Omics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people's lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.

A Systematic Review of Proteomics in Obesity: Unpacking the Molecular Puzzle

PURPOSE OF REVIEW

The present study aims to review the existing literature to identify pathophysiological proteins in obesity by conducting a systematic review of proteomics studies. Proteomics may reveal the mechanisms of obesity development and clarify the links between obesity and related diseases, improving our comprehension of obesity and its clinical implications.

RECENT FINDINGS

Most of the molecular events implicated in obesity development remain incomplete. Proteomics stands as a powerful tool for elucidating the intricate interactions among proteins in the context of obesity. This methodology has the potential to identify proteins involved in pathological processes and to evaluate changes in protein abundance during obesity development, contributing to the identification of early disease predisposition, monitoring the effectiveness of interventions and improving disease management overall. Despite many non-targeted proteomic studies exploring obesity, a comprehensive and up-to-date systematic review of the molecular events implicated in obesity development is lacking. The lack of such a review presents a significant challenge for researchers trying to interpret the existing literature. This systematic review was conducted following the PRISMA guidelines and included sixteen human proteomic studies, each of which delineated proteins exhibiting significant alterations in obesity. A total of 41 proteins were reported to be altered in obesity by at least two or more studies. These proteins were involved in metabolic pathways, oxidative stress responses, inflammatory processes, protein folding, coagulation, as well as structure/cytoskeleton. Many of the identified proteomic biomarkers of obesity have also been reported to be dysregulated in obesity-related disease. Among them, seven proteins, which belong to metabolic pathways (aldehyde dehydrogenase and apolipoprotein A1), the chaperone family (albumin, heat shock protein beta 1, protein disulfide-isomerase A3) and oxidative stress and inflammation proteins (catalase and complement C3), could potentially serve as biomarkers for the progression of obesity and the development of comorbidities, contributing to personalized medicine in the field of obesity. Our systematic review in proteomics represents a substantial step forward in unravelling the complexities of protein alterations associated with obesity. It provides valuable insights into the pathophysiological mechanisms underlying obesity, thereby opening avenues for the discovery of potential biomarkers and the development of personalized medicine in obesity.

Exploring the Link between Oxidative Stress, Selenium Levels, and Obesity in Youth

Obesity is a worldwide increasing concern. Although in adults this is easily estimated with the body mass index, in children, who are constantly growing and whose bodies are changing, the reference points to assess weight status are age and gender, and need corroboration with complementary data, making their quantification highly difficult. The present review explores the interaction spectrum of oxidative stress, selenium status, and obesity in children and adolescents. Any factor related to oxidative stress that triggers obesity and, conversely, obesity that induces oxidative stress are part of a vicious circle, a complex chain of mechanisms that derive from each other and reinforce each other with serious health consequences. Selenium and its compounds exhibit key antioxidant activity and also have a significant role in the nutritional evaluation of obese children. The balance of selenium intake, retention, and metabolism emerges as a vital aspect of health, reflecting the complex interactions between diet, oxidative stress, and obesity. Understanding whether selenium status is a contributor to or a consequence of obesity could inform nutritional interventions and public health strategies aimed at preventing and managing obesity from an early age.

Elevated Immunoglobulin E Serum Levels: Possible Underlying Factors That Can Cause an Inborn Error of Immunity in the Pediatric Population with Recurrent Infections

Elevated immunoglobulin E (IgE) levels are commonly associated with allergies. However, high IgE levels are also found in several other infectious and non-infectious disorders. Elevated IgE levels typically suggest allergies, eczema, or recurrent skin infections. Hyperimmunoglobulin E (hyper-IgE) levels typically reflect a monogenic atopic condition or inborn immune defects with an atopic phenotype. The aim of our research is to investigate and observe the clinical characteristics of children with increased IgE levels who have previously manifested infectious diseases. Furthermore, the retrospective study considers other factors, such as demographic characteristics (sex, area/environment, and age), and their effect on IgE serum levels. To answer this question, we conducted a one-year hospital-based retrospective study that included 200 hospitalized children who had at least two viral or bacterial infections in the six months preceding hospitalization. Measurements of IgE and allergen panels (respiratory and digestive) using blood samples revealed that individuals who tested positive for the body's synthesis of hyper-IgE were not observably allergic to any potential allergens despite having higher total serum IgE. According to the results, there was a strong correlation between elevated IgE serum levels and a history of infectious diseases among the patients.

