Genetic associations with longevity are on average stronger in females than in males.

It is long observed that females tend to live longer than males in nearly every country. However, the underlying mechanism remains elusive. In this study, we discovered that genetic associations with longevity are on average stronger in females than in males through bio-demographic analyses of genome-wide association studies (GWAS) dataset of 2178 centenarians and 2299 middle-age controls of Chinese Longitudinal Healthy Longevity Study (CLHLS). This discovery is replicated across North and South regions of China, and is further confirmed by North-South discovery/replication analyses of different and independent datasets of Chinese healthy aging candidate genes with CLHLS participants who are not in CLHLS GWAS, including 2972 centenarians and 1992 middle-age controls. Our polygenic risk score analyses of eight exclusive groups of sex-specific genes, analyses of sex-specific and not-sex-specific individual genes, and Genome-wide Complex Trait Analysis using all SNPs all reconfirm that genetic associations with longevity are on average stronger in females than in males. Our discovery/replication analyses are based on genetic datasets of in total 5150 centenarians and compatible middle-age controls, which comprises the worldwide largest sample of centenarians. The present study's findings may partially explain the well-known male-female health-survival paradox and suggest that genetic variants may be associated with different reactions between males and females to the same vaccine, drug treatment and/or nutritional intervention. Thus, our findings provide evidence to steer away from traditional view that "one-size-fits-all" for clinical interventions, and to consider sex differences for improving healthcare efficiency. We suggest future investigations focusing on effects of interactions between sex-specific genetic variants and environment on longevity as well as biological function.

THE USES AND ABUSES OF AN AGE-PERIOD-COHORT METHOD: ON THE LINEAR ALGEBRA AND STATISTICAL PROPERTIES OF INTRINSIC AND RELATED ESTIMATORS.

As a sophisticated and popular age-period-cohort method, the Intrinsic Estimator (IE) and related estimators have evoked intense debate in demography, sociology, epidemiology and statistics. This study aims to provide a more holistic review and critical assessment of the overall methodological significance of the IE and related estimators in age-period-cohort analysis. We derive the statistical properties of the IE from a linear algebraic perspective, provide more precise mathematical proofs relevant to the current debate, and demonstrate the essential, yet overlooked, link between the IE and classical statistical tools that have been employed by scholars for decades. This study offers guidelines for the future use of the IE and related estimators in demographic research. The exposition of the IE and related estimators may help redirect, if not settle, the logic of the debate.

Optimizing Count Responses in Surveys: A Machine-learning Approach.

Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses from a (semisupervised) machine-learning perspective. This article uses Poisson multinomial mixture models to conceptualize the data-generating process of count responses with grouping and right censoring and demonstrates the link between grouping-scheme choices and asymptotic distributions of the Poisson mixture. To search for the optimal grouping scheme maximizing objective functions of the Fisher information (matrix), an innovative three-step M algorithm is then proposed to process infinitely many grouping schemes based on Bayesian A-, D-, and E-optimalities. A new R package is developed to implement this algorithm and evaluate grouping schemes of count responses. Results show that an optimal grouping scheme not only leads to a more efficient sampling design but also outperforms a nonoptimal one even if the latter has more groups.

Recent trends in US mortality in early and middle adulthood: racial/ethnic disparities in inter-cohort patterns.

