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Dietary transitions in China have undergone rapid changes in over the last three decades. The purpose of this study is to describe trends in the macronutrient consumption, the sources of those nutrients and the diet quality among Chinese adults.
Longitudinal China Health and Nutrition Survey (CHNS) cohort analysis. Main outcomes are dietary energy intake from total carbohydrate, protein and fat and their subtypes, as well as food sources of carbohydrates, protein, and fat, and the China Dietary Guidelines Index 2018 (CDGI-2018).
CHNS (1991, 2000, 2009 and 2015).
Data from the longitudinal 1991, 2000, 2009 and 2015 CHNS of adults aged 18 years or older who had complete demographic information.
The estimated mean energy intake from total carbohydrate decreased from 62·6 % to 50·6 % between 1991 and 2015, while the mean energy intake from total protein increased from 12·6 % to just 13·1 % and the mean energy intake from total fat significantly increased from 24·0 % to 35·8 % (P < 0·001 for trend). Decreases were observed in evaluated mean energy from low-quality carbohydrates (from 53·6 % to 41·7 %) and incomplete protein (from 9·3 % to 7·5 %), while increases were seen in estimated mean energy from high-quality protein (from 3·3 % to 5·5 %), high-quality fat (from 9·1 % to 16·7 %) and low-quality fat (from 14·9 % to 19·0 %). Low-quality carbohydrates, primarily those derived from refined grains, decreased from 52·2 % to 36·2 %. The diet quality as measured by CDGI-2018 improved, with the estimated mean increasing from 41·7 to 52·4 (P < 0·01 for trend).
For Chinese adults, there was a significant change in the macronutrient composition over the previous few decades. The percentage of energy consumed from carbohydrates significantly decreased; however, the percentage of energy consumed from total fat significantly increased. Additionally, the diet quality remains suboptimal.
From 21 January 2020 to 9 February 2020, three family clusters involving 31 patients with coronavirus disease 2019 were identified in Wenzhou, China. The epidemiological and clinical characteristics of the family cluster patients were analysed and compared with those of 43 contemporaneous sporadic cases. The three index cases transmitted the infection to 28 family members 2–10 days before illness onset. Overall, 28 of the 41 sporadic cases and three of 31 patients in the family clusters came back from Wuhan (65.12 vs. 9.68%, P< 0.001). In terms of epidemiological characters and clinical symptoms, no significant differences were observed between the family cluster and sporadic cases. However, the lymphocyte counts of sporadic cases were significantly lower than those of family cluster cases ((1.32 ± 0.55) × 109/l vs. (1.63 ± 0.70) × 109/l, P = 0.037), and the proportion of hypoalbuminaemia was higher in sporadic cases (18/43, 41.86%) than in the family clusters (6/31, 19.35%) (P < 0.05). Within the family cluster, the second- and third-generation cases had milder clinical manifestations, without severe conditions, compared with the index and first-generation cases, indicating that the virulence gradually decreased following passage through generations within the family clusters. Close surveillance, timely recognition and isolation of the suspected or latent patient is crucial in preventing family cluster infection.
Animal studies have suggested that Mn might be associated with some components of the metabolic syndrome (MetS). A few epidemiological studies have assessed dietary Mn intake and its association with the risk of the MetS and its components among Chinese adults. In this study, we assessed daily dietary Mn intake and its relationship with MetS risk among Chinese adults in Zhejiang Province using data from the 5th Chinese National Nutrition and Health Survey (2010–2012). A total of 2111 adults were included. Dietary Mn intake was assessed using 3-d 24-h dietary recalls; health-related data were obtained by questionnaire surveys, physical examinations and laboratory assessments. The mean intake of Mn was 6·07 (sd 2·94) mg/d for men (n 998) and 5·13 (sd 2·65) mg/d for women (n 1113). Rice (>42 %) was the main food source of Mn. The prevalence of the MetS was 28·0 % (590/2111). Higher Mn intake was associated with a decreased risk of the MetS in men (Q4 v. Q1 OR 0·62; 95 % CI 0·42, 0·92; Ptrend=0·043) but an increased risk in women (Q4 v. Q1 OR 1·56; 95 % CI 1·02, 2·45; Ptrend=0·078). In addition, Mn intake was inversely associated with abdominal obesity (Ptrend=0·016) and hypertriacylglycerolaemia (Ptrend=0·029) in men, but positively associated with low HDL-cholesterol in both men (Ptrend=0·003) and women (Ptrend<0·001). Our results suggest that higher Mn intakes may be protective against the MetS in men. The inverse association between Mn intake and the MetS in women might be due to the increased risk for low HDL-cholesterol.
The aim of the present study was to explore the influencing factors of urinary iodine concentration (UIC) and the relationship between iodised salt concentration and UIC in order to give suggestions for the surveillance of iodine nutrition status. For this purpose, a multi-stage cluster sampling technique was employed in the present cross-sectional study. Correlations between UIC and salt iodine concentration were evaluated by Spearmen's correlation analysis. Risk factors of having a lower UIC were identified by logistic regression analysis, and the equations of UIC and salt iodine concentration were fitted by curve regression analysis. The median UIC was found to be 162·0 (25th–75th percentile 98·2–248·6) μg/l. The UIC was correlated with salt iodine concentration (Spearman's ρ = 0·144, P< 0·05). The multiple logistic regression analysis found the following influencing factors for having a lower UIC: age (OR 0·98, 95 % CI 0·98, 0·98, P< 0·05); sex (OR 0·81, 95 % CI 0·71, 0·92, P< 0·05); education level (OR 0·87, 95 % CI 0·83, 0·90, P< 0·05); status of occupation (OR 0·91, 95 % CI 0·86, 0·96, P< 0·05); occupation (OR 1·03, 95 % CI 1·00, 1·05, P< 0·05); pickled food (OR 1·24, 95 % CI 1·08, 1·42, P< 0·05); salt iodine concentration (OR 1·03, 95 % CI 1·02, 1·03, P< 0·05). The curve regression analysis found that UIC (y) and salt iodine concentration (x) could be expressed by the following equation: y= 1·5772x1·4845. In conclusion, the median UIC of individuals in Zhejiang Province falls within optimal status as recommended by the WHO/UNICEF/International Council for Control of IDD. To maintain optimal iodine nutrition status, salt iodine concentration should be in the range of 16·4 to 34·3 mg/kg.
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