(+)/(-)-Gerbeloid A, a pair of unprecedented coumarin-based polycyclic meroterpenoid enantiomers from Gerbera piloselloides: Structural elucidation, semi-synthesis, and lipid-lowering activity

A pair of coumarin-based polycyclic meroterpenoid enantiomers (+)/(-)-gerbeloid A [(+)-1a and (-)-1b] were isolated from the medicinal plant Gerbera piloselloides, which have a unique caged oxatricyclo [4.2.2.03,8] decene scaffold. Their planar and three-dimensional structures were exhaustively characterized by comprehensive spectroscopic data and X-ray diffraction analysis. Guided by the hypothetical biosynthetic pathway, the biomimetic synthesis of racemic 1 was achieved using 4-hydroxy-5-methylcoumarin and citral as the starting material via oxa-6π electrocyclization and intramolecular [2 + 2] photocycloaddition. Subsequently, the results of the biological activity assay demonstrated that both (+)-1a and (-)-1b exhibited potent lipid-lowering effects in 3T3-L1 adipocytes and the high-fat diet zebrafish model. Notably, the lipid-lowering activity of (+)-1a is better than that of (-)-1b at the same concentration, and molecular mechanism study has shown that (+)-1a and (-)-1b impairs adipocyte differentiation and stimulate lipolysis by regulating C/EBPα/PPARγ signaling and Perilipin signaling in vitro and in vivo. Our findings provide a promising drug model molecule for the treatment of obesity.

Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study

There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.

Effects of Reactive Oxygen and Nitrogen Species on Male Fertility

Significance: In recent decades, male fertility has been severely reduced worldwide. The causes underlying this decline are multifactorial, and include, among others, genetic alterations, changes in the microbiome, and the impact of environmental pollutants. Such factors can dysregulate the physiological levels of reactive species of oxygen (ROS) and nitrogen (RNS) in the patient, generating oxidative and nitrosative stress that impairs fertility. Recent Advances: Recent studies have delved into other factors involved in the dysregulation of ROS and RNS levels, such as diet, obesity, persistent infections, environmental pollutants, and gut microbiota, thus leading to new strategies to solve male fertility problems, such as consuming prebiotics to regulate gut flora or treating psychological conditions. Critical Issues: The pathways where ROS or RNS may be involved as modulators are still under investigation. Moreover, the extent to which treatments can rescue male infertility as well as whether they may have side effects remains, in most cases, to be elucidated. For example, it is known that prescription of antioxidants to treat nitrosative stress can alter sperm chromatin condensation, which makes DNA more exposed to ROS and RNS, and may thus affect fertilization and early embryo development. Future Directions: The involvement of extracellular vesicles, which might play a crucial role in cell communication during spermatogenesis and epididymal maturation, and the relevance of other factors such as sperm epigenetic signatures should be envisaged in the future.

New Mediators in the Crosstalk between Different Adipose Tissues

Adipose tissue is a multifunctional organ that regulates many physiological processes such as energy homeostasis, nutrition, the regulation of insulin sensitivity, body temperature, and immune response. In this review, we highlight the relevance of the different mediators that control adipose tissue activity through a systematic review of the main players present in white and brown adipose tissues. Among them, inflammatory mediators secreted by the adipose tissue, such as classical adipokines and more recent ones, elements of the immune system infiltrated into the adipose tissue (certain cell types and interleukins), as well as the role of intestinal microbiota and derived metabolites, have been reviewed. Furthermore, anti-obesity mediators that promote the activation of beige adipose tissue, e.g., myokines, thyroid hormones, amino acids, and both long and micro RNAs, are exhaustively examined. Finally, we also analyze therapeutic strategies based on those mediators that have been described to date. In conclusion, novel regulators of obesity, such as microRNAs or microbiota, are being characterized and are promising tools to treat obesity in the future.

Multiomics Picture of Obesity in Young Adults

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

COVID-19 and Comorbidities: What Has Been Unveiled by Metabolomics?

The COVID-19 pandemic has brought about diverse impacts on the global population. Individuals with comorbidities were more susceptible to the severe symptoms caused by the virus. Within the crisis scenario, metabolomics represents a potential area of science capable of providing relevant information for understanding the metabolic pathways associated with the intricate interaction between the viral disease and previous comorbidities. This work aims to provide a comprehensive description of the scientific production pertaining to metabolomics within the specific context of COVID-19 and comorbidities, while highlighting promising areas for exploration by those interested in the subject. In this review, we highlighted the studies of metabolomics that indicated a variety of metabolites associated with comorbidities and COVID-19. Furthermore, we observed that the understanding of the metabolic processes involved between comorbidities and COVID-19 is limited due to the urgent need to report disease outcomes in individuals with comorbidities. The overlap of two or more comorbidities associated with the severity of COVID-19 hinders the comprehension of the significance of each condition. Most identified studies are observational, with a restricted number of patients, due to challenges in sample collection amidst the emergent situation.

