Physical inactivity and obesity: a vicious circle

OBJECTIVE: Physical activity (PA) begins to decline in adolescence with a concomitant increase in weight. We hypothesized that a vicious circle may arise between decreasing PA and weight gain from adolescence to early adulthood. METHODS AND PROCEDURES: PA and self-perceived physical fitness assessed in adolescents (16-18 years of age) were used to predict the development of obesity (BMI > or =30 kg/m(2)) and abdominal obesity (waist >/=88 cm in females and > or =102 cm in males) at age 25 in 4,240 twin individuals (90% of twins born in Finland, 1975-1979). Ten 25-year-old monozygotic (MZ) twin pairs who were discordant for obesity (with a 16 kg weight difference) were then carefully evaluated for current PA (using a triaxial accelerometer), total energy expenditure (TEE, assessed by means of the doubly labeled water (DLW) method), and basal metabolic rate (BMR, assessed by indirect calorimetry). RESULTS: Physical inactivity in adolescence strongly predicted the risk for obesity (odds ratio (OR) 3.9, 95% confidence interval (CI) 1.4-10.9) and abdominal obesity (4.8, 1.9-12.0) at age 25, even after adjusting for baseline and current BMI. Poor physical fitness in adolescence also increased the risk for overall obesity (5.1, 2.0-12.7) and abdominal obesity (3.2, 1.5-6.7) in adulthood. Physical inactivity was both causative and secondary to the development of obesity discordance in the MZ pairs. TEE did not differ between the MZ co-twins. PA was lower whereas BMR was higher in the obese co-twins. DISCUSSION: Physical inactivity in adolescence strongly and independently predicts total (and especially) abdominal obesity in young adulthood, favoring the development of a self-perpetuating vicious circle of obesity and physical inactivity. Physical activity should therefore be seriously recommended for obesity prevention in the young

Use of Genome-Wide Expression Data to Mine the "Gray Zone" of GWA Studies Leads to Novel Candidate Obesity Genes

To get beyond the "low-hanging fruits'' so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N=77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N=21,000) revealed a significant deviation of P-values from the expected (P=4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of similar to 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity.Genomics, epigenetics, population genetics and bioinformatic

FGF-21 as a biomarker for muscle-manifesting mitochondrial respiratory chain deficiencies: a diagnostic study.

Contains fulltext : 95893.pdf (publisher's version ) (Closed access)BACKGROUND: Muscle biopsy is the gold standard for diagnosis of mitochondrial disorders because of the lack of sensitive biomarkers in serum. Fibroblast growth factor 21 (FGF-21) is a growth factor with regulatory roles in lipid metabolism and the starvation response, and concentrations are raised in skeletal muscle and serum in mice with mitochondrial respiratory chain deficiencies. We investigated in a retrospective diagnostic study whether FGF-21 could be a biomarker for human mitochondrial disorders. METHODS: We assessed samples from adults and children with mitochondrial disorders or non-mitochondrial neurological disorders (disease controls) from seven study centres in Europe and the USA, and recruited healthy volunteers (healthy controls), matched for age where possible, from the same centres. We used ELISA to measure FGF-21 concentrations in serum or plasma samples (abnormal values were defined as >200 pg/mL). We compared these concentrations with values for lactate, pyruvate, lactate-to-pyruvate ratio, and creatine kinase in serum or plasma and calculated sensitivity, specificity, and positive and negative predictive values for all biomarkers. FINDINGS: We analysed serum or plasma from 67 patients (41 adults and 26 children) with mitochondrial disorders, 34 disease controls (22 adults and 12 children), and 74 healthy controls. Mean FGF-21 concentrations in serum were 820 (SD 1151) pg/mL in adult and 1983 (1550) pg/mL in child patients with respiratory chain deficiencies and 76 (58) pg/mL in healthy controls. FGF-21 concentrations were high in patients with mitochondrial disorders affecting skeletal muscle but not in disease controls, including those with dystrophies. In patients with abnormal FGF-21 concentrations in serum, the odds ratio of having a muscle-manifesting mitochondrial disease was 132.0 (95% CI 38.7-450.3). For the identification of muscle-manifesting mitochondrial disease, the sensitivity was 92.3% (95% CI 81.5-97.9%) and specificity was 91.7% (84.8-96.1%). The positive and negative predictive values for FGF-21 were 84.2% (95% CI 72.1-92.5%) and 96.1 (90.4-98.9%). The accuracy of FGF-21 to correctly identify muscle-manifesting respiratory chain disorders was better than that for all conventional biomarkers. The area under the receiver-operating-characteristic curve for FGF-21 was 0.95; by comparison, the values for other biomarkers were 0.83 lactate (p=0.037, 0.83 for pyruvate (p=0.015), 0.72 for the lactate-to-pyruvate ratio (p=0.0002), and 0.77 for creatine kinase (p=0.013). INTERPRETATION: Measurement of FGF-21 concentrations in serum identified primary muscle-manifesting respiratory chain deficiencies in adults and children and might be feasible as a first-line diagnostic test for these disorders to reduce the need for muscle biopsy. FUNDING: Sigrid Juselius Foundation, Jane and Aatos Erkko Foundation, Molecular Medicine Institute of Finland, University of Helsinki, Helsinki University Central Hospital, Academy of Finland, Novo Nordisk, Arvo and Lea Ylppo Foundation.1 september 201

