Systemic inflammation in midlife is associated with late-life functional limitations: The Atherosclerosis Risk in Communities Study

Systemic inflammation in midlife is associated with late-life functional limitations: The Atherosclerosis Risk in Communities Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Systemic inflammation in midlife is associated with late-life functional limitations: The Atherosclerosis Risk in Communities Study Yao Tong, Yu Jia, Aobo Gong, Fanghui Li, Rui Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3794413/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Background Functional limitations seriously affect the quality of life of individuals. Systemic inflammation generally coexists with functional limitations. This study aims to investigate the association between systemic inflammation in midlife and the risk of functional limitations in late-life. Methods and Results A total of 10,044 participants were included in a cohort study, with an average age of 53.9 ± 5.7 years at baseline. After a median follow-up time of 9.0 years, the prevalence of impaired activities of daily living (ADLs), instrumental activities of daily living (IADLs) and lower limb function (LEF) was 14.7%, 21.6%, and 50.3%, respectively. The values of four inflammatory biomarkers were used to calculate the state of inflammation composite score. Compared with the lowest quartile of the inflammation composite score, the highest quartile exhibited odds of impaired ADLs (OR = 1.589, 95% CI: 1.335–1.892), impaired IADLs (OR = 1.426, 95% CI: 1.228–1.657), and impaired LEF (OR = 1.728, 95% CI: 1.526–1.957). The association between systemic inflammation and functional limitations was partly mediated by cardiac and brain functions. Conclusion The present study showed that systemic inflammation in midlife was associated with a higher risk of late-life functional limitations. Protecting vital organ functions in midlife may reduce the risk of future functional limitations. Trial registration: www.clinicaltrials.gov; Unique identifier: NCT00005131. Health sciences/Medical research Health sciences/Medical research/Experimental models of disease Systemic inflammation Functional limitations Disability ARIC study Public health Organ dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Functional limitations are defined as basic age-related physical dysfunction. Functional limitations include the ability to carry out self-care and domestic life activities, athletic ability and the ability to participate in social labor 1 . In United States, approximately 20–30% of adults aged 60–69 have one or more functional limitations, while the proportion of adults aged 80 and above increases 2-fold 2,3 . The number of the elderly with functional limitations will continue to increase with aging. Functional limitations are considered as a major precursor of disability 4,5 , imposing a heavy burden on late-life quality of individuals and public healthcare services 6 . Functional limitations have been widely shown to be a key prognostic determinant of adverse outcomes 7–9 . Therefore, we have an urgent front-burner to predict and prevent functional limitations, and find modifiable risk factors for targeted intervention. Systemic inflammation refers to inflammation caused by infectious or non-infectious injury that can not be locally restricted by organisms 10 . Systemic inflammation and functional limitations share the common risk factors and pathophysiological pathways. It is widely accepted to use biomarkers of peripheral blood to describe the state of systemic inflammation. Several cross-sectional studies reported that high-level peripheral inflammatory biomarkers commonly coexists with functional limitations 11,12 . Moreover, prospective studies have found that high-level peripheral inflammatory biomarkers were associated with functional limitations in the elderly 13,14 . However, these studies were designed as cross-sectional studies or exhibited a short follow-up time. And advanced age interacts with high-level inflammatory biomarkers and a high incidence of functional limitations, which make these association can not well established. Moreover, previous studies only evaluate inflammation by single biomarkers, which could not accurately reflect the systemic inflammation status. Therefore, this study used composite biomarkers from the middle-aged populations to elucidate the causal relationship between inflammation. A slew of studies have shown that several important inflammatory pathways are involved in the aging and injury of various organs 15,16 , suggesting that inflammation may lead to impairment of physical function by damaging organ function. However, few studies have explored the impact of organ function on the relationship between inflammation and physical functional limitations. Therefore, our study aims to explore the association between mid-life systemic inflammation and late-life functional limitations, and whether this association is mediated by organ functions impairment. 2. Methods 2.1 Study Design and population A prospective cohort study was conducted using the Atherosclerosis Risk in Communities (ARIC) study population from four communities in the United States 17 . A total of 15,792 participants, aged between 45 and 64 years, were recruited in 1987–1989 (Visit 1) and followed up every three years. Visit 2 was conducted in 1990–1992, Visit 3 in 1993–1995 and Visit 4 in 1996–1998. The ARIC study was approved by the institutional review committee of all participating institutions with the informed consent from all participants. Functional limitations were assessed for the first time in Visit 4. In our study, Visit 1 was regarded as the baseline, part of the data from Visit 2 was collected, and Visit 4 was regarded as the research outcome. Among participants in Visit 4 (n = 15,028), exclusion criteria were as follows: absence of an inflammatory biomarkers assessment (n = 336), absence of one or more covariates (n = 1111), and absence of an assessment on functional limitations (n = 3537). Finally, a total of 10,044 participants were enrolled in this study. 2.2 Inflammatory biomarkers The physiological response of biomarkers to stress varies across individuals. One biomarker may be compensated by the effective function of other regions, and may not accurately describe the overall physiological function and the body's response to stress 18 . It is unclear whether a single measurement can adequately capture inflammation chronicity. Thus, the use of an inflammation composite score alleviated this possibility. Therefore, four biomarkers in plasma collected from Visit 1 were used to describe the state of systemic inflammation, including white blood cell count (after log-transformed for corrected skewness), von Willebrand factor, fibrinogen and Factor VIII. The inflammation composite score refers to the average value of four biomarkers after standardization to z-score. The selection of these markers is largely dependent on their availability in the ARIC study and that the inflammation composite score was often used in other studies 19 . In this study, hypersensitive-C-reactive proteins (hs-CRP) in blood sample from Visit 1 and 2 was used as a sub-indicator of systemic inflammation. There was no difference between the relevant conditions of the two measurements. The hs-CRP was classified as “low” or “elevated”, with a cutoff value of 3.0 mg/L 20 . According to the hs-CRP values from Visit 1 to Visit 2, the following four longitudinal patterns of change were established: ( 1 ) Consistent low hs-CRP: both were at low level; ( 2 ) Ascending hs-CRP: first at low level and then at elevated level; ( 3 ) Descending hs-CRP: first at elevated level and then at low level; ( 4 ) Consistent elevated hs-CRP: both were at elevated level. 2.3 Functional limitations At Visit 4, the functional status assessment was completed in the form of a self-reported questionnaire containing the designated activities, as was done in the third National Health and Nutrition Examination Survey 4 . These activities included activities of daily living (eating, dressing, getting up, and walking) 21 , instrumental activities of daily living (cooking, housework, and financial management) 22 and lower limb functions (standing up from a chair without arm support, bending or kneeling, walking 1/4 miles, going upstairs, and carrying 10 pounds) 23 . Participants were required to indicate the level of difficulty in conducting these activities. When participants could not answer for themselves, proxies of participants could answer those questions with high degree of reliability. The final results were reported as "no limits" or "function impaired". Functional limitations were defined as three categories: impaired activities of daily living (ADLs), impaired instrumental activities of daily living (IADLs) and impaired lower limb function (LEF). 2.4 Assessment of Multiple Organ Function Organ function was assessed at Visit 4, including cardiac function, brain function, lung function, liver function, and kidney function. Cardiac function was assessed using N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP), which was measured in plasma using electrochemiluminescence immunoassay on an automatic Cobase411 analyzer (Roche diagnosis). Brain function was assessed using the global composite cognition score, including delayed word recall test (DWRT), digit symbol substitution test (DSST), and word fluency test (WFT). The composite cognition score was the average value of the three tests after standardization to z-score 24–27 . Lung function was assessed using the ratio of forced expiratory volume in one second / forced vital capacity (FEV1/FVC). The values were measured by the standardized Collins Survey II spirometer 28 . Liver function was assessed using plasma alaninetransaminase (ALT). Kidney function was assessed using estimated glomerular filtration rate (eGFR) which was calculated using creatinine in the Eq. 2 9 . 