The association between baseline and long-term status of metabolic score for insulin resistance index and the incidence of cardiometabolic multimorbidity

The association between baseline and long-term status of metabolic score for insulin resistance index and the incidence of cardiometabolic multimorbidity | 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 Research Article The association between baseline and long-term status of metabolic score for insulin resistance index and the incidence of cardiometabolic multimorbidity Man Yang, Jia Liu, Suwen Shen, Qian Shen, Yaqi Liu, Yun Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5068082/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2025 Read the published version in BMC Public Health → Version 1 posted 14 You are reading this latest preprint version Abstract Background: Cardiometabolic multimorbidity (CMM) is concurrently associated with a reduction in life expectancy and an increased propensity for all-cause mortality. Our objective was to evaluate the correlations between the baseline and longitudinal metabolic score for insulin resistance index (METS-IR) and the incidence of cardiometabolic multimorbidity (CMM) within a middle-aged and older Chinese population cohort.. Methods and Results: A total of 8,050 participants were enrolled and included in the analytical dataset for this study. Long-term status of METS-IR were defined as updated mean METS-IR and high METS-IR exposure duration. Updated mean METS-IR was defined as the mean of the two METS-IR measurements. High METS-IR exposure duration was defined as the times of visits with a high METS-IR among the 2 visits, quantified as 0 year, 2 years and 4 years according to the optimal cut points from the receiver operating characteristic curves of the two METS-IR measurements, respectively. The outcome was defined as the occurrence of CMM, characterized by the presence of two or more cardiometabolic disorders as self-reported by participants, encompassing conditions such as diabetes, stroke, and cardiac events. During 6-year visit, 540 participants experienced CMM. Substantially elevated incidences of CMM were observed in participants belonging to the highest tertiles of both baseline and updated mean METS-IR. After multivariable adjustment, the odds ratios (ORs) with 95% confidence intervals (CIs) for CMM were 2.94 (CI: 2.04-4.22) for those in the highest baseline METS-IR tertile and 3.26 (CI: 1.90-5.59) for those in the highest updated mean METS-IR tertile, relative to participants in the lowest tertiles. Multivariable-adjusted spline regression models showed a linear association of baseline METS-IR ( P linearity <0.0001) and updated mean METS-IR ( P linearity <0.0001) with CMM. Moreover, participants with 2 and 4 years high METS-IR exposure duration had increased risk of CMM (ORs [95% CIs]: 2.45 [1.52-3.96] and 3.46 [2.18-5.51], respectively), compared with the reference of those with unexposed group. Conclusion: This study proved elevated baseline METS-IR, updated mean METS-IR, especially high METS-IR exposure duration was associated with CMM incidence among middle-aged and older Chinese. Cardiometabolic multimorbidity METS-IR insulin resistance CHARLS long-term status Figures Figure 1 Figure 2 Introduction Multimorbidity, the most common chronic condition experienced by adults, which refers to the co-occurrence of multiple chronic disease or conditions 1 . It is becoming a global health challenge which have led to the decline in quality of life and greater use of health-care resources 2 . As one such representative multimorbidity, cardiometabolic multimorbidity (CMM) is defined as the co-existence of two or three cardiometabolic diseases (CMDs), which including diabetes mellitus (DM), stroke and heart disease 3 . Previous studies from around the world has consistently shown that CMM is linked to a shortened lifespan and an increased risk of all-cause mortality 3–5 . Therefore, the efforts to prevent CMM by reducing risk factors are of substantial relevance to both public health initiatives and medical practice. Insulin resistance (IR) signifies a decline or dysfunction in insulin sensitivity within peripheral tissues, as evidenced by compromised glucose uptake and oxidative metabolism. In addition, as a metabolic risk factor, IR itself is a prominent characteristic of cardiovascular and metabolic diseases, including hyperglycemia, atherosclerosis, diabetes mellitus and sroke 6–8 . While the euglycaemic-hyperinsulinaemic clamp (EHC) is broadly acknowledged as the gold-standard method and benchmark for evaluating IR, its adoption in routine clinical practice may be difficult due to its inherent complexities, time-intensive nature, and substantial resource requirements 9 . Thus, it is particularly important to recognize rapidly available and reliable IR markers. Recently, a number of methodologies incorporating simple routine biochemical markers have been introduced for the assessment of IR, including indices such as the Triglyceride Glucose (TyG) index and the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL) 10,11 . Nevertheless, these indices overlook the influence of cholesterol levels and nutritional status on the pathogenesis of the disease. Recently, the metabolic score for insulin resistance (METS-IR) index has been developed, which combined fasting plasma glucose (FPG), fasting triglycerides (TG), fasting high‐density lipoprotein cholesterol (HDL-C) and body mass index (BMI) mirroring nutritional status. This index has been shown to exhibit a high degree of agreement with the EHC 12 . Moreover, accumulative researches suggested that METS-IR was related to cardiometabolic disorders 13–15 . However, these preceding investigations have exclusively examined the association between METS-IR and specific diseases, furthermore, the assessment of METS-IR was conducted at a single time point, limiting the scope of the findings. Therefore, we aimed to explore the association of baseline and long-term MET-IR with CMM using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods Study participants The research involved a sample of middle-aged and elderly individuals from the CHARLS—a persistent, nationally representative, prospective, and longitudinal investigation based on the population of China. Details of the study design have been published elsewhere 16 . A total of 17,708 participants from 10,257 households across 28 provinces in China were enrolled at the baseline phase (2011-2012, wave 1), utilizing a multistage stratified probability proportional-to-size sampling approach. Participants in the CHARLS were monitored biennially through in-person interviews facilitated by computer-assisted personal interviewing technology. Four consecutive follow-ups were executed from 2013-2014 (wave 2), 2015-2016 (wave 3), and 2017-2018 (wave 4) for the enduring participants. Ethical approval for all the CHARLS waves was granted from the Institutional Review Board (IRB) at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015; the IRB approval number for biomarker collection, was IRB00001052-11014. The details of the CHARLS data are available on the website (http://charls.pku.edu.cn/en). In our study, participants were excluded if they had missing survey information on the MET-IR at baseline (i.e., 2011-2012) or if they had known physician-diagnosed CMM (e.g., diabetes mellitus, stroke or cardiac events) during 2011-2015. We first included 9962 participants with fasting METS-IR information at baseline and then excluded 322 participants <45 years, 567 participants with previous CMM or without CMM information and 1023 participants lost to follow up. A total of 8050 respondents were eligible for analysis of the baseline METS-IR. In subsequent analysis of updated mean METS-IR and high METS-IR exposure duration, we further excluded 2945 participants without fasting METS-IR information and 612 participants who occurred CMM or didn’t offer CMM information during 2013-2016. Finally, a total of 4493 participants were eligible for the analysis of updated mean METS-IR and high METS-IR exposure duration (Fig. 1). The CHARLS was granted ethical clearance by the Institutional Review Board of Peking University. All participants provided written informed consent prior to their inclusion in the study. The research adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for transparent and comprehensive reporting. Data collection and measurements Baseline data encompassing demographic characteristics, lifestyle risk factors, medical history, and medication utilization were gathered from the participants. History of chronic disease was determined based on participants' self-disclosures of diagnoses confirmed by healthcare professionals. Three blood pressure (BP) was measured with an electronic sphygmomanometer (Omron HEM-7200 Monitor) after 5 min of rest in the sitting position. Mean of the three BP measurements was used in the analyses. High inflammatory status was defined as high-sensitivity C-reactive protein (hsCRP) ≥ 3 mg/L according to previous studies 17 . Assessment of renal function was based on estimated glomerular filtration rate (eGFR) calculated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation with adjusted coefficient of 1.1 for the Chinese population 18 . In accordance with the Kidney Disease: Improving Global Outcomes guidelines 19 , we defined normal renal function as eGFR ≥ 90 ml/min/1.73 m 2 and abnormal renal function as eGFR ≥ 90 ml/min/1.73 m 2 . Grip strength from the dominant hand was determined using a handgrip dynamometer (YuejianTM WL-1000 dynamometer) twice, and the average was used. Participants were instructed to perform five consecutive sit-to-stand repetitions on a chair at their quickest pace, with the chair-rising time being meticulously recorded using a stopwatch for each repetition 20 . The summary score for the balance test was categorized as follows: a score of 1 was assigned if the participant failed to complete either the semi-tandem or side-by-side tests to the maximum duration; a score of 2 was given for those who did not complete the semi-tandem test to the maximum time but managed to complete the side-by-side test to the maximum duration; a score of 3 was designated for participants who completed the semi-tandem test to the maximum duration but were unable to finish the full-tandem test within the expected time; and a score of 4 was allocated to those who completed both the semi-tandem and full-tandem tests to the maximum duration 21 . Blood samples were meticulously collected from each participant by proficient medical personnel from the Chinese Center for Disease Control and Prevention (China CDC), adhering to a standardized protocol. Participants were instructed to undergo an overnight fast prior to blood collection; however, blood samples were also obtained from those who had not fasted, with their fasting status meticulously recorded as a variable within the dataset. Over 92% of respondents who gave blood reported that they were fasting. Complete blood count (CBC) test was measured on automated analyzers available at county CDC stations or town/village health centers. All the plasma samples including triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), fasting glucose (FBG) were measured at the Youanmen Center for Clinical Laboratory of Capital Medical University. Body mass index (BMI) was caculated as weight divided by height squared (kg/m 2 ). METS-IR was calculated as (ln(2*FBG +TG)*BMI))/(ln(HDL-C) 12 . The MET-IR measurements obtained during two visits (wave 1 and wave 3) from CHARLS were utilized to evaluate the long-term status of the METS-IR. Long-term status of METS-IR were defined as updated mean METS-IR and high METS-IR exposure duration. In the present analysis, updated mean METS-IR was defined as the mean of the METS-IR measurements from 2 visits. High METS-IR exposure duration was defined as the times of visits with a high METS-IR (over the cutoff mentioned in the Statistical analysis) among the 2 visits, quantified as 0 year, 2 years and 4 years. Assessment of CMM The identification of chronic conditions diagnosed by physicians was determined through self-reported data collected at the baseline assessment and subsequent follow-up surveys (for diabetes or high blood sugar, “Have you been diagnosed with diabetes or high blood sugar by a doctor?”, for heart attack, coronary heart disease, or other heart problems, “Have you been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems by a doctor?” and for stroke, “Have you been diagnosed with stroke by a doctor?”). Participants who responded affirmatively to the pertinent query were deemed to have a cardiometabolic condition. Of those, CMM was defined as participants who reported suffering from at least two cardiometabolic conditions 22 . Statistical analysis Participants in the study were categorized into tertiles based on their baseline METS-IR values, the updated mean METS-IR values, as well as across three distinct exposure durations characterized by high METS-IR, respectively. Baseline demographic and clinical attributes were examined across the various participant groups. The optimal cutoff for METS-IR associated with CMM incidence was determined using the receiver operating characteristic curves of METS-IR at wave 1 (≥34.81) and wave 3 (≥36.87), respectively. Multivariable logistic regression models were used to estimate the risk of CMM associated with baseline METS-IR, updated mean METS-IR and high METS-IR exposure duration, respectively. Tests for linear trend in risk across baseline METS-IR, updated mean METS-IR and high METS-IR exposure duration were performed using these category as continuous variables. The odds ratios (ORs) and 95% confidence intervals (CIs) were computed for each 1-standard deviation (SD) increase in both baseline and updated mean METS-IR. All analyses were conducted with adjustments for the following covariates: age, sex, rural residency, current smoking status, and current alcohol consumption in Model 1; adding medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease, stomach/digestive disease, arthritis, asthma, psychological problem, memory problem) and medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor) in model 2; adding waist circumference (WC), systolic BP, low-density lipoproteins cholesterol (LDL-C), eGFR, uric acid (UC) and hsCRP in model 3; adding dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score in model 4. The impact of both baseline and updated mean METS-IR on the risk for cardiometabolic multimorbidity (CMM) was investigated utilizing ordinal logistic regression analyses. Nonparametric restricted cubic splines were used to examine the shape of the association of baseline METS-IR and updated mean METS-IR with CMM with 3 knots (at the 10th, 50th and 90th percentiles of baseline METS-IR and updated mean METS-IR, respectively). We assessed the capacity of baseline METS-IR, updated mean METS-IR, and the duration of high METS-IR exposure to enhance risk stratification when added to a basic model that includes well-established risk factors. The Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI) were computed to quantify this effect 23 . To test the robustness of our findings, we performed several sensitivity analyses by excluding (1) participants with death, (2) participants with abnormal BMI (≥ 28 kg/m2), FBG (≥ 126 mg/dL), TG (≥150mg/dL) and HDL-C (< 35 mg/dL). Furthermore, we conducted subgroup analyses, stratified by sex, age (<65 and ≥65 years), current smoking, current drinking, history of hypertension and dyslipidemia, baseline hsCRP (<3.0 mg/L and ≥3.0 mg/L) and baseline eGFR (<90 ml/min/1.73 m 2 and ≥ 90 ml/min/1.73 m 2 ) in multivariable adjusted logistic regression models. This study did not employ sample weights, as prior research has demonstrated comparable outcomes whether weights were applied or not 24,25 . Two-sided P value <0.05 was considered to be significant. Data analysis was performed using SAS statistical software (version 9.4). Results Baseline characteristics A total of 8050 participants (4330 men and 3720 women; mean age 58.78±9.08 years) were enrolled in the current study. Participants with high baseline METS-IR, in contrast to those with low METS-IR, were more frequently younger, female, and residing in urban areas. They also exhibited lower rates of smoking and alcohol consumption, as well as a reduced use of anti-hypertensive and lipid-lowering