Association between relative fat mass, estimated pulse wave velocity and intrinsic capacity among the older people

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This study aims to investigate relationship of estimated pulse wave velocity (ePWV), relative fat mass (RFM) and IC in the older adults. Methods This study is based on the China Health and Retirement Longitudinal Study (CHARLS), including 2,598 individuals aged 60 and above from 2011 to 2015. Cross-lagged models and generalized estimation equation were employed to assess the associations between ePWV, RFM, and IC. Results Elevated ePWV showed significantly poorer IC (B = -0.047, 95% CI: -0.087 to -0.008, P = 0.020), while increased RFM demonstrated better IC (B = 0.020, 95% CI: 0.010 to 0.031, P < 0.001). Furthermore, ePWV was significantly associated with poorer cognitive function, reduced locomotor activity, impaired respiratory function, severe sensory deficits, and a lower risk of depression. Moreover, RFM was linked to superior cognitive performance, reduced depressive symptoms, enhanced respiratory function, increased handgrip strength, better sensory function, and decreased locomotor activity. In non-hypertensive individuals, ePWV showed no significant association with IC. Among hypertensive subjects, RFM was not significantly correlated with IC or cognitive function. Cross-lagged model demonstrated that baseline RFM (β = 0.428, P < 0.001) and ePWV (β = -0.091, P < 0.001) significantly predicted IC at follow-up. Conclusion The study found that ePWV is negatively correlated with IC, while RFM is positively associated with IC in the older people. The findings enable development of targeted interventions by healthcare providers focusing on adjustable determinants to slow IC decline. Relative fat mass Estimated pulse wave velocity Intrinsic capacity Older people Figures Figure 1 Figure 2 1.Introduction The global population is undergoing rapid aging, presenting significant societal and economic challenges. By the end of 2024, China’s population aged 60 and above had reached 310 million, accounting for 22% of the total population [ 1 ]. The aging process is characterized by a gradual deterioration of physiological resilience, which negatively impacts both physical and cognitive capacities. To combat age-related challenges, WHO’s World Report on Ageing and Health established healthy aging as maintaining functional ability for wellbeing in later life. The new concept, intrinsic capacity (IC), was defined as the composite of all the physical and mental capacities that an individual can draw upon throughout the lifespan [ 2 ]. Accumulating empirical evidence establishes intrinsic capacity as a robust predictor of critical health outcomes, particularly incident disability and all-cause mortality [ 3 , 4 ]. However, the factors associated with IC are still uncertain. Arterial stiffness is a hallmark of the aging process and an essential manifestation of vascular aging. The noninvasive measurement of central arterial stiffness by carotid-femoral pulse wave velocity (cfPWV) is currently regarded as the gold standard [ 5 ]. cfPWV’s reliance on specialized hardware, operator skill, and strict protocols restricts its real-world clinical scalability. Vlachopoulos et al. recently suggested that estimated pulse wave velocity (ePWV) may serve as a viable substitute for cfPWV, proving its independent association with cardiovascular risk beyond conventional predictors [ 6 ]. A growing body of research indicates that ePWV shows robust correlations with cognitive performance, chronic disease, disability and mortality [ 7 – 11 ]. Furthermore, ePWV mediated the detrimental effects of impaired insulin sensitivity on biological aging indicators, suggesting arterial stiffness may be an important pathway linking metabolic dysfunction to premature aging [ 12 ]. In middle-aged and older Chinese adults, fluctuations in in ePWV may be associated with an increased risk of sarcopenia, suggesting its potential as a predictive indicator [ 13 ]. Vascular aging assessed by ePWV is a significant biomarker for the risk and prognosis of depression [ 14 ]. While current evidence suggests that ePWV may modulate healthy aging, the relationship between ePWV and IC remains unexplored. Relative fat mass (RFM) represents a novel anthropometric index derived from a linear equation to assess total body fat percentage in adult populations. [ 15 ]. RFM correlated better with dual-energy X-ray absorptiometry-based measurements of whole-body adipose tissue percentage than body mass index (BMI) did and strongly correlated with trunk adipose tissue levels [ 16 ]. Besides the advantages mentioned, RFM has demonstrated significant predictive value for cardiometabolic disease risk in the general population [ 17 ]. A population-based study in the Dutch revealed that higher RFM levels were significantly associated with increased risk of all-cause mortality [ 18 ]. Moreover, higher RFM values were associated with poorer cognitive scores in older men [ 19 ]. Analysis of NHANES data (2005–2018) revealed that RFM showed a stronger association with depression than both BMI and waist circumference (WC) [ 20 ]. Although no direct evidence currently links RFM to ePWV, existing study suggests that WC correlates with arterial stiffness [ 21 ]. Moreover, longitudinal studies indicate RFM serves as a robust predictor of hypertension onset among Chinese adults [ 22 ]. In light of these findings, RFM shows great potential in vascular aging or IC. This study utilizes data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of the middle-aged and elderly Chinese population. We aim to investigate the associations of RFM, vascular aging and intrinsic capacity among Chinese older adults. 2.Method 2.1 Study population The CHARLS, a prospective nationwide study, includes adults above 45 years from 450 communities across 28 Chinese provinces, with data collection repeated every two years since 2011. [ 23 ]. This study conducted a retrospective analysis of CHARLS data collected between 2011 and 2015.The exclusion criteria for the present study were as follows: 1) individuals aged less than 60 years old; 2) missing data on RFM, ePWV and IC in 2011; 3) lost to follow-up in 2015; 4) missing data on RFM, ePWV and IC in 2015. As described in the Fig. 1 , 2598 older people were included in the final analysis. 2.2 Definitions of IC Following López-Ortiz et al.'s established protocol, IC was defined and examined across five principal dimensions: cognitive, psychological, locomotor, sensory, and vitality [ 24 ]. The assessment of locomotion was conducted using the SPPB, which incorporated chair stand tests, gait speed assessment, and balance examinations. [ 25 ]. Sensory function was assessed through participant-reported hearing and vision impairments. [ 26 , 27 ]. The operational definition of vitality incorporated pulmonary function (peak flow test) and dominant hand strength measurement [ 28 ]. The evaluation of psychological status utilized the Center for Epidemiological Studies Depression Scale-10 (CES-D10), a validated 10-item depression screening instrument [ 29 ]. Mini-Mental State Examination (MMSE) were used to quantify cognitive functioning [ 30 ]. Intrinsic capacity (IC) domains were categorized into three ordinal levels (0–2): 0 = severely impaired, 1 = partially impaired, and 2 = slightly impaired or fully preserved. The composite IC score, derived from all five domains, ranged from 0 (complete impairment) to 10 (peak function), with increasing values reflecting better-preserved capacity [ 24 ]. Detailed specifications for IC domain, including definitions, assessment protocols, and stratification criteria, are systematically presented in Table S1 . The data used to calculate the IC index were collected in two waves: 2011 and 2015 wave. 2.3 ePWV Calculation ePWV, a non-invasive estimation of pulse wave velocity, is computed based on age and mean blood pressure (MBP), which is determined from systolic (SBP) and diastolic blood pressure (DBP) measurements [ 6 ]. The formula for calculating ePWV is as follows: ePWV = 9.587 − 0.402 × age + 4.560 × 10 − 3 × age 2 -2.621× 10 − 5 × age 2 ×MBP + 3.176 × 10 − 3 × MBP × age – 1.832 × 10 − 2 × MBP MBP = DBP + 0.4 × (SBP-DBP) 2.4 RFM Calculation RFM, a validated proxy for whole-body adiposity, was calculated using the following formula: 64 − (20 × height/WC) + (12 × sex); sex equals 0 for men and 1 for women [ 16 ]. Height and WC are measured in the same units. 2.5 Covariates At baseline, the covariates selected were factors known to be associated with the risk of IC [ 31 ]. Sociodemographic characteristics comprised age, gender (male/female), educational attainment (categorized as illiterate, primary school or less, or junior high school and above), marital status (married vs. other), and area of residence (urban/rural). Health-related variables encompassed: current smoking (yes/no), alcohol intake frequency (never, drink but less than once a month, and drink more than once a month), social engagement (yes/no), sleep duration at night and daytime napping, self-reported physician-diagnosed chronic conditions (hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease) and medications taken for these chronic diseases (hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease). 