2.5 Baseline Covariates At baseline, the following covariates were assessed based on self-report or medical record evidence from participants: age, gender, race, income, education, hours of metabolic equivalent of task (MET-hour) per week, prevalence of medical comorbidity (hypertension, diabetes, coronary heart disease, heart failure, cancer, and chronic obstructive pulmonary disease), and medication use. Total cholesterol, high density lipoprotein, low density lipoprotein and triglycerides were measured by enzymatic analysis. Hypertension was defined as blood pressure ≥ 140/90 mmHg, or a history of medication. Diabetes was defined as fasting glucose ≥ 126.0 mg/dl or non-fasting glucose ≥ 200.0 mg/dl, or a history of medication or insulin therapy. Coronary heart disease was defined as acute coronary syndrome, or chronic coronary artery disease, or a history of medication or coronary revascularization. Chronic obstructive pulmonary disease was identified as FEV1/FVC < 70% after bronchodilator, or a history of medication. Heart failure and cancer were diagnosed with the evidence from medical records. 2.6 Statistical analysis In this study, continuous variables were represented with median (25th − 75th) or mean ± standard deviation, and categorical variables were represented with numbers (percentages). Continuous variables were compared using Mann–Whitney U test or ANOVA, and categorical variables were compared using chi-square test. Logistic regression models were conducted to assess the association between the inflammation composite score at Visit 1, hs-CRP at Visit 1, and longitudinal pattern of hs-CRP (from Visit 1 to Visit 2) with functional limitations (impaired ADLs, IADLs and LEF), respectively. Two regression models were established to examine the independent role of these relationships. Model 1 was adjusted according to age, gender, race, education, income, and MET-hour/week. Model 2 was additionally adjusted according to the prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medications. Furthermore, a logistic regression analysis in Model 2 was conducted to assess the effect modification between the inflammation composite score and functional limitations of age, gender and race subgroups. The value of multiplicative interaction term < 0.05 was considered statistically significant. According to the procedure recommended by Hayes 30 , Pathway analysis was carried out through the structural equation Model 4 to calculate the indirect effects of the inflammation composite score on functional limitations, including cardiac function (NT-proBNP was log-transformed to correct for skewness), brain function (composite cognition score), lung function (FEV1/FVC), liver function (ALT), and kidney function (eGFR). In structural equation modeling, cross-products from estimated mediation effects were considered statistically significant when confidence intervals (CIs) did not include zero. Our study used the standardized regression coefficients (β) to report point estimates of direct and indirect effects. A two-tailed P value of < 0.05 was considered statistically significant. SPSS Statistics (version 36.0, IBM Corp, Armonk, USA), PROCESS (version 3.5 for SPSS) and R software (version 3.5.0, Vienna, Austria) were used for data analysis. 3. Results 3.1 Baseline characteristics The 10,044 participants with an average age of 53.9 ± 5.7 years were categorized into quartiles (Q1, Q2, Q3 and Q4) based on the inflammation composite score. Table 1 shows the baseline participant characteristics. Increased inflammation composite score was associated with aging, female, African American (still lower than Caucasians), lower income and education levels, unhealthy behaviors, higher levels of cardiovascular biomarkers, and higher odds of complications (P < 0.001 for all). Table 1 Baseline participant characteristics stratified by groups of inflammation composite score at Visit 1 (1987–1989). Characteristic Inflammation Composite Score P Q1 Q2 Q3 Q4 N 2511 2511 2511 2511 Demographic Variables Age, years 52.7 ± 5.5 53.7 ± 5.6 54.4 ± 5.8 55.0 ± 5.7 < 0.001 Female 1323(52.7) 1363(54.3) 1363(54.3) 1477(58.8) < 0.001 Race (African American) 414(16.5) 434(17.3) 537(21.4) 723(28.8) < 0.001 Income, US $ < 0.001 35 000 1419(56.5) 1277(50.9) 1160(46.2) 915(36.4) Education < 0.001 Less than high school 298(11.9) 416(16.6) 513(20.4) 659(26.2) High school 782(31.1) 803(32.0) 792(31.5) 810(32.3) College 1431(57.0) 1292(51.5) 1206(48.0) 1042(41.5) Smoking < 0.001 Never 1267(50.5) 1170(46.6) 1074(42.8) 925(36.8) Ever 953(38.0) 856(34.1) 811(32.3) 772(30.7) Current 291(11.6) 485(19.3) 626(24.9) 814(32.4) Drinking < 0.001 Never 513(20.4) 565(22.5) 650(25.9) 692(27.6) Ever 339(13.5) 398(15.9) 434(17.3) 521(20.8) Current 1659(66.1) 1548(61.7) 1427(56.8) 1298(51.7) MET-hour, /week 16.8 ± 18.9 15.4 ± 18.3 14.6 ± 18.0 12.3 ± 16.5 < 0.001 Physiological & Lab Variables Body mass index, kg/m 2 26.1 ± 4.1 26.9 ± 4.5 27.9 ± 5.2 29.1 ± 6.0 < 0.001 SBP, mmHg 116.8 ± 16.3 119.0 ± 17.7 119.9 ± 17.3 122.0 ± 18.0 < 0.001 DBP, mmHg 72.7 ± 10.5 72.8 ± 10.8 73.2 ± 10.7 73.6 ± 10.9 0.015 Total cholesterol, mmol/l 5.4 ± 1.0 5.5 ± 1.0 5.6 ± 1.1 5.6 ± 1.1 < 0.001 HDL-C, mmol/l 1.4 ± 0.5 1.4 ± 0.4 1.3 ± 0.4 1.3 ± 0.4 < 0.001 LDL-C, mmol/l 3.4 ± 1.0 3.5 ± 1.0 3.6 ± 1.0 3.7 ± 1.0 < 0.001 Triglycerides, mmol/l 1.3 ± 0.7 1.4 ± 0.7 1.5 ± 0.7 1.5 ± 0.8 < 0.001 Creatinine, mg/dl 0.7 ± 0.2 0.7 ± 0.3 0.8 ± 0.4 0.8 ± 0.6 < 0.001 Blood glucose,mmol/l 5.5 ± 1.1 5.7 ± 1.3 5.8 ± 1.7 6.4 ± 2.5 < 0.001 Chronic Medical Conditions Hypertension 478(19.0) 579(23.1) 656(26.1) 880(35.1) < 0.001 Diabetes mellitus 66(2.6) 110(4.4) 182(7.3) 360(14.3) < 0.001 Coronary heart disease 48(1.9) 76(3.0) 83(3.3) 140(5.6) < 0.001 Heart Failure 45(1.8) 68(2.7) 76(3.0) 135(5.4) < 0.001 Cancer 111(4.4) 128(5.1) 138(5.5) 145(5.8) 0.153 Chronic Obstructive Pulmonary Disease 74(3.0) 81(3.2) 118(4.7) 157(6.3) < 0.001 Medication Anti-inflammatory (regular use) 1172(46.7) 1212(48.3) 1103(43.9) 1196(47.6) 0.011 Cholesterol lowering (last 2 weeks) 53(2.1) 60(2.4) 69(2.8) 87(3.5) 0.018 Values are displayed as N (%) for categorical and mean ± SD for continuous variable. MET, metabolic equivalent of task; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol. 3.2 Inflammation and functional limitations After a median follow-up time of 9.0 years, the prevalence of impaired ADLs, IADLs and LEF was 14.7%, 21.6%, and 50.3%, respectively. The higher the inflammation composite score, the higher risk of functional limitations after approximately 9 years (P < 0.001, Fig. 1 ). In the adjusted logistic regression model, compared with Q1 with a low inflammation composite score, Q4 exhibited a higher odds of impaired ADLs (OR = 1.589, 95% CI: 1.335–1.892, p < 0.001), impaired IADLs (OR = 1.426, 95% CI: 1.228–1.657, p < 0.001), and impaired LEF (OR = 1.728, 95% CI: 1.526–1.957, p < 0.001). Notably, the odds ratio of impaired ADLs and impaired LEF showed an gradually upward trend with the increase of inflammation composite score (Table 2 ). Table 2 Adjusted odds ratios (OR) for the association of inflammation composite score with impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF) . HLS Unadjusted Model 1 Model 2 OR (95%CI) P OR (95%CI) P OR (95%CI) P Impaired ADLs < 0.001 < 0.001 < 0.001 Q1 Ref. - Ref. - Ref. - Q2 1.347 (1.131–1.604) 0.001 1.247 (1.046–1.488) 0.014 1.212 (1.013–1.450) 0.036 Q3 1.594 (1.344–1.889) < 0.001 1.393 (1.172–1.656) < 0.001 1.319 (1.105–1.574) 0.002 Q4 2.341 (1.991–2.753) < 0.001 1.857 (1.571–2.195) < 0.001 1.589 (1.335–1.892) < 0.001 Impaired IADLs < 0.001 < 0.001 < 0.001 Q1 Ref. - Ref. - Ref. - Q2 1.410 (1.220–1.630) < 0.001 1.286 (1.109–1.492) 0.001 1.238 (1.065–1.439) 0.005 Q3 1.452 (1.256–1.677) < 0.001 1.223 (1.055–1.419) 0.008 1.135 (0.975–1.321) 0.104 Q4 2.233 (1.945–2.564) < 0.001 1.676 (1.452–1.936) < 0.001 1.426 (1.228–1.657) < 0.001 Impaired LEF < 0.001 < 0.001 < 0.001 Q1 Ref. - Ref. - Ref. - Q2 1.422 (1.271–1.591) < 0.001 1.315 (1.170–1.478) < 0.001 1.271 (1.129–1.431) < 0.001 Q3 1.835 (1.641–2.054) < 0.001 1.603 (1.425–1.802) < 0.001 1.489 (1.321–1.679) < 0.001 Q4 2.580 (2.302–2.890) < 0.001 2.001 (1.775–2.255) < 0.001 1.728 (1.526–1.957) < 0.001 Model 1: adjusted by age, sex, race, education ( high school), annual household income ( 35 000), and physical activity defined by MET-hour/week. Model 2: adjusted by model 1 plus, prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medication use. Unexplained variables are regarded as continuous variables. OR, odds ratio; CI, confidence interval. In the adjusted logistic regression model, compared with the group with low hs-CRP level at Visit 1, participants initially with elevated hs-CRP had a higher odds of activities of impaired ADLs (OR = 2.076, 95% CI: 1.829–2.357, p < 0.001), impaired IADLs (OR = 1.741, 95% CI: 1.561–1.941, p < 0.001), and impaired LEF (OR = 1.884, 95% CI: 1.719–2.066, p < 0.001) (Fig. 2 ). Furthermore, longitudinal pattern of changes in hs-CRP affected late-life functional limitations. In the adjusted logistic regression model, compared with consistent low hs-CRP, participants with ascending hs-CRP, descending hs-CRP, and consistent elevated hs-CRP exhibited a higher risk of functional limitations, respectively (Fig. 3 ). 