Integrative Personal Omics Profiles during Periods of Weight Gain and Loss

Body weight variability as a predictor of cardiovascular outcomes in type 1 diabetes: A nationwide cohort study

AIM

Intraindividual body weight variability (BWV), that is, the degree of weight fluctuations over time, is associated with an increased risk of cardiovascular diseases (CVDs) in multiple settings. The impact of BWV on cardiovascular risk in type 1 diabetes (T1D) remains unclear, despite the issues relative to weight management in individuals with this condition.

MATERIALS AND METHODS

Using data from the Swedish National Diabetes Register, we identified individuals with T1D and without CVD at baseline with at least three measurements of body weight taken over three consecutive years. We estimated BWV as quartiles of the standard deviation of weight measures and explored its longitudinal association with the incidence of CVD during a 12.7 ± 4.6 year follow-up through adjusted Cox regression models. The primary endpoint was the composite of nonfatal myocardial infarction, nonfatal stroke and all-cause mortality. We modelled the function of risk in relation to the magnitude of BWV, testing also whether weight trends, that is, increasing, stable or decreasing, age, sex and glycaemic control modified the association between BWV and the outcome.

RESULTS

Among the 36 333 individuals with T1D in the register, we identified 19 373 individuals with at least three measures of body weight and without CVD at baseline. Participants with the highest BWV had a 42% increased risk of reaching the primary endpoint compared to those with the lowest BWV (hazard ratio [HR] = 1.42, 95% confidence interval [CI]: 1.24-1.62). In addition, high BWV was significantly associated with a 51% increased risk of all-cause mortality (HR = 1.51, 95% CI: 1.28-1.78), a 37% increased risk of peripheral artery disease (HR = 1.37, 95% CI: 1.06-1.77) and a 55% increased risk of hospitalization for heart failure (HR = 1.55, 95% CI: 1.20-2.01). BWV showed a quasi-linear association with the primary endpoint. No interaction was observed when comparing subgroups for weight trends, sex or degree of glycaemic control. In the subgroup of elderly individuals, the association of BWV with the primary endpoint was no longer significant.

CONCLUSIONS

High BWV is associated with an increased risk of CVD and all-cause mortality in individuals with T1D, independently of canonical risk factors. Weight trends, sex and glycaemic control do not modify such association while older age attenuates it.

Combined Association of Plasma Metabolites with Body Mass Index and Physical Activity Level

Metabolomic analysis of the changes in plasma metabolites in obesity along with physical activity interaction may contribute to disease diagnosis and treatment. We sought to make a comprehensive assessment of the plasma metabolite profile of subjects with a lean (n = 20, BMI = 22.3) or overweight/obese (n = 29, BMI = 29) body mass index (BMI) and low (n = 33, IPAQ = 842) or high (n = 16, IPAQ = 6935) index of physical activity questionnaire (IPAQ), using an untargeted metabolomic approach. Two-way analysis of variance was applied to the data obtained from liquid chromatography-mass spectrometry analyses and resulted in 64 metabolites, mainly responsible for the data variance among the different groups. Finally, a complex network approach reveals the most relevant metabolites. The majority of the relevant metabolites are oxidized species of phospholipids. Most species of phosphatidylcholine and a species of phosphatidylglycerol were found to be decreased in obese subjects, while most species of phosphatidylethanolamine, phosphatidylserine, and phosphatidylinositol were increased. Only a single species each of prostaglandin, phosphatidylglycerol, and phosphatidylinositol were modulated by IPAQ, but interaction effects between BMI and IPAQ were found for most of the metabolites in the combination of obese BMI with low IPAQ.

Modulation of Serum Metabolic Profiles by Bifidobacterium breve BBr60 in Obesity: A Randomized Controlled Trial

Obesity, a prevalent metabolic disorder in youth, leads to complications and economic strain. Gut dysbiosis significantly contributes to obesity and metabolic issues. Bifidobacterium breve, a probiotic strain, may help regulate gut dysbiosis and benefit obese individuals. However, more research is needed on its effect on serum metabolism. A total of 75 overweight or obese young adults (aged 19-45) participated in the current study, and were randomly divided into probiotic and placebo groups using a random number table. Both groups received dietary guidance and underwent twelve weeks of intervention with either oral Bifidobacterium breve BBr60 (BBr60) or a placebo. After the intervention, collection and analysis of fasting serum samples were conducted using mass spectrometry coupled with liquid chromatography. Analyses of associations were conducted in order to determine the correlations between key serum metabolites and clinical obesity indicators, aiming to understand the influence of BBr60. Due to 10 participants dropping out for personal reasons, the study included 32 and 33 participants in the placebo and the BBr60 groups, respectively. The BBr60 intervention significantly regulated 134 serum metabolites and influenced crucial metabolic pathways in obesity management (p < 0.05), including ascorbate and aldarate metabolism for oxidative stress reduction, cholesterol metabolism for lipid regulation, parathyroid hormone synthesis, secretion and action for the endocrine system, oxidative phosphorylation for enhanced energy efficiency, and glycolysis/gluconeogenesis for glucose metabolism. Analysis showed a positive relationship between fasting blood glucose (FBG), aspartate aminotransferase (AST), total protein (TP), and the content of 5-Methyl DL-glutamate (p < 0.05). Similarly, body mass index (BMI), weight, and body fat percentage (BFP) were positively linked to serum metabolites (1-Hydroxycyclohexyl) acetic acid, and 5-Oxooctanoic acid (p < 0.05). Significant associations of AST levels with key serum metabolites in cholesterol metabolism pathways further suggest BBr60's potential to improve liver function and overall metabolic health in overweight or obese individuals. These findings support BBr60's effectiveness in modulating serum metabolic profiles and suggest it may improve liver function and BMI in overweight or obese individuals by regulating key serum metabolites.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

Lipid metabolism mediates the association between body mass index change and bone mineral density: The Taizhou imaging study

BACKGROUND

Limited research explores the impact of body mass index (BMI) change on osteoporosis, regarding the role of lipid metabolism. We aimed to cross-sectionally investigate these relationships in 820 Chinese participants aged 55-65 from the Taizhou Imaging Study.

