A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Er-Dong-Xiao-Ke decoction regulates lipid metabolism via PPARG-mediated UCP2/AMPK signaling to alleviate diabetic meibomian gland dysfunction

ETHNOPHARMACOLOGICAL RELEVANCE

Meibomian gland dysfunction (MGD), complicated by type 2 diabetes, is associated with a high incidence of ocular surface disease, and no effective drug treatment exists. Diabetes mellitus (DM) MGD shows a notable disturbance in lipid metabolism. Er-Dong-Xiao-Ke decoction (EDXKD) has important functions in nourishing yin, clearing heat, and removing blood stasis, which are effective in the treatment of DM MGD.

AIM OF THE STUDY

To observe the therapeutic effect of EDXKD on DM MGD and its underlying molecular mechanism.

MATERIALS AND METHODS

After establishing a type 2 DM (T2DM)-induced MGD rat model, different doses of EDXKD and T0070907 were administered. The chemical constituents of EDXKD were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the molecular mechanism of EDXKD in treating DM MGD was predicted using network pharmacology. Lipid metabolism in DM meibomian glands (MGs) was analyzed using LC-MS/MS, and lipid biomarkers were screened and identified. Histological changes and lipid accumulation in MGs were detected by staining, and Peroxisome proliferator-activated receptor gamma (PPARG) expression in MG acinar cells was detected by immunofluorescence. The expression of lipid metabolism-related factors was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) or western blotting.

RESULTS

EDXKD reduced lipid accumulation in the MGs and improved the ocular surface index in DM MGD rats. The main active components of EDXKD had advantages in lipid regulation. Additionally, the PPARG signaling pathway was the key pathway of EDXKD in the treatment of DM MGD. Twelve lipid metabolites were biomarkers of EDXKD in the treatment of DM MGD, and glycerophospholipid metabolism was the main pathway of lipid regulation. Moreover, EDXKD improved lipid deposition in the acini and upregulated the expression of PPARG. Further, EDXKD regulated the PPARG-mediated UCP2/AMPK signaling network, inhibited lipid production, and promoted lipid transport.

CONCLUSION

EDXKD is an effective treatment for MGD in patients with T2DM. EDXKD can regulate lipids by regulating the PPARG-mediated UCP2/AMPK signaling network, as it reduced lipid accumulation in the MGs of DM MGD rats, promoted lipid metabolism, and improved MG function and ocular surface indices.

Comparing the metabolic signatures of obesity defined by waist circumference, waist-hip ratio, or BMI

OBJECTIVE

Measuring obesity is crucial for assessing health risks and developing effective prevention and treatment strategies. The most common methods used to measure obesity include BMI, waist circumference, and waist-hip ratio. This study aimed to determine the metabolic signatures associated with each measure of obesity in the Qatari population.

METHODS

Metabolomics profiling was conducted to identify, quantify, and characterize metabolites in serum samples from the study participants. Inverse rank normalization, principal component analysis, and orthogonal partial least square-discriminant analysis were used to analyze the metabolomics data.

RESULTS

This study revealed significant differences in metabolites associated with obesity based on different measurements. In men, phosphatidylcholine and phosphatidylethanolamine metabolites were significantly enriched in individuals classified as having obesity based on the waist-hip ratio. In women, significant changes were observed in leucine, isoleucine, and valine metabolism metabolites. Unique metabolites were found in the different categorization groups that could serve as biomarkers for assessing many obesity-related disorders.

CONCLUSIONS

This study identified unique metabolic signatures associated with obesity based on different measurements in the Qatari population. These findings contribute to a better understanding of the molecular pathways involved in obesity and may have implications for developing personalized prevention and treatment strategies.

The Hydrophilic Metabolite UMP Alleviates Obesity Traits through a HIF2α-ACER2-Ceramide Signaling Axis

Metabolic abnormalities contribute to the pathogenesis of obesity and its complications. Yet, the understanding of the interactions between critical metabolic pathways that underlie obesity remains to be improved, in part owing to the lack of comprehensive metabolomics studies that reconcile data from both hydrophilic and lipophilic metabolome analyses that can lead to the identification and characterization of key signaling networks. Here, the study conducts a comprehensive metabolomics analysis, surveying lipids and hydrophilic metabolites of the plasma and omental adipose tissue of obese individuals and the plasma and epididymal adipose tissue of mice. Through these approaches, it is found that a significant accumulation of ceramide due to inhibited sphingolipid catabolism, while a significant reduction in the levels of uridine monophosphate (UMP), is critical to pyrimidine biosynthesis. Further, it is found that UMP administration restores sphingolipid homeostasis and can reduce obesity in mice by reversing obesity-induced inhibition of adipocyte hypoxia inducible factor 2a (Hif2α) and its target gene alkaline ceramidase 2 (Acer2), so as to promote ceramide catabolism and alleviate its accumulation within cells. Using adipose tissue Hif2α-specific knockout mice, the study further demonstrates that the presence of UMP can alleviate obesity through a HIF2α-ACER2-ceramide pathway, which can be a new signaling axis for obesity improvement.

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.

Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC-MS and LC-MS

BACKGROUND

Untargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) and identified possible diagnostic markers.

METHODS

Forty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC-MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia.

RESULTS

According to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC-MS and 85 metabolites by LC-MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817-0.957), 0.836 (95% CI = 0.751-0.921), and 0.805 (95% CI = 0.717-0.893), respectively.

CONCLUSIONS

These findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.

Alanine to glycine ratio is a novel predictive biomarker for type 2 diabetes mellitus

AIM

We aimed to evaluate the metabolite ratios that could predict the clinical incidence or remission of type 2 diabetes mellitus (T2D).

METHODS

The Cox proportional hazards regression model was used to assess 1813 individuals without T2D to test the predictive value of metabolite ratios for T2D incidence and 451 newly diagnosed T2D for remission. The receiver operating characteristic curve analysis was performed to determine the best cut-off values for the metabolite ratios. Survival analyses were performed to compare the four subgroups defined by baseline metabolite ratios and clinical status of obesity.

RESULTS

The alanine/glycine was the most significant marker for T2D incidence (hazard ratio per SD: 1.24; p < .001). On the other hand, metabolite hydroxy sphingomyelin C22:2 was most specific for T2D remission (hazard ratio per SD: 1.32; p = .029). Survival analysis of T2D incidence among the subgroups defined by the combination of alanine/glycine and obesity showed the group with a high alanine/glycine and obesity had the highest risk of T2D incidence (p < .001). The alanine/glycine as a T2D risk marker was also validated in the independent external data.

CONCLUSIONS

The combination of obesity and the alanine/glycine ratio can be used to evaluate the diabetes risk.

Evaluating the causal relationship between human blood metabolites and gastroesophageal reflux disease

BACKGROUND

Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome.

AIM

To analyze of the relationship between 486 blood metabolites and GERD.

METHODS

Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD.

