Biodemography became one of the most innovative and fastest growing areas in demography. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent methodological advances in biodemography and their diverse practical applications. Three major topics are covered: (1) computational approaches to reconstruction of age patterns of incidence of geriatric diseases and other characteristics such as recovery rates at the population level using Medicare claims data; (2) methodological advances in genetic and genomic biodemography and applications to research on genetic determinants of longevity and health; and (3) biodemographic models for joint analyses of time-to-event data and longitudinal measurements of biomarkers collected in longitudinal studies on aging. We discuss how such data and methodology can be used in a comprehensive prediction model for joint analyses of incomplete datasets that take into account the wide spectrum of factors affecting health and mortality transitions including genetic factors and hidden mechanisms of aging-related changes in physiological variables in their dynamic connection with health and survival. 1. Introduction The field of biodemography focuses on development and applications of analytic approaches aimed at integrating biological knowledge and traditional demographic methods to investigate variability in mortality and morbidity across populations and between individuals. Biodemography of aging, in particular, investigates the impact of aging on longevity and health. Although biodemography is a relatively young scientific discipline, it rapidly became one of the most innovative and fastest growing areas in demography with a history of substantial achievements up to date and with great opportunities and new promises for the future [1–8]. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent publications by our research group that contributed both to methodological advances in biodemography and their diverse practical applications. Three major topics are covered. Section 2.1 discusses recent developments in computational approaches
References
[1]
J. R. Carey and J. W. Vaupel, “Biodemography,” in Handbook of Population, D. Poston and M. Micklin, Eds., pp. 625–658, Kluwer Academic/Plenum Publishers, New York, NY, USA, 2005.
[2]
J. R. Carey, “Biodemography: research prospects and directions,” Demographic Research, vol. 19, article 50, pp. 1749–1757, 2008.
[3]
K. Christensen, “Human biodemography: some challenges and possibilities for aging research,” Demographic Research, vol. 19, no. 43, pp. 1575–1586, 2008.
[4]
H. Kaplan and M. Gurven, “Top-down and bottom-up research in biodemography,” Demographic Research, vol. 19, article 44, pp. 1587–1602, 2008.
[5]
S. Vasunilashorn and E. M. Crimmins, “Biodemography: integrating disciplines to explain aging,” in Handbook of Theories of Aging, V. L. Bengtson, D. Gans, N. M. Putney, and M. Silverstein, Eds., pp. 63–85, Springer, New York, NY, USA, 2008.
[6]
K. W. Wachter, “Biodemography comes of age,” Demographic Research, vol. 19, article 40, pp. 1501–1512, 2008.
[7]
E. Crimmins, J. K. Kim, and S. Vasunilashorn, “Biodemography: new approaches to understanding trends and differences in population health and mortality,” Demography, vol. 47, no. 1, pp. S41–S64, 2010.
[8]
J. W. Vaupel, “Biodemography of human ageing,” Nature, vol. 464, no. 7288, pp. 536–542, 2010.
[9]
C. DeNavas-Walt, B. D. Proctor, and J. C. Smith, U.S. Census Bureau, Current Population Reports, P60-243, Income, Poverty, and Health Insurance Coverage in the United States: 2011, U.S. Government Printing Office, Washington, DC, USA, 2012.
[10]
I. Akushevich, J. Kravchenko, S. Ukraintseva, K. Arbeev, and A. I. Yashin, “Age patterns of incidence of geriatric disease in the U.S. elderly population: medicare-based analysis,” Journal of the American Geriatrics Society, vol. 60, no. 2, pp. 323–327, 2012.
[11]
I. Akushevich, J. Kravchenko, S. Ukraintseva, K. Arbeev, and A. Yashin, “Population-based analysis of incidence rates of cancer and noncancer chronic diseases in the US elderly using NLTCS/medicare-linked database,” ISRN Geriatrics, vol. 2013, Article ID 943418, 15 pages, 2013.
[12]
I. Akushevich, J. Kravchenko, S. Ukraintseva, K. Arbeev, and A. I. Yashin, “Circulatory diseases in the U.S. elderly in the linked national long-term care survey-medicare satabase: population-based analysis of incidence, comorbidity, and disability,” Research on Aging, vol. 35, no. 4, pp. 437–458, 2013.
[13]
I. Akushevich, J. Kravchenko, S. Ukraintseva, K. Arbeev, and A. I. Yashin, “Time trends of incidence of age-associated diseases in the US elderly population: Medicare-based analysis,” Age and Ageing, vol. 42, no. 4, pp. 494–500, 2013.
[14]
C. F. Mendes de Leon, D. T. Gold, T. A. Glass, L. Kaplan, and L. K. George, “Disability as a function of social networks and support in elderly African Americans and whites: the duke EPESE 1986-1992,” Journals of Gerontology B: Psychological Sciences and Social Sciences, vol. 56, no. 3, pp. S179–S190, 2001.
[15]
L. C. Giles, P. A. Metcalf, G. F. V. Glonek, M. A. Luszcz, and G. R. Andrews, “The effects of social networks on disability in older Australians,” Journal of Aging and Health, vol. 16, no. 4, pp. 517–538, 2004.
[16]
M.-A. Escobar-Bravo, D. Puga-González, and M. Martín-Baranera, “Protective effects of social networks on disability among older adults in Spain,” Archives of Gerontology and Geriatrics, vol. 54, no. 1, pp. 109–116, 2012.
[17]
S. V. Ukraintseva, K. G. Arbeev, I. Akushevich et al., “Trade-offs between cancer and other diseases: do they exist and influence longevity?” Rejuvenation Research, vol. 13, no. 4, pp. 387–396, 2010.
[18]
R. Tabarés-Seisdedos, N. Dumont, A. Baudot et al., “No paradox, no progress: inverse cancer comorbidity in people with other complex diseases,” The Lancet Oncology, vol. 12, no. 6, pp. 604–608, 2011.
[19]
J. A. Driver, A. Beiser, R. Au et al., “Inverse association between cancer and Alzheimer's disease: results from the Framingham Heart Study,” British Medical Journal, vol. 344, Article ID e1442, 2012.
[20]
I. Akushevich, J. Kravchenko, S. Ukraintseva, K. Arbeev, A. Kulminski, and A. I. Yashin, “Morbidity risks among older adults with pre-existing age-related diseases,” Experimental Gerontology, vol. 48, no. 12, pp. 1395–1401, 2013.
[21]
A. I. Yashin, S. V. Ukraintseva, I. V. Akushevich, K. G. Arbeev, A. Kulminski, and L. Akushevich, “Trade-off between cancer and aging: what role do other diseases play? Evidence from experimental and human population studies,” Mechanisms of Ageing and Development, vol. 130, no. 1-2, pp. 98–104, 2009.
[22]
A. M. Kulminski, I. Culminskaya, S. V. Ukraintseva et al., “Trade-off in the effects of the apolipoprotein E polymorphism on the ages at onset of CVD and cancer influences human lifespan,” Aging Cell, vol. 10, no. 3, pp. 533–541, 2011.
[23]
I. Akushevich, J. Kravchenko, S. Ukraintseva, K. Arbeev, and A. I. Yashin, “Recovery and survival from aging-associated diseases,” Experimental Gerontology, vol. 48, no. 8, pp. 824–830, 2013.
[24]
A. Yashin, I. Akushevich, S. Ukraintseva, L. Akushevich, K. Arbeev, and A. Kulminski, “Trends in survival and recovery from stroke: evidence from the national long-term care survey/medicare data,” Stroke, vol. 41, no. 3, pp. 563–565, 2010.
[25]
C. D. Mathers and J. M. Robine, “How good is Sullivan's method for monitoring changes in population health expectancies,” Journal of Epidemiology and Community Health, vol. 51, no. 1, pp. 80–86, 1997.
[26]
H. Putter, M. Fiocco, and R. B. Geskus, “Tutorial in biostatistics: competing risks and multi-state models,” Statistics in Medicine, vol. 26, no. 11, pp. 2389–2430, 2007.
[27]
D. P. Goldman, P. G. Shekelle, J. Bhattacharya, et al., “Health status and medical treatment of the future elderly,” Final Report TR-169-CMS, RAND Corporation, Santa Monica, Calif, USA, 2004.
[28]
J. Lubitz, “Health, technology, and medical care spending,” Health Affairs, vol. 24, pp. W5R81–W85R85, 2005.
[29]
I. Akushevich, J. Kravchenko, L. Akushevich, S. Ukraintseva, K. Arbeev, and A. I. Yashin, “Medical cost trajectories and onsets of cancer and noncancer diseases in US elderly population,” Computational and Mathematical Methods in Medicine, vol. 2011, Article ID 857892, 14 pages, 2011.
[30]
R. Suzman, “Prologue: research on the demography and economics of aging,” Demography, vol. 47, no. 1, pp. S1–S4, 2010.
[31]
A. I. Yashin, G. De Benedictis, J. W. Vaupel et al., “Genes, demography, and life span: the contribution of demographic data in genetic studies on aging and longevity,” American Journal of Human Genetics, vol. 65, no. 4, pp. 1178–1193, 1999.