METHODS

We used the baseline data collected between 2013 and 2018. T-score was calculated by standardizing bone mineral density and was used for osteoporosis and osteopenia diagnosis. Multinomial logistic regression was used to examine the effect of BMI change on bone health status. Multivariable linear regression was employed to identify the metabolites corrected with BMI change and T-score. Exploratory factor analysis (EFA) and mediation analysis were conducted to ascertain the involvement of the metabolites.

RESULTS

BMI increase served as a protective factor against osteoporosis (OR = 0.79[0.71-0.88], P-value<0.001) and osteopenia (OR = 0.88[0.82-0.95], P-value<0.001). Eighteen serum metabolites were associated with both BMI change and T-score. Specifically, high-density lipoprotein (HDL) substructures demonstrated negative correlations (β = -0.08 to -0.06 and - 0.12 to -0.08, respectively), while very low-density lipoprotein (VLDL) substructions showed positive correlations (β = 0.09 to 0.10 and 0.10 to 0.11, respectively). The two lipid factors (HDL and VLDL) extracted by EFA acted as mediators between BMI change and T-score (Prop. Mediated = 8.16% and 10.51%, all P-value<0.01).

CONCLUSION

BMI gain among Chinese aged 55-65 is beneficial for reducing the risk of osteoporosis. The metabolism of HDL and VLDL partially mediates the effect of BMI change on bone loss. Our research offers novel insights into the prevention of osteoporosis, approached from the perspective of weight management and lipid metabolomics.

Altered proteome profiles related to visceral adiposity may mediate the favorable effect of green Mediterranean diet: the DIRECT-PLUS trial

OBJECTIVE

The objective of this study was to explore the effects of a green Mediterranean (green-MED) diet, which is high in dietary polyphenols and green plant-based protein and low in red/processed meat, on cardiovascular disease and inflammation-related circulating proteins and their associations with cardiometabolic risk parameters.

METHODS

In the 18-month weight loss trial Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS), 294 participants with abdominal obesity were randomized to basic healthy dietary guidelines, Mediterranean (MED), or green-MED diets. Both isocaloric MED diet groups consumed walnuts (28 g/day), and the green-MED diet group also consumed green tea (3-4 cups/day) and green shakes (Mankai plant shake, 500 mL/day) and avoided red/processed meat. Proteome panels were measured at three time points using Olink CVDII.

RESULTS

At baseline, a dominant protein cluster was significantly related to higher phenotypic cardiometabolic risk parameters, with the strongest associations attributed to magnetic resonance imaging-assessed visceral adiposity (false discovery rate of 5%). Overall, after 6 months of intervention, both the MED and green-MED diets induced improvements in cardiovascular disease and proinflammatory risk proteins (p < 0.05, vs. healthy dietary guidelines), with the green-MED diet leading to more pronounced beneficial changes, largely driven by dominant proinflammatory proteins (IL-1 receptor antagonist protein, IL-16, IL-18, thrombospondin-2, leptin, prostasin, galectin-9, and fibroblast growth factor 21; adjusted for age, sex, and weight loss; p < 0.05). After 18 months, proteomics cluster changes presented the strongest correlations with visceral adiposity reduction.

CONCLUSIONS

Proteomics clusters may enhance our understanding of the favorable effect of a green-MED diet that is enriched with polyphenols and low in red/processed meat on visceral adiposity and cardiometabolic risk.

Metabolic liability for weight gain in early adulthood

While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.

Integrative multi-omics profiling in human decedents receiving pig heart xenografts

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.

Influence of supplementation with iron and probiotic bacteria Lactobacillus plantarum and Lactobacillus curvatus on selected parameters of inflammatory state in rats on a high-fat iron-deficient diet

BACKGROUND

A high-fat (HF) diet, diet iron deficiency and iron supplementation may affect inflammatory parameters. Probiotics influence both iron metabolism and inflammation. We compared the inflammatory state in rats on a HF iron-deficient diet receiving oral iron, Lactobacillus plantarum and Lactobacillus curvatus in different combinations.

METHODS

This was a two-stage experiment. In groups C (n = 8) and HF (n = 8), rats ate a control or HF diet, respectively, for 16 weeks. In the group HFDEF (n = 48), rats ate a HF iron-deficient diet for 8 weeks (first stage) and were subsequently divided into 6 groups (n = 8 each) receiving the following for a further 8 weeks (second stage): HFDEF - a HF iron-deficient diet; HFDEFFe - a HF iron-deficient diet with iron; HFDEFLp and HFDEFLc - a HF iron-deficient diet with L. plantarum or L. curvatus, respectively; and HFDEFFeLp and HFDEFFeLc - a HF iron-deficient diet with iron and L. plantarum or L. curvatus, respectively. Body composition analysis and blood sampling was performed. Markers of iron status and levels of total antioxidant status (TAS), C-reactive protein (CRP), tumour necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) were measured in the blood.