medications. Moreover, this group had a higher incidence of hypertension and dyslipidemia, but a lower occurrence of lung disease, stomach or digestive disorders, and asthma. They also demonstrated higher values for BMI, waist circumference (WC), systolic blood pressure (SBP), blood glucose, low-density lipoprotein cholesterol (LDL-C), uric acid (UC), high-sensitivity C-reactive protein (hsCRP), and grip strength of the dominant hand (Table 1). Similar results were observed when participants were categorized by updated mean METS-IR and high METS-IR exposure duration (Table 2 and 3).) Association of baseline METS-IR with incident CMM Over the course of a 6-year follow-up period, 540 participants (6.71%) developed CMM. Notably, the incidence of CMM was significantly greater in the subset of participants with higher baseline METS-IR values. After adjustment for potential confounding factors, with each SD (9.22) of METS-IR increasing, the risk of incident CMM elevated by 82% (OR: 1.82, 95%CI: 1.57-2.11). In the categorical analysis, the multivariable-adjusted ORs for CMM in the T2 and T3 groups, as compared to the T1 group, were 1.54 (95% CI: 1.10-2.17) and 2.94 (95% CI: 2.04-4.22), respectively, with a statistically significant trend (P trend < 0.0001) (Table 4). Analysis using multivariable-adjusted spline regression models revealed a linear relationship between baseline METS-IR and the incidence of CMM ( P linearity <0.0001) (Fig. 2A). Adding baseline METS-IR tertiles to a model containing conventional risk factors significantly improved risk reclassification for CMM (continuous NRI was 29.11% [p <0.0001] and IDI was 0.53% [p =0.002]) (Table 5). Association of long-term status of METS-IR with incident CMM Similarly, a significant association was found between the updated mean METS-IR and incident CMM when the aforementioned analysis was replicated. The multivariable-adjusted ORs and 95% CIs for the highest tertile of updated mean METS-IR relative to the lowest tertile were 3.17 (95% CI: 1.49-6.75) for the incidence of CMM. Each 1-SD (9.62) increment in updated mean METS-IR was associated with 99% (OR: 1.99, 95%CI: 1.53-2.59) increased risk of CMM (Table 4). Multivariable-adjusted spline regression models showed a linear association of updated mean METS-IR with CMM incidence ( P linearity <0.0001) (Fig. 2B). Incorporating updated mean METS-IR tertiles into a model that already includes conventional risk factors yielded a significant enhancement in the risk reclassification for CMM (continuous NRI was 40.18% [p <0.0001] and IDI was 0.62% [p =0.01]) (Table 5). The incidence rates of cardiometabolic multimorbidity (CMM) were elevated to 6.23% and 8.91% at 2-year and 4-year durations of high METS-IR exposure, respectively, compared to the unexposed group (0-year exposure) which had an incidence rate of 2.63%. After multivariable adjustment, compared with unexposed group (0 year), risk of CMM was significantly higher in those with 2 years group (OR: 2.45, 95%CI: 1.52-3.96) and 4 years group (OR: 3.46, 95%CI: 2.18-5.51), respectively (Table 4). Adding three high METS-IR exposure duration to a model containing conventional risk factors significantly improved risk reclassification for CMM (continuous NRI was 40.56% [ p <0.0001] and IDI was 0.76% [ p =0.006]) (Table 5). Additional analyses Sensitivity analyses yielded results aligned with the primary analysis. Even after excluding participants who had deceased by Wave 4 and limiting the sample to those with normal levels of BMI, FBG, TG, and HDL-C, the findings remained consistent with the initial analysis (Table 6). In order to delve deeper into the association between the baseline and long-term status of METS-IR and the incidence of CMM, a series of subgroup analyses were performed. None of the subgroups, including the sex, age, current drinking status, current smoking status, history of hypertension, history of dyslipidemia, high-sensitivity C-reactive protein and estimated glomerular filtration rate subgroups, profoundly changed the relationship between the baseline and long-term status of METS-IR and CMM incidence (all P for interaction>0.05) (Table 7-9). Discussion Our prospective study insights into the prognostic significance of baseline and prolonged METS-IR status in predicting cardiometabolic risk among middle-aged and elderly individuals of the Chinese population. The findings not only contribute to the extension of knowledge regarding the longevity of cardiometabolic risk but also enhance our understanding of the underlying pathophysiological mechanisms. In this prospective study among CHARLS participants, we documented that elevated baseline METS-IR, updated mean METS-IR and high METS-IR exposure duration during follow-up was independently associated with increased risk of CMM. The integration of baseline or long-term METS-IR measurements into the conventional risk factors model significantly enhanced the risk reclassification for CMM, as indicated by the NRI and IDI. Moreover, the observed association remained robust following both sensitivity analyses and examination within various subgroups. To the best of our knowledge, this is the first study to prospectively evaluate the predictive significance of both baseline and long-term status of METS-IR on the incidence of cardiometabolic diseases among middle-aged and older adults in China, utilizing data from a nationally representative panel. METS-IR, a new non-insulin-based index, is calculated on clinical markers including FBG, TG, HDL-C and BMI. METS-IR is a practical alternative to insulin-related indices, which is primarily due to the fact that serum insulin levels are not regularly assessed in routine clinical settings. Previous studies were usually based on predicting role of METS-IR in the development of specific disease, including CVD and DM. In a study consisted of 6489 participants aged 35-70 years without a history of CVD during a median follow-up of 10.6 years, elevated METS-IR was found to be independently associated with incident CVD 14 . A study conducted in Mexico City showed that individuals who developed type 2 diabetes mellitus (T2DM) had elevated baseline METS-IR, and the likelihood of developing incident T2DM increased consistently with higher percentile METS-IR 12 . Aforementioned researches were based on a single METS-IR measurement, only a few studies are based on the change or long-term status of METS-IR. Tian et al. found that the potential for future CVD incidence was linked to the cumulative exposure to METS-IR, as well as the timing and progression of METS-IR 26 . Similarly, data from a rural Chinese area showed that an elevation in METS-IR and a decrease in METS-IR over a 6-year period were both independently associated with an increased risk of developing T2DM 27 . Nevertheless, the existing research has largely focused on evaluating the relationship between METS-IR and specific health outcomes within select a certain group of population, rather than examining the broader implications of METS-IR for cardiometabolic health among the general community-dwelling population. It is worth mentioning that the influence of baseline and long-term status of METS-IR on CMM has not been ascertained. Taking into account the variability of METS-IR over time, which might engender regression dilution bias and and thereby impact the accuracy of the findings, employing serial assessments of long-term METS-IR is likely to yield results that are both more reliable and more robust, enhancing the overall validity of the finding. In the present study, the dose-response association still existed when using updated mean METS-IR and long-term status of METS-IR to evaluate. More importantly, the updated mean METS-IR and long-term status of METS-IR seemed to be more significantly associated with CMM than baseline METS-IR. Consequently, our findings underscore the importance of long-term surveillance of METS-IR within clinical settings, suggesting that such monitoring could be instrumental in extending the period of METS-IR remission among middle-aged and elderly individuals of the Chinese population. Although the precise mechanisms underlying the association between METS-IR and CMM are not yet fully elucidated, several speculative explanations have been suggested. One explanation suggests that considering the involvement of BMI, METS-IR might be a superior indicator for assessing IR in adipose tissue, muscle, and the liver 28 . Abundant adipose tissue not only elevates metabolic risk but is also linked to elevated blood glucose concentrations and reduced levels HDL-C 29 . Hypertriglyceridemia exacerbates this by raising free fatty acid (FFA) levels, which can disrupt insulin signaling and prompt oxidative stress in the tissues, leading to IR in the bone and live 30 . Inflammations caused by high cumulative IR could affect blood glucose and enhance the formation of atherosclerosis-associated foam cells and vulnerable plaques 31–33 . Another explanation suggests that the increased platelet aggregation, adhesion, and activation, as indicated by the higher IR, contributed to the occlusion of blood vessels, leading to disturbances in hemodynamic 34,35 . Our research boasts several notable strengths, including the extensive scale and national representativeness of the prospective study conducted across China, coupled with an impressive participant response rate and rigorous adjustment for potential confounders within our multivariable models. As a result, the present investigation affords high-caliber evidence regarding the association between METS-IR and cardiometabolic risk. Several limitations also need to be mentioned. First, The CHARLS study was conducted exclusively on a Chinese population, and thus, the findings drawn from our research may not be directly applicable or generalizable to diverse populations. Second, CMM was derived from participants' self-reported physician diagnoses, which may cause information bias, although this method has been widely adopted in epidemiologic study 30 . Third, the METS-IR was not measured in wave 2 or wave 4, precluding the analysis of its trajectory over time. Moreover, it is important to note that the current analysis was not preplanned as part of the original study protocol. This observational analysis could be influenced by potential biases and confounding factors which we did not account for in our adjustments. Consequently, the outcomes of our investigation primarily serve to postulate hypotheses that will need to be explored and validated in subsequent research initiatives. In conclusion, this study proved elevated baseline METS-IR, especially high METS-IR exposure duration was associated with CMM incidence among middle-aged and older Chinese. Our findings indicate that this simple index may be useful for identifying individuals at high risk CMM in advance, and emphasize the importance in long-term monitoring of METS-IR in clinical practice. Declarations Financial support The research was supported by the Medical Key Discipline Program of Wuxi Health Commission (LCZX2021006); Top Talent Support Program of Wuxi Taihu Talents Plan; Top Talent Support programme for Young and Middle-aged Scientists (HB2023095); General Programme of Wuxi Medical Center, Nanjing Medical University (WMCG202303); the Youth Foundation of Wuxi Health and Family Planning Commission (Q202267, Q202365) Human Ethics and Consent to Participate declarations The CHARLS survey was conducted in line with the Declaration of Helsinki. The ethics application for collecting data on human subjects in CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015), and all CHARLS participants provided written informed consent. This study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Authors contributions Conceptualization, Man Yang and Yun Qian; Data curation, Qian Shen and Yaqi Liu; Formal analysis, Man Yang, Suwen Shen and Yaqi Liu; Funding acquisition, Jia Liu and Yun Qian; Writing – original draft, Man Yang and Jia Liu; Writing – review & editing, Yun Qian. All authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor. Clinical Trial Number Not Clinical Trial. Declaration of competing interest The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript. Acknowledgements This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version D as of June 2021 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01AG030153, RC2AG036619, R03 AG043052). For more information, please refer to www.g2aging.org. Data Availability declaration This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version D as of June 2021 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01AG030153, RC2AG036619, R03 AG043052). For more information, please refer to www.g2aging.org. References Tinetti, M. E., Fried, T. R. & Boyd, C. M. Designing health care for the most common chronic condition--multimorbidity. JAMA 307 , 2493–2494 (2012). Han, Y. et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J 42 , 3374–3384 (2021). Emerging Risk Factors Collaboration et al. Association of Cardiometabolic Multimorbidity With Mortality. JAMA 314 , 52–60 (2015). Canoy, D. et al. 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Characteristics of the study participants according to the tertiles of baseline METS-IR Characteristics Baseline METS-IR p value <30.96 <30.96 <30.96 Subjects, n (%) 2656 (32.99) 2655 (32.98) 2739 (34.03) Demographics Age, years 60.88 ± 9.56 58.22 ± 8.84 57.28 ± 8.44 <0.0001 Male, n (%) 1387 (52.22) 1224 (46.10) 1109 (40.49) <0.0001 Rural, n (%) 1978 (74.47) 1783 (67.16) 1537 (56.12) <0.0001 Current smoking, n (%) 996 (38.06) 779 (29.71) 616 (22.70) <0.0001 Current drinking, n (%) 1027 (38.68) 911 (34.33) 758 (27.69) <0.0001 Medical history Hypertension, n (%) 418 (15.80) 578 (21.87) 1030 (37.70) <0.0001 Dyslipidemia, n (%) 102 (3.89) 179 (6.84) 410 (15.26) <0.0001 Chronic kidney disease, n (%) 153 (5.80) 155 (5.86) 134 (4.90) 0.23 Malignant tumor, n (%) 18 (0.68) 20 (0.75) 27 (0.99) 0.42 Lung disease, n (%) 338 (12.75) 213 (8.04) 202 (7.38) <0.0001 Liver disease, n (%) 96 (3.63) 87 (3.30) 98 (3.59) 0.77 Stomach/digestive disease, n (%) 666 (25.13) 610 (22.98) 516 (18.85) <0.0001 Arthritis, n (%) 906 (34.16) 892 (33.62) 985 (36.04) 0.15 Asthma, n (%) 142 (5.36) 97 (3.67) 99 (3.63) 0.002 Psychological problem, n (%) 34 (1.28) 37 (1.40) 29 (1.06) 0.53 Memory problem, n (%) 30 (1.13) 33 (1.24) 33 (1.21) 0.93 Medicine history Anti-hypertensive drugs, n (%) 260 (9.83) 406 (15.37) 806 (29.50) <0.0001 Lipid-lowering drugs, n (%) 40 (1.53) 78 (2.98) 227 (8.45) <0.0001 Oncology drugs or treatment, n (%) 15 (0.56) 13 (0.49) 23 (0.84) 0.23 Clinical features Body mass index, kg/m 2 20.08 (18.87, 21.23) 23.21 (22.07, 24.42) 26.76 (25.08, 28.67) <0.0001 Waist circumference, cm 76.40 (72.20, 81.00) 84.20 (80.00, 89.00) 93.45 (88.40, 99.00) <0.0001 Systolic blood pressure, mmHg 121.50 (110.00, 136.50) 124.50 (112.50, 139.00) 130.50 (118.00, 145.00) <0.0001 Blood glucose, mg/dL 98.28 (91.62, 106.20) 101.16 (94.32, 109.62) 106.02 (98.10, 117.90) <0.0001 Low-density lipoproteins cholesterol, mg/dL 111.34 (91.24, 113.93) 117.53 (97.42, 139.56) 117.14 (94.72, 142.27) <0.0001 Estimated glomerular filtration rate, ml/min/1.73 m 2 106.54 (96.98, 113.93) 107.99 (98.32, 115.65) 107.65 (96.65, 114.87) 0.0006 Uric acid, mg/dL 4.11 (3.46, 4.94) 4.21 (3.54, 5.03) 4.53 (3.78, 5.40) <0.0001 High-sensitivity C-reactive protein, mg/L 0.74 (0.43, 1.55) 0.92 (0.52, 1.89) 1.35 (0.75, 2.69) <0.0001 Dominant hand grip strength, kg 30.00 (24.00, 37.00) 31.00 (25.00, 40.00) 31.20 (25.00, 40.00) <0.0001 Chair-rising time, s 10.18 (8.07, 12.60) 10.05 (7.97, 12.54) 10.03 (8.19, 12.50) 0.54 Lung function peak flow, ml 260.00 (180.00, 360.00) 300.00 (210.00, 380.00) 300.00 (210.00, 380.00) <0.0001 Balance test summary score 4.00 (4.00, 4.00) 4.00 (4.00, 4.00) 4.00 (4.00, 4.00) 0.71 Continuous variables are expressed as mean ± SD (normal distribution) or median (interquartile range) (not normal distribution) and compared using F tests (normal distribution) or Wilcoxon rank-sum tests (not normal distribution) as appropriate. Categorical variables are expressed as number (percent) and compared using χ 2 tests. Table 2. Characteristics of the study participants according to the tertiles of updated mean METS-IR. Characteristics Updated mean METS-IR p value <31.18 31.18-37.31 ≥37.31 Subjects, n (%) 1484 (33.03) 1481 (32.96) 1528 (34.01) Demographics Age, years 60.85 ± 8.88 58.10 ± 8.49 56.95 ± 8.05 0.0003 