CONCLUSION

Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

Glucuronic acid is a novel source of pentosidine, associated with schizophrenia

Pentosidine (PEN) is an advanced glycation end-product (AGEs), where a fluorescent cross-link is formed between lysine and arginine residues in proteins. Accumulation of PEN is associated with aging and various diseases. We previously reported that a subpopulation of patients with schizophrenia showed PEN accumulation in the blood, having severe clinical features. PEN is thought to be produced from glucose, fructose, pentoses, or ascorbate. However, patients with schizophrenia with high PEN levels present no elevation of these precursors of PEN in their blood. Therefore, the molecular mechanisms underlying PEN accumulation and the molecular pathogenesis of schizophrenia associated with PEN accumulation remain unclear. Here, we identified glucuronic acid (GlcA) as a novel precursor of PEN from the plasma of subjects with high PEN levels. We demonstrated that PEN can be generated from GlcA, both in vitro and in vivo. Furthermore, we found that GlcA was associated with the diagnosis of schizophrenia. Among patients with high PEN, the proportion of those who also have high GlcA is 25.6%. We also showed that Aldo-keto reductase (AKR) activity to degrade GlcA was decreased in patients with schizophrenia, and its activity was negatively correlated with GlcA levels in the plasma. This is the first report to show that PEN is generated from GlcA. In the future, this finding will contribute to understanding the molecular pathogenesis of not only schizophrenia but also other diseases with PEN accumulation.

Integrated mass spectrometry imaging and metabolomics reveals sublethal effects of indoxacarb on the red fire ant Solenopsis invicta

BACKGROUND

Indoxacarb, representing an efficient insecticide, is normally made into a bait to spread the poison among red fire ants so that it can be widely applied in the prevention and control of Solenopsis invicta. However, the potential toxicity mechanism of S. invicta in response to indoxacarb remains to be explored. In this study, we integrated mass spectrometry imaging (MSI) and untargeted metabolomics methods to reveal disturbed metabolic expression levels and spatial distribution within the whole-body tissue of S. invicta treated with indoxacarb.

RESULTS

Metabolomics results showed a significantly altered level of metabolites after indoxacarb treatment, such as carbohydrates, amino acids and pyrimidine and derivatives. Additionally, the spatial distribution and regulation of several crucial metabolites resulting from the metabolic pathway and lipids can be visualized using label-free MSI methods. Specifically, xylitol, aspartate, and uracil were distributed throughout the whole body of S. invicta, while sucrose-6'-phosphate and glycerol were mainly distributed in the abdomen of S. invicta, and thymine was distributed in the head and chest of S. invicta. Taken together, the integrated MSI and metabolomics results indicated that the toxicity mechanism of indoxacarb in S. invicta is closely associated with the disturbance in several key metabolic pathways, such as pyrimidine metabolism, aspartate metabolism, pentose and glucuronate interconversions, and inhibited energy synthesis.

CONCLUSION

Collectively, these findings provide a new perspective for the understanding of toxicity assessment between targeted organisms S. invicta and pesticides. © 2023 Society of Chemical Industry.

Food Polyphenols and Type II Diabetes Mellitus: Pharmacology and Mechanisms

Type II diabetes mellitus and its related complications are growing public health problems. Many natural products present in our diet, including polyphenols, can be used in treating and managing type II diabetes mellitus and different diseases, owing to their numerous biological properties. Anthocyanins, flavonols, stilbenes, curcuminoids, hesperidin, hesperetin, naringenin, and phenolic acids are common polyphenols found in blueberries, chokeberries, sea-buckthorn, mulberries, turmeric, citrus fruits, and cereals. These compounds exhibit antidiabetic effects through different pathways. Accordingly, this review presents an overview of the most recent developments in using food polyphenols for managing and treating type II diabetes mellitus, along with various mechanisms. In addition, the present work summarizes the literature about the anti-diabetic effect of food polyphenols and evaluates their potential as complementary or alternative medicines to treat type II diabetes mellitus. Results obtained from this survey show that anthocyanins, flavonols, stilbenes, curcuminoids, and phenolic acids can manage diabetes mellitus by protecting pancreatic β-cells against glucose toxicity, promoting β-cell proliferation, reducing β-cell apoptosis, and inhibiting α-glucosidases or α-amylase. In addition, these phenolic compounds exhibit antioxidant anti-inflammatory activities, modulate carbohydrate and lipid metabolism, optimize oxidative stress, reduce insulin resistance, and stimulate the pancreas to secrete insulin. They also activate insulin signaling and inhibit digestive enzymes, regulate intestinal microbiota, improve adipose tissue metabolism, inhibit glucose absorption, and inhibit the formation of advanced glycation end products. However, insufficient data are available on the effective mechanisms necessary to manage diabetes.

Obesity, Gut Microbiota, and Metabolome: From Pathophysiology to Nutritional Interventions

Obesity is a disorder identified by an inappropriate increase in weight in relation to height and is considered by many international health institutions to be a major pandemic of the 21st century. The gut microbial ecosystem impacts obesity in multiple ways that yield downstream metabolic consequences, such as affecting systemic inflammation, immune response, and energy harvest, but also the gut-host interface. Metabolomics, a systematized study of low-molecular-weight molecules that take part in metabolic pathways, represents a serviceable method for elucidation of the crosstalk between hosts' metabolism and gut microbiota. In the present review, we confer about clinical and preclinical studies exploring the association of obesity and related metabolic disorders with various gut microbiome profiles, and the effects of several dietary interventions on gut microbiome composition and the metabolome. It is well established that various nutritional interventions may serve as an efficient therapeutic approach to support weight loss in obese individuals, yet no agreement exists in regard to the most effective dietary protocol, both in the short and long term. However, metabolite profiling and the gut microbiota composition might represent an opportunity to methodically establish predictors for obesity control that are relatively simple to measure in comparison to traditional approaches, and it may also present a tool to determine the optimal nutritional intervention to ameliorate obesity in an individual. Nevertheless, a lack of adequately powered randomized trials impedes the application of observations to clinical practice.

Serum, Urine, and Fecal Metabolome Alterations in the Gut Microbiota in Response to Lifestyle Interventions in Pediatric Obesity: A Non-Randomized Clinical Trial

Pediatric obesity is associated with alterations in the gut microbiota and its metabolites. However, how they influence obesity and the effect of lifestyle interventions remains unknown.. In this non-randomized clinical trial, we analyzed metabolomes and microbial features to understand the associated metabolic pathways and the effect of lifestyle interventions on pediatric obesity. Anthropometric/biochemical data and fasting serum, urine, and fecal samples were collected at baseline and after an eight-week, weight-reduction lifestyle modification program. Post-intervention, children with obesity were classified into responder and non-responder groups based on changes in total body fat. At baseline, serum L-isoleucine and uric acid levels were significantly higher in children with obesity compared with those in normal-weight children and were positively correlated with obesogenic genera. Taurodeoxycholic and tauromuricholic α + β acid levels decreased significantly with obesity and were negatively correlated with obesogenic genera. Branched-chain amino acid and purine metabolisms were distinguished metabolic pathways in the obese group. Post-intervention, urinary myristic acid levels decreased significantly in the responder group, showing a significant positive correlation with Bacteroides. Fatty acid biosynthesis decreased significantly in the responder group. Thus, lifestyle intervention with weight loss is associated with changes in fatty acid biosynthesis, and myristic acid is a possible therapeutic target for pediatric obesity.