RESULTS

TAS was higher in the HFDEF group (756.57 ± 489.53 ng mL-1) versus the HFDEFLc group (187.04 ± 47.84 ng mL-1; P = 0.022). No more differences were found between groups, or in TAS, CRP, TNF-α and IL-6 concentrations. Also, no differences were found between groups for alanine and aspartate aminotransferases, glucose, total cholesterol, low- and high-density lipoproteins and triglycerides. TAS level was positively correlated with ferritin concentration, IL-6 with TAS and TNF-α with hepcidin level.

CONCLUSIONS

Supplementation with L. plantarum, L. curvatus and iron in combinations exerts no influence on inflammatory status, lipid profile, hepatic function and serum fasting glucose in rats on a HF iron-deficient diet. © 2024 Society of Chemical Industry.

Description of the fasted serum metabolomic signature of lean and obese cats at maintenance and of obese cats under energy restriction

This study aimed to investigate the serum metabolomic profile of obese and lean cats as well as obese cats before and after energy restriction for weight loss. Thirty cats, 16 obese (body condition score 8 to 9/9) and 14 lean (body condition score 4 to 5/9), were fed a veterinary weight loss food during a 4-week period of weight maintenance (L-MAINT and O-MAINT). The 16 obese cats were then energy restricted by a 60% energy intake reduction with the same food for a 10-week period (O-RESTRICT). Fasted serum metabolites were measured using nuclear magnetic resonance and direct infusion mass spectrometry after the maintenance period for L-MAINT and O-MAINT cats and after the energy restriction period for O-RESTRICT and compared between groups using a two-sided t-test. Obese cats lost 672 g ± 303 g over the 10-week restriction period, representing a weight loss rate of 0.94 ± 0.28% per week. Glycine, l-alanine, l-histidine, l-glutamine, 2-hydroxybutyrate, isobutryric acid, citric acid, creatine, and methanol were greater in O-RESTRICT compared to O-MAINT. There was a greater concentration of long-chain acylcarnitines in O-RESTRICT compared to both O-MAINT and L-MAINT, and greater total amino acids compared to O-MAINT. Glycerol and 3-hydroxybutyric acid were greater in O-MAINT compared to L-MAINT, as were several lysophosphatidylcholines. Thus, energy restriction resulted in increased dispensable amino acids in feline serum which could indicate alterations in amino acid partitioning. An increase in lipolysis was not evident, though greater circulating acylcarnitines were observed, suggesting that fatty acid oxidation rates may have been greater under calorie restriction. More research is needed to elucidate energy metabolism and substrate utilization, specifically fatty acid oxidation and methyl status, during energy restriction in strict carnivorous cats to optimize weight loss.

Precise prediction of metabolites patterns using machine learning approaches in distinguishing honey and sugar diets fed to mice

As a natural sweetener produced by honey bees, honey was recognized as being healthier for consumption than table sugar. Our previous study also indicated thatmetaboliteprofiles in mice fed honey and mixedsugardiets aredifferent. However, it is still noteworthy about the batch-to-batch consistency of the metabolic differences between two diet types. Here, the machine learning (ML) algorithms were applied to complement and calibrate HPLC-QTOF/MS-based untargeted metabolomics data. Data were generated from three batches of mice that had the same treatment, which can further mine the metabolite biomarkers. Random Forest and Extra-Trees models could better discriminate between honey and mixed sugar dietary patterns under five-fold cross-validation. Finally, SHapley Additive exPlanations tool identified phosphatidylethanolamine and phosphatidylcholine as reliable metabolic biomarkers to discriminate the honey diet from the mixed sugar diet. This study provides us new ideas for metabolomic analysis of larger data sets.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome

Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.

Genetic architecture of adiposity measures among Asians: Findings from GWAS

Adiposity has gradually become a global public threat over the years with drastic increase in the attributable deaths and disability adjusted life years (DALYs). Given an increased metabolic risk among Asians as compared to Europeans for any given body mass index (BMI) and considering the differences in genetic architecture between them, the present review aims to summarize the findings from genome-wide scans for various adiposity indices and related anthropometric measures from Asian populations. The search for related studies, published till February 2022, were made on PubMed and GWAS Catalog using search strategy built with relevant keywords joined by Boolean operators. It was recorded that out of a total of 47 identified studies, maximum studies are from Korean population (n = 14), followed by Chinese (n = 7), and Japanese (n = 6). Nearly 200 loci have been identified for BMI, 660 for height, 16 for weight, 28 for circumferences (waist and hip), 32 for ratios (waist hip ratio [WHR] and thoracic hip ratio [THR]), 5 for body fat, 16 for obesity, and 28 for adiposity-related blood markers among Asians. It was observed that though, most of the loci were unique for each trait, there were 3 loci in common to BMI and WHR. Apart from validation of variants identified in European setting, there were many novel loci discovered in Asian populations. Notably, 125 novel loci form Asian studies have been reported for BMI, 47 for height, 5 for waist circumference, and 2 for adiponectin level to the existing knowledge of the genetic framework of adiposity and related measures. It is necessary to examine more advanced adiposity measures, specifically of relevance to abdominal adiposity, a major risk factor for cardiometabolic disorders among Asians. Moreover, in spite of being one continent, there is diversity among different ethnicities across Asia in terms of lifestyle, climate, geography, genetic structure and consequently the phenotypic manifestations. Hence, it is also important to consider ethnic specific studies for identifying and validating reliable genetic variants of adiposity measures among Asians.

Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment

DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.

Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

Machine learning in precision diabetes care and cardiovascular risk prediction

Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.

Gut microbial carbohydrate metabolism contributes to insulin resistance

Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes1,2. Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance3-9. In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host's overall energy extraction10, thereby playing a role in the pathogenesis of obesity and prediabetes3,4,6. Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host-microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

CsMYC2 is involved in the regulation of phenylpropanoid biosynthesis induced by trypsin in cucumber (Cucumis sativus) during storage

Trypsin has a new activity of scavenging superoxide anion and generating hydrogen peroxide. Trypsin can significantly improve the storage quality of C. sativus. To illustrate the mechanism of trypsin-induced resistance in fruits and vegetables, an integrated analysis of widely targeted metabolomics and transcriptomics was carried out. Transcriptomic results showed that 1068 genes highly related to phenylpropanoid biosynthesis gathered in the brown module were obtained by WGCNA. In KEGG analysis, differentially expressed genes (DEGs) were also highly enriched in EIP (Environmental Information Processing) pathways "Plant hormone signal transduction (map04075)" and "MAPK signaling pathway-plant (map04016)". Next, 87 genes were identified as the leading edge by GSEA analysis. So far, CsMYC2 was highlighted as a key transcription factor that regulates phenylpropanoid biosynthesis identified by GSEA and WGCNA. Furthermore, the major route of biosynthesis of phenylpropanoid compounds including coumarins, lignins, chlorogenic acid, flavonoids, and derivatives regulated by trypsin was also illustrated by both transcriptomic and metabolomic data. Results of O2PLS showed that CsMYC2 was positively correlated with Rosmarinic acid-3-O-glucoside, Epigallocatechin, Quercetin-3-O-sophoroside (Baimaside), and so on. Correlation between CsMYC2, phenylpropanoid related genes, and metabolites in C. sativus was illustrated by co-expression networks. Roles of CsMYC2 were further checked in C. sativus by VIGS. The results of this study might give new insight into the exploration of the postharvest resistance mechanism of C. sativus induced by trypsin and provide useful information for the subsequent mining of resistance genes in C. sativus.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Pathway-guided monitoring of the disease course in bladder cancer with longitudinal urine proteomics

BACKGROUND

Monitoring bladder cancer over time requires invasive and costly procedures. Less invasive approaches are required using readily available biological samples such as urine. In this study, we demonstrate a method for longitudinal analysis of the urine proteome to monitor the disease course in patients with bladder cancer.

METHODS

We compared the urine proteomes of patients who experienced recurrence and/or progression (n = 13) with those who did not (n = 17). We identified differentially expressed proteins within various pathways related to the hallmarks of cancer. The variation of such pathways during the disease course was determined using our differential personal pathway index (dPPi) calculation, which could indicate disease progression and the need for medical intervention.

RESULTS

Seven hallmark pathways are used to develop the dPPi. We demonstrate that we can successfully longitudinally monitor the disease course in bladder cancer patients through a combination of urine proteomic analysis and the dPPi calculation, over a period of 62 months.

CONCLUSIONS

Using the information contained in the patient's urinary proteome, the dPPi reflects the individual's course of bladder cancer, and helps to optimise the use of more invasive procedures such as cystoscopy.

Repercussions of absolute and time-rated BMI "yo-yo" fluctuations on cardiovascular stress-related morbidities within the vascular-metabolic CUN cohort

Aims

The association between body mass index (BMI) fluctuation and BMI fluctuation rate with cardiovascular stress morbidities in a Caucasian European cohort was evaluated to ascertain the impact of weight cycling.

Methods

A total of 4,312 patients of the Vascular-Metabolic CUN cohort (VMCUN cohort) were examined and followed up during 9.35 years ( ± 4.39). Cox proportional hazard ratio analyses were performed to assess the risk of developing cardiovascular stress-related diseases (CVDs) across quartiles of BMI fluctuation, measured as the average successive variability (ASV) (ASV = |BMIt0 - BMIt1| + |BMIt1 - BMIt2| + |BMIt2-BMIt3| +…+ |BMItn - 1 - BMItn|/n - 1), and quartiles of BMI fluctuation rate (ASV/year).

Results

There were 436 incident cases of CVD-associated events involving 40,323.32 person-years of follow-up. A progressively increased risk of CVD in subjects with greater ASV levels was found. Also, a higher level of ASV/year was significantly associated with an increased risk of developing CVD stress independent of confounding factors with a value of 3.71 (95% CI: 2.71-5.07) for those in the highest quartile and 1.82 (95% CI: 1.33-2.50) for those in the third quartile.

Conclusions

The BMI fluctuation rate seems to be a better predictor than BMI fluctuation of the potential development of cardiovascular stress morbidities. The time-rated weight fluctuations are apparently more determinant in increasing the risk of a CVD than the weight fluctuation itself, which is remarkable in subjects under "yo-yo" weight patterns for precision medicine.

Gut microbiota analyses of Saudi populations for type 2 diabetes-related phenotypes reveals significant association

BACKGROUND

Large-scale gut microbiome sequencing has revealed key links between microbiome dysfunction and metabolic diseases such as type 2 diabetes (T2D). To date, these efforts have largely focused on Western populations, with few studies assessing T2D microbiota associations in Middle Eastern communities where T2D prevalence is now over 20%. We analyzed the composition of stool 16S rRNA from 461 T2D and 119 non-T2D participants from the Eastern Province of Saudi Arabia. We quantified the abundance of microbial communities to examine any significant differences between subpopulations of samples based on diabetes status and glucose level.

