@article {12601, title = {A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking}, journal = {Nature Aging}, volume = {2}, year = {2022}, pages = {644{\textendash}661}, abstract = {Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show that technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components (PCs) from CpG-level data as input for biological age prediction. Our retrained PC versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or previous knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of PC-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies and clinical trials of aging interventions.}, keywords = {Aging, Bioinformatics, computational models, DNA Methylation, predictive markers}, doi = {10.1038/s43587-022-00248-2}, author = {Higgins-Chen, Albert T. and Thrush, Kyra L. and Wang, Yunzhang and Minteer, Christopher J. and Kuo, Pei-Lun and Wang, Meng and Niimi, Peter and Sturm, Gabriel and Lin, Jue and Ann Zenobia Moore and Bandinelli, Stefania and Vinkers, Christiaan H. and Vermetten, Eric and Rutten, Bart P. F. and Geuze, Elbert and Okhuijsen-Pfeifer, Cynthia and van der Horst, Marte and Schreiter, Stefanie and Gutwinski, Stefan and Luykx, Jurjen J. and Picard, Martin and Ferrucci, Luigi and Eileen M. Crimmins and Boks, Marco P. and H{\"a}gg, Sara and Hu-Seliger, Tina T. and Morgan E. Levine} } @article {12568, title = {Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study.}, journal = {Immunity \& Ageing}, volume = {19}, year = {2022}, pages = {33}, abstract = {

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 na{\"\i}ve T-cells (T) and increase in memory T-cells (T). 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: T/(T (Central Memory) + T (Effector Memory) + T (Effector)) (referred as T/T) 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 + T and CD8 + T/T 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 + T/T and CD4 + T~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 + T/T and CD4 + T was inversely associated with multimorbidity. For CD4 + T/T, 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 + T subset were very similar to the associations seen with the CD4 + T/T. CD4 + T/T and CD4 + T 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 + T/T and CD4 + T 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.

