Effects of obesity on the lipid and metabolite profiles of young adults by serum 1H-NMR spectroscopy

Highlights of three metabolites HDL and reduction in blood pressure values after dietary fiber supplementation in overweight and obese normotensive women: a metabolomic study

INTRODUCTION

The prevalence of hypertension and obesity are a worldwide concern.

OBJETIVES

Assess the metabolites profile after intervention with mixed dietary fiber in overweight and obese normotensive women.

METHODS

This is a randomized double blind placebo-controlled study. Through a simple randomization process, two groups were allocated, with eleven women (group 1) receiving 12 g of mixed dietary fiber and thirteen women (group 2) receiving 12 g of placebo (corn starch) for eight weeks. Anthropometric and biochemical tests and lifestyle were analyzed. As for evaluation metabolomics, used a 1H NMR. The data matrix generated 96 samples and 225 variables, which was exported in the ASCII format for the "The Unscrumbler" statistics software (version 9.7, CAMO Process).

RESULTS

After the intervention with mixed dietary fiber, significant differences were observed between the main types of metabolites, referring to the increase in the relative peak areas of in three HDL metabolites 4.94 ppm (0.0086*), HDL 1.28 ppm (0 .0337*), HDL 0.88 ppm (0.0224*) and an α-glucose metabolite 4.90 ppm (0.0106) and the reduction in systolic blood pressure (SBP) (0.0292*) of 7 mmHg in the reference range and in the placebo group there was a reduction in SBP (0.0118*) of 4 mmHg and of a choline metabolite 3.65 ppm (0.0266*), which does not call into question the validity of these results in the literature.

CONCLUSION

The synergism of the functions of these statistically highlighted metabolites contributed to prevention the increase in SBP after fiber intervention in overweight and obese normotensive women.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food

The definition of metabolic syndrome (MetS) has undergone several changes over the years due to the difficulty in establishing universal criteria for it. Underlying the disorders related to MetS is almost invariably a pro-inflammatory state related to altered glucose metabolism, which could lead to elevated cardiovascular risk. Indeed, the complications closely related to MetS are cardiovascular diseases (CVDs) and type 2 diabetes (T2D). It has been observed that the predisposition to metabolic syndrome is modulated by complex interactions between human microbiota, genetic factors, and diet. This review provides a summary of the last decade of literature related to three principal aspects of MetS: (i) the syndrome's definition and classification, pathophysiology, and treatment approaches; (ii) prediction and diagnosis underlying the biomarkers identified by means of advanced methodologies (NMR, LC/GC-MS, and LC, LC-MS); and (iii) the role of foods and food components in prevention and/or treatment of MetS, demonstrating a possible role of specific foods intake in the development of MetS.

Advanced Molecular Imaging (MRI/MRS/1H NMR) for Metabolic Information in Young Adults with Health Risk Obesity

BACKGROUND

Obesity or being overweight is a medical condition of abnormal body fat accumulation which is associated with a higher risk of developing metabolic syndrome. The distinct body fat depots on specific parts of the anatomy have unique metabolic properties and different types of regional excessive fat distribution can be a disease hazard. The aim of this study was to identify the metabolome and molecular imaging phenotypes among a young adult population.

METHODS

The amount and distribution of fat and lipid metabolites profile in the abdomen, liver, and calf muscles of 46 normal weight, 17 overweight, and 13 obese participants were acquired using MRI and MR spectroscopy (MRS), respectively. The serum metabolic profile was obtained using proton NMR spectroscopy. NMR spectra were integrated into seven integration regions, which reflect relative metabolites.

RESULTS

A significant metabolic disorder symptom appeared in the overweight and obese group, and increased lipid deposition occurred in the abdomen, hepatocytes, and muscles that were statistically significant. Overall, the visceral fat depots had a marked influence on dyslipidemia biomarkers, blood triglyceride (r = 0.592, p < 0.001), and high-density lipoprotein cholesterol (r = -0.484, p < 0.001). Intrahepatocellular lipid was associated with diabetes predictors for hemoglobin (HbA1c%; r = 0.379, p < 0.001) and for fasting blood sugar (r = 0.333, p < 0.05). The lipid signals in serum triglyceride and glucose signals gave similar correspondence to biochemical lipid profiles.

CONCLUSIONS

This study proves the association between alteration in metabolome in young adults, which is the key population for early prevention of obesity and metabolic syndrome. This study suggests that dyslipidemia prevalence is influenced mainly by the visceral fat depot, and liver fat depot is a key determinant for glucose metabolism and hyperglycemia. Moreover, noninvasive advanced molecular imaging completely elucidated the impact of fat distribution on the anthropometric and laboratory parameters, especially indices of the metabolic syndrome biomarkers in young adults.

