Predictive Performance of a Polygenic Risk Score for Incident Ischemic Stroke in a Healthy Older Population

Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke

BACKGROUND

Current risk assessment for ischemic stroke (IS) is limited to clinical variables. We hypothesize that polygenic scores (PGS) of IS (PGSIS) and IS-associated diseases such as atrial fibrillation (AF), venous thromboembolism (VTE), coronary artery disease (CAD), hypertension (HTN), and Type 2 diabetes (T2D) may improve the performance of IS risk assessment.

METHODS

Incident IS was followed for 479,476 participants in the UK Biobank who did not have an IS diagnosis prior to the recruitment. Lifestyle variables (obesity, smoking and alcohol) at the time of study recruitment, clinical diagnoses of IS-associated diseases, PGSIS, and five PGSs for IS-associated diseases were tested using the Cox proportional-hazards model. Predictive performance was assessed using the C-statistic and net reclassification index (NRI).

RESULTS

During a median average 12.5-year follow-up, 8374 subjects were diagnosed with IS. Known clinical variables (age, gender, clinical diagnoses of IS-associated diseases, obesity, and smoking) and PGSIS were all independently associated with IS (P < 0.001). In addition, PGSIS and each PGS for IS-associated diseases was also independently associated with IS (P < 0.001). Compared to the clinical model, a joint clinical/PGS model improved the C-statistic for predicting IS from 0.71 to 0.73 (P < 0.001) and significantly reclassified IS risk (NRI = 0.017, P < 0.001), and 6.48% of subjects were upgraded from low to high risk.

CONCLUSIONS

Adding PGSs of IS and IS-associated diseases to known clinical risk factors statistically improved risk assessment for IS, demonstrating the supplementary value of inherited susceptibility measurement . However, its clinical utility is likely limited due to modest improvements in predictive values.

A Polygenic Score Predicts Caries Experience in Elderly Swedish Adults

Caries is a partially heritable disease, raising the possibility that a polygenic score (PS, a summary of an individual's genetic propensity for disease) might be a useful tool for risk assessment. To date, PS for some diseases have shown clinical utility, although no PS for caries has been evaluated. The objective of the study was to test whether a PS for caries is associated with disease experience or increment in a cohort of Swedish adults. A genome-wide PS for caries was trained using the results of a published genome-wide association meta-analysis and constructed in an independent cohort of 15,460 Swedish adults. Electronic dental records from the Swedish Quality Registry for Caries and Periodontitis (SKaPa) were used to compute the decayed, missing, and filled tooth surfaces (DMFS) index and the number of remaining teeth. The performance of the PS was evaluated by testing the association between the PS and DMFS at a single dental examination, as well as between the PS and the rate of change in DMFS. Participants in the highest and lowest deciles of PS had a mean DMFS of 63.5 and 46.3, respectively. A regression analysis confirmed this association where a 1 standard deviation increase in PS was associated with approximately 4-unit higher DMFS (P < 2 × 10-16). Participants with the highest decile of PS also had greater change in DMFS during follow-up. Results were robust to sensitivity analysis, which adjusted for age, age squared, sex, and the first 20 genetic principal components. Mediation analysis suggested that tooth loss was a strong mediating factor in the association between PS and DMFS but also supported a direct genetic effect on caries. In this cohort, there are clinically meaningful differences in DMFS between participants with high and low PS for caries. The results highlight the potential role of genomic data in improving caries risk assessment.

Plasma protein signatures of adult asthma

BACKGROUND

Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

METHODS

Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)).

RESULTS

Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis.

