Fsp27, a member of the Cide family proteins, was shown to localize to lipid droplet and promote lipid storage in adipocytes. We aimed to understand the biological role of Fsp27 in regulating adipose tissue differentiation, insulin sensitivity and energy balance. Fsp27 −/− mice and Fsp27/lep double deficient mice were generated and we examined the adiposity, whole body metabolism, BAT and WAT morphology, insulin sensitivity, mitochondrial activity, and gene expression changes in these mouse strains. Furthermore, we isolated mouse embryonic fibroblasts (MEFs) from wildtype and Fsp27 −/− mice, followed by their differentiation into adipocytes in vitro. We found that Fsp27 is expressed in both brown adipose tissue (BAT) and white adipose tissue (WAT) and its levels were significantly elevated in the WAT and liver of leptin-deficient ob/ob mice. Fsp27 −/− mice had increased energy expenditure, lower levels of plasma triglycerides and free fatty acids. Furthermore, Fsp27 −/− and Fsp27/lep double-deficient mice are resistant to diet-induced obesity and display increased insulin sensitivity. Moreover, white adipocytes in Fsp27 −/− mice have reduced triglycerides accumulation and smaller lipid droplets, while levels of mitochondrial proteins, mitochondrial size and activity are dramatically increased. We further demonstrated that BAT-specific genes and key metabolic controlling factors such as FoxC2, PPAR and PGC1α were all markedly upregulated. In contrast, factors inhibiting BAT differentiation such as Rb, p107 and RIP140 were down-regulated in the WAT of Fsp27 −/− mice. Remarkably, Fsp27 −/− MEFs differentiated in vitro show many brown adipocyte characteristics in the presence of the thyroid hormone triiodothyronine (T3). Our data thus suggest that Fsp27 acts as a novel regulator in vivo to control WAT identity, mitochondrial activity and insulin sensitivity.

Background: Hyperuricemia predisposes to gout, which may result in tophi, kidney stones, or urate nephropathy even kidney failure. Many metabolic risk factors and disorders has been recognized as a key risk factor contributing to development of hyperuricemia. Aim: To determine the prevalence of hyperuricemia and its association with adiposity and dyslipidemia. Methods: We recruited non-hospitalized participants (aged ≥35 years) in Xinjiang, a northwest part of China based on the Cardiovascular Risk Survey (CRS 2008(CRS -2012. Information of general health status, seafood or internal organs intake and history of disease were obtained by using an interview-based questionnaire. The levels of serum uric acid (sUA) and creatinine and lipid profiles were measured. A multivariate logistic regression model was performed to assess the association between prevalence of hyperuricemia and adiposity and dyslipidemia. (Continued on next page)Results: This study recruited 16,611 participants, and 14,618 was included (mean age of 50.5 ± 12.6 years, 46.6% was males). The study population comprised three ethnic groups with 39.4% of Han, 32.6% of Uygur and 28% of Kazakh Chinese. The overall prevalence of hyperuricemia was 9.1% (95% CI: 8.6 to 9.6) and it was11.8% in men was 6.7% in women. The three ethnic groups also had different hyperuricemia prevalence with 15.4% in Han, 4.6% in Uygur and 5.5% in Kazakh Chinese, which corresponding to a respective mean sUA levels of 306.2 ± 86.9, 249.4 ± 76.1 and 259.8 ± 78.7 μmol/L. Participants with diabetes, hypertension or hypertriglyceridemia and higher blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC) had higher levels of sUA (P < 0.001 respectively). Multivariate logistic regression analysis revealed that age, gender, ethnicity, drinking, obesity, waist circumference, TG (≥2.26 mmol/L), TC (≥6.22 mmol/L) are major risk factors for hyperuricemia. Compared to the 35-44-year age group [adjusted odds ratio (AOR) = 1], the risk of hyperuricemia increased 1.61-fold in the 65-74-year age group (AOR = 1.61, 95% CI: 1.34-1.91; P < 0.001), and 1.71-fold in the 75-and older age group (AOR = 1.71, 95% CI: 1.27-2.29; P < 0.001). There was a 1.45-fold higher risk of hyperuricemia in men (AOR = 1.45, 95% CI: 1.24-1.68; P < 0.001) compared to women. Further, the risk of hyperuricemia increased significantly with drinking (AOR = 1.36; 95% CI: 1.16-1.61; P < 0.001), overweight (AOR = 1.25; 95% CI: 1.06-1.48; P = 0.01), obesity (AOR = 1.28; 95% CI: 1.10-1.49; P < 0.001), waist circumference (AOR = 1.48; 95% CI: 1.24-1.78; P < 0.001), TC (≥6.22 mmol/L, AOR = 1.45; 95% CI: 1.19-1.75; P < 0.001), TG (≥2.26 mmol/L, AOR = 2.74; 95% CI: 2.39-3.14; P < 0.001). Conclusions: These findings documented that the hyperuricemia is prevalent in the economically developing regions of northwest China. Hyperuricemia is associated with advanced age, male ender and general metabolic and cardiovascular risk factors. Obesity and dyslip...

