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Importance: The link between familial loss of a loved one and long-term health decline is complex and not fully understood., Objective: To test associations of losing a parent, sibling, child, or partner or spouse with accelerated biological aging., Design, Setting, and Participants: Data from the National Longitudinal Study of Adolescent to Adult Health, a US population-based longitudinal cohort study, were analyzed. Participants were enrolled from 1994 to 1995 for wave 1, while in grades 7 to 12, and followed up through wave 5 in 2018. The study analyzed participant reports of loss collected at each wave from 1 to 5 over 24 years and used a banked wave 5 blood sample for subsequent DNA methylation testing and epigenetic clock calculation from 2018 to 2024. Data were analyzed from January 2022 to July 2024., Exposure: Loss of biological parents or parental figures, partners or spouses, siblings, or children at waves 1 to 3 or during childhood, adolescence (aged <18 years), or adulthood at wave 4 to wave 5 (aged 18-43 years)., Main Outcomes and Measures: Biological aging assessed from blood DNA methylation using the Horvath, PhenoAge, GrimAge, and DunedinPACE epigenetic clocks at wave 5., Results: Data from 3963 participants were analyzed, with a weighted mean (range) age of 38.36 (36.78-39.78) years at wave 5; 2370 (50.3%) were male, 720 (15.97%) were Black, 400 (8.18%) were Hispanic, and 2642 (72.53%) were White. Nearly 40% of participants experienced loss by wave 5 when they were aged 33 to 43 years, and participants who were Black (379 participants [56.67%]), Hispanic (152 participants [41.38%]), and American Indian (18 participants [56.08%]) experienced a greater proportion of losses compared with White participants (884 participants [34.09%]). Those who experienced 2 or more losses tended to have older biological ages for several of the clocks (PhenoAge β = 0.15; 95% CI, 0.02 to 0.28; GrimAge β = 0.27; 95% CI, 0.09 to 0.45; DunedinPACE β = 0.22; 95% CI, 0.10 to 0.34) compared with those with no losses. In contrast, there were no associations with 2 or more losses for the Horvath clock (β = -0.08; 95% CI, -0.23 to 0.06)., Conclusions and Relevance: This study reveals associations between various measures of loss experienced from childhood to adulthood and biological aging in a diverse sample of the US population. These findings underscore the potentially enduring impact of loss on biological aging even before middle age and may contribute to understanding racial and ethnic disparities in health and mortality.

To test the hypothesis that early-life adversity accelerates the pace of biological aging, we analyzed data from the Dutch Hunger Winter Families Study (DHWFS, N = 951). DHWFS is a natural-experiment birth-cohort study of survivors of in-utero exposure to famine conditions caused by the German occupation of the Western Netherlands in Winter 1944 to 1945, matched controls, and their siblings. We conducted DNA methylation analysis of blood samples collected when the survivors were aged 58 to quantify biological aging using the DunedinPACE, GrimAge, and PhenoAge epigenetic clocks. Famine survivors had faster DunedinPACE, as compared with controls. This effect was strongest among women. Results were similar for GrimAge, although effect-sizes were smaller. We observed no differences in PhenoAge between survivors and controls. Famine effects were not accounted for by blood-cell composition and were similar for individuals exposed early and later in gestation. Findings suggest in-utero undernutrition may accelerate biological aging in later life., Competing Interests: Competing interests statement:D.W.B. is listed as an inventor on the Duke University and University of Otago Invention DunedinPACE, which is licensed to TruDiagnostic.

