A newly developed circadian imbalance index (CII) and risk of cardiovascular-kidney-metabolic disease in the UK biobank

A newly developed circadian imbalance index (CII) and risk of cardiovascular-kidney-metabolic disease in the UK biobank | 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 A newly developed circadian imbalance index (CII) and risk of cardiovascular-kidney-metabolic disease in the UK biobank Jing Zhang, Dat Thien Tran, Tala El Ghoul, Susanne Strohmaier, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722286/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in European Journal of Epidemiology → Version 1 posted 5 You are reading this latest preprint version Abstract Background To examine the association between combined circadian imbalance related traits and cardiovascular-kidney-metabolic (CKM) disease risk, and their potential interaction with night shift work. Methods This study included 191,764 UK Biobank participants without major chronic diseases who were actively working at baseline (2006–2010). Several factors indicative of a propensity for circadian misalignment were combined to create the circadian imbalance index (CII), with each factor (evening chronotype, sleep ≥ 9 or ≤ 6 hours/day, high neuroticism (score ≥ 7), caffeinated coffee consumption 0 or ≥ 5 cups/day, and vitamin D < 50 nmol/L) contributing one point if present, yielding a composite scale ranging from 0 to 5. CKM outcome (type 2 diabetes, cardiovascular diseases, chronic kidney diseases) identified by ICD codes, self-reports, or death records. Cox models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the multivariable (MV)-adjusted association between the CII and CKM risk, including effect modification by night shift work. Results During a median follow-up of 13.5 years (through 2022), 16,907 incident CKM cases were identified. Among participants with European ancestry, for highest versus lowest (0–1) CII, the MV-adjusted risk of CKM was 1.95 (95%CI: 1.70–2.23; P trend <0.001). A significant positive relationship between CII and CKM risk was also observed in participants of Asian (HR = 2.03, 95%CI, 1.07–3.86; P trend =0.02), but not African ancestry (HR = 1.43, 95%CI, 0.67–3.06; P trend =0.66). Risks were higher in shift and night workers than day workers. Among Europeans, the HR for highest CII combined with current night shift work was 2.22 (95%CI, 1.95–2.53), with significant additive interaction ( P < 0.05). Conclusions In this large prospective study, circadian imbalance index (CII) was associated with higher CKM risk in Europeans and Asians. Among Europeans, high CII plus night shift work posed the greatest risk. Maintaining low CII may help prevent CKM, especially in night shift workers. nightshift work chronotype caffeinated coffee consumption vitamin D sleep duration cardiovascular-kidney-metabolic health cardiovascular disease chronic kidney disease type 2 diabetes Figures Figure 1 INTRODUCTION Internal biological clocks are endogenous regulators that optimally align physiology and behavior with the solar day, following an intrinsic circadian rhythm [ 1 ]. The molecular mechanisms of the clock regulate nearly every aspect of human physiology, including the sleep-wake cycle, core body temperature, secretion of hormones (e.g., cortisol and melatonin), and behavioral factors such as cognition and mood [ 2 ]. Circadian imbalance i.e. a propensity to circadian misalignment is widespread, impacting an increasing number of people. Circadian misalignment is not limited to individuals who work non-daytime schedules [ 3 ]; rather, it reflects inconsistencies between internal biological rhythms and external environmental or behavioral cues, and is influenced by factors such as chronotype [ 4 ], sleep timing [ 5 ], personality traits notably neuroticism [ 6 ], caffeine intake [ 7 ], and vitamin D levels, which are biologically linked with melatonin pathways and serve as a proxy for outdoor sunlight exposure [ 8 , 9 ]. Furthermore, there is accumulating observational evidence that an evening chronotype [ 10 ], short or long sleep duration [ 11 ], neuroticism [ 12 ], atypical coffee consumption [ 13 ], and vitamin D insufficiency [ 14 ] each are associated with an increased risk of cardio-metabolic diseases. Prior research has primarily assessed these circadian-related traits in isolation, without accounting for their combined effects on cardio-metabolic disease risk. In the past few decades, the population of shift workers has increased tremendously. Today, up to 18% of workers in the European region reported working shifts [ 15 ]. Shift work, especially night shifts characterized by irregular sleep patterns and atypical exposure to environmental light, represents a pronounced form of circadian misalignment [ 16 ]. This disruption of circadian rhythms has been associated with a range of adverse health outcomes including cardio-metabolic disease [ 17 , 18 ], breast and prostate cancer [ 19 , 20 ], and chronic kidney disease [ 21 ]. However, research examining the interaction between shift work and circadian-related traits such as sleep duration or chronotype in relation to cardiovascular disease remains limited and yields inconsistent findings [ 22 , 23 ]. In 2022, circulatory diseases accounted for close to one-third of all deaths in the European Union [ 24 ]. Cardiovascular diseases frequently coexist with type 2 diabetes and chronic kidney disease, collectively termed cardiovascular-kidney-metabolic (CKM) diseases, reflecting shared underlying mechanisms including dysglycemia, dyslipidemia, hypertension, and obesity [ 25 , 26 ]. When one system is impaired, it can exacerbate dysfunction in the other, increasing the risk of subsequent health complications and mortality [ 27 ]. To our knowledge, no prior study has assessed the link between a composite score for circadian misalignment and risk for incident CKM, or how night shift work influences their relationship [ 28 ]. Using data from the UK Biobank, we evaluated the association between a combination of circadian imbalance-related factors, characterized by a novel circadian imbalance index (CII), and the risk of CKM disease, in the whole cohort and when stratified by the night shift work status. METHODS Study cohort The UK Biobank is a large-scale prospective study, which began in 2006, enrolling half a million residents of the United Kingdom, aged 40 to 69 years [ 29 ]. All participants completed an initial baseline assessment, which included sociodemographic psychosocial, physical, and lifestyle information, as well as the collection of biological samples [ 29 ]. The UK Biobank can track participants’ health outcomes through various national datasets, including Hospital Episode Statistics and national death and cancer registries. The National Research Ethics Service approved the UK Biobank study (ref. 11/NW/0382), and all participants provided written informed consent. Assessment of circadian imbalance related factors The UK Biobank collected data on chronotype, sleep duration, neuroticism, and caffeinated coffee consumption through a baseline self-response questionnaire. Specifically, chronotype preference was assessed using the question, ‘Do you consider yourself to be?’ with responses of (1) definitely a ‘morning’ person, (2) more a ‘morning’ than ‘evening’ person, (3) more an ‘evening’ person than a ‘morning’ person, (4) definitely an ‘evening’ person, (5) do not know. Sleep duration was self-reported through the question ‘About how many hours sleep do you get in every 24 hours? (Please include naps)’. Neuroticism was assessed using 12 questions from the Eysenck Personality Inventory Neuroticism Scale (EPIN-R) [ 30 ]. The neuroticism score was calculated by summing the number of “Yes” responses across these questions, resulting in a single integer score for each participant ranging from 0 to 12. Consumption of caffeinated coffee was assessed using two dietary questions. Participants were first asked, ‘How many cups of coffee do you drink each DAY? (Include decaffeinated coffee)’. Response options included a specific number of cups, ‘<1’, ‘do not know’ or ‘Prefer not to answer’. For those who reported drinking at least one cup of coffee per day, a follow-up question was asked: ‘What type of coffee do you usually drink?’ with response options of ‘Decaffeinated coffee (any type)’, ‘Instant coffee’, ‘Ground coffee (include espresso, filter etc.)’ or ‘other type of coffee’. For our analysis, we categorized caffeinated coffee consumption into the following groups: 0, 1, 2, 3, 4, >=5 cups/day, with participants reporting decaffeinated coffee intake classified into the 0 cups/day group. Serum concentrations of 25(OH)D (nmol/L) were determined using the chemiluminescent immunoassay method (DiaSorin Liaison XL). Detailed information regarding the measurement of biochemical markers and quality assessment are provided elsewhere [ 31 ]. Construction of Circadian Imbalance Index (CII) We conducted literature searches and relied on previously published systematic reviews [ 8 , 28 , 32 ] and reports [ 33 ] to identify factors of potential interest for our definition of circadian imbalance. Based on relevance to the circadian system and availability within the UK Biobank, we chose a set of five circadian imbalance-related factors (chronotype, sleep duration, neuroticism, caffeinated coffee intake, and serum vitamin D concentration) to derive a Circadian Imbalance Index (CII). Specifically, morning chronotypes have previously been associated with better circadian alignment whereas evening chronotype are more prone to misalignment [ 34 ]. Additionally, both short ( 9 h) sleep durations have been linked to increased circadian imbalance [ 35 ]. Individuals with high neuroticism tend to report greater variability in sleep-wake patterns and appear more susceptible to misalignment [ 6 ]. Moderate caffeine consumption may support more stable circadian alignment by enhancing the internal clock’s responsiveness to environmental cues [ 7 ]. Low vitamin D levels have also been associated with circadian misalignment, potentially due to its role in melatonin biosynthesis and signaling pathways that regulate the sleep–wake cycle [ 8 , 36 ]. For each factor, participants were assigned one point if they exhibited the following characteristics: evening chronotype (including “definitely an ‘evening’ person” or “more an ‘evening’ person than a ‘morning’ person”) [ 37 ]; short or long sleep duration (≥ 9 hours or ≤ 6 hours/day) [ 38 ]; high neuroticism score (≥ 7) [ 30 ]; low serum vitamin D concentration (< 50 nmol/L) [ 39 ]; and atypical caffeinated coffee intake (none or ≥ 5 cups/day, approximately 400mg caffeine) [ 40 ]. Notably, given that approximately 80% of the global population consumes caffeine daily [ 41 ], complete avoidance of caffeinated coffee may reflect underlying caffeine sensitivity rather than a typical behavioral pattern. Participants with alternative expressions of these factors were assigned a zero for each respective category. The CII was calculated by summing the points across all five factors and ranged from 0 to 5, with a higher index indicating greater propensity to circadian imbalance. For interaction analyses, we categorized individuals into three circadian imbalance groups: ‘low CII’ (0 ≤ CII ≤ 1); ‘intermediate CII’ (2 ≤ CII ≤ 3); and ‘high CII’ (4 ≤ CII ≤ 5). Assessment of night shift work At baseline from 2006 to 2010, participants who were in paid employment or self-employed were asked whether their primary job involved shift work, defined as work schedules outside typical daytime hours (9am-5pm). Participants who answered ‘yes’ were further asked if their job included night shifts, characterized by working during normal sleeping hours (12am to 6am). Responses to the two questions included the following options: ‘never/rarely’, ‘sometimes’, ‘usually’, ‘always’, along with ‘prefer not to answer’ and ‘do not know’. Based on these responses and consistent with existing research [ 42 ], participants were categorized into one of the following work status groups: ‘day workers’, ‘shift workers’ (i.e., those who never or rarely worked night shifts), and ‘night shift workers’ (i.e., those who reported working sometimes, usually, or always night shifts). To further assess their lifetime employment history, between July and September 2015, all participants were invited via email to complete an online occupational history questionnaire. A subset of participants responded, providing detailed employment histories on all jobs they had held, including the duration of each job and details about shift work schedules. Using this data and findings from previous studies, we calculated three key night shift work metrics: duration of night shift work (total number of years spent working night shifts), cumulative exposure (total number of night shifts over a lifetime), and intensity of night shift work (average number of night shifts per month) [ 43 , 44 ]. Assessment of outcome The primary outcome was defined as time to any incident CKM disease, which included type 2 diabetes (T2D), cardiovascular disease (CVD) or chronic kidney disease (CKD). These outcomes were identified through the national death registry, hospital inpatient records and self-reports. The national death registry and hospital impatient records were classified based on the 10th edition of the International Classification of Diseases (ICD-10) [ 45 ] as follows: type 2 diabetes: E11 (non-insulin-dependent diabetes mellitus); cardiovascular disease: I50 (heart failure), I21-I25 (ischemic heart disease) and I60-I64 (stroke); chronic kidney disease: N18 (kidney failure) and I12-I13 (hypertensive renal disease) [ 46 ]. Assessment of covariables We considered a range of demographic, health and lifestyle factors [ 43 , 47 ] that were all assessed at baseline, as potential confounders in our analyses. They included age, sex, average total household income before tax, education level, recruitment season, smoking status, alcohol consumption, physical activity, body mass index (BMI), hypertension and elevated cholesterol. For missing covariable data, we applied sex-specific median values for continuous variables and introduced a missing indicator for categorical variables. To ensure accurate ancestry classification, we based our classification on detailed genetically derived, rather than self-reported ancestry. Ancestry estimates are described in the Genetic Ancestry Assessment Supplement. A detailed description of all other covariables is provided in Supplemental Table 1. Analytic sample From the total UK Biobank cohort, we excluded individuals with prevalent cancer or CKM disease at baseline (N = 69,562), as well as those with missing responses – including "prefer not to answer" – for circadian imbalance related variables (N = 124,505). Additionally, participants who were not in paid employment or self-employed at baseline (N = 116,447) were excluded. These exclusions resulted in a final analytic sample of 191,764 individuals for the analysis considering current night shift exposure. Among these, a subset of 47,843 participants of European ancestry completed an online employment history questionnaire in 2015, which was used to assess lifetime exposure to night shift work (Supplemental Fig. 1). In analyses using this subset, participants of non-European ancestry were excluded due to limited case numbers. Statistical analyses The follow-up period extended from the date of enrollment (2006–2010) until the first occurrence of either a diagnosis of CKM disease or a censoring event, which included death, withdrawal from the study, or the end of the designated follow-up period. Region-specific designated follow-up end dates were applied: October 31, 2022, for England, August 31, 2022, for Wales and May 31, 2022, for Scotland. To assess the relationship between the CII and incidence of CKM, we employed Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The proportional hazards assumption was tested using Schoenfeld residuals and no indications of violations were observed (Supplemental Fig. 2). The main analyses were conducted stratified by three main genetic ancestry groups (European, Asian and African). In addition to stratifying, in sensitivity analyses, we also adjusted for ethnicity in the overall analytic sample; however, due to the predominance of European ancestry (87%), results were highly similar to those in the stratum of participants with European ancestry only. Guided by prior research [ 43 , 47 ], we considered four models serially adjusting for potential covariables: Model 1 accounted for age and sex; model 2 additionally included socioeconomic indicators, specifically average total household income before tax and education level; model 3 further incorporated lifestyle factors, including smoking status, alcohol consumption, and physical activity. Finally, to account for its potential mediating role in the CII-CKM relationship [ 48 ], BMI was introduced separately in model 4. Although these potential confounders and mediators were assessed at baseline only, it appears reasonable to assume that they would likely remain on the same trajectory through follow-up. To evaluate potential linear trends, the CII was additionally analyzed as a continuous variable in various models as specified above. In subgroup analyses, we first performed sex-stratified analyses within the overall study population. Among the women, we further considered menopausal status as a potential confounder but because results remained virtually unchanged, did not retain it in our models. Subsequently, among individuals of European ancestry, we performed additional analyses stratified by current work status (as reported in the baseline questionnaire) and lifetime exposure to night shift work (as assessed in the 2015 occupational questionnaire). To assess potential multiplicative interaction, we included a product term of CII group and night shift work status in the regression models and compared the − 2 log-likelihood values of models with and without the inclusion of a product term. To further explore whether the associations between CII and CKM disease risk varied according to night shift work status, participants were categorized into nine groups based on the combination of night shift work status (day worker, shift worker and night shift worker) and CII level (low, intermediate and high). Hazard ratios (HRs) for incident CKM disease were then estimated for each group, using dayworkers with low CII as the reference category. To evaluate additive interaction [ 49 ], we calculated the relative excess risk to interaction (RERI) and the attributable proportion (AP), along with their corresponding 95% confidence intervals. Details regarding the formulas used for assessing additive interaction are provided in the Supplementary Statistical Methods. We subsequently conducted similar analyses among the smaller set of participants with available lifetime-employment history data, repeating the