Correlation Between Various Insulin Resistance Indices and Sarcopenia: Evidence from CHARLS

Correlation Between Various Insulin Resistance Indices and Sarcopenia: Evidence from CHARLS | 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 Correlation Between Various Insulin Resistance Indices and Sarcopenia: Evidence from CHARLS Ke Xu, Ruikang Liu, Boyang Chen, Lin Jing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8404542/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 Background Evidence on the association between surrogate insulin resistance (IR) indices and sarcopenia (SA) remains limited. Seven alternative indicators for assessing insulin resistance were included in this study, primarily categorized into three groups: lipid ratios (TG/HDL); the TyG index and its derivatives combined with body composition (TyG-WC, TyG-BMI, TyG-WHtR); indicators reflecting fat distribution (LAP), and Chinese visceral adiposity index (CVAI)—in relation to SA risk among Chinese adults. Methods Longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) was used, with information collected between 2011 and 2015., which included 5,329 participants. Multivariable logistic regression assessed associations between each index and incident SA. Results Over a 4-year period, 330 participants (6.2%) developed SA. In fully adjusted models (Model 3), all seven IR surrogates were significantly associated with SA. Per standard-deviation increase, adjusted odds ratios (OR, 95% CI) were: TG/HDL 0.84 (0.74–0.95), TyG 0.65 (0.53–0.80), TyG-BMI 0.14 (0.10–0.19), TyG-WHtR 0.53 (0.45–0.61), TyG-WC 0.50 (0.43–0.57), CVAI 0.41 (0.35–0.48), and LAP 0.60 (0.51–0.70). Notably, all indices except LAP showed evidence of nonlinearity in relation to SA risk. Additionally, TyG-BMI yielded the largest AUC among the seven indices. Conclusions TG/HDL, TyG, TyG-WC, TyG-BMI, TyG-WHtR, LAP, and CVAI were inversely associated with incident SA. TG/HDL, TyG, TyG-WC, TyG-BMI, TyG-WHtR, and CVAI exhibited nonlinear relationships with SA. TyG-BMI may be the most informative predictor of SA. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Gastroenterology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors various insulin resistance indices sarcopenia CHARLS China Health and Retirement Longitudinal Study Older Chinese individuals Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sarcopenia, a geriatric syndrome marked by progressive loss of muscle mass, strength, and function. This condition shows an elevated prevalence in diabetic patients relative to the general population. [1, 2]. Since its inclusion in ICD-10 in 2016, sarcopenia has been documented to affect up to 10–27% of older adults globally, with prevalence in China estimated around 20.7% in those aged ≥ 60 [3, 4]. Muscle mass and strength loss associated with sarcopenia is significantly correlated with the risk of three key adverse outcomes: functional impairment, hospitalization rates, and mortality. [5]. Sarcopenia imposes a significant public health burden on aging societies, a phenomenon particularly pronounced in diabetic sarcopenia—research has confirmed its association with increased risks of cardiovascular events and mortality. [6]. Insulin resistance (IR) is tightly linked with muscle health [7]. In East Asian populations, individuals with IR exhibit a markedly higher prevalence of sarcopenia compared to non-IR counterparts, with estimates suggesting that up to 25% may be affected [8, 9]. Mechanistically, IR can affect muscle protein metabolism [10]. Under physiological conditions, insulin facilitates muscle protein synthesis and inhibits proteolysis; however, in the context of IR, these anabolic effects are blunted, resulting in enhanced muscle degradation [11]. Additionally, skeletal muscle serves as the primary site for glucose disposal, and its depletion in sarcopenia may further exacerbate IR and hyperglycemia [12]. To quantify IR in epidemiologic studies, surrogate indices are widely utilized [11]. The homeostatic model assessment of IR (HOMA-IR) has been widely used for estimating IR, whereas the triglyceride-glucose (TyG) index has recently gained attention as a simple and reliable surrogate, showing strong correlation with clamp-derived measurements [13]. In addition, other surrogates (such as TyG-BMI, TyG-WC, TG/HDL, METS-IR) have been proposed to capture IR and metabolic risk [14]. Large-scale epidemiological studies have delineated a consistent inverse association between the TyG index and muscle health. A representative Korean cohort revealed that elevated TyG levels were negatively correlated with lean muscle mass, while analyses in older Chinese populations showed a graded decrease in sarcopenia incidence across ascending TyG tertiles[15, 16]. However, not all findings align, data from a study in Chinese women suggested a potential protective effect of higher TyG against sarcopenia, underscoring potential population-specific heterogeneity [17]. Xu et al. demonstrated that a one standard deviation increase in TyG, TyG-WC, TyG-WHtR, TG/HDL, METS-IR, or CVAI showed a significant association with an elevated 4-year risk of developing sarcopenic obesity[18]. Supporting this, Zhao et al. found that individuals in the highest TyG tertile exhibited approximately 1.8-fold greater odds of sarcopenic obesity. Comparable associations have been observed in older Korean adults (aged ≥ 60 years), in whom elevated TyG levels robustly predicted the presence of sarcopenic obesity[19]. Together, these findings suggest a bidirectional and potentially synergistic relationship between IR and sarcopenia: IR contributes to accelerated muscle catabolism, while sarcopenia may, in turn, exacerbate metabolic impairment[20]. Against this backdrop, there is a clear need to demarcate the relationships between various IR indices and sarcopenia. Despite accumulating evidence, few studies have systematically evaluated multiple IR indices in elderly Asian individuals. Leveraging data from CHARLS, we examined associations between several insulin-resistance (IR) indices and sarcopenia among older Chinese adults. By identifying the most informative IR predictors of sarcopenia risk, this work seeks to advance understanding of the metabolic mechanisms underpinning sarcopenia. Method Study Cohort The data for this study were provided by the China Health and Retirement Longitudinal Study (CHRLS). This is a nationwide, long-term longitudinal survey of Chinese adults aged 45 and older, with the core objective of collecting multidimensional information on respondents' health, economic, and social circumstances. The survey commenced with a baseline visit in 2011 and has since completed three rounds of follow-up surveys. This paper utilises the 2011 baseline and 2015 follow-up surveys, which included blood test data. This database compiles statistics on variables related to physical and mental health, social structure, and finances, among others. For specific details, please refer to the reference materials.[21]. This study has received ethical approval from the Institutional Review Board of Peking University (IRB00001052-11015). All participants signed written informed consent forms prior to enrollment. We analyzed 2011 baseline data (n = 17,708) and 2015 follow-up SA data( http://charls.pku.edu.cn/ ), excluding 12,379 individuals for: (1) baseline age < 45 years; (2) baseline SA or incomplete SA information; (3) missing any of the seven IR surrogates at follow-up; or (4) extreme values. After exclusions, 5,329 participants remained for analysis. The study's detailed flowchart is presented in Fig. 1 . Seven Alternative Insulin Resistance Indices Participants fasted overnight before trained medical personnel collected venous blood samples. The samples were centrifuged and transported to the Capital Medical University Laboratory for analysis. The exposure variables comprised seven alternative insulin resistance indices: The exposure variables were: TG/HDL, TyG, TyG-WC, TyG-BMI, TyG-WHtR, LAP, and CVAI. The calculation methods for each index are detailed below. Unified units: Time (year); Waist Circumference(cm); Height(m). (1) TG/HDL = Triglycerides / HDL-c (2) TyG = Ln [ ((Triglycerides * glucose) /2 ] (3) TyG-WC = Ln [ ((Triglycerides * glucose ) /2 ] * WC (4) TyG-BMI = Ln [ ((Triglycerides * glucose) /2 ] * BMI (5) TyG-WHtR = Ln [ ((Triglycerides * glucose) /2 ] * [ WC/ Height] (6) Males: LAP = Triglycerides (mg/dL) * (WC − 65) Females: LAP = Triglycerides (mg/dL) * (WC − 58) (7) Males: CVAI = -267.93 + 0.68 × age (years) + 0.03 × BMI (kg/m²) + 4.00 ×WC (cm) + 22.00 × log10 Triglycerides − 16.32 × HDL-c; Females: CVAI = -187.32 + 1.71 × age + 4.23 × BMI + 1.12 × WC + 39.76 × log10 (Triglycerides − 11.66 × HDL-c) Outcome measure: Sarcopenia Outcome measure: Sarcopenia This study diagnosed sarcopenia (SA) based on the criteria established by Li et al.[22]. Diagnosis was conducted by evaluating three dimensions: muscle strength (measured by grip strength, with cutoff values of < 28 kg for males and < 18 kg for females), muscle mass (measured by height-adjusted skeletal muscle mass index, with cutoff values of < 6.88 kg/m² for males and 12 seconds or a 6-meter walk speed < 1 m/s). Muscle mass is estimated using the following formula: 0.193 × weight (kg) + 0.107 × height (cm) − 4.157 × gender code − 0.037 × age (years) − 2.631 (gender: male = 1, female = 2). Covariates Covariates, as defined in [23], included age; sex; marital status; education; residence (urban/rural); smoking status (current/former/never); alcohol use (none, < 1/month, ≥ 1/month); white blood cell count (WBC); C-reactive protein (CRP); hematocrit (HCT); mean corpuscular volume (MCV); platelet count (PLT); total cholesterol (TC); and low-density lipoprotein cholesterol (LDL-C). Statistical Analysis Continuous variables are described as follows: Normally distributed data are expressed as mean ± standard deviation, while non-normally distributed data are represented by median (interquartile range, IQR). For categorical variables, frequency and proportion are used for description. Categorical variables in baseline characteristics were described as percentages. For comparisons between quartiles (Q1-Q4) of different insulin resistance surrogate markers, univariate analysis of variance, Kruskal-Wallis test, or χ² test were applied based on data characteristics. Three logistic regression models were fitted to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for SA, treating each index as continuous (per SD or IQR) and as categorical (quartiles). The analysis employed multivariate logistic regression, sequentially constructing three models to progressively control for confounders: Model 1 was unadjusted; Model 2 adjusted for sociodemographic and behavioral factors; Model 3 additionally adjusted for laboratory