Healthy Lifestyle including Sleep Patterns, Genetic Risk, and Risk of Cardiometabolic Multimorbidity

Healthy Lifestyle including Sleep Patterns, Genetic Risk, and Risk 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 Healthy Lifestyle including Sleep Patterns, Genetic Risk, and Risk of Cardiometabolic Multimorbidity Siqi Liu, Ninghao Huang, Yueying Li, Tao Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5425238/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Emerging evidence suggests a link between sleep patterns, healthy lifestyles, and the risk of cardiometabolic outcomes. However, few studies have explored the association between lifestyles that incorporate healthy sleep patterns and genetic factors with the risk of cardiometabolic multimorbidity (CMM). Objective/Aim : To prospectively investigate the associations between a healthy lifestyle including sleep patterns, genetic risk, and the risk of cardiometabolic multimorbidity and cardiometabolic diseases. Methods: A total of 382,448 UK Biobank participants were included in this study. A modified healthy lifestyle score (mHLS) was defined by healthy sleep patterns, diet, physical activity, smoking, alcohol consumption, and Body Mass Index (BMI). Weighted genetic risk score (GRS) for cardiometabolic outcomes was calculated. Cox proportional hazards models were used to estimate the associations between a healthy lifestyle and cardiometabolic outcomes. Results: During a median follow-up of 8.7 years, 13,388 participants developed CMM, and 54,381 participants developed at least one of those cardiometabolic diseases (CMDs) that contribute to CMM. After adjusting for major confounders, the modified healthy lifestyle score was significantly associated with lower risks of CMM and CMDs, with hazard ratios (HRs) of 0.81 (95% confidence interval [CI]: 0.74-0.90) and 0.84 (95% CI: 0.79-0.88) for the highest versus lowest mHLS groups, respectively. The association between mHLS and CMDs was stronger in women and individuals aged 65 and above ( p = 0.006 and p = 0.04, respectively). Additionally, there was an additive interaction between mHLS and GRS on CMDs, with a relative excess risk due to interaction (RERI) of 0.057 (95% CI: 0.007, 0.106). Conclusion: Our results show that adherence to a healthy lifestyle, including healthy sleep patterns, is associated with lower risks of CMM and CMDs. Women and individuals over 65 may benefit more. A healthy lifestyle can also modify the genetic predisposition to CMDs. Cardiometabolic multimorbidity lifestyle sleep pattern genetic risk cohort study Figures Figure 1 Figure 2 Figure 3 Introduction Cardiometabolic diseases are among the leading causes of morbidity and mortality in adults, with increasing numbers and burdens 1 , 2 . The presence of cardiometabolic multimorbidity (CMM), where two or more diseases of diabetes, coronary heart disease (CHD), and stroke coexist 3 , exacerbates these challenges, contributing to significant declines in quality of life, reduced life expectancy, and increased healthcare resource utilization. 4 , 5 Lifestyle factors such as a healthy diet and appropriate physical exercise have been proven to be associated with a reduced risk of cardiometabolic outcomes, including CMM or the occurrence of at least one CMD (CMDs). 6 – 10 Study based on UK Biobank showed that adhering to a comprehensive healthy lifestyle, which includes a healthy diet, physical exercise, smoking cessation, moderate alcohol consumption, and maintaining a healthy BMI, significantly reduces the risk of type 2 diabetes. 11 Another cohort study based on the China Kadoorie Biobank also indicated that maintaining a comprehensive healthy lifestyle in terms of smoking, alcohol consumption, diet, physical exercise, and body weight is crucial for the management of CMM and CMDs. 3 Sleep is another important lifestyle factor affecting cardiometabolic diseases; sleep duration and quality are associated with the risk of diabetes and cardiovascular diseases. 12 Research by Li et al. found that sleep patterns, including chronotype, sleep duration, insomnia, snoring, and daytime sleepiness, are significantly related to cardiac issues. 13 However, few studies have explored the impact of a comprehensive healthy lifestyle that includes sleep patterns on cardiometabolic outcomes. Both genetic and lifestyle factors influence disease risk. 14 Zhao et al.'s study indicates a significant interaction between genetic susceptibility and environmental factors in the development of diabetes. 11 Another cohort study involving 140,000 participants showed that the risk of coronary heart disease due to high genetic susceptibility can be partially mitigated by a healthy lifestyle. 15 , 16 However, the relationship between genetic susceptibility and a comprehensive lifestyle (including sleep), concerning the risk of CMDs and CMM remains unclear. Therefore, in this study based on 500,000 adults from the UK Biobank, we aimed to investigate the association of comprehensive healthy lifestyle factors, including sleep patterns, with the incidence of CMM and CMDs. We also established genetic scores related to cardiometabolic outcomes and explored the potential interactions and joint associations between lifestyle factors and genetic susceptibility in the occurrence of cardiometabolic outcomes. Method Study population The UK Biobank study is a large prospective cohort study comprising 500,000 participants, whose design and characteristics have been previously described. 17 , 18 In brief, participants from England, Scotland, and Wales were recruited between 2006 and 2010, provided their health-related information, including environment, lifestyle, etc., via touchscreen questionnaire, and underwent physical examinations, biological samples, and genetic data collection. All participants provided written informed consent. The UK Biobank study was approved by the National Information Governance Board for Health and Social Care in England and Wales, the Community Health Index Advisory Group in Scotland, and the Northwest Multicenter Research Ethics Committee. The current study conducted under UK Biobank application number 44430, was approved by the Ethical Committee of Peking University (Beijing, China). Assessment of Healthy Lifestyle Based on the guidance of the American Heart Association and previous research, 11 , 19 the current study incorporates healthy sleep patterns into the framework of a healthy diet, physical activity, alcohol consumption, smoking, and a healthy BMI to construct the modified healthy lifestyle score. For each factor, the following conditions are considered healthy and receive a score of 1, otherwise 0: No current smoking (including previous and never); 150 minutes of moderate to vigorous activity/75 minutes of vigorous activity per week, or 5 days of moderate activity/1 day of vigorous activity per week; alcohol consumption ≤ 4g per day for women and ≤ 28g per day for men; adequate intake of fruits, vegetables, fish, meat, and processed meat; body mass index > 18.5 and ≤ 30 kg/m²; at least 3 out of 5 aspects of chronotype preference, sleep duration, insomnia, snoring, and daytime sleepiness are considered healthy. For detailed information, see the electronic supplementary material eTable 1. Assessment of outcomes The primary outcome of this study was CMM followed up to October 2022, defined as the presence of at least two CMDs: type 2 diabetes, stroke, and coronary heart disease. CMDs (the occurrence of at least one CMD) was considered another outcome. These CMDs were classified based on the 10th edition of the International Classification of Diseases. Detailed disease definitions are provided in the electronic supplementary material eTable 2. Hospital data were obtained through linkage with the Health Episode Statistics records for England and Wales, and the Scottish Morbidity Records for Scotland. Genotype data The UK Biobank team performed genotyping, calculation, and quality control processes, as previously described. 20 Using the method by Li et al., 13 weighted genetic risk values for specific CMD were calculated, including 74 independent single nucleotide polymorphisms (SNPs) related to CHD, 403 related to type 2 diabetes, and 90 related to stroke. Individual disease genetic risk values were assigned scores of 1, 2, or 3 based on tertiles and then summed to obtain the genetic risk score (GRS). Participants were classified into low, intermediate, or high genetic risk of cardiometabolic outcomes according to the tertiles of GRS. Assessment of other covariates Participants' age and sex information were collected at the baseline visit to the assessment center. The Townsend deprivation index was assigned based on participants' postcodes. Height and weight were measured to calculate BMI. Medication history for cholesterol, blood pressure, diabetes, or exogenous hormones (for women), and family history of cardiometabolic diseases (father, mother, and siblings) were recorded via a touchscreen questionnaire. Statistical analysis Due to the small number of participants with a modified healthy lifestyle score of 0 or 1, we combined them into one group. Differences between groups were compared using one-way ANOVA or chi-square tests as appropriate. Baseline characteristics of participants with different mHLS were presented as mean ± standard deviation or median (interquartile range) for continuous variables, and percentages for categorical variables. We used restricted cubic