3.3 Subgroup Analysis Figure 4 showed a significant interaction between races (P for interaction: 0.016, 0.004 and 0.037, respectively). Compared with African Americans, Caucasians had a higher risk of mid-life inflammation composite score associated with late-life functional limitations. For participants aged < 54 years, the inflammation composite score revealed a higher risk associated with impaired LEF (P for interaction = 0.002), and the risk associated with impaired ADLs was higher but not significant(P for interaction = 0.059). The association between inflammation composite score and functional limitations was consistent among the gender subgroups (P for interaction > 0.05). 3.4 Path analysis Path analysis showed that the inflammation composite score and functional limitations were significantly associated with NT-proBNP and composite cognition score (p < 0.01). FEV1/FVC, ALT and eGFR were not shown as mediators. Table 3 showed pathway estimates and 95% CIs of the mediation model. Figure 5 showed the effects of inflammation composite score on impaired ADLs, impaired IADLs, and impaired LEF was partly mediated by NT-proBNP (5.8%, 9.3%, and 7.2%; respectively) and composite cognition score (19.5%, 28.2%, and 14.3%; respectively). Table 3 Direct and indirect effects of inflammation composite score on impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF) . Effect Variables Impaired ADLs Impaired IADLs Impaired LEF β coefficient 95%CI SE β coefficient 95%CI SE β coefficient 95%CI SE Direct inflammation composite score 0.349 0.263–0.435 0.044 0.275 0.199–0.352 0.039 0.461 0.394–0.528 0.034 Indirect NT-proBNP 0.027 0.010–0.098 0.014 0.041 0.026–0.104 0.013 0.042 0.021–0.089 0.021 Indirect Composite cognition score 0.091 0.027–0.189 0.078 0.124 0.086–0.173 0.096 0.084 0.032–0.125 0.060 NT-proBNP, N-terminal pro-B-type natriuretic peptide; CI, confidence interval; SE, standard error. 4. DISCUSSION In this study, participants with a higher level of mid-life systemic inflammation exhibited a gradually increasing risk of late-life functional limitations. Assuming causality, participants with a higher overall inflammation composite score may have a 40–70% increased risk of adverse outcomes after approximately 9 years. Higher hs-CRP levels may increase the risk of late-life functional limitations by nearly 2-folds. The pattern of consistently elevated or increasing or descending hs-CRP, associated with an increased chance of functional limitation compared to consistently low levels. In the present study, individuals with elevated hs-CRP lasting 3 years were at the greatest risk, followed by individuals with initially low hs-CRP and elevated 3 years later, which supported the hypothesis that long-term inflammation is an independent etiological role for functional limitations 31–33 . Furthermore, in addition to existing systemic inflammation, underlying long-term inflammation may play a role in the development of physiological functional limitations 34 . Subgroup analysis demonstrated that the association between inflammation composite score and functional limitations was consistent irrespective of gender. However, this association was stronger among Caucasians than African Americans. There has been little research on the underlying factors that contribute to functional limitations caused by inflammation in African Americans. Racial differences in the regulation of inflammatory signaling pathways 35 , chronic disease burden, and non-physiological factors (such as socio-economic status, healthcare access and affordability) 36 may explain the race-based differences observed in this study. More importantly, systemic inflammation at a relatively young age exhibited a higher risk of functional limitation a few years later, suggesting that early exposure to systemic inflammation has a higher risk of functional limitations. Furthermore, the morbidity of multiple chronic diseases shows an accelerated trend in midlife. In our study, adjusting medical comorbidities played a strongly weakening effect on the association between systemic inflammation and functional limitations. Mid-life inflammation may serve as an important common biological mechanism of late-life multimorbidity and functional limitations 37 . Systemic inflammation may lead to diseases of various important organs, such as heart, brain, lung, liver, and kidney 33,38 . Previous studies have demonstrated that cardiac function has been independently associated with functional limitations, and brain function may be a stronger predictor 39–42 . The relationship between other systemic diseases and functional limitations has not been proved to be independent. According to our findings, the increased risk of functional limitations caused by systemic inflammation may be mediated by cardiac function and brain function. Lung, liver, and kidney are not involved in this pathway. Indirect effects mediated by cardiac function and brain function accounted for 5–10% and 14–28%, respectively. In general, systemic inflammation partially causes functional limitations through the heart and brain, which can reveal important intervention and therapy targets. Investigators should consider planning preventive programs to protect the heart and brain functions of middle-aged individuals with systemic inflammation, so as to reduce the risk of functional limitations and even disability. 5. Limitations There are several limitations in this study. First, in addition to the inflammatory biomarkers used in this study participating in systemic inflammatory responses, some biomarkers are also involved in other biological regulation pathways, leading to the failure of inflammation compound score to fully represent the mechanism of systemic inflammation. Second, functional impaired status in the study was measured only at Visit 4 and not at baseline, and thus cannot demonstrate causality. Third, the midlife hs-CRP pattern from Visit 1 to visit 2 was not fully representative of the overall follow-up trend. Fourth, important parameters were selected for each organ function, but the assessment was not sufficiently comprehensive. Fifth, in pathway analysis, only the effects of vital organ function have been studied without assessing the motor system, which may account for a certain proportion of the pathway. 6. Conclusions In this study individuals with mid-life systemic inflammation were at increased risk of late-life functional limitations. The association, which was stronger in Caucasians and relatively younger ages, was partly mediated by cardiac and brain function. For individuals with systemic inflammation, protection on heart and brain function in midlife may be conducive to reducing the risk of functional limitations and improving quality of life in the future. Declarations Ethical statement This study was conducted in conformity to the Declaration of Helsinki and was approved by the Human Ethical Committee of West China Hospital of Sichuan University. The ARIC study was approved by the institutional review committee of all participating institutions with the informed consent from all participants. Funding This work was supported financially by grants from Sichuan Science and Technology Program (No. 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2021YFQ0062, 2022JDRC0148), Sichuan Provincial Health Commission (No. ZH2022-101), Sichuan University West China Nursing Discipline Development Special Fund Project (No. HXHL21016) Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://biolincc.nhlbi.nih.gov/home/. Author contribution YT and YJ conceived the study design. YT, YJ and AG collected the epidemiological and clinical data. YT, YJ, FL, and AG summarized data and performed the statistical analysis. YT and YJ interpreted the data and drafted the manuscript. RZ participated in the design of the study, acquired the data, and helped to revise the manuscript. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. All authors contributed to the article and approved the submitted version. Acknowledgments The authors thank the staff and participants of the ARIC study and BioLINCC for their important contributions. Disclosure The authors report no disclosures relevant to the manuscript. References Freedman, V. A. Adopting the ICF language for studying late-life disability: a field of dreams? J Gerontol A Biol Sci Med Sci 64 , 1172-1174; discussion 1175-1176 (2009). Holmes, J., Powell-Griner, E., Lethbridge-Cejku, M. & Heyman, K. 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CDC/AHA Workshop on Markers of Inflammation and Cardiovascular Disease: Application to Clinical and Public Health Practice: clinical use of inflammatory markers in patients with cardiovascular diseases: a background paper. Circulation 110 , e560-567 (2004). Nagi, S. Z. An epidemiology of disability among adults in the United States. Milbank Mem Fund Q Health Soc 54 , 439-467 (1976). Rosow, I. & Breslau, N. A Guttman health scale for the aged. J Gerontol 21 , 556-559 (1966). Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A. & Jaffe, M. W. Studies of Illness in the Aged. The Index of Adl: A Standardized Measure of Biological and Psychosocial Function. JAMA 185 , 914-919 (1963). Li, D. et al. Plasma phospholipid very-long-chain SFAs in midlife and 20-year cognitive change in the Atherosclerosis Risk in Communities (ARIC): a cohort study. Am J Clin Nutr 111 , 1252-1258 (2020). Knopman, D. S. & Ryberg, S. A verbal memory test with high predictive accuracy for dementia of the Alzheimer type. Arch Neurol 46 , 141-145 (1989). Jaeger, J. Digit Symbol Substitution Test: The Case for Sensitivity Over Specificity in Neuropsychological Testing. J Clin Psychopharmacol 38 , 513-519 (2018). Pendleton, M. G., Heaton, R. K., Lehman, R. A. & Hulihan, D. Diagnostic utility of the Thurstone Word Fluency Test in neuropsychological evaluations. J Clin Neuropsychol 4 , 307-317 (1982). Rabe, K. F. et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 176 , 532-555 (2007). Parrinello, C. M. et al. Recalibration of blood analytes over 25 years in the atherosclerosis risk in communities study: impact of recalibration on chronic kidney disease prevalence and incidence. Clin Chem 61 , 938-947 (2015). Igartua, J. J. & Hayes, A. F. Mediation, Moderation, and Conditional Process Analysis: Concepts, Computations, and Some Common Confusions. Span J Psychol 24 , e49 (2021). Singh, T. & Newman, A. B. Inflammatory markers in population studies of aging. Ageing Res Rev 10 , 319-329 (2011). Mekli, K., Nazroo, J. Y., Marshall, A. D., Kumari, M. & Pendleton, N. Proinflammatory genotype is associated with the frailty phenotype in the English Longitudinal Study of Ageing. Aging Clin Exp Res 28 , 413-421 (2016). Furman, D. et al. Chronic inflammation in the etiology of disease across the life span. Nat Med 25 , 1822-1832 (2019). Candore, G., Caruso, C. & Colonna-Romano, G. Inflammation, genetic background and longevity. Biogerontology 11 , 565-573 (2010). Quinones, A. R. et al. Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLoS One 14 , e0218462 (2019). Mahajan, S. et al. Trends in Differences in Health Status and Health Care Access and Affordability by Race and Ethnicity in the United States, 1999-2018. JAMA 326 , 637-648 (2021). Friedman, E. M., Christ, S. L. & Mroczek, D. K. Inflammation Partially Mediates the Association of Multimorbidity and Functional Limitations in a National Sample of Middle-Aged and Older Adults: The MIDUS Study. J Aging Health 27 , 843-863 (2015). Liberale, L. et al. Inflammation, Aging, and Cardiovascular Disease: JACC Review Topic of the Week. J Am Coll Cardiol 79 , 837-847 (2022). Dodge, H. H. et al. Cognitive impairment as a strong predictor of incident disability in specific ADL-IADL tasks among community-dwelling elders: the Azuchi Study. Gerontologist 45 , 222-230 (2005). Stuck, A. E. et al. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Soc Sci Med 48 , 445-469 (1999). Kelly-Hayes, M., Jette, A. M., Wolf, P. A., D'Agostino, R. B. & Odell, P. M. Functional limitations and disability among elders in the Framingham Study. Am J Public Health 82 , 841-845 (1992). Tas, U. et al. Incidence and risk factors of disability in the elderly: the Rotterdam Study. Prev Med 44 , 272-278 (2007). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 May, 2024 Reviews received at journal 22 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers agreed at journal 07 Mar, 2024 Reviewers invited by journal 28 Dec, 2023 Editor assigned by journal 28 Dec, 2023 Editor invited by journal 25 Dec, 2023 Submission checks completed at journal 25 Dec, 2023 First submitted to journal 22 Dec, 2023 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-3794413","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":263660655,"identity":"b85ce18f-2631-4eb0-a6b9-5486b4c99563","order_by":0,"name":"Yao Tong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Tong","suffix":""},{"id":263660656,"identity":"0ad73c29-cbe2-425b-9ed5-840d4edfc503","order_by":1,"name":"Yu Jia","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jia","suffix":""},{"id":263660657,"identity":"2061f178-c643-498a-81b9-a8d929337bd9","order_by":2,"name":"Aobo Gong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Aobo","middleName":"","lastName":"Gong","suffix":""},{"id":263660658,"identity":"71751416-2063-4f0a-8759-4274442c69ad","order_by":3,"name":"Fanghui Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Fanghui","middleName":"","lastName":"Li","suffix":""},{"id":263660659,"identity":"9e03744c-d0a6-4648-9221-8b043b3f2063","order_by":4,"name":"Rui Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYPACCQY2+cMHDD4Y2MgRr4Vfgi2hcEZBmjHx9kjO4DH4zPPhcCJBlfLtvYdfV7ZZMBjcbjDcbGPAnMDAfvjoBnxaGHvOpVmebZNgMLhzINk4x4Atj4EnLe0GPi3MEjlmho0gLQcSjgG18BQzSPCY4dXCJv8GpiWx/beFgURiAyEtPBI8xg9BWiRnJDMYMxgYENYiwZNjxthwDhjIPMcYDHsMEozZCPlFvv2M8ceGsjoGNvb+DwY//vyX42c/fAyvFpB3JIBEfQOcS0A5CDB/IELRKBgFo2AUjGQAAHj4RKMpX+RdAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2023-12-23 01:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3794413/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3794413/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-68724-w","type":"published","date":"2024-07-29T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49063060,"identity":"7e8e6e34-2fb4-441c-8446-76404cd088ed","added_by":"auto","created_at":"2024-01-02 14:32:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170064,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence of impaired (A) activities of daily living (ADLs), (B) instrumental ADLs (IADLs), and (C) lower-extremity function (LEF) grouped by inflammation composite score.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3794413/v1/deef02570c40b989ac410f9b.png"},{"id":49063064,"identity":"cb897162-1641-452b-84ad-1f2b3e6cb03d","added_by":"auto","created_at":"2024-01-02 14:32:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":292708,"visible":true,"origin":"","legend":"\u003cp\u003eUnadjusted and adjusted odds ratios (OR) for the association of hs-CRP level with impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF).\u003c/p\u003e\n\u003cp\u003eModel 1: adjusted by age, sex, race, education (\u0026lt;high school, high school, or \u0026gt;high school), annual household income (\u0026lt;16 000, 16 000 to 35 000, \u0026gt;35 000), and physical activity defined by MET-hour/week.\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted by model 1 plus, prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medication use.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3794413/v1/1b4ac7c63813de022c4d74de.png"},{"id":49063911,"identity":"8db6a994-2784-4bed-a764-af712280e162","added_by":"auto","created_at":"2024-01-02 14:40:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260719,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted probability of impaired (A) activities of daily living (ADLs), (B) instrumental ADLs (IADLs), and (C) lower-extremity function (LEF) based on longitudinal pattern of changes in hs-CRP levels. Model was adjusted by age, sex, race, education (\u0026lt;high school, high school, or \u0026gt;high school), annual household income (\u0026lt;16 000, 16 000 to 35 000, \u0026gt;35 000), physical activity defined by MET-hour/week, prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medication use.\u003c/p\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.001 compared to the consistent low hs-CRP group\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3794413/v1/5ac94002c7699d18f221701e.png"},{"id":49064406,"identity":"f716391f-0618-48dc-88da-ceb85ebebfce","added_by":"auto","created_at":"2024-01-02 14:48:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":408423,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted odds ratios (OR) for the association of inflammation composite score with impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF) in different subgroups. Model was adjusted by age, sex, race, education (\u0026lt;high school, high school, or \u0026gt;high school), annual household income (\u0026lt;16 000, 16 000 to 35 000, \u0026gt;35 000), physical activity defined by MET-hour/week, prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medication use.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3794413/v1/a18370c05d9ad3839824345e.png"},{"id":49063063,"identity":"b43f03d3-46bc-4981-9a52-1dddb54b2676","added_by":"auto","created_at":"2024-01-02 14:32:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":851317,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Which organs mediate the association between systemic inflammation and functional limitations. (B) Direct and indirect effects of systemic inflammation on impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3794413/v1/adbcea6bad41239f6715ae3f.png"},{"id":61793317,"identity":"15a6c21c-dd5d-44d8-8c13-28fcd341acd8","added_by":"auto","created_at":"2024-08-05 16:10:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2983764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3794413/v1/99a45c7b-94e6-4f8e-9e22-3d479befcb61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systemic inflammation in midlife is associated with late-life functional limitations: The Atherosclerosis Risk in Communities Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFunctional limitations are defined as basic age-related physical dysfunction. Functional limitations include the ability to carry out self-care and domestic life activities, athletic ability and the ability to participate in social labor\u003csup\u003e1\u003c/sup\u003e. In United States, approximately 20\u0026ndash;30% of adults aged 60\u0026ndash;69 have one or more functional limitations, while the proportion of adults aged 80 and above increases 2-fold\u003csup\u003e2,3\u003c/sup\u003e. The number of the elderly with functional limitations will continue to increase with aging. Functional limitations are considered as a major precursor of disability\u003csup\u003e4,5\u003c/sup\u003e, imposing a heavy burden on late-life quality of individuals and public healthcare services\u003csup\u003e6\u003c/sup\u003e. Functional limitations have been widely shown to be a key prognostic determinant of adverse outcomes\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. Therefore, we have an urgent front-burner to predict and prevent functional limitations, and find modifiable risk factors for targeted intervention.