Association Between Sleep Patterns and the Risk of Cardiometabolic Diseases or Cardiometabolic Multimorbidity and the Mediating Role of Metabolic Biomarkers: A Prospective Study of the UK Biobank Cohort
Poor sleep pattern was associated with increased risk of CMDs and CMM, respectively, and differences in metabolites might partially explain the association between sleep pattern and cardiometabolic health.
Abstract
Aim: To identify the relationship between joint sleep patterns and the incidence of cardiometabolic diseases (CMDs) and multimorbidity (CMM) as well as the mediating effects of plasma metabolites. Methods: This prospective study included 190,827 participants from the UK Biobank. The joint sleep pattern was evaluated by the healthy sleep score according to five sleep factors (chronotype, sleep duration, insomnia, snoring, and excessive daytime sleepiness). Cox proportional hazards models were utilized to evaluate the relationship between sleep-related metabolites and the incidence of CMM and CMDs. Mediation analyses were conducted to quantify the potential mediating effects of circulating metabolites. Results: After a median follow-up of 13.5 years, 24,602 incident cases of CMDs and 2740 incident cases of CMM were identified. Per 1-decrement of sleep score was associated with 10.5% and 15.1% increased risks of CMDs and CMM, respectively. However, sleep was not associated with the progression from the first disease to CMM. A total of 152 metabolites were identified to be associated with sleep patterns, among which lipoproteins emerged as the predominant class. Most of the metabolites showed mediation effects on the association between sleep and outcomes. Conclusions: Poor sleep pattern was associated with increased risk of CMDs and CMM. Differences in metabolites, especially lipoproteins, might partially explain the association between sleep pattern and cardiometabolic health.
ABSTRACT Aims To prospectively examine how combined sleep behaviors affect vascular complications in type 2 diabetes, whether these associations are modified by disease severity, as well as the mediating roles of clinical biomarkers. Methods A total of 12,456 participants with type 2 diabetes from the UK Biobank were included. A sleep score was defined by: sleep duration, chronotype, insomnia, snoring, and daytime sleepiness. Cox regression models were used to examine the association between healthy sleep score and risk of vascular complications. Results Over 13.5 years, 1,878 macrovascular and 3,066 microvascular complications occurred. Compared with a healthy sleep score of 0–1, the multivariable‐adjusted HRs (95% CIs) of the score 4–5 were 0.75 (0.63, 0.89) for macrovascular complications [coronary artery disease 0.72 (0.57, 0.91), stroke 0.92 (0.66, 1.28), peripheral artery disease 0.79 (0.56, 1.10)]; 0.86 (0.75, 0.99) for microvascular complications [neuropathy 0.62 (0.47, 0.83), nephropathy 1.01 (0.84, 1.21), retinopathy 0.84 (0.68, 1.03)]. The aggregated diabetes severity score (P interaction = 0.037) significantly modified the association with microvascular complications. Total cholesterol, LDL‐C, triglycerides, apolipoprotein B, and CRP collectively explained 11.34% (4.24%, 37.70%) of macrovascular associations [including 11.14% (2.85%, 43.03%) for coronary artery disease], while total bilirubin explained 3.57% (0.43%, 20.63%) of microvascular associations, collectively with triglycerides, GGT, and CRP mediating 9.33% (3.95%, 27.58%) for diabetic neuropathy. Conclusions A healthy sleep pattern is associated with lower risks of vascular complications in type 2 diabetes, independent of traditional risk factors, and the association with microvascular complications is modified by diabetes severity. Lipid profiles and inflammatory factors partly mediate the favorable association.
Yanqiu Zou, Xia Jiang, Jinyu Zhou et al.· Journal of Diabetes Investig...· 0 citations
Background Obstructive sleep apnea (OSA) is a significant contributor to cardiometabolic diseases, yet its burden and metabolic consequences remain underexplored in the United Arab Emirates (UAE). Additionally, little is known about how OSA risk relates to early metabolic and renal abnormalities in young adults. This study evaluated the association between OSA risk and a comprehensive profile of cardiometabolic and renal biomarkers in a large cohort of young Emirati adults. Methods This cross-sectional study included 5,242 participants from the UAE Healthy Future Study. OSA risk was assessed using the STOP-Bang questionnaire and classified as no-to-low (score 0–2) or intermediate-to-high (score ≥3). Biomarkers assessed included HbA1c, lipid profile, ApoA, ApoB, C-reactive protein (CRP), serum creatinine, and urine microalbumin. Group differences were examined using t-tests and χ2 tests. Multivariable linear regression models adjusted for sociodemographic, lifestyle, and clinical covariates were used to assess associations between OSA risk and biomarker levels. Sensitivity analyses were conducted excluding participants with diabetes or dyslipidaemia. Results The mean age was 27.5 years, and 30% of participants were classified as intermediate-to-high OSA risk. The intermediate-to-high OSA risk group exhibited greater adiposity, higher blood pressure, and more adverse cardiometabolic profiles than the no-to-low risk group. After adjustment, intermediate-to-high OSA risk was associated with elevated HbA1c (β = 0.18%, 95% CI: 0.14–0.23), higher total cholesterol, LDL-C, triglycerides, and ApoB, and lower HDL-C and ApoA (all p < 0.001). Inflammatory and renal markers were similarly elevated, including CRP (β = 0.14 mg/dL, 95% CI: 0.10, 0.18), serum creatinine (β = 0.11 mg/dL, 95% CI: 0.09, 0.13), and urine microalbumin (β = 3.61 mg/dL, 95% CI: 1.64, 5.60). Sex-stratified analyses showed higher median blood pressure and creatinine in males, and higher HDL-C and CRP in females. Sensitivity analyses produced consistent results for most biomarkers, except for urine microalbumin, which became non-significant. Conclusions Elevated OSA risk was consistently associated with adverse cardiometabolic and renal profiles in young adults. These findings suggest that STOP-Bang may serve as a useful screening tool for early risk detection, warranting validation in longitudinal studies.
Manal Taimah, Amar S. Ahmad, Y. Idaghdour et al.· Frontiers in sleep· 0 citations
OBJECTIVES
We examined the association between sleep duration and quality with cardiometabolic risk factors, assessed whether these associations varied by socioeconomic status, and identified prevalent risk factor combinations in those with poor sleep health.
METHODS
We conducted a cross-sectional analysis of 25,433 adults using the 2022 National Health Interview Survey data. Sleep health exposures included self-reported sleep duration (<7, 7-9, >9 h), sleep quality measures (frequency of feeling well rested, difficulty falling asleep, difficulty staying asleep), and a composite sleep health indicator. Cardiometabolic risk factors included overweight/obesity, diabetes, hypertension, and hyperlipidemia. We examined the mean count of cardiometabolic risk factors (individual range: 0-4) and patterns of cardiometabolic risk factor combinations across sleep measures using Poisson regression with marginal standardization for covariates, accounting for the complex survey design.
RESULTS
Population-average cardiometabolic risk factor count was 9% higher among short sleepers, 15% to 20% higher across poor sleep quality measures, and 18% higher among those without healthy composite sleep compared with healthy reference groups. Associations between difficulty staying asleep and mean cardiometabolic risk factor count were stronger among those with lower SES (p-interaction ≤0.001) on the ratio scale. Poor sleep quality was associated with more severe CMRF combinations and patterns, while sleep duration showed heterogeneous associations across cardiometabolic outcomes.
CONCLUSION
Poor sleep quality and short sleep duration were associated with higher population-level cardiometabolic risk factor burden. Associations for difficulty staying asleep were stronger among individuals with lower socioeconomic status. Poor sleep quality was also associated with more severe cardiometabolic risk factor combinations, suggesting sleep quality may be a potential target for multimorbidity prevention.
K. Vashist, Laura Ward, Amit J. Shah et al.· Sleep Health· 0 citations
OBJECTIVES
Healthy sleep is associated with lower stroke risk, but the underlying metabolic mechanisms remain poorly understood. We investigated whether a nuclear magnetic resonance-derived metabolic signature mediates this association.
METHODS
This prospective cohort study included 200,729 UK Biobank participants (mean age 56.48±8.10 years, 53.80% female) free of stroke at baseline. A healthy sleep score (0-6) was derived from 6 behaviors as follows: chronotype, sleep duration, insomnia, snoring, daytime dozing, and ease of getting up. Sleep-related metabolites among 249 plasma nuclear magnetic resonance biomarkers were selected using elastic net regression and combined into a metabolic signature score. Associations with incident stroke were estimated using Cox models, and mediation was assessed using counterfactual analyses over a median 12.6 -year follow-up.
RESULTS
During 2,530,392 person-years, 5140 incident strokes occurred. Elastic net identified 35 sleep-related metabolites, mainly reflecting lower glycoprotein acetyls and VLDL particles and higher polyunsaturated fatty acids, including DHA and linoleic acid. Each 1-point higher healthy sleep score was associated with lower risk of stroke (HR 0.97; 95% CI, 0.95-0.99). Each 1-SD higher metabolic signature score was associated with a reduced risk of stroke (HR 0.93; 95% CI, 0.90-0.96), and the highest versus lowest tertile had lower risk (HR 0.86; 95% CI, 0.79-0.93). The signature mediated 8.63% (95% CI, 3.65%-18.25%) of the sleep-stroke association, with omega-3 fatty acids, HDL subfractions, amino acids, and GlycA as major contributors.
CONCLUSIONS
A nuclear magnetic resonance-derived metabolic signature partially mediates the protective effect of healthy sleep on stroke risk through lipid metabolism, amino acid homeostasis, and systemic inflammation pathways, highlighting potential metabolic targets for stroke prevention in individuals with sleep disturbances.
Zhe Xu, Jinxiang Zhang, Zhixing Fan et al.· Sleep Health· 0 citations
BACKGROUND
Cardiometabolic multimorbidity (CMM) poses a substantial public health burden in aging populations. However, limited evidence exists regarding the clustering of cardiometabolic conditions or the interplay between sleep and frailty in CMM. This study aimed to explore the independent and synergistic associations of these factors with CMM, which may inform targeted preventive strategies.
METHODS
This cross-sectional study analyzed data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Self-reported sleep quality was categorized as good, fair, or bad. Frailty was quantified using a 35-item deficit accumulation frailty index (FI), dichotomized as non-frail (FI ≤ 0.25) or frail (FI > 0.25). Multivariable logistic regression models assessed associations between sleep quality, frailty, and CMM (≥ 2 concurrent cardiometabolic conditions). Multiplicative interaction was tested via likelihood ratio tests, while additive interaction was quantified using the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI).
RESULTS
Among 8,592 participants (mean age 83.17 ± 11.00 years; 48.0% male), CMM prevalence was 21.6% (n = 1,854). Females exhibited higher CMM prevalence than males (54.7% vs. 45.3%), with significant sex- and age-specific disparities (e.g., 65-74 years: females 28.1% vs. males 21.3%; 75-84 years: females 28.5% vs. males 23.5%; all P < 0.05). In adjusted models, bad sleep quality was independently associated with higher odds of CMM (bad vs. good: adjusted odds ratio [aOR] = 1.84, 95% CI: 1.55-2.18; fair vs. good: aOR = 1.22, 95% CI: 1.06-1.40), with a dose-response relationship (P < 0.001). Frailty demonstrated the strongest association (frail vs. non-frail: aOR = 6.87, 95% CI: 5.70-8.28). Notably, frail individuals with bad sleep quality had 9.18-fold higher odds of CMM (95% CI: 6.63-12.72) compared to non-frail good sleepers, with 21% of CMM cases attributable to this interaction (RERI = 1.09, 95% CI: 0.08-2.10; AP = 0.21, 95% CI: 0.06-0.36; SI = 1.35, 95% CI: 1.03-1.67).
CONCLUSIONS
Sleep quality and frailty are associated with CMM, suggesting that their combined assessment may enhance predictive accuracy for CMM risk. These findings underscore the need to prioritize high-risk elderly individuals for integrated interventions addressing sleep disturbances and frailty, thereby facilitating the transition from reactive to proactive cardiometabolic care models.
Siyu Bing, Weida Liu, Shuting Song et al.· BMC Geriatrics· 0 citations
BACKGROUND
The long-term associations between cardiometabolic multimorbidity (CMM) and depressive symptoms (DS) onset and symptom-state reversion, as well as the potential role of sleep duration in these associations, remain unclear.
METHODS
We analyzed 14,468 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 8551 from the English Longitudinal Study of Ageing (ELSA). DS was assessed using the CES-D scale; reversion was defined as a transition from DS to non-DS based on CES-D thresholds. Multi-state Markov models estimated transitions among non-DS, DS, and death according to baseline CMM and sleep duration (poor sleep duration defined as <6 or ≥9 h). Cox models evaluated associations with DS onset and reversion.
RESULTS
In 14,468 Chinese participants (age 59.3 ± 9.5 years; 52.3% female), compared with those without CMDs, participants with one CMD or CMM had 13% and 29% higher hazards of transitioning from non-DS to DS (HR = 1.13 [1.05-1.22]; HR = 1.29 [1.09-1.54]) and 10% and 21% lower hazards of transitioning from DS to non-DS (HR = 0.90 [0.84-0.97]; HR = 0.79 [0.67-0.94]). In 8551 UK participants (age 66.1 ± 9.4 years; 56.2% female), the corresponding higher hazards were 10% and 21% (HR = 1.10 [1.02-1.20]; HR = 1.21 [1.04-1.41]), while hazards of symptom-state reversion were 17% and 26% lower (HR = 0.83 [0.76-0.91]; HR = 0.74 [0.63-0.87]). Significant additive interactions between CMM and unhealthy sleep duration were observed (P for additive interaction <0.001).
CONCLUSIONS
CMM is associated with higher hazards of DS onset and lower hazards of transitioning from DS to non-DS. Compared with individuals with good sleep duration, these associations were stronger among those with poor sleep duration.
Rui Zhang, Xueqin Sun, Guanzhong Caotian et al.· Journal of Affective Disorde...· 0 citations
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