Introduction

Patients suffering from chronic cardiovascular or metabolic diseases, such as diabetes, hypertension, and obesity, frequently also experience comorbid chronic kidney disease (CKD). Conversely, individuals with CKD face a heightened risk of developing cardiovascular disease (CVD), which can manifest in various forms, including coronary artery disease (CAD), heart failure (HF), arrhythmias, and sudden cardiac mortality (SCD)1. The long-term outcomes for patients with both cardiac and kidney diseases (cardio-kidney events, CKE) tend to be worse than for those with either condition alone.

Cardiovascular-kidney-metabolic (CKM) syndrome was first proposed in a new American Heart Association (AHA) scientific statement to address the complex association between heart, kidney, and metabolism2. Based on metabolic risk factors, the stages of CKM syndrome range from stage 0 (no risk factors) to stage 4 (clinical CVD), leading to multiorgan disorder and an increased risk of adverse CVD and CKD outcomes2. In addition, poor health in CKM is a major determinant of all-cause mortality, and the risk of developing all-cause mortality increases with the progression of the CKM stage, especially in young people3. Thus, early identification and treatment of metabolic risk factors may hold the potential to alleviate the future medical and economic burden of CKE and even all-cause mortality.

In the internal environment of humans, metabolic substances such as lipids, carbohydrates, and proteins exhibit a certain circadian rhythm to sustain the energy cycle and supply needed for normal activities of humans4,5,6. In recent decades, alterations in lifestyle, such as increased use of artificial light sources, excessive intake of energy, controlled environment temperature, sleep disorders, and great work pressure, have resulted in disruption of circadian rhythm, and this disruption accelerates the progression of chronic diseases and leads to a vicious cycle7,8. Circadian syndrome (CircS), a concept evolved from metabolic syndrome (MetS), is defined by a cluster of chronic conditions, including obesity, hypertension, dyslipidemia, hyperglycemia, sleep disorders, and depression. Research has shown that CircS is a more significant predictor of CVD prevalence and incidence than MetS9. Additionally, CircS is linked to the progression of CKD and a rapid decline in kidney function10. Furthermore, accumulating evidence further indicates that the constellation of metabolic abnormalities comprising MetS contributes to premature mortality in later life11. However, there is no data available concerning the relationship between CircS and CKE or all-cause mortality.

Here, we use a large UK Biobank (UKB) database to investigate the relationship between CircS and the combined results of CKE or all-cause mortality. Our research results indicate that CircS significantly associate with increased risk of the primary outcome, and depression was the strongest component contributor.

Method

Study design and participants

This study draws on data from UKB, prospective cohorts conducted in the United Kingdom12. From 2006 to 2010, over 500,000 participants aged 37 to 73 were recruited from 22 assessment centers across England, Scotland, and Wales. All participants completed a structured touch-screen questionnaire and underwent comprehensive baseline assessments, including detailed sociodemographic profiling, lifestyle evaluations, health status examinations, and physical assessments. The study received ethical approval from the Northwest Multi-Centre Research Ethics Committee, with written informed consent obtained from all participants prior to their involvement in the study. For this study, permission to access and analyses the UK Biobank data was approved under the application numbered 134973 and 335191. Therefore, we do not need to apply for IRB to approve this study separately.

Collection of data

Sociodemographic information and clinical data are primarily collected through ACE touchscreen questionnaires, baseline physical examinations, and disease-related hospital admission records. Indicators of body measurement: standing height was measured by using the Seca 202 device; weight was measured by a variety of means during the initial Assessment Centre visit; the Omron device was used to measure blood pressure twice, separated by a few moments. About biochemical indicators: glycosylated hemoglobin (HbA1c) was measured by HPLC analysis on a Bio-Rad VARIANT II Turbo; total cholesterol (TC) and triglyceride (TG) were measured by CHO-POD analysis on a Beckman Coulter AU5800; high-density lipoprotein cholesterol (HDL-C) was measured by enzyme immunoinhibiting analysis on a Beckman Coulter AU5800; low-density lipoprotein cholesterol (LDL-C) was measured by enzymatic protective selection analysis on a Beckman Coulter AU5800; serum creatinine (SCr) was measured by enzymatic analysis on a Beckman Coulter AU5800. Diagnoses of medical conditions, such as ischemic heart disease (IHD), heart failure (HF), atrial fibrillation (AF), cerebrovascular disease, peripheral artery disease, CKD, and depression are identified using linked mortality data and hospital admission statistics. Cases are defined according to the International Classification of Diseases, Tenth Revision (ICD-10) codes (Supplementary Table 1).