\u003c/p\u003e \u003cp\u003eSystemic inflammation refers to inflammation caused by infectious or non-infectious injury that can not be locally restricted by organisms\u003csup\u003e10\u003c/sup\u003e. Systemic inflammation and functional limitations share the common risk factors and pathophysiological pathways. It is widely accepted to use biomarkers of peripheral blood to describe the state of systemic inflammation. Several cross-sectional studies reported that high-level peripheral inflammatory biomarkers commonly coexists with functional limitations\u003csup\u003e11,12\u003c/sup\u003e. Moreover, prospective studies have found that high-level peripheral inflammatory biomarkers were associated with functional limitations in the elderly\u003csup\u003e13,14\u003c/sup\u003e. However, these studies were designed as cross-sectional studies or exhibited a short follow-up time. And advanced age interacts with high-level inflammatory biomarkers and a high incidence of functional limitations, which make these association can not well established. Moreover, previous studies only evaluate inflammation by single biomarkers, which could not accurately reflect the systemic inflammation status. Therefore, this study used composite biomarkers from the middle-aged populations to elucidate the causal relationship between inflammation.\u003c/p\u003e \u003cp\u003eA slew of studies have shown that several important inflammatory pathways are involved in the aging and injury of various organs\u003csup\u003e15,16\u003c/sup\u003e, suggesting that inflammation may lead to impairment of physical function by damaging organ function. However, few studies have explored the impact of organ function on the relationship between inflammation and physical functional limitations. Therefore, our study aims to explore the association between mid-life systemic inflammation and late-life functional limitations, and whether this association is mediated by organ functions impairment.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and population\u003c/h2\u003e \u003cp\u003eA prospective cohort study was conducted using the Atherosclerosis Risk in Communities (ARIC) study population from four communities in the United States\u003csup\u003e17\u003c/sup\u003e. A total of 15,792 participants, aged between 45 and 64 years, were recruited in 1987\u0026ndash;1989 (Visit 1) and followed up every three years. Visit 2 was conducted in 1990\u0026ndash;1992, Visit 3 in 1993\u0026ndash;1995 and Visit 4 in 1996\u0026ndash;1998. The ARIC study was approved by the institutional review committee of all participating institutions with the informed consent from all participants. Functional limitations were assessed for the first time in Visit 4. In our study, Visit 1 was regarded as the baseline, part of the data from Visit 2 was collected, and Visit 4 was regarded as the research outcome. Among participants in Visit 4 (n\u0026thinsp;=\u0026thinsp;15,028), exclusion criteria were as follows: absence of an inflammatory biomarkers assessment (n\u0026thinsp;=\u0026thinsp;336), absence of one or more covariates (n\u0026thinsp;=\u0026thinsp;1111), and absence of an assessment on functional limitations (n\u0026thinsp;=\u0026thinsp;3537). Finally, a total of 10,044 participants were enrolled in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inflammatory biomarkers\u003c/h2\u003e \u003cp\u003eThe physiological response of biomarkers to stress varies across individuals. One biomarker may be compensated by the effective function of other regions, and may not accurately describe the overall physiological function and the body's response to stress\u003csup\u003e18\u003c/sup\u003e. It is unclear whether a single measurement can adequately capture inflammation chronicity. Thus, the use of an inflammation composite score alleviated this possibility. Therefore, four biomarkers in plasma collected from Visit 1 were used to describe the state of systemic inflammation, including white blood cell count (after log-transformed for corrected skewness), von Willebrand factor, fibrinogen and Factor VIII. The inflammation composite score refers to the average value of four biomarkers after standardization to z-score. The selection of these markers is largely dependent on their availability in the ARIC study and that the inflammation composite score was often used in other studies\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, hypersensitive-C-reactive proteins (hs-CRP) in blood sample from Visit 1 and 2 was used as a sub-indicator of systemic inflammation. There was no difference between the relevant conditions of the two measurements. The hs-CRP was classified as \u0026ldquo;low\u0026rdquo; or \u0026ldquo;elevated\u0026rdquo;, with a cutoff value of 3.0 mg/L\u003csup\u003e20\u003c/sup\u003e. According to the hs-CRP values from Visit 1 to Visit 2, the following four longitudinal patterns of change were established: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Consistent low hs-CRP: both were at low level; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Ascending hs-CRP: first at low level and then at elevated level; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Descending hs-CRP: first at elevated level and then at low level; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Consistent elevated hs-CRP: both were at elevated level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional limitations\u003c/h2\u003e \u003cp\u003eAt Visit 4, the functional status assessment was completed in the form of a self-reported questionnaire containing the designated activities, as was done in the third National Health and Nutrition Examination Survey\u003csup\u003e4\u003c/sup\u003e. These activities included activities of daily living (eating, dressing, getting up, and walking)\u003csup\u003e21\u003c/sup\u003e, instrumental activities of daily living (cooking, housework, and financial management)\u003csup\u003e22\u003c/sup\u003e and lower limb functions (standing up from a chair without arm support, bending or kneeling, walking 1/4 miles, going upstairs, and carrying 10 pounds)\u003csup\u003e23\u003c/sup\u003e. Participants were required to indicate the level of difficulty in conducting these activities. When participants could not answer for themselves, proxies of participants could answer those questions with high degree of reliability. The final results were reported as \"no limits\" or \"function impaired\". Functional limitations were defined as three categories: impaired activities of daily living (ADLs), impaired instrumental activities of daily living (IADLs) and impaired lower limb function (LEF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Assessment of Multiple Organ Function\u003c/h2\u003e \u003cp\u003eOrgan function was assessed at Visit 4, including cardiac function, brain function, lung function, liver function, and kidney function. Cardiac function was assessed using N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP), which was measured in plasma using electrochemiluminescence immunoassay on an automatic Cobase411 analyzer (Roche diagnosis). Brain function was assessed using the global composite cognition score, including delayed word recall test (DWRT), digit symbol substitution test (DSST), and word fluency test (WFT). The composite cognition score was the average value of the three tests after standardization to z-score\u003csup\u003e24\u0026ndash;27\u003c/sup\u003e. Lung function was assessed using the ratio of forced expiratory volume in one second / forced vital capacity (FEV1/FVC). The values were measured by the standardized Collins Survey II spirometer\u003csup\u003e28\u003c/sup\u003e. Liver function was assessed using plasma alaninetransaminase (ALT). Kidney function was assessed using estimated glomerular filtration rate (eGFR) which was calculated using creatinine in the Eq.\u0026nbsp;2\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Baseline Covariates\u003c/h2\u003e \u003cp\u003eAt baseline, the following covariates were assessed based on self-report or medical record evidence from participants: age, gender, race, income, education, hours of metabolic equivalent of task (MET-hour) per week, prevalence of medical comorbidity (hypertension, diabetes, coronary heart disease, heart failure, cancer, and chronic obstructive pulmonary disease), and medication use. Total cholesterol, high density lipoprotein, low density lipoprotein and triglycerides were measured by enzymatic analysis.