Determinants of cognitive performance and decline in 20 diverse ethno-regional groups: A COSMIC collaboration cohort study

dataset

posted on 2019-07-23, 17:26 authored by Darren M. Lipnicki, Steve R. Makkar, John D. Crawford, Anbupalam Thalamuthu, Nicole A. Kochan, Maria Fernanda Lima-Costa, Erico Castro-Costa, Cleusa Pinheiro Ferri, Carol Brayne, Blossom Stephan, Juan J. Llibre-Rodriguez, Jorge J. Llibre-Guerra, Adolfo J. Valhuerdi-Cepero, Richard B. Lipton, Mindy J. Katz, Carol A. Derby, Karen Ritchie, Marie-Laure Ancelin, Isabelle Carrière, Nikolaos Scarmeas, Mary Yannakoulia, Georgios M. Hadjigeorgiou, Linda Lam, Wai-chi Chan, Ada Fung, Antonio Guaita, Roberta Vaccaro, Annalisa Davin, Ki Woong Kim, Ji Won Han, Seung Wan Suh, Steffi G. Riedel-Heller, Susanne Roehr, Alexander Pabst, Martin van Boxtel, Sebastian Köhler, Kay Deckers, Mary Ganguli, Erin P. Jacobsen, Tiffany F. Hughes, Kaarin J. Anstey, Nicolas Cherbuin, Mary N. Haan, Allison E. Aiello, Kristina Dang, Shuzo Kumagai, Tao Chen, Kenji Narazaki, Tze Pin Ng, Qi Gao, Ma Shwe Zin Nyunt, Marcia Scazufca, Henry Brodaty, Katya Numbers, Julian N. Trollor, Kenichi Meguro, Satoshi Yamaguchi, Hiroshi Ishii, Antonio Lobo, Raul Lopez-Anton, Javier Santabárbara, Yvonne Leung, Jessica W. Lo, Gordana Popovic, Perminder S. Sachdev

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.