Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study

BACKGROUND: Family studies and heritability estimates provide evidence for a genetic contribution to variation in the human life span. METHODS:We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participants from 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards, logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate [greater than or equal to]80%, minor allele frequency [greater than or equal to]10%, Hardy-Weinberg test p [greater than or equal to] 0.001).RESULTS:In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10-5). The two sets of SNPs were in high linkage disequilibrium (minimum r2 = 0.58). The top 30 SNPs for generalized estimating equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002), SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983 near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a and rs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. CONCLUSION: Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of the associations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication as more genes and biologic pathways are proposed as contributing to longevity and healthy aging

Early-Adulthood Cardiovascular Disease Risk Factor Profiles Among Individuals With and Without Diabetes in the Framingham Heart Study

OBJECTIVE Many studies of diabetes have examined risk factors at the time of diabetes diagnosis instead of considering the lifetime burden of adverse risk factor levels. We examined the 30-year cardiovascular disease (CVD) risk factor burden that participants have up to the time of diabetes diagnosis. RESEARCH DESIGN AND METHODS Among participants free of CVD, incident diabetes cases (fasting plasma glucose ≥126 mg/dL or treatment) occurring at examinations 2 through 8 (1979–2008) of the Framingham Heart Study Offspring cohort were age- and sex-matched 1:2 to controls. CVD risk factors (hypertension, high LDL cholesterol, low HDL cholesterol, high triglycerides, obesity) were measured at the time of diabetes diagnosis and at time points 10, 20, and 30 years prior. Conditional logistic regression was used to compare risk factor levels at each time point between diabetes cases and controls. RESULTS We identified 525 participants with new-onset diabetes who were matched to 1,049 controls (mean age, 60 years; 40% women). Compared with those without diabetes, individuals who eventually developed diabetes had higher levels of hypertension (odds ratio [OR], 2.2; P = 0.003), high LDL (OR, 1.5; P = 0.04), low HDL (OR, 2.1; P = 0.0001), high triglycerides (OR, 1.7; P = 0.04), and obesity (OR, 3.3; P < 0.0001) at time points 30 years before diabetes diagnosis. After further adjustment for BMI, the ORs for hypertension (OR, 1.9; P = 0.02) and low HDL (OR, 1.7; P = 0.01) remained statistically significant. CONCLUSIONS CVD risk factors are increased up to 30 years before diagnosis of diabetes. These findings highlight the importance of a life course approach to CVD risk factor identification among individuals at risk for diabetes

Does adding information on job strain improve risk prediction for coronary heart disease beyond the standard Framingham risk score? The Whitehall II study

Background Guidelines for coronary heart disease (CHD) prevention recommend using multifactorial risk prediction algorithms, particularly the Framingham risk score. We sought to examine whether adding information on job strain to the Framingham model improves its predictive power in a low-risk working population

On the combination of omics data for prediction of binary outcomes

Enrichment of predictive models with new biomolecular markers is an important task in high-dimensional omic applications. Increasingly, clinical studies include several sets of such omics markers available for each patient, measuring different levels of biological variation. As a result, one of the main challenges in predictive research is the integration of different sources of omic biomarkers for the prediction of health traits. We review several approaches for the combination of omic markers in the context of binary outcome prediction, all based on double cross-validation and regularized regression models. We evaluate their performance in terms of calibration and discrimination and we compare their performance with respect to single-omic source predictions. We illustrate the methods through the analysis of two real datasets. On the one hand, we consider the combination of two fractions of proteomic mass spectrometry for the calibration of a diagnostic rule for the detection of early-stage breast cancer. On the other hand, we consider transcriptomics and metabolomics as predictors of obesity using data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort, from Finland

An Obesity Dietary Quality Index Predicts Abdominal Obesity in Women: Potential Opportunity for New Prevention and Treatment Paradigms

