Polysocial and Polygenic Risk Scores and All-Cause Dementia, Alzheimer's Disease, and Vascular Dementia

BACKGROUND

To establish a polysocial risk score (PsRS) incorporating various social factors for capturing the dementia risk and investigate the benefits of favorable social conditions across different genetic backgrounds.

METHODS

This prospective cohort study comprised 345 439 participants initially free of dementia from the UK Biobank. A total of 10 social factors were summed to create a PsRS. A polygenic risk score (PRS) was constructed based on genome-wide significant variants.

RESULTS

During a median follow-up of 12.5 years, we documented 4 595 incident all-cause dementia events including 2 067 Alzheimer's disease (AD) events and 1 028 vascular dementia (VD) events. Each additional PsRS was associated with a 19% increased risk of all-cause dementia (hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.17 to 1.21), a 13% increased risk of AD (1.13; 1.10 to 1.16), and a 24% increased risk of VD (1.24; 1.19 to 1.29). 29% (24% to 33%) of dementia cases, 22% (14% to 29%) of AD cases, and 39% (28% to 48%) of VD cases were associated with a disadvantageous social environment. In addition, among participants at a high genetic risk, the low social risk was linked to a lower incidence rate of all-cause dementia, AD, and VD compared to those who had a high social risk, with reductions of 67.8%, 64.5%, and 84.2%, respectively.

CONCLUSIONS

The PsRS could be effectively used in discriminating individuals at high risk of dementia. Around a quarter of dementia events could have a connection with a disadvantageous social environment, especially for those genetically susceptible to dementia.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management

PURPOSE OF REVIEW

To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management.

RECENT FINDINGS

Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.

Associations of polysocial risk score, lifestyle and genetic factors with incident type 2 diabetes: a prospective cohort study

AIM/HYPOTHESIS

We aimed to investigate the association between polysocial risk score (PsRS), an estimator of individual-level exposure to cumulative social risks, and incident type 2 diabetes in the UK Biobank study.

METHODS

This study includes 319,832 participants who were free of diabetes, cardiovascular disease and cancer at baseline in the UK Biobank study. The PsRS was calculated by counting the 12 social determinants of health from three social risk domains (namely socioeconomic status, psychosocial factors, and neighbourhood and living environment) that had a statistically significant association with incident type 2 diabetes after Bonferroni correction. A healthy lifestyle score was calculated using information on smoking status, alcohol intake, physical activity, diet quality and sleep quality. A genetic risk score was calculated using 403 SNPs that showed significant genome-wide associations with type 2 diabetes in people of European descent. The Cox proportional hazards model was used to analyse the association between the PsRS and incident type 2 diabetes.

RESULTS

During a median follow-up period of 8.7 years, 4427 participants were diagnosed with type 2 diabetes. After adjustment for major confounders, an intermediate PsRS (4-6) and high PsRS (≥7) was associated with higher risks of developing type 2 diabetes with the HRs being 1.38 (95% CI 1.26, 1.52) and 2.02 (95% CI 1.83, 2.22), respectively, compared with those with a low PsRS (≤3). In addition, an intermediate to high PsRS accounted for approximately 34% (95% CI 29, 39) of new-onset type 2 diabetes cases. A healthy lifestyle slightly, but significantly, mitigated PsRS-related risks of type 2 diabetes (p_interaction=0.030). In addition, the additive interactions between PsRS and genetic predisposition led to 15% (95% CI 13, 17; p<0.001) of new-onset type 2 diabetes cases (p_interaction<0.001).

CONCLUSIONS/INTERPRETATION

A higher PsRS was related to increased risks of type 2 diabetes. Adherence to a healthy lifestyle may attenuate elevated diabetes risks due to social vulnerability. Genetic susceptibility and disadvantaged social status may act synergistically, resulting in additional risks for type 2 diabetes.

Development and validation of a polysocial risk score for atherosclerotic cardiovascular disease

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Social determinants of health may improve identification of atherosclerotic cardiovascular disease – beyond traditional risk factors.

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We provide the first, validated, polysocial risk score – the PsRS – for atherosclerotic cardiovascular disease.

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PsRS is a robust tool to quantify cumulative social disadvantage.

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PsRS offers unique opportunities to improve cardiovascular risk prediction algorithms.

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Our findings may help highlight, and address disparities in cardiovascular disease.

Objective

To date, the extent to which social determinants of health (SDOH) may help identify individuals with atherosclerotic cardiovascular disease (ASCVD) – beyond traditional risk factors – has not been quantified using a cumulative social disadvantage approach. The objective of this study was to develop, and validate, a polysocial risk score (PsRS) for prevalent ASCVD in a nationally representative sample of adults in the United States (US).

Methods

We used data from the 2013–2017 National Health Interview Survey. A total of 38 SDOH were identified from the database. Stepwise and criterion-based selection approaches were applied to derive PsRS, after adjusting for traditional risk factors. Logistic regression models were fitted to assign risk scores to individual SDOH, based on relative effect size magnitudes. PsRS was calculated by summing risk scores for individual SDOH, for each participant; and validated using a separate validation cohort.

Results

Final sample comprised 164,696 adults. PsRS included 7 SDOH: unemployment, inability to pay medical bills, low income, psychological distress, delayed care due to lack of transport, food insecurity, and less than high school education. PsRS ranged from 0–20 and exhibited excellent calibration and discrimination. Individuals with the highest PsRS (5^th quintile) had nearly 4-fold higher ASCVD prevalence, relative to those with the lowest risk scores (1^st quintile). Area under receiver operating curve (AU-ROC) for PsRS with SDOH alone was 0.836. Addition of SDOH to the model with only demographic and clinical risk factors (AU-ROC=0.852) improved overall discriminatory power, with AU-ROC for final PsRS (demographics + clinical + SDOH) = 0.862.

Conclusions

Cumulatively, SDOH may help identify individuals with ASCVD, beyond traditional cardiovascular risk factors. In this study, we provide a unique validated PsRS for ASCVD in a national sample of US adults. Future study should target development of similar scores in diverse populations, and incorporate longitudinal study designs.