Association Between Baseline Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia in Middle-Aged and Older Chinese Adults: Findings from the CHARLS Cohort

Association Between Baseline Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia in Middle-Aged and Older Chinese Adults: Findings from the CHARLS Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Baseline Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia in Middle-Aged and Older Chinese Adults: Findings from the CHARLS Cohort Lei Shi, Hanzhe Li, Jing Ren, Jinyu Wu, Qin Zhang, Yuting Tian, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8642201/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 This study aimed to comprehensively investigate the association between the Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia.We included 6,494 CHARLS (China Health and Retirement Longitudinal Study) participants aged ≥ 45 years who were free of sarcopenia at baseline (2011) and were followed prospectively until 2015.Logistic regression estimated incident sarcopenia odds ratios (ORs) with 95% confidence intervals (CIs) across BUN/Cr levels, adjusting for sociodemographic characteristics, lifestyle, and health-relevant covariates. Restricted cubic splines modeled the dose-response relationship flexibly. Subgroup analyses by age, sex, and other factors were performed. Over 4 years, 673 participants (10.4%) developed sarcopenia. These individuals were older (62.5 ± 7.9 vs 56.6 ± 7.8 years) and had higher baseline BUN/Cr ratios (22.7 ± 7.6 vs 20.7 ± 6.6) than those without sarcopenia (p < 0.001). Each interquartile range increase in BUN/Cr was associated with a 17% higher sarcopenia odds after full adjustment (OR 1.17, 95% CI 1.05–1.31, p = 0.006). Participants in the highest BUN/Cr quartile had a 57% increased risk compared to the lowest quartile (OR 1.57, 95% CI 1.22–2.03). A roughly linear exposure-response relationship was observed (p = 0.013), with consistent associations across all subgroups. Prospectively, elevated baseline BUN/Cr independently predicted 4-year incident sarcopenia in this nationally representative cohort. Figures Figure 1 Figure 2 Figure 3 Introduction Sarcopenia is an age-related syndrome characterized by progressive loss of skeletal muscle mass and strength, leading to adverse outcomes such as physical disability, poor quality of life, and higher mortality [ 1 ]. It is formally recognized by consensus groups including the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGS) [ 2 – 4 ]. Despite growing awareness, identifying early biomarkers of sarcopenia risk remains an important research goal. The blood urea nitrogen to creatinine ratio (BUN/Cr ratio) is a common clinical measure traditionally used to evaluate hydration status and kidney function. Beyond these uses, recent evidence suggests that an elevated BUN/Cr ratio may also reflect underlying changes in muscle metabolism [ 5 ]. Serum creatinine is produced by muscle tissue, and lower creatinine (for a given level of urea) can indicate reduced muscle mass or protein intake [ 6 ]. Conversely, elevated urea relative to creatinine may signal increased protein catabolism or poor nutritional status [ 7 ]. For example, Gunst J et al. reported that critically ill trauma patients with prolonged intensive care stays had markedly increased BUN/Cr ratios, consistent with significant muscle wasting [ 8 ]. Likewise, among hemodialysis patients, a higher urea-to-creatinine ratio was associated with worse nutritional status and lower muscle mass [ 9 ]. In community-dwelling populations, cross-sectional studies have hinted at a link between BUN/Cr and frailty or sarcopenia. Chen et al. recently demonstrated that in older Chinese adults, a higher BUN/Cr ratio was linearly related to weaker grip strength and, above a threshold, to slower chair-stand times, suggesting an association with physical frailty [ 10 ]. However, it remains unclear whether an elevated BUN/Cr ratio predicts the future development of sarcopenia, as longitudinal data are lacking. We therefore conducted a prospective analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the association between baseline BUN/Cr ratio and incident sarcopenia in middle-aged and older Chinese adults. We hypothesized that individuals with higher BUN/ Cr ratios at baseline would have greater risk of developing sarcopenia over follow-up. Methods Study Design and Population This study utilized data from CHARLS, an ongoing nationally representative cohort of Chinese adults aged 45 years and older [ 11 ]. The CHARLS study began with a baseline survey in 2011–2012 (Wave 1) including 17,708 participants, and participants are followed up two to three years [ 11 ]. The present analysis included respondents who participated in both the baseline examination (2011) and the 2015 follow-up (Wave 3), the only two waves with available data on BUN, creatinine, and sarcopenia assessments. We excluded individuals younger than 45, those with chronic kidney disease (CKD) at baseline (estimated glomerular filtration rate < 60 mL/min/1.73 m²), and those who had sarcopenia at baseline. Participants with missing key covariate data or who were lost to follow-up were also excluded. Exposure Measurement Fasting blood samples were collected at baseline. Central laboratory analysis quantified serum BUN and creatinine using standardized enzymatic assays. The BUN/creatinine ratio was derived by dividing serum urea nitrogen (mg/dL) by serum creatinine (mg/dL). Analytically, BUN-to-creatinine ratios were quantified as continuous measures and categorical quartiles. We also calculated the interquartile range (IQR) of the BUN/Cr ratio and treated an increase of one IQR as a unit of exposure for continuous models. Outcome Assessment The outcome was incident sarcopenia at the 2015 follow-up. Incident sarcopenia (2015) was diagnosed per AWGS 2019 criteria, requiring reduced appendicular skeletal muscle mass plus either dynapenia or physical performance decline [ 3 ]. Height-adjusted ASM thresholds for low muscle mass were sex-specific: <6.88 kg/m² (male) and < 5.69 kg/m² (female), based on Chinese population-validated prediction. Grip strength was quantified using a dynamometer, with dynapenia defined per AWGS 2019 criteria as < 28 kg (male)and 12 seconds) and the habitual gait velocity test (with a cutoff of < 1.0 m/s); failure in either test indicated functional impairment. Participants meeting the criteria for dynamometric impairment or validated mobility deficit met diagnostic criteria for sarcopenia [ 12 ]. Those who expired or were lost to follow-up before the 2015 exam were treated as non-assessable for sarcopenia and thus were not included in the analysis. Covariates Baseline sociodemographic and clinical variables were documented through standardized questionnaires, physical examinations, and laboratory assays. Collected demographics included chronological age, biological sex, settlement type (rural/urban), education level (categorized as ≤ primary, high school, or ≥ college), and partnership status (classified as married or non-married, with the latter including widowed, separated, or never-married individuals). Lifestyle behaviors incorporated tobacco use, classified as current smoker, ex-smoker, or non-smoker, alcohol consumption frequency (< 1 drink/month, ≥ 1 drink/month, or never drinks alcohol), and average nightly sleep duration (< 7 hours vs ≥ 7 hours). Health measurements included body mass index (BMI, kg/m²), waist circumference (cm), systolic and diastolic blood pressure (SBP and DBP, mmHg).We calculated estimated glomerular filtration rate (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration equation and defined CKD as eGFR < 60 mL/min/1.73 m² [ 13 ]; as noted, those with CKD were excluded. Presence of diabetes mellitus (DM) was defined by self-reported physician diagnosis. Serum lipid profiles (triglycerides (TG), high-density lipoprotein (HDL) cholesterol) were measured from blood samples. Statistical Analysis Baseline characteristics of participants are presented by sarcopenia outcome status (developed sarcopenia vs did not develop) and were compared using Student’s t -test for continuous measures and χ ² test (or chi-squared test) for categorical factors. Logistic regression models were constructed to estimate the odds ratio (OR) and 95% confidence interval (CI) for incident sarcopenia associated with baseline BUN/Cr ratio. Three models were constructed: Model 1 (unadjusted), Model 2 (adjusted for sociodemographic variables such as age, gender, educational attainment, place of residence, and marital status), and Model 3 (full model, which further incorporated tobacco use, alcohol consumption, sleep duration, systolic and diastolic blood pressure, and diabetes mellitus status on the basis of Model 2). In these models, BUN/Cr was analyzed per IQR increase and by quartile (with the lowest quartile as reference). Linear trend testing assigned quartile-specific BUN/Cr medians to participants, with these values analyzed as continuous variables in models. Dose-response patterns between BUN/Cr and sarcopenia were modeled via restricted cubic splines (3 knots), fully adjusted for covariates. The non-linearity was tested by the significance of the spline’s second-order term. Stratified analyses examined effect heterogeneity across prespecified subgroups: age (< 65/≥65 years), sex, and other key covariates. Interaction terms between BUN/Cr (per IQR) and subgroup indicators were incorporated into the fully adjusted model to test for effect modification. A two-tailed p < 0.05 constituted statistical significance. All statistical operations were implemented in R software (version 4.4.3). Results Baseline Characteristics The study cohort (N = 6,494) exhibited mean age 57.2 ± 8.0 years and contained 51.2% males. During the 4-year follow-up, 673 individuals (10.4%) developed sarcopenia. Table 1 compares baseline characteristics of those who did and did not develop sarcopenia. Participants who developed sarcopenia were significantly older at baseline (mean 62.5 vs 56.6 years, p < 0.001) and included a higher percentage of females (69.4% vs 46.4%, p < 0.001). They had markedly lower educational levels (86.2% with primary school or less, vs 64.8% in those without sarcopenia, p < 0.001) and lived in rural areas more frequently (89.0% vs 81.7%, p < 0.001). Those who developed sarcopenia had a lower baseline eGFR on average (91.9 vs 94.7 mL/min/1.73mkg/m², p < 0.001), although all were non-CKD by study design. There were no differences in baseline BMI (approximately 25.3 kg/m² in both groups, p = 0.200), but the