spline analysis to examine the association between continuous mHLS and outcomes. Cox proportional hazards models were constructed to analyze the association between mHLS and outcomes, with time-to-event as the timescale and follow-up time defined from the recruitment date to the date of disease diagnosis, death, or loss to follow-up, whichever came first. Schoenfeld residuals were used to test the proportional hazards assumption. Model 0 was unadjusted, Model 1 adjusted for age, sex, ethnicity, and assessment center, and Model 2 further adjusted for Townsend index, education, family history of cardiometabolic disease, and medication history. We analyzed the relationship between the five individual factors constituting the mHLS and outcomes. Stratified analyses for the main analyses were performed, and potential interactions between mHLS and age, sex, ethnicity, education, and family history of cardiometabolic disease were explored by adding multiplicative interaction terms. We also investigated the potential interaction and joint association between healthy lifestyle and genetic risk on cardiometabolic outcomes. Additive interaction was assessed using relative excess risk due to interaction (RERI) and attributable proportion (AP), with the following equations: RERI = HR ++ - HR +− - HR −+ +1 and AP = RERI/HR ++ . Multiplicative interaction was assessed by adding interaction terms in the model and using the likelihood ratio test. We also conducted several sensitivity analyses. First, since previous studies indicated obesity as a mediator between healthy lifestyle and cardiometabolic outcomes, 21 we further adjusted for BMI/waist-to-hip ratio (WHR). Second, we re-categorized the mHLS and examined its association with outcomes. Finally, we excluded participants who developed cardiometabolic diseases within the first two years of follow-up to avoid reverse causation when exploring the association between mHLS and cardiometabolic outcomes and reassessed the joint association between mHLS and genetic risk. Statistical analyses were performed with Stata/MP version 16.0 (StataCorp, USA). Statistical significance was set at p < 0.05. Results Baseline characteristics stratified by the modified Healthy Lifestyle Score During a median follow-up of 8.7 years (IQR 8.1–9.3 years; 3,304,914 total person-years), we documented 13,388 incident CMM, and 54,381 incident cardiometabolic diseases. The baseline characteristics of participants entering the initial analysis, categorized by the modified healthy lifestyle score, are shown in Table 1 . Participants with higher adherence to a healthy lifestyle tend to be older, have higher socioeconomic status (Townsend Index), higher education levels, lower BMI and waist-hip ratio, and a lower proportion of family history of CMM. Additionally, women and white individuals are more likely to maintain a healthy lifestyle. Table 1 Baseline characteristics of participants stratified by the modified Healthy Lifestyle Score Modified Healthy Lifestyle Score p- value 0–1 2 3 4 5 6 Participants (%) 7,785(2.04) 28,393(7.42) 68,689(17.96) 113,311(29.63) 114,291(29.88) 49,979(13.07) Age(years) 54.7 ± 7.8 55.6 ± 7.9 56.0 ± 8.0 56.3 ± 8.1 56.6 ± 8.1 57.2 ± 8.1 < 0.001 Male 4,125(53.0) 14,822(52.2) 34,036(49.6) 52,229(46.1) 48,785(42.7) 20,156(40.3) < 0.001 White race 6,952(89.3) 25,199(88.8) 61,158(89.0) 101,240(89.3) 102,108(89.3) 44,908(89.9) < 0.001 Townsend deprivation index -0.1 ± 3.5 -0.6 ± 3.3 -1.1 ± 3.2 -1.5 ± 3.0 -1.8 ± 2.8 -2.0 ± 2.7 < 0.001 Higher education‡ 1,639(21.1) 6,862(24.2) 19,292(28.1) 37,143(32.8) 43,041(37.7) 21,032(42.1) < 0.001 BMI 32.7 ± 5.9 31.0 ± 5.7 29.2 ± 5.3 27.3 ± 4.5 25.9 ± 3.5 25.0 ± 2.6 < 0.001 WHR 0.9 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 0.8 ± 0.1 < 0.001 With a family history of CMM 1,390(17.9) 4,739(16.7) 10,549(15.4) 16,504(14.6) 15,753(13.8) 6,776(13.6) < 0.001 Abbreviations: CMM, cardiometabolic multimorbidity; BMI, body mass index; WHR, waist-to-hip ratio; SD, standard deviation. Continuous variables were displayed as means ± SDs, and categorical variables were displayed as n(%). * p values were obtained from the analysis of variance for continuous variables and the X 2 test for categorical variables. ‡Higher education refers to a college/university degree or other professional qualifications. Modified Healthy Lifestyle Score and cardiometabolic outcomes The association between the modified healthy lifestyle score and the risk of CMM is shown in Table 2 . The modified healthy lifestyle score was inversely associated with the risk of both CMM and CMDs ( p < 0.001 in all models). In the fully adjusted model 2, each one-unit increase in the modified healthy lifestyle score was associated with HRs (95% CI) for CMM and CMDs of 0.96 (0.94–0.97) and 0.96 (0.96–0.97), respectively. Compared to participants with a mHLS of 0–1, those with a mHLS of 6 have fully adjusted HRs (95% CI) of 0.81 (0.74–0.90) for CMM and 0.84 (0.79–0.88) for CMDs. As shown in Fig. 1 , dose-response analysis indicates that the association between the modified healthy lifestyle score and both CMM and CMDs is more likely to be linear rather than nonlinear ( p for non-linearity = 0.795 and 0.255, respectively). Table 2 Associations between the modified Healthy Lifestyle Score and cardiometabolic outcomes Modified Healthy Lifestyle Score p -trend Per unit of index 0–1 2 3 4 5 6 Cardiometabolic multimorbidity Cases (person-years) 710 (5,456) 1,935 (15,096) 3,336 (26,737) 3,872 (31,905) 2,665 (22,414) 870 (7,436) Model 0* Reference 1.00 (0.92–1.09) 0.97 (0.89–1.05) 0.91 (0.84–0.99) 0.89 (0.82–0.96) 0.86 (0.78–0.95) < 0.001 0.96 (0.95–0.98) Model 1† Reference 0.96 (0.88–1.05) 0.91 (0.84–0.98) 0.86 (0.79–0.93) 0.81 (0.74–0.88) 0.77 (0.70–0.86) < 0.001 0.95 (0.93–0.96) Model 2‡ Reference 0.97 (0.89–1.05) 0.93 (0.85–1.01) 0.88 (0.81–0.95) 0.84 (0.77–0.91) 0.81 (0.74–0.90) < 0.001 0.96 (0.94–0.97) Cardiometabolic diseases Cases (person-years) 2,008 (13,703) 6,078 (42,737) 12,078 (85,797) 16,002 (116,969) 13,186 (98,695) 5,029 (38,078) Model 0* Reference 0.98 (0.93–1.03) 0.97 (0.93–1.02) 0.92 (0.88–0.96) 0.90 (0.85–0.94) 0.88 (0.84–0.93) < 0.001 0.97 (0.96–0.98) Model 1† Reference 0.94 (0.90–0.99) 0.92 (0.88–0.96) 0.85 (0.82–0.90) 0.82 (0.78–0.86) 0.80 (0.76–0.84) < 0.001 0.95 (0.95–0.96) Model 2‡ Reference 0.95 (0.91-1.00) 0.94 (0.89–0.98) 0.88 (0.84–0.92) 0.85 (0.81–0.89) 0.84 (0.79–0.88) < 0.001 0.96 (0.96–0.97) *Model 0: Cox proportional regression without adjustment. †Model 1: Cox proportional regression adjusted for age, sex, race, and recruitment assessment center. ‡Model 2: Cox proportional regression adjusted for Model 1, socioeconomic status, education, family history of cardiometabolic multimorbidity, and medication history. Cardiometabolic diseases refer to the presence of at least one of the following conditions: type 2 diabetes, coronary heart disease, or stroke. The associations between each binary component (healthy or not) of the modified healthy lifestyle score and the risks of CMM and CMDs are shown in eTable3. In the fully adjusted model 2, having a healthy sleep pattern, healthy alcohol consumption, healthy BMI, and healthy physical activity were independently associated with a 5%, 5%, 10%, and 7% reduction in the risk of CMM, respectively. For CMDs, having a healthy sleep pattern, healthy diet, no current smoking, healthy BMI, and healthy physical activity were independently associated with a 5%, 3%, 3%, 9%, and 6% reduction in risk, respectively. Sub-group analysis Subgroup analysis showed that the association between the modified healthy lifestyle score and the occurrence of CMDs was stronger in elderly individuals (≥ 65 years) and females (elderly individuals, 0.96; 95% CI, 0.95–0.97; younger individuals, 0.97; 95% CI, 0.96–0.98; p = 0.006; male individuals, 0.96; 95% CI, 0.95–0.97; female individuals, 0.95; 95% CI, 0.94–0.96; p = 0.04). Modified Healthy Lifestyle Score, Genetic Risk Score, and cardiometabolic outcomes In the fully adjusted model 2, participants with high genetic risk scores had an increased risk of CMDs (HR 1.03, 95% CI: 0.99–1.08) compared to participants with a low genetic risk score, while the risk of CMM did not show a statistically significant difference (eTable4). A healthy lifestyle was associated with a lower risk of cardiometabolic outcomes across all GRS groups, with a more pronounced risk reduction observed in the groups with high genetic risk. (Fig. 2 ). As shown in Table 3 , there was a slight but statistically significant additive interaction between the mHLS and GRS on CMDs, with the RERI for CMDs being 0.057 (95% CI: 0.007,0.106), p = 0.026. The joint association of the modified healthy lifestyle score and the GRS with the risk of cardiometabolic diseases shows that compared to participants with high genetic risk and poor lifestyle, those with low genetic risk and a healthy lifestyle had a 16% lower risk of cardiometabolic diseases (HR [95% CI]: 0.16 [0.10, 0.22]). There was also a multiplicative interaction between GRS and mHLS in CMDs ( p = 0.022). Table 3 Attributing effects to additive interaction between GRS and mHLS on the risk of cardiometabolic outcomes. Additive Interaction Cardiometabolic Multimorbidity Cardiometabolic diseases Estimate (95%CI) p value Estimate (95%CI) p value Relative excess risk due to interaction 0.077(-0.279,0.182) 0.150 0.057(0.007,0.106) 0.026 Attributable proportion for modified healthy lifestyle score 0.871(0.070,0.911) 0.101 