Male, n (%) 820 (55.26) 652 (44.02) 610 (39.92) <0.0001 Rural, n (%) 1131 (76.21) 994 (67.21) 893 (58.44) <0.0001 Current smoking, n (%) 584 (39.92) 414 (28.24) 337 (22.27) <0.0001 Current drinking, n (%) 891 (39.85) 500 (33.76） 427 (27.96) <0.0001 Medical history Hypertension, n (%) 232 (15.72) 310 (21.02) 563 (36.94) <0.0001 Dyslipidemia, n (%) 56 (3.83) 112 (7.70) 216 (14.30) <0.0001 Chronic kidney disease, n (%) 74 (5.02) 80 (5.43) 74 (4.86) 0.77 Malignant tumor, n (%) 7 (0.47) 11 (0.74) 11 (0.72) 0.59 Lung disease, n (%) 196 (13.23) 124 (8.38) 114 (7.47) <0.0001 Liver disease, n (%) 55 (3.73) 44 (2.98) 47 (3.09) 0.47 Stomach/digestive disease, n (%) 363 (24.51) 337 (22.77) 276 (18.09) <0.0001 Arthritis, n (%) 485 (32.73) 480 (32.43) 528 (34.60) 0.39 Asthma, n (%) 80 (5.41) 55 (3.72) 58 (3.81) 0.04 Psychological problem, n (%) 21 (1.42) 16 (1.09) 16 (1.05) 0.59 Memory problem, n (%) 22 (1.49) 5 (0.34) 10 (0.66) 0.002 Medicine history Anti-hypertensive drugs, n (%) 150 (10.16) 198 (13.42) 426 (27.95) <0.0001 Lipid-lowering drugs, n (%) 22 (1.50) 52 (3.57) 124 (8.21) <0.0001 Oncology drugs or treatment, n (%) 9 (0.61) 7 (0.47) 12 (0.79) 0.55 Clinical features Body mass index, kg/m 2 20.04 (18.88, 21.18) 23.15 (22.04, 24.32) 26.71 (25.11, 28.74) <0.0001 Waist circumference, cm 76.40 (72.00, 81.00) 84.00 (80.00, 88.60) 93.60 (88.40, 99.00) <0.0001 Systolic blood pressure, mmHg 121.00 (109.50, 135.00) 123.50 (112.00, 138.50) 130.00 (117.50, 145.00) <0.0001 Blood glucose, mg/dL 99.00 (91.80, 107.10) 100.26 (94.14, 108.54) 104.40 (96.84, 113.58) <0.0001 Low-density lipoproteins cholesterol, mg/dL 110.18 (89.69, 131.06) 116.37 (96.65, 139.56) 117.53 (95.10, 140.72) <0.0001 Estimated glomerular filtration rate, ml/min/1.73 m 2 107.36 (98.48, 113.96) 108.70 (99.40, 115.94) 108.29 (97.69, 115.20) 0.008 Uric acid, mg/dL 4.08 (3.43, 4.92) 4.11 (3.51, 4.96) 4.43 (3.67, 5.3) <0.0001 High-sensitivity C-reactive protein, mg/L 0.72 (0.44, 1.57) 0.89 (0.52, 1.83) 1.27 (0.70, 2.39) <0.0001 Dominant hand grip strength, kg 30.00 (24.30, 37.00) 31.00 (25.00, 39.60) 32.00 (25.50, 40.00) <0.0001 Chair-rising time, s 10.12 (8.00, 12.60) 10.12 (8.01, 12.62) 10.12 (8.29, 12.62) 0.52 Lung function peak flow, ml 260.00 (190.00, 360.00) 300.00 (210.00, 380.00) 300.00 (220.00, 380.00) <0.0001 Balance test summary score 4.00 (4.00, 4.00) 4.00 (4.00, 4.00) 4.00 (4.00, 4.00) 0.31 Continuous variables are expressed as mean ± SD (normal distribution) or median (interquartile range) (not normal distribution) and compared using F tests (normal distribution) or Wilcoxon rank-sum tests (not normal distribution) as appropriate. Categorical variables are expressed as number (percent) and compared using χ 2 tests. Table 3. Characteristics of the study participants according to the high MET-IR exposure duration. Characteristics High MET-IR exposure duration p value 0 year 2 years 4 years Subjects, n (%) 2243 (49.92) 802 (17.85) 1448 (32.23) Demographics Age, years 59.94 ± 8.90 58.44 ± 8.38 56.67 ± 7.95 <0.0001 Male, n (%) 1158 (51.63) 349 (43.52) 575 (39.71) <0.0001 Rural, n (%) 1653 (73.70) 519 (64.71) 846 (58.43) <0.0001 Current smoking, n (%) 801 (36.20) 207 (26.07) 327 (22.79) <0.0001 Current drinking, n (%) 844 (37.64) 268 (33.42) 406 (28.06) <0.0001 Medical history Hypertension, n (%) 360 (16.13) 215 (26.88) 530 (36.73) <0.0001 Dyslipidemia, n (%) 104 (4.70) 76 (9.67) 204 (14.26) <0.0001 Chronic kidney disease, n (%) 115 (5.16) 46 (5.76) 67 (4.64) 0.50 Malignant tumor, n (%) 10 (0.45) 8 (1.00) 11 (0.76) 0.20 Lung disease, n (%) 267 (11.92) 59 (7.37) 108 (7.46) <0.0001 Liver disease, n (%) 78 (3.49) 23 (2.88) 45 (3.12) 0.65 Stomach/digestive disease, n (%) 548 (24.46) 176 (21.97) 252 (17.43) <0.0001 Arthritis, n (%) 732 (32.66) 264 (32.96) 497 (34.37) 0.55 Asthma, n (%) 106 (4.74) 33 (4.13) 54 (3.74) 0.33 Psychological problem, n (%) 31 (1.39) 9 (1.12) 13 (0.90) 0.40 Memory problem, n (%) 27 (1.21) 1 (0.13) 9 (0.62) 0.009 Medicine history Anti-hypertensive drugs, n (%) 231 (10.35) 141 (17.63) 402 (27.86) <0.0001 Lipid-lowering drugs, n (%) 45 (2.03) 35 (4.45) 118 (8.25) <0.0001 Oncology drugs or treatment, n (%) 10 (0.45) 6 (0.75) 12 (0.83) 0.31 Clinical features Body mass index, kg/m 2 20.97 (19.46, 88.33) 23.88 (22.69, 25.17) 26.76 (25.16, 28.79) <0.0001 Waist circumference, cm 78.80 (74.00, 83.20) 86.55 (82.00, 91.00) 93.80 (88.20, 99.00) <0.0001 Systolic blood pressure, mmHg 122.00 (110.00, 135.50) 125.50 (114.00, 140.00) 130.00 (117.00, 144.50) <0.0001 Blood glucose, mg/dL 99.18 (92.16, 107.10) 102.06 (95.04, 110.16) 104.22 (96.57, 113.49) <0.0001 Low-density lipoproteins cholesterol, mg/dL 112.89 (92.78, 133.76) 116.37 (93.56, 139.67) 117.14 (95.49, 141.50) 0.0002 Estimated glomerular filtration rate, ml/min/1.73 m 2 107.55 (98.67, 114.53) 108.15 (97.38, 115.20) 108.71 (98.47, 115.78) 0.10 Uric acid, mg/dL 4.08 (3.42, 4.93) 4.22 (3.62, 5.03) 4.40 (3.68, 5.30) <0.0001 High-sensitivity C-reactive protein, mg/L 0.77 (0.45, 1.64) 0.97 (0.56, 1.99) 1.26 (0.70, 2.38) <0.0001 Dominant hand grip strength, kg 30.00 (24.50, 38.00) 30.50 (25.00, 39.00) 32.00 (25.50, 40.00) <0.0001 Chair-rising time, s 10.07 (7.99, 12.60) 10.24 (8.03, 12.72) 10.11 (8.34, 12.57) 0.48 Lung function peak flow, ml 270.00 (200.00, 365.00) 295.00 (200.00, 370.00) 300.00 (220.00, 390.00) <0.0001 Balance test summary score 4.00 (4.00, 4.00) 4.00 (4.00, 4.00) 4.00 (4.00, 4.00) 0.15 Continuous variables are expressed as mean ± SD (normal distribution) or median (interquartile range) (not normal distribution) and compared using F tests (normal distribution) or Wilcoxon rank-sum tests (not normal distribution) as appropriate. Categorical variables are expressed as number (percent) and compared using χ 2 tests. Table 4. Odds ratios and 95% confidence intervals of CMM according to METS-IR. Case (%) Age, sex-adjusted Model 1 Model 2 Model 3 Model 4 Baseline METS-IR T1 (<30.96) 75 (2.82) 1.00 1.00 1.00 1.00 1.00 T2 (30.96-37.56) 139 (5.24) 2.04 (1.53-2.72) 2.04 (1.52-2.72) 1.92 (1.42-2.60) 1.71 (1.23-2.36) 1.54 (1.10-2.17) T3 (≥37.56) 326 (11.90) 5.10 (3.92-6.23) 5.03 (3.85-6.57) 3.68 (2.76-4.92) 3.19 (2.26-4.50) 2.94 (2.04-4.22) p for trend <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Each SD (9.22) increase 1.77 (1.62-1.94) 1.75 (1.59-1.92) 1.42 (1.28-1.58) 1.82 (1.59-2.09) 1.82 (1.57-2.11) Updated mean METS-IR T1 (<31.18) 38 (2.56) 1.00 1.00 1.00 1.00 1.00 T2 (31.18-37.31) 62 (4.19) 1.76 (1.16-2.67) 1.80 (1.19-2.75) 1.73 (1.12-2.67) 1.52 (0.95-2.42) 1.56 (0.93-2.61) T3 (37.31) 138 (9.03) 4.12 (2.83-6.00) 4.33 (2.95-6.36) 3.50 (2.32-5.28) 2.95 (1.79-4.86) 3.26 (1.90-5.59) p for trend <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Each SD (9.62) increase 1.38 (1.17-1.64) 1.39 (1.17-1.65) 1.19 (1.07-1.32) 1.95 (1.52-2.50) 1.99 (1.53-2.59) High METS-IR exposure duration 0 year 59 (2.63) 1.00 1.00 1.00 1.00 1.00 2 years 50 (6.23) 2.54 (1.73-3.75) 2.67 (1.80-3.95) 2.46 (1.64-3.70) 2.39 (1.53-3.72) 2.45 (1.52-3.96) 4 years 129 (8.91) 3.91 (2.83-5.40) 4.11 (2.95-5.73) 3.37 (2.36-4.81) 3.07 (1.99-4.76) 3.46 (2.18-5.51) p for trend <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Optimal cut points for METS-IR at wave 1 (≥34.81) and wave 3 (≥36.87) were obtained from the receiver operating characteristic curves. Model 1: adjusted for age, sex, rural region, current smoking, current drinking. Model 2: adjusted for model 1 + medical history (hypertension, dyslipidemia, diabetes mellitus, chronic kidney disease, malignant tumor, lung disease, liver disease, stomach/digestive disease, arthritis, asthma, psychological problem, memory problem) + medicine history (taking any medicine or treatment for hypertensive, dyslipidemia, diabetes mellitus, cardio-cerebrovascular disease and malignant tumor). Model 3: adjusted for model 2 + body mass index, waist circumference, systolic blood pressure, low-density lipoproteins cholesterol, blood glucose, estimated glomerular filtration rate, uric acid and high-sensitivity C-reactive protein. Model 4: adjusted for model 3 + dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score. Table 5. Reclassification statistics for CMM by METS-IR. Continuous NRI (95% CI), % P value IDI (95% CI), % P value Conventional model Reference Reference Conventional model + baseline METS-IR tertiles 29.11 (19.08-39.14) <0.0001 0.53 (0.19-0.87) 0.002 Conventional model + updated mean METS-IR tertiles 40.18 (25.71-54.65) <0.0001 0.62 (0.14-1.10) 0.01 Conventional model + high MET-IR exposure duration 40.56 (25.95-55.18) <0.0001 0.76 (0.22-1.29) 0.006 Abbreviations: CI = confidence interval; NRI = net reclassification improvement; IDI = integrated discrimination index. Optimal cut points for METS-IR at wave 1 (≥34.81) and wave 3 (≥36.87) were obtained from the receiver operating characteristic curves. Conventional model included age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate, high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score unless the variable was excluded. Table 6. Odds ratios and 95% confidence intervals of CMM to METS-IR in sensitivity analysis. Case (%) Age, sex-adjusted Model 1 Model 2 Model 3 Model 4 Baseline METS-IR T1 (<30.96) 63 (2.84) 1.00 1.00 1.00 1.00 1.00 T2 (30.96-37.56) 95 (5.12) 2.06 (1.48-2.86) 2.03 (1.45-2.84) 1.78 (1.25-2.54) 1.89 (1.27-2.82) 1.76 (1.15-2.68) T3 (≥37.56) 46 (8.08) 3.45 (2.31-5.14) 3.37 (2.24-5.07) 2.32 (1.48-3.61) 2.59 (1.54-4.37) 2.53 (1.44-4.42) p for trend <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Each SD (9.22) increase 2.61 (1.99-2.43) 2.58 (1.96-3.41) 1.95 (1.45-2.63) 2.16 (1.51-3.08) 2.18 (1.49-3.19) Updated mean METS-IR T1 (<31.18) 33 (2.71) 1.00 1.00 1.00 1.00 1.00 T2 (31.18-37.31) 43 (4.06) 1.67 (1.04-2.67) 1.65 (1.02-2.67) 1.51 (0.91-2.52) 1.77 (1.01-3.14) 1.87 (0.99-3.53) T3 (37.31) 28 (6.81) 2.99 (1.76-5.08) 3.04 (1.77-5.22) 2.14 (1.18-3.89) 2.74 (1.38-5.46) 3.17 (1.49-6.75) p for trend <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Each SD (9.62) increase 2.48 (1.67-3.69) 2.52 (1.68-3.80) 1.83 (1.19-2.83) 2.13 (1.28-3.55) 2.44 (1.38-4.35) High MET-IR exposure duration 0 year 50 (2.72) 1.00 1.00 1.00 1.00 1.00 2 years 29 (6.76) 2.72 (1.89-4.38) 2.79 (1.72-4.52) 2.46 (1.46-4.14) 2.78 (1.57-4.93) 2.80 (1.49-5.26) 4 years 25 (5.95) 2.58 (1.56-4.27) 2.67 (1.60-4.46) 1.98 (1.14-.344) 2.46 (1.32-4.58) 2.93 (1.50-5.71) p for trend <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Optimal cut points for METS-IR at wave 1 (≥34.81) and wave 3 (≥36.87) were obtained from the receiver operating characteristic curves. Model 1: adjusted for age, sex, rural region, current smoking, current drinking. Model 2: adjusted for model 1 + medical history (hypertension, dyslipidemia, diabetes mellitus, chronic kidney disease, malignant tumor, lung disease, liver disease, stomach/digestive disease, arthritis, asthma, psychological problem, memory problem) + medicine history (taking any medicine or treatment for hypertensive, dyslipidemia, diabetes mellitus, cardio-cerebrovascular disease and malignant tumor). Model 3: adjusted for model 2 + body mass index, waist circumference, systolic blood pressure, low-density lipoproteins cholesterol, blood glucose, estimated glomerular filtration rate, uric acid and high-sensitivity C-reactive protein. Model 4: adjusted for model 3 + dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score. Table 7. Subgroup analysis of ORs (95 % CIs) of CMM according to baseline METS-IR. METS-IR T1 <30.96 T2 30.96-37.56 T3 ≥37.56 p interaction Sex 0.45 Male 1.00 1.54 (0.93-2.55) 3.32 (1.92-5.76) Female 1.00 1.62 (1.01-2.60) 2.81 (1.72-4.60) Age 0.89 <65 1.00 1.89 (1.21-2.95) 3.18 (1.98-5.09) ≥65 1.00 1.18 (0.66-2.11) 3.10 (1.67-5.75) Current drinking 0.35 Yes 1.00 1.58 (0.87-2.89) 3.37 (1.78-6.37) No 1.00 1.55 (1.02-2.36) 2.85 (1.83-4.45) Current smoking 0.08 Yes 1.00 2.00 (1.06-3.77) 4.94 (2.44-10.01) No 1.00 1.38 (0.91-2.08) 2.44 (1.60-3.74) History of hypertension Yes 1.00 1.32 (0.79-2.19) 2.08 (1.23-3.52) 0.02 No 1.00 1.62 (1.01-2.61) 3.61 (2.16-6.04) History of dyslipidemia 0.35 Yes 1.00 1.46 (0.52-4.12) 1.83 (0.64-5.22) No 1.00 1.52 (1.05-2.20) 3.17 (2.15-4.68) High-sensitivity C-reactive protein 0.73 <3.0 mg/L 1.00 1.53 (1.05-2.23) 2.72 (1.81-4.07) ≥3.0 mg/L 1.00 1.52 (0.63-3.67) 3.66 (1.49-9.03) Estimated glomerular filtration rate 0.86 <90 ml/min/1.73 m 2 1.00 1.78 (0.75-4.19) 2.88 (1.19-7.01) ≥90 ml/min/1.73 m 2 1.00 1.50 (1.03-2.19) 2.92 (1.95-4.37) NA: the sample is not enough. In the multivariate models, confounding factors such age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate , high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score were included unless the variable was used as a subgroup variable. Table 8. Subgroup analysis of ORs (95 % CIs) of CMM according to updated mean METS-IR. Updated mean METS-IR T1 <31.18 T2 31.18-37.31 T3 ≥37.31 p interaction Sex 0.22 Male 1.00 1.74 (0.79-3.84) 5.37 (2.37-12.14) Female 1.00 1.44 (0.72-2.91) 2.20 (1.05-4.62) Age 0.21 <65 1.00 1.88 (0.96-3.71) 3.92 (1.96-7.81) ≥65 1.00 1.63 (0.68-3.94) 2.67 (0.96-7.43) Current drinking 0.75 Yes 1.00 1.55 (0.67-3.58) 3.25 (1.33-7.95) No 1.00 1.60 (0.82-3.12) 3.47 (1.74-6.94) Current smoking 0.25 Yes 1.00 1.38 (0.51-.73) 6.78 (2.42-18.96) No 1.00 1.63 (0.87-3.06) 2.54 (1.32-4.91) History of hypertension 0.13 Yes 1.00 1.26 (0.56-2.84) 2.28 (1.02-5.10) No 1.00 1.73 (0.86-3.48) 3.90 (1.81-8.44) History of dyslipidemia 0.99 Yes 1.00 1.13 (0.19-6.73) 2.32 (0.42-12.71) No 1.00 1.67 (0.96-2.90) 3.56 (1.99-6.35) High-sensitivity C-reactive protein 0.32 <3.0 mg/L 1.00 1.79 (1.00-3.22) 3.59 (1.93-6.68) ≥3.0 mg/L 1.00 0.89 (0.28-2.85) 1.84 (0.55-6.13) Estimated glomerular filtration rate 0.59 <90 ml/min/1.73 m 2 1.00 1.42 (0.37-5.54) 1.71 (0.37-7.85) ≥90 ml/min/1.73 m 2 1.00 1.64 (0.93-2.89) 3.65 (2.03-6.56) NA: the sample is not enough. In the multivariate models, confounding factors such age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate, high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score were included unless the variable was used as a subgroup variable. Table 9. Subgroup analysis of ORs (95 % CIs) of CMM according to high MET-IR exposure duration. High MET-IR exposure duration 0 year 2 years 4 years p interaction Sex 0.09 Male 1.00 2.86 (1.30-6.26) 6.21 (3.04-12.70) Female 1.00 2.17 (1.16-4.06) 2.19 (1.17-4.09) Age 0.30 <65 1.00 3.34 (1.83-6.08) 4.08 (2.29-7.26) ≥65 1.00 1.82 (0.71-4.68) 3.00 (1.17-7.74) Current drinking 0.96 Yes 1.00 2.23 (0.95-5.25) 3.85 (1.72-8.60) No 1.00 2.65 (1.46-4.80) 3.53 (1.98-6.31) Current smoking 0.13 Yes 1.00 2.74 (1.01-7.47) 8.00 (3.25-19.69) No 1.00 2.38 (1.35-4.17) 2.60 (1.49-4.54) History of hypertension 0.08 Yes 1.00 2.12 (1.02-4.39) 2.50 (1.24-5.03) No 1.00 2.40 (1.23-4.69) 4.18 (2.19-7.98) History of dyslipidemia 0.92 Yes 1.00 3.03 (0.64-14.29) 3.12 (0.75-13.02) No 1.00 2.39 (1.42-4.03) 3.61 (2.19-5.96) High-sensitivity C-reactive protein 0.37 <3.0 mg/L 1.00 2.54 (1.48-4.35) 3.50 (2.06-5.95) ≥3.0 mg/L 1.00 2.22 (0.72-6.86) 2.58 (0.91-7.34) Estimated glomerular filtration rate 0.36 <90 ml/min/1.73 m 2 1.00 1.93 (0.52-7.10) 1.53 (0.38-6.07) ≥90 ml/min/1.73 m 2 1.00 2.63 (1.55-4.45) 3.96 (2.39-6.56) In the multivariate models, confounding factors such age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate, high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score were included unless the variable was used as a subgroup variable. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers invited by journal 17 Oct, 2024 Editor assigned by journal 14 Oct, 2024 Editor invited by journal 18 Sep, 2024 Submission checks completed at journal 17 Sep, 2024 First submitted to journal 17 Sep, 2024 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. 