Natalia Palacios1,2,3, Jong Soo Lee4, Tammy Scott5, Rachel S Kelly6, Shilpa N Bhupathiraju2,6, Sherman J Bigornia7, Katherine L Tucker8. 1. Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, USA. 2. Department of Nutrition, Harvard University School of Public Health, Boston, MA, USA. 3. Geriatric Research Education Clinical Center, Department of Veterans Affairs, ENRM VA Hospital, Bedford, MA, USA. 4. Department of Mathematical Sciences, University of Massachusetts Lowell, Lowell, MA, USA. 5. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA. 6. Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. 7. University of New Hampshire, Department of Agriculture, Nutrition, and Food Systems. 8. Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell, Lowell, MA, USA.
Abstract
BACKGROUND: Minorities, including mainland Puerto Ricans, are impacted disproportionally by Alzheimer's disease (AD), dementia, and cognitive decline. Studying blood metabolomics in this population has the potential to probe the biological underpinnings of this health disparity. OBJECTIVE: We performed a comprehensive analysis of circulating plasma metabolites in relation to cognitive function in 736 participants from the Boston Puerto Rican Health Study (BPRHS) who underwent untargeted mass-spectrometry based metabolomics analysis and had undergone a battery of in-person cognitive testing at baseline. METHODS: After relevant exclusions, 621 metabolites were examined. We used multivariable regression, adjusted for age, sex, education, apolipoprotein E genotype, smoking, and Mediterranean dietary pattern, to identify metabolites related to global cognitive function in our cohort. LASSO machine learning was used in a complementary analysis to identify metabolites that could discriminate good from poor extremes of cognition. We also conducted sensitivity analyses: restricted to participants without diabetes, and to participants with good adherence to Mediterranean diet. RESULTS: Of 621 metabolites, FDR corrected (p < 0.05) multivariable linear regression identified 3 metabolites positively, and 10 negatively, associated with cognitive function in the BPRHS. In a combination of FDR-corrected linear regression, logistic regression regularized via LASSO, and sensitivity analyses restricted to participants without diabetes, and with good adherence to the Mediterranean diet, β-cryptoxanthin plasma concentration was consistently associated with better cognitive function and N-acetylisoleucine and tyramine O-sulfate concentrations were consistently associated with worse cognitive function. CONCLUSION: This untargeted metabolomics study identified potential biomarkers for cognitive function in a cohort of Puerto Rican older adults.
BACKGROUND: Minorities, including mainland Puerto Ricans, are impacted disproportionally by Alzheimer's disease (AD), dementia, and cognitive decline. Studying blood metabolomics in this population has the potential to probe the biological underpinnings of this health disparity. OBJECTIVE: We performed a comprehensive analysis of circulating plasma metabolites in relation to cognitive function in 736 participants from the Boston Puerto Rican Health Study (BPRHS) who underwent untargeted mass-spectrometry based metabolomics analysis and had undergone a battery of in-person cognitive testing at baseline. METHODS: After relevant exclusions, 621 metabolites were examined. We used multivariable regression, adjusted for age, sex, education, apolipoprotein E genotype, smoking, and Mediterranean dietary pattern, to identify metabolites related to global cognitive function in our cohort. LASSO machine learning was used in a complementary analysis to identify metabolites that could discriminate good from poor extremes of cognition. We also conducted sensitivity analyses: restricted to participants without diabetes, and to participants with good adherence to Mediterranean diet. RESULTS: Of 621 metabolites, FDR corrected (p < 0.05) multivariable linear regression identified 3 metabolites positively, and 10 negatively, associated with cognitive function in the BPRHS. In a combination of FDR-corrected linear regression, logistic regression regularized via LASSO, and sensitivity analyses restricted to participants without diabetes, and with good adherence to the Mediterranean diet, β-cryptoxanthin plasma concentration was consistently associated with better cognitive function and N-acetylisoleucine and tyramine O-sulfate concentrations were consistently associated with worse cognitive function. CONCLUSION: This untargeted metabolomics study identified potential biomarkers for cognitive function in a cohort of Puerto Rican older adults.
Entities:
Keywords:
Cognitive function; Puerto Ricans; diabetes; metabolomics
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