Sarcopenia Associated with All-Cause Mortality Risk

By Shuping Zhang, Zhaoyang Zeng

Affiliations

1. Department of Endocrinology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Hubei, China

doi: 10.29271/jcpsp.2025.12.1536

ABSTRACT
Objective: To examine the association of sarcopenia with elevated all-cause mortality risk.
Study Design: Descriptive study.
Place and Duration of the Study: Department of Endocrinology, the First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Hubei, China, from December 2024 to April 2025.
Methodology: Data were obtained from 11,176 eligible adults who participated in the National Health and Nutrition Examination Survey (NHANES) conducted from 1999 to 2006. The sarcopenia index (SI) was computed as the ratio of appendicular skeletal muscle mass (ASM, kg), measured using dual-energy x-ray absorptiometry, to body mass index (BMI, kg/m²). The cohort was stratified into quartiles based on the SI (Q1-Q4). Mortality follow-up lasted until December 31, 2019. A multivariable Cox proportional hazards model was used to evaluate the association between the SI and all-cause mortality. Non-linear relationships were assessed using restricted cubic splines, and subgroup analyses were performed to assess effect modification.
Results: After accounting for demographic, socio-economic, lifestyle, and clinical factors, there was an inverse, non-linear relationship between SI and all-cause mortality. Specifically, the mortality risk in the Q4 group (highest SI quartile) was 24% lower than in the Q1 group (lowest SI quartile; p <0.05). In participants with metabolic abnormalities, such as overweight/obese, hypertension, or impaired glucose metabolism, higher SI was significantly linked to reduced mortality. Moreover, using the Kaplan-Meier analysis, the Q4 group featured the best prognosis, while the Q1 group had the lowest survival rate.
Conclusion: Sarcopenia, assessed by the SI, has a significant correlation with all-cause mortality. Moreover, a strong association between them was observed in populations with metabolic abnormalities.

Key Words: Sarcopenia, Sarcopenia index, Mortality, Metabolic abnormalities, Nutrition surveys, Cohort study.

INTRODUCTION

Sarcopenia is an age-related syndrome characterised by both a progressive reduction in skeletal muscle mass and a concomitant impairment of muscle strength and physical function. Its prevalence varies significantly across diagnostic criteria.¹ Pooled analyses estimate an overall prevalence of 11%, ranging from 18 to 66% among hospitalised patients.^2-4 This condition is strongly linked to increased all-cause mortality and elevates the likelihood of fall-related injuries, cognitive impairment, and cardiovascular events, highlighting its significance as a public health concern.^5-8

In current clinical evaluations, the standard approach for assessing sarcopenia-related parameters involves the use of ASM/height² (where ASM represents appendicular skeletal muscle mass).

 In addition, the novel sarcopenia index (SI), defined as ASM/ BMI (where BMI denotes body mass index), is also recommended, particularly for obese populations.⁹ Emerging evidence confirms that sarcopenia, assessed using the novel SI, is linked to metabolic syndrome, liver fibrosis, and other conditions.^10,11 Notably, a study conducted by Moon et al. in Korea demonstrated that the novel SI provides superior predictive power for mortality among older adults.¹² However, existing research has several limitations: sample sizes are generally small, study populations are predominantly Asian, and the dose-response relationship between SI and mortality risk is underexplored, particularly in Western populations. Furthermore, the association between the SI and all-cause mortality among individuals with endocrine and metabolic disorders, such as dysglycaemia or hypertension, remains unknown.

Leveraging data from a substantial sample within the National Health and Nutrition Examination Survey (NHANES), this research aimed to examine the link between SI and all-cause mortality risk. It also aimed to examine potential heterogeneity effects across diverse population subgroups and establish a non-linear dose-response relationship model. The results will provide new epidemiological insights to refine risk assessment  strategies  for  sarcopenia.

