-free mass ratio (all P \u0026lt; .001 for linear trend); (D) mean trunk fat mass (P \u0026lt; .001 overall and males, and P = .003 females for linear trend); (E) mean appendicular muscle mass (all P \u0026lt; .001 for linear trend); and (F) mean trunk fat mass to appendicular muscle mass ratio (all P \u0026lt; .001 for linear trend).\u003c/p\u003e\n\u003cp\u003eAll estimates were standardized to the 2000 US Census population using 6 age and sex categories: males aged 20-39, 40-59, and ≥ 60 years and females aged 20-39, 40-59, and ≥ 60 years. Linear and polynomial models were used to test linear and nonlinear trends.\u003c/p\u003e\n\u003cp\u003eThe homogeneity of trends among sex subgroups was tested using an interaction term of time × sex in the regression models.\u003c/p\u003e\n\u003cp\u003eError bars indicate 95% CIs\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7423254/v1/3489c81a41ad9b7690c3bf64.jpg"},{"id":92189593,"identity":"e95c8ad0-585b-4578-8973-b47e082a68dd","added_by":"auto","created_at":"2025-09-25 14:58:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184355,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in prevalence of sarcopenic obesity in U.S adults\u003c/p\u003e\n\u003cp\u003e(A) Prevalence of general SO defined by fat-to-fat-free mass ratio (P \u0026lt; .001 for linear trend in overall and females); (B) Prevalence of general SO defined by fat-to-fat-free mass ratio (P = .015, P = .003, and P = .048 for linear trend in White, Black, and Asian individuals, respectively); (C) Prevalence of general SO defined by fat-to-fat-free mass ratio (P = .021 and P = .003 for linear trend in individuals with aged \u0026lt;40 years and 40-59 years, respectively); (D) Prevalence of abdominal SO defined by trunk fat-to-appendicular muscle mass ratio (P \u0026lt; .001 for linear trend in overall and females); (E) Prevalence of abdominal SO defined by trunk fat-to-appendicular muscle mass ratio (P = .002 for linear trend in White, P = .038 for linear trend in Hispanic, and P = .023 for nonlinear trend in Black); (F) Prevalence of abdominal SO defined by trunk fat-to-appendicular muscle mass ratio (P = .045 and P \u0026lt; .001 for linear trend in individuals with aged \u0026lt;40 years and 40-59 years, respectively).\u003c/p\u003e\n\u003cp\u003eAll estimates were standardized to the 2000 US Census population using 6 age and sex categories: males aged 20-39, 40-59, and ≥ 60 years and females aged 20-39, 40-59, and ≥ 60 years. Linear and polynomial models were used to test linear and nonlinear trends in prevalence of SO defined by fat-to-fat-free mass ratio and trunk fat-to-appendicular muscle mass ratio.\u003c/p\u003e\n\u003cp\u003eThe homogeneity of trends among racial and ethnic or sex subgroups was tested using an interaction term of time × race and ethnicity or sex in the regression models. Error bars indicate 95% CIs. SO: sarcopenic obesity; FM: fat mass; FFM: fat-free mass; TrFM: Trunk fat mass; ASM: appendicular muscle mass.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7423254/v1/13713db76b520fd5532596a2.jpg"},{"id":92189586,"identity":"c9708be3-6e4e-44d3-bdff-472828afbcc1","added_by":"auto","created_at":"2025-09-25 14:58:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":252159,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of body composition indices with all-cause mortality\u003c/p\u003e\n\u003cp\u003eHazard ratios (HRs) were scaled relative to the median value of body composition indices, where HR=1. The spline curve illustrates how mortality risk changes as body composition indices deviates from reference level. Estimates adjusted for age, sex, height, race/ethnicity, education level, race and ethnicity, education, family income-to-poverty ratio, home ownership, type of health insurance, high-density lipoprotein cholesterol, and total cholesterol, and mutually adjusted for fat mass or free fat mass. HR=hazard ratio.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7423254/v1/7529d5aa1baa3555415f9c99.jpg"},{"id":92189494,"identity":"b9a7f2f1-09ab-4fef-baeb-50030feaa9cf","added_by":"auto","created_at":"2025-09-25 14:58:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111633,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves by sarcopenic obesity status for all-cause and cause-specific mortality\u003c/p\u003e\n\u003cp\u003e(A) Survival curve of general SO defined by fat-to-fat-free mass ratio and all-cause mortality;\u003c/p\u003e\n\u003cp\u003e(B) Survival curve of general SO defined by fat-to-fat-free mass ratio and cardiovascular mortality;\u003c/p\u003e\n\u003cp\u003e(C) Survival curve of general SO defined by fat-to-fat-free mass ratio and cancer mortality;\u003c/p\u003e\n\u003cp\u003e(D) Survival curve of abdominal SO defined by trunk fat-to-appendicular muscle mass ratio and all-cause mortality;\u003c/p\u003e\n\u003cp\u003e(E) Survival curve of abdominal SO defined by trunk fat-to-appendicular muscle mass ratio and cardiovascular mortality;\u003c/p\u003e\n\u003cp\u003e(F) Survival curve of abdominal SO defined by trunk fat-to-appendicular muscle mass ratio and cancer mortality.\u003c/p\u003e\n\u003cp\u003eAll Estimates adjusted for age, sex, height, race/ethnicity, education level, race and ethnicity, education, family income-to-poverty ratio, home ownership, type of health insurance, high-density lipoprotein cholesterol, and total cholesterol.\u003c/p\u003e\n\u003cp\u003eSO: sarcopenic obesity.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7423254/v1/01e03cfa7252135f7ae652dc.jpg"},{"id":99793896,"identity":"c5dd376b-5958-49f1-a658-3e65e9e972f6","added_by":"auto","created_at":"2026-01-08 13:33:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7423254/v1/80358166-7d2a-4bfc-b8b7-9e7043ba3dc4.pdf"},{"id":92189560,"identity":"4b562872-6b5a-4af1-bce7-3f5199e88896","added_by":"auto","created_at":"2025-09-25 14:58:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1806824,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7423254/v1/e05913d37fe8ee71efdaa707.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Twenty-Year Temporal Trends in Sarcopenic Obesity and Mortality Risk: The Evolving Landscape of Body Composition in US Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObesity persists as a critical global public health challenge, with the prevalence of adult obesity\u0026mdash;defined by body mass index (BMI)\u0026mdash;rising markedly over the past decade\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Yet, growing evidence indicates that BMI alone is an insufficient metric, failing to capture nuances in body fat distribution and muscle composition, which can lead to the misclassification of obesity phenotypes and suboptimal metabolic disease risk assessment\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In recognition of these limitations, the European Association for the Study of Obesity (EASO) has introduced a revised diagnostic framework emphasizing body composition as a key determinant of obesity\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Concurrently, the European Society for Clinical Nutrition and Metabolism (ESPEN) and EASO have highlighted the clinical significance of the concurrent decline in muscle mass and increase in adiposity, termed sarcopenic obesity\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Although prior cross-sectional studies estimate a global sarcopenic obesity prevalence of 11% (95% CI: 10\u0026ndash;13%)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, the lack of longitudinal data and standardized diagnostic criteria has obscured long-term trends and hindered epidemiological insights. Establishing unified diagnostic standards is thus imperative to elucidate the temporal dynamics of body composition changes and sarcopenic obesity prevalence, offering a clearer perspective on its epidemiological trajectory and progression.