2015-rottensteiner.pdf: “Physical activity, fitness, glucose homeostasis, and brain morphology in twins”, Mirva Rottensteiner, Tuija Leskinen, Eini Niskanen, Sari Aaltonen, Sara Mutikainen, Jan Wikgren, Kauko Heikkilä, Vuokko Kovanen, Heikki Kainulainen, Jaakko Kaprio, Ina Tarkka, Urho Kujala (2015):
Purpose: The main aim of the present study (FITFATTWIN) was to investigate how physical activity level is associated with body composition, glucose homeostasis, and brain morphology in young adult male monozygotic twin pairs discordant for physical activity.
Methods: From a population-based twin cohort, we systematically selected 10 young adult male monozygotic twin pairs (age range, 32–36 yr) discordant for leisure time physical activity during the past 3 yr. On the basis of interviews, we calculated a mean sum index for leisure time and commuting activity during the past 3 yr (3-yr LTMET index expressed as MET-hours per day). We conducted extensive measurements on body composition (including fat percentage measured by dual-energy x-ray absorptiometry), glucose homeostasis including homeostatic model assessment index and insulin sensitivity index (Matsuda index, calculated from glucose and insulin values from an oral glucose tolerance test), and whole brain magnetic resonance imaging for regional volumetric analyses.
Results: According to pairwise analysis, the active twins had lower body fat percentage (p = 0.029) and homeostatic model assessment index (p = 0.031) and higher Matsuda index (p = 0.021) compared with their inactive co-twins. Striatal and prefrontal cortex (subgyral and inferior frontal gyrus) brain gray matter volumes were larger in the nondominant hemisphere in active twins compared with those in inactive co-twins, with a statistical threshold of p < 0.001.
Conclusions: Among healthy adult male twins in their mid-30s, a greater level of physical activity is associated with improved glucose homeostasis and modulation of striatum and prefrontal cortex gray matter volume, independent of genetic background. The findings may contribute to later reduced risk of type 2 diabetes and mobility limitations.
2016-plomin.pdf#page=10: “Top 10 Replicated Findings From Behavioral Genetics”, Robert Plomin, John C. DeFries, Valerie S. Knopik, Jenae M. Neiderhiser (2016):
Finding 7. Most measures of the “environment” show substantial genetic influence
Although it might seem a peculiar thing to do, measures of the environment widely used in psychological science—such as parenting, social support, and life events—can be treated as dependent measures in genetic analyses. If they are truly measures of the environment, they should not show genetic influence. To the contrary, in 1991, Plomin and Bergeman conducted a review of the first 18 studies in which environmental measures were used as dependent measures in genetically sensitive designs and found evidence for genetic influence for these measures of the environment. Substantial genetic influence was found for objective measures such as videotaped observations of parenting as well as self-report measures of parenting, social support, and life events. How can measures of the environment show genetic influence? The reason appears to be that such measures do not assess the environment independent of the person. As noted earlier, humans select, modify, and create environments correlated with their genetic behavioral propensities such as personality and psychopathology (McAdams, Gregory, & Eley, 2013). For example, in studies of twin children, parenting has been found to reflect genetic differences in children’s characteristics such as personality and psychopathology (Avinun & Knafo, 2014; Klahr & Burt, 2014; Plomin, 1994).
Since 1991, more than 150 articles have been published in which environmental measures were used in genetically sensitive designs; they have shown consistently that there is substantial genetic influence on environmental measures, extending the findings from family environments to neighborhood, school, and work environments. Kendler and Baker (2007) conducted a review of 55 independent genetic studies and found an average heritability of 0.27 across 35 diverse environmental measures (confidence intervals not available). Meta-analyses of parenting, the most frequently studied domain, have shown genetic influence that is driven by child characteristics (Avinun & Knafo, 2014) as well as by parent characteristics (Klahr & Burt, 2014). Some exceptions have emerged. Not surprisingly, when life events are separated into uncontrollable events (e.g., death of a spouse) and controllable life events (e.g., financial problems), the former show nonsignificant genetic influence. In an example of how all behavioral genetic results can differ in different cultures, Shikishima, Hiraishi, Yamagata, Neiderhiser, and Ando (2012) compared parenting in Japan and Sweden and found that parenting in Japan showed more genetic influence than in Sweden, consistent with the view that parenting is more child centered in Japan than in the West.
Researchers have begun to use GCTA to replicate these findings from twin studies. For example, GCTA has been used to show substantial genetic influence on stressful life events (Power et al., 2013) and on variables often used as environmental measures in epidemiological studies such as years of schooling (C. A. Rietveld, Medland, et al., 2013). Use of GCTA can also circumvent a limitation of twin studies of children. Such twin studies are limited to investigating within-family (twin-specific) experiences, whereas many important environmental factors such as socioeconomic status (SES) are the same for two children in a family. However, researchers can use GCTA to assess genetic influence on family environments such as SES that differ between families, not within families. GCTA has been used to show genetic influence on family SES (Trzaskowski et al., 2014) and an index of social deprivation (Marioni et al., 2014).