0.584(0.180,0.988) 0.005 Attributable proportion for genetic risk score 0.012(-0.171,0.896) 0.478 0.127(-0.132,0.385) 0.391 Attributable proportion for addictive interaction 0.117(-0.238,0.872) 0.468 0.289(0.112,0.967) 0.124 Relative excess risk due to interaction and attributable proportions were estimated based on hazard ratio and proportion hazard ratio, respectively. All adjusted for age, sex, race, recruitment assessment center, socioeconomic status, education, family history of cardiometabolic multimorbidity and medication history. Cardiometabolic diseases refer to the presence of at least one of the following conditions: type 2 diabetes, coronary heart disease, or stroke. Sensitivity analysis In the sensitivity analysis, after further adjusting for BMI or WHR, the association between the modified healthy lifestyle score and CMM and CMDs remained robust (Supplemental eTable 6.1 and 6.2). When the modified healthy lifestyle score was grouped into tertiles, its association with cardiometabolic outcomes also remained stable (Supplemental eTable 7). Excluding cardiometabolic outcomes occurring within the first two years of follow-up, the association between the modified healthy lifestyle score and CMM and CMDs was consistent against reverse causation (Supplemental eTable 8). The joint and interaction analyses of the modified healthy lifestyle score and genetic risk were similar to previous results ( p for additive interaction with cardiometabolic diseases = 0.024). Discussion In the current large prospective cohort, we found that a healthy lifestyle, including sleep patterns, was significantly associated with a lower risk of developing cardiometabolic multimorbidity and at least one cardiometabolic disease. Healthy sleep patterns, BMI, and physical activity were independently associated with a lower risk of CMM and CMDs; healthy alcohol consumption was independently associated with a lower risk of CMM, and healthy diet and smoking status were independently associated with a lower risk of CMDs. The association between a healthy lifestyle and CMDs was stronger in women and older adults. A healthy lifestyle significantly reduces the risk of CMDs associated with genetic predisposition. Smoking, alcohol consumption, healthy diet, physical activity, and BMI have frequently been considered components of a healthy lifestyle. Consistent with the findings of the present study, adherence to these healthy factors, whether individually or collectively, was associated with a reduced risk of diabetes, 22 stroke, 23 and other cardiometabolic diseases. 3 , 24 Studies examining CMM as an outcome in relation to a healthy lifestyle also demonstrated similar results. The study, 25 which included smoking, alcohol consumption, healthy diet, and physical activity as part of a healthy lifestyle, investigated the risk of CMM and the onset of the first cardiometabolic disease in 8,270 middle-aged UK participants and found that a healthy lifestyle was associated with a reduced risk of these outcomes. A pooled analysis of multiple cohorts from Europe and the USA found that high BMI was associated with an increased risk of CMM, aligning with our findings on the relationship between lifestyle factors and CMM. 26 Additionally, a healthy sleep pattern, included in this study as an additional lifestyle factor, was associated with a reduced risk of cardiovascular issues. 13 , 27 , 28 This may be related to unhealthy sleep patterns causing overactivity of the sympathetic nervous system 29 and dysregulation in neuroendocrine and proinflammatory hormone release 30 . Studies incorporating healthy sleep into the definition of a healthy lifestyle, similar to the present study, found that this comprehensive healthy lifestyle was associated with a reduced risk of type 2 diabetes. 31 Previous cohort studies by Hu et al. involving nurses and health professionals showed that adherence to a healthy lifestyle, including sleep, is associated with a reduced risk of, stroke, and CHD, which are included in the current study, and cardiovascular disease (CVD). 32 CVD shares common risk factors, such as hyperglycemia and dyslipidemia, with cardiometabolic diseases and CMM. 33 Our results, along with previous studies, underscore the importance of improving lifestyle factors, including sleep patterns, in high-risk populations to prevent cardiometabolic diseases and support positive interventions in these lifestyle areas to reduce the risk of developing such diseases. Our stratified analysis revealed that the association between a healthy lifestyle and the risk of developing at least one CMD is stronger in women and older adults (65 + years). This suggests that disease prevention efforts through healthy lifestyle promotion should be more targeted based on gender and age. Previous research has found similar results. Women adhering to a healthy lifestyle tend to have a longer health expectancy 34 and lower CVD risk 35 compared to men. Another meta-analysis indicated that the impact of lifestyle factors, such as sleep, is greater in older adults than in younger adults. 36 The reasons for these stratified differences are not entirely clear. Gender differences might be due to women's higher propensity to adopt healthy lifestyles, as shown in baseline data. Age-related differences might be because a healthy lifestyle can reduce inflammation, thereby lowering the risk of cardiometabolic diseases, 37 and older adults generally have higher levels of inflammation. 38 Interestingly, we observed both additive and multiplicative interactions between the healthy lifestyle score and genetic predisposition regarding CMDs risk. Since additive interaction on risks is more relevant for both clinical decisions and public health, we therefore focused more on the additive interaction observed between lifestyle and genetic risk. Our results suggest that adopting a healthy lifestyle, including good sleep patterns, can partially mitigate the risk of CMDs associated with genetic susceptibility. Previous studies on type 2 diabetes risk among Chinese cohorts reported no interaction between lifestyle (without sleep pattern) and genetic factors, 39 possibly due to differences in study populations and lifestyle factors considered. Overall, whether there is an interaction between genetic risk and a healthy lifestyle regarding cardiometabolic diseases remains inconclusive, necessitating further evidence. In this study, the genetic risk score did not show a statistical association with CMM, possibly because the SNPs used to construct the GRS are significantly associated with individual cardiometabolic diseases rather than CMM. Future research should explore SNPs associated with CMM to gain deeper insights into its pathogenesis and prevention strategies. To the best of our knowledge, this is the first study to investigate the relationship between a healthy lifestyle, including sleep patterns, and the risk of cardiometabolic multimorbidity and cardiometabolic diseases as a class. Lifestyle factors are interrelated, changes in one aspect of lifestyle, or even within subcomponents, can influence others. 40 – 42 Therefore, a comprehensive assessment of lifestyle is crucial when exploring disease risk. Additionally, the large sample size of the UK Biobank provides a significant advantage, allowing us to explore the interaction between genetic factors and lifestyle in the development of CMDs and CMM. This study has several limitations. First, lifestyle factors, including sleep patterns, were self-reported via questionnaires at baseline, which may introduce recall bias and lack repeated measurements, potentially leading to inaccurate estimates. Second, the SNPs used to construct the genetic risk score were associated with individual cardiometabolic diseases rather than CMM, necessitating future identification of SNPs independently related to CMM. Third, the study population was primarily from the UK, which may limit the generalizability of the findings to other populations. Lastly, despite sensitivity analyses supporting robustness, observational studies cannot establish causality between healthy lifestyles and cardiometabolic outcomes. Conclusion In summary, our study findings suggest that adopting a healthy lifestyle, including maintaining healthy sleep patterns, is associated with reduced risks of CMM and CMDs. Women and individuals aged 65 years and older may particularly benefit. Embracing a healthy lifestyle may mitigate genetic predispositions to cardiometabolic diseases. Our research further supports the importance of comprehensive healthy lifestyles in preventing and managing cardiometabolic outcomes. Declarations Data availability The data used in this study is available in a public database. Specifically, the resources from the UK Biobank under Application Number 44430 were utilized, which can be accessed at www.ukbiobank.ac.uk/. Funding This research was funded by the National Key R&D Program of China (2019YFC2003401), the National Natural Science Foundation of China (82173499), and the High-Performance Computing Platform of Peking University. The sponsors were not involved in the study's design, data collection, analysis, interpretation, or the writing of the report. There were no publication restrictions imposed by the sponsors. Ethics approval and consent to participate The UK Biobank Study was approved by the Northwest Multi-centre Research Ethics Committee (REC reference for UK Biobank 11/NW/0274). Besides, all the participants have written informed consent. Author’s relationships and activities The authors confirm that they have no affiliations or engagements that could affect, or be perceived to affect, their research. Contribution statement SL and TH conceived and designed the research. SL, NH, and TH had complete access to all the data in the study and are accountable for the integrity and accuracy of the data and analysis. SL conducted the data analysis and drafted the manuscript. 