\u003c/p\u003e\u003cp\u003eThe association between obesity (defined by BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) and elevated mortality risk is well-documented across epidemiological studies. Large-scale cohort analyses and meta-analyses consistently report increased cardiovascular and all-cause mortality in this population, with Zembic et al.'s multi-cohort European study reporting a hazard ratio (HR) of 1.30 (95% CI: 1.19\u0026ndash;1.60) for obese individuals\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Emerging evidence highlights sarcopenic obesity\u0026mdash;a phenotype characterized by concurrent low muscle mass and high adiposity\u0026mdash;as a clinically significant risk factor. Observational studies link this condition to higher mortality and morbidity, including Mirzai et al.'s findings on cardiovascular disease risk in older adults\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, and Kim et al.'s NHANES-based analysis associating it with all-cause and cardiovascular mortality\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, research on sarcopenic obesity remains limited by methodological challenges. Most studies rely on cross-sectional designs, which hinder causal inference, and focus on non-representative or clinical populations\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e,. Additionally, heterogeneous diagnostic criteria and cut-off values impede comparability across studies\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Large-scale longitudinal studies investigating the relationship between sarcopenic obesity and mortality remain scarce. Despite growing recognition of its clinical significance, the lack of standardized diagnostic criteria and robust prospective cohorts has hindered accurate assessment of sarcopenic obesity's disease burden and prognostic value\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Comprehensive, well-designed cohort studies with extended follow-up periods are urgently needed to clarify the mortality risks associated with this condition.\u003c/p\u003e\u003cp\u003eThis study utilized eight cross-sectional datasets from NHANES 1999\u0026ndash;2018 to analyze temporal trends in body composition indices and sarcopenic obesity over nearly two decades. By linking with National Death Index data, a cohort study was constructed examine the impact of body composition-defined sarcopenic obesity on all-cause and cause-specific mortality. The findings will provide important epidemiological evidence for risk stratification and preventive interventions for sarcopenic obesity.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Participants\u003c/h2\u003e\u003cp\u003eNHANES is dedicated to assessing the health and nutritional status of the general population in the United States. Employing a stratified, multi-stage probability sampling method, it selects a series of nationally-representative samples across cross-sections. Since 1999\u0026ndash;2000, this survey has been conducted in two-year cycles. For the current analysis, data from eight out of ten cycles, spanning from 1999\u0026ndash;2000 to 2017\u0026ndash;2018 were utilized, excluding the cycles of 2007\u0026ndash;2008 and 2009\u0026ndash;2010 due to the absence of body composition data during those cycles. The study was approved by the National Center for Health Statistics Institutional Review Board, and all participants signed informed consent. Mortality status of the NHANES participants was ascertained by probabilistic matching with the National Death Index through December 31, 2019\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we analyzed individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years (n\u0026thinsp;=\u0026thinsp;55,081). After excluding those with missing body composition data (n\u0026thinsp;=\u0026thinsp;28,205) or missing person-years (n\u0026thinsp;=\u0026thinsp;47), the final analytical cohort comprised 26,829 participants (\u003cb\u003eFig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Body composition\u003c/h2\u003e\u003cp\u003eWhole-body DXA scanning was performed to test body composition indicators in this study using Hologic fan beam densitometers (Hologic, Bedford, Massachusetts, USA) by trained technicians according to the International Society of Clinical Densitometry\u0026rsquo;s (ISCD) standard operating procedure\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Data from DXA included values for fat mass (g), and fat-free mass (bone mineral content and lean mass) (g) of the whole body and different parts of the body. Appendicular muscle mass was calculated as fat-free mass of the limbs minus the bone mineral content of the limbs, fat-to-fat-free mass ratio was calculated as whole-fat mass divided by the fat-free mass ratio, and trunk fat-to-appendicular muscle mass ratio was calculated as trunk fat mass divided by the appendicular muscle mass.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Definition of sarcopenic obesity\u003c/h2\u003e\u003cp\u003eBased on DXA data, the study employed two distinct methods to define sarcopenic obesity: general sarcopenic obesity and abdominal sarcopenic obesity. The difference between these two methods lies in the specific metrics used for definition, but both approaches classify individuals as having sarcopenic obesity based on predetermined cut-off values for the respective metrics. These cut-off values were defined by age-standardized reference curves derived from DXA data of 13,236 participants in the NHANES 1999\u0026ndash;2004 study. General sarcopenic obesity was defined using the fat-to-fat-free mass ratio model, based on the ratio of total fat mass to total fat-free mass\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Cutoff points of the reference curve for this method are delineated as follows: ratios falling below the 15th percentile, between the 15th and 85th percentiles, between the 85th and 95th percentiles, or exceeding the 95th percentile. Groups with fat-to-fat-free mass ratio surpassing the 95th percentile are deemed to belong to the sarcopenic obesity category\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Abdominal myogenic obesity is defined by the ratio of trunk fat mass to appendicular muscle mass, using the trunk fat to limb muscle mass ratio curve\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Covariates\u003c/h2\u003e\u003cp\u003eBody measurements were collected during a mobile physical examination. Interviews were conducted using standardized questionnaires to obtain detailed information on covariates including race/ethnicity, educational level, race and ethnicity, family income-to-poverty ratio, home ownership, and type of health insurance. Serum samples were collected during the physical examination. Serum total cholesterol (TC) was measured enzymatically, and high-density lipoprotein cholesterol (HDL-C) by direct immunoassay.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Ascertainment of death\u003c/h2\u003e\u003cp\u003eData on the primary cause of death was used for case definition, according to International Classification of Diseases 10th Revision (ICD-10) codes. The primary outcomes for this study were mortality from (1) all causes, (2) cardiovascular diseases: codes I00\u0026ndash;I09 (Acute rheumatic fever and chronic rheumatic heart diseases), I11 (hypertensive heart disease), I13 (hypertensive heart and renal disease), I20\u0026ndash;I25 (ischemic heart disease), I26\u0026ndash;I51 (other heart diseases), and I60\u0026ndash;I69 (cerebrovascular diseases), and (3) cancer (codes C00\u0026ndash;C97).