sarcopenia group showed lower waist circumference values (79.5 vs 87.4 cm, p < 0.001). Regarding lifestyle factors, individuals who developed sarcopenia had a lower probability of being current smokers or alcohol consumers and a higher tendency to report never having smoked or consumed alcohol (p < 0.001), which mirrors the higher female proportion in this group. They also had slightly shorter sleep duration (6.1 vs 6.4 hours on average, p < 0.001). Notably, the baseline BUN/Cr ratio was higher among those who developed sarcopenia (mean 22.7 ± 7.6) compared to those who did not (20.7 ± 6.6, p < 0.001). When categorized into quartiles, only 16.2% of those who became sarcopenic were in the lowest BUN/Cr quartile at baseline, versus 26.0% of those who did not develop sarcopenia, whereas 33.7% of the sarcopenia group were in the highest quartile compared to 24.0% of the non-sarcopenia group (p < 0.001 for overall distribution). Table 1 CHARLS cohort (2011–2015): Baseline profiles according to sarcopenia incidence Characteristic Overall (N = 6,494) No Sarcopenia (n = 5,821) Incident Sarcopenia (n = 673) p -value Age, years (mean ± SD) 57.2 ± 8.0 56.6 ± 7.8 62.5 ± 7.9 < 0.001 Female sex, n (%) 3,167(48.8) 2,700 (46.4) 467 (69.4) < 0.001 Male sex, n (%) 3,327(51.2) 3,121 (53.6) 206 (30.6) < 0.001 Education, n (%) < 0.001 College or above 212 (3.3) 200 (3.4) 12 (1.8) < 0.001 High school 1,931(29.7) 1,850 (31.8) 81 (12.0) < 0.001 Primary school or below 4,351(66.9) 3,771 (64.8) 580 (86.2) < 0.001 Residence, n (%) < 0.001 City/Town 1,137(17.5) 1,063 (18.3) 74 (11.0) < 0.001 Village 5,357(82.5) 4,758 (81.7) 599 (89.0) < 0.001 Married, n (%) 5,918(91.1) 5,345 (91.8) 573 (85.1) < 0.001 Never-married/ Separated/Widowed, n (%) 576 (8.9) 476 (8.2) 100 (14.9) < 0.001 Current smoker, n (%) 2,003(31.5) 1,851 (31.8) 152 (22.6) < 0.001 Ex-smoker, n (%) 564 (8.7) 525 (9.0) 39 (5.8) 0.006 Non-smoker, n (%)** 3,795(59.7) 3,322 (58.4) 473(70.3) < 0.001 Alcohol < 1 drink/ month, n (%) 560 (8.6) 526 (9.0) 34 (5.1) < 0.001 Alcohol ≥ 1 drink/ month, n (%) 1,359(20.9) 1,270 (21.8) 89 (13.2) < 0.001 Never drink, n (%)** 4,575(70.5) 4,025 (69.2) 550 (81.7) < 0.001 Table 1 (continued) Characteristic Overall (N = 6,494) No Sarcopenia (n = 5,821) Incident Sarcopenia (n = 673) p -value Sleep duration < 7h, n (%) 2,972(45.7) 2,653 (45.6) 319 (47.4) 0.413 Sleep duration ≥ 7h, n (%) 3,522(54.3) 3,168 (54.4) 354 (52.6) 0.413 Systolic BP, mmHg (mean ± SD) 129.0 ± 20.6 129.0 ± 20.5 128.1 ± 21.7 0.340 Diastolic BP, mmHg (mean ± SD) 75.9 ± 12.1 76.3 ± 12.1 72.5 ± 11.5 < 0.001 Diabetes (DM) present, n (%) 417 (6.5) 376 (6.5) 41 (6.1) 0.794 Estimated GFR, mL/ min/1.73m 2 94.4 ± 12.6 94.7 ± 12.6 91.9 ± 12.0 < 0.001 Body mass index, kg/ m 2 25.4 ± 3.5 25.6 ± 3.6 25.3 ± 2.8 0.200 Waist circumference, cm 86.6 ± 11.8 87.4 ± 11.7 79.5 ± 10.0 < 0.001 Triglycerides, mg/dL 139.0 ± 112.4 140.6 ± 113.8 124.8 ± 98.5 0.001 HDL cholesterol, mg/dL 49.6 ± 14.6 49.0 ± 14.3 55.1 ± 16.4 < 0.001 BUN/Cr ratio (raw), mean ± SD 20.9 ± 6.8 20.7 ± 6.6 22.7 ± 7.6 < 0.001 BUN/Cr ratio Quartile 1 (lowest) n (%) 1,624(25.0) 1,515 (26.0) 109 (16.2) < 0.001 BUN/Cr ratio Quartile 2, n (%) 1,624(25.0) 1,482 (25.5) 142 (21.1) 0.002 BUN/Cr ratio Quartile 3, n (%) 1,623(25.0) 1,428 (24.5) 195 (29.0) 0.004 BUN/Cr ratio Quartile 4 (highest), n (%) 1,623(25.0) 1,396 (24.0) 227 (33.7) < 0.001 a Group differences between sarcopenia and non-sarcopenia groups were assessed using t-tests for continuous measures and χ² tests for categorical variables b p -value for overall distribution of categories (from chi-square test) c Among the 673 incident sarcopenia cases, 467 were female (who have lower smoking rates), contributing to the high percentage of “never smokers” in this group Relationship Between BUN/Cr Ratio and Incident Sarcopenia Higher baseline BUN/Cr ratios showed a connection with an elevated risk of sarcopenia onset. Table 2 presents the ORs for incident sarcopenia by BUN/ Cr, under different levels of covariate adjustment. The unadjusted model (Model 1) indicated a 1.35-fold sarcopenia odds elevation (95%CI 1.25–1.47, p < 0.001) per 6.5-unit IQR rise in BUN/Cr ratio. The highest BUN/Cr quartile associated with more than twice the increased sarcopenia odds versus the lowest quartile (OR 2.26, 95% CI 1.78–2.88). This relationship stayed significant, though weakened, upon adjustment for demographic factors (Model 2: OR per IQR 1.18, 95% CI 1.08–1.30) and additionally for lifestyle and health factors (Model 3: OR per IQR 1.17, 95% CI 1.05–1.31). After comprehensive adjustment, the highest BUN/Cr quartile exhibited greater odds of sarcopenia (OR 1.57, 95%CI 1.22–2.03, p = 0.0006) versus the lowest BUN/Cr quartile. Quartile 3 was also at higher risk (OR ~ 1.48, 95% CI 1.15–1.92, p = 0.003), whereas Quartile 2 suggested a non-significant 10% increase (OR ~ 1.10, 95% CI 0.84–1.45, p = 0.47). There was a significant linear trend of increasing sarcopenia incidence with higher BUN/Cr quartiles (p for trend < 0.001 in all models). Baseline BUN/Cr demonstrated independent predictive value for sarcopenia following comprehensive confounder adjustment. Table 2 Odds ratios for incident sarcopenia according to baseline BUN/Cr ratio BUN/Cr Ratio Exposure Model 1:Unadjusted OR(95% CI) Model 2:Adjusted OR (95% CI) Model 3:Fully Adjusted OR (95% CI) Per IQR increase 1.35 (1.25–1.47) *** 1.18 (1.08–1.30) *** 1.17 (1.05–1.31) ** Quartile 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Quartile 2 1.33 (1.03–1.73) * 1.18 (0.92–1.53) 1.10 (0.84–1.45) Quartile 3 1.90 (1.49–2.43) *** 1.38 (1.08–1.77) ** 1.48 (1.15–1.92) ** Quartile 4 (highest) 2.26 (1.78–2.88) *** 1.63 (1.27–2.10) *** 1.57 (1.22–2.03) *** P for trend (quartiles) < 0.001 < 0.001 < 0.001 p < 0.05, p < 0.01, p < 0.001. Model 2: Sociodemographic adjustment (age, sex, education, urban/rural residence, marital status) Model 3: Model 2 covariates with behavioral extension (smoking status, drinking frequency, sleep duration) and metabolic extension (SBP, DBP, diabetes) To visualize the dose-response relationship, we plotted a restricted cubic spline of the fully adjusted association between baseline BUN/Cr and the odds of sarcopenia (Fig. 2 ). The spline curve demonstrates that the probability of sarcopenia rises as BUN/Cr ratios increase. The relationship appeared approximately linear over the observed range of BUN/Cr. There was no significant deviation from linearity in the dose- response (p for non-linear trend = 0.217). However, the overall association was significant (p for overall association = 0.013), reinforcing that higher BUN/Cr values correspond to higher risk of sarcopenia [ 14 ]. The spline suggests a slight increase in risk begins around the middle ranges of BUN/Cr (around 18–20) and becomes more pronounced at the upper end of the BUN/Cr distribution. The 95% confidence band (shaded area) widens at extreme BUN/Cr values, reflecting fewer participants with very low or very high ratios. We conducted stratified analyses to assess whether the association between BUN/Cr and sarcopenia risk was modified by demographic or health factors. As illustrated in Fig. 3 , the positive association between higher BUN/Cr (per IQR increment) and incident sarcopenia was consistently observed across all subgroups examined. There were no statistically significant interactions by age (< 65 vs ≥ 65 years, p_interaction = 0.24), sex (female vs male, p_interaction = 0.36), residence (urban vs rural, p_interaction = 0.35), or other factors including education level, smoking and drinking status, or sleep duration (all p_interaction > 0.1). The magnitude of the OR for BUN/Cr varied slightly: for instance, the association tended to be somewhat stronger in males (OR ~ 1.29) than females (OR ~ 1.13) and in those living in villages (OR ~ 1.24) versus cities (OR ~ 0.84), but these differences were not significant. Notably, even in subgroups traditionally at lower risk of sarcopenia (e.g., younger than 65, or male sex), a higher BUN/Cr still showed a trend toward increased sarcopenia odds. This indicates that the BUN/Cr–sarcopenia relationship is broadly applicable across different population segments [ 15 , 16 ]. Discussion In this prospective cohort analysis involving middle-aged and elderly Chinese participants, our findings indicated that a raised baseline BUN/Cr ratio correlated with increased incident sarcopenia risk over 4 years. This relationship remained significant following adjustment for numerous potential confounding factors, suggesting that the BUN/ Cr ratio may capture an aspect of health – likely related to muscle status or nutritional/metabolic state – that predisposes individuals to sarcopenia. To our knowledge, this is the first longitudinal evidence linking BUN/Cr ratio with incident sarcopenia in a general population. Our findings extend previous cross-sectional observations and clinical studies. Chen et al. demonstrated that higher BUN/Cr ratios correlated with poorer physical performance (weaker grip strength and slower chair stands) in older Chinese adults at a single time point [ 3 ]. We now demonstrate that this biomarker also has predictive value for new-onset sarcopenia, indicating its potential usefulness in risk stratification [ 17 ]. The magnitude of risk we observed (OR ~ 1.17 per IQR, or ~ 1.6-fold higher odds for the top vs bottom quartile) is comparable to, or slightly lower than, some well-known risk factors for sarcopenia such as age or sex. For example, in our data being ≥ 65 years old or female correlated with approximately a 2–3-fold greater likelihood of sarcopenia (Table 1 ). A high BUN/Cr appears to convey additional risk independent of these factors. The BUN/Cr ratio is influenced by multiple physiological processes, and its link to sarcopenia likely reflects a combination of mechanisms. One important contributor is muscle mass - lower muscle mass leads to lower creatinine generation [ 18 ], raising the BUN/Cr ratio if urea production is unchanged [ 5 ]. Indeed, serum creatinine (when renal function is normal) has long been recognized as a surrogate marker for muscle mass [ 19 ]. However, creatinine alone can be confounded by kidney function variations [ 5 ]. The advantage of the BUN/Cr ratio is that it incorporates urea levels, which tend to rise in states of increased catabolism or protein breakdown. Haines et al. proposed the term “urea-corrected creatinine” to reflect that an elevated urea-to-creatinine ratio serves as a biochemical signature of muscle catabolism in critical illness [ 20 ]. In their ICU study, trauma patients with persistent muscle wasting had sharply higher UCR