Abstract
People progressively lose muscle strength with aging, a process that can be accelerated by malnutrition, comorbidities, and physical inactivity. Low muscle strength has been linked to increased mortality risk. This study aimed to investigate the association between low muscle strength and all-cause mortality and to identify key risk factors using handgrip strength as a measure in a large adult cohort. Participants aged ≥20 years with available handgrip strength data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014 cycles were included. Among 8608 participants, 3747 were alive and 384 (55.2%) deceased males, and 4178 were alive and 299 (44.8%) deceased females. Categorical variables were compared using the χ2 test, and continuous variables were analyzed using a complex samples general linear model. Univariate and multivariate Cox regression analyses estimated the relative risk of all-cause mortality, and the Kaplan–Meier method with log-rank test assessed survival differences. Multivariate Cox regression indicated that low muscle strength increased the risk of all-cause mortality (adjusted hazard ratio, 1.656; 95% confidence interval, 1.039–2.640; P = .035). Other significant factors included age ≥ 65 years, male sex, low income, underweight, physical inactivity, diabetes mellitus, cancer, and chronic kidney disease (CKD). While muscle strength limitations were comparable between males and females (P > .05), older adults and participants with CKD exhibited markedly lower strength. These findings highlight the importance of maintaining muscle strength, particularly among older adults and individuals with chronic diseases such as CKD, diabetes, and cancer, as a potential strategy to support healthy aging and longevity. Further research is warranted to examine whether interventions that enhance muscle strength can reduce mortality risk.
Keywords: handgrip strength, mortality, muscle strength, risk factors, sarcopenia
1. Introduction
Loss of muscle strength is a common process of aging, with an estimated annual decline of 2.5 to 4% in lower muscle strength after the age of 75.[1] Age-related reductions in muscle or lean mass that impede active and healthy aging are referred to as sarcopenia, a condition associated with increased mortality, elevated healthcare costs, and diminished quality of life.[2] Sarcopenia is typically diagnosed based on 2 components: low muscle mass and low muscle function.[3] However, research has shown that muscle strength declines at a rate 2 to 5 times faster than muscle mass,[1] and reduced muscle strength more accurately predicts adverse outcomes in sarcopenia than does muscle mass alone.[4] As a result, some experts advocate defining sarcopenia based primarily on skeletal muscle function, particularly strength,[5] although consensus on its definition remains lacking.[6]
A portion of the age-related decline in muscle strength is attributable to factors such as muscle disuse, disease, and malnutrition.[7,8] Chronic diseases, in particular, exacerbate the natural deterioration of skeletal muscle with age, and it is estimated that up to 92% of older adults have at least one chronic condition.[9] Among these, chronic kidney disease (CKD) is a prevalent age-related illness that is often characterized as a manifestation of accelerated or premature aging.[10] In individuals with CKD, factors such as systemic inflammation, hormonal imbalances, impaired protein metabolism, and sedentary behavior contribute to reduced muscle mass and strength.[3,11] Furthermore, in this population, low muscle strength, decreased muscle mass, and reduced physical performance are all associated with adverse outcomes, including increased mortality, higher hospitalization rates, systemic inflammation, cardiovascular events, and diminished quality of life.[12] Poor nutrition and inactivity further accelerate declines in muscle function, which in early stages may be reversed through appropriate dietary and exercise interventions[13]; however, if left unaddressed, muscle dysfunction may become irreversible.[14]
Handgrip strength (HGS) is widely recognized as a reliable indicator of physical decline and biological aging.[15,16] Importantly, HGS reflects overall body strength and offers a simple, safe, and effective method for assessing physical status, particularly in older populations.[17,18] Age and gender are major determinants of HGS, with strength typically decreasing with age[19,20] and being consistently lower in females.[21] HGS is sensitive to changes in nutritional status,[22] responds more rapidly to nutritional deficits than anthropometric measures, and shows strong associations with both sarcopenia and frailty.[23,24] Accordingly, HGS has become a standard metric in clinical assessments.
Although previous studies have demonstrated a link between low muscle strength and increased mortality,[4] the influence of additional factors on mortality among individuals with low muscle strength remains unclear. Therefore, this study aimed to investigate the association between low muscle strength and all-cause mortality and to identify contributing risk factors in a population that underwent HGS testing. The results may help inform strategies to reduce mortality related to low muscle strength.
2. Methods
2.1. Data source
This retrospective cohort study utilized data from the 2011 to 2014 cycles of the National Health and Nutrition Examination Survey (NHANES), a nationally representative surveillance program designed to assess the health and nutritional status of the noninstitutionalized U.S. population. The 2011 to 2014 cycles were selected because they were the most recent cycles at the time that included complete handgrip strength (HGS) testing, mortality follow-up eligibility, and comprehensive dietary, biochemical, and demographic data. NHANES has been conducted since the early 1960s, using a sophisticated, multistage probability sampling design to ensure representativeness. The total U.S. population sample represented in NHANES is approximately 220 million. Detailed information on survey methodology, sampling design, and implementation can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.html).
2.2. Ethics approval
All NHANES participants provided written informed consent, and the study protocol was approved by the Institutional Review Board (IRB) of the National Center for Health Statistics (NCHS). The publicly available NHANES data used in this secondary analysis are de-identified, and the present study was additionally reviewed and approved by the Institutional Review Board of Chung Shan Medical University Hospital, Taiwan (IRB No. CS1-22004).
2.3. Study participants
This study analyzed data from 19,931 individuals enrolled in the NHANES cycles from 2011 to 2012 and 2013 to 2014. These cycles were selected based on the availability of both handgrip strength (HGS) data and linked mortality follow-up information. After applying inclusion and exclusion criteria, a total of 8608 participants were included in the final analysis. Participants were eligible if they were aged 20 years or older, had complete HGS measurements, and were eligible for mortality linkage, which is determined by the National Center for Health Statistics based on sufficient identifying data (e.g., Social Security number, date of birth, etc). Participants were excluded if they had missing mortality status, incomplete HGS data, or failed to complete the 2-day dietary recall with appropriate sample weights (WTDR2D). Individuals with physiologically implausible or extreme HGS values, as determined by NHANES quality control protocols, were also excluded. All-cause mortality status was determined through linkage to the National Death Index, with follow-up available through December 31, 2019. The final analytic cohort and selection process are detailed in Figure 1.
Figure 1.
Flow chart for participant selection.
2.4. Study variables
2.4.1. Muscle strength
Muscle strength data were obtained from the handgrip strength (HGS) test files, following standardized protocols detailed in the NHANES Muscle Strength Procedure Manual (https://wwwn.cdc.gov/nchs/data/nhanes/public/2011/manuals/Muscle_Strength_Proc_Manual.pdf).
Data from the 2011 to 2014 NHANES cycles were selected because these were the most recent survey waves that included complete HGS measurements together with linked mortality data available through the National Death Index (NDI).