Serum metabolomics profiling by proton nuclear magnetic resonance spectroscopy reveals sexual dimorphism and masculinization of intermediate metabolism in women with polycystic ovary syndrome (PCOS)

BACKGROUND

The polycystic ovary syndrome (PCOS) is associated with insulin resistance, obesity and cardiometabolic comorbidities. We here challenged the hypothesis, using state-of-the art proton nuclear magnetic resonance spectroscopy metabolomics profiling, that androgen excess in women induces also a certain masculinization of intermediate metabolism that is modulated by obesity.

METHODS

Participants were 53 Caucasian young adults, including 17 women with classic PCOS consisting of hyperandrogenism and ovulatory dysfunction, 17 non-hyperandrogenic women presenting with regular menses, and 19 healthy men, selected in order to be similar in terms of age and body mass index (BMI). Half of the subjects had obesity defined by a body mass index ≥ 30 kg/m². Subjects maintained the same diet unrestricted in carbohydrates for 3 days before sampling and maintained their lifestyle and exercise patterns prior and during the study. Plasma samples were submitted to proton nuclear magnetic resonance spectroscopy metabolomics profiling.

RESULTS

Obesity associated a metabolomics profile mainly characterized by increased branched chain and aromatic aminoacids. Regardless of obesity, this unfavorable profile also characterized men as compared with control women, and was shared by women with PCOS. Notably, the negative impact of obesity on metabolomics profile was restricted to women, with obese men showing no further deterioration when compared with their non-obese counterparts.

CONCLUSIONS

Serum metabolomics profiling by proton nuclear magnetic resonance spectroscopy reveals sexual dimorphism, and masculinization of intermediate metabolism in women with PCOS, further suggesting a role for sex and sex hormones in the regulation of intermediate metabolism.

Childhood Obesity and the Cryptic Language of the Microbiota: Metabolomics’ Upgrading

The growing obesity epidemic in childhood is increasingly concerning for the related physical and psychological consequences, with a significant impact on health care costs in both the short and the long term. Nonetheless, the scientific community has not yet completely clarified the complex metabolic mechanisms underlying body weight alterations. In only a small percentage of cases, obesity is the result of endocrine, monogenic, or syndromic causes, while in much more cases, lifestyle plays a crucial role in obesity development. In this context, the pediatric age appears to be of considerable importance as prevention strategies together with early intervention can represent important therapeutic tools not only to counteract the comorbidities that increasingly affect children but also to hinder the persistence of obesity in adulthood. Although evidence in the literature supporting the alteration of the microbiota as a critical factor in the etiology of obesity is abundant, it is not yet fully defined and understood. However, increasingly clear evidence is emerging regarding the existence of differentiated metabolic profiles in obese children, with characteristic metabolites. The identification of specific pathology-related biomarkers and the elucidation of the altered metabolic pathways would therefore be desirable in order to clarify aspects that are still poorly understood, such as the consequences of the interaction between the host, the diet, and the microbiota. In fact, metabolomics can characterize the biological behavior of a specific individual in response to external stimuli, offering not only an eventual effective screening and prevention strategy but also the possibility of evaluating adherence and response to dietary intervention.

Cross-sectional relationships between general and central adiposity and plasma amino acids in Chinese adults

Adiposity is an important determinant of blood metabolites, but little is known about the variations of blood amino acids according to general and central adiposity status among Chinese population. This study included 187 females and 322 males who were cancer-free subjects randomly selected from two cohorts in Shanghai, China. Participants' plasma concentrations of amino acids were measured by ultra-performance liquid chromatography coupled to tandem mass spectrometry. Linear regression models were used to examine the cross-sectional correlations between general and central adiposity and amino acid levels. A total of 35 amino acids in plasma were measured in this study. In females, alanine, aspartic acid and pyroglutamic acid were positively correlated with general adiposity. In males, glutamic acid, aspartic acid, valine and pyroglutamic acid showed positive correlations, and glutamine, serine and glycine showed negative correlations with both general and central adiposity; phenylalanine, isoleucine and leucine were positively correlated and N-phenylacetylglutamine was negatively correlated with general adiposity; asparagine was negatively correlated with central adiposity. In summary, general adiposity and central adiposity were correlated with the concentrations of specific plasma amino acids among cancer-free female and male adults in China. Adiposity-metabolite characteristics and relationships should be considered when studying blood biomarkers for adiposity-related health outcomes.

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.

Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes

Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.

The relationship between islet β-cell function and metabolomics in overweight patients with Type 2 diabetes

A cross-sectional study was performed using metabolomics in overweight patients with Type 2 diabetes (T2D) at different stages of the disease. We aimed to identify potential metabolites for assessing islet β-cell function in order to investigate the correlation between islet β-cell dysfunction and metabolite changes in overweight patients with T2D. We selected 60 overweight adults (24 ≤ body mass index [BMI] < 28 kg/m2) with T2D who had been admitted to our hospital. The participants were equally divided into three groups according to disease duration: H1 (duration ≤ 5 years), H2 (5 years < duration ≤ 10 years), and H3 (duration > 10 years). Questionnaires, physical examinations, laboratory tests, and imaging studies were administered to all participants. The modified homeostasis model of assessment (HOMA) index was calculated using fasting C-peptide levels, and metabolite assays were performed using mass spectrometry. The results showed that HOMA-β and visceral fat area (VFA) were negatively correlated with diabetes duration. The VFA was positively correlated with arginine, cysteine, methionine, proline, and succinyl/methylmalonylcarnitine levels. The HOMA-β was negatively correlated with the serine and tetradecanoyldiacylcarnitine levels, and positively correlated with the aspartic acid, cysteine, homocysteine, piperamide, proline, and valine levels. The HOMA-IR was negatively correlated with hydroxypalmitoylcarnitine levels and positively correlated with the myristoylcarnitine levels. Thus, at different stages of T2D progression in overweight patients, serine, aspartic acid, cysteine, homocysteine, piperamide, proline, valine, and tetradecanoyldiacylcarnitine may be associated with HOMA-β and represent potential novel biomarkers for evaluating islet β-cell function.

