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Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using “second and third generation clocks,” PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes. [ABSTRACT FROM AUTHOR]

The Health and Retirement Study (HRS) is a nationally representative longitudinal survey of more than 37 000 individuals over age 50 in 23 000 households in the USA. The survey, which has been fielded every 2 years since 1992, was established to provide a national resource for data on the changing health and economic circumstances associated with ageing at both individual and population levels. Its multidisciplinary approach is focused on four broad topics—income and wealth; health, cognition and use of healthcare services; work and retirement; and family connections. HRS data are also linked at the individual level to administrative records from Social Security and Medicare, Veteran’s Administration, the National Death Index and employer-provided pension plan information. Since 2006, data collection has expanded to include biomarkers and genetics as well as much greater depth in psychology and social context. This blend of economic, health and psychosocial information provides unprecedented potential to study increasingly complex questions about ageing and retirement. The HRS has been a leading force for rapid release of data while simultaneously protecting the confidentiality of respondents. Three categories of data—public, sensitive and restricted—can be accessed through procedures described on the HRS website (hrsonline.isr.umich.edu). [ABSTRACT FROM PUBLISHER]

Background: Dyslipidemia, which is characterized by an unfavorable lipid profile, is a key risk factor for cardiovascular disease (CVD). Understanding the relationships between epigenetic aging and lipid levels may help guide early prevention and treatment efforts for dyslipidemia. Methods: We used weighted linear regression to cross-sectionally investigate the associations between five measures of epigenetic age acceleration estimated from whole blood DNA methylation (HorvathAge Acceleration, HannumAge Acceleration, PhenoAge Acceleration, GrimAge Acceleration, and DunedinPACE) and four blood lipid measures (total cholesterol (TC), LDL-C, HDL-C, and triglycerides (TG)) in 3,813 participants (mean age = 70 years) from the Health and Retirement Study (HRS). As a sensitivity analysis, we examined the same associations in participants who fasted prior to the blood draw (n = 2,531) and in participants who did not take lipid-lowering medication (n = 1,869). Using interaction models, we also examined whether demographic factors including age, sex, and educational attainment modified the relationships between epigenetic age acceleration and blood lipids. Results: After adjusting for age, race/ethnicity, sex, fasting status, and lipid-lowering medication use, greater epigenetic age acceleration was associated with lower TC, HDL-C, and LDL-C, and higher TG (p < 0.05), although the effect sizes were relatively small (e.g., < 7 mg/dL of TC per standard deviation in epigenetic age acceleration). GrimAge acceleration and DunedinPACE associations with all lipids remained significant after further adjustment for body mass index, smoking status, and educational attainment. These associations were stronger in participants who fasted and who did not use lipid-lowering medication, particularly for LDL-C. We observed the largest number of interactions between DunedinPACE and demographic factors, where the associations with lipids were stronger in younger participants, females, and those with higher educational attainment. Conclusion: Multiple measures of epigenetic age acceleration are associated with blood lipid levels in older adults. A greater understanding of how these associations differ across demographic groups can help shed light on the relationships between aging and downstream cardiovascular diseases. The inverse associations between epigenetic age and TC and LDL-C could be due to sample limitations or non-linear relationships between age and these lipids, as both TC and LDL-C decrease faster at older ages. [ABSTRACT FROM AUTHOR]