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e\u003cp\u003e All statistical analyses accounted for NHANES complex survey design factors, including sample weights, stratification, and clustering, following NHANES guidelines. Age- and sex-adjusted means or proportions of sarcopenic obesity were calculated for each of the 8 two-year cycles from 1999\u0026ndash;2000 to 2017\u0026ndash;2018, excluding the 2007\u0026ndash;2008 and 2009\u0026ndash;2010 cycles. Adjustments for age and sex used direct standardization to the 2000 US Census population, with categories for men and women aged 20\u0026ndash;39, 40\u0026ndash;59, and 60 or older. Linear regression assessed age- and sex-adjusted means, while logistic regression evaluated proportions, testing linear and nonlinear trends and group differences. Nonlinearity was tested by adding a quadratic term to the regression models, and homogeneity of secular trends among subgroups was tested using an interaction term for time and subgroup.\u003c/p\u003e\u003cp\u003eRestricted cubic splines with four knots at the 25th, 50th, 75th, and 95th centiles were used to flexibly model the association between the mean body composition indices across all cycles and mortality. Kaplan-Meier survival curves were generated for all-cause and cause-specific mortality, stratified by sarcopenic obesity status. Multivariable Cox regression examined the associations of sarcopenic obesity with mortality, adjusting for known or suspected confounders, including age, sex, height, race/ethnicity, education level, family income-to-poverty ratio, home ownership, health insurance type, HDL-C, and TC. Fat mass, fat-free mass, and muscle mass were further adjusted for each other in additional models. Stratified analyses by sex, age, and race assessed potential modifiers of the association between sarcopenic obesity and all-cause mortality. All statistical tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Analyses were conducted using R 4.3.4 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org/\u003c/span\u003e\u003cspan address=\"http://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Due to the potential for type I errors from multiple comparisons, results from secondary analyses and outcomes should be considered exploratory.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Characteristics of participants\u003c/h2\u003e\u003cp\u003eThe mean age ranged from 38.7 to 51.4 years, with the proportion of males varying from 49.6% to 53.3% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The percentage of individuals with a high school education or less decreased from 39.4% in 1999\u0026ndash;2000 to 16.6% in 2017\u0026ndash;2018, while those with college degrees or higher increased from 15.6% to 28.0%. Home ownership ranged from 50.3% to 68.2%, and the proportion without medical insurance varied from 19.0% to 31.0%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants in the National Health and Nutrition Examination Surveys (NHANES), 1999\u0026ndash;2018 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1999\u0026ndash;2000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2001\u0026ndash;2002\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2003\u0026ndash;2004\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2005\u0026ndash;2006\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2011\u0026ndash;2012\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal No. of participants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2044(49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2268(50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2238(50.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1832(51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1388(53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1486(49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1307(49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e978(49.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean (SD), y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.0(18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.0(18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.4(19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.3(14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.7(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.2(11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39.0(11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.4(11.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group, y \u003csup\u003eb\u003c/sup\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=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1311(31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1493(33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1418(31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1502(41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1371(52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1478(49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1351(51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e999(50.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1248(30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1499(33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1316(29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1433(40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1235(47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1505(50.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1267(48.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e967(49.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1561(37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1522(33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1706(38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e647(18.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace and ethnicity, No. (%) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1850\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1834(45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2359(53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2366(55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1626(47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e934(37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1210(42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e752(29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e580(31.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e800(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e879(20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e876(20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e902(26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e664(26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e536(18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e539(21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e376(20.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1360(34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1137(26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1007(23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e898(26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e517(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e727(25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e853(33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e492(26.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e401(15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e394(13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e379(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e402(21.