Lipnicki DMMakkar SRCrawford JDThalamuthu AKochan NALima-Costa MFCastro-Costa EFerri CPBrayne CStephan BLlibre-Rodriguez JJLlibre-Guerra JJValhuerdi-Cepero AJLipton RBKatz MJDerby CARitchie KAncelin M-LCarrière IScarmeas NYannakoulia MHadjigeorgiou GMLam LChan W-CFung AGuaita AVaccaro RDavin AKim KWHan JWSuh SWRiedel-Heller SGRoehr SPabst Avan Boxtel MKöhler SDeckers KGanguli MJacobsen EPHughes TFAnstey KJCherbuin NHaan MNAiello AEDang KKumagai SChen TNarazaki KNg TPGao QNyunt MSZScazufca MBrodaty HNumbers KTrollor JNMeguro KYamaguchi SIshii HLobo ALopez-Anton RSantabárbara JLeung YLo JWPopovic GSachdev PSfor Cohort Studies of Memory in an International Consortium (COSMIC)2024-01-292024-07-252019-07-232024-01-292024-07-252019-07Lipnicki DM, Makkar SR, Crawford JD, Thalamuthu A, Kochan NA, Lima-Costa MF, Castro-Costa E, Ferri CP, Brayne C, Stephan B, Llibre-Rodriguez JJ, Llibre-Guerra JJ, Valhuerdi-Cepero AJ, Lipton RB, Katz MJ, Derby CA, Ritchie K, Ancelin M-L, Carrière I, Scarmeas N, Yannakoulia M, Hadjigeorgiou GM, Lam L, Chan W-C, Fung A, Guaita A, Vaccaro R, Davin A, Kim KW, Han JW, Suh SW, Riedel-Heller SG, Roehr S, Pabst A, van Boxtel M, Köhler S, Deckers K, Ganguli M, Jacobsen EP, Hughes TF, Anstey KJ, Cherbuin N, Haan MN, Aiello AE, Dang K, Kumagai S, Chen T, Narazaki K, Ng TP, Gao Q, Nyunt MSZ, Scazufca M, Brodaty H, Numbers K, Trollor JN, Meguro K, Yamaguchi S, Ishii H, Lobo A, Lopez-Anton R, Santabárbara J, Leung Y, Lo JW, Popovic G, Sachdev PS, for Cohort Studies of Memory in an International Consortium (COSMIC) . (2019). Determinants of cognitive performance and decline in 20 diverse ethno-regional groups: A COSMIC collaboration cohort study.. PLoS Med. 16. 7. (pp. e1002853-).1549-1277https://mro.massey.ac.nz/handle/10179/70908Background 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.(c) The author/shttps://creativecommons.org/licenses/by/4.0/Age FactorsAgedAged, 80 and overCognitionCognitive DysfunctionComorbidityDiabetes MellitusEthnicityExerciseFemaleHealth EducationHumansMaleMiddle AgedRisk AssessmentRisk FactorsSmokingStrokeJournal article10.1371/journal.pmed.10028531549-1676CC BY 4.0journal-articlee1002853-https://www.ncbi.nlm.nih.gov/pubmed/31335910PMEDICINE-D-19-00731