HGS was measured using a calibrated handgrip dynamometer, with participants instructed to exert maximal grip strength while standing. Each hand was tested 3 times, alternating between hands, with a 60-second rest period between measurements for the same hand. The combined HGS (kg) was calculated as the sum of the highest recorded grip strength from both the dominant and nondominant hands. To determine the average HGS, this sum was divided by 2.
Low muscle strength was defined according to the Foundation for the National Institutes of Health Sarcopenia Project criteria, with thresholds set at <26 kg for men and <18 kg for women.^(26)*** These cutoffs are widely applied in population-based research and are recommended for use in older adults and clinical populations to standardize sarcopenia diagnosis.
2.5. Primary outcome: all-cause mortality
Mortality information was obtained from the public-use linked mortality files, which connect NHANES survey data to death certificate records in the NDI. Eligibility for mortality follow-up required participants to have provided sufficient identifying information for probabilistic matching with the NDI database. Each eligible participant was assigned a vital status code, indicating whether they were alive or deceased at the end of follow-up.
The mortality linkage file used in this study reflects follow-up through December 31, 2019, providing up to 8 years of observation depending on the participant’s original survey cycle. All-cause mortality status was determined using the MORTSTAT variable, where a value of “1” indicated death from any cause during follow-up.
2.6. Covariates
The NHANES database provided comprehensive demographic, anthropometric, lifestyle, clinical, and laboratory variables that were selected a priori based on their potential associations with both muscle strength and mortality, as supported by prior literature.[25] Demographic factors included age (years), gender, race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, and other race), marital status (married/living with partner vs other), education level (less than high school, high school graduate, some college, or college graduate), and the poverty-income ratio (PIR), representing the ratio of family income to the U.S. poverty threshold in the survey year.
Anthropometric measures – height (cm), arm circumference (cm), waist circumference (cm), and body mass index (BMI, kg/m2) – were collected using standardized NHANES protocols. BMI was categorized according to World Health Organization criteria as underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥30.0 kg/m2). Lifestyle factors included cigarette smoking (current, former, never), alcohol consumption (yes/no), and physical activity, which was derived from self-reported leisure-time and occupational activities. Physical activity levels were quantified as metabolic equivalent (MET)-minutes/week, with participants categorized as active (MET ≥ 600) or inactive (MET < 600) following World Health Organization recommendations.[26]
Comorbidities were selected based on their established links with both muscle function and survival outcomes. These included diabetes mellitus (DM), hypertension, cardiovascular diseases (CVD), chronic obstructive pulmonary disease (COPD), hyperlipidemia, cancer, and CKD. Specifically, CVD encompassed congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, and stroke, while COPD included emphysema and chronic bronchitis. Hyperlipidemia was defined as total cholesterol ≥ 200 mg/dL, and CKD was identified by a urine albumin-to-creatinine ratio ≥ 30 mg/g. All comorbidities were determined from NHANES interviewer-administered medical condition questionnaires unless otherwise specified.
Laboratory biomarkers included albumin (g/L), creatinine (µmol/L), phosphorus (mmol/L), blood urea nitrogen (mmol/L), hemoglobin (g/dL), alanine aminotransferase (U/L), aspartate aminotransferase (U/L), creatine phosphokinase (IU/L), lactate dehydrogenase (U/L), and serum 25-hydroxyvitamin D (nmol/L). Detailed specimen collection and processing procedures are described in the NHANES Laboratory/Medical Technologists Procedures Manual.
Dietary nutrient intake – total energy (kcal), protein (g), carbohydrate (g), sugar (g), fiber (g), fat (g), calcium (mg), vitamin C (mg), vitamin D (µg), iron (mg), and potassium (mg) – was obtained from 2 nonconsecutive 24-hour dietary recall interviews. Only participants who completed both days of dietary assessment were included. The mean of the 2 days’ intakes was used to represent habitual consumption, and total energy intake was normalized to 2000 kcal/day to control for overall caloric intake when evaluating nutrient-specific associations.
2.7. Statistical analyses
The basic characteristics of the participants are presented as unweighted counts (weighted %) for categorical variables and means ± standard error for continuous variables. The chi-square test was used to assess differences between categorical variables, while the complex samples general linear model was employed to evaluate differences in continuous variables. Univariate and multivariate Cox regression analyses were performed to estimate the relative risk of all-cause mortality. Variables with significant P-values (<.05) from the univariate analysis were selected for further evaluation using Cox proportional hazards models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. Survival curves were generated using the Kaplan–Meier method, and differences between groups were assessed using the log-rank test. All analyses, including those for WTDR2D, stratum, and primary sampling units, were conducted in accordance with the guidelines provided by the National Center for Health Statistics. Furthermore, we performed complex sampling design analysis to address oversampling, nonresponse, and noncoverage, and to provide nationally representative estimates. Subgroup analyses were conducted based on age, gender, and CKD status to examine differences between these groups. Additionally, a sensitivity analysis was performed using BMI-adjusted muscle strength (grip strength divided by BMI) to verify the consistency of the findings. Low muscle strength was defined as <1 m2 for men and <0.56 m2 for women. This alternative definition, although less commonly applied, accounts for inter-individual differences in body composition and has been adopted in several recent population-based epidemiological studies. All statistical tests were two-sided and assessed at a significance level of 0.05. Statistical analyses were conducted using the complex samples module of the Statistical Package for the Social Sciences version 22.0 (IBM Corp., Armonk).
3. Results
3.1. Baseline characteristics of the study population
A total of 8608 participants met the eligibility criteria for this study. Weighted baseline characteristics are summarized in Table 1. The mean handgrip strength was significantly lower in deceased participants compared with those alive at follow-up (30.15 ± 0.689 kg vs 36.77 ± 0.196 kg; P < .001). The prevalence of low muscle strength was also markedly higher among deceased individuals (19.4% vs 1.8%; P < .001). Compared with survivors, deceased participants were more likely to be older, male, White, widowed/divorced/separated, underweight, current smokers, light or moderate alcohol consumers, and physically inactive (all P < .05). They also tended to have lower educational attainment and household income, as well as a higher prevalence of chronic conditions, including DM, hypertension, CVD, COPD, cancer, and CKD (all P < .05).
Table 1.
