The polymorphisms of the PPARD gene modify post-training body mass and biochemical parameter changes in women

Metabolic Biomarkers in Adults with Type 2 Diabetes: The Role of PPAR-γ2 and PPAR-β/δ Polymorphisms

Glucose and lipid metabolism regulation by the peroxisome proliferator-activated receptors (PPARs) has been extensively reported. However, the role of their polymorphisms remains unclear.

OBJECTIVE

To determine the relation between PPAR-γ2 rs1801282 (Pro12Ala) and PPAR-β/δ rs2016520 (+294T/C) polymorphisms and metabolic biomarkers in adults with type 2 diabetes (T2D).

MATERIALS AND METHODS

We included 314 patients with T2D. Information on anthropometric, fasting plasma glucose (FPG), HbA1c and lipid profile measurements was taken from clinical records. Genomic DNA was obtained from peripheral blood. End-point PCR was used for PPAR-γ2 rs1801282, while for PPAR-β/δ rs2016520 the PCR product was digested with Bsl-I enzyme. Data were compared with parametric or non-parametric tests. Multivariate models were used to adjust for covariates and interaction effects.

RESULTS

minor allele frequency was 12.42% for PPAR-γ2 rs1801282-G and 13.85% for PPAR-β/δ rs2016520-C. Both polymorphisms were related to waist circumference; they showed independent effects on HbA1c, while they interacted for FPG; carriers of both PPAR minor alleles had the highest values. Interactions between FPG and polymorphisms were identified in their relation to triglyceride level.

CONCLUSIONS

PPAR-γ2 rs1801282 and PPAR-β/δ rs2016520 polymorphisms are associated with anthropometric, glucose, and lipid metabolism biomarkers in T2D patients. Further research is required on the molecular mechanisms involved.

C Allele of the PPARδ+294T>C Polymorphism Confers a Higher Risk of Hypercholesterolemia, but not Obesity and Insulin Resistance: A Systematic Review and Meta-Analysis

The relationships of the PPARα Leu162Val and PPARδ+294 T>C polymorphisms with metabolic indexes have been reported to be inconsistent and even contradictory. The meta-analysis was conducted to clarify the relationships between the two variants and the indexes of obesity, insulin resistance, and blood lipids. PubMed, Google Scholar, Embase, and Cochrane Library were searched for eligible studies. Standardized mean difference with 95% confidence interval was calculated to estimate the differences in the metabolic indexes between the genotypes of the Leu162Val and+294 T>C polymorphisms. Heterogeneity among studies was assessed by Cochran's x2-based Q-statistic test. Publication bias was identified by using Begg's test. Forty-one studies (44 585 subjects) and 33 studies (23 018 subjects) were identified in the analyses for the Leu162Val and+294 T>C polymorphisms, respectively. C allele carriers of the+294 T>C polymorphism had significantly higher levels of total cholesterol and low-density lipoprotein cholesterol than TT homozygotes in the whole population. Notably, C allele carriers of the+294 T>C polymorphism had significantly higher levels of triglycerides and total cholesterol in East Asians, but lower levels of triglycerides in West Asians than TT homozygotes. Regarding the Leu162Val polymorphism, it was found that Val allele carriers had significantly higher levels of blood glucose than Leu/Leu homozygotes only in European Caucasians. The meta-analysis demonstrates that C allele of the+294 T>C polymorphism in PPARδ gene confers a higher risk of hypercholesterolemia, which may partly explain the relationship between this variant and coronary artery disease.