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PLoS Med . 2015;12(3):e1001779. doi:10.1371/journal.pmed.1001779 Metabolic effects of sleep disruption, links to obesity and diabetes - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/24937041/ Diet, lifestyle, and the risk of type 2 diabetes mellitus in women - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/11556298/ Rutten-Jacobs LC, Larsson SC, Malik R, et al. Genetic risk, incident stroke, and the benefits of adhering to a healthy lifestyle: cohort study of 306 473 UK Biobank participants. BMJ . Published online October 24, 2018:k4168. doi:10.1136/bmj.k4168 Rotating night shift work and adherence to unhealthy lifestyle in predicting risk of type 2 diabetes: results from two large US cohorts of female nurses - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/30464025/ Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/29782486/ Kivimäki M, Kuosma E, Ferrie JE, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health . 2017;2(6):e277-e285. doi:10.1016/S2468-2667(17)30074-9 Fan M, Sun D, Zhou T, et al. Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants. Eur Heart J . 2020;41(11):1182-1189. doi:10.1093/eurheartj/ehz849 Li X, Xue Q, Wang M, et al. Adherence to a Healthy Sleep Pattern and Incident Heart Failure: A Prospective Study of 408 802 UK Biobank Participants. Circulation . 2021;143(1):97-99. doi:10.1161/CIRCULATIONAHA.120.050792 Sympathetic Nervous System, Sleep, and Hypertension | Current Hypertension Reports. Accessed July 13, 2024. https://link.springer.com/article/10.1007/s11906-018-0874-y Khot SP, Taylor BL, Longstreth WT, Brown AF. Sleep Health as a Determinant of Disparities in Stroke Risk and Health Outcome. Stroke . 2023;54(2):595-604. doi:10.1161/STROKEAHA.122.039524 Song Z, Yang R, Wang W, et al. Association of healthy lifestyle including a healthy sleep pattern with incident type 2 diabetes mellitus among individuals with hypertension. Cardiovasc Diabetol . 2021;20(1):239. doi:10.1186/s12933-021-01434-z Healthy Lifestyle Score Including Sleep Duration and Cardiovascular Disease Risk - ScienceDirect. Accessed July 13, 2024. https://www.sciencedirect.com/science/article/abs/pii/S074937972200112X Wong ND, Sattar N. Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention. Nat Rev Cardiol . 2023;20(10):685-695. doi:10.1038/s41569-023-00877-z Li Y, Schoufour J, Wang DD, et al. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study. BMJ . 2020;368:l6669. doi:10.1136/bmj.l6669 Healthy lifestyle behaviours and cardiovascular mortality among Japanese men and women: the Japan collaborative cohort study - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/22334626/ Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/20469800/ The association between healthy lifestyle score and risk of metabolic syndrome in Iranian adults: a cross-sectional study - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/36647030/ Age-related immune alterations and cerebrovascular inflammation - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/34711943/ Li H, Khor CC, Fan J, et al. Genetic risk, adherence to a healthy lifestyle, and type 2 diabetes risk among 550,000 Chinese adults: results from 2 independent Asian cohorts. Am J Clin Nutr . 2020;111(3):698-707. doi:10.1093/ajcn/nqz310 Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/32701986/ Comprehensive Lifestyle Modification Intervention to Improve Chronic Disease Risk Factors and Quality of Life in Cancer Survivors - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/30067063/ The impact of nutrition and lifestyle modification on health - ScienceDirect. Accessed July 13, 2024. https://www.sciencedirect.com/science/article/abs/pii/S0953620521003290 Additional Declarations No competing interests reported. Supplementary Files eTablesandeFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5425238","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383298987,"identity":"6552acbe-8162-4f69-b8ae-82bb135a4183","order_by":0,"name":"Siqi Liu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Liu","suffix":""},{"id":383298988,"identity":"a7c431b1-bfa3-40df-9399-67f132e0468c","order_by":1,"name":"Ninghao Huang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Ninghao","middleName":"","lastName":"Huang","suffix":""},{"id":383298989,"identity":"e4a93aa2-bd05-4e9b-819b-56111a9c86f9","order_by":2,"name":"Yueying Li","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yueying","middleName":"","lastName":"Li","suffix":""},{"id":383298991,"identity":"d636bf81-d401-4bde-b1dd-14c190dc31aa","order_by":3,"name":"Tao Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACAxiDX4IhAUQzNhCnBahYcgbJWgxuQAQIazGXSH728OsPOznj2w1PN/Mw2MhuOMD87AE+LZYz0syNZRKSjc3uHEi7zcOQZrzhAJu5AT4tBjcSzKQlEpgTt91IAGk5nLjhAA+bBH4t6d+AWuoTN88Aa/lPjJYcM8kPCUDDJcBaDhCh5cybMmmGtOPGEkC/3JxjkGw88zCbGX4tx9O3Sf6wqZbjn92TduNNhZ1s3/HmZ3i1gAAzD5jiSYBEEzMh9UDA+ANMsR8gQu0oGAWjYBSMRAAAHnBMUKlmqFUAAAAASUVORK5CYII=","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-11-10 09:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5425238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5425238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71560762,"identity":"de5782fc-1e40-439f-8f44-b26152540bc3","added_by":"auto","created_at":"2024-12-16 16:53:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121952,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between the modified Healthy Lifestyle Score and cardiometabolic outcomes.\u003c/p\u003e\n\u003cp\u003eModels were adjusted for age, sex, race, recruitment assessment center, socioeconomic status, education, family history of cardiometabolic multimorbidity, and medication history.\u003c/p\u003e\n\u003cp\u003eFor CMM (Cardiometabolic Multimorbidity),\u003cem\u003e p\u003c/em\u003e for non-linearity =0.795; for CMDs, \u003cem\u003ep\u003c/em\u003e for non-linearity =0.255.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5425238/v1/746a7b7e701b34e0d1f3a9c9.png"},{"id":71561365,"identity":"bd02e054-67a3-443e-b062-51b0f5769992","added_by":"auto","created_at":"2024-12-16 17:01:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93709,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between the modified Healthy Lifestyle Score and cardiometabolic outcomes in three genetic risk categories\u003c/p\u003e\n\u003cp\u003eModel 1 was adjusted for age, sex, race, and recruitment assessment center. Model 2 was adjusted for Model 1, socioeconomic status, education, family history of CMM, and medication history. Error bars represent 95% CIs.\u003c/p\u003e\n\u003cp\u003eCMM: Cardiometabolic multimorbidity; CMDs: Cardiometabolic diseases.\u003c/p\u003e\n\u003cp\u003eCardiometabolic diseases refer to the presence of at least one of the following conditions: type 2 diabetes, coronary heart disease, or stroke.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5425238/v1/1972fda6fde7203b2c0ad185.png"},{"id":71560764,"identity":"03234aa0-8106-4901-bea5-d432942afec0","added_by":"auto","created_at":"2024-12-16 16:53:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431143,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of cardiometabolic outcomes according to the modified Healthy Lifestyle score and genetic risk\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; All HRs (95% CIs) were adjusted for age, sex, race, recruitment assessment center, socioeconomic status, education, family history of cardiometabolic multimorbidity, and medication history.\u003c/p\u003e\n\u003cp\u003eCardiometabolic diseases refer to the presence of at least one of the following conditions: type 2 diabetes, coronary heart disease, or stroke.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5425238/v1/c1cb63df3e2d09d6867c7163.png"},{"id":73720485,"identity":"479a6851-b4b5-4425-8952-4747e526cce5","added_by":"auto","created_at":"2025-01-14 02:31:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1381795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5425238/v1/832a56d6-c04e-4908-981e-8e2d62d7f1f9.pdf"},{"id":71560763,"identity":"3dfa975d-130c-486c-89fb-e020ac5db17e","added_by":"auto","created_at":"2024-12-16 16:53:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":238795,"visible":true,"origin":"","legend":"","description":"","filename":"eTablesandeFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5425238/v1/9d460550e9a995b325bad245.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Healthy Lifestyle including Sleep Patterns, Genetic Risk, and Risk of Cardiometabolic Multimorbidity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiometabolic diseases are among the leading causes of morbidity and mortality in adults, with increasing numbers and burdens\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The presence of cardiometabolic multimorbidity (CMM), where two or more diseases of diabetes, coronary heart disease (CHD), and stroke coexist\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, exacerbates these challenges, contributing to significant declines in quality of life, reduced life expectancy, and increased healthcare resource utilization.