METHODOLOGY

This study employed a prospective longitudinal cohort design to analyse data from the NHANES, which is a nationally representative survey that uses stratified multistage probability sampling to assess U.S. adult health through standardised questionnaires and physical examinations. The inclusion criteria were as follows: enrollment in the NHANES during the 1999–2006 cycles, aged ≥20 years at baseline, and a non- pregnant status at the baseline assessment. Of the 19,142 eligible participants initially identified, 20 were excluded due to missing mortality status, 3,472 due to unavailable SI values (calculated from ASM and BMI), 1,292 due to baseline malig- nancies, 105 who died within the first year of follow-up, and 3,077 due to incomplete covariate data. Following NHANES quality control protocols, participants with imputation indicator variables coded as 2 (indicating highly variable imputed muscle mass data) were further excluded. The final analytical cohort comprised 11,176 eligible participants. The study protocol was approved by the Research Ethics Review Board of the National Centre for Health Statistics (NCHS), and informed consent was obtained from all NHANES participants. The analysis of this de- identified public dataset was granted an exemption by the Institutional Review Board of Yichang Central People's Hospital, Yichang, China.

The primary exposure was the SI, calculated as ASM (kg), measured using dual-energy x-ray absorptiometry (DEXA), divided by BMI (kg/m²). Gender-specific cut-offs were used to define sarcopenia (men <0.789; women <0.512). Participants with a weight above 136.08 kg or a height exceeding 195.58 cm, which are the thresholds for reliable DEXA measurements, were excluded. The primary outcome was all-cause mortality, determined through connection with the National Death Index, with follow-up on December 31, 2019.

Continuous variables included age, HbA1c, total cholesterol, and systolic and diastolic blood pressure (SBP and DBP). The categorical covariates were categorised into several domains: demographic indicators included gender, race, marital status, and education; lifestyle-related factors included smoking status and alcohol consumption; and an economic metric included poverty- income ratio (PIR). Detailed categorisations of these variables are provided in Table I. Guided by approaches adopted in previous studies, these variables were selected for their crucial clinical significance and possible confounding effects.

R software (version 4.4.3) was used to perform all statistical analyses. For data representation, continuous variables were presented as mean ± standard deviation if normally distri- buted. Continuous variables with non-normal distribution were presented as median with 25^th and 75^th percentiles. Categorical variables were summarised using frequencies and percentages. Group comparisons were conducted using appropriate statistical methods, including ANOVA, the Kruskal-Wallis test, and the Chi-square test. The Kaplan-Meier curves with log-rank tests were used to conduct survival analysis for group comparisons. To decipher the connection between SI and all-cause mortality, multivariable Cox proportional hazards models were used. The unadjusted Model 1 was pitted against Model 2, where age, gender, and race/ethnicity were considered. Subsequently, Model 3 underwent more extensive adjustment, factoring in variables related to socioeconomic status (education, marital status, PIR), lifestyle behaviours (smoking, alcohol consumption), and clinical measurements (HbA1c, SBP, total cholesterol). Potential non-linear associations between SI and mortality were explored using restricted cubic splines (RCS). Subgroup analyses were conducted to assess effect modification using interaction terms, evaluated using likelihood ratio tests (p for interaction). All covariates were adjusted for, except the grouping variable. All statistical tests were two- tailed, with significance set at p <0.05.

RESULTS

The study included 11,176 participants. Based on the SI, the weighted prevalence of sarcopenia was 8.9% among men and 8.1% among women. When stratified by quartiles based on SI (Q1-Q4), significant differences were observed across all baseline characteristics (all p <0.001). Participants with higher SI values were generally younger, more often male, and had more favourable socioeconomic profiles, including higher levels of education and income. Metabolically, the Q3 and Q4 groups demonstrated superior profiles, with the Q4 group showing particularly favourable values for HbA1c (5.4% ± 0.8), total cholesterol (196 ± 42 mg/dL), SBP (123 ± 15 mmHg), and BMI (26.2 ± 4.4 kg/m²). Significant variations were also noted in lifestyle factors, including smoking and alcohol consumption patterns, across the quartiles based on SI (Table Ⅰ).