2.6 Statistical analysis Data are expressed as mean ± SD for continuous variables and n (%) for categorical variables. Group differences (significant capacity loss, declining capacity, and stable capacity) were assessed using either chi-square tests or ANOVA as appropriate. Generalized estimation equation (GEE) models were used to examine the associations of RFM, ePWV and IC. This approach accounts for the correlation between the repeated measures within a person. The GEE parameter estimates were expressed as the odds ratios (ORs) and the 95% confidence intervals (95% CIs). A P -value < 0.05 was considered to indicate statistical significance. For the longitudinal analyses, we constructed two models: Model 1 adjusted for age and sex; Model 2 adjusted for age, sex, residence, marital status, educational level, smoking, drinking status, social engagement, sleep duration at night and daytime napping, hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease and medication for hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease. In order to examine the complex interplay among RFM, ePWV, and IC in older adults, we constructed a cross-lagged model, incorporating factors such as age, sex, residence, marital status, educational level, smoking, drinking status, active social engagement, nighttime sleep duration, nap, hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease and medication for hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease as covariates. The following goodness-of-fit indices were employed to evaluate model fit: chi-square statistic, comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). Model fit was considered acceptable when CFI ≥ 0.90, RMSEA < 0.05, and SRMR < 0.08 [ 32 ]. All the analysis will be conducted by Mplus version 8.3 and SPSS 25.0. 3.Results 3.1 Baseline Characteristics Table 1 displays the baseline characteristics of eligible participants (n = 2,598). The mean age of participants was 66.16 (5.23), comprising 1409 men and 1189 women. In the analytic sample, 13.4% of participants exhibited significant loss of capacity, while 77.8% showed declining capacity. The mean ePWV and RFM of total population was 10.91 (1.53) and 32.10 (8.85), respectively. Compared to significant loss of capacity, individuals with stable capacity were more likely to be younger, male, high education level, and long nighttime sleep duration. Table 1 Baseline Characteristics of study population in 2011. Variables Significant loss of capacity (n = 347) Declining capacity (n = 2021) Stable capacity (n = 230) P Age 68.56 ± 6.08 65.99 ± 5.04 63.99 ± 3.99 < 0.001 Male 135 (38.9) 1138 (56.3) 136 (59.1) < 0.001 Rural 265 (76.4) 1349 (66.7) 122 (53.0) < 0.001 Education level Below Primary School 195 (56.2) 512 (25.3) 22 (9.6) < 0.001 Primary school 133 (38.3) 1105 (54.7) 110 (47.8) Middle school and above 19 (5.5) 404 (20.0) 98 (42.6) Married 255 (73.5) 1718 (85.0) 203 (88.3) < 0.001 Smoking 105 (30.3) 691 (34.2) 75 (32.6) 0.001 Alcohol consumption Never 64 (18.4) 551 (27.3) 56 (24.3) < 0.001 Less than once a month 16 (4.6) 138 (6.8) 32 (13.9) More than once a month. 267 (77.0) 1332 (65.9) 142 (61.7) Social activities 137 (39.5) 1054 (52.2) 151 (65.7) < 0.001 Nighttime sleep 5.48 ± 2.28 6.30 ± 1.84 6.70 ± 1.64 < 0.001 Nap 0.00 (0.00, 60.00) 3.00 (0, 60.00) 30 (0, 60.00) 0.080 Chronic disease Hypertension 105 (30.3) 578 (28.7) 61 (26.6) 0.628 Dyslipidemia 32 (9.4) 203 (10.2) 28 (12.3) 0.519 Diabetes or high blood sugar 26 (7.5) 122 (6.1) 21 (9.3) 0.140 Cancer 3 (0.9) 15 (0.7) 1 (0.4) 0.817 Chronic lung diseases 76 (22.0) 268 (13.3) 9 (3.9) < 0.001 Heart disease 66 (19.1) 271 (13.5) 27 (11.8) 0.013 Stroke 10 (2.9) 46 (2.3) 3 (1.3) 0.464 Kidney disease 34 (9.9) 133 (6.6) 3 (1.3) < 0.001 Medication for chronic disease Hypertension 81 (23.3) 466 (23.1) 50 (21.7) 0.890 Dyslipidemia 22 (6.3) 113 (5.6) 16 (7.0) 0.635 Diabetes or high blood sugar 16 (4.6) 81 (4.0) 16 (7.0) 0.112 Cancer 2 (0.6) 10 (0.5) 1 (0.4) 0.970 Chronic lung diseases 62 (17.9) 185 (9.2) 4 (8.9) < 0.001 Heart disease 51 (14.7) 183 (9.1) 14 (6.1) 0.001 Stroke 5 (1.4) 38 (1.9) 2 (0.9) Kidney disease 25 (7.2) 73 (3.6) 1 (0.4) < 0.001 ePWV 11.02 ±1.70 10.51 ± 1.46 10.22 ± 1.24 < 0.001 RFM 33.59 ± 9.00 31.71 ± 8.75 31.51 ± 7.94 0.001 3.2 The relationship of ePWV, RFM and IC in 2011 ~ 2015 evaluated by GEE. As shown in Table 2 , participants with elevated ePWV showed poorer IC (B = -0.047, 95% CI: -0.087 to -0.008, P = 0.020), reduced cognitive function performance (B = -0.175, 95% CI: -0.266~-0.084, P < 0.001), lower level of depression symptom (B = -0.317, 95% CI: -0.266~-0.084, P < 0.001), lower SPPB scores (B = -0.101, 95% CI: -0.148~-0.053, P < 0.001), diminished respiratory function (B = -5.286, 95% CI: -7.890~-2.682, P < 0.001) and greater sensory impairment (B = -0.015, 95% CI: -0.029~-0.002, P = 0.025). Individuals with elevated RFM demonstrated slightly higher IC scores (B = 0.020, 95% CI: 0.010 ~ 0.031, P < 0.001), better cognitive function performance (B = 0.047, 95% CI: 0.021 ~ 0.072, P < 0.001), lower level of depression symptom (B = -0.075, 95% CI: -0.117~-0.033, P = 0.001), reduced SPPB scores (B = -0.023, 95% CI: -0.034~-0.012, P < 0.001), greater respiratory function (B = 0.745, 95% CI: 0.071 ~ 1.438, P = 0.031), increased hand grip strength (B = 0.097, 95% CI: 0.045 ~ 0.148, P < 0.001), enhanced sensory function (B = 0.004, 95% CI: 0.001 ~ 0.008, P = 0.013). Table 2 Association of ePWV, RFM and IC among older people in 2011 ~ 2015 ePWV RFM Model 1 Model 2 Model 1 Model 2 B(95%CI) B(95%CI) B(95%CI) B(95%CI) IC -0.090 (-0.132~-0.048) ** -0.047 (-0.087~-0.008) * 0.034 (0.023 ~ 0.044) ** 0.020 (0.010 ~ 0.031) ** Cognition -0.281 (-0.385~-0.178) ** -0.175 (-0.266~-0.084) ** 0.092 (0.063 ~ 0.120) ** 0.047 (0.021 ~ 0.072) ** Psychological -0.179 (-0.343~-0.015) * -0.317 (-0.476~-0.158) ** -0.106 (-0.152~-0.060) ** -0.075 (-0.117~-0.033) * Sensory -0.019 (-0.032~-0.006) * -0.015 (-0.029~-0.002) * 0.004 (0.001 ~ 0.007) * 0.004 (0.001 ~ 0.008) * Locomotion -0.126 (-0.172~-0.080) ** -0.101 (-0.148~-0.053) ** -0.018 (-0.029~-0.007) * -0.023 (-0.034~-0.012) ** Vitality Respiratory function -5.694 (-8.266~-3.121) * -5.286 (-7.890~-2.682) ** 1.249 (0.544 ~ 1.954) * 0.745 (0.071 ~ 1.438) * Hand Grip strength -0.109 (-0.294 ~ 0.076) -0.014 (-0.211 ~ 0.183) 0.106 (0.057 ~ 0.155) ** 0.097 (0.045 ~ 0.148) ** Model 1: adjusted gender and age Model 2: adjusted gender, age, residence, education, marital status, smoking, drinking, social activities, nighttime sleep duration, nap, hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease, medication for hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease. * P < 0.05; ** P < 0.001 3.3 Bidirectional relationship between ePWV, RFM and IC by cross-lagged model Figure 2 demonstrated robust model fit (CFI = 0.943, SRMR = 0.020, RMSEA = 0.046) and highlighted significant associations: RFM1 was positively linked to ePWV (rT1 = 0.237, P < 0.001; rT2 = 0.205, P < 0.001) and IC (rT1 = 0.102, P = 0.005; rT2 = 0.121, P < 0.001) across both time points. RFM at T1 significantly predicted ePWV at T2 (β = 0.021, P = 0.007), IC2 at T2 (β = 0.021, P < 0.001). ePWV at T1 significantly predicted RFM at T2 (β = 0.725, P < 0.001), IC2 at T2 (β = -0.091, P < 0.001). 3.4 Subgroup analysis As shown in Table S2, RFM showed a significant positive association with IC (B = 0.027, 95% CI: 0.013 ~ 0.041, P < 0.001), while ePWV demonstrated no statistically significant relationship with IC in men. In women, ePWV was significant positive association with IC (B = -0.069, 95% CI: -0.130~-0.009, P = 0.026). Conversely, RFM was significant negative association with IC (B = 0.015, 95% CI: 0.000 ~ 0.030, P = 0.046) in women. Among the individuals with hypertension, ePWV was negatively associated with IC (B = -0.115, 95% CI: -0.183~-0.047, P = 0.001), RFM was not significantly associated with IC (Table S3). However, among the participants without hypertension, ePWV was not significantly associated with IC, RFM was significantly positive with IC (B = 0.024, 95% CI: 0.012 ~ 0.037, P < 0.001). In the terms of hypertension, ePWV was negatively associated with IC (B = -0.105, 95% CI: -0.184~-0.027, P = 0.009), RFM was not significantly associated with IC (Table S4). Furthermore, RFM at T1 significantly predicted ePWV at T2 and IC at T2 among men and women (Figure S1 and Figure S2). Meanwhile, ePWV at T1 significantly predicted RFM at T2 and IC at T2 among men and women. 4.Discussion This study revealed ePWV was significantly associated with poorer IC, characterized by global cognitive decline, attenuated depressive symptoms, reduced physical performance, respiratory impairment, and sensory deficits. Conversely, RFM demonstrated protective associations, including marginally better IC, enhanced cognitive function, fewer depressive symptoms, preserved respiratory capacity, greater muscular strength, and superior sensory performance—despite reduced locomotor activity. Furthermore, the results demonstrate that both ePWV and RFM significantly predict IC in a bidirectional manner among older people. The study indicates that increased arterial stiffness measured by ePWV is associated with diminished cognitive function, whereas elevated adiposity demonstrates a potentially protective association. Arterial stiffness is linked to age-related cognitive dysfunction [ 33 ]. Across ethnic groups, higher ePWV predicted poorer cognitive performance and accelerated decline, independent of demographic factors, vascular risks, and cerebral small vessel disease markers [ 11 ]. Advancing age physiologically manifests through arterial stiffening, atherosclerotic plaque accumulation, and consequent attenuation of central blood flow [ 34 ]. This may aggravate cognitive dysfunction. Additionally, increased arterial stiffness correlates with poorer pulmonary function and physical performance in older adults, consistent with prior observations. [ 35 , 36 ]. This present study showed that arterial stiffness was related to sensory loss. This may be attributed to the close bidirectional vicious cycle relationship between vascular stiffness and hypertension. Hypertension is a major risk factor that contributes to pathophysiological changes in the cochlea, ultimately leading to hearing loss [ 37 , 38 ]. Moreover, higher arterial