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLifestyle factors such as a healthy diet and appropriate physical exercise have been proven to be associated with a reduced risk of cardiometabolic outcomes, including CMM or the occurrence of at least one CMD (CMDs).\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Study based on UK Biobank showed that adhering to a comprehensive healthy lifestyle, which includes a healthy diet, physical exercise, smoking cessation, moderate alcohol consumption, and maintaining a healthy BMI, significantly reduces the risk of type 2 diabetes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Another cohort study based on the China Kadoorie Biobank also indicated that maintaining a comprehensive healthy lifestyle in terms of smoking, alcohol consumption, diet, physical exercise, and body weight is crucial for the management of CMM and CMDs.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Sleep is another important lifestyle factor affecting cardiometabolic diseases; sleep duration and quality are associated with the risk of diabetes and cardiovascular diseases.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Research by Li et al. found that sleep patterns, including chronotype, sleep duration, insomnia, snoring, and daytime sleepiness, are significantly related to cardiac issues.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e However, few studies have explored the impact of a comprehensive healthy lifestyle that includes sleep patterns on cardiometabolic outcomes.\u003c/p\u003e \u003cp\u003eBoth genetic and lifestyle factors influence disease risk.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Zhao et al.'s study indicates a significant interaction between genetic susceptibility and environmental factors in the development of diabetes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Another cohort study involving 140,000 participants showed that the risk of coronary heart disease due to high genetic susceptibility can be partially mitigated by a healthy lifestyle.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e However, the relationship between genetic susceptibility and a comprehensive lifestyle (including sleep), concerning the risk of CMDs and CMM remains unclear.\u003c/p\u003e \u003cp\u003eTherefore, in this study based on 500,000 adults from the UK Biobank, we aimed to investigate the association of comprehensive healthy lifestyle factors, including sleep patterns, with the incidence of CMM and CMDs. We also established genetic scores related to cardiometabolic outcomes and explored the potential interactions and joint associations between lifestyle factors and genetic susceptibility in the occurrence of cardiometabolic outcomes.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe UK Biobank study is a large prospective cohort study comprising 500,000 participants, whose design and characteristics have been previously described.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e In brief, participants from England, Scotland, and Wales were recruited between 2006 and 2010, provided their health-related information, including environment, lifestyle, etc., via touchscreen questionnaire, and underwent physical examinations, biological samples, and genetic data collection. All participants provided written informed consent. The UK Biobank study was approved by the National Information Governance Board for Health and Social Care in England and Wales, the Community Health Index Advisory Group in Scotland, and the Northwest Multicenter Research Ethics Committee. The current study conducted under UK Biobank application number 44430, was approved by the Ethical Committee of Peking University (Beijing, China).\u003c/p\u003e \u003cp\u003eAssessment of Healthy Lifestyle\u003c/p\u003e \u003cp\u003eBased on the guidance of the American Heart Association and previous research,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e the current study incorporates healthy sleep patterns into the framework of a healthy diet, physical activity, alcohol consumption, smoking, and a healthy BMI to construct the modified healthy lifestyle score. For each factor, the following conditions are considered healthy and receive a score of 1, otherwise 0: No current smoking (including previous and never); 150 minutes of moderate to vigorous activity/75 minutes of vigorous activity per week, or 5 days of moderate activity/1 day of vigorous activity per week; alcohol consumption\u0026thinsp;\u0026le;\u0026thinsp;4g per day for women and \u0026le;\u0026thinsp;28g per day for men; adequate intake of fruits, vegetables, fish, meat, and processed meat; body mass index\u0026thinsp;\u0026gt;\u0026thinsp;18.5 and \u0026le;\u0026thinsp;30 kg/m\u0026sup2;; at least 3 out of 5 aspects of chronotype preference, sleep duration, insomnia, snoring, and daytime sleepiness are considered healthy. For detailed information, see the electronic supplementary material eTable 1.\u003c/p\u003e \u003cp\u003eAssessment of outcomes\u003c/p\u003e \u003cp\u003eThe primary outcome of this study was CMM followed up to October 2022, defined as the presence of at least two CMDs: type 2 diabetes, stroke, and coronary heart disease. CMDs (the occurrence of at least one CMD) was considered another outcome. These CMDs were classified based on the 10th edition of the International Classification of Diseases. Detailed disease definitions are provided in the electronic supplementary material eTable 2. Hospital data were obtained through linkage with the Health Episode Statistics records for England and Wales, and the Scottish Morbidity Records for Scotland.\u003c/p\u003e \u003cp\u003eGenotype data\u003c/p\u003e \u003cp\u003eThe UK Biobank team performed genotyping, calculation, and quality control processes, as previously described.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Using the method by Li et al.,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e weighted genetic risk values for specific CMD were calculated, including 74 independent single nucleotide polymorphisms (SNPs) related to CHD, 403 related to type 2 diabetes, and 90 related to stroke. Individual disease genetic risk values were assigned scores of 1, 2, or 3 based on tertiles and then summed to obtain the genetic risk score (GRS). Participants were classified into low, intermediate, or high genetic risk of cardiometabolic outcomes according to the tertiles of GRS.\u003c/p\u003e \u003cp\u003eAssessment of other covariates\u003c/p\u003e \u003cp\u003eParticipants' age and sex information were collected at the baseline visit to the assessment center. The Townsend deprivation index was assigned based on participants' postcodes. Height and weight were measured to calculate BMI. Medication history for cholesterol, blood pressure, diabetes, or exogenous hormones (for women), and family history of cardiometabolic diseases (father, mother, and siblings) were recorded via a touchscreen questionnaire.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDue to the small number of participants with a modified healthy lifestyle score of 0 or 1, we combined them into one group. Differences between groups were compared using one-way ANOVA or chi-square tests as appropriate. Baseline characteristics of participants with different mHLS were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) for continuous variables, and percentages for categorical variables. We used restricted cubic spline analysis to examine the association between continuous mHLS and outcomes. Cox proportional hazards models were constructed to analyze the association between mHLS and outcomes, with time-to-event as the timescale and follow-up time defined from the recruitment date to the date of disease diagnosis, death, or loss to follow-up, whichever came first. Schoenfeld residuals were used to test the proportional hazards assumption. Model 0 was unadjusted, Model 1 adjusted for age, sex, ethnicity, and assessment center, and Model 2 further adjusted for Townsend index, education, family history of cardiometabolic disease, and medication history. We analyzed the relationship between the five individual factors constituting the mHLS and outcomes. Stratified analyses for the main analyses were performed, and potential interactions between mHLS and age, sex, ethnicity, education, and family history of cardiometabolic disease were explored by adding multiplicative interaction terms. We also investigated the potential interaction and joint association between healthy lifestyle and genetic risk on cardiometabolic outcomes. Additive interaction was assessed using relative excess risk due to interaction (RERI) and attributable proportion (AP), with the following equations: RERI\u0026thinsp;=\u0026thinsp;HR\u003csub\u003e++\u003c/sub\u003e - HR\u003csub\u003e+\u0026minus;\u003c/sub\u003e - HR\u003csub\u003e\u0026minus;+\u003c/sub\u003e +1 and AP\u0026thinsp;=\u0026thinsp;RERI/HR\u003csub\u003e++\u003c/sub\u003e. Multiplicative interaction was assessed by adding interaction terms in the model and using the likelihood ratio test.