Association of Psychobehavioral Variables With HOMA-IR and BMI Differs for Men and Women With Prediabetes in the PREVIEW Lifestyle Intervention

OBJECTIVE Stress, sleep, eating behavior, and physical activity are associated with weight change and insulin resistance (IR). The aim of this analysis was the assessment of the overall and sex-specific associations of psychobehavioral variables throughout the 3-year PREVIEW intervention using the homeostatic model assessment of IR (HOMA-IR), BMI, and length of time in the study. RESEARCH DESIGN AND METHODS Associations of psychobehavioral variables, including stress, mood, eating behavior, physical activity (PA), and sleep, with BMI, HOMA-IR, and time spent in the study were assessed in 2,184 participants with prediabetes and overweight/obesity (n = 706 men; n = 1,478 women) during a 3-year lifestyle intervention using linear mixed modeling and general linear modeling. The study was a randomized multicenter trial using a 2 x 2 diet-by-PA design. RESULTS Overall, cognitive restraint and PA increased during the intervention compared with baseline, whereas BMI, HOMA-IR, disinhibition, hunger, and sleepiness decreased (all P &lt; 0.05). Cognitive restraint and PA were negatively, whereas disinhibition, hunger, stress, and total mood disturbance were positively, associated with both BMI and HOMA-IR. Sleep duration, low sleep quality, total mood disturbance, disinhibition, and hunger scores were positively associated with HOMA-IR for men only. Participants who dropped out at 6 months had higher stress and total mood disturbance scores at baseline and throughout their time spent in the study compared with study completers. CONCLUSIONS Eating behavior and PA, control of stress, mood disturbance, and sleep characteristics were associated with BMI, HOMA-IR, and time spent in the study, with different effects in men and women during the PREVIEW lifestyle intervention study.</p

Appraisal of Triglyceride-Related Markers as Early Predictors of Metabolic Outcomes in the PREVIEW Lifestyle Intervention: A Controlled Post-hoc Trial

Background: Individuals with pre-diabetes are commonly overweight and benefit from dietary and physical activity strategies aimed at decreasing body weight and hyperglycemia. Early insulin resistance can be estimated via the triglyceride glucose index {TyG = Ln [TG (mg/dl) x fasting plasma glucose (FPG) (mg/dl)/2]} and the hypertriglyceridemic-high waist phenotype (TyG-waist), based on TyG x waist circumference (WC) measurements. Both indices may be useful for implementing personalized metabolic management. In this secondary analysis of a randomized controlled trial (RCT), we aimed to determine whether the differences in baseline TyG values and TyG-waist phenotype predicted individual responses to type-2 diabetes (T2D) prevention programs.Methods: The present post-hoc analyses were conducted within the Prevention of Diabetes through Lifestyle intervention and population studies in Europe and around the world (PREVIEW) study completers (n = 899), a multi-center RCT conducted in eight countries (NCT01777893). The study aimed to reduce the incidence of T2D in a population with pre-diabetes during a 3-year randomized intervention with two sequential phases. The first phase was a 2-month weight loss intervention to achieve & GE;8% weight loss. The second phase was a 34-month weight loss maintenance intervention with two diets providing different amounts of protein and different glycemic indices, and two physical activity programs with different exercise intensities in a 2 x 2 factorial design. On investigation days, we assessed anthropometrics, glucose/lipid metabolism markers, and diet and exercise questionnaires under standardized procedures.Results: Diabetes-related markers improved during all four lifestyle interventions. Higher baseline TyG index (p Conclusions: Two novel indices of insulin resistance (TyG and TyG-waist) may allow for a more personalized approach to avoiding progression to T2D.<br

