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Basic Science and Pathogenesis.

Razaq O Durodoye1, Timothy H Ciesielski1, Penelope Benchek2

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Summary
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Race and ethnicity capture non-genetic risk factors for late-onset Alzheimer's disease (LOAD). These factors, including non-medical drivers of health, independently associate with LOAD risk beyond genetic contributions.

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Area of Science:

  • Genetics and Genomics
  • Neuroscience
  • Public Health

Background:

  • Race and ethnicity (R/E) are frequently used to stratify participants in genetic studies of late-onset Alzheimer's disease (LOAD).
  • R/E categories help identify risk alleles, quantify effect size variations, and assess etiologic differences across groups.
  • R/E can act as a proxy for unmeasured extrinsic risk factors, such as the lived experience of race/ethnicity and non-medical drivers of health (NMDH).

Purpose of the Study:

  • To estimate the non-genetic contributions of R/E to LOAD risk.
  • To adjust for genetic risk factors and ancestry while assessing the impact of R/E.

Main Methods:

  • Utilized genetic and demographic data from 42,015 Alzheimer's Disease Genetic Consortium (ADGC) participants across diverse R/E groups.
  • Performed logistic regression, adjusting for APOE genotype, age, sex, and genetic ancestry, to determine NMDH contributions captured by R/E.
  • Employed K-means clustering to identify genetically distinct groups and re-evaluated regression models using these clusters.

Main Results:

  • East Asian (EAS), non-Hispanic White (NHW), and Hispanic/Latino (HIS) participants showed significantly increased odds of LOAD compared to non-Hispanic Black (NHB) individuals (ORs: 2.43, 2.07, 2.04, respectively).
  • K-means clustering identified five genetic ancestries: African, East Asian, European, Amerindian, and genetically admixed Latino.
  • R/E remained a significant predictor of LOAD risk even after adjusting for genetic factors, indicating it captures risk beyond genetics.

Conclusions:

  • Extrinsic factors, specifically NMDH captured through R/E, are independently associated with LOAD risk.
  • This study highlights the importance of considering socio-environmental factors in Alzheimer's disease research.