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1618(39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1378(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1309(29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e904(25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e467(17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e555(18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e534(20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e326(16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school graduate and some college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1850(45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2230(49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2334(52.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1923(53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1396(53.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1622(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1390(53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1088(55.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e640(15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e901(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e789(17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e753(21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e743(28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e805(27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e694(26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e551(28.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily income-to-poverty ratio, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1755\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e725(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e712(16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e769(18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e599(17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e642(26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e641(23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e508(21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e341(19.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;100%-299%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1505(42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1729(41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1844(44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1296(37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e895(36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1011(36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1062(44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e732(41.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e300%-499%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e712(20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e907(21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e886(21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e826(24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e483(19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e567(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e426(17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e363(20.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;500%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e592(16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e855(20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e686(16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e722(21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e426(17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e545(19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e405(16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e319(18.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome ownership, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOwned home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2613(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3038(68.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2874(65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2292(64.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1305(50.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1597(54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1354(53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e999(52.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRented home or other arrangement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1362(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1327(31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1411(34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1176(35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1199(49.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1268(45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1147(46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e847(47.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCovered by health insurance, No. (%) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3202(78.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3603(80.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3532(80.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2614(73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1797(69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2145(71.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1971(75.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1556(79.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e858(21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e846(19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e867(19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e964(26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e808(31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e835(28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e643(24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e403(20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRefused/Missing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3(0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4(0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7(0.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e The study included 8 out of 10 rounds of data from 1999 to 2018 for analysis, and no body composition data from two rounds of surveys from 2007\u0026ndash;2008 and 2009\u0026ndash;2010.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e Body composition information was not collected from individuals aged 60 and above starting in NHANES 2011\u0026ndash;2012.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003e NHANES collected self-reported race data in 6 categories (American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, or other) and data on Hispanic origin. Non-Hispanic Asian was listed as a race and ethnicity group starting in NHANES 2011\u0026ndash;2012. American Indian or Alaska Native and Native Hawaiian or Pacific Islander were not listed as separate race and ethnicity groups in the data sets.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ed\u003c/sup\u003e Answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Are you covered by health insurance or some other kind of health care plan?