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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-5068082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478260548,"identity":"6d150539-df03-49fc-9861-b982ae17bd94","order_by":0,"name":"Man Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFAD9sbGhx9I08JzuNlYgjQtEultAjzEKDQ4fvbwqxs1d6INbj5sY5BgsJPTbSCk5UxemnXOsWe5G24ntj0oYEg2NjtASMuBHDPjHLbDIC3tBhIMBxK3EdRy/g1Qyz+glpsH2yR4iNJyI8f4cW4bUMsNRiK1SN54Y8ac23c4d+aZRGAgGxDhF77zOcafc74dzu07fvzhww8VdnIEtSgcYGCTgDJA7iSgHATkGxiYP0AZo2AUjIJRMAqwAwA3pk4WPXAUrQAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Man","middleName":"","lastName":"Yang","suffix":""},{"id":478260549,"identity":"8a44e72b-e5f0-4d81-9a19-9da21f258367","order_by":1,"name":"Jia Liu","email":"","orcid":"","institution":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":478260550,"identity":"dab34c98-2451-4aa7-98fb-4a32d31bc15a","order_by":2,"name":"Suwen Shen","email":"","orcid":"","institution":"Department of Medical Administration, Suzhou Industrial Park Medical and Health Management Center","correspondingAuthor":false,"prefix":"","firstName":"Suwen","middleName":"","lastName":"Shen","suffix":""},{"id":478260551,"identity":"b216133c-3226-45a4-b91a-43a015226a32","order_by":3,"name":"Qian Shen","email":"","orcid":"","institution":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Shen","suffix":""},{"id":478260552,"identity":"c1fc4ef7-ccb3-4920-bc97-50285b09ea02","order_by":4,"name":"Yaqi Liu","email":"","orcid":"","institution":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yaqi","middleName":"","lastName":"Liu","suffix":""},{"id":478260553,"identity":"c1a6a02b-3471-448e-8058-622647807abd","order_by":5,"name":"Yun Qian","email":"","orcid":"","institution":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2024-09-11 04:07:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5068082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5068082/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-24480-8","type":"published","date":"2025-10-28T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87917588,"identity":"3d6225ac-27f0-4fd8-9645-011dbe3cc578","added_by":"auto","created_at":"2025-07-30 11:18:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99500,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants’ selection.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5068082/v1/47a9f1798eaa38787e95a73b.jpg"},{"id":87917587,"identity":"f85c62fc-f41e-49f0-85b3-57b25ce88ebe","added_by":"auto","created_at":"2025-07-30 11:18:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74279,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted odds radios of CMM according to METS-IR in respondents. (A) baseline METS-IR; (B) updated mean METS-IR. Odds ratios and 95% confidence intervals derived from restricted cubic spline regression, with knots placed at the 10th, 50th and 90th percentiles of METS-IR. The reference point for METS-IR is the median of the first tertile (baseline METS-IR: 27.92; updated mean METS-IR: 28.25). Odds radios were adjusted for the same variables as model 4 in Table 4 unless the variable was excluded.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5068082/v1/bdfab4aff4ed7736cb5af472.jpg"},{"id":95041236,"identity":"c89a53f1-d4b3-4e5d-9675-0832c8504035","added_by":"auto","created_at":"2025-11-03 16:11:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1891254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5068082/v1/c7fd2ac7-0f95-4c13-a947-9efb53d3d733.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between baseline and long-term status of metabolic score for insulin resistance index and the incidence of cardiometabolic multimorbidity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultimorbidity, the most common chronic condition experienced by adults, which refers to the co-occurrence of multiple chronic disease or conditions\u003csup\u003e1\u003c/sup\u003e. It is becoming a global health challenge which have led to the decline in quality of life and greater use of health-care resources\u003csup\u003e2\u003c/sup\u003e. As one such representative multimorbidity, cardiometabolic multimorbidity (CMM) is defined as the co-existence of two or three cardiometabolic diseases (CMDs), which including diabetes mellitus (DM), stroke and heart disease\u003csup\u003e3\u003c/sup\u003e. Previous studies from around the world has consistently shown that CMM is linked to a shortened lifespan and an increased risk of all-cause mortality\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. Therefore, the efforts to prevent CMM by reducing risk factors are of substantial relevance to both public health initiatives and medical practice.\u003c/p\u003e\n\u003cp\u003eInsulin resistance (IR) signifies a decline or dysfunction in insulin sensitivity within peripheral tissues, as evidenced by compromised glucose uptake and oxidative metabolism. In addition, as a metabolic risk factor, IR itself is a prominent characteristic of cardiovascular and metabolic diseases, including hyperglycemia, atherosclerosis, diabetes mellitus and sroke\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. While the euglycaemic-hyperinsulinaemic clamp (EHC) is broadly acknowledged as the gold-standard method and benchmark for evaluating IR, its adoption in routine clinical practice may be difficult due to its inherent complexities, time-intensive nature, and substantial resource requirements\u003csup\u003e9\u003c/sup\u003e. Thus, it is particularly important to recognize rapidly available and reliable IR markers. Recently, a number of methodologies incorporating simple routine biochemical markers have been introduced for the assessment of IR, including indices such as the Triglyceride Glucose (TyG) index and the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL)\u003csup\u003e10,11\u003c/sup\u003e. Nevertheless, these indices overlook the influence of cholesterol levels and nutritional status on the pathogenesis of the disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, the metabolic score for insulin resistance (METS-IR) index has been developed, which combined fasting plasma glucose (FPG), fasting triglycerides (TG), fasting high‐density lipoprotein cholesterol (HDL-C) and body mass index (BMI) mirroring nutritional status. This index has been shown to exhibit a high degree of agreement with the EHC\u003csup\u003e12\u003c/sup\u003e. Moreover, accumulative researches suggested that METS-IR was related to cardiometabolic disorders\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e. However, these preceding investigations have exclusively examined the association between METS-IR and specific diseases, furthermore, the assessment of METS-IR was conducted at a single time point, limiting the scope of the findings. Therefore, we aimed to explore the association of baseline and long-term MET-IR with CMM using data from the China Health and Retirement Longitudinal Study (CHARLS).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research involved a sample of middle-aged and elderly individuals from the CHARLS\u0026mdash;a persistent, nationally representative, prospective, and longitudinal investigation based on the population of China. Details of the study design have been published elsewhere\u003csup\u003e16\u003c/sup\u003e. A total of 17,708 participants from 10,257 households across 28 provinces in China were enrolled at the baseline phase (2011-2012, wave 1), utilizing a multistage stratified probability proportional-to-size sampling approach. Participants in the CHARLS were monitored biennially through in-person interviews facilitated by computer-assisted personal interviewing technology. Four consecutive follow-ups were executed from 2013-2014 (wave 2), 2015-2016 (wave 3), and 2017-2018 (wave 4) for the enduring participants. Ethical approval for all the CHARLS waves was granted from the Institutional Review Board (IRB) at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015; the IRB approval number for biomarker collection, was IRB00001052-11014. The details of the CHARLS data are available on the website (http://charls.pku.edu.cn/en).\u003c/p\u003e\n\u003cp\u003eIn our study, participants were excluded if they had missing survey information on the MET-IR at baseline (i.e., 2011-2012) or if they had known physician-diagnosed CMM (e.g., diabetes mellitus, stroke or cardiac events) during 2011-2015. We first included 9962 participants with fasting METS-IR information at baseline and then excluded 322 participants \u0026lt;45 years, 567 participants with previous CMM or without CMM information and 1023 participants lost to follow up. A total of 8050 respondents were eligible for analysis of the baseline METS-IR. In subsequent analysis of updated mean METS-IR and high METS-IR exposure duration, we further excluded 2945 participants without fasting METS-IR information and 612 participants who occurred CMM or didn\u0026rsquo;t offer CMM information during 2013-2016. Finally, a total of 4493 participants were eligible for the analysis of updated mean METS-IR and high METS-IR exposure duration (Fig. 1). The CHARLS was granted ethical clearance by the Institutional Review Board of Peking University. All participants provided written informed consent prior to their inclusion in the study. The research adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for transparent and comprehensive reporting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline data encompassing demographic characteristics, lifestyle risk factors, medical history, and medication utilization were gathered from the participants. History of chronic disease was determined based on participants\u0026apos; self-disclosures of diagnoses confirmed by healthcare professionals. Three blood pressure (BP) was measured with an electronic sphygmomanometer (Omron HEM-7200 Monitor) after 5 min of rest in the sitting position. Mean of the three BP measurements was used in the analyses. High inflammatory status was defined as high-sensitivity C-reactive protein (hsCRP) \u0026ge; 3 mg/L according to previous studies\u003csup\u003e17\u003c/sup\u003e. Assessment of renal function was based on estimated glomerular filtration rate (eGFR) calculated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation with adjusted coefficient of 1.1 for the Chinese population\u003csup\u003e18\u003c/sup\u003e. In accordance with the Kidney Disease: Improving Global Outcomes guidelines\u003csup\u003e19\u003c/sup\u003e, we defined normal renal function as eGFR \u0026ge; 90\u0026nbsp;ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e and abnormal renal function as eGFR \u0026ge; 90\u0026nbsp;ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. Grip strength from the dominant hand was determined using a handgrip dynamometer (YuejianTM WL-1000 dynamometer) twice, and the average was used. Participants were instructed to perform five consecutive sit-to-stand repetitions on a chair at their quickest pace, with the chair-rising time being meticulously recorded using a stopwatch for each repetition\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e. The summary score for the balance test was categorized as follows: a score of 1 was assigned if the participant failed to complete either the semi-tandem or side-by-side tests to the maximum duration; a score of 2 was given for those who did not complete the semi-tandem test to the maximum time but managed to complete the side-by-side test to the maximum duration; a score of 3 was designated for participants who completed the semi-tandem test to the maximum duration but were unable to finish the full-tandem test within the expected time; and a score of 4 was allocated to those who completed both the semi-tandem and full-tandem tests to the maximum duration\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBlood samples were meticulously collected from each participant by proficient medical personnel from the Chinese Center for Disease Control and Prevention (China CDC), adhering to a standardized protocol. Participants were instructed to undergo an overnight fast prior to blood collection; however, blood samples were also obtained from those who had not fasted, with their fasting status meticulously recorded as a variable within the dataset. Over 92% of respondents who gave blood reported that they were fasting. Complete blood count (CBC) test was measured on automated analyzers available at county CDC stations or town/village health centers. All the plasma samples including triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), fasting glucose (FBG) were measured at the Youanmen Center for Clinical Laboratory of Capital Medical University. Body mass index (BMI) was caculated as weight divided by height squared (kg/m\u003csup\u003e2\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMETS-IR was calculated as (ln(2*FBG +TG)*BMI))/(ln(HDL-C)\u003csup\u003e12\u003c/sup\u003e. The MET-IR measurements obtained during two visits (wave 1 and wave 3) from CHARLS were utilized to evaluate the long-term status of the METS-IR. Long-term status of METS-IR were defined as updated mean METS-IR and high METS-IR exposure duration. In the present analysis, updated mean METS-IR was defined as the mean of the METS-IR measurements from 2 visits. High METS-IR exposure duration was defined as the times of visits with a high METS-IR (over the cutoff mentioned in the Statistical analysis) among the 2 visits, quantified as 0 year, 2 years and 4 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of CMM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of chronic conditions diagnosed by physicians was determined through self-reported data collected at the baseline assessment and subsequent follow-up surveys (for diabetes or high blood sugar, \u0026ldquo;Have you been diagnosed with diabetes or high blood sugar by a doctor?\u0026rdquo;, for heart attack, coronary heart disease, or other heart problems,\u0026nbsp;\u0026ldquo;Have you been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems by a doctor?\u0026rdquo; and for stroke, \u0026ldquo;Have you been diagnosed with stroke by a doctor?\u0026rdquo;). Participants who responded affirmatively to the pertinent query were deemed to have a cardiometabolic condition. Of those, CMM was defined as participants who reported suffering from at least two cardiometabolic conditions\u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in the study were categorized into tertiles based on their baseline METS-IR values, the updated mean METS-IR values, as well as across three distinct exposure durations characterized by high METS-IR, respectively. Baseline demographic and clinical attributes were examined across the various participant groups. The optimal cutoff for METS-IR associated with CMM incidence was determined using the receiver operating characteristic curves of METS-IR at wave 1 (\u0026ge;34.81) and wave 3 (\u0026ge;36.87), respectively.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression models were used to estimate the risk of CMM associated with baseline METS-IR, updated mean METS-IR and high METS-IR exposure duration, respectively. Tests for linear trend in risk across baseline METS-IR, updated mean METS-IR and high METS-IR exposure duration were performed using these category as continuous variables.\u0026nbsp;The odds ratios (ORs) and 95% confidence intervals (CIs) were computed for each 1-standard deviation (SD) increase in both baseline and updated mean METS-IR. All analyses were conducted with adjustments for the following covariates: age, sex, rural residency, current smoking status, and current alcohol consumption in Model 1; adding medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease, stomach/digestive disease, arthritis, asthma, psychological problem, memory problem) and medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor) in model 2; adding waist circumference (WC), systolic BP, low-density lipoproteins cholesterol (LDL-C), eGFR, uric acid (UC) and hsCRP in model 3; adding dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score in model 4. The impact of both baseline and updated mean METS-IR on the risk for cardiometabolic multimorbidity (CMM) was investigated utilizing ordinal logistic regression analyses. Nonparametric restricted cubic splines were used to examine the shape of the association of baseline METS-IR and updated mean METS-IR with CMM with 3 knots (at the 10th, 50th and 90th percentiles of baseline METS-IR and updated mean METS-IR, respectively). We assessed the capacity of baseline METS-IR, updated mean METS-IR, and the duration of high METS-IR exposure to enhance risk stratification when added to a basic model that includes well-established risk factors. The Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI) were computed to quantify this effect\u003csup\u003e23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test the robustness of our findings, we performed several sensitivity analyses by excluding (1) participants with death, (2) participants with abnormal BMI (\u0026ge; 28 kg/m2), FBG (\u0026ge; 126 mg/dL), TG (\u0026ge;150mg/dL) and HDL-C (\u0026lt; 35 mg/dL). Furthermore, we conducted subgroup analyses, stratified by sex, age (\u0026lt;65 and \u0026ge;65 years), current smoking, current drinking, history of hypertension and dyslipidemia, baseline hsCRP (\u0026lt;3.0 mg/L and \u0026ge;3.0 mg/L) and baseline eGFR (\u0026lt;90\u0026nbsp;ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e and \u0026ge; 90\u0026nbsp;ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) in multivariable adjusted logistic regression models. This study did not employ sample weights, as prior research has demonstrated comparable outcomes whether weights were applied or not\u003csup\u003e24,25\u003c/sup\u003e. Two-sided \u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue \u0026lt;0.05 was considered to be significant. Data analysis was performed using SAS statistical software (version 9.4).