values over time [ 20 ]. Another study found that an early rise in BUN/ Cr predicted subsequent rapid muscle loss in sepsis patients [ 21 ]. These clinical data align with our finding that even in community-dwelling individuals, a relatively high BUN/Cr may signal an underlying catabolic or low-muscle state that evolves into sarcopenia. Nutritional factors could also play a role. In particular, multi-nutrient supplements stand out as safe, easily tolerated, and effective complements, contributing to improvements in lean mass, strength, and general muscle quality among the elderly [ 22 , 23 ]. A high BUN/Cr ratio can indicate low protein intake or malnutrition in certain contexts, as demonstrated by Tufan et al., who showed that malnourished dialysis patients had higher Urea/Cr ratios along with lower albumin and BMI [ 7 ]. In our study, participants who developed sarcopenia did have some markers suggestive of poorer nutrition or health status at baseline – for instance, lower albumin (reflected indirectly by higher HDL and lower TG, which sometimes correlate with malnutrition) and smaller waist circumference despite similar BMI, possibly indicating lower muscle for the same weight. Anna Picca et al. reported that Inverse correlations were found between albumin, hemoglobin, and both frailty and sarcopenia [ 24 ]. It is conceivable that individuals with subtle chronic undernutrition or illness have both higher BUN/Cr and are on a trajectory of muscle loss [ 17 ]. Acute dehydration can transiently increase BUN/Cr. Extended periods of dehydration are typical among older populations and correlated with likely sarcopenia. However, an acute fluid deficit is less likely to persist long enough to influence long-term sarcopenia risk, and we adjusted for baseline hydration-related variables (such as blood pressure and reported health conditions) [ 25 ]. Nevertheless, we cannot entirely exclude that BUN/Cr might also be capturing aspects of renal function or perfusion not fully accounted for, even though we excluded those with clinical CKD. It is notable that baseline eGFR was slightly lower in those who became sarcopenic (Table 1 ), which might suggest that even within “normal” range, lower kidney function (hence higher BUN/Cr) and sarcopenia share common determinants (e.g. aging, vascular factors) [ 26 ]. Future research could explore the interplay between renal function decline and muscle decline, as both are common in older adults (the “renal-sarcopenia syndrome”). From a clinical perspective, our results highlight the potential utility of the BUN/Cr ratio as a simple screening tool for sarcopenia risk [ 27 ]. Given that BUN and creatinine are routinely measured in metabolic panels, the ratio is readily available without additional cost or testing. An older adult with an elevated BUN/Cr (for example, in the top quartile, > ~24 in our cohort) might benefit from closer evaluation of muscle strength and nutritional status, even if their values are considered within normal limits for renal function. Early interventions such as dietary protein supplementation and resistance exercise could then be implemented to possibly mitigate future sarcopenia onset [ 28 ]. In resource-limited settings or large population screenings, BUN/Cr could serve as an efficient first pass to identify high-risk individuals for further geriatric assessment. This study has several strengths, including the prospective design, large sample from a well- characterized national cohort, and standardized assessment of sarcopenia using established criteria. We also adjusted for an extensive set of covariates, reducing the likelihood of confounding. The use of restricted cubic splines provided insight into the dose-response pattern without imposing a linear assumption. However, we acknowledge several limitations. First, sarcopenia was assessed only at baseline and 4-year follow-up; some misclassification is possible if participants developed and perhaps recovered from sarcopenia in the interim. Nevertheless, such transient cases are likely few, and our outcome definition based on objective muscle measures is robust. Second, the BUN/Cr ratio was measured at a single baseline time point, which might not capture long-term patterns in protein intake or muscle metabolism. Random measurement error in BUN or creatinine would tend to attenuate the observed associations. Third, despite multivariable adjustment, residual confounding cannot be ruled out. For instance, we did not have detailed dietary data; differences in protein intake or quality could influence both BUN levels and muscle health [ 29 ]. Similarly, inflammatory status (e.g., CRP levels) was not measured and could link to both higher BUN (due to catabolism) and sarcopenia progression [ 30 ]. Fourth, generalizability may be limited to similar populations; all participants were Chinese, and cultural or genetic factors may modulate both BUN/Cr and sarcopenia risk [ 31 ]. Finally, around 24% of the baseline CHARLS sample was excluded due to missing data or loss to follow-up, which could introduce bias if those excluded had different BUN/Cr– sarcopenia relationships. We note that those lost to follow-up tended to be older and in poorer health; if anything, inclusion of those individuals might have strengthened the associations, but this remains speculative. In conclusion, our findings suggest that the BUN/Cr ratio — a readily available laboratory indicator — is elevated by either an increase in blood urea nitrogen or a decrease in creatinine, and can independently predict the development of sarcopenia in older adults [ 32 , 33 ]. This supports the notion that higher BUN/Cr captures a state of muscle protein catabolism or frailty that predisposes to muscle loss over time. Screening for elevated blood urea nitrogen/creatinine ratio (BUN/Cr) can help identify individuals at risk of sarcopenia, who may benefit from early preventive interventions such as nutritional supplementation and exercise programs, and in particular, targeted nutritional supplements combined with low-intensity exercise can improve muscle quality in the elderly [ 34 ]. Reports in recent years have also confirmed that dietary and nutritional adjustments focused on gut microbiota may lessen the prevalence of sarcopenia [ 35 ]. Further studies are warranted to confirm these results in other populations and to explore the biological mechanisms linking BUN/Cr with muscle aging. If validated, the BUN/Cr ratio could be incorporated into risk assessment models for sarcopenia and healthy aging. Conclusion Within an extensive prospective cohort of Chinese adults, our results revealed that an elevated baseline urea nitrogen-to-creatinine ratio was significantly linked to an elevated risk of incident sarcopenia over 4 years. This association persisted after controlling for demographics, lifestyle factors, and health status. Our study provides new evidence that the BUN/Cr ratio – a straightforward and routinely measured biomarker – may serve as an early warning indicator of sarcopenia. Incorporating BUN/Cr ratio into sarcopenia risk screening could facilitate earlier identification and intervention for individuals at risk, potentially helping to delay or prevent the onset of sarcopenia and its adverse outcomes. These findings should be confirmed in diverse populations, and further research should investigate whether lowering an elevated BUN/Cr (through nutritional or medical interventions) might translate into a reduced risk of sarcopenia and improved muscle health in aging. Declarations Funding Not applicable. Data availability The CHARLS datasets are accessible for download via the official CHARLS website at http://charls.pku.edu.cn/en . Ethics approval and consent to participate The data utilized in this study were endorsed by the Biomedical Ethics Review Committee of Peking University. Written informed consent was provided by all participants, and the assigned ethical approval code is IRB00001052-11015. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Department of Gastroenterology, Biliary-Pancreatic Center, Affiliated Hospital Southwest Medical University, Luzhou, 646000,China; 2 Department of Gastroenterology, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu, 610000, China 3 Department of Nursing, Affliated Hospital of Southwest Medical University, Luzhou, 646000, China 4 Department of Gastroenterology, The First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, 615000, China Author Contribution Lei Shi and Jinyu Wu designed the study, supervised the project, and revised the manuscript. Hanzhe Li and Jinyu Wu collected and analyzed data, and drafted the manuscript. Jing Ren and Qin Zhang assisted in data curation and verification. Yuting Tian and Yuxing Yang conducted literature review and contributed to the introduction. Jiaxun Xie and Jingyuan Liao participated in result interpretation and figure preparation. Yi Liu and Lü Muhan provided insights on study methodology. Xiaowei Tang reviewed the manuscript and approved the final version. All authors have reviewed and approved the final version of the manuscript. Acknowledgement The present study drew upon data from the China Health and Retirement Longitudinal Study (CHARLS). We thank the CHARLS research team sincerely for their dedicated efforts and invaluable contributions to the implementation of this project. Data Availability The CHARLS datasets are accessible for download via the official CHARLS website at http://charls.pku.edu.cn/en. References Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393(10191):2636–46. https://doi.org/10.1016/s0140-6736(19)31138-9 . Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–23. https://doi.org/10.1093/ageing/afq034 . Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300–e3072. https://doi.org/10.1016/j.jamda.2019.12.012 . Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, Chou MY, Chen LY, Hsu PS, Krairit O, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15(2):95–101. https://doi.org/10.1016/j.jamda.2013.11.025 . Gao H, Wang J, Zou X, Zhang K, Zhou J, Chen M. High blood urea nitrogen to creatinine ratio is associated with increased risk of sarcopenia in patients with chronic obstructive pulmonary disease. Exp Gerontol. 