Multicomponent (bio)markers for obesity risk prediction: a scoping review protocol

INTRODUCTION

Despite international efforts, the number of individuals struggling with obesity is still increasing. An important aspect of obesity prevention relates to identifying individuals at risk at early stage, allowing for timely risk stratification and initiation of countermeasures. However, obesity is complex and multifactorial by nature, and one isolated (bio)marker is unlikely to enable an optimal risk stratification and prognosis for the individual; rather, a combined set is required. Such a multicomponent interpretation would integrate biomarkers from various domains, such as classical markers (eg, anthropometrics, blood lipids), multiomics (eg, genetics, proteomics, metabolomics), lifestyle and behavioural attributes (eg, diet, physical activity, sleep patterns), psychological traits (mental health status such as depression) and additional host factors (eg, gut microbiota diversity), also by means of advanced interpretation tools such as machine learning. In this paper, we will present a protocol that will be employed for a scoping review that attempts to summarise and map the state-of-the-art in the area of multicomponent (bio)markers related to obesity, focusing on the usability and effectiveness of such biomarkers.

METHODS AND ANALYSIS

PubMed, Scopus, CINAHL and Embase databases will be searched using predefined key terms to identify peer-reviewed articles published in English until January 2024. Once downloaded into EndNote for deduplication, CADIMA will be employed to review and select abstracts and full-text articles in a two-step procedure, by two independent reviewers. Data extraction will then be carried out by several independent reviewers. Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and Peer Review of Electronic Search Strategies guidelines will be followed. Combinations employing at least two biomarkers from different domains will be mapped and discussed.

ETHICS AND DISSEMINATION

Ethical approval is not required; data will rely on published articles. Findings will be published open access in an international peer-reviewed journal. This review will allow guiding future directions for research and public health strategies on obesity prevention, paving the way towards multicomponent interventions.

Unraveling Adipose Tissue Dysfunction: Molecular Mechanisms, Novel Biomarkers, and Therapeutic Targets for Liver Fat Deposition

Adipose tissue (AT), once considered a mere fat storage organ, is now recognized as a dynamic and complex entity crucial for regulating human physiology, including metabolic processes, energy balance, and immune responses. It comprises mainly two types: white adipose tissue (WAT) for energy storage and brown adipose tissue (BAT) for thermogenesis, with beige adipocytes demonstrating the plasticity of these cells. WAT, beyond lipid storage, is involved in various metabolic activities, notably lipogenesis and lipolysis, critical for maintaining energy homeostasis. It also functions as an endocrine organ, secreting adipokines that influence metabolic, inflammatory, and immune processes. However, dysfunction in WAT, especially related to obesity, leads to metabolic disturbances, including the inability to properly store excess lipids, resulting in ectopic fat deposition in organs like the liver, contributing to non-alcoholic fatty liver disease (NAFLD). This narrative review delves into the multifaceted roles of WAT, its composition, metabolic functions, and the pathophysiology of WAT dysfunction. It also explores diagnostic approaches for adipose-related disorders, emphasizing the importance of accurately assessing AT distribution and understanding the complex relationships between fat compartments and metabolic health. Furthermore, it discusses various therapeutic strategies, including innovative therapeutics like adipose-derived mesenchymal stem cells (ADMSCs)-based treatments and gene therapy, highlighting the potential of precision medicine in targeting obesity and its associated complications.

Combined untargeted and targeted lipidomics approaches reveal potential biomarkers in type 2 diabetes mellitus cynomolgus monkeys

BACKGROUND

The significantly increasing incidence of type 2 diabetes mellitus (T2DM) over the last few decades triggers the demands of T2DM animal models to explore the pathogenesis, prevention, and therapy of the disease. The altered lipid metabolism may play an important role in the pathogenesis and progression of T2DM. However, the characterization of molecular lipid species in fasting serum related to T2DM cynomolgus monkeys is still underrecognized.

METHODS

Untargeted and targeted LC-mass spectrometry (MS)/MS-based lipidomics approaches were applied to characterize and compare the fasting serum lipidomic profiles of T2DM cynomolgus monkeys and the healthy controls.

