Marking up childhood obesity

Metabolic fingerprinting has been shown to be a powerful tool for exploring Biomarkers in a range of disorders and the pathophysiological mechanisms of disease. A new study has now applied the technique to childhood obesity to intriguing effect.

Childhood obesity is a growing problem in the developed world, with potentially far-reaching long-term health implications including increased incidence of type 2 diabetes mellitus and cardiovascular diseases. Indeed, while other health indicators have apparently been consigned to the history books leading to more people living well into old age, today's youngsters may well reverse that trend if the challenged of this public-health crisis is not met urgently.

"Screening, prevention and early treatment of childhood obesity are very important, explain researchers in the March 2010 issue of the Journal of Pharmaceutical and Biomedical Analysis. Maomao Zeng, Yizeng Liang, Hongdong Li, Mei Wang, Bing Wang, Xian Chen, Neng Zhou, Dongsheng Cao, Jing Wu of the Central South University, in Changsha, and Yulin Normal College in Yulin, China, explain that elucidating the causes and processes involved in the development of childhood obesity are critical.

BMI (body-mass index), blood glucose, total triglycerides, total cholesterol, high density lipoprotein, and low density lipoprotein are commonly determined in the ongoing monitoring of obesity and related complications.

"Metabolic pattern discrimination and biomarker screening for childhood obesity may be effective for this issue," the say, "Since metabolic fingerprinting has proved to be a powerful tool for exploring systemic metabolic changes and biomarker candidates of [other] diseases. They suggest that it might also shed light on the pathophysiological process of childhood obesity.

Previous researchers have investigated the potential of metabolomic studies in obesity. Until now, it has not been used in research into childhood obesity specifically.

Wu and colleagues point out that metabolic fingerprinting research requires information-rich analytical data, which can be obtained using the likes of high efficiency gas chromatographic (GC) separation techniques and mass spectrometry (MS) for metabolite identification. Multivariate statistical analysis of GC/MS data, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) have been used widely in metabolic fingerprinting for identification of biomarkers for given conditions.

The team has now successfully demonstrated that a new multivariate strategy for metabolic fingerprinting using GC/MS data can reveal important disturbances in the metabolic patterns of childhood obesity as well as potentially uncovering significant biomarkers.

As such, the team obtained plasma samples from normal weight, overweight and obese children and applied GC/MS to these. Uncorrelated linear discriminant analysis (ULDA) was then used to analyse the data. New variables with optimal discriminatory ability and low redundancy, as well as canonical correlation analysis (CCA) were employed in looking for metabolic perturbations and biomarkers.

ULDA revealed that the metabolic patterns of the three groups - normal weight, overweight, and obese - were different. They discovered that several metabolites, including isoleucine, glyceric acid, serine, 2,3,4-trihydroxybutyric acid and phenylalanine had potential as biomarkers of childhood obesity by both ULDA and CCA (canonical correlation analysis). The CCA work showed a direct correlation between metabolic patterns and clinical parameters, and suggest that waist to hip ratio taken together with TG, TC, HDL, and LDL measurements are the most important parameters associated closely with the metabolic perturbations of childhood obesity. Moreover, once again, this work highlights just how inadequate is BMI as an indicator of health.

"The results have demonstrated that the proposed metabolic fingerprinting approach may be a useful tool for discovering metabolic abnormalities and possible biomarkers for childhood obesity," the team concludes. As the approach matures the team expects to obtain useful information that might allow healthcare providers to address the problem of metabolic abnormalities in children with obesity.