Introduction: Native American communities suffer disproportionately from elevated metal exposures and increased risk for cardiovascular diseases and diabetes. DNA methylation is a sensitive biomarker of aging-related processes and novel epigenetic-based "clocks" can be used to estimate accelerated biological aging that may underlie increased risk. Metals alter DNA methylation, yet little is known about their individual and combined impact on epigenetic age acceleration. Our objective was to investigate the associations of metals on several DNA methylation-based aging measures in the Strong Heart Study (SHS) cohort., Methods: Blood DNA methylation data from 2,301 SHS participants was used to calculate age acceleration of epigenetic clocks (PhenoAge, GrimAge, DunedinPACE, Hannum, Horvath). Urinary metals [arsenic (As), cadmium (Cd), tungsten (W), zinc (Zn), selenium (Se), molybdenum (Mo)] were creatinine-adjusted and categorized into quartiles. We examined associations of individual metals through linear regression models and used Bayesian Kernel Machine Regression (BKMR) for the impact of the total metal mixture on epigenetic age acceleration., Results: The mixture of nonessential metals (W, As, Cd) was associated with greater GrimAge acceleration and DunedinPACE, while the essential metal mixture (Se, Zn, Mo) was associated with lower epigenetic age acceleration. Cd was associated with increased epigenetic age acceleration across all clocks and BKMR analysis suggested nonlinear associations between Se and DunedinPACE, GrimAge, and PhenoAge acceleration. No interactions between individual metals were observed. The associations between Cd, Zn, and epigenetic age acceleration were greater in never smokers in comparison to current/former smokers., Conclusion: Nonessential metals were positively associated with greater epigenetic age acceleration, with strongest associations observed between Cd and DunedinPACE and GrimAge acceleration. In contrast, essential metals were associated with lower epigenetic aging. Examining the influence of metal mixtures on epigenetic age acceleration can provide insight into metals and aging-related diseases., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose of Review: Acceleration of biological processes of aging is hypothesized to drive excess morbidity and mortality in socially disadvantaged populations. DNA methylation measures of biological aging provide tools for testing this hypothesis., Recent Findings: Next-generation DNA methylation measures of biological aging developed to predict mortality risk and physiological decline are more predictive of morbidity and mortality than the original epigenetic clocks developed to predict chronological age. These new measures show consistent evidence of more advanced and faster biological aging in people exposed to socioeconomic disadvantage and may be able to record the emergence of socially determined health inequalities as early as childhood. Next-generation DNA methylation measures of biological aging also indicate race/ethnic disparities in biological aging. More research is needed on these measures in samples of non-Western and non-White populations. New DNA methylation measures of biological aging open opportunities for refining inference about the causes of social disparities in health and devising policies to eliminate them. Further refining measures of biological aging by including more diversity in samples used for measurement development is a critical priority for the field., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Biological aging is a proposed mechanism through which social determinants drive health disparities. We conducted proof-of-concept testing of 8 DNA-methylation (DNAm) and blood-chemistry quantifications of biological aging as mediators of disparities in healthspan between Black and White participants in the 2016 wave of the Health and Retirement Study (n = 9,005). We quantified biological aging from 4 DNAm "clocks" (Horvath, Hannum, PhenoAge, and GrimAge clock), a DNAm pace-of-aging measure (DunedinPoAm), and 3 blood-chemistry measures (PhenoAge, Klemera-Doubal method biological age, and homeostatic dysregulation). We quantified Black-White disparities in healthspan from cross-sectional and longitudinal data on physical performance tests, self-reported limitations in activities of daily living, and physician-diagnosed chronic diseases, self-rated health, and survival. DNAm and blood-chemistry quantifications of biological aging were moderately correlated (Pearson's r = 0.1-0.4). The GrimAge clock, DunedinPoAm, and all 3 blood-chemistry measures were associated with healthspan characteristics (e.g., mortality effect-size hazard ratios were 1.71-2.32 per standard deviation of biological aging) and showed evidence of more advanced/faster biological aging in Black participants than in White participants (Cohen's d = 0.4-0.5). These measures accounted for 13%-95% of Black-White differences in healthspan-related characteristics. Findings suggest that reducing disparities in biological aging can contribute to building health equity., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously, we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al., 2020). Here, we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome)., Methods: We used data from the Dunedin Study 1972-1973 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets., Results: DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge., Conclusions: DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience., Funding: This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1., Competing Interests: DB, AC, DC, KS, RP, TM is listed as an inventor on a Duke University and University of Otago invention that was licensed to a commercial entity, LA, AB, KC, XG, EH, HH, RH, MK, DK, JM, JS, PV, CW, BW No competing interests declared

Background and Objectives: Children who grow up in socioeconomic disadvantage face increased burden of disease and disability throughout their lives. One hypothesized mechanism for this increased burden is that early-life disadvantage accelerates biological processes of aging, increasing vulnerability to subsequent disease. To evaluate this hypothesis and the potential impact of preventive interventions, measures are needed that can quantify early acceleration of biological aging in childhood., Methods: Saliva DNA methylation and socioeconomic circumstances were measured in N = 600 children and adolescents aged 8 to 18 years (48% female) participating in the Texas Twin Project. We measured pace of biological aging using the DunedinPoAm DNA methylation algorithm, developed to quantify the pace-of-aging-related decline in system integrity. We tested if children in more disadvantaged families and neighborhoods exhibited a faster pace of aging as compared with children in more affluent contexts., Results: Children living in more disadvantaged families and neighborhoods exhibited a faster DunedinPoAm-measured pace of aging ( r = 0.18; P = .001 for both). Latinx-identifying children exhibited a faster DunedinPoAm-measured pace of aging compared with both White- and Latinx White-identifying children, consistent with higher levels of disadvantage in this group. Children with more advanced pubertal development, higher BMI, and more tobacco exposure exhibited faster a faster DunedinPoAm-measured pace of aging. However, DunedinPoAm-measured pace of aging associations with socioeconomic disadvantage were robust to control for these factors., Conclusions: Children growing up under conditions of socioeconomic disadvantage exhibit a faster pace of biological aging. DNA methylation pace of aging might be useful as a surrogate end point in evaluation of programs and policies to address the childhood social determinants of lifelong health disparities., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2021 by the American Academy of Pediatrics.)

