Abstract
Although sarcopenia is associated with various factors, the specific relationship between social determinants of health (SDoH) and sarcopenia, as well as mortality, remains unclear. This study seeks to examine how SDoH relates to both sarcopenia and mortality in individuals diagnosed with sarcopenia. The National Health and Nutrition Examination Survey provided data covering the period from 2003 to 2006. Assessment of sarcopenia was conducted using dual-energy X-ray absorptiometry. The SDoH factors considered encompassed education, marital status, household income, food security, health insurance coverage, employment status, home ownership, and healthcare access. Weighted multivariate logistic regression was applied in a cross-sectional analysis to estimate the association between SDoH and sarcopenia, while restricted cubic spline (RCS) regression was used to assess potential nonlinearity. Cox proportional hazards models and RCS were employed in the cohort study to investigate the associations between SDoH and all-cause as well as premature mortality. This analysis comprised 5210 participants in total. Cross-sectional analysis revealed that as SDoH scores increased as a continuous variable, the likelihood of sarcopenia significantly rose (odds ratio [OR] = 1.163, 95% confidence interval [CI]: 1.065–1.270, P = .003). When categorized variable, compared to participants with lower SDoH scores (category: none), those with higher scores (category: 6) were more susceptible to sarcopenia (OR = 2.250, 95% CI: 1.085–4.667, P = .034). The RCS curve suggested that SDoH was positively and linearly associated with the likelihood of developing sarcopenia. The cohort study revealed that among individuals with sarcopenia, higher SDoH scores were associated with an increased probability of all-cause mortality (adjusted hazard ratio [aHR] = 1.20, 95% CI: 1.10–1.30, P < .0001) and premature mortality (aHR = 1.41, 95% CI: 1.25–1.59, P < .0001). The RCS curve indicated that SDoH was significantly and positively linearly related to both all-cause and premature mortality in individuals at high risk for sarcopenia. Findings from this study demonstrated a notable association between SDoH and sarcopenia. Adverse SDoH are associated with increased all-cause and premature mortality. Developing innovative public health policies and interventions addressing SDoH is essential to mitigating sarcopenia and reducing mortality in affected populations.
Keywords: all-cause mortality, NHANES, Premature mortality, Sarcopenia, Social determinants of health
1. Introduction
Sarcopenia is a significant global public health issue, affecting approximately 8% to 16% of the world’s population.[1] Its prevalence has been increasing due to aging, chronic diseases, and other contributing factors, making it a key risk factor for mortality.[2,3] Sarcopenia is defined as the progressive loss of skeletal muscle mass and function associated with aging.[4] It is a disease state characterized by a decline in muscle mass and strength due to multiple factors, which consequently leads to reduced physical function and mobility. Despite its common occurrence, sarcopenia is often overlooked due to its gradual onset and subtle symptoms. However, it has profound health consequences, including an increased risk of falls, exacerbation of chronic diseases, and a decline in overall quality of life.[5]
Social determinants of health (SDoH) encompass a range of nonmedical factors that significantly impact health outcomes. These determinants include economic conditions, educational attainment, living environments, social support networks, and access to healthcare, forming the foundational framework that influences health disparities.[6,7] Individuals exposed to adverse SDoH may be at an increased risk of developing diseases and experiencing premature death. The Healthy People 2030 initiative in the U.S. seeks to enhance overall well-being and advance health equity by tackling negative SDoH.[8] Therefore, achieving better population health requires a dual focus on both medical treatment and the broader social context.
SDoH are among the most critical modifiable risk factors in the development of sarcopenia. Growing research interest has been directed at understanding the relationship between sarcopenia and adverse SDoH. Given the expanding body of evidence highlighting the comprehensive influence of SDoH on sarcopenia progression and mortality, it is essential to investigate their specific associations. By analyzing data from the National Health and Nutrition Examination Survey (NHANES), we investigated the relationship between SDoH and sarcopenia, as well as their impact on mortality and premature death. By identifying adverse social factors associated with sarcopenia, our findings aim to provide healthcare providers with critical insights to inform timely and effective preventive strategies.
2. Methods
2.1. Data source and study population
This study utilized data from the NHANES dataset, which consists of 5 major components: demographics, dietary information, physical examinations, laboratory test results, and questionnaire responses.[9,10] All NHANES procedures received formal approval from the National Center for Health Statistics Research Ethics Review Board, and informed consent was obtained from all study participants before participation.[11] Furthermore, all research activities strictly abided by the established guidelines and regulations.
The initial enrollment for the NHANES 2003 to 2006 cycles comprised 11,021 individuals. However, due to missing data for SDoH (n = 3353) (including employment status, marital status, home ownership, health insurance status, healthcare access, family income-to-poverty ratio), smoking status (n = 307), red cell distribution width (RDW) (n = 1662), lymphocytes (n = 150), alcohol consumption (n = 20), hypertension (n = 212), waist circumference (n = 2), total bilirubin (n = 22), HbA1c (n = 68), cardiovascular disease (CVD) (n = 14), and diabetes mellitus (DM) (n = 1), these participants were excluded from the analysis. Consequently, our final analytical sample included 5210 participants. Figure 1 outlines the participant selection process and provides detailed information.
Figure 1.
Flow chart of the study population. HbA1c = glycated hemoglobin, NHANES = National Health and Nutrition Examination Survey, PIR = poverty income ratio, SDoH = social determinants of health.
