The number of individuals suffering from fatty liver is increasing worldwide, leading to interest in the noninvasive study of liver fat. Magnetic resonance spectroscopy (MRS) is a powerful tool that allows direct quantification of metabolites in tissue or areas of interest. MRS has been applied in both research and clinical studies to assess liver fat noninvasively in vivo. MRS has also demonstrated excellent performance in liver fat assessment with high sensitivity and specificity compared to biopsy and other imaging modalities. Because of these qualities, MRS has been generally accepted as the reference standard for the noninvasive measurement of liver steatosis. MRS is an evolving technique with high potential as a diagnostic tool in the clinical setting. This review aims to provide a brief overview of the MRS principle for liver fat assessment and its application, and to summarize the current state of MRS study in comparison to other techniques.

Young adulthood is increasingly considered as a vulnerable age group for significant weight gain, and it is apparent that there is an increasing number of new cases of metabolic syndrome developing among this population. This study included 60 young adult volunteers (18–26 years old). All participants obtained a calculated total abdominal fat percentage, subcutaneous fat percentage, and visceral fat percentage using a semiautomatic segmentation technique from T1-weighted magnetic resonance imaging (MRI) images of the abdomen. The results show strongest correlation between abdominal fat and BMI (r = 0.824) followed by subcutaneous fat (r = 0.768), and visceral fat (r = 0.633) respectively, (p < 0.001 for all, after having been adjusted for age and gender). Among anthropometric measurements, waist circumference showed strong correlation with all fat compartments (r = 0.737 for abdominal, r = 0.707 for subcutaneous fat, and r = 0.512 for visceral fat; p < 0.001 for all). The results obtained from examining the blood revealed that there was a moderate positive correlation relationship between all fat compartments with triglyceride, high-density lipoprotein, and fasting glucose levels (p < 0.05 for all). This study suggests that both BMI and waist circumference could be used to assess the fat compartments and treatment targets to reduce the risk of metabolic disorders and health risks in the young adult population.

(1) Since the obesity prevalence rate has been consistently increasing, it is necessary to find an effective way to prevent and treat it. Although progress is being made to reduce obesity in the young adult population, a better understanding of obesity-related metabolomics and related biochemical mechanisms is urgently needed for developing appropriate screening strategies. Therefore, the aim of this study is to identify the serum metabolic profile associated with young adult obesity and its metabolic phenotypes. (2) Methods: The serum metabolic profile of 30 obese and 30 normal-weight young adults was obtained using proton nuclear magnetic resonance spectroscopy (¹H NMR). ¹H NMR spectra were integrated into 24 integration regions, which reflect relative metabolites, and were used as statistical variables. (3) Results: The obese group showed increased levels of lipids, glucose, glutamate, N-acetyl glycoprotein, alanine, lactate, 3 hydroxybutyrate and branch chain amino acid (BCAA), and decreased levels of choline as compared with the normal-weight group. Non-hyperlipidemia obese adults showed lower levels of lipids and lactate, glutamate, acetoacetate, N-acetyl glycoprotein, isoleucine, and higher levels of choline and glutamine, as compared with hyperlipidemic obese adults. (4) Conclusions: This study reveals valuable findings in the field of metabolomics and young adult obesity. We propose several serum biomarkers that distinguish between normal weight and obese adults, i.e., glutamine (higher in the normal group, p < 0.05), and lactate, BCAAs, acetoacetate and 3-hydroxybutyrate (higher in the obese group, p < 0.05). In addition, visceral fat and serum TG, glutamate, acetoacetate, N-acetyl glycoprotein, unsaturated lipid, isoleucine, and VLDL/LDL are higher (p < 0.05) in the obese with hyperlipidemia. Therefore, they can be used as biomarkers to identify these two types of obesity.