Large-scale epigenome-wide association meta-analyses have identified multiple 'signatures'' of smoking. Drawing on these findings, we describe the construction of a polyepigenetic DNA methylation score that indexes smoking behavior and that can be utilized for multiple purposes in population health research. To validate the score, we use data from two birth cohort studies: The Dunedin Longitudinal Study, followed to age-38 years, and the Environmental Risk Study, followed to age-18 years. Longitudinal data show that changes in DNA methylation accumulate with increased exposure to tobacco smoking and attenuate with quitting. Data from twins discordant for smoking behavior show that smoking influences DNA methylation independently of genetic and environmental risk factors. Physiological data show that changes in DNA methylation track smoking-related changes in lung function and gum health over time. Moreover, DNA methylation changes predict corresponding changes in gene expression in pathways related to inflammation, immune response, and cellular trafficking. Finally, we present prospective data about the link between adverse childhood experiences (ACEs) and epigenetic modifications; these findings document the importance of controlling for smoking-related DNA methylation changes when studying biological embedding of stress in life-course research. We introduce the polyepigenetic DNA methylation score as a tool both for discovery and theory-guided research in epigenetic epidemiology.

Evaluation of biological age, as opposed to chronological age, is of high relevance for interventions to increase healthy ageing. Highly reproducible age-associated DNA methylation (DNAm) changes can be integrated into algorithms for epigenetic age predictions. These predictors have mostly been trained to correlate with chronological age, but they are also indicative for biological ageing. For example, accelerated epigenetic age of blood is associated with higher risk of all-cause mortality in later life. The perceived age of facial images (face-age) is also associated with all-cause mortality and other ageing-associated traits. In this study, we therefore tested the hypothesis that an epigenetic predictor for biological age might be trained on face-age as a surrogate for biological age rather than on chronological age. Our data demonstrate that facial ageing does not correlate with either the epigenetic clock or blood-based DNAm measures.

Variation in DNA methylation is being increasingly associated with health and disease outcomes. Although DNA methylation is hypothesized to be a mechanism by which both genetic and non-genetic factors can influence the regulation of gene expression, little is known about the extent to which DNA methylation at specific sites is influenced by heritable as well as environmental factors. We quantified DNA methylation in whole blood at age 18 in a birth cohort of 1,464 individuals comprising 426 monozygotic (MZ) and 306 same-sex dizygotic (DZ) twin pairs. Site-specific levels of DNA methylation were more strongly correlated across the genome between MZ than DZ twins. Structural equation models revealed that although the average contribution of additive genetic influences on DNA methylation across the genome was relatively low, it was notably elevated at the highly variable sites characterized by intermediate levels of DNAm that are most relevant for epigenetic epidemiology. Sites at which variable DNA methylation was most influenced by genetic factors were significantly enriched for DNA methylation quantitative trait loci (mQTL) effects, and overlapped with sites where inter-individual variation correlates across tissues. Finally, we show that DNA methylation at sites robustly associated with environmental exposures such as tobacco smoking and obesity is also influenced by additive genetic effects, highlighting the need to control for genetic background in analyses of exposure-associated DNA methylation differences. Estimates of the contribution of genetic and environmental influences to DNA methylation at all sites profiled in this study are available as a resource for the research community (http://www.epigenomicslab.com/online-data-resources)., Competing Interests: The authors have declared that no competing interests exist.