stratified analysis based on lifetime exposure to night shift work. In parallel, we performed joint and interaction analyses to examine whether lifetime night shift work modified the association between CII categories and CKM disease risk. All statistical analyses were performed using R software, version 4.3.1 (R foundation for Statistical Computing). All P-values were two-sided, with a threshold of P < 0.05 considered statistically significant. RESULTS Population characteristics Table 1 presents baseline characteristics of the study participants by level of CII. Among the 191,764 participants, 66,962 (34.9%), 65,765 (34.3%), 42,246 (22.0%), 14,468 (7.5%), and 2,323 (1.2%) had a CII of 0–1, 2, 3, 4, and 5, respectively. The mean age of participants was 52 years (SD = 7), 51% were women, 87% were of European ancestry, and 83% were employed in non-shift work occupations. Overall, participants with higher CII were more likely to be women, to undertake night shift work, and have lower educational attainment and household income. They were also more likely to currently smoke, to exercise less, and to have a higher BMI. Table 1 Characteristics of 191,764 participants from the UK biobank*, overall and according to category of Circadian Imbalance Index (CII). Baseline Characteristic Overall Circadian Imbalance Index 0–1 2 3 4 5 Number of participants 191,764 66,962 65,765 42,246 14,468 2,323 Female % (N) 50.84 (97,490) 48.19 (32,271) 50.95 (33,506) 52.90 (22,350) 55.45 (8,023) 57.68 (1,340) Age (years) (mean(SD)) 52.36 (7.03) 52.97 (7.10) 52.37 (7.01) 51.84 (6.95) 51.29 (6.79) 50.59 (6.71) Ethnicity % (N) African 1.35 (2,583) 0.45 (333) 1.36 (897) 2.24 (946) 2.41 (349) 2.50 (58) Asian 1.82 (3,481) 0.84 (562) 2.05 (1,350) 2.61 (1,103) 2.75 (398) 2.93 (68) European 86.67 (166,194) 89.49 (59,921) 86.21 (56,698) 84.27 (35,600) 83.34 (12,058) 82.52 (1,917) Other 10.17 (19,506) 9.178 (6,146) 10.37 (6,820) 10.88 (4,597) 11.49 (1,663) 12.05 (280) Night shift work status % (N) Day workers 83.47 (160,068) 86.06 (57,629) 83.78 (55,096) 81.30 (34,347) 78.17 (11,309) 72.62 (1,687) Shift workers 8.10 (15,529) 7.25 (4,856) 8.09 (5,323) 8.78 (3,709) 9.52 (1,378) 11.32 (263) Night shift workers 8.43 (16,167) 6.69 (4,477) 8.13 (5,346) 9.92 (4,190) 12.31 (1,781) 16.06 (373) Current smokers % (N) 10.37 (19,892) 7.707 (5,161) 10.18 (6,697) 12.51 (5,283) 15.97 (2,311) 18.94 (440) Daily or almost daily drinker % (N) 20.07 (38,489) 21.94 (14,691) 19.64 (12,915) 18.62 (7,868) 18.14 (2,625) 16.79 (390) Household income % (N) £100,000 7.64 (14,644) 9.20 (6,158) 7.75 (5,095) 6.09 (2,573) 5.11 (739) 3.40 (79) Unknown 7.94 (15,230) 7.84 (5,248) 8.02 (5,275) 7.96 (3,361) 8.14 (1,178) 7.23 (168) College education % (N) 39.19 (75,151) 41.05 (27,487) 39.78 (26,163) 37.18 (15,707) 34.82 (5,038) 32.54 (756) Physical activity % (N) Low (< 10 MET-h/week) 15.94 (30,570) 13.09 (8,767) 16.31 (10,728) 18.28 (7,722) 19.64 (2,842) 22.00 (511) Middle (10 ~ 50 MET-h/week) 43.08 (82,603) 44.86 (30,038) 43.31 (28,486) 41.75 (17,639) 38.75 (5,606) 35.90 (834) High (> 50 MET-h/week) 24.77 (47,504) 27.49 (18,409) 24.22 (15,925) 22.51 (9,509) 21.89 (3,167) 21.27 (494) Unknown 16.21 (31,087) 14.56 (9,748) 16.16 (10,626) 17.46 (7,376) 19.72 (2,853) 20.84 (484) Hypertension % (N) 41.16 (78,939) 41.58 (27,842) 41.32 (27,177) 40.68 (17,186) 40.30 (5,831) 38.87 (903) Self-report hyperlipidemia % (N) 7.13 (13,668) 6.83 (4,570) 7.04 (4,628) 7.49 (3,164) 7.76 (1,123) 7.88 (183) BMI (kg/m 2 ) (mean(SD)) 27.15 (4.63) 26.59 (4.15) 27.21 (4.64) 27.58 (4.92) 27.97 (5.21) 28.69 (5.78) BMI: body mass index; SD: standard deviation. * No restrictions based on ethnicity were implemented. Prospective association between CII and CKM disease risk During a median follow-up period of 13.5 years, a total of 16,907 incident cases of CKM disease were documented. Table 2 presents the associations between CII and CKM disease risk across genetic ancestry groups, as estimated by multivariable-adjusted models. Among individuals of either European or Asian ancestry, higher CII values were consistently associated with a higher risk of CKM disease compared to those with a CII of 0–1, across all models ( P trend < 0.05). Following adjustments for age, sex, household income and education level in Model 2, the HRs for Europeans and Asians were slightly attenuated but remained statistically significant. Specifically for the Europeans, the HRs (95% CI) for CII values of 2, 3, 4, and 5 were 1.26 (1.21–1.31), 1.42 (1.36–1.49), 1.65 (1.55–1.76), and 1.95 (1.70–2.23), respectively. Among Asian participants, compared with those with a CII of 0–1, the multivariable-adjusted HRs (95% CI) for CII of 2 to 5 were 1.41 (1.07–1.86), 1.49 (1.13–1.97), 1.64 (1.17–2.31), and 2.03 (1.07–3.86), respectively, in Model 2. Further adjustment for smoking status, alcohol consumption, and physical activity (Model 3), as well as body mass index (Model 4), attenuated these associations somewhat, yet they remained significant among Europeans and Asians. In contrast, no significant association was observed between CII and CKM disease risk among participants of African ancestry. Table 2 Prospective associations between Circadian Imbalance Index (CII) and risk of incident Cardiovascular-Kidney-Metabolic disease among 191,764 participants from the UK Biobank, stratified by genetic ancestry. Ethnicity CII Case/N Model 1 a Model 2 b Model 3 c Model 4 d HR (95%CI) P trend HR (95%CI) P trend HR (95%CI) P trend HR (95%CI) P trend European (N = 166,194) 0–1 4 474/59 921 Reference < 0.001 Reference < 0.001 Reference < 0.001 Reference < 0.001 2 4 979/56 698 1.27 (1.22–1.32) 1.26 (1.21–1.31) 1.22 (1.17–1.27) 1.14 (1.09–1.18) 3 3 394/35 600 1.47 (1.40–1.53) 1.42 (1.36–1.49) 1.34 (1.28–1.40) 1.21 (1.16–1.27) 4 1 269/12 058 1.74 (1.63–1.85) 1.65 (1.55–1.76) 1.53 (1.44–1.63) 1.34 (1.26–1.42) 5 221/ 1 917 2.08 (1.82–2.38) 1.95 (1.70–2.23) 1.75 (1.53–2.01) 1.42 (1.24–1.63) Asian (N = 3,481) 0–1 67/ 562 Reference < 0.001 Reference 0.002 Reference 0.006 Reference 0.024 2 211/1 350 1.42 (1.08–1.87) 1.41 (1.07–1.86) 1.39 (1.06–1.84) 1.37 (1.04–1.80) 3 183/1 103 1.51 (1.14-2.00) 1.49 (1.13–1.97) 1.44 (1.09–1.91) 1.36 (1.03–1.81) 4 68/ 398 1.71 (1.22–2.39) 1.64 (1.17–2.31) 1.58 (1.13–2.23) 1.49 (1.06–2.09) 5 11/ 68 2.13 (1.12–4.05) 2.03 (1.07–3.86) 1.91 (1.00-3.64) 1.82 (0.95–3.48) African (N = 2,583) 0–1 42/333 Reference 0.326 Reference 0.353 Reference 0.442 Reference 0.662 2 141/897 1.39 (0.99–1.96) 1.38 (0.98–1.95) 1.33 (0.94–1.87) 1.29 (0.91–1.82) 3 142/946 1.32 (0.94–1.86) 1.30 (0.92–1.84) 1.25 (0.89–1.77) 1.26 (0.89–1.78) 4 52/349 1.32 (0.88–1.98) 1.31 (0.87–1.97) 1.27 (0.84–1.90) 1.16 (0.77–1.74) 5 8/ 58 1.46 (0.68–3.11) 1.43 (0.67–3.06) 1.36 (0.63–2.91) 1.24 (0.58–2.65) a Model 1 includes sex and age b Model 2 includes variables in Model 1 and household income and education c Model 3 includes variables in Model 2 and smoking status, alcohol consumption and physical activity d Model 4 includes variables in Model 3 and body mass index (BMI) In sensitivity analyses, we additionally adjusted for hypertension and hyperlipidemia, as well as recruitment season (Supplemental Table 2); excluded those with missing information on any of the covariables that were considered (Supplemental Table 3); and excluded participants with a report of incident CKM within the first 2 years of follow-up (Supplemental Table 4); all results consistently demonstrated that higher CII levels were associated with higher risks of CKM diseases. Further, in analysis adjusting (rather than stratifying) for genetic ethnicity results were similar to those in the stratum of participants with European ancestry only (Supplemental Table 5). In addition, we found that each individual circadian trait was independently associated with an elevated risk of CKM disease. Moreover, within the European ancestry group, the overall CII was significantly associated with the risk of each individual CKM disease component. Detailed results of these analyses are provided in Supplemental Table 12–15, and Supplemental Fig. 3. Although in gender-stratified analyses, the association between CII and CKM disease risk appeared stronger among women compared to men in Model 2, there was no statistically significant effect modification by gender ( P interaction = 0.666) (Supplemental Table 6). Further stratification by genetic ancestry (Supplemental Table 7) revealed that the HRs in Model 2 were particularly elevated among European women and Asian men. No significant associations were observed within African ancestry gender subgroups. Additive and multiplicative interactions of CII and night shift work Among individuals of European ancestry, a significant dose-response relationship between the CII and CKM disease risk was observed across all categories of work status, with both shift workers and night shift workers exhibiting elevated CKM disease risk (Table 3 ). Specifically, compared to participants with a CII of 0–1, the multivariable-adjusted HRs (95%CIs) for those with CII of 5 in Model 2 were 1.82 (1.54–2.15) among day workers, 2.57 (1.82–3.62) among shift workers, and 1.86 (1.34–2.57) among night shift workers. Consistent patterns of significant associations between CII and CKM were also observed in a subset of participants who provided detailed lifetime occupational histories, particularly among those who had worked night shifts for over 20 years or engaged in night shift work for at least 8 nights per month (Supplemental Tables 8–9). Table 3 Prospective associations between Circadian Imbalance Index (CII) and risk of incident Cardiovascular-Kidney-Metabolic disease among 166,194 European ancestry participants from the UK biobank, stratified by work status. Group CII Case/N Model 1 a Model 2 b Model 3 c Model 4 d HR (95%CI) P trend HR (95%CI) P trend HR (95%CI) P trend HR (95%CI) P trend Day workers (N = 139,853) 0–1 3 761/51 699 Reference < 0.001 Reference < 0.001 Reference < 0.001 Reference < 0.001 2 4 020/47 953 1.25 (1.19–1.30) 1.23 (1.18–1.29) 1.20 (1.15–1.25) 1.12 (1.07–1.17) 3 2 634/29 267 1.43 (1.36–1.50) 1.39 (1.32–1.46) 1.32 (1.25–1.38) 1.19 (1.13–1.25) 4 930/ 9 525 1.66 (1.55–1.79) 1.60 (1.49–1.72) 1.49 (1.38–1.60) 1.30 (1.21–1.40) 5 144/ 1 409 1.92 (1.62–2.26) 1.82 (1.54–2.15) 1.64 (1.39–1.94) 1.36 (1.15–1.61) Shift workers (N = 13,029) 0–1 377/4 236 Reference < 0.001 Reference < 0.001 Reference < 0.001 Reference < 0.001 2 453/4 407 1.25 (1.09–1.43) 1.24 (1.08–1.42) 1.19 (1.04–1.37) 1.12 (0.98–1.29) 3 373/3 036 1.58 (1.37–1.83) 1.54 (1.34–1.78) 1.46 (1.26–1.69) 1.33 (1.15–1.53) 4 132/1 137 1.59 (1.31–1.95) 1.55 (1.27–1.89) 1.41 (1.15–1.72) 1.25 (1.02–1.53) 5 36/ 213 2.68 (1.90–3.78) 2.57 (1.82–3.62) 2.34 (1.66–3.31) 1.88 (1.33–2.66) Night shift workers (N = 13,312) 0–1 336/3 986 Reference < 0.001 Reference < 0.001 Reference < 0.001 Reference < 0.001 2 506/4 338 1.46 (1.27–1.68) 1.45 (1.26–1.66) 1.40 (1.22–1.61) 1.28 (1.11–1.47) 3 387/3 297 1.51 (1.31–1.75) 1.48 (1.28–1.71) 1.39 (1.20–1.61) 1.22 (1.05–1.41) 4 207/1 396 2.01 (1.69–2.39) 1.93 (1.62–2.30) 1.81 (1.52–2.15) 1.60 (1.34–1.90) 5 41/ 295 1.94 (1.40–2.68) 1.86 (1.34–2.57) 1.65 (1.19–2.29) 1.29 (0.93–1.79) a Model 1 includes sex and age b Model 2 includes variables in Model 1 and household income and education c Model 3 includes variables in Model 2 and smoking status, alcohol consumption and physical activity d Model 4 includes variables in Model 3 and body mass index (BMI) * ‘Shift workers’ were those who worked shift work, but never or rarely worked night shifts, ‘Night shift workers’ were those who report working night shifts sometimes, usually, or always. * ‘Shift workers’ were those who work shift works, but never or rarely worked night shifts, ‘Night shift workers’ were those who report working night shifts sometimes, usually, or always. We further assessed the joint association of night shift work status and CII categories with the CKM disease outcome. Within each work status group, a higher CII was consistently associated with an elevated risk of CKM disease in a dose-response manner. Specifically, compared to the reference group (day workers with low CII of 0–1), night shift workers with an intermediate CII (HR: 1.69; 95% CI: 1.57–1.81) or a high CII (HR: 2.22; 95% CI: 1.95–2.53) exhibited significantly elevated risk of CKM disease. A similarly elevated hazard ratio was also observed among shift workers with intermediate or high CII (Fig. 1 , Table 4 ). Although the test for multiplicative interaction was not statistically significant ( P interaction = 0.238), we observed a significant additive interaction between night shift work and both intermediate (2–3) or high CII (4–5) (Table 4 ). Specifically, for night shift workers with a high CII, the relative excess risk due to interaction (RERI) was estimated at 0.456 (95% CI: 0.138–0.775), and the attributable proportion (AP) was 0.205 (95% CI: 0.083–0.327) (Table 4 ). These findings indicate a 45.6% excess relative risk attributable to the additive interaction between night shift work and high CII, with 20.5% (95% CI: 8.3–32.7%) of the CKM disease risk in this group being attributable to their combined effect (Table 4 ). Furthermore, similar patterns of joint associations were observed when other metrics of night shift work, including duration and intensity, were considered (Supplemental Fig. 4, Supplemental Tables 10–11). Notably, significant additive interactions were also observed between a high CII and long-term night shift work (≥20 years), as well as between a high CII and high-intensity night shift work (≥ 8 nights per month) (Supplemental Tables 10–11). Table 4 Multivariable adjusted hazard ratios with 95% CI, RERI and AP for additive interaction between Circadian Imbalance Index (CII) and shift work status for cardiovascular-kidney-metabolic disease among European ancestry UKB participants, stratified by categories of circadian imbalance index (CII) and night shift work status, n = 166,194. Characteristic N Case HR (95% CI) 1 P value RERI (95%CI) 2 AP (95%CI) 3 Low CII (0–1) Day workers 51,699 3,761 Reference Ref. Ref. Shift workers 4,236 377 1.17 (1.05, 1.30) 0.004 - - Night Shift Workers 3,986 336 1.15 (1.03, 1.28) 0.016 - - Middle CII (2–3) Day workers 77,220 6,654 1.29 (1.24, 1.34) < 0.001 - - Shift workers 7,443 826 1.59 (1.48, 1.72) < 0.001 0.132 (-0.034–0.298) 0.083 (-0.018–0.184) Night Shift Workers 7,635 893 1.69 (1.57, 1.81) < 0.001 0.249 (0.079–0.419) 0.148 (0.052–0.243) High CII (4–5) Day workers 10,934 1,074 1.62 (1.51, 1.73) < 0.001 - - Shift workers 1,350 168 1.98 (1.69, 2.31) < 0.001 0.185 (-0.152–0.522) 0.094 (-0.065–0.252) Night Shift Workers 1,691 248 2.22 (1.95, 2.53) < 0.001 0.456 (0.138–0.775) 0.205 (0.083–0.327) 1 HR = Hazard Ratio, CI = Confidence Interval 2 RERI = relative excess risk due to the interaction 3 AP = attributable proportion due to the interaction * Model adjusted for sex, age, household income and education * To estimate the RERI and AP, the low Circadian Imbalance Index (0–1) and the day worker group were the reference categories. ‘Shift workers’ were those who work shift works, but never or rarely worked night shifts, ‘Night shift workers’ were those who report working night shifts sometimes, usually, or always. DISCUSSION In this study, we observed that higher propensity for circadian imbalance, as described by our newly developed circadian imbalance index (CII), was significantly associated with a higher risk of CKM disease. Moreover, compared to participants with low circadian imbalance, those with higher CII showed a progressively elevated risk of CKM disease across all metrics of night shift work status (duration, intensity, and cumulative number of lifetime night shift work). Participants who worked night shifts and were classified into the high CII group (4–5) exhibited the greatest risk when compared to day workers in the low CII group (0–1). Furthermore, we identified a significant additive interaction between CII categories and night shift work status on CKM risk. This interaction was particularly pronounced among individuals with extended night shift duration (≥ 20 years) and high night shift intensity (≥ 8 nights per month). Comparison with other studies To the best of our knowledge, this is the first prospective cohort study to investigate the relationships between a newly developed CII, integrating evening chronotype, short or long sleep duration, scoring higher on the neuroticism spectrum, atypical caffeinated coffee intake and low serum concentration vitamin D, and the risk of CKM disease. Though prior studies have explored the associations of night shift work, chronotype, and sleep duration with various chronic health outcomes independently [ 50 – 53 ], the potential additive effect of multiple circadian-related traits, as captured by the CII, on the risk of CKM diseases has not been previously evaluated. One previous study found that evening chronotype was significantly associated with cardiovascular health in night-shift workers [ 54 ]. Additionally, Young et al. proposed that night shift workers with long or short sleep duration had higher blood pressure [ 55 ]. In this study, we newly constructed a CII by taking into account the combined impact of five circadian imbalance traits on CKM risk, which reflects the most comprehensive circadian imbalance evaluation to date. We found CII to be associated with a higher risk of CKM among the women but not the men of European ancestry in our study, whereas the opposite pattern was observed among Asian ancestry, though none of these interactions reached statistical significance, and so these results should be interpreted with caution. While a previous small experiment has suggested that circadian misalignment might have a stronger effect on metabolic disorder among women compared with men [ 56 ], gender differences regarding metabolic disorder syndrome have been mixed in numerous countries [ 57 ]. Alternatively, there may be gender differences in the types of occupations held by men and women across different regions of the world, particularly with respect to the intensity and nature of shift work, including night shifts. Overall, these secondary gender-ancestry subgroup analyses require further exploration. We observed a higher risk of CKM disease with increasing CII among night shift workers, especially among those with longer lifetime duration in terms of years worked night shifts, and greater intensity of night shifts. Similar to our finding, several previous studies assessing more detailed metrics of night shift work have described an increased risk of type 2 diabetes [ 43 ] or cardiovascular disease [ 58 ] with longer duration or greater intensity of night shift work. Our findings highlight the additive interaction between night shift work status and CII on CKM disease risk. Specifically, the combination of both night shift work and high CII would result in an additional 20.5% of CKM cases. Consistently, we also observed that the significantly higher risk of CKM disease associated with longer lifetime duration or