Nutrition & Metabolism最新文献

Background: The hemoglobin glycation index (HGI) and its association with mortality risk in the United States adults remain insufficiently understood. This research explores potential links between HGI levels and all-cause mortality using nationally representative data.

Methods: >The relationship between HGI and mortality was investigated using data from National Health and Nutrition Examination Survey (NHANES) (1999-2018) covering 19,287 U.S. adults. HGI was calculated via linear regression of glycated hemoglobin A1c (HbA1c) on fasting plasma glucose (FPG). The National Death Index was utilized to link mortality outcomes, with tracking continuing through December 31, 2019. To explore sex-specific associations, we utilized weighted Cox regression models with multivariable adjustments, along with restricted cubic splines and segmented Cox analyses. Robustness was confirmed through stratified analyses and sensitivity tests.

Results: In U.S. males, HGI showed a U-shaped association with mortality (threshold: -0.131). Below this, lower HGI was protective (hazard ratio (HR) 0.52; 95% confidence interval (CI) 0.42-0.66); above it, higher HGI increased risk (HR 1.34; 95% CI 1.16-1.55). In females, an L-shaped pattern emerged, where higher HGI below the threshold correlated with a decreased likelihood of mortality (HR 0.48; 95% CI 0.40-0.59). These sex-specific associations were verified through stratified and sensitivity analyses.

Conclusions: The cohort study observed a U-shaped pattern between HGI and all-cause mortality in U.S. males, whereas an L-shaped association was found in females. These sex-specific patterns warrant further investigation to explore clinical implications.

In response to Campbell and Cappuccio's comments, this letter clarifies and defends the appropriate use of spot-urine sodium estimates in population-level and exploratory analyses. The critique misrepresents key aspects of the study design and overstates the limitations of spot-urine equations. While acknowledging that these methods are unsuitable for precise individual assessment, they perform adequately for ranking individuals and examining population-level associations, as supported by extensive validation studies. The reported associations with metabolic traits were internally consistent, biologically plausible, and in agreement with findings from studies using 24-hour urine collections. Dismissing such analyses as "invalid" undermines constructive scientific dialogue and disregards the exploratory value of transparent, hypothesis-generating research.

Background: Dietary carotenoids may have independent positive impacts on a range of health-related outcomes such as obesity. The purpose of this study was to examine the associations between individual and total dietary carotenoids and the likelihood of being overweight or obese.

Methods: Dietary intakes of 4202 participants of the PERSIAN cohort study were investigated by a 237-item FFQ, and carotenoid intake was determined using the USDA food databases. Models of logistic regression were applied to assess the association between obesity and overweight and dietary intake of carotenoids after adjusting for confounders.

Results: Higher total carotenoid intake was associated with reduced risk of overweight (energy-adjusted OR: 0.647, 95% CI: 0.429-0.975, P = 0.037) and obesity (energy-adjusted OR: 0.395, 95% CI: 0.244-0.640, P < 0.001). Specific individual carotenoids, including β-carotene (OR: 0.510, 95% CI: 0.356-0.731, P < 0.001), lutein/zeaxanthin (OR: 0.479, 95% CI: 0.331-0.692, P < 0.001), and phytoene (OR = 0.450, 95% CI = 0.306-0.661, P < 0.001) exhibited stronger inverse associations compared to lycopene and astaxanthin.

Conclusion: Diets rich in specific carotenoids, particularly β-carotene, lutein, and phytoene, may reduce the risk of both overweight and obesity. Future research ought to examine the mechanisms behind the relations of carotenoids with public health issues.

Background: Diet is a determinant of metabolic health, partly through its effects on the gut microbiome, which influences nutrient metabolism, inflammation, and energy balance. We investigated the mediating role of gut microbiome features in the association between dietary quality and metabolic risk.

Methods: In this cross-sectional study, we included 269 adults aged 25-76 years with heterogeneous metabolic profiles, BMI ranging from 17.5 to 47.6 kg/m², and fasting glucose levels between 5.6 and 6.9 mmol/L. Dietary quality was assessed using the Healthy Eating Index (HEI-MON), the Planetary Health Diet Index (PHEI-MON), and the alternate Mediterranean Diet Score (aMED), derived from food-frequency questionnaires and three-day food records. Metabolic risk was quantified using a continuous metabolic syndrome score (cMetS) incorporating waist circumference, mean arterial pressure, HDL cholesterol, triglycerides, and fasting glucose. Microbiome composition (16 S rRNA gene sequencing) and predicted SCFA pathways were analyzed using adjusted multiple linear regression, PERMANOVA, and differential abundance analysis. Mediation analyses examined microbial features as potential mediators of the association between diet and metabolic risk.

Results: Higher HEI-MON, PHEI-MON, and aMED were associated with lower cMetS (q < 0.01). Christensenellaceae R7 group and Ruminococcaceae NK4A214 group were enriched with higher dietary quality and lower cMetS (q < 0.1), whereas Lachnoclostridium were associated with lower diet quality and higher cMetS (q < 0.1). The Enterotype Dysbiosis Score (EDS) correlated inversely with dietary quality (PHEI-MON q = 0.04) and positively with cMetS (q = 0.04). Butyrate-synthesis pathways were more abundant in individuals with higher dietary quality (q < 0.05) and inversely associated with cMetS (q < 0.05). Mediation analysis indicated that the Ruminococcaceae NK4A214 group, the Christensenellaceae R7 group, and Lachnoclostridium accounted for up to 16% of the association between diet and metabolic risk.

Conclusion: Better dietary quality is associated with lower metabolic risk and positive gut microbiome signatures across taxonomic, functional, and stability-related aspects. Certain taxa statistically mediated these associations, highlighting gut microbiome features that may contribute to observed links between dietary patterns and metabolic health.