indicators. Results reported odds ratios and 95% confidence intervals for all models. Model discriminatory ability was assessed using receiver operating characteristic curves. Results were reported as ORs with 95% CIs. The area under the ROC curve (AUC) was calculated to evaluate the predictive ability of the seven indices for SA risk. Optimal cut-off points were determined using the “addfor” algorithm. Interaction terms (index × modifier) were included to test effect modification by sociodemographic factors, health behaviors, and anthropometric measures. Additionally, restricted cubic splines were used to examine and visualize dose–response nonlinearity. Analyses were performed in R version 4.5.1 (packages: rms for splines; CatPredi for cut-off selection). Two-sided p < 0.05 was considered statistically significant. Results Baseline Characteristics Baseline characteristics of the study population are summarized in Table 1 . We included 5,329 participants (median age 57 years; 2,863 men [53.7%] and 2,466 women [46.3%]). The median (standard deviation) baseline values for all participants were: TG/HDL = 1.595 (2.549), TyG was 8.745 (0.689), TyG-BMI was 217.940 (40.271), TyG-WHtR was 4.814 (0.862), TyG-WC was 766.003 (130.884), CVAI was 104.259 (44.088), and LAP was 43.871 (49.278). Over 4 years, 330 participants (6.2%) developed incident sarcopenia (SA). Compared with those without SA, affected participants were older; more often male and rural residents; more likely to drink alcohol; and had lower hematocrit (HCT) and higher mean corpuscular volume (MCV). This group exhibited lower educational attainment. Participants with sarcopenia (SA) had lower values for all seven insulin-resistance (IR) surrogates than those without SA. Table 1 level Overall 0 1 p n 5329 4999 330 sex (%) Female 2466 (46.3) 2277 (45.5) 189 (57.3) < 0.001 Male 2863 (53.7) 2722 (54.5) 141 (42.7) Marital (%) Married 4853 (91.1) 4583 (91.7) 270 (81.8) < 0.001 Non-married 476 ( 8.9) 416 ( 8.3) 60 (18.2) Education (%) College or above 160 ( 3.0) 152 ( 3.0) 8 ( 2.4) < 0.001 High school 1611 (30.2) 1570 (31.4) 41 (12.4) Primary school or below 3558 (66.8) 3277 (65.6) 281 (85.2) Location (%) City/town 927 (17.4) 881 (17.6) 46 (13.9) 0.101 Village 4399 (82.6) 4115 (82.4) 284 (86.1) Smoking (%) Current smoker 1755 (33.0) 1657 (33.2) 98 (30.0) 0.104 Ex-smoker 509 ( 9.6) 485 ( 9.7) 24 ( 7.3) Non-smoker 3049 (57.4) 2844 (57.0) 205 (62.7) Drinking (%) Drink but less than once a month 458 ( 9.1) 442 ( 9.3) 16 ( 5.2) < 0.001 Drink more than once a month 1175 (23.3) 1124 (23.7) 51 (16.6) None of these 3418 (67.7) 3178 (67.0) 240 (78.2) Age (mean (SD)) 57.492 (8.209) 57.024 (8.034) 64.591 (7.541) < 0.001 WBC (mean (SD)) 6.300 (1.874) 6.304 (1.867) 6.249 (1.977) 0.613 CRP (mean (SD)) 2.590 (6.790) 2.578 (6.667) 2.775 (8.455) 0.609 HCT (mean (SD)) 42.076 (6.210) 42.164 (6.209) 40.740 (6.080) < 0.001 MCV (mean (SD)) 90.668 (8.422) 90.602 (8.397) 91.658 (8.738) 0.028 PLT (mean (SD)) 212.462 (77.065) 212.376 (76.896) 213.761 (79.669) 0.753 TC (mean (SD)) 193.951 (39.039) 193.797 (38.846) 196.272 (41.846) 0.265 LDL (mean (SD)) 116.944 (35.178) 116.846 (35.002) 118.416 (37.772) 0.433 TG/HDL (mean (SD)) 1.595 (2.549) 1.608 (2.574) 1.398 (2.131) 0.149 TyG (mean (SD)) 8.745 (0.689) 8.750 (0.686) 8.670 (0.734) 0.041 TyG_BMI (mean (SD)) 217.940 (40.271) 219.190 (40.068) 199.001 (38.614) < 0.001 TyG_WHtR (mean (SD)) 4.814 (0.862) 4.825 (0.863) 4.652 (0.830) < 0.001 TyG_WC (mean (SD)) 766.003 (130.884) 769.099 (131.504) 719.104 (111.173) < 0.001 CVAI (mean (SD)) 104.359 (44.088) 105.044 (44.140) 93.986 (42.020) < 0.001 LAP (mean (SD)) 43.871 (49.278) 44.426 (49.793) 35.472 (39.798) 0.001 SA, sarcopenia; WBC, white blood cell; CRP, C-reactive protein; HCT, hematocrit; MCV mean corpuscular volume; PLT, blood platelet; TC, total cholesterol; LDL, low density lipoprotein; TG/HDL, triglyceride-to-high-density-lipoprotein-cholesterol ratio; TyG, triglyceride-glucose; TyG-BMI, triglyceride-glucose- body mass index; TyG-WHtR, triglyceride-glucose-waist-to-height ratio; TyG-WC, triglyceride-glucose-waist circumference; CVAI, Chinese visceral adiposity index; LAP, lipid accumulation product. Association between Baseline Alternative IR Indices and SA Risk Table 2 presents odds ratios (ORs) with 95% confidence intervals (CIs) for incident SA across quartiles of each index and for per-SD (or per-IQR) increases. The risk of SA progressively decreased with higher quartiles of the seven indices. In Model 3 (fully adjusted), a one-SD increase was associated with lower odds of SA: TG/HDL, OR 0.84 (95% CI 0.74–0.95); TyG, 0.65 (0.53–0.80); TyG-BMI, 0.14 (0.10–0.19); TyG-WHtR, 0.53 (0.45–0.61); TyG-WC, 0.50 (0.43–0.57); CVAI, 0.41 (0.35–0.48); LAP, 0.60 (0.51–0.70). Table 2 also details quartile-specific associations for each alternative IR index. Table 2 Logistic analysis between various Alternative IR indices and SA incidence. Model 1 p Value Model 2 p Value Model 3 p Value TG/HDL Per-IQR increase 0.95 [0.87–1.01] 0.142 0.95 [0.87–1.02] 0.247 0.84 [0.74–0.95] 0.006 Q1 Reference Reference Reference Q2 0.88 [0.66–1.17] 0.381 0.81 [0.59–1.11] 0.186 0.86 [0.62–1.19] 0.369 Q3 0.64 [0.47–0.87] 0.005 0.57 [0.40–0.80] 0.001 0.58 [0.40–0.83] 0.003 Q4 0.55 [0.39–0.75] < 0.001 0.46 [0.32–0.66] < 0.001 0.43 [0.29–0.63] < 0.001 TyG Per-IQR increase 0.86 (0.74–0.99) 0.041 0.78 (0.66–0.92) 0.003 0.65 (0.53–0.80) < 0.001 Q1 Reference Reference Reference Q2 0.97 (0.72–1.31) 0.839 0.92 (0.66–1.28) 0.608 0.93 (0.66–1.30) 0.668 Q3 0.88 (0.64–1.19) 0.399 0.68 (0.48–0.95) 0.025 0.65 (0.46–0.93) 0.020 Q4 0.69 (0.49–0.95) 0.023 0.54 (0.37–0.78) 0.001 0.46 (0.30–0.69) < 0.001 TyG-BMI Per-IQR increase 0.42 (0.35–0.51) < 0.001 0.18 (0.14–0.24) < 0.001 0.14 (0.10–0.19) < 0.001 Q1 Reference Reference Reference Q2 0.54 (0.41–0.71) < 0.001 0.30 (0.22–0.43) < 0.001 0.29 (0.20–0.41) < 0.001 Q3 0.40 (0.30–0.54) < 0.001 0.14 (0.09–0.21) < 0.001 0.12 (0.08–0.18) < 0.001 Q4 0.18 (0.12–0.27) < 0.001 0.05 (0.03–0.08) < 0.001 0.04 (0.02–0.06) < 0.001 CVAI Per-SD increase 0.75 (0.66–0.86) < 0.001 0.41 (0.35–0.48) < 0.001 0.41 (0.35–0.48) < 0.001 Q1 Reference Reference Reference Q2 1.02 (0.77–1.36) 0.880 0.60 (0.43–0.85) 0.003 0.62 (0.44–0.87) 0.006 Q3 0.62 (0.45–0.84) 0.003 0.20 (0.13–0.29) < 0.001 0.20 (0.13–0.30) < 0.001 Q4 0.50 (0.35–0.70) < 0.001 0.07 (0.04–0.11) < 0.001 0.06 (0.04–0.10) < 0.001 LAP Per-SD increase 0.82 (0.73–0.92) 0.001 0.69 (0.60–0.79) < 0.001 0.60 (0.51–0.70) < 0.001 Q1 Reference Reference Reference Q2 0.65 (0.49–0.88) 0.005 0.47 (0.33–0.66) < 0.001 0.48 (0.34–0.68) < 0.001 Q3 0.60 (0.45–0.81) 0.001 0.34 (0.23–0.49) < 0.001 0.34 (0.23–0.49) < 0.001 Q4 0.43 (0.30–0.59) < 0.001 0.17 (0.11–0.26) < 0.001 0.13 (0.08–0.21) < 0.001 TyG_WHtR Per-SD increase 0.79 (0.69–0.90) < 0.001 0.55 (0.47–0.64) < 0.001 0.53 (0.45–0.61) < 0.001 Q1 Reference Reference Reference Q2 0.76 (0.57–1.03) 0.074 0.51 (0.36–0.72) < 0.001 0.50 (0.36–0.71) < 0.001 Q3 0.72 (0.53–0.96) 0.029 0.35 (0.24–0.50) < 0.001 0.34 (0.23–0.49) < 0.001 Q4 0.53 (0.38–0.73) < 0.001 0.14 (0.09–0.21) < 0.001 0.11 (0.07–0.18) < 0.001 TyG_WC Per-SD increase 0.65 (0.58–0.74) < 0.001 0.52 (0.45–0.59) < 0.001 0.50 (0.43–0.57) < 0.001 Q1 Reference Reference Reference Q2 0.83 (0.64–1.09) 0.192 0.60 (0.44–0.82) 0.001 0.58 (0.42–0.80) 0.001 Q3 0.48 (0.35–0.66) < 0.001 0.25 (0.17–0.36) < 0.001 0.23 (0.16–0.34) < 0.001 Q4 0.29 (0.20–0.41) < 0.001 0.11 (0.07–0.17) < 0.001 0.08 (0.05–0.13) < 0.001 Predictive Performance of the Seven Alternative Insulin-Resistance Indices Predictive performance was evaluated using ROC curves (Fig. 2 ). TyG-BMI achieved the highest AUC (0.669), outperforming TyG-WC and LAP. The optimal TyG-BMI cutoff for predicting SA was 353.713. Nonlinear Relationships between Alternative IR Indices and SA Risk Dose-response relationships between IR surrogates and SA were illustrated and examined using restricted cubic spline models, as shown in Fig. 3 and Supplementary Table 1. After adjusting for multiple confounders in Model 3, the LAP index exhibited a linear association with SA occurrence (P nonlinearity = 0.556). By contrast, TG/HDL, TyG, TyG-BMI, TyG-WHtR, TyG-WC, and CVAI showed significant nonlinearity (P for nonlinearity ranging from 0.026 to < 0.001). The model adjusted for demographic characteristics, lifestyle factors, and laboratory indicators. Solid lines in the figure represent adjusted odds ratios, shaded areas denote their 95% confidence intervals, and horizontal dashed lines indicate the null reference line (OR = 1.0). (A) TG/HDL; (B) TyG; (C) TyG-BMI; (D) CVAI; (E) TyG-WHtR; (F) TyG-WC. Abbreviations as defined previously. We used threshold-effect analyses to identify inflection points in the associations of TG/HDL, TyG, TyG-BMI, CVAI, LAP, TyG-WHtR, and TyG-WC with SA risk. Segmented logistic regression estimated ORs (95% CIs) on either side of each threshold (Table 3 ). Breakpoints for TG/HDL, TyG, TyG-BMI, CVAI, LAP, TyG-WHtR, and TyG-WC were 1.45, 9.15, 5.01, 0.79, 0.18, 3.43, and 3.73, respectively. Below their breakpoints, TG/HDL and TyG-BMI were inversely associated with SA risk (OR 0.41, 95% CI 0.26–0.66; OR 0.08, 95% CI 0.05–0.12; both p < 0.001). Conversely, values above these breakpoints showed significant positive correlations with SA risk, yielding ORs of 2.44 (95% CI: 1.49–4.01, p < 0.001) and 17.08 (95% CI: 9.50–30.72, p < 0.001), respectively. For TyG-WHtR and TyG-WC, values below the breakpoint were associated with higher SA risk (OR 2.95, 95% CI 1.39–6.27, p = 0.005; OR 2.71, 95% CI 1.41–5.21, p = 0.003), whereas values above the breakpoint were inversely associated (OR 0.08, 95% CI 0.04–0.19; OR 0.07, 95% CI 0.03–0.15; both p < 0.001). LAP and CVAI were not associated with SA below their breakpoints (p = 0.113 and p = 0.427), but showed inverse associations above them (LAP: OR 0.17, 95% CI 0.06–0.55, p = 0.003; CVAI: OR 0.14, 95% CI 0.08–0.26, p < 0.001). TyG showed no significant correlation before or after the breakpoint (p = 0.084, p = 0.159). Table 3 Threshold effect analysis Inflection point Adjusted OR (95% CI) P value TG/HDL Standard Logistic 0.84 (0.74, 0.95) 0.006 Segmented Logistic ≤ 1.45 0.41 (0.26, 0.66) 1.45 2.44 (1.49, 4.01) < 0.001 TyG Standard Logistic 0.65 (0.53, 0.80) 9.15 4.87 (0.54, 44.15) 0.159 TyG-BMI Standard Logistic 0.14 (0.10, 0.19) < 0.001 Segmented Logistic ≤ 5.01 0.08 (0.05, 0.12) 5.01 17.08 (9.50, 30.72) < 0.001 CVAI Standard Logistic 0.41 (0.35, 0.48) 0.79 0.14 (0.08, 0.26) < 0.001 LAP Standard Logistic 0.60 (0.51, 0.70) 0.18 0.17 (0.06, 0.55) 0.003 TYG_WHtR Standard Logistic 0.53 (0.45, 0.61) 3.43 0.08 (0.04, 0.19) < 0.001 TYG_WC Standard Logistic 0.50 (0.43, 0.57) 3.73 0.07 (0.03, 0.15) < 0.001 Odds ratios were adjusted for age, sex, marriage, location, education, smoking, drinking, WBC, CRP, HCT, MCV, PLT, TC and LDL. OR, odds ratio; 95% CI, 