Waist Circumference and BMI Are Strongly Correlated with MRI-Derived Fat Compartments in Young Adults

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.

Identification of Metabolic Phenotypes in Young Adults with Obesity by 1H NMR Metabolomics of Blood Serum

(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.

Choline Metabolites, Hydroxybutyrate and HDL after Dietary Fiber Supplementation in Overweight/Obese Hypertensive Women: A Metabolomic Study

Metabolomics has been increasingly used to evaluate metabolic changes associated with morbidities. The objective of this study is to assess the metabolic profile before and after intervention with mixed dietary fiber in overweight and obese hypertensive women. This is an intervention study, and the sample consists of 14 women aged 28 to 58 years. An intervention with 12 g of mixed soluble and insoluble fiber is performed for a period of eight weeks. Serum metabolites are identified using a Bruker ¹H NMR spectrometer at 400 MHz. Multivariate data analysis, including principal component analysis (PCA), is used to differentiate the two groups. After supplementation with dietary fiber, there is a significant increase in the peak intensity values of the metabolites HDL-C (0.0010*), choline (0.0012*) and hydroxybutyrate (0.0010*) as well as a decrease in systolic (0.0013*) and diastolic (0.0026*) blood pressure. The analysis of the metabolomic profile allows the identification of metabolites that have been associated in the literature with hypertension and excess weight (choline, hydroxybutyrate and amino acids) and with fiber intake (choline, hydroxybutyrate and amino acids) in addition to an increase in HDL-C. The increase in the detection of the described metabolites possibly occurs due to the presence of pathologies and the use of fiber in the intervention, which also contributes to elevated HDL-c and reduced blood pressure.

Insulin negatively regulates dedifferentiation of mouse adipocytes in vitro

Insulin plays an important role during adipogenic differentiation of animal preadipocytes and the maintenance of mature phenotypes. However, its role and mechanism in dedifferentiation of adipocyte remains unclear. This study investigated the effects of insulin on dedifferentiation of mice adipocytes, and the potential mechanisms. The preadipocytes were isolated from the subcutaneous white adipose tissue of wild type (WT), TNFα gene mutant (TNFα-/-), leptin gene spontaneous point mutant (db/db) and TNFα-/-/db/db mice and were then induced for differentiation. Interestingly, dedifferentiation of these adipocytes occurred once removing exogenous insulin from the adipogenic medium. As characteristics of dedifferentiation of the adipocytes, downregulation of adipogenic markers, upregulation of stemness markers and loss of intracellular lipids were observed from the four genotypes. Notably, dedifferentiation was occurring earlier if the insulin signal was blocked. These dedifferentiated cells regained the potentials of the stem cell-like characteristics. There is no significant difference in the characteristics of the dedifferentiation between the adipocytes. Overall, the study provided evidence that insulin plays a negative regulatory role in the dedifferentiation of adipocytes. We also confirmed that both dedifferentiation of mouse adipocytes, and effect of the insulin on this process were independent of the cell genotypes, while it is a widespread phenomenon in the adipocytes.

Serum Metabolomic Profiles Associated With Untreated Metabolic Syndrome Patients in the Chinese Population

Metabolomics is a promising technology for elucidating the mechanisms of metabolic syndrome (MetS). However, measurements in patients with MetS under different conditions vary. Metabolomics experiments in different populations and pathophysiological conditions are, therefore, indispensable. We performed a serum metabolomics investigation in untreated patients with MetS in the Chinese population. Untreated patients with MetS were recruited to this study. Metabolites were measured using a traditional ¹H nuclear magnetic resonance (NMR) experiment followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS‐DA). Key metabolic pathways were identified by searching the Kyoto Encyclopedia of Genes and Genomes Pathway Database. A total of 28 patients with MetS and 30 healthy subjects were enrolled. All patients were untreated because they were unaware of or neglected to treat their MetS. By ¹H NMR, we identified 49 known substances. Following PCA and OPLS‐DA, 36 metabolites were confirmed to be closely associated with MetS compared with the control group; 33 metabolites were increased, whereas 3 metabolites were reduced. Importantly, 14 metabolites that changed in the serum of these untreated patients with MetS were previously unreported. Pathway analysis revealed the top 15 metabolic pathways associated with untreated MetS, which included 3 amino acid metabolic pathways. Our data suggest that untreated patients exhibit a worse pathophysiologic manifestation, which may result in more rapid progression of MetS. Thus, we propose that health education be reinforced to improve the public’s knowledge, attitude, and practice regarding MetS. The rates of “untreated” patients due to unawareness and neglect must be reduced immediately.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.