Basal fat oxidation and after a peak oxygen consumption test in obese women with a beta2 adrenoceptor gene polymorphism

Differential lipid metabolism outcomes associated with ADRB2 gene polymorphisms in response to two dietary interventions in overweight/obese subjects

BACKGROUND AND AIMS

A precise nutrigenetic management of hypercholesterolemia involves the understanding of the interactions between the individual's genotype and dietary intake. The aim of this study was to analyze the response to two dietary energy-restricted interventions on cholesterol changes in carriers of two ADRB2 polymorphisms.

METHODS AND RESULTS

A 4-month nutritional intervention was conducted involving two different hypo-energetic diets based on low-fat (LF) and moderately high-protein (MHP) dietary patterns. A total of 107 unrelated overweight/obese individuals were genotyped for two ADRB2 non-synonymous polymorphisms: Arg16Gly (rs1042713) and Gln27Glu (rs1042714). Genotyping was performed by next-generation sequencing and haplotypes were phenotypically screened. Anthropometric measurements and the biochemical profile were assessed by conventional methods. Both diets induced cholesterol decreases at the end of both nutritional interventions. Interestingly, phenotypical differences were observed according to the Arg16Gly polymorphism. Within the MHP group, Gly16Gly homozygotes had lower reductions in total cholesterol (-6.5 mg/dL vs. -24.2 mg/dL, p = 0.009), LDL-c levels (-1.4 mg/dL vs. -16.5 mg/dL, p = 0.005), and non-HDL-c (-4.5 mg/dL vs. -21.5 mg/dL, p = 0.008) than Arg16 allele carriers. Conversely, within the LF group, Gly16Gly homozygotes underwent similar falls in total cholesterol (-18.5 mg/dL vs. -18.7 mg/dL, ns), LDL-c levels (-9.7 mg/dL vs. -13.1 mg/dL, ns), and non-HDL-c (-15.3 mg/dL vs. -15.7 mg/dL, ns) than Arg16 allele carriers. The Gln27Glu polymorphism and the Gly16/Glu27 haplotype showed similar, but not greater effects.

CONCLUSIONS

An energy-restricted LF diet could be more beneficial than a MHP diet to reduce serum cholesterol, LDL-c, and non-HDL-c among Gly16Gly genotype carriers. CLINICALTRIALS.GOV: Identifier: NCT02737267.

Human-specific SNP in obesity genes, adrenergic receptor beta2 (ADRB2), Beta3 (ADRB3), and PPAR γ2 (PPARG), during primate evolution

Adrenergic-receptor beta2 (ADRB2) and beta3 (ADRB3) are obesity genes that play a key role in the regulation of energy balance by increasing lipolysis and thermogenesis. The Glu27 allele in ADRB2 and the Arg64 allele in ADRB3 are associated with abdominal obesity and early onset of non-insulin-dependent diabetes mellitus (NIDDM) in many ethnic groups. Peroxisome proliferator-activated receptor γ (PPARG) is required for adipocyte differentiation. Pro12Ala mutation decreases PPARG activity and resistance to NIDDM. In humans, energy-expense alleles, Gln27 in ADRB2 and Trp64 in ADRB3, are at higher frequencies than Glu27 and Arg64, respectively, but Ala12 in PPARG is at lower frequency than Pro12. Adaptation of humans for lipolysis, thermogenesis, and reduction of fat accumulation could be considered by examining which alleles in these genes are dominant in non-human primates (NHP). All NHP (P. troglodytes, G. gorilla, P. pygmaeus, H. agilis and macaques) had energy-thrifty alleles, Gly16 and Glu27 in ADRB2, and Arg64 in ADRB3, but did not have energy-expense alleles, Arg16, Gln27 and Trp64 alleles. In PPARG gene, all NHP had large adipocyte accumulating type, the Pro12 allele.

CONCLUSIONS

These results indicate that a tendency to produce much more heat through the energy-expense alleles developed only in humans, who left tropical rainforests for savanna and developed new features in their heat-regulation systems, such as reduction of body hair and increased evaporation of water, and might have helped the protection of entrails from cold at night, especially in glacial periods.