\u003c/p\u003e \u003cp\u003eHypertension was defined as blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg, or a history of medication. Diabetes was defined as fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126.0 mg/dl or non-fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;200.0 mg/dl, or a history of medication or insulin therapy. Coronary heart disease was defined as acute coronary syndrome, or chronic coronary artery disease, or a history of medication or coronary revascularization. Chronic obstructive pulmonary disease was identified as FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70% after bronchodilator, or a history of medication. Heart failure and cancer were diagnosed with the evidence from medical records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, continuous variables were represented with median (25th \u0026minus;\u0026thinsp;75th) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables were represented with numbers (percentages). Continuous variables were compared using Mann\u0026ndash;Whitney U test or ANOVA, and categorical variables were compared using chi-square test.\u003c/p\u003e \u003cp\u003eLogistic regression models were conducted to assess the association between the inflammation composite score at Visit 1, hs-CRP at Visit 1, and longitudinal pattern of hs-CRP (from Visit 1 to Visit 2) with functional limitations (impaired ADLs, IADLs and LEF), respectively. Two regression models were established to examine the independent role of these relationships. Model 1 was adjusted according to age, gender, race, education, income, and MET-hour/week. Model 2 was additionally adjusted according to the prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medications. Furthermore, a logistic regression analysis in Model 2 was conducted to assess the effect modification between the inflammation composite score and functional limitations of age, gender and race subgroups. The value of multiplicative interaction term\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eAccording to the procedure recommended by Hayes\u003csup\u003e30\u003c/sup\u003e, Pathway analysis was carried out through the structural equation Model 4 to calculate the indirect effects of the inflammation composite score on functional limitations, including cardiac function (NT-proBNP was log-transformed to correct for skewness), brain function (composite cognition score), lung function (FEV1/FVC), liver function (ALT), and kidney function (eGFR). In structural equation modeling, cross-products from estimated mediation effects were considered statistically significant when confidence intervals (CIs) did not include zero. Our study used the standardized regression coefficients (β) to report point estimates of direct and indirect effects.\u003c/p\u003e \u003cp\u003eA two-tailed \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. SPSS Statistics (version 36.0, IBM Corp, Armonk, USA), PROCESS (version 3.5 for SPSS) and R software (version 3.5.0, Vienna, Austria) were used for data analysis.\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\u003eThe 10,044 participants with an average age of 53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 years were categorized into quartiles (Q1, Q2, Q3 and Q4) based on the inflammation composite score. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline participant characteristics. Increased inflammation composite score was associated with aging, female, African American (still lower than Caucasians), lower income and education levels, unhealthy behaviors, higher levels of cardiovascular biomarkers, and higher odds of complications (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all).\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 participant characteristics stratified by groups of inflammation composite score at Visit 1 (1987\u0026ndash;1989).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eInflammation Composite Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\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\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic Variables\u003c/b\u003e\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1323(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1363(54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1363(54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1477(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eRace (African American)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e414(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e434(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e537(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e723(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eIncome, US\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e\u0026lt;16 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e469(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e608(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e16 000\u0026ndash;35 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e825(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e880(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e882(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e988(39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e\u0026gt;35 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1419(56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1277(50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1160(46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e915(36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eEducation\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e513(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e659(26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e782(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e803(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e792(31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e810(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1431(57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1292(51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1206(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1042(41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eSmoking\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1267(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1170(46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1074(42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e925(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e953(38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e856(34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e811(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e772(30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e485(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e626(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e814(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eDrinking\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e513(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e565(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e650(25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e692(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e434(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e521(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1659(66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1548(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1427(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1298(51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eMET-hour, /week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysiological \u0026amp; Lab Variables\u003c/b\u003e\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\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\u003eTotal cholesterol, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eHDL-C, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eLDL-C, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eTriglycerides, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eCreatinine, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eBlood glucose,mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic Medical Conditions\u003c/b\u003e\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e579(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e656(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e880(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eHeart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.153\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\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAnti-inflammatory (regular use)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1172(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1212(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1103(43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1196(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\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\u003eCholesterol lowering (last 2 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues are displayed as N (%) for categorical and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMET, metabolic equivalent of task; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Inflammation and functional limitations\u003c/h2\u003e \u003cp\u003eAfter a median follow-up time of 9.0 years, the prevalence of impaired ADLs, IADLs and LEF was 14.7%, 21.6%, and 50.3%, respectively. The higher the inflammation composite score, the higher risk of functional limitations after approximately 9 years (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the adjusted logistic regression model, compared with Q1 with a low inflammation composite score, Q4 exhibited a higher odds of impaired ADLs (OR\u0026thinsp;=\u0026thinsp;1.589, 95% CI: 1.335\u0026ndash;1.892, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), impaired IADLs (OR\u0026thinsp;=\u0026thinsp;1.426, 95% CI: 1.228\u0026ndash;1.657, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and impaired LEF (OR\u0026thinsp;=\u0026thinsp;1.728, 95% CI: 1.526\u0026ndash;1.957, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the odds ratio of impaired ADLs and impaired LEF showed an gradually upward trend with the increase of inflammation composite score (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eAdjusted odds ratios (OR) for the association of inflammation composite score with impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF) .