Baseline characteristics of study participants (unweighted sample sizes and weighted %).
| Variables | Alive (n = 7925) | Deceased (n = 683) | P-value |
|---|---|---|---|
| Muscle strength | |||
| Grip (kg), mean ± SE | 36.77 ± 0.196 | 30.15 ± 0.689 | <.001 |
| Low muscle strength (yes) | 191 (01.8) | 154 (19.4) | <.001 |
| Demographic | |||
| Age (yr) | |||
| 65+ | 1305 (13.8) | 465 (62.7) | <.001 |
| 45–64 | 2703 (36.6) | 155 (26.5) | |
| <45 | 3917 (49.6) | 63 (10.8) | |
| Gender | |||
| Male | 3747 (48.1) | 384 (55.2) | .002 |
| Female | 4178 (51.9) | 299 (44.8) | |
| Race | |||
| Others | 2871 (22.9) | 114 (11.0) | <.001 |
| Non-Hispanic Black | 1861 (11.6) | 169 (11.8) | |
| Non-Hispanic White | 3193 (65.6) | 400 (77.2) | |
| Education | |||
| Under 12th grade | 1390 (13.7) | 217 (22.8) | <.001 |
| High school graduate | 1590 (20.5) | 176 (26.6) | |
| College or above | 4449 (65.8) | 286 (50.6) | |
| Marriage | |||
| Never married | 1579 (20.1) | 69 (11.2) | <.001 |
| Widowed/Divorced/Separated | 1448 (16.8) | 284 (37.0) | |
| Married/Living with partner | 4403 (63.1) | 328 (51.8) | |
| Income ratio | |||
| 0.00–1.30 | 2493 (24.5) | 258 (31.2) | <.001 |
| 1.31–3.50 | 2476 (32.5) | 259 (44.2) | |
| >3.50 (richest) | 2380 (43.0) | 126 (24.6) | |
| Body composite | |||
| Height (cm), mean ± SE | 168.9 ± 0.233 | 167.0 ± 0.631 | .005 |
| Arm circumference (cm), mean ± SE | 33.21 ± 0.095 | 32.24 ± 0.320 | .007 |
| Waist circumference (cm), mean ± SE | 98.38 ± 0.346 | 104.4 ± 1.214 | <.001 |
| BMI (kg/m2) | |||
| Underweight | 141 (01.5) | 19 (03.2) | .034 |
| Overweight | 2515 (32.6) | 200 (31.6) | |
| Obese | 2910 (35.6) | 254 (40.0) | |
| Normal | 2312 (30.3) | 187 (25.3) | |
| Lifestyle | |||
| Smoking | |||
| Smoker | 3111 (41.2) | 404 (60.3) | <.001 |
| Nonsmoker | 4575 (58.8) | 276 (39.7) | |
| Alcohol drinking | |||
| Heavy drinker | 2226 (39.1) | 112 (26.2) | .002 |
| Light/moderate drinker | 3423 (47.5) | 293 (59.3) | |
| Nondrinker | 1216 (13.4) | 89 (14.5) | |
| Physical activity | |||
| Inactive | 4139 (49.7) | 511 (73.5) | <.001 |
| Active | 3786 (50.3) | 172 (26.5) | |
| Comorbidities (yes) | |||
| DM | 1024 (09.7) | 230 (30.7) | <.001 |
| Hypertension | 2517 (29.5) | 463 (65.2) | <.001 |
| Hyperlipidemia | 2904 (39.7) | 223 (38.0) | .638 |
| CVD | 557 (06.4) | 235 (32.1) | <.001 |
| COPD | 437 (06.3) | 98 (15.9) | <.001 |
| Cancer | 594 (09.2) | 166 (29.1) | <.001 |
| CKD | 750 (07.6) | 236 (32.5) | <.001 |
| Laboratory, mean ± SE | |||
| Albumin, blood (g/L) | 43.24 ± 0.082 | 41.39 ± 0.220 | <.001 |
| Creatinine, blood (µmol/L) | 77.58 ± 0.420 | 99.22 ± 2.880 | <.001 |
| Phosphorus (mmol/L) | 1.232 ± 0.004 | 1.241 ± 0.013 | .477 |
| Blood urea nitrogen, BUN (mmol/L) | 4.563 ± 0.034 | 6.108 ± 0.161 | <.001 |
| Hemoglobin (g/dL) | 14.19 ± 0.037 | 13.72 ± 0.120 | <.001 |
| Alanine aminotransferase, ALT (U/L) | 25.23 ± 0.309 | 25.58 ± 3.007 | .909 |
| Aspartate aminotransferase, AST (U/L) | 25.28 ± 0.249 | 28.62 ± 1.577 | .044 |
| Creatine phosphokinase, CPK (IU/L) | 145.5 ± 3.151 | 125.6 ± 11.29 | .084 |
| Lactate dehydrogenase, LDH (U/L) | 124.0 ± 0.589 | 139.1 ± 1.807 | <.001 |
| Serum 25-(OH)-D (nmol/L) | |||
| < 75 | 5240 (61.4) | 357 (51.9) | .019 |
| >125 | 246 (04.3) | 30 (05.6) | |
| 75–125 | 2161 (34.3) | 258 (42.5) | |
| Dietary/nutrition (per d), mean ± SE | |||
| Energy (kcal) | 2119 ± 15.52 | 1940 ± 39.06 | <.001 |
| Protein (g) | 83.36 ± 0.596 | 74.86 ± 1.664 | <.001 |
| Carbohydrate (g) | 255.0 ± 2.036 | 235.9 ± 5.012 | .002 |
| Sugar (g) | 110.9 ± 1.304 | 103.7 ± 2.525 | .013 |
| Fiber (g) | 17.68 ± 0.180 | 16.01 ± 0.438 | .001 |
| Fat (g) | 80.85 ± 0.694 | 73.29 ± 1.948 | .001 |
| Calcium (mg) | 986.0 ± 8.828 | 861.1 ± 33.58 | .001 |
| Vitamin C (mg) | 82.49 ± 2.147 | 77.51 ± 3.830 | .274 |
| Vitamin D (mg) | 4.693 ± 0.088 | 4.903 ± 0.250 | .446 |
| Iron (mg) | 15.26 ± 0.112 | 14.63 ± 0.468 | .198 |
| Potassium (mg) | 2704 ± 24.66 | 2572 ± 56.01 | .032 |
BMI = body mass index, CKD = chronic kidney diseases, COPD = chronic obstructive pulmonary disease, CVD = cardiovascular disease, DM = diabetic mellitus, Hyperlipidemia = total cholesterol ≥200 mg/dL, SE = standard error.
In terms of laboratory parameters, deceased participants had significantly lower serum albumin, hemoglobin, and 25-hydroxyvitamin D levels, along with higher levels of creatinine, blood urea nitrogen, aspartate aminotransferase, and lactate dehydrogenase (all P < .05). Nutritionally, they also exhibited significantly lower overall dietary intake of macronutrients and micronutrients (all P < .05).
