To examine the independent and joint associations of physical activity (PA) and dietary quality (HEI-2020) with the presence of advanced cardiovascular-kidney-metabolic syndrome (CKM) in older U.S. adults.
DesignA cross-sectional study.
SiteThe 2007–2020 National Health and Nutrition Examination Survey (NHANES).
ParticipantsFive thousand thirty-six individuals aged ≥60 years (2051 with advanced CKM).
Main measurementsPA was measured using MET-min/week, and dietary quality was assessed via the HEI-2020 score. Weighted logistic regression, restricted cubic spline (RCS) analysis, mediation, and stratified analyses.
ResultsSufficient PA (OR=0.66, 95% CI: 0.54–0.80) and HEI-2020 ≥60 (OR=0.82, 95% CI: 0.67–0.99) were independently associated with lower odds of advanced CKM, with the strongest association observed for the combination of both factors (OR=0.49, 95% CI: 0.38–0.64). RCS analysis revealed a nonlinear association between PA and advanced CKM, while HEI-2020 showed a linear inverse association. Mediation analysis indicated that BMI and HDL-C accounted for 15.7% and 23.1% of these associations, respectively. Stratified analyses suggested stronger associations among individuals aged ≥70 years, women, and widowed, divorced, or separated individuals.
ConclusionPhysical activity and dietary quality were jointly associated with lower odds of advanced CKM in older adults, and these associations may involve metabolic and inflammatory pathways. Multidimensional lifestyle strategies may be beneficial for populations with a higher burden of CKM, although longitudinal studies are needed to clarify temporality.
Examinar las asociaciones independientes y conjuntas de la actividad física (AF) y la calidad de la dieta (HEI-2020) con la presencia de síndrome cardiovascular-reno-metabólico avanzado (CKM) en adultos mayores de Estados Unidos.
DiseñoEstudio transversal.
EmplazamientoEstudio transversal con datos de NHANES 2007-2020.
Participantes5.036 individuos ≥60 años.
Mediciones principalesLa AF se midió en MET-min/semana y la calidad de la dieta mediante la puntuación HEI-2020. Se aplicaron regresión logística ponderada, análisis con splines cúbicos restringidos, mediación y análisis estratificados.
ResultadosUna AF suficiente (OR=0,66; IC 95%: 0,54–0,80) y un HEI-2020 ≥60 (OR=0,82; IC 95%: 0,67–0,99) se asociaron independientemente con menor probabilidad de CKM avanzado, observándose la asociación más fuerte cuando coexistían ambos factores (OR=0,49; IC 95%: 0,38–0,64). La relación entre AF y CKM avanzado fue no lineal, mientras que la de HEI-2020 fue lineal inversa. El IMC y el c-HDL mediaron parcialmente estas asociaciones. Las asociaciones fueron más fuertes en personas de ≥70 años, mujeres y personas viudas, divorciadas o separadas.
ConclusiónLa AF y la calidad de la dieta se asociaron conjuntamente con menor probabilidad de CKM avanzado en adultos mayores, posiblemente a través de vías metabólicas e inflamatorias.
Cardiovascular-kidney-metabolic syndrome (CKM) is a systemic comorbidity highlighting the interplay between metabolic risk factors, cardiovascular disease, and chronic kidney disease, which often progress synergistically.1 The American Heart Association stages CKM from 0 to 4, with advanced stages (3–4) signifying substantial organ damage, higher mortality, and resource use,2 making them a critical focus for intervention.3 Older adults (≥60 years) are particularly vulnerable due to age-related physiological decline and multimorbidity.4 Identifying modifiable factors to mitigate advanced CKM risk in this population is therefore a public health priority.