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 8050 participants (4330 men and 3720 women; mean age 58.78±9.08 years) were enrolled in the current study. Participants with high baseline METS-IR, in contrast to those with low METS-IR, were more frequently younger, female, and residing in urban areas. They also exhibited lower rates of smoking and alcohol consumption, as well as a reduced use of anti-hypertensive and lipid-lowering medications. Moreover, this group had a higher incidence of hypertension and dyslipidemia, but a lower occurrence of lung disease, stomach or digestive disorders, and asthma. They also demonstrated higher values for BMI, waist circumference (WC), systolic blood pressure (SBP), blood glucose, low-density lipoprotein cholesterol (LDL-C), uric acid (UC), high-sensitivity C-reactive protein (hsCRP), and grip strength of the dominant hand (Table 1). Similar results were observed when participants were categorized by updated mean METS-IR and high METS-IR exposure duration (Table 2 and 3).)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of baseline METS-IR with\u003c/strong\u003e \u003cstrong\u003eincident CMM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver the course of a 6-year follow-up period, 540 participants (6.71%) developed CMM. Notably, the incidence of CMM was significantly greater in the subset of participants with higher baseline METS-IR values. After adjustment for potential confounding factors, with each SD (9.22) of METS-IR increasing, the risk of incident CMM elevated by 82% (OR: 1.82, 95%CI: 1.57-2.11). In the categorical analysis, the multivariable-adjusted ORs for CMM in the T2 and T3 groups, as compared to the T1 group, were 1.54 (95% CI: 1.10-2.17) and 2.94 (95% CI: 2.04-4.22), respectively, with a statistically significant trend (P\u003csub\u003etrend\u003c/sub\u003e \u0026lt; 0.0001) (Table 4). Analysis using multivariable-adjusted spline regression models revealed a linear relationship between baseline METS-IR and the incidence of CMM (\u003cem\u003eP\u003csub\u003elinearity\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e\u0026lt;0.0001) (Fig. 2A). Adding baseline METS-IR tertiles to a model containing conventional risk factors significantly improved risk reclassification for CMM (continuous NRI was 29.11% [p \u0026lt;0.0001] and IDI was 0.53% [p =0.002]) (Table 5). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of long-term status of METS-IR with incident CMM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, a significant association was found between the updated mean METS-IR and incident CMM when the aforementioned analysis was replicated. The multivariable-adjusted ORs and 95% CIs for the highest tertile of updated mean METS-IR relative to the lowest tertile were 3.17 (95% CI: 1.49-6.75) for the incidence of CMM. Each 1-SD (9.62) increment in updated mean METS-IR was associated with 99% (OR: 1.99, 95%CI: 1.53-2.59) increased risk of CMM (Table 4). Multivariable-adjusted spline regression models showed a linear association of updated mean METS-IR with CMM incidence (\u003cem\u003eP\u003csub\u003elinearity\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e\u0026lt;0.0001) (Fig. 2B). Incorporating updated mean METS-IR tertiles into a model that already includes conventional risk factors yielded a significant enhancement in the risk reclassification for CMM (continuous NRI was 40.18% [p \u0026lt;0.0001] and IDI was 0.62% [p =0.01]) (Table 5).\u003c/p\u003e\n\u003cp\u003eThe incidence rates of cardiometabolic multimorbidity (CMM) were elevated to 6.23% and 8.91% at 2-year and 4-year durations of high METS-IR exposure, respectively, compared to the unexposed group (0-year exposure) which had an incidence rate of 2.63%. After multivariable adjustment, compared with unexposed group (0 year), risk of CMM was significantly higher in those with 2 years group (OR: 2.45, 95%CI: 1.52-3.96) and 4 years group (OR: 3.46, 95%CI: 2.18-5.51), respectively (Table 4). Adding three high METS-IR exposure duration to a model containing conventional risk factors significantly improved risk reclassification for CMM (continuous NRI was 40.56% [\u003cem\u003ep\u003c/em\u003e \u0026lt;0.0001] and IDI was 0.76% [\u003cem\u003ep\u0026nbsp;\u003c/em\u003e=0.006]) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses yielded results aligned with the primary analysis. Even after excluding participants who had deceased by Wave 4 and limiting the sample to those with normal levels of BMI, FBG, TG, and HDL-C, the findings remained consistent with the initial analysis (Table 6).\u003c/p\u003e\n\u003cp\u003eIn order to delve deeper into the association between the baseline and long-term status of METS-IR and the incidence of CMM, a series of subgroup analyses were performed. None of the subgroups, including the sex, age, current drinking status, current smoking status, history of hypertension, history of dyslipidemia, high-sensitivity C-reactive protein and estimated glomerular filtration rate subgroups, profoundly changed the relationship between the baseline and long-term status of METS-IR and CMM incidence (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026gt;0.05) (Table 7-9).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur prospective study insights into the prognostic significance of baseline and prolonged METS-IR status in predicting cardiometabolic risk among middle-aged and elderly individuals of the Chinese population. The findings not only contribute to the extension of knowledge regarding the longevity of cardiometabolic risk but also enhance our understanding of the underlying pathophysiological mechanisms. In this prospective study among CHARLS participants, we documented that elevated baseline METS-IR, updated mean METS-IR and high METS-IR exposure duration during follow-up was independently associated with increased risk of CMM. The integration of baseline or long-term METS-IR measurements into the conventional risk factors model significantly enhanced the risk reclassification for CMM, as indicated by the NRI and IDI. Moreover, the observed association remained robust following both sensitivity analyses and examination within various subgroups. To the best of our knowledge, this is the first study to prospectively evaluate the predictive significance of both baseline and long-term status of METS-IR on the incidence of cardiometabolic diseases among middle-aged and older adults in China, utilizing data from a nationally representative panel.\u003c/p\u003e\n\u003cp\u003eMETS-IR, a new non-insulin-based index, is calculated on clinical markers including FBG, TG, HDL-C and BMI. METS-IR is a practical alternative to insulin-related indices, which is primarily due to the fact that serum insulin levels are not regularly assessed in routine clinical settings. Previous studies were usually based on predicting role of METS-IR in the development of specific disease, including CVD and DM. In a study consisted of 6489 participants aged 35-70 years without a history of CVD during a median follow-up of 10.6 years, elevated METS-IR was found to be independently associated with incident CVD\u003csup\u003e14\u003c/sup\u003e. A study conducted in Mexico City showed that individuals who developed type 2 diabetes mellitus (T2DM) had elevated baseline METS-IR, and the likelihood of developing incident T2DM increased consistently with higher percentile METS-IR\u003csup\u003e12\u003c/sup\u003e. Aforementioned researches were based on a single METS-IR measurement, only a few studies are based on the change or long-term status of METS-IR. Tian et al. found that the potential for future CVD incidence was linked to the cumulative exposure to METS-IR, as well as the timing and progression of METS-IR\u003csup\u003e26\u003c/sup\u003e. Similarly, data from a rural Chinese area showed that an elevation in METS-IR and a decrease in METS-IR over a 6-year period were both independently associated with an increased risk of developing T2DM\u003csup\u003e27\u003c/sup\u003e. Nevertheless, the existing research has largely focused on evaluating the relationship between METS-IR and specific health outcomes within select a certain group of population, rather than examining the broader implications of METS-IR for cardiometabolic health among the general community-dwelling population.\u003c/p\u003e\n\u003cp\u003eIt is worth mentioning that the influence of baseline and long-term status of METS-IR on CMM has not been ascertained. Taking into account the variability of METS-IR over time, which might engender regression dilution bias and and thereby impact the accuracy of the findings,\u0026nbsp;employing serial assessments of long-term METS-IR is likely to yield results that are both more reliable and more robust, enhancing the overall validity of the finding. In the present study, the dose-response association still existed when using updated mean METS-IR and long-term status of METS-IR to evaluate. More importantly, the updated mean METS-IR and long-term status of METS-IR seemed to be more significantly associated with CMM than baseline METS-IR. Consequently, our findings underscore the importance of long-term surveillance of METS-IR within clinical settings, suggesting that such monitoring could be instrumental in extending the period of METS-IR remission among middle-aged and elderly individuals of the Chinese population.\u003c/p\u003e\n\u003cp\u003eAlthough the precise mechanisms underlying the association between METS-IR and CMM are not yet fully elucidated, several speculative explanations have been suggested. One explanation suggests that considering the involvement of BMI, METS-IR might be a superior indicator for assessing IR in adipose tissue, muscle, and the liver\u003csup\u003e28\u003c/sup\u003e. Abundant adipose tissue not only elevates metabolic risk but is also linked to elevated blood glucose concentrations and reduced levels HDL-C\u003csup\u003e29\u003c/sup\u003e. Hypertriglyceridemia exacerbates this by raising free fatty acid (FFA) levels, which can disrupt insulin signaling and prompt oxidative stress in the tissues, leading to IR in the bone and live\u003csup\u003e30\u003c/sup\u003e. Inflammations caused by high cumulative IR could affect blood glucose and enhance the formation of atherosclerosis-associated foam cells and vulnerable plaques\u003csup\u003e31–33\u003c/sup\u003e. Another explanation suggests that the increased platelet aggregation, adhesion, and activation, as indicated by the higher IR, contributed to the occlusion of blood vessels, leading to disturbances in hemodynamic\u003csup\u003e34,35\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur research boasts several notable strengths, including the extensive scale and national representativeness of the prospective study conducted across China, coupled with an impressive participant response rate and rigorous adjustment for potential confounders within our multivariable models. As a result, the present investigation affords high-caliber evidence regarding the association between METS-IR and cardiometabolic risk. Several limitations also need to be mentioned. First, The CHARLS study was conducted exclusively on a Chinese population, and thus, the findings drawn from our research may not be directly applicable or generalizable to diverse populations. Second, CMM was derived from participants' self-reported physician diagnoses, which may cause information bias, although this method has been widely adopted in epidemiologic study\u003csup\u003e30\u003c/sup\u003e. Third, the METS-IR was not measured in wave 2 or wave 4, precluding the analysis of its trajectory over time. Moreover, it is important to note that the current analysis was not preplanned as part of the original study protocol. This observational analysis could be influenced by potential biases and confounding factors which we did not account for in our adjustments. Consequently,\u0026nbsp;the outcomes of our investigation primarily serve to postulate hypotheses that will need to be explored and validated in subsequent research initiatives.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study proved elevated baseline METS-IR, especially high METS-IR exposure duration was associated with CMM incidence among middle-aged and older Chinese. Our findings indicate that this simple index may be useful for identifying individuals at high risk CMM in advance, and emphasize the importance in long-term monitoring of METS-IR in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by the Medical Key Discipline Program of Wuxi Health Commission (LCZX2021006); Top Talent Support Program of Wuxi Taihu Talents Plan; Top Talent Support programme for Young and Middle-aged Scientists (HB2023095); General Programme of Wuxi Medical Center, Nanjing Medical University (WMCG202303); the Youth Foundation of Wuxi Health and Family Planning Commission (Q202267, Q202365)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS survey was conducted in line with the Declaration of Helsinki. The ethics application for collecting data on human subjects in CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052\u0026ndash;11015), and all CHARLS participants provided written informed consent. This study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Man Yang and Yun Qian; Data curation, Qian Shen and Yaqi Liu; Formal analysis, Man Yang, Suwen Shen and Yaqi Liu; Funding acquisition, Jia Liu and Yun Qian; Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft, Man Yang and Jia Liu; Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing, Yun Qian.\u003c/p\u003e\n\u003cp\u003eAll authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Clinical Trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version D as of June 2021 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01AG030153, RC2AG036619, R03 AG043052). For more information, please refer to www.g2aging.org.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version D as of June 2021 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01AG030153, RC2AG036619, R03 AG043052). For more information, please refer to www.g2aging.org.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTinetti, M. E., Fried, T. R. \u0026amp; Boyd, C. M. 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Frailty Index and Cardiovascular Disease among Middle-Aged and Older Chinese Adults: A Nationally Representative Cross-Sectional and Follow-Up Study. \u003cem\u003eJ Cardiovasc Dev Dis\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 228 (2022).\u003c/li\u003e\n\u003cli\u003eYao, S.-S. \u003cem\u003eet al.\u003c/em\u003e Prevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study. \u003cem\u003eJ Gerontol A Biol Sci Med Sci\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 1974\u0026ndash;1980 (2020).\u003c/li\u003e\n\u003cli\u003eTian, X. \u003cem\u003eet al.\u003c/em\u003e Magnitude and time course of insulin resistance accumulation with the risk of cardiovascular disease: an 11-years cohort study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 339 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, M. \u003cem\u003eet al.\u003c/em\u003e Association of metabolic score for insulin resistance and its 6-year change with incident type 2 diabetes mellitus. \u003cem\u003eJ Diabetes\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 725\u0026ndash;734 (2021).\u003c/li\u003e\n\u003cli\u003eLow, S. \u003cem\u003eet al.\u003c/em\u003e The role of triglyceride glucose index in development of Type 2 diabetes mellitus. \u003cem\u003eDiabetes Res Clin Pract\u003c/em\u003e \u003cstrong\u003e143\u003c/strong\u003e, 43\u0026ndash;49 (2018).\u003c/li\u003e\n\u003cli\u003eAbraham, T. 