3.2. Factors associated with all-cause mortality
Table 2 presents the crude and adjusted hazard ratios (aHRs) for all-cause mortality from the Cox proportional hazards regression analyses. After adjusting for key confounders, low muscle strength remained significantly associated with an increased risk of mortality (adjusted HR [aHR] = 1.656; 95% CI: 1.039–2.640; P = .035). Factors independently associated with a higher risk of mortality included advanced age (≥65 years vs <45 years: aHR = 4.019; 95% CI: 2.221–7.273; P < .001), male gender (aHR = 2.172; 95% CI: 1.498–3.150; P < .001), low income (PIR 0.00–1.30 vs >3.50: aHR = 2.121; 95% CI: 1.185–3.796; P = .013; PIR 1.31–3.50 vs >3.50: aHR = 1.783; 95% CI: 1.153–2.755; P = .011), greater waist circumference (aHR = 1.042; 95% CI: 1.022–1.063; P < .001), underweight status (aHR = 4.254; 95% CI: 2.056–8.801; P < .001), physical inactivity (aHR = 1.399; 95% CI: 1.003–1.950; P = .048), and the presence of comorbidities such as DM (aHR = 1.407; 95% CI: 1.047–1.891; P = .025), cancer (aHR = 1.869; 95% CI: 1.411–2.477; P < .001), and CKD (aHR = 2.002; 95% CI: 1.341–2.988; P = .001). In contrast, nonwhite race (aHR = 0.503; 95% CI: 0.314–0.807; P = .006), larger arm circumference (aHR = 0.874; 95% CI: 0.824–0.928; P < .001), and higher serum albumin levels (aHR = 0.943; 95% CI: 0.892–0.997; P = .039) were associated with a significantly lower risk of mortality.
Table 2.
Cox proportional hazards regression analyses for all-cause mortality.
| Variables | All-cause mortality | |
|---|---|---|
| Crude HR (95% CI) | Adjusted HR (95% CI) | |
| Low muscle strength (Ref = No) | ||
| Yes | 9.908 (7.850–12.50) | 1.656 (1.039–2.640) |
| Demographic | ||
| Age (yr) (Ref < 45) | ||
| 65+ | 17.99 (12.18–26.58) | 4.019 (2.221–7.273) |
| 45–64 | 3.247 (2.071–5.091) | 1.363 (0.742–2.503) |
| Gender (Ref = Female) | ||
| Male | 1.313 (1.110–1.554) | 2.172 (1.498–3.150) |
| Race (Ref = Non-Hispanic White) | ||
| Others | 0.424 (0.297–0.607) | 0.503 (0.314–0.807) |
| Non-Hispanic Black | 0.877 (0.664–1.158) | 0.817 (0.547–1.220) |
| Education (Ref = college or above) | ||
| Under 12th grade | 2.073 (1.405–3.060) | 0.934 (0.590–1.480) |
| High school graduate | 1.652 (1.175–2.323) | 0.973 (0.637–1.487) |
| Marriage (Ref = Married) | ||
| Never married | 0.684 (0.453–1.031) | 1.524 (0.833–2.790) |
| Widowed/Divorced/Separated | 2.570 (2.048–3.227) | 1.265 (0.838–1.912) |
| Income ratio (Ref > 3.50 (richest)) | ||
| 0.00–1.30 | 2.142 (1.538–2.983) | 2.121 (1.185–3.796) |
| >1.30–3.50 | 2.296 (1.594–3.309) | 1.783 (1.153–2.755) |
| Body composite | ||
| Height (cm) | 0.982 (0.970–0.994) | 0.996 (0.970–1.023) |
| Arm circumference (cm) | 0.963 (0.937–0.990) | 0.874 (0.824–0.928) |
| Waist circumference (cm) | 1.020 (1.011–1.028) | 1.042 (1.022–1.063) |
| BMI (kg/m2) (Ref = Normal) | ||
| Underweight | 2.379 (1.392–4.064) | 4.254 (2.056–8.801) |
| Overweight | 1.168 (0.841–1.621) | 1.100 (0.690–1.754) |
| Obese | 1.357 (1.006–1.831) | 0.964 (0.533–1.743) |
| Lifestyle | ||
| Smoking (Ref = nonsmoker) | ||
| Smoker | 2.075 (1.546–2.786) | 1.323 (0.962–1.819) |
| Alcohol drinking (Ref = nondrinker) | ||
| Heavy drinker | 0.618 (0.386–0.989) | 1.327 (0.737–2.389) |
| Light/moderate drinker | 1.140 (0.739–1.760) | 1.175 (0.820–1.683) |
| Physical activity (Ref = Active) | ||
| Inactive | 2.752 (2.245–3.374) | 1.399 (1.003–1.950) |
| Comorbidities (Ref = No) | ||
| DM | 3.810 (3.037–4.780) | 1.407 (1.047–1.891) |
| Hypertension | 4.253 (3.294–5.492) | 1.307 (0.880–1.941) |
| Hyperlipidemia | 0.903 (0.669–1.218) | – |
| CVD | 6.066 (4.600–7.999) | 1.003 (0.704–1.429) |
| COPD | 2.574 (1.822–3.638) | 1.202 (0.798–1.809) |
| Cancer | 3.793 (2.757–5.219) | 1.869 (1.411–2.477) |
| CKD | 5.267 (3.866–7.176) | 2.002 (1.341–2.988) |
| Laboratory | ||
| Albumin, blood (g/L) | 0.865 (0.840–0.892) | 0.943 (0.892–0.997) |
| Creatinine, blood (µmol/L) | 1.004 (1.003–1.005) | 1.001 (0.996–1.006) |
| Phosphorus (mmol/L) | 1.390 (0.659–2.932) | - |
| Blood urea nitrogen, BUN (mmol/L) | 1.255 (1.215–1.296) | 1.073 (0.973–1.183) |
| Hemoglobin (g/dL) | 0.814 (0.742–0.892) | 0.909 (0.814–1.015) |
| Alanine aminotransferase, ALT (U/L) | 1.001 (0.987–1.015) | – |
| Aspartate aminotransferase, AST (U/L) | 1.005 (1.003–1.008) | 1.004 (0.999–1.008) |
| Creatine phosphokinase, CPK (IU/L) | 0.999 (0.997–1.001) | – |
| Lactate dehydrogenase, LDH (U/L) | 1.005 (1.003–1.007) | 1.004 (0.999–1.009) |
| Serum 25-(OH)-D (nmol/L) (Ref = 75–125) | ||
| <75 | 0.701 (0.532–0.923) | 1.043 (0.715–1.521) |
| >125 | 1.035 (0.591–1.813) | 0.517 (0.232–1.155) |
| Dietary/nutrition (per day)* | ||
| Energy (kcal) | 0.997 (0.995–0.999) | 1.003 (0.991–1.015) |
| Protein (g) | 0.927 (0.895–0.961) | 0.970 (0.887–1.060) |
| Carbohydrate (g) | 0.981 (0.969–0.992) | 1.012 (0.919–1.114) |
| Sugar (g) | 0.979 (0.963–0.995) | 0.996 (0.933–1.065) |
| Fiber (g) | 0.803 (0.705–0.913) | 0.968 (0.621–1.510) |
| Fat (g) | 0.943 (0.911–0.977) | 0.931 (0.832–1.042) |
| Calcium (mg) | 0.995 (0.991–0.998) | 0.996 (0.991–1.001) |
| Vitamin C (mg) | 0.991 (0.974–1.009) | – |
| Vitamin D (mg) | 1.093 (0.879–1.360) | – |
| Iron (mg) | 0.890 (0.746–1.061) | – |
| Potassium (mg) | 0.999 (0.998–1.000) | 1.001 (0.998–1.004) |
BMI = body mass index, CI = confidence interval, CKD = chronic kidney diseases, COPD = chronic obstructive pulmonary disease, CVD = cardiovascular disease, DM = diabetic mellitus, HR = hazard ratio, Hyperlipidemia = total cholesterol ≥ 200 mg/dL.