greater intensity of night shift work was further amplified among individuals in the high CII group. These findings raise the possibility that keeping a low CII among night shift workers, especially those with long-term or high-intensity night shift exposure, may be an effective strategy for reducing the risk of CKM disease. From a public health perspective, the CII may serve as a useful tool for helping night shift workers assess their degree of circadian imbalance, identify those at higher risk of CKM, and guide the development of personalized strategies for CKM disease prevention. Clinical trials would be required to test the efficacy and safety of any new intervention strategies. Potential mechanisms Night shift work and the traits in our study that we used to define circadian imbalance are likely to share serval potential underlying mechanisms involved in CKM disease risk. To date, exposure to night shift work remains the most common and extreme observational model of circadian misalignment in human studies [ 59 ], and, similar to circadian imbalance traits, is typically chronic in nature. Night shift work has been associated with an increase in inflammatory markers e.g., level of C-reactive protein (CRP), tumor necrosis factor (TNF-α), and interleukin-6 (IL-6) levels [ 60 ], which in turn increase risk of chronic inflammatory conditions such as type 2 diabetes [ 61 ] and obesity [ 62 ]. Circadian disruption has further been associated with hormonal changes in appetite regulation, including reduced leptin and elevated ghrelin levels, which contribute to weight gain and metabolic dysregulation [ 63 ]. On a molecular level, virtually all mammalian cell types have a functional circadian clock including clock and period genes, such as CLOCK , BMAL1 PER1 , PER2 , PER3 , CRY1 , and CRY2 [ 64 , 65 ]. Oscillation and dysregulated expression of the molecular circadian clock has been linked to atherosclerosis, insulin resistance, dampening of blood pressure rhythmicity, and reduced production of vasoactive hormones and neurotransmitters [ 66 , 67 ]. Experimental evidence shows that mice with CLOCK gene mutations exhibited disrupted feeding and activity rhythms under ad libitum conditions, leading to obesity and metabolic syndrome [ 68 ]. These studies point to the potential mediating effects of obesity, highlighting the importance of our modeling strategy, adding BMI separately in model 4. Caffeine consumption and bright light / vitamin D have been linked to melatonin secretion and circadian rhythms [ 8 , 69 ]. Sleep-wake cycle disturbances and exposure to irregular light-dark patterns, as commonly seen in night shift work, may further impair synthesis of cortisol and melatonin [ 70 , 71 ]. Previous studies consistently support melatonin’s anti-inflammatory, antihypertensive, and oxidative activity and its possibility to reduce the risk of cardiometabolic disease, including type 2 diabetes and hypertension [ 72 , 73 ]. Therefore, reduced melatonin levels resulting from chronic circadian imbalance or night shift work maybe represent another underlying pathway for the observed associations in our study. Lastly, some lifestyle behaviors such as smoking, sedentary behavior, and irregular meal timing could also be potential contributors to CKM disease. Further studies are needed to explore the pathophysiological pathways underlying the interaction between night shift work and circadian imbalance related traits on CKM disease risk. Strengths and limitations The main strengths of this study include its prospective study design, large sample size, and long-term follow up. More importantly, the integration of several circadian imbalance-related traits allowed for a more comprehensive assessment of their potential impact on CKM disease risk. Further, we were able to assess their relationship among different ancestries. Another major novelty of this study is that it is the first to investigate the joint association of night shift work and circadian imbalance related traits with the risk of CKM disease. We also provide novel and unique insight by using detailed shift work metrics including lifetime years and intensity of night shift work. Although uncontrolled confounding remains a limitation in any observational study, the extensive data collection in the UK Biobank allowed for detailed control of potential confounders and mediators. The present study also has several limitations. First, information on night shift work and most circadian imbalance related factors was self-reported (with the exception of vitamin D which was measured in serum), thus exposure misclassification potentially exists. However, such misclassification would likely be random to outcome status, resulting in attenuation of the effect estimations and underestimation of the observed associations. Second, we dichotomized five circadian imbalance related traits to create CII and assigned equal weight to each trait, which might result in loss of information and study power. In addition, information was not available on consumption of caffeinated tea and daily timing of coffee consumption. Furthermore, the CII may not have fully captured all relevant circadian imbalance related traits, such as light exposure during the day, meal timing, or timing of physical activity [ 74 , 75 ], potentially overlooking other important aspects of circadian disruption. Further, the UK Biobank’s healthy volunteer bias may limit the generalizability of our findings to less healthy populations [ 76 ]. Finally, although the present study included participants of different ancestries from the UK Biobank, the small number of individuals of African ancestry limited the statistical power for analyses within this group. Conclusion and public health implications In summary, a newly derived circadian imbalance index, CII, integrating evening chronotype, short or long sleep duration, high neuroticism score, atypical caffeine consumption, and low vitamin D levels, was associated with increased CKM disease risk among European and Asian ancestries. Notably, CII and night shift work were jointly associated with a higher risk of CKM disease, and there was an additive association of night shift work and high CII on CKM disease risk. Our findings highlight the possibility that cases of CKM disease could be prevented by reducing high CII scores, which reflect a greater burden of circadian imbalance related traits. The benefits may be particularly pronounced among night shift worker or those with longer duration or greater intensity of night shift exposure. Intervention trials and mechanistic research are warranted to extend this research and clarify the underlying biological mechanisms. Abbreviations CKM Cardiovascular-Kidney-Metabolic CII Circadian Imbalance Index HRs Hazard Ratios CIs Confidence Intervals T2D Type 2 Diabetes CVD Cardiovascular Disease CKD Chronic Kidney Disease ICD-10 International Classification of Diseases BMI Body Mass Index RERI Relative Excess Risk to Interaction AP Attributable Proportion CRP C-reactive protein TNF-α tumor necrosis factor IL-6 interleukin-6 levels Declarations Ethics approval and consent to participate The National Research Ethics Service approved the UK Biobank study (ref. 11/NW/0382), and all participants provided written informed consent. Consent for publication Not applicable Availability of data and materials The data underlying this article cannot be shared publicly. However, researchers are encouraged to apply to access to the UK Biobank resource for health-related research that serves the public interest. The statistical R code and technical processes are available from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This study was supported by European Union, European Research Council (ERC) Advanced Grant CLOCKrisk (grant number 101053225), Department of Epidemiology, Medical University of Vienna to PI Eva Schernhammer. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Authors' contributions Contributors: JZ, ES were involved in the study conception and design. ES provided funding, and JZ analyzed and interpreted the data. DT supported JZ with data analyses. SS, MZ, and ES provided statistical expertise. JZ drafted the manuscript. All authors participated in the interpretation of the results and critically reviewed the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. JZ and ES have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; they are the guarantors. Acknowledgments This research was conducted using the UK Biobank resource under application number 48576. We thank all the participants and staff of the UK Biobank for enabling us to conduct this research. References Mohawk JA, Green CB, Takahashi JS. Central and peripheral circadian clocks in mammals. Annu Rev Neurosci. 2012;35:445–62. Gamble KL, et al. Circadian clock control of endocrine factors. Nat Rev Endocrinol. 2014;10(8):466–75. Fishbein AB, Knutson KL, Zee PC. Circadian disruption and human health. J Clin Invest, 2021. 131(19). Montaruli A, et al. Biological rhythm and chronotype: new perspectives in health. Biomolecules. 2021;11(4):487. Murray JM, et al. Sleep regularity is associated with sleep-wake and circadian timing, and mediates daytime function in delayed sleep-wake phase disorder. Sleep Med. 2019;58:93–101. DeYoung CG, et al. Morning people are stable people: Circadian rhythm and the higher-order factors of the Big Five. Pers Indiv Differ. 2007;43(2):267–76. Burke TM, et al. Effects of caffeine on the human circadian clock in vivo and in vitro. Sci Transl Med. 2015;7(305):305ra146. Yousefi T, et al. Molecular pathways and biological roles of melatonin and vitamin D; effects on immune system and oxidative stress. Int Immunopharmacol. 2024;143(Pt 3):113548. Webb AR, et al. The role of sunlight exposure in determining the vitamin D status of the U.K. white adult population. Br J Dermatol. 2010;163(5):1050–5. Li T et al. Prospective study of the association between chronotype and cardiometabolic risk among Chinese young adults. BMC Public Health, 2023. 23(1): p. 1966. Knutson KL. Sleep duration and cardiometabolic risk: a review of the epidemiologic evidence. Best Pract Res Clin Endocrinol Metab. 2010;24(5):731–43. Lee LO, et al. Neuroticism, Worry, and Cardiometabolic Risk Trajectories: Findings From a 40-Year Study of Men. J Am Heart Assoc. 2022;11(3):e022006. Ungvari Z, Kunutsor SK. Coffee consumption and cardiometabolic health: a comprehensive review of the evidence. Geroscience. 2024;46(6):6473–510. Song Y, Wang L. Vitamin D and cardiometabolic disease: From observation to intervention. Curr Nutr Rep. 2012;1:55–63. Employees working shifts as a percentage of the total of employees . 2024; Available from: https://ec.europa.eu/eurostat/databrowser/view/lfsa_ewpshi/default/table?lang=en Ansu Baidoo V, Knutson KL. Associations between circadian disruption and cardiometabolic disease risk: a review. Obesity. 2023;31(3):615–24. Hemmer A, et al. The effects of shift work on cardio-metabolic diseases and eating patterns. Nutrients. 2021;13(11):4178. Torquati L, et al. Shift work and the risk of cardiovascular disease. A systematic review and meta-analysis including dose–response relationship. Scand J Work Environ Health. 2018;44(3):229–38. Schernhammer ES, et al. Rotating night shifts and risk of breast cancer in women participating in the nurses' health study. J Natl Cancer Inst. 2001;93(20):1563–8. Dickerman BA, et al. Sleep disruption, chronotype, shift work, and prostate cancer risk and mortality: a 30-year prospective cohort study of Finnish twins. Cancer Causes Control. 2016;27:1361–70. Lee Y, et al. A longitudinal study of working hours and chronic kidney disease in healthy workers: The Kangbuk Samsung Health Study. J Occup Health. 2021;63(1):e12266. Hulsegge G, et al. Shift work, chronotype and the risk of cardiometabolic risk factors. Eur J Public Health. 2019;29(1):128–34. Hu P, et al. Joint Association of Sleep Onset Time and Sleep Duration With Cardiometabolic Health Outcome. J Am Heart Assoc. 2024;13(12):e034165. Circulatory diseases – main cause of death in 2022 . 2025; Available from: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20250321-2?utm_source=chatgpt.com Sarafidis P, et al. SGLT-2 inhibitors and GLP-1 receptor agonists for nephroprotection and cardioprotection in patients with diabetes mellitus and chronic kidney disease. A consensus statement by the EURECA-m and the DIABESITY working groups of the ERA-EDTA. Nephrol Dialysis Transplantation. 2019;34(2):208–30. Rangaswami J, et al. Cardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association. Circulation. 2019;139(16):e840–78. Cherney DZ, et al. Impact of cardio-renal-metabolic comorbidities on cardiovascular outcomes and mortality in type 2 diabetes mellitus. Am J Nephrol. 2020;51(1):74–82. Ansu Baidoo V, Knutson KL. Associations between circadian disruption and cardiometabolic disease risk: A review. Obes (Silver Spring). 2023;31(3):615–24. Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. Smith DJ, et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS ONE. 2013;8(11):e75362. Biobank U. serum_biochemistry . Available from: https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf Landolt HP. CIRCADIAN RHYTHMS. Caffeine, the circadian clock, and sleep. Science. 2015;349(6254):1289. Cable J, et al. Sleep and circadian rhythms: pillars of health-a Keystone Symposia report. Ann N Y Acad Sci. 2021;1506(1):18–34. Brooks C, et al. The impact of chronotype on circadian rest-activity rhythm and sleep characteristics across the week. Chronobiol Int. 2021;38(11):1575–90. Baron K, et al. 0017 Circadian Phase Alignment and Sleep Duration in Overweight Adults: Associations with Cardiometabolic Risk Factors. Sleep. 2024;47(Supplement1):A7–8. Muscogiuri G, et al. The lullaby of the sun: the role of vitamin D in sleep disturbance. Sleep Med. 2019;54:262–5. Jones SE, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. 2019;10(1):343. Lao XQ, et al. Sleep quality, sleep duration, and the risk of coronary heart disease: a prospective cohort study with 60,586 adults. J Clin Sleep Med. 2018;14(1):109–17. Martelli M, et al. Shift work and serum vitamin D levels: a systematic review and meta-analysis. Int J Environ Res Public Health. 2022;19(15):8919. Wikoff D, et al. Systematic review of the potential adverse effects of caffeine consumption in healthy adults, pregnant women, adolescents, and children. Food Chem Toxicol. 2017;109:585–648. Heckman MA, Weil J. Gonzalez de Mejia, Caffeine (1, 3, 7-trimethylxanthine) in foods: a comprehensive review on consumption, functionality, safety, and regulatory matters . J Food Sci. 2010;75(3):R77–87. Wang N, et al. Long-term night shift work is associated with the risk of atrial fibrillation and coronary heart disease. Eur Heart J. 2021;42(40):4180–8. Vetter C, et al. Night shift work, genetic risk, and type 2 diabetes in the UK biobank. Diabetes Care. 2018;41(4):762–9. Dat Thien Tran. Susanne Strohmaier; Magdalena Żebrowska; Tianyi Huang; Kyriaki Papantoniou; Susan Redline; Richa Saxena; Martin K Rutter, E.S., Risk of Cardiovascular-kidney-metabolic diseases in the UK Biobank: influences of night shift work, sleep duration, and chronotype. Under review. ICD-10: international statistical classification of diseases and related health problems : tenth revision , 2nd ed . 2019: World health organization. World Health Organization’s guidelines of ICD-10 . Available from: https://www.who.int/standards/classifications/classification-of-diseases Shan Z et al. 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. BMJ, 2018. 363. Guo T et al. The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study. Eclinicalmedicine, 2024. 69. Li R, Chambless L. Test for additive interaction in proportional hazards models. Ann Epidemiol. 2007;17(3):227–36. Morris CJ et al. Circadian misalignment increases cardiovascular disease risk factors in humans. Proceedings of the National Academy of Sciences, 2016. 113(10): pp. E1402-E1411. Yang L, et al. Shift work and the risk of cardiometabolic multimorbidity among patients with hypertension: a prospective cohort study of UK biobank. J Am Heart Association. 2022;11(17):e025936. Shan Z, et al. 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. BMJ. 2018;363:k4641. Mason IC, et al. Impact of circadian disruption on glucose metabolism: implications for type 2 diabetes. Diabetologia. 2020;63(3):462–72. Jiang J, et al. Effects of chronotype on sleep, mood and cardiovascular circadian rhythms in rotating night shift medical workers. Int Arch Occup Environ Health. 2024;97(4):461–71. Kanki M, et al. Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants. Nat Commun. 2023;14(1):7096. Qian J et al. Sex differences in the circadian misalignment effects on energy regulation. Proceedings of the National Academy of Sciences, 2019. 116(47): pp. 23806–23812. Jiang B, et al. Age and gender-specific distribution of metabolic syndrome components in East China: role of hypertriglyceridemia in the SPECT-China study. Lipids Health Dis. 2018;17:1–11. Vetter C, et al. Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank. Diabetes Care. 2018;41(4):762–9. Vetter C. Circadian disruption: What do we actually mean? Eur J Neurosci. 2020;51(1):531–50. Wright KP Jr., et al. Influence of sleep deprivation and circadian misalignment on cortisol, inflammatory markers, and cytokine balance. Brain Behav Immun. 2015;47:24–34. Wang X, et al. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2013;36(1):166–75. Ellulu MS, et al. Obesity and inflammation: the linking mechanism and the complications. Arch Med Sci. 2017;13(4):851–63. Gonnissen HK, et al. Effect of a phase advance and phase delay of the 24-h cycle on energy metabolism, appetite, and related hormones. Am J Clin Nutr. 2012;96(4):689–97. Hogenesch JB, et al. Characterization of a subset of the basic-helix-loop-helix-PAS superfamily that interacts with components of the dioxin signaling pathway. J Biol Chem. 1997;272(13):8581–93. Allada R, et al. Stopping time: the genetics of fly and mouse circadian clocks. Annu Rev Neurosci. 2001;24(1):1091–119. Curtis AM et al. Circadian variation of blood pressure and the vascular response to asynchronous stress. Proceedings of the National Academy of Sciences, 2007. 104(9): pp. 3450–3455. Lefta M, et al. Development of dilated cardiomyopathy in Bmal1-deficient mice. Am J Physiol Heart Circ Physiol. 2012;303(4):H475–85. Turek FW, et al. Obesity and metabolic syndrome in circadian Clock mutant mice. Science. 2005;308(5724):1043–5. Wright KP Jr., et al. Caffeine and light effects on nighttime melatonin and temperature levels in sleep-deprived humans. Brain Res. 1997;747(1):78–84. Refinetti R. Integration of biological clocks and rhythms. Compr Physiol. 2011;2(2):1213–39. Ulhôa M, et al. Shift work and endocrine disorders. Int J Endocrinol. 2015;2015(1):826249. McMullan CJ, et al. Melatonin secretion and the incidence of type 2 diabetes. JAMA. 2013;309(13):1388–96. Li Y, et al. Use of melatonin supplements and risk of type 2 diabetes and cardiovascular diseases in the USA: insights from three prospective cohort studies. Lancet Diabetes Endocrinol. 