CONCLUSION

This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

The ASPREE Healthy Ageing Biobank: Methodology and participant characteristics

ASPirin in Reducing Events in the Elderly (ASPREE), a placebo-controlled prevention trial of low dose aspirin, provided the opportunity to establish a biospecimen biobank from initially healthy persons aged 70+ years for future research. The ASPREE Healthy Ageing Biobank (ASPREE Biobank) collected, processed and stored blood and urine samples at -80degC or under nitrogen vapour at two timepoints, three years apart, from a willing subset of Australian ASPREE participants. Written informed consent included separate opt-in questions for biomarker and genetic testing. Fractionated blood and urine were aliquoted into multiple low-volume, barcoded cryotubes for frozen storage within 4 hours of collection. Specially designed and outfitted mobile laboratories provided opportunities for participation by people in regional and rural areas. Detailed, high quality demographic, physiological and clinical data were collected annually through the ASPREE trial. 12,219 participants contributed blood/urine at the first timepoint, 10,617 of these older adults provided 3-year follow-up samples, and an additional 1,712 provided saliva for DNA. The mean participant age was 74 years, 54% were female and 46% lived outside major cities. Despite geographical and logistical challenges, nearly 100% of blood/urine specimens were processed and frozen within 4 hours of collection into >1.4 million aliquots. After a median of 4.7 years, major clinical events among ASPREE Biobank participants included 332 with dementia, 613 with cardiovascular disease events, 1259 with cancer, 357 with major bleeds and 615 had died. The ASPREE Biobank houses and curates a large number of biospecimens collected prior to the clinical manifestations of major disease, and 3-year follow-up samples, all linked to high quality, extensive phenotypic information. This provides the opportunity to identify or validate diagnostic, prognostic and predictive biomarkers, and potentially study biological effectors, of ageing-related diseases or maintenance of older-age good health.

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

Genetics in Ischemic Stroke: Current Perspectives and Future Directions

Ischemic stroke is a heterogeneous condition influenced by a combination of genetic and environmental factors. Recent advancements have explored genetics in relation to various aspects of ischemic stroke, including the alteration of individual stroke occurrence risk, modulation of treatment response, and effectiveness of post-stroke functional recovery. This article aims to review the recent findings from genetic studies related to various clinical and molecular aspects of ischemic stroke. The potential clinical applications of these genetic insights in stratifying stroke risk, guiding personalized therapy, and identifying new therapeutic targets are discussed herein.

Integrating polygenic and clinical risks to improve stroke risk stratification in prospective Chinese cohorts

The utility of the polygenic risk score (PRS) to identify individuals at higher risk of stroke beyond clinical risk remains unclear, and we clarified this using Chinese population-based prospective cohorts. Cox proportional hazards models were used to estimate the 10-year risk, and Fine and Gray's models were used for hazard ratios (HRs), their 95% confidence intervals (CIs), and the lifetime risk according to PRS and clinical risk categories. A total of 41,006 individuals aged 30-75 years with a mean follow-up of 9.0 years were included. Comparing the top versus bottom 5% of the PRS, the HR was 3.01 (95%CI 2.03-4.45) in the total population, and similar findings were observed within clinical risk strata. Marked gradients in the 10-year and lifetime risk across PRS categories were also found within clinical risk categories. Notably, among individuals with intermediate clinical risk, the 10-year risk for those in the top 5% of the PRS (7.3%, 95%CI 7.1%-7.5%) reached the threshold of high clinical risk (⩾7.0%) for initiating preventive treatment, and this effect of the PRS on refining risk stratification was evident for ischemic stroke. Even among those in the top 10% and 20% of the PRS, the 10-year risk would also exceed this level when aged ⩾50 and ⩾60 years, respectively. Overall, the combination of the PRS with the clinical risk score improved the risk stratification within clinical risk strata and distinguished actual high-risk individuals with intermediate clinical risk.

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

Assessing the performance of genetic risk score for stratifying risk of post-sepsis cardiovascular complications

Background

Patients with sepsis are at increased risk for cardiovascular complications, including myocardial infarction (MI), ischemic stroke (IS), and venous thromboembolism (VTE). Our objective is to assess whether genetic risk score (GRS) can differentiate risk for these complications.

Methods

A population-based prospective cohort of 483,177 subjects, derived from the UK Biobank, was followed for diagnosis of sepsis and its complications (MI, IS, and VTE) after the study recruitment. GRS for each complication was calculated based on established risk-associated single nucleotide polymorphisms (SNPs). Time to incident MI, IS, and VTE was compared between subjects with or without sepsis and GRS risk groups using Kaplan-Meier log-rank test and Cox-regression analysis.