Analysis with an increase in 10 units.
3.3. Subgroup analysis
3.3.1. Patients with CKD
In the CKD subgroup, low muscle strength was significantly associated with a higher risk of all-cause mortality after adjustment (aHR = 2.496; 95% CI: 1.502–4.150; P = .001), whereas no statistically significant association was observed among participants without CKD (aHR = 1.571; 95% CI: 0.917–2.692; P > .05) (Table 3). Most mortality-associated factors differed between participants with and without CKD. In the CKD subgroup, no HR was estimated for males due to an insufficient number of mortality events, leading to model non-convergence and indicating limited statistical power for this category.
Table 3.
Cox proportional hazards regression analyses for all-cause mortality stratified by chronic kidney diseases (CKD).
| Variables | Adjusted HR (95% CI) | |
|---|---|---|
| With CKD | Without CKD | |
| Low muscle strength (Ref = No) | ||
| Yes | 2.496 (1.502–4.150) | 1.571 (0.917–2.692) |
| Demographic | ||
| Age (yr) (Ref < 45) | ||
| 65+ | 7.646 (2.574–22.70) | 3.053 (1.672–5.573) |
| 45–64 | 4.718 (1.853–12.01) | 0.952 (0.545–1.665) |
| Gender (Ref = Female) | ||
| Male | – | 3.050 (2.070–4.494) |
| Race (Ref = Non-Hispanic White) | ||
| Others | 0.615 (0.396–0.955) | 0.542 (0.344–0.852) |
| Non-Hispanic Black | 0.695 (0.396–1.220) | 0.765 (0.512–1.144) |
| Education (Ref = college or above) | ||
| Under 12th grade | – | 0.990 (0.547–1.792) |
| High school graduate | – | 1.170 (0.760–1.801) |
| Marriage (Ref = Married) | ||
| Never married | 1.453 (0.574–3.680) | 1.450 (0.841–2.499) |
| Widowed/Divorced/Separated | 1.046 (0.649–1.685) | 1.692 (1.207–2.371) |
| Income ratio (Ref > 3.50 (richest)) | ||
| 0.00–1.30 | – | 2.236 (1.311–3.812) |
| >1.30–3.50 | – | 1.730 (1.017–2.945) |
| Body composite | ||
| Arm circumference (cm) | – | 0.854 (0.800–0.911) |
| Waist circumference (cm) | 1.018 (1.006–1.030) | 1.041 (1.019–1.064) |
| Lifestyle | ||
| Smoking (Ref = nonsmoker) | ||
| Smoker | 1.410 (0.821–2.422) | 1.426 (1.013–2.009) |
| Physical activity (Ref = Active) | ||
| Inactive | 1.313 (0.822–2.095) | 1.355 (1.000–1.836) |
| Comorbidities (Ref = No) | ||
| DM | 1.490 (0.993–2.236) | 1.040 (0.695–1.555) |
| Hypertension | 1.130 (0.720–1.776) | 1.507 (1.052–2.157) |
| Hyperlipidemia | – | – |
| CVD | 0.985 (0.612–1.584) | 1.010 (0.662–1.542) |
| COPD | – | 1.314 (0.892–1.934) |
| Cancer | 2.315 (1.414–3.792) | 1.809 (1.350–2.423) |
| Laboratory | ||
| Albumin, blood (g/L) | 0.959 (0.891–1.033) | 0.939 (0.894–0.985) |
| Creatinine, blood (µmol/L) | 1.002 (0.999–1.005) | 1.002 (0.992–1.012) |
| Phosphorus (mmol/L) | – | – |
| Blood urea nitrogen, BUN (mmol/L) | 1.000 (0.926–1.079) | 1.028 (0.890–1.187) |
| Hemoglobin (g/dL) | 0.937 (0.764–1.149) | 0.919 (0.793–1.065) |
| Alanine aminotransferase, ALT (U/L) | 0.999 (0.995–1.004) | 0.987 (0.968–1.005) |
| Aspartate aminotransferase, AST (U/L) | 1.006 (0.995–1.017) | 1.005 (0.998–1.012) |
| Creatine phosphokinase, CPK (IU/L) | 0.996 (0.992–1.000) | – |
| Lactate dehydrogenase, LDH (U/L) | 1.016 (1.010–1.022) | 1.007 (1.002–1.012) |
| Serum 25-(OH)-D (nmol/L) (Ref = 75–125) | ||
| <75 | – | 1.004 (0.731–1.380) |
| >125 | – | 0.502 (0.204–1.236) |
| Dietary/nutrition (per d)* | ||
| Energy (kcal) | 1.003 (0.999–1.007) | – |
| Protein (g) | – | 0.947 (0.888–1.009) |
| Fiber (g) | – | 0.945 (0.777–1.150) |
| Calcium (mg) | 0.995 (0.990–1.001) | – |
CI = confidence interval, COPD = chronic obstructive pulmonary disease, CVD = cardiovascular disease, DM = diabetic mellitus, HR = hazard ratio.
*Variables included only in the model with CKD. Not included in the non-CKD group due to missing data or non-significance.
3.3.2. Age
In the age-stratified analysis, low muscle strength was significantly associated with an increased risk of all-cause mortality in participants aged ≥ 65 years after adjustment (aHR = 1.854; 95% CI: 1.195–2.876; P = .007) (Fig. 2; Table S1, Supplemental Digital Content, https://links.lww.com/MD/Q364). No statistically significant association was observed in participants aged < 45 years (aHR = 2.354; 95% CI: 0.331–16.73) or 45 to 64 years (aHR = 2.534; 95% CI: 0.891–7.204) after adjustment (P > .05), indicating that the mortality impact of low muscle strength is more pronounced in older adults.
Figure 2.
Impact of low muscle strength on survival probability, stratified by time and age for individuals ≥65 yr.
3.3.3. Gender
In the gender-stratified analysis, low muscle strength was associated with a higher risk of all-cause mortality in both males and females; however, this association was not statistically significant after adjustment (males: aHR = 1.618; 95% CI: 0.917–2.855; females: aHR = 1.329; 95% CI: 0.689–2.561; P > .05) (Table S2, Supplemental Digital Content, https://links.lww.com/MD/Q364). Several factors significantly differed between sexes in their association with mortality, including older age, marital status (widowed, divorced, or separated), low income, underweight, DM, CKD, cancer, low albumin levels, and daily energy intake (P < .05).