Physical activity (PA) and dietary quality are key modifiable lifestyle factors independently linked to cardiometabolic and renal health.5 The Healthy Eating Index-2020 (HEI-2020) provides a robust measure of dietary alignment with health guidelines.6 However, evidence on their combined effect on advanced CKM, especially in older adults, remains scarce.7 Most studies focus on single behaviors or specific disease outcomes, leaving a gap regarding synergistic effects and underlying mechanisms.8 Potential mediators like body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and frailty are often examined as confounders rather than causal pathways.9 Furthermore, social determinants of health (e.g., socioeconomic status) may modify intervention effectiveness, suggesting heterogeneous benefits across subgroups.10
Therefore, using nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2007–2020, this study aimed to: investigate the independent and joint associations of PA and HEI-2020 with advanced CKM among U.S. adults aged ≥60 years, We focused on adults aged ≥60 years because this threshold is commonly used to define older adults in population-based epidemiology and enables evaluation of early older age, while maintaining adequate sample size and precision within NHANES; explore potential mediating roles of BMI, HDL-C, and other biomarkers; and examine effect modification by key sociodemographic factors.
MethodsThis cross-sectional analysis utilized data from the 2007–2020 National Health and Nutrition Examination Survey (NHANES), a nationally representative survey employing a complex, multi-stage sampling design. The NHANES protocol was approved by the NCHS Ethics Review Board, and all participants provided informed consent. NHANES data are publicly available and can be accessed through the CDC/NCHS website. The released datasets are de-identified and do not contain direct personal identifiers.
From 75,402 initial participants, we excluded those aged under 60 years, and those with missing data for CKM staging or HEI-2020 score. The final analytical sample comprised 5036 older adults (≥60 years). The participant selection flowchart is depicted in Fig. 1.
Physical activity (PA) was assessed using the Global Physical Activity Questionnaire (GPAQ). Self-reported activities across work, transportation, and leisure domains were converted into weekly metabolic equivalent (MET)-minutes per established guidelines.11 Participants were categorized into three groups based on American PA guidelines: inactive (0MET-min/week), insufficiently active (1–599MET-min/week), and sufficiently active (≥600MET-min/week).
Advanced CKM syndrome was defined as stages 3–4, based on the 2023 AHA CKM consensus statement (detailed criteria in Supplementary Table S1). Dietary quality was assessed using the Healthy Eating Index-2020 (HEI-2020), which aligns with the Dietary Guidelines for Americans. Participants were categorized as having a good diet (HEI-2020 ≥60) or poor diet (HEI-2020 <60), consistent with established clinical thresholds.
Based on prior literature, we adjusted for the following potential confounders: age, gender, race, education, marital status, poverty-income ratio (PIR), smoking, alcohol use, sleep duration, sedentary time, and sleep disorders. We also examined several biological markers as potential mediators in the pathway between lifestyle and advanced CKM: body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), oxidative balance score (OBS), frailty score, and estimated glucose disposal rate (eGDR). Calculation methods for composite scores are in Supplementary Table S2.
All analyses accounted for the complex NHANES survey design using appropriate weights, strata, and primary sampling units (PSUs). We described baseline characteristics using weighted means (95% CIs) for continuous variables and weighted proportions (%) for categorical variables, with group comparisons made using design-adjusted tests. We employed weighted logistic regression to examine associations of PA, HEI-2020, and their combination with advanced CKM, using three sequential models: Model 1 (unadjusted); Model 2 (adjusted for age, gender, race, education, PIR); Model 3 (further adjusted for lifestyle factors). Dose–response relationships were assessed with restricted cubic splines (RCS). Stratified analyses tested effect modification, and mediation analysis evaluated potential pathways. To evaluate model adequacy, we conducted diagnostic checks appropriate for survey-weighted logistic regression. Multicollinearity among covariates was assessed using variance inflation factors (VIF), with VIF <5 considered acceptable. We also examined the linearity-in-the-logit assumption for continuous covariates and reviewed residual and influence diagnostics to identify potentially influential observations. All analyses were conducted in R version 4.3.0, with statistical significance set at P<0.05 (two-sided).
