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  1. Article ; Online: Would You Screen This Patient for Cognitive Impairment? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center.

    Burns, Risa B / Barry, Michael J / Blacker, Deborah / Kanjee, Zahir

    Annals of internal medicine

    2023  Volume 176, Issue 10, Page(s) 1405–1412

    Abstract: Dementia, according to the American Psychiatric Association' ... ...

    Abstract Dementia, according to the American Psychiatric Association's
    MeSH term(s) Humans ; Aged ; Teaching Rounds ; Cognitive Dysfunction/diagnosis ; Mass Screening ; Cognition ; Dementia/diagnosis
    Language English
    Publishing date 2023-10-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 336-0
    ISSN 1539-3704 ; 0003-4819
    ISSN (online) 1539-3704
    ISSN 0003-4819
    DOI 10.7326/M23-1808
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  2. Article ; Online: Eliminating racial disparities in dementia risk by equalizing education quality: A sensitivity analysis.

    Liu, Chelsea / Murchland, Audrey R / VanderWeele, Tyler J / Blacker, Deborah

    Social science & medicine (1982)

    2022  Volume 312, Page(s) 115347

    Abstract: Background: Higher risk of dementia among racial/ethnic minorities compared to White populations in the U.S. has been attributed to life-course exposures to adverse conditions such as lower educational attainment, but most studies have not considered ... ...

    Abstract Background: Higher risk of dementia among racial/ethnic minorities compared to White populations in the U.S. has been attributed to life-course exposures to adverse conditions such as lower educational attainment, but most studies have not considered additional disparities in education quality. We sought to determine the extent to which disparities in dementia would be reduced had different racial groups received the same quality of education, with no change to present disparities in educational attainment.
    Methods: We conducted a literature review to assess whether and how measures of educational attainment and quality are utilized in the development of norms for standard cognitive screening measures. In a separate search of the literature, we identified estimates of relationships between race, education quality and dementia; and calculated the adjusted association between race and dementia had education quality been equalized between Black and White participants.
    Results: Most norms for cognitive measures included educational attainment, but few addressed quality. Our search identified relevant parameter estimates: 44.3% of Black participants and 10.5% of White participants had "limited literacy" (<9th grade reading level, a potential marker of poor education quality), which was associated with a 53% greater hazard of dementia compared with "adequate literacy" (≥ 9th grade reading level) after adjusting for educational attainment. Applying these parameters to a hazard ratio of 1.37 (95%CI: 1.12,1.67) for the risk of dementia comparing Black to White participants, we obtained an adjusted hazard ratio of 1.17 (0.96,1.43), a 54% reduction.
    Discussion: Present studies are limited in their consideration of education quality. Our work using available measures from the literature suggests that if education quality were equalized across groups by race, without changing disparities in attainment, racial disparities in dementia would be reduced by about half. Future work should seek to consistently incorporate education quality in order to better understand the sources of disparities.
    MeSH term(s) Dementia/epidemiology ; Educational Status ; Ethnicity ; Humans ; Racial Groups
    Language English
    Publishing date 2022-09-12
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 4766-1
    ISSN 1873-5347 ; 0037-7856 ; 0277-9536
    ISSN (online) 1873-5347
    ISSN 0037-7856 ; 0277-9536
    DOI 10.1016/j.socscimed.2022.115347
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  3. Article ; Online: Food for thought.

    Blacker, Deborah

    JAMA neurology

    2013  Volume 70, Issue 8, Page(s) 967–968

    MeSH term(s) Amyloid beta-Peptides/cerebrospinal fluid ; Amyloid beta-Peptides/metabolism ; Apolipoprotein E4/cerebrospinal fluid ; Apolipoprotein E4/genetics ; Diet/adverse effects ; Female ; Genotype ; Humans ; Lipid Metabolism/genetics ; Male ; Peptide Fragments/metabolism
    Chemical Substances Amyloid beta-Peptides ; Apolipoprotein E4 ; Peptide Fragments ; amyloid beta-protein (40-42)
    Language English
    Publishing date 2013-08
    Publishing country United States
    Document type Comment ; Editorial ; Research Support, N.I.H., Extramural
    ZDB-ID 2702023-X
    ISSN 2168-6157 ; 2168-6149
    ISSN (online) 2168-6157
    ISSN 2168-6149
    DOI 10.1001/jamaneurol.2013.3288
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  4. Article ; Online: To meat or not to meat? Processed meat and risk of dementia.