RESULTS

In this study we performed the largest microbiome study ever conducted in Saudi Arabia, as well as the first-ever characterization of gut microbiota T2D versus non-T2D in this population. We observed overall positive enrichment within diabetics compared to healthy individuals and amongst diabetic participants; those with high glucose levels exhibited slightly more positive enrichment compared to those at lower risk of fasting hyperglycemia. In particular, the genus Firmicutes was upregulated in diabetic individuals compared to non-diabetic individuals, and T2D was associated with an elevated Firmicutes/Bacteroidetes ratio, consistent with previous findings.

CONCLUSION

Based on diabetes status and glucose levels of Saudi participants, relatively stable differences in stool composition were perceived by differential abundance and alpha diversity measures. However, community level differences are evident in the Saudi population between T2D and non-T2D individuals, and diversity patterns appear to vary from well-characterized microbiota from Western cohorts. Comparing overlapping and varying patterns in gut microbiota with other studies is critical to assessing novel treatment options in light of a rapidly growing T2D health epidemic in the region. As a rapidly emerging chronic condition in Saudi Arabia and the Middle East, T2D burdens have grown more quickly and affect larger proportions of the population than any other global region, making a regional reference T2D-microbiome dataset critical to understanding the nuances of disease development on a global scale.

Gut microbiota analysis for prediction of clinical relapse in Crohn's disease

The role of intestinal bacterial microbiota has been described as key in the pathophysiology of Crohn's disease (CD). CD is characterized by frequent relapses after periods of remission which are not entirely understood. In this paper, we investigate whether the heterogeneity in microbiota profiles in CD patients could be a suitable predictor for these relapses. This prospective observational study involved 259 CD patients, in which 41 provided an additional total of 62 consecutive fecal samples, with an average interval of 25 weeks in between each of these samples. Fecal microbiota was analyzed by massive genomic sequencing through 16 S rRNA amplicon sampling. We found that our 259 CD patients could be split into three distinct subgroups of microbiota (G1, G2, G3). From G1 to G3, we noticed a progressive decrease in alpha diversity (p ≤ 0.0001) but no change in the fecal calprotectin (FC) level. Focusing on the 103 consecutive samples from 41 CD patients, we showed that the patients microbiota profiles were remarkably stable over time and associated with increasing symptom severity. Investigating further this microbiota/severity association revealed that the first signs of aggravation are (1) a loss of the main anti-inflammatory Short-Chain Fatty Acids (SCFAs) Roseburia, Eubacterium, Subdoligranumum, Ruminococcus (P < 0.05), (2) an increase in pro-inflammatory pathogens Proteus, Finegoldia (P < 0.05) while (3) an increase of other minor SCFA producers such as Ezakiella, Anaerococcus, Megasphaera, Anaeroglobus, Fenollaria (P < 0.05). Further aggravation of clinical signs is significantly linked to the subsequent loss of these minor SCFAs species and to an increase in other proinflammatory Proteobacteria such as Klebsiella, Pseudomonas, Salmonella, Acinetobacter, Hafnia and proinflammatory Firmicutes such as Staphylococcus, Enterococcus, Streptococcus. (P < 0.05). To our knowledge, this is the first study (1) specifically identifying subgroups of microbiota profiles in CD patients, (2) relating these groups to the evolution of symptoms over time and (3) showing a two-step process in CD symptoms' worsening. This paves the way towards a better understanding of patient-to-patient heterogeneity, as well as providing early warning signals of future aggravation of the symptoms and eventually adapting empirically treatments.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and leptin-deficient obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity.

Annual Dynamics of Blood Lipid Parameters in Highly Qualified Physical Training

The purpose of the paper is to study and analyse the annual dynamics of blood lipid parameters in highly qualified physical training. An experiment is a leading method for studying this problem that allows considering the problem comprehensively and in practice, as well as a comparison method, which makes it possible to analyse common features and differences as well as consider the dynamics of blood lipid parameters. Athletes who developed endurance or strength to a greater extent had no significant differences in many blood parameters. However, the groups of athletes who developed only strength had a more pronounced anisocytosis. In addition, it was possible to identify a correlation between the parameters of red blood cells and trained sports results. It was concluded that the highest indicators of the number of red blood cells, haemoglobin and average haemoglobin concentration in red blood cells were observed in strength training, and the lowest-in athletes training speed indicators. The article is of practical value for future research in the field of medicine and regenerative physiotherapy.

Omics technologies in poultry health and productivity - part 2: future applications in the poultry industry

The increasing global demand for poultry products, together with the growing consumer concerns related to bird health and welfare, pose a significant challenge to the poultry industry. Therefore, the poultry industry is increasingly implementing novel technologies to optimize and enhance bird welfare and productivity. This second part of a bipartite review on omics technologies in poultry health and productivity highlights the implementation of specific diagnostic biomarkers based on omics-research in the poultry industry, as well as the potential integration of multi-omics in future poultry production. A general discussion of the use of multiple omics technologies in poultry research is provided in part 1. To date, approaches focusing on one or more omics type are widely used in poultry research, but the implementation of these omics techniques in poultry production is not expected in the near future. However, great potential lays in the development of diagnostic tests based on disease- or gut health-specific biomarkers, which are identified through omics research. As the cost of omics technologies is rapidly decreasing, implementation of multi-omics measurements in routine poultry monitoring systems might be feasible in the more distant future. Therefore, the opportunities, challenges and requirements to enable the integration of multi-omics-based monitoring of bird health and productivity in future poultry production are discussed.