BACKGROUND: A striking increase in the all-cause mortality of US middle-aged non-Hispanic Whites in the past two decades has been documented by previous studies. The inter-cohort patterns in US mortality, as well as their racial/ethnic disparities, are still unclear. METHODS: Using official mortality data, we study US annual mortality rates for ages 25-54 from 1990 to 2016 by gender and race/ethnicity. We conduct an age-period-cohort analysis to disentangle the period and cohort forces driving the absolute changes in mortality across cohorts. Nine leading causes of death are also explored to explain the inter-cohort mortality patterns and their racial/ethnic disparities. RESULTS: We find cohort-specific elevated mortality trends for gender- and race/ethnicity-specific populations. For non-Hispanic Blacks and Hispanics, Baby Boomers have increased mortality trends compared with other cohorts. For non-Hispanic White females, it is late-Gen Xers and early-Gen Yers for whom the mortality trends are higher than other cohorts. For non-Hispanic White males, the elevated mortality pattern is found for Baby Boomers, late-Gen Xers, and early-Gen Yers. The mortality pattern among Baby Boomers is at least partially driven by mortality related to drug poisoning, suicide, external causes, chronic obstructive pulmonary disease and HIV/AIDS for all race and gender groups affected. The elevated mortality patterns among late-Gen Xers and early-Gen Yers are at least partially driven by mortality related to drug poisonings and alcohol-related diseases for non-Hispanic Whites. Differential patterns of drug poisoning-related mortality play an important role in the racial/ethnic disparities in these mortality patterns. CONCLUSIONS: We find substantial racial/ethnic disparities in inter-cohort mortality patterns. Our findings also point to the unique challenges faced by younger generations.

Sex Differences in Genetic Associations With Longevity.

IMPORTANCE: Sex differences in genetic associations with human longevity remain largely unknown; investigations on this topic are important for individualized health care. OBJECTIVE: To explore sex differences in genetic associations with longevity. DESIGN SETTING AND PARTICIPANTS: This population-based case-control study used sex-specific genome-wide association study and polygenic risk score (PRS) analyses to examine sex differences in genetic associations with longevity. Five hundred sixty-four male and 1614 female participants older than 100 years were compared with a control group of 773 male and 1526 female individuals aged 40 to 64 years. All were Chinese Longitudinal Healthy Longevity Study participants with Han ethnicity who were recruited in 1998 and 2008 to 2014. MAIN OUTCOMES AND MEASURES: Sex-specific loci and pathways associated with longevity and PRS measures of joint effects of sex-specific loci. RESULTS: Eleven male-specific and 11 female-specific longevity loci (P < 10-5) and 35 male-specific and 25 female-specific longevity loci (10-5 ≤ P < 10-4) were identified. Each of these loci's associations with longevity were replicated in north and south regions of China in one sex but were not significant in the other sex (P = .13-.97), and loci-sex interaction effects were significant (P < .05). The associations of loci rs60210535 of the LINC00871 gene with longevity were replicated in Chinese women (P = 9.0 × 10-5) and US women (P = 4.6 × 10-5) but not significant in Chinese and US men. The associations of the loci rs2622624 of the ABCG2 gene were replicated in Chinese women (P = 6.8 × 10-5) and European women (P = .003) but not significant in both Chinese and European men. Eleven male-specific pathways (inflammation and immunity genes) and 34 female-specific pathways (tryptophan metabolism and PGC-1α induced) were significantly associated with longevity (P < .005; false discovery rate < 0.05). The PRS analyses demonstrated that sex-specific associations with longevity of the 4 exclusive groups of 11 male-specific and 11 female-specific loci (P < 10-5) and 35 male-specific and 25 female-specific loci (10-5 ≤P < 10-4) were jointly replicated across north and south discovery and target samples. Analyses using the combined data set of north and south showed that these 4 groups of sex-specific loci were jointly and significantly associated with longevity in one sex (P = 2.9 × 10-70 to 1.3 × 10-39) but not jointly significant in the other sex (P = .11 to .70), while interaction effects between PRS and sex were significant (P = 4.8 × 10-50 to 1.2 × 10-16). CONCLUSION AND RELEVANCE: The sex differences in genetic associations with longevity are remarkable, but have been overlooked by previously published genome-wide association studies on longevity. This study contributes to filling this research gap and provides a scientific basis for further investigating effects of sex-specific genetic variants and their interactions with environment on healthy aging, which may substantially contribute to more effective and targeted individualized health care for male and female elderly individuals.

Latent Trajectories of Cross-National Homicide Trends: Structural Characteristics of Underlying Groups.