Pathophysiology of type 2 diabetes and the impact of altered metabolic interorgan crosstalk

Diabetes is a complex and multifactorial disease that affects millions of people worldwide, reducing the quality of life significantly, and results in grave consequences for our health care system. In type 2 diabetes (T2D), the lack of β-cell compensatory mechanisms overcoming peripherally developed insulin resistance is a paramount factor leading to disturbed blood glucose levels and lipid metabolism. Impaired β-cell functions and insulin resistance have been studied extensively resulting in a good understanding of these pathways but much less is known about interorgan crosstalk, which we define as signaling between tissues by secreted factors. Besides hormones and organokines, dysregulated blood glucose and long-lasting hyperglycemia in T2D is associated with changes in metabolism with metabolites from different tissues contributing to the development of this disease. Recent data suggest that metabolites, such as lipids including free fatty acids and amino acids, play important roles in the interorgan crosstalk during the development of T2D. In general, metabolic remodeling affects physiological homeostasis and impacts the development of T2D. Hence, we highlight the importance of metabolic interorgan crosstalk in this review to gain enhanced knowledge of the pathophysiology of T2D, which may lead to new therapeutic approaches to treat this disease.

Lipidomics in gestational diabetes mellitus

PURPOSE OF REVIEW

Epidemiological and mechanistic studies have reported relationships between blood lipids, mostly measured by traditional method in clinical settings, and gestational diabetes mellitus (GDM). Recent advances of high-throughput lipidomics techniques have made available more comprehensive lipid profiling in biological samples. This review aims to summarize evidence from prospective studies in assessing relations between blood lipids and GDM, and discuss potential underlying mechanisms.

RECENT FINDINGS

Mass spectrometry and nuclear magnetic resonance spectroscopy-based analytical platforms are extensively used in lipidomics research. Epidemiological studies have identified multiple novel lipidomic biomarkers that are associated with risk of GDM, such as certain types of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, cholesterol, and lipoproteins. However, the findings are inconclusive mainly due to the heterogeneities in study populations, sample sizes, and analytical platforms. Mechanistic evidence indicates that abnormal lipid metabolism may be involved in the pathogenesis of GDM by impairing pancreatic β-cells and inducing insulin resistance through several etiologic pathways, such as inflammation and oxidative stress.

SUMMARY

Lipidomics is a powerful tool to study pathogenesis and biomarkers for GDM. Lipidomic biomarkers and pathways could help to identify women at high risk for GDM and could be potential targets for early prevention and intervention of GDM.

Using HbA1c measurements and the Finnish Diabetes Risk Score to identify undiagnosed individuals and those at risk of diabetes in primary care

BACKGROUND

Prevalence of prediabetes and type 2 diabetes mellitus (T2DM) is increasing worldwide. The objective of this study was to determine the proportion of people in Northern Iceland with prediabetes, at risk of developing T2DM or with manifest undiagnosed T2DM, as this information is lacking in Iceland.

METHODS

A cross-sectional study. Clients of the three largest primary health care centres in the Health Care Institution of North Iceland (HSN) were invited to participate if fulfilling the following inclusion criteria: a) aged between 18 and 75 years, b) not diagnosed with diabetes, c) speaking and understanding Icelandic or English fluently and d) living in the included service area. Data collection took place via face-to-face interviews between 1 March 2020 and 15 May 2021. Participation included answering the Finnish Diabetes Risk Score (FINDRISC), measuring the HbA1c levels and background information.

RESULTS

Of the 220 participants, 65.9% were women. The mean age was 52.1 years (SD ± 14.1) and FINDRISC scores were as follows: 47.3% scored ≤8 points, 37.2% scored between 9 and 14 points, and 15.5% scored between 15 and 26 points. The mean HbA1c levels in mmol/mol, were 35.5 (SD ± 3.9) for men and 34.4 (SD ± 3.4) for women, ranging from 24 to 47. Body mass index ≥30 kg/m² was found in 32% of men and 35.9% of women. Prevalence of prediabetes in this cohort was 13.2%. None of the participants had undiagnosed T2DM. Best sensitivity and specificity for finding prediabetes was by using cut-off points of ≥11 on FINDRISC, which gave a ROC curve of 0.814.

CONCLUSIONS

The FINDRISC is a non-invasive and easily applied screening instrument for prediabetes. Used in advance of other more expensive and invasive testing, it can enable earlier intervention by assisting decision making, health promotion actions and prevention of the disease burden within primary health care.

TRIAL REGISTRATION

This study is a pre-phase of the registered study "Effectiveness of Nurse-coordinated Follow up Program in Primary Care for People at risk of T2DM" at www.

CLINICALTRIALS

gov (NCT01688359). Registered 30 December 2020.

Metabolic remodeling of glycerophospholipids acts as a signature of dulaglutide and liraglutide treatment in recent-onset type 2 diabetes mellitus

Aims

As metabolic remodeling is a pathological characteristic in type 2 diabetes (T2D), we investigate the roles of newly developed long-acting glucagon-like peptide-1 receptor agonists (GLP-1RAs) such as dulaglutide and liraglutide on metabolic remodeling in patients with recent-onset T2D.

Methods

We recruited 52 cases of T2D and 28 control cases in this study. In the patient with T2D, 39 cases received treatment with dulaglutide and 13 cases received treatment with liraglutide. Using untargeted metabolomics analysis with broad-spectrum LC-MS, we tracked serum metabolic changes of the patients from the beginning to the end of follow-up (12th week).

Results

We identified 198 metabolites that were differentially expressed in the patients with T2D, compared to the control group, in which 23 metabolites were significantly associated with fasting plasma glucose. Compared to pre-treatment, a total of 46 and 45 differentially regulated metabolites were identified after treatments with dulaglutide and liraglutide, respectively, in which the most differentially regulated metabolites belong to glycerophospholipids. Furthermore, a longitudinal integration analysis concurrent with diabetes case-control status revealed that metabolic pathways, such as the insulin resistance pathway and type 2 diabetes mellitus, were enriched after dulaglutide and liraglutide treatments. Proteins such as GLP-1R, GNAS, and GCG were speculated as potential targets of dulaglutide and liraglutide.

Conclusions

In total, a metabolic change in lipids existed in the early stage of T2D was ameliorated after the treatments of GLP-1RAs. In addition to similar effects on improving glycemic control, remodeling of glycerophospholipid metabolism was identified as a signature of dulaglutide and liraglutide treatments.

The association between acylcarnitine and amino acids profile and metabolic syndrome and its components in Iranian adults: Data from STEPs 2016

BACKGROUND

Evidence, albeit with conflicting results, has suggested that cardiometabolic risk factors, including obesity, type 2 diabetes (T2D), dyslipidemia, and hypertension, are highly associated with changes in metabolic signature, especially plasma amino acids and acylcarnitines levels. Here, we aimed to evaluate the association of circulating levels of amino acids and acylcarnitines with metabolic syndrome (MetS) and its components in Iranian adults.

METHODS

This cross-sectional study was performed on 1192 participants from the large-scale cross-sectional study of Surveillance of Risk Factors of non-communicable diseases (NCDs) in Iran (STEP 2016). The circulating levels of amino acids and acylcarnitines were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in individuals with MetS (n=529) and without MetS (n=663).

