Development and validation of decision rules models to stratify coronary artery disease, diabetes, and hypertension risk in preventive care: cohort study of returning UK Biobank participants

AbstractBackgroundA wide range of predictive models exist that predict risk of common lifestyle conditions. However, these have not focused on identifying pre-clinical higher risk groups that would benefit from lifestyle interventions and do not include genetic risk scores. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension in the general population against commonly used clinical risk scoring tools.Methods and findingsOf all individuals recruited between 2006 and 2010 from the UK Biobank study for whom re-measurement data were available, 60782 were included in the analyses (mean age 56.3 (7.59), 51.2% female). Follow-up data were available until 2016. Three decision rules models with three risk strata were developed and tested for an association with incident disease. Hazard ratios (HR with 95% confidence interval) for incident CAD, T2D, and hypertension were calculated from survival models. Model performance in discriminating between higher risk individuals suitable for lifestyle intervention and individuals at low risk was assessed using the area under the receiver operating characteristic curve (AUROC).From the initial baseline measurement until the follow-up re-measurements, 500 incident CAD cases, 1005 incident T2D cases, and 2379 incident hypertension cases were ascertained. The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increase in risk of CAD, T2D, and hypertension, respectively (P < 0.001 for all), and the risk increased significantly between the three strata for all three conditions (P < 0.05). Risk stratification based on decision rules identified both a low risk group which would not have benefited from lifestyle intervention (only 1.3% incident disease across all models), as well as a high risk group where 72%, 81.5%, and 74% of those who developed disease within 8 years would have been recommended lifestyle intervention. Based on genetic risk alone, we identified not only a high risk group, but also a group at elevated risk for all health conditions. This study was limited by the restricted number of participants with follow-up data, and the lack of ethnic diversity in the UK Biobank cohort.ConclusionIn this analysis of returning UK Biobank participants, we found that decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores were superior at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions compared to commonly used clinical risk scores.

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Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care: Cohort Study of Returning UK Biobank Participants

Journal of Personalized Medicine ◽

10.3390/jpm11121322 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1322

Author(s):

José Castela Forte ◽

Pytrik Folkertsma ◽

Rahul Gannamani ◽

Sridhar Kumaraswamy ◽

Sarah Mount ◽

...

Keyword(s):

Genetic Risk ◽

Risk Group ◽

Decision Rules ◽

Health Conditions ◽

Risk Scores ◽

Lifestyle Interventions ◽

Uk Biobank ◽

Clinical Risk ◽

Physical Measurements ◽

Artery Disease

Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.

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Chromosomal scale length variation of germline DNA can predict individual cancer risk

10.1101/303339 ◽

2018 ◽

Author(s):

Chris Toh ◽

James P. Brody

Keyword(s):

Cancer Risk ◽

Genetic Risk ◽

Germ Line ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Length Variation ◽

Uk Biobank ◽

Genetic Risk Scores ◽

The Uk ◽

Scale Length

AbstractInherited factors are thought to be responsible for a substantial fraction of many different forms of cancer. However, individual cancer risk cannot currently be well quantified by analyzing germ line DNA. Most analyses of germline DNA focus on the additive effects of single nucleotide polymorphisms (SNPs) found. Here we show that chromosomal-scale length variation of germline DNA can be used to predict whether a person will develop cancer. In two independent datasets, the Cancer Genome Atlas (TCGA) project and the UK Biobank, we could classify whether or not a patient had a certain cancer based solely on chromosomal scale length variation. In the TCGA data, we found that all 32 different types of cancer could be predicted better than chance using chromosomal scale length variation data. We found a model that could predict ovarian cancer in women with an area under the receiver operator curve, AUC=0.89. In the UK Biobank data, we could predict breast cancer in women with an AUC=0.83. This method could be used to develop genetic risk scores for other conditions known to have a substantial genetic component and complements genetic risk scores derived from SNPs.