Relative to studies of U.S. homicide trends, few have investigated cross-national trends. We explore hidden heterogeneity across a sample of 82 nations between 1980 and 2010, and examine (a) what distinct latent trajectories are represented among these nations? and (b) what structural factors characterize these latent trajectory groups? World Health Organization mortality data were used for the trajectory analyses wherein three distinct groups were identified. Structural characteristics of each group are compared to determine which factors account for their trajectories. Characteristics that predicted group placement include a development index, divorced males, female labor force participation, and Latin American region.

The Importance of the Baby Boom Cohort and the Great Recession in Understanding Age, Period, and Cohort Patterns in Happiness.

Twenge, Sherman, and Lyubomirsky (TSL) claim that long-term cultural changes have increased young adults' happiness while reducing mature adults' happiness. To establish their conclusion, TSL use trend analyses, as well as more sophisticated mixed-effects models, but their analyses are problematic. In particular, TSL's trend analyses ignore a crucial cohort effect: well-known lower happiness among baby boomers. Furthermore, their data aggregation obscures the ephemerality of a recent period effect: the Great Recession. Finally, TSL overlook a key finding of their mixed-effects models that both pre- and post-Boomer cohorts became happier as they aged from young to mature adults. Our reanalyses of the data establish that the Baby Boomer cohort, the short-lived Great Recession, and unfortunate data aggregation account for TSL's results. The well-established, long-term relationship between age and happiness remains as it has been for decades despite any cultural shifts that may have occurred disfavoring mature adults.

Novel loci and pathways significantly associated with longevity.

Only two genome-wide significant loci associated with longevity have been identified so far, probably because of insufficient sample sizes of centenarians, whose genomes may harbor genetic variants associated with health and longevity. Here we report a genome-wide association study (GWAS) of Han Chinese with a sample size 2.7 times the largest previously published GWAS on centenarians. We identified 11 independent loci associated with longevity replicated in Southern-Northern regions of China, including two novel loci (rs2069837-IL6; rs2440012-ANKRD20A9P) with genome-wide significance and the rest with suggestive significance (P < 3.65 × 10(-5)). Eight independent SNPs overlapped across Han Chinese, European and U.S. populations, and APOE and 5q33.3 were replicated as longevity loci. Integrated analysis indicates four pathways (starch, sucrose and xenobiotic metabolism; immune response and inflammation; MAPK; calcium signaling) highly associated with longevity (P ≤ 0.006) in Han Chinese. The association with longevity of three of these four pathways (MAPK; immunity; calcium signaling) is supported by findings in other human cohorts. Our novel finding on the association of starch, sucrose and xenobiotic metabolism pathway with longevity is consistent with the previous results from Drosophilia. This study suggests protective mechanisms including immunity and nutrient metabolism and their interactions with environmental stress play key roles in human longevity.

Age-Specific Variation in Adult Mortality Rates in Developed Countries.

This paper investigates historical changes in both single-year-of-age adult mortality rates and variation of the single-year mortality rates around expected values within age intervals over the past two centuries in 15 developed countries. We apply an integrated Hierarchical Age-Period-Cohort-Variance Function Regression Model to data from the Human Mortality Database. We find increasing variation of the single-year rates within broader age intervals over the life course for all countries, but the increasing variation slows down at age 90 and then increases again after age 100 for some countries; the variation significantly declined across cohorts born after the early 20th century; and the variation continuously declined over much of the last two centuries but has substantially increased since 1980. Our further analysis finds the recent increases in mortality variation are not due to increasing proportions of older adults in the population, trends in mortality rates, or disproportionate delays in deaths from degenerative and man-made diseases, but rather due to increasing variations in young and middle-age adults.

Interaction Between the FOXO1A-209 Genotype and Tea Drinking Is Significantly Associated with Reduced Mortality at Advanced Ages.