stiffness is associated with age-related macular degeneration, glaucoma, retinal vein occlusion and retinopathy (diabetic and hypertensive) [ 39 ]. Prior research showed that older adults with type 2 diabetes who had depressive symptoms tended to have higher pulse wave velocity or aortic stiffness [ 40 ], which is inconsistent with our findings. There is a significant relationship between higher ePWV and depressive symptoms in men. This may be attributable to the coexistence of arterial stiffness and microvascular pathology in older adults, which induces nonspecific alterations in cognitive and affective functions, thereby masking typical depressive symptoms. The complex mutual association between depression and vascular burden may have multiple underlying mechanisms, including inflammation, endothelial dysfunction, and hyperactivity of the hypothalamic-pituitary-adrenal axis [ 41 ]. Previous study showed that sympathetic baroreflex sensitivity was inversely correlated with carotid artery stiffness in older people [ 42 ]. People with major depressive disorder are accompanied by sustained sympathetic hyperactivity [ 43 ]. In hypertensive populations, the association between arterial stiffness and depressive symptoms was not statistically significant. The average ePWV in non-hypertensive individuals was significantly lower than that in hypertensive patients in this study. In the non-hypertensive, accelerated pulse wave velocity may enhance organ perfusion efficiency, which may potentially ameliorate depressive symptoms. Overall, arterial stiffness may contribute to the progressive decline of intrinsic capacity in older adults. The association between obesity and cognitive function in older adults remains inconsistent. Prior studies indicate obesity is associated with greater risks of cognitive decline and dementia development [ 44 ]. A higher RFM was linked to lower cognitive function in older American males [ 19 ], which is inconsistent with our own findings. Evidence from the “obesity paradox” indicates that obesity, as measured by BMI or WC, may be associated with reduced cognitive decline in some populations [ 45 , 46 ]. In the CHARLS cohort, elevated visceral adiposity index (VAI) levels correlated with better global cognitive performance and enhanced episodic memory[ 46 ]. Adipokines, bioactive hormones and cytokines secreted by adipose tissue, may mediate this association through their effects on inflammatory pathways, insulin sensitivity, and metabolic regulation. Evidence suggests that adipokines like adiponectin and leptin support brain health by reducing neuroinflammation and stimulating neurogenesis, which could underlie their positive association with cognition. [ 47 ]. However, among older people with hypertension, no significant associations were observed between RFM and cognitive performance in this study. This phenomenon might be attributed to hypertension potentially attenuating the protective effects of adipose tissue. In present study, individuals, who were with elevated RFM, were at a lower risk of experiencing depression symptoms. Previous study showed that a significant nonlinear association was observed between body roundness index (BRI) scores and depressive symptom risk, manifesting in an inverted “L” shape in older Chinese individuals [ 48 ]. BRI is a new geometric index that estimates total and visceral fat by combining height and waist circumference. Overweight and mild obesity were associated with reduced odds of depressive symptoms in older adults [ 48 ]. Several research works have recognized overweight and obesity as predisposing factors for depression [ 49 ]. However, “obesity paradox” suggests that for elderly individuals, the optimal body mass index may fall within the overweight or mildly obese range. Fat mass and obesity associated gene (FTO), an RNA demethylase, can be found in the hippocampus. Overexpression of FTO has been found to have antidepressant effects in mice [ 50 ]. Furthermore, RFM was positively associated with hand grip strength, indicating that moderate amounts of fat contribute to grip strength. Prior research found that every 1-unit increment in BRI corresponded to a 0.38-unit gain in handgrip strength among U.S. adults aged ≥ 20 years [ 51 ]. Experimental studies using adipocyte-deficient mouse models demonstrated significant reductions in both muscle mass and strength compared to wild-type controls [ 52 ]. Muscle functional capacity returned to baseline levels following adipose tissue restoration to 10% of physiological fat mass, and this restoration was fully mediated by leptin, suggesting that leptin plays a key role in the maintenance of muscle mass and strength in adipose tissue [ 52 ]. However, RFM is negatively correlated with physical performance, indicating that higher adiposity is associated with lower physical performance. Previous studies have demonstrated that overweight and obese older adults exhibit poorer physical performance [ 53 ]. Notably, reduced leg strength was observed specifically in Italian adults aged ≥ 65 years with severe obesity. Additionally, individuals with elevated RFM experienced better respiratory function in this study. Higher obesity-related indices correlated with improved baseline lung function but accelerated decline during follow-up [ 54 ]. The shape of non-linear associations was also found when the relation between obesity-related index values and lung function values was evaluated in the cross-sectional study [ 55 ]. Overall, fat mass may contribute to the progressive increase of intrinsic capacity in older adults. There are several limitations in the current study. First, excluding participants with missing data on IC, ePWV, RFM, and those lost to follow-up may have introduced selection bias and reduced the final sample size, limiting the study’s statistical power. Second, as this study utilized observational data, the observed associations might have been influenced by confounding variables. To mitigate this potential bias, we incorporated as many relevant covariates as possible in our analysis. Third, despite adjusting for multiple covariates, residual confounding from unmeasured factors—such as dietary habits, adipokines and genetic predisposition—may persist. These aspects should be addressed in future research. Fourth, since IC, ePWV, and RFM data were only available up to 2015, our analysis was limited to a 4-year follow-up, restricting our ability to assess the long-term association between ePWV, RFM, and IC. Future research should prolong the follow-up period to gain deeper insights into the long-term dynamics of these variables. Furthermore, while the cross-lagged panel model allows for assessing directional relationships between variables, it does not provide conclusive evidence of causation. Future studies should employ experimental or intervention designs to verify potential causal relationships. In conclusion, we found older adults with reduced relative fat mass and increased arterial stiffness face an elevated risk of intrinsic capacity decline. This study identifies modifiable factors influencing intrinsic capacity in older adults. The findings enable personalized strategies for intrinsic capacity preservation to optimize healthy aging outcomes and development of targeted interventions by healthcare providers focusing on adjustable determinants to slow IC decline. Declarations Conflicts of interest The authors declare no conflicts of interest. Ethics approval and consent to participate The CHARLS study has gotten the approval for interviewing respondents and collecting data by the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015), and the informed consent was required to sign by the respondents. Consent for publication: Not applicable. Clinical trial number Not applicable. Funding statement This work was supported by Philosophy and Social Science Project of Hubei Provincial Department of Education (no.24Y098). Author Contribution Xinhong Zhu and Xiaohong Zhang: conceptualization; funding acquisition, writing–original draft, writing–review & editing. Jiayu He: writing–original draft. Xiaoming Zhang, Heqing Wang, Nibo Chen, Yumeng Zhao and Jingyi Li: data curation, formal analysis, resources. Acknowledgement We thank the China Health and Retirement Longitudinal Study team for providing data. We thank all participants in the CHARLS. We thank all volunteers and staff involved in this research. Data Availability The data used in the study are accessible to be downloaded publicly at https://charls.charlsdata.com/pages/data/111/zh-cn.html. References National Bureau of Statistics of China. https://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202501/t20250117_1958332.html Huang ZT, Lai ETC, Luo Y, et al. 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Association of estimated pulse wave velocity with cognitive function in a multiethnic diverse population: The Northern Manhattan Study. Alzheimers Dement. 2024;20(7):4903–13. Lu S, Xiong YJ, Meng XD, et al. Association between eGDR and accelerated aging: the mediating role of ePWV. Diabetol Metab Syndr. 2025;17(1):170. Song D, Miao J, Zhang Y, et al. Relationship between estimated pulse wave velocity and the risk of future sarcopenia in middle-aged and older Chinese adults: evidence from the China Health and Retirement Longitudinal Study. Front Cardiovasc Med. 2025;12:1494635. Feng Y-T, Pei J-Y, Wang Y-P, et al. Association between depression and vascular aging: a comprehensive analysis of predictive value and mortality risks. J Affect Disord. 2024;367:632–9. Suthahar N, Bergman RN, de Boer RA. Replacing body mass index with relative fat mass to accurately estimate adiposity. Nat Rev Endocrinol. 2025;21(7):393–4. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage A cross-sectional study in American adult individuals. Sci Rep. 2018;8(1):10980. Suthahar N, Wang K, Zwartkruis VW, et al. Associations of relative fat mass, a new index of adiposity, with type-2 diabetes in the general population. Eur J Intern Med. 2023;109:73–8. Suthahar N, Zwartkruis V, Geelhoed B, et al. Associations of relative fat mass and BMI with all-cause mortality: Confounding effect of muscle mass. Obes (Silver Spring). 2024;32(3):603–11. Liu L, Wu A, Yang S. Association between relative fat mass and cognitive function among US older men: NHANES 2011–2014. Lipids Health Dis. 2025;24(1):166. Zhu X, Yue Y, Li L, et al. The relationship between depression and relative fat mass (RFM): A population-based study. J Affect Disord. 2024;356:323–8. Kim HL, Joh HS, Lim WH et al. Associations of Estimated Pulse Wave Velocity with Body Mass Index and Waist Circumference among General Korean Adults. Metabolites, 2023. 13(10). Yu P, Huang T, Hu S, et al. Predictive value of relative fat mass algorithm for incident hypertension: a 6-year prospective study in Chinese population. BMJ Open. 2020;10(10):e038420. Zhao Y, Hu Y, Smith JP, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. López-Ortiz S, Lista S, Peñín-Grandes S, et al. Defining and assessing intrinsic capacity in older people: A systematic review and a proposed scoring system. Ageing Res Rev. 2022;79:101640. Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85–94. Gutiérrez-Robledo LM, García-Chanes RE, Pérez-Zepeda MU. Allostatic Load as a Biological Substrate to Intrinsic Capacity: A Secondary Analysis of CRELES. J Nutr Health Aging. 2019;23(9):788–95. Guo Z, Chen Y, Koirala B, et al. Intrinsic capacity trajectories and cardiovascular disease incidence among Chinese older adults: a population-based prospective cohort study. BMC Geriatr. 2025;25(1):269. Stolz E, Mayerl H, Freidl W, et al. Intrinsic Capacity Predicts Negative Health Outcomes in Older Adults. J Gerontol Biol Sci Med Sci. 2022;77(1):101–5. Andresen EM, Malmgren JA, Carter WB, et al. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10(2):77–84. Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. Wei X, Chen Y, Qin J, et al. Factors associated with the intrinsic capacity in older adults: A scoping review. J Clin Nurs. 2024;33(5):1739–50. Xia Y, Yang Y, CFI RMSEA. TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behav Res Methods. 2019;51(1):409–28. Liu Q, Fang J, Cui C, et al. Association of Aortic Stiffness and Cognitive Decline: A Systematic Review and Meta-Analysis. Front Aging Neurosci. 2021;13:680205. Mitchell GF, van Buchem MA, Sigurdsson S, et al. Arterial stiffness, pressure and flow pulsatility and brain structure and function: the Age, Gene/Environment Susceptibility–Reykjavik study. Brain. 2011;134(Pt 11):3398–407. Costanzo L, Pedone C, Battistoni F, et al. Relationship between FEV(1) and arterial stiffness in elderly people with chronic obstructive pulmonary disease. Aging Clin Exp Res. 2017;29(2):157–64. Cilhoroz BT, Heckel AR, DeBlois JP, et al. Arterial stiffness and augmentation index are associated with balance function in young adults. Eur J Appl Physiol. 2023;123(4):891–9. Przewoźny T, Gójska-Grymajło A, Kwarciany M, et al. Hypertension and cochlear hearing loss. Blood Press. 2015;24(4):199–205. Hou Y, Liu B. Relationship Between Hypertension and Hearing Loss: Analysis of the Related Factors. Clin Interv Aging. 2024;19:845–56. Beros AL, Sluyter JD, Scragg R. Association of arterial stiffness and eye disease: a systematic review and meta-analysis. BMJ Open Ophthalmol, 2025. 10(1). Moh MC, Low S, Ng TP, et al. Association between depressive symptoms and pulse wave velocity is mediated by increased adiposity in older adults with type 2 diabetes. J Psychiatry Neurosci. 2021;46(1):E176–83. Cui L, Li S, Wang S, et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduct Target Ther. 2024;9(1):30. Okada Y, Galbreath MM, Shibata S, et al. Relationship between sympathetic baroreflex sensitivity and arterial stiffness in elderly men and women. Hypertension. 2012;59(1):98–104. Barton DA, Dawood T, Lambert EA, et al. Sympathetic activity in major depressive disorder: identifying those at increased cardiac risk? J Hypertens. 2007;25(10):2117–24. Dye L, Boyle NB, Champ C, et al. The relationship between obesity and cognitive health and decline. Proc Nutr Soc. 2017;76(4):443–54. Tang X, Zhao W, Lu M, et al. Relationship between Central Obesity and the incidence of Cognitive Impairment and Dementia from Cohort Studies Involving 5,060,687 Participants. Neurosci Biobehav Rev. 2021;130:301–13. Zeng Z, Huang K, Cen Y, et al. Elevated visceral adiposity index linked to improved cognitive function in middle-aged and elderly Chinese: evidence from the China health and retirement longitudinal study. Front Aging Neurosci. 2023;15:1270239. Thacker EL, Karki R, Gabor R, et al. Leptin, adiponectin, body mass index, and incident cognitive impairment. J Alzheimers Dis. 2025;105(1):90–106. Wang Y, Yang Y, Wang W, et al. The Relationship Between BRI and Depressive Symptoms in Chinese Older Adults: A CLHLS-Based Study. Int J Methods Psychiatr Res. 2024;33(4):e70009. Scott KM, McGee MA, Wells JE, et al. Obesity and mental disorders in the adult general population. J Psychosom Res. 2008;64(1):97–105. Liu S, Xiu J, Zhu C, et al. Fat mass and obesity-associated protein regulates RNA methylation associated with depression-like behavior in mice. Nat Commun. 2021;12(1):6937. Wei Z, Yu T, Jin X, et al. The association between body roundness index and handgrip strength and muscle quality index: A cross-sectional study. PLoS ONE. 2025;20(5):e0322928. Collins KH, Gui C, Ely EV, et al. Leptin mediates the regulation of muscle mass and strength by adipose tissue. J Physiol. 2022;600(16):3795–817. De Stefano F, Zambon S, Giacometti L, et al. Obesity, Muscular Strength, Muscle Composition and Physical Performance in an Elderly Population. J Nutr Health Aging. 2015;19(7):785–91. Hsu YE, Chen SC, Geng JH et al. Obesity-Related Indices Are Associated with Longitudinal Changes in Lung Function: A Large Taiwanese Population Follow-Up Study. Nutrients, 2021. 13(11). Zhang RH, Zhou JB, Cai YH, et al. Non-linear association of anthropometric measurements and pulmonary function. Sci Rep. 2021;11(1):14596. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7121744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500044606,"identity":"dca290bb-1421-4532-ac89-235a266f7208","order_by":0,"name":"Xinhong Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYFACHsYHHyoOJEA4bMRpYTaccYZELWzSvG2kaJGfkXvYcOa8O3kGx08nMHwoO8zAP7sBvxbGnnOJDz5ue1ZscCZ3A+OMc4cZJO4cwK+Fmb3H2HDmtsOJG27wbmDmbTvMYCCRgF8LGzOPmTTvHKiWv8Ro4WHvAWppgGphJEaLBM8ZY8MZxw4XSwL9crDnXDqPxA0CWuRn5Bg++FBzOI/v+NmND36UWcvxzyCgBQUcALmUBPWjYBSMglEwCnABAN2KSSpl8QdNAAAAAElFTkSuQmCC","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xinhong","middleName":"","lastName":"Zhu","suffix":""},{"id":500044607,"identity":"037b7509-c33f-4901-9609-48b4a330e08b","order_by":1,"name":"Jiayu He","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"He","suffix":""},{"id":500044608,"identity":"7d08a70e-4e88-459d-ad23-2890edea6c49","order_by":2,"name":"Xiaoming Zhang","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Zhang","suffix":""},{"id":500044609,"identity":"bf748c95-2548-4251-8ebe-90f9de44cc72","order_by":3,"name":"Heqing Wang","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Heqing","middleName":"","lastName":"Wang","suffix":""},{"id":500044610,"identity":"4fe555ad-c51b-4870-972d-ac3ec2db7fd6","order_by":4,"name":"Nibo Chen","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nibo","middleName":"","lastName":"Chen","suffix":""},{"id":500044611,"identity":"65169af1-1ecc-4b4c-b646-339510281d94","order_by":5,"name":"Yumeng Zhao","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yumeng","middleName":"","lastName":"Zhao","suffix":""},{"id":500044612,"identity":"baa432e3-1333-4b77-9225-9fe73ed65fea","order_by":6,"name":"Jingyi Li","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Li","suffix":""},{"id":500044613,"identity":"33c79721-a035-43e2-b17e-0aa085cb7c95","order_by":7,"name":"Xiaohong Zhang","email":"","orcid":"","institution":"Hubei Provincial Hospital of Traditional Chinese Medicine (Affiliated Hospital of Hubei University of Chinese Medicine)","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-07-14 13:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7121744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7121744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89416598,"identity":"d90ea02e-1aa6-4e14-9d08-5236a26ade31","added_by":"auto","created_at":"2025-08-19 17:20:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":426478,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7121744/v1/0628d069c1103df2b780b81a.jpeg"},{"id":89416599,"identity":"bd4f3ba4-34ab-43a2-b580-efa48a8b566c","added_by":"auto","created_at":"2025-08-19 17:20:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":411232,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged panel models of ePWV, RFM and IC.