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Summary: Background: The relationship between sugar‐free beverage (SFB) intake and childhood obesity among Chinese children is unknown. Objectives: To describe the status of SFB consumption among children and adolescents in China and assess the association between SFB intake and different types of obesity. Methods: The study was based on the baseline data of an ongoing cohort project named Evaluation and Monitoring on School‐based Nutrition and Growth in Shenzhen (EMSNGS). Food frequency questionnaires were used to collect information on SFB consumption in 3227 students aged 9–17. Physical and clinical examinations were conducted by trained investigators and clinicians. Multivariable binary logistic regression models were performed to assess the association between SFB intake and general obesity, overweight/obesity, abdominal obesity, metabolically unhealthy overweight (MUOW)/metabolically unhealthy obesity (MUO). Results: The median age of the participants was 13.28 years. Among the participants, 55.2% were boys, and 66.1% were adolescents. The median SFB consumption was 16.67 mL/d. After adjusting for potential confounding factors, each 100 mL increase in daily SFB intake was associated with an increased risk of overweight/obesity (OR = 1.14; 95%CI: 1.06–1.23), abdominal obesity (OR = 1.12; 95%CI: 1.03–1.23), and MUOW/MUO (OR = 1.12; 95%CI: 1.02–1.21), respectively. Stratified analyses showed that family income may have an impact on the association between SFB intake and overweight/obesity (P for interaction = 0.021) and abdominal obesity (P for interaction = 0.031). Conclusion: SFB intake was positively associated with childhood obesity in Chinese children, particularly among individuals with high‐income families. [ABSTRACT FROM AUTHOR]

Background. The association between atherogenic index of plasma (AIP) and hyperuricemia remains indistinct. This study was aimed to examine the relationship between AIP and hyperuricemia among the middle-aged and the elderly Chinese population. Methods. Datasets were retrieved from the China Health and Retirement Longitudinal Study (CHARLS) survey conducted in 2011 and 2015. 13,021 participants in the CHARLS in 2011 and 7,017 participants involved both in 2011 and 2015 were included, respectively. The measurement of AIP and hyperuricemia was based on the test of fasting blood. Association between AIP and hyperuricemia was assessed by logistic regression, and the nonlinear association was examined by restricted cubic splines (RCS). The cutoff point of AIP was calculated using receiver operator curve (ROC). 1 : 1 propensity score matching (PSM) was adopted to further explore the relationship between AIP and hyperuricemia. Results. In the section of a cross-sectional study, a positive association between AIP and hyperuricemia was found. The odds ratios (ORs) of hyperuricemia were 1.00 (reference), 1.52 (1.10–2.10), 1.80 (1.31–2.47), and 3.81 (2.84–5.11). Nonlinear association was not detected using RCS analysis. There were 664 hyperuricemia cases during the four years follow-up. The hyperuricemia prevalence was 9.5%. In the fully adjusted longitudinal analysis, the ORs for hyperuricemia across the quartiles of AIP were 1.00 (reference), 1.00 (0.74–1.37), 1.59 (1.20–2.11), and 2.55 (1.94–3.35), respectively. In the longitudinal analysis after PSM, the OR of hyperuricemia were 1.91 (1.45, 2.51) and 1.92 (1.45, 2.54) in the univariate and multivariate model, respectively. Conclusion. AIP can predict the prevalence of hyperuricemia in the Chinese middle-aged and elderly population. [ABSTRACT FROM AUTHOR]

Backgrounds. Acute kidney injury (AKI) is a prevalent nonneurological complication in patients with traumatic brain injury (TBI). We designed this study to explore the association between serum uric acid (SUA) level and the occurrence of AKI following TBI. Methods. This is a retrospective single-center study. A total of 479 patients admitted with TBI were included in this study. We utilized SUA and other risk factors for AKI to construct a predictive model by performing multivariate logistic regression. 374 patients and 105 patients were, respectively, divided into a training set and validation set. The predictive value of the single SUA and constructed model was evaluated by drawing a receiver operating characteristic (ROC) curve. AKI was diagnosed according to the KIDGO criteria. Results. 79 (21.12%) patients were diagnosed with AKI in the training cohort. The patients in the AKI group are older than those in the non-AKI group (p = 0.01). And the Glasgow Coma Scale (GCS) of the AKI group was lower than that of the non-AKI group (p < 0.001). In a multivariate logistic regression analysis, we found that heart rate (p = 0.041), shock (p = 0.018), serum creatinine (p < 0.001), and serum uric acid (SUA) (p < 0.001) were significant risk factors for AKI. Bivariate correlation analyses showed that serum creatinine was moderately positively correlated with SUA (r = 0.523 , p < 0.001). Finally, the area under the receiver operating characteristic curve (AUC) of SUA for predicting AKI in the training set and validation set was 0.850 (0.805-0.895) and 0.869 (0.801-0.938), respectively. Conclusions. SUA is an effective risk factor for AKI following TBI. Combining SUA with serum creatinine could more accurately identify TBI patients with high risk of developing AKI. [ABSTRACT FROM AUTHOR]