\u0026rdquo;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Trends of body composition from 1999\u0026ndash;2000 to 2017\u0026ndash;2018\u003c/h2\u003e\u003cp\u003eFrom 1999\u0026ndash;2000 to 2017\u0026ndash;2018, age- and sex-adjusted mean fat mass decreased from 28.1 kg (95% CI, 27.3\u0026ndash;28.9) to 26.5 kg (95% CI, 25.5\u0026ndash;27.5), while fat-free mass increased from 52.7 kg (95% CI, 52.1\u0026ndash;53.2) to 54.2 kg (95% CI, 53.5\u0026ndash;54.9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Appendicular muscle mass rose from 22.1 kg (95% CI, 21.9\u0026ndash;22.4) to 22.8 kg (95% CI, 22.5\u0026ndash;23.2), and trunk fat mass declined from 14.2 kg (95% CI, 13.7\u0026ndash;14.6) to 12.8 kg (95% CI, 12.2\u0026ndash;13.3). Correspondingly, the fat-to-fat-free mass ratio decreased from 0.55 (95% CI, 0.54\u0026ndash;0.56) to 0.50 (95% CI, 0.49\u0026ndash;0.52), and the trunk fat-to-appendicular muscle mass ratio declined from 0.66 (95% CI, 0.64\u0026ndash;0.68) to 0.58 (95% CI, 0.56\u0026ndash;0.60) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for linear trend). These trends were consistent across non-Hispanic White and Black populations (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for subgroup homogeneity) (\u003cb\u003eFig S2\u003c/b\u003e), while Hispanics showed divergent patterns and non-Hispanic Asians exhibited lower absolute values but similar fat-to-muscle ratios. Age-stratified analysis revealed declining fat and muscle mass in participants under 60 years, whereas those over 60 years demonstrated increases in both parameters (\u003cb\u003eFig S3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Prevalence of sarcopenic obesity from 1999\u0026ndash;2000 to 2017\u0026ndash;2018\u003c/h2\u003e\u003cp\u003eGeneral sarcopenic obesity decreased from 6.2% (95% CI, 5.4%\u0026ndash;7.1%) to 4.9% (95% CI, 3.6%\u0026ndash;11.9%), and abdominal sarcopenic obesity from 6.2% (95% CI, 4.8%\u0026ndash;7.9%) to 3.6% (95% CI, 2.6%\u0026ndash;4.8%) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sex-stratified analysis revealed contrasting patterns, with prevalence increasing among males but decreasing more substantially among females for general sarcopenic obesity, though both sexes demonstrated overall downward trends for both definitions. Racial disparities were evident, with significant decreases among non-Hispanic White and Black individuals but increases among Asians for both definitions. The prevalence of abdominal sarcopenic obesity increased among older adults (\u0026ge;\u0026thinsp;60 years) (Detailed data in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Association of body composition with all-cause and cause-specific mortality\u003c/h2\u003e\u003cp\u003eDuring median 11.3-year follow-up (IQR: 5.6\u0026ndash;16.8 years), 4117 deaths occurred, including 1284 cardiovascular and 927 cancer deaths. Fat mass and trunk fat mass demonstrated reverse J-shaped associations with all-cause mortality, with lowest risk at fat-free mass of 55\u0026ndash;80 kg and increased risk at both extremes. Appendicular muscle mass below 22 kg correlated positively with mortality, while higher values showed protective effects. Fat-to-muscle ratios exhibited U-shaped patterns with elevated mortality risk at both extremes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For cause-specific mortality, fat mass and trunk fat mass were positively correlated with cardiovascular and cancer mortality, with similar associations for the fat-to-muscle ratios. Higher muscle mass was associated with a lower risk of cancer mortality. For cardiovascular mortality, both excessive and insufficient fat-free mass or appendicular skeletal muscle mass increased risk (\u003cb\u003eFig S4-S5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Association of sarcopenic obesity with all-cause and cause-specific mortality outcomes\u003c/h2\u003e\u003cp\u003eCompared to non-sarcopenic obesity, general sarcopenic obesity conferred increased risks of all-cause mortality (HR: 1.29; 95% CI: 1.13\u0026ndash;1.47) and cardiovascular mortality (HR: 1.57; 95% CI: 1.25\u0026ndash;1.97). Abdominal sarcopenic obesity demonstrated even greater mortality risks, with cardiovascular mortality HR of 1.83 (95% CI: 1.47\u0026ndash;2.29) and cancer mortality HR of 1.68 (95% CI: 1.27\u0026ndash;2.22) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In subgroup analyses, general sarcopenic obesity was not significantly associated with all-cause mortality in females, individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years and Non-Hispanic Blacks compared to non-sarcopenic obesity. In contrast, abdominal sarcopenic obesity was associated with increased all-cause mortality risk in women (HR: 1.55; 95% CI: 1.30\u0026ndash;1.85), individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years (HR: 1.76; 95% CI: 1.08\u0026ndash;2.86) and Non-Hispanic Blacks (HR: 3.41; 95% CI: 2.15\u0026ndash;5.41). For cardiovascular mortality, general sarcopenic obesity showed no significant association in women, individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years and Non-Hispanic Blacks, while abdominal sarcopenic obesity was linked to increased risk in all subgroups. For cancer mortality, general sarcopenic obesity was not associated with any subgroup, whereas abdominal sarcopenic obesity increased risk in women, individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, and both Non-Hispanic Whites and Non-Hispanic Blacks (\u003cb\u003eTables S2-S3\u003c/b\u003e). Adjusted Kaplan-Meier curves demonstrated significantly reduced survival for both sarcopenic obesity definitions across all mortality outcomes compared to non-sarcopenic obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHazard ratios (95% CI) for all-cause and cause-specific mortality in sarcopenic obesity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo. of deaths/total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMortality rate (%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFat mass-Fat-free mass ratio \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4102/25456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283/1373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28(1.13,1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.29(1.13,1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTrunk fat mass-Appendicular muscle mass ratio \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4060/25500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325/1329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51(1.35,1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.54(1.36,1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCardiovascular mortality\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFat mass-Fat-free mass ratio \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1277/22632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104/1194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.56(1.28,1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.57(1.25,1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTrunk fat mass-Appendicular muscle mass ratio \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1273/22714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108/1112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.85(1.52,2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.83(1.47,2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCancer mortality\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFat mass-Fat-free mass ratio \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e926/22281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52/1142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16(0.88,1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.14(0.83,1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTrunk fat mass-Appendicular muscle mass ratio \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e909/22350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69/1073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.69(1.32,2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.68(1.27,2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Mortality rates are presented as per 1000 person-years.