RESULTS

Multivariate analysis revealed that 196 and 64 lipid molecules differentially expressed in serum samples using untargeted and targeted lipidomics as the comparison between the disease group and healthy group, respectively. Furthermore, the comparative analysis of differential serum lipid metabolites obtained by untargeted and targeted lipidomics approaches, four common serum lipid species (phosphatidylcholine [18:0_22:4], lysophosphatidylcholine [14:0], phosphatidylethanolamine [PE] [16:1_18:2], and PE [18:0_22:4]) were identified as potential biomarkers and all of which were found to be downregulated. By analyzing the metabolic pathway, glycerophospholipid metabolism was associated with the pathogenesis of T2DM cynomolgus monkeys.

CONCLUSION

The study found that four downregulated serum lipid species could serve as novel potential biomarkers of T2DM cynomolgus monkeys. Glycerophospholipid metabolism was filtered out as the potential therapeutic target pathway of T2DM progression. Our results showed that the identified biomarkers may offer a novel tool for tracking disease progression and response to therapeutic interventions.

Methods for Assessing Health Outcomes Associated with Food Insecurity in the United States College Student Population: A Narrative Review

In the United States, college students experience disproportionate food insecurity (FI) rates compared to the national prevalence. The experience of acute and chronic FI has been associated with negative physical and mental health outcomes in this population. This narrative review aims to summarize the current methodologies for assessing health outcomes associated with the experience of FI in college students in the United States. To date, assessing the health outcomes of FI has predominately consisted of subjective assessments, such as self-reported measures of dietary intake, perceived health status, stress, depression, anxiety, and sleep behaviors. This review, along with the emergence of FI as an international public health concern, establishes the need for novel, innovative, and objective biomarkers to evaluate the short- and long-term impacts of FI on physical and mental health outcomes in college students. The inclusion of objective biomarkers will further elucidate the relationship between FI and a multitude of health outcomes to better inform strategies for reducing the pervasiveness of FI in the United States college student population.

Multifaceted effects of obesity on cancer immunotherapies: Bridging preclinical models and clinical data

Obesity, defined by excessive body fat, is a highly complex condition affecting numerous physiological processes, such as metabolism, proliferation, and cellular homeostasis. These multifaceted effects impact cells and tissues throughout the host, including immune cells as well as cancer biology. Because of the multifaceted nature of obesity, common parameters used to define it (such as body mass index in humans) can be problematic, and more nuanced methods are needed to characterize the pleiotropic metabolic effects of obesity. Obesity is well-accepted as an overall negative prognostic factor for cancer incidence, progression, and outcome. This is in part due to the meta-inflammatory and immunosuppressive effects of obesity. Immunotherapy is increasingly used in cancer therapy, and there are many different types of immunotherapy approaches. The effects of obesity on immunotherapy have only recently been studied with the demonstration of an "obesity paradox", in which some immune therapies have been demonstrated to result in greater efficacy in obese subjects despite the direct adverse effects of obesity and excess body fat acting on the cancer itself. The multifactorial characteristics that influence the effects of obesity (age, sex, lean muscle mass, underlying metabolic conditions and drugs) further confound interpretation of clinical data and necessitate the use of more relevant preclinical models mirroring these variables in the human scenario. Such models will allow for more nuanced mechanistic assessment of how obesity can impact, both positively and negatively, cancer biology, host metabolism, immune regulation, and how these intersecting processes impact the delivery and outcome of cancer immunotherapy.

Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes

BACKGROUND

A healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk.

METHODS

In a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models.

RESULTS

The metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38-0.77 for dichotomous HL, and 0.22, 0.11-0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42-0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31-1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile.

CONCLUSIONS

In an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD.

TRIAL REGISTRATION

The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).

Hesperetin attenuates the expression of markers of adipose tissue fibrosis in pre-adipocytes

BACKGROUND

Excessive extracellular matrix (ECM) deposition in adipose tissue is a hallmark of fibrosis, leading to disrupted adipose tissue homeostasis and metabolic dysfunction. Hesperetin, a flavonoid compound, has shown promising anti-inflammatory, anti-obesity and anti-diabetic properties. Therefore, we investigated the anti-fibrotic effects of hesperetin, through targeting ECM components and matrix metalloproteinase enzymes.