The Influence of the Differentiation of Genes Encoding Peroxisome Proliferator-Activated Receptors and Their Coactivators on Nutrient and Energy Metabolism

Genetic components may play an important role in the regulation of nutrient and energy metabolism. In the presence of specific genetic variants, metabolic dysregulation may occur, especially in relation to the processes of digestion, assimilation, and the physiological utilization of nutrients supplied to the body, as well as the regulation of various metabolic pathways and the balance of metabolic changes, which may consequently affect the effectiveness of applied reduction diets and weight loss after training. There are many well-documented studies showing that the presence of certain polymorphic variants in some genes can be associated with specific changes in nutrient and energy metabolism, and consequently, with more or less desirable effects of applied caloric reduction and/or exercise intervention. This systematic review focused on the role of genes encoding peroxisome proliferator-activated receptors (PPARs) and their coactivators in nutrient and energy metabolism. The literature review prepared showed that there is a link between the presence of specific alleles described at different polymorphic points in PPAR genes and various human body characteristics that are crucial for the efficacy of nutritional and/or exercise interventions. Genetic analysis can be a valuable element that complements the work of a dietitian or trainer, allowing for the planning of a personalized diet or training that makes the best use of the innate metabolic characteristics of the person who is the subject of their interventions.

Relationships between maternal perfluoroalkyl substance levels, polymorphisms of receptor genes, and adverse birth outcomes in the Hokkaido birth cohort study, Japan

We assessed the associations between perfluorooctanesulfonate (PFOS) and perfluorooctanoate (PFOA) levels in third trimester maternal serum, the maternal genotypes of genes encoding nuclear receptors, and birth outcomes. We studied a prospective birth cohort of healthy pregnant Japanese women (n = 372) recruited in Sapporo between July 2002 and October 2005. We analyzed PFOS and PFOA levels using liquid chromatography-tandem mass spectrometry and analyzed 13 single nucleotide polymorphisms (SNPs) of proliferator-activated receptor alpha, gamma, gamma coactivator 1A, delta, constitutive androstane receptor, liver X receptor alpha, and beta (LXRB) using real-time polymerase reaction (PCR). We employed multiple linear regression models to establish the influences of log₁₀-transformed PFOS and PFOA levels and maternal genotypes on birth size. In female infants, we identified interactions between PFOS levels, the maternal genotype of LXRB (rs1405655), and birth weight. The estimated mean changes in birth weight in response to PFOS levels, the maternal genotype LXRB (rs1405655)-TC/CC (compared to TT), and their interactions were -502.9 g (95 % confidence interval [CI] = -247.3, -758.5 g), -526.3 g (95 % CI = -200.7, -852.0 g), and 662.1 g (95 % CI = 221.0, 1,103.2 g; p_int = 0.003), respectively. Interactions between PFOS levels and the maternal genotype of LXRB (rs1405655) also significantly affected birth chest circumference and the Ponderal index (p_int = 0.037 and 0.005, respectively). Thus, interactions between PFOS levels and the maternal genotype of LXRB (rs1405655) affects birth sizes in female infants. We found that certain SNPs modify the effects of PFOS levels on birth size.

Associations between maternal mono-(2-ethylhexyl) phthalate levels, nuclear receptor gene polymorphisms, and fatty acid levels in pregnant Japanese women in the Hokkaido study

We assessed how the interaction between mono-(2-ethylhexyl) phthalate (MEHP) in maternal sera and the maternal genotypes associated with nuclear receptors affect fatty acid levels in a prospective birth cohort study of pregnant Japanese individuals (n = 437) recruited in Sapporo between 2002 and 2005. We analyzed MEHP and fatty acids using gas chromatography-mass spectrometry. Thirteen single nucleotide polymorphisms of peroxisome proliferator-activated receptor (PPAR) alpha, PPAR gamma (PPARG), PPARG coactivator 1A (PPARGC1A), PPAR delta, constitutive androstane receptor, liver X receptor (LXR) alpha, and LXR beta (LXRB) were analyzed using real-time PCR. Multiple linear regression models were used to confirm the influence of log₁₀-transformed MEHP levels and maternal genotypes on log₁₀-transformed fatty acid levels. When the effects of the interaction between MEHP levels and the maternal PPARGC1A (rs8192678) genotype on oleic acid levels were evaluated, the estimated changes (95 % confidence intervals) in oleic acid levels against MEHP levels, maternal PPARGC1A (rs8192678)-GA/AA genotype, and the interaction between them showed a mean reduction of 0.200 (0.079, 0.322), mean reduction of 0.141 (0.000, 0.283), and mean increase of 0.145 (0.010, 0.281), respectively, after adjusting for the perfluorooctanesulfonate level. The effects of the interaction between MEHP levels and maternal LXRB (rs2303044) genotype on linoleic acid levels was also significant (p_int = 0.010). In conclusion, the interaction between MEHP and the maternal genotypes PPARGC1A (rs8192678) and LXRB (rs2303044) decreased fatty acid levels. Further, the interaction between MEHP and PPARGC1A (rs8192678) may have a greater effect on fatty acid levels than the interaction between PFOS and PPARGC1A.

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study

BACKGROUND

The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions.

OBJECTIVE

This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone.

METHODS

A cohort of 393 participants enrolled in Digbi Health's personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data.

RESULTS

Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS

Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants' genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficacy.

Associations among perfluorooctanesulfonic/perfluorooctanoic acid levels, nuclear receptor gene polymorphisms, and lipid levels in pregnant women in the Hokkaido study

The effect of interactions between perfluorooctanesulfonic (PFOS)/perfluorooctanoic acid (PFOA) levels and nuclear receptor genotypes on fatty acid (FA) levels, including those of triglycerides, is not clear understood. Therefore, in the present study, we aimed to analyse the association of PFOS/PFOA levels and single-nucleotide polymorphisms (SNPs) in nuclear receptors with FA levels in pregnant women. We analysed 504 mothers in a birth cohort between 2002 and 2005 in Japan. Serum PFOS/PFOA and FA levels were measured using liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Maternal genotypes in PPARA (rs1800234; rs135561), PPARG (rs3856806), PPARGC1A (rs2970847; rs8192678), PPARD (rs1053049; rs2267668), CAR (rs2307424; rs2501873), LXRA (rs2279238) and LXRB (rs1405655; rs2303044; rs4802703) were analysed. When gene-environment interaction was considered, PFOS exposure (log₁₀ scale) decreased palmitic, palmitoleic, and oleic acid levels (log₁₀ scale), with the observed β in the range of - 0.452 to - 0.244; PPARGC1A (rs8192678) and PPARD (rs1053049; rs2267668) genotypes decreased triglyceride, palmitic, palmitoleic, and oleic acid levels, with the observed β in the range of - 0.266 to - 0.176. Interactions between PFOS exposure and SNPs were significant for palmitic acid (P_int = 0.004 to 0.017). In conclusion, the interactions between maternal PFOS levels and PPARGC1A or PPARD may modify maternal FA levels.

Exercise Response Efficiency: A Novel Way to Enhance Population Health?

The rates of obesity and its related comorbidities have increased substantially over the last 30 years, with approximately 35% of all US adults now classified as obese. Whilst the causes of obesity are both complex and multifactorial, one contributor is a reduction in leisure time physical activity, with no concurrent reduction in energy intake. Physical activity interventions have been demonstrated to promote fat loss, yet more than 50% of US adults undertake no leisure time physical activity at all, with a lack of time and enjoyment often cited as the main drivers of rising inactivity levels. Furthermore, recent evidence has demonstrated that a sub-group of individuals may experience no improvement in a given fitness or health-related measure following a specific training programme, suggesting that there may be optimal exercise types for different groups of individuals. In this paper, we introduce the concept of exercise response efficiency, whereby individuals are matched to the training type from which they are most likely to derive the greatest improvements for the least time commitment. We propose that a more precise targeting of exercise interventions is likely to drive more rapid improvements in health, thereby promoting exercise adherence and enjoyment, whilst simultaneously reducing obesity and mortality risks. Such an innovation would, we suggest, confer important public health benefits.