2.2. Assessments of SDoH
According to conventional classification, the SDoH of interest are divided into favorable and unfavorable conditions: employment status (employed, student, or retired vs unemployed), household income and poverty ratio (≥300% vs <300%), food security status (fully secure vs marginal, low, or very low security), healthcare access (having at least 1 regular healthcare facility vs none or relying on the emergency department), health insurance coverage (private vs government-provided or uninsured), educational attainment (high school graduate or higher vs below high school), homeownership status (owning a home vs renting or other living arrangements), and marital status (married or cohabiting vs single or not cohabiting).[12]
A cumulative SDoH score was generated by summing the 8 binary SDoH indicators, ranging from 0 to 8, where each favorable SDoH indicator was assigned a value of 0 and each adverse SDoH indicator was assigned a value of 1.[13] This method enables us to examine the association between cumulative adverse SDoH and sarcopenia, as well as the impact of cumulative adverse SDoH on individual all-cause mortality and premature all-cause mortality rates. Given the limited number of participants reporting 6, 7, or 8 adverse factors, we combined those with 6 or more into 1 category, leading to a cumulative SDoH variable spanning from 0 to 6 or more.[14] In the cohort study, a stratified analysis was conducted where the cumulative SDoH variable was categorized into ≥2 and <2 adverse SDoH domains, where ≥2 represented the median number of adverse SDoH domains.
2.3. Assessments of sarcopenia
The NHANES study measured body composition with the Hologic QDR-4500A fan-beam densitometer (Hologic, Inc.) utilizing dual-energy X-ray absorptiometry (DXA).[15,16]
Inclusion criteria: Participants aged ≥18 years. Exclusion criteria: To ensure safety, individuals who were pregnant, with a body weight >136 kg, or with a height >196 cm were excluded from the DXA measurements.
Complete DXA measurements were obtained from the NHANES 2003 to 2006 cycles. The commonly used appendicular skeletal muscle mass was defined as the sum of lean mass in the arms and legs.[17] The skeletal muscle mass index was calculated as appendicular skeletal muscle mass divided by body mass index (BMI), in accordance with the Foundation for the National Institutes of Health guidelines for sarcopenia. Sarcopenia was defined based on sex-specific cutoff values for the skeletal muscle mass index established by Foundation for the National Institutes of Health (0.789 for men, 0.512 for women).[18]
2.4. Mortality and premature mortality data
This study employed the probabilistic record linkage method provided by the National Center for Health Statistics to obtain mortality data from the 2003 to 2006 NHANES cohort. Follow-up data were available until December 31, 2019. Mortality was defined according to the International Classification of Diseases, 10th Revision.[19,20]
Follow-up duration was calculated as the period spanning from the date of the initial NHANES interview to either the last known date of life status for each participant or the end of follow-up. Premature mortality was defined as death occurring before the age of 75, which approximates the average lifespan of U.S. adults over the study period.[21] In sensitivity analyses, we examined the relationship between SDoH and premature mortality at alternative age cutoffs of 65, and 70 years.
2.5. Covariate assessment
The covariates in this study encompassed factors that have been suggested or demonstrated in prior research to be associated with SDoH or sarcopenia, such as demographic variables, results from blood tests, lifestyle choices, and existing comorbid conditions.[22] The demographic characteristics considered in this study included age, sex (male and female), race/ethnicity (White, Black, and other, with multiracial individuals included in the latter category), BMI, height, waist circumference, and poverty income ratio. Blood test results included white blood cell count, monocyte count, neutrophil count, lymphocyte count, hemoglobin level, RDW, HbA1c, bilirubin level, albumin level, and uric acid. Lifestyle factors included alcohol consumption and smoking. Comorbidities included DM, hypertension, hyperlipidemia, and CVD. The primary analysis included 8 SDoH indicators from the 2003 to 2006 NHANES cycles: employment status, family income-to-poverty ratio (calculated by dividing household income by the poverty threshold specific to household size), food security (assessed based on responses to ten questions regarding food inaccessibility, with complete accessibility defined as no affirmative responses), education level, healthcare accessibility (whether the participant had a regular healthcare facility or provider for medical advice rather than relying on emergency care), health insurance status (private vs government or uninsured), home ownership, and marital/cohabitation status.[23]
2.6. Statistical analysis
All analyses followed the NHANES analysis and reporting guidelines.[24] Given NHANES’ complex sampling design, mobile examination center weights were incorporated into all analyses as required for NHANES data analysis. Continuous variables were presented as means ± standard deviations, while categorical variables were expressed as percentages.[25] Weighted Student t tests were applied to assess differences in continuous variables between groups, whereas weighted chi-square tests were used for categorical variables.[26,27] The association between SDoH and sarcopenia was analyzed using logistic regression,[28] with findings expressed as adjusted odds ratios (OR) and 95% confidence intervals (CI). Model 1 accounted solely for SDoH adjustments. Model 2 was adjusted for SDoH, age, race, and sex. Model 3 was expanded to incorporate additional covariates, including age, race, sex, DM, waist circumference, BMI, alcohol consumption, HbA1c, hypertension, albumin, RDW, bilirubin, and CVD. Restricted cubic spline (RCS) regression was used to evaluate the dose–response relationship between statistically significant SDoH indicators and sarcopenia risk with Model 3.[29] Additionally, subgroup analyses were performed to assess study heterogeneity by stratifying participants based on age, alcohol consumption, BMI, DM, hypertension, and smoking status.
A multivariate Cox regression model was utilized in the cohort study to examine the association between SDoH and all-cause mortality, as well as premature mortality, among individuals with sarcopenia.[30] Adjusted hazard ratios (aHR) with 95% CIs were computed using Cox regression models.[31] Kaplan–Meier survival analyses were performed to compare the cumulative risks of all-cause mortality and premature mortality between participants with <2 and ≥2 adverse SDoH domains.[32] All analyses were performed using R version 4.2.2 (Posit PBC, formerly RStudio, Inc.). We also now list the key R packages used: survey, rms, ggplot2, and survival. Statistical significance was defined as a 2-sided P-value <.05.