2024;12(6):404–13. Xu Y, Zhang, Ding W-q. Association of light at night with cardiometabolic disease: A systematic review and meta-analysis. Environ Pollut. 2024;342:123130. McHill AW, et al. Later circadian timing of food intake is associated with increased body fat. Am J Clin Nutr. 2017;106(5):1213–9. Fry A, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017;186(9):1026–34. Supplementary Files SupplementaryMaterials259.docx Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in European Journal of Epidemiology → Version 1 posted Reviewers agreed at journal 02 Oct, 2025 Reviewers invited by journal 02 Oct, 2025 Editor invited by journal 01 Oct, 2025 Editor assigned by journal 27 Sep, 2025 First submitted to journal 26 Sep, 2025 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-7722286","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523683646,"identity":"406b93a6-d6d5-4070-a876-6d084eface57","order_by":0,"name":"Jing Zhang","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-8828-499X","institution":"Medical University of Vienna: Medizinische Universitat Wien","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":523683647,"identity":"453d07f0-2d9c-4667-ac54-0ef998c4baa4","order_by":1,"name":"Dat Thien Tran","email":"","orcid":"","institution":"Medical University of Vienna: Medizinische Universitat Wien","correspondingAuthor":false,"prefix":"","firstName":"Dat","middleName":"Thien","lastName":"Tran","suffix":""},{"id":523683648,"identity":"cb831218-254f-49f6-9a2c-e50369ee4c36","order_by":2,"name":"Tala El Ghoul","email":"","orcid":"","institution":"Medical University of Vienna: Medizinische Universitat Wien","correspondingAuthor":false,"prefix":"","firstName":"Tala","middleName":"El","lastName":"Ghoul","suffix":""},{"id":523683649,"identity":"4422fb5c-fedb-475d-acab-177af0bf3f12","order_by":3,"name":"Susanne Strohmaier","email":"","orcid":"","institution":"Medical University of Vienna: Medizinische Universitat Wien","correspondingAuthor":false,"prefix":"","firstName":"Susanne","middleName":"","lastName":"Strohmaier","suffix":""},{"id":523683650,"identity":"07cbcc5d-ed2d-48e3-bd30-564e1813f576","order_by":4,"name":"Magdalena Żebrowska","email":"","orcid":"","institution":"Medical University of Vienna: Medizinische Universitat Wien","correspondingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"","lastName":"Żebrowska","suffix":""},{"id":523683651,"identity":"ddb60ce4-f22c-43c3-ba09-679d98ab3037","order_by":5,"name":"Susan Redline","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Redline","suffix":""},{"id":523683652,"identity":"98180299-34ec-4e24-ab0c-72432aab1cf0","order_by":6,"name":"Richa Saxena","email":"","orcid":"","institution":"Brigham and Women's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Richa","middleName":"","lastName":"Saxena","suffix":""},{"id":523683653,"identity":"b9cc3a0b-b414-441c-9f16-47406f69e0d0","order_by":7,"name":"Martin K. Rutter","email":"","orcid":"","institution":"The University of Manchester Faculty of Biology Medicine and Health","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"K.","lastName":"Rutter","suffix":""},{"id":523683654,"identity":"e105abcf-d731-4a44-b063-cd5b9b0adadd","order_by":8,"name":"Eva S. Schernhammer","email":"","orcid":"","institution":"Medical University of Vienna: Medizinische Universitat Wien","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"S.","lastName":"Schernhammer","suffix":""}],"badges":[],"createdAt":"2025-09-26 13:26:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7722286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7722286/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10654-026-01373-7","type":"published","date":"2026-02-21T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93574557,"identity":"ec628e67-8855-48e1-a47b-fedf477f2526","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12656,"visible":true,"origin":"","legend":"","description":"","filename":"ejepEJEPD2501778.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/4bb0536d42db6531392b7ed8.xml"},{"id":93574559,"identity":"44f0eef4-6614-4479-ab5f-0461382b0152","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1025,"visible":true,"origin":"","legend":"","description":"","filename":"EJEPD250177835402.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/937fcdf53a8b4f670052c860.xml"},{"id":93574558,"identity":"a32806fb-ae70-4492-bc49-4c835008c8fa","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":824,"visible":true,"origin":"","legend":"","description":"","filename":"EJEPD2501778Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/229cfb2f60d3b275db08bf71.xml"},{"id":93574565,"identity":"878982e6-2f11-4e75-aad4-4e130cad4fa6","added_by":"auto","created_at":"2025-10-15 09:16:36","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190605,"visible":true,"origin":"","legend":"","description":"","filename":"EJEPD25017780enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/83871b91eb8df16d0ce5e02e.xml"},{"id":93574560,"identity":"2b1c4333-6aae-4a69-9f6f-bd8493a7f944","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10227,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/21baa6849d27587fa31c123d.png"},{"id":93574563,"identity":"93061c2e-344e-402b-beaa-f97caf98a2a3","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9524,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/2d1586c8f86a42eb6ee53266.png"},{"id":93574561,"identity":"0ed09974-3b9d-4ed6-8c63-7ad9abb29ead","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":188335,"visible":true,"origin":"","legend":"","description":"","filename":"EJEPD25017780structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/e64363cb4dec6170185c49f9.xml"},{"id":93574562,"identity":"3f94f76a-b4e3-4626-b870-11def118432c","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201985,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/df8a889519f509ba0ad2439b.html"},{"id":93574556,"identity":"95edf2a7-77d9-4d64-9383-71e969c1c35c","added_by":"auto","created_at":"2025-10-15 09:16:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45662,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable adjusted hazard ratios and 95% confidence intervals of cardiovascular-kidney-metabolic disease according to joint categories of the Circadian Imbalance Index (CII) and night shift work status for European ancestry participants, in multivariable model adjusted for sex, age, household income and education, n=166,194.\u003c/p\u003e\n\u003cp\u003e* ‘Shift workers’ were those who work shift works, but never or rarely worked night shifts, ‘Night shift workers’ were those who report working night shifts sometimes, usually, or always.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/8242d38629cbd781c64ab1ca.png"},{"id":103252253,"identity":"a612bbb7-8f26-484c-a2bd-383455c8cfd7","added_by":"auto","created_at":"2026-02-23 16:13:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1662465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/60de8c15-4499-4ca9-9b9a-bb806fbfdf1f.pdf"},{"id":93574564,"identity":"4dc64cfd-de5e-4ad3-afd2-f4edd63f8683","added_by":"auto","created_at":"2025-10-15 09:16:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":677293,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials259.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722286/v1/3e3824f82fdc929d0eb614dc.docx"}],"financialInterests":"","formattedTitle":"A newly developed circadian imbalance index (CII) and risk of cardiovascular-kidney-metabolic disease in the UK biobank","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eInternal biological clocks are endogenous regulators that optimally align physiology and behavior with the solar day, following an intrinsic circadian rhythm [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The molecular mechanisms of the clock regulate nearly every aspect of human physiology, including the sleep-wake cycle, core body temperature, secretion of hormones (e.g., cortisol and melatonin), and behavioral factors such as cognition and mood [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCircadian imbalance i.e. a propensity to circadian misalignment is widespread, impacting an increasing number of people. Circadian misalignment is not limited to individuals who work non-daytime schedules [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; rather, it reflects inconsistencies between internal biological rhythms and external environmental or behavioral cues, and is influenced by factors such as chronotype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], sleep timing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], personality traits notably neuroticism [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], caffeine intake [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and vitamin D levels, which are biologically linked with melatonin pathways and serve as a proxy for outdoor sunlight exposure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, there is accumulating observational evidence that an evening chronotype [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], short or long sleep duration [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], neuroticism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], atypical coffee consumption [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and vitamin D insufficiency [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] each are associated with an increased risk of cardio-metabolic diseases. Prior research has primarily assessed these circadian-related traits in isolation, without accounting for their combined effects on cardio-metabolic disease risk.\u003c/p\u003e\u003cp\u003eIn the past few decades, the population of shift workers has increased tremendously. Today, up to 18% of workers in the European region reported working shifts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Shift work, especially night shifts characterized by irregular sleep patterns and atypical exposure to environmental light, represents a pronounced form of circadian misalignment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This disruption of circadian rhythms has been associated with a range of adverse health outcomes including cardio-metabolic disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], breast and prostate cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and chronic kidney disease [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, research examining the interaction between shift work and circadian-related traits such as sleep duration or chronotype in relation to cardiovascular disease remains limited and yields inconsistent findings [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn 2022, circulatory diseases accounted for close to one-third of all deaths in the European Union [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cardiovascular diseases frequently coexist with type 2 diabetes and chronic kidney disease, collectively termed cardiovascular-kidney-metabolic (CKM) diseases, reflecting shared underlying mechanisms including dysglycemia, dyslipidemia, hypertension, and obesity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. When one system is impaired, it can exacerbate dysfunction in the other, increasing the risk of subsequent health complications and mortality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To our knowledge, no prior study has assessed the link between a composite score for circadian misalignment and risk for incident CKM, or how night shift work influences their relationship [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUsing data from the UK Biobank, we evaluated the association between a combination of circadian imbalance-related factors, characterized by a novel circadian imbalance index (CII), and the risk of CKM disease, in the whole cohort and when stratified by the night shift work status.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy cohort\u003c/h2\u003e\u003cp\u003eThe UK Biobank is a large-scale prospective study, which began in 2006, enrolling half a million residents of the United Kingdom, aged 40 to 69 years [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. All participants completed an initial baseline assessment, which included sociodemographic psychosocial, physical, and lifestyle information, as well as the collection of biological samples [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The UK Biobank can track participants\u0026rsquo; health outcomes through various national datasets, including Hospital Episode Statistics and national death and cancer registries. The National Research Ethics Service approved the UK Biobank study (ref. 11/NW/0382), and all participants provided written informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of circadian imbalance related factors\u003c/h3\u003e\n\u003cp\u003eThe UK Biobank collected data on chronotype, sleep duration, neuroticism, and caffeinated coffee consumption through a baseline self-response questionnaire. Specifically, chronotype preference was assessed using the question, \u0026lsquo;Do you consider yourself to be?\u0026rsquo; with responses of (1) definitely a \u0026lsquo;morning\u0026rsquo; person, (2) more a \u0026lsquo;morning\u0026rsquo; than \u0026lsquo;evening\u0026rsquo; person, (3) more an \u0026lsquo;evening\u0026rsquo; person than a \u0026lsquo;morning\u0026rsquo; person, (4) definitely an \u0026lsquo;evening\u0026rsquo; person, (5) do not know. Sleep duration was self-reported through the question \u0026lsquo;About how many hours sleep do you get in every 24 hours? (Please include naps)\u0026rsquo;. Neuroticism was assessed using 12 questions from the Eysenck Personality Inventory Neuroticism Scale (EPIN-R) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The neuroticism score was calculated by summing the number of \u0026ldquo;Yes\u0026rdquo; responses across these questions, resulting in a single integer score for each participant ranging from 0 to 12. Consumption of caffeinated coffee was assessed using two dietary questions. Participants were first asked, \u0026lsquo;How many cups of coffee do you drink each DAY? (Include decaffeinated coffee)\u0026rsquo;. Response options included a specific number of cups, \u0026lsquo;\u0026lt;1\u0026rsquo;, \u0026lsquo;do not know\u0026rsquo; or \u0026lsquo;Prefer not to answer\u0026rsquo;. For those who reported drinking at least one cup of coffee per day, a follow-up question was asked: \u0026lsquo;What type of coffee do you usually drink?\u0026rsquo; with response options of \u0026lsquo;Decaffeinated coffee (any type)\u0026rsquo;, \u0026lsquo;Instant coffee\u0026rsquo;, \u0026lsquo;Ground coffee (include espresso, filter etc.)\u0026rsquo; or \u0026lsquo;other type of coffee\u0026rsquo;. For our analysis, we categorized caffeinated coffee consumption into the following groups: 0, 1, 2, 3, 4, \u0026gt;=5 cups/day, with participants reporting decaffeinated coffee intake classified into the 0 cups/day group. Serum concentrations of 25(OH)D (nmol/L) were determined using the chemiluminescent immunoassay method (DiaSorin Liaison XL). Detailed information regarding the measurement of biochemical markers and quality assessment are provided elsewhere [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eConstruction of Circadian Imbalance Index (CII)\u003c/h3\u003e\n\u003cp\u003eWe conducted literature searches and relied on previously published systematic reviews [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and reports [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to identify factors of potential interest for our definition of circadian imbalance. Based on relevance to the circadian system and availability within the UK Biobank, we chose a set of five circadian imbalance-related factors (chronotype, sleep duration, neuroticism, caffeinated coffee intake, and serum vitamin D concentration) to derive a Circadian Imbalance Index (CII). Specifically, morning chronotypes have previously been associated with better circadian alignment whereas evening chronotype are more prone to misalignment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, both short (\u0026lt;\u0026thinsp;7 h) and long (\u0026gt;\u0026thinsp;9 h) sleep durations have been linked to increased circadian imbalance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Individuals with high neuroticism tend to report greater variability in sleep-wake patterns and appear more susceptible to misalignment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moderate caffeine consumption may support more stable circadian alignment by enhancing the internal clock\u0026rsquo;s responsiveness to environmental cues [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Low vitamin D levels have also been associated with circadian misalignment, potentially due to its role in melatonin biosynthesis and signaling pathways that regulate the sleep\u0026ndash;wake cycle [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor each factor, participants were assigned one point if they exhibited the following characteristics: evening chronotype (including \u0026ldquo;definitely an \u0026lsquo;evening\u0026rsquo; person\u0026rdquo; or \u0026ldquo;more an \u0026lsquo;evening\u0026rsquo; person than a \u0026lsquo;morning\u0026rsquo; person\u0026rdquo;) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]; short or long sleep duration (\u0026ge;\u0026thinsp;9 hours or \u0026le;\u0026thinsp;6 hours/day) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; high neuroticism score (\u0026ge;\u0026thinsp;7) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; low serum vitamin D concentration (\u0026lt;\u0026thinsp;50 nmol/L) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; and atypical caffeinated coffee intake (none or \u0026ge;\u0026thinsp;5 cups/day, approximately 400mg caffeine) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Notably, given that approximately 80% of the global population consumes caffeine daily [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], complete avoidance of caffeinated coffee may reflect underlying caffeine sensitivity rather than a typical behavioral pattern. Participants with alternative expressions of these factors were assigned a zero for each respective category. The CII was calculated by summing the points across all five factors and ranged from 0 to 5, with a higher index indicating greater propensity to circadian imbalance. For interaction analyses, we categorized individuals into three circadian imbalance groups: \u0026lsquo;low CII\u0026rsquo; (0\u0026thinsp;\u0026le;\u0026thinsp;CII\u0026thinsp;\u0026le;\u0026thinsp;1); \u0026lsquo;intermediate CII\u0026rsquo; (2\u0026thinsp;\u0026le;\u0026thinsp;CII\u0026thinsp;\u0026le;\u0026thinsp;3); and \u0026lsquo;high CII\u0026rsquo; (4\u0026thinsp;\u0026le;\u0026thinsp;CII\u0026thinsp;\u0026le;\u0026thinsp;5).