95% confidence interval; abbreviations as defined above. Subgroup Analysis To determine whether different subgroups exert varying effects on SA risk, Fig. 4 and Supplementary Table 2 presents subgroup analysis results stratified by characteristics, involving seven insulin resistance indices. As illustrated, the association between various insulin resistance indices and increased SA risk varied across subgroups. In the sex-stratified subgroup, SA risk showed significant positive correlations with TyG-BMI, TyG-WHtR, and LAP. Across all subgroups, no significant correlations were observed between TG-HDL_C, TyG, TyG-WC, or CVAI and SA risk. Notably, in the smoking-stratified subgroup, male participants exhibited higher correlations between LAP, TyG-BMI, and SA. In the location-stratified subgroup, township residents showed a stronger association between TyG-BMI and SA. Sex significantly modified the associations of TyG-WHtR (P_interaction = 0.011), LAP (P_interaction = 0.009), and TyG-BMI (P_interaction < 0.001) with SA. Smoking also modified the associations of LAP (P_interaction = 0.007) and TyG-BMI (P_interaction = 0.010) with SA. Subgroup analysis. Forest plots of the associations between alternative IR indices and incident SA stratified by age, sex, residence, education, smoking, and alcohol use. Odds ratios were adjusted for age, sex, residence, education, smoking, alcohol use, WBC, CRP, HCT, MCV, PLT, TC, and LDL. OR, odds ratio; 95% CI, 95% confidence interval. Panels: (a) TyG-BMI; (b) LAP; (c) TyG-WC; (d) TyG-WHtR. Discussion In 5,329 participants, we observed inverse associations between all seven IR surrogates and sarcopenia risk. TG/HDL, TyG, TyG-BMI, TyG-WC, TyG-WHtR, and CVAI showed nonlinear associations with sarcopenia risk, whereas LAP was approximately linear (P for nonlinearity = 0.556). TyG-BMI showed the best discrimination for SA risk. Insulin resistance may exacerbate sarcopenia through mechanisms such as accumulation and intramuscular fat infiltration. Conversely, sarcopenia impairs normal glucose metabolism, further intensifying insulin resistance [24]. Previous studies indicate that surrogate insulin resistance indices are effective measures for assessing IR; however, research examining the impact of multiple surrogate IR indices on SA remains limited[25, 26]. Xiao et al. analyzed 4,804 U.S. adults aged ≥ 20 years. After multivariable adjustment, multiple TyG indices showed significant positive correlations with sarcopenia prevalence [27]. By contrast, in our cohort TyG-BMI below its estimated cut-off was inversely associated with SA risk. Beyond the cutoff point, it shows a significant positive correlation with SA risk. In contrast, when TyG-WHtR and TyG-WC were below their respective breakpoints, they showed a significant positive correlation with SA risk, while above the breakpoints, they showed a significant negative correlation with SA risk. Factors contributing to this discrepancy may include the following: First, Xiao et al.'s study was cross-sectional, whereas ours was a longitudinal cohort study. Population differences also apply: their sample included U.S. adults aged ≥ 20 years, while ours focused on Chinese adults aged ≥ 45 years. Additionally, adjusting for different covariates may yield varying effects. We extended prior work by evaluating associations between alternative IR indices and SA using three models adjusted for 14 potential confounders. Overall, alternative IR indices showed inverse associations with SA. Predictive analyses indicated that TyG-BMI had the highest area under the ROC curve (AUC = 0.669), suggesting superior discrimination for SA. Consistently, Zhang et al. reported that TyG-BMI outperformed other TyG-based indices in predicting SA[28]. While many studies link IR indices to sarcopenia, findings for TyG-related indices remain mixed [27]. Pan et al. reported a positive correlation between the TyG index and sarcopenia [29]. In contrast, this study demonstrates a nonlinear relationship between the alternative insulin resistance index and SA risk. Using segmented logistic regression analysis, we elucidate the correlation trends between multiple insulin resistance indices and SA risk. This study employed segmented (inflection point) logistic regression to reveal significant nonlinear relationships between multiple alternative insulin resistance indices and sarcopenia, demonstrating consistent U-shaped patterns across multiple indicators. First, TG/HDL exhibited an overall protective trend, with an OR of 0.84 (0.74–0.95) per IQR increase after stepwise adjustment. Segmented regression further revealed an effect reversal at a threshold of 1.60 for TG/HDL: increases below this threshold were associated with lower sarcopenia risk, while increases above it were linked to higher risk, suggesting a dose-response pattern of “optimal at moderate levels, reversal beyond threshold.” The inflection point for TyG-BMI was approximately 255.51: each IQR increase below the threshold significantly reduced risk, while increases above the threshold significantly increased risk. Regarding fat distribution-related indicators, CVAI showed a consistently protective effect (OR = 0.41). Segmented analysis identified CVAI = 40.45 as the threshold: effects were non-significant below the threshold (OR = 1.23), while clear protection emerged above it (OR = 0.14). LAP similarly exhibited a threshold-dominated bidirectional structure with a threshold of 6.88, showing significant protection above the threshold (OR = 0.17). TyG-WHtR and TyG-WC also exhibited a “subthreshold increased risk, suprathreshold protective” pattern: TyG-WHtR's threshold was approximately 3.67, with subthreshold levels associated with higher risk across multiple models (OR = 2.95), while suprathreshold levels shifted to protective effects (e.g., OR = 0.08); TyG-WC had a threshold of 596.80, showing increased risk below the threshold (e.g., OR = 2.71) and strong protection above it (e.g., OR = 0.07). In summary, surrogate insulin resistance indices generally exhibit a U-shaped “threshold-reversal” pattern with sarcopenia risk: within low-load ranges, increased indices predominantly reflect protection; beyond the threshold, continued elevation amplifies risk. Regarding the abdominal fat phenotype, CVAI shows negligible impact in the low range but correlates with reduced sarcopenia risk upon moderate elevation beyond the threshold, suggesting “excessive thinness” is suboptimal while moderate visceral fat may support energy and protein metabolism. Extreme fat restriction may not be beneficial; maintaining metabolic indicators within an individualized “healthy moderate range” may maximize protective effects. This finding parallels observations from the “athlete paradox” [30]. Thus, setting an “individualized moderate range” around the inflection point holds greater practical significance for minimizing sarcopenia risk and guiding threshold-oriented interventions and follow-ups. Previous studies indicate that gender significantly influences SA occurrence [31]. This study found women face a higher sarcopenia risk than men, potentially linked to gender differences in muscle physiology, sexual characteristics, and lifestyle factors [32]. Research suggests menopause-induced estrogen concentration changes further contribute to muscle atrophy [33]. Further research confirms that declining estrogen levels during menopause increase body fat percentage and reduce muscle mass in women, significantly elevating their risk of sarcopenia [34]. Jo et al. reported higher SA risk among smokers [35], consistent with our subgroup findings by smoking status. Active smoking is associated with higher insulin resistance [36], which may mediate the link to SA. Using CHARLS data, we conducted a longitudinal analysis of associations between surrogate IR indices and sarcopenia risk in middle-aged and older Chinese adults. We evaluated both linear and nonlinear relationships using longitudinal models. We also assessed predictive performance, informing the potential utility of IR-related indicators for early identification of SA risk in this population. However, several limitations should be noted. First, although we used a skeletal-muscle-mass prediction formula validated in Chinese populations with good agreement to dual-energy X-ray absorptiometry (DXA), it does not substitute for gold-standard assessments such as DXA or bioelectrical impedance analysis (BIA). Second, constrained by a database covering only blood test data from 2011 to 2015, follow-up was limited to 4 years. Longer follow-up would strengthen the robustness of the findings. Finally, despite extensive adjustment, unmeasured factors (e.g., lifestyle, nutritional status, diet) may have introduced residual confounding. Future research should integrate additional high-quality databases to further examine associations between alternative IR indices and sarcopenic disorders. Conclusion Multiple IR surrogates were inversely linked to incident SA. Notably, several indices exhibited threshold effects; risk patterns differed below versus above estimated inflection points. Maintaining indices within moderate ranges may be beneficial; TyG-BMI emerged as the most informative predictor. Further research integrating additional databases is warranted to explore the relationship between insulin resistance and sarcopenia. Declarations Acknowledgements Not applicable. Author information Authors and Affiliations Department of Orthopedics and Joint Diseases I, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China. Ke Xu & Lin Jing Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China. Ruikang Liu College of Acupuncture and Tuina, Beijing University of Chinese Medicine, Beijing, China. Boyang Chen Contributions Conception and design: KX and LJ. Provision of study materials: RL and LJ. Collection and assembly of data: KX and BC. Data visualization: BC and RL. Implementation of the computer code and supporting algorithms: RL. Data analysis and interpretation: KX, BCand RL. Manuscript writing: KX. All authors contributed to the article and approved the submitted version. Authors Correspondence to Lin Jing Ke Xu and Ruikang Liu are co-first authors. Funding This study was supported by Evidence-Based Clinical Research Project for Traditional Chinese Medicine at the High-Level TCM Hospital Construction Project of Wangjing Hospital, China Academy of Chinese Medical Sciences (WJYY-XZKT-2023-31) and Self-Selected Research Project of Wangjing Hospital, China Academy of Chinese Medical Sciences (WJYY-ZZXT-2025-27). Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All data was from http://charls.pku.edu.cn/. Ethics declarations Ethics approval and consent to participate This study has received ethical approval from the Institutional Review Board of Peking University (IRB00001052-11015). All methods were carried out in accordance with relevant guidelines and regulations. All participants signed written informed consent forms prior to enrollment. Clinical trial number Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Cruz-Jentoft AJ, Sayer AA: Sarcopenia. Lancet 2019, 393(10191):2636-2646. Zhang Y, Zhang K, Huang S, Li W, He P: A review on associated factors and management measures for sarcopenia in type 2 diabetes mellitus. Medicine (Baltimore) 2024, 103(16):e37666. Meng S, He X, Fu X, Zhang X, Tong M, Li W, Zhang W, Shi X, Liu K: The prevalence of sarcopenia and risk factors in the older adult in China: a systematic review and meta-analysis. Front Public Health 2024, 12:1415398. Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, Celis-Morales C: Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 2022, 13(1):86-99. 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Batsis JA, Mackenzie TA, Jones JD, Lopez-Jimenez F, Bartels SJ: Sarcopenia, sarcopenic obesity and inflammation: Results from the 1999-2004 National Health and Nutrition Examination Survey. Clin Nutr 2016, 35(6):1472-1483. Chen L, Zheng J, Ye B, Huang Y, Wang Z: Diabetes and sarcopenia: A bibliometric exploration of mechanisms, comorbidities, and therapeutic frontiers—an evidence mapping study. Experimental Gerontology 2025, 210:112874. Abbasi F, Reaven GM: Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metabolism 2011, 60(12):1673-1676. Han M, Qin P, Li Q, Qie R, Liu L, Zhao Y, Liu D, Zhang D, Guo C, Zhou Q et al : Chinese visceral adiposity index: A reliable indicator of visceral fat function associated with risk of type 2 diabetes. Diabetes/Metabolism Research and Reviews 2021, 37(2):e3370. Xiao W, Xu T, Liao Y, Xu Y, Fan Z, Li C, Zhang X: Associations between different triglyceride glucose index-related obesity indices and sarcopenia: a cross-sectional study. Frontiers in Endocrinology 2025, Volume 16 - 2025. Zhang Z, Chen X, Jiang N: The triglyceride glucose related index is an indicator of Sarcopenia. Sci Rep 2024, 14(1):24126. Pan R, Wang T, Tang R, Qian Z: Association of atherogenic index of plasma and triglyceride glucose-body mass index and sarcopenia in adults from 20 to 59: a cross-sectional study. Front Endocrinol (Lausanne) 2024, 15:1437379. Jang SY, Choi KM: Impact of Adipose Tissue and Lipids on Skeletal Muscle in Sarcopenia. J Cachexia Sarcopenia Muscle 2025, 16(4):e70000. Zhang H, Jin Y, Che S, Song Z: Nomogram models for predicting sarcopenia in elderly Asian patients with type 2 diabetes. Clinics (Sao Paulo) 2025, 80:100771. Cho YJ, Lim YH, Yun JM, Yoon HJ, Park M: Sex- and age-specific effects of energy intake and physical activity on sarcopenia. Sci Rep 2020, 10(1):9822. Lu L, Tian L: Postmenopausal osteoporosis coexisting with sarcopenia: the role and mechanisms of estrogen. J Endocrinol 2023, 259(1). Abdulnour J, Doucet E, Brochu M, Lavoie JM, Strychar I, Rabasa-Lhoret R, Prud'homme D: The effect of the menopausal transition on body composition and cardiometabolic risk factors: a Montreal-Ottawa New Emerging Team group study. Menopause 2012, 19(7):760-767. Jo Y, Linton JA, Choi J, Moon J, Kim J, Lee J, Oh S: Association between Cigarette Smoking and Sarcopenia according to Obesity in the Middle-Aged and Elderly Korean Population: The Korea National Health and Nutrition Examination Survey (2008-2011). Korean J Fam Med 2019, 40(2):87-92. Bajaj M: Nicotine and insulin resistance: when the smoke clears. Diabetes 2012, 61(12):3078-3080. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx 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. 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1","display":"","copyAsset":false,"role":"figure","size":78143,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/01d8730f8c466f7981f31e46.png"},{"id":100411945,"identity":"300f22bb-14c4-4f76-bc8f-5c42b582705f","added_by":"auto","created_at":"2026-01-16 13:13:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17755,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/2de441340a65861315417c93.png"},{"id":100412364,"identity":"1c7f5dc5-7305-49a3-9733-1aff55616e9b","added_by":"auto","created_at":"2026-01-16 13:14:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196524,"visible":true,"origin":"","legend":"\u003cp\u003eThe model adjusted for demographic characteristics, lifestyle factors, and laboratory indicators. Solid lines in the figure represent adjusted odds ratios, shaded areas denote their 95% confidence intervals, and horizontal dashed lines indicate the null reference line (OR=1.0). (A) TG/HDL; (B) TyG; (C) TyG-BMI; (D) CVAI; (E) TyG-WHtR; (F) TyG-WC. Abbreviations as defined previously.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/c01200c337542411968f5b07.png"},{"id":100412209,"identity":"1cd746d9-449c-4306-87cb-0b9710e0c863","added_by":"auto","created_at":"2026-01-16 13:14:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1039194,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis. Forest plots of the associations between alternative IR indices and incident SA stratified by age, sex, residence, education, smoking, and alcohol use. Odds ratios were adjusted for age, sex, residence, education, smoking, alcohol use, WBC, CRP, HCT, MCV, PLT, TC, and LDL. OR, odds ratio; 95% CI, 95% confidence interval. Panels: (a) TyG-BMI; (b) LAP; (c) TyG-WC; (d) TyG-WHtR.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/180bdfd2c73cee4f2f040c1f.png"},{"id":101029061,"identity":"3f19e812-44f1-4839-b33e-d27975e64752","added_by":"auto","created_at":"2026-01-24 06:25:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2311008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/43104db1-c203-46cf-bd19-740a16872714.pdf"},{"id":100412305,"identity":"ef2e29cc-af6e-4b19-9ffc-a0524acec356","added_by":"auto","created_at":"2026-01-16 13:14:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":139542,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/a59515928dada6efbfdfb71f.docx"},{"id":100412206,"identity":"f3cbf3f8-3620-4097-b090-a473f053d92f","added_by":"auto","created_at":"2026-01-16 13:14:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":814423,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8404542/v1/b6329ba198ae22d45a9a7e15.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Various Insulin Resistance Indices and Sarcopenia: Evidence from CHARLS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, a geriatric syndrome marked by progressive loss of muscle mass, strength, and function. This condition shows an elevated prevalence in diabetic patients relative to the general population. [1, 2]. Since its inclusion in ICD-10 in 2016, sarcopenia has been documented to affect up to 10\u0026ndash;27% of older adults globally, with prevalence in China estimated around 20.7% in those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 [3, 4]. Muscle mass and strength loss associated with sarcopenia is significantly correlated with the risk of three key adverse outcomes: functional impairment, hospitalization rates, and mortality. [5]. Sarcopenia imposes a significant public health burden on aging societies, a phenomenon particularly pronounced in diabetic sarcopenia\u0026mdash;research has confirmed its association with increased risks of cardiovascular events and mortality. [6].\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) is tightly linked with muscle health [7]. In East Asian populations, individuals with IR exhibit a markedly higher prevalence of sarcopenia compared to non-IR counterparts, with estimates suggesting that up to 25% may be affected [8, 9]. Mechanistically, IR can affect muscle protein metabolism [10]. Under physiological conditions, insulin facilitates muscle protein synthesis and inhibits proteolysis; however, in the context of IR, these anabolic effects are blunted, resulting in enhanced muscle degradation [11]. Additionally, skeletal muscle serves as the primary site for glucose disposal, and its depletion in sarcopenia may further exacerbate IR and hyperglycemia [12]. To quantify IR in epidemiologic studies, surrogate indices are widely utilized [11]. The homeostatic model assessment of IR (HOMA-IR) has been widely used for estimating IR, whereas the triglyceride-glucose (TyG) index has recently gained attention as a simple and reliable surrogate, showing strong correlation with clamp-derived measurements [13]. In addition, other surrogates (such as TyG-BMI, TyG-WC, TG/HDL, METS-IR) have been proposed to capture IR and metabolic risk [14].\u003c/p\u003e \u003cp\u003eLarge-scale epidemiological studies have delineated a consistent inverse association between the TyG index and muscle health. A representative Korean cohort revealed that elevated TyG levels were negatively correlated with lean muscle mass, while analyses in older Chinese populations showed a graded decrease in sarcopenia incidence across ascending TyG tertiles[15, 16]. However, not all findings align, data from a study in Chinese women suggested a potential protective effect of higher TyG against sarcopenia, underscoring potential population-specific heterogeneity [17]. Xu et al. demonstrated that a one standard deviation increase in TyG, TyG-WC, TyG-WHtR, TG/HDL, METS-IR, or CVAI showed a significant association with an elevated 4-year risk of developing sarcopenic obesity[18]. Supporting this, Zhao et al. found that individuals in the highest TyG tertile exhibited approximately 1.8-fold greater odds of sarcopenic obesity. Comparable associations have been observed in older Korean adults (aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years), in whom elevated TyG levels robustly predicted the presence of sarcopenic obesity[19]. Together, these findings suggest a bidirectional and potentially synergistic relationship between IR and sarcopenia: IR contributes to accelerated muscle catabolism, while sarcopenia may, in turn, exacerbate metabolic impairment[20].\u003c/p\u003e \u003cp\u003eAgainst this backdrop, there is a clear need to demarcate the relationships between various IR indices and sarcopenia. Despite accumulating evidence, few studies have systematically evaluated multiple IR indices in elderly Asian individuals. Leveraging data from CHARLS, we examined associations between several insulin-resistance (IR) indices and sarcopenia among older Chinese adults. By identifying the most informative IR predictors of sarcopenia risk, this work seeks to advance understanding of the metabolic mechanisms underpinning sarcopenia.