Participants screening and exclusion criteria

We screened 502,301 participants from the baseline survey conducted between 2006 and 2010. The participants excluded from the study met any of the following criteria: 1) missing data on CircS or on source of inpatient follow-up data (preventing ascertainment of the correct censor date); 2) lack of information on age, gender, or ethnicity; 3) missing blood samples or related test data; 4) diagnosed with CKE prior to recruitment; 5) had a primary outcome within the first 24 months of follow-up. Finally, we included 295,378 participants, excluding 175,018 individuals who lacked relevant data, 29,999 individuals who had been diagnosed with CKE prior to recruitment, and 1906 participants who had an event within the first 24 months of follow-up. See Fig. 1 for details. To include at least one estimated glomerular filtration rate (eGFR) value in the follow-up, we established sub-cohort that was followed up to 2012–2013, as detailed in supplementary methods and supplementary fig. 1.

Fig. 1: Flow diagram of study participants.
figure 1

SBP systolic blood pressure, DBP diastolic blood pressure, WC waist circumference.

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Circadian syndrome definition

The details of defining CircS were as follows: (1) The threshold for elevated waist circumference (WC) was based on the National Cholesterol Education Program Adult Treatment Panel III, set at ≥102 cm for men and ≥88 cm for women13. (2) Elevated TG was defined as ≥150 mg/dL (1.7 mmol/L). (3) Reduced HDL-C levels were defined as <40 mg/dL (1.0 mmol/L) in men and <50 mg/dL (1.3 mmol/L) in women. (4) Elevated blood pressure was defined as systolic blood pressure (SBP) ≥ 130 mmHg and/or diastolic blood pressure (DBP) ≥ 85 mmHg and/or the use of anti-hypertensive drugs. (5) We used HbA1c as a proxy measure of glucose, based on the recommendations of the American Diabetes Association, with a cut point of HbA1c ≥ 5.7% (39 mmol/mol) or the use of hypoglycemic medications can be defined as populations with hyperglycemia (Supplementary Table 2). The two additional components were defined as follows: (6) Short sleep duration was defined as participants’ average night sleep duration <6 h/day in the past month. (7) Depression was defined according to the ICD-10 codes: F32.x, F33.x, F34.x, F38.x, and F39.x. CircS was defined as meeting 4 or more components. Having ≥3 of the first five components was defined as having MetS.

Potential covariates collection

Covariates include potential confounders thought to be in the relationship between exposure (CircS) and primary outcome. Age (years) and sex (male, female) were defined using data recorded and/or self-reported by the National Health Service. Household income, current smoking status (smokers vs. non-smokers), current drinking status (drinkers vs. non-drinkers), and educational level (classified according to England’s education score system) were measured using a touch-screen questionnaire. We use metabolic equivalent (MET) to quantify physical activity (PA). Based on total MET-hours per week, the population was divided into three levels: low intensity PA: <10 MET-hours/week; moderate intensity PA: 10-50 MET-hours/week; high intensity PA: >50 MET-hours/week. See supplementary methods for details. The diet score was derived from the following dietary factors: vegetable intake ≥4 tablespoons/day, fruit intake ≥3 pieces/day, fish intake ≥twice a week, unprocessed red meat intake ≤twice a week, and processed meat intake ≤twice a week. Each beneficial dietary factor received one point, with the healthy eating score ranging from 0 to 5.

Primary outcome

The primary outcome was CKE or all-cause mortality. CKE is defined as having both CVD and CKD. CVD (including IHD, HF, AF, cerebrovascular disease, or peripheral artery disease) and CKD were diagnosed using ICD-10 codes extracted from hospital inpatient records, with details provided in Supplementary Table 1. All-cause mortality was determined through records from the National Health Service (NHS) Information Centre for England and Wales and the NHS Central Register for Scotland. At baseline, CKD diagnosis also included an eGFR, calculated using the CKD-EPI creatinine equation14. Mild CKD was defined as eGFR <90 mL/min/1.73 m².