\u003c/p\u003e\n\u003ch3\u003eAssessment of night shift work\u003c/h3\u003e\n\u003cp\u003eAt baseline from 2006 to 2010, participants who were in paid employment or self-employed were asked whether their primary job involved shift work, defined as work schedules outside typical daytime hours (9am-5pm). Participants who answered \u0026lsquo;yes\u0026rsquo; were further asked if their job included night shifts, characterized by working during normal sleeping hours (12am to 6am). Responses to the two questions included the following options: \u0026lsquo;never/rarely\u0026rsquo;, \u0026lsquo;sometimes\u0026rsquo;, \u0026lsquo;usually\u0026rsquo;, \u0026lsquo;always\u0026rsquo;, along with \u0026lsquo;prefer not to answer\u0026rsquo; and \u0026lsquo;do not know\u0026rsquo;. Based on these responses and consistent with existing research [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], participants were categorized into one of the following work status groups: \u0026lsquo;day workers\u0026rsquo;, \u0026lsquo;shift workers\u0026rsquo; (i.e., those who never or rarely worked night shifts), and \u0026lsquo;night shift workers\u0026rsquo; (i.e., those who reported working sometimes, usually, or always night shifts).\u003c/p\u003e\u003cp\u003eTo further assess their lifetime employment history, between July and September 2015, all participants were invited via email to complete an online occupational history questionnaire. A subset of participants responded, providing detailed employment histories on all jobs they had held, including the duration of each job and details about shift work schedules. Using this data and findings from previous studies, we calculated three key night shift work metrics: duration of night shift work (total number of years spent working night shifts), cumulative exposure (total number of night shifts over a lifetime), and intensity of night shift work (average number of night shifts per month) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssessment of outcome\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was defined as time to any incident CKM disease, which included type 2 diabetes (T2D), cardiovascular disease (CVD) or chronic kidney disease (CKD). These outcomes were identified through the national death registry, hospital inpatient records and self-reports. The national death registry and hospital impatient records were classified based on the 10th edition of the International Classification of Diseases (ICD-10) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] as follows: type 2 diabetes: E11 (non-insulin-dependent diabetes mellitus); cardiovascular disease: I50 (heart failure), I21-I25 (ischemic heart disease) and I60-I64 (stroke); chronic kidney disease: N18 (kidney failure) and I12-I13 (hypertensive renal disease) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAssessment of covariables\u003c/h2\u003e\u003cp\u003eWe considered a range of demographic, health and lifestyle factors [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] that were all assessed at baseline, as potential confounders in our analyses. They included age, sex, average total household income before tax, education level, recruitment season, smoking status, alcohol consumption, physical activity, body mass index (BMI), hypertension and elevated cholesterol. For missing covariable data, we applied sex-specific median values for continuous variables and introduced a missing indicator for categorical variables. To ensure accurate ancestry classification, we based our classification on detailed genetically derived, rather than self-reported ancestry. Ancestry estimates are described in the Genetic Ancestry Assessment Supplement. A detailed description of all other covariables is provided in Supplemental Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalytic sample\u003c/h3\u003e\n\u003cp\u003eFrom the total UK Biobank cohort, we excluded individuals with prevalent cancer or CKM disease at baseline (N\u0026thinsp;=\u0026thinsp;69,562), as well as those with missing responses \u0026ndash; including \"prefer not to answer\" \u0026ndash; for circadian imbalance related variables (N\u0026thinsp;=\u0026thinsp;124,505). Additionally, participants who were not in paid employment or self-employed at baseline (N\u0026thinsp;=\u0026thinsp;116,447) were excluded. These exclusions resulted in a final analytic sample of 191,764 individuals for the analysis considering current night shift exposure. Among these, a subset of 47,843 participants of European ancestry completed an online employment history questionnaire in 2015, which was used to assess lifetime exposure to night shift work (Supplemental Fig.\u0026nbsp;1). In analyses using this subset, participants of non-European ancestry were excluded due to limited case numbers.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eThe follow-up period extended from the date of enrollment (2006\u0026ndash;2010) until the first occurrence of either a diagnosis of CKM disease or a censoring event, which included death, withdrawal from the study, or the end of the designated follow-up period. Region-specific designated follow-up end dates were applied: October 31, 2022, for England, August 31, 2022, for Wales and May 31, 2022, for Scotland. To assess the relationship between the CII and incidence of CKM, we employed Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The proportional hazards assumption was tested using Schoenfeld residuals and no indications of violations were observed (Supplemental Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThe main analyses were conducted stratified by three main genetic ancestry groups (European, Asian and African). In addition to stratifying, in sensitivity analyses, we also adjusted for ethnicity in the overall analytic sample; however, due to the predominance of European ancestry (87%), results were highly similar to those in the stratum of participants with European ancestry only. Guided by prior research [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], we considered four models serially adjusting for potential covariables: Model 1 accounted for age and sex; model 2 additionally included socioeconomic indicators, specifically average total household income before tax and education level; model 3 further incorporated lifestyle factors, including smoking status, alcohol consumption, and physical activity. Finally, to account for its potential mediating role in the CII-CKM relationship [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], BMI was introduced separately in model 4. Although these potential confounders and mediators were assessed at baseline only, it appears reasonable to assume that they would likely remain on the same trajectory through follow-up. To evaluate potential linear trends, the CII was additionally analyzed as a continuous variable in various models as specified above.\u003c/p\u003e\u003cp\u003eIn subgroup analyses, we first performed sex-stratified analyses within the overall study population. Among the women, we further considered menopausal status as a potential confounder but because results remained virtually unchanged, did not retain it in our models. Subsequently, among individuals of European ancestry, we performed additional analyses stratified by current work status (as reported in the baseline questionnaire) and lifetime exposure to night shift work (as assessed in the 2015 occupational questionnaire). To assess potential multiplicative interaction, we included a product term of CII group and night shift work status in the regression models and compared the \u0026minus;\u0026thinsp;2 log-likelihood values of models with and without the inclusion of a product term.\u003c/p\u003e\u003cp\u003eTo further explore whether the associations between CII and CKM disease risk varied according to night shift work status, participants were categorized into nine groups based on the combination of night shift work status (day worker, shift worker and night shift worker) and CII level (low, intermediate and high). Hazard ratios (HRs) for incident CKM disease were then estimated for each group, using dayworkers with low CII as the reference category.\u003c/p\u003e\u003cp\u003eTo evaluate additive interaction [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], we calculated the relative excess risk to interaction (RERI) and the attributable proportion (AP), along with their corresponding 95% confidence intervals. Details regarding the formulas used for assessing additive interaction are provided in the Supplementary Statistical Methods. We subsequently conducted similar analyses among the smaller set of participants with available lifetime-employment history data, repeating the stratified analysis based on lifetime exposure to night shift work. In parallel, we performed joint and interaction analyses to examine whether lifetime night shift work modified the association between CII categories and CKM disease risk. All statistical analyses were performed using R software, version 4.3.1 (R foundation for Statistical Computing). All P-values were two-sided, with a threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePopulation characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents baseline characteristics of the study participants by level of CII. Among the 191,764 participants, 66,962 (34.9%), 65,765 (34.3%), 42,246 (22.0%), 14,468 (7.5%), and 2,323 (1.2%) had a CII of 0\u0026ndash;1, 2, 3, 4, and 5, respectively. The mean age of participants was 52 years (SD\u0026thinsp;=\u0026thinsp;7), 51% were women, 87% were of European ancestry, and 83% were employed in non-shift work occupations. Overall, participants with higher CII were more likely to be women, to undertake night shift work, and have lower educational attainment and household income. They were also more likely to currently smoke, to exercise less, and to have a higher BMI.\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\u003eCharacteristics of 191,764 participants from the UK biobank*, overall and according to category of Circadian Imbalance Index (CII).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBaseline Characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eCircadian Imbalance Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of participants\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191,764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66,962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65,765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42,246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14,468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.84 (97,490)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.19 (32,271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.95 (33,506)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.90 (22,350)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.45 (8,023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.68 (1,340)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years) (mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.36 (7.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.97 (7.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.37 (7.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.84 (6.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.29 (6.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.59 (6.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfrican\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.35 (2,583)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45 (333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36 (897)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.24 (946)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.41 (349)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.50 (58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82 (3,481)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84 (562)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.05 (1,350)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.61 (1,103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.75 (398)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.93 (68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.67 (166,194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.49 (59,921)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.21 (56,698)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.27 (35,600)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.34 (12,058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e82.52 (1,917)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.17 (19,506)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.178 (6,146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.37 (6,820)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.88 (4,597)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.49 (1,663)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.05 (280)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNight shift work status % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.47 (160,068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.06 (57,629)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.78 (55,096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.30 (34,347)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.17 (11,309)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.62 (1,687)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShift workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.10 (15,529)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.25 (4,856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.09 (5,323)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.78 (3,709)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.52 (1,378)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.32 (263)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight shift workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.43 (16,167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.69 (4,477)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.13 (5,346)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.92 (4,190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.31 (1,781)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.06 (373)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent smokers % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.37 (19,892)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.707 (5,161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.18 (6,697)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.51 (5,283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.97 (2,311)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.94 (440)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDaily or almost daily drinker % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.07 (38,489)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.94 (14,691)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.64 (12,915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.62 (7,868)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.14 (2,625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.79 (390)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold income % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; \u0026pound;18,000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.35 (16,016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.52 (4,365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.18 (5,378)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.89 (4,180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.17 (1,761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.29 (332)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026pound;18,000 ~ \u0026pound;100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.07 (145,874)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.45 (51,191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.05 (50,017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.06 (32,132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.58 (10,790)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75.08 (1,744)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt; \u0026pound;100,000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.64 (14,644)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.20 (6,158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.75 (5,095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.09 (2,573)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.11 (739)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.40 (79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.94 (15,230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.84 (5,248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.02 (5,275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.96 (3,361)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.14 (1,178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.23 (168)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCollege education % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.19 (75,151)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.05 (27,487)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.78 (26,163)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.18 (15,707)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.82 (5,038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.54 (756)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical activity % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (\u0026lt;\u0026thinsp;10 MET-h/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.94 (30,570)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.09 (8,767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.31 (10,728)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.28 (7,722)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.64 (2,842)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.00 (511)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle (10\u0026thinsp;~\u0026thinsp;50 MET-h/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.08 (82,603)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.86 (30,038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.31 (28,486)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.75 (17,639)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.75 (5,606)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.90 (834)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh (\u0026gt;\u0026thinsp;50 MET-h/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.77 (47,504)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.49 (18,409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.22 (15,925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.51 (9,509)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.89 (3,167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.27 (494)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.21 (31,087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.56 (9,748)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.16 (10,626)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.46 (7,376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.72 (2,853)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.84 (484)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.16 (78,939)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.58 (27,842)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.32 (27,177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.68 (17,186)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.30 (5,831)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.87 (903)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-report hyperlipidemia % (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.13 (13,668)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.83 (4,570)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.04 (4,628)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.49 (3,164)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.76 (1,123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.88 (183)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e) (mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.15 (4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.59 (4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.21 (4.