\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImpaired ADLs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.347 (1.131\u0026ndash;1.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.247 (1.046\u0026ndash;1.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.212 (1.013\u0026ndash;1.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.594 (1.344\u0026ndash;1.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.393 (1.172\u0026ndash;1.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.319 (1.105\u0026ndash;1.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.341 (1.991\u0026ndash;2.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.857 (1.571\u0026ndash;2.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.589 (1.335\u0026ndash;1.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImpaired IADLs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.410 (1.220\u0026ndash;1.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.286 (1.109\u0026ndash;1.492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.238 (1.065\u0026ndash;1.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.452 (1.256\u0026ndash;1.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.223 (1.055\u0026ndash;1.419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.135 (0.975\u0026ndash;1.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.233 (1.945\u0026ndash;2.564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.676 (1.452\u0026ndash;1.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.426 (1.228\u0026ndash;1.657)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImpaired LEF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.422 (1.271\u0026ndash;1.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.315 (1.170\u0026ndash;1.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.271 (1.129\u0026ndash;1.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.835 (1.641\u0026ndash;2.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.603 (1.425\u0026ndash;1.802)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.489 (1.321\u0026ndash;1.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.580 (2.302\u0026ndash;2.890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.001 (1.775\u0026ndash;2.255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.728 (1.526\u0026ndash;1.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: adjusted by age, sex, race, education (\u0026lt;\u0026thinsp;high school, high school, or \u0026gt;\u0026thinsp;high school), annual household income (\u0026lt;\u0026thinsp;16 000, 16 000 to 35 000, \u0026gt;\u0026thinsp;35 000), and physical activity defined by MET-hour/week.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: adjusted by model 1 plus, prevalent of hypertension, diabetes, coronary heart disease, heart failure, cancer, chronic obstructive pulmonary disease, total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides, cholesterol-lowering and anti-inflammatory medication use.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eUnexplained variables are regarded as continuous variables. OR, odds ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the adjusted logistic regression model, compared with the group with low hs-CRP level at Visit 1, participants initially with elevated hs-CRP had a higher odds of activities of impaired ADLs (OR\u0026thinsp;=\u0026thinsp;2.076, 95% CI: 1.829\u0026ndash;2.357, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), impaired IADLs (OR\u0026thinsp;=\u0026thinsp;1.741, 95% CI: 1.561\u0026ndash;1.941, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and impaired LEF (OR\u0026thinsp;=\u0026thinsp;1.884, 95% CI: 1.719\u0026ndash;2.066, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, longitudinal pattern of changes in hs-CRP affected late-life functional limitations. In the adjusted logistic regression model, compared with consistent low hs-CRP, participants with ascending hs-CRP, descending hs-CRP, and consistent elevated hs-CRP exhibited a higher risk of functional limitations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed a significant interaction between races (P for interaction: 0.016, 0.004 and 0.037, respectively). Compared with African Americans, Caucasians had a higher risk of mid-life inflammation composite score associated with late-life functional limitations. For participants aged\u0026thinsp;\u0026lt;\u0026thinsp;54 years, the inflammation composite score revealed a higher risk associated with impaired LEF (P for interaction\u0026thinsp;=\u0026thinsp;0.002), and the risk associated with impaired ADLs was higher but not significant(P for interaction\u0026thinsp;=\u0026thinsp;0.059). The association between inflammation composite score and functional limitations was consistent among the gender subgroups (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Path analysis\u003c/h2\u003e \u003cp\u003ePath analysis showed that the inflammation composite score and functional limitations were significantly associated with NT-proBNP and composite cognition score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). FEV1/FVC, ALT and eGFR were not shown as mediators. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed pathway estimates and 95% CIs of the mediation model. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showed the effects of inflammation composite score on impaired ADLs, impaired IADLs, and impaired LEF was partly mediated by NT-proBNP (5.8%, 9.3%, and 7.2%; respectively) and composite cognition score (19.5%, 28.2%, and 14.3%; respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect and indirect effects of inflammation composite score on impaired activities of daily living (ADLs), instrumental ADLs (IADLs), and lower-extremity function (LEF) .\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eImpaired ADLs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eImpaired IADLs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eImpaired LEF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eβ coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003einflammation composite score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u0026ndash;0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.199\u0026ndash;0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.394\u0026ndash;0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNT-proBNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u0026ndash;0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.026\u0026ndash;0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.021\u0026ndash;0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite cognition score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u0026ndash;0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.086\u0026ndash;0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.032\u0026ndash;0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNT-proBNP, N-terminal pro-B-type natriuretic peptide; CI, confidence interval; SE, standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn this study, participants with a higher level of mid-life systemic inflammation exhibited a gradually increasing risk of late-life functional limitations. Assuming causality, participants with a higher overall inflammation composite score may have a 40\u0026ndash;70% increased risk of adverse outcomes after approximately 9 years. Higher hs-CRP levels may increase the risk of late-life functional limitations by nearly 2-folds.