The last follow-up dates were October 31, 2022, for participants in England; July 31, 2021, for those in Scotland; and February 28, 2018, for those in Wales. During the follow-up period, participants experienced either a new CKE, mortality, or reached their last follow-up date, whichever occurred first. The diagnosis date for incident CKE was defined as the later of the two diagnosis dates between CVD and CKD. The primary outcome was a composite of incident CKE or all-cause mortality.

In the sub-cohort dataset, CKD were based on ICD-10 codes, or measurements of eGFR <90 mL/min/1.73 m2 during the first follow-up, whichever came first.

Statistics and reproducibility

At baseline, the population was categorized into two groups based on the presence or absence of CircS, and the differences between these groups were analyzed. Normally distributed data are presented as mean ± standard deviation, while non-normally distributed data are expressed as median (range). The independent sample t-test was employed for parametric data, and the chi-square test was applied for nonparametric data.

The Cox proportional hazards regression models were utilized to assess the relationships between CircS and primary outcome, with results expressed as hazard ratios (HR) and 95% confidence interval (CI). Furthermore, patients with CircS were stratified into four groups based on the number of CircS components (scores of 4, 5, 6, and 7), with those scoring 4 serving as the reference group. Two adjustment models were applied in the analyses. Model 1 accounted for age (years) and gender, while model 2 extended adjustments to include additional covariates: current smoking status, current drinking status, educational level, diet score, physical activity, and socioeconomic status.

Additionally, we conducted a series of sensitivity analyses: constructing sub-cohort, evaluating individual components of the primary composite outcome (including IHD, HF, AF, cerebrovascular disease, peripheral artery disease, CKD, CKE, and all-cause mortality), further excluding participants who developed incident events within the first five years of follow-up, and redefining CKD as an eGFR <60 mL/min/1.73 m², repeating the primary analysis again. Furthermore, stratified analyses were performed to assess potential effect modification by gender and age, aiming to minimize the influence of confounding factors. Age stratification was based on a cut-off of 60 years, dividing participants into two groups: <60 years and ≥60 years.

Statistical analysis was carried out by R 4.3.1 and SPSS 25. A two-sided P value < 0.05 was considered statistically significant.

Results

Baseline characteristics of participants

A total of 295,378 participants (55.7% female, median age 58.0 years) were included in this study (Supplementary Data 1). In the baseline population, 31,001 participants were in the CircS group, and 264,377 were in the non-CircS group. In a median follow-up period of 13.6 years, 28,027 participants experienced a primary outcome event. Among these, 4418 participants (14.3%) were from the CircS group, which was significantly higher than the rate in the non-CircS group (P < 0.001). In terms of demographic characteristics, the CircS group was, on average, older and had a higher proportion of males compared to the non-CircS group. Both groups were predominantly White (over 90%), but the CircS group showed slightly higher proportions of Black and other ethnicities. For socioeconomically and in lifestyle factors, the CircS group had a greater prevalence of individuals with lower household incomes (<£18,000), higher educational attainment, smoking habits, low-intensity physical activity, and poorer dietary scores (all with P < 0.001). In terms of physical examination, the median WC, BMI, SBP, and DBP were higher in the CircS group than in the non-CircS group (P < 0.001). Additionally, higher levels of biochemical markers such as HbA1c and TG were observed in the CircS group (P < 0.001), while HDL-C were lower. Additionally, the CircS group exhibited elevated levels of biochemical markers, such as HbA1c and TG, while their HDL-C levels were lower (all with P < 0.001).

The association between CircS and primary outcome

In Cox regression analyses, the results indicated that CircS was independently associated with an elevated risk of the primary outcome. After adjusting for confounding factors, the presence of CircS was associated with an HR of 1.379 (95% CI, 1.319–1.441) (P < 0.001) (Table 1). In the population diagnosed with CircS (≥4), we found that the prevalence of the primary outcome gradually increased with the increase in the number of CircS components (4, 5, 6, 7) (13.64%, 15.50%, 18.65%, 24.05%, respectively). Compared to the lowest CircS score of 4, the risk of 5, 6, 7 is gradually increased, the HR were 1.147 (95% CI, 1.045–1.258, P = 0.004 ), 1.382 (95% CI, 1.102–1.734, P = 0.005), and 2.287 (95% CI, 1.261–4.148, P = 0.006)  (Table 2). This analysis indicated a dose-response relationship, showing that a higher CircS score was associated with an increased risk of the primary outcome.

Table 1 Association between CircS and primary outcome in UK Biobank
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Table 2 Relationship between the score of CircS and primary outcome in UK Biobank
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The relationship between each component of CircS and primary outcome showed that WC (HR, 1.223; 95% CI, 1.184–1.263, P < 0.001), TG (HR, 1.060; 95% CI, 1.027–1.094, P < 0.001), HDL-C (HR, 1.212; 95% CI, 1.168–1.258, P < 0.001), blood pressure (HR, 1.133; 95% CI, 1.097–1.170, P < 0.001), blood glucose (HR, 1.338; 95% CI, 1.292–1.387, P < 0.001), short sleep (HR, 1.180; 95% CI, 1.103–1.263, P < 0.001), and depression (HR, 1.518; 95% CI, 1.426–1.616, P < 0.001) were independently associated with primary outcome after adjusting for confounding factors (Fig. 2). Among these components, depression emerged as the most significant factor, followed closely by blood glucose and WC. To eliminate the possibility of reverse causality, we excluded emerging events within the first 5 years of follow-up and still found a strong link between depression and the primary outcome (Supplementary Fig. 2).

Fig. 2: Association of CircS with primary outcome based on circadian syndrome components in UK Biobank.
figure 2

HR Hazard ratio; values are HR (95% CI) derived from Cox regression models. CI confidence interval, HDL-C High density lipoprotein cholesterol, CircS circadian syndrome. X-axes was presented on a log scale. Adjusted age, gender, smoking, drinking, education, diet score, physical activity, and socioeconomic status.

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Because CircS partially overlapped with MetS diagnoses, the analysis also presented an association between MetS and primary outcome (HR, 1.271; 95% CI, 1.228–1.315, P < 0.001) (Supplementary Table 3).

Sensitivity and subgroup analysis

Sensitivity analyses showed significant associations between CircS and the risk of the following individual diseases: IHD (HR, 1.400; 95% CI, 1.279–1.534, P < 0.001), HF (HR, 1.380; 95% CI, 1.171–1.627, P < 0.001), CKD (HR, 1.753; 95% CI, 1.324–2.319, P < 0.001), CKE (HR, 1.143; 95% CI, 1.044–1.251, P = 0.004), and all-cause mortality (HR, 1.460; 95% CI, 1.390–1.533, P < 0.001). These findings align with the risk of primary outcome observed in the primary analysis (Fig. 3). However, there was no association between CircS and atrial fibrillation, cerebrovascular disease, and peripheral artery disease.

Fig. 3: Association of CircS with risks of components of cardio-kidney events or all-cause mortality in UK Biobank.
figure 3

HR Hazard ratio; values are HR (95% CI) derived from Cox regression models. CI confidence interval, CircS circadian syndrome, IHD ischemic heart disease, HF heart failure, AF atrial fibrillation, CKD chronic kidney disease. X-axes was presented on a log scale. Adjusted age, gender, smoking, drinking, education, diet score, physical activity, and socioeconomic status.

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In our sub-cohort analysis, we observed that CircS continued to be linked to the primary outcome in Model 1 (Supplementary Table 4). Furthermore, after excluding participants who experienced new events within the first five years of follow-up, the results remained consistent with those of the primary analysis (Supplementary Table 5). The findings were also stable when we defined chronic kidney disease (CKD) using either an eGFR of less than 60 mL/min/1.73 m² or ICD-10 codes (Supplementary Table 6).

Subgroup analyses were performed according to stratified variables such as age and gender (see Supplementary Fig. 2). After adjusting for multiple variables, we observed stronger associations in the subgroup of individuals aged ≤60 years (HR, 1.554; 95% CI, 1.442–1.675, P < 0.001) (Supplementary Fig. 3).

The association between CircS and CKE

We conducted further analysis on the relationship between CircS and CKE. In cases of newly diagnosed CKE, CVD and CKD appeared in different sequences. To better understand the relationship between CircS and CKE, we classified CKE outcomes into two categories: CVD-first and CKD-first. Our findings indicated that individuals with CircS had a significantly higher risk of experiencing CVD-first (HR, 2.061; 95% CI, 1.389–3.058, P < 0.001) compared to those with CKD-first (HR, 1.112; 95% CI, 1.014–1.221, P = 0.025) (Table 3).

Table 3 Association between CircS and CKE with CVD-first or CKD-first in UK Biobank
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In addition, based on the characteristics of CKM4 stage, we divided the population with CKE into CVD without renal failure and CVD with renal failure. Results showed that CircS had significantly higher predictive value for participants with renal failure (HR, 4.115; 95% CI, 2.146–7.887, P < 0.001), instead of participants without renal failure (HR, 1.117; 95% CI, 1.019–1.224, P = 0.018) (Supplementary Table 7).

Discussion

In this large population-based study, our data, showed that (1) CircS is a strong risk factor for the composite outcome of CKE or all-cause mortality; (2) Individuals with a greater number of CircS components presented higher risks of the composite outcome of CKE or all-cause mortality; (3) Of the seven components of CircS, depression had the greatest impact on the composite outcome; (4) CircS is a risk factor for CKE and have a greater impact on CKE with CVD-first and CKE with renal failure. Therefore, our findings provide additional information regarding the association of CircS and CKE, which may support the novel concept of CKM syndrome proposed recently.

The circadian system has been highlighted as a major regulator of almost every human health and metabolism aspect15. Two additional components, short sleep and depression, were added to the components of MetS to define CircS, requiring more stringent criteria7. It has been established that CircS and MetS are both drivers for CVD, which are also involved in CKD9,10,16,17.

Some studies indicate that MetS, depression, and short sleep duration all contribute to an increased risk of all-cause mortality11,18,19,20. In this study, individuals with CircS presented higher risks to develop the composite outcome of CKE or all-cause mortality, as well as each of the individual components. Notably, as the number of CircS components increased, so did the incidence of the composite outcome of CKE or all-cause mortality, suggesting a positive correlation between the number of CircS components and the risk of CKE or all-cause mortality.

In addition, Shi et al. demonstrated that, in comparison to MetS, CircS serves as a more robust predictor of CVD, as evidenced by data from CHARLS9 and the National Health and Nutrition Examination Survey (NHANES)17. Xiong et al. reported that the prevalence of CKD was notably higher in the CircS cohort compared to both the normal and MetS groups (5.03% vs. 3.06% vs. 3.87%)21. Currently, there is a noticeable gap in research that directly compares the predictive value of CircS and MetS regarding the combined outcomes of CKE or all-cause mortality. Our data suggest that CircS may possess a superior capability for predicting the composite outcome of CKE or all-cause mortality compared to MetS. The observed difference may be attributed to the two additional components: short sleep and depression. Existing literature have substantiated the correlation between both short sleep and depressive disorders with an increased risk of prevalent CVD, CKD, and all-cause mortality18,19,20,22,23,24,25. Moreover, our study also found that depression showed the strongest effect on primary outcomes compared to the other components of CircS. As previous studies stated, depression may drive progression or exacerbation of MetS, including glucose intolerance, hyperlipidemia, and weight gain, leading to the development of CVD26,27. Yu et al. demonstrated a positive correlation between cardiorenal syndrome and depression in the American population, and this correlation was not mediated by lipid indexes such as triglyceride glucose index and residual cholesterol index28. In addition, two large prospective cohort studies of Chinese adults have also found that depression is associated with higher risk of all-cause mortality and cardiovascular mortality, and that these associations are independent of sociodemographic factors, lifestyle factors, and health status18. These results suggest that depression has predictive value for the occurrence and development of CKE or even mortality, which requires widespread clinical attention.