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Model 1: adjusted for age; Model 2: adjusted for age, sex, height, race/ethnicity, education level, race and ethnicity, education, family income-to-poverty ratio, home ownership, type of health insurance, high-density lipoprotein cholesterol, and total cholesterol.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e According to the gender and age curves of fat-lean ratio, the group with a fat mass to free fat mass ratio above the 95th centile is considered as the sarcopenic obesity group. The definition of sarcopenic obesity was based on the ratio between truncal fat mass and appendicular muscle mass were similar to the fat-lean ratio approach.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eSO: sarcopenic obesity\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the most recent national trend estimates of body composition and sarcopenic obesity from 1999\u0026ndash;2000 to 2017\u0026ndash;2018, highlighting significant disparities among racial, ethnic, and age groups. Notably, higher total and trunk fat mass were strongly associated with increased risks of all-cause and cardiovascular mortality, whereas greater appendicular muscle mass exhibited protective effects against all-cause and cancer mortality. Furthermore, sarcopenic obesity was independently linked to elevated all-cause and cause-specific mortality risks, with abdominal sarcopenic obesity demonstrating particularly pronounced associations.\u003c/p\u003e\u003cp\u003eConsistent with previous research among U.S. adults from 2011\u0026ndash;2018\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, we observed decreased mean fat mass alongside increased fat-free mass and appendicular muscle mass. Furthermore, our analysis revealed significant racial, ethnic, and age disparities. Non-Hispanic Black individuals demonstrated the highest absolute fat mass, fat-free mass, and appendicular muscle mass, yet maintained favorable fat-to-muscle ratios, particularly for trunk fat-to-appendicular muscle mass, with declining trends over time. Conversely, Hispanic and non-Hispanic Asian populations exhibited lower absolute values but higher fat-to-muscle ratios with increasing trends over time, indicating greater susceptibility to sarcopenic obesity. This pattern may explain the increased metabolic risks\u0026mdash;including insulin resistance, dyslipidemia, and elevated cholesterol\u0026mdash;observed at lower BMI thresholds in Hispanic and Asian populations compared to other groups\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Notably, non-Hispanic Asians were the only group demonstrating increases across all body composition parameters, potentially reflecting escalating obesity-related health burdens in this population. Age-stratified analysis revealed contrasting trends: adults aged 20\u0026ndash;59 years showed decreases in both fat and muscle mass, while those\u0026thinsp;\u0026ge;\u0026thinsp;60 years exhibited increases in both parameters, with disproportionate fat mass accumulation driving elevated trunk fat-to-appendicular muscle mass ratios. Our sarcopenic obesity prevalence estimates, using two distinct diagnostic ratios, align with recent North American meta-analyses reporting\u0026thinsp;\u0026lt;\u0026thinsp;8% prevalence\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, though published estimates vary widely (3.2%-26.3%)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, likely reflecting diagnostic heterogeneity and population differences\u003c/p\u003e\u003cp\u003ePrevious studies investigating the association between body composition and mortality have employed diverse measurement approaches, including both direct and indirect methods\u003csup\u003e[\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. However, only a limited number of these studies utilized dual-energy X-ray absorptiometry (DXA) for body composition assessment\u003csup\u003e[\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, and the majority focused on specific populations\u003csup\u003e[\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, resulting in inconsistent findings regarding these relationships. Our findings demonstrated a reverse J-shaped association between fat mass and all-cause mortality, with elevated risks at both extremes, while appendicular muscle mass showed inverse associations with mortality risk. These results align with previous body composition-mortality analyses from 1988\u0026ndash;2014\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The increased mortality risk associated with low fat mass may reflect loss of metabolically beneficial adipose depots, including gluteofemoral fat and brown adipose tissue, which confer protective lipid and glucose profiles and reduce cardiovascular-metabolic risk\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Trunk fat mass demonstrated stronger associations with all-cause and cause-specific mortality than total fat mass, consistent with NHANES 1999\u0026ndash;2006 findings\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, suggesting central adiposity as a primary mortality determinant. Our cardiovascular mortality findings regarding fat-free mass align with male cohort studies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, though all-cause and cancer mortality associations differed. Higher fat-free mass and appendicular muscle mass were consistently protective against all-cause and cancer mortality. Discrepancies with previous studies may reflect methodological differences, including sex-specific variations and reference point selection. Unlike prospective cohorts using 25th percentile lean mass references\u0026mdash;potentially obscuring protective effects\u0026mdash;our 50th percentile reference and separate appendicular muscle mass analysis revealed stronger protective associations. Recent evidence supports our finding linking reduced muscle mass to elevated cancer-specific mortality\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Thus, our study highlights the importance of maintaining higher appendicular muscle mass in cancer patients and moderate levels in those with cardiovascular disease.\u003c/p\u003e\u003cp\u003eOur analysis revealed a significant elevation in all-cause and cause-specific mortality among individuals with sarcopenic obesity. These findings align with recent meta-analyses, which further demonstrate that sarcopenic obesity\u0026mdash;unlike obesity alone\u0026mdash;is associated with heightened risks of all-cause and cardiovascular mortality compared to metabolically healthy individuals\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Our findings corroborate recent large-scale studies underscoring the prognostic relevance of sarcopenic obesity. In a NHANES-based analysis, Kim et al. observed markedly elevated all-cause mortality (HR: 1.57; 95% CI: 1.41\u0026ndash;1.75) and cardiovascular mortality (HR: 1.63; 95% CI: 1.34\u0026ndash;1.98) among individuals with low muscle mass and obesity, aligning closely with our results for general sarcopenic obesity\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Further supporting our conclusions, Benz et al. reported that sarcopenic obesity with altered body composition components was linked to a substantially higher 10-year mortality risk (HR: 1.94\u0026ndash;2.84; 95% CI: 1.60\u0026ndash;4.11), reinforcing the consistency of mortality risk across varying definitions of the condition\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Subgroup analyses