Background
China has the largest obese population in the world, and the prevalence of central obesity is increasing dramatically in China. Moreover, the rapid economic growth of China in recent decades has led to rapid urbanization in rural China. However, studies comparing the prevalence trends of different types of obesity and the association of obesity with hypertension between urban and rural areas in China are very scarce, and most studies have focused only on the difference in the prevalence of overweight and general obesity or hypertension among rural and urban populations. Therefore, the focus of this study was to examine the shifts in the overall distribution of the prevalence of different types of obesity and to estimate the risk of hypertension in different types of obesity among urban and rural adults aged 18–65 years.
Methods
Seven iterations of the China Health and Nutrition Survey (CHNS), conducted in 1993, 1997, 2000, 2004, 2006, 2009 and 2011, were used in this study. A total of 53,636 participants aged 18–65 years were included. Obesity was classified into three types based on body mass index (BMI) and waist circumference (WC). A log-binomial model was constructed to estimate the prevalence ratio (PR) of hypertension with three types of obesity.
Results
The age-standardized prevalence of central obesity only, general obesity only, and both central and general obesity increased from 15.8, 0.2 and 2.9% in 1993 to 30.3, 0.9 and 10.3% in 2011, respectively. The prevalence of central obesity only (urban vs. rural: 20.8% vs. 13.4% in 1993, 29.6% vs. 30.6% in 2011) and both central and general obesity (urban vs. rural: 3.5% vs. 2.5% in 1993, 10.0% vs. 10.6% in 2011) in rural adults exceeded that in urban adults in 2011. Participants with both central and general obesity had the highest risk for incident hypertension compared with those with normal body measurements (adjusted PR, urban: 2.30 (95%
CI
, 2.01–2.63), rural: 2.50 (95%
CI
, 2.25–2.77)).
Conclusions
Both WC and BMI should be considered measures of obesity and targeted in hypertension prevention. More attention should be paid to the incidence of central obesity in adults in rural China.
map with 6970 markers and a total map length of 1823.1 centimorgan (cM), on which 837 QTLs were projected. These QTLs were then integrated into 87 meta-quantitative trait loci (MQTLs) by meta-analysis, and the 95 % confidence intervals (CI) of them were smaller than the mean value of the original QTLs. Also, 30 MQTLs covered 47 of the 54 QTLs detected from the cross between Nipponbare and H71D in this study. Among them, the two major and stable QTLs, spp10.1 and sd10.1, were found to be included in MQTL10.4. The three other major QTLs, pl3.1, sb2.1, and sb10.1, were included in MQTL3.3, MQTL2.2, and MQTL10.3, respectively. A total of 21 of the 87 MQTLs' phenotypic variation were >20 %. In total, 24 candidate genes were found in 15 MQTLs that spanned physical intervals <0.2 Mb, including genes that have been cloned previously, e.g., EP3, LP, MIP1, HTD1, DSH1, and OsPNH1. However, it would be beneficial to identify a greater number of candidate genes from these MQTLs. Mining new genes that modulate yield and its related traits would assist researchers to better understand the relevant molecular mechanisms. The MQTLs found in this study that have small physical and genetic intervals are useful not only for marker-assisted selection and pyramiding, but they also provide important information of rice yield and related gene mining for future research.Keywords QTL analysis · Meta-analysis · Rice panicle traits · Yield · MQTL Communicated by B. Yang.Y. Wu and M. Huang are contributed equally to this work.
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Knee osteoarthritis (KOA) is a chronic joint bone disease characterized by inflammatory destruction and hyperplasia of bone. Its main clinical symptoms are joint mobility difficulties and pain, severe cases can lead to limb paralysis, which poses major pressure to the quality of life and mental health of patients, but also brings serious economic burden to society. The occurrence and development of KOA is influenced by many factors, including systemic factors and local factors. The joint biomechanical changes caused by aging, trauma and obesity, abnormal bone metabolism caused by metabolic syndrome, the effects of cytokines and related enzymes, genetic and biochemical abnormalities caused by plasma adiponectin, etc. all directly or indirectly lead to the occurrence of KOA. However, there is little literature that systematically and comprehensively integrates macro‐ and microscopic KOA pathogenesis. Therefore, it is necessary to comprehensively and systematically summarize the pathogenesis of KOA in order to provide a better theoretical basis for clinical treatment.
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