\u003c/p\u003e \u003cp\u003eWe also conducted several sensitivity analyses. First, since previous studies indicated obesity as a mediator between healthy lifestyle and cardiometabolic outcomes,\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e we further adjusted for BMI/waist-to-hip ratio (WHR). Second, we re-categorized the mHLS and examined its association with outcomes. Finally, we excluded participants who developed cardiometabolic diseases within the first two years of follow-up to avoid reverse causation when exploring the association between mHLS and cardiometabolic outcomes and reassessed the joint association between mHLS and genetic risk.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed with Stata/MP version 16.0 (StataCorp, USA). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics stratified by the modified Healthy Lifestyle Score\u003c/p\u003e \u003cp\u003eDuring a median follow-up of 8.7 years (IQR 8.1\u0026ndash;9.3 years; 3,304,914 total person-years), we documented 13,388 incident CMM, and 54,381 incident cardiometabolic diseases. The baseline characteristics of participants entering the initial analysis, categorized by the modified healthy lifestyle score, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants with higher adherence to a healthy lifestyle tend to be older, have higher socioeconomic status (Townsend Index), higher education levels, lower BMI and waist-hip ratio, and a lower proportion of family history of CMM. Additionally, women and white individuals are more likely to maintain a healthy lifestyle.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants stratified by the modified Healthy Lifestyle Score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eModified Healthy Lifestyle Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,785(2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,393(7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68,689(17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113,311(29.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114,291(29.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49,979(13.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,125(53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,822(52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34,036(49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52,229(46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48,785(42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,156(40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,952(89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,199(88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61,158(89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101,240(89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102,108(89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44,908(89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,639(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,862(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,292(28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37,143(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43,041(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21,032(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith a family history of CMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,390(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,739(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,549(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,504(14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,753(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6,776(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: CMM, cardiometabolic multimorbidity; BMI, body mass index; WHR, waist-to-hip ratio; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eContinuous variables were displayed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs, and categorical variables were displayed as n(%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*\u003cem\u003ep\u003c/em\u003e values were obtained from the analysis of variance for continuous variables and the X\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026Dagger;Higher education refers to a college/university degree or other professional qualifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModified Healthy Lifestyle Score and cardiometabolic outcomes\u003c/p\u003e \u003cp\u003eThe association between the modified healthy lifestyle score and the risk of CMM is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The modified healthy lifestyle score was inversely associated with the risk of both CMM and CMDs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in all models). In the fully adjusted model 2, each one-unit increase in the modified healthy lifestyle score was associated with HRs (95% CI) for CMM and CMDs of 0.96 (0.94\u0026ndash;0.97) and 0.96 (0.96\u0026ndash;0.97), respectively. Compared to participants with a mHLS of 0\u0026ndash;1, those with a mHLS of 6 have fully adjusted HRs (95% CI) of 0.81 (0.74\u0026ndash;0.90) for CMM and 0.84 (0.79\u0026ndash;0.88) for CMDs. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, dose-response analysis indicates that the association between the modified healthy lifestyle score and both CMM and CMDs is more likely to be linear rather than nonlinear (\u003cem\u003ep\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.795 and 0.255, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between the modified Healthy Lifestyle Score and cardiometabolic outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eModified Healthy Lifestyle Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePer unit of index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCardiometabolic multimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases (person-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710 (5,456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,935 (15,096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,336 (26,737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,872 (31,905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,665 (22,414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e870 (7,436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.92\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.89\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91 (0.84\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.82\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86 (0.78\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (0.95\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.88\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.84\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.79\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81 (0.74\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.70\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95 (0.93\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.89\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.85\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 (0.81\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84 (0.77\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.74\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (0.94\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiometabolic diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases (person-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,008 (13,703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,078 (42,737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,078 (85,797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,002 (116,969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,186 (98,695)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,029 (38,078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.93\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.93\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.85\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.84\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97 (0.96\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.90\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.82\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82 (0.78\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.76\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95 (0.95\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.91-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.89\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 (0.84\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.81\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84 (0.79\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (0.96\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Model 0: Cox proportional regression without adjustment.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026dagger;Model 1: Cox proportional regression adjusted for age, sex, race, and recruitment assessment center.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026Dagger;Model 2: Cox proportional regression adjusted for Model 1, socioeconomic status, education, family history of cardiometabolic multimorbidity, and medication history.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCardiometabolic diseases refer to the presence of at least one of the following conditions: type 2 diabetes, coronary heart disease, or stroke.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe associations between each binary component (healthy or not) of the modified healthy lifestyle score and the risks of CMM and CMDs are shown in eTable3. In the fully adjusted model 2, having a healthy sleep pattern, healthy alcohol consumption, healthy BMI, and healthy physical activity were independently associated with a 5%, 5%, 10%, and 7% reduction in the risk of CMM, respectively. For CMDs, having a healthy sleep pattern, healthy diet, no current smoking, healthy BMI, and healthy physical activity were independently associated with a 5%, 3%, 3%, 9%, and 6% reduction in risk, respectively.