Objective: DNA methylation has been proposed as an epigenetic mechanism by which early-life experiences become "embedded" in the genome and alter transcriptional processes to compromise health. The authors sought to investigate whether early-life victimization stress is associated with genome-wide DNA methylation., Method: The authors tested the hypothesis that victimization is associated with DNA methylation in the Environmental Risk (E-Risk) Longitudinal Study, a nationally representative 1994-1995 birth cohort of 2,232 twins born in England and Wales and assessed at ages 5, 7, 10, 12, and 18 years. Multiple forms of victimization were ascertained in childhood and adolescence (including physical, sexual, and emotional abuse; neglect; exposure to intimate-partner violence; bullying; cyber-victimization; and crime)., Results: Epigenome-wide analyses of polyvictimization across childhood and adolescence revealed few significant associations with DNA methylation in peripheral blood at age 18, but these analyses were confounded by tobacco smoking and/or did not survive co-twin control tests. Secondary analyses of specific forms of victimization revealed sparse associations with DNA methylation that did not replicate across different operationalizations of the same putative victimization experience. Hypothesis-driven analyses of six candidate genes in the stress response (NR3C1, FKBP5, BDNF, AVP, CRHR1, SLC6A4) did not reveal predicted associations with DNA methylation in probes annotated to these genes., Conclusions: Findings from this epidemiological analysis of the epigenetic effects of early-life stress do not support the hypothesis of robust changes in DNA methylation in victimized young people. We need to come to terms with the possibility that epigenetic epidemiology is not yet well matched to experimental, nonhuman models in uncovering the biological embedding of stress.

To test the hypothesis that early-life adversity accelerates the pace of biological aging, we analyzed data from the Dutch Hunger Winter Families Study (DHWFS, N = 951). DHWFS is a natural-experiment birth-cohort study of survivors of in-utero exposure to famine conditions caused by the German occupation of the Western Netherlands in Winter 1944 to 1945, matched controls, and their siblings. We conducted DNA methylation analysis of blood samples collected when the survivors were aged 58 to quantify biological aging using the DunedinPACE, GrimAge, and PhenoAge epigenetic clocks. Famine survivors had faster DunedinPACE, as compared with controls. This effect was strongest among women. Results were similar for GrimAge, although effect-sizes were smaller. We observed no differences in PhenoAge between survivors and controls. Famine effects were not accounted for by blood-cell composition and were similar for individuals exposed early and later in gestation. Findings suggest in-utero undernutrition may accelerate biological aging in later life. [ABSTRACT FROM AUTHOR]

Objective: People who eat healthier diets are less likely to develop dementia, but the biological mechanism of this protection is not well understood. We tested the hypothesis that healthy diet protects against dementia because it slows the pace of biological aging. Methods: We analyzed Framingham Offspring Cohort data. We included participants ≥60 years‐old, free of dementia and having dietary, epigenetic, and follow‐up data. We assessed healthy diet as long‐term adherence to the Mediterranean‐Dash Intervention for Neurodegenerative Delay diet (MIND, over 4 visits spanning 1991–2008). We measured the pace of aging from blood DNA methylation data collected in 2005–2008 using the DunedinPACE epigenetic clock. Incident dementia and mortality were defined using study records compiled from 2005 to 2008 visit through 2018. Results: Of n = 1,644 included participants (mean age 69.6, 54% female), n = 140 developed dementia and n = 471 died over 14 years of follow‐up. Greater MIND score was associated with slower DunedinPACE and reduced risks for dementia and mortality. Slower DunedinPACE was associated with reduced risks for dementia and mortality. In mediation analysis, slower DunedinPACE accounted for 27% of the diet‐dementia association and 57% of the diet‐mortality association. Interpretation: Findings suggest that slower pace of aging mediates part of the relationship of healthy diet with reduced dementia risk. Monitoring pace of aging may inform dementia prevention. However, a large fraction of the diet‐dementia association remains unexplained and may reflect direct connections between diet and brain aging that do not overlap other organ systems. Investigation of brain‐specific mechanisms in well‐designed mediation studies is warranted. ANN NEUROL 2024;95:1069–1079 [ABSTRACT FROM AUTHOR]