    Yeh, Tian-Shin / Blacker, Deborah / Ascherio, Alberto

    The American journal of clinical nutrition

    2021  Volume 114, Issue 1, Page(s) 7–8

    MeSH term(s) Dementia/etiology ; Humans ; Meat ; Meat Products
    Language English
    Publishing date 2021-05-07
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 280048-2
    ISSN 1938-3207 ; 0002-9165
    ISSN (online) 1938-3207
    ISSN 0002-9165
    DOI 10.1093/ajcn/nqab139
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  5. Article ; Online: Further evaluation of narrative description as a measure of cognitive function in Alzheimer's disease.

    Reeves, Stephanie M / Williams, Victoria / Blacker, Deborah / Woods, Russell L

    Neuropsychology

    2022  Volume 37, Issue 7, Page(s) 801–812

    Abstract: Objective: The narrative description (ND) test objectively measures the ability to understand and describe visual scenes. As subtle differences in speech occur early in cognitive decline, we analyzed linguistic features for their utility in detecting ... ...

    Abstract Objective: The narrative description (ND) test objectively measures the ability to understand and describe visual scenes. As subtle differences in speech occur early in cognitive decline, we analyzed linguistic features for their utility in detecting cognitive impairment and predicting downstream decline.
    Method: Participants (
    Results: Many of the linguistic-feature metrics were related to cognitive status. Many of the linguistic features could distinguish between the cognitively normal group and the mild cognitive impairment (MCI) and Dementia groups. The area under the curve (AUC) for ND score alone was 0.74, with a nonsignificant increase to 0.78 when adding mean word length. Among participants with subjective cognitive decline (SCD) at the first visit, a smaller number of words plus more interjections or a lower ND score at baseline were predictive of future cognitive decline.
    Conclusions: While many linguistic features were associated with cognitive status, and some were able to detect early cognitive impairment or predictive of future cognitive decline, all the features we tested seem to have been captured by the ND score. Thus, adding linguistic measures to the ND test score did not add to its value in assessing current or predicting future cognitive status. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
    MeSH term(s) Humans ; Alzheimer Disease/complications ; Alzheimer Disease/diagnosis ; Alzheimer Disease/psychology ; Cognition ; Cognitive Dysfunction/psychology ; Dementia/complications ; Speech ; Neuropsychological Tests
    Language English
    Publishing date 2022-12-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1042412-x
    ISSN 1931-1559 ; 0894-4105
    ISSN (online) 1931-1559
    ISSN 0894-4105
    DOI 10.1037/neu0000884
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  6. Article ; Online: AI-assisted prediction of differential response to antidepressant classes using electronic health records.

    Sheu, Yi-Han / Magdamo, Colin / Miller, Matthew / Das, Sudeshna / Blacker, Deborah / Smoller, Jordan W

    NPJ digital medicine

    2023  Volume 6, Issue 1, Page(s) 73

    Abstract: Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after ... ...

    Abstract Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
    Language English
    Publishing date 2023-04-26
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-023-00817-8
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  7. Article ; Online: Temporal characterization of Alzheimer's Disease with sequences of clinical records.

    Estiri, Hossein / Azhir, Alaleh / Blacker, Deborah L / Ritchie, Christine S / Patel, Chirag J / Murphy, Shawn N

    EBioMedicine

    2023  Volume 92, Page(s) 104629

    Abstract: Background: Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision ... ...