Background. Links between dietary quality and abdominal obesity are poorly understood. Objective. To examine the association between an obesity-specific dietary quality index and abdominal obesity risk in women. Methods. Over 12 years, we followed 288 Framingham Offspring/Spouse Study women, aged 30–69 years, without metabolic syndrome risk factors, cardiovascular disease, cancer, or diabetes at baseline. An 11-nutrient obesity-specific dietary quality index was derived using mean ranks of nutrient intakes from 3-day dietary records. Abdominal obesity (waist circumference >88 cm) was assessed during follow-up. Results. Using multiple logistic regression, women with poorer dietary quality were more likely to develop abdominal obesity compared to those with higher dietary quality (OR 1.87; 95% CI, 1.01, 3.47; P for trend = .048) independent of age, physical activity, smoking, and menopausal status. Conclusions. An obesity-specific dietary quality index predicted abdominal obesity in women, suggesting targets for dietary quality assessment, intervention, and treatment to address abdominal adiposity

Genetic Correlates of Longevity and Selected Age-related Phenotypes: A Genome-wide Association Study in the Framingham Study

Background: Family studies and heritability estimates provide evidence for a genetic contribution to variation in the human life span. Methods: We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participants from 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards, logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001). Results: In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10^-5). The two sets of SNPs were in high linkage disequilibrium (minimum r2 = 0.58). The top 30 SNPs for generalized estimating equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002), SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983 near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a and rs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Conclusion: Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of the associations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication as more genes and biologic pathways are proposed as contributing to longevity and healthy aging

A reference relative time-scale as an alternative to chronological age for cohorts with long follow-up

Background: Epidemiologists have debated the appropriate time-scale for cohort survival studies; chronological age or time-on-study being two such time-scales. Importantly, assessment of risk factors may depend on the choice of time-scale. Recently, chronological or attained age has gained support but a case can be made for a ‘reference relative time-scale’ as an alternative which circumvents difficulties that arise with this and other scales. The reference relative time of an individual participant is the integral of a reference population hazard function between time of entry and time of exit of the individual. The objective here is to describe the reference relative time-scale, illustrate its use, make comparison with attained age by simulation and explain its relationship to modern and traditional epidemiologic methods. Results: A comparison was made between two models; a stratified Cox model with age as the time-scale versus an un-stratified Cox model using the reference relative time-scale. The illustrative comparison used a UK cohort of cotton workers, with differing ages at entry to the study, with accrual over a time period and with long follow-up. Additionally, exponential and Weibull models were fitted since the reference relative time-scale analysis need not be restricted to the Cox model. A simulation study showed that analysis using the reference relative time-scale and analysis using chronological age had very similar power to detect a significant risk factor and both were equally unbiased. Further, the analysis using the reference relative time-scale supported fully-parametric survival modelling and allowed percentile predictions and mortality curves to be constructed. Conclusions: The reference relative time-scale was a viable alternative to chronological age, led to simplification of the modelling process and possessed the defined features of a good time-scale as defined in reliability theory. The reference relative time-scale has several interpretations and provides a unifying concept that links contemporary approaches in survival and reliability analysis to the traditional epidemiologic methods of Poisson regression and standardised mortality ratios. The community of practitioners has not previously made this connection

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies

ABSTRACT: BACKGROUND: Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. METHODS: We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. RESULTS: We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. CONCLUSIONS: We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application

Performance of a multianalyte test as an aid for the diagnosis of ovarian cancer in symptomatic women

Background: Concomitant with the development of in vitro diagnostic multivariate index assays (IVDMIAs) to improve the diagnostic efficiency of ovarian cancer detection is the need to identify appropriate biostatistical approaches to assess improvements in risk predication. In this study, we assessed the utility of three different approaches for comparing diagnostic efficiency of an ovarian cancer multivariate assay in a retrospective case control phase 2 biomarker trial. The control cohort included both disease-free women and women with benign gynecological conditions to more accurately reflect the target population of symptomatic women