[32]
A. I. Yashin, G. de Benedictis, J. W. Vaupel et al., “Genes and longevity: lessons from studies of centenarians,” Journals of Gerontology A: Biological Sciences and Medical Sciences, vol. 55, no. 7, pp. B319–B328, 2000.
[33]
K. G. Arbeev, S. V. Ukraintseva, L. S. Arbeeva, I. Akushevich, A. M. Kulminski, and A. I. Yashin, “Evaluation of genotype-specific survival using joint analysis of genetic and non-genetic subsamples of longitudinal data,” Biogerontology, vol. 12, no. 2, pp. 157–166, 2011.
[34]
A. I. Yashin, K. G. Arbeev, and D. Wu, “How the quality of GWAS of human lifespan and health span can be improved,” Frontiers in Genetics, vol. 4, p. 125, 2013.
[35]
J. Hardy and A. Singleton, “Genomewide association studies and human disease,” The New England Journal of Medicine, vol. 360, no. 17, pp. 1759–1768, 2009.
[36]
K. L. Lunetta, R. B. D'Agostino Sr., D. Karasik et al., “Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the framingham study,” BMC Medical Genetics, vol. 8, no. 1, article S13, 2007.
[37]
A. B. Newman, S. Walter, K. L. Lunetta et al., “A Meta-analysis of four genome-wide association studies of survival to age 90 years or older: the cohorts for heart and aging research in genomic epidemiology consortium,” Journals of Gerontology A: Biological Sciences and Medical Sciences, vol. 65, no. 5, pp. 478–487, 2010.
[38]
S. Walter, G. Atzmon, E. W. Demerath, et al., “A genome-wide association study of aging,” Neurobiology of Aging, vol. 32, no. 11, pp. 2109.e15–2109.e28, 2011.
[39]
J. Deelen, M. Beekman, H. W. Uh et al., “Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited,” Aging Cell, vol. 10, no. 4, pp. 686–698, 2011.
[40]
A. Nebel, R. Kleindorp, A. Caliebe et al., “A genome-wide association study confirms APOE as the major gene influencing survival in long-lived individuals,” Mechanisms of Ageing and Development, vol. 132, no. 6-7, pp. 324–330, 2011.
[41]
B. Maher, “Personal genomes: the case of the missing heritability,” Nature, vol. 456, no. 7218, pp. 18–21, 2008.
[42]
T. A. Manolio, F. S. Collins, N. J. Cox et al., “Finding the missing heritability of complex diseases,” Nature, vol. 461, no. 7265, pp. 747–753, 2009.
[43]
M. Slatkin, “Epigenetic inheritance and the missing heritability problem,” Genetics, vol. 182, no. 3, pp. 845–850, 2009.
[44]
P. M. Visscher, W. G. Hill, and N. R. Wray, “Heritability in the genomics era—concepts and misconceptions,” Nature Reviews Genetics, vol. 9, no. 4, pp. 255–266, 2008.
[45]
A. I. Yashin, D. Q. Wu, K. G. Arbeev, and S. V. Ukraintseva, “Joint influence of small-effect genetic variants on human longevity,” Aging, vol. 2, no. 9, pp. 612–620, 2010.
[46]
A. B. Mitnitski, A. J. Mogilner, and K. Rockwood, “Accumulation of deficits as a proxy measure of aging,” TheScientificWorldJournal, vol. 1, pp. 323–336, 2001.
[47]
A. Kulminski, A. Yashin, S. Ukraintseva et al., “Accumulation of health disorders as a systemic measure of aging: findings from the NLTCS data,” Mechanisms of Ageing and Development, vol. 127, no. 11, pp. 840–848, 2006.
[48]
A. I. Yashin, K. G. Arbeev, A. Kulminski, I. Akushevich, L. Akushevich, and S. V. Ukraintseva, “Health decline, aging and mortality: how are they related?” Biogerontology, vol. 8, no. 3, pp. 291–302, 2007.
[49]
A. I. Yashin, D. Wu, K. G. Arbeev, E. Stallard, K. C. Land, and S. V. Ukraintseva, “How genes influence life span: the biodemography of human survival,” Rejuvenation Research, vol. 15, no. 4, pp. 374–380, 2012.
[50]
B. L. Strehler and A. S. Mildvan, “General theory of mortality and aging,” Science, vol. 132, pp. 14–21, 1960.
[51]
J. W. Vaupel and A. I. Yashin, “Repeated resuscitation: how lifesaving alters life tables,” Demography, vol. 24, no. 1, pp. 123–135, 1987.