METHODS

3T3-L1 cells were cultured in DMEM, containing 10% FBS and 1% penicillin/streptomycin. Cells were treated with a range of hesperetin concentrations, and the cell viability was determined using MTT assay. Subsequently, the expression of genes encoding collagen VI, osteopontin, matrix metalloproteinase-2 (Mmp-2) and Mmp-9 was analyzed using specific primers and real-time PCR technique. To evaluate protein levels of collagen VI and osteopontin, Western blotting was performed.

RESULTS

Hesperetin affected the viability of 3T3-L1 adipocytes with IC50 of 447.4 µM, 339.2 µM and 258.8 µM (24 h, 48 and 72 h, respectively). Hesperetin significantly reduced the gene and protein expression of both collagen VI and osteopontin in 3T3-L1 pre-adipocytes, in a time- and dose-dependent manner. Hesperetin was also able to cause a remarkable decline in gene expression of Mmp2 and Mmp9.

CONCLUSION

Hesperetin could potently reduce the production of markers of adipose tissue fibrosis and might be considered a potential anti-fibrotic compound in obesity. Thus, hesperetin has the potency to be used for the treatment of obesity-associated fibrosis.

A transcriptomic and proteomic atlas of obesity and type 2 diabetes in cynomolgus monkeys

Obesity and type 2 diabetes (T2D) remain major global healthcare challenges, and developing therapeutics necessitates using nonhuman primate models. Here, we present a transcriptomic and proteomic atlas of all the major organs of cynomolgus monkeys with spontaneous obesity or T2D in comparison to healthy controls. Molecular changes occur predominantly in the adipose tissues of individuals with obesity, while extensive expression perturbations among T2D individuals are observed in many tissues such as the liver and kidney. Immune-response-related pathways are upregulated in obesity and T2D, whereas metabolism and mitochondrial pathways are downregulated. Moreover, we highlight some potential therapeutic targets, including SLC2A1 and PCSK1 in obesity as well as SLC30A8 and SLC2A2 in T2D. Our study provides a resource for exploring the complex molecular mechanism of obesity and T2D and developing therapies for these diseases, with limitations including lack of hypothalamus, isolated islets of Langerhans, longitudinal data, and body fat percentage.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

The metabolomic profiling of total fat and fat distribution in a multi-cohort study of women and men

Currently studies aiming for the comprehensive metabolomics profiling of measured total fat (%) as well as fat distribution in both sexes are lacking. In this work, bioimpedance analysis was applied to measure total fat (%) and fat distribution (trunk to leg ratio). Liquid chromatography-mass spectrometry-based untargeted metabolomics was employed to profile the metabolic signatures of total fat (%) and fat distribution in 3447 participants from three Swedish cohorts (EpiHealth, POEM and PIVUS) using a discovery-replication cross-sectional study design. Total fat (%) and fat distribution were associated with 387 and 120 metabolites in the replication cohort, respectively. Enriched metabolic pathways for both total fat (%) and fat distribution included protein synthesis, branched-chain amino acids biosynthesis and metabolism, glycerophospholipid metabolism and sphingolipid metabolism. Four metabolites were mainly related to fat distribution: glutarylcarnitine (C5-DC), 6-bromotryptophan, 1-stearoyl-2-oleoyl-GPI (18:0/18:1) and pseudouridine. Five metabolites showed different associations with fat distribution in men and women: quinolinate, (12Z)-9,10-dihydroxyoctadec-12-enoate (9,10-DiHOME), two sphingomyelins and metabolonic lactone sulfate. To conclude, total fat (%) and fat distribution were associated with a large number of metabolites, but only a few were exclusively associated with fat distribution and of those metabolites some were associated with sex*fat distribution. Whether these metabolites mediate the undesirable effects of obesity on health outcomes remains to be further investigated.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Modulation of Gut Microbiota through Low-Calorie and Two-Phase Diets in Obese Individuals

Different nutritional regimens have been reported to exert beneficial effects on obesity through the regulation of the composition and function of gut microbiota. In this context, we conducted in obese subjects two dietary interventions consisting of a low-calorie and two-phase (ketogenic plus low-calorie) diet for 8 weeks. Anthropometric and clinical parameters were evaluated at baseline and following the two diets, and gut microbiota composition was assessed by 16S rRNA gene sequencing. A significant reduction was observed for abdominal circumference and insulin levels in the subjects following the two-phase diet. Significant differences in gut microbial composition were observed after treatment compared to the baseline. Both diets induced taxonomic shifts including a decrease in Proteobacteria, which are recognized as dysbiosis markers and enrichment of Verrucomicrobiaceae, which has recently emerged as an effective probiotic. An increase in Bacteroidetes, constituting the so-called good bacteria, was observable only in the two-phase diet. These findings provide evidence that a targeted nutritional regimen and an appropriate use of probiotics can modulate gut microbiota to reach a favorable composition and achieve the balance often compromised by different pathologies and conditions, such as obesity.

Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity

INTRODUCTION

Obesity is a multifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not a single country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without a comprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes.

OBJECTIVE

The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how a multi-omics approach can be fundamental for the implementation of precision obesity management.

RESULTS

The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity.

CONCLUSION

Integrating multi-omics approaches will enable a paradigm shift from the one size fits all approach towards precision obesity management, i.e. (1) precision prevention of the onset of obesity, (2) precision medicine and tailored treatment of obesity, and (3) precision risk reduction and prevention of secondary diseases related to obesity.

Understanding the adipose tissue acetylome in obesity and insulin resistance

Obesity is a widely prevalent pathology with a high exponential growth worldwide. Altered lipid accumulation by adipose tissue is one of the main causes of obesity and exploring lipid homeostasis in this tissue may represent a source for the identification of possible therapeutic targets. The study of the proteome and the post-translational modifications of proteins, specifically acetylation due to its involvement in energy metabolism, may be of great interest to understand the molecular mechanisms involved in adipose tissue dysfunction in obesity. The objective of this study was to characterize the subcutaneous and omental adipose tissue acetylome in conditions of obesity and insulin resistance and to describe the importance of acetylation of key molecules in adipose tissue to use them as therapeutic targets. The results describe for the first time the acetylome of subcutaneous and omental adipose tissue under physiological and physiopathological conditions such as obesity and insulin resistance. New evidence showed different acetylation patterns between two main depots and highlight the molecular complexity of adipose tissue. Results showed changes in FABP4 acetylation in subcutaneous fat in relation to insulin resistance, thus unveiling a potential marker of depot-specific dysfunctional expansion in obesity-associated metabolic disease. Furthermore, it is shown that the acetylation of FABP4 affects its function, modulating the capacity of differentiation in adipocytes. In conclusion, this study demonstrates a profound, depot-specific alteration of adipose tissue acetylome, wherein the acetylation of FABP4 may play a key role in adipocyte differentiation and lipid accumulation.

Empowering consumers to PREVENT diet-related diseases through OMICS sciences (PREVENTOMICS): protocol for a parallel double-blinded randomised intervention trial to investigate biomarker-based nutrition plans for weight loss

INTRODUCTION

Personalised nutrition holds immense potential over conventional one-size-fits-all approaches for preventing and treating diet-related diseases, such as obesity. The current study aims to examine whether a personalised nutritional plan produces more favourable health outcomes than a standard approach based on general dietary recommendations in subjects with overweight or obesity and elevated waist circumference.

METHODS AND ANALYSIS

This project is a 10-week parallel, double-blinded randomised intervention trial. We plan to include 100 adults aged 18-65 years interested in losing weight, with body mass index ≥27 but<40 kg/m² and elevated waist circumference (males >94 cm; females >80 cm). Participants will be categorised into one of five predefined 'clusters' based on their individual metabolic biomarker profile and genetic background, and will be randomised in a 1:1 ratio to one of two groups: (1) personalised plan group that will receive cluster-specific meals every day for 6 days a week, in conjunction with a personalised behavioural change programme via electronic push notifications; or (2) control group that will receive meals following the general dietary recommendations in conjunction with generic health behaviour prompts. The primary outcome is the difference between groups (personalised vs control) in the change in fat mass from baseline. Secondary outcomes include changes in weight and body composition, fasting blood glucose and insulin, lipid profile, adipokines, inflammatory biomarkers, and blood pressure. Other outcomes involve measures of physical activity and sleep patterns, health-related quality of life, dietary intake, eating behaviour, and biomarkers of food intake. The effect of the intervention on the primary outcome will be analysed by means of linear mixed models.

ETHICS AND DISSEMINATION

The protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated through peer-reviewed publications, conference presentations and media outlets.

TRIAL REGISTRATION NUMBER

NCT04590989.

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

PURPOSE OF REVIEW

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes - based on circulating biomarkers - that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

RECENT FINDINGS

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials. Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.