Background: Polygenic scores are a strategy to aggregate the small, additive effects of single nucleotide polymorphisms across the genome. With phenotypes like Alzheimer's disease, which have a strong and well-established genomic locus (APOE), the cumulative effect of genetic variants outside of this area has not been well established in a population-representative sample. Methods: Here we examine the association between polygenic scores for Alzheimer's disease both with and without the APOE region (chr19: 45,384,477 to 45,432,606, build 37/hg 19) at different P value thresholds and dementia. We also investigate the addition of APOE-ε4 carrier status and its effect on the polygenic score—dementia association in the Health and Retirement Study using generalized linear models accounting for repeated measures by individual and use a binomial distribution, logit link, and unstructured correlation structure. Results: In a large sample of European ancestry participants of the Health and Retirement Study (n = 9872) with an average of 5.2 (standard deviation 1.8) visit spaced two years apart, we found that including the APOE region through weighted variants in a polygenic score was insufficient to capture the large amount of risk attributed to this region. We also found that a polygenic score with a P value threshold of 0.01 had the strongest association with the odds of dementia in this sample (odds ratio = 1.10 95%CI 1.0 to 1.2). Conclusion: We recommend removing the APOE region from polygenic score calculation and treating the APOE locus as an independent covariate when modeling dementia. We also recommend using a moderately conservative P value threshold (e.g. 0.01) when creating polygenic scores for Alzheimer's disease on dementia. These recommendations may help elucidate relationships between polygenic scores and regions of strong significance for phenotypes similar to Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Recent genome wide association studies have identified 89 common genetic variants robustly associated with ischemic stroke and primarily located in non-coding regions. To evaluate the contribution of coding variants, which are mostly rare, we performed an exome array analysis on 106,101 SNPs for 9721 ischemic stroke cases from the SiGN Consortium, and 12,345 subjects with no history of stroke from the Health Retirement Study and SiGN consortium. We identified 15 coding variants significantly associated with all ischemic stroke at array-wide threshold (i.e., p < 4.7 × 10−7), including two common SNPs in ABO that have previously been associated with stroke. Twelve of the remaining 13 variants were extremely rare in European Caucasians (MAF < 0.1%) and the associations were driven by African American samples. There was no evidence for replication of these associations in either TOPMed Stroke samples (n = 5613 cases) or UK Biobank (n = 5874 stroke cases), although power to replicate was very low given the low allele frequencies of the associated variants and a shortage of samples from diverse ancestries. Our study highlights the need for acquiring large, well-powered diverse cohorts to study rare variants, and the technical challenges using array-based genotyping technologies for rare variant genotyping. [ABSTRACT FROM AUTHOR]

The epigenome likely interacts with traditional and genetic risk factors to influence blood pressure. We evaluated whether 13 previously reported DNA methylation sites (CpGs) are associated with systolic (SBP) or diastolic (DBP) blood pressure, both individually and aggregated into methylation risk scores (MRS), in 3070 participants (including 437 African ancestry (AA) and 2021 European ancestry (EA), mean age = 70.5 years) from the Health and Retirement Study. Nine CpGs were at least nominally associated with SBP and/or DBP after adjusting for traditional hypertension risk factors (p < 0.05). MRSSBP was positively associated with SBP in the full sample (β = 1.7 mmHg per 1 standard deviation in MRSSBP; p = 2.7 × 10−5) and in EA (β = 1.6; p = 0.001), and MRSDBP with DBP in the full sample (β = 1.1; p = 1.8 × 10−6), EA (β = 1.1; p = 7.2 × 10−5), and AA (β = 1.4; p = 0.03). The MRS and BP-genetic risk scores were independently associated with blood pressure in EA. The effects of both MRSs were weaker with increased age (pinteraction < 0.01), and the effect of MRSDBP was higher among individuals with at least some college education (pinteraction = 0.02). In AA, increasing MRSSBP was associated with higher SBP in females only (pinteraction = 0.01). Our work shows that MRS is a potential biomarker of blood pressure that may be modified by traditional hypertension risk factors. [ABSTRACT FROM AUTHOR]

