Purpose: Few studies have summarized findings for the effect of metformin on obesity indices. Therefore, we aimed to conduct a systematic review and meta-analysis on the effect of metformin on obesity indices among children and adolescents.Methods: Relevant articles published up to September 2018 were searched in SCOPUS, Medline, and Google Scholar using appropriate keywords. All clinical trials that examined the effect of metformin on obesity indices in children and adolescents were included.Results: Overall, 38 studies, including 2199 participants (39.75% male and 60.25% female), were included. The pooled results indicated that metformin significantly reduced BMI [weighted mean difference (WMD): -1.07 kg/m 2 ; 95% confidence interval (CI): -1.43 to -0.72]. Same findings were found for waist circumference (WC) (WMD: -1.93 cm; 95% CI: -2.69 to -1.16). Metformin also reduced body weight in all participants (WMD: -2.51 kg; 95% CI: -3.14 to -1.89). Moreover, it reduced body fat mass in patients with overweight or obesity (WMD: -1.90%; 95% CI: -3.25 to -0.56) and chronic diseases (WMD: -1.41%; 95% CI: -2.23 to -0.58), but not among those with growth problems. Metformin therapy did not affect lean body mass (LBM) in patients with overweight or obesity and growth problems; however, it reduced LBM in patients with chronic diseases (WMD: -1.49 kg; 95% CI: -2.69 to -0.30).Conclusions: We found a significant reduction in BMI, body weight, WC, and fat mass following administration with metformin. However, the effect of metformin on LBM was not significant. Further studies are required to confirm these findings.

Diabetes prevalence and incidence among youth have been increasing globally. Type 1 Diabetes (T1D) in children or adolescents accounts for 5-10% of all diagnosed cases of diabetes. Emerging evidence indicates that genetic factors, especially genes in the human leukocyte antigen region, are not the only factors involved in the predisposition of an individual to T1D. The pathogenesis and development of T1D is driven by both genetic predisposition and environmental factors. Studies indicate that gut microbiota is one of the potential environmental influencers involved in the pathophysiology of TID. Gut microbiota mediates the development of diabetes by altering intestinal permeability, modifying intestinal immunity, and molecular mimicry. The gut microbial diversity, taxonomic profile, and functional potential of gut microbes are significantly altered in individuals with T1D as compared to healthy individuals. However, studies are still needed to identify the specific microbes and microbial metabolites that are involved in the development and pathogenesis of T1D. This will help the development of microbiome-based therapeutic strategies for the prevention and treatment of T1D. The present review article highlights the following: (i) the current knowledge and knowledge gaps in understanding the association between T1D and gut microbiome specifically focusing on the composition and functional potential of gut microbiome in children and adolescents, (ii) the possible mechanisms involved in gut microbiome-mediated T1D pathogenesis, and (iii) challenges and future direction in this field.

No abstract

No abstract

Three-dimensional (3D) anthropometry has become an emerging field with the advancement of 3D body scanning technology over the recent years. Conventional method of anthropometry data measurement is time consuming and error-prone. The 3D whole-body surface scanner, which was initially developed for the clothing industry, contributes to healthcare application by providing accurate anthropometric measurements and data visualization in body size, shape and skin surface area. The body scanner utilized 3D photogrammetry technology to generate high quality digital anthropometric information about shapes and sizes of an individual. The aim of the study is to investigate body size and shape of Malaysian females. A cross-sectional study was conducted using random sampling technique. Recruitment of subjects were randomized and based on age and ethnicity. A preliminary study of 160 female subjects was carried out. In addition to the scanning procedures using the body scanner, selected manual anthropometric measurements for height, weight and skinfold thickness were obtained using manual methods and demographic data such as age and ethnic groups were recorded. All manual measurement followed the requirements and procedures stated in the respective ISO documents, involving body posture, landmark and the instrument used. Descriptive statistical analysis for body dimensions and calculations were carried out. The 3D scanners undoubtedly produced highly accurate, consistent and repeatable measurements. However, the scan data can be slightly different from traditional anthropometric data especially for circumference measurements such as chest, waist and hip. Therefore, scanned anthropometric data and manual measurement data are more useful together. We envisage that this study would be beneficial for healthcare providers for growth monitoring and early diagnosis of health problems related to obesity especially for the Malaysian population. Although the study focuses on the healthcare sector, datasets can also be utilized by the industry in the design and production of consumer products related on the human body.