[52]
A. I. Yashin, D. Wu, K. G. Arbeev, and S. V. Ukraintseva, “Polygenic effects of common single-nucleotide polymorphisms on life span: when association meets causality,” Rejuvenation Research, vol. 15, no. 4, pp. 381–394, 2012.
[53]
A. M. Kulminski, I. Culminskaya, K. G. Arbeev, S. V. Ukraintseva, L. Arbeeva, and A. I. Yashin, “Trade-off in the effect of the APOE gene on the ages at onset of cardiocascular disease and cancer across ages, gender, and human generations,” Rejuvenation Research, vol. 16, no. 1, pp. 28–34, 2013.
[54]
A. M. Kulminski, K. G. Arbeev, K. Christensen et al., “Biogenetic mechanisms predisposing to complex phenotypes in parents may function differently in their children,” The Journals of Gerontology A: Biological sciences and medical sciences, vol. 68, no. 7, pp. 760–768, 2013.
[55]
A. M. Kulminski, I. Culminskaya, K. G. Arbeev et al., “The role of lipid-related genes, aging-related processes, and environment in healthspan,” Aging Cell, vol. 12, no. 2, pp. 237–246, 2013.
[56]
A. I. Yashin, K. G. Arbeev, S. V. Ukraintseva, I. Akushevich, and A. Kulminski, “Patterns of aging-related changes on the way to 100: an approach to studying aging, mortality, and longevity from longitudinal data,” The North American Actuarial Journal, vol. 16, no. 4, pp. 403–433, 2012.
[57]
A. I. Yashin, I. V. Akushevich, K. G. Arbeev, L. Akushevich, S. V. Ukraintseva, and A. Kulminski, “Insights on aging and exceptional longevity from longitudinal data: novel findings from the Framingham heart study,” Age, vol. 28, no. 4, pp. 363–374, 2006.
[58]
K. G. Arbeev, S. V. Ukraintseva, I. Akushevich et al., “Age trajectories of physiological indices in relation to healthy life course,” Mechanisms of Ageing and Development, vol. 132, no. 3, pp. 93–102, 2011.
[59]
A. I. Yashin, K. G. Arbeev, D. Wu et al., “How lifespan associated genes modulate aging changes: lessons from analysis of longitudinal data,” Frontiers in Genetics, vol. 4, article 3, 2013.
[60]
A. I. Yashin, K. G. Arbeev, S. V. Ukraintseva, I. Akushevich, and A. Kulminski, Patterns of Aging Related Changes on the Way to 100: An Approach to Studying Aging, Mortality, and Longevity from Longitudinal Data. 2011 Living to 100 Monograph, Society of Actuaries Monograph M-LI11-1, Schaumburg, Ill, USA, 2011.
[61]
A. I. Yashin, K. G. Arbeev, I. Akushevich et al., “Dynamic determinants of longevity and exceptional health,” Current Gerontology and Geriatrics Research, vol. 2010, Article ID 381637, 13 pages, 2010.
[62]
K. G. Arbeev, S. V. Ukraintseva, and A. M. Kulminski, “Effect of the APOE polymorphism and age trajectories of physiological variables on mortality: application of genetic stochastic process model of aging,” Scientifica, vol. 2012, Article ID 568628, 14 pages, 2012.
[63]
R. L. Prentice, “Covariate measurement errors and parameter estimation in a failure time regression model,” Biometrika, vol. 69, no. 2, pp. 331–342, 1982.
[64]
M. J. Sweeting and S. G. Thompson, “Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture,” Biometrical Journal, vol. 53, no. 5, pp. 750–763, 2011.
[65]
A. A. Tsiatis and M. Davidian, “Joint modeling of longitudinal and time-to-event data: an overview,” Statistica Sinica, vol. 14, no. 3, pp. 809–834, 2004.
[66]
P. J. Diggle, I. Sousa, and A. G. Chetwynd, “Joint modelling of repeated measurements and time-to-event outcomes: the fourth Armitage lecture,” Statistics in Medicine, vol. 27, no. 16, pp. 2981–2998, 2008.
[67]
J. G. Ibrahim, H. Chu, and L. M. Chen, “Basic concepts and methods for joint models of longitudinal and survival data,” Journal of Clinical Oncology, vol. 28, no. 16, pp. 2796–2801, 2010.
[68]
I. Sousa, “A review on joint modelling of longitudinal measurements and time-to-event,” RevStat: Statistical Journal, vol. 9, no. 1, pp. 57–81, 2011.
