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We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, A\u03b21\u201342 and A\u03b21\u201340 (used as A\u03b242\/A\u03b240 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants\u2019 brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors (APOE, single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65\u201380 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance (R\u00b2 = 0.15) to MRI (R\u00b2 = 0.18) and cardiovascular measures (R\u00b2 = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction (R\u00b2 = 0.26 and 0.27). For amyloid positive individuals A\u03b242\/A\u03b240, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while A\u03b242\/A\u03b240 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population\u2019s socio-demographics, brain and cardiovascular health.<\/jats:p>","DOI":"10.1093\/braincomms\/fcae300","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:56:39Z","timestamp":1725530199000},"source":"Crossref","is-referenced-by-count":5,"title":["Can integration of Alzheimer\u2019s plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5746-0994","authenticated-orcid":false,"given":"Robel K","family":"Gebre","sequence":"first","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Rochester, MN 55905 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I","family":"Hofrenning","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott A","family":"Przybelski","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aivi T","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy G","family":"Lesnick","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey L","family":"Gunter","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-6217","authenticated-orcid":false,"given":"Alicia","family":"Algeciras-Schimnich","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6544-066X","authenticated-orcid":false,"given":"David S","family":"Knopman","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Rochester, MN 55905 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary M","family":"Machulda","sequence":"additional","affiliation":[{"name":"Department of Psychology, Mayo Clinic , Rochester, MN 55905 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