3. Results
3.1. Cross-sectional study
3.1.1. Baseline characteristics
A total of 5210 NHANES participants from the 2003 to 2006 cycles were analyzed in this study. Figure 1 provides a visual representation of the study selection process. Significant demographic and clinical differences were observed between participants with and without sarcopenia. The prevalence of sarcopenia among the study participants was 13.59% (n = 708).The mean age of sarcopenic participants (55.0 years) was significantly higher than that of non-sarcopenic participants (43.3 years). The proportion of females was 50.6% in the non-sarcopenia group and 45.0% in the sarcopenia group. Compared to non-sarcopenic individuals, participants with sarcopenia exhibited: older age; higher BMI; lower height; larger waist circumference; and more severe poverty. Furthermore, sarcopenic participants had: higher white blood cell count; higher neutrophil count; lower lymphocyte count; higher RDW; higher HbA1c; lower serum albumin; higher uric acid levels; lower education levels; higher alcohol consumption; higher prevalence of DM, hypertension, hyperlipidemia, and CVD; and a significantly higher SDoH burden. Baseline characteristics and statistically significant variables (P < .05) are summarized in Table 1.
Table 1.
Baseline characteristics of participants in the study investigating the association between SDoH and sarcopenia based on NHANES data.
| Variable | Total | Non-sarcopenia | Sarcopenia | P value | |
|---|---|---|---|---|---|
| 1 | Age | 44.389 (0.370) | 43.312 (0.391) | 55.086 (0.782) | <.0001 |
| 2 | Sex | .036 | |||
| 3 | Female | 2493 (50.116) | 2174 (50.629) | 319 (45.021) | |
| 4 | Male | 2717 (49.884) | 2328 (49.371) | 389 (54.979) | |
| 5 | Ethnic group | <.0001 | |||
| 6 | Black | 979 (9.220) | 951 (9.923) | 28 (2.233) | |
| 7 | Other | 1515 (16.833) | 1145 (15.312) | 370 (31.949) | |
| 8 | White | 2716 (73.947) | 2406 (74.765) | 310 (65.818) | |
| 9 | BMI (kg/m2) | 27.451 (0.132) | 27.045 (0.140) | 31.482 (0.275) | <.0001 |
| 10 | Height (cm) | 169.152 (0.177) | 169.983 (0.173) | 160.895 (0.455) | <.0001 |
| 11 | Waist circumference (cm) | 95.190 (0.355) | 94.155 (0.382) | 105.472 (0.737) | <.0001 |
| 12 | PIR | 3.152 (0.054) | 3.202 (0.056) | 2.656 (0.070) | <.0001 |
| 13 | WBC | 7.280 (0.048) | 7.256 (0.053) | 7.525 (0.112) | .046 |
| 14 | MON | 7.775 (0.042) | 7.778 (0.041) | 7.750 (0.107) | .775 |
| 15 | NEU | 58.425 (0.157) | 58.326 (0.160) | 59.402 (0.437) | .02 |
| 16 | LYM | 30.261 (0.131) | 30.372 (0.140) | 29.165 (0.390) | .007 |
| 17 | HB | 14.630 (0.048) | 14.635 (0.048) | 14.586 (0.087) | .522 |
| 18 | RDW | 12.579 (0.019) | 12.550 (0.019) | 12.870 (0.044) | <.0001 |
| 19 | HbA1c | 5.418 (0.017) | 5.374 (0.017) | 5.858 (0.059) | <.0001 |
| 20 | Bilirubin total (mg/dL) | 0.760 (0.008) | 0.764 (0.009) | 0.728 (0.018) | .051 |
| 21 | Albumin (g/L) | 42.916 (0.092) | 43.041 (0.096) | 41.670 (0.151) | <.0001 |
| 22 | Uric acid (mg/dL) | 5.319 (0.022) | 5.283 (0.021) | 5.680 (0.076) | <.0001 |
| 23 | Smoke | <.001 | |||
| 24 | Former | 1301 (24.099) | 1063 (23.214) | 238 (32.886) | |
| 25 | Never | 2608 (49.484) | 2256 (49.638) | 352 (47.959) | |
| 26 | Now | 1301 (26.417) | 1183 (27.148) | 118 (19.154) | |
| 27 | Alcohol.user | <.0001 | |||
| 28 | Former | 1027 (15.857) | 814 (14.581) | 213 (28.525) | |
| 29 | Heavy | 1099 (22.608) | 971 (23.065) | 128 (18.077) | |
| 30 | Mild | 1657 (33.945) | 1464 (34.337) | 193 (30.055) | |
| 31 | Moderate | 790 (17.297) | 730 (18.059) | 60 (9.727) | |
| 32 | Never | 637 (10.293) | 523 (9.958) | 114 (13.617) | |
| 33 | DM | <.0001 | |||
| 34 | DM | 643 (8.778) | 437 (7.000) | 206 (26.447) | |
| 35 | No | 4289 (86.345) | 3843 (88.505) | 446 (65.022) | |
| 36 | Pre-DM | 277 (4.864) | 221 (4.495) | 56 (8.530) | |
| 37 | Hypertension | <.0001 | |||
| 38 | No | 3250 (67.451) | 2934 (69.482) | 316 (47.278) | |
| 39 | Yes | 1960 (32.549) | 1568 (30.518) | 392 (52.722) | |
| 40 | Hyperlipidemia | <.0001 | |||
| 41 | No | 1527 (30.684) | 1419 (32.284) | 108 (14.793) | |
| 42 | Yes | 3683 (69.316) | 3083 (67.716) | 600 (85.207) | |
| 43 | CVD | <.0001 | |||
| 44 | No | 4703 (93.133) | 4144 (94.383) | 559 (80.718) | |
| 45 | Yes | 507 (6.867) | 358 (5.617) | 149 (19.282) | |
| 46 | SDoH | 2.088 (0.063) | 2.045 (0.066) | 2.514 (0.093) | <.0001 |
| 47 | Employment | .008 | |||
| 48 | Employed, student, retired | 4086 (80.298) | 3547 (80.789) | 539 (75.419) | |
| 49 | Not employed | 1124 (19.702) | 955 (19.211) | 169 (24.581) | |
| 50 | PIR | <.0001 | |||
| 51 | <3 | 2984 (45.440) | 2461 (43.895) | 523 (60.789) | |
| 52 | ≥3 | 2226 (54.560) | 2041 (56.105) | 185 (39.211) | |
| 53 | Food.security | .012 | |||
| 54 | Full food security | 4131 (84.780) | 3591 (85.142) | 540 (81.183) | |
| 55 | Marginal, low, or very low | 1079 (15.220) | 911 (14.858) | 168 (18.817) | |
| 56 | Education | <.0001 | |||
| 57 | High school or more | 3848 (84.163) | 3461 (85.555) | 387 (70.572) | |
| 58 | Less than high school | 1359 (15.812) | 1038 (14.445) | 321 (29.428) | |
| 59 | Access.to.healthcare | .07 | |||
| 60 | No routine place, or ER/hospital/other | 963 (17.337) | 852 (17.634) | 111 (14.393) | |
| 61 | Routine place to go for healthcare | 4247 (82.663) | 3650 (82.366) | 597 (85.607) | |
| 62 | Health.insurance | <.0001 | |||
| 63 | Government or no insurance | 2239 (33.242) | 1844 (31.819) | 395 (47.379) | |
| 64 | Private insurance | 2971 (66.758) | 2658 (68.181) | 313 (52.621) | |
| 65 | Housing.instability | .021 | |||
| 66 | Own home | 3439 (70.486) | 2927 (69.981) | 512 (75.509) | |
| 67 | Rent or other arrangement | 1771 (29.514) | 1575 (30.019) | 196 (24.491) | |
| 68 | Marital.status | .742 | |||
| 69 | Married or living with a partner | 3321 (67.489) | 2856 (67.391) | 465 (68.461) | |
| 70 | Not married nor living with a partner | 1889 (32.511) | 1646 (32.609) | 243 (31.539) |
BMI = body mass index, CVD = cardiovascular disease, DM = diabetes mellitus, HB = hemoglobin, HbA1c = glycated hemoglobin, LYM = Lymphocyte count, MON = monocyte count, NEU = neutrophil count, PIR = poverty income ratio, RDW = red cell distribution width, WBC = white blood cell count.
3.1.2. Association between SDoH and sarcopenia
SDoH were categorized into 7 groups for analysis. When regarded as a continuous variable, SDoH showed a significant positive association with sarcopenia risk. According to the results of Model 1, the OR was 1.137 (95% CI: 1.077–1.201), with a statistically significant P value of <.0001. Even after adjustment in Model 2, the observed association remained significant, with an odds ratio of 1.182 (95% CI: 1.107–1.263) and a P value below .0001. After further adjustments in Model 3, a positive association was still observed (OR = 1.163, 95% CI: 1.065–1.270, P = .003).
When analyzed as a categorical variable, participants in the highest SDoH group (6) had a greater risk of developing sarcopenia compared to those in the lowest SDoH group (no). In Model 1, a significant association was observed (OR = 1.977, 95% CI: 1.306–2.992, P = .002). This increased to OR = 2.547 (95% CI: 1.546–4.197, P < .001) in Model 2 and remained elevated in Model 3 (OR = 2.250, 95% CI: 1.085–4.667, P = .034). The detailed associations between SDoH and sarcopenia risk are presented in Table 2.
Table 2.
Analysis of continuous and categorical variables related to SDoH and sarcopenia: Results from the 2003 to 2006 National Health and Nutrition Examination Survey (NHANES).
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| Character | 95% CI | P | 95% CI | P | 95% CI | P |
| No | Ref | Ref | Ref | |||
| 1 | 1.330 (0.978–1.808) | .068 | 1.248 (0.888–1.754) | .190 | 1.268 (0.774, 2.076) | .293 |
| 2 | 1.336 (0.875–2.041) | .171 | 1.199 (0.790–1.821) | .375 | 1.014 (0.603, 1.706) | .950 |
| 3 | 1.789 (1.130–2.834) | .015 | 1.730 (1.083–2.764) | .024 | 1.643 (0.936, 2.884) | .075 |
| 4 | 2.550 (1.621–4.010) | <.001 | 2.664 (1.640–4.330) | <.001 | 2.456 (1.307, 4.616) | .012 |
| 5 | 1.775 (1.081–2.913) | .025 | 2.037 (1.217–3.407) | .009 | 1.953 (1.006, 3.791) | .049 |
| 6 | 1.977 (1.306–2.992) | .002 | 2.547 (1.546–4.197) | <.001 | 2.250 (1.085, 4.667) | .034 |
| P for trend (character 2 integer) | <.0001 | <.0001 | .003 | |||
| SDoH | 1.137 (1.077–1.201) | <.0001 | 1.182 (1.107–1.263) | <.0001 | 1.163 (1.065–1.270) | .003 |
SDoH.
Model 1: SDoH.
Model 2: SDoH, sex, ethnic group, age.
Model 3: SDoH, sex, age, ethnic group, DM, waist circumference (cm), BMI (kg/m2), alcohol user, HbA1c, Hypertension, albumin (g/L), RDW, bilirubin total (mg/dl), CVD.