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.58 (4.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.97 (5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.69 (5.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI: body mass index; SD: standard deviation. * No restrictions based on ethnicity were implemented.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProspective association between CII and CKM disease risk\u003c/h2\u003e\u003cp\u003eDuring a median follow-up period of 13.5 years, a total of 16,907 incident cases of CKM disease were documented. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between CII and CKM disease risk across genetic ancestry groups, as estimated by multivariable-adjusted models. Among individuals of either European or Asian ancestry, higher CII values were consistently associated with a higher risk of CKM disease compared to those with a CII of 0\u0026ndash;1, across all models (\u003cem\u003eP\u003c/em\u003e \u003csub\u003e\u003cem\u003etrend\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.05). Following adjustments for age, sex, household income and education level in Model 2, the HRs for Europeans and Asians were slightly attenuated but remained statistically significant. Specifically for the Europeans, the HRs (95% CI) for CII values of 2, 3, 4, and 5 were 1.26 (1.21\u0026ndash;1.31), 1.42 (1.36\u0026ndash;1.49), 1.65 (1.55\u0026ndash;1.76), and 1.95 (1.70\u0026ndash;2.23), respectively. Among Asian participants, compared with those with a CII of 0\u0026ndash;1, the multivariable-adjusted HRs (95% CI) for CII of 2 to 5 were 1.41 (1.07\u0026ndash;1.86), 1.49 (1.13\u0026ndash;1.97), 1.64 (1.17\u0026ndash;2.31), and 2.03 (1.07\u0026ndash;3.86), respectively, in Model 2. Further adjustment for smoking status, alcohol consumption, and physical activity (Model 3), as well as body mass index (Model 4), attenuated these associations somewhat, yet they remained significant among Europeans and Asians. In contrast, no significant association was observed between CII and CKM disease risk among participants of African ancestry.\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\u003eProspective associations between Circadian Imbalance Index (CII) and risk of incident Cardiovascular-Kidney-Metabolic disease among 191,764 participants from the UK Biobank, stratified by genetic ancestry.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCase/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModel 4\u003csup\u003ed\u003c/sup\u003e\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;166,194)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 474/59 921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 979/56 698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27 (1.22\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.26 (1.21\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.22 (1.17\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.14 (1.09\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 394/35 600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 (1.40\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.42 (1.36\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.34 (1.28\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.21 (1.16\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 269/12 058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.74 (1.63\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.65 (1.55\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.53 (1.44\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.34 (1.26\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221/ 1 917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.08 (1.82\u0026ndash;2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.95 (1.70\u0026ndash;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.75 (1.53\u0026ndash;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.42 (1.24\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAsian\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;3,481)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67/ 562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211/1 350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42 (1.08\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.41 (1.07\u0026ndash;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39 (1.06\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.37 (1.04\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e183/1 103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51 (1.14-2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49 (1.13\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.44 (1.09\u0026ndash;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.36 (1.03\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68/ 398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.71 (1.22\u0026ndash;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64 (1.17\u0026ndash;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.58 (1.13\u0026ndash;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.49 (1.06\u0026ndash;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11/ 68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.13 (1.12\u0026ndash;4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.03 (1.07\u0026ndash;3.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.91 (1.00-3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.82 (0.95\u0026ndash;3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAfrican\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;2,583)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42/333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141/897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39 (0.99\u0026ndash;1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38 (0.98\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.33 (0.94\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.29 (0.91\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142/946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32 (0.94\u0026ndash;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.30 (0.92\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.25 (0.89\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.26 (0.89\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52/349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32 (0.88\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31 (0.87\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.27 (0.84\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.16 (0.77\u0026ndash;1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8/ 58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46 (0.68\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.43 (0.67\u0026ndash;3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.36 (0.63\u0026ndash;2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.24 (0.58\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1 includes sex and age\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 2 includes variables in Model 1 and household income and education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eModel 3 includes variables in Model 2 and smoking status, alcohol consumption and physical activity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eModel 4 includes variables in Model 3 and body mass index (BMI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn sensitivity analyses, we additionally adjusted for hypertension and hyperlipidemia, as well as recruitment season (Supplemental Table\u0026nbsp;2); excluded those with missing information on any of the covariables that were considered (Supplemental Table\u0026nbsp;3); and excluded participants with a report of incident CKM within the first 2 years of follow-up (Supplemental Table\u0026nbsp;4); all results consistently demonstrated that higher CII levels were associated with higher risks of CKM diseases. Further, in analysis adjusting (rather than stratifying) for genetic ethnicity results were similar to those in the stratum of participants with European ancestry only (Supplemental Table\u0026nbsp;5). In addition, we found that each individual circadian trait was independently associated with an elevated risk of CKM disease. Moreover, within the European ancestry group, the overall CII was significantly associated with the risk of each individual CKM disease component. Detailed results of these analyses are provided in Supplemental Table\u0026nbsp;12\u0026ndash;15, and Supplemental Fig.\u0026nbsp;3.\u003c/p\u003e\u003cp\u003eAlthough in gender-stratified analyses, the association between CII and CKM disease risk appeared stronger among women compared to men in Model 2, there was no statistically significant effect modification by gender (\u003cem\u003eP\u003c/em\u003e\u003csub\u003einteraction\u003c/sub\u003e= 0.666) (Supplemental Table\u0026nbsp;6). Further stratification by genetic ancestry (Supplemental Table\u0026nbsp;7) revealed that the HRs in Model 2 were particularly elevated among European women and Asian men. No significant associations were observed within African ancestry gender subgroups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAdditive and multiplicative interactions of CII and night shift work\u003c/h2\u003e\u003cp\u003eAmong individuals of European ancestry, a significant dose-response relationship between the CII and CKM disease risk was observed across all categories of work status, with both shift workers and night shift workers exhibiting elevated CKM disease risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, compared to participants with a CII of 0\u0026ndash;1, the multivariable-adjusted HRs (95%CIs) for those with CII of 5 in Model 2 were 1.82 (1.54\u0026ndash;2.15) among day workers, 2.57 (1.82\u0026ndash;3.62) among shift workers, and 1.86 (1.34\u0026ndash;2.57) among night shift workers. Consistent patterns of significant associations between CII and CKM were also observed in a subset of participants who provided detailed lifetime occupational histories, particularly among those who had worked night shifts for over 20 years or engaged in night shift work for at least 8 nights per month (Supplemental Tables\u0026nbsp;8\u0026ndash;9).\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\u003eProspective associations between Circadian Imbalance Index (CII) and risk of incident Cardiovascular-Kidney-Metabolic disease among 166,194 European ancestry participants from the UK biobank, stratified by work status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCase/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModel 4\u003csup\u003ed\u003c/sup\u003e\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eDay workers\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;139,853)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 761/51 699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 020/47 953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25 (1.19\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.23 (1.18\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.20 (1.15\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.12 (1.07\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 634/29 267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43 (1.36\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.39 (1.32\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.32 (1.25\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.19 (1.13\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e930/ 9 525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66 (1.55\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.60 (1.49\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.49 (1.38\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.30 (1.21\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144/ 1 409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92 (1.62\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.82 (1.54\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.64 (1.39\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.36 (1.15\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eShift workers\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;13,029)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e377/4 236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e453/4 407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25 (1.09\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.24 (1.08\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.19 (1.04\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.12 (0.98\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e373/3 036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.58 (1.37\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.54 (1.34\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.46 (1.26\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.33 (1.15\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132/1 137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.31\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.55 (1.27\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.41 (1.15\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.25 (1.02\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36/ 213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.68 (1.90\u0026ndash;3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.57 (1.82\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.34 (1.66\u0026ndash;3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.88 (1.33\u0026ndash;2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eNight shift workers\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;13,312)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e336/3 986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e506/4 338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46 (1.27\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.45 (1.26\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.40 (1.22\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.28 (1.11\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387/3 297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51 (1.31\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.48 (1.28\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39 (1.20\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.22 (1.05\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207/1 396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01 (1.69\u0026ndash;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.93 (1.62\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.81 (1.52\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.60 (1.34\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41/ 295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94 (1.40\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.86 (1.34\u0026ndash;2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.65 (1.19\u0026ndash;2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.29 (0.93\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1 includes sex and age\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 2 includes variables in Model 1 and household income and education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eModel 3 includes variables in Model 2 and smoking status, alcohol consumption and physical activity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eModel 4 includes variables in Model 3 and body mass index (BMI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e* \u0026lsquo;Shift workers\u0026rsquo; were those who worked shift work, but never or rarely worked night shifts, \u0026lsquo;Night shift workers\u0026rsquo; were those who report working night shifts sometimes, usually, or always.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e* \u0026lsquo;Shift workers\u0026rsquo; were those who work shift works, but never or rarely worked night shifts, \u0026lsquo;Night shift workers\u0026rsquo; were those who report working night shifts sometimes, usually, or always.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe further assessed the joint association of night shift work status and CII categories with the CKM disease outcome. Within each work status group, a higher CII was consistently associated with an elevated risk of CKM disease in a dose-response manner. Specifically, compared to the reference group (day workers with low CII of 0\u0026ndash;1), night shift workers with an intermediate CII (HR: 1.69; 95% CI: 1.57\u0026ndash;1.81) or a high CII (HR: 2.22; 95% CI: 1.95\u0026ndash;2.53) exhibited significantly elevated risk of CKM disease. A similarly elevated hazard ratio was also observed among shift workers with intermediate or high CII (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although the test for multiplicative interaction was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u003csub\u003einteraction\u003c/sub\u003e = 0.238), we observed a significant additive interaction between night shift work and both intermediate (2\u0026ndash;3) or high CII (4\u0026ndash;5) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, for night shift workers with a high CII, the relative excess risk due to interaction (RERI) was estimated at 0.456 (95% CI: 0.138\u0026ndash;0.775), and the attributable proportion (AP) was 0.205 (95% CI: 0.083\u0026ndash;0.327) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings indicate a 45.6% excess relative risk attributable to the additive interaction between night shift work and high CII, with 20.5% (95% CI: 8.3\u0026ndash;32.7%) of the CKM disease risk in this group being attributable to their combined effect (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, similar patterns of joint associations were observed when other metrics of night shift work, including duration and intensity, were considered (Supplemental Fig.