yielded critical clinical insights. Notably, abdominal sarcopenic obesity was associated with greater mortality risks than general sarcopenic obesity\u0026mdash;a finding consistent with growing evidence emphasizing the prognostic significance of fat distribution. For instance, Lee et al. demonstrated that body composition-defined sarcopenic obesity exhibited stronger mortality associations in younger adults and women, paralleling our results where abdominal sarcopenic obesity remained significant in women and individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years, whereas general sarcopenic obesity did not\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. This pattern suggests that central fat accumulation coupled with muscle loss may constitute a more pathogenic phenotype, particularly among younger populations in whom traditional cardiovascular risk factors are less prevalent. The stronger associations observed for abdominal sarcopenic obesity are consistent with the established pathophysiology of visceral adiposity and deteriorating muscle quality. Emerging mechanistic evidence indicates that visceral fat deposition drives chronic inflammation and insulin resistance, potentially exacerbating muscle protein catabolism and functional decline\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This biological plausibility reinforces our clinical observations, positioning abdominal sarcopenic obesity as a high-risk phenotype that merits early and targeted intervention alongside rigorous monitoring.\u003c/p\u003e\u003cp\u003eThis study offers several key strengths. First, we employed a large, nationally representative sample with a broad age distribution, bolstering statistical power and enhancing the generalizability of our findings. Second, comprehensive stratified analyses\u0026mdash;categorized by race/ethnicity, age, and sex\u0026mdash;uncovered distinct variations in body composition and sarcopenic obesity patterns across demographic subgroups. Third, our methodological approach addresses critical gaps in the literature by examining two-decade temporal trends in body composition and elucidating the mortality implications of sarcopenic obesity. Nevertheless, certain limitations should be noted. The lack of DXA data for the 2007\u0026ndash;2008 and 2009\u0026ndash;2010 survey cycles restricted comparative analyses across these periods. Furthermore, the absence of body composition measurements for adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years during 2011\u0026ndash;2018 precluded precise estimation of sarcopenic obesity prevalence in older populations. These data constraints necessitate cautious interpretation of temporal trends and prevalence estimates, particularly among elderly participants.\u003c/p\u003e\u003cp\u003eOverall, this study reveals favorable 20-year trends in body composition, marked by a decline in sarcopenic obesity prevalence. Across racial/ethnic and age subgroups, health improvements were most pronounced in non-Hispanic White and Black populations, whereas Hispanic, non-Hispanic Asian, and older adults (\u0026ge;\u0026thinsp;60 years) exhibited concerning rises in fat-to-muscle ratios. Importantly, we identified a robust association between sarcopenic obesity and mortality, highlighting the critical role of maintaining a lower trunk fat-to-appendicular muscle mass ratio for reducing mortality risk.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMagliano DJ, Chen L, Carstensen B et al (2022) Trends in all-cause mortality among people with diagnosed diabetes in high-income settings: a multicountry analysis of aggregate data[J]. Lancet Diabetes Endocrinol 10(2):112\u0026ndash;119\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai H, Alsalhe TA, Chalghaf N et al (2020) The global burden of disease attributable to high body mass index in 195 countries and territories, 1990\u0026ndash;2017: An analysis of the Global Burden of Disease Study[J]. PLoS Med 17(7):e1003198\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarrow JS (1988) Three limitations of body mass index[J]. Am J Clin Nutr 47(3):553\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalumbo AM, Jacob CM, Khademioore S et al (2025) Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis[J]. Obes Rev 26(6):e13894\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBusetto L, Dicker D, Fr\u0026uuml;hbeck G et al (2024) A new framework for the diagnosis, staging and management of obesity in adults[J]. Nat Med\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonini LM, Busetto L, Bischoff SC et al (2022) Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement[J]. Clin Nutr 41(4):990\u0026ndash;1000\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao Q, Mei F, Shang Y et al (2021) Global prevalence of sarcopenic obesity in older adults: A systematic review and meta-analysis[J]. Clin Nutr 40(7):4633\u0026ndash;4641\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZembic A, Eckel N, Stefan N et al (2021) An Empirically Derived Definition of Metabolically Healthy Obesity Based on Risk of Cardiovascular and Total Mortality[J]. JAMA Netw Open 4(5):e218505\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMirzai S, Carbone S, Batsis JA et al (2024) Sarcopenic Obesity and Cardiovascular Disease: An Overlooked but High-Risk Syndrome[J]. Curr Obes Rep 13(3):532\u0026ndash;544\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim D, Lee J, Park R et al (2024) Association of low muscle mass and obesity with increased all-cause and cardiovascular disease mortality in US adults[J]. J Cachexia Sarcopenia Muscle 15(1):240\u0026ndash;254\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonini LM, Busetto L, Bauer JM et al (2020) Critical appraisal of definitions and diagnostic criteria for sarcopenic obesity based on a systematic review[J]. Clin Nutr 39(8):2368\u0026ndash;2388\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu B, Du Y, Wu Y et al (2021) Trends in obesity and adiposity measures by race or ethnicity among adults in the United States 2011-18: population based study[J]. BMJ 372:n365\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan Z, Stephan BCM, Donini LM et al (2024) Exploring the association between sarcopenic obesity and cardiovascular risk: a summary of findings from longitudinal studies and potential mechanisms[J]. Proc Nutr Soc, : 1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei S, Nguyen TT, Zhang Y et al (2023) Sarcopenic obesity: epidemiology, pathophysiology, cardiovascular disease, mortality, and management[J]. Front Endocrinol (Lausanne) 14:1185221\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Center for Health Statistics (2019) [J] 2019\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong H, Yan Y, Liu J et al (2021) Reference centiles for evaluating total body fat development and fat distribution by dual-energy x-ray absorptiometry among children and adolescents aged 3\u0026ndash;18 years[J]. Clin Nutr 40(3):1289\u0026ndash;1295\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewiecki EM, Binkley N, Morgan SL et al (2016) Best Practices for Dual-Energy X-ray Absorptiometry Measurement and Reporting: International Society for Clinical Densitometry Guidance[J]. J Clin Densitom 19(2):127\u0026ndash;140\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiervo M, Prado CM, Mire E et al (2015) Body composition indices of a load-capacity model: gender- and BMI-specific reference curves[J]. Public Health Nutr 18(7):1245\u0026ndash;1254\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Aller C, Lara J, Stephan BCM et al (2019) Sarcopenic obesity and overall mortality: Results from the application of