2022;169:111960. https://doi.org/10.1016/j.exger.2022.111960 . Bartholomae E, Knurick J, Johnston CS. Serum creatinine as an indicator of lean body mass in vegetarians and omnivores. Front Nutr. 2022;9:996541. https://doi.org/10.3389/fnut.2022.996541 . Tufan F, Yıldız A, Dogan I, Yıldız D, Sevinir Ş. Urea to creatinine ratio: a forgotten marker of poor nutritional state in patients undergoing hemodialysis treatment. Aging Male. 2015;18(1):49–53. https://doi.org/10.3109/13685538.2014.908281 . Gunst J, Kashani KB, Hermans G. The urea-creatinine ratio as a novel biomarker of critical illness-associated catabolism. Intensive Care Med. 2019;45(12):1813–5. https://doi.org/10.1007/s00134-019-05810-y . Tanaka S, Ninomiya T, Taniguchi M, Tokumoto M, Masutani K, Ooboshi H, Kitazono T, Tsuruya K. Impact of blood urea nitrogen to creatinine ratio on mortality and morbidity in hemodialysis patients: The Q-Cohort Study. Sci Rep. 2017;7(1):14901. https://doi.org/10.1038/s41598-017-14205-2 . Chen XX, Chen ZX, Zhou WJ, Wang Y, Su J, Zhou Q. Blood urea nitrogen to creatinine ratio is associated with physical frailty in older-aged Chinese: a cross-sectional study. Aging Clin Exp Res. 2023;35(3):581–9. https://doi.org/10.1007/s40520-022-02332-4 . Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. https://doi.org/10.1093/ije/dys203 . Bernabei R, Landi F, Calvani R, Cesari M, Del Signore S, Anker SD, Bejuit R, Bordes P, Cherubini A, Cruz-Jentoft AJ, et al. Multicomponent intervention to prevent mobility disability in frail older adults: randomised controlled trial (SPRINTT project). BMJ. 2022;377:e068788. https://doi.org/10.1136/bmj-2021-068788 . Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. https://doi.org/10.7326/0003-4819-150-9-200905050-00006 . Lin T, Jiang T, Huang X, Xu P, Liang R, Song Q, Tu X, Zhao Y, Huang L, Yue J, et al. Diagnostic test accuracy of serum creatinine and cystatin C-based index for sarcopenia: a systematic review and meta-analysis. Age Ageing. 2024;53(1):afad252. https://doi.org/10.1093/ageing/afad252 . Yang H, Huang Y, Jiang G, Duan Z, Du R, Hao Y, Huang W, Liu X. Sex differences in the association between sarcopenia index and sarcopenia: a cross-sectional study from a Chinese community-based population. Eur Geriatr Med. 2025;16(1):55–65. https://doi.org/10.1007/s41999-024-01111-w . Wilkinson TJ, Baker LA, Watson EL, Smith AC, Yates T. Diagnostic accuracy of a 'sarcopenia index' based on serum biomarkers creatinine and cystatin C in 458,702 UK Biobank participants. Clin Nutr ESPEN. 2024;63:207–13. https://doi.org/10.1016/j.clnesp.2024.06.041 . He L, Shi K, Chen X, Gao M, Han Y, Fang Y. Blood Profiles of Community-Dwelling People with Sarcopenia: Analysis Based on the China Health and Retirement Longitudinal Study. Gerontology. 2024;70(6):561–71. https://doi.org/10.1159/000537936 . Baxmann AC, Ahmed MS, Marques NC, Menon VB, Pereira AB, Kirsztajn GM, Heilberg IP. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol. 2008;3(2):348–54. https://doi.org/10.2215/cjn.02870707 . Ávila M, Mora Sánchez MG, Bernal Amador AS, Paniagua R. The Metabolism of Creatinine and Its Usefulness to Evaluate Kidney Function and Body Composition in Clinical Practice. Biomolecules. 2025;15(1):41. https://doi.org/10.3390/biom15010041 . Haines RW, Zolfaghari P, Wan Y, Pearse RM, Puthucheary Z, Prowle JR. Elevated urea-to-creatinine ratio provides a biochemical signature of muscle catabolism and persistent critical illness after major trauma. Intensive Care Med. 2019;45(12):1718–31. https://doi.org/10.1007/s00134-019-05760-5 . Jiang J, Chen H, Meng SS, Pan C, Xie JF, Guo FM. Early urea-to-creatinine ratio to predict rapid muscle loss in critically ill patients with sepsis: a single-center retrospective observational study. BMC Anesthesiol. 2025;25(1):26. https://doi.org/10.1186/s12871-025-02892-8 . Nilsson MI, Mikhail A, Lan L, Di Carlo A, Hamilton B, Barnard K, Hettinga BP, Hatcher E, Tarnopolsky MG, Nederveen JP, et al. A Five-Ingredient Nutritional Supplement and Home-Based Resistance Exercise Improve Lean Mass and Strength in Free-Living Elderly. Nutrients. 2020;12(8):2391. https://doi.org/10.3390/nu12082391 . Dent E, Morley JE, Cruz-Jentoft AJ, Arai H, Kritchevsky SB, Guralnik J, Bauer JM, Pahor M, Clark BC, Cesari M, et al. International Clinical Practice Guidelines for Sarcopenia (ICFSR): Screening, Diagnosis and Management. J Nutr Health Aging. 2018;22(10):1148–61. https://doi.org/10.1007/s12603-018-1139-9 . Picca A, Coelho-Junior HJ, Calvani R, Marzetti E, Vetrano DL. Biomarkers shared by frailty and sarcopenia in older adults: A systematic review and meta-analysis. Ageing Res Rev. 2022;73:101530. https://doi.org/10.1016/j.arr.2021.101530 . Atciyurt K, Heybeli C, Smith L, Veronese N, Soysal P. The prevalence, risk factors and clinical implications of dehydration in older patients: a cross-sectional study. Acta Clin Belg. 2024;79(1):12–8. https://doi.org/10.1080/17843286.2023.2275922 . Wang C, Guo X, Xu X, Liang S, Wang W, Zhu F, Wang S, Wu J, Zhang L, Sun X, et al. Association between sarcopenia and frailty in elderly patients with chronic kidney disease. J Cachexia Sarcopenia Muscle. 2023;14(4):1855–64. https://doi.org/10.1002/jcsm.13275 . Page A, Flower L, Prowle J, Puthucheary Z. Novel methods to identify and measure catabolism. Curr Opin Crit Care. 2021;27(4):361–6. https://doi.org/10.1097/mcc.0000000000000842 . Rondanelli M, Cereda E, Klersy C, Faliva MA, Peroni G, Nichetti M, Gasparri C, Iannello G, Spadaccini D, Infantino V, et al. Improving rehabilitation in sarcopenia: a randomized-controlled trial utilizing a muscle-targeted food for special medical purposes. J Cachexia Sarcopenia Muscle. 2020;11(6):1535–47. https://doi.org/10.1002/jcsm.12532 . van Vliet S, Burd NA, van Loon LJ. The Skeletal Muscle Anabolic Response to Plant- versus Animal-Based Protein Consumption. J Nutr. 2015;145(9):1981–91. https://doi.org/10.3945/jn.114.204305 . Lin S, Chen X, Cheng Y, Huang H, Yang F, Bao Z, Fan Y. C-Reactive Protein Level as a Novel Serum Biomarker in Sarcopenia. Mediators Inflamm. 2024;2024:3362336. https://doi.org/10.1155/2024/3362336 . Mayhew AJ, Amog K, Phillips S, Parise G, McNicholas PD, de Souza RJ, Thabane L, Raina P. The prevalence of sarcopenia in community-dwelling older adults, an exploration of differences between studies and within definitions: a systematic review and meta-analyses. Age Ageing. 2019;48(1):48–56. https://doi.org/10.1093/ageing/afy106 . Inoue T, Shimizu A, Murotani K, Satake S, Matsui Y, Arai H, Maeda K. Exploring biomarkers of osteosarcopenia in older adults attending a frailty clinic. Exp Gerontol. 2023;172:112047. https://doi.org/10.1016/j.exger.2022.112047 . Petermann-Rocha F, Gray SR, Pell JP, Celis-Morales C, Ho FK. Biomarkers Profile of People With Sarcopenia: A Cross-sectional Analysis From UK Biobank. J Am Med Dir Assoc. 2020;21(12). https://doi.org/10.1016/j.jamda.2020.05.005 . :2017.e1–2017.e9. Rondanelli M, Klersy C, Terracol G, Talluri J, Maugeri R, Guido D, Faliva MA, Solerte BS, Fioravanti M, Lukaski H, et al. Whey protein, amino acids, and vitamin D supplementation with physical activity increases fat-free mass and strength, functionality, and quality of life and decreases inflammation in sarcopenic elderly. Am J Clin Nutr. 2016;103(3):830–40. https://doi.org/10.3945/ajcn.115.113357 . Gong H, Duan S, Lin X, Huang S. The association between Dietary Index for Gut Microbiota and sarcopenia: the mediating role of Dietary Inflammatory Index. Front Nutr. 2025;12:1514209. https://doi.org/10.3389/fnut.2025.1514209 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8642201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577363629,"identity":"c5d86150-d06b-4c4e-8f55-5e9dc875b5dd","order_by":0,"name":"Lei 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Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zhang","suffix":""},{"id":577363655,"identity":"1c6dc182-2475-44ba-bea9-97660a369418","order_by":5,"name":"Yuting Tian","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Tian","suffix":""},{"id":577363658,"identity":"272344f5-aea3-4d7d-b4ac-02c4f66a1065","order_by":6,"name":"Yuxing Yang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxing","middleName":"","lastName":"Yang","suffix":""},{"id":577363661,"identity":"c8df5c61-d0b8-491f-9979-08cf41244f58","order_by":7,"name":"Jiaxun Xie","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxun","middleName":"","lastName":"Xie","suffix":""},{"id":577363662,"identity":"ef7ba0db-415e-4e09-8461-f4d643bdf280","order_by":8,"name":"Yi Liu","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""},{"id":577363669,"identity":"a39fb43d-d3fe-475b-ad41-a780f78ef566","order_by":9,"name":"Jingyuan Liao","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingyuan","middleName":"","lastName":"Liao","suffix":""},{"id":577363671,"identity":"dcbec412-724e-46be-ac66-d7fec1968432","order_by":10,"name":"Xiaowei Tang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Tang","suffix":""},{"id":577363672,"identity":"0b96aa85-8918-4eee-87c9-051273d6a1a1","order_by":11,"name":"Lü Muhan","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lü","middleName":"","lastName":"Muhan","suffix":""}],"badges":[],"createdAt":"2026-01-19 18:17:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8642201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8642201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101648558,"identity":"41cd565f-e19c-488e-9564-cd9be27486dc","added_by":"auto","created_at":"2026-02-02 08:59:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62250,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant inclusion and exclusion from the study. A total of 17,708 individuals were examined at baseline. After sequential exclusions of those aged \u0026lt;45 (n = 602), those with baseline CKD (eGFR \u0026lt; 60; n = 398), those with baseline sarcopenia (n = 2,420), those with missing baseline demographic data (n = 52), and those lost to follow-up by 2015 (n = 5,306) or missing key laboratory data (n = 2,436), the final analytic sample included 6,494 participants.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8642201/v1/101296ba07268fed5159c840.png"},{"id":101648528,"identity":"2b864bce-fbd3-4c53-a1c5-35a75cda7a48","added_by":"auto","created_at":"2026-02-02 08:58:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10838,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline showing the adjusted dose–response association between baseline BUN/Cr ratio and odds ratio of incident sarcopenia. The solid red line represents the OR for sarcopenia (right y-axis) as a function of baseline BUN/Cr ratio (x-axis), while the shaded region denotes the 95% confidence interval. This model incorporates adjustments for all covariates (Model 3). The horizontal dashed line denotes OR = 1 (no association). P for overall association = 0.0133; p for non-linear trend = 0.2174.