Kexin Jiang

Background: There is growing evidence that diet and aging are associated; however, few studies have examined the relationship between macronutrient subtypes and phenotypic age acceleration, and the extent to which BMI (body mass index) mediates this association is unclear. Methods: This study included 6911 individuals who were 20 years or older and had participated in the National Health and Nutrition Examination Survey. Daily macronutrient intakes were calculated and classified by the quartile of their subtypes. PhenoAgeAccel was calculated as an aging index using nine chemistry biomarkers. Multivariable linear regression and isocaloric substitution effects were used to evaluate the association of macronutrients with PhenoAgeAccel. Mediation analyses were used to examine the mediation role of BMI in the association. Results: After adjusting for the potential covariates, the consumption of high-quality carbohydrates (β = −1.01, 95% CI: −1.91, −0.12), total protein (β = −2.00, 95% CI: −3.16, −0.84), and plant protein (β = −1.65, 95% CI: −2.52, −0.78) was negatively correlated with PhenoAgeAccel; the consumption of SFAs (β = 1.77, 95% CI: 0.72, 2.81) was positively correlated with PhenoAgeAccel. For every serving of low-quality carbohydrates/animal protein and other calories replaced by one serving of high-quality carbohydrates/plant protein, PhenoAgeAccel would be reduced by about 25 percent. The ratio between BMI-mediated high-quality carbohydrates and PhenoAgeAccel accounted for 19.76% of the total effect, while the ratio between BMI-mediated total fat and PhenoAgeAccel accounted for 30.78% of the total effect. Conclusions: Different macronutrient consumption subtypes are related to PhenoAgeAccel, which is partially mediated by BMI, depending on the quality of macronutrients. Replacing low-quality macronutrients with high-quality macronutrients might slow aging.

OBJECTIVES: The magnesium depletion score (MDS) is considered more reliable than traditional approaches for predicting magnesium deficiency in humans. We explored the associations of MDS and dietary magnesium intake with diabetes.METHODS: We obtained data from 18,853 participants in the National Health and Nutrition Examination Survey 2011-2018. Using multivariate regression and stratified analysis, we investigated the relationships of both MDS and magnesium intake with diabetes. To compute prevalence ratios (PRs), we employed modified Poisson or log-binomial regression. We characterized the non-linear association between magnesium intake and diabetes using restricted cubic spline analysis.RESULTS: Participants with MDS ≥2 exhibited a PR of 1.26 (95% confidence interval [CI], 1.19 to 1.34) for diabetes. Per-standard deviation (SD) increase in dietary magnesium intake was associated with a lower prevalence of diabetes (PR, 0.91; 95% CI, 0.87 to 0.96). Subgroup analyses revealed a positive association between MDS ≥2 and diabetes across all levels of dietary magnesium intake, including the lowest (PR, 1.35; 95% CI, 1.18 to 1.55), middle (PR, 1.23; 95% CI, 1.12 to 1.35), and highest tertiles (PR, 1.25; 95% CI, 1.13 to 1.37; pinteraction<0.001). Per-SD increase in magnesium intake was associated with lower diabetes prevalence in participants with MDS <2 (PR, 0.92; 95% CI, 0.87 to 0.98) and those with MDS ≥2 (PR, 0.91; 95% CI, 0.84 to 0.98; pinteraction=0.030).CONCLUSIONS: MDS is associated with diabetes, particularly among individuals with low magnesium intake. Adequate dietary magnesium intake may reduce diabetes risk, especially in those with high MDS.