Ola Sternäng

Challenging the notion of an early-onset of cognitive decline.

Salthouse claims that cognitive aging starts around 20 years of age. The basis for this claim is cross-sectional data. He dismisses longitudinal data, which typically show the cognitive decline to start much later, around 60 years of age. He states that longitudinal data cannot be trusted because they are flawed. There is a confounding between the effects of maturation and retest effects. We challenge Salthouses strong claim on four accounts.

Examination of the processing speed account in a population-based longitudinal study with narrow age cohort design

The processing speed account suggests that general slowing of mental processing speed results in an overall decline, especially age-related decline, in other cognitive domains. Support for the speed account comes mainly from cross-sectional studies with participants that vary in age (age-heterogeneous samples). This study investigated how well variations in processing speed predict change of episodic recall in a longitudinal framework and examined with the Narrow Age Cohort (NAC) design. Data were obtained from Betula, a population-based longitudinal study. Both 5-year (n= 490; Time 3 - Time 4) and 10-year follow-up results (n= 608; Time 1 - Time 3) were used. In both samples, which were subjected to prospective dementia screening, we found considerably weaker associations in longitudinal data compared to cross-sectional, and also weaker associations in age-homogeneous than in age-heterogeneous samples. The results provide little support for the speed account.

Examination of the Common Cause Account in a Population-Based Longitudinal Study with Narrow Age Cohort Design

Background: The common cause account suggests that there is a third factor causing aging effects in both sensory and cognitive functioning, hypothesized to be the integrity of the central nervous system [Lindenberger and Baltes; Psychol Aging 1994;9:339–355]. Importantly, the common cause account was developed based on cross-sectional data, which are especially biased by cohort effects. However, cohort effects can be controlled for in narrow age cohort (NAC) designs and by longitudinal examination. Findings from the few longitudinal studies that have studied the relation between age-related changes in sensory and cognitive functions are complex and give only partial support to the common cause account. Objective: The present paper examines the common cause account within a longitudinal setting. Method: Our study is unique in the sense that it tests the common cause account within a longitudinal NAC design using data from the Betula project. The participants (n = 1,057) were in the age range of 45–90 years. Results: The findings indicate that the relationship between sensory and memory functioning in both a longitudinal age-heterogeneous and a longitudinal NAC design are much weaker than that detected by an age-heterogeneous cross-sectional design. Conclusion: The demonstrated weak age-associated sensory-cognitive link raises questions regarding the explanatory value of the common cause account and related theoretical accounts for accounting for age-related cognitive changes.

APOE and lipid level synergy effects on declarative memory functioning in adulthood

This study of the general population examined interactions of the gene Apolipoprotein E (APOE) and/or lipid levels, and their effects on cognitive change. A MANCOVA model based on longitudinal data ...

What Is the Role of Apolipoprotein E for Cognitive Functioning Across the Lifespan

This study investigated APOE genotype and lipid levels in relation to some cognitive variables in children. However, there are other relevant cognitive variables remaining for study, such as executive functions and long-term declarative memory (episodic and semantic memory), often studied in connection to APOE in older adults. Design is also crucial to understand how the APOE gene affects cognitive functions across the lifespan. Longitudinal studies (such as ALSPAC) are important in this respect. Taylor et al. found no evidence that the negative effects on cognitive functioning of the APOE e4 allele appear as early as in the age span 7–14 years. Most studies of APOE involve older people, and therefore, the current study by Taylor et al. (3) on children represents a piece of important information that adds to the total picture of the role of APOE during the lifespan. Further research is needed to understand why the effects of APOE e4 change from childhood to older age.

Genetic and Environmental Influences on Longitudinal Trajectories of Functional Biological Age : Comparisons Across Gender

We used an alternate age variable, functional biological age (fBioAge), which was based on performance on functional body measures. The aim was to examine development of fBioAge across the adult life span, and to also examine potential gender differences and genetic and environmental influences on change with age. We used longitudinal data (n = 740; chronological age (ChronAge) range 45–85 at baseline) from the Swedish Adoption/Twin Study of Aging. The rate of increase in fBioAge was twice as fast after ChronAge 75 than before. fBioAge was higher in women than in men. fBioAge was fairly equally influenced by genetic and environmental factors. Whereas the rate of ChronAge cannot vary across time, gender, or individual, our analyses demonstrate that fBioAge does capture these within and between individual differences in aging, providing advantages for fBioAge in the study of aging effects.

Everyday Health among Older People : A Comparison between Two Countries with Variant Life Conditions

This study described health factors of importance for everyday health, such as pain, tiredness, and sleeping problems, in a cross-national context. Data for persons 60+ years were obtained from the Poverty and Health in Aging study, Bangladesh, and the Swedish National Study on Aging and Care-Blekinge. The strongest associations with everyday health in Sweden were found for pain and tiredness, while in Bangladesh they were financial status, tiredness, and sleeping problems. As similarities were found regarding the associations of tiredness on everyday health, tiredness may be a universal predictor of everyday health in older adults irrespective of country context.

Longitudinal trends in functional biological age : impact of lifestyle factors

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.

Contents Vol. 56, 2010

Behavioural Science Section K.J. Anstey, Canberra L. Clare, Bangor D. Gerstorf, University Park, Pa. J.D. Henry, Sydney T. Hess, Raleigh, N.C. S.M. Hofer, Victoria I. Kryspin Exner, Wien D.C. Park, Dallas, Tex. K. Ritchie, Montpellier J. Smith, Ann Arbor, Mich. Experimental Section C. Bertoni-Freddari, Ancona R. Faragher, Brighton C. Franceschi, Bologna T. Fülöp, Sherbrooke L. Gavrilov, Chicago, Ill. L. Haynes, Saranac Lake, N.Y. K. Hirokawa, Tokyo G.J. Lithgow, Novato, Calif. M. Rose, Irvine, Calif. A. Viidik, Wien J. Vijg, Bronx, N.Y.