Background Inflammation and sleep disturbances increase the risk of multiple diseases, including cardiovascular disease, type 2 diabetes and dementia. Since diet plays a significant role in inflammatory responses and sleep quality, this study aimed to investigate the association of a plant‐based diet index (PDI) with sleep quality and inflammatory markers in overweight and obese women. Methods 390 overweight and obese women aged 18‐48 years participated in this cross‐sectional study. A validated food frequency questionnaire (FFQ) was used to create an overall PDI, healthful plant‐based diet index (hPDI) and unhealthful plant‐based diet index (uPDI). Sleep quality was assessed by Pittsburgh Sleep Quality Index (PSQI). Higher scores on the PSQI were indicative of poor sleep. Anthropometric measurements and serum concentrations of high‐sensitivity C‐reactive protein (hs‐CRP), interleukin 1 beta (IL‐1β) and transforming growth factor‐beta (TGF‐β) were evaluated. Linear regression models were used to determine the association between exposure and outcomes. Results After taking potential confounders into account, we found a significant inverse association between adherence to hPDI and hs‐CRP (β = −0.14, 95% confidence interval [CI]: −0.22,0.06, P = .001) and a significant positive association between uPDI and hs‐CRP (β = 0.13, 95% CI: 0.05,0.21, P = .001). Overall, PDI was significantly associated with TGF‐β (β = 2.04, 95% CI: 0.54,3.55, P = .008). No association was detected between PDI indices and IL‐1β. Higher adherence to uPDI was significantly associated with higher PSQI score (lower sleep quality) (β= 0.20, 95% CI:0.007,0.40, P = .04). A significant positive association was found between TGF‐β (β = 0.05, 95% CI:0.005,0.10, P = .03) and hs‐CRP (β = 0.32, 95% CI:0.02,0.62, P = .03) with PSQI. Conclusion Our findings indicated a significant association between adherence to a plant‐based diet with inflammation and sleep quality in obese and overweight females. What is already known about this topic? Sleep is an essential part of life, and sleep quality has a significant impact on individual well‐being and performance. There is a bidirectional relationship between disturbed sleep and elevated levels of inflammatory markers. Diet plays a major part in sleep quality and its related health consequences. Plant‐based diets are associated with lower risk of chronic diseases such as coronary artery disease (CAD), type 2 diabetes, obesity and reduced level of inflammation. What does this article add? Adherence to a healthful plant‐based diet is associated with lower level of hs‐CRP, while adherence to an unhealthful plant‐based diet is associated with higher concentrations of hs‐CRP. Adherence to an unhealthful plant‐based diet is associated with lower sleep quality.

Background Previous studies have shown that the minor allele (C allele) for Cry 1 rs2287161, may be associated with increased risk of cardiovascular diseases (CVDs). Low resting metabolic rate (RMR) caused by the diet has been shown to have, potentially, unfavorable effects on obesity. This study sought to investigate the interactions between the Cry 1 Gene and fat intake on RMR in women with overweight of obesity. Methods This comparative cross-sectional study was conducted on 377 Iranian women with overweight of obesity. A food frequency questionnaire (FFQ), with 147 items, was used to assess dietary intake. Individuals were categorized into two groups based on the rs2287161 genotype. Body composition, dietary intake, and RMR were assessed for all participants. Results There was a significant difference between genotypes for fasting blood sugar (FBS) (P = 0.04), fat free mass (FFM) (P = 0.0009), RMR per FFM (P = 0.05), RMR per body mass index (BMI) (P = 0.02), and RMR deviation (P = 0.01). Our findings also showed significant interactions between total fat and C allele carrier group on RMR per kg body weight, RMR per body surface area (BSA), RMR per FFM, and RMR deviation (P for interaction < 0.1), in addition to a significant interaction between CC + CG group genotype and polyunsaturated fatty acids (PUFA) intake on RMR per BMI (P for interaction =0.00) and RMR per kg (P for interaction = 0.02) and RMR per BSA (P = 0.07), compared to the GG group, after control for confounder factors. Conclusion These results highlight that dietary compositions, gene variants, and their interaction, should be acutely considered in lower RMR.

Background: We aimed to investigate the association between the energy density (ED) of diet and body composition components in Iranian adults.Methods: We conducted a cross-sectional study on 267 adults in Tehran. We obtained ED (kcal/g) using the two most common methods: ED1, ED from foods only with the exclusion of all beverages and ED2, from foods and all beverages. Body composition was measured using a multifrequency bio-impedance analysis. To find a strong association, we used both the linear and binary regression analysis in the three adjusted models.Results: The mean of ED1 and ED2 was 1.34 ± 0.23 and 0.89 ± 0.20 kcal/g, respectively. Increasing the ED of diet in both methods was associated with a high intake of dietary fat, of saturated fatty acid (SFA), of monounsaturated fatty acid (MUFA), of polyunsaturated fatty acid (PUFA), of oleic and linoleic acids, accompanied by a low intake of fruits, vegetables, and some vitamins and minerals. There was a significant positive relationship between fat-free mass index (FFMI) and ED1 (β = 4.44, p = 0.02). However, we found no significant association between the consumption of ED1 and fat mass index (FMI) (0.28; 95% CI 0.08, 0.98; p = 0.07), and abdominal obesity (0.91; 95% CI 0.43, 1.94; p = 0.82). Also, ED2 had no association with FMI (0.86; 95% CI 0.26, 2.80; p = 0.81) and abdominal obesity (0.78; 95% CI 0.35, 1.72; p = 0.54). No significant associations were found between ED and other anthropometric indices and body composition components after considering the confounders.Conclusion: This study supports the positive association between ED and poor dietary quality. However, our findings did not show significant associations of dietary energy density (DED) with anthropometric indices and body composition components. Further well-designed studies are required to investigate the exact link between DED and body composition.

Accumulating evidence regarding the effect of artichoke on lipid profile is equivocal. We updated a previous meta‐analysis on the effect of artichoke extract supplementation on lipid profile and performed dose–response analysis. We searched PubMed, Scopus, Web of Science, and Cochrane Library from inception to June 2021 using relevant keywords. Papers from identified articles were collected. Two researchers rated the certainty in the estimates using the GRADE approach. Combining 15 effect sizes from 14 studies based on the random‐effects analysis, we found that artichoke significantly reduced TG (weighed mean difference [WMD]: −17.01 mg/dl, 95% CI: −23.88, −10.13, p = .011), TC (WMD: −17.01 mg/dl, 95% CI: −23.88, −10.13, p < .001), and LDL‐C (WMD: −17.48 mg/dl, 95%CI: −25.44, −9.53, p < .001). No significant effect of artichoke on HDL‐C level was detected (WMD: 0.78 mg/dl, 95%CI: −0.93, 2.49, p = .371). Combining the two effect sizes revealed that artichoke juice supplementation significantly reduced TG (WMD: −3.34 mg/dl, 95%CI: −5.51, −1.17, p = .003), TC (WMD: −18.04 mg/dl, 95%CI: −20.30, −15.78, p < .001), LDL‐C (WMD: −1.75 mg/dl, 95%CI: −3.02, −0.48, p = .007), and HDL‐C levels (WMD: −4.21 mg/dl, 95%CI: −5.49, −2.93, p < .001). In conclusion, we found that artichoke supplementation may favor CVD prevention by acting in improving the lipid profile.

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