\u0026nbsp;4, Supplemental Tables\u0026nbsp;10\u0026ndash;11). Notably, significant additive interactions were also observed between a high CII and long-term night shift work (\u0026ge;20 years), as well as between a high CII and high-intensity night shift work (\u0026ge;\u0026thinsp;8 nights per month) (Supplemental Tables\u0026nbsp;10\u0026ndash;11).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable adjusted hazard ratios with 95% CI, RERI and AP for additive interaction between Circadian Imbalance Index (CII) and shift work status for cardiovascular-kidney-metabolic disease among European ancestry UKB participants, stratified by categories of circadian imbalance index (CII) and night shift work status, n\u0026thinsp;=\u0026thinsp;166,194.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003csup\u003e1\u003c/sup\u003e\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\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRERI (95%CI)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAP (95%CI)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow CII (0\u0026ndash;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51,699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShift workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17 (1.05, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight Shift Workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (1.03, 1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMiddle CII (2\u0026ndash;3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77,220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29 (1.24, 1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShift workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.48, 1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.132 (-0.034\u0026ndash;0.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.083 (-0.018\u0026ndash;0.184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight Shift Workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.69 (1.57, 1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.249 (0.079\u0026ndash;0.419)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.148 (0.052\u0026ndash;0.243)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh CII (4\u0026ndash;5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62 (1.51, 1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShift workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.98 (1.69, 2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.185 (-0.152\u0026ndash;0.522)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.094 (-0.065\u0026ndash;0.252)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight Shift Workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.22 (1.95, 2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.456 (0.138\u0026ndash;0.775)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.205 (0.083\u0026ndash;0.327)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e2\u003c/sup\u003eRERI = relative excess risk due to the interaction\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e3\u003c/sup\u003eAP = attributable proportion due to the interaction\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003eModel adjusted for sex, age, household income and education\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003eTo estimate the RERI and AP, the low Circadian Imbalance Index (0\u0026ndash;1) and the day worker group were the reference categories. \u0026lsquo;Shift workers\u0026rsquo; were those who work shift works, but never or rarely worked night shifts, \u0026lsquo;Night shift workers\u0026rsquo; were those who report working night shifts sometimes, usually, or always.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we observed that higher propensity for circadian imbalance, as described by our newly developed circadian imbalance index (CII), was significantly associated with a higher risk of CKM disease. Moreover, compared to participants with low circadian imbalance, those with higher CII showed a progressively elevated risk of CKM disease across all metrics of night shift work status (duration, intensity, and cumulative number of lifetime night shift work). Participants who worked night shifts and were classified into the high CII group (4–5) exhibited the greatest risk when compared to day workers in the low CII group (0–1). Furthermore, we identified a significant additive interaction between CII categories and night shift work status on CKM risk. This interaction was particularly pronounced among individuals with extended night shift duration (≥ 20 years) and high night shift intensity (≥ 8 nights per month).\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eComparison with other studies\u003c/h2\u003e\u003cp\u003eTo the best of our knowledge, this is the first prospective cohort study to investigate the relationships between a newly developed CII, integrating evening chronotype, short or long sleep duration, scoring higher on the neuroticism spectrum, atypical caffeinated coffee intake and low serum concentration vitamin D, and the risk of CKM disease. Though prior studies have explored the associations of night shift work, chronotype, and sleep duration with various chronic health outcomes independently [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e–\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], the potential additive effect of multiple circadian-related traits, as captured by the CII, on the risk of CKM diseases has not been previously evaluated. One previous study found that evening chronotype was significantly associated with cardiovascular health in night-shift workers [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Additionally, Young et al. proposed that night shift workers with long or short sleep duration had higher blood pressure [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this study, we newly constructed a CII by taking into account the combined impact of five circadian imbalance traits on CKM risk, which reflects the most comprehensive circadian imbalance evaluation to date.\u003c/p\u003e\u003cp\u003eWe found CII to be associated with a higher risk of CKM among the women but not the men of European ancestry in our study, whereas the opposite pattern was observed among Asian ancestry, though none of these interactions reached statistical significance, and so these results should be interpreted with caution. While a previous small experiment has suggested that circadian misalignment might have a stronger effect on metabolic disorder among women compared with men [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], gender differences regarding metabolic disorder syndrome have been mixed in numerous countries [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Alternatively, there may be gender differences in the types of occupations held by men and women across different regions of the world, particularly with respect to the intensity and nature of shift work, including night shifts. Overall, these secondary gender-ancestry subgroup analyses require further exploration.\u003c/p\u003e\u003cp\u003eWe observed a higher risk of CKM disease with increasing CII among night shift workers, especially among those with longer lifetime duration in terms of years worked night shifts, and greater intensity of night shifts. Similar to our finding, several previous studies assessing more detailed metrics of night shift work have described an increased risk of type 2 diabetes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] or cardiovascular disease [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] with longer duration or greater intensity of night shift work.\u003c/p\u003e\u003cp\u003eOur findings highlight the additive interaction between night shift work status and CII on CKM disease risk. Specifically, the combination of both night shift work and high CII would result in an additional 20.5% of CKM cases. Consistently, we also observed that the significantly higher risk of CKM disease associated with longer lifetime duration or greater intensity of night shift work was further amplified among individuals in the high CII group. These findings raise the possibility that keeping a low CII among night shift workers, especially those with long-term or high-intensity night shift exposure, may be an effective strategy for reducing the risk of CKM disease. From a public health perspective, the CII may serve as a useful tool for helping night shift workers assess their degree of circadian imbalance, identify those at higher risk of CKM, and guide the development of personalized strategies for CKM disease prevention. Clinical trials would be required to test the efficacy and safety of any new intervention strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePotential mechanisms\u003c/h2\u003e\u003cp\u003eNight shift work and the traits in our study that we used to define circadian imbalance are likely to share serval potential underlying mechanisms involved in CKM disease risk. To date, exposure to night shift work remains the most common and extreme observational model of circadian misalignment in human studies [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and, similar to circadian imbalance traits, is typically chronic in nature. Night shift work has been associated with an increase in inflammatory markers e.g., level of C-reactive protein (CRP), tumor necrosis factor (TNF-α), and interleukin-6 (IL-6) levels [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], which in turn increase risk of chronic inflammatory conditions such as type 2 diabetes [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and obesity [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Circadian disruption has further been associated with hormonal changes in appetite regulation, including reduced leptin and elevated ghrelin levels, which contribute to weight gain and metabolic dysregulation [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. On a molecular level, virtually all mammalian cell types have a functional circadian clock including clock and period genes, such as \u003cem\u003eCLOCK\u003c/em\u003e, \u003cem\u003eBMAL1 PER1\u003c/em\u003e, \u003cem\u003ePER2\u003c/em\u003e, \u003cem\u003ePER3\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, and \u003cem\u003eCRY2\u003c/em\u003e [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Oscillation and dysregulated expression of the molecular circadian clock has been linked to atherosclerosis, insulin resistance, dampening of blood pressure rhythmicity, and reduced production of vasoactive hormones and neurotransmitters [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Experimental evidence shows that mice with \u003cem\u003eCLOCK\u003c/em\u003e gene mutations exhibited disrupted feeding and activity rhythms under ad libitum conditions, leading to obesity and metabolic syndrome [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. These studies point to the potential mediating effects of obesity, highlighting the importance of our modeling strategy, adding BMI separately in model 4.\u003c/p\u003e\u003cp\u003eCaffeine consumption and bright light / vitamin D have been linked to melatonin secretion and circadian rhythms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Sleep-wake cycle disturbances and exposure to irregular light-dark patterns, as commonly seen in night shift work, may further impair synthesis of cortisol and melatonin [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Previous studies consistently support melatonin’s anti-inflammatory, antihypertensive, and oxidative activity and its possibility to reduce the risk of cardiometabolic disease, including type 2 diabetes and hypertension [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Therefore, reduced melatonin levels resulting from chronic circadian imbalance or night shift work maybe represent another underlying pathway for the observed associations in our study. Lastly, some lifestyle behaviors such as smoking, sedentary behavior, and irregular meal timing could also be potential contributors to CKM disease. Further studies are needed to explore the pathophysiological pathways underlying the interaction between night shift work and circadian imbalance related traits on CKM disease risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe main strengths of this study include its prospective study design, large sample size, and long-term follow up. More importantly, the integration of several circadian imbalance-related traits allowed for a more comprehensive assessment of their potential impact on CKM disease risk. Further, we were able to assess their relationship among different ancestries. Another major novelty of this study is that it is the first to investigate the joint association of night shift work and circadian imbalance related traits with the risk of CKM disease. We also provide novel and unique insight by using detailed shift work metrics including lifetime years and intensity of night shift work. Although uncontrolled confounding remains a limitation in any observational study, the extensive data collection in the UK Biobank allowed for detailed control of potential confounders and mediators.\u003c/p\u003e\u003cp\u003eThe present study also has several limitations. First, information on night shift work and most circadian imbalance related factors was self-reported (with the exception of vitamin D which was measured in serum), thus exposure misclassification potentially exists. However, such misclassification would likely be random to outcome status, resulting in attenuation of the effect estimations and underestimation of the observed associations. Second, we dichotomized five circadian imbalance related traits to create CII and assigned equal weight to each trait, which might result in loss of information and study power. In addition, information was not available on consumption of caffeinated tea and daily timing of coffee consumption. Furthermore, the CII may not have fully captured all relevant circadian imbalance related traits, such as light exposure during the day, meal timing, or timing of physical activity [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], potentially overlooking other important aspects of circadian disruption. Further, the UK Biobank’s healthy volunteer bias may limit the generalizability of our findings to less healthy populations [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Finally, although the present study included participants of different ancestries from the UK Biobank, the small number of individuals of African ancestry limited the statistical power for analyses within this group.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion and public health implications","content":"\u003cp\u003eIn summary, a newly derived circadian imbalance index, CII, integrating evening chronotype, short or long sleep duration, high neuroticism score, atypical caffeine consumption, and low vitamin D levels, was associated with increased CKM disease risk among European and Asian ancestries. Notably, CII and night shift work were jointly associated with a higher risk of CKM disease, and there was an additive association of night shift work and high CII on CKM disease risk. Our findings highlight the possibility that cases of CKM disease could be prevented by reducing high CII scores, which reflect a greater burden of circadian imbalance related traits. The benefits may be particularly pronounced among night shift worker or those with longer duration or greater intensity of night shift exposure. Intervention trials and mechanistic research are warranted to extend this research and clarify the underlying biological mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCKM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular-Kidney-Metabolic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCII\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCircadian Imbalance Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHRs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard Ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCIs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eT2D\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eType 2 Diabetes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic Kidney Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eICD-10\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRERI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelative Excess Risk to Interaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAttributable Proportion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF-α\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor necrosis factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterleukin-6 levels\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe National Research Ethics Service approved the UK Biobank study (ref. 11/NW/0382), and all participants provided written informed consent.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data underlying this article cannot be shared publicly. However, researchers are encouraged to apply to access to the UK Biobank resource for health-related research that serves the public interest. The statistical R code and technical processes are available from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by European Union, European Research Council (ERC) Advanced Grant CLOCKrisk (grant number 101053225), Department of Epidemiology, Medical University of Vienna to PI Eva Schernhammer. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eContributors: JZ, ES were involved in the study conception and design. ES provided funding, and JZ analyzed and interpreted the data. DT supported JZ with data analyses. SS, MZ, and ES provided statistical expertise. JZ drafted the manuscript. All authors participated in the interpretation of the results and critically reviewed the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. JZ and ES have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; they are the guarantors.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis research was conducted using the UK Biobank resource under application number 48576. We thank all the participants and staff of the UK Biobank for enabling us to conduct this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMohawk JA, Green CB, Takahashi JS. Central and peripheral circadian clocks in mammals. Annu Rev Neurosci. 2012;35:445\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGamble KL, et al. Circadian clock control of endocrine factors. Nat Rev Endocrinol. 