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8642201/v1/36cf9bc4b2db0d82faf7aadb.png"},{"id":101648534,"identity":"aa88c9b4-eef5-44d4-8021-b9e5294d5633","added_by":"auto","created_at":"2026-02-02 08:58:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15835,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of incident sarcopenia risk associations with baseline blood urea nitrogen-to-creatinine ratios across subgroups. Odds ratios (blue squares) and 95% CIs (horizontal lines) per one IQR increase in BUN/Cr are shown for each subgroup, from logistic models adjusted for all covariates except the subgroup stratifier. The vertical solid line is the overall fully adjusted OR (reference line at OR = 1). P_interaction values indicate the significance of interaction between BUN/Cr and subgroup factor.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8642201/v1/8bbdbf84ba4962176e38228d.png"},{"id":101880432,"identity":"29d6fa07-e1ee-4198-b0ef-084b2662ea4c","added_by":"auto","created_at":"2026-02-04 15:01:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":897659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8642201/v1/6cb185fe-91b8-4d01-8d5e-5e2e76957188.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Baseline Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia in Middle-Aged and Older Chinese Adults: Findings from the CHARLS Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is an age-related syndrome characterized by progressive loss of skeletal muscle mass and strength, leading to adverse outcomes such as physical disability, poor quality of life, and higher mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is formally recognized by consensus groups including the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGS) [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite growing awareness, identifying early biomarkers of sarcopenia risk remains an important research goal.\u003c/p\u003e \u003cp\u003eThe blood urea nitrogen to creatinine ratio (BUN/Cr ratio) is a common clinical measure traditionally used to evaluate hydration status and kidney function. Beyond these uses, recent evidence suggests that an elevated BUN/Cr ratio may also reflect underlying changes in muscle metabolism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Serum creatinine is produced by muscle tissue, and lower creatinine (for a given level of urea) can indicate reduced muscle mass or protein intake [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conversely, elevated urea relative to creatinine may signal increased protein catabolism or poor nutritional status [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, Gunst J \u003cem\u003eet al.\u003c/em\u003e reported that critically ill trauma patients with prolonged intensive care stays had markedly increased BUN/Cr ratios, consistent with significant muscle wasting [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Likewise, among hemodialysis patients, a higher urea-to-creatinine ratio was associated with worse nutritional status and lower muscle mass [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In community-dwelling populations, cross-sectional studies have hinted at a link between BUN/Cr and frailty or sarcopenia. Chen et al. recently demonstrated that in older Chinese adults, a higher BUN/Cr ratio was linearly related to weaker grip strength and, above a threshold, to slower chair-stand times, suggesting an association with physical frailty [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, it remains unclear whether an elevated BUN/Cr ratio predicts the future development of sarcopenia, as longitudinal data are lacking. We therefore conducted a prospective analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the association between baseline BUN/Cr ratio and incident sarcopenia in middle-aged and older Chinese adults. We hypothesized that individuals with higher BUN/ Cr ratios at baseline would have greater risk of developing sarcopenia over follow-up.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis study utilized data from CHARLS, an ongoing nationally representative cohort of Chinese adults aged 45 years and older [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The CHARLS study began with a baseline survey in 2011\u0026ndash;2012 (Wave 1) including 17,708 participants, and participants are followed up two to three years [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The present analysis included respondents who participated in both the baseline examination (2011) and the 2015 follow-up (Wave 3), the only two waves with available data on BUN, creatinine, and sarcopenia assessments. We excluded individuals younger than 45, those with chronic kidney disease (CKD) at baseline (estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;), and those who had sarcopenia at baseline. Participants with missing key covariate data or who were lost to follow-up were also excluded.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure Measurement\u003c/h3\u003e\n\u003cp\u003eFasting blood samples were collected at baseline. Central laboratory analysis quantified serum BUN and creatinine using standardized enzymatic assays. The BUN/creatinine ratio was derived by dividing serum urea nitrogen (mg/dL) by serum creatinine (mg/dL). Analytically, BUN-to-creatinine ratios were quantified as continuous measures and categorical quartiles. We also calculated the interquartile range (IQR) of the BUN/Cr ratio and treated an increase of one IQR as a unit of exposure for continuous models.\u003c/p\u003e\n\u003ch3\u003eOutcome Assessment\u003c/h3\u003e\n\u003cp\u003eThe outcome was incident sarcopenia at the 2015 follow-up. Incident sarcopenia (2015) was diagnosed per AWGS 2019 criteria, requiring reduced appendicular skeletal muscle mass plus either dynapenia or physical performance decline [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Height-adjusted ASM thresholds for low muscle mass were sex-specific: \u0026lt;6.88 kg/m\u0026sup2; (male) and \u0026lt;\u0026thinsp;5.69 kg/m\u0026sup2; (female), based on Chinese population-validated prediction. Grip strength was quantified using a dynamometer, with dynapenia defined per AWGS 2019 criteria as \u0026lt;\u0026thinsp;28 kg (male)and \u0026lt;\u0026thinsp;18 kg (female). Mobility assessment included the 5-repetition chair stand test (with a time cutoff of \u0026gt;\u0026thinsp;12 seconds) and the habitual gait velocity test (with a cutoff of \u0026lt;\u0026thinsp;1.0 m/s); failure in either test indicated functional impairment. Participants meeting the criteria for dynamometric impairment or validated mobility deficit met diagnostic criteria for sarcopenia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Those who expired or were lost to follow-up before the 2015 exam were treated as non-assessable for sarcopenia and thus were not included in the analysis.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eBaseline sociodemographic and clinical variables were documented through standardized questionnaires, physical examinations, and laboratory assays. Collected demographics included chronological age, biological sex, settlement type (rural/urban), education level (categorized as \u0026le;\u0026thinsp;primary, high school, or \u0026ge;\u0026thinsp;college), and partnership status (classified as married or non-married, with the latter including widowed, separated, or never-married individuals). Lifestyle behaviors incorporated tobacco use, classified as current smoker, ex-smoker, or non-smoker, alcohol consumption frequency (\u0026lt;\u0026thinsp;1 drink/month, \u0026ge;\u0026thinsp;1 drink/month, or never drinks alcohol), and average nightly sleep duration (\u0026lt;\u0026thinsp;7 hours vs\u0026thinsp;\u0026ge;\u0026thinsp;7 hours). Health measurements included body mass index (BMI, kg/m\u0026sup2;), waist circumference (cm), systolic and diastolic blood pressure (SBP and DBP, mmHg).We calculated estimated glomerular filtration rate (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration equation and defined CKD as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; as noted, those with CKD were excluded. Presence of diabetes mellitus (DM) was defined by self-reported physician diagnosis. Serum lipid profiles (triglycerides (TG), high-density lipoprotein (HDL) cholesterol) were measured from blood samples.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics of participants are presented by sarcopenia outcome status (developed sarcopenia vs did not develop) and were compared using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for continuous measures and \u003cem\u003eχ\u003c/em\u003e\u0026sup2; test (or chi-squared test) for categorical factors. Logistic regression models were constructed to estimate the odds ratio (OR) and 95% confidence interval (CI) for incident sarcopenia associated with baseline BUN/Cr ratio. Three models were constructed: Model 1 (unadjusted), Model 2 (adjusted for sociodemographic variables such as age, gender, educational attainment, place of residence, and marital status), and Model 3 (full model, which further incorporated tobacco use, alcohol consumption, sleep duration, systolic and diastolic blood pressure, and diabetes mellitus status on the basis of Model 2). In these models, BUN/Cr was analyzed per IQR increase and by quartile (with the lowest quartile as reference). Linear trend testing assigned quartile-specific BUN/Cr medians to participants, with these values analyzed as continuous variables in models. Dose-response patterns between BUN/Cr and sarcopenia were modeled via restricted cubic splines (3 knots), fully adjusted for covariates. The non-linearity was tested by the significance of the spline\u0026rsquo;s second-order term. Stratified analyses examined effect heterogeneity across prespecified subgroups: age (\u0026lt;\u0026thinsp;65/\u0026ge;65 years), sex, and other key covariates. Interaction terms between BUN/Cr (per IQR) and subgroup indicators were incorporated into the fully adjusted model to test for effect modification. A two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 constituted statistical significance. All statistical operations were implemented in R software (version 4.4.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eThe study cohort (N\u0026thinsp;=\u0026thinsp;6,494) exhibited mean age 57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0 years and contained 51.2% males. During the 4-year follow-up, 673 individuals (10.4%) developed sarcopenia. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares baseline characteristics of those who did and did not develop sarcopenia. Participants who developed sarcopenia were significantly older at baseline (mean 62.5 vs 56.6 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and included a higher percentage of females (69.4% vs 46.