3.4. Sensitivity analysis
In the sensitivity analysis, we applied an alternative definition of low muscle strength (“BMI-adjusted muscle strength”) to evaluate the robustness of our findings. Although low muscle strength was associated with an increased risk of all-cause mortality, the association did not remain statistically significant after adjustment (aHR = 1.196; 95% CI: 0.784–1.825; P > .05) (Table S3, Supplemental Digital Content, https://links.lww.com/MD/Q364). Consistent with the primary analysis, several factors were significantly associated with increased mortality risk, including older age, male gender, low income, physical inactivity, greater waist circumference, DM, cancer, and CKD. Conversely, nonwhite race, larger arm circumference, and higher serum albumin levels were associated with a reduced risk of mortality (Table S3, Supplemental Digital Content, https://links.lww.com/MD/Q364).
4. Discussion
Using NHANES, a nationally representative dataset, this retrospective cohort study found that low muscle strength was the main factor associated with all-cause mortality, consistent with findings from other studies, such as the study by Metter et al[27] on healthy men. Falling is one mechanism linking low muscle strength to mortality. Sarcopenic subjects, of which low muscle strength was an important factor, had a higher mortality risk than non-sarcopenic subjects and fell more often.[28] Low strength and muscle mass contribute to balance impairment,[29] which in turn leads to incidents of falling.[30]
Other significant factors included age ≥ 65 years, males, low income, underweight, physical inactivity, DM, cancer, and CKD. Age ≥ 65 years is consistent with the findings of previous studies.[31] The high prevalence of osteoporosis and malnutrition among older adults[32–34] heightens the vulnerability to fractures, which may result in the need for hospitalization. HGS as a reflection of low muscle strength also exhibits a correlation with patients experiencing prolonged hospital stays.[35,36] Given that hospitalization is a contributing factor to the depletion of muscle mass and strength,[37] this recurring pattern of functional deterioration and readmission could potentially contribute to mortality. Males were at a higher risk of all-cause mortality, consistent with other studies.[38] However, the subgroup analysis revealed no significant association between gender and low muscle strength. Significant research has reported similarities between low muscle strength and physical inactivity[31] and DM,[39] which could both potentially contribute to age-related muscle loss. Conversely, the decrease in skeletal muscle, which is the primary tissue that responds to insulin, might lead to insulin resistance, which in turn increases the risk of CVD and other metabolic diseases.[40–43] Cancer is also associated with reduced muscle strength.[44] Routine handgrip strength assessments in older adults and patients with CKD could serve as early screening tools to initiate timely nutritional and physical interventions.
While this study concentrated on muscle strength, in CKD this may be associated with loss of muscle mass. CKD has increased protein degradation and reduced protein synthesis, resulting in a state of negative protein balance, which eventually leads to a nutritional disturbance known as protein energy wasting, primarily attributed to malnutrition.[45] This is associated with the loss of muscle mass resulting in sarcopenia and cachexia.[46,47] Muscle loss in CKD patients is also linked to worse quality of life and outcomes, as well as increased hospitalization and mortality.³ However, declining muscle mass is not always parallel with declining muscle strength in older adults,[48] which suggests that further study is needed to address the status of muscle strength in CKD.
We detected no significant association with all-cause mortality when we replaced low muscle strength with BMI-adjusted muscle strength. This finding shows that BMI was not a major factor; moreover, one could be below the normal weight with adequate muscle mass, whereas others could be obese with low muscle mass. Increased body fat may accompany the loss of muscle mass, leading to marked weakness despite normal weight, a condition known as sarcopenic obesity.[49] Body composition is a more relevant factor than the total weight relative to height. This finding may suggest that body composition, rather than BMI alone, plays a more critical role in determining functional status and mortality risk.
This study has some strengths. As a large cohort study with detailed and comprehensive clinical and demographic data, this study allowed for the analysis of many factors related to all-cause mortality. The HGS test is recommended due to its satisfactory intra- and intertester reliability,[50,51] which provides a strong basis for this study. Additionally, the study accounts for survey weights, strata, and primary sampling units, ensuring that the estimates are nationally representative and reducing biases from oversampling or nonresponse.
This study has some limitations. First, it did not specify the year of death, corresponding age, or participants’ state of residence. Second, the small sample size (0.04% of the total population) and the imbalance between alive (n = 7925) and deceased (n = 683) participants may affect the results. However, the use of nationally representative NHANES data and comprehensive covariate adjustments enhances generalizability. Larger studies are needed to further investigate the association between low muscle strength and mortality, as well as strategies to reverse muscle strength loss, particularly in older adults and patients with renal disorders.
5. Recommendations
Nutritional interventions characterized by energy and protein supplementation can improve the nutritional intake of malnourished or sarcopenic patients.[52,53]Protein supplementation immediately after exercise has the best effect on muscle protein synthesis.[54,55] Strong evidence in older adults without health issues shows that exercise, namely resistance training and physical activity, has positive effects on muscle growth, strength, and performance.[56] Augmenting protein consumption can enhance the use of ingested amino acids for the creation of new proteins.[57,58] Exercise has anti-inflammatory benefits and leads to improved cardiovascular characteristics, including higher cardiac output and enhanced blood volume.[59]
6. Conclusion
In conclusion, this nationally representative analysis demonstrates that low muscle strength is independently associated with a higher risk of all-cause mortality, with the strongest effect observed among older adults and individuals with CKD. Given the simplicity, reliability, and low cost of handgrip strength measurement, incorporating routine muscle strength assessment into clinical and community health programs could facilitate early identification of high-risk individuals. Such efforts would enable timely, targeted interventions – including nutritional optimization and structured resistance training – to preserve muscle function and potentially improve survival outcomes.
The present findings also contribute to the growing evidence base on sarcopenia by highlighting the differential impact of low muscle strength across age and CKD status, underscoring the importance of personalized prevention strategies. Future research should adopt a multidimensional evaluation of sarcopenia, integrating muscle mass and physical performance measures, to further elucidate the mechanisms linking muscle weakness to mortality. Policymakers and healthcare providers are encouraged to prioritize muscle health within preventive care frameworks, particularly for aging populations and those with chronic diseases.
Author contributions
Conceptualization: Mei-Fei Hsieh.
Methodology: Mei-Fei Hsieh.
Formal analysis: Mei-Fei Hsieh.
Statistical interpretation: Shiow-Ing Wang.
Supervision: Hsiu-Fen Hsieh, Mei-Zen Huang, Hsiu-Hung Wang.
Validation: Shiow-Ing Wang.
Writing – original draft: Mei-Fei Hsieh.
Writing – review & editing: Hsiu-Fen Hsieh, Mei-Zen Huang, Hsiu-Hung Wang.
Supplementary Material
Abbreviations:
- aHR
- adjusted hazard ratio
- AST
- aspartate aminotransferase
- BMI
- body mass index
- CI
- confidence interval
- CKD
- chronic kidney disease
- COPD
- chronic obstructive pulmonary disease
- CVD
- cardiovascular disease
- DM
- diabetes mellitus
- HGS
- hand grip strength
- HR
- hazard ratio
- MET
- metabolic equivalent
- NHANES
- National Health and Nutrition Examination Survey
- PIR
- poverty-income ratio.
All authors read and approved the final manuscript.