Delayed processing of blood samples impairs the accuracy of mRNA-based biomarkers

The transcriptome of peripheral white blood cells (PWBCs) are indicators of an organism's physiological state, thus making them a prime biological sample for mRNA-based biomarker discovery. Here, we designed an experiment to evaluate the impact of delayed processing of whole blood samples on gene transcript abundance in PWBCs. We hypothesized that storing blood samples for 24 h at 4 °C would cause RNA degradation resulting in altered transcriptome profiles. There were no statistical differences in RNA quality parameters among samples processed after one, three, six, or eight hours post collection. Additionally, no significant differences were noted in RNA quality parameters or gene transcript abundance between samples collected from the jugular and coccygeal veins. However, samples processed after 24 h of storage had a lower RNA integrity number value (P = 0.03) in comparison to those processed after one hour of storage. Using RNA-sequencing, we identified four and 515 genes with differential transcript abundance in samples processed after storage for eight and 24 h, respectively, relative to samples processed after one hour. Sequencing coverage of transcripts was similar between samples from the 24-h and one-hour groups, thus showing no indication of RNA degradation. This alteration in transcriptome profiles can impair the accuracy of mRNA-based biomarkers, therefore, blood samples collected for mRNA-based biomarker discovery should be refrigerated immediately and processed within six hours post-sampling.

Circulating miRNAs Are Associated with Inflammation Biomarkers in Children with Overweight and Obesity: Results of the I.Family Study

Increasing data suggest that overnutrition-induced obesity may trigger an inflammatory process in adipose tissue and upturn in the innate immune system. Numerous players have been involved in governing the inflammatory response, including epigenetics. Among epigenetic players, miRNAs are emerging as crucial regulators of immune cell development, immune responses, autoimmunity, and inflammation. In this study, we aimed at identifying the involvement of candidate miRNAs in relation to inflammation-associated biomarkers in a subsample of European children with overweight and obesity participating in the I.Family study. The study sample included individuals with increased adiposity since this condition contributes to the early occurrence of chronic low-grade inflammation. We focused on the acute-phase reagent C-reactive protein (CRP) as the primary outcome and selected cytokines as plausible biomarkers of inflammation. We found that chronic low-grade CRP elevation shows a highly significant association with miR-26b-3p and hsa-miR-576-5p in boys. Furthermore, the association of CRP with hsa-miR-10b-5p and hsa-miR-31-5p is highly significant in girls. We also observed major sex-related associations of candidate miRNAs with selected cytokines. Except for IL-6, a significant association of hsa-miR-26b-3p and hsa-miR-576-5p with TNF-α, IL1-Ra, IL-8, and IL-15 levels was found exclusively in boys. The findings of this exploratory study suggest sex differences in the association of circulating miRNAs with inflammatory response biomarkers, and indicate a possible role of miRNAs among the candidate epigenetic mechanisms related to the process of low-grade inflammation in childhood obesity.

Integrated, data-driven health management: A step closer to personalized and predictive healthcare

Integrated, data-driven health management provides a roadmap to personalized and predictive healthcare. In this issue of Cell Systems, Marabita et al. showcase the application of data-driven, individualized lifestyle coaching to promote health and present an interpretable view of human health by integrating deep molecular, digital health, and clinical data.

Offering Fiber-Enriched Foods Increases Fiber Intake in Adults With or Without Cardiometabolic Risk: A Randomized Controlled Trial

Introduction

Previous efforts to increase fiber intake in the general population were disappointing despite growing awareness of the multiple benefits of a high fiber intake. Aim of the study was to investigate the acceptance and consumption of fiber-enriched foods.

Methods

One hundred and fifteen middle-aged healthy individuals with and without elevated waist circumference (> 102 cm in males and > 88 cm in females) were recruited and randomized to an intervention or an age- and sex-matched control group. Subjects assigned to the intervention group were invited to select fiber-enriched foods from a broad portfolio of products to increase fiber intake by 10 g/day. Control subjects could choose items from the same food basket without fiber enrichment. The primary outcome was the increase in dietary fiber intake, and secondary outcomes were changes in cardiometabolic risk factors, microbiota composition, food choices, and consumer acceptance of the fiber-enriched foods.

Results

Compared to baseline, daily fiber intake increased from 22.5 ± 8.0 to 34.0 ± 9.6 g/day after 4 weeks (p < 0.001) and to 36.0 ± 8.9 g/day after 12 weeks (p < 0.001) in the intervention group, whereas fiber intake remained unchanged in the control group. Participants rated the taste of the food products as pleasant without group differences. In both groups, the most liked foods included popular convenience foods such as pretzel breadstick, pizza salami, and pizza vegetarian. After 12 weeks of intervention, there were minor improvements in plasma lipids and parameters of glucose metabolism in both the intervention and control group compared to baseline, but no differences between the two groups. Increased fiber consumption resulted in an increased (p < 0.001) relative abundance of Tannerellaceae.

Conclusions

Fiber-enrichment of popular foods increases fiber intake in a middle-aged population with and without cardiometabolic risk and may provide a simple, novel strategy to increase fiber intake in the population.

