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Objective: To analyze food carbon footprint and its socio-demographic disparities among adults in China., Methods: A total of 12 777 adults aged 18 years and above from the China Health and Nutrition Survey in 2018 who have completed dietary and socio-demographic data were analyzed. The information of food intake were collected by 24 h recalls combined with the weighing of household seasonings. Food consumption was converted into energy intake by the China Food Composition Table. Carbon footprint of 26 food groups were calculated by the food carbon footprint database based on life-cycle assessment(LCA), multinomial logit model was used to analyze the association of socio-demographic factors and food carbon footprint., Results: Average food carbon footprint were decreased with increasing age while increased with increasing income and education levels, and was higher among male than that among female, was higher among urban residents than that among rural residents, was higher in the south than that in the north. Multinomial logit analysis showed that compared with people aged 18-44, the likelihood of occurring high carbon footprint in 60y and above group were 29%(OR=0.71, 95%CI 0.61-0.83) lower than that occurring low carbon footprint. Women were 11%(OR=0.89, 95%CI 0.81-0.99) and 25%(OR=0.75, 95%CI 0.67-0.84) less likely to appear medium and high carbon footprint than low carbon footprint, compared with their male counterparts. In comparison to people living in cities, rural dwellers were 24%(OR=0.76, 95%CI 0.69-0.85) and 38%(OR=0.62, 95%CI 0.55-0.70) less likely to appear medium and high carbon footprint than low carbon footprint. People in the south were 3.89 times(95%CI 3.52-4.30) and 11.35 times(95%CI 10.01-12.88) more likely to occur medium and high carbon footprint than low carbon footprint, compared with people in the north. Participants were more likely to occur medium carbon footprint and high carbon footprint with the increasing income level(OR>1), and were more likely to occur high carbon footprint with the increasing education level(OR>1)., Conclusion: The food carbon footprint of adults in China in 2018 show different socio-demographic disparities, gender, income and education level are significant factors.

Objective: To analyze the prevalence of cardio-metabolic(CM) risk in women aged 15-49 years in 4 provinces of China and the influence of socioeconomic factors on them., Methods: A total of 2851 women aged 15-49 years from Community-based Cohort Study on Nervous System Disease in 2018 were selected. Obesity, central obesity, elevated triglyceride(TG), elevated total cholesterol(TC), decreased high-density lipoprotein cholesterol(HDL-C), elevated low-density lipoprotein cholesterol(LDL-C), elevated blood pressure, elevated blood glucose and risk factor aggregation were analyzed. χ~2 test was used for univariate analysis, multinomial Logit model was used to evaluate the relationship between socioeconomic factors and CM risk factors, and Cochran-Armitage trend test was used for trend analysis., Results: The detection rate of CM risk factors in this study from high to low were central obesity(26.76%), overweight(22.41%), pre central obesity(17.47%), decreased HDL-C(15.36%), elevated TG(11.78%), borderline elevated TC(11.40%), borderline elevated TG(11.12%), elevated blood pressure(9.71%) and hypertension(9.12%). The prevalence rates of CM risk factors were different among different age groups, income groups and education levels(P<0.05). In addition to decreased HDL-C, the prevalence of other metabolic risk factors increased with age(P_(trend)<0.05). With the improvement of educational level, the prevalence rates of overweight, obesity, central obesity, central obesity, elevated TG, decreased HDL-C, elevated blood pressure, hypertension, elevated blood pressure and diabetes showed a downward trend(P_(trend)<0.05). Multinomial Logit model showed that the rick of metabolic risk factors in the age group of 40 to 49 years old was higher than that in the younger age group aged 15-29 years, and was more significant in hypertension, elevated blood pressure and elevated blood glucose, which were 8.51 times(95% CI 5.45-13.27), 3.14 times(95%CI 2.20-4.48)and 2.66(95% CI 1.52-4.66)times of the younger age group, respectively. Women with high-income level have a higher risk of borderline elevated TC, elevated TC and borderline elevated LDL-C(OR=1.85, 95% CI 1.44-2.38;OR=2.01, 95% CI 1.25-3.22;OR=2.16, 95% CI 1.61-2.90), but the lower risk of overweight and elevated blood pressure(OR=0.79, 95% CI 0.64-0.98;OR=0.69, 95% CI 0.50-0.94). The risk of obesity, hypertension and diabetes of people with college degree or above was about 50% lower than those with junior high school education or below(OR=0.52, 95% CI 0.35-0.78;OR=0.43, 95% CI 0.27-0.67; OR=0.52, 95% CI 0.28-0.96)., Conclusion: Central obesity, overweight, pre central obesity and HDL-C decrease were prominent CM risk factors in women aged 15-49 years in four provinces of China in 2018. The detection rate of CM risk factors is higher in women of high age group or low education level.