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P=1 × 10) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 × 10). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies

Age- and sex-specific causal effects of adiposity on cardiovascular risk factors

88siObservational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.noneFall, T.; Hägg, S.; Ploner, A.; Mägi, R.; Fischer, K.; Draisma, H.H.; Sarin, A.-P.; Benyamin, B.; Ladenvall, C.; Åkerlund, M.; Kals, M.; Esko, T.; Nelson, C.P.; Kaakinen, M.; Huikari, V.; Mangino, M.; Meirhaeghe, A.; Kristiansson, K.; Nuotio, M.-L.; Kobl, M.; Grallert, H.; Dehghan, A.; Kuningas, M.; de Vries, P.S.; de Bruijn, R.F.; Willems, S.M.; Heikkilä, K.; Silventoinen, K.; Pietiläinen, K.H.; Legry, V.; Giedraitis, V.; Goumidi, L.; Syvänen, A.-C.; Strauch, K.; Koenig, W.; Lichtner, P.; Herder, C.; Palotie, A.; Menni, C.; Uitterlinden, A.G.; Kuulasmaa, K.; Havulinna, A.S.; Moreno, L.A.; Gonzalez-Gross, M.; Evans, A.; Tregouet, D.-A.; Yarnell, J.W.; Virtamo, J.; Ferrières, J.; Veronesi, G.; Perola, M.; Arveiler, D.; Brambilla, P.; Lind, L.; Kaprio, J.; Hofman, A.; Stricker, B.H.; van Duijn, C.M.; Ikram, M.A.; Franco, O.H.; Cottel, D.; Dallongeville, J.; Hall, A.S.; Jula, A.; Tobin, M.D.; Penninx, B.W.; Peters, A.; Gieger, C.; Samani, N.J.; Montgomery, G.W.; Whitfield, J.B.; Martin, N.G.; Groop, L.; Spector, T.D.; Magnusson, P.K.; Amouyel, P.; Boomsma, D.I.; Nilsson, P.M.; Järvelin, M.-R.; Lyssenko, V.; Metspalu, A.; Strachan, D.P.; Salomaa, V.; Ripatti, S.; Pedersen, N.L.; Prokopenko, I.; Mccarthy, M.I.; Ingelsson, E.Fall, T.; Hägg, S.; Ploner, A.; Mägi, R.; Fischer, K.; Draisma, H. H.; Sarin, A. P.; Benyamin, B.; Ladenvall, C.; Åkerlund, M.; Kals, M.; Esko, T.; Nelson, C. P.; Kaakinen, M.; Huikari, V.; Mangino, M.; Meirhaeghe, A.; Kristiansson, K.; Nuotio, M. L.; Kobl, M.; Grallert, H.; Dehghan, A.; Kuningas, M.; de Vries, P. S.; de Bruijn, R. F.; Willems, S. M.; Heikkilä, K.; Silventoinen, K.; Pietiläinen, K. H.; Legry, V.; Giedraitis, V.; Goumidi, L.; Syvänen, A. C.; Strauch, K.; Koenig, W.; Lichtner, P.; Herder, C.; Palotie, A.; Menni, C.; Uitterlinden, A. G.; Kuulasmaa, K.; Havulinna, A. S.; Moreno, L. A.; Gonzalez Gross, M.; Evans, A.; Tregouet, D. A.; Yarnell, J. W.; Virtamo, J.; Ferrières, J.; Veronesi, Giovanni; Perola, M.; Arveiler, D.; Brambilla, P.; Lind, L.; Kaprio, J.; Hofman, A.; Stricker, B. H.; van Duijn, C. M.; Ikram, M. A.; Franco, O. H.; Cottel, D.; Dallongeville, J.; Hall, A. S.; Jula, A.; Tobin, M. D.; Penninx, B. W.; Peters, A.; Gieger, C.; Samani, N. J.; Montgomery, G. W.; Whitfield, J. B.; Martin, N. G.; Groop, L.; Spector, T. D.; Magnusson, P. K.; Amouyel, P.; Boomsma, D. I.; Nilsson, P. M.; Järvelin, M. R.; Lyssenko, V.; Metspalu, A.; Strachan, D. P.; Salomaa, V.; Ripatti, S.; Pedersen, N. L.; Prokopenko, I.; Mccarthy, M. I.; Ingelsson, E