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Introduction: An analysis of the ethnocultural and socioeconomic composition of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants is needed to assess the generalizability of ADNI data to diverse populations.Methods: ADNI data collected between October 2004 and November 2020 were used to determine ethnocultural and educational composition of the sample and differences in the following metrics: screening, screen fails, enrollment, biomarkers.Results: Of 3739 screened individuals, 11% identified as being from ethnoculturally underrepresented populations (e.g., Black, Latinx) and 16% had <12 years of education.Of 2286 enrolled participants, 11% identified as ethnoculturally underrepresented individuals and 15% had <12 years of education. This participation is considerably lower than US Census data for adults 60+ (ethnoculturally underrepresented populations: 25%; <12 years of education: 4%). Individuals with <12 years of education failed screening at a higher rate. Discussion: Our findings suggest that ADNI results may not be entirely generalizable to ethnoculturally diverse and low education populations.

Background Differences in CSF and plasma Alzheimer’s disease biomarkers between African Americans (AA), Latinos (LA), and non‐Hispanic Whites (NHW) have been reported. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) may offer insights into ethnoracial biomarker differences important for understanding disease trajectory variations among diverse populations. Method We queried the ADNI database for all participants with available plasma pTau181 and NfL (Simoa) and linked CSF biomarkers within 1 year of blood draw (Aβ42, pTau181, T‐tau, Elecsys). Baseline data for 47 AA and 43 LA with plasma biomarkers were matched to separate groups of NHW (1:3 nearest neighbor matching on propensity score), considering age, sex, education, CDR, APOE ε4 alleles and family history of dementia (Table 1). Cross‐sectional analyses compared biomarker levels between matched cohorts without (non‐parametric Mann‐Whitney U‐test) and with adjustment for demographics, clinical, biomarker, and medical characteristics (multi‐linear regression). Statistical threshold was set at two‐tailed p<0.05. Result Unadjusted comparisons between AA and NHW revealed no significant differences in plasma NfL [29.3 (IQR: 21.6‐42.5) vs 35.5 (27.0‐49.6), p=.12], plasma pTau181 [14.5 (9.4‐22.9) vs 15.5 (10.1‐22.8), p=.83], CSF Aβ42 [933.1 (649.5‐1590.8) vs 926.7 (677.7‐1697.8), p=.83], CSF T‐tau [214.6 (152.4‐290.7) vs 251.9 (194.5‐351.4), p=.17] or CSF pTau181 [19.6 (14.4‐27.3) vs 22.7 (17.0‐33.3), p=.32] (Fig. 1a‐b). No significant differences were identified in any of the biomarkers after adjustment for relevant covariates. Unadjusted comparisons between LA and NHW revealed no significant differences in plasma NfL [36.7 (24.4‐50.4) vs 35.6 (25.6‐47.1), p=1], plasma pTau181 [18.0 (11.3‐25.0) vs 15.7 (10.9‐23.4), p=.81], or CSF Aβ42 [852.7 (787.1‐1213.0) vs 951.6 (651.1‐1530.5), p=1]. A trend towards lower CSF T‐Tau [216.5 (147.1‐270.5) vs 257.2 (197.2‐360.9), p=.076] and CSF pTau181 [19.4 (13.6‐27.9) vs 24.7 (17.5‐33.4), p=.076] in LA was observed (Fig. 2a‐b). After adjustment for covariates, LA were found to have significantly lower levels of CSF T‐tau (β=0.154, p<.05) and CSF pTau181 (β=0.164, p<.05), while differences in plasma markers were not significant. Conclusion No significant biomarker differences between AA and NHW were observed. Lower CSF pTau181 and CSF T‐Tau in LA compared to NHW after covariate adjustment may represent subtle differences driven by biological factors or unmeasured sociocultural determinants (income, occupation, neighborhood environment); larger studies are needed.