ResultsAs summarized in Table 1, among 5036 participants (≥60 years), 2051 were identified with advanced CKM. Compared to those without CKM, participants with advanced CKM were significantly older (73.23 vs. 67.39 years) and more likely to be male (57.2% vs. 38.1%) (both P<0.0001). They also had lower educational attainment and a higher prevalence of being unmarried, physically inactive (42.7% vs. 27.0%), and having a poor diet (HEI <60; 73.4% vs. 67.0%) (all P<0.05).
Weighted baseline characteristics of participants with or without advanced cardiovascular-kidney-metabolic (CKM) syndrome.
| Variables | Overall(n = 5036) | Advanced CKM syndrome (stages 3 or 4) | P-value | |
|---|---|---|---|---|
| No(n = 2985) | Yes(n = 2051) | |||
| Age | 69.50 (69.21, 69.79) | 67.39 (67.09, 67.68) | 73.23 (72.83, 73.63) | <0.0001 |
| Gender (%) | <0.0001 | |||
| Male | 2459 (45.00) | 1222 (38.10) | 1237 (57.21) | |
| Female | 2577 (55.00) | 1763 (61.90) | 814 (42.79) | |
| Race (%) | 0.5227 | |||
| Non-Hispanic White | 2548 (79.95) | 1360 (79.94) | 1188 (79.95) | |
| Non-Hispanic Black | 1077 (7.91) | 662 (7.53) | 415 (8.59) | |
| Mexican American | 539 (3.54) | 358 (3.63) | 181 (3.39) | |
| Other Hispanic | 518 (3.50) | 357 (3.73) | 161 (3.10) | |
| Other race | 354 (5.10) | 248 (5.17) | 106 (4.97) | |
| Marital (%) | <0.0001 | |||
| Married/living with partner | 2974 (65.76) | 1815 (67.63) | 1159 (62.46) | |
| Widowed/divorced/separated | 1794 (30.43) | 987 (27.97) | 807 (34.78) | |
| Never married | 264 (3.81) | 180 (4.40) | 84 (2.77) | |
| Education (%) | <0.0001 | |||
| Below high school | 656 (6.77) | 351 (5.33) | 305 (9.33) | |
| High school | 1913 (35.21) | 1037 (30.84) | 876 (42.93) | |
| Above high school | 2458 (58.02) | 1594 (63.83) | 864 (47.74) | |
| Smoking (%) | <0.0001 | |||
| Never | 2468 (48.99) | 1595 (53.12) | 873 (41.67) | |
| Former | 1939 (40.80) | 1056 (38.08) | 883 (45.60) | |
| Now | 623 (10.22) | 330 (8.80) | 293 (12.73) | |
| Drinking (%) | <0.0001 | |||
| Never | 739 (13.76) | 441 (13.12) | 298 (14.93) | |
| Former | 1034 (20.71) | 507 (16.79) | 527 (27.79) | |
| Now | 2713 (65.52) | 1743 (70.09) | 970 (57.28) | |
| Physical activity (%) | <0.0001 | |||
| 0Met-min/week | 1901 (32.66) | 953 (26.98) | 948 (42.69) | |
| 1–599Met-min/week | 782 (16.22) | 499 (17.12) | 283 (14.61) | |
| ≥600Met-min/week | 2353 (51.13) | 1533 (55.89) | 820 (42.70) | |
| HEI-2020 (%) | 0.0006 | |||
| <60 | 3499 (69.34) | 1980 (67.05) | 1519 (73.39) | |
| ≥60 | 1537 (30.66) | 1005 (32.95) | 532 (26.61) | |
| PA–HEI-2020 (%) | <0.0001 | |||
| No PA and poor diet | 1901 (32.66) | 953 (26.98) | 948 (42.69) | |
| Insufficient PA and poor diet | 536 (11.25) | 326 (11.46) | 210 (10.88) | |
| Insufficient PA and healthy diet | 246 (4.97) | 173 (5.67) | 73 (3.73) | |