}, keywords = {Adaptive immunity, biological aging, immune aging, multimorbidity}, issn = {1742-4933}, doi = {10.1186/s12979-022-00290-z}, author = {Ramasubramanian, Ramya and Meier, Helen C S and Vivek, Sithara and Klopack, Eric and Eileen M. Crimmins and Jessica Faul and Nikolich-{\v Z}ugich, Janko and Bharat Thyagarajan} } @article {12877, title = {The role of cohabitation on adaptive and innate immune cell profiles in the Health and Retirement Study}, journal = {American Journal of Clinical Pathology}, volume = {158}, year = {2022}, pages = {S2}, abstract = {Immune cells distribution is shaped by numerous factors including environmental factors, age, and genetics. Cohabitation has been associated with similar microbiomes, possibly due to dietary patterns and exposure to similar pathogens but has not been studied in the context of adaptive and innate immune systems previously. We used immunophenotyping data of 2283 households with participants living in the same household and compared it to 2283 randomly generated pairs of participants from the Health and Retirement study. The adaptive immune cells (subsets of T-cells and B-cells), and innate immune cells (monocytes, natural killer cells, and neutrophils) were compressed to two coordinates using multidimensional scaling. The Euclidean distances between participants in the same household were compared to the distances between the random pairs of participants using two sample independent t-tests. The mean distances of the immune coordinate points for adaptive immune cells between participants in the same household were lower than the randomly paired participants (p-value \< 0.0001) and the variability of intra-household distances was lower than the random pairs (IQR: 7.18 vs 8.99). For the innate immune cells, the mean distances between participants in the same household were slightly lower than the randomly paired participants (p-value = 0.03) but the variability of the intra-household distances was higher than the random pairs (IQR: 4.08 vs 3.65). Variability in the adaptive immune system among participants living in the same household were substantially lower indicating the influence of shared environmental conditions in determining the adaptive immune profiles.}, keywords = {adaptive immune cells, Cohabitation, Households}, doi = {10.1093/ajcp/aqac126.002}, author = {Ramasubramanian, Ramya and Meier, Helen and Eileen M. Crimmins and Jessica Faul and Bharat Thyagarajan} } @inbook {Laditka2020, title = {Microsimulation of Health Expectancies, Life Course Health, and Health Policy Outcomes}, booktitle = {International Handbooks of Population: International Handbook of Health Expectancies}, volume = {9}, year = {2020}, pages = {129{\textendash}138}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Basel}, abstract = {Active life expectancy measures life expectancy and the proportions of remaining life with and without disease or disability. Microsimulation, a useful tool for life course research, estimates active life expectancy by simulating individual lifetime health biographies, where the individual{\textquoteright}s status in one or more outcomes is known for each measured unit of life. In this chapter we describe how researchers use microsimulation to study active life expectancy, focusing on research of the past 20 years. We summarize the microsimulation process. We describe how researchers model current and future population health, calculate new active life expectancy measures, and forecast effects of policy change. We illustrate the application of microsimulation to active life expectancy research with a study of interval need, a measure of need for health care and other services focused on resource use. We describe strengths of microsimulation, considerations regarding its use, and directions for future research.}, keywords = {Active life expectancy, Forecasting, Health expectancy, health policy, Population Health}, isbn = {978-3-030-37668-0}, doi = {10.1007/978-3-030-37668-0_9}, author = {Laditka, Sarah B. and Laditka, James N. and Jagger, Carol}, editor = {Jagger, Carol and Eileen M. Crimmins and Saito, Yasuhiko and De Carvalho Yokota, Renata Tiene and Van Oyen, Herman and Robine, Jean-Marie} } @article {10828, title = {Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study}, journal = {Scientific Reports}, volume = {10}, year = {2020}, pages = {8759}, abstract = {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.}, keywords = {Bioinformatics, high-throughput screening}, isbn = {2045-2322}, doi = {10.1038/s41598-020-65016-x}, author = {Hunter-Schlichting, DeVon and Lane, John and Cole, Benjamin and Flaten, Zachary and Barcelo, Helene and Ramasubramanian, Ramya and Cassidy, Erin and Jessica Faul and Eileen M. Crimmins and Pankratz, Nathan and Bharat Thyagarajan} } @article {Robinette388, title = {Differential vulnerability to neighbourhood disorder: a gene{\texttimes}environment interaction study}, journal = {Journal of Epidemiology \& Community Health}, volume = {73}, year = {2019}, pages = {388{\textendash}392}, abstract = {Background Type 2 diabetes (T2D) is preventable, it is increasing in prevalence and it is a major risk factor for morbidity and mortality. Importantly, residents of neighbourhoods with high levels of disorder are more likely to develop T2D than those living in less disordered neighbourhoods and neighbourhood disorder may exacerbate genetic risk for T2D.Method We use genetic, self-reported neighbourhood, and health data from the Health and Retirement Study. We conducted weighted logistic regression analyses in which neighbourhood disorder, polygenic scores for T2D and their interaction predicted T2D.Results Greater perceptions of neighbourhood disorder (OR=1.11, p\<0.001) and higher polygenic scores for T2D (OR=1.42, p\<0.001) were each significantly and independently associated with an increased risk of T2D. Furthermore, living in a neighbourhood perceived as having high levels of disorder exacerbated genetic risk for T2D (OR=1.10}, keywords = {Diabetes, Genetics, neighborhood}, issn = {0143-005X}, doi = {10.1136/jech-2018-211373}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6935762/}, author = {Jennifer W Robinette and Jason D Boardman and Eileen M. Crimmins} } @article {9960, title = {Differential vulnerability to neighbourhood disorder: a gene{\texttimes}environment interaction study}, journal = {Journal of Epidemiology and Community Health}, volume = {73}, year = {2019}, month = {05/2019}, pages = {388-392}, abstract = {Background: Type 2 diabetes (T2D) is preventable, it is increasing in prevalence and it is a major risk factor for morbidity and mortality. Importantly, residents of neighbourhoods with high levels of disorder are more likely to develop T2D than those living in less disordered neighbourhoods and neighbourhood disorder may exacerbate genetic risk for T2D. Method: We use genetic, self-reported neighbourhood, and health data from the Health and Retirement Study. We conducted weighted logistic regression analyses in which neighbourhood disorder, polygenic scores for T2D and their interaction predicted T2D. Results: Greater perceptions of neighbourhood disorder (OR=1.11, p<0.001) and higher polygenic scores for T2D (OR=1.42, p<0.001) were each significantly and independently associated with an increased risk of T2D. Furthermore, living in a neighbourhood perceived as having high levels of disorder exacerbated genetic risk for T2D (OR=1.10, p=0.001). This significant gene{\texttimes}environment interaction was observed after adjusting for years of schooling, age, gender, levels of physical activity and obesity. Conclusion: Findings in the present study suggested that minimising people{\textquoteright}s exposure to vandalism, vacant buildings, trash and circumstances viewed by residents as unsafe may reduce the burden of this prevalent chronic health condition, particularly for subgroups of the population who carry genetic liability for T2D.}, keywords = {Diabetes, Genetics, Neighborhoods}, issn = {0143-005X}, doi = {10.1136/jech-2018-211373}, url = {http://jech.bmj.com/lookup/doi/10.1136/jech-2018-211373https://syndication.highwire.org/content/doi/10.1136/jech-2018-211373}, author = {Jennifer W Robinette and Jason D Boardman and Eileen M. Crimmins} } @article {10596, title = {Perceived neighborhood social cohesion and cardiometabolic risk: a gene x environment study}, journal = {BIODEMOGRAPHY AND SOCIAL BIOLOGY}, volume = {65}, year = {2019}, pages = {1-15}, abstract = {People living in socially cohesive neighborhoods generally have better health. We extend this research by evaluating the hypothesis that perceived neighborhood cohesion may influence health by attenuating genetic liability for cardiometabolic risk factors. Using data from the Health and Retirement Study (n = 6615; mean age 69.7), we conducted a gene x environment interaction study hypothesizing that perceived neighborhood cohesion would attenuate the link between polygenic scores for waist-to-hip ratio (WHR) and body mass index and a measure of multisystem cardiometabolic risk (systolic and diastolic blood pressure, heart rate, A1c, C-reactive protein, and total and high-density lipoprotein cholesterol). In support of the hypothesis, results indicated that among people perceiving low neighborhood cohesion, higher WHR polygenic scores were associated with greater cardiometabolic risk. In contrast, the genetic-cardiometabolic risk link was much attenuated among those living in neighborhoods perceived as socially cohesive. Our results support community-level interventions to enhance the social cohesiveness of individuals{\textquoteright} neighborhoods which may provide health benefits by reducing the risks associated with known genetic risk factors.}, keywords = {BODY-MASS INDEX; BLOOD-PRESSURE; OBESITY; DETERMINANTS; PREDICTION;}, issn = {1948-5565}, doi = {10.1080/19485565.2019.1568672}, author = {Jennifer W Robinette and Jason D Boardman and Eileen M. Crimmins} } @article {8936, title = {Clinical Trials Targeting Aging and Age-Related Multimorbidity}, journal = {The Journals of Gerontology Series A: Biological Sciences and Medical Sciences}, volume = {72}, year = {2017}, pages = {355-361}, abstract = {Background: There is growing interest in identifying interventions that may increase health span by targeting biological processes underlying aging. The design of efficient and rigorous clinical trials to assess these interventions requires careful consideration of eligibility criteria, outcomes, sample size, and monitoring plans. Methods: Experienced geriatrics researchers and clinical trialists collaborated to provide advice on clinical trial design. Results: Outcomes based on the accumulation and incidence of age-related chronic diseases are attractive for clinical trials targeting aging. Accumulation and incidence rates of multimorbidity outcomes were developed by selecting at-risk subsets of individuals from three large cohort studies of older individuals. These provide representative benchmark data for decisions on eligibility, duration, and assessment protocols. Monitoring rules should be sensitive to targeting aging-related, rather than disease-specific, outcomes. Conclusions: Clinical trials targeting aging are feasible, but require careful design consideration and monitoring rules.}, keywords = {Chronic disease, Clinical trials, Older Adults}, issn = {1079-5006}, doi = {10.1093/gerona/glw220}, url = {https://academic.oup.com/biomedgerontology/article-lookup/doi/10.1093/gerona/glw220https://academic.oup.com/biomedgerontology/article/2328606/Clinical-Trials-Targeting-Aging-and-AgeRelated}, author = {Mark A. Espeland and Eileen M. Crimmins and Brandon R. Grossardt and Jill P. Crandall and Jonathan A. L. Gelfond and Tamara B Harris and Stephen B Kritchevsky and JoAnn E Manson and Jennifer G Robinson and Walter A Rocca and Temprosa, Marinella and Thomas, Fridtjof and Robert B Wallace and Barzilai, Nir} } @inbook {5244, title = {Physical and biological indicators of health and functioning in U.S. oldest old}, booktitle = {Annual Review of Gerontology and Geriatrics}, volume = {33}, year = {2013}, pages = {193-215}, publisher = {Springer}, organization = {Springer}, address = {New York}, abstract = {This chapter examines biomarkers of aging, including indicators of physical performance and biomarkers of physiological dysregulation, among a representative national sample of U.S. adults aged 80 years and older with the aim of addressing three questions about the oldest old U.S. population: (a) How do levels of biomarkers of aging vary by age? (b) Are biomarkers of aging patterned by gender, race, ethnicity, and education? and (c) Which biomarkers of aging predict health and mortality. Data for this study come from the Health and Retirement Study (HRS). This large population-based study of U.S. adults aged 80 years and older provides confirmation of the importance of biomarkers of aging for understanding health and longevity in the oldest old. The results indicate that physical functioning declines across age groups even among the oldest old, and that the oldest adults are more likely to have levels of physical performance, inflammation, and organ function that are considered to be high risk for poor health outcomes. In addition, social disparities in physical functioning continue to be evident among the oldest segment of the U.S. population. We also provide evidence for the value of several biomarkers of aging in predicting poor health outcomes among the oldest old. In particular, indicators of high risk for walking dysfunction and dysregulation in the lungs and kidneys were found to predict hospitalization and short-term mortality.}, keywords = {End of life decisions, Health Conditions and Status, Healthcare}, author = {Jennifer A Ailshire and Eileen M. Crimmins}, editor = {Robine, Jean-Marie and Jagger, Carol and Eileen M. Crimmins} } @article {7561, title = {Do biological measures mediate the relationship between education and health: A comparative study.}, journal = {Soc Sci Med}, volume = {72}, year = {2011}, month = {2011 Jan}, pages = {307-15}, publisher = {72}, abstract = {