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Associations between insulin resistance and thrombotic risk factors in high-risk South Asian subjects. \u003cem\u003eDiabet Med\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 651\u0026ndash;655 (2003).\u003c/li\u003e\n\u003cli\u003eRusinek, H. \u003cem\u003eet al.\u003c/em\u003e Cerebral perfusion in insulin resistance and type 2 diabetes. \u003cem\u003eJ Cereb Blood Flow Metab\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 95\u0026ndash;102 (2015).\u003c/li\u003e\n\u003c/ol\u003e "},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics of the study participants according to the tertiles of baseline METS-IR\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eBaseline METS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;30.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;30.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;30.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSubjects, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2656 (32.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2655 (32.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2739 (34.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.88 \u0026plusmn; 9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.22 \u0026plusmn; 8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.28 \u0026plusmn; 8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1387 (52.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1224 (46.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1109 (40.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1978 (74.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1783 (67.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1537 (56.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e996 (38.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e779 (29.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e616 (22.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1027 (38.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e911 (34.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e758 (27.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Hypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e418 (15.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e578 (21.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1030 (37.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102 (3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179 (6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e410 (15.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153 (5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e155 (5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134 (4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalignant tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338 (12.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e213 (8.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202 (7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Liver disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96 (3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Stomach/digestive disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e666 (25.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e610 (22.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e516 (18.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Arthritis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e906 (34.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e892 (33.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e985 (36.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsthma, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142 (5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97 (3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological problem, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Memory problem, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedicine history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Anti-hypertensive drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260 (9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e406 (15.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e806 (29.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Lipid-lowering drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 (1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78 (2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e227 (8.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Oncology drugs or treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Body mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.08 (18.87, 21.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.21 (22.07, 24.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.76 (25.08, 28.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Waist circumference, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.40 (72.20, 81.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.20 (80.00, 89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.45 (88.40, 99.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Systolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121.50 (110.00, 136.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124.50 (112.50, 139.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130.50 (118.00, 145.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBlood glucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.28 (91.62, 106.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.16 (94.32, 109.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106.02 (98.10, 117.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-density lipoproteins cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111.34 (91.24, 113.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117.53 (97.42, 139.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117.14 (94.72, 142.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEstimated glomerular filtration rate, ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.54 (96.98, 113.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107.99 (98.32, 115.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107.65 (96.65, 114.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUric acid, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.11 (3.46, 4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.21 (3.54, 5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.53 (3.78, 5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-sensitivity C-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74 (0.43, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92 (0.52, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35 (0.75, 2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDominant hand grip strength, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00 (24.00, 37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.00 (25.00, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.20 (25.00, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChair-rising time, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.18 (8.07, 12.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.05 (7.97, 12.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.03 (8.19, 12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung function peak flow, ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260.00 (180.00, 360.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300.00 (210.00, 380.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300.00 (210.00, 380.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBalance test summary score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables are expressed as mean \u0026plusmn; SD (normal distribution) or median (interquartile range) (not normal distribution) and compared using F tests (normal distribution) or Wilcoxon rank-sum tests (not normal distribution) as appropriate.\u003c/p\u003e\n\u003cp\u003eCategorical variables are expressed as number (percent) and compared using \u0026chi;\u003csup\u003e2\u003c/sup\u003e tests.\u003c/p\u003e\n\u003cp\u003eTable 2. Characteristics of the study participants according to the tertiles of updated mean METS-IR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eUpdated mean METS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;31.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.18-37.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;37.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSubjects, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1484 (33.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1481 (32.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1528 (34.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.85 \u0026plusmn; 8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.10 \u0026plusmn; 8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.95 \u0026plusmn; 8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e820 (55.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e652 (44.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e610 (39.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1131 (76.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e994 (67.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e893 (58.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e584 (39.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e414 (28.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e337 (22.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e891 (39.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e500 (33.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e427 (27.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Hypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e232 (15.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e310 (21.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e563 (36.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56 (3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112 (7.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e216 (14.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74 (5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74 (4.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalignant tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e196 (13.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124 (8.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114 (7.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Liver disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Stomach/digestive disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e363 (24.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e337 (22.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e276 (18.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Arthritis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e485 (32.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480 (32.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e528 (34.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsthma, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (3.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological problem, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Memory problem, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedicine history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Anti-hypertensive drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150 (10.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198 (13.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e426 (27.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Lipid-lowering drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52 (3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124 (8.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Oncology drugs or treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Body mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.04 (18.88, 21.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.15 (22.04, 24.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.71 (25.11, 28.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Waist circumference, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.40 (72.00, 81.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.00 (80.00, 88.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.60 (88.40, 99.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Systolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121.00 (109.50, 135.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123.50 (112.00, 138.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130.00 (117.50, 145.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBlood glucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.00 (91.80, 107.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.26 (94.14, 108.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104.40 (96.84, 113.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-density lipoproteins cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110.18 (89.69, 131.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.37 (96.65, 139.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117.53 (95.10, 140.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEstimated glomerular filtration rate, ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107.36 (98.48, 113.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.70 (99.40, 115.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108.29 (97.69, 115.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUric acid,\u0026nbsp;mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.08 (3.43, 4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.11 (3.51, 4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.43 (3.67, 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-sensitivity C-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72 (0.44, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.52, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27 (0.70, 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDominant hand grip strength, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00 (24.30, 37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.00 (25.00, 39.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.00 (25.50, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChair-rising time, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.12 (8.00, 12.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.12 (8.01, 12.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.12 (8.29, 12.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung function peak flow, ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260.00 (190.00, 360.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300.00 (210.00, 380.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300.00 (220.00, 380.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBalance test summary score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables are expressed as mean \u0026plusmn; SD (normal distribution) or median (interquartile range) (not normal distribution) and compared using F tests (normal distribution) or Wilcoxon rank-sum tests (not normal distribution) as appropriate.\u003c/p\u003e\n\u003cp\u003eCategorical variables are expressed as number (percent) and compared using \u0026chi;\u003csup\u003e2\u003c/sup\u003e tests.\u003c/p\u003e\n\u003cp\u003eTable 3. Characteristics of the study participants according to the\u0026nbsp;high MET-IR exposure duration.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eHigh MET-IR exposure duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSubjects, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2243 (49.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e802 (17.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1448 (32.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.94 \u0026plusmn; 8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.44 \u0026plusmn; 8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.67 \u0026plusmn; 7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1158 (51.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e349 (43.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e575 (39.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1653 (73.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e519 (64.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e846 (58.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e801 (36.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e207 (26.