Risk of Incident Dementia According to Metabolic Health and Obesity Status in Late Life: A Population-Based Cohort Study

Ji Yeon Lee, Kyungdo Han, Eugene Han, Gyuri Kim, Hanna Cho, Kwang Joon Kim, Byung Wan Lee, Eun Seok Kang, Bong Soo Cha, Carol Brayne, Yong Ho Lee

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

The risk for dementia among subjects who are obese with normal metabolic profiles, or called metabolically healthy obese (MHO), remains uninvestigated. Objective: To determine the association between late-life metabolic health and obesity status and risk of incident dementia. Design: Retrospective cohort study. Setting: The National Health Insurance System, Republic of Korea. Patients: A total of 12,296,863 adults.50 years old who underwent health examinations from 2009 to 2012 without baseline history of dementia. Main Outcome Measure: Incident overall dementia, Alzheimer's disease (AD), and vascular dementia (VaD). Results: Among subjects 60 years old, 363,932 (6.4%) developed dementia during a median followup of 65 months (interquartile range 51 to 74months). TheMHO group showed the lowest incidence of overall dementia [hazard ratio (HR) 0.85; 95%CI, 0.84 to 0.86] andAD(HR0.87; 95%CI, 0.86 to 0.88), but not VaD, comparedwith themetabolically healthy nonobese group. All components ofmetabolic syndrome except obesity significantly elevated the risk of dementia, and these associationsweremore pronounced in VaD. In particular, being underweight dramatically increased the risk of dementia.

Original languageEnglish
Article numberjcem_201801491
Pages (from-to)2942-2952
Number of pages11
JournalJournal of Clinical Endocrinology and Metabolism
Volume104
Issue number7
DOIs
Publication statusPublished - 2019 Mar 21

Bibliographical note

Funding Information:
Financial Support: This research was supported by a grant from the Basic Science Research Program through a National Research Foundation of Korea grant, funded by the Ministry of Science and ICT (NRF-2016R1A5A1010764, to Y.H.L.), and an Institute for Information & Communications Technology Promotiongrant,fundedbytheKoreangovernment(no.2017-0-01779, “A Machine Learning and Statistical Inference Frame-work for Explainable Artificial Intelligence,” to K.J.K.).

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Endocrinology
  • Clinical Biochemistry
  • Biochemistry, medical

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