\u003c/p\u003e \u003cp\u003eThe pattern of consistently elevated or increasing or descending hs-CRP, associated with an increased chance of functional limitation compared to consistently low levels. In the present study, individuals with elevated hs-CRP lasting 3 years were at the greatest risk, followed by individuals with initially low hs-CRP and elevated 3 years later, which supported the hypothesis that long-term inflammation is an independent etiological role for functional limitations\u003csup\u003e31\u0026ndash;33\u003c/sup\u003e. Furthermore, in addition to existing systemic inflammation, underlying long-term inflammation may play a role in the development of physiological functional limitations\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubgroup analysis demonstrated that the association between inflammation composite score and functional limitations was consistent irrespective of gender. However, this association was stronger among Caucasians than African Americans. There has been little research on the underlying factors that contribute to functional limitations caused by inflammation in African Americans. Racial differences in the regulation of inflammatory signaling pathways\u003csup\u003e35\u003c/sup\u003e, chronic disease burden, and non-physiological factors (such as socio-economic status, healthcare access and affordability)\u003csup\u003e36\u003c/sup\u003e may explain the race-based differences observed in this study. More importantly, systemic inflammation at a relatively young age exhibited a higher risk of functional limitation a few years later, suggesting that early exposure to systemic inflammation has a higher risk of functional limitations. Furthermore, the morbidity of multiple chronic diseases shows an accelerated trend in midlife. In our study, adjusting medical comorbidities played a strongly weakening effect on the association between systemic inflammation and functional limitations. Mid-life inflammation may serve as an important common biological mechanism of late-life multimorbidity and functional limitations\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSystemic inflammation may lead to diseases of various important organs, such as heart, brain, lung, liver, and kidney\u003csup\u003e33,38\u003c/sup\u003e. Previous studies have demonstrated that cardiac function has been independently associated with functional limitations, and brain function may be a stronger predictor\u003csup\u003e39\u0026ndash;42\u003c/sup\u003e. The relationship between other systemic diseases and functional limitations has not been proved to be independent. According to our findings, the increased risk of functional limitations caused by systemic inflammation may be mediated by cardiac function and brain function. Lung, liver, and kidney are not involved in this pathway. Indirect effects mediated by cardiac function and brain function accounted for 5\u0026ndash;10% and 14\u0026ndash;28%, respectively. In general, systemic inflammation partially causes functional limitations through the heart and brain, which can reveal important intervention and therapy targets. Investigators should consider planning preventive programs to protect the heart and brain functions of middle-aged individuals with systemic inflammation, so as to reduce the risk of functional limitations and even disability.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThere are several limitations in this study. First, in addition to the inflammatory biomarkers used in this study participating in systemic inflammatory responses, some biomarkers are also involved in other biological regulation pathways, leading to the failure of inflammation compound score to fully represent the mechanism of systemic inflammation. Second, functional impaired status in the study was measured only at Visit 4 and not at baseline, and thus cannot demonstrate causality. Third, the midlife hs-CRP pattern from Visit 1 to visit 2 was not fully representative of the overall follow-up trend. Fourth, important parameters were selected for each organ function, but the assessment was not sufficiently comprehensive. Fifth, in pathway analysis, only the effects of vital organ function have been studied without assessing the motor system, which may account for a certain proportion of the pathway.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn this study individuals with mid-life systemic inflammation were at increased risk of late-life functional limitations. The association, which was stronger in Caucasians and relatively younger ages, was partly mediated by cardiac and brain function. For individuals with systemic inflammation, protection on heart and brain function in midlife may be conducive to reducing the risk of functional limitations and improving quality of life in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in conformity to the Declaration of Helsinki and was approved by the Human Ethical Committee of West China Hospital of Sichuan University. The ARIC study was approved by the institutional review committee of all participating institutions with the informed consent from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported financially by grants from\u0026nbsp;Sichuan Science and Technology Program (No. 2023YFS0027,\u0026nbsp;2023YFS0240,\u0026nbsp;2023YFS0074,\u0026nbsp;2023NSFSC1652, 2022YFS0279,\u0026nbsp;2021YFQ0062, 2022JDRC0148), Sichuan Provincial Health Commission\u0026nbsp;(No.\u0026nbsp;ZH2022-101),\u0026nbsp;Sichuan University West China Nursing Discipline Development Special Fund Project (No. HXHL21016)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://biolincc.nhlbi.nih.gov/home/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYT\u0026nbsp;and YJ\u0026nbsp;conceived the study design.\u0026nbsp;YT,\u0026nbsp;YJ\u0026nbsp;and\u0026nbsp;AG\u0026nbsp;collected the epidemiological and clinical data.\u0026nbsp;YT,\u0026nbsp;YJ, FL, and\u0026nbsp;AG\u0026nbsp;summarized data and performed the statistical\u0026nbsp;analysis.\u0026nbsp;YT\u0026nbsp;and YJ interpreted the data and drafted the\u0026nbsp;manuscript. RZ\u0026nbsp;participated in the design of the study,\u0026nbsp;acquired the data, and helped to revise the manuscript. All the\u0026nbsp;authors have accepted responsibility for the entire content of\u0026nbsp;this submitted manuscript and approved submission. All authors\u0026nbsp;contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff and participants of the ARIC study and BioLINCC for their important contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no disclosures relevant to the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFreedman, V. A. Adopting the ICF language for studying late-life disability: a field of dreams? \u003cem\u003eJ Gerontol A Biol Sci Med Sci\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 1172-1174; discussion 1175-1176 (2009).\u003c/li\u003e\n\u003cli\u003eHolmes, J., Powell-Griner, E., Lethbridge-Cejku, M. \u0026amp; Heyman, K. 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Incidence and risk factors of disability in the elderly: the Rotterdam Study. \u003cem\u003ePrev Med\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 272-278 (2007).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Systemic inflammation, Functional limitations, Disability, ARIC study, Public health, Organ dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-3794413/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3794413/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional limitations seriously affect the quality of life of individuals. Systemic inflammation generally coexists with functional limitations. This study aims to investigate the association between systemic inflammation in midlife and the risk of functional limitations in late-life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10,044 participants were included in a cohort study, with an average age of 53.9 ± 5.7 years at baseline. After a median follow-up time of 9.0 years, the prevalence of impaired activities of daily living (ADLs), instrumental activities of daily living (IADLs) and lower limb function (LEF) was 14.7%, 21.6%, and 50.3%, respectively. The values of four inflammatory biomarkers were used to calculate the state of inflammation composite score. Compared with the lowest quartile of the inflammation composite score, the highest quartile exhibited odds of impaired ADLs (OR = 1.589, 95% CI: 1.335–1.892), impaired IADLs (OR = 1.426, 95% CI: 1.228–1.657), and impaired LEF (OR = 1.728, 95% CI: 1.526–1.957). The association between systemic inflammation and functional limitations was partly mediated by cardiac and brain functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study showed that systemic inflammation in midlife was associated with a higher risk of late-life functional limitations. Protecting vital organ functions in midlife may reduce the risk of future functional limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e www.clinicaltrials.gov; Unique identifier: NCT00005131.\u003c/p\u003e","manuscriptTitle":"Systemic inflammation in midlife is associated with late-life functional limitations: The Atherosclerosis Risk in Communities Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 14:32:19","doi":"10.21203/rs.3.rs-3794413/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-21T06:04:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-22T07:45:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1660f5ed-f174-4530-bc85-b708955e6e29","date":"2024-03-12T16:00:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"421bd659-5c59-406d-bf06-a5278020c82d","date":"2024-03-07T09:26:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-28T16:10:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-28T16:09:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-12-25T10:11:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-25T10:06:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-12-23T01:53:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3613a9b-75c4-41a2-997e-5b6d809e2a43","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":27807142,"name":"Health sciences/Medical research"},{"id":27807143,"name":"Health sciences/Medical research/Experimental models of disease"}],"tags":[],"updatedAt":"2024-08-05T15:59:41+00:00","versionOfRecord":{"articleIdentity":"rs-3794413","link":"https://doi.org/10.1038/s41598-024-68724-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-07-29 15:57:00","publishedOnDateReadable":"July 29th, 2024"},"versionCreatedAt":"2024-01-02 14:32:19","video":"","vorDoi":"10.1038/s41598-024-68724-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-68724-w","workflowStages":[]},"version":"v1","identity":"rs-3794413","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3794413","identity":"rs-3794413","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}