Despite a myriad of studies examining the relationship between socioeconomic status and health outcomes, few have assessed the extent to which biological markers of chronic disease account for social disparities in health. Studies that have examined this issue have generally been based on surveys in wealthy countries that include a small set of clinical markers of cardiovascular disease. The availability of recent data from nationally representative surveys of older adults in Costa Rica and Taiwan that collected a rich set of biomarkers comparable to those in a recent US survey permits us to explore these associations across diverse populations. Similar regression models were estimated on three data sets - the Social Environment and Biomarkers of Aging Study in Taiwan, the Costa Rican Study on Longevity and Healthy Aging, and the Health and Retirement Study in the USA - in order to assess (1) the strength of the associations between educational attainment and a broad range of biomarkers; and (2) the extent to which these biomarkers account for the relationships between education and two measures of health status (self-rated health, functional limitations) in older populations. The estimates suggest non-systematic and weak associations between education and high risk biomarker values in Taiwan and Costa Rica, in contrast to generally negative and significant associations in the US, especially among women. The results also reveal negligible or modest contributions of the biomarkers to educational disparities in the health outcomes. The findings are generally consistent with previous research suggesting stronger associations between socioeconomic status and health in wealthy countries than in middle-income countries and may reflect higher levels of social stratification in the US. With access to an increasing number of longitudinal biosocial surveys, researchers may be better able to distinguish true variations in the relationship between socioeconomic status and health across different settings from methodological differences.

