Metabolic syndrome (MetS) is increasingly prevalent, and the relationship between dietary magnesium and MetS remains controversial. Therefore, we aimed to explore the association and dose-response relationship between dietary magnesium intake and MetS and its single component. The sample was adults aged 18 years and above who participated in at least two follow-up surveys in 2009, 2015 and 2018. Food consumption data were collected from three consecutive 24-h dietary recalls. The multivariate Cox proportional risk regression model and restricted cubic spline (RCS) model were used to analyze the association and dose-response relationship between dietary magnesium intake and MetS and its components. In our study, 6104 subjects were included, with a total follow-up of 37,173.36 person-years, and the incidence was 33.16%. Cox regression analysis showed that the multivariable-adjusted Hazard Ratio (HR) for MetS comparing the highest to the lowest quintile of dietary magnesium intake was 0.84 (95% confidence intervals [CI] = 0.71–0.99). Central obesity, elevated TG, elevated blood pressure and elevated blood glucose were reduced by 18%, 41%, 20% and 42%, respectively. The risk of decreased HDL-C was reduced by 23% in the third quintile of dietary magnesium intake, with a slightly increased risk in the highest group. RCS analysis showed that the overall and non-linear associations between dietary magnesium and MetS and its components were statistically significant, the risk of them decreased significantly when magnesium intake was lower than 280 mg/day, and then the curve leveled off or slightly increased.

Magnesium is an essential mineral for the human body and a cofactor or activator for more than 300 enzymatic reactions, including blood glucose control and insulin release. Diabetes is a well-known global burden of disease with increasing global prevalence. In China, the prevalence of diabetes in adults is higher than the global average. Evidence shows that magnesium is a predictor of insulin resistance and diabetes. However, the majority of studies focus on dietary magnesium instead of serum magnesium concentration. We study the correlation of serum magnesium levels with insulin resistance and Type 2 diabetes. In this prospective cohort study, we included 5044 participants aged 18 years and older without insulin resistance (IR) and diabetes at the baseline from China Health and Nutrition Survey (CHNS). A fasting blood sample was taken for the measurement of both types of magnesium, fasting blood glucose, hemoglobin A1c (HbA1c), and fasting insulin. The homeostatic model (HOMA-IR) was calculated. Demographic characteristics of participants, and risk factors such as intensity of physical activities, smoking status, drinking habit, and anthropometric information were recorded. IR was defined as HOMA-IR ≥ 2.5, and Type 2 diabetes mellitus was defined as fasting plasma glucose ≥ 7.0 mmol/L or HbA1c ≥ 6.5%, or a self-reported diagnosis or treatment of diabetes. A total of 1331 incident insulin resistance events and 429 incident diabetic events were recorded during an average follow-up of 5.8 years. The serum magnesium concentration was categorized into quintiles. After adjusting for relevant covariates, the third quintile of serum magnesium (0.89–0.93 mmol/L) was correlated with 29% lower risk of incident insulin resistance (hazard ratio = 0.71, 95% CI 0.58, 0.86) and with a lower risk of Type 2 diabetes. Multivariable-adjusted hazard ratios (95% confidence intervals) for insulin resistance were compared with the lowest quintile of serum magnesium (<0.85). We found similar results when evaluating serum magnesium as a continuous measure. Restricted cubic spline (RCS) curves showed a nonlinear dose–response correlation in both serum magnesium levels and insulin resistance, and in serum magnesium levels and Type 2 diabetes. Lower serum magnesium concentration was associated with a higher risk of insulin resistance and diabetes.