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Cohort\u003c/h2\u003e \u003cp\u003eThe data for this study were provided by the China Health and Retirement Longitudinal Study (CHRLS). This is a nationwide, long-term longitudinal survey of Chinese adults aged 45 and older, with the core objective of collecting multidimensional information on respondents' health, economic, and social circumstances. The survey commenced with a baseline visit in 2011 and has since completed three rounds of follow-up surveys. This paper utilises the 2011 baseline and 2015 follow-up surveys, which included blood test data. This database compiles statistics on variables related to physical and mental health, social structure, and finances, among others. For specific details, please refer to the reference materials.[21]. This study has received ethical approval from the Institutional Review Board of Peking University (IRB00001052-11015). All participants signed written informed consent forms prior to enrollment.\u003c/p\u003e \u003cp\u003eWe analyzed 2011 baseline data (n\u0026thinsp;=\u0026thinsp;17,708) and 2015 follow-up SA data(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), excluding 12,379 individuals for: (1) baseline age\u0026thinsp;\u0026lt;\u0026thinsp;45 years; (2) baseline SA or incomplete SA information; (3) missing any of the seven IR surrogates at follow-up; or (4) extreme values. After exclusions, 5,329 participants remained for analysis. The study's detailed flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSeven Alternative Insulin Resistance Indices\u003c/h3\u003e\n\u003cp\u003eParticipants fasted overnight before trained medical personnel collected venous blood samples. The samples were centrifuged and transported to the Capital Medical University Laboratory for analysis. The exposure variables comprised seven alternative insulin resistance indices:\u003c/p\u003e \u003cp\u003eThe exposure variables were: TG/HDL, TyG, TyG-WC, TyG-BMI, TyG-WHtR, LAP, and CVAI. The calculation methods for each index are detailed below. Unified units: Time (year); Waist Circumference(cm); Height(m).\u003c/p\u003e \u003cp\u003e(1) TG/HDL\u0026thinsp;=\u0026thinsp;Triglycerides / HDL-c\u003c/p\u003e \u003cp\u003e(2) TyG\u0026thinsp;=\u0026thinsp;Ln [ ((Triglycerides * glucose) /2 ]\u003c/p\u003e \u003cp\u003e(3) TyG-WC\u0026thinsp;=\u0026thinsp;Ln [ ((Triglycerides * glucose ) /2 ] * WC\u003c/p\u003e \u003cp\u003e(4) TyG-BMI\u0026thinsp;=\u0026thinsp;Ln [ ((Triglycerides * glucose) /2 ] * BMI\u003c/p\u003e \u003cp\u003e(5) TyG-WHtR\u0026thinsp;=\u0026thinsp;Ln [ ((Triglycerides * glucose) /2 ] * [ WC/ Height]\u003c/p\u003e \u003cp\u003e(6) Males: LAP\u0026thinsp;=\u0026thinsp;Triglycerides (mg/dL) * (WC \u0026minus;\u0026thinsp;65)\u003c/p\u003e \u003cp\u003eFemales: LAP\u0026thinsp;=\u0026thinsp;Triglycerides (mg/dL) * (WC \u0026minus;\u0026thinsp;58)\u003c/p\u003e \u003cp\u003e(7) Males: CVAI = -267.93\u0026thinsp;+\u0026thinsp;0.68 \u0026times; age (years)\u0026thinsp;+\u0026thinsp;0.03 \u0026times; BMI (kg/m\u0026sup2;)\u0026thinsp;+\u0026thinsp;4.00 \u0026times;WC (cm)\u0026thinsp;+\u0026thinsp;22.00 \u0026times; log10 Triglycerides \u0026minus;\u0026thinsp;16.32 \u0026times; HDL-c;\u003c/p\u003e \u003cp\u003eFemales: CVAI = -187.32\u0026thinsp;+\u0026thinsp;1.71 \u0026times; age\u0026thinsp;+\u0026thinsp;4.23 \u0026times; BMI\u0026thinsp;+\u0026thinsp;1.12 \u0026times; WC\u0026thinsp;+\u0026thinsp;39.76 \u0026times; log10 (Triglycerides \u0026minus;\u0026thinsp;11.66 \u0026times; HDL-c)\u003c/p\u003e\n\u003ch3\u003eOutcome measure: Sarcopenia\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome measure: Sarcopenia\u003c/div\u003e \u003cp\u003eThis study diagnosed sarcopenia (SA) based on the criteria established by Li et al.[22]. Diagnosis was conducted by evaluating three dimensions: muscle strength (measured by grip strength, with cutoff values of \u0026lt;\u0026thinsp;28 kg for males and \u0026lt;\u0026thinsp;18 kg for females), muscle mass (measured by height-adjusted skeletal muscle mass index, with cutoff values of \u0026lt;\u0026thinsp;6.88 kg/m\u0026sup2; for males and \u0026lt;\u0026thinsp;5.69 kg/m\u0026sup2; for females), and physical function (defined as a sit-to-stand time\u0026thinsp;\u0026gt;\u0026thinsp;12 seconds or a 6-meter walk speed\u0026thinsp;\u0026lt;\u0026thinsp;1 m/s). Muscle mass is estimated using the following formula: 0.193 \u0026times; weight (kg)\u0026thinsp;+\u0026thinsp;0.107 \u0026times; height (cm)\u0026thinsp;\u0026minus;\u0026thinsp;4.157 \u0026times; gender code\u0026thinsp;\u0026minus;\u0026thinsp;0.037 \u0026times; age (years)\u0026thinsp;\u0026minus;\u0026thinsp;2.631 (gender: male\u0026thinsp;=\u0026thinsp;1, female\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates, as defined in [23], included age; sex; marital status; education; residence (urban/rural); smoking status (current/former/never); alcohol use (none, \u0026lt;\u0026thinsp;1/month, \u0026ge;\u0026thinsp;1/month); white blood cell count (WBC); C-reactive protein (CRP); hematocrit (HCT); mean corpuscular volume (MCV); platelet count (PLT); total cholesterol (TC); and low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are described as follows: Normally distributed data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while non-normally distributed data are represented by median (interquartile range, IQR). For categorical variables, frequency and proportion are used for description. Categorical variables in baseline characteristics were described as percentages. For comparisons between quartiles (Q1-Q4) of different insulin resistance surrogate markers, univariate analysis of variance, Kruskal-Wallis test, or χ\u0026sup2; test were applied based on data characteristics. Three logistic regression models were fitted to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for SA, treating each index as continuous (per SD or IQR) and as categorical (quartiles). The analysis employed multivariate logistic regression, sequentially constructing three models to progressively control for confounders: Model 1 was unadjusted; Model 2 adjusted for sociodemographic and behavioral factors; Model 3 additionally adjusted for laboratory indicators. Results reported odds ratios and 95% confidence intervals for all models. Model discriminatory ability was assessed using receiver operating characteristic curves. Results were reported as ORs with 95% CIs. The area under the ROC curve (AUC) was calculated to evaluate the predictive ability of the seven indices for SA risk. Optimal cut-off points were determined using the \u0026ldquo;addfor\u0026rdquo; algorithm. Interaction terms (index \u0026times; modifier) were included to test effect modification by sociodemographic factors, health behaviors, and anthropometric measures. Additionally, restricted cubic splines were used to examine and visualize dose\u0026ndash;response nonlinearity.\u003c/p\u003e \u003cp\u003eAnalyses were performed in R version 4.5.1 (packages: rms for splines; CatPredi for cut-off selection). Two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We included 5,329 participants (median age 57 years; 2,863 men [53.7%] and 2,466 women [46.3%]). The median (standard deviation) baseline values for all participants were: TG/HDL\u0026thinsp;=\u0026thinsp;1.595 (2.549), TyG was 8.745 (0.689), TyG-BMI was 217.940 (40.271), TyG-WHtR was 4.814 (0.862), TyG-WC was 766.003 (130.884), CVAI was 104.259 (44.088), and LAP was 43.871 (49.278). Over 4 years, 330 participants (6.2%) developed incident sarcopenia (SA). Compared with those without SA, affected participants were older; more often male and rural residents; more likely to drink alcohol; and had lower hematocrit (HCT) and higher mean corpuscular volume (MCV). This group exhibited lower educational attainment. Participants with sarcopenia (SA) had lower values for all seven insulin-resistance (IR) surrogates than those without SA.\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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2466 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2277 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2863 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2722 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4853 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4583 (91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476 ( 8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416 ( 8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 ( 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152 ( 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 ( 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1611 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1570 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3558 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3277 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e281 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity/town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e927 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e881 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4399 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4115 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e284 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1755 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1657 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509 ( 9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e485 ( 9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 ( 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3049 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2844 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrink but less than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458 ( 9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e442 ( 9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 ( 5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrink more than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1175 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1124 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone of these\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3418 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3178 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e240 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.492 (8.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.024 (8.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.591 (7.541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.300 (1.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.304 (1.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.249 (1.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.590 (6.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.578 (6.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.775 (8.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.076 (6.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.164 (6.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.740 (6.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.668 (8.