This study was based on publicly available de-identified data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014 cycles. Therefore, ethical approval and informed consent were not required.
The authors have no funding and conflicts of interest to disclose.
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available for this article.
How to cite this article: Hsieh M-F, Wang S-I, Hsieh H-F, Huang M-Z, Wang H-H. Low muscle strength and mortality: Key risk factors in the National Health and Nutrition Examination Survey retrospective cohort study. Medicine 2025;104:42(e45284).
Contributor Information
Shiow-Ing Wang, Email: hhwang@kmu.edu.tw.
Hsiu-Fen Hsieh, Email: hsiufen96@kmu.edu.tw.
Mei-Zen Huang, Email: meizen@ntin.edu.tw.
References
- [1].Mitchell WK, Williams J, Atherton P, Larvin M, Lund J, Narici M. Sarcopenia, dynapenia, and the impact of advancing age on human skeletal muscle size and strength; a quantitative review. Front Physiol. 2012;3:260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Westbury LD, Beaudart C, Bruyère O, et al. ; International Musculoskeletal Ageing Network. Recent sarcopenia definitions-prevalence, agreement and mortality associations among men: findings from population-based cohorts. J Cachexia Sarcopenia Muscle. 2023;14:565–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Sabatino A, Cuppari L, Stenvinkel P, Lindholm B, Avesani CM. Sarcopenia in chronic kidney disease: what have we learned so far? J Nephrol. 2021;34:1347–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Leong DP, Teo KK, Rangarajan S, et al. ; Prospective Urban Rural Epidemiology (PURE) Study investigators. Prospective urban rural epidemiology (PURE) study investigators. Prognostic value of grip strength: findings from the prospective urban rural epidemiology (PURE) study. Lancet. 2015;386:266–73. [DOI] [PubMed] [Google Scholar]
- [5].Sayer AA, Cruz-Jentoft A. Sarcopenia definition, diagnosis and treatment: consensus is growing. Age Ageing. 2022;51:afac220. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Kirk B, Cawthon PM, Arai H, et al. ; Global Leadership Initiative in Sarcopenia (GLIS) group. The conceptual definition of sarcopenia: delphi consensus from the global leadership initiative in sarcopenia (GLIS). Age Ageing. 2024;53:afae052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Aartolahti E, Lönnroos E, Hartikainen S, Häkkinen A. Long-term strength and balance training in prevention of decline in muscle strength and mobility in older adults. Aging Clin Exp Res. 2020;32:59–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Tieland M, Trouwborst I, Clark BC. Skeletal muscle performance and ageing. J Cachexia Sarcopenia Muscle. 2018;9:3–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Hung WW, Ross JS, Boockvar KS, Siu AL. Recent trends in chronic disease, impairment and disability among older adults in the United States. BMC Geriatr. 2011;11:47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Zhang Y, Yu C, Li X. Kidney aging and chronic kidney disease. Int J Mol Sci . 2024;25:6585. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Moorthi RN, Avin KG. Clinical relevance of sarcopenia in chronic kidney disease. Curr Opin Nephrol Hypertens. 2017;26:219–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Ribeiro HS, Neri SR, Oliveira JS, Bennett PN, Viana JL, Lima RM. Association between sarcopenia and clinical outcomes in chronic kidney disease patients: a systematic review and meta-analysis. Clin Nutr. 2022;41:1131–40. [DOI] [PubMed] [Google Scholar]
- [13].Ali AM, Kunugi H. Screening for sarcopenia (physical frailty) in the COVID-19 era. Int J Endocrinol. 2021;2021:5563960. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Crosignani S, Sedini C, Calvani R, Marzetti E, Cesari M. Sarcopenia in primary care: screening, diagnosis, management. J Frailty Aging. 2021;10:226–32. [DOI] [PubMed] [Google Scholar]
- [15].Reichenheim ME, Lourenço RA, Nascimento JS, et al. Normative reference values of handgrip strength for Brazilian older people aged 65 to 90 years: evidence from the multicenter fibra-BR study. PLoS One. 2021;16:e0250925. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Nara K, Kumar P, Rathee R, et al. Grip strength performance as a determinant of body composition, muscular strength and cardiovascular endurance. J Phys Educ Sports. 2022;22:1618–25. [Google Scholar]
- [17].Kim J. Handgrip strength to predict the risk of all-cause and premature mortality in Korean adults: a 10-year cohort study. Int J Environ Res Public Health. 2021;19:39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Choe YR, Jeong JR, Kim YP. Grip strength mediates the relationship between muscle mass and frailty. J Cachexia Sarcopenia Muscle. 2020;11:441–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Dağ F, Erdoğan AT. Gender and age differences in absolute and relative handgrip strength of the Turkish population aged 8–27 years. Hand Surg Rehabil. 2020;39:556–63. [DOI] [PubMed] [Google Scholar]
- [20].Pan PJ, Lin CH, Yang NP, et al. Normative data and associated factors of hand grip strength among elderly individuals: the Yilan Study, Taiwan. Sci Rep. 2020;10:6611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Zhuang CL, Zhang FM, Li W, et al. Associations of low handgrip strength with cancer mortality: a multicentre observational study. J Cachexia Sarcopenia Muscle. 2020;11:1476–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Barbosa MV, Santos MP D, Leite JA, Rodrigues VD, De Pinho NB, Martucci RB. Association between functional aspects and health-related quality of life in patients with colorectal cancer: can handgrip strength be the measure of choice in clinical practice? Support Care Cancer. 2023;31:144. [DOI] [PubMed] [Google Scholar]
- [23].Byambaa A, Altankhuyag I, Damdinbazar O, Jadamba T, Byambasukh O. Anthropometric and body circumference determinants for hand grip strength: a population-based Mon-timeline study. J Aging Res. 2023;2023:6272743. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Doyev R, Axelrod R, Keinan-Boker L, et al. Energy intake is highly associated with handgrip strength in community-dwelling elderly adults. J Nutr. 2021;151:1249–55. [DOI] [PubMed] [Google Scholar]
- [25].Studenski SA, Peters KW, Alley DE, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69:547–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Wu S, Fisher-Hoch SP, Reininger B, McCormick JB. Recommended levels of physical activity are associated with reduced risk of the metabolic syndrome in Mexican–Americans. PLoS One. 2016;11:e0152896. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Metter EJ, Talbot LA, Schrager M, Conwit R. Skeletal muscle strength as a predictor of all-cause mortality in healthy men. J Gerontol A Biol Sci Med Sci. 2002;57:B359–65. [DOI] [PubMed] [Google Scholar]
- [28].Beaudart C, Zaaria M, Pasleau FO, Reginster JY, Bruyère O. Health outcomes of sarcopenia: a systematic review and meta-analysis. PLoS One. 2017;12:e0169548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Bijlsma AY, Pasma JH, Lambers D, et al. Muscle strength rather than muscle mass is associated with standing balance in elderly outpatients. J Am Med Dir Assoc. 2013;14:493–8. [DOI] [PubMed] [Google Scholar]