ObjectiveTo explore the association between egg intake and cardiometabolic factors (CMFs) in Chinese adults.MethodThe subjects were 6,182 adults aged 18–64 who had complete survey data and had no CMFs at baseline. Egg intake was assessed with 3 days−24 h dietary recalls in all waves of the China Health and Nutrition Survey (CHNS). Multivariate Cox proportional risk regression model and restricted cubic spline (RCS) model were used to analyze the association and dose-response relationship between egg intake and CMFs.ResultsOf the 6,182 participants who did not have metabolic syndrome (MetS) at baseline, 1,921 developed this disease during an average follow-up of 5.71 years, with an incidence of 31.07%. Central obesity, elevated TG, decreased HDL-C, elevated blood pressure and elevated plasma glucose were 38.65, 26.74, 30.21, 40.64, and 30.64%, respectively. After adjusting for demographic characteristics, lifestyle, energy and BMI, using the lowest quintile (Q1) as a reference, the risk of central obesity, elevated TG, decreased HDL-C, and elevated plasma glucose in the highest quintile (Q5) were reduced by 15% (HR = 0.85, 95% CI = 0.73–0.98, P = 0.16), 33% (HR = 0.67, 95% CI = 0.57–0.78), 25% (HR = 0.75, 95% CI = 0.63 0.90, p = 0.05), and 28% (HR = 0.72, 95% CI = 0.63–0.83, p &lt; 0.05), respectively. The risk of elevated blood pressure was reduced by 26% in the fourth quintile (HR = 0.74, 95% CI = 0.64–0.85, P = 0.85). RCS analysis show that the overall correlation and nonlinear relationship between egg intake and CMFs were statistically significant (P &lt; 0.05). When the intake was lower than 20 g/days, the risk of MetS, central obesity, elevated blood pressure and elevated plasma glucose were negatively correlated with egg intake, while elevated TG was negatively correlated with eggs when the intake was lower than 60 g/days. There was no statistically significant association between egg intake and CMFs at higher egg intake.ConclusionThere was a U-shaped association between egg intake and CMFs in Chinese adults.

Objectives To evaluate the cardiovascular health (CVH) status of the elderly and analyze the effects of dietary patterns and demographic characteristics on CVH. Methods A total of 4299 individuals aged 60 years and above from the China Health and Nutrition Survey in 2018 were selected as the research objects. Cluster analysis was used to analyze the dietary patterns. The definition of "Life’s Essential 8" of CVH released by American Heart Association (AHA)in 2022 was used to evaluate CVH status. Finally, multinomial logit model was used to analyze the impact of demographic economic characteristics on CVH. Results Three dietary patterns were obtained by cluster analysis. In pattern 1, the intake of wheat, other grains, tubers and legumes was higher. Pattern 2 was dominated by high intake of aquatic products, vegetables and fruits; Pattern 3 was dominated by higher intake of rice and livestock meat. The total CVH score was 68.50, and sleep and blood pressure had the highest and lowest scores (85.85 and 37.64). Pattern 1 and Pattern 2 have slightly higher CVH scores. There were 16%-18% of the elderly with high CVH, and there was no significant difference in the distribution of high, moderate and low CVH among the three patterns (p=0.29). More than 50% of the elderly have 3-4 ideal metrics, 0.2% of the elderly have all 8 metrics reached the ideal state only in pattern 1. Multinomial logit analysis showed that the elderly in pattern 2 had 6-8 ideal metrics, which was 1.81 times higher than that in pattern 1; The presence of 6-8 ideal metrics in female was 3.42 times higher than that in male; Those with a college degree and above have 6-8 ideal metrics, which was 1.99 times of those with a primary school degree and below. Compared with 60-69 years, the presence of 6-8 ideal metrics in 70 years and above was 35% lower (OR=0.65,95%=0.49-0.87). The presence of 6-8 ideal metrics in high income group were 31% lower than those in low income group (OR=0.69,95%=0.47-1.00). Conclusions The elderly in China were in moderate CVH. Dietary pattern characterized by higher intake of aquatic products, vegetables and fruits were more likely to have more ideal CVH metrics. It is necessary to take targeted intervention measures for the elderly and health factors with low scores to promote the improvement of CVH status.