The aim of the current meta-analysis was to evaluate the accuracy of using BMI based on self-reported height and weight (BMIsr) to estimate the prevalence of overweight and obesity among children and adolescents.

A systematic literature search was conducted to select studies that compared the prevalence rates of overweight and obesity based on BMIsr and BMIm (BMI based on measured height and weight). A random-effect model was assumed to estimate summary prevalence rates and prevalence ratio (PR).

Thirty-seven studies were included. The aggregated prevalence of overweight and obesity based on BMIsr (0·190, 95 % CI 0·163, 0·221) was significantly lower than that based on BMIm (0·233, 95 % CI 0·203, 0·265). The pooled mean PR was 0·823 (95 % CI 0·775, 0·875). Moderator analyses showed that the underestimation was related to gender, age, weight status screened (overweight v. obesity) and weight status screening tool.

BMIsr may produce less biased results under some conditions than others. Future researchers using BMIsr may consider these findings and avoid the conditions that could lead to more severe underestimation of the prevalence of overweight and obesity among children and adolescents.

Early identification of patients with bipolar disorder during their first depressive episode is beneficial to the outcome of the disorder and treatment, but traditionally this has been a great challenge to clinicians. Recently, brain-derived neurotrophic factor (BDNF) has been suggested to be involved in the pathophysiology of bipolar disorder and major depressive disorder (MDD), but it is not clear whether BDNF levels can be used to predict bipolar disorder among patients in their first major depressive episode.

To explore whether BDNF levels can differentiate between MDD and bipolar disorder in the first depressive episode.

A total of 203 patients with a first major depressive episode as well as 167 healthy controls were recruited. After 3 years of bi-annual follow-up, 164 patients with a major depressive episode completed the study, and of these, 21 were identified as having bipolar disorder and 143 patients were diagnosed as having MDD. BDNF gene expression and plasma levels at baseline were compared among the bipolar disorder, MDD and healthy control groups. Logistic regression and decision tree methods were applied to determine the best model for predicting bipolar disorder at the first depressive episode.

At baseline, patients in the bipolar disorder and MDD groups showed lower BDNF mRNA levels (P<0.001 and P = 0.02 respectively) and plasma levels (P = 0.002 and P = 0.01 respectively) compared with healthy controls. Similarly, BDNF levels in the bipolar disorder group were lower than those in the MDD group. These results showed that the best model for predicting bipolar disorder during a first depressive episode was a combination of BDNF mRNA levels with plasma BDNF levels (receiver operating characteristics (ROC) = 0.80, logistic regression; ROC = 0.84, decision tree).

Our findings suggest that BDNF levels may serve as a potential differential diagnostic biomarker for bipolar disorder in a patient's first depressive episode.