Background: Cellular changes in adaptive immune system accompany the process of aging and contribute to an aging-related immune phenotype (ARIP) characterized by decrease in naïve T-cells (TN) and increase in memory T-cells (TM). A population-representative marker of ARIP and its associations with biological aging and age-related chronic conditions have not been studied previously. Methods: We developed two ARIP indicators based on well understood age-related changes in T cell distribution: TN/(TCM (Central Memory) + TEM (Effector Memory) + TEFF (Effector)) (referred as TN/TM) in CD4 + and CD8 + T-cells. We compared them with existing ARIP measures including CD4/CD8 ratio and CD8 + TN cells by evaluating associations with chronological age and the Klemera Doubal measure of biological age (measured in years) using linear regression, multimorbidity using multinomial logistic regression and two-year mortality using logistic regression. Results: CD8 + TN and CD8 + TN/TM had the strongest inverse association with chronological age (beta estimates: -3.41 and -3.61 respectively; p-value < 0.0001) after adjustment for sex, race/ethnicity and CMV status. CD4 + TN/TM and CD4 + TN had the strongest inverse association with biological age (β = -0.23; p = 0.003 and β = -0.24; p = 0.004 respectively) after adjustment for age, sex, race/ethnicity and CMV serostatus. CD4/CD8 ratio was not associated with chronological age or biological age. CD4 + TN/TM and CD4 + TN was inversely associated with multimorbidity. For CD4 + TN/TM, people with 2 chronic conditions had an odds ratio of for 0.74 (95%CI: 0.63–0.86 p = 0.0003) compared to those without any chronic conditions while those with 3 chronic conditions had an odds ratio of 0.75 (95% CI: 0.63–0.90; p = 0.003) after adjustment for age, sex, race/ethnicity, CMV serostatus, smoking, and BMI. The results for the CD4 + TN subset were very similar to the associations seen with the CD4 + TN/TM. CD4 + TN/TM and CD4 + TN were both associated with two-year mortality (OR = 0.80 (95% CI: 0.67–0.95; p = 0.01) and 0.81 (0.70–0.94; p = 0.01), respectively). Conclusion: CD4 + TN/TM and CD4 + TN had a stronger association with biological age, age-related morbidity and mortality compared to other ARIP measures. Future longitudinal studies are needed to evaluate the utility of the CD4 + subsets in predicting the risk of aging-related outcomes. [ABSTRACT FROM AUTHOR]

Objectives: This project evaluated if by focusing on process changes and tool use rather than key indicator rates, the use of evidence-based therapies in patients with acute myocardial infarction (AMI) would increase.Background: The use of tools designed to improve quality of care in the American College of Cardiology AMI Guidelines Applied in Practice Pilot Project resulted in improved adherence to evidence-based therapies for patients, but overall, tool use was modest.Methods: The current project, implemented in five hospitals, was modeled after the previous project, but with greater emphasis on tool use. This allowed early identification of barriers to tool use and strategies to overcome barriers. Main outcome measures were AMI quality indicators in pre-measurement (January 1, 2001 to June 30, 2001) and post-measurement (December 15, 2001 to March 31, 2002) samples.Results: One or more tools were used in 93% of patients (standard orders = 82%, and discharge document = 47%). Tool use was associated with significantly higher adherence to most discharge quality indicator rates with increases in aspirin, angiotensin-converting enzyme inhibitors, and smoking cessation and dietary counseling. Patients undergoing coronary artery bypass grafting (CABG) had low rates of discharge indicators. Patients undergoing percutaneous coronary revascularization were more likely to receive evidence-based therapies.Conclusions: These data validate the results of the pilot project that quality of AMI care can be improved through the use of guideline-based tools. Identifying and overcoming barriers to tool use led to substantially higher rates of tool use. The low rates of adherence to quality indicators in patients undergoing CABG suggest that these patients should be particularly targeted for quality improvement efforts. [Copyright &y& Elsevier]

Background: In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years.Methods: For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50-75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell's optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts.Results: The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model's prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity.Conclusions: A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions. [ABSTRACT FROM AUTHOR]

Traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. Therefore, we developed an approach that utilizes a previously published automated algorithm (OpenCyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (FlowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). Our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies. [ABSTRACT FROM AUTHOR]

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10−300, 2.1 × 10−6, 2.5 × 10−10 and 1.8 × 10−10, respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months' less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been. [ABSTRACT FROM AUTHOR]

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective—population neuroscience—that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas. [ABSTRACT FROM AUTHOR]