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). They had markedly lower educational levels (86.2% with primary school or less, vs 64.8% in those without sarcopenia, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lived in rural areas more frequently (89.0% vs 81.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Those who developed sarcopenia had a lower baseline eGFR on average (91.9 vs 94.7 mL/min/1.73mkg/m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although all were non-CKD by study design. There were no differences in baseline BMI (approximately 25.3 kg/m\u0026sup2; in both groups, p\u0026thinsp;=\u0026thinsp;0.200), but the sarcopenia group showed lower waist circumference values (79.5 vs 87.4 cm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding lifestyle factors, individuals who developed sarcopenia had a lower probability of being current smokers or alcohol consumers and a higher tendency to report never having smoked or consumed alcohol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which mirrors the higher female proportion in this group. They also had slightly shorter sleep duration (6.1 vs 6.4 hours on average, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the baseline BUN/Cr ratio was higher among those who developed sarcopenia (mean 22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6) compared to those who did not (20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When categorized into quartiles, only 16.2% of those who became sarcopenic were in the lowest BUN/Cr quartile at baseline, versus 26.0% of those who did not develop sarcopenia, whereas 33.7% of the sarcopenia group were in the highest quartile compared to 24.0% of the non-sarcopenia group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for overall distribution).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCHARLS cohort (2011\u0026ndash;2015): Baseline profiles according to sarcopenia incidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6,494)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5,821)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncident Sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;673)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,167(48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,700 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467 (69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,327(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,121 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eEducation, n (%)\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=\"char\" char=\".\" colname=\"c5\"\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\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (1.8)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,931(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,850 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (12.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,351(66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,771 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e580 (86.2)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, n (%)\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=\"char\" char=\".\" colname=\"c5\"\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\u003eCity/Town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,137(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,063 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (11.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,357(82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,758 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599 (89.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,918(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,345 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573 (85.1)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever-married/ Separated/Widowed, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (14.9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,003(31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,851 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152 (22.6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e525 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker, n (%)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,795(59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,322 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473(70.3)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u0026thinsp;\u0026lt;\u0026thinsp;1 drink/ month, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e526 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (5.1)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u0026thinsp;\u0026ge;\u0026thinsp;1 drink/ month, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,359(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,270 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (13.2)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drink, n (%)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,575(70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,025 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550 (81.7)\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(continued)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;6,494)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5,821)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncident Sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;673)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u0026thinsp;\u0026lt;\u0026thinsp;7h, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,972(45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,653 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e319 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u0026thinsp;\u0026ge;\u0026thinsp;7h, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,522(54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,168 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e354 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mmHg (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128.1\u0026thinsp;\u0026plusmn;\u0026thinsp;21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mmHg (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (DM) present, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e417 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e376 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated GFR, mL/ min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/ m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.0\u0026thinsp;\u0026plusmn;\u0026thinsp;112.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140.6\u0026thinsp;\u0026plusmn;\u0026thinsp;113.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.8\u0026thinsp;\u0026plusmn;\u0026thinsp;98.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Cr ratio (raw), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Cr ratio Quartile 1 (lowest) n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,624(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,515 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109 (16.2)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Cr ratio Quartile 2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,624(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,482 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Cr ratio Quartile 3, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,623(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,428 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Cr ratio Quartile 4 (highest), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,623(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,396 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227 (33.7)\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Group differences between sarcopenia and non-sarcopenia groups were assessed using t-tests for continuous measures and χ\u0026sup2; tests for categorical variables\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e-value for overall distribution of categories (from chi-square test)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ec\u003c/sup\u003e Among the 673 incident sarcopenia cases, 467 were female (who have lower smoking rates), contributing to the high percentage of \u0026ldquo;never smokers\u0026rdquo; in this group\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRelationship Between BUN/Cr Ratio and Incident Sarcopenia\u003c/h3\u003e\n\u003cp\u003eHigher baseline BUN/Cr ratios showed a connection with an elevated risk of sarcopenia onset. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the ORs for incident sarcopenia by BUN/ Cr, under different levels of covariate adjustment. The unadjusted model (Model 1) indicated a 1.35-fold sarcopenia odds elevation (95%CI 1.25\u0026ndash;1.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) per 6.5-unit IQR rise in BUN/Cr ratio. The highest BUN/Cr quartile associated with more than twice the increased sarcopenia odds versus the lowest quartile (OR 2.26, 95% CI 1.78\u0026ndash;2.88). This relationship stayed significant, though weakened, upon adjustment for demographic factors (Model 2: OR per IQR 1.18, 95% CI 1.08\u0026ndash;1.30) and additionally for lifestyle and health factors (Model 3: OR per IQR 1.17, 95% CI 1.05\u0026ndash;1.31). After comprehensive adjustment, the highest BUN/Cr quartile exhibited greater odds of sarcopenia (OR 1.57, 95%CI 1.22\u0026ndash;2.03, p\u0026thinsp;=\u0026thinsp;0.0006) versus the lowest BUN/Cr quartile. Quartile 3 was also at higher risk (OR\u0026thinsp;~\u0026thinsp;1.48, 95% CI 1.15\u0026ndash;1.92, p\u0026thinsp;=\u0026thinsp;0.003), whereas Quartile 2 suggested a non-significant 10% increase (OR\u0026thinsp;~\u0026thinsp;1.10, 95% CI 0.84\u0026ndash;1.45, p\u0026thinsp;=\u0026thinsp;0.47). There was a significant linear trend of increasing sarcopenia incidence with higher BUN/Cr quartiles (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in all models). Baseline BUN/Cr demonstrated independent predictive value for sarcopenia following comprehensive confounder adjustment.