}, keywords = {Biomarkers, Blood pressure, Body Mass Index, Cholesterol, Costa Rica, Educational Status, Female, Health Status Disparities, Health Status Indicators, Health Surveys, Humans, Male, Middle Aged, Qualitative Research, Taiwan, United States}, issn = {1873-5347}, doi = {10.1016/j.socscimed.2010.11.004}, author = {Goldman, Noreen and Cassio M. Turra and Rosero-Bixby, Luis and David R Weir and Eileen M. Crimmins} } @inbook {5246, title = {Links Between Biomarkers and Mortality}, booktitle = {International Handbook of Adult Mortality}, volume = {2}, year = {2011}, note = {Times Cited: 2}, pages = {381-398}, publisher = {Springer}, organization = {Springer}, abstract = {A biomarker is an objectively measured indicator of a physiological state. Biomarkers include indicators of genotype, normal biological processes, pathogenic processes, and pharmacologic responses to a therapeutic intervention (Biomarkers Definitions Working Group 2001; National Heart Lung and Blood Institute 2007). Biomarkers can serve as objective indicators of health status within a sample, indicators of health change over time, and, with comparable measurement, indicators of differences across populations. They signal disease status, early physiological dysregulation preceding disease, or change in organ reserve or functioning. And they can clarify how the social, psychological, and behavioral factors traditionally examined in social science research get under the skin to influence biology and subsequent health outcomes (Crimmins and Seeman 2001, 2004; Crimmins et al. 2008a; Seeman and Crimmins 2001).}, keywords = {Demographics, Health Conditions and Status, Healthcare}, isbn = {978-90-481-9995-2}, doi = {10.1007/978-90-481-9996-9_18}, author = {Eileen M. Crimmins and Vasunilashorn, Sarinnapha}, editor = {Rebecca G Rogers and Eileen M. Crimmins} } @article {7026, title = {The impact of obesity on active life expectancy in older American men and women.}, journal = {Gerontologist}, volume = {45}, year = {2005}, month = {2005 Aug}, pages = {438-44}, publisher = {45}, abstract = {

