Back

Cardiometabolic Indicators of Cognitive Impairment in The Cameron County Hispanic Cohort

Musfee, F. I.; Aggarwal, S. S.; Maroufy, V.; McCormick, J.; Fisher-Hoch, S.; Savitz, S. I.

2025-01-09 epidemiology
10.1101/2025.01.08.25320180 medRxiv
Show abstract

IntroductionCognitive impairment (CI) and its related risk factors (e.g., diabetes and stroke) are highly prevalent among Hispanic/Latinos (H/L); however, prior research in H/L focused on aging individuals ([&ge;]65 years of age). We conducted a prospective study in a younger cohort of H/L (majority <65 years old) from the Cameron County Hispanic Cohort (CCHC) to comprehensively assess the associations between a wide-range of cardiometabolic health indicators with CI. MethodsWe identified a total of 1240 CCHC subjects with complete Mini-mental status exam (MMSE) data at study baseline and at 5-year follow-up. The outcome (i.e., CI) was based on MMSE scores of less than 24. We conducted univariate associations for multiple cardiometabolic indicators with CI; and mixed logistic regression models to estimate odds ratios for the associations between cardiometabolic indicators and CI adjusted for age, education, prior stroke, and APOE gene. ResultsThe majority (89.9%) of the participants were <65 years old. A total of 117 subjects had CI at baseline (9.4%). Baseline study cohort showed that Individuals with CI were older with a lower education performance, and were more likely to be diabetic with lower mean levels of Low-density Lipoprotein, and a higher mean systolic blood pressure. Diabetes significantly increased the odds for CI (OR:2.11, 95%CI:1.26-3.52) from the adjusted multivariate mixed logistic models. ConclusionsThis analysis showed that diabetes was an important indicator for CI regardless of age, education, or APOE gene status. These findings highlight the higher burden of cardiometabolic risk factors on CI in the CCHC cohort.

Published in Journal of Alzheimer’s Disease (predicted rank #3) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.