BMI = body mass index, CI = confidence interval, CVD = cardiovascular disease, DM = diabetes mellitus, HbA1c = glycated hemoglobin, RDW = red cell distribution width.
A RCS curve was used to visualize the relationship between SDoH and sarcopenia, as shown in Figure 2. A notable positive linear correlation was identified between SDoH and sarcopenia.
Figure 2.
Restricted cubic spline (RCS) curve for the association between SDoH and sarcopenia. OR = odds ratio, SDoH = social determinants of health.
The findings indicate a significant relationship between adverse SDoH and sarcopenia risk across both genders. As detailed in Figure S1, Supplemental Digital Content 1, both males and females exhibited a significant positive linear link between adverse SDoH and an elevated risk of sarcopenia.
3.1.3. Subgroup analysis of the association with sarcopenia
To further explore the association between SDoH and sarcopenia in different populations, we conducted a subgroup analysis stratified by age (<45 and ≥45 years), alcohol consumption (former, never, mild, moderate, and heavy), BMI (<30 and ≥30 kg/m2), diabetes status (no, prediabetes, and diabetes), hypertension (yes and no), and smoking status (yes and no). Our findings revealed that the association between SDoH and sarcopenia was more pronounced among alcohol consumers. The absence of significant interactions between SDoH and other variables supports the reliability of our study’s findings. However, individuals with diabetes, hypertension, and those aged 45 years or older were more susceptible to sarcopenia than other participants. The detailed results are presented in Table S1, Supplemental Digital Content 2.
3.1.4. Subgroup analysis of the association of SDoH with sarcopenia
In the subgroup analysis of social determinants of health (SDoH) components, our findings indicated that the effects of these determinants on sarcopenia did not significantly differ across subgroups, with P values all surpassing the .05 threshold. Nevertheless, the majority of social determinants – including employment status, food security, access to healthcare services, and housing stability - demonstrated significant associations with the risk of sarcopenia. Detailed findings are presented in Table S2, Supplemental Digital Content 3.
3.2. Cohort study
3.2.1. Baseline characteristics
This cohort study included 708 participants with sarcopenia; 389 (54.9%) were male. During the study, 272 deaths (38.4%) were recorded. The average length of follow-up was 147.2 months, with a range of 122.5 to 186.2 months (interquartile range). Notably, a significant trend of increasing mortality was observed across SDoH categories, with higher mortality rates in the high SDoH group (P < .01). The detailed results are presented in Table 3.
Table 3.
Baseline characteristics of participants in a cohort study based on NHANES data on the association between SDoH and sarcopenia-related mortality.
| Variable | Total | Survivors | Non-survivors | P value | |
|---|---|---|---|---|---|
| 1 | Age | 55.086 (0.782) | 49.259 (0.896) | 68.762 (1.121) | <.0001 |
| 2 | Sex | .011 | |||
| 3 | Female | 319 (45.021) | 217 (49.906) | 102 (33.557) | |
| 4 | Male | 389 (54.979) | 219 (50.094) | 170 (66.443) | |
| 5 | Ethnic group | <.0001 | |||
| 6 | Black | 28 (2.233) | 17 (2.129) | 11 (2.479) | |
| 7 | Other | 370 (31.949) | 274 (39.539) | 96 (14.134) | |
| 8 | White | 310 (65.818) | 145 (58.332) | 165 (83.387) | |
| 9 | BMI (kg/m2) | 31.482 (0.275) | 31.900 (0.309) | 30.502 (0.382) | .002 |
| 10 | Height (cm) | 160.895 (0.455) | 159.965 (0.561) | 163.079 (0.693) | .002 |
| 11 | Waist circumference (cm) | 105.472 (0.737) | 104.938 (0.884) | 106.725 (0.862) | .099 |
| 12 | PIR | 2.656 (0.070) | 2.742 (0.083) | 2.454 (0.155) | .131 |
| 13 | WBC | 7.525 (0.112) | 7.444 (0.097) | 7.714 (0.294) | .385 |
| 14 | RDW | 12.870 (0.044) | 12.738 (0.050) | 13.179 (0.084) | <.001 |
| 15 | MON | 7.750 (0.107) | 7.429 (0.138) | 8.504 (0.133) | <.0001 |
| 16 | LYM | 29.165 (0.390) | 30.162 (0.496) | 26.824 (0.670) | <.001 |
| 17 | NEU | 59.402 (0.437) | 58.667 (0.576) | 61.125 (0.706) | .016 |
| 18 | HB | 14.586 (0.087) | 14.619 (0.116) | 14.507 (0.109) | .488 |
| 19 | HbA1c | 5.858 (0.059) | 5.755 (0.072) | 6.099 (0.102) | .009 |
| 20 | Bilirubin total (mg/dL) | 0.728 (0.018) | 0.704 (0.021) | 0.782 (0.023) | .008 |
| 21 | Albumin (g/L) | 41.670 (0.151) | 41.977 (0.204) | 40.950 (0.156) | <.001 |
| 22 | Uric acid (mg/dL) | 5.680 (0.076) | 5.559 (0.084) | 5.966 (0.119) | .004 |