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Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank

10.1101/096214 ◽

2016 ◽

Cited By ~ 1

Author(s):

Amy E. Taylor ◽

Marcus R. Munafò

Keyword(s):

Genetic Risk ◽

Coffee Consumption ◽

Risk Scores ◽

Tea Consumption ◽

Uk Biobank ◽

Beverage Consumption ◽

Dietary Recall ◽

Genetic Risk Scores ◽

Decaffeinated Coffee ◽

The Uk

AbstractBackgroundGenetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.AimsTo improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.MethodsWe tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.ResultsGenetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.ConclusionsGenetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.

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Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

10.1101/448597 ◽

2018 ◽

Author(s):

Kristi Läll ◽

Maarja Lepamets ◽

Marili Palover ◽

Tõnu Esko ◽

Andres Metspalu ◽

...

Keyword(s):

Breast Cancer ◽

Genetic Risk ◽

Odds Ratio ◽

Population Based ◽

Risk Scores ◽

Full Potential ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Risk Scores ◽

The Uk

AbstractBackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.MethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.ResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.ConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

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Abstract P879: Differences in Statistical Performance of Polygenic Risk Scores for Cardiovascular Disease Across Different Race/Ethnicities

Stroke ◽

10.1161/str.52.suppl_1.p879 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Julian N Acosta ◽

Cameron Both ◽

Natalia Szejko ◽

Stacy Brown ◽

Kevin N Sheth ◽

...

Keyword(s):

Cardiovascular Disease ◽

Logistic Regression ◽

Genetic Risk ◽

Regression Models ◽

Risk Scores ◽

Uk Biobank ◽

Polygenic Risk ◽

Logistic Regression Models ◽

The Uk ◽

Significant Health

Introduction: Genome-wide association studies have identified numerous genetic risk variants for stroke and myocardial infarction (MI) in Europeans. However, the limited applicability of these results to non-Europeans due to racial/ethnic differences in the genetic architecture of cardiovascular disease (CVD), coupled with the limited availability of genomic data in non-Europeans, may create significant health disparities now that genomic-based precision medicine is a reality. We tested the hypothesis that the performance of polygenic risk scores (PRS) for CVD differ in Europeans versus non-Europeans. Methods: We conducted a nested study within the UK Biobank, a prospective, population-based study that enrolled ~500,000 participants across the UK. For this study, we identified self-reported black participants and randomly matched them 1:1 by age and sex with white participants. We created a PRS using previously discovered loci for stroke and MI. We then tested whether this PRS representing the aggregate polygenic susceptibility to CVD yielded similar precision in black versus white participants in logistic regression models. Results: Of the 502,536 participants enrolled in the UK Biobank, 8,061 were self-reported blacks, with 7,644 having available data for our analyses. We randomly matched these participants with white individuals, leading to a total sample size of 15,288 (mean age 51.9 [SD 8.1], female 8,722 [57%]). The total number of events was 741 overall, with 363 happening in blacks and 378 happening in whites. In logistic regression models including age, sex, and 5 principal components, the statistical precision (e.g. narrower confidence intervals) for the PRS was substantially higher for whites (OR 1.22, 95%CI 1.08 - 1.37; p<0.0001) compared to blacks (OR 1.24, 95%CI 1.05-1.47; p=0.01). Secondary analyses using genetically-determined ancestry yielded similar results. Conclusion: Because CVD-related PRSs are derived mainly using genetic risk factors identified in populations of European ancestry, their statistical performance is lower in non-European populations. This asymmetry can lead to significant health disparities now that these tools are being evaluated in multiple precision medicine approaches.

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Healthy lifestyle, genetic risk, and incidence of cancer: A prospective cohort study of 13 cancer types

10.1101/2021.12.07.21267341 ◽

2021 ◽

Author(s):

Stephanie Byrne ◽

Terry Boyle ◽

Muktar Ahmed ◽

Sang Hong Lee ◽

Beben Benyamin ◽

...