Few articles have investigated the impact of long-term meat intake trends and their changes during follow-up on the risk of type 2 diabetes (T2D). We aimed to explore the long-term trajectories of meat intake and determine its association with T2D risk in Chinese adults. This study used seven rounds of data from the China Health and Nutrition Survey (1997, 2000, 2004, 2006, 2009, 2015, and 2018), and 4464 adults aged 18 years or older were analyzed. The group-based trajectory modeling was used to identify meat intake trajectories over 21 years. Multivariate Cox proportional hazard and restricted cubic spline models were used to analyze the association and dose-response relationship between meat intake and T2D. Four trajectory groups were identified: "low-increase intake group" (Group 1), "moderate-increase intake group" (Group 2), "medium-stable intake group" (Group 3), and "high intake group" (Group 4). Compared with Group 2, Group 4 was associated with a higher risk of developing T2D (hazard ratio 2.37 [95% CI 1.41-3.98]). After adjusting for demographic characteristics, lifestyle, total energy intake, waist circumference, and systolic blood pressure, and using the third quintile as a reference, the risk of T2D was increased by 46% in the lowest quintile with meat intake (hazard ratio 1.46 [95% CI 1.07-2.01]) and by 41% in the highest quintile with meat intake (HR 1.41 [95% CI 1.03-1.94]). A U-shape was observed between meat intake and T2D risk ( p for nonlinear < 0.001). When the intake was lower than 75 g/day, the risk of T2D was negatively correlated with meat intake, while the risk of T2D was positively correlated with meat intake when the intake was higher than 165 g/day. We identified four trajectory groups of meat intake from 1997 to 2018, which were associated with different risks of developing T2D. A U-shaped association was observed between meat intake and T2D in Chinese adults.

Objective: To evaluate the cardiovascular health status of Chinese adults and explore the related influencing factors., Methods: Adults aged 20-59 from the China Health and Nutrition Survey covering 15 provinces and cities in 2018 were selected as the study subjects. The average score of 8 health factors was calculated using the definition of the "Life&apos;s Essential 8" for cardiovascular health released by the American Heart Association in 2022. Cardiovascular health was classified according to the score. Multinomial logit model was used to analyze the impact of demographic characteristics on cardiovascular health., Results: A total of 5977 subjects were included in this study. The total score of cardiovascular health was 73.62. The single factor analysis showed that sleep and physical activity scored higher(91.69 and 87.39, respectively), while blood pressure and diet scored lower(58.88 and 44.16, respectively). Clustering analysis found that only 0.45% of the people with full score of all eight indicators, The average number of full score indicators was 4.22, and the full score indicators were more among 20-44, female, college or above, and urban residents. In addition, the number of people with high, moderate and poor cardiovascular health status were 35.70%, 59.58% and 4.72%, respectively. Multinomial logit analysis showed that compared with low cardiovascular health status, the people with high in 45-59 were 66%(95% CI 0.25-0.45) lower than those in 20-44. Women with moderate and high were 9.93(95%CI 6.44-15.33) and 41.82(95% CI 26.87-65.08) times higher than men, respectively. The moderate cardiovascular health of the people with middle income was 1.47(95% CI 1.07-2.01) times higher than those with low income. People in the south with moderate and high were 31%(95% CI 0.53-0.91) and 38%(95% CI 0.46-0.82) lower than those in the north, respectively., Conclusion: Adults in our country were in the moderate cardiovascular health. People aged 20-44, female, people with higher income and living in the north had better cardiovascular health.

Objective: To analyze the dietary patterns of Chinese adults in 15 provinces and cities in China., Methods: A total of 9208 adults aged 18-64 from the "China Health and Nutrition Survey" in 2018 were selected as subjects. Food consumption data were collected through 3 d-24 h dietary recalls and weighing household cooking oil and condiments, and the average daily dietary nutrient intake was calculated by the food composition table. Clustering analysis was used to analyze dietary patterns in different regions, and data from the 2009 China Health and Nutrition Survey were used for comparison., Results: Three dietary patterns were obtained in this study, including northern pattern, coastal pattern and southern pattern. The analysis of demographic characteristics showed that the intake of cereals, vegetables and livestock were statistically different among different age groups, and livestock and poultry intake was higher in the 18-49 years; Fruit and milk were higher in women and other foods were higher in men; The intakes of cereals, tubers, legumes and vegetables were higher in rural areas, people with low income and low education levels, and other foods were higher in urban areas, people with high income and high education levels, especially milk. Compared with the dietary guidelines, it was found that the proportion of insufficient intake of dark vegetables, fruits and nuts in three patterns and milk, eggs in the southern pattern and aquatic products in the northern pattern was up to 80%, the other food intake was in 50%-80%; The proportion of excessive intake of livestock and poultry meat in the southern mode was significantly higher, reaching 78%. Nutrient analysis showed that the intake of carbohydrate, dietary fiber and carbohydrate energy ratio, vitamin E, magnesium and manganese were higher in the northern pattern; The intakes of protein, protein energy ratio, riboflavin, potassium, sodium, calcium, phosphorus, copper and selenium were higher in the coastal pattern; The southernpattern had a higher intake of energy, fat, fat energy ratio, niacin, retinol, zinc and vitamin C. In addition, the comparison between 2009 and 2018 showed that the intake of most foods except milk has decreased to varying degrees in the past decade, while the intake of some foods has increased., Conclusion: The 15 provinces in China were divided into three dietary patterns. Unbalanced intake of food and nutrients was common among them, and the intake was influenced by different demographic characteristics. In addition, most food intake has declined over the past decade.

Objective: To explore the association between egg intake and cardiometabolic factors (CMFs) in Chinese adults., Method: The 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., Results: Of 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 < 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 < 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., Conclusion: There was a U -shaped association between egg intake and CMFs in Chinese adults., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jiao, Li, Jiang, Wang, Wang, Hao, Jia, Wang, Wang, Zhang and Ding.)

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.