PURPOSE: The purpose of this article is to estimate the effect of obesity on both the length of life and length of nondisabled life for older Americans.

DESIGN AND METHODS: Using data from the first 3 waves of the Asset and Health Dynamics Among the Oldest Old (AHEAD) survey, this article develops estimates of total, active, and disabled life expectancy for obese and nonobese older men and women. We used the Interpolation of Markov Chains (IMaCh) method to estimate the average number of years obese and nonobese older persons can expect to live with and without activity of daily living (ADL) disability.

RESULTS: Our findings indicate that obesity has little effect on life expectancy in adults aged 70 years and older. However, the obese are more likely to become disabled. This means that obese older adults live both more years and a higher proportion of their remaining lives disabled.

IMPLICATIONS: The lack of significant differences in life expectancy by obesity status among the old suggests that obesity-related death is less of a concern than disability in this age range. Given steady increases in obesity among Americans at all ages, future disability rates may be higher than anticipated among older U.S. adults. In order to reduce disability among future cohorts of older adults, more research is needed on the causes and treatment of obesity and evaluations done on interventions to accomplish and maintain weight loss.

}, keywords = {Activities of Daily Living, Aged, Female, Health Status, Health Surveys, Humans, Life Expectancy, Life Tables, Logistic Models, Male, Markov chains, Obesity, Risk Factors, United States}, issn = {0016-9013}, doi = {10.1093/geront/45.4.438}, author = {Sandra L Reynolds and Saito, Yasuhiko and Eileen M. Crimmins} } @article {6610, title = {Do medical conditions affect cognition in older adults?}, journal = {Health Psychol}, volume = {17}, year = {1998}, month = {1998 Nov}, pages = {504-12}, publisher = {17}, abstract = {

Analyses of a nationally representative sample who completed a list recall task (weighted n = 6,446) and 2 mental status tasks (weighted n = 6,646) were conducted to determine whether specific medical conditions such as high blood pressure and diabetes as well as general health ratings predict cognitive performance in adults aged 70 to 103. Presence of stroke and poorer health ratings predicted poorer performance on the 3 tasks. Presence of diabetes predicted poorer performance on recall and 1 mental status task. Age interacted with medical conditions including high blood pressure and diabetes in predicting mental status, with condition-related deficits confined to the younger end of the age continuum. Global health ratings interacted with age, with poorer ratings associated with worse mental status in the younger participants. Findings suggest that stroke and diabetes are associated with cognitive deficits. Some deficits are more pronounced in younger old adults with high blood pressure and poorer health ratings.

}, keywords = {Aged, Aged, 80 and over, Aging, Cerebrovascular Disorders, Cognition, Diabetes Complications, Diabetes Mellitus, Female, Health Status, Humans, Hypertension, Male, Mental Health}, issn = {0278-6133}, doi = {10.1037//0278-6133.17.6.504}, url = {https://pubmed.ncbi.nlm.nih.gov/9848800/}, author = {Elizabeth Zelinski and Eileen M. Crimmins and Sandra L Reynolds and Teresa Seeman} }