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e327 (22.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e844 (37.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e268 (33.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e406 (28.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Hypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e360 (16.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e215 (26.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e530 (36.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104 (4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76 (9.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e204 (14.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115 (5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67 (4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalignant tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e267 (11.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108 (7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Liver disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78 (3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Stomach/digestive disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e548 (24.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176 (21.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e252 (17.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Arthritis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e732 (32.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e264 (32.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e497 (34.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAsthma, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106 (4.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54 (3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological problem, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Memory problem, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedicine history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Anti-hypertensive drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e231 (10.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141 (17.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e402 (27.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Lipid-lowering drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45 (2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118 (8.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Oncology drugs or treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Body mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.97 (19.46, 88.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.88 (22.69, 25.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.76 (25.16, 28.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Waist circumference, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.80 (74.00, 83.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.55 (82.00, 91.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.80 (88.20, 99.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Systolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122.00 (110.00, 135.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.50 (114.00, 140.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130.00 (117.00, 144.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBlood glucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.18 (92.16, 107.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.06 (95.04, 110.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104.22 (96.57, 113.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-density lipoproteins cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.89 (92.78, 133.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.37 (93.56, 139.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117.14 (95.49, 141.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEstimated glomerular filtration rate, ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107.55 (98.67, 114.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.15 (97.38, 115.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108.71 (98.47, 115.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUric acid,\u0026nbsp;mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.08 (3.42, 4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.22 (3.62, 5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.40 (3.68, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-sensitivity C-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77 (0.45, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.56, 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26 (0.70, 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDominant hand grip strength, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00 (24.50, 38.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.50 (25.00, 39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.00 (25.50, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChair-rising time, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.07 (7.99, 12.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.24 (8.03, 12.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.11 (8.34, 12.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung function peak flow, ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e270.00 (200.00, 365.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e295.00 (200.00, 370.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300.00 (220.00, 390.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBalance test summary score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.00 (4.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables are expressed as mean \u0026plusmn; SD (normal distribution) or median (interquartile range) (not normal distribution) and compared using F tests (normal distribution) or Wilcoxon rank-sum tests (not normal distribution) as appropriate.\u003c/p\u003e\n\u003cp\u003eCategorical variables are expressed as number (percent) and compared using \u0026chi;\u003csup\u003e2\u003c/sup\u003e tests.\u003c/p\u003e\n\u003cp\u003eTable 4. Odds ratios and 95% confidence intervals of CMM according to METS-IR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAge, sex-adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline METS-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT1 (\u0026lt;30.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e75 (2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT2 (30.96-37.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e139 (5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.04 (1.53-2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.04 (1.52-2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.92 (1.42-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.71 (1.23-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.54 (1.10-2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT3 (\u0026ge;37.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e326 (11.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e5.10 (3.92-6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5.03 (3.85-6.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.68 (2.76-4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.19 (2.26-4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.94 (2.04-4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eEach SD (9.22) increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.77 (1.62-1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.75 (1.59-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.42 (1.28-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.82 (1.59-2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.82 (1.57-2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpdated mean METS-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT1 (\u0026lt;31.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e38 (2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT2 (31.18-37.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e62 (4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.76 (1.16-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.80 (1.19-2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.73 (1.12-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.52 (0.95-2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.56 (0.93-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT3 (37.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e138 (9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e4.12 (2.83-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.33 (2.95-6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.50 (2.32-5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.95 (1.79-4.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e3.26 (1.90-5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eEach SD (9.62) increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.38 (1.17-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.39 (1.17-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.19 (1.07-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.95 (1.52-2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.99 (1.53-2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh METS-IR exposure duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e0 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e59 (2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e50 (6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.54 (1.73-3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.67 (1.80-3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.46 (1.64-3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.39 (1.53-3.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.45 (1.52-3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e129 (8.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3.91 (2.83-5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.11 (2.95-5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.37 (2.36-4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.07 (1.99-4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e3.46 (2.18-5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOptimal cut points for METS-IR at wave 1 (\u0026ge;34.81) and wave 3 (\u0026ge;36.87) were obtained from the receiver operating characteristic curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1: adjusted for age, sex, rural region, current smoking, current drinking.\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for model 1 + medical history (hypertension, dyslipidemia, diabetes mellitus, chronic kidney disease, malignant tumor, lung disease, liver disease, stomach/digestive disease, arthritis, asthma, psychological problem, memory problem) + medicine history (taking any medicine or treatment for hypertensive, dyslipidemia, diabetes mellitus, cardio-cerebrovascular disease and malignant tumor).\u003c/p\u003e\n\u003cp\u003eModel 3: adjusted for model 2 + body mass index, waist circumference, systolic blood pressure, low-density lipoproteins cholesterol, blood glucose, estimated glomerular filtration rate, uric acid and high-sensitivity C-reactive protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 4: adjusted for model 3 + dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Reclassification statistics for CMM by METS-IR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;NRI (95% CI), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eIDI (95% CI), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eConventional model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eConventional model + baseline METS-IR tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e29.11 (19.08-39.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.53 (0.19-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eConventional model + updated mean METS-IR tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e40.18 (25.71-54.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.62 (0.14-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eConventional model + high MET-IR exposure duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e40.56 (25.95-55.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.76 (0.22-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI = confidence interval; NRI = net reclassification improvement; IDI = integrated discrimination index.\u003c/p\u003e\n\u003cp\u003eOptimal cut points for METS-IR at wave 1 (\u0026ge;34.81) and wave 3 (\u0026ge;36.87) \u0026nbsp;were obtained from the receiver operating characteristic curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConventional model included age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate, high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score unless the variable was excluded.\u003c/p\u003e\n\u003cp\u003eTable 6. Odds ratios and 95% confidence intervals of CMM\u0026nbsp;to METS-IR in sensitivity analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAge, sex-adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline METS-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT1 (\u0026lt;30.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e63 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT2 (30.96-37.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e95 (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.06 (1.48-2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.03 (1.45-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.78 (1.25-2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.89 (1.27-2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.76 (1.15-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT3 (\u0026ge;37.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e46 (8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3.45 (2.31-5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.37 (2.24-5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.32 (1.48-3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.59 (1.54-4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.53 (1.44-4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eEach SD (9.22) increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.61 (1.99-2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.58 (1.96-3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.95 (1.45-2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.16 (1.51-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.18 (1.49-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpdated mean METS-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT1 (\u0026lt;31.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e33 (2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT2 (31.18-37.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e43 (4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.67 (1.04-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.65 (1.02-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.51 (0.91-2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.77 (1.01-3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.87 (0.99-3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT3 (37.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e28 (6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.99 (1.76-5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.04 (1.77-5.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.14 (1.18-3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.74 (1.38-5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e3.17 (1.49-6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eEach SD (9.62) increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.48 (1.67-3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.52 (1.68-3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.83 (1.19-2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.13 (1.28-3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.44 (1.38-4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh MET-IR exposure duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e0 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e50 (2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e29 (6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.72 (1.89-4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.79 (1.72-4.