TY - JOUR
T1 - Determinants of cognitive performance and decline in 20 diverse ethno-regional groups
T2 - A COSMIC collaboration cohort study
AU - Lipnicki, Darren M.
AU - Makkar, Steve R.
AU - Crawford, John D.
AU - Thalamuthu, Anbupalam
AU - Kochan, Nicole A.
AU - Lima-Costa, Maria Fernanda
AU - Castro-Costa, Erico
AU - Ferri, Cleusa Pinheiro
AU - Brayne, Carol
AU - Stephan, Blossom
AU - Llibre-Rodriguez, Juan J.
AU - Llibre-Guerra, Jorge J.
AU - Valhuerdi-Cepero, Adolfo J.
AU - Lipton, Richard B.
AU - Katz, Mindy J.
AU - Derby, Carol A.
AU - Ritchie, Karen
AU - Ancelin, Marie Laure
AU - Carrière, Isabelle
AU - Scarmeas, Nikolaos
AU - Yannakoulia, Mary
AU - Hadjigeorgiou, Georgios M.
AU - Lam, Linda
AU - Chan, Wai Chi
AU - Fung, Ada
AU - Guaita, Antonio
AU - Vaccaro, Roberta
AU - Davin, Annalisa
AU - Kim, Ki Woong
AU - Han, Ji Won
AU - Suh, Seung Wan
AU - Riedel-Heller, Steffi G.
AU - Roehr, Susanne
AU - Pabst, Alexander
AU - van Boxtel, Martin
AU - Köhler, Sebastian
AU - Deckers, Kay
AU - Ganguli, Mary
AU - Jacobsen, Erin P.
AU - Hughes, Tiffany F.
AU - Anstey, Kaarin J.
AU - Cherbuin, Nicolas
AU - Haan, Mary N.
AU - Aiello, Allison E.
AU - Dang, Kristina
AU - Kumagai, Shuzo
AU - Chen, Tao
AU - Narazaki, Kenji
AU - Ng, Tze Pin
AU - Gao, Qi
AU - Nyunt, Ma Shwe Zin
AU - Scazufca, Marcia
AU - Brodaty, Henry
AU - Numbers, Katya
AU - Trollor, Julian N.
AU - Meguro, Kenichi
AU - Yamaguchi, Satoshi
AU - Ishii, Hiroshi
AU - Lobo, Antonio
AU - Lopez-Anton, Raúl
AU - Santabárbara, Javier
AU - Leung, Yvonne
AU - Lo, Jessica W.
AU - Popovic, Gordana
AU - Sachdev, Perminder S.
AU - Cohort Studies of Memory in an International Consortium (COSMIC)
N1 - Funding for COSMIC comes from a National Health and Medical Research Council of Australia Program Grant (ID 1093083) (PSS, HB), the National Institute On Aging of the National Institutes of Health under Award Number RF1AG057531 (PSS, MG, RBL, KR, KWK, HB), and philanthropic contributions to The Dementia Momentum Fund (UNSW Project ID PS38235) (PSS, HB). Funding for each of the contributing studies is as follows: The Brazilian Ministry of Health (Department of Science and Technology), the Brazilian Ministry of Science and Technology (National Fund for Scientific and Technological Development, Funding of Studies, Brazilian National Research Council) and the Minas Gerais State Research Foundation (MFLC, ECC); Major awards from the Medical Research Council and the Department of Health, UK (CB); The Wellcome Trust Foundation (GR066133 and GR08002) and the Cuban Ministry of Public Health (JJLR); Supported in part by National Institutes of Health grants NIA 2 P01 AG03949, the Leonard and Sylvia Marx Foundation, and the Czap Foundation (RBL, MJK); Novartis (KR, MLA); IIRG-09133014 from the Alzheimer’s Association; 189 10276/8/9/2011 from the ESPA-EU program Excellence Grant (ARISTEIA), which is co-funded by the European Social Fund and Greek National resources, and ΔΥ2β/οικ.51657/14.4.2009 from the Ministry for Health and Social Solidarity (Greece) (NS); The Mei Family Trust (LL); Financed with own funds and supported in part by "Federazione Alzheimer Italia", Milan, Italy (AG); The Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea [Grant No. HI09C1379 (A092077)] (KWK); The Interdisciplinary Centre for Clinical Research at the University of Leipzig (Interdisziplinäres Zentrum für Klinische Forschung/IZKF; grant 01KS9504) (SGRH); Grant # R01AG07562 from the National Institute on Aging, National Institutes of Health, United States Department of Health and Human Services (MG); National Health and Medical Research Council of Australia grants 973302, 179805, 157125 and 1002160 (KA); NIH grants AG12975, T32 AG049663, ES023451 (MNH); Carolina Population Center (CPC) Funding: CPC Center grant (the P2C Center grant from NIH): P2C HD050924. CPC NICHD-NRSA Population Research Training (the T32 Training grant from NIH): T32 HD007168, Biosocial Training Grant: T32 HD091058 (AEA); JSPS KAKENHI Grant Number JP17K09146 (SK); Agency for Science Technology and Research (A*STAR) Biomedical Research Council (BMRC) [Grants: 03/1/21/17/214 and 08/1/21/19/567] and the National Medical Research Council [Grant: NMRC/1108/2007] (TPN); The Wellcome Trust Foundation and FAPESP, São Paulo, Brazill (MS); National Health & Medical Research Council of Australia Program Grant (ID 350833) (PSS, HB); Supported by grants from the Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spanish Ministry of Economy and Competitiveness, Madrid, Spain (grants 94/1562, 97/1321E, 98/0103, 01/0255, 03/0815, 06/0617, G03/128), and the Fondo Europeo de Desarrollo Regional (FEDER) of the European Union and Gobierno de Aragón, Group #19 (AL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funders.
