TY - JOUR
T1 - Determinants of cognitive performance and decline in 20 diverse ethno-regional groups
T2 - A COSMIC collaboration cohort study
AU - Cohort Studies of Memory in an International Consortium (COSMIC)
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
PY - 2019/7/23
Y1 - 2019/7/23
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 -