RESULTS

The higher plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asp to Asn were significantly associated with MetS, whereas lower circulating levels of Gly, Ser, His, Asn, and citrulline were significantly associated with MetS. As for plasma levels of free carnitine and acylcarnitines, higher levels of short-chain acylcarnitines (C2, C3, C4DC), free carnitine (C0), and long-chain acylcarnitines (C16, C18OH) were significantly associated with MetS. Principal component analysis (PCA) showed that factor 3 (Tyr, Leu, Val, Met, Trp, Phe, Thr) [OR:1.165, 95% CI: 1.121-1.210, P<0.001], factor 7 (C0, C3, C4) [OR:1.257, 95% CI: 1.150-1.374, P<0.001], factor 8 (Gly, Ser) [OR:0.718, 95% CI: 0.651-0.793, P< 0.001], factor 9 (Ala, Pro, C4DC) [OR:1.883, 95% CI: 1.669-2.124, P<0.001], factor 10 (Glu, Asp, C18:2OH) [OR:1.132, 95% CI: 1.032-1.242, P= 0.009], factor 11 (citrulline, ornithine) [OR:0.862, 95% CI: 0.778-0.955, P= 0.004] and 13 (C18OH, C18:1 OH) [OR: 1.242, 95% CI: 1.042-1.480, P= 0.016] were independently correlated with metabolic syndrome.

CONCLUSION

Change in amino acid, and acylcarnitines profiles were seen in patients with MetS. Moreover, the alteration in the circulating levels of amino acids and acylcarnitines is along with an increase in MetS component number. It also seems that amino acid and acylcarnitines profiles can provide valuable information on evaluating and monitoring MetS risk. However, further studies are needed to establish this concept.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

The metabolomics of a protein kinase C delta (PKCδ) knock-out mouse model

INTRODUCTION

PKCδ is ubiquitously expressed in mammalian cells and its dysregulation plays a key role in the onset of several incurable diseases and metabolic disorders. However, much remains unknown about the metabolic pathways and disturbances induced by PKC deficiency, as well as the metabolic mechanisms involved.

OBJECTIVES

This study aims to use metabolomics to further characterize the function of PKC from a metabolomics standpoint, by comparing the full serum metabolic profiles of PKC deficient mice to those of wild-type mice.

METHODS

The serum metabolomes of PKCδ knock-out mice were compared to that of a wild-type strain using a GCxGC-TOFMS metabolomics research approach and various univariate and multivariate statistical analyses.

RESULTS

Thirty-seven serum metabolite markers best describing the difference between PKCδ knock-out and wild-type mice were identified based on a PCA power value > 0.9, a t-test p-value < 0.05, or an effect size > 1. XERp prediction was also done to accurately select the metabolite markers within the 2 sample groups. Of the metabolite markers identified, 78.4% (29/37) were elevated and 48.65% of these markers were fatty acids (18/37). It is clear that a total loss of PKCδ functionality results in an inhibition of glycolysis, the TCA cycle, and steroid synthesis, accompanied by upregulation of the pentose phosphate pathway, fatty acids oxidation, cholesterol transport/storage, single carbon and sulphur-containing amino acid synthesis, branched-chain amino acids (BCAA), ketogenesis, and an increased cell signalling via N-acetylglucosamine.

CONCLUSION

The charaterization of the dysregulated serum metabolites in this study, may represent an additional tool for the early detection and screening of PKCδ-deficiencies or abnormalities.

Glucagon-like peptide-1 analog liraglutide leads to multiple metabolic alterations in diet-induced obese mice

Liraglutide, a glucagon-like peptide-1 analog, has beneficial metabolic effects in patients with type 2 diabetes and obesity. Although the high efficacy of liraglutide as an anti-diabetic and anti-obesity drug is well known, liraglutide-induced metabolic alterations in diverse tissues remain largely unexplored. Here, we report the changes in metabolic profiles induced by a 2-week subcutaneous injection of liraglutide in diet-induced obese mice fed a high-fat diet for 8 weeks. Our comprehensive metabolomic analyses of the hypothalamus, plasma, liver, and skeletal muscle showed that liraglutide intervention led to various metabolic alterations in comparison with diet-induced obese or nonobese mice. We found that liraglutide remarkably coordinated not only fatty acid metabolism in the hypothalamus and skeletal muscle but also amino acid and carbohydrate metabolism in plasma and liver. Comparative analyses of metabolite dynamics revealed that liraglutide rewired intertissue metabolic correlations. Our study points to a previously unappreciated metabolic alteration by liraglutide in several tissues, which may underlie its therapeutic effects within and across the tissues.

Plasma metabolomic signatures of obesity and risk of type 2 diabetes

OBJECTIVE

The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D.

METHODS

In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk.

RESULTS

A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001).

CONCLUSIONS

Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.

BMI and lipidomic biomarkers with risk of gestational diabetes in pregnant women

OBJECTIVE

The study aimed to identify BMI-related lipids and to explore the role of lipids linking BMI and gestational diabetes mellitus (GDM).

METHODS

Plasma lipidome, height, and weight were measured in early pregnancy among 1008 women. Pearson correlation analyses and least absolute shrinkage and selection operator regression (LASSO) were performed to identify BMI-associated lipids. Based on these lipids, a lipid score was created using LASSO, and its association with GDM risk was evaluated by conditional logistic regression. The causal relationships between BMI and lipids were tested by Mendelian randomization analysis with genotyping data. Mediation analysis was conducted to evaluate the mediating effect of lipids on the association of BMI with GDM.

RESULTS

Of 366 measured lipids, BMI was correlated with 28 lipids, which mainly belong to glycerophospholipids and glycerolipids. A total of 10 lipid species were associated with BMI, and a lipid score was established. A causal relationship between BMI and lysophosphatidylcholine 14:0 was observed. The lipid score was associated with a 1.69-fold increased risk of GDM per 1-point increment (95% CI: 1.33-2.15). Furthermore, BMI-associated lipids might explain 66.4% of the relationship between BMI and GDM.

CONCLUSIONS

Higher BMI in early pregnancy was associated with altered lipid metabolism that may contribute to the increased risk of GDM.

Untargeted approach to investigating the metabolomics profile of type 2 diabetes emiratis

Type 2 Diabetes (T2D) is expected to be the seventh most significant cause of death worldwide by 2030. Although research into its mechanism has received the attention it deserves, our understanding of T2D is still limited. This case-control study employs untargeted metabolomics to explore novel T2D plasma biomarkers in the Emirati population. Ninety-two UAE nationals were included in the cohort, with fifty T2D and forty-two non-T2D profiles. Participants were then stratified into three groups based on metabolic profiles, clinically verified diabetic status, and current HbA1c values: namely controlled diabetics, uncontrolled diabetics and prediabetics, and non-diabetics. The study identified fifteen significant differentially abundant metabolites between the uncontrolled diabetics group and the prediabetics or controlled diabetics group. Interestingly, some metabolites essential for the corticosteroid and thyroid signaling pathways were found to be significantly elevated in poorly controlled T2D, including cortisol, glycocholic acid, bile acids, thyroxine, and the tryptophan metabolite, 5-hydroxyindoleacetic acid. These findings align with those from prior western cohorts and suggest an intriguing linkage between T2D glycemic control and thyroid and adrenal signaling that may provide new diagnostic and prognostic indicators. RESEARCH SIGNIFICANCE: This study investigates the underlooked metabolomic role and correlation with T2D in the UAE population. The report indicates fifteen significant differentially abundant metabolites between on diabetics, uncontrolled diabetics and or controlled diabetics or prediabetics. This panel of metabolites such as thyroxine and corticosteroids should be considered further as potential diagnostic or prognostic biomarkers for T2D in the region.

The Harm of Metabolically Healthy Obese and the Effect of Exercise on Their Health Promotion

Obesity and obesity-related diseases [type 2 diabetes, cardiovascular disease (CVD), and cancer] are becoming more common, which is a major public health concern. Metabolically healthy obesity (MHO) has become a type of obesity, accounting for a large proportion of obese people. MHO is still harmful to health. It was discovered that MHO screening criteria could not well reflect health hazards, whereas visceral fat, adiponectin pathway, oxidative stress, chronic inflammation, and histological indicators at the microlevel could clearly distinguish MHO from health control, and the biological pathways involved in these micro indicators were related to MHO pathogenesis. This review reveals that MHO’s micro metabolic abnormality is the initial cause of the increase of disease risk in the future. Exploring the biological pathway of MHO is important in order to develop an effective mechanism-based preventive and treatment intervention strategy. Exercise can correct the abnormal micro metabolic pathway of MHO, regulate metabolic homeostasis, and enhance metabolic flexibility. It is a supplementary or possible alternative to the traditional healthcare prevention/treatment strategy as well as an important strategy for reducing MHO-related health hazards.

Metabolomic profile of combined healthy lifestyle behaviours in humans: A systematic review

A combination of healthy lifestyle behaviours (i.e. regular physical activity, nutritious diet, no smoking, moderate alcohol, and healthy body mass) has been consistently associated with beneficial health outcomes including reduced risk of cardiometabolic diseases. Metabolomic profiles, characterized by distinct sets of biomarkers, have been described for healthy lifestyle behaviours individually and in combination. However, recent literature calls for systematic evaluation of these heterogenous data to identify potential clinical biomarkers relating to a combined healthy lifestyle. The objective was to systematically review existing literature on the metabolomic profile of combined healthy lifestyle behaviours. MEDLINE, EMBASE and Cochrane databases were searched through March 2022. Studies in humans outlining the metabolomic profile of a combination of two or more healthy lifestyle behaviours were included. Collectively, the metabolomic profile following regular adherence to combined healthy lifestyle behaviours points to a positive association with beneficial fatty acids and phosphocreatine, and inverse associations with triglycerides, trimethylamine N-oxide, and acylcarnitines. The findings suggest that a unique metabolomic profile is associated with combined healthy lifestyle behaviours. Additional research is warranted to further describe this metabolomic profile using targeted and untargeted metabolomic approaches along with uniform definitions of combined healthy lifestyle variables across populations. This article is protected by copyright. All rights reserved.

Targeted metabolomics suggests a probable role of the FTO gene in the kynurenine pathway in prediabetes

Background

Genome-wide association studies have identified the alpha-ketoglutarate dependent dioxygenase gene (FTO) as the first susceptibility gene of obesity. In the present study, we utilized targeted metabolomics in an attempt to further elucidate mechanisms underlying the action of the FTO gene.

Methods

This study was part of a health survey of employees of the Electricity Generating Authority of Thailand (n = 79, 10 female and 69 male). Targeted metabolomics was performed by liquid chromatography-mass spectrometry using Biocrates AbsoluteIDQ-p180 kit. Genotyping of FTO rs9939609 was performed by real-time PCR (TaqMan™ MGB probes).

Results

Using OPLS-DA variable importance in projection (VIP), tryptophan was found to be among the metabolites with the 10 highest VIP scores. Pearson's correlation analysis showed that kynurenine and tryptophan were positively correlated only in subjects with the rs9939609 A allele (n = 32, r = 0.56, p < 0.001) and the correlation coefficients were significantly higher in subjects having the A allele than in those without the A allele (p < 0.05). Moreover, the kynurenine/tryptophan ratio was significantly associated with the presence of the A allele, independently of body mass index and sex.

Conclusions

The FTO gene is likely to influences the conversion of tryptophan to kynurenine.

N6-Acetyl-L-Lysine and p-Cresol as Key Metabolites in the Pathogenesis of COVID-19 in Obese Patients

Despite the growing number of the vaccinated population, COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global health burden. Obesity, a metabolic syndrome affecting one-third of the population, has proven to be a major risk factor for COVID-19 severe complications. Several studies have identified metabolic signatures and disrupted metabolic pathways associated with COVID-19, however there are no reports evaluating the role of obesity in the COVID-19 metabolic regulation. In this study we highlight the involvement of obesity metabolically in affecting SARS-CoV-2 infection and the consequent health complications, mainly cardiovascular disease. We measured one hundred and forty-four (144) metabolites using ultra high-performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS) to identify metabolic changes in response to SARS-CoV-2 infection, in lean and obese COVID-19 positive (n=82) and COVID-19 negative (n=24) patients. The identified metabolites are found to be mainly correlating with glucose, energy and steroid metabolisms. Further data analysis indicated twelve (12) significantly yet differentially abundant metabolites associated with viral infection and health complications, in COVID-19 obese patients. Two of the detected metabolites, n6-acetyl-l-lysine and p-cresol, are detected only among the COVID-19 cohort, exhibiting significantly higher levels in COVID-19 obese patients when compared to COVID-19 lean patients. These metabolites have important roles in viral entry and could explain the increased susceptibility of obese patients. On the same note, a set of six metabolites associated with antiviral and anti-inflammatory functions displayed significantly lower abundance in COVID-19 obese patients. In conclusion, this report highlights the plasma metabolome of COVID-19 obese patients as a metabolic feature and signature to help improve clinical outcomes. We propose n6-acetyl-l-lysine and p-cresol as potential metabolic markers which warrant further investigations to better understand their involvement in different metabolic pathways in COVID-19.

Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes

Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.