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratios for incident sarcopenia according to baseline BUN/Cr ratio\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Cr Ratio Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1:Unadjusted OR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2:Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3:Fully Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer IQR increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.25\u0026ndash;1.47) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.08\u0026ndash;1.30) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.05\u0026ndash;1.31) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 (1.03\u0026ndash;1.73) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (0.92\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.84\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.90 (1.49\u0026ndash;2.43) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.08\u0026ndash;1.77) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48 (1.15\u0026ndash;1.92) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.26 (1.78\u0026ndash;2.88) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63 (1.27\u0026ndash;2.10) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57 (1.22\u0026ndash;2.03) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend (quartiles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eModel 2: Sociodemographic adjustment (age, sex, education, urban/rural residence, marital status)\u003c/p\u003e \u003cp\u003eModel 3: Model 2 covariates with behavioral extension (smoking status, drinking frequency, sleep duration) and metabolic extension (SBP, DBP, diabetes)\u003c/p\u003e \u003cp\u003eTo visualize the dose-response relationship, we plotted a restricted cubic spline of the fully adjusted association between baseline BUN/Cr and the odds of sarcopenia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The spline curve demonstrates that the probability of sarcopenia rises as BUN/Cr ratios increase. The relationship appeared approximately linear over the observed range of BUN/Cr. There was no significant deviation from linearity in the dose- response (p for non-linear trend\u0026thinsp;=\u0026thinsp;0.217). However, the overall association was significant (p for overall association\u0026thinsp;=\u0026thinsp;0.013), reinforcing that higher BUN/Cr values correspond to higher risk of sarcopenia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The spline suggests a slight increase in risk begins around the middle ranges of BUN/Cr (around 18\u0026ndash;20) and becomes more pronounced at the upper end of the BUN/Cr distribution. The 95% confidence band (shaded area) widens at extreme BUN/Cr values, reflecting fewer participants with very low or very high ratios.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted stratified analyses to assess whether the association between BUN/Cr and sarcopenia risk was modified by demographic or health factors. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the positive association between higher BUN/Cr (per IQR increment) and incident sarcopenia was consistently observed across all subgroups examined. There were no statistically significant interactions by age (\u0026lt;\u0026thinsp;65 vs\u0026thinsp;\u0026ge;\u0026thinsp;65 years, p_interaction\u0026thinsp;=\u0026thinsp;0.24), sex (female vs male, p_interaction\u0026thinsp;=\u0026thinsp;0.36), residence (urban vs rural, p_interaction\u0026thinsp;=\u0026thinsp;0.35), or other factors including education level, smoking and drinking status, or sleep duration (all p_interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.1). The magnitude of the OR for BUN/Cr varied slightly: for instance, the association tended to be somewhat stronger in males (OR\u0026thinsp;~\u0026thinsp;1.29) than females (OR\u0026thinsp;~\u0026thinsp;1.13) and in those living in villages (OR\u0026thinsp;~\u0026thinsp;1.24) versus cities (OR\u0026thinsp;~\u0026thinsp;0.84), but these differences were not significant. Notably, even in subgroups traditionally at lower risk of sarcopenia (e.g., younger than 65, or male sex), a higher BUN/Cr still showed a trend toward increased sarcopenia odds. This indicates that the BUN/Cr\u0026ndash;sarcopenia relationship is broadly applicable across different population segments [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort analysis involving middle-aged and elderly Chinese participants, our findings indicated that a raised baseline BUN/Cr ratio correlated with increased incident sarcopenia risk over 4 years. This relationship remained significant following adjustment for numerous potential confounding factors, suggesting that the BUN/ Cr ratio may capture an aspect of health \u0026ndash; likely related to muscle status or nutritional/metabolic state \u0026ndash; that predisposes individuals to sarcopenia. To our knowledge, this is the first longitudinal evidence linking BUN/Cr ratio with incident sarcopenia in a general population.\u003c/p\u003e \u003cp\u003eOur findings extend previous cross-sectional observations and clinical studies. Chen et al. demonstrated that higher BUN/Cr ratios correlated with poorer physical performance (weaker grip strength and slower chair stands) in older Chinese adults at a single time point [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. We now demonstrate that this biomarker also has predictive value for new-onset sarcopenia, indicating its potential usefulness in risk stratification [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The magnitude of risk we observed (OR\u0026thinsp;~\u0026thinsp;1.17 per IQR, or ~\u0026thinsp;1.6-fold higher odds for the top vs bottom quartile) is comparable to, or slightly lower than, some well-known risk factors for sarcopenia such as age or sex. For example, in our data being \u0026ge;\u0026thinsp;65 years old or female correlated with approximately a 2\u0026ndash;3-fold greater likelihood of sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A high BUN/Cr appears to convey additional risk independent of these factors.\u003c/p\u003e \u003cp\u003eThe BUN/Cr ratio is influenced by multiple physiological processes, and its link to sarcopenia likely reflects a combination of mechanisms. One important contributor is muscle mass - lower muscle mass leads to lower creatinine generation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], raising the BUN/Cr ratio if urea production is unchanged [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Indeed, serum creatinine (when renal function is normal) has long been recognized as a surrogate marker for muscle mass [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, creatinine alone can be confounded by kidney function variations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The advantage of the BUN/Cr ratio is that it incorporates urea levels, which tend to rise in states of increased catabolism or protein breakdown. Haines et al. proposed the term \u0026ldquo;urea-corrected creatinine\u0026rdquo; to reflect that an elevated urea-to-creatinine ratio serves as a biochemical signature of muscle catabolism in critical illness [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In their ICU study, trauma patients with persistent muscle wasting had sharply higher UCR values over time [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Another study found that an early rise in BUN/ Cr predicted subsequent rapid muscle loss in sepsis patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These clinical data align with our finding that even in community-dwelling individuals, a relatively high BUN/Cr may signal an underlying catabolic or low-muscle state that evolves into sarcopenia.\u003c/p\u003e \u003cp\u003eNutritional factors could also play a role. In particular, multi-nutrient supplements stand out as safe, easily tolerated, and effective complements, contributing to improvements in lean mass, strength, and general muscle quality among the elderly [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A high BUN/Cr ratio can indicate low protein intake or malnutrition in certain contexts, as demonstrated by Tufan et al., who showed that malnourished dialysis patients had higher Urea/Cr ratios along with lower albumin and BMI [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In our study, participants who developed sarcopenia did have some markers suggestive of poorer nutrition or health status at baseline \u0026ndash; for instance, lower albumin (reflected indirectly by higher HDL and lower TG, which sometimes correlate with malnutrition) and smaller waist circumference despite similar BMI, possibly indicating lower muscle for the same weight. Anna Picca et al. reported that Inverse correlations were found between albumin, hemoglobin, and both frailty and sarcopenia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is conceivable that individuals with subtle chronic undernutrition or illness have both higher BUN/Cr and are on a trajectory of muscle loss [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Acute dehydration can transiently increase BUN/Cr. Extended periods of dehydration are typical among older populations and correlated with likely sarcopenia. However, an acute fluid deficit is less likely to persist long enough to influence long-term sarcopenia risk, and we adjusted for baseline hydration-related variables (such as blood pressure and reported health conditions) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Nevertheless, we cannot entirely exclude that BUN/Cr might also be capturing aspects of renal function or perfusion not fully accounted for, even though we excluded those with clinical CKD. It is notable that baseline eGFR was slightly lower in those who became sarcopenic (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which might suggest that even within \u0026ldquo;normal\u0026rdquo; range, lower kidney function (hence higher BUN/Cr) and sarcopenia share common determinants (e.g. aging, vascular factors) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Future research could explore the interplay between renal function decline and muscle decline, as both are common in older adults (the \u0026ldquo;renal-sarcopenia syndrome\u0026rdquo;).\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, our results highlight the potential utility of the BUN/Cr ratio as a simple screening tool for sarcopenia risk [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Given that BUN and creatinine are routinely measured in metabolic panels, the ratio is readily available without additional cost or testing. An older adult with an elevated BUN/Cr (for example, in the top quartile, \u0026gt; ~24 in our cohort) might benefit from closer evaluation of muscle strength and nutritional status, even if their values are considered within normal limits for renal function. Early interventions such as dietary protein supplementation and resistance exercise could then be implemented to possibly mitigate future sarcopenia onset [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In resource-limited settings or large population screenings, BUN/Cr could serve as an efficient first pass to identify high-risk individuals for further geriatric assessment.