How can polygenic inheritance be used in population screening for common diseases?

Advances in genomics have near-term impact on diagnosis and management of monogenic disorders. For common complex diseases, the use of genomic information from multiple loci (polygenic model) is generally not useful for diagnosis and individual prediction. In principle, the polygenic model could be used along with other risk factors in stratified population screening to target interventions. For example, compared to age-based criterion for breast, colorectal, and prostate cancer screening, adding polygenic risk and family history holds promise for more efficient screening with earlier start and/or increased frequency of screening for segments of the population at higher absolute risk than an established screening threshold; and later start and/or decreased frequency of screening for segments of the population at lower risks. This approach, while promising, faces formidable challenges for building its evidence base and for its implementation in practice. Currently, it is unclear whether or not polygenic risk can contribute enough discrimination to make stratified screening worthwhile. Empirical data are lacking on population-based age-specific absolute risks combining genetic and non-genetic factors, on impact of polygenic risk genes on disease natural history, as well as information on comparative balance of benefits and harms of stratified interventions. Implementation challenges include difficulties in integration of this information in the current health-care system in the United States, the setting of appropriate risk thresholds, and ethical, legal, and social issues. In an era of direct-to-consumer availability of personal genomic information, the public health and health-care systems need to prepare for an evidence-based integration of this information into population screening

Impact of Communicating Familial Risk of Diabetes on Illness Perceptions and Self-Reported Behavioral Outcomes: A randomized controlled trial

OBJECTIVE: To assess the potential effectiveness of communicating familial risk of diabetes on illness perceptions and self-reported behavioral outcomes. RESEARCH DESIGN AND METHODS: Individuals with a family history of diabetes were randomized to receive risk information based on familial and general risk factors (n = 59) or general risk factors alone (n = 59). Outcomes were assessed using questionnaires at baseline, 1 week, and 3 months. RESULTS: Compared with individuals receiving general risk information, those receiving familial risk information perceived heredity to be a more important cause of diabetes (P <0.01) at 1-week follow-up, perceived greater control over preventing diabetes (P <0.05), and reported having eaten more healthily (P = 0.01) after 3 months. Behavioral intentions did not differ between the groups. CONCLUSIONS: Communicating familial risk increased personal control and, thus, did not result in fatalism. Although the intervention did not influence intentions to change behavior, there was some evidence to suggest it increases healthy behavio

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information

Reflection on modern methods: Revisiting the area under the ROC Curve

The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility

An Electrocardiogram-Based Risk Equation for Incident Cardiovascular Disease From the National Health and Nutrition Examination Survey.

Importance Electrocardiography (ECG) may detect subclinical cardiovascular disease (CVD) in asymptomatic individuals, but its role in assessing adverse events beyond traditional risk factors is not clear. Interval and vector data that are commonly available on modern ECGs may offer independent prognostic information that improves risk classification. Objectives To derive and validate a CVD risk equation based on ECG metrics and to determine its incremental benefit in addition to the Framingham risk score (FRS). Design, Setting, and Participants This study included 3640 randomly selected community-based adults aged 40 to 74 years without known CVD from the First National Health and Nutrition Examination Survey (NHANES I) cohort (1971-1975) and 6329 from the NHANES III cohort (1988-1994). Participants were sampled from across the United States. A risk score to assess incident nonfatal and fatal CVD events was derived based on computer-generated ECG data, including frontal P, R, and T axes; heart rate; and PR, QRS, and QT intervals from NHANES I. The most prognostic variables, along with age and sex, were incorporated into the NHANES ECG risk equation. The equation was evaluated in the NHANES III cohort for an independent validation. Follow-up in the NHANES III cohort was completed on December 31, 2006. Data for this study were analyzed from August 11, 2015, to May 20, 2016. Main Outcomes and Measures The primary end point was CVD death. Secondary outcomes included 10-year ischemic heart disease and all-cause death. Results The final study sample included 9969 participants (4714 men [47.3%]; 5255 women [52.7%]; mean [SD] age, 55.3 [10.1] years) from both cohorts. Frontal T axis, heart rate, and heart rate–corrected QT interval were the most significant ECG factors in the NHANES I cohort. In the validation cohort (NHANES III), the equation provided for prognostic information for fatal CVD with a hazard ratio (HR) of 3.23 (95% CI, 2.82-3.72); the C statistic was 0.79 (95% CI, 0.76-0.81). When added to the FRS in Cox proportional hazards regression models, the categorical (1%, 5%, and 10% cutoffs) net reclassification improvement was 24%. When the FRS and ECG scores were combined in a single model, the C statistic improved by 0.04 (95% CI, 0.02-0.06) to 0.80 (95% CI, 0.77-0.82). Similar improvements were noted when the ECG score was added to the pooled cohort equation. When the equation for prognostic information about ischemic heart disease and all-cause death was evaluated, the results were similar. Conclusions and Relevance An ECG risk score based on age, sex, heart rate, frontal T axis, and QT interval assesses the risk for CVD and compares favorably with the FRS alone in an independent cohort of asymptomatic individuals. Although the ECG risk equation is low cost, further research is needed to ascertain whether this additional step in risk stratification may improve prevention efforts and reduce CVD events