[69]
L. Wu, W. Liu, G. Y. Yi, and Y. Huang, “Analysis of longitudinal and survival data: joint modeling, inference methods, and issues,” Journal of Probability and Statistics, vol. 2012, Article ID 640153, 17 pages, 2012.
[70]
D. Rizopoulos, Joint Models for Longitudinal and Time-to-Event Data with Applications in R, Chapman and Hall/CRC, Boca Raton, Fla, USA, 2012.
[71]
T. E. Seeman, B. S. McEwen, J. W. Rowe, and B. H. Singer, “Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 8, pp. 4770–4775, 2001.
[72]
J. A. Troncale, “The aging process: physiologic changes and pharmacologic implications,” Postgraduate Medicine, vol. 99, no. 5, pp. 111–114, 1996.
[73]
J. Lund, P. Tedesco, K. Duke, J. Wang, S. K. Kim, and T. E. Johnson, “Transcriptional profile of aging in C. elegans,” Current Biology, vol. 12, no. 18, pp. 1566–1573, 2002.
[74]
D. M. Hall, L. Xu, V. J. Drake et al., “Aging reduces adaptive capacity and stress protein expression in the liver after heat stress,” Journal of Applied Physiology, vol. 89, no. 2, pp. 749–759, 2000.
[75]
M. M. Rankin and J. A. Kushner, “Adaptive β-cell proliferation is severely restricted with advanced age,” Diabetes, vol. 58, no. 6, pp. 1365–1372, 2009.
[76]
B. Strehler, Time, Cells, and Aging, Academic Press, London, UK, 1962.
[77]
G. V. Semenchenko, A. A. Khazaeli, J. W. Curtsinger, and A. I. Yashin, “Stress resistance declines with age: analysis of data from a survival experiment with Drosophila melanogaster,” Biogerontology, vol. 5, no. 1, pp. 17–30, 2004.
[78]
S. V. Ukraintseva and A. I. Yashin, “Individual aging and cancer risk: how are they related?” Demographic Research, vol. 9, no. 8, pp. 163–195, 2003.
[79]
A. I. Yashin, K. G. Arbeev, I. Akushevich, A. Kulminski, L. Akushevich, and S. V. Ukraintseva, “Stochastic model for analysis of longitudinal data on aging and mortality,” Mathematical Biosciences, vol. 208, no. 2, pp. 538–551, 2007.
[80]
A. A. Cohen, E. Milot, J. Yong et al., “A novel statistical approach shows evidence for multi-system physiological dysregulation during aging,” Mechanisms of Ageing and Development, vol. 134, no. 3-4, pp. 110–117, 2013.
[81]
K. G. Arbeev, I. Akushevich, A. M. Kulminski et al., “Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data,” Journal of Theoretical Biology, vol. 258, no. 1, pp. 103–111, 2009.
[82]
A. I. Yashin, K. G. Arbeev, I. Akushevich et al., “The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span,” Physics of Life Reviews, vol. 9, no. 2, pp. 177–188, 2012.
[83]
A. I. Yashin, K. G. Arbeev, I. Akushevich et al., “Modeling longitudinal data on health aging and life span,” Physics of Life Reviews, vol. 9, no. 2, pp. 195–197, 2012.
[84]
I. Akushevich, K. Arbeev, S. Ukraintseva, and A. Yashin, “Theory of individual health histories and dependent competing risks,” in JSM Proceedings, Section on Risk Analysis, pp. 5385–5399, 2011.
[85]
A. I. Yashin, I. Akushevich, K. G. Arbeev, A. Kulminski, and S. Ukraintseva, “Joint analysis of health histories, physiological state, and survival,” Mathematical Population Studies, vol. 18, no. 4, pp. 207–233, 2011.
[86]
A. I. Yashin, I. Akushevich, K. G. Arbeev, A. Kulminski, and S. V. Ukraintseva, “New approach for analyzing longitudinal data on health, physiological state, and survival collected using different observational plans,” in Proceedings of the Joint Statistical Meetings, Section on Government Statistics (JSM '11), pp. 5336–5350, Alexandria, Va, USA, 2011.
[87]
A. I. Yashin, I. Akushevich, K. Arbeev, A. Kulminski, and S. Ukraintseva, “Chapter 19. Methodological aspects of studying human aging, health, and mortality,” in Applied Demography and Public Health, N. Hoque, M. A. McGehee, and B. S. Bradshaw, Eds., pp. 337–355, Springer, Dordrecht, The Netherlands, 2013.