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Ukraintseva S, Duan H, Holmes R, Bagley O, Wu D, Yashkin A, Kulminski A, Akushevich I, Whitson H, Stallard E, Yashin A, Arbeev K. Patterns of Aging Changes in Bodyweight May Predict Alzheimer's Disease. J Alzheimers Dis 2024; 97:163-170. [PMID: 38108347 PMCID: PMC10789330 DOI: 10.3233/jad-220998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/10/2023] [Indexed: 12/19/2023]
Abstract
Relationships between patterns of aging-changes in bodyweight and AD are not fully understood. We compared mean age-trajectories of weight between those who did and did not develop late-onset-AD, and evaluated impact of age at maximum weight (AgeMax), and slope of decline in weight, on AD risk. Women with late-onset-AD had lower weight three or more decades before AD onset, and ∼10 years younger AgeMax, compared to AD-free women. APOE4 carriers had younger AgeMax and steeper slope. Older AgeMax and flatter slope predicted lower AD risk. Premature decline in weight could be a sign of accelerated physical aging contributing to AD.
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Affiliation(s)
- Svetlana Ukraintseva
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Hongzhe Duan
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Rachel Holmes
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Olivia Bagley
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Deqing Wu
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Arseniy Yashkin
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Alexander Kulminski
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Igor Akushevich
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Heather Whitson
- Center for Aging and Human Development, Duke University, Durham, NC, USA
- Geriatrics Research, Education, and Clinical Center (GRECC), Durham VA Medical Center, Durham, NC, USA
| | - Eric Stallard
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Anatoliy Yashin
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
| | - Konstantin Arbeev
- Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC, USA
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Liu TL, Woodward JM, Kowalkowski M, Taylor YJ, Gutnik B, Mangieri DA. Assessing healthcare outcomes among patients with dementia requiring hospitalization for COVID-19: An observational study. J Am Geriatr Soc 2023; 71:970-973. [PMID: 36268968 PMCID: PMC9874898 DOI: 10.1111/jgs.18093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/22/2022] [Accepted: 09/26/2022] [Indexed: 01/29/2023]
Affiliation(s)
- Tsai-Ling Liu
- Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, North Carolina, USA
| | | | - Marc Kowalkowski
- Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, North Carolina, USA
| | - Yhenneko J Taylor
- Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, North Carolina, USA
| | - Bella Gutnik
- Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, North Carolina, USA
| | - Deanna A Mangieri
- Atrium Health Senior Care, Atrium Health, Charlotte, North Carolina, USA
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Shen J, Chen H, Zhou T, Zhang S, Huang L, Lv X, Ma Y, Zheng Y, Yuan C. Long-term Weight Change and its Temporal Relation to Later-life Dementia in the Health and Retirement Study. J Clin Endocrinol Metab 2022; 107:e2710-e2716. [PMID: 35420682 PMCID: PMC9202702 DOI: 10.1210/clinem/dgac229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Indexed: 11/19/2022]
Abstract
CONTEXT Weight loss among middle-aged and older adults has been associated with a higher risk of subsequent dementia. However, most studies have limited follow-up durations or suboptimal control for the potential influence of physical frailty (PF). OBJECTIVE Our study aimed to investigate the long-term and temporal relations of weight change to risk of dementia among middle-aged and older adults in the United States. METHODS A total of 5985 participants aged 65 years and older were included from the Health and Retirement Study. History of long-term weight change was calculated using 9 repeated body mass index measurements during 1992-2008. We then followed participants' dementia status from 2008 to 2018. Multivariable Cox proportional hazard models were used. RESULTS During the study follow-up period (mean = 7.54 years), a total of 682 (11.40%) dementia cases were documented. After adjustment for basic demographic and lifestyle factors, participants with weight loss (median: -0.23 kg/m2 per year) were at a significantly higher risk of dementia (HR = 1.60; 95% CI, 1.33, 1.92), compared with the stable weight group (median: 0.11 kg/m2 per year). This association was attenuated but remained strong and significant after further adjustment for PF (HR = 1.57; 95% CI, 1.30, 1.89). Significant association was observed for weight loss assessed approximately 14 to 18 years preceding dementia diagnosis (HR = 1.30; 95% CI, 1.07, 1.58), and was consistent closer to diagnosis. CONCLUSION Both recent and remote weight loss were associated with a higher risk of later-life dementia among middle-aged and older adults independent of PF status.
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Affiliation(s)
| | | | - Tianjing Zhou
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Simei Zhang
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Liyan Huang
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaozhen Lv
- Beijing Dementia Key Lab, National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University), Peking University Institute of Mental Health (Sixth Hospital), Beijing, China
| | - Yuan Ma
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Yan Zheng
- Yan Zheng, MD, PhD, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, 200043, China.
| | - Changzheng Yuan
- Correspondence: Changzheng Yuan, ScD, School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, China.
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Zhang JJ, Li L, Liu D, Hu FF, Cheng GR, Xu L, Yan PT, Tian Y, Hu H, Yu YF, Gan XG, An LN, Zhang B, Qian J, Fu LY, Cheng X, Lian PF, Zou MJ, Chen C, Wu QM, Zeng Y. Urban-Rural Disparities in the Association Between Body Mass Index and Cognitive Impairment in Older Adults: A Cross-Sectional Study in Central China. J Alzheimers Dis 2021; 83:1741-1752. [PMID: 34459393 DOI: 10.3233/jad-210295] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Some studies have demonstrated an association between low and high body mass index (BMI) and an increased risk of dementia. However, only a few of these studies were performed in rural areas. OBJECTIVE This cross-sectional study investigated the associations between BMI and cognitive impairment among community-dwelling older adults from rural and urban areas. METHODS 8,221 older persons enrolled in the Hubei Memory & Ageing Cohort Study (HMACS) were recruited. Sociodemographic and lifestyle data, comorbidities, physical measurements, and clinical diagnoses of cognitive impairment were analyzed. Logistic regression was performed to assess the associations of BMI categories with cognitive impairment. A series of sensitivity analyses were conducted to test whether reverse causality could influence our results. RESULTS Being underweight in the rural-dwelling participants increased the risk of cognitive impairment. Being overweight was a protective factor in rural-dwelling participants aged 65-69 years and 75-79 years, whereas being underweight was significantly associated with cognitive impairment (OR, 1.37; 95% CI: 1.03-1.83; p < 0.05). Sensitivity analyses support that underweight had an additive effect on the odds of cognitive impairment and was related to risk of dementia. Interaction test revealed that the differences between urban/rural in the relationship between BMI and cognitive impairment are statistically significant. CONCLUSION Associations between BMI and cognitive impairment differ among urban/rural groups. Older people with low BMI living in rural China are at a higher risk for dementia than those living in urban areas.
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Affiliation(s)
- Jing-Jing Zhang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Lin Li
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Dan Liu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Fei-Fei Hu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Gui-Rong Cheng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Lang Xu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ping-Ting Yan
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Yuan Tian
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Heng Hu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ya-Fu Yu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Xu-Guang Gan
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Li-Na An
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Bo Zhang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Jin Qian
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Li-Yan Fu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Xi Cheng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Peng-Fei Lian
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ming-Jun Zou
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Cong Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Qing-Ming Wu
- Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yan Zeng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
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