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7121744/v1/819cdfc23f3d12edaaeb2642.jpeg"},{"id":91308136,"identity":"21e3da51-b766-4075-9abb-a6dc3ed33501","added_by":"auto","created_at":"2025-09-15 06:47:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1743142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7121744/v1/ad33b2ac-1155-46b2-849c-e2959d02b890.pdf"},{"id":89416603,"identity":"6a4c98d1-3bcf-4d65-9cd1-71b9c83baf04","added_by":"auto","created_at":"2025-08-19 17:20:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":322451,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-7121744/v1/0f1baaace0ac6924f1baa2c4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between relative fat mass, estimated pulse wave velocity and intrinsic capacity among the older people","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eThe global population is undergoing rapid aging, presenting significant societal and economic challenges. By the end of 2024, China\u0026rsquo;s population aged 60 and above had reached 310\u0026nbsp;million, accounting for 22% of the total population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The aging process is characterized by a gradual deterioration of physiological resilience, which negatively impacts both physical and cognitive capacities. To combat age-related challenges, WHO\u0026rsquo;s World Report on Ageing and Health established healthy aging as maintaining functional ability for wellbeing in later life. The new concept, intrinsic capacity (IC), was defined as the composite of all the physical and mental capacities that an individual can draw upon throughout the lifespan [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accumulating empirical evidence establishes intrinsic capacity as a robust predictor of critical health outcomes, particularly incident disability and all-cause mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the factors associated with IC are still uncertain.\u003c/p\u003e\u003cp\u003eArterial stiffness is a hallmark of the aging process and an essential manifestation of vascular aging. The noninvasive measurement of central arterial stiffness by carotid-femoral pulse wave velocity (cfPWV) is currently regarded as the gold standard [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. cfPWV\u0026rsquo;s reliance on specialized hardware, operator skill, and strict protocols restricts its real-world clinical scalability. Vlachopoulos et al. recently suggested that estimated pulse wave velocity (ePWV) may serve as a viable substitute for cfPWV, proving its independent association with cardiovascular risk beyond conventional predictors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A growing body of research indicates that ePWV shows robust correlations with cognitive performance, chronic disease, disability and mortality [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, ePWV mediated the detrimental effects of impaired insulin sensitivity on biological aging indicators, suggesting arterial stiffness may be an important pathway linking metabolic dysfunction to premature aging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In middle-aged and older Chinese adults, fluctuations in in ePWV may be associated with an increased risk of sarcopenia, suggesting its potential as a predictive indicator [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Vascular aging assessed by ePWV is a significant biomarker for the risk and prognosis of depression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While current evidence suggests that ePWV may modulate healthy aging, the relationship between ePWV and IC remains unexplored.\u003c/p\u003e\u003cp\u003eRelative fat mass (RFM) represents a novel anthropometric index derived from a linear equation to assess total body fat percentage in adult populations. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. RFM correlated better with dual-energy X-ray absorptiometry-based measurements of whole-body adipose tissue percentage than body mass index (BMI) did and strongly correlated with trunk adipose tissue levels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Besides the advantages mentioned, RFM has demonstrated significant predictive value for cardiometabolic disease risk in the general population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A population-based study in the Dutch revealed that higher RFM levels were significantly associated with increased risk of all-cause mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, higher RFM values were associated with poorer cognitive scores in older men [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Analysis of NHANES data (2005\u0026ndash;2018) revealed that RFM showed a stronger association with depression than both BMI and waist circumference (WC) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although no direct evidence currently links RFM to ePWV, existing study suggests that WC correlates with arterial stiffness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, longitudinal studies indicate RFM serves as a robust predictor of hypertension onset among Chinese adults [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In light of these findings, RFM shows great potential in vascular aging or IC.\u003c/p\u003e\u003cp\u003eThis study utilizes data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of the middle-aged and elderly Chinese population. We aim to investigate the associations of RFM, vascular aging and intrinsic capacity among Chinese older adults.\u003c/p\u003e"},{"header":"2.Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eThe CHARLS, a prospective nationwide study, includes adults above 45 years from 450 communities across 28 Chinese provinces, with data collection repeated every two years since 2011. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study conducted a retrospective analysis of CHARLS data collected between 2011 and 2015.The exclusion criteria for the present study were as follows: 1) individuals aged less than 60 years old; 2) missing data on RFM, ePWV and IC in 2011; 3) lost to follow-up in 2015; 4) missing data on RFM, ePWV and IC in 2015. As described in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2598 older people were included in the final analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Definitions of IC\u003c/h2\u003e\u003cp\u003eFollowing L\u0026oacute;pez-Ortiz et al.'s established protocol, IC was defined and examined across five principal dimensions: cognitive, psychological, locomotor, sensory, and vitality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The assessment of locomotion was conducted using the SPPB, which incorporated chair stand tests, gait speed assessment, and balance examinations. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Sensory function was assessed through participant-reported hearing and vision impairments. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The operational definition of vitality incorporated pulmonary function (peak flow test) and dominant hand strength measurement [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The evaluation of psychological status utilized the Center for Epidemiological Studies Depression Scale-10 (CES-D10), a validated 10-item depression screening instrument [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Mini-Mental State Examination (MMSE) were used to quantify cognitive functioning [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Intrinsic capacity (IC) domains were categorized into three ordinal levels (0\u0026ndash;2): 0\u0026thinsp;=\u0026thinsp;severely impaired, 1\u0026thinsp;=\u0026thinsp;partially impaired, and 2\u0026thinsp;=\u0026thinsp;slightly impaired or fully preserved. The composite IC score, derived from all five domains, ranged from 0 (complete impairment) to 10 (peak function), with increasing values reflecting better-preserved capacity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Detailed specifications for IC domain, including definitions, assessment protocols, and stratification criteria, are systematically presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The data used to calculate the IC index were collected in two waves: 2011 and 2015 wave.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 ePWV Calculation\u003c/h2\u003e\u003cp\u003eePWV, a non-invasive estimation of pulse wave velocity, is computed based on age and mean blood pressure (MBP), which is determined from systolic (SBP) and diastolic blood pressure (DBP) measurements [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The formula for calculating ePWV is as follows:\u003c/p\u003e\u003cp\u003eePWV\u0026thinsp;=\u0026thinsp;9.587\u0026thinsp;\u0026minus;\u0026thinsp;0.402 \u0026times; age\u0026thinsp;+\u0026thinsp;4.560 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026times; age \u003csup\u003e2\u003c/sup\u003e -2.621\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u0026times; age \u003csup\u003e2\u003c/sup\u003e \u0026times;MBP\u0026thinsp;+\u0026thinsp;3.176 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026times; MBP \u0026times; age \u0026ndash; 1.832 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e \u0026times; MBP\u003c/p\u003e\u003cp\u003eMBP\u0026thinsp;=\u0026thinsp;DBP\u0026thinsp;+\u0026thinsp;0.4 \u0026times; (SBP-DBP)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 RFM Calculation\u003c/h2\u003e\u003cp\u003eRFM, a validated proxy for whole-body adiposity, was calculated using the following formula: 64 \u0026minus; (20 \u0026times; height/WC) + (12 \u0026times; sex); sex equals 0 for men and 1 for women [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Height and WC are measured in the same units.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Covariates\u003c/h2\u003e\u003cp\u003eAt baseline, the covariates selected were factors known to be associated with the risk of IC [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Sociodemographic characteristics comprised age, gender (male/female), educational attainment (categorized as illiterate, primary school or less, or junior high school and above), marital status (married vs. other), and area of residence (urban/rural). Health-related variables encompassed: current smoking (yes/no), alcohol intake frequency (never, drink but less than once a month, and drink more than once a month), social engagement (yes/no), sleep duration at night and daytime napping, self-reported physician-diagnosed chronic conditions (hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease) and medications taken for these chronic diseases (hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables and n (%) for categorical variables. Group differences (significant capacity loss, declining capacity, and stable capacity) were assessed using either chi-square tests or ANOVA as appropriate.