PFOA regulate adenosine receptors and downstream concentration-response cAMP-PKA pathway revealed by integrated omics and molecular dynamics analyses

As an important pollutant, perfluorooctane acid (PFOA) has been widely concerned and reported by thousands of times, while less is known about the concentration-response pathway of PFOA. The aim of the present work was to reveal the concentration-response mechanism of PFOA in human cells. Omics results showed that calcium-related pathways play key roles in PFOA injury mechanisms. The results of GO and KEGG analyses showed that the cAMP signaling pathway was presented as the top one in all of the regulatory patterns and concentrations groups of PFOA. In the cAMP signaling pathway, the adenosine A₁ receptor (ADORA1) recognized the low concentration of PFOA and induced pathway "G_i-cAMP-PKA" to decrease the concentration of cAMP. This indicated that the low concentration of PFOA may promote breast hyperplasia and inhibit lactation. While adenosine A_2A receptor (ADORA2A) recognized the high concentration of PFOA and induced pathway "G_S-AC-cAMP-RKA" to increase the concentration of cAMP, induce cell damage and may lead to the deterioration of breast cancer. The results of molecular dynamics simulation showed that PFOA could bind to ADORA1 and ADORA2A, thus cause subsequent signal transduction. Furthermore, considering the strong binding ability of PFOA with ADORA1, PFOA tends to bind to ADORA1 at a low concentration. On the other side, PFOA at high concentration will continue to bind to another receptor protein, ADORA2A, and activate subsequent signaling pathways. Combined analyses of transcriptomic and proteomic revealed that different concentrations of PFOA regulate cellular calcium-related pathways. The cAMP pathway showed a concentration-response effect of PFOA. After treatment with different concentrations of PFOA, ADORA1 and ADORA2A were activated respectively, showing opposite cellular effects, leading to kinds of breast lesions. In the nervous system, PFOA might induce a variety of nervous system diseases. The present work was an exploration on the toxicological mechanism of PFOA, providing important information on the health impacts of PFOA in humans.

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.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Proteome-wide associations with short- and long-term weight loss and regain after Roux-en-Y gastric bypass surgery

OBJECTIVE

Gastric bypass surgery results in long-term weight loss. Small studies have examined protein changes during rapid weight loss (up to 1 or 2 years post surgery). This study tested whether short-term changes were maintained after 12 years.

METHODS

A 12-year follow-up, protein-wide association study of 1,297 SomaLogic aptamer-based plasma proteins compared short- (2-year) and long-term (12-year) protein changes in 234 individuals who had gastric bypass surgery with 144 nonintervened individuals with severe obesity.

RESULTS

There were 51 replicated 12-year protein changes that differed between the surgery and nonsurgery groups. Adjusting for change in BMI, only 12 proteins remained significant, suggesting that BMI change was the primary reason for most protein changes and not non-BMI-related surgical effects. Protein changes were related to BMI changes during both weight-loss and weight-regain periods. The significant proteins were associated primarily with lipid, uric acid, or resting energy expenditure clinical variables and metabolic pathways. Eight protein changes were associated with 12-year diabetes remission, including apolipoprotein M, sex hormone binding globulin, and adiponectin (p < 3.5 × 10^-5 ).

CONCLUSIONS

This study showed that most short-term postsurgical changes in proteins were maintained at 12 years. Systemic protection pathways, including inflammation, complement, lipid, and adipocyte pathways, were related to the long-term benefits of gastric bypass surgery.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

Design and Methods of the Validating Injury to the Renal Transplant Using Urinary Signatures (VIRTUUS) Study in Children

Lack of noninvasive diagnostic and prognostic biomarkers to reliably detect early allograft injury poses a major hindrance to long-term allograft survival in pediatric kidney transplant recipients.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Chromatin accessibility associates with protein-RNA correlation in human cancer

Although alterations in chromatin structure are known to exist in tumors, how these alterations relate to molecular phenotypes in cancer remains to be demonstrated. Multi-omics profiling of human tumors can provide insight into how alterations in chromatin structure are propagated through the pathway of gene expression to result in malignant protein expression. We applied multi-omics profiling of chromatin accessibility, RNA abundance, and protein abundance to 36 human thyroid cancer primary tumors, metastases, and patient-match normal tissue. Through quantification of chromatin accessibility associated with active transcription units and global protein expression, we identify a local chromatin structure that is highly correlated with coordinated RNA and protein expression. In particular, we identify enhancers located within gene-bodies as predictive of correlated RNA and protein expression, that is independent of overall transcriptional activity. To demonstrate the generalizability of these findings we also identify similar results in an independent cohort of human breast cancers. Taken together, these analyses suggest that local enhancers, rather than distal enhancers, are likely most predictive of cancer gene expression phenotypes. This allows for identification of potential targets for cancer therapeutic approaches and reinforces the utility of multi-omics profiling as a methodology to understand human disease.