The analysis revealed a stable inverse link between the SI and all-cause mortality in all models (Models 1 and 2: p for trend <0.001; Model 3: p = 0.002). In the crude Model 1, participants in the highest SI quartile (Q4 group) showed a 57% lower mortality risk compared to the reference group (Q1 group; HR = 0.43, 95% CI: 0.38-0.49). This protective association remained significant after adjusting for demographic factors (age, gender, and race/ethnicity) in Model 2 (HR = 0.65, 95% CI: 0.53-0.80), with further adjustment for socioeconomic status, behavioural factors, and clinical parameters in Model 3 (HR = 0.76, 95% CI: 0.61-0.94). Continuous analyses revealed a dose-response relationship, where each 1-unit increase in the SI was associated with hazard ratios of 0.22 (95% CI: 0.18-0.28), 0.36 (95% CI: 0.23-0.54), and 0.51 (95% CI: 0.33-0.78) in Models 1–3, respectively (Table II). The RCS analysis indicated a significant non-linear association between the SI and the risk of all-cause mortality (p for non-linearity = 0.035), showing a progressively lower risk with increasing the SI (Figure 1A). Significant variations in survival curves across groups were observed in the Kaplan-Meier analysis (log-rank test; p <0.001). Throughout the follow-up period, individuals in the Q4 group demonstrated the most favourable survival outcomes, achieving the highest survival rates. In contrast, those in the Q1 group experienced the poorest survival outcomes, with significantly lower survival rates (Figure 1B).

Table I: Baseline characteristics of participants stratified by the SI.

Characteristics	Q1 Group (≤0.617) (n = 2,680)	Q2 Group (0.617-0.769) (n = 2,776)	Q3 Group (0.769-0.941) (n = 2,821)	Q4 Group (>0.941) (n = 2,899)	p-values
Demographic information	-	-	-	-	-
Age (years)	56 (41, 68)	45 (33, 61)	47 (34, 62)	39 (29, 50)	<0.001
Gender: female	2658 (49.4)	2176 (40.4)	528 (9.8)	22 (0.4)	<0.001
Race, ethnicity, n (%)	-	-	-	-	<0.001
Non-hispanic white	937 (35.7)	530 (20.2)	803 (30.6)	354 (13.5)
Non-hispanic black	151 (31.7)	109 (22.9)	126 (26.4)	91 (19.1)
Mexican American	1204 (21.8)	1418 (25.7)	1374 (24.9)	1520 (27.6)
Other races, including multi-racial	294 (13.5)	619 (28.4)	412 (18.9)	856 (39.3)
Other hispanic	94 (24.9)	100 (26.5)	106 (28.0)	78 (20.6)
Education level, n (%)	-	-	-	-	<0.001
Less than high school	1006 (30.3)	658 (19.8)	908 (27.3)	754 (22.7)
High school or equivalent	690 (26.1)	692 (26.2)	639 (24.2)	623 (23.6)
College graduate or higher	984 (18.9)	1549 (29.8)	1274 (24.5)	1399 (26.9)
Marital status, n (%)	-	-	-	-	<0.001
Married	1448 (22.9)	1583 (25.0)	1772 (28.0)	1527 (24.1)
Never married	248 (13.2)	727 (38.7)	456 (24.3)	446 (23.8)
Others	984 (33.1)	589 (19.8)	593 (20.0)	803 (27.1)
Poverty income ratio, n (%)	-	-	-	-	<0.001
<1.3	888 (29.1)	644 (21.1)	759 (24.9)	761 (24.9)
1.3-3.5	1075 (25.2)	1058 (24.8)	1080 (25.3)	1058 (24.8)
≥3.5	717 (18.6)	1197 (31.1)	982 (25.5)	957 (24.8)
Anthropometry and biomarkers	-	-	-	-	-
HbA1c level (%)	5.7 ± 1.1	5.6 ± 1.0	5.7 ± 1.1	5.4 ± 0.8	<0.001
Cholesterol level (mg/d)	210 ± 41	200 ± 40	201 ± 42	196 ± 42	<0.001
SBP (mmHg)	131 ± 23	124 ± 22	125 ± 19	123 ± 15	<0.001
DBP (mmHg)	70 ± 14	71 ± 12	72 ± 14	73 ± 12	<0.001
BMI (kg/m2)	30.8 ± 6.5	28.1 ± 6.2	28.0 ± 5.5	26.2 ± 4.4	<0.001
Lifestyle factors	-	-	-	-	-
Current smokers	409 (15.3)	911 (34.0)	728 (27.2)	630 (23.5)	<0.001
Current drinkers	1052 (15.6)	2158 (32.1)	1914 (28.4)	1606 (23.9)	<0.001
*Categorical variables are expressed as counts and percentages (n, %). For continuous variables, normal data are presented as means ± SD, while non-normal data are shown as medians (25th, 75th percentiles). p-values were calculated using the Kruskal-Wallis test for age, ANOVA for other continuous variables, and Chi-square tests for categorical variables.