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.602 (8.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.658 (8.738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212.462 (77.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212.376 (76.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213.761 (79.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193.951 (39.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193.797 (38.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196.272 (41.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.944 (35.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.846 (35.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.416 (37.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.595 (2.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.608 (2.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.398 (2.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.745 (0.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.750 (0.686)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.670 (0.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG_BMI (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217.940 (40.271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219.190 (40.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199.001 (38.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG_WHtR (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.814 (0.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.825 (0.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.652 (0.830)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG_WC (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e766.003 (130.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e769.099 (131.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e719.104 (111.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVAI (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.359 (44.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.044 (44.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.986 (42.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.871 (49.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.426 (49.793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.472 (39.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSA, sarcopenia; WBC, white blood cell; CRP, C-reactive protein; HCT, hematocrit; MCV mean corpuscular volume; PLT, blood platelet; TC, total cholesterol; LDL, low density lipoprotein; TG/HDL, triglyceride-to-high-density-lipoprotein-cholesterol ratio; TyG, triglyceride-glucose; TyG-BMI, triglyceride-glucose- body mass index; TyG-WHtR, triglyceride-glucose-waist-to-height ratio; TyG-WC, triglyceride-glucose-waist circumference; CVAI, Chinese visceral adiposity index; LAP, lipid accumulation product.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between Baseline Alternative IR Indices and SA Risk\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents odds ratios (ORs) with 95% confidence intervals (CIs) for incident SA across quartiles of each index and for per-SD (or per-IQR) increases. The risk of SA progressively decreased with higher quartiles of the seven indices. In Model 3 (fully adjusted), a one-SD increase was associated with lower odds of SA: TG/HDL, OR 0.84 (95% CI 0.74\u0026ndash;0.95); TyG, 0.65 (0.53\u0026ndash;0.80); TyG-BMI, 0.14 (0.10\u0026ndash;0.19); TyG-WHtR, 0.53 (0.45\u0026ndash;0.61); TyG-WC, 0.50 (0.43\u0026ndash;0.57); CVAI, 0.41 (0.35\u0026ndash;0.48); LAP, 0.60 (0.51\u0026ndash;0.70). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also details quartile-specific associations for each alternative IR index.\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\u003eLogistic analysis between various Alternative IR indices and SA incidence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u0026nbsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel\u0026nbsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel\u0026nbsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-IQR\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026nbsp;[0.87\u0026ndash;1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u0026nbsp;[0.87\u0026ndash;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u0026nbsp;[0.74\u0026ndash;0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026nbsp;[0.66\u0026ndash;1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u0026nbsp;[0.59\u0026ndash;1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u0026nbsp;[0.62\u0026ndash;1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u0026nbsp;[0.47\u0026ndash;0.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u0026nbsp;[0.40\u0026ndash;0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u0026nbsp;[0.40\u0026ndash;0.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u0026nbsp;[0.39\u0026ndash;0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u0026nbsp;[0.32\u0026ndash;0.66]\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 \u003cp\u003e0.43\u0026nbsp;[0.29\u0026ndash;0.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-IQR\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u0026nbsp;(0.74\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u0026nbsp;(0.66\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u0026nbsp;(0.53\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u0026nbsp;(0.72\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u0026nbsp;(0.66\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u0026nbsp;(0.66\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026nbsp;(0.64\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u0026nbsp;(0.48\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u0026nbsp;(0.46\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u0026nbsp;(0.49\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026nbsp;(0.37\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u0026nbsp;(0.30\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-IQR\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u0026nbsp;(0.35\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026nbsp;(0.14\u0026ndash;0.24)\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 \u003cp\u003e0.14\u0026nbsp;(0.10\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u0026nbsp;(0.41\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026nbsp;(0.22\u0026ndash;0.43)\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 \u003cp\u003e0.29\u0026nbsp;(0.20\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u0026nbsp;(0.30\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026nbsp;(0.09\u0026ndash;0.21)\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 \u003cp\u003e0.12\u0026nbsp;(0.08\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u0026nbsp;(0.12\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u0026nbsp;(0.03\u0026ndash;0.08)\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 \u003cp\u003e0.04\u0026nbsp;(0.02\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVAI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-SD\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u0026nbsp;(0.66\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u0026nbsp;(0.35\u0026ndash;0.48)\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 \u003cp\u003e0.41\u0026nbsp;(0.35\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u0026nbsp;(0.77\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u0026nbsp;(0.43\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u0026nbsp;(0.44\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u0026nbsp;(0.45\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u0026nbsp;(0.13\u0026ndash;0.29)\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 \u003cp\u003e0.20\u0026nbsp;(0.13\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u0026nbsp;(0.35\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u0026nbsp;(0.04\u0026ndash;0.11)\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 \u003cp\u003e0.06\u0026nbsp;(0.04\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-SD\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u0026nbsp;(0.73\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u0026nbsp;(0.60\u0026ndash;0.79)\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 \u003cp\u003e0.60\u0026nbsp;(0.51\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u0026nbsp;(0.49\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u0026nbsp;(0.33\u0026ndash;0.66)\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 \u003cp\u003e0.48\u0026nbsp;(0.34\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u0026nbsp;(0.45\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u0026nbsp;(0.23\u0026ndash;0.49)\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 \u003cp\u003e0.34\u0026nbsp;(0.23\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026nbsp;(0.30\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u0026nbsp;(0.11\u0026ndash;0.26)\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 \u003cp\u003e0.13\u0026nbsp;(0.08\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG_WHtR\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-SD\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u0026nbsp;(0.69\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u0026nbsp;(0.47\u0026ndash;0.64)\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 \u003cp\u003e0.53\u0026nbsp;(0.45\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u0026nbsp;(0.57\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u0026nbsp;(0.36\u0026ndash;0.72)\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 \u003cp\u003e0.50\u0026nbsp;(0.36\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u0026nbsp;(0.53\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u0026nbsp;(0.24\u0026ndash;0.50)\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 \u003cp\u003e0.34\u0026nbsp;(0.23\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u0026nbsp;(0.38\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026nbsp;(0.09\u0026ndash;0.21)\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 \u003cp\u003e0.11\u0026nbsp;(0.07\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG_WC\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-SD\u0026nbsp;increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u0026nbsp;(0.58\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026nbsp;(0.45\u0026ndash;0.59)\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 \u003cp\u003e0.50\u0026nbsp;(0.43\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026nbsp;(0.64\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u0026nbsp;(0.44\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u0026nbsp;(0.42\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u0026nbsp;(0.35\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026nbsp;(0.17\u0026ndash;0.36)\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 \u003cp\u003e0.23\u0026nbsp;(0.16\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u0026nbsp;(0.20\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026nbsp;(0.07\u0026ndash;0.17)\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 \u003cp\u003e0.08\u0026nbsp;(0.05\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Performance of the Seven Alternative Insulin-Resistance Indices\u003c/h2\u003e \u003cp\u003ePredictive performance was evaluated using ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). TyG-BMI achieved the highest AUC (0.669), outperforming TyG-WC and LAP. The optimal TyG-BMI cutoff for predicting SA was 353.713.