\u003c/p\u003e\u003cp\u003eGeneralized estimation equation (GEE) models were used to examine the associations of RFM, ePWV and IC. This approach accounts for the correlation between the repeated measures within a person. The GEE parameter estimates were expressed as the odds ratios (ORs) and the 95% confidence intervals (95% CIs). A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance. For the longitudinal analyses, we constructed two models: Model 1 adjusted for age and sex; Model 2 adjusted for age, sex, residence, marital status, educational level, smoking, drinking status, social engagement, sleep duration at night and daytime napping, hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease and medication for hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease.\u003c/p\u003e\u003cp\u003eIn order to examine the complex interplay among RFM, ePWV, and IC in older adults, we constructed a cross-lagged model, incorporating factors such as age, sex, residence, marital status, educational level, smoking, drinking status, active social engagement, nighttime sleep duration, nap, hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease and medication for hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease as covariates. The following goodness-of-fit indices were employed to evaluate model fit: chi-square statistic, comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). Model fit was considered acceptable when CFI\u0026thinsp;\u0026ge;\u0026thinsp;0.90, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.08 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All the analysis will be conducted by Mplus version 8.3 and SPSS 25.0.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the baseline characteristics of eligible participants (n\u0026thinsp;=\u0026thinsp;2,598). The mean age of participants was 66.16 (5.23), comprising 1409 men and 1189 women. In the analytic sample, 13.4% of participants exhibited significant loss of capacity, while 77.8% showed declining capacity. The mean ePWV and RFM of total population was 10.91 (1.53) and 32.10 (8.85), respectively. Compared to significant loss of capacity, individuals with stable capacity were more likely to be younger, male, high education level, and long nighttime sleep duration.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of study population in 2011.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificant loss of capacity\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;347)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeclining capacity\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2021)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStable capacity\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;230)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.56 \u0026plusmn; 6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.99 \u0026plusmn; 5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.99 \u0026plusmn; 3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1138 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e265 (76.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1349 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (53.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow Primary School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e512 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1105 (54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e404 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (73.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1718 (85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203 (88.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e691 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e551 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than once a month.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e267 (77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1332 (65.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1054 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151 (65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNighttime sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.48 \u0026plusmn; 2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.30 \u0026plusmn; 1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.70 \u0026plusmn; 1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00, 60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (0, 60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (0, 60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e578 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes or high blood sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e268 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e271 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedication for chronic disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e466 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes or high blood sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e183 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.02 \u0026plusmn;1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.51 \u0026plusmn; 1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.22 \u0026plusmn; 1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.59 \u0026plusmn; 9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.71 \u0026plusmn; 8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.51 \u0026plusmn; 7.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 The relationship of ePWV, RFM and IC in 2011\u0026thinsp;~\u0026thinsp;2015 evaluated by GEE.\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, participants with elevated ePWV showed poorer IC (B = -0.047, 95% CI: -0.087 to -0.008, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), reduced cognitive function performance (B = -0.175, 95% CI: -0.266~-0.084, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower level of depression symptom (B = -0.317, 95% CI: -0.266~-0.084, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower SPPB scores (B = -0.101, 95% CI: -0.148~-0.053, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diminished respiratory function (B = -5.286, 95% CI: -7.890~-2.682, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and greater sensory impairment (B = -0.015, 95% CI: -0.029~-0.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). Individuals with elevated RFM demonstrated slightly higher IC scores (B\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.010\u0026thinsp;~\u0026thinsp;0.031, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), better cognitive function performance (B\u0026thinsp;=\u0026thinsp;0.047, 95% CI: 0.021\u0026thinsp;~\u0026thinsp;0.072, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower level of depression symptom (B = -0.075, 95% CI: -0.117~-0.033, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), reduced SPPB scores (B = -0.023, 95% CI: -0.034~-0.012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), greater respiratory function (B\u0026thinsp;=\u0026thinsp;0.745, 95% CI: 0.071\u0026thinsp;~\u0026thinsp;1.438, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), increased hand grip strength (B\u0026thinsp;=\u0026thinsp;0.097, 95% CI: 0.045\u0026thinsp;~\u0026thinsp;0.148, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), enhanced sensory function (B\u0026thinsp;=\u0026thinsp;0.004, 95% CI: 0.001\u0026thinsp;~\u0026thinsp;0.008, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of ePWV, RFM and IC among older people in 2011\u0026thinsp;~\u0026thinsp;2015\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eePWV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRFM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB(95%CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.090 (-0.132~-0.048) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.047 (-0.087~-0.008) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034 (0.023\u0026thinsp;~\u0026thinsp;0.044) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020 (0.010\u0026thinsp;~\u0026thinsp;0.031) **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.281 (-0.385~-0.178) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.175 (-0.266~-0.084) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.092 (0.063\u0026thinsp;~\u0026thinsp;0.120) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.047 (0.021\u0026thinsp;~\u0026thinsp;0.072) **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.179 (-0.343~-0.015) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.317 (-0.476~-0.158) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.106 (-0.152~-0.060) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.075 (-0.117~-0.033) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019 (-0.032~-0.006) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.015 (-0.029~-0.002) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004 (0.001\u0026thinsp;~\u0026thinsp;0.007) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004 (0.001\u0026thinsp;~\u0026thinsp;0.008) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocomotion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.126 (-0.172~-0.080) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.101 (-0.148~-0.053) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.018 (-0.029~-0.007) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.023 (-0.034~-0.012) **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory function\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.694 (-8.266~-3.121) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.286 (-7.890~-2.682) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.249 (0.544\u0026thinsp;~\u0026thinsp;1.954) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.745 (0.071\u0026thinsp;~\u0026thinsp;1.438) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHand Grip strength\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.109 (-0.294\u0026thinsp;~\u0026thinsp;0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.014 (-0.211\u0026thinsp;~\u0026thinsp;0.183)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106 (0.057\u0026thinsp;~\u0026thinsp;0.155) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.097 (0.045\u0026thinsp;~\u0026thinsp;0.148) **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: adjusted gender and age\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: adjusted gender, age, residence, education, marital status, smoking, drinking, social activities, nighttime sleep duration, nap, hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease, medication for hypertension, dyslipidemia, diabetes or high blood sugar, cancer, chronic lung diseases, heart disease, stroke, and kidney disease.