novel models of body composition phenotypes to the National Health and Nutrition Examination Survey 1999\u0026ndash;2004[J]. Clin Nutr 38(1):264\u0026ndash;270\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng W, Mclerran DF, Rolland B et al (2011) Association between body-mass index and risk of death in more than 1 million Asians[J]. N Engl J Med 364(8):719\u0026ndash;729\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWulan SN, Westerterp KR, Plasqui G (2010) Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians[J]. Maturitas 65(4):315\u0026ndash;319\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C, Wong PY, Chung YL et al (2023) Deciphering the obesity paradox in the elderly: A systematic review and meta-analysis of sarcopenic obesity[J]. Obes Rev 24(2):e13534\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Bahat G, Bauer J et al (2019) Sarcopenia: revised European consensus on definition and diagnosis[J]. Age Ageing 48(4):601\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBatsis JA, Mackenzie TA, Jones JD et al (2016) Sarcopenia, sarcopenic obesity and inflammation: Results from the 1999\u0026ndash;2004 National Health and Nutrition Examination Survey[J]. Clin Nutr 35(6):1472\u0026ndash;1483\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurdock DJ, Wu N, Grimsby JS et al (2022) The prevalence of low muscle mass associated with obesity in the USA[J]. Skelet Muscle 12(1):26\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee DH, Keum N, Hu FB et al (2018) Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study[J]. BMJ 362:k2575\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCesari M, Pahor M, Lauretani F et al (2009) Skeletal muscle and mortality results from the InCHIANTI Study[J]. J Gerontol Biol Sci Med Sci 64(3):377\u0026ndash;384\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewman AB, Kupelian V, Visser M et al (2006) Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort[J]. J Gerontol Biol Sci Med Sci 61(1):72\u0026ndash;77\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToss F, Wiklund P, Nordstr\u0026ouml;m P et al (2012) Body composition and mortality risk in later life[J]. Age Ageing 41(5):677\u0026ndash;681\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRolland Y, Gallini A, Cristini C et al (2014) Body-composition predictors of mortality in women aged\u0026thinsp;\u0026ge;\u0026thinsp;75 y: data from a large population-based cohort study with a 17-y follow-up[J]. Am J Clin Nutr 100(5):1352\u0026ndash;1360\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWijnhoven HA, Snijder MB, Van Schueren B-D (2012) Region-specific fat mass and muscle mass and mortality in community-dwelling older men and women[J]. Gerontology 58(1):32\u0026ndash;40\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpahillari A, Mukamal KJ, Defilippi C et al (2016) The association of lean and fat mass with all-cause mortality in older adults: The Cardiovascular Health Study[J]. Nutr Metab Cardiovasc Dis 26(11):1039\u0026ndash;1047\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadwal R, Leslie WD, Lix LM et al (2016) Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study[J]. Ann Intern Med 164(8):532\u0026ndash;541\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZong G, Zhang Z, Yang Q et al (2016) Total and regional adiposity measured by dual-energy X-ray absorptiometry and mortality in NHANES 1999\u0026ndash;2006[J]. Obes (Silver Spring) 24(11):2414\u0026ndash;2421\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBigaard J, Frederiksen K, Tj\u0026oslash;nneland A et al (2004) Body fat and fat-free mass and all-cause mortality[J]. Obes Res 12(7):1042\u0026ndash;1049\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M, Zhang Z, Zhou C et al (2022) Predicted fat mass and lean mass in relation to all-cause and cause-specific mortality[J]. J Cachexia Sarcopenia Muscle 13(2):1064\u0026ndash;1075\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecher T, Palanisamy S, Kramer DJ et al (2021) Brown adipose tissue is associated with cardiometabolic health[J]. Nat Med 27(1):58\u0026ndash;65\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaajendiran A, Ooi G, Bayliss J et al (2019) Identification of Metabolically Distinct Adipocyte Progenitor Cells in Human Adipose Tissues[J]. Cell Rep 27(5):1528\u0026ndash;1540e7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEitmann S, Matrai P, Hegyi P et al (2024) Obesity paradox in older sarcopenic adults - a delay in aging: A systematic review and meta-analysis[J]. Ageing Res Rev 93:102164\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Xie X, Dou Q et al (2019) Association of sarcopenic obesity with the risk of all-cause mortality among adults over a broad range of different settings: a updated meta-analysis[J]. BMC Geriatr 19(1):183\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenz E, Pinel A, Guillet C et al (2024) Sarcopenia and Sarcopenic Obesity and Mortality Among Older People[J]. JAMA Netw Open 7(3):e243604\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee H, Chung HS, Kim YJ et al (2023) Association between body composition and the risk of mortality in the obese population in the United States[J]. Front Endocrinol (Lausanne) 14:1257902\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Body composition, sarcopenic obesity, mortality","lastPublishedDoi":"10.21203/rs.3.rs-7423254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7423254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBody composition changes and sarcopenic obesity represent emerging health concerns with mortality implications. We analyzed 26,829 adults from NHANES 1999\u0026ndash;2018 to examine temporal trends in sarcopenic obesity prevalence and mortality associations. Sarcopenic obesity was defined using fat-to-fat-free mass ratio (general) and trunk fat-to-appendicular muscle mass ratio (abdominal). Age- and sex-adjusted analyses revealed overall declining prevalence of general sarcopenic obesity (6.2% to 4.9%) and abdominal sarcopenic obesity (6.2% to 3.6%) from 1999\u0026ndash;2018. However, stratified analyses showed increasing prevalence among Asian populations and older adults (\u0026ge;\u0026thinsp;60 years) for abdominal sarcopenic obesity. Multivariable Cox regression demonstrated that general sarcopenic obesity was associated with elevated all-cause mortality [HR: 1.29 (95% CI: 1.13\u0026ndash;1.47)] and cardiovascular mortality [HR: 1.57 (95% CI: 1.25\u0026ndash;1.97)], while abdominal sarcopenic obesity showed stronger associations with cardiovascular [HR: 1.83 (95% CI: 1.47\u0026ndash;2.29)] and cancer mortality [HR: 1.68 (95% CI: 1.27\u0026ndash;2.22)]. These findings highlight demographic-specific trends in sarcopenic obesity prevalence and underscore the independent prognostic value of body composition phenotypes for mortality risk assessment.\u003c/p\u003e","manuscriptTitle":"Twenty-Year Temporal Trends in Sarcopenic Obesity and Mortality Risk: The Evolving Landscape of Body Composition in US Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 05:14:48","doi":"10.21203/rs.3.rs-7423254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a21f1740-130b-44f4-b881-4fea2ceab9c0","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55222232,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":55222233,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"}],"tags":[],"updatedAt":"2026-01-06T18:05:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 05:14:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7423254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7423254","identity":"rs-7423254","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}