On the basis of the genotypic/phenotypic data from Chinese Longitudinal Healthy Longevity Survey (CLHLS) and Cox proportional hazard model, the present study demonstrates that interactions between carrying FOXO1A-209 genotypes and tea drinking are significantly associated with lower risk of mortality at advanced ages. Such a significant association is replicated in two independent Han Chinese CLHLS cohorts (p = 0.028-0.048 in the discovery and replication cohorts, and p = 0.003-0.016 in the combined dataset). We found the associations between tea drinking and reduced mortality are much stronger among carriers of the FOXO1A-209 genotype compared to non-carriers, and drinking tea is associated with a reversal of the negative effects of carrying FOXO1A-209 minor alleles, that is, from a substantially increased mortality risk to substantially reduced mortality risk at advanced ages. The impacts are considerably stronger among those who carry two copies of the FOXO1A minor allele than those who carry one copy. On the basis of previously reported experiments on human cell models concerning FOXO1A-by-tea-compounds interactions, we speculate that results in the present study indicate that tea drinking may inhibit FOXO1A-209 gene expression and its biological functions, which reduces the negative impacts of FOXO1A-209 gene on longevity (as reported in the literature) and offers protection against mortality risk at oldest-old ages. Our empirical findings imply that the health outcomes of particular nutritional interventions, including tea drinking, may, in part, depend upon individual genetic profiles, and the research on the effects of nutrigenomics interactions could potentially be useful for rejuvenation therapies in the clinic or associated healthy aging intervention programs.

Puzzling role of genetic risk factors in human longevity: "risk alleles" as pro-longevity variants.

Complex diseases are major contributors to human mortality in old age. Paradoxically, many genetic variants that have been associated with increased risks of such diseases are found in genomes of long-lived people, and do not seem to compromise longevity. Here we argue that trade-off-like and conditional effects of genes can play central role in this phenomenon and in determining longevity. Such effects may occur as result of: (i) antagonistic influence of gene on the development of different health disorders; (ii) change in the effect of gene on vulnerability to death with age (especially, from "bad" to "good"); (iii) gene-gene interaction; and (iv) gene-environment interaction, among other factors. A review of current knowledge provides many examples of genetic factors that may increase the risk of one disease but reduce chances of developing another serious health condition, or improve survival from it. Factors that may increase risk of a major disease but attenuate manifestation of physical senescence are also discussed. Overall, available evidence suggests that the influence of a genetic variant on longevity may be negative, neutral or positive, depending on a delicate balance of the detrimental and beneficial effects of such variant on multiple health and aging related traits. This balance may change with age, internal and external environments, and depend on genetic surrounding. We conclude that trade-off-like and conditional genetic effects are very common and may result in situations when a disease "risk allele" can also be a pro-longevity variant, depending on context. We emphasize importance of considering such effects in both aging research and disease prevention.

Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones.

Previously, Reither et al. (2015) demonstrated that hierarchical age-period-cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide "misleading evidence dressed up as science." Despite such strong words, B&J show no curiosity about their own simulated data--and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the "true" DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy--testing HAPC models with data simulated from contrived DGPs that violate important assumptions--is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences.

Multicollinearity in hierarchical linear models.

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.

Should age-period-cohort studies return to the methodologies of the 1970s?

Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods - hierarchical APC (HAPC) modeling - to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question - along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that "solid theory" is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies.

Implications of changes in households and living arrangements for future home-based care needs and costs for disabled elders in China.

OBJECTIVES: To better understand future home-based care needs and costs for disabled elders in China. METHOD: To further develop and apply the ProFamy extended cohort-component method and the most recent census and survey data. RESULTS: (a) Chinese disabled elders and the annual growth rate of the percentage of national gross domestic product (GDP) devoted to home-based care costs for disabled elders will increase much more rapidly than the growth of total elderly population; (b) home-based care needs and costs for disabled oldest-old aged 80+ will increase much faster than that for disabled young-old aged 65-79 after 2030; (c) disabled unmarried elders living alone and their home-based care costs increase substantially faster than those disabled unmarried elders living with children; (d) percent of rural disabled oldest-old will be substantially higher than that of rural population after 2030; (e) sensitivity analyses show that possible changes in mortality and elderly disability status are the major direct factors affecting home-based care needs and costs; (f) caregivers resources under the universal two-child policy will be substantially better than that under the rigorous fertility policy unchanged. DISCUSSION: We discuss policy recommendations concerning pathways to healthy aging with relatively reduced care costs, including reductions of the prevalence of disability, gender equality, the universal two-child policy and resources of caregivers, encouragements of rural-to-urban family migration and elder's residential proximity to their adult children, and remarriages of not-married elders.