Quality More Than Quantity: The Use of Carbohydrates in High-Fat Diets to Tackle Obesity in Growing Rats

Childhood obesity prevention is important to avoid obesity and its comorbidities into adulthood. Although the energy density of food has been considered a main obesogenic factor, a focus on food quality rather that the quantity of the different macronutrients is needed. Therefore, this study investigates the effects of changing the quality of carbohydrates from rapidly to slowly digestible carbohydrates on metabolic abnormalities and its impact on obesity in growing rats fed a high-fat diet (HFD). Growing rats were fed on HFD containing carbohydrates with different digestion rates: a HFD containing rapid-digesting carbohydrates (OBE group) or slow-digesting carbohydrates (ISR group), for 4 weeks and the effect on the metabolism and signaling pathways were analyzed in different tissues. Animals from OBE group presented an overweight/obese phenotype with a higher body weight gain and greater accumulation of fat in adipose tissue and liver. This state was associated with an increase of HOMA index, serum diacylglycerols and triacylglycerides, insulin, leptin, and pro-inflammatory cytokines. In contrast, the change of carbohydrate profile in the diet to one based on slow digestible prevented the obesity-related adverse effects. In adipose tissue, GLUT4 was increased and UCPs and PPARγ were decreased in ISR group respect to OBE group. In liver, GLUT2, FAS, and SRBP1 were lower in ISR group than OBE group. In muscle, an increase of glycogen, GLUT4, AMPK, and Akt were observed in comparison to OBE group. In conclusion, this study demonstrates that the replacement of rapidly digestible carbohydrates for slowly digestible carbohydrates within a high-fat diet promoted a protective effect against the development of obesity and its associated comorbidities.

Circulating Palmitoyl Sphingomyelin Is Associated With Cardiovascular Disease in Individuals With Type 2 Diabetes: Findings From the China Da Qing Diabetes Study

OBJECTIVE

To investigate the association of potential cardiovascular disease (CVD) biomarkers in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We enrolled 120 participants (aged 61.5-69.5 years) with type 2 diabetes and 60 (aged 62.5-73.5 years) with normal glucose tolerance in the discovery group from the original Da Qing Diabetes Study. Their diabetes status was confirmed in 1986; then, the participants were followed over 23 years to collect CVD outcome data. Untargeted and targeted metabolomics analyses based on ultra-high-performance liquid chromatography-tandem mass spectrometry were used to identify potential markers. Multivariable regression analysis was used to evaluate the association between metabolites and CVD outcomes. An independent group of 335 patients (aged 67.0-77.0 years) with diabetes was used for biomarker validation.

RESULTS

In the discovery group, untargeted metabolomics analysis found 16 lipids and fatty acids metabolites associated with CVD risk in patients with diabetes, with palmitoyl sphingomyelin (PSM) having the strongest association. Plasma PSM concentrations were significantly higher in cases of diabetes with CVD than without (41.68 ± 10.47 vs. 9.69 ± 1.47 μg/mL; P < 0.0001). The odds ratio (OR) of CVD for 1 µg/mL PSM change was 1.19 (95% CI 1.13-1.25) after adjustment of clinical confounders. The validation study confirmed that PSM was significantly associated with increased CVD risk in diabetes (OR 1.22 [95% CI 1.16-1.30]).

CONCLUSIONS

Changes in lipid and fatty acid content were significantly associated with CVD risk in the Chinese population with diabetes. PSM is a potential biomarker of increased CVD risk in diabetes.

Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity

Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

Multimorbidity and obesity in older adults from six low- and middle-income countries

Prevalence of multimorbidity (i.e., ≥2 chronic conditions), chronic diseases, and obesity is increasing in low- and middle-income countries (LMICs), posing a potential threat to the health of older adults living in these areas. This study hence investigates the unexplored association between obesity and multimorbidity among older adults from LMICs. Cross-sectional, community-based data from the WHO Study on global Ageing and adult health (SAGE) were analysed. The sample consisted of 20,198 individuals aged ≥60 years [Mean age (SD) = 69.3 (13.1) years; 54.1% female] from China, India, Ghana, Mexico, Russia, South Africa. Twelve chronic conditions were assessed. Multivariable logistic regression was conducted to assess the association between BMI ≥ 30 kg/m², high waist circumference (WC, cut-points > 102 cm for males and > 88 cm for females) and multimorbidity. After adjusting for potential confounders, overall, BMI ≥ 30 kg/m² was associated with 1.43 (95%CI = 1.21-1.69) times higher odds for multimorbidity, while this estimate for high WC was 1.50 (95%CI = 1.21-1.86). Significant associations emerged between obesity measures and five out of twelve chronic conditions. Results from this study underline the need to reduce obesity among older adults in areas where its prevalence is increasing, as it is associated with increased odds for multimorbidity. Future longitudinal research in this setting is required to assess the impact of obesity reduction on multimorbidity incidence.

Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women

BACKGROUND

Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations.

OBJECTIVES

We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM.

METHODS

We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations.

RESULTS

Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators.

CONCLUSIONS

Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.

Maternal Metabolites Associated With Gestational Diabetes Mellitus and a Postpartum Disorder of Glucose Metabolism

CONTEXT

Gestational diabetes is associated with a long-term risk of developing a disorder of glucose metabolism. However, neither the metabolic changes characteristic of gestational diabetes in a large, multi-ancestry cohort nor the ability of metabolic changes during pregnancy, beyond glucose levels, to identify women at high risk for progression to a disorder of glucose metabolism has been examined.

OBJECTIVE

This work aims to identify circulating metabolites present at approximately 28 weeks' gestation associated with gestational diabetes mellitus (GDM) and development of a disorder of glucose metabolism 10 to 14 years later.

METHODS

Conventional clinical and targeted metabolomics analyses were performed on fasting and 1-hour serum samples following a 75-g glucose load at approximately 28 weeks' gestation from 2290 women who participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Postpartum metabolic traits included fasting and 2-hour plasma glucose following a 75-g glucose load, insulin resistance estimated by the homeostasis model assessment of insulin resistance, and disorders of glucose metabolism (prediabetes and type 2 diabetes) during the HAPO Follow-Up Study.

RESULTS

Per-metabolite analyses identified numerous metabolites, ranging from amino acids and carbohydrates to fatty acids and lipids, before and 1-hour after a glucose load that were associated with GDM as well as development of a disorder of glucose metabolism and metabolic traits 10 to 14 years post partum. A core group of fasting and 1-hour metabolites mediated, in part, the relationship between GDM and postpartum disorders of glucose metabolism, with the fasting and 1-hour metabolites accounting for 15.7% (7.1%-30.8%) and 35.4% (14.3%-101.0%) of the total effect size, respectively. For prediction of a postpartum disorder of glucose metabolism, the addition of circulating fasting or 1-hour metabolites at approximately 28 weeks' gestation showed little improvement in prediction performance compared to clinical factors alone.

CONCLUSION

The results demonstrate an association of multiple metabolites with GDM and postpartum metabolic traits and begin to define the underlying pathophysiology of the transition from GDM to a postpartum disorder of glucose metabolism.