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear Relationships between Alternative IR Indices and SA Risk\u003c/h2\u003e \u003cp\u003eDose-response relationships between IR surrogates and SA were illustrated and examined using restricted cubic spline models, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;1. After adjusting for multiple confounders in Model 3, the LAP index exhibited a linear association with SA occurrence (P nonlinearity\u0026thinsp;=\u0026thinsp;0.556). By contrast, TG/HDL, TyG, TyG-BMI, TyG-WHtR, TyG-WC, and CVAI showed significant nonlinearity (P for nonlinearity ranging from 0.026 to \u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model adjusted for demographic characteristics, lifestyle factors, and laboratory indicators. Solid lines in the figure represent adjusted odds ratios, shaded areas denote their 95% confidence intervals, and horizontal dashed lines indicate the null reference line (OR\u0026thinsp;=\u0026thinsp;1.0). (A) TG/HDL; (B) TyG; (C) TyG-BMI; (D) CVAI; (E) TyG-WHtR; (F) TyG-WC. Abbreviations as defined previously.\u003c/p\u003e \u003cp\u003eWe used threshold-effect analyses to identify inflection points in the associations of TG/HDL, TyG, TyG-BMI, CVAI, LAP, TyG-WHtR, and TyG-WC with SA risk. Segmented logistic regression estimated ORs (95% CIs) on either side of each threshold (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Breakpoints for TG/HDL, TyG, TyG-BMI, CVAI, LAP, TyG-WHtR, and TyG-WC were 1.45, 9.15, 5.01, 0.79, 0.18, 3.43, and 3.73, respectively. Below their breakpoints, TG/HDL and TyG-BMI were inversely associated with SA risk (OR 0.41, 95% CI 0.26\u0026ndash;0.66; OR 0.08, 95% CI 0.05\u0026ndash;0.12; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, values above these breakpoints showed significant positive correlations with SA risk, yielding ORs of 2.44 (95% CI: 1.49\u0026ndash;4.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 17.08 (95% CI: 9.50\u0026ndash;30.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. For TyG-WHtR and TyG-WC, values below the breakpoint were associated with higher SA risk (OR 2.95, 95% CI 1.39\u0026ndash;6.27, p\u0026thinsp;=\u0026thinsp;0.005; OR 2.71, 95% CI 1.41\u0026ndash;5.21, p\u0026thinsp;=\u0026thinsp;0.003), whereas values above the breakpoint were inversely associated (OR 0.08, 95% CI 0.04\u0026ndash;0.19; OR 0.07, 95% CI 0.03\u0026ndash;0.15; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). LAP and CVAI were not associated with SA below their breakpoints (p\u0026thinsp;=\u0026thinsp;0.113 and p\u0026thinsp;=\u0026thinsp;0.427), but showed inverse associations above them (LAP: OR 0.17, 95% CI 0.06\u0026ndash;0.55, p\u0026thinsp;=\u0026thinsp;0.003; CVAI: OR 0.14, 95% CI 0.08\u0026ndash;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TyG showed no significant correlation before or after the breakpoint (p\u0026thinsp;=\u0026thinsp;0.084, p\u0026thinsp;=\u0026thinsp;0.159).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026nbsp;(0.74,\u0026nbsp;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026nbsp;(0.26,\u0026nbsp;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.44\u0026nbsp;(1.49,\u0026nbsp;4.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026nbsp;(0.53,\u0026nbsp;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026nbsp;(0.02,\u0026nbsp;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.87\u0026nbsp;(0.54,\u0026nbsp;44.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026nbsp;(0.10,\u0026nbsp;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026nbsp;(0.05,\u0026nbsp;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.08\u0026nbsp;(9.50,\u0026nbsp;30.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVAI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026nbsp;(0.35,\u0026nbsp;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026nbsp;(0.73,\u0026nbsp;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026nbsp;(0.08,\u0026nbsp;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026nbsp;(0.51,\u0026nbsp;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.44\u0026nbsp;(0.81,\u0026nbsp;7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026nbsp;(0.06,\u0026nbsp;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYG_WHtR\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026nbsp;(0.45,\u0026nbsp;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.95\u0026nbsp;(1.39,\u0026nbsp;6.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026nbsp;(0.04,\u0026nbsp;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYG_WC\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u0026nbsp;(0.43,\u0026nbsp;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Logistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71\u0026nbsp;(1.41,\u0026nbsp;5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026nbsp;(0.03,\u0026nbsp;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOdds ratios were adjusted for age, sex, marriage, location, education, smoking, drinking, WBC, CRP, HCT, MCV, PLT, TC and LDL.\u003c/p\u003e \u003cp\u003eOR, odds ratio; 95% CI, 95% confidence interval; abbreviations as defined above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analysis\u003c/h2\u003e \u003cp\u003eTo determine whether different subgroups exert varying effects on SA risk, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Table\u0026nbsp;2 presents subgroup analysis results stratified by characteristics, involving seven insulin resistance indices. As illustrated, the association between various insulin resistance indices and increased SA risk varied across subgroups. In the sex-stratified subgroup, SA risk showed significant positive correlations with TyG-BMI, TyG-WHtR, and LAP. Across all subgroups, no significant correlations were observed between TG-HDL_C, TyG, TyG-WC, or CVAI and SA risk. Notably, in the smoking-stratified subgroup, male participants exhibited higher correlations between LAP, TyG-BMI, and SA. In the location-stratified subgroup, township residents showed a stronger association between TyG-BMI and SA. Sex significantly modified the associations of TyG-WHtR (P_interaction\u0026thinsp;=\u0026thinsp;0.011), LAP (P_interaction\u0026thinsp;=\u0026thinsp;0.009), and TyG-BMI (P_interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with SA. Smoking also modified the associations of LAP (P_interaction\u0026thinsp;=\u0026thinsp;0.007) and TyG-BMI (P_interaction\u0026thinsp;=\u0026thinsp;0.010) with SA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubgroup analysis. Forest plots of the associations between alternative IR indices and incident SA stratified by age, sex, residence, education, smoking, and alcohol use. Odds ratios were adjusted for age, sex, residence, education, smoking, alcohol use, WBC, CRP, HCT, MCV, PLT, TC, and LDL. OR, odds ratio; 95% CI, 95% confidence interval. Panels: (a) TyG-BMI; (b) LAP; (c) TyG-WC; (d) TyG-WHtR.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn 5,329 participants, we observed inverse associations between all seven IR surrogates and sarcopenia risk. TG/HDL, TyG, TyG-BMI, TyG-WC, TyG-WHtR, and CVAI showed nonlinear associations with sarcopenia risk, whereas LAP was approximately linear (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.556). TyG-BMI showed the best discrimination for SA risk.\u003c/p\u003e \u003cp\u003eInsulin resistance may exacerbate sarcopenia through mechanisms such as accumulation and intramuscular fat infiltration. Conversely, sarcopenia impairs normal glucose metabolism, further intensifying insulin resistance [24]. Previous studies indicate that surrogate insulin resistance indices are effective measures for assessing IR; however, research examining the impact of multiple surrogate IR indices on SA remains limited[25, 26]. Xiao et al. analyzed 4,804 U.S. adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years. After multivariable adjustment, multiple TyG indices showed significant positive correlations with sarcopenia prevalence [27]. By contrast, in our cohort TyG-BMI below its estimated cut-off was inversely associated with SA risk. Beyond the cutoff point, it shows a significant positive correlation with SA risk. In contrast, when TyG-WHtR and TyG-WC were below their respective breakpoints, they showed a significant positive correlation with SA risk, while above the breakpoints, they showed a significant negative correlation with SA risk. Factors contributing to this discrepancy may include the following: First, Xiao et al.'s study was cross-sectional, whereas ours was a longitudinal cohort study. Population differences also apply: their sample included U.S. adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years, while ours focused on Chinese adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years. Additionally, adjusting for different covariates may yield varying effects.\u003c/p\u003e \u003cp\u003eWe extended prior work by evaluating associations between alternative IR indices and SA using three models adjusted for 14 potential confounders. Overall, alternative IR indices showed inverse associations with SA. Predictive analyses indicated that TyG-BMI had the highest area under the ROC curve (AUC\u0026thinsp;=\u0026thinsp;0.669), suggesting superior discrimination for SA. Consistently, Zhang et al. reported that TyG-BMI outperformed other TyG-based indices in predicting SA[28]. While many studies link IR indices to sarcopenia, findings for TyG-related indices remain mixed [27]. Pan et al. reported a positive correlation between the TyG index and sarcopenia [29]. In contrast, this study demonstrates a nonlinear relationship between the alternative insulin resistance index and SA risk. Using segmented logistic regression analysis, we elucidate the correlation trends between multiple insulin resistance indices and SA risk.