| Adequate PA and poor diet | 1526 (33.08) | 942 (35.10) | 584 (29.50) | |
| Adequate PA and healthy diet | 827 (18.05) | 591 (20.80) | 236 (13.20) | |
| Sleep disorder (%) | 0.0848 | |||
| Yes | 595 (17.83) | 317 (16.54) | 278 (20.05) | |
| No | 3223 (82.17) | 1923 (83.46) | 1300 (79.95) | |
| PIR | 3.12 (3.02, 3.22) | 3.35 (3.24, 3.46) | 2.71 (2.60, 2.83) | <0.0001 |
| Physical activity time, min/week | 775.65 (726.18, 825.12) | 789.40 (727.49, 851.31) | 744.66 (660.38, 828.95) | 0.4113 |
| Sedentary time, min/day | 406.13 (384.43, 427.83) | 398.06 (366.49, 429.63) | 419.85 (401.02, 438.68) | 0.2284 |
| Sleep time, h/day | 7.30 (7.23, 7.36) | 7.29 (7.22, 7.36) | 7.31 (7.19, 7.44) | 0.6896 |
| BMI, kg/m2 | 29.31 (29.02, 29.61) | 29.15 (28.76, 29.53) | 29.61 (29.21, 30.00) | 0.0963 |
| eGDR, ml/min/1.73m2 | 6.37 (6.26, 6.49) | 6.81 (6.67, 6.95) | 5.58 (5.43, 5.73) | <0.0001 |
| HDL_C, mg/dL | 56.93 (55.88, 57.97) | 59.92 (58.68, 61.16) | 51.76 (50.61, 52.92) | <0.0001 |
| HEI-2020 | 54.38 (53.79, 54.97) | 55.10 (54.39, 55.80) | 53.11 (52.35, 53.86) | <0.0001 |
| OBS | 20.30 (19.96, 20.65) | 21.29 (20.90, 21.67) | 18.57 (18.12, 19.01) | <0.0001 |
| FRAILTY_SCORE | 0.18 (0.18, 0.19) | 0.15 (0.14, 0.15) | 0.24 (0.24, 0.25) | <0.0001 |
For continuous variables: survey-weighted mean (95% CI), while categorical variables were described using frequencies and percentages. Abbreviations: PIR, poverty income ratio; HDL-C, high-density lipoprotein cholesterol; eGDR, estimated glucose disposal rate; OBS, oxidative balance score.
Weighted logistic regression models were used to assess independent associations (Table 2). In the fully adjusted model (Model 3), compared to inactive individuals, insufficient and sufficient PA were associated with 27% (OR=0.73, 95% CI: 0.56–0.94) and 34% (OR=0.66, 95% CI: 0.54–0.80) lower odds of advanced CKM, respectively. A HEI-2020 score ≥60 was associated with an 18% risk reduction (OR=0.82, 95% CI: 0.67–0.99) compared to a score <60.
Independent association of PA level and dietary health with advanced CKM.
| Model 1OR (95% Cl) P-value | Model 2OR (95% Cl) P-value | Model 3OR (95% Cl) P-value | |
|---|---|---|---|
| PA | |||
| 0MET | Ref | Ref | Ref |
| 1–599MET | 0.57 (0.48, 0.68) <0.0001 | 0.69 (0.56, 0.84) 0.0004 | 0.73 (0.56, 0.94) 0.0153 |
| ≥600MET | 0.54 (0.48, 0.61) <0.0001 | 0.63 (0.54, 0.74) <0.0001 | 0.66 (0.54, 0.80) <0.0001 |
| HEI-2020 | |||
| <60 | Ref | Ref | Ref |
| ≥60 | 0.69 (0.61, 0.78) <0.0001 | 0.70 (0.60, 0.82) <0.0001 | 0.82 (0.67, 0.99) 0.0348 |
Abbreviations: PA, physical activity; OR, odds ratio; Cl, confidence interval; Model 1 adjust for: none; Model 2 adjust for: age, gender, race, education, PIR; Model 3 adjust for: age, gender, race, education, PIR, smoking status, alcohol status, sedentary time, sleep time, sleep disorder.