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Bidirectional relationship between ePWV, RFM and IC by cross-lagged model\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated robust model fit (CFI\u0026thinsp;=\u0026thinsp;0.943, SRMR\u0026thinsp;=\u0026thinsp;0.020, RMSEA\u0026thinsp;=\u0026thinsp;0.046) and highlighted significant associations: RFM1 was positively linked to ePWV (rT1\u0026thinsp;=\u0026thinsp;0.237, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; rT2\u0026thinsp;=\u0026thinsp;0.205, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and IC (rT1\u0026thinsp;=\u0026thinsp;0.102, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; rT2\u0026thinsp;=\u0026thinsp;0.121, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across both time points. RFM at T1 significantly predicted ePWV at T2 (β\u0026thinsp;=\u0026thinsp;0.021, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), IC2 at T2 (β\u0026thinsp;=\u0026thinsp;0.021, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ePWV at T1 significantly predicted RFM at T2 (β\u0026thinsp;=\u0026thinsp;0.725, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IC2 at T2 (β = -0.091, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subgroup analysis\u003c/h2\u003e\u003cp\u003eAs shown in Table S2, RFM showed a significant positive association with IC (B\u0026thinsp;=\u0026thinsp;0.027, 95% CI: 0.013\u0026thinsp;~\u0026thinsp;0.041, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while ePWV demonstrated no statistically significant relationship with IC in men. In women, ePWV was significant positive association with IC (B = -0.069, 95% CI: -0.130~-0.009, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). Conversely, RFM was significant negative association with IC (B\u0026thinsp;=\u0026thinsp;0.015, 95% CI: 0.000\u0026thinsp;~\u0026thinsp;0.030, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) in women. Among the individuals with hypertension, ePWV was negatively associated with IC (B = -0.115, 95% CI: -0.183~-0.047, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), RFM was not significantly associated with IC (Table S3). However, among the participants without hypertension, ePWV was not significantly associated with IC, RFM was significantly positive with IC (B\u0026thinsp;=\u0026thinsp;0.024, 95% CI: 0.012\u0026thinsp;~\u0026thinsp;0.037, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the terms of hypertension, ePWV was negatively associated with IC (B = -0.105, 95% CI: -0.184~-0.027, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), RFM was not significantly associated with IC (Table S4). Furthermore, RFM at T1 significantly predicted ePWV at T2 and IC at T2 among men and women (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure S2). Meanwhile, ePWV at T1 significantly predicted RFM at T2 and IC at T2 among men and women.\u003c/p\u003e\u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eThis study revealed ePWV was significantly associated with poorer IC, characterized by global cognitive decline, attenuated depressive symptoms, reduced physical performance, respiratory impairment, and sensory deficits. Conversely, RFM demonstrated protective associations, including marginally better IC, enhanced cognitive function, fewer depressive symptoms, preserved respiratory capacity, greater muscular strength, and superior sensory performance\u0026mdash;despite reduced locomotor activity. Furthermore, the results demonstrate that both ePWV and RFM significantly predict IC in a bidirectional manner among older people.\u003c/p\u003e\u003cp\u003eThe study indicates that increased arterial stiffness measured by ePWV is associated with diminished cognitive function, whereas elevated adiposity demonstrates a potentially protective association. Arterial stiffness is linked to age-related cognitive dysfunction [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Across ethnic groups, higher ePWV predicted poorer cognitive performance and accelerated decline, independent of demographic factors, vascular risks, and cerebral small vessel disease markers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Advancing age physiologically manifests through arterial stiffening, atherosclerotic plaque accumulation, and consequent attenuation of central blood flow [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This may aggravate cognitive dysfunction. Additionally, increased arterial stiffness correlates with poorer pulmonary function and physical performance in older adults, consistent with prior observations. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This present study showed that arterial stiffness was related to sensory loss. This may be attributed to the close bidirectional vicious cycle relationship between vascular stiffness and hypertension. Hypertension is a major risk factor that contributes to pathophysiological changes in the cochlea, ultimately leading to hearing loss [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, higher arterial stiffness is associated with age-related macular degeneration, glaucoma, retinal vein occlusion and retinopathy (diabetic and hypertensive) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Prior research showed that older adults with type 2 diabetes who had depressive symptoms tended to have higher pulse wave velocity or aortic stiffness [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which is inconsistent with our findings. There is a significant relationship between higher ePWV and depressive symptoms in men. This may be attributable to the coexistence of arterial stiffness and microvascular pathology in older adults, which induces nonspecific alterations in cognitive and affective functions, thereby masking typical depressive symptoms. The complex mutual association between depression and vascular burden may have multiple underlying mechanisms, including inflammation, endothelial dysfunction, and hyperactivity of the hypothalamic-pituitary-adrenal axis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous study showed that sympathetic baroreflex sensitivity was inversely correlated with carotid artery stiffness in older people [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. People with major depressive disorder are accompanied by sustained sympathetic hyperactivity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In hypertensive populations, the association between arterial stiffness and depressive symptoms was not statistically significant. The average ePWV in non-hypertensive individuals was significantly lower than that in hypertensive patients in this study. In the non-hypertensive, accelerated pulse wave velocity may enhance organ perfusion efficiency, which may potentially ameliorate depressive symptoms. Overall, arterial stiffness may contribute to the progressive decline of intrinsic capacity in older adults.\u003c/p\u003e\u003cp\u003eThe association between obesity and cognitive function in older adults remains inconsistent. Prior studies indicate obesity is associated with greater risks of cognitive decline and dementia development [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A higher RFM was linked to lower cognitive function in older American males [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which is inconsistent with our own findings. Evidence from the \u0026ldquo;obesity paradox\u0026rdquo; indicates that obesity, as measured by BMI or WC, may be associated with reduced cognitive decline in some populations [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the CHARLS cohort, elevated visceral adiposity index (VAI) levels correlated with better global cognitive performance and enhanced episodic memory[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Adipokines, bioactive hormones and cytokines secreted by adipose tissue, may mediate this association through their effects on inflammatory pathways, insulin sensitivity, and metabolic regulation. Evidence suggests that adipokines like adiponectin and leptin support brain health by reducing neuroinflammation and stimulating neurogenesis, which could underlie their positive association with cognition. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, among older people with hypertension, no significant associations were observed between RFM and cognitive performance in this study. This phenomenon might be attributed to hypertension potentially attenuating the protective effects of adipose tissue. In present study, individuals, who were with elevated RFM, were at a lower risk of experiencing depression symptoms. Previous study showed that a significant nonlinear association was observed between body roundness index (BRI) scores and depressive symptom risk, manifesting in an inverted \u0026ldquo;L\u0026rdquo; shape in older Chinese individuals [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. BRI is a new geometric index that estimates total and visceral fat by combining height and waist circumference. Overweight and mild obesity were associated with reduced odds of depressive symptoms in older adults [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Several research works have recognized overweight and obesity as predisposing factors for depression [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, \u0026ldquo;obesity paradox\u0026rdquo; suggests that for elderly individuals, the optimal body mass index may fall within the overweight or mildly obese range. Fat mass and obesity associated gene (FTO), an RNA demethylase, can be found in the hippocampus. Overexpression of FTO has been found to have antidepressant effects in mice [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Furthermore, RFM was positively associated with hand grip strength, indicating that moderate amounts of fat contribute to grip strength. Prior research found that every 1-unit increment in BRI corresponded to a 0.38-unit gain in handgrip strength among U.S. adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Experimental studies using adipocyte-deficient mouse models demonstrated significant reductions in both muscle mass and strength compared to wild-type controls [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Muscle functional capacity returned to baseline levels following adipose tissue restoration to 10% of physiological fat mass, and this restoration was fully mediated by leptin, suggesting that leptin plays a key role in the maintenance of muscle mass and strength in adipose tissue [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, RFM is negatively correlated with physical performance, indicating that higher adiposity is associated with lower physical performance. Previous studies have demonstrated that overweight and obese older adults exhibit poorer physical performance [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Notably, reduced leg strength was observed specifically in Italian adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years with severe obesity. Additionally, individuals with elevated RFM experienced better respiratory function in this study. Higher obesity-related indices correlated with improved baseline lung function but accelerated decline during follow-up [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The shape of non-linear associations was also found when the relation between obesity-related index values and lung function values was evaluated in the cross-sectional study [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Overall, fat mass may contribute to the progressive increase of intrinsic capacity in older adults.\u003c/p\u003e\u003cp\u003eThere are several limitations in the current study. First, excluding participants with missing data on IC, ePWV, RFM, and those lost to follow-up may have introduced selection bias and reduced the final sample size, limiting the study\u0026rsquo;s statistical power. Second, as this study utilized observational data, the observed associations might have been influenced by confounding variables. To mitigate this potential bias, we incorporated as many relevant covariates as possible in our analysis. Third, despite adjusting for multiple covariates, residual confounding from unmeasured factors\u0026mdash;such as dietary habits, adipokines and genetic predisposition\u0026mdash;may persist. These aspects should be addressed in future research. Fourth, since IC, ePWV, and RFM data were only available up to 2015, our analysis was limited to a 4-year follow-up, restricting our ability to assess the long-term association between ePWV, RFM, and IC. Future research should prolong the follow-up period to gain deeper insights into the long-term dynamics of these variables. Furthermore, while the cross-lagged panel model allows for assessing directional relationships between variables, it does not provide conclusive evidence of causation. Future studies should employ experimental or intervention designs to verify potential causal relationships.\u003c/p\u003e\u003cp\u003eIn conclusion, we found older adults with reduced relative fat mass and increased arterial stiffness face an elevated risk of intrinsic capacity decline. This study identifies modifiable factors influencing intrinsic capacity in older adults. The findings enable personalized strategies for intrinsic capacity preservation to optimize healthy aging outcomes and development of targeted interventions by healthcare providers focusing on adjustable determinants to slow IC decline.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThe CHARLS study has gotten the approval for interviewing respondents and collecting data by the Biomedical Ethics Review Committee of Peking University (IRB00001052\u0026ndash;11015), and the informed consent was required to sign by the respondents.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent for publication:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003eThis work was supported by Philosophy and Social Science Project of Hubei Provincial Department of Education (no.24Y098).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXinhong Zhu and Xiaohong Zhang: conceptualization; funding acquisition, writing\u0026ndash;original draft, writing\u0026ndash;review \u0026amp; editing. Jiayu He: writing\u0026ndash;original draft. Xiaoming Zhang, Heqing Wang, Nibo Chen, Yumeng Zhao and Jingyi Li: data curation, formal analysis, resources.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the China Health and Retirement Longitudinal Study team for providing data. We thank all participants in the CHARLS. We thank all volunteers and staff involved in this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in the study are accessible to be downloaded publicly at https://charls.charlsdata.com/pages/data/111/zh-cn.html.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNational Bureau of Statistics of China. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202501/t20250117_1958332.html\u003c/span\u003e\u003cspan address=\"https://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202501/t20250117_1958332.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang ZT, Lai ETC, Luo Y, et al. Social determinants of intrinsic capacity: A systematic review of observational studies. 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Nutrients, 2021. 13(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang RH, Zhou JB, Cai YH, et al. Non-linear association of anthropometric measurements and pulmonary function. Sci Rep. 2021;11(1):14596.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Relative fat mass, Estimated pulse wave velocity, Intrinsic capacity, Older people","lastPublishedDoi":"10.21203/rs.3.rs-7121744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7121744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChina\u0026rsquo;s accelerating population aging has led to the challenge of intrinsic capacity (IC) decline. This study aims to investigate relationship of estimated pulse wave velocity (ePWV), relative fat mass (RFM) and IC in the older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study is based on the China Health and Retirement Longitudinal Study (CHARLS), including 2,598 individuals aged 60 and above from 2011 to 2015. Cross-lagged models and generalized estimation equation were employed to assess the associations between ePWV, RFM, and IC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eElevated ePWV showed significantly poorer IC (B = -0.047, 95% CI: -0.087 to -0.008, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), while increased RFM demonstrated better IC (B\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.010 to 0.031, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, ePWV was significantly associated with poorer cognitive function, reduced locomotor activity, impaired respiratory function, severe sensory deficits, and a lower risk of depression. Moreover, RFM was linked to superior cognitive performance, reduced depressive symptoms, enhanced respiratory function, increased handgrip strength, better sensory function, and decreased locomotor activity. In non-hypertensive individuals, ePWV showed no significant association with IC. Among hypertensive subjects, RFM was not significantly correlated with IC or cognitive function. Cross-lagged model demonstrated that baseline RFM (β\u0026thinsp;=\u0026thinsp;0.428, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ePWV (β = -0.091, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly predicted IC at follow-up.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe study found that ePWV is negatively correlated with IC, while RFM is positively associated with IC in the older people. The findings enable development of targeted interventions by healthcare providers focusing on adjustable determinants to slow IC decline.\u003c/p\u003e","manuscriptTitle":"Association between relative fat mass, estimated pulse wave velocity and intrinsic capacity among the older people","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 17:20:52","doi":"10.21203/rs.3.rs-7121744/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fd8367d-c2be-460a-a609-583163ef1321","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T06:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 17:20:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7121744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7121744","identity":"rs-7121744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}