Profound Perturbation in the Metabolome of a Canine Obesity and Metabolic Disorder Model

Canine models are increasingly being used in metabolic studies due to their physiological similarity with humans. The present study aimed to identify changes in metabolic pathways and biomarkers with potential clinical utility in a canine model of obesity and metabolic disorders induced by a high-fat diet (HFD). Eighteen male beagles were included in this study, 9 of which were fed a HFD for 24 weeks, and the remaining 9 were fed normal chow (NC) during the same period. Plasma and urine samples were collected at weeks 12 and 24 for untargeted metabolomic analysis. Dogs fed a HFD showed a gradual body weight increase during the feeding period and had hyperlipidemia, increased leukocyte counts, and impaired insulin sensitivity at week 24. Plasma and urine metabonomics analysis displayed clear separations between the HFD-fed and NC-fed dogs. A total of 263 plasma metabolites varied between the two groups, including stearidonic acid, linolenic acid, carnitine, long-chain ceramide, 3-methylxanthine, and theophylline, which are mainly engaged in fatty acid metabolism, sphingolipid metabolism, and caffeine metabolism. A total of 132 urine metabolites related to HFD-induced obesity and metabolic disorders were identified, including 3-methylxanthine, theophylline, pyridoxal 5'-phosphate, and harmine, which participate in pathways such as caffeine metabolism and vitamin digestion and absorption. Eight metabolites with increased abundance (e.g., 3-methylxanthine, theophylline, and harmine) and 4 metabolites with decreased abundance (e.g., trigonelline) in both the plasma and urine of the HFD-fed dogs were identified. In conclusion, the metabolomic analysis revealed molecular events underlying a canine HFD model and identified several metabolites as potential targets for the prevention and treatment of obesity-related metabolic disorders.

AdipoAtlas: Mapping out human white adipose tissue

In this issue of Cell Reports Medicine, Lange and colleagues1 significantly improve lipid identification accuracy, detection, and quantification to provide AdipoAtlas, an in-depth lipidomic profile of human white adipose tissue (WAT). Importantly, they define obesity-mediated lipid alterations, which may provide insight into the etiology of associated diseases.

MD, Eckel-Passow J, Acosta A. Precision Medicine for Obesity

Obesity is a multifactorial disease with a variable and underwhelming weight loss response to current treatment approaches. Precision medicine proposes a new paradigm to improve disease classification based on the premise of human heterogeneity, with the ultimate goal of maximizing treatment effectiveness, tolerability, and safety. Recent advances in high-throughput biochemical assays have contributed to the partial characterization of obesity's pathophysiology, as well as to the understanding of the role that intrinsic and environmental factors, and their interaction, play in its development and progression. These data have led to the development of biological markers that either are being or will be incorporated into strategies to develop personalized lines of treatment for obesity. There are currently many ongoing initiatives aimed at this; however, much needs to be resolved before precision obesity medicine becomes common practice. This review aims to provide a perspective on the currently available data of high-throughput technologies to treat obesity.

Plasma glycerophospholipid profile, erythrocyte n-3 PUFAs, and metabolic syndrome incidence: a prospective study in Chinese men and women

BACKGROUND

Animal studies have highlighted critical roles of glycerophospholipid (GP) metabolism in various metabolic syndrome (MetS)-related features such as dyslipidemia, obesity, and insulin resistance. However, human prospective studies of associations between circulating GPs and risks of MetS are scarce.

OBJECTIVES

We aimed to investigate whether GPs are associated with incidence of MetS in a well-established cohort.

METHODS

A total of 1243 community-dwelling Chinese aged 50-70 y without MetS at baseline and followed up for 6 y were included in current analyses. A total of 145 plasma GPs were quantified by high-throughput targeted lipidomics. MetS was defined using the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans.

RESULTS

After 6 y, 429 participants developed MetS. Eleven GPs, especially those with long-chain polyunsaturated fatty acids (LCPUFAs) or very-long-chain polyunsaturated fatty acids (VLCPUFAs) at the sn-2 position, including 1 phosphatidylcholine (PC) [PC(18:0/22:6)], 9 phosphatidylethanolamines (PEs) [PE(16:0/22:6), PE(18:0/14:0), PE(18:0/18:1), PE(18:0/18:2), PE(18:0/20:3), PE(18:0/22:5), PE(18:0/22:6), PE(18:1/22:6), and PE(18:2/22:6)], and 1 phosphatidylserine (PS) [PS(18:0/18:0)], were positively associated with incident MetS (RRs: 1.16-1.30 per SD change; Bonferroni-corrected P < 0.05). In network analysis, the strongest positive association for MetS incidence was evidenced in a module mainly composed of PEs containing C22:6 and PSs [RR: 1.21; 95% CI: 1.12, 1.31 per SD change; Bonferroni-corrected P < 0.05]. This association was more pronounced in participants with lower erythrocyte total n-3 PUFA concentrations [Bonferroni-corrected Pinter(P value for the interaction)< 0.05].