Results

During an average of 12.6 years of follow-up, 10,757 (2.23%) developed sepsis. Patients with sepsis had an overall higher risk than non-sepsis subjects for each complication, but the risk differed by time after a sepsis diagnosis; exceedingly high in short-term (0-30 days), considerably high in mid-term (31 days to 2 years), and reduced in long-term (>2 years). Furthermore, in White subjects, GRS was a significant predictor of complications, independent of sepsis and other risk factors. For example, GRS_MI further differentiated their risk in patients with sepsis; 3.49, 4.73, and 9.03% in those with low- (<0.5), intermediate- (0.5-1.99), high- GRS_MI (≥2.0), P_trend  < 0.001.

Conclusion

Risk for post-sepsis cardiovascular complications differed considerably by time after a sepsis diagnosis and GRS. These findings, if confirmed in other ancestry-specific populations, may guide personalized management for preventing post-sepsis cardiovascular complications.

Polygenic Risk, Midlife Life's Simple 7, and Lifetime Risk of Stroke

Background

Recent genetic discoveries in stroke have unleashed the potential of using genetic information for risk prediction and health interventions aimed at disease prevention. We sought to estimate the lifetime risk of stroke (LTRS) by levels of genetic risk and to investigate whether optimal cardiovascular health can offset the negative impact of high genetic risk on lifetime risk of stroke.

Methods and Results

Study participants were 11 568 middle‐aged adults (56% women, 23% Black adults), who were free of stroke at baseline and were followed up for a median of 28 years. The remaining LTRS was estimated according to levels of genetic risk based on a validated stroke polygenic risk score, and to levels of cardiovascular health based on the American Heart Association Life's Simple 7 recommendations. At age 45, individuals with high, intermediate, and low polygenic risk score had a remaining LTRS of 23.2% (95% CI, 20.8%–25.5%), 13.8% (95% CI, 11.7%–15.8%), and 9.6% (95% CI, 7.3%–11.8%), respectively. Those with both a high genetic risk and an inadequate Life's Simple 7 experienced the highest LTRS: 24.8% (95% CI, 22.0%–27.6%). Across all polygenic risk score categories, those with an optimal Life's Simple 7 had a ≈30% to 43% lower LTRS than those with an inadequate Life's Simple 7. This corresponded to almost 6 additional years lived free of stroke.

Conclusions

The LTRS varies by levels of polygenic risk and cardiovascular health. Maintaining an optimal cardiovascular health can partially offset a high genetic risk, emphasizing the importance of modifiable risk factors and illustrating the potential of personalizing genetic risk information to motivate lifestyle changes for stroke prevention.

Social isolation, social support and loneliness as predictors of cardiovascular disease incidence and mortality

BACKGROUND

Poor social health is associated with increased risk of cardiovascular disease (CVD). Recent research suggests that different social health domains should be considered separately as the implications for health and possible interventions may differ.

AIM

To assess social isolation, low social support and loneliness as predictors of CVD.

METHODS

Secondary analysis of 11,486 community-dwelling, Australians, aged 70 years and over, free of CVD, dementia, or significant physical disability, from the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Social isolation, social support (Revised Lubben Social Network Scale), and loneliness were assessed as predictors of CVD using Cox proportional-hazard regression. CVD events included fatal CVD, heart failure hospitalization, myocardial infarction and stroke. Analyses were adjusted for established CVD risk factors.

RESULTS

Individuals with poor social health were 42 % more likely to develop CVD (p = 0.01) and twice as likely to die from CVD (p = 0.02) over a median 4.5 years follow-up. Interaction effects indicated that poorer social health more strongly predicted CVD in smokers (HR 4.83, p = 0.001, p-interaction = 0.01), major city dwellers (HR 1.94, p < 0.001, p-interaction=0.03), and younger older adults (70-75 years; HR 2.12, p < 0.001, p-interaction = 0.01). Social isolation (HR 1.66, p = 0.04) and low social support (HR 2.05, p = 0.002), but not loneliness (HR 1.4, p = 0.1), predicted incident CVD. All measures of poor social health predicted ischemic stroke (HR 1.73 to 3.16).