2014;10(8):466\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFishbein AB, Knutson KL, Zee PC. \u003cem\u003eCircadian disruption and human health.\u003c/em\u003e J Clin Invest, 2021. 131(19).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontaruli A, et al. Biological rhythm and chronotype: new perspectives in health. Biomolecules. 2021;11(4):487.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurray JM, et al. Sleep regularity is associated with sleep-wake and circadian timing, and mediates daytime function in delayed sleep-wake phase disorder. Sleep Med. 2019;58:93\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeYoung CG, et al. Morning people are stable people: Circadian rhythm and the higher-order factors of the Big Five. Pers Indiv Differ. 2007;43(2):267\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurke TM, et al. Effects of caffeine on the human circadian clock in vivo and in vitro. Sci Transl Med. 2015;7(305):305ra146.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYousefi T, et al. Molecular pathways and biological roles of melatonin and vitamin D; effects on immune system and oxidative stress. Int Immunopharmacol. 2024;143(Pt 3):113548.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWebb AR, et al. The role of sunlight exposure in determining the vitamin D status of the U.K. white adult population. Br J Dermatol. 2010;163(5):1050\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T et al. \u003cem\u003eProspective study of the association between chronotype and cardiometabolic risk among Chinese young adults.\u003c/em\u003e BMC Public Health, 2023. 23(1): p. 1966.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnutson KL. Sleep duration and cardiometabolic risk: a review of the epidemiologic evidence. Best Pract Res Clin Endocrinol Metab. 2010;24(5):731\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee LO, et al. Neuroticism, Worry, and Cardiometabolic Risk Trajectories: Findings From a 40-Year Study of Men. J Am Heart Assoc. 2022;11(3):e022006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUngvari Z, Kunutsor SK. Coffee consumption and cardiometabolic health: a comprehensive review of the evidence. Geroscience. 2024;46(6):6473\u0026ndash;510.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong Y, Wang L. Vitamin D and cardiometabolic disease: From observation to intervention. Curr Nutr Rep. 2012;1:55\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eEmployees working shifts as a percentage of the total of employees\u003c/em\u003e. 2024; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ec.europa.eu/eurostat/databrowser/view/lfsa_ewpshi/default/table?lang=en\u003c/span\u003e\u003cspan address=\"https://ec.europa.eu/eurostat/databrowser/view/lfsa_ewpshi/default/table?lang=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnsu Baidoo V, Knutson KL. Associations between circadian disruption and cardiometabolic disease risk: a review. Obesity. 2023;31(3):615\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHemmer A, et al. The effects of shift work on cardio-metabolic diseases and eating patterns. Nutrients. 2021;13(11):4178.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorquati L, et al. Shift work and the risk of cardiovascular disease. A systematic review and meta-analysis including dose\u0026ndash;response relationship. Scand J Work Environ Health. 2018;44(3):229\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchernhammer ES, et al. Rotating night shifts and risk of breast cancer in women participating in the nurses' health study. J Natl Cancer Inst. 2001;93(20):1563\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDickerman BA, et al. Sleep disruption, chronotype, shift work, and prostate cancer risk and mortality: a 30-year prospective cohort study of Finnish twins. Cancer Causes Control. 2016;27:1361\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y, et al. A longitudinal study of working hours and chronic kidney disease in healthy workers: The Kangbuk Samsung Health Study. J Occup Health. 2021;63(1):e12266.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHulsegge G, et al. Shift work, chronotype and the risk of cardiometabolic risk factors. Eur J Public Health. 2019;29(1):128\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu P, et al. Joint Association of Sleep Onset Time and Sleep Duration With Cardiometabolic Health Outcome. J Am Heart Assoc. 2024;13(12):e034165.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eCirculatory diseases \u0026ndash; main cause of death in 2022\u003c/em\u003e. 2025; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20250321-2?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20250321-2?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarafidis P, et al. SGLT-2 inhibitors and GLP-1 receptor agonists for nephroprotection and cardioprotection in patients with diabetes mellitus and chronic kidney disease. A consensus statement by the EURECA-m and the DIABESITY working groups of the ERA-EDTA. Nephrol Dialysis Transplantation. 2019;34(2):208\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRangaswami J, et al. Cardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association. Circulation. 2019;139(16):e840\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCherney DZ, et al. Impact of cardio-renal-metabolic comorbidities on cardiovascular outcomes and mortality in type 2 diabetes mellitus. Am J Nephrol. 2020;51(1):74\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnsu Baidoo V, Knutson KL. Associations between circadian disruption and cardiometabolic disease risk: A review. Obes (Silver Spring). 2023;31(3):615\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith DJ, et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS ONE. 2013;8(11):e75362.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiobank U. \u003cem\u003eserum_biochemistry\u003c/em\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandolt HP. CIRCADIAN RHYTHMS. Caffeine, the circadian clock, and sleep. Science. 2015;349(6254):1289.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCable J, et al. Sleep and circadian rhythms: pillars of health-a Keystone Symposia report. Ann N Y Acad Sci. 2021;1506(1):18\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooks C, et al. The impact of chronotype on circadian rest-activity rhythm and sleep characteristics across the week. Chronobiol Int. 2021;38(11):1575\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaron K, et al. 0017 Circadian Phase Alignment and Sleep Duration in Overweight Adults: Associations with Cardiometabolic Risk Factors. Sleep. 2024;47(Supplement1):A7\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuscogiuri G, et al. The lullaby of the sun: the role of vitamin D in sleep disturbance. Sleep Med. 2019;54:262\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones SE, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. 2019;10(1):343.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLao XQ, et al. Sleep quality, sleep duration, and the risk of coronary heart disease: a prospective cohort study with 60,586 adults. J Clin Sleep Med. 2018;14(1):109\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartelli M, et al. Shift work and serum vitamin D levels: a systematic review and meta-analysis. Int J Environ Res Public Health. 2022;19(15):8919.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWikoff D, et al. Systematic review of the potential adverse effects of caffeine consumption in healthy adults, pregnant women, adolescents, and children. Food Chem Toxicol. 2017;109:585\u0026ndash;648.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeckman MA, Weil J. Gonzalez de Mejia, \u003cem\u003eCaffeine (1, 3, 7-trimethylxanthine) in foods: a comprehensive review on consumption, functionality, safety, and regulatory matters\u003c/em\u003e. J Food Sci. 2010;75(3):R77\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang N, et al. Long-term night shift work is associated with the risk of atrial fibrillation and coronary heart disease. Eur Heart J. 2021;42(40):4180\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVetter C, et al. Night shift work, genetic risk, and type 2 diabetes in the UK biobank. Diabetes Care. 2018;41(4):762\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDat Thien Tran. Susanne Strohmaier; Magdalena Żebrowska; Tianyi Huang; Kyriaki Papantoniou; Susan Redline; Richa Saxena; Martin K Rutter, E.S., \u003cem\u003eRisk of Cardiovascular-kidney-metabolic diseases in the UK Biobank: influences of night shift work, sleep duration, and chronotype.\u003c/em\u003e Under review.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eICD-10: international statistical classification of diseases and related health problems : tenth revision\u003c/em\u003e, \u003cem\u003e2nd ed\u003c/em\u003e. 2019: World health organization.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eWorld Health Organization\u0026rsquo;s guidelines of ICD-10\u003c/em\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/standards/classifications/classification-of-diseases\u003c/span\u003e\u003cspan address=\"https://www.who.int/standards/classifications/classification-of-diseases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShan Z et al. 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. BMJ, 2018. 363.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo T et al. The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study. Eclinicalmedicine, 2024. 69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi R, Chambless L. Test for additive interaction in proportional hazards models. Ann Epidemiol. 2007;17(3):227\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorris CJ et al. \u003cem\u003eCircadian misalignment increases cardiovascular disease risk factors in humans.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2016. 113(10): pp. E1402-E1411.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang L, et al. Shift work and the risk of cardiometabolic multimorbidity among patients with hypertension: a prospective cohort study of UK biobank. J Am Heart Association. 2022;11(17):e025936.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShan Z, et al. 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. BMJ. 2018;363:k4641.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMason IC, et al. Impact of circadian disruption on glucose metabolism: implications for type 2 diabetes. Diabetologia. 2020;63(3):462\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang J, et al. Effects of chronotype on sleep, mood and cardiovascular circadian rhythms in rotating night shift medical workers. Int Arch Occup Environ Health. 2024;97(4):461\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanki M, et al. Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants. Nat Commun. 2023;14(1):7096.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQian J et al. \u003cem\u003eSex differences in the circadian misalignment effects on energy regulation.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2019. 116(47): pp. 23806\u0026ndash;23812.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang B, et al. Age and gender-specific distribution of metabolic syndrome components in East China: role of hypertriglyceridemia in the SPECT-China study. Lipids Health Dis. 2018;17:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVetter C, et al. Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank. Diabetes Care. 2018;41(4):762\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVetter C. Circadian disruption: What do we actually mean? Eur J Neurosci. 2020;51(1):531\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright KP Jr., et al. Influence of sleep deprivation and circadian misalignment on cortisol, inflammatory markers, and cytokine balance. Brain Behav Immun. 2015;47:24\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, et al. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2013;36(1):166\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllulu MS, et al. Obesity and inflammation: the linking mechanism and the complications. Arch Med Sci. 2017;13(4):851\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonnissen HK, et al. Effect of a phase advance and phase delay of the 24-h cycle on energy metabolism, appetite, and related hormones. Am J Clin Nutr. 2012;96(4):689\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHogenesch JB, et al. Characterization of a subset of the basic-helix-loop-helix-PAS superfamily that interacts with components of the dioxin signaling pathway. J Biol Chem. 1997;272(13):8581\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllada R, et al. Stopping time: the genetics of fly and mouse circadian clocks. Annu Rev Neurosci. 2001;24(1):1091\u0026ndash;119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCurtis AM et al. \u003cem\u003eCircadian variation of blood pressure and the vascular response to asynchronous stress.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2007. 104(9): pp. 3450\u0026ndash;3455.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLefta M, et al. Development of dilated cardiomyopathy in Bmal1-deficient mice. Am J Physiol Heart Circ Physiol. 2012;303(4):H475\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurek FW, et al. Obesity and metabolic syndrome in circadian Clock mutant mice. Science. 2005;308(5724):1043\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright KP Jr., et al. Caffeine and light effects on nighttime melatonin and temperature levels in sleep-deprived humans. Brain Res. 1997;747(1):78\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRefinetti R. Integration of biological clocks and rhythms. Compr Physiol. 2011;2(2):1213\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUlh\u0026ocirc;a M, et al. Shift work and endocrine disorders. Int J Endocrinol. 2015;2015(1):826249.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMullan CJ, et al. Melatonin secretion and the incidence of type 2 diabetes. JAMA. 2013;309(13):1388\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, et al. Use of melatonin supplements and risk of type 2 diabetes and cardiovascular diseases in the USA: insights from three prospective cohort studies. Lancet Diabetes Endocrinol. 2024;12(6):404\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu Y, Zhang, Ding W-q. Association of light at night with cardiometabolic disease: A systematic review and meta-analysis. Environ Pollut. 2024;342:123130.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcHill AW, et al. Later circadian timing of food intake is associated with increased body fat. Am J Clin Nutr. 2017;106(5):1213\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFry A, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017;186(9):1026\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"nightshift work, chronotype, caffeinated coffee consumption, vitamin D, sleep duration, cardiovascular-kidney-metabolic health, cardiovascular disease, chronic kidney disease, type 2 diabetes ","lastPublishedDoi":"10.21203/rs.3.rs-7722286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine the association between combined circadian imbalance related traits and cardiovascular-kidney-metabolic (CKM) disease risk, and their potential interaction with night shift work.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study included 191,764 UK Biobank participants without major chronic diseases who were actively working at baseline (2006\u0026ndash;2010). Several factors indicative of a propensity for circadian misalignment were combined to create the circadian imbalance index (CII), with each factor (evening chronotype, sleep\u0026thinsp;\u0026ge;\u0026thinsp;9 or \u0026le;\u0026thinsp;6 hours/day, high neuroticism (score\u0026thinsp;\u0026ge;\u0026thinsp;7), caffeinated coffee consumption 0 or \u0026ge;\u0026thinsp;5 cups/day, and vitamin D\u0026thinsp;\u0026lt;\u0026thinsp;50 nmol/L) contributing one point if present, yielding a composite scale ranging from 0 to 5. CKM outcome (type 2 diabetes, cardiovascular diseases, chronic kidney diseases) identified by ICD codes, self-reports, or death records. Cox models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the multivariable (MV)-adjusted association between the CII and CKM risk, including effect modification by night shift work.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring a median follow-up of 13.5 years (through 2022), 16,907 incident CKM cases were identified. Among participants with European ancestry, for highest versus lowest (0\u0026ndash;1) CII, the MV-adjusted risk of CKM was 1.95 (95%CI: 1.70\u0026ndash;2.23; \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u0026lt;0.001). A significant positive relationship between CII and CKM risk was also observed in participants of Asian (HR\u0026thinsp;=\u0026thinsp;2.03, 95%CI, 1.07\u0026ndash;3.86; \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e=0.02), but not African ancestry (HR\u0026thinsp;=\u0026thinsp;1.43, 95%CI, 0.67\u0026ndash;3.06; \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e=0.66). Risks were higher in shift and night workers than day workers. Among Europeans, the HR for highest CII combined with current night shift work was 2.22 (95%CI, 1.95\u0026ndash;2.53), with significant additive interaction (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this large prospective study, circadian imbalance index (CII) was associated with higher CKM risk in Europeans and Asians. Among Europeans, high CII plus night shift work posed the greatest risk. Maintaining low CII may help prevent CKM, especially in night shift workers.\u003c/p\u003e","manuscriptTitle":"A newly developed circadian imbalance index (CII) and risk of cardiovascular-kidney-metabolic disease in the UK biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 09:16:30","doi":"10.21203/rs.3.rs-7722286/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-02T12:21:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T07:51:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Epidemiology","date":"2025-10-01T18:48:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T18:47:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Epidemiology","date":"2025-09-26T09:24:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"34ebddb6-9f76-49be-bfb4-fb9c620a606d","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:07:59+00:00","versionOfRecord":{"articleIdentity":"rs-7722286","link":"https://doi.org/10.1007/s10654-026-01373-7","journal":{"identity":"european-journal-of-epidemiology","isVorOnly":false,"title":"European Journal of Epidemiology"},"publishedOn":"2026-02-21 15:58:17","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-10-15 09:16:30","video":"","vorDoi":"10.1007/s10654-026-01373-7","vorDoiUrl":"https://doi.org/10.1007/s10654-026-01373-7","workflowStages":[]},"version":"v1","identity":"rs-7722286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722286","identity":"rs-7722286","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}