| 23 | Smoke | <.0001 | |||
| 24 | Former | 238 (32.886) | 113 (27.457) | 125 (45.630) | |
| 25 | Never | 352 (47.959) | 254 (55.636) | 98 (29.942) | |
| 26 | Now | 118 (19.154) | 69 (16.907) | 49 (24.428) | |
| 27 | Alcohol user | .164 | |||
| 28 | Former | 213 (28.525) | 114 (24.870) | 99 (37.103) | |
| 29 | Heavy | 128 (18.077) | 94 (20.265) | 34 (12.941) | |
| 30 | Mild | 193 (30.055) | 121 (29.639) | 72 (31.032) | |
| 31 | Moderate | 60 (9.727) | 38 (10.479) | 22 (7.962) | |
| 32 | Never | 114 (13.617) | 69 (14.748) | 45 (10.961) | |
| 33 | DM | <.001 | |||
| 34 | DM | 206 (26.447) | 101 (20.782) | 105 (39.745) | |
| 35 | No | 446 (65.022) | 297 (70.797) | 149 (51.469) | |
| 36 | Pre-DM | 56 (8.530) | 38 (8.422) | 18 (8.786) | |
| 37 | Hypertension | <.001 | |||
| 38 | No | 316 (47.278) | 230 (52.411) | 86 (35.230) | |
| 39 | Yes | 392 (52.722) | 206 (47.589) | 186 (64.770) | |
| 40 | Hyperlipidemia | .885 | |||
| 41 | No | 108 (14.793) | 68 (14.634) | 40 (15.167) | |
| 42 | Yes | 600 (85.207) | 368 (85.366) | 232 (84.833) | |
| 43 | CVD | <.0001 | |||
| 44 | No | 559 (80.718) | 386 (88.768) | 173 (61.824) | |
| 45 | Yes | 149 (19.282) | 50 (11.232) | 99 (38.176) | |
| 46 | SDoH | 2.514 (0.093) | 2.465 (0.116) | 2.629 (0.136) | .36 |
| 47 | SDOHX | .009 | |||
| 48 | <2 | 174 (37.237) | 117 (41.066) | 57 (28.249) | |
| 49 | ≥2 | 534 (62.763) | 319 (58.934) | 215 (71.751) | |
| 50 | Employment | .738 | |||
| 51 | Employed, student, retired | 539 (75.419) | 326 (75.935) | 213 (74.208) | |
| 52 | Not employed | 169 (24.581) | 110 (24.065) | 59 (25.792) | |
| 53 | PIR | .145 | |||
| 54 | <3 | 523 (60.789) | 311 (58.267) | 212 (66.706) | |
| 55 | ≥3 | 185 (39.211) | 125 (41.733) | 60 (33.294) | |
| 56 | Food security | .004 | |||
| 57 | Full food security | 540 (81.183) | 309 (78.216) | 231 (88.146) | |
| 58 | Marginal, low, or very low | 168 (18.817) | 127 (21.784) | 41 (11.854) | |
| 59 | Education | .89 | |||
| 60 | High school or more | 387 (70.572) | 232 (70.726) | 155 (70.210) | |
| 61 | Less than high school | 321 (29.428) | 204 (29.274) | 117 (29.790) | |
| 62 | Access to healthcare | .01 | |||
| 63 | No routine place, or ER/hospital/other | 111 (14.393) | 94 (17.628) | 17 (6.803) | |
| 64 | Routine place to go for healthcare | 597 (85.607) | 342 (82.372) | 255 (93.197) | |
| 65 | Health insurance | .129 | |||
| 66 | Government or no insurance | 395 (47.379) | 239 (44.274) | 156 (54.668) | |
| 67 | Private insurance | 313 (52.621) | 197 (55.726) | 116 (45.332) | |
| 68 | Housing.instability | .639 | |||
| 69 | Own home | 512 (75.509) | 307 (74.881) | 205 (76.984) | |
| 70 | Rent or other arrangement | 196 (24.491) | 129 (25.119) | 67 (23.016) | |
| 71 | Marital status | <.001 | |||
| 72 | Married or living with a partner | 465 (68.461) | 314 (73.893) | 151 (55.710) | |
| 73 | Not married nor living with a partner | 243 (31.539) | 122 (26.107) | 121 (44.290) |
BMI = body mass index, CVD = cardiovascular disease, DM = diabetes mellitus, HB = hemoglobin, HbA1c = glycated hemoglobin, LYM = Lymphocyte count, MON = monocyte count, NEU = neutrophil count, PIR = poverty income ratio, RDW = red cell distribution width, WBC = white blood cell count.
3.2.2. Association between SDoH and all-cause and premature mortality in individuals at risk for sarcopenia
The association between SDoH and all-cause as well as premature mortality among sarcopenia individuals is shown in Table 4. In the continuous variable SDoH model, after adjusting for various covariates, the results for all-cause mortality were as follows: Model 1: aHR = 1.04 (95% CI: 0.96–1.12), P = .35, Model 2: aHR = 1.21 (95% CI: 1.11–1.33), P < .0001, Model 3: aHR = 1.20 (95% CI: 1.10–1.30), P < .0001. For premature mortality: Model 1: aHR = 1.17 (95% CI: 1.02–1.33), P = .02, Model 2: aHR = 1.37 (95% CI: 1.19–1.58), P < .0001, Model 3: aHR = 1.41 (95% CI: 1.25–1.59), P < .0001. In the categorical SDoH model, after adjusting for various covariates, compared to individuals with SDoH <2, those with SDoH ≥2 had an elevated risk of all-cause mortality: Model 1: aHR = 1.64 (95% CI: 1.15–2.32), P = .01, Model 2: aHR = 1.68 (95% CI: 1.20–2.34), P = .002, Model 3: aHR = 1.72 (95% CI: 1.32–2.23), P < .0001.Similarly, for premature mortality, individuals with SDoH ≥ 2 exhibited an increased risk compared to those with SDoH <2: Model 1: aHR = 2.27 (95% CI: 0.95–5.45), P = .07, Model 2: aHR = 2.98 (95% CI: 1.25–7.11), P = .01, Model 3: aHR = 3.59 (95% CI: 1.68–7.67), P < .001.