Lifestyle modifies the relationship between body composition and adrenergic receptor genetic polymorphisms, ADRB2, ADRB3 and ADRA2B: a secondary analysis of a randomized controlled trial of physical activity among postmenopausal women

Genetic variations in the adrenergic receptor (ADR) have been associated with body composition in cross-sectional studies. Recent findings suggest that ADR variants may also modify body composition response to lifestyle. We assessed the role of ADR variants in body composition response to 12 months of resistance training versus control in previously sedentary postmenopausal women. Randomized trial completers were genotyped for A2B (Glu9/12) by fragment length analysis, and B2 (Gln27Glu) and B3 (Trp64Arg) by TaqMan (n = 148, 54% hormone therapy users). Associations between genotypes and body composition, by dual energy X-ray absorptiometry, were analyzed using univariate models. There was no main effect of individual genes on change in body composition, however, gene x exercise interactions were observed for A2B (Glu9/12) and B2 (Gln27Glu) on change in lean soft tissue (LST, p = 0.02); exercisers on the A2B (Glu9-) background gained LST compared to a loss among controls over 12 months (p < 0.05), with no significant intervention effect on the A2B (Glu9+) background. Similarly, there was a significant LST gain with exercise on the B2 (Glu27+) background compared to loss among controls and no intervention effect on the B2 (Glu27-) background. A non-significant association between total body fat (TBF) and B3 (Trp64Arg) persisted among sedentary controls only when intervention groups were separated (%TBF gain with B3 (Arg64+) carriage, p = 0.03); exercisers lost TBF regardless of genotype. In summary, effect modification by lifestyle was demonstrated on ADRA2B, B2, and B3 genetic backgrounds. Individuals with certain ADR genotypes may be more vulnerable to adverse changes in body composition with sedentary behavior, thus these candidate genes warrant further study.

The human gene map for performance and health-related fitness phenotypes: the 2006-2007 update

This update of the human gene map for physical performance and health-related fitness phenotypes covers the research advances reported in 2006 and 2007. The genes and markers with evidence of association or linkage with a performance or a fitness phenotype in sedentary or active people, in responses to acute exercise, or for training-induced adaptations are positioned on the map of all autosomes and sex chromosomes. Negative studies are reviewed, but a gene or a locus must be supported by at least one positive study before being inserted on the map. A brief discussion on the nature of the evidence and on what to look for in assessing human genetic studies of relevance to fitness and performance is offered in the introduction, followed by a review of all studies published in 2006 and 2007. The findings from these new studies are added to the appropriate tables that are designed to serve as the cumulative summary of all publications with positive genetic associations available to date for a given phenotype and study design. The fitness and performance map now includes 214 autosomal gene entries and quantitative trait loci plus seven others on the X chromosome. Moreover, there are 18 mitochondrial genes that have been shown to influence fitness and performance phenotypes. Thus,the map is growing in complexity. Although the map is exhaustive for currently published accounts of genes and exercise associations and linkages, there are undoubtedly many more gene-exercise interaction effects that have not even been considered thus far. Finally, it should be appreciated that most studies reported to date are based on small sample sizes and cannot therefore provide definitive evidence that DNA sequence variants in a given gene are reliably associated with human variation in fitness and performance traits.

Performance of a neuro-fuzzy model in predicting weight changes of chronic schizophrenic patients exposed to antipsychotics

Artificial intelligence has become a possible solution to resolve the problem of loss of information when complexity of a disease increases. Obesity phenotypes are observable clinical features of drug-naive schizophrenic patients. In addition, atypical antipsychotic medications may cause these unwanted effects. Here we examined the performance of neuro-fuzzy modeling (NFM) in predicting weight changes in chronic schizophrenic patients exposed to antipsychotics. Two hundred and twenty inpatients meeting DSMIV diagnosis of schizophrenia, treated with antipsychotics, either typical or atypical, for more than 2 years, were recruited. All subjects were assessed in the same study period between mid-November 2003 and mid-April 2004. The baseline and first visit's physical data including weight, height and circumference were used in this study. Clinical information (Clinical Global Impression and Life Style Survey) and genotype data of five single nucleotide polymorphisms were also included as predictors. The subjects were randomly assigned into the first group (105 subjects) and second group (115 subjects), and NFM was performed by using the FuzzyTECH 5.54 software package, with a network-type structure constructed in the rule block. A complete learned model trained from merged data of the first and second groups demonstrates that, at a prediction error of 5, 93% subjects with weight gain were identified. Our study suggests that NFM is a feasible prediction tool for obesity in schizophrenic patients exposed to antipsychotics, with further improvements required.

Interaction between genes and lifestyle factors on obesity

Obesity originates from a failure of the body-weight control systems, which may be affected by changing environmental influences. Basically, the obesity risk depends on two important mutually-interacting factors: (1) genetic variants (single-nucleotide polymorphisms, haplotypes); (2) exposure to environmental risks (diet, physical activity etc.). Common single-nucleotide polymorphisms at candidate genes for obesity may act as effect modifiers for environmental factors. More than 127 candidate genes for obesity have been reported and there is evidence to support the role of twenty-two genes in at least five different populations. Gene-environment interactions imply that the synergy between genotype and environment deviates from either the additive or multiplicative effect (the underlying model needs to be specified to appraise the nature of the interaction). Unravelling the details of these interactions is a complex task. Emphasis should be placed on the accuracy of the assessment methods for both genotype and lifestyle factors. Appropriate study design (sample size) is crucial in avoiding false positives and ensuring that studies have enough power to detect significant interactions, the ideal design being a nested case-control study within a cohort. A growing number of studies are examining the influence of gene-environmental interactions on obesity in either epidemiological observational or intervention studies. Positive evidence has been obtained for genes involved in adiposity, lipid metabolism or energy regulation such as PPARgamma2 (Pro12Ala), beta-adrenoceptor 2 (Gln27Glu) or uncoupling proteins 1, 2 and 3. Variants on other genes relating to appetite regulation such as melanocortin and leptin receptors have also been investigated. Examples of some recently-identified interactions are discussed.

