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
ABSTRACT Epigenetic aging biomarkers are well‐established hallmarks of biological aging, yet their metabolic underpinnings remain largely unexplored. Here, we characterized metabolic signatures associated with five epigenetic aging biomarkers (HorvathAge, HannumAge, DNAmPhenoAge, DunedinPACE, and DNAmTL) and examined their clinical relevance and potential determinants in 7162 Chinese older adults from two cohorts (primary and validation). We observed both shared and distinct metabolic associations across epigenetic aging biomarkers. Metabolic signatures of epigenetic aging biomarkers were derived using elastic net regression, showing moderate correlations with the corresponding epigenetic aging biomarkers (r = 0.21–0.36 in internal testing set, p < 0.05), with external replication further validating metabolic signatures of DNAmPhenoAge, DunedinPACE, and DNAmTL (r = 0.18–0.29, p < 0.05). These five metabolic signatures of epigenetic age acceleration (EAA) exhibited 279 significant associations with aging‐related phenotypes including higher disease risk, poorer health status, and adverse clinical indicators. Gallstones, chronic kidney disease, and hepatitis, along with renal‐, hepatic‐ and metabolic‐related clinical indicators, were consistently associated with multiple metabolic signatures of EAA. Smoking status, alcohol consumption, body mass index (BMI), and physical activity were identified as modifiable lifestyle factors associated with metabolic signatures of EAA, with BMI showing the most consistent associations. Metabolic signatures of DunedinPACE and DNAmPhenoAA exhibited the most extensive associations with aging‐related phenotypes and modifiable lifestyle factors in both primary and validation cohorts. These findings provide novel insights into the metabolic correlates of epigenetic aging biomarkers and underscore the potential of metabolomics‐informed metrics of epigenetic aging as informative indicators of physiological decline and lifestyle effects.
The Met-TL may serve as a promising biomarker of cellular aging, particularly in identifying liver dysfunction, facilitating the development of personalized aging monitoring and anti-aging intervention strategies.
Tian-Pei Ma, Xun-Ying Zhao, Ke Jiang et al.· The journals of gerontology....· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
