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10.1038/s41467-025-66407-2 http://scihub22266oqcxt.onion/10.1038/s41467-025-66407-2 41390485!?!41390485 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41390485&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Nat+Commun 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41390485|t=2025. Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies |pdf=|usr=}}{{41390485}} gab.com Text Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=41390485 #FOAvip Accelerometers allow objective measures of dimensions (rest-activity rhythm (RAR), daytime activity, sleep, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3,991, mean age=69.4years) and UK Biobank (N = 54,995, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented), one high-intensity physical activity, and four poor RAR (one characterised by late chronotype, two by low activity but opposite sleep profiles, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic, behavioural and health-related factors. Findings were similar in these two independent cohort studies, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR, daytime activity, sleep, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health. Twit Text FOAVip Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=41390485 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41390485 #FOAvip English Wikipedia <ref>{{Cite pmid|41390485}}</ref> Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies #MMPMID41390485 Vidil S; Danilevicz IM; Dugravot A; Fayosse A; Landre B; van Hees V; Chen M; Singh-Manoux A; Sabia S Nat Commun 2025[Dec]; ? (?): ? PMID41390485show ga Accelerometers allow objective measures of dimensions (rest-activity rhythm (RAR), daytime activity, sleep, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3,991, mean age=69.4years) and UK Biobank (N = 54,995, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented), one high-intensity physical activity, and four poor RAR (one characterised by late chronotype, two by low activity but opposite sleep profiles, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic, behavioural and health-related factors. Findings were similar in these two independent cohort studies, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR, daytime activity, sleep, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health. ?Circadian rhythm profiles derived from accelerometer measures of the s(epmc).pdf DeepDyve Pubget Overpricing