\u003c/p\u003e \u003cp\u003eSub-group analysis\u003c/p\u003e \u003cp\u003eSubgroup analysis showed that the association between the modified healthy lifestyle score and the occurrence of CMDs was stronger in elderly individuals (\u0026ge;\u0026thinsp;65 years) and females (elderly individuals, 0.96; 95% CI, 0.95\u0026ndash;0.97; younger individuals, 0.97; 95% CI, 0.96\u0026ndash;0.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; male individuals, 0.96; 95% CI, 0.95\u0026ndash;0.97; female individuals, 0.95; 95% CI, 0.94\u0026ndash;0.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e\n\u003ch3\u003eModified Healthy Lifestyle Score, Genetic Risk Score, and cardiometabolic outcomes\u003c/h3\u003e\n\u003cp\u003eIn the fully adjusted model 2, participants with high genetic risk scores had an increased risk of CMDs (HR 1.03, 95% CI: 0.99\u0026ndash;1.08) compared to participants with a low genetic risk score, while the risk of CMM did not show a statistically significant difference (eTable4). A healthy lifestyle was associated with a lower risk of cardiometabolic outcomes across all GRS groups, with a more pronounced risk reduction observed in the groups with high genetic risk. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there was a slight but statistically significant additive interaction between the mHLS and GRS on CMDs, with the RERI for CMDs being 0.057 (95% CI: 0.007,0.106), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026. The joint association of the modified healthy lifestyle score and the GRS with the risk of cardiometabolic diseases shows that compared to participants with high genetic risk and poor lifestyle, those with low genetic risk and a healthy lifestyle had a 16% lower risk of cardiometabolic diseases (HR [95% CI]: 0.16 [0.10, 0.22]). There was also a multiplicative interaction between GRS and mHLS in CMDs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAttributing effects to additive interaction between GRS and mHLS on the risk of cardiometabolic outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive Interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCardiometabolic Multimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCardiometabolic diseases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative excess risk due to interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.077(-0.279,0.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057(0.007,0.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributable proportion for modified healthy lifestyle score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.871(0.070,0.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584(0.180,0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributable proportion for genetic risk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012(-0.171,0.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127(-0.132,0.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributable proportion for addictive interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.117(-0.238,0.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289(0.112,0.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eRelative excess risk due to interaction and attributable proportions were estimated based on hazard ratio and proportion hazard ratio, respectively.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAll adjusted for age, sex, race, recruitment assessment center, socioeconomic status, education, family history of cardiometabolic multimorbidity and medication history.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCardiometabolic diseases refer to the presence of at least one of the following conditions: type 2 diabetes, coronary heart disease, or stroke.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eIn the sensitivity analysis, after further adjusting for BMI or WHR, the association between the modified healthy lifestyle score and CMM and CMDs remained robust (Supplemental eTable 6.1 and 6.2). When the modified healthy lifestyle score was grouped into tertiles, its association with cardiometabolic outcomes also remained stable (Supplemental eTable 7). Excluding cardiometabolic outcomes occurring within the first two years of follow-up, the association between the modified healthy lifestyle score and CMM and CMDs was consistent against reverse causation (Supplemental eTable 8). The joint and interaction analyses of the modified healthy lifestyle score and genetic risk were similar to previous results (\u003cem\u003ep\u003c/em\u003e for additive interaction with cardiometabolic diseases\u0026thinsp;=\u0026thinsp;0.024).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current large prospective cohort, we found that a healthy lifestyle, including sleep patterns, was significantly associated with a lower risk of developing cardiometabolic multimorbidity and at least one cardiometabolic disease. Healthy sleep patterns, BMI, and physical activity were independently associated with a lower risk of CMM and CMDs; healthy alcohol consumption was independently associated with a lower risk of CMM, and healthy diet and smoking status were independently associated with a lower risk of CMDs. The association between a healthy lifestyle and CMDs was stronger in women and older adults. A healthy lifestyle significantly reduces the risk of CMDs associated with genetic predisposition.\u003c/p\u003e \u003cp\u003eSmoking, alcohol consumption, healthy diet, physical activity, and BMI have frequently been considered components of a healthy lifestyle. Consistent with the findings of the present study, adherence to these healthy factors, whether individually or collectively, was associated with a reduced risk of diabetes,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e stroke,\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and other cardiometabolic diseases.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Studies examining CMM as an outcome in relation to a healthy lifestyle also demonstrated similar results. The study,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e which included smoking, alcohol consumption, healthy diet, and physical activity as part of a healthy lifestyle, investigated the risk of CMM and the onset of the first cardiometabolic disease in 8,270 middle-aged UK participants and found that a healthy lifestyle was associated with a reduced risk of these outcomes. A pooled analysis of multiple cohorts from Europe and the USA found that high BMI was associated with an increased risk of CMM, aligning with our findings on the relationship between lifestyle factors and CMM.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Additionally, a healthy sleep pattern, included in this study as an additional lifestyle factor, was associated with a reduced risk of cardiovascular issues.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e This may be related to unhealthy sleep patterns causing overactivity of the sympathetic nervous system\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and dysregulation in neuroendocrine and proinflammatory hormone release\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Studies incorporating healthy sleep into the definition of a healthy lifestyle, similar to the present study, found that this comprehensive healthy lifestyle was associated with a reduced risk of type 2 diabetes.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious cohort studies by Hu et al. involving nurses and health professionals showed that adherence to a healthy lifestyle, including sleep, is associated with a reduced risk of, stroke, and CHD, which are included in the current study, and cardiovascular disease (CVD).\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e CVD shares common risk factors, such as hyperglycemia and dyslipidemia, with cardiometabolic diseases and CMM.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Our results, along with previous studies, underscore the importance of improving lifestyle factors, including sleep patterns, in high-risk populations to prevent cardiometabolic diseases and support positive interventions in these lifestyle areas to reduce the risk of developing such diseases.\u003c/p\u003e \u003cp\u003eOur stratified analysis revealed that the association between a healthy lifestyle and the risk of developing at least one CMD is stronger in women and older adults (65\u0026thinsp;+\u0026thinsp;years). This suggests that disease prevention efforts through healthy lifestyle promotion should be more targeted based on gender and age. Previous research has found similar results. Women adhering to a healthy lifestyle tend to have a longer health expectancy\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and lower CVD risk\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e compared to men. Another meta-analysis indicated that the impact of lifestyle factors, such as sleep, is greater in older adults than in younger adults.