Global Vascular Risk Score and CAIDE Dementia Risk Score Predict Cognitive Function in the Northern Manhattan Study

Background. Cardiovascular comorbidities have been associated with cognitive decline in the general population. Objectives. To evaluate the associations between cardiovascular risk and neuropsychological performances in MS. Methods. This is a retrospective study, including 69 MS patients. For all patients, we calculated the Framingham risk score, which provides the 10-year probability of developing macrovascular disease, using age, sex, diabetes, smoking, systolic blood pressure, and cholesterol levels as input variables. Cognitive function was examined with the Brief International Cognitive Assessment for MS (BICAMS), including the Symbol Digit Modalities Test (SDMT), the California Verbal Learning Test-II (CVLT-II), and the Brief Visuospatial Memory Test-Revised (BVMT-R). Results. Each point increase of the Framingham risk score corresponded to 0.21 lower CVLT-II score. Looking at Framingham risk score components, male sex and higher total cholesterol levels corresponded to lower CVLT scores (Coeff = −8.54; 95%CI = −15.51, −1.57; and Coeff = −0.11; 95%CI = −0.20, −0.02, respectively). No associations were found between cardiovascular risk and SDMT or BVMT-R. Conclusions. In our exploratory analyses, cardiovascular risk was associated with verbal learning dysfunction in MS. Lifestyle and pharmacological interventions on cardiovascular risk factors should be considered carefully in the management of MS, given the possible effects on cognitive function.

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All-cause Dementia Prediction by Machine Learning: The Health, Aging, and Body Composition Study

Innovation in Aging ◽

10.1093/geroni/igaa057.1575 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 487-487

Author(s):

Chenkai Wu ◽

Xurui Jin

Keyword(s):

Machine Learning ◽

Body Composition ◽

Risk Prediction ◽

Risk Score ◽

Body Composition Study ◽

C Statistic ◽

Dementia Risk ◽

Health Aging ◽

Composition Study ◽

Traditional Approaches

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

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Association of midlife vascular risk and AD biomarkers with subsequent cognitive decline

Neurology ◽

10.1212/wnl.0000000000010946 ◽

2020 ◽

Vol 95 (23) ◽

pp. e3093-e3103

Author(s):

Corinne Pettigrew ◽

Anja Soldan ◽

Jiangxia Wang ◽

Mei-Cheng Wang ◽

Karissa Arthur ◽

...

Keyword(s):

Cognitive Decline ◽

Risk Score ◽

Synergistic Effects ◽

Vascular Risk Factor ◽

Rate Of Change ◽

Csf Biomarkers ◽

Vascular Risk ◽

Cognitively Normal ◽

T Tau

ObjectiveTo determine whether vascular risk and Alzheimer disease (AD) biomarkers have independent or synergistic effects on cognitive decline and whether vascular risk is associated with the accumulation of AD pathology as measured by change in biomarkers over time.MethodsAt baseline, participants (n = 168) were cognitively normal and primarily middle-aged (mean 56.4 years, SD 10.9 years) and had both vascular risk factor status and proximal CSF biomarkers available. Baseline vascular risk was quantified with a composite vascular risk score reflecting the presence or absence of hypertension, hypercholesterolemia, diabetes, current smoking, and obesity. CSF biomarkers of β-amyloid (Aβ)1–42, total tau (t-tau), and phosphorylated tau (p-tau) were used to create dichotomous high and low AD biomarker groups (based on Aβ1–42 and tau). Linear mixed-effects models were used to examine change in a cognitive composite score (mean follow-up 13.9 years) and change in CSF biomarkers (mean follow-up 4.2 years).ResultsThere was no evidence of a synergistic relationship between the vascular risk score and CSF AD biomarkers and cognitive decline. Instead, the vascular risk score (estimate −0.022, 95% confidence interval [CI] −0.043 to −0.002, p = 0.03) and AD biomarkers (estimate −0.060, 95% CI −0.096 to −0.024, p = 0.001) were independently and additively associated with cognitive decline. In addition, the vascular risk score was unrelated to levels of or rate of change in CSF Aβ1–42, t-tau, or p-tau.ConclusionsThe results of this observational cohort study suggest that vascular risk and biomarkers of AD pathology, when measured in midlife, act along independent pathways and underscore the importance of accounting for multiple risk factors for identifying cognitively normal individuals at the greatest risk of cognitive decline.

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Vascular Risk Factors and Cognitive Function

Brain and Heart Dynamics ◽

10.1007/978-3-030-28008-6_62 ◽

2020 ◽

pp. 953-972

Author(s):

Enrico Mossello ◽

Niccolò Marchionni

Keyword(s):

Risk Factors ◽

Cognitive Function ◽

Vascular Risk Factors ◽

Vascular Risk

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Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

10.31234/osf.io/5yvjr ◽

2021 ◽

Author(s):

Melis Anatürk ◽

Raihaan Patel ◽

Georgios Georgiopoulos ◽

Danielle Newby ◽

Anya Topiwala ◽

...

Keyword(s):

At Risk ◽

Cardiovascular Risk ◽

Risk Score ◽

Disease Risk ◽

Risk Index ◽

Risk Scores ◽

Uk Biobank ◽

Dementia Risk ◽

History Of ◽

The Uk

INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

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