Mediating Roles of hsCRP, TNF-α and Adiponectin on the Associations between Body Fat and Fatty Liver Disease among Overweight and Obese Adults

Simple Summary

Body fat has been reported to be related to a higher risk of fatty liver disease (FLD). However, few studies have explored the mediating roles of inflammatory biomarkers or adipokines on the relationships. This study examined the potential mediating effects of high sensitivity C-reactive protein (hsCRP), tumor necrosis factor-α (TNF-α) and adiponectin (APN) in relationships between body fat and FLD in overweight and obese adults. Additionally, gender and age differences were demonstrated. It was concluded that hsCRP has a significant mediating effect on the association between body fat percentage and FLD in females independent of potential covariates. It was also demonstrated that the mediation effect of hsCRP was only significant and more profound in relatively older adults (36–56 years, 38.3%), not significant in the young ones (19–35 years). TNF-α and APN were not significantly associated with body fat percentage or FLD, with no mediating effect on the association between body fat percentage and FLD observed in either gender. In conclusion, hsCRP was a potential mediator on the association between adiposity and FLD, and this mediation is gender-specific and age-specific. The authors hope that the findings could contribute to the further exploration of the inflammatory-related mechanism of obesity-associated FLD.

Abstract

Body fat has been reported to be associated with a higher risk of fatty liver disease (FLD). However, few studies have explored the mediating roles of an inflammatory biomarker or adipokine on the relationships. Here, we examined the potential mediating roles of high sensitivity C-reactive protein (hsCRP), tumor necrosis factor-α (TNF-α) and adiponectin (APN) in relationships between body fat and FLD in overweight and obese adults. Additionally, gender differences will be investigated. In total, 1221 participants aged 19–56 years were included in our study. Body fat percentage was measured with Dual Energy X-ray Absorptiometry (DEXA) and FLD by abdominal ultrasound. Mediation analysis was performed to assess the mediating effect of hsCRP, TNF-α and APN on the associations between BF (%) and FLD by gender differences. We found that hsCRP was significantly associated with body fat percentage in both genders (b = 0.2014, p < 0.0001 and b = 0.1804, p < 0.0001 for male and female, respectively), while hsCRP was associated with FLD only in the female group (b = 0.1609, p = 0.0109) but not in male group (b = 0.4800, p = 0.0603). We observed that hsCRP has a significant mediating effect on the association between body fat percentage and FLD (b = 0.0290, p = 0.0201, mediation ratio: 13.6%) in the female group independent of potential covariates (age, smoking, alcohol drinking and physical activity). TNF-α was not significantly associated with body fat percentage or FLD, with no mediating effect on the association between body fat percentage and FLD in either gender. In conclusion, there is a gender-specific mediation role of hsCRP in the association between body fat and FLD. HsCRP was a potential mediator on the association between adiposity and FLD in the female gender, but not in the male gender. Higher body fat was associated with a higher risk of FLD, and the inflammation level might play a potential mediating role in the association between body fat and FLD among female overweight and obese adults.

Variability in body weight and the risk of cardiovascular complications in type 2 diabetes: results from the Swedish National Diabetes Register

Background

There is a high incidence of cardiovascular disease in diabetes. Weight variability has been reported as independent risk factor for cardiovascular disease in the general population and preliminarily also in people with type 2 diabetes.

Methods

Using data from the Swedish National Diabetes Register the possible link between visit-to-visit body weight variability and the risk of cardiovascular complications among people with type 2 diabetes and without prevalent cardiovascular diseases at baseline has been evaluated. Overall, 100,576 people with type 2 diabetes, with at least five measurements of body weight taken over three consecutive years, were included. Variability was expressed as quartiles of the standard deviation of the measures during the three years. The primary composite outcome included non-fatal myocardial infarction, non-fatal stroke, and all-cause mortality and was assessed during five years following the first 3 years of exposure to weight variability.

Results

After adjusting for known cardiovascular risk factors, the risk of the primary composite outcome significantly increased with increasing body weight variability [upper quartile HR = 1.45; 95% confidence interval 1.39–1.52]. Furthermore, elevated body weight variability was associated with almost all the other cardiovascular complications considered (non-fatal myocardial infarction, non-fatal stroke, all-cause mortality, peripheral arterial disease, peripheral vascular angioplasty, hospitalization for heart failure, foot ulcer, and all-cause mortality).

Conclusions

High body weight variability predicts the development of cardiovascular complications in type 2 diabetes. These data suggest that any strategy to reduce the body weight in these subjects should be aimed at maintaining the reduction in the long-term, avoiding oscillations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01360-0.

Psychological Functioning and Health Behaviors Associated with Weight Loss Patterns up to 13.7 Years After Weight Loss Surgery

Weight loss surgery produces dramatic health improvements immediately after surgery, including rapid declines in diabetes. However, less is known about its long-term effects. 124 St. Vincent Bariatric Center patients completed questionnaires on weight and psychological functioning a mean of 7.7 and 13.7 years post-surgery (T1 and T2, respectively). Because mean weight data may mask differing weight trajectories, participants were categorized based on weight over time. Most participants underwent Roux-En-Y gastric bypass (90.3%) and were Caucasian (96%), female (81.5%), and married (69.1%). Mean age at T2 was 64; mean %EWL was 64.9%. Most patients fit into one of three weight change patterns, reaching weight nadir, and regaining by T1 and then, by T2, experiencing (1) Weight Loss (n = 36), (2) Weight Maintenance (n = 37), or (3) Continued Weight Gain (n = 39). Groups differed significantly on body satisfaction, weighing frequency, and conscientiousness, with Weight Gainers significantly lower than other groups on conscientiousness and body satisfaction, and Weight Losers reporting higher frequency of weighing than Maintainers. Bariatric patients can maintain substantial weight loss and positive psychological functioning for many years post-surgery, although weight regain is associated with less body satisfaction. Conscientiousness may signify medical adherence, whereas frequent weighing may be a behavior that promotes ongoing weight loss.