GxE interactions between FOXO genotypes and drinking tea are significantly associated with prevention of cognitive decline in advanced age in China.

Logistic regression analysis based on data from 822 Han Chinese oldest old aged 92+ demonstrated that interactions between carrying FOXO1A-266 or FOXO3-310 or FOXO3-292 and tea drinking at around age 60 or at present time were significantly associated with lower risk of cognitive disability at advanced ages. Associations between tea drinking and reduced cognitive disability were much stronger among carriers of the genotypes of FOXO1A-266 or FOXO3-310 or FOXO3-292 compared with noncarriers, and it was reconfirmed by analysis of three-way interactions across FOXO genotypes, tea drinking at around age 60, and at present time. Based on prior findings from animal and human cell models, we postulate that intake of tea compounds may activate FOXO gene expression, which in turn may positively affect cognitive function in the oldest old population. Our empirical findings imply that the health benefits of particular nutritional interventions, including tea drinking, may, in part, depend upon individual genetic profiles.

Household and living arrangement projections at the subnational level: an extended cohort-component approach.

This article presents the core methodological ideas and empirical assessments of an extended cohort-component approach (known as the "ProFamy model"), and applications to simultaneously project household composition, living arrangements, and population sizes-gender structures at the subnational level in the United States. Comparisons of projections from 1990 to 2000 using this approach with census counts in 2000 for each of the 50 states and Washington, DC show that 68.0 %, 17.0 %, 11.2 %, and 3.8 % of the absolute percentage errors are <3.0 %, 3.0 % to 4.99 %, 5.0 % to 9.99 %, and ≥10.0 %, respectively. Another analysis compares average forecast errors between the extended cohort-component approach and the still widely used classic headship-rate method, by projecting number-of-bedrooms-specific housing demands from 1990 to 2000 and then comparing those projections with census counts in 2000 for each of the 50 states and Washington, DC. The results demonstrate that, compared with the extended cohort-component approach, the headship-rate method produces substantially more serious forecast errors because it cannot project households by size while the extended cohort-component approach projects detailed household sizes. We also present illustrative household and living arrangement projections for the five decades from 2000 to 2050, with medium-, small-, and large-family scenarios for each of the 50 states; Washington, DC; six counties of southern California; and the Minneapolis-St. Paul metropolitan area. Among many interesting numerical outcomes of household and living arrangement projections with medium, low, and high bounds, the aging of American households over the next few decades across all states/areas is particularly striking. Finally, the limitations of the present study and potential future lines of research are discussed.

Assessing the Significance of Cohort and Period Effects in Hierarchical Age-Period-Cohort Models: Applications to Verbal Test Scores and Voter Turnout in U.S. Presidential Elections.

In recently developed hierarchical age-period-cohort (HAPC) models, inferential questions arise: How can one assess or judge the significance of estimates of individual cohort and period effects in such models? And how does one assess the overall statistical significance of the cohort and/or the period effects? Beyond statistical significance is the question of substantive significance. This paper addresses these questions. In the context of empirical applications of linear and generalized linear mixed-model specifications of HAPC models using data on verbal test scores and voter turnout in U.S. presidential elections, respectively, we describe a two-step approach and a set of guidelines for assessing statistical significance. The guidelines include assessments of patterns of effects and statistical tests both for the effects of individual cohorts and time periods as well as for entire sets of cohorts and periods. The empirical applications show strong evidence that trends in verbal test scores are primarily cohort driven, while voter turnout is primarily a period phenomenon.