Keyword(s):

Genetic Risk ◽

Prospective Cohort ◽

Cox Regression ◽

Healthy Lifestyle ◽

Risk Scores ◽

Unhealthy Lifestyle ◽

Cancer Types ◽

The Uk ◽

Risk Of Cancer

AbstractBackgroundGenetic and lifestyle factors are related to the risk of cancer, but it is unclear whether a healthy lifestyle can offset genetic risk. Our aim was to investigate this for 13 cancer types using data from the UK Biobank prospective cohort.MethodsIn 2006-2010, participants aged 37-73 years were assessed and followed until 2015-2019. Analyses were restricted to those of European ancestries with no history of malignant cancer (n=195,822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined (‘overall cancer’), and a healthy lifestyle score was calculated from current recommendations. Relationships with cancer incidence were examined using Cox regression, adjusting for relevant confounders. Interactions between HLI and PRSs were assessed.ResultsThere were 15,240 incident cancers during the 1,926,987 person-years of follow-up (median follow-up= 10.2 years). After adjusting for confounders, an unhealthy lifestyle was associated with a higher risk of overall cancer [lowest vs highest tertile hazard ratio (95% confidence interval) = 1.32(1.26, 1.37)] and eight cancer types. The greatest increased risks were seen for cancers of the lung [3.5(2.96,4.15)], bladder [2.03 (1.57, 2.64)], and pancreas [1.98 (1.54,2.55)]. Positive additive interactions were observed, suggesting a healthy lifestyle may partially offset genetic risk of colorectal, breast, and pancreatic cancers, and may completely offset genetic risk of lung and bladder cancers.ConclusionsA healthy lifestyle is beneficial for most cancers and may offset genetic risk of some cancers. These findings have important implications for those genetically predisposed to these cancers and population strategies for cancer prevention.

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Exploring Various Polygenic Risk Scores for Skin Cancer in the Phenomes of the Michigan Genomics Initiative and the UK Biobank with a Visual Catalog:PRSWeb

10.1101/384909 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lars G. Fritsche ◽

Lauren J. Beesley ◽

Peter VandeHaar ◽

Robert B. Peng ◽

Maxwell Salvatore ◽

...

Keyword(s):

Skin Cancer ◽

Cell Carcinoma ◽

Genetic Risk ◽

Population Based ◽

Risk Scores ◽

Uk Biobank ◽

Polygenic Risk ◽

Cancer Subtypes ◽

Construction Methods ◽

The Uk

AbstractPolygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog calledPRSwebthat provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.Author summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patient’s risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases’ shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

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Can Autism be Detected at 18 Months?

The British Journal of Psychiatry ◽

10.1192/bjp.161.6.839 ◽

1992 ◽

Vol 161 (6) ◽

pp. 839-843 ◽

Cited By ~ 472

Author(s):

Simon Baron-Cohen ◽

Jane Allen ◽

Christopher Gillberg

Keyword(s):

High Risk ◽

Genetic Risk ◽

Risk Group ◽

Pretend Play ◽

Social Play ◽

High Risk Group ◽

High Genetic Risk ◽

Health Visitors ◽

New Instrument

Autism is currently detected only at about three years of age. This study aimed to establish if detection of autism was possible at 18 months of age. We screened 41 18–month-old toddlers who were at high genetic risk for developing autism, and 50 randomly selected 18–month-olds, using a new instrument, the CHAT, administered by GPs or health visitors. More than 80% of the randomly selected 18–month-old toddlers passed on all items, and none failed on more than one of pretend play, protodeclarative pointing, joint-attention, social interest, and social play. Four children in the high-risk group failed on two or more of these five key types of behaviour. At follow-up at 30 months of age, the 87 children who had passed four or more of these key types of behaviour at 18 months of age had continued to develop normally. The four toddlers who had failed on two or more of these key types of behaviour at 18 months received a diagnosis of autism by 30 months.

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Associations Between Major Psychiatric Disorder Polygenic Risk Scores and Blood-Based Markers in UK Biobank

10.1101/2020.11.06.20227066 ◽

2020 ◽

Author(s):

Michael D.E. Sewell ◽

Xueyi Shen ◽

Lorena Jiménez-Sánchez ◽

Amelia J. Edmondson-Stait ◽

Claire Green ◽

...