Table II: The associative link between the SI and all-cause mortality risk.

SI	Model 1		Model 2		Model 3
	HR (95% CI)	p-values	HR (95% CI)	p-values	HR (95% CI)	p-values
Q1	1.00 (Reference)		1.00 (Reference)		1.00 (Reference)
Q2	0.73 (0.66 ~ 0.81)	<0.001	0.89 (0.78 ~ 1.01)	0.08	0.90 (0.79 ~ 1.03)	0.114
Q3	0.81 (0.73 ~ 0.90)	<0.001	0.72 (0.60 ~ 0.86)	<0.001	0.79 (0.66 ~ 0.94)	0.008
Q4	0.43 (0.38 ~ 0.49)	<0.001	0.65 (0.53 ~ 0.80)	<0.001	0.76 (0.61 ~ 0.94)	0.012
Per 1-unit	0.22 (0.18 ~ 0.28)	<0.001†	0.36 (0.23 ~ 0.54)	<0.001†	0.51 (0.33 ~ 0.78)	0.002†
*Data in the models are presented as hazard ratios (95% confidence interval). Model 1 Crude model; Model 2: Adjusted considering age, racial ethnicity, and gender; Model 3: Further adjusted for education level, marital status, PIR, smoking status, alcohol consumption habits, and clinical parameters, including HbA1c, SBP, and total cholesterol. †p for trend.   Figure 1: Hazard ratios and survival analysis for all-cause mortality. (A) A hazard ratio analysis using RCS for all participants, with the likelihood ratio test (p = 0.035). The analysis adjusted for the same confounding factors as the main analysis. (B) Survival curve for all participants.

Between-group comparisons (Q2-Q4 vs. Q1) and the p for trend were derived from Wald tests within the Cox models: group p-values were derived from individual coefficient tests, while trend p-values were calculated by treating SI as a continuous variable.

Subgroup analyses revealed significant heterogeneity in the SI-mortality association by race/ethnicity (p = 0.010), PIR (p = 0.012), and BMI categories (p = 0.012). The protective effect of the SI was particularly pronounced in non-Hispanic Whites, middle-income individuals (PIR 1.3-3.5), and those categorised as overweight or obese (BMI between 25 and 30, and ≥30 kg/m²). Notably, consistent inverse associations were observed in participants with hypertension (HR = 0.52, 95% CI: 0.31-0.88) and dysglycaemia (HbA1c ≥5.7%; HR = 0.27, 95% CI: 0.14-0.53), although interaction terms were nonsignificant (p for interaction = 0.275 and 0.631, respectively).