ObjectiveOverweight and obesity have been shown to be related to multiple chronic conditions, leading to a heavy economic burden on society throughout the world. This study aims to estimate the prevalence of overweight and obesity and determine potential influencing factors among adults in Xinjiang, northwest China.DesignA community-based observational study.SettingThe First Affiliated Hospital of Xinjiang Medical University.MethodsIn total, 14 618 adult participants (7799 males; 6819 females) aged over 35 years were recruited from the Cardiovascular Risk Survey conducted in 2010. Data were obtained from face-to-face interviews and physical examinations. The sample was used to estimate the prevalence of overweight (body mass index (BMI) 24–28 kg/m2) and obesity (BMI ≥28 kg/m2) in Xinjiang Province. Influencing factors were analysed based on statistical methods.ResultsIn Xinjiang Province, the overall prevalence of overweight was 36.5% (male 40.1%; female 33.4%), and the prevalence of obesity was 26.5% (male 27.2%; female 25.8%). The prevalence of both overweight and obesity were higher in women than in men (p<0.001). The main influencing factors for overweight and obesity were sex, age, race, marital status, education level, occupation, smoking, drinking, hypertension, diabetes and dyslipidaemia (p<0.05).ConclusionsThis study estimated that the prevalence of overweight and obesity among adult residents of Xinjiang Province, northwest China, was high. These data suggest that efforts related to the prevention and control of overweight and obesity should be a public health priority in northwest China.

This study presents a rapid and low-cost method to detect thyroid dysfunction using serum Raman spectroscopy combined with support vector machine (SVM). The serum samples taken from 34 thyroid dysfunction patients and 40 healthy volunteers were measured in this study. Tentative assignments of the Raman bands in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was used for feature extraction and reduced the dimension of high-dimension spectral data; then, SVM was employed to establish an effective discriminant model. To improve the efficiency and accuracy of the SVM discriminant model, we proposed artificial fish coupled with uniform design (AFUD) algorithm to optimize the SVM parameters. The average accuracy of 30 discriminant results reached 82.74%, and the average optimization time was 0.45 s. The results demonstrate that the serum Raman spectroscopy technique combined with the AFUD-SVM discriminant model has great potential for the detection of thyroid dysfunction. This technique could be used to develop a portable, rapid, and low-cost device for detecting thyroid function to meet the needs of individuals and communities.

BackgroundDiabetes is a major global public health problem driven by a high prevalence of metabolic risk factors.ObjectiveTo describe the differences of metabolic risk factors of type 2 diabetes, as well as glycemic control and complicated diabetic complications between rural and urban Uygur residents in Xinjiang Uygur Autonomous Region of China.MethodsThis comparative cross-sectional study, conducted among 2879 urban and 918 rural participants in Xinjiang, China, assessed the metabolic risk factors of diabetes and related complications differences between urban and rural settlements.ResultsCompared to rural areas, urban participants had higher education level and more average income, little physical activity, less triglycerides and higher HDL-c (p < 0.05 respectively). Differences in metabolic risk factors by urban/rural residence included overweight or obesity, triglycerides (≥1.71mmol/l), HDL-c (< 1.04 mmol/l), alcohol intake, and physical inactivity (p < 0.01 respectively). There was significant difference regarding the prevalence of HbA1c >8% (48.1% versus 54.5%, p = 0.019) between rural and urban diabetic participants. No significant difference in the prevalence of type 2 diabetic complications between urban and rural participants (74.9% versus 72.2%; p = 0.263) was detected. Compared to rural participants, the most prevalent modifiable risk factors associated with diabetic complications in urban participants were obesity (BMI ≥ 28 Kg/m2), HDL-c (< 1.04 mmol/l), physical inactivity and irregular eating habits (p = 0.035, p = 0.001, p < 0.001, and p = 0.013, respectively).ConclusionsUrban settlers were significantly more likely to have metabolic risk factors highlighting the need for public health efforts to improve health outcomes for these vulnerable populations. Diabetes related complications risk factors were prevalent amongst rural and urban diabetes settlers.

BackgroundRecent studies have shown osteocalcin (OC) plays an important role in regulating glucose and lipid metabolism. Thus, the aim of this study was to investigate the association of OC with glucose and lipid metabolism in patients with type 2 diabetes mellitus (T2DM) in the Chinese Han and Uygur population.MethodsA total of 1397 T2DM patients (705 Han and 692 Uygur T2DM patients) were enrolled in the present study. Lipid profile, glucose metabolic indices and total OC (TOC) were measured. Homeostasis model assessment of β-cells function (HOMA-β), insulin sensitivity (HOMA-IS) and insulin resistance (HOMA-IR) were also calculated in all participants. Pearson/Spearman correlation analysis and multivariate stepwise regression analysis were adopted to test the relationships between OC and those parameters.ResultsUygur T2DM patients had significantly higher body mass index (BMI), hemoglobin A1C (HbA1C) and lower TOC compared with their Han counterparts (all P < 0.05). HbA1C was negatively associated with TOC in all Uygur and Han T2DM patients (Total: Uygur: t = −3.468, P = 0.001; Han: t = −4.169, P < 0.001). BMI was inversely associated with TOC in all Uygur T2DM patients (Males: t = −2.893, P = 0.014; Females: t = −2.250, P = 0.027, respectively). Multivariate stepwise regression analysis showed that TOC was positively correlated with HOMA-β in the Uygur male group (β = 2.101, P = 0.040) and negatively associated with BMI in all Uygur T2DM patients (Males: β = −1.563, P = 0.011; Females: β = −1.284, P = 0.016, respectively). No significant differences were observed between TOC and lipid profiles in all participants (all P > 0.05).ConclusionThere were differences in the associations between TOC and glucose metabolism in Han and Uygur T2DM patients, indicating genetic factors may play a role in modulating OC and glucose metabolism in different ethnic population.

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The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm −1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.