People with higher genetic predisposition to obesity are more susceptible to cardiovascular diseases (CVDs) and healthy plant-based foods may be associated with reduced risks of obesity and other metabolic markers. We investigated whether healthy plant-foods-rich dietary patterns might have inverse associations with cardiometabolic risk factors in participants at genetically elevated risk of obesity. For this cross-sectional study, 377 obese and overweight women were chosen from health centers in Tehran, Iran. We calculated a healthy plant-based diet index (h-PDI) in which healthy plant foods received positive scores, and unhealthy plant and animal foods received reversed scores. A genetic risk score (GRS) was developed based on 3 polymorphisms. The interaction between GRS and h-PDI on cardiometabolic traits was analyzed using a generalized linear model (GLM). We found significant interactions between GRS and h-PDI on body mass index (BMI) (p = 0.02), body fat mass (p = 0.04), and waist circumference (p = 0.056). There were significant gene-diet interactions for healthful plant-derived diets and BMI-GRS on high-sensitivity C-reactive protein (p = 0.03), aspartate aminotransferase (p = 0.04), alanine transaminase (p = 0.05), insulin (p = 0.04), and plasminogen activator inhibitor 1 (p = 0.002). Adherence to h-PDI was more strongly related to decreased levels of the aforementioned markers among participants in the second or top tertile of GRS than those with low GRS. These results highlight that following a plant-based dietary pattern considering genetics appears to be a protective factor against the risks of cardiometabolic abnormalities.
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Creating a complex balance between dietary composition, circadian rhythm, and the hemostasis control of energy is important for managing diseases. Therefore, we aimed to determine the interaction between cryptochrome circadian clocks 1 polymorphism and energy-adjusted dietary inflammatory index (E-DII) on high-sensitivity C-reactive protein in women with central obesity. This cross-sectional study recruited 220 Iranian women aged 18–45 with central obesity. The 147-item semi-quantitative food frequency questionnaire was used to assess the dietary intakes, and the E-DII score was calculated. Anthropometric and biochemical measurements were determined. By polymerase chain response-restricted length polymorphism method, cryptochrome circadian clocks 1 polymorphism was assigned. Participants were categorized into three groups based on the E-DII score, then categorized according to cryptochrome circadian clocks 1 genotypes. The mean and standard deviation of age, BMI, and high-sensitivity C-reactive protein (hs-CRP) were 35.61 ± 9.57 years, 30.97 ± 4.16 kg/m2, and 4.82 ± 5.16 mg/dL, respectively. The interaction of the CG genotype and E-DII score had a significant association with higher hs-CRP level compared to GG genotype as the reference group (β, 1.19; 95% CI, 0.11–2.27; p value, 0.03). There was a marginally significant association between the interaction of the CC genotype and the E-DII score with higher hs-CRP level compared to the GG genotype as the reference group (β, 0.85; 95% CI, −0.15 to 1.86; p value, 0.05). There is probably positive interaction between CG, CC genotypes of cryptochrome circadian clocks 1, and E-DII score on the high-sensitivity C-reactive protein level in women with central obesity.
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Resting metabolic rate (RMR) accounts for most daily energy expenditure. The low carbohydrate diet (LCD) attenuates decreases in RMR. This study aims to investigate the relationship between an LCD and RMR status among overweight and obese women. We enrolled 291 overweight and obese women in this cross-sectional study. Body mass index (BMI), fat mass, fat-free mass, visceral fat, and insulin level were assessed. RMR was measured using indirect calorimetry. LCD score (LCDS) was measured using a validated semi-quantitative food frequency questionnaire. Analysis of variance, independent sample t-test, and Multinomial logistic regression tests were used. Results showed no relationship between LCDS and deviation of normal RMR (DNR) even after adjust for confounders (increased [Inc.] RMR: odds ratio [OR], 0.97; 95% confidence interval [CI], 0.92–1.01; p = 0.20; decreased [Dec.] RMR: OR, 0.97; 95% CI, 0.94–1.00; p = 0.14). Some components of LCDS had no significant association with DNR, such as carbohydrate and Dec. RMR in adjusted model (OR, 1.62; 95% CI, 0.98–1.37; p = 0.08) and monounsaturated fatty acids and Dec. RMR in adjusted model (OR, 0.48; 95% CI, 0.21–1.10, p = 0.08). However, refined grains had a significant association with Inc. RMR in crude model (OR, 0.87; 95% CI, 0.77–0.99, p = 0.04). There is no association between LCDS and RMR status.
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