Contribution of Metabolomics to the Understanding of NAFLD and NASH Syndromes: A Systematic Review

Several differential panels of metabolites have been associated with the presence of metabolic syndrome and its related conditions, namely non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). This study aimed to perform a systematic review to summarize the most recent finding in terms of circulating biomarkers following NAFLD/NASH syndromes. Hence, the research was focused on NAFLD/NASH studies analysed by metabolomics approaches. Following Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a systematic search was conducted on the PubMed database. The inclusion criteria were (i) publication date between 2010 and 2021, (ii) presence of the combination of terms: metabolomics and NAFLD/NASH, and (iii) published in a scholarly peer-reviewed journal. Studies were excluded from the review if they were (i) single-case studies, (ii) unpublished thesis and dissertation studies, and (iii) not published in a peer-reviewed journal. Following these procedures, 10 eligible studies among 93 were taken into consideration. The metabolisms of amino acids, fatty acid, and vitamins were significantly different in patients affected by NAFLD and NASH compared to healthy controls. These findings suggest that low weight metabolites are an important indicator for NAFLD/NASH syndrome and there is a strong overlap between NAFLD/NASH and the metabolic syndrome. These findings may lead to new perspectives in early diagnosis, identification of novel biomarkers, and providing novel targets for pharmacological interventions.

Sex-Specific Differences in Lysine, 3-Hydroxybutyric Acid and Acetic Acid in Offspring Exposed to Maternal and Postnatal High Linoleic Acid Diet, Independent of Diet

BACKGROUND

Linoleic acid (LA) is an essential polyunsaturated fatty acid (PUFA) that is required for foetal growth and development. Excess intake of LA can be detrimental for metabolic health due to its pro-inflammatory properties; however, the effect of a diet high in LA on offspring metabolites is unknown. In this study, we aimed to determine the role of maternal or postnatal high linoleic acid (HLA) diet on plasma metabolites in adult offspring.

METHODS

Female Wistar Kyoto (WKY) rats were fed with either low LA (LLA) or HLA diet for 10 weeks prior to conception and during gestation/lactation. Offspring were weaned at postnatal day 25 (PN25), treated with either LLA or HLA diets and sacrificed at PN180. Metabolite analysis was performed in plasma samples using Nuclear Magnetic Resonance.

RESULTS

Maternal and postnatal HLA diet did not alter plasma metabolites in male and female adult offspring. There was no specific clustering among different treatment groups as demonstrated by principal component analysis. Interestingly, there was clustering among male and female offspring independent of maternal and postnatal dietary intervention. Lysine was higher in female offspring, while 3-hydroxybutyric acid and acetic acid were significantly higher in male offspring.

CONCLUSION

In summary, maternal or postnatal HLA diet did not alter the plasma metabolites in the adult rat offspring; however, differences in metabolites between male and female offspring occurred independently of dietary intervention.

Salivary Metabolites are Promising Non-Invasive Biomarkers of Hepatocellular Carcinoma and Chronic Liver Disease

Background

Hepatocellular carcinoma (HCC) is a leading causes of cancer mortality worldwide. Improved tools are needed for detecting HCC so that treatment can begin as early as possible. Current diagnostic approaches and existing biomarkers, such as alpha-fetoprotein (AFP) lack sensitivity, resulting in too many false negative diagnoses. Machine-learning may be able to identify combinations of biomarkers that provide more robust predictions and improve sensitivity for detecting HCC. We sought to evaluate whether metabolites in patient saliva could distinguish those with HCC, cirrhosis, and those with no documented liver disease.

Methods and Results

We tested 125 salivary metabolites from 110 individuals (43 healthy, 37 HCC, 30 cirrhosis) and identified 4 metabolites that displayed significantly different abundance between groups (FDR P <.2). We also developed four tree-based, machine-learning models, optimized to include different numbers of metabolites, that were trained using cross-validation on 99 patients and validated on a withheld test set of 11 patients. A model using 12 metabolites -octadecanol, acetophenone, lauric acid, 1-monopalmitin, dodecanol, salicylaldehyde, glycyl-proline, 1-monostearin, creatinine, glutamine, serine and 4-hydroxybutyric acid- had a cross-validated sensitivity of 84.8%, specificity of 92.4% and correctly classified 90% of the HCC patients in the test cohort. This model outperformed previously reported sensitivities and specificities for AFP (20-100ng/ml) (61%, 86%) and AFP plus ultrasound (62%, 88%).

Conclusions and Impact

Metabolites detectable in saliva may represent products of disease pathology or a breakdown in liver function. Notably, combinations of salivary metabolites derived from machine-learning may serve as promising non-invasive biomarkers for the detection of HCC.

Prospective analysis of circulating metabolites and endometrial cancer risk

BACKGROUND

Endometrial cancer is strongly associated with obesity and dysregulation of metabolic factors such as estrogen and insulin signaling are causal risk factors for this malignancy. To identify additional novel metabolic pathways associated with endometrial cancer we performed metabolomic analyses on pre-diagnostic plasma samples from 853 case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC).

METHODS

A total of 129 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexoses, and sphingolipids) were measured by liquid chromatography-mass spectrometry. Conditional logistic regression estimated the associations of metabolites with endometrial cancer risk. An analysis focusing on clusters of metabolites using the bootstrap lasso method was also employed.

RESULTS

After adjustment for body mass index, sphingomyelin [SM] C18:0 was positively (OR1SD: 1.18, 95% CI: 1.05-1.33), and glycine, serine, and free carnitine (C0) were inversely (OR1SD: 0.89, 95% CI: 0.80-0.99; OR1SD: 0.89, 95% CI: 0.79-1.00 and OR1SD: 0.91, 95% CI: 0.81-1.00, respectively) associated with endometrial cancer risk. Serine, C0 and two sphingomyelins were selected by the lasso method in >90% of the bootstrap samples. The ratio of esterified to free carnitine (OR1SD: 1.14, 95% CI: 1.02-1.28) and that of short chain to free acylcarnitines (OR1SD: 1.12, 95% CI: 1.00-1.25) were positively associated with endometrial cancer risk. Further adjustment for C-peptide or other endometrial cancer risk factors only minimally altered the results.

CONCLUSION

These findings suggest that variation in levels of glycine, serine, SM C18:0 and free carnitine may represent specific pathways linked to endometrial cancer development. If causal, these pathways may offer novel targets for endometrial cancer prevention.

Metabolic flexibility during normal pregnancy allows appropriate adaptation during gestation independently of BMI

BACKGROUND & AIMS

Overweight and obesity in reproductive-age women hasten the development of insulin resistance and increase risk for deterioration of pregnancy metabolism. These pregnancy-associated metabolic changes are similar to those of the metabolic syndrome. Thus, some metabolic flexibility must allow appropriate adaptation to the metabolic load that pregnancy imposes. We evaluated metabolic flexibility during uncomplicated pregnancy in women with pre-gestational normal weight or overweight.

METHODS

In 20 women with singleton pregnancies, pre-pregnancy BMI was categorized as normal-weight (Nw) or overweight (Ow). The women were seen quarterly, and fasting and postprandial blood samples were collected at each visit. Indirect fasting and/postprandial calorimetry was performed to evaluate metabolic flexibility (Δrespiratory quotient (RQ) = RQ_postprandial - RQ_fasting).

RESULTS

In the first trimester, metabolic flexibility was lower in the Ow group compared to the Nw group (0.031 ± 0.0131 vs 0.077 ± 0.018, respectively) without a statistically significant difference (p = 0.053). