Subgroup analyses and interaction tests demonstrated that the association between CMI and sleep disorders remained consistent across different subgroups, and no significant modification of this relationship was observed by other covariates except for body mass index.
Abstract
Sleep disorders have already been recognized as a public health concern, with evidence suggesting that lipid metabolism significantly affects sleep quality and circadian rhythms. Cardiometabolic index (CMI), a novel lipid index, has been confirmed to be associated with obesity-associated metabolic disorders, such as diabetes and cardiovascular disorders. Despite these findings, the exact relationship between CMI and sleep disorders remains to be fully validated. Consequently, our objective was to investigate the association between CMI and sleep disorders using data from the National Health and Nutrition Examination Survey (NHANES). Based on data from NHANES spanning 2015 to 2020, this study employed weighted multivariate logistic regression and restricted cubic spline (RCS) regression analysis to investigate the relationship between CMI and sleep disorders. Additionally, receiver operating characteristic analysis and the area under the curve were also utilized to assess the predictive performance of CMI for sleep disorders. Subgroup analyses and interaction tests were conducted to examine the consistency of this association across different populations. A positive association between CMI and sleep disorders was found in a cohort comprising 5817 participants aged 20 and above, which was further verified to be nonlinear through RCS regression analysis. Within the context of a fully adjusted model, when CMI was considered as a continuous variable, each one-unit increase was linked to a 17% increase in the prevalence of sleep disorders (odds ratio [OR] = 1.17; 95% CI: 1.06–1.29; P = .004). Upon stratifying CMI into quartiles, it was observed that participants in the highest quartile exhibited an 81% greater risk of developing sleep disorders compared to those in the lowest quartile (OR = 1.81; 95% confidence interval [CI]: 1.29–2.54; P = .002). Subgroup analyses and interaction tests demonstrated that the association between CMI and sleep disorders remained consistent across different subgroups, and no significant modification of this relationship was observed by other covariates except for body mass index. An elevated CMI is correlated with a higher probability of sleep disorders among individuals in the United States. Given the difficulty in establishing a causal relationship between the 2, further extensive prospective studies are necessary to investigate the role of CMI in the development of sleep disorders.
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.
Duqiu Liu, Yi Guo, Guochen Li et al.· Healthcare· 0 citations
Sleep disorders and abnormal sleep duration were associated with diabetic kidney disease among Americans aged 45 and older, and sleep-related factors may warrant consideration in the comprehensive clinical assessment of individuals with diabetes.
Lingjiang Liu, Shibei Zhang, Junlin Wu et al.· European Journal of Medical...· 0 citations
It is found that changes in sleep duration were linked to gastrointestinal diseases, with depression playing a mediating role, which suggests that changes in sleep duration and mental health may help to prevent gastrointestinal disturbances.
Jian-hao Zheng, Lingyan Qiu, D. Shi et al.· Medicine· 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
Background: Metabolic syndrome (MS) significantly contributes to premature mortality in schizophrenia, which is driven by multiple biological and behavioral factors. While individual risk factors are known, comprehensive studies examining the combined influence of biological markers (adiponectin) and behavioral factors (sleep, lifestyle) in the Indian context are limited. Methods: In this cross-sectional, observational study, 130 patients diagnosed with schizophrenia were assessed for MS using modified National Cholesterol Education Program Adult Treatment Panel III criteria. We evaluated symptom severity using the Brief Psychiatric Rating Scale, Sleep quality using the Pittsburgh Sleep Quality Index, and lifestyle behaviors using the Health-Promoting Lifestyle Profile-II. Blood samples were analyzed for serum adiponectin and metabolic profiles to diagnose MS. Results: The MS rate was 24.6%. Bivariate analyses revealed that patients with MS had a significantly longer time spent in exacerbation (p = .03), a longer treatment duration (p = .01), lower serum adiponectin levels (p = .02), poorer sleep quality (p = .001), and lower physical activity and nutritional habit scores (p = .01). In the multivariable logistic regression model, poorer sleep quality (OR = 1.17, 95% CI = 1.06–1.30, p = .002) and a lower physical activity and nutritional habit score (OR = 0.92, 95% CI = 0.86–0.98, p = .01) emerged as significant predictors of MS. Conclusions: One-fourth of the patients with schizophrenia had MS in this cohort. Notably, modifiable behavioral factors—specifically sleep quality and lifestyle—were stronger predictors of metabolic risk. This signifies the vital need to integrate routine screening for cardiometabolic risk factors and to apply therapeutic strategies into the standard of care for schizophrenia.
Smit Ratilal Makwana, N. Nebhinani, Pranshu Singh et al.· Indian Journal of Psychologi...· 0 citations
BACKGROUND
Building on evidence that diet may ameliorate sleep disorders, and following the introduction of the EAT-Lancet Planetary Health Diet in 2019 to promote human health and environmental sustainability, this study aimed to investigate the potential association between the planetary health diet and the risk of sleep disorders.
METHODS
This study utilized data from the UK Biobank and included 165866 participants (mean baseline age 55.8 years; 53.5% female). The planetary health diet index (PHDI) was calculated using a 24-hour dietary recall, with adherence quantified using three scoring methods. Sleep disorders were identified using ICD-10 code G47 from linked health records. Cox proportional hazards regression was used to estimate the association between PHDI and sleep disorder risk.
RESULTS
During a median follow-up of 13.9 years, 3200 participants developed sleep disorders, of whom 1282 (40.1%) were female. At baseline, 78113 (47.1%) participants scored below the Knuppel PHDI mean, 83258 (50.2%) below the Stubbendorff PHDI mean, and 89528 (54.0%) below the Kesse-Guyot PHDI mean. Compared with the lowest adherence group, participants in the highest adherence group for the Knuppel index had a lower risk of sleep disorders (HR = 0.827, 95% CI: 0.733-0.933). Similar associations were observed for Stubbendorff (HR = 0.796, 95% CI: 0.712-0.891) and Kesse-Guyot (HR = 0.838, 95% CI: 0.754-0.931).
CONCLUSIONS
Higher adherence to the planetary health diet was associated with a reduced risk of sleep disorders, supporting the potential role of dietary recommendations in sleep health.