PY - 2019/7
Y1 - 2019/7
N2 - Background: With no effective treatments for cognitive decline or dementia, improving the evidence base for modifiable risk factors is a research priority. This study investigated associations between risk factors and late-life cognitive decline on a global scale, including comparisons between ethno-regional groups.Methods and findings: We harmonized longitudinal data from 20 population-based cohorts from 15 countries over 5 continents, including 48,522 individuals (58.4% women) aged 54–105 (mean = 72.7) years and without dementia at baseline. Studies had 2–15 years of follow-up. The risk factors investigated were age, sex, education, alcohol consumption, anxiety, apolipoprotein E ε4 allele (APOE*4) status, atrial fibrillation, blood pressure and pulse pressure, body mass index, cardiovascular disease, depression, diabetes, self-rated health, high cholesterol, hypertension, peripheral vascular disease, physical activity, smoking, and history of stroke. Associations with risk factors were determined for a global cognitive composite outcome (memory, language, processing speed, and executive functioning tests) and Mini-Mental State Examination score. Individual participant data meta-analyses of multivariable linear mixed model results pooled across cohorts revealed that for at least 1 cognitive outcome, age (B = −0.1, SE = 0.01), APOE*4 carriage (B = −0.31, SE = 0.11), depression (B = −0.11, SE = 0.06), diabetes (B = −0.23, SE = 0.10), current smoking (B = −0.20, SE = 0.08), and history of stroke (B = −0.22, SE = 0.09) were independently associated with poorer cognitive performance (p < 0.05 for all), and higher levels of education (B = 0.12, SE = 0.02) and vigorous physical activity (B = 0.17, SE = 0.06) were associated with better performance (p < 0.01 for both). Age (B = −0.07, SE = 0.01), APOE*4 carriage (B = −0.41, SE = 0.18), and diabetes (B = −0.18, SE = 0.10) were independently associated with faster cognitive decline (p < 0.05 for all). Different effects between Asian people and white people included stronger associations for Asian people between ever smoking and poorer cognition (group by risk factor interaction: B = −0.24, SE = 0.12), and between diabetes and cognitive decline (B = −0.66, SE = 0.27; p < 0.05 for both). Limitations of our study include a loss or distortion of risk factor data with harmonization, and not investigating factors at midlife.Conclusions: These results suggest that education, smoking, physical activity, diabetes, and stroke are all modifiable factors associated with cognitive decline. If these factors are determined to be causal, controlling them could minimize worldwide levels of cognitive decline. However, any global prevention strategy may need to consider ethno-regional differences.
AB - Background: With no effective treatments for cognitive decline or dementia, improving the evidence base for modifiable risk factors is a research priority. This study investigated associations between risk factors and late-life cognitive decline on a global scale, including comparisons between ethno-regional groups.Methods and findings: We harmonized longitudinal data from 20 population-based cohorts from 15 countries over 5 continents, including 48,522 individuals (58.4% women) aged 54–105 (mean = 72.7) years and without dementia at baseline. Studies had 2–15 years of follow-up. The risk factors investigated were age, sex, education, alcohol consumption, anxiety, apolipoprotein E ε4 allele (APOE*4) status, atrial fibrillation, blood pressure and pulse pressure, body mass index, cardiovascular disease, depression, diabetes, self-rated health, high cholesterol, hypertension, peripheral vascular disease, physical activity, smoking, and history of stroke. Associations with risk factors were determined for a global cognitive composite outcome (memory, language, processing speed, and executive functioning tests) and Mini-Mental State Examination score. Individual participant data meta-analyses of multivariable linear mixed model results pooled across cohorts revealed that for at least 1 cognitive outcome, age (B = −0.1, SE = 0.01), APOE*4 carriage (B = −0.31, SE = 0.11), depression (B = −0.11, SE = 0.06), diabetes (B = −0.23, SE = 0.10), current smoking (B = −0.20, SE = 0.08), and history of stroke (B = −0.22, SE = 0.09) were independently associated with poorer cognitive performance (p < 0.05 for all), and higher levels of education (B = 0.12, SE = 0.02) and vigorous physical activity (B = 0.17, SE = 0.06) were associated with better performance (p < 0.01 for both). Age (B = −0.07, SE = 0.01), APOE*4 carriage (B = −0.41, SE = 0.18), and diabetes (B = −0.18, SE = 0.10) were independently associated with faster cognitive decline (p < 0.05 for all). Different effects between Asian people and white people included stronger associations for Asian people between ever smoking and poorer cognition (group by risk factor interaction: B = −0.24, SE = 0.12), and between diabetes and cognitive decline (B = −0.66, SE = 0.27; p < 0.05 for both). Limitations of our study include a loss or distortion of risk factor data with harmonization, and not investigating factors at midlife.Conclusions: These results suggest that education, smoking, physical activity, diabetes, and stroke are all modifiable factors associated with cognitive decline. If these factors are determined to be causal, controlling them could minimize worldwide levels of cognitive decline. However, any global prevention strategy may need to consider ethno-regional differences.
UR - http://www.scopus.com/inward/record.url?scp=85070472369&partnerID=8YFLogxK
U2 - 10.1371/journal.pmed.1002853
DO - 10.1371/journal.pmed.1002853
M3 - Journal article
C2 - 31335910
AN - SCOPUS:85070472369
SN - 1549-1277
VL - 16
JO - PLoS Medicine
JF - PLoS Medicine
IS - 7
M1 - e1002853
ER -