The combined association of PA and diet with advanced CKM is shown in Table 3. In the fully adjusted model, the group with both adequate PA and a healthy diet (HEI-2020 ≥60) showed the strongest protective effect (OR=0.49, 95% CI: 0.38–0.64). Independent inverse associations were also observed for insufficient PA with a healthy diet (OR=0.53, 95% CI: 0.34–0.81) and adequate PA with a poor diet (OR=0.69, 95% CI: 0.55–0.85), though these effects were weaker than the combined association.
Joint association of PA level and dietary health with advanced CKM.
| Model 1OR (95% Cl) P-value | Model 2OR (95% Cl) P-value | Model 3OR (95% Cl) P-value | |
|---|---|---|---|
| No PA and poor diet | Ref | Ref | Ref |
| Insufficient PA and poor diet | 0.65 (0.53, 0.79) <0.0001 | 0.76 (0.60, 0.96) 0.0208 | 0.79 (0.59, 1.06) 0.1151 |
| Insufficient PA and healthy diet | 0.42 (0.32, 0.57) <0.0001 | 0.53 (0.37, 0.76) 0.0005 | 0.53 (0.34, 0.81) 0.0038 |
| Adequate PA and poor diet | 0.62 (0.54, 0.71) <0.0001 | 0.75 (0.63, 0.89) 0.0010 | 0.69 (0.55, 0.85) 0.0007 |
| Adequate PA and healthy diet | 0.40 (0.34, 0.48) <0.0001 | 0.46 (0.37, 0.58) <0.0001 | 0.49 (0.38, 0.64) <0.0001 |
Abbreviations: PA–HEI-2020, physical activity combined with HEI-2020; OR, odds ratio; Cl, confidence interval; PA, physical activity. Model 1 adjust for: none; Model 2 adjust for: age, gender, race, education, PIR; Model 3 adjust for: age, gender, race, education, PIR, smoking status, alcohol status, sedentary time, sleep time, sleep disorder.
Restricted cubic spline analyses revealed distinct dose–response patterns (Fig. 2). PA showed a significant nonlinear relationship with advanced CKM risk (P-nonlinear=0.001), characterized by rapid risk reduction up to approximately 2500MET-min/week followed by a plateau. In contrast, HEI-2020 demonstrated a linear inverse association (P-nonlinear=0.596), with progressively lower risk at higher diet quality scores.
Nonlinear relationship between physical activity and dietary health indices and risk of advanced CKM syndrome (RCS analysis). (A–C) The nonlinear relationship between physical activity (PA-MET) and risk of advanced CKM syndrome. (D–F) The nonlinear relationship between HEI-2020 and risk of advanced CKM syndrome (D–F) for the nonlinear relationship between HEI-2020 and risk of advanced CKM syndrome.
Stratified analyses assessed heterogeneity in the association between combined PA-diet exposure and advanced CKM (Supplementary Table S3). The protective association of maintaining both adequate PA and a healthy diet was consistent across most subgroups, with particularly strong risk reductions observed in adults ≥70 years (OR=0.52, 95% CI: 0.36–0.75), women (OR=0.49, 95% CI: 0.29–0.82), and widowed, divorced, or separated individuals (OR=0.44, 95% CI: 0.27–0.71). Significant effect modification was observed for marital status (P-interaction=0.012) and income level (P-interaction=0.004), indicating enhanced benefits in socially disadvantaged groups.
In the mediation analysis, we explored potential pathways underlying the associations of PA and dietary quality (HEI-2020) with advanced CKM. Mediation was assessed using regression models for each mediator and survey-weighted logistic regression models for advanced CKM, adjusted using the fully adjusted covariate set (Model 3). As shown in Fig. 3A, BMI significantly mediated the association between PA and advanced CKM (indirect effect=−0.0014, 95% CI: −0.0024 to −0.0007, P<0.0001), explaining 15.7% of the total effect. As shown in Fig. 3B, HDL-C mediated the association between HEI-2020 and advanced CKM (indirect effect=−0.0063, 95% CI: −0.0106 to −0.0021, P=0.002), accounting for 23.1% of the total effect. Additionally, EGDR, frailty score, and OBS showed statistically significant indirect effects between PA and advanced CKM (P<0.0001), while their direct effects were not significant or the total effect was marginally significant; these results are summarized in Supplementary Table S4.
Analysis of the mediating role of PA and HEI-2020 with advanced CKM. Abbreviations: PA, physical activity; HEI-2020, Healthy Eating Index 2020; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; advanced CKM, cardiometabolic kidney disease; IE, indirect effect; DE, direct effect; TE, total effect; CI, confidence interval.
Stratified analyses (Supplementary Tables S5 and S6) showed stronger protective associations of adequate PA in subgroups including adults ≥70 years (OR=0.55), males (OR=0.58), and low-income individuals (OR=0.59). Healthy diet (HEI-2020 ≥60) showed significant protective effects primarily in unmarried (OR=0.16) and middle-income individuals (OR=0.64), indicating socioeconomic moderation of dietary benefits.
DiscussionThis study, based on the large representative population data from NHANES 2007–2020, is the first to systematically assess the individual and combined associations of PA and dietary quality on the risk of advanced CKM syndrome in elderly individuals, and to explore potential mediating mechanisms. It highlights the potential importance of lifestyle-related factors for advanced CKM prevention and management.
Our results demonstrate that both higher physical activity levels and better dietary quality are independently associated with a significantly lower odds of advanced CKM syndrome, with a synergistic effect observed when combined. After multivariable adjustment, individuals meeting the recommended PA level (≥600MET-min/week) had a 34% lower odds of advanced CKM, while those with a HEI-2020 score ≥60 had an 18% lower odds, consistent with previous evidence linking lifestyle modifications to improved cardiorenal-metabolic profiles.12 Importantly, the protective association was strongest when both factors were present, with 51% odds reduction – greater than the risk reductions associated with either adequate PA alone (31%) or healthy diet alone (47%). This suggests a synergistic relationship between these health behaviors, supporting the consideration of integrated lifestyle strategies rather than isolated approaches.13 These findings align with the “Lifestyle Mosaic Model,” which posits that multidimensional health behaviors collectively form a more comprehensive barrier against chronic diseases.14
The restricted cubic spline analysis revealed a nonlinear relationship between physical activity and advanced CKM, with the most pronounced inverse association observed at moderate intensity levels (approximately 2500–3000MET-min/week), beyond which the association plateaued. This pattern is consistent with previous studies reporting diminishing marginal returns of PA beyond a certain threshold, possibly due to inflammatory responses,15 oxidative balance rebound, and elevated metabolic load at higher intensities.16 In older adults, high-intensity exercise may also pose risks such as renal function fluctuation or arrhythmia, which could contribute to the slight risk rebound observed at the upper end of the PA range.17 In contrast, HEI-2020 scores exhibited a linear inverse association with advanced CKM odds, suggesting that each incremental improvement in diet quality may be associated with continued improvements. This aligns with findings from the MESA study and other cohorts,18 where sustained adherence to high-quality dietary patterns – such as the DASH and Mediterranean diets – was associated with progressive reductions in kidney function decline.19 The inverse association of diet may operate through modulation of systemic inflammation, metabolic homeostasis, glucose regulation, and gut microbiota.20
Stratified analyses indicated that the combined associations of adequate PA and healthy diet were particularly pronounced among older adults (≥70 years) and those who were widowed, divorced, or separated. These groups often experience accelerated physiological decline or emotional isolation,21 which may amplify the inverse association of integrated lifestyle modifications.22 Notably, the strongest association was observed in unmarried individuals, suggesting that limited social support may enhance the association of structured health behaviors. Furthermore, socioeconomic and gender differences moderated the observed associations.23 Low-income individuals showed substantial lower odds from joint PA and dietary categories, likely due to their greater baseline vulnerability and limited access to health resources.24 In terms of gender, men exhibited stronger associations to the combined lifestyle joint exposure, whereas dietary quality alone did not significantly show a significant association in women, possibly reflecting sex-specific differences in metabolism, hormonal profiles, or health-related behaviors.25 These findings highlight the need for future studies to elucidate the mechanisms behind combined behavioral patterns and to develop tailored strategies accounting for demographic and socioeconomic contexts.26