BACKGROUND: Inorganic arsenic (As) may increase the risk of cardiovascular disease (CVD) and all-cause mortality through accelerated aging, which can be estimated using epigenetic-based measures. OBJECTIVES: We evaluated three DNA methylation-based aging measures (PhenoAge, GrimAge, DunedinPACE) (epigenetic aging measures) as potential mediators of the previously reported association of As exposure with CVD incidence, CVD mortality, and all-cause mortality in the Strong Heart Study (SHS), an epidemiological cohort of American Indian adults. METHODS: Blood DNA methylation and urinary As levels were measured in 2,323 SHS participants (41.5% men, mean age of 55 years old). PhenoAge and GrimAge values were calculated using a residual-based method. We tested the association of urinary As with epigenetic aging measures using linear regression, the association of epigenetic aging measures with the three health outcomes using additive hazards models, and the mediation of As-related CVD incidence, CVD mortality, and all-cause mortality by epigenetic aging measures using the product of coefficients method. RESULTS: SHS participants with higher vs. lower urinary As levels had similar PhenoAge age, older GrimAge age, and faster DunedinPACE. An interquartile range increase in urinary As was associated with higher of PhenoAge age acceleration [mean difference (95% confidence interval)=0.48 (0.17, 0.80) years], GrimAge age acceleration [0.80 (0.60, 1.00) years], and DunedinPACE [0.011 (0.005, 0.018)], after adjusting for age, sex, center location, genetic components, smoking status, and body mass index. Of the 347 incident CVD events per 100,000 person-years associated with a doubling in As exposure, 21.3% (9.1, 57.1) and 22.6% (9.5, 56.9), were attributable to differences in GrimAge and DunedinPACE, respectively. DISCUSSION: Arsenic exposure was associated with older GrimAge and faster DunedinPACE measures of biological age. Furthermore, accelerated biological aging measured from DNA methylation accounted for a relevant fraction of As-associated risk for CVD, CVD mortality, and all-cause mortality in the SHS, supporting the role of As in accelerated aging. Research of the biological underpinnings can contribute to a better understanding of the role of aging in arsenic-related disease. [ABSTRACT FROM AUTHOR]

Calorie restriction (CR) increases healthy life span and is accompanied by slowing or reversal of aging-associated DNA methylation (DNAm) changes in animal models. In the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIETM) human trial, we evaluated associations of CR and changes in whole-blood DNAm. CALERIETM randomized 220 healthy, nonobese adults in a 2:1 allocation to 2 years of CR or ad libitum (AL) diet. The average CR in the treatment group through 24 months of follow-up was 12%. Whole blood (baseline, 12, and 24 months) DNAm profiles were measured. Epigenome-wide association study (EWAS) analysis tested CR-induced changes from baseline to 12 and 24 months in the n  = 197 participants with available DNAm data. CR treatment was not associated with epigenome-wide significant (false discovery rate [FDR] < 0.05) DNAm changes at the individual-CpG-site level. Secondary analysis of sets of CpG sites identified in published EWAS revealed that CR induced DNAm changes opposite to those associated with higher body mass index and cigarette smoking (p  < .003 at 12- and 24-month follow-ups). In contrast, CR altered DNAm at chronological-age-associated CpG sites in the direction of older age (p  < .003 at 12- and 24-month follow-ups). Although individual CpG site DNAm changes in response to CR were not identified, analyses of sets CpGs identified in prior EWAS revealed CR-induced changes to blood DNAm. Altered CpG sets were enriched for insulin production, glucose tolerance, inflammation, and DNA-binding and DNA-regulation pathways, several of which are known to be modified by CR. DNAm changes may contribute to CR effects on aging. [ABSTRACT FROM AUTHOR]

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub (http://github.com/dayoonkwon/BioAge), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience. [ABSTRACT FROM AUTHOR]

Intervention studies in animals suggest molecular changes underlying age-related disease and disability can be slowed or reversed. To speed translation of these so-called "geroprotective" therapies to prevent age-related disease and disability in humans, biomarkers are needed that can track changes in the rate of human aging over the course of intervention trials. Algorithm methods that measure biological processes of aging from combinations of DNA methylation marks or clinical biomarkers show promise. To identify next steps for establishing utility of these algorithm-based measures of biological aging for geroprotector trials, we considered the history a candidate biomarker of aging that has received substantial research attention, telomere length. Although telomere length possesses compelling biology to recommend it as a biomarker of aging, mixed research findings have impeded clinical and epidemiologic translation. Strengths of telomeres that should be established for algorithm biomarkers of aging are correlation with chronological age across the lifespan, prediction of disease, disability, and early death, and responsiveness to risk and protective exposures. Key challenges in telomere research that algorithm biomarkers of aging must address are measurement precision and reliability, establishing links between longitudinal rates of change across repeated measurements and aging outcomes, and clarity over whether the biomarker is a causal mechanism of aging. These strengths and challenges suggest a research agenda to advance translation of algorithm-based aging biomarkers: establish validity in young-adult and midlife individuals; test responsiveness to exposures that shorten or extend healthy lifespan; and conduct repeated-measures longitudinal studies to test differential rates of change. [ABSTRACT FROM AUTHOR]