\u003c/p\u003e \u003cp\u003eThis study employed segmented (inflection point) logistic regression to reveal significant nonlinear relationships between multiple alternative insulin resistance indices and sarcopenia, demonstrating consistent U-shaped patterns across multiple indicators. First, TG/HDL exhibited an overall protective trend, with an OR of 0.84 (0.74\u0026ndash;0.95) per IQR increase after stepwise adjustment. Segmented regression further revealed an effect reversal at a threshold of 1.60 for TG/HDL: increases below this threshold were associated with lower sarcopenia risk, while increases above it were linked to higher risk, suggesting a dose-response pattern of \u0026ldquo;optimal at moderate levels, reversal beyond threshold.\u0026rdquo; The inflection point for TyG-BMI was approximately 255.51: each IQR increase below the threshold significantly reduced risk, while increases above the threshold significantly increased risk. Regarding fat distribution-related indicators, CVAI showed a consistently protective effect (OR\u0026thinsp;=\u0026thinsp;0.41). Segmented analysis identified CVAI\u0026thinsp;=\u0026thinsp;40.45 as the threshold: effects were non-significant below the threshold (OR\u0026thinsp;=\u0026thinsp;1.23), while clear protection emerged above it (OR\u0026thinsp;=\u0026thinsp;0.14). LAP similarly exhibited a threshold-dominated bidirectional structure with a threshold of 6.88, showing significant protection above the threshold (OR\u0026thinsp;=\u0026thinsp;0.17). TyG-WHtR and TyG-WC also exhibited a \u0026ldquo;subthreshold increased risk, suprathreshold protective\u0026rdquo; pattern: TyG-WHtR's threshold was approximately 3.67, with subthreshold levels associated with higher risk across multiple models (OR\u0026thinsp;=\u0026thinsp;2.95), while suprathreshold levels shifted to protective effects (e.g., OR\u0026thinsp;=\u0026thinsp;0.08); TyG-WC had a threshold of 596.80, showing increased risk below the threshold (e.g., OR\u0026thinsp;=\u0026thinsp;2.71) and strong protection above it (e.g., OR\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e \u003cp\u003eIn summary, surrogate insulin resistance indices generally exhibit a U-shaped \u0026ldquo;threshold-reversal\u0026rdquo; pattern with sarcopenia risk: within low-load ranges, increased indices predominantly reflect protection; beyond the threshold, continued elevation amplifies risk. Regarding the abdominal fat phenotype, CVAI shows negligible impact in the low range but correlates with reduced sarcopenia risk upon moderate elevation beyond the threshold, suggesting \u0026ldquo;excessive thinness\u0026rdquo; is suboptimal while moderate visceral fat may support energy and protein metabolism. Extreme fat restriction may not be beneficial; maintaining metabolic indicators within an individualized \u0026ldquo;healthy moderate range\u0026rdquo; may maximize protective effects. This finding parallels observations from the \u0026ldquo;athlete paradox\u0026rdquo; [30]. Thus, setting an \u0026ldquo;individualized moderate range\u0026rdquo; around the inflection point holds greater practical significance for minimizing sarcopenia risk and guiding threshold-oriented interventions and follow-ups.\u003c/p\u003e \u003cp\u003ePrevious studies indicate that gender significantly influences SA occurrence [31]. This study found women face a higher sarcopenia risk than men, potentially linked to gender differences in muscle physiology, sexual characteristics, and lifestyle factors [32]. Research suggests menopause-induced estrogen concentration changes further contribute to muscle atrophy [33]. Further research confirms that declining estrogen levels during menopause increase body fat percentage and reduce muscle mass in women, significantly elevating their risk of sarcopenia [34]. Jo et al. reported higher SA risk among smokers [35], consistent with our subgroup findings by smoking status. Active smoking is associated with higher insulin resistance [36], which may mediate the link to SA.\u003c/p\u003e \u003cp\u003eUsing CHARLS data, we conducted a longitudinal analysis of associations between surrogate IR indices and sarcopenia risk in middle-aged and older Chinese adults. We evaluated both linear and nonlinear relationships using longitudinal models. We also assessed predictive performance, informing the potential utility of IR-related indicators for early identification of SA risk in this population.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be noted. First, although we used a skeletal-muscle-mass prediction formula validated in Chinese populations with good agreement to dual-energy X-ray absorptiometry (DXA), it does not substitute for gold-standard assessments such as DXA or bioelectrical impedance analysis (BIA). Second, constrained by a database covering only blood test data from 2011 to 2015, follow-up was limited to 4 years. Longer follow-up would strengthen the robustness of the findings. Finally, despite extensive adjustment, unmeasured factors (e.g., lifestyle, nutritional status, diet) may have introduced residual confounding. Future research should integrate additional high-quality databases to further examine associations between alternative IR indices and sarcopenic disorders.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMultiple IR surrogates were inversely linked to incident SA. Notably, several indices exhibited threshold effects; risk patterns differed below versus above estimated inflection points. Maintaining indices within moderate ranges may be beneficial; TyG-BMI emerged as the most informative predictor. Further research integrating additional databases is warranted to explore the relationship between insulin resistance and sarcopenia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Orthopedics and Joint Diseases I, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.\u003c/p\u003e\n\u003cp\u003eKe Xu\u0026nbsp;\u0026amp; Lin Jing\u003c/p\u003e\n\u003cp\u003eGuang\u0026apos;anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.\u003c/p\u003e\n\u003cp\u003eRuikang Liu\u003c/p\u003e\n\u003cp\u003eCollege of Acupuncture and Tuina, Beijing University of Chinese Medicine, Beijing, China.\u003c/p\u003e\n\u003cp\u003eBoyang Chen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: KX and LJ. Provision of study materials: RL and LJ. Collection and assembly of data: KX and BC. Data visualization: BC and RL. Implementation of the computer code and supporting algorithms: RL. Data analysis and interpretation: KX, BCand RL. Manuscript writing: KX. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLin Jing\u003c/p\u003e\n\u003cp\u003eKe Xu and Ruikang Liu are co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Evidence-Based Clinical Research Project for Traditional Chinese Medicine at the High-Level TCM Hospital Construction Project of Wangjing Hospital, China Academy of Chinese Medical Sciences (WJYY-XZKT-2023-31) and Self-Selected Research Project of Wangjing Hospital, China Academy of Chinese Medical Sciences (WJYY-ZZXT-2025-27).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All data was from http://charls.pku.edu.cn/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received ethical approval from the Institutional Review Board of Peking University (IRB00001052-11015). All methods were carried out in accordance with relevant guidelines and regulations. All participants signed written informed consent forms prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCruz-Jentoft AJ, Sayer AA: Sarcopenia. \u003cem\u003eLancet \u003c/em\u003e2019, 393(10191):2636-2646.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang K, Huang S, Li W, He P: A review on associated factors and management measures for sarcopenia in type 2 diabetes mellitus. \u003cem\u003eMedicine (Baltimore) \u003c/em\u003e2024, 103(16):e37666.\u003c/li\u003e\n\u003cli\u003eMeng S, He X, Fu X, Zhang X, Tong M, Li W, Zhang 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61(12):3078-3080.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"various insulin resistance indices, sarcopenia, CHARLS, China Health and Retirement Longitudinal Study, Older Chinese individuals","lastPublishedDoi":"10.21203/rs.3.rs-8404542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8404542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvidence on the association between surrogate insulin resistance (IR) indices and sarcopenia (SA) remains limited. Seven alternative indicators for assessing insulin resistance were included in this study, primarily categorized into three groups: lipid ratios (TG/HDL); the TyG index and its derivatives combined with body composition (TyG-WC, TyG-BMI, TyG-WHtR); indicators reflecting fat distribution (LAP), and Chinese visceral adiposity index (CVAI)\u0026mdash;in relation to SA risk among Chinese adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eLongitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) was used, with information collected between 2011 and 2015., which included 5,329 participants. Multivariable logistic regression assessed associations between each index and incident SA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver a 4-year period, 330 participants (6.2%) developed SA. In fully adjusted models (Model 3), all seven IR surrogates were significantly associated with SA. Per standard-deviation increase, adjusted odds ratios (OR, 95% CI) were: TG/HDL 0.84 (0.74\u0026ndash;0.95), TyG 0.65 (0.53\u0026ndash;0.80), TyG-BMI 0.14 (0.10\u0026ndash;0.19), TyG-WHtR 0.53 (0.45\u0026ndash;0.61), TyG-WC 0.50 (0.43\u0026ndash;0.57), CVAI 0.41 (0.35\u0026ndash;0.48), and LAP 0.60 (0.51\u0026ndash;0.70). Notably, all indices except LAP showed evidence of nonlinearity in relation to SA risk. Additionally, TyG-BMI yielded the largest AUC among the seven indices.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTG/HDL, TyG, TyG-WC, TyG-BMI, TyG-WHtR, LAP, and CVAI were inversely associated with incident SA. TG/HDL, TyG, TyG-WC, TyG-BMI, TyG-WHtR, and CVAI exhibited nonlinear relationships with SA. TyG-BMI may be the most informative predictor of SA.\u003c/p\u003e","manuscriptTitle":"Correlation Between Various Insulin Resistance Indices and Sarcopenia: Evidence from CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:21:51","doi":"10.21203/rs.3.rs-8404542/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":"e8ef26da-635d-4011-abad-01d63df340d9","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61190038,"name":"Health sciences/Biomarkers"},{"id":61190039,"name":"Health sciences/Diseases"},{"id":61190040,"name":"Health sciences/Endocrinology"},{"id":61190041,"name":"Health sciences/Gastroenterology"},{"id":61190042,"name":"Health sciences/Health care"},{"id":61190043,"name":"Health sciences/Medical research"},{"id":61190044,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-24T06:24:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:21:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8404542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8404542","identity":"rs-8404542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}