Table 4.
Analysis of SDoH- and sarcopenia-related all-cause and premature mortality: results from the 2003 to 2006 National Health and Nutrition Examination Survey (NHANES).
| All-cause mortality | ||||||
|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||||
| Character | 95% CI | P | 95% CI | P | 95% CI | P |
| <2 | Ref | Ref | Ref | |||
| ≥2 | 1.64 (1.15–2.32) | .01 | 1.68 (1.20–2.34) | .002 | 1.72 (1.32–2.23) | <.0001 |
| SDoH | 1.04 (0.96–1.12) | .35 | 1.21 (1.11–1.33) | <.0001 | 1.20 (1.10–1.30) | <.0001 |
| Premature mortality | ||||||
| <2 | Ref | Ref | Ref | |||
| ≥2 | 2.27 (0.95–5.45) | .07 | 2.98 (1.25–7.11) | .01 | 3.59 (1.68–7.67) | <.001 |
| SDoH | 1.17 (1.02–1.33) | .02 | 1.37 (1.19–1.58) | <.0001 | 1.41 (1.25–1.59) | <.0001 |
SDoH.
Model 1: SDoH.
Model 2: SDoH, sex, ethnic group, age.
Model 3: SDoH, sex, age, ethnic group, DM, waist circumference (cm), BMI (kg/m2), alcohol user, HbA1c, hypertension, albumin (g/L), RDW, total bilirubin (mg/dl), CVD.
BMI = body mass index, CI = confidence interval, CVD = cardiovascular disease, DM = diabetes mellitus, HbA1c = glycated Hemoglobin, RDW = red cell distribution width, SDoH = social determinants of health.
The RCS analysis, as shown in Figure 3, indicates a significant linear relationship in a positive direction between SDoH and sarcopenia-related all-cause mortality.
Figure 3.
Restricted cubic spline (RCS) curve for the association between SDoH and all-cause mortality among individuals with sarcopenia. HR = hazard ratio, SDoH = social determinants of health.
Kaplan–Meier survival curves indicated that individuals with SDoH ≥2 had significantly higher all-cause mortality (Fig. 4) and premature mortality.
Figure 4.
The Kaplan–Meier survival curve demonstrates that individuals with an SDoH score of ≥2 have significantly higher all-cause mortality. SDoH = social determinants of health.
The restricted cubic spline curve shows that, in the female population, there is no statistically significant association between SDoH score and all-cause mortality. However, in the male population, the SDoH score is positively and linearly associated with an increased risk of all-cause mortality, even after adjusting for multiple covariates (Model 3). The corresponding results are presented in Figure S2, Supplemental Digital Content 4.
4. Discussion
With the continued aging of the global population, public health concerns are increasingly prominent.[33,34] Among the numerous health challenges individuals face, sarcopenia and its associated mortality rates represent critical issues.[35] This study, utilizing data from the 2003 to 2006 National Health and Nutrition Examination Survey (NHANES), found that, after making adjustments for potential risk factors, SDoH was independently associated with sarcopenia. A significant positive linear association was found between SDoH and sarcopenia.
Furthermore, our results suggest a significant association between SDoH and an increased risk of all-cause and premature mortality in individuals with sarcopenia. This study is the 1st to assess the relationship between SDoH and all-cause mortality in sarcopenia populations, as well as its link to premature mortality. These findings suggest that SDoH is associated with both the occurrence of sarcopenia and increased all-cause and premature mortality in affected individuals.
4.1. The relationship between social determinants of health (SDoH) and sarcopenia
This study demonstrates that adverse SDoH is strongly associated with a higher prevalence of sarcopenia. Important socioeconomic factors, such as employment status, household income, food security, level of education, marital status, access to healthcare, health insurance coverage, and homeownership, were found to significantly influence sarcopenia development. A potential explanation for this association is that financial hardship may reduce individuals’ ability to prioritize proper nutrition.[36] Economic constraints can limit access to high-quality protein and essential nutrients vital for maintaining muscle mass and function.[37] These findings highlight the importance of implementing strong income security policies for low-income populations, particularly among those exposed to multiple adverse SDoH factors. Sarcopenia is also strongly associated with food security and health insurance status. Food security encompasses not only access to sufficient food but also food safety, hygienic food processing, and nutritional adequacy. Studies suggest that exposure to harmful foodborne substances, such as excessive organophosphate intake, may contribute to metabolic disruptions and hormonal imbalances, increasing sarcopenia risk.[38] A lack of health insurance may delay early sarcopenia detection and hinder access to effective medical interventions.[39] In contrast, long-term care insurance can provide rehabilitation support, such as physical therapy and exercise programs, which help preserve muscle function. These findings underscore the importance of expanding government health insurance coverage to mitigate sarcopenia risk. Additionally, education level and marital status are also closely associated with sarcopenia prevalence.
A particularly noteworthy finding of this study is that cumulative disadvantage in SDoH has a significant impact on sarcopenia. Individuals with 6 or more adverse SDoH factors are 2.25 times more likely to develop sarcopenia compared to those without any adverse SDoH factors. This suggests that multiple unfavorable SDoH factors interact synergistically to compromise individual health. For instance, low-income individuals often experience food insecurity, characterized by unstable food access and inadequate nutrient intake, which negatively affects health.[40] Moreover, lower education levels are often linked to reduced cognitive awareness of health-related issues and lower enrollment in health insurance programs.[41] Individuals with limited education may also struggle to afford private health insurance, preventing them from fully benefiting from available healthcare services.[42]
4.2. The relationship between SDoH and all-cause and premature mortality
Our research highlights a strong connection between SDoH and elevated risks of all-cause and premature mortality in individuals vulnerable to sarcopenia. Socioeconomic factors, such as education level, income, and employment, significantly drive inequalities in mortality rates. Addressing these disparities is a crucial step toward achieving greater health equity.[43] However, it is essential to consider the interdependence of all 8 SDoH indicators, as they exert combined effects on health outcomes.