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The reasons for these stratified differences are not entirely clear. Gender differences might be due to women's higher propensity to adopt healthy lifestyles, as shown in baseline data. Age-related differences might be because a healthy lifestyle can reduce inflammation, thereby lowering the risk of cardiometabolic diseases,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and older adults generally have higher levels of inflammation.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eInterestingly, we observed both additive and multiplicative interactions between the healthy lifestyle score and genetic predisposition regarding CMDs risk. Since additive interaction on risks is more relevant for both clinical decisions and public health, we therefore focused more on the additive interaction observed between lifestyle and genetic risk. Our results suggest that adopting a healthy lifestyle, including good sleep patterns, can partially mitigate the risk of CMDs associated with genetic susceptibility. Previous studies on type 2 diabetes risk among Chinese cohorts reported no interaction between lifestyle (without sleep pattern) and genetic factors,\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e possibly due to differences in study populations and lifestyle factors considered. Overall, whether there is an interaction between genetic risk and a healthy lifestyle regarding cardiometabolic diseases remains inconclusive, necessitating further evidence. In this study, the genetic risk score did not show a statistical association with CMM, possibly because the SNPs used to construct the GRS are significantly associated with individual cardiometabolic diseases rather than CMM. Future research should explore SNPs associated with CMM to gain deeper insights into its pathogenesis and prevention strategies.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to investigate the relationship between a healthy lifestyle, including sleep patterns, and the risk of cardiometabolic multimorbidity and cardiometabolic diseases as a class. Lifestyle factors are interrelated, changes in one aspect of lifestyle, or even within subcomponents, can influence others.\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Therefore, a comprehensive assessment of lifestyle is crucial when exploring disease risk. Additionally, the large sample size of the UK Biobank provides a significant advantage, allowing us to explore the interaction between genetic factors and lifestyle in the development of CMDs and CMM.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, lifestyle factors, including sleep patterns, were self-reported via questionnaires at baseline, which may introduce recall bias and lack repeated measurements, potentially leading to inaccurate estimates. Second, the SNPs used to construct the genetic risk score were associated with individual cardiometabolic diseases rather than CMM, necessitating future identification of SNPs independently related to CMM. Third, the study population was primarily from the UK, which may limit the generalizability of the findings to other populations. Lastly, despite sensitivity analyses supporting robustness, observational studies cannot establish causality between healthy lifestyles and cardiometabolic outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study findings suggest that adopting a healthy lifestyle, including maintaining healthy sleep patterns, is associated with reduced risks of CMM and CMDs. Women and individuals aged 65 years and older may particularly benefit. Embracing a healthy lifestyle may mitigate genetic predispositions to cardiometabolic diseases. Our research further supports the importance of comprehensive healthy lifestyles in preventing and managing cardiometabolic outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data used in this study is available in a public database. Specifically, the resources from the UK Biobank under Application Number 44430 were utilized, which can be accessed at www.ukbiobank.ac.uk/.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Key R\u0026amp;D Program of China (2019YFC2003401), the National Natural Science Foundation of China (82173499), and the High-Performance Computing Platform of Peking University. The sponsors were not involved in the study\u0026apos;s design, data collection, analysis, interpretation, or the writing of the report.\u0026nbsp;There were no publication restrictions imposed by the sponsors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe UK Biobank Study was approved by the Northwest Multi-centre Research\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Committee (REC reference for UK Biobank 11/NW/0274). Besides, all the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eparticipants have written informed consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author\u0026rsquo;s relationships and activities\u003c/p\u003e\n\u003cp\u003eThe authors confirm that they have no affiliations or engagements that could affect, or be perceived to affect, their research.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Contribution statement\u003c/p\u003e\n\u003cp\u003eSL and TH conceived and designed the research. SL, NH, and TH had complete access to all the data in the study and are accountable for the integrity and accuracy of the data and analysis. SL conducted the data analysis and drafted the manuscript. All authors were involved in the statistical analysis, offered critical feedback during the manuscript\u0026apos;s preparation, and approved the final version for publication. TH serves as the study\u0026apos;s guarantor.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMolecular Biomarkers for Cardiometabolic Disease: Risk Assessment in Young Individuals - PubMed. Accessed July 13, 2024. https://pubmed.ncbi.nlm.nih.gov/37289904/\u003c/li\u003e\n\u003cli\u003eN S, Jmr G, W A. Improving prevention strategies for cardiometabolic disease. \u003cem\u003eNat Med\u003c/em\u003e. 2020;26(3). doi:10.1038/s41591-020-0786-7\u003c/li\u003e\n\u003cli\u003eHan Y, Hu Y, Yu C, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. \u003cem\u003eEur Heart J\u003c/em\u003e. 2021;42(34):3374-3384. doi:10.1093/eurheartj/ehab413\u003c/li\u003e\n\u003cli\u003eDi Angelantonio E, Kaptoge S, Wormser D, et al. 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Accessed July 13, 2024. https://www.sciencedirect.com/science/article/abs/pii/S0953620521003290\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic multimorbidity, lifestyle, sleep pattern, genetic risk, cohort study","lastPublishedDoi":"10.21203/rs.3.rs-5425238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5425238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEmerging evidence suggests a link between sleep patterns, healthy lifestyles, and the risk of cardiometabolic outcomes. However, few studies have explored the association between lifestyles that incorporate healthy sleep patterns and genetic factors with the risk of cardiometabolic multimorbidity (CMM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective/Aim\u003c/strong\u003e: To prospectively investigate the associations between a healthy lifestyle including sleep patterns, genetic risk, and the risk of cardiometabolic multimorbidity and cardiometabolic diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 382,448 UK Biobank participants were included in this study. A modified healthy lifestyle score (mHLS) was defined by healthy sleep patterns, diet, physical activity, smoking, alcohol consumption, and Body Mass Index (BMI). Weighted genetic risk score (GRS) for cardiometabolic outcomes was calculated. Cox proportional hazards models were used to estimate the associations between a healthy lifestyle and cardiometabolic outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e During a median follow-up of 8.7 years, 13,388 participants developed CMM, and 54,381 participants developed at least one of those cardiometabolic diseases (CMDs) that contribute to CMM. After adjusting for major confounders, the modified healthy lifestyle score was significantly associated with lower risks of CMM and CMDs, with hazard ratios (HRs) of 0.81 (95% confidence interval [CI]: 0.74-0.90) and 0.84 (95% CI: 0.79-0.88) for the highest versus lowest mHLS groups, respectively. The association between mHLS and CMDs was stronger in women and individuals aged 65 and above (\u003cem\u003ep\u003c/em\u003e = 0.006 and \u003cem\u003ep\u003c/em\u003e = 0.04, respectively). Additionally, there was an additive interaction between mHLS and GRS on CMDs, with a relative excess risk due to interaction (RERI) of 0.057 (95% CI: 0.007, 0.106).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our results show that adherence to a healthy lifestyle, including healthy sleep patterns, is associated with lower risks of CMM and CMDs. Women and individuals over 65 may benefit more. A healthy lifestyle can also modify the genetic predisposition to CMDs.\u003c/p\u003e","manuscriptTitle":"Healthy Lifestyle including Sleep Patterns, Genetic Risk, and Risk of Cardiometabolic Multimorbidity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 16:53:44","doi":"10.21203/rs.3.rs-5425238/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6468ab12-d7ea-42a1-8218-bd4b94b82b3c","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-14T02:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 16:53:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5425238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5425238","identity":"rs-5425238","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}