- [30].Muir SW, Berg K, Chesworth B, Klar N, Speechley M. Quantifying the magnitude of risk for balance impairment on falls in community-dwelling older adults: a systematic review and meta-analysis. J Clin Epidemiol. 2010;63:389–406. [DOI] [PubMed] [Google Scholar]
- [31].Akehurst E, Scott D, Rodriguez JP, et al. Associations of sarcopenia components with physical activity and nutrition in Australian older adults performing exercise training. BMC Geriatr. 2021;21:276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Gingrich A, Volkert D, Kiesswetter E, et al. Prevalence and overlap of sarcopenia, frailty, cachexia and malnutrition in older medical inpatients. BMC Geriatr. 2019;19:120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Beaudart C, Sanchez-Rodriguez D, Locquet M, Reginster JY, Lengelé L, Bruyère O. Malnutrition as a strong predictor of the onset of sarcopenia. Nutrients. 2019;11:2883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Wang YJ, Wang Y, Zhan JK, et al. Sarco-osteoporosis: prevalence and association with frailty in Chinese community-dwelling older adults. Int J Endocrinol. 2015;2015:482940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Roberts HC, Syddall HE, Sparkes J, et al. Grip strength and its determinants among older people in different healthcare settings. Age Ageing. 2014;43:241–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Hamasaki H, Kawashima Y, Katsuyama H, Sako A, Goto A, Yanai H. Association of handgrip strength with hospitalization, cardiovascular events, and mortality in Japanese patients with type 2 diabetes. Sci Rep. 2017;7:7041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Van Ancum JM, Scheerman K, Jonkman NH, et al. Change in muscle strength and muscle mass in older hospitalized patients: a systematic review and meta-analysis. Exp Gerontol. 2017;92:34–41. [DOI] [PubMed] [Google Scholar]
- [38].Crimmins EM, Shim H, Zhang YS, Kim JK. Differences between men and women in mortality and the health dimensions of the morbidity process. Clin Chem. 2019;65:135–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39].Lin JA, Hou JD, Wu SY. Effect of sarcopenia on mortality in type 2 diabetes: a long-term follow-up propensity score-matched diabetes cohort study. J Clin Med. 2022;11:4424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [40].Fried LP, Tangen CM, Walston J, et al. ; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56. [DOI] [PubMed] [Google Scholar]
- [41].Syddall H, Roberts HC, Evandrou M, Cooper C, Bergman H, Sayer AA. Prevalence and correlates of frailty among community-dwelling older men and women: findings from the Hertfordshire cohort study. Age Ageing. 2010;39:197–203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [42].Rasmussen BB, Fujita S, Wolfe RR, et al. Insulin resistance of muscle protein metabolism in aging. FASEB J. 2006;20:768–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [43].Sayer AA, Syddall HE, Dennison EM, et al. ; Hertfordshire Cohort. Grip strength and the metabolic syndrome: findings from the Hertfordshire cohort study. QJM. 2007;100:707–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [44].Anjanappa M, Corden M, Green A, et al. Sarcopenia in cancer: risking more than muscle loss. Tech Innov Patient Support Radiat Oncol. 2020;16:50–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [45].Massini G, Caldiroli L, Molinari P, Carminati FMI, Castellano G, Vettoretti S. Nutritional strategies to prevent muscle loss and sarcopenia in chronic kidney disease: what do we currently know? Nutrients. 2023;15:3107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [46].Ferreira MF, Böhlke M, Pauletto MB, Frühauf IR, Gonzalez MC. Sarcopenia diagnosis using different criteria as a predictor of early mortality in patients undergoing hemodialysis. Nutrition. 2022;95:111542. [DOI] [PubMed] [Google Scholar]
- [47].Yoshikoshi S, Yamamoto S, Suzuki Y, et al. Associations between dynapenia, cardiovascular hospitalizations, and all‐cause mortality among patients on haemodialysis. J Cachexia Sarcopenia Muscle. 2022;13:2417–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [48].Riviati N, Indra B. Relationship between muscle mass and muscle strength with physical performance in older adults: a systematic review. SAGE Open Med. 2023;11:20503121231214650. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [49].Deschenes MR. Effects of aging on muscle fibre type and size. Sports Med. 2004;34:809–24. [DOI] [PubMed] [Google Scholar]
- [50].Karagiannis C, Savva C, Korakakis V, et al. Test–retest reliability of handgrip strength in patients with chronic obstructive pulmonary disease. COPD. 2020;17:568–74. [DOI] [PubMed] [Google Scholar]
- [51].Sugiyama T, Whitney DG, Schmidt M, et al. Measuring grip strength in adolescents and adults with cerebral palsy in a clinic setting: feasibility, reliability, and clinical associations. Dev Med Child Neurol. 2023;66:87–94. [DOI] [PubMed] [Google Scholar]
- [52].Bauer J, Biolo G, Cederholm T, et al. Evidence-based recommendations for optimal dietary protein intake in older people: a position paper from the PROT-AGE study group. J Am Med Dir Assoc. 2013;14:542–59. [DOI] [PubMed] [Google Scholar]
- [53].Deutz N, Bauer J, Barazzoni R, Biolo G, Boirie Y, Bosy-Westphal A. Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN expert group. Clin Nutr. 2014;33:929–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [54].Witard O, Jackman S, Breen L, Smith K, Selby A, Tipton K. Myofibrillar muscle protein synthesis rates subsequent to a meal in response to increasing doses of whey protein at rest and after resistance exercise. Am J Clin Nutr. 2014;99:86–95. [DOI] [PubMed] [Google Scholar]
- [55].Smiles W, Areta J, Coffey V, et al. Modulation of autophagy signaling with resistance exercise and protein ingestion following short-term energy deficit. Am J Physiol Regul Integr Comp Physiol. 2015;309:R603–12. [DOI] [PubMed] [Google Scholar]
- [56].Jadczak A, Makwana N, Luscombe-Marsh N, Visvanathan R, Schultz T. Effectiveness of exercise interventions on physical function in community-dwelling frail older people: an umbrella review of systematic reviews. JBI Database System Rev Implement Rep. 2016;14:93–102. [DOI] [PubMed] [Google Scholar]
- [57].Moore D, Tang J, Burd N, Rerecich T, Tarnopolsky M, Phillips S. Differential stimulation of myofibrillar and sarcoplasmic protein synthesis with protein ingestion at rest and after resistance exercise. J Physiol. 2009;587:897–904. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [58].Pennings B, Koopman R, Beelen M, Senden J, Saris W, van Loon L. Exercising before protein intake allows for greater use of dietary protein-derived amino acids for de novo muscle protein synthesis in both young and elderly men. Am J Clin Nutr. 2011;93:322–31. [DOI] [PubMed] [Google Scholar]
- [59].Lo JH, U KP, Yiu T, Ong MT, Lee WY. Sarcopenia: current treatments and new regenerative therapeutic approaches. J Orthop Translat. 2020;23:38–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.