CONCLUSIONS

Among healthy older adults, social isolation and low social support may be more important than loneliness as cardiovascular risk factors. Social health domains should be considered in future CVD risk prediction models.

Clinical utility of polygenic risk scores for coronary artery disease

Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.

A Polygenic Risk Score Predicts Incident Prostate Cancer Risk in Older Men but Does Not Select for Clinically Significant Disease

Despite the high prevalence of prostate cancer in older men, the predictive value of a polygenic risk score (PRS) remains uncertain in men aged ≥70 years. We used a 6.6 million-variant PRS to predict the risk of incident prostate cancer in a prospective study of 5701 men of European descent aged ≥70 years (mean age 75 years) enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. The study endpoint was prostate cancer, including metastatic or non-metastatic disease, confirmed by an expert panel. After excluding participants with a history of prostate cancer at enrolment, we used a multivariable Cox proportional hazards model to assess the association between the PRS and incident prostate cancer risk, adjusting for covariates. Additionally, we examined the distribution of Gleason grade groups by PRS group to determine if a higher PRS was associated with higher grade disease. We tested for interaction between the PRS and aspirin treatment. Logistic regression was used to independently assess the association of the PRS with prevalent (pre-trial) prostate cancer, reported in medical histories. During a median follow-up time of 4.6 years, 218 of the 5701 participants (3.8%) were diagnosed with prostate cancer. The PRS predicted incident risk with a hazard ratio (HR) of 1.52 per standard deviation (SD) (95% confidence interval (CI) 1.33-1.74, p < 0.001). Men in the top quintile of the PRS distribution had an almost three times higher risk of prostate cancer than men in the lowest quintile (HR = 2.99 (95% CI 1.90-4.27), p < 0.001). However, a higher PRS was not associated with a higher Gleason grade groups. We found no interaction between aspirin treatment and the PRS for prostate cancer risk. The PRS was also associated with prevalent prostate cancer (odds ratio = 1.80 per SD (95% CI 1.65-1.96), p < 0.001).While a PRS for prostate cancer is strongly associated with incident risk in men aged ≥70 years, the clinical utility of the PRS as a biomarker is currently limited by its inability to select for clinically significant disease.

The Incremental Prognostic Value of Hepatocyte Growth Factor in First-Ever Acute Ischemic Stroke: An Early Link Between Growth Factor and Interleukins

Hepatocyte growth factor (HGF) is a potential prognostic factor for acute ischemic stroke (AIS). In this study, we sought to validate its earlier predictive accuracy within 24 h for first-ever AIS. Moreover, as HGF interacts with interleukins, their associations may lead to novel immunomodulatory therapeutic strategies. Patients with first-ever AIS (n = 202) within 24 h were recruited. Plasma HGF and related interleukin concentrations were measured by multiplex immunoassays. The primary and secondary outcomes were major disability (modified Rankin scale score ≥3) at 3 months after AIS and death, respectively. Elastic net regression was applied to screen variables associated with stroke outcome; binary multivariable logistic analysis was then used to explore the relationship between HGF level and stroke outcome. After multivariate adjustment, upregulated HGF levels were associated with an increased risk of the primary outcome (odds ratio, 7.606; 95% confidence interval, 3.090–18.726; p < 0.001). Adding HGF to conventional risk factors significantly improved the predictive power for unfavorable outcomes (continuous net reclassification improvement 37.13%, p < 0.001; integrated discrimination improvement 8.71%, p < 0.001). The area under the receiver operating characteristic curve value of the traditional model was 0.8896 and reached 0.9210 when HGF was introduced into the model. An elevated HGF level may also be a risk factor for mortality within 3 months poststroke. The HGF level was also positively correlated with IL-10 and IL-16 levels, and HGF before interaction with all interleukins was markedly negatively correlated with the lymphocyte/neutrophil ratio. HGF within 24 h may have prognostic potential for AIS. Our findings reinforce the link between HGF and interleukins.