CVD is often concomitant with CKD and metabolic risk factors such as obesity and diabetes29. CKM syndrome involves both individuals at risk for CVD due to metabolic risk factors, CKD, or both29. Despite the recommendation for a holistic strategy rather than focusing on individual diseases, there is still a lack of comprehensive guidelines for screening, preventing, and managing patients with CKM syndrome. In this study, we did additional analysis with CKE as an individual outcome. Our study represents the first investigation to establish the relationship between CircS and CKE within the UKB cohort, revealing CircS as a risk factor for CKE. CKM stage 4 is the final stage of CKM, which ultimately combines CKD (with or without chronic renal failure) and CVD on the basis of various metabolic risk factors. Based on this, we stratified CKE into CVD without renal failure and CVD with renal failure. The findings demonstrated that CircS exhibits significantly higher predictive value for patients with renal failure comorbidity compared to those without renal impairment. A recent study by Huang et al. demonstrated that patients in stage 4 of CKM syndrome had the greatest risk of depression and anxiety compared with those without loneliness and in stage 0, suggesting a mutual and influential relationship between depression and metabolic factors in patients with CKM30. Short sleep also has been shown to be associated with a greater risk for adverse cardiometabolic outcomes, since short sleep duration persisted from pregnancy to 2 to 7 years after delivery31. The two additional components of CircS, short sleep and depression, are not indicators for diagnosing CKM syndrome. However, the positive correlation between CircS and CKE identified in our study provides theoretical validation and conceptual extension of the CKM framework. Therefore, in addition to traditional risk factors, such as obesity, diabetes, and hyperlipidemia, it is important and urgent for targeted interventions aimed at improving sleep and mental health among populations at increased risk of CKM syndrome.

This study has several strengths. First, the database for this study originated from a large prospective cohort with a rigorous study design and a large sample size. In addition, to the best of our knowledge, this is the first study to assess the relationship between CircS and the composite outcome of CKE or all-cause mortality. Our study explored the positive correlation between CircS and CKE, providing a theoretical validation and extension of the CKM concept. The results of a series of sensitivity analyses were generally consistent, suggesting that our findings are reliable.

There are some limitations in this study. First, the UKB relies on diagnostic codes from hospitalized patients, national procedure registries, and death records to ascertain disease endpoints, which may result in endpoint misclassification. Second, in this study, the use of baseline measurements to define CircS in cohort studies (due to the lack of biomarker data at the time of the follow-up assessment) and the assessment of events several years later limits our ability to interpret changes in CircS status over time. However, prior work supports the validity of integrating baseline biomarker data, demonstrating that these data are highly stable over time32. Third, we used HbA1c as a proxy for fasting glucose, which differs from the Harmonized Criteria for MetS and CircS; however, the American Diabetes Association recommendations support using this measure as an appropriate proxy for glucose values. Fourth, in this study, we defined one of the definitions of CKD as eGFR <90 mL/min/1.73 m2, including patients with mild renal insufficiency, whereas many studies define CKD as eGFR <60 mL/min/1.73 m2. However, many studies have shown that mild renal insufficiency heightens the risk of adverse outcomes during hospitalization for patients with cardiovascular and cerebrovascular diseases33,34,35,36. In order to reduce the risk of adverse health outcomes as early as possible, participants with mild renal insufficiency were eventually included in our study. Further sensitivity analysis showed that the results were consistent with those of the main analysis, even if eGFR <60 mL/min/1.73 m2 was defined as CKD. Fifth, we excluded 175,018 participants at baseline and a further 1,906 patients at follow-up, which may have introduced selection bias. Finally, given the observational design of this study, residual confounding and other non‐causal explanations remain.

Conclusion

In conclusion, CircS can strongly predict CKE or all-cause of mortality and present superior predictive capability compared with MetS. Our findings offer evidence supporting the longitudinal association between CircS and CKE or all-cause of mortality, highlighting the potential benefits of targeting and ameliorating CircS components to mitigate CKE and all-cause of mortality. It also provides theoretical validation and conceptual extension for CKM framework.