Mediation analysis identified BMI and HDL-C as significant mediators in the pathways linking PA and HEI-2020, respectively, to advanced CKM odds. BMI accounted for 15.7% of the total association of PA, suggesting that part of PA's inverse association operates through weight regulation. Previous studies have established obesity as a key risk factor for advanced CKM, contributing to muscle loss, insulin resistance, and chronic inflammation. Moreover, PA may activate AMPK and PPAR signaling pathways, further improving metabolic health and body composition.27 In the dietary pathway, HEI-2020 exerted its inverse association partly by raising HDL-C levels, which mediated 23.1% of the total association. HDL-C plays multiple potentially beneficial functions, including anti-inflammatory, antioxidant, and reverse cholesterol transport functions.24 Low HDL-C is common in atherosclerosis, diabetes, and chronic kidney disease, and evidence from the Framingham study supports dietary quality as a positive correlate of HDL-C, highlighting its role in chronic disease lower risk.18 We also examined EGDR, OBS, and frailty score as potential mediators. These factors have been implicated in metabolic health and aging-related diseases.28 For instance, EGDR reflects insulin sensitivity and is closely tied to cardiorenal-metabolic function, while frailty is strongly associated with chronic inflammation and sarcopenia, serving as an independent associated factor of advanced CKM.29 However, in our analysis, these variables exhibited significant indirect effects but nonsignificant direct effects, indicating possible complete mediation or the presence of unmeasured moderators. These findings illustrate the complex network through which lifestyle factors influence multisystem metabolic health.30
This study has several limitations. Its cross-sectional design precludes causal inference, as reverse causality may exist (e.g., advanced CKM could reduce PA and diet quality). Self-reported PA and dietary data are subject to measurement bias. Future longitudinal studies using objective measures (e.g., accelerometers, detailed dietary records) and incorporating genetic and environmental covariates would help clarify these relationships and enhance generalizability.
ConclusionThis study demonstrates that both physical activity and good dietary quality are independently associated with reduced risk of advanced CKM in older adults, with evidence of a synergistic protective effect when combined. These associations appear to be partially mediated through BMI and HDL-C pathways. Future longitudinal research should validate these findings and explore implementation strategies for integrated lifestyle interventions in high-risk geriatric populations.
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Older adults are a high-risk population for advanced cardiovascular-kidney-metabolic (CKM) syndrome.
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Physical activity and diet quality are established lifestyle factors linked to cardiometabolic and renal health.
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Evidence regarding their joint effect on advanced CKM, especially in the elderly, remains limited.
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This is the first study to identify a synergistic association between combined physical activity and good dietary quality with markedly lower risk of advanced CKM (OR=0.49).
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We identified BMI and HDL-C as significant mediators and revealed stronger benefits in socially vulnerable subgroups (e.g., unmarried, low-income).
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The findings support multidimensional lifestyle interventions and highlight the value of integrating physiological and social factors in public health strategies.
NHANES protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board (ERB), and written informed consent was obtained from all participants. The present study is a secondary analysis of publicly available, de-identified data and therefore did not require additional institutional review. All analyses were conducted in accordance with relevant guidelines and the principles of the Declaration of Helsinki.
FundingThis study was supported by the Major Sports Research Project of the Jiangsu Provincial Sports Bureau (ST251106).
Conflicts of interestThe authors declare no conflicts of interest.
We thank all authors for their contributions to the article.
The following are the supplementary data to this article:
