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Data from the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium were used to examine predictions of different models of gene-by-environment interaction to understand how genetic variance in self-rated health (SRH) varies at different levels of financial strain. A total of 11,359 individuals from 10 twin studies in Australia, Sweden, and the United States contributed relevant data, including 2,074 monozygotic and 2,623 dizygotic twin pairs. Age ranged from 22 to 98 years, with a mean age of 61.05 (SD = 13.24). A factor model was used to create a harmonized measure of financial strain across studies and items. Twin analyses of genetic and environmental variance for SRH incorporating age, age2, sex, and financial strain moderators indicated significant financial strain moderation of genetic influences on self-rated health. Moderation results did not differ across sex or country. Genetic variance for SRH increased as financial strain increased, matching the predictions of the diathesis-stress and social comparison models for components of variance. Under these models, environmental improvements would be expected to reduce genetically based health disparities.

Health care in Sweden usually focuses on single diseases and is less prepared to treat people with multimorbidity. Combining different national quality registries (NQR) can provide a broader picture of health care, preventive care, and health-promoting care of older people with multimorbidity. The aim of this study was to explore how NQRs can be used in gerontological health care research. A descriptive mixed-method study, including a case report and statistical analysis, was performed. Nine National Quality Registries, three health registries and a social care registry maintained by the Swedish government were individually matched to an older population (birth year 1896 to 1958) in the Swedish Twin Registry (n≈44000). Factor analysis demonstrated the people tended to fall into one of 4 clusters of registries: stroke/dementia/hip, heart/diabetes, arthritis-related, and other. Men were more highly represented in the heart/diabetes cluster. People in the arthritis cluster had the lowest mean age; people in the stroke/dementia/hip cluster had the highest mean age. 161 people were identified as “most ill elderly” (according to the definition by the Swedish government). Two of them were randomly selected for case reports. Each case appeared in 6–7 of the health quality registries, providing sufficient information to track their progression through the health care system until the point of death. The results highlight both pros and cons of using the quality registries as the basis for analysis and “registry-enriched” research designs, but more research needs to be performed to understand how NQRs can be used in gerontological research.

Composites of measures of biological aging (e.g., Anstey and colleagues, Wahlin and colleagues) can be more meaningful than simple chronological age and provide insights into the aging process and its covariates. The Swedish Adoption/Twin Study of Aging provides longitudinal data on measures of vision, hearing, gait, grip strength, and lung function from 642 individuals ranging in age from 47 to 87 at wave 1. Individuals were included who participated in at least one of 5 measurement waves covering 16 years of follow-up; 69% participated in at least 3 waves. The 5 measures are combined and transformed to T-scores to create FBioAge. A two-slope age-based latent growth curve model (LGCM) was applied to the data (note that results for a time-based LGCM were similar). Phenotypic analyses indicated an inflection point in rates of change at age 75: the rate of increase in FBioAge was twice as fast after age 75, compared with prior to age 75. Analysis of the impact of several covariates on the LGCM parameters indicated that most impacted the intercept, only. Thus, on average higher (i.e., older) FBioAge was indicated for women, individuals with less education, smokers, drinkers, individuals who reported more illnesses, and individuals who reported poorer subjective health. Two variables impact the rate of change in FBioAge. Faster rate of change was predictive of mortality and childhood SES impacted the rate of change prior to age 75, only. In future research we will examine how FBioAge relates longitudinally to aging-sensitive functions, such as cognitive abilities.

A Swedish version of the National Geographic Smell Survey (Wysocki & Gilbert, 1989) was completed by 127 twin pairs from the Swedish Adaption/Twin Study of Aging, Twins ranged in age from 45 to 89 years. Quantitative genetic analysis of four measures of o, Addresses: Finkel D, Indiana Univ SE, Sch Social Sci, New Albany, IN 47150 USA. Indiana Univ SE, Sch Social Sci, New Albany, IN 47150 USA. Karolinska Inst, Dept Med Epidemiol, Stockholm, Sweden. Karolinska Inst, Div Geriatr Med, Stockholm, Sweden. Univ So

The aim of this study was to examine the influences of age, gender, cognitive abilities, and personality styles on odor identification. Participants (N = 532) aged 45-87 years from the Swedish Adoption/Twin Study of Aging were assessed with a Swedish vers, Addresses: Larsson M, Stockholm Gerontol Res Ctr, Box 6401, S-113 Stockholm, Sweden. Uppsala Univ, Dept Psychol, Uppsala, Sweden. Karolinska Inst, Dept Clin Neurosci NEUROTEC, Stockholm, Sweden. Karolinska Inst, Dept Med Epidemiol, Stockholm, Sweden. Indi

Addresses: Indiana Univ SE, Div Social Sci, New Albany, IN 47150 USA. Karolinska Inst, Div Genet Epidemiol, S-17177 Stockholm, Sweden. Univ So Calif, Dept Psychol, Los Angeles, CA 90089 USA. Karolinska Inst, Dept Clin Neurosci & Family Med, Div Geriatr Me