Lifestyle Profiling Using Wearables and Prediction of Glucose Metabolism in Individuals with Normoglycemia or Prediabetes.

Autor:	Park H; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A.; Department of International Health, Johns Hopkins Bloomberg School of Public Health, MD 21205, U.S.A., Metwally AA; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., Delfarah A; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., Wu Y; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., Perelman D; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., Rodgar M; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., Mayer C; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., Celli A; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A., McLaughlin T; Department of Medicine, Stanford University, Stanford, CA 94305, U.S.A., Mignot E; Center for Sleep, Sciences and Medicine, Stanford University School of Medicine, Palo Alto, CA 94304, U.S.A., Snyder M; Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Sep 06. Date of Electronic Publication: 2024 Sep 06.
DOI:	10.1101/2024.09.05.24312545
Abstrakt:	This study examined the relationship between lifestyles (diet, sleep, and physical activity) and glucose responses at a personal level. 36 healthy adults in the Bay Area were monitored for their lifestyles and glucose levels using wearables and continuous glucose monitoring (NCT03919877). Gold-standard metabolic tests were conducted to phenotype metabolic characteristics. Through the lifestyle data (2,307 meals, 1,809 nights, and 2,447 days) and 231,206 CGM readings from metabolically-phenotyped individuals with normoglycemia or prediabetes, we found: 1) eating timing was associated with hyperglycemia, muscle insulin resistance (IR), and incretin dysfunction, whereas nutrient intakes were not; 2) timing of increased activity in muscle IS and IR participants was associated with differential benefits of glucose control; 3) Integrated ML models using lifestyle factors predicted distinct metabolic characteristics (muscle, adipose IR or incretin dysfunction). Our data indicate the differential impact of lifestyles on glucose regulation among individuals with different metabolic phenotypes, highlighting the value of personalized lifestyle modifications. Competing Interests: Competing Interests. A.A.M. is currently an employee of Google, and A.D. is an employee of Calico Life Sciences. D.P. and T.M. are members of the scientific advisory board of January AI. M.P.S. is a co-founder and a member of the scientific advisory board of Personalis, Qbio, January AI, SensOmics, Protos, Mirvie, RTHM and Iollo. He is on the scientific advisory board of Danaher, Neuvivo and Jupiter. All other authors declare no financial or non-financial competing interests.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu