FTO gene-lifestyle interactions on serum adiponectin concentrations and central obesity in a Turkish population.

The aim of the study was to investigate whether lifestyle factors modify the association between fat mass and obesity-associated (FTO) gene single nucleotide polymorphisms (SNPs) and obesity in a Turkish population. The study included 400 unrelated individuals, aged 24-50 years recruited in a hospital setting. Dietary intake and physical activity were assessed using 24-hour dietary recall and self-report questionnaire, respectively. A genetic risk score (GRS) was developed using FTO SNPs, rs9939609 and rs10163409. Body mass index and fat mass index were significantly associated with FTO SNP rs9939609 (p = 0.001 and p = 0.002, respectively) and GRS (p = 0.002 and p = 0.003, respectively). The interactions between SNP rs9939609 and physical activity on adiponectin concentrations, and SNP rs10163409 and dietary protein intake on increased waist circumference were statistically significant (Pinteraction = 0.027 and Pinteraction = 0.044, respectively). Our study has demonstrated that the association between FTO SNPs and central obesity might be modified by lifestyle factors in this Turkish population.

Dietary Total Polyphenol, Flavonoid, and Lignan Intakes Are Associated with Obesity and Diabetes-Related Traits: A Case-Control Study.

Purpose: Dietary phytochemicals have been under examination as adjuvants for the prevention and treatment of obesity and diabetes. This study aimed at examining the potential associations of dietary "Phytochemical Index" (PI) and polyphenol intake with obesity and diabetes-related parameters. Materials and Methods: The case-control study involved 331 participants (156 overweight/obese and 175 normal weight), aged 18-50 years. Dietary intake was assessed using the 24-hr dietary recall method, and the PI score was calculated as the percentage of energy intake provided by phytochemical-rich foods. Polyphenol intakes were calculated using Phenol-Explorer and U.S. Department of Agriculture databases. Anthropometrical measurements were taken, serum glucose, insulin, and lipid profiles were analyzed, homeostatic model assessment for insulin resistance (HOMA-IR) was calculated, and blood pressure was measured. Linear regression analyses were used to examine the potential associations. Results: Participants with higher PI scores had higher total and some sub-classes polyphenol intakes compared with lower ones (P < 0.05, for each). Dietary PI score was not associated with any of the anthropometrical measurements; however, total polyphenol and flavonoids intakes were inversely associated with body mass index (ß = -0.269, P = 0.049; ß = -0.262, P = 0.048; respectively), waist circumference (ß = -0.127, P = 0.021; ß = -0.130, P = 0.016; respectively), and waist-to-hip ratio (ß = -20.724, P = 0.032; ß = -22.199, P = 0.018; respectively) after adjusting for potential confounders. Either dietary PI score or total and sub-class polyphenol intakes were not associated with a better metabolic profile, except for the lignan intake, which was inversely associated with HOMA-IR (ß = -0.048, P = 0.011). Conclusions: Higher dietary polyphenol intake may have potential in the prevention of obesity and diabetes, and validated practical tools are essential for the assessment of polyphenol intake in clinical practice.

The effects of shift work on sleep quality, diet quality, and obesity-related factors: A male population study.

Recent studies suggest that shift working may trigger health issues through disrupted circadian rhythms and altered eating habits. This study aimed to examine the possible associations of shift work with sleep quality, diet quality and obesity-related parameters. This study was conducted with 150 males (75 shift and daytime workers) aged 24-50 years. Dietary intake was determined using the 24-hour recall method, and dietary quality was evaluated using the Healthy Eating Index-2010 (HEI-2010). Anthropometrical measurements and body composition were assessed. The Pittsburgh Sleep Quality Index was used to evaluate sleep quality. Fasting and postprandial glucose and insulin, and fasting lipid levels were analyzed. HOMA-IR was calculated, and blood pressure was measured. The total HEI-2010 score of shift workers (52.6 [45.4-58.8]) was lower than the daytime workers (55.9 [49.1-64.9]) (p = 0.016). Among individuals with good sleep quality, the total HEI-2010 score, seafood and plant proteins, fatty acids, and sodium scores were lower in shift workers compared to others (p < 0.05 for each). Non-obese shift workers had higher body weight, waist circumference, waist/hip ratio, VLDL-cholesterol, triglyceride, postprandial glucose, and insulin levels compared to daytime workers (p < 0.05 for each). This study demonstrates that shift working is associated with lower diet quality independent of sleep quality.

The Triglycerides-Glucose Index Shows a Stronger Correlation with Serum Adiponectin Levels than Homeostasis Model Assessment of Insulin Resistance and Quantitative Insulin Sensitivity Check Index.

Purpose: To evaluate the association between diverse surrogate markers of insulin resistance and adiponectin concentrations. Methods: Four hundred healthy participants were included. Two different cohorts were formed according to the body mass index (BMI) values. Group 1 (n = 200) consisted of individuals with normal BMI values (18.50-24.99 kg/m2), whereas in Group 2 (n = 200) there were overweight or obese individuals (BMI ≥25.00 kg/m2). Homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), and triglycerides-glucose index (TyG) were calculated. Serum adiponectin levels were measured by ELISA. A correlation analysis was performed to assess the association between serum adiponectin and HOMA-IR, QUICKI, and TyG. Results: Participants in Group 2 were older (age in years: Group 1, 33.3 ± 6.8 vs. Group 2, 36.4 ± 7.0, P < 0.001). There was no gender difference between groups. Overweight or obese participants had higher BMI, waist circumference, fat mass, fat ratio, fasting plasma glucose, fasting plasma insulin, triglycerides, total cholesterol, and low-density lipoprotein cholesterol values, whereas high-density lipoprotein cholesterol was higher in participants with normal BMI measures. Overweight or obese subjects were more insulin resistant (higher TyG index and HOMA-IR) and less insulin sensitive (lower QUICKI), P < 0.001 for all. Serum adiponectin levels were lower in Group 2 (serum adiponectin in ng/mL: Group 1, 11,880 ± 6838 vs. Group 2, 9115 ± 5766, P < 0.001). The correlation between TyG index and adiponectin was stronger than the correlation between QUICKI and adiponectin, and HOMA-IR and adiponectin (r for TyG and adiponectin -0.408, r for QUICKI and adiponectin 0.394, r for HOMA-IR and adiponectin -0.268, respectively, P < 0.001 for all correlations). Conclusions: TyG has a stronger association with adiponectin than HOMA-IR and QUICKI.

Interaction between Dietary Fat Intake and Metabolic Genetic Risk Score on 25-Hydroxyvitamin D Concentrations in a Turkish Adult Population.

Previous studies have pointed out a link between vitamin D status and metabolic traits, however, consistent evidence has not been provided yet. This cross-sectional study has used a nutrigenetic approach to investigate the interaction between metabolic-genetic risk score (GRS) and dietary intake on serum 25-hydroxyvitamin D [25(OH)D] concentrations in 396 unrelated Turkish adults, aged 24-50 years. Serum 25(OH)D concentration was significantly lower in those with a metabolic-GRS ≥ 1 risk allele than those with a metabolic-GRS < 1 risk allele (p = 0.020). A significant interaction between metabolic-GRS and dietary fat intake (energy%) on serum 25(OH)D levels was identified (Pinteraction = 0.040). Participants carrying a metabolic-GRS ≥ 1 risk allele and consuming a high fat diet (≥38% of energy = 122.3 ± 52.51 g/day) had significantly lower serum 25(OH)D concentration (p = 0.006) in comparison to those consuming a low-fat diet (<38% of energy = 82.5 ± 37.36 g/d). In conclusion, our study suggests a novel interaction between metabolic-GRS and dietary fat intake on serum 25(OH)D level, which emphasises that following the current dietary fat intake recommendation (<35% total fat) could be important in reducing the prevalence of vitamin D deficiency in this Turkish population. Nevertheless, further larger studies are needed to verify this interaction, before implementing personalized dietary recommendations for the maintenance of optimal vitamin D status.