Keyword(s):

Genetic Risk ◽

Smoking Status ◽

Hdl Cholesterol ◽

Risk Scores ◽

Uk Biobank ◽

Linear Regression Models ◽

Polygenic Risk ◽

Bbb Permeability ◽

The Uk ◽

Peripheral Markers

AbstractBackgroundMajor depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD) have both shared and discrete genetic risk factors and abnormalities in blood-based measures of inflammation and blood-brain barrier (BBB) permeability. The relationships between such genetic architectures and blood-based markers are however unclear. We investigated relationships between polygenic risk scores for these disorders and peripheral biomarkers in the UK Biobank cohort.MethodsWe calculated polygenic risk scores (PRS) for samples of n = 367,329 (MDD PRS), n = 366,465 (SCZ PRS), and n = 366,383 (BD PRS) individuals from the UK Biobank cohort. We examined associations between each disorder PRS and 62 blood markers, using two generalized linear regression models: ‘minimally adjusted’ controlling for variables including age and sex, and ‘fully adjusted’ including additional lifestyle covariates such as alcohol and smoking status.Results12/62, 13/62 and 9/62 peripheral markers were significantly associated with MDD, SCZ and BD PRS respectively for both models. Most associations were disorder PRS-specific, including several immune-related markers for MDD and SCZ. We also identified several BBB-permeable marker associations, including vitamin D for all three disorder PRS, IGF-1 and triglycerides for MDD PRS, testosterone for SCZ PRS, and HDL cholesterol for BD PRS.ConclusionsThis study suggests that MDD, SCZ and BD have shared and distinct peripheral markers associated with disorder-specific genetic risk. The results implicate BBB permeability disruptions in all three disorders and inflammatory dysfunction in MDD and SCZ, and enrich our understanding of potential underlying pathophysiological mechanisms in major psychiatric disorders.

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Genome-Wide Association Study of Microscopic Colitis in the UK Biobank Confirms Immune-Related Pathogenesis

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjz104 ◽

2019 ◽

Vol 13 (12) ◽

pp. 1578-1582 ◽

Cited By ~ 5

Author(s):

Harry D Green ◽

Robin N Beaumont ◽

Amanda Thomas ◽

Benjamin Hamilton ◽

Andrew R Wood ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Genetic Risk ◽

Genome Wide Association Study ◽

Microscopic Colitis ◽

Risk Scores ◽

Uk Biobank ◽

Genome Wide ◽

Downstream Analysis ◽

The Uk

Abstract Background and Aims The causes of microscopic colitis are currently poorly understood. Previous reports have found clinical associations with coeliac disease and genetic associations at the human leukocyte antigen [HLA] locus on the ancestral 8.1 haplotype. We investigated pharmacological and genetic factors associated with microscopic colitis in the UK Biobank. Methods In total, 483 European UK Biobank participants were identified by ICD10 coding, and a genome-wide association study was performed using BOLT-LMM, with a sensitivity analysis performed excluding potential confounders. The HLA*IMP:02 algorithm was used to estimate allele frequency at 11 classical HLA genes, and downstream analysis was performed using FUMA. Genetic overlap with inflammatory bowel disease [Crohn’s disease and ulcerative colitis] was investigated using genetic risk scores. Results We found significant phenotypic associations with smoking status, coeliac disease and the use of proton-pump inhibitors but not with other commonly reported pharmacological risk factors. Using the largest sample size to date, we confirmed a recently reported association with the MHC Ancestral 8.1 Haplotype. Downstream analysis suggests association with digestive tract morphogenesis. By calculating genetic risk scores, we also report suggestive evidence of shared genetic risk with Crohn’s disease, but not with ulcerative colitis. Conclusions This report confirms the role of genetic determinants in the HLA in the pathogenesis of microscopic colitis. The genetic overlap with Crohn’s disease suggests a common underlying mechanism of disease.

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