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including the prospective design, large sample from a well- characterized national cohort, and standardized assessment of sarcopenia using established criteria. We also adjusted for an extensive set of covariates, reducing the likelihood of confounding. The use of restricted cubic splines provided insight into the dose-response pattern without imposing a linear assumption.\u003c/p\u003e \u003cp\u003eHowever, we acknowledge several limitations. First, sarcopenia was assessed only at baseline and 4-year follow-up; some misclassification is possible if participants developed and perhaps recovered from sarcopenia in the interim. Nevertheless, such transient cases are likely few, and our outcome definition based on objective muscle measures is robust. Second, the BUN/Cr ratio was measured at a single baseline time point, which might not capture long-term patterns in protein intake or muscle metabolism. Random measurement error in BUN or creatinine would tend to attenuate the observed associations. Third, despite multivariable adjustment, residual confounding cannot be ruled out. For instance, we did not have detailed dietary data; differences in protein intake or quality could influence both BUN levels and muscle health [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, inflammatory status (e.g., CRP levels) was not measured and could link to both higher BUN (due to catabolism) and sarcopenia progression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Fourth, generalizability may be limited to similar populations; all participants were Chinese, and cultural or genetic factors may modulate both BUN/Cr and sarcopenia risk [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Finally, around 24% of the baseline CHARLS sample was excluded due to missing data or loss to follow-up, which could introduce bias if those excluded had different BUN/Cr\u0026ndash; sarcopenia relationships. We note that those lost to follow-up tended to be older and in poorer health; if anything, inclusion of those individuals might have strengthened the associations, but this remains speculative.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings suggest that the BUN/Cr ratio \u0026mdash; a readily available laboratory indicator \u0026mdash; is elevated by either an increase in blood urea nitrogen or a decrease in creatinine, and can independently predict the development of sarcopenia in older adults [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This supports the notion that higher BUN/Cr captures a state of muscle protein catabolism or frailty that predisposes to muscle loss over time. Screening for elevated blood urea nitrogen/creatinine ratio (BUN/Cr) can help identify individuals at risk of sarcopenia, who may benefit from early preventive interventions such as nutritional supplementation and exercise programs, and in particular, targeted nutritional supplements combined with low-intensity exercise can improve muscle quality in the elderly [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Reports in recent years have also confirmed that dietary and nutritional adjustments focused on gut microbiota may lessen the prevalence of sarcopenia [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Further studies are warranted to confirm these results in other populations and to explore the biological mechanisms linking BUN/Cr with muscle aging. If validated, the BUN/Cr ratio could be incorporated into risk assessment models for sarcopenia and healthy aging.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWithin an extensive prospective cohort of Chinese adults, our results revealed that an elevated baseline urea nitrogen-to-creatinine ratio was significantly linked to an elevated risk of incident sarcopenia over 4 years. This association persisted after controlling for demographics, lifestyle factors, and health status. Our study provides new evidence that the BUN/Cr ratio \u0026ndash; a straightforward and routinely measured biomarker \u0026ndash; may serve as an early warning indicator of sarcopenia. Incorporating BUN/Cr ratio into sarcopenia risk screening could facilitate earlier identification and intervention for individuals at risk, potentially helping to delay or prevent the onset of sarcopenia and its adverse outcomes. These findings should be confirmed in diverse populations, and further research should investigate whether lowering an elevated BUN/Cr (through nutritional or medical interventions) might translate into a reduced risk of sarcopenia and improved muscle health in aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003eData availability\u003c/p\u003e \u003cp\u003eThe CHARLS datasets are accessible for download via the official CHARLS website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/en\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eEthics approval and consent to participate\u003c/p\u003e \u003cp\u003e The data utilized in this study were endorsed by the Biomedical Ethics Review Committee of Peking University. Written informed consent was provided by all participants, and the assigned ethical approval code is IRB00001052-11015.\u003c/p\u003e \u003cp\u003e Consent for publication\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003eCompeting interests\u003c/p\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003cp\u003eAuthor details\u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eDepartment of Gastroenterology, Biliary-Pancreatic Center, Affiliated Hospital Southwest Medical University, Luzhou, 646000,China;\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003eDepartment of Gastroenterology, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu, 610000, China\u003c/p\u003e \u003cp\u003e \u003csup\u003e3\u003c/sup\u003eDepartment of Nursing, Affliated Hospital of Southwest Medical University, Luzhou, 646000, China\u003c/p\u003e \u003cp\u003e \u003csup\u003e4\u003c/sup\u003eDepartment of Gastroenterology, The First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, 615000, China\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLei Shi and Jinyu Wu designed the study, supervised the project, and revised the manuscript. Hanzhe Li and Jinyu Wu collected and analyzed data, and drafted the manuscript. Jing Ren and Qin Zhang assisted in data curation and verification. Yuting Tian and Yuxing Yang conducted literature review and contributed to the introduction. Jiaxun Xie and Jingyuan Liao participated in result interpretation and figure preparation. Yi Liu and L\u0026uuml; Muhan provided insights on study methodology. Xiaowei Tang reviewed the manuscript and approved the final version. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe present study drew upon data from the China Health and Retirement Longitudinal Study (CHARLS). We thank the CHARLS research team sincerely for their dedicated efforts and invaluable contributions to the implementation of this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe CHARLS datasets are accessible for download via the official CHARLS website at http://charls.pku.edu.cn/en.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393(10191):2636\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(19)31138-9\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(19)31138-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM, et al. 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Front Nutr. 2025;12:1514209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2025.1514209\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1514209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8642201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8642201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to comprehensively investigate the association between the Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia.We included 6,494 CHARLS (China Health and Retirement Longitudinal Study) participants aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years who were free of sarcopenia at baseline (2011) and were followed prospectively until 2015.Logistic regression estimated incident sarcopenia odds ratios (ORs) with 95% confidence intervals (CIs) across BUN/Cr levels, adjusting for sociodemographic characteristics, lifestyle, and health-relevant covariates. Restricted cubic splines modeled the dose-response relationship flexibly. Subgroup analyses by age, sex, and other factors were performed. Over 4 years, 673 participants (10.4%) developed sarcopenia. These individuals were older (62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 vs 56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8 years) and had higher baseline BUN/Cr ratios (22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6 vs 20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6) than those without sarcopenia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each interquartile range increase in BUN/Cr was associated with a 17% higher sarcopenia odds after full adjustment (OR 1.17, 95% CI 1.05\u0026ndash;1.31, p\u0026thinsp;=\u0026thinsp;0.006). Participants in the highest BUN/Cr quartile had a 57% increased risk compared to the lowest quartile (OR 1.57, 95% CI 1.22\u0026ndash;2.03). A roughly linear exposure-response relationship was observed (p\u0026thinsp;=\u0026thinsp;0.013), with consistent associations across all subgroups. Prospectively, elevated baseline BUN/Cr independently predicted 4-year incident sarcopenia in this nationally representative cohort.\u003c/p\u003e","manuscriptTitle":"Association Between Baseline Blood Urea Nitrogen-to-Creatinine Ratio and Incident Sarcopenia in Middle-Aged and Older Chinese Adults: Findings from the CHARLS Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 08:56:32","doi":"10.21203/rs.3.rs-8642201/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":"3768e9e1-332c-41a6-ab44-8ed5d8637c4e","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T05:45:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 08:56:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8642201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8642201","identity":"rs-8642201","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}