Expanding health insurance coverage is significantly associated with lower mortality rates. By reducing financial barriers to healthcare, insurance coverage helps prevent medical delays and avoidable deaths.[44,45] Additionally, health insurance facilitates access to mental health services, prescription medications, and rehabilitation therapies, which are particularly crucial in managing chronic conditions. Effective chronic disease management – including routine medical checkups, medication adherence, and lifestyle interventions – can substantially reduce mortality risks.[46,47]
We also observed that marital status and food security play critical roles in determining mortality risk. Being married or cohabiting is linked to higher survival rates, potentially due to shared economic resources, increased social support, and reduced psychological stress.[48] Furthermore, our study, in alignment with previous research, found that adults experiencing food insecurity were approximately twice as likely to face an increased risk of all-cause and premature mortality compared to those with stable food security.[49] The mechanisms underlying this association likely involve both inadequate intake of essential nutrients and unhealthy dietary patterns. Therefore, beyond traditional food assistance programs, additional efforts should focus on optimizing protein and nutrient intake to mitigate these risks.
In adults in the United States, 2 or more adverse SDoH factors are significantly associated with an increased risk of premature death. In contrast, individuals without adverse SDoH factors exhibit significantly lower mortality rates, emphasizing the importance of promoting optimal SDoH conditions. Notably, although individual SDoH components are independently associated with mortality, the cumulative burden of multiple adverse SDoH factors has a much greater impact on all-cause mortality and premature mortality than single lifestyle and biological risk factors. This finding is especially relevant given that adverse SDoH factors tend to cluster in the same individuals, amplifying their harmful effects.[50]
Furthermore, we found that the association between SDoH and all-cause mortality was stronger in men than in women. This finding aligns with previous studies, which have also observed a stronger correlation in men. In most cases, when faced with adverse SDoH, men tend to prioritize increasing household income over their own health, often reducing physical activity due to work demands and limiting their own nutritional intake to ensure adequate nutrition for their families. Additionally, men are less likely to seek medical care, further increasing their mortality risk.[51]
The findings of this study have important implications for clinical practice and public health policy. First, sarcopenia – a chronic condition closely linked to health and aging – is strongly associated with SDoH. Therefore, public health interventions should account for socioeconomic factors, particularly targeting low-income and less-educated populations, by implementing tailored muscle health promotion programs. In patients with chronic diseases, in addition to standard drug treatment, improving living conditions, eliminating adverse SDoH, and placing more emphasis on muscle health may effectively delay the onset of sarcopenia, thereby improving quality of life and reducing the risk of death.
5. Advantages and limitations
Advantages: This study has several key strengths. First, we utilized a large-scale study design and carried out a comprehensive survey on a nationally representative sample of U.S. adults, enhancing the reliability and comprehensiveness of our findings. Second, we performed RCS analysis, which clearly illustrated a positive linear association between SDoH and sarcopenia. Third, after adjusting for potential confounding factors, the results are more generalizable to real-world contexts.
Limitations: However, the study has some limitations. First, as an observational study, it cannot establish causal relationships. Second, although we adjusted for multiple confounding variables, the possibility of residual confounding cannot be entirely ruled out. Third, there may be biases in the assessment of social factors. Therefore, future studies should adopt longitudinal designs to confirm the relationship between SDoH and sarcopenia and conduct experimental research interventions to reduce bias and ensure result reliability. Fourth, due to the short study period and small sample size, the findings may be unstable and require validation in larger sample studies.
6. Conclusion
In summary, this study highlights the significant impact of SDoH on sarcopenia and mortality, offering valuable data for future policy development and providing a foundation for future intervention strategies.
Author contributions
Conceptualization: Xia Chen.
Data curation: Huanyong Tian.
Investigation: Xia Chen.
Methodology: Guodong Wang.
Software: Guodong Wang.
Supervision: Tian Lv.
Writing – original draft: Huanyong Tian.
Writing – review & editing: Tian Lv.
Abbreviations:
- aHR
- adjusted hazard ratio
- BMI
- body mass index
- CI
- confidence interval
- CVD
- cardiovascular disease
- DM
- diabetes mellitus
- DXA
- dual-energy X-ray absorptiometry
- HbA1c
- glycated hemoglobin
- IQR
- interquartile range
- NHANES
- National Health and Nutrition Examination Survey
- OR
- odds ratio
- RCS
- restricted cubic spline
- RDW
- red cell distribution width
- SDoH
- social determinants of health
- SMI
- skeletal muscle mass index
All statistical analyses in this study were conducted using R software version 4.2.2 (http://www.r-project.org/).
Ethical approval was not required for this study under local laws and institutional policies. Moreover, as per national regulations and institutional guidelines, written informed consent from participants or their legal representatives was not mandated.
The authors have no funding and conflicts of interest to declare.
All data generated or analyzed during this study are included in this published article (and its supplementary information files).
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000049638).
How to cite this article: Chen X, Tian H, Wang G, Lv T. Social determinants of health and sarcopenia in adults: Insights from the National Health and Nutrition Examination Survey. Medicine 2026;105:27(e49638).
XC and HT contributed to this article equally.
Contributor Information
Xia Chen, Email: 490102889@qq.com.
Huanyong Tian, Email: tianhuanyong@qq.com.
Guodong Wang, Email: 18268786752@163.com.
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