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.46 (1.46-4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.78 (1.57-4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.80 (1.49-5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e25 (5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.58 (1.56-4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.67 (1.60-4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.98 (1.14-.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.46 (1.32-4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.93 (1.50-5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOptimal cut points for METS-IR at wave 1 (\u0026ge;34.81) and wave 3 (\u0026ge;36.87) were obtained from the receiver operating characteristic curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1: adjusted for age, sex, rural region, current smoking, current drinking.\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for model 1 + medical history (hypertension, dyslipidemia, diabetes mellitus, chronic kidney disease, malignant tumor, lung disease, liver disease, stomach/digestive disease, arthritis, asthma, psychological problem, memory problem) + medicine history (taking any medicine or treatment for hypertensive, dyslipidemia, diabetes mellitus, cardio-cerebrovascular disease and malignant tumor).\u003c/p\u003e\n\u003cp\u003eModel 3: adjusted for model 2 + body mass index, waist circumference, systolic blood pressure, low-density lipoproteins cholesterol, blood glucose, estimated glomerular filtration rate, uric acid and high-sensitivity C-reactive protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 4: adjusted for model 3 + dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score.\u003c/p\u003e\n\u003cp\u003eTable 7.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSubgroup analysis of ORs (95 % CIs) of CMM according to baseline METS-IR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMETS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003e\u0026lt;30.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003e30.96-37.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003cp\u003e\u0026ge;37.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csub\u003einteraction\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.54 (0.93-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.32 (1.92-5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.62 (1.01-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.81 (1.72-4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.89 (1.21-2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.18 (1.98-5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18 (0.66-2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.10 (1.67-5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.58 (0.87-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.37 (1.78-6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.55 (1.02-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.85 (1.83-4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00 (1.06-3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.94 (2.44-10.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38 (0.91-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.44 (1.60-3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.32 (0.79-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.08 (1.23-3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.62 (1.01-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.61 (2.16-6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of dyslipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.46 (0.52-4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.83 (0.64-5.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52 (1.05-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.17 (2.15-4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-sensitivity C-reactive protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;3.0\u0026nbsp;mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.53 (1.05-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.72 (1.81-4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3.0 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52 (0.63-3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.66 (1.49-9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated glomerular filtration rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.78 (0.75-4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.88 (1.19-7.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50 (1.03-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.92 (1.95-4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNA: the sample is not enough.\u003c/p\u003e\n\u003cp\u003eIn the multivariate models, confounding factors such age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate , high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score were included unless the variable was used as a subgroup variable.\u003c/p\u003e\n\u003cp\u003eTable 8.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSubgroup analysis of ORs (95 % CIs) of CMM according to updated mean METS-IR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUpdated mean METS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003e\u0026lt;31.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003e31.18-37.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003cp\u003e\u0026ge;37.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csub\u003einteraction\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.74 (0.79-3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.37 (2.37-12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.44 (0.72-2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20 (1.05-4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.88 (0.96-3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.92 (1.96-7.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.63 (0.68-3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.67 (0.96-7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.55 (0.67-3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.25 (1.33-7.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.60 (0.82-3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.47 (1.74-6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38 (0.51-.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.78 (2.42-18.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.63 (0.87-3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.54 (1.32-4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.26 (0.56-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.28 (1.02-5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.73 (0.86-3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.90 (1.81-8.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of dyslipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.13 (0.19-6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.32 (0.42-12.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67 (0.96-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.56 (1.99-6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-sensitivity C-reactive protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;3.0\u0026nbsp;mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.79 (1.00-3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.59 (1.93-6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3.0 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.28-2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.84 (0.55-6.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated glomerular filtration rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42 (0.37-5.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.71 (0.37-7.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.64 (0.93-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.65 (2.03-6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNA: the sample is not enough.\u003c/p\u003e\n\u003cp\u003eIn the multivariate models, confounding factors such age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate, high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score were included unless the variable was used as a subgroup variable.\u003c/p\u003e\n\u003cp\u003eTable 9.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSubgroup analysis of ORs (95 % CIs) of CMM according to high MET-IR exposure duration.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh MET-IR exposure duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csub\u003einteraction\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.86 (1.30-6.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.21 (3.04-12.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.17 (1.16-4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.19 (1.17-4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.34 (1.83-6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.08 (2.29-7.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.82 (0.71-4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00 (1.17-7.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.23 (0.95-5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.85 (1.72-8.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.65 (1.46-4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.53 (1.98-6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.74 (1.01-7.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.00 (3.25-19.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.38 (1.35-4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.60 (1.49-4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.12 (1.02-4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.50 (1.24-5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.40 (1.23-4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.18 (2.19-7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of dyslipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.03 (0.64-14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.12 (0.75-13.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.39 (1.42-4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.61 (2.19-5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-sensitivity C-reactive protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;3.0 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.54 (1.48-4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.50 (2.06-5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3.0 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.22 (0.72-6.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.58 (0.91-7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated glomerular filtration rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.93 (0.52-7.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.53 (0.38-6.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.63 (1.55-4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.96 (2.39-6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the multivariate models, confounding factors such age, sex, rural region, current smoking, current drinking, active physical activity, medical history (hypertension, dyslipidemia, chronic kidney disease, malignant tumor, lung disease, liver disease and stomach/digestive disease), medicine history (taking any medicine or treatment for hypertensive, dyslipidemia and malignant tumor), systolic blood pressure, low-density lipoproteins cholesterol, estimated glomerular filtration rate, high-sensitivity C-reactive protein, dominant hand grip strength, chair-rising time, lung function peak flow and balance test summary score were included unless the variable was used as a subgroup variable.\u003c/p\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic multimorbidity, METS-IR, insulin resistance, CHARLS, long-term status","lastPublishedDoi":"10.21203/rs.3.rs-5068082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5068082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCardiometabolic multimorbidity (CMM) is concurrently associated with a reduction in life expectancy and an increased propensity for all-cause mortality. Our objective was to evaluate the correlations between the baseline and longitudinal metabolic score for insulin resistance index (METS-IR) and the incidence of cardiometabolic multimorbidity (CMM) within a middle-aged and older Chinese population cohort..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results: \u003c/strong\u003eA total of 8,050 participants were enrolled and included in the analytical dataset for this study. Long-term status of METS-IR were defined as updated mean METS-IR and high METS-IR exposure duration. Updated mean METS-IR was defined as the mean of the two METS-IR measurements. High METS-IR exposure duration was defined as the times of visits with a high METS-IR among the 2 visits, quantified as 0 year, 2 years and 4 years according to the optimal cut points from the receiver operating characteristic curves of the two METS-IR measurements, respectively. The outcome was defined as the occurrence of CMM, characterized by the presence of two or more cardiometabolic disorders as self-reported by participants, encompassing conditions such as diabetes, stroke, and cardiac events. During 6-year visit, 540 participants experienced CMM. Substantially elevated incidences of CMM were observed in participants belonging to the highest tertiles of both baseline and updated mean METS-IR. After multivariable adjustment, the odds ratios (ORs) with 95% confidence intervals (CIs) for CMM were 2.94 (CI: 2.04-4.22) for those in the highest baseline METS-IR tertile and 3.26 (CI: 1.90-5.59) for those in the highest updated mean METS-IR tertile, relative to participants in the lowest tertiles. Multivariable-adjusted spline regression models showed a linear association of baseline METS-IR (\u003cem\u003eP\u003c/em\u003e\u003csub\u003elinearity\u003c/sub\u003e \u0026lt;0.0001) and updated mean METS-IR (\u003cem\u003eP\u003c/em\u003e\u003csub\u003elinearity\u003c/sub\u003e \u0026lt;0.0001) with CMM. Moreover, participants with 2 and 4 years high METS-IR exposure duration had increased risk of CMM (ORs [95% CIs]: 2.45 [1.52-3.96] and 3.46 [2.18-5.51], respectively), compared with the reference of those with unexposed group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study proved elevated baseline METS-IR, updated mean METS-IR, especially high METS-IR exposure duration was associated with CMM incidence among middle-aged and older Chinese.\u003c/p\u003e","manuscriptTitle":"The association between baseline and long-term status of metabolic score for insulin resistance index and the incidence of cardiometabolic multimorbidity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 11:18:03","doi":"10.21203/rs.3.rs-5068082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-30T07:52:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-29T23:09:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306600873400867369621970640820165312302","date":"2025-06-29T22:58:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316694415700529624737472446854475836676","date":"2025-06-27T13:03:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T18:11:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T03:50:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49881842529753086587897816921665563198","date":"2025-05-11T03:23:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219754954239649866686201970347322414239","date":"2025-05-05T14:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296949086778303850881059134293087745541","date":"2025-05-01T23:38:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-17T07:19:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-14T14:42:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-18T06:24:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-18T03:17:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-09-18T03:15:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5aaacdd3-e40c-4e77-810f-12f7d37c0627","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:09:02+00:00","versionOfRecord":{"articleIdentity":"rs-5068082","link":"https://doi.org/10.1186/s12889-025-24480-8","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-10-28 15:58:09","publishedOnDateReadable":"October 28th, 2025"},"versionCreatedAt":"2025-07-30 11:18:03","video":"","vorDoi":"10.1186/s12889-025-24480-8","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24480-8","workflowStages":[]},"version":"v1","identity":"rs-5068082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5068082","identity":"rs-5068082","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}