CONCLUSIONS

Elevated plasma concentrations of GPs, especially PEs with LCPUFAs or VLCPUFAs at the sn-2 position, are associated with higher risk of incident MetS. Future studies are merited to confirm our findings.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention

OBJECTIVE

Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention.

METHODS

Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed.

RESULTS

A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways.

CONCLUSIONS

These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

Metabolic signatures of greater body size and their associations with risk of colorectal and endometrial cancers in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

The mechanisms underlying the obesity-cancer relationship are incompletely understood. This study aimed to characterise metabolic signatures of greater body size and to investigate their association with two obesity-related malignancies, endometrial and colorectal cancers, and with weight loss within the context of an intervention study.

METHODS

Targeted mass spectrometry metabolomics data from 4326 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 17 individuals from a single-arm pilot weight loss intervention (Intercept) were used in this analysis. Metabolic signatures of body size were first determined in discovery (N = 3029) and replication (N = 1297) sets among EPIC participants by testing the associations between 129 metabolites and body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) using linear regression models followed by partial least squares analyses. Conditional logistic regression models assessed the associations between the metabolic signatures with endometrial (N = 635 cases and 648 controls) and colorectal (N = 423 cases and 423 controls) cancer risk using nested case-control studies in EPIC. Pearson correlation between changes in the metabolic signatures and weight loss was tested among Intercept participants.

RESULTS

After adjustment for multiple comparisons, greater BMI, WC, and WHR were associated with higher levels of valine, isoleucine, glutamate, PC aa C38:3, and PC aa C38:4 and with lower levels of asparagine, glutamine, glycine, serine, lysoPC C17:0, lysoPC C18:1, lysoPC C18:2, PC aa C42:0, PC ae C34:3, PC ae C40:5, and PC ae C42:5. The metabolic signature of BMI (OR1-sd 1.50, 95% CI 1.30-1.74), WC (OR1-sd 1.46, 95% CI 1.27-1.69), and WHR (OR1-sd 1.54, 95% CI 1.33-1.79) were each associated with endometrial cancer risk. Risk of colorectal cancer was positively associated with the metabolic signature of WHR (OR1-sd: 1.26, 95% CI 1.07-1.49). In the Intercept study, a positive correlation was observed between weight loss and changes in the metabolic signatures of BMI (r = 0.5, 95% CI 0.06-0.94, p = 0.03), WC (r = 0.5, 95% CI 0.05-0.94, p = 0.03), and WHR (r = 0.6, 95% CI 0.32-0.87, p = 0.01).

CONCLUSIONS

Obesity is associated with a distinct metabolic signature comprising changes in levels of specific amino acids and lipids which is positively associated with both colorectal and endometrial cancer and is potentially reversible following weight loss.

Omics Approaches in Adipose Tissue and Skeletal Muscle Addressing the Role of Extracellular Matrix in Obesity and Metabolic Dysfunction

Extracellular matrix (ECM) remodeling plays important roles in both white adipose tissue (WAT) and the skeletal muscle (SM) metabolism. Excessive adipocyte hypertrophy causes fibrosis, inflammation, and metabolic dysfunction in adipose tissue, as well as impaired adipogenesis. Similarly, disturbed ECM remodeling in SM has metabolic consequences such as decreased insulin sensitivity. Most of described ECM molecular alterations have been associated with DNA sequence variation, alterations in gene expression patterns, and epigenetic modifications. Among others, the most important epigenetic mechanism by which cells are able to modulate their gene expression is DNA methylation. Epigenome-Wide Association Studies (EWAS) have become a powerful approach to identify DNA methylation variation associated with biological traits in humans. Likewise, Genome-Wide Association Studies (GWAS) and gene expression microarrays have allowed the study of whole-genome genetics and transcriptomics patterns in obesity and metabolic diseases. The aim of this review is to explore the molecular basis of ECM in WAT and SM remodeling in obesity and the consequences of metabolic complications. For that purpose, we reviewed scientific literature including all omics approaches reporting genetic, epigenetic, and transcriptomic (GWAS, EWAS, and RNA-seq or cDNA arrays) ECM-related alterations in WAT and SM as associated with metabolic dysfunction and obesity.