    Abstract Background: Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD.
    Methods: We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts.
    Findings: In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts.
    Interpretation: We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling.
    Funding: National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).
    MeSH term(s) Humans ; Alzheimer Disease/diagnosis ; Retrospective Studies ; Algorithms ; Machine Learning ; Electronic Health Records
    Language English
    Publishing date 2023-05-27
    Publishing country Netherlands
    Document type Review ; Journal Article
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2023.104629
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  8. Article ; Online: Interplay Between Chronic Kidney Disease, Hypertension, and Stroke: Insights From a Multivariable Mendelian Randomization Analysis.

    Kelly, Dearbhla M / Georgakis, Marios K / Franceschini, Nora / Blacker, Deborah / Viswanathan, Anand / Anderson, Christopher D

    Neurology

    2023  Volume 101, Issue 20, Page(s) e1960–e1969

    Abstract: Background and objectives: Chronic kidney disease (CKD) increases the risk of stroke, but the extent through which this association is mediated by hypertension is unknown. We leveraged large-scale genetic data to explore causal relationships between CKD, ...

    Abstract Background and objectives: Chronic kidney disease (CKD) increases the risk of stroke, but the extent through which this association is mediated by hypertension is unknown. We leveraged large-scale genetic data to explore causal relationships between CKD, hypertension, and cerebrovascular disease phenotypes.
    Methods: We used data from genome-wide association studies of European ancestry to identify genetic proxies for kidney function (CKD diagnosis, estimated glomerular filtration rate [eGFR], and urinary albumin-to-creatinine ratio [UACR]), systolic blood pressure (SBP), and cerebrovascular disease (ischemic stroke and its subtypes and intracerebral hemorrhage). We then conducted univariable, multivariable, and mediation Mendelian randomization (MR) analyses to investigate the effect of kidney function on stroke risk and the proportion of this effect mediated through hypertension.
    Results: Univariable MR revealed associations between genetically determined lower eGFR and risk of all stroke (odds ratio [OR] per 1-log decrement in eGFR, 1.77; 95% CI 1.31-2.40;
    Discussion: Our results demonstrate an independent causal effect of impaired kidney function, as assessed by decreased eGFR, on stroke risk, particularly LAS, even when controlled for SBP. Targeted prevention of kidney disease could lower atherosclerotic stroke risk independent of hypertension.
    MeSH term(s) Humans ; Mendelian Randomization Analysis ; Genome-Wide Association Study ; Stroke/epidemiology ; Stroke/genetics ; Cerebrovascular Disorders/complications ; Renal Insufficiency, Chronic/epidemiology ; Renal Insufficiency, Chronic/genetics ; Hypertension/epidemiology ; Hypertension/genetics ; Hypertension/complications ; Ischemic Stroke/complications
    Language English
    Publishing date 2023-09-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 207147-2
    ISSN 1526-632X ; 0028-3878
    ISSN (online) 1526-632X
    ISSN 0028-3878
    DOI 10.1212/WNL.0000000000207852
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  9. Article ; Online: Promise and peril of claims-based dementia ascertainment in causal inference.

    Festa, Natalia / Moura, Lidia Mvr / Blacker, Deborah / Newhouse, Joseph P / Hsu, John

    BMJ evidence-based medicine

    2023  Volume 28, Issue 4, Page(s) 222–225

    MeSH term(s) Humans ; Causality ; Dementia/epidemiology
    Language English
    Publishing date 2023-05-02
    Publishing country England
    Document type Journal Article
    ISSN 2515-4478
    ISSN (online) 2515-4478
    DOI 10.1136/bmjebm-2022-112134
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  10. Article ; Online: Brain Exercise and Brain Outcomes: Does Cognitive Activity Really Work to Maintain Your Brain?

    Blacker, Deborah / Weuve, Jennifer

    JAMA psychiatry

    2018  Volume 75, Issue 7, Page(s) 703–704

    MeSH term(s) Adult ; Aged ; Brain ; Cognition ; Dementia ; Exercise ; Humans ; Middle Aged
    Language English
    Publishing date 2018-05-30
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2701203-7
    ISSN 2168-6238 ; 2168-622X
    ISSN (online) 2168-6238
    ISSN 2168-622X
    DOI 10.1001/jamapsychiatry.2018.0656
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