Racial and ethnic differences in plasma p-tau217 ratio biomarker eligibility rates in a preclinical AD trial with lecanemab.

IF 4.4 Q1 CLINICAL NEUROLOGY
Doris P Molina-Henry, Rema Raman, Andy Liu, Oliver Langford, Joel B Braunstein, Philip B Verghese, Venky Venkatesh, Shobha Dhadda, Michael Irrizary, Joshua D Grill, Keith Johnson, Robert A Rissman, Paul Aisen, Reisa A Sperling
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Abstract

Introduction: Consistent predictive performance across racial and ethnic groups is essential to the use of plasma biomarkers as screening tools in preclinical Alzheimer's disease trials.

Methods: Logistic regression examined racial and ethnic group differences in plasma eligibility using an algorithm that included Phosphorylated tau217 to non-phosphorylated tau217 ratio, amyloid beta 42/40, age, and apolipoprotein E to predict > 18 Centiloids on amyloid imaging in cognitively unimpaired individuals.

Results: Among 6437 participants screened, with non-Hispanic (NH) White as the reference group, odds ratios of plasma ineligibility were 2.88 (95% confidence interval [CI]: 1.40-6.96) for Hispanic Black, 1.60 (95% CI: 1.33-1.92) for Hispanic White, 2.10 (95% CI: 1.37-3.38) for NH Asian, and 1.59 (95% CI: 1.27-2.0) for NH Black participants. Positron emission tomography (PET) eligibility rates did not differ among those who were plasma eligible.

Discussion: Differential rates of plasma eligibility, but consistent PET eligibility among plasma-eligible participants, were observed, supporting the use of universal biomarker cutpoints across race and ethnic groups.Highlights: Underrepresented racial and ethnic groups had lower rates of plasma eligibility compared to non-Hispanic White individuals based on a plasma screening algorithm that included the phosphorylated tau 217 ratio.Among plasma-eligible participants, amyloid positron emission tomography eligibility rates did not differ by racial and ethnic group.Plasma biomarker tests may provide equivalent effectiveness for identifying imaging biomarker eligible, cognitively unimpaired individuals across racial and ethnic groups.

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莱卡耐单抗临床前AD试验中血浆p-tau217比值生物标志物合格率的种族和民族差异
在临床前阿尔茨海默病试验中,血浆生物标志物作为筛选工具的使用对于跨种族和族裔群体的一致预测性能至关重要。方法:使用一种包括磷酸化tau217与非磷酸化tau217比率、淀粉样蛋白β 42/40、年龄和载脂蛋白E的算法,Logistic回归检验了种族和民族在血浆适格性方面的差异,以预测认知功能未受损个体淀粉样蛋白成像上的bbb18 Centiloids。结果:在筛选的6437名参与者中,以非西班牙裔(NH)白人为参照组,西班牙裔黑人血浆不合格的比值比为2.88(95%可信区间[CI]: 1.40-6.96),西班牙裔白人为1.60 (95% CI: 1.33-1.92), NH亚洲参与者为2.10 (95% CI: 1.37-3.38), NH黑人参与者为1.59 (95% CI: 1.27-2.0)。正电子发射断层扫描(PET)的合格率在符合血浆条件的患者中没有差异。讨论:观察到血浆适格率不同,但血浆适格率一致,PET适格率一致,支持在种族和民族群体中使用通用生物标志物切点。重点:基于包括磷酸化tau 217比率的血浆筛选算法,与非西班牙裔白人相比,代表性不足的种族和族裔群体的血浆适格率较低。在符合血浆条件的参与者中,淀粉样蛋白正电子发射断层扫描的合格率没有种族和民族群体的差异。血浆生物标志物测试可以为识别不同种族和民族群体中符合成像生物标志物条件的认知未受损个体提供同等的有效性。
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来源期刊
CiteScore
7.80
自引率
7.50%
发文量
101
审稿时长
8 weeks
期刊介绍: Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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