DISCUSSION

Through this prospective large-scale cohort study, a significant inverse association was identified between elevated SI and reduced all-cause mortality, demonstrating a non-linear dose-response relationship. Importantly, the SI maintained its protective effect against mortality, even among individuals with metabolic abnormalities, including those with overweight/obesity, hypertension, or dysglycaemia.

Current clinical practice demonstrates substantial heterogeneity in how sarcopenia is defined and assessed. It is recommended that handgrip strength, combined with ASM adjusted for height squared, be used for diagnosis, as this approach is linked with a significantly elevated mortality risk.¹³ Similarly, studies using the Sarcopenia Definitions and Outcomes Consortium criteria reported a 5.3% prevalence of Sarcopenia among white male populations, with clear associa-tions to all-cause mortality.¹⁴ These findings are consistent with the results of the present study, reinforcing the significance of the relationship. This study introduces an innovative methodology by using the ASM/BMI ratio to diagnose sarcopenia, determining the weighted prevalence of this condition in the population to be 8.5%. To the best of the authors’ knowledge, this study is the first to comprehensively assess and establish the strength of the association between this diagnostic approach and all-cause mortality risk.

Chronic low-grade inflammation serves as a core pathological mechanism in sarcopenia, primarily mediated by pro-inflammatory cytokines, such as IL-6 and TNF-α. These cytokines activate the NF-κB pathway, accelerating proteolytic breakdown, and promoting muscle wasting.¹⁵ This process elevates mortality risk through multifaceted pathways, including metabolic dysregulation, sustained inflammation, and functional decline.¹⁶ Furthermore, sarcopenia has been linked to multiple health consequences, including vulnerability to falls leading to fractures, a higher probability of hospitalisation, and elevated risk of cardiovascular events, all of which contribute to higher mortality through both direct and indirect mechanisms.^17-20

Subgroup analyses demonstrated significant inverse associations between the SI and mortality risk among individuals with dysglycaemia (HbA1c ≥5.7%), hypertension, and overweight/obesity, consistent with previous reports. These findings align with the established evidence. Yamaguchi et al. identified the synergistic amplification of mortality risk when hypertension coexists with sarcopenia in older adults, while Lin et al. reported that individuals with Sarcopenia and type 2 diabetes face a higher risk of death.^21,22 Notably, Ha et al. revealed that obesity increases sarcopenia-related mortality.²³ Collectively, these data underscore the critical need for proactive sarcopenia screening and diagnosis, particularly in populations with pre-existing metabolic abnormalities, where multiple risk factors may compound and accelerate adverse outcomes.

This study benefits from the large sample size and extended follow-up of the NHANES database, enhancing the robustness of the findings through comprehensive adjustment for sociodemographic, metabolic, and behavioural covariates. Furthermore, it explored the non-linear association between SI and all-cause mortality and assessed heterogeneity across diverse subgroups. However, this study has several limitations. First, the generalisability of the findings may be limited, as the study population primarily comprised U.S. individuals. Second, although extensive covariate adjustments are made, the observational design cannot preclude residual confounding. Finally, the assessment of SI at a single time point prevented the investigation of its longitudinal changes and their potential impact on the outcomes.

CONCLUSION

The findings of this investigation strongly suggest a significant association between sarcopenia and a higher risk of all- cause mortality, especially among individuals with metabolic abnormalities (hypertension, dysglycaemia, or overweight/ obesity).

ETHICAL APPROVAL:
The Research Ethics Review Board at the National Centre for Health Statistics approved the study protocols, verifying adherence to ethical standards for data collection and participant involvement. As the publicly available data were collected under appropriate ethical oversight, no additional approval was required for the current analysis.

PATIENTS’ CONSENT:
Informed consent was obtained from all NHANES participants.

COMPETING INTEREST:
The authors declared no conflict of interest.

AUTHORS’ CONTRIBUTION:
SZ: Collected and analysed the data and prepared the initial draft of the manuscript.
ZZ: Contributed to the conception, design, and critical review of the study and ensured accountability for all aspects.
Both authors approved the final version of the manuscript to be published.

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