Use of an Alzheimer’s disease polygenic risk score to identify mild cognitive impairment in adults in their 50s

Abstract Background The combinatorial effect of multiple genetic factors calculated as a polygenic risk score (PRS) has been studied to predict disease progression to Alzheimer’s disease (AD) from mild cognitive impairment (MCI). Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the APOE gene. These studies may have missed the APOE genotype-specific predictability of PRS for disease progression to AD. Methods We analyzed 732 MCI from the Alzheimer’s Disease Neuroimaging Initiative cohort, including those who progressed to AD within 5 years post-baseline (n = 270) and remained stable as MCI (n = 462). The predictability of PRS including and excluding the APOE region (PRS+APOE and PRS−APOE) on the conversion to AD and its interaction with the APOE ε4 carrier status were assessed using Cox regression analyses. Results PRS+APOE (hazard ratio [HR] 1.468, 95% CI 1.335–1.615) and PRS−APOE (HR 1.293, 95% CI 1.157–1.445) were both associated with a significantly increased risk of MCI progression to dementia. The interaction between PRS+APOE and APOE ε4 carrier status was significant with a P-value of 0.0378. The association of PRSs with the progression risk was stronger in APOE ε4 non-carriers (PRS+APOE: HR 1.710, 95% CI 1.244–2.351; PRS−APOE: HR 1.429, 95% CI 1.182–1.728) than in APOE ε4 carriers (PRS+APOE: HR 1.167, 95% CI 1.005–1.355; PRS−APOE: HR 1.172, 95% CI 1.020–1.346). Conclusions PRS could predict the conversion of MCI to dementia with a stronger association in APOE ε4 non-carriers than APOE ε4 carriers. This indicates PRS as a potential genetic predictor particularly for MCI with no APOE ε4 alleles.

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Correction: Alzheimer’s disease polygenic risk score as a predictor of conversion from mild-cognitive impairment

Translational Psychiatry ◽

10.1038/s41398-019-0503-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Sultan Chaudhury ◽

Keeley J. Brookes ◽

Tulsi Patel ◽

Abigail Fallows ◽

Tamar Guetta-Baranes ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Risk Score ◽

Polygenic Risk Score ◽

Polygenic Risk

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[P3-114]: ALZHEIMER'S DISEASE POLYGENIC RISK SCORE PREDICTS MILD COGNITIVE IMPAIRMENT (MCI) IN MIDDLE AGE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2017.06.1325 ◽

2017 ◽

Vol 13 (7S_Part_20) ◽

pp. P978-P978

Author(s):

William S. Kremen ◽

Mark W. Logue ◽

Matthew Panizzon ◽

Jeremy A. Elman ◽

Nathan Gillespie ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Risk Score ◽

Middle Age ◽

Polygenic Risk Score ◽

Polygenic Risk

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Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-210700 ◽

2021 ◽

pp. 1-13

Author(s):

Hongliang Liu ◽

Michael Lutz ◽

Sheng Luo ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Risk Scores ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Increased Risk ◽

Risk Of Progression ◽

Clinical Changes

Background: Mild cognitive impairment (MCI) is a heterogeneous condition and MCI patients are at increased risk of progression to dementia due to Alzheimer’s disease (AD). Objective: In this study, we aim to evaluate the associations between polygenic risk scores (PRSs) and 1) time to AD progression from MCI, 2) changes in longitudinal cognitive impairment, and 3) biomarkers from cerebrospinal fluid and imaging. Methods: We constructed PRS by using 40 independent non-APOE SNPs from well-replicated AD GWASs and tested its association with the progression time from MCI to AD by using 767 MCI patients from the ADNI study and 1373 patients from the NACC study. PRSs calculated with other methods were also computed. Results: We found that the PRS constructed with SNPs that reached genome-wide significance predicted the progression from MCI to AD (beta = 0.182, se = 0.061, p = 0.003) after adjusting for the demographic and clinical variables. This association was replicated in the NACC dataset (beta = 0.094, se = 0.037, p = 0.009). Further analyses revealed that PRS was associated with the increased ADAS-Cog11/ADAS-Cog13/ADASQ4 scores, tau/ptau levels, and cortical amyloid burdens (PIB and AV45), but decreased hippocampus and entorhinal cortex volumes (p < 0.05). Mediation analysis showed that the effect of PRS on the increased risk of AD may be mediated by Aβ 42 (beta = 0.056, SE = 0.026, p = 0.036). Conclusion: Our findings suggest that PRS can be useful for the prediction of time to AD and other clinical changes after the diagnosis of MCI.

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Interaction of greenness and polygenic risk score of Alzheimer's disease on risk of cognitive impairment

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148767 ◽

2021 ◽

pp. 148767

Author(s):

Xurui Jin ◽

Chang Shu ◽

Yi Zeng ◽

Liming Liang ◽

John S. Ji

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Risk Score ◽

Polygenic Risk Score ◽

Polygenic Risk

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P4-240: Using polygenic risk score to predict Alzheimer's disease

Alzheimer s & Dementia ◽

10.1016/j.jalz.2015.08.069 ◽

2015 ◽

Vol 11 (7S_Part_19) ◽

pp. P872-P872 ◽

Cited By ~ 1

Author(s):

Valentina Escott-Price ◽

Rebecca Sims ◽

Denise Harold ◽

Maria Vronskaya ◽

Peter Holmans ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Risk Score ◽

Polygenic Risk Score ◽

Polygenic Risk

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12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

Brain Communications ◽

10.1093/braincomms/fcab167 ◽

2021 ◽

Author(s):

McKenna E Williams ◽

Jeremy A Elman ◽

Linda K McEvoy ◽

Ole A Andreassen ◽

Anders M Dale ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Mean Diffusivity ◽

Area Under The Curve ◽

Polygenic Risk Score ◽

Age Difference ◽

Cognitively Normal ◽

Brain Age

Abstract Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and 2) a novel gray matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply aging-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P=0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently-derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step toward improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

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