Genotype-specific weight loss treatment advice: how close are we?

Obesity, whose prevalence is continually rising, is one of the world’s greatest health care burdens. This multifactorial condition is associated with many obesity-related conditions, such as type 2 diabetes, dyslipidemia, and cardiovascular disease. Weight loss is a significant challenge facing those wishing to reduce their disease risk. Of course, like obesity itself, weight loss is a complex phenomenon dependent on many environmental and genetic influences, and thus individual responses to weight loss interventions are incredibly variable. Currently, there are 3 major interventions used to reduce weight: diet, exercise, and pharmacotherapy. The findings from studies examining gene–diet (nutrigenetic), gene–exercise (actigenetic), and gene–pharmaceutical (pharmacogenetic) interactions, although not clinically applicable at this time, are gaining awareness. This review article summarizes the current evidence to support the contribution of DNA sequence variation in genes related to energy balance (expenditure and intake) in the response to weight loss intervention. There is no doubt that replication using more rigorous study designs that include the study of interactions between multiple genes and interventions is required to move towards the development of genotype-specific weight loss treatment strategies.

The human gene map for performance and health-related fitness phenotypes: the 2005 update

The current review presents the 2005 update of the human gene map for physical performance and health-related fitness phenotypes. It is based on peer-reviewed papers published by the end of 2005. The genes and markers with evidence of association or linkage with a performance or fitness phenotype in sedentary or active people, in adaptation to acute exercise, or for training-induced changes are positioned on the genetic map of all autosomes and the X chromosome. Negative studies are reviewed, but a gene or locus must be supported by at least one positive study before being inserted on the map. By the end of 2000, in the early version of the gene map, 29 loci were depicted. In contrast, the 2005 human gene map for physical performance and health-related phenotypes includes 165 autosomal gene entries and QTL, plus five others on the X chromosome. Moreover, there are 17 mitochondrial genes in which sequence variants have been shown to influence relevant fitness and performance phenotypes. Thus, the map is growing in complexity. Unfortunately, progress is slow in the field of genetics of fitness and performance, primarily because the number of laboratories and scientists focused on the role of genes and sequence variations in exercise-related traits continues to be quite limited.

Genes, lifestyles and obesity

OBJECTIVE

To review the role of genes and lifestyle factors, particularly dietary habits and physical activity patterns, in obesity risk as well as their potential interactions.

DESIGN AND MEASUREMENTS

A descriptive report of a number of genes definitely ascribed or potentially implicated in excessive fat accumulation leading to obesity as assessed by different research approaches (Mendelian transmission, genetic animal models, epidemiological association/linkage studies and genome-wide scans). Also, the involvement of macronutrient intake and composition (fat/carbohydrate) as well as the role of activity-linked energy expenditure in obesity onset is reviewed.

RESULTS

Examples of the role of the genotype as well as of the dietary macronutrient composition/intake and sedentary/low energy cost of physical activities in obesity prevalence are reported.

CONCLUSIONS

Both genes and everyday life environmental factors such as cultural and social mediated food intake and reduced domestic and living work activities are involved in the obesity pandemia. The occurrence of gene x gene and gene x environmental factors interactions makes it more difficult to interpret the specific roles of genetics and lifestyle in obesity risk.

The human gene map for performance and health-related fitness phenotypes: the 2003 update

This review presents the 2003 update of the human gene map for physical performance and health-related fitness phenotypes. It is based on peer-reviewed papers published by the end of 2003 and includes association studies with candidate genes, genome-wide scans with polymorphic markers, and single-gene defects causing exercise intolerance to variable degrees. The genes and markers with evidence of association or linkage with a performance or fitness phenotype in sedentary or active people, in adaptation to acute exercise, or for training-induced changes are positioned on the genetic map of all autosomes and the X chromosome. Negative studies are reviewed but a gene or locus must be supported by at least one positive study before being inserted on the map. By the end of 2000, 29 loci were depicted on the first edition of the map. In contrast, the 2003 human gene map for physical performance and health-related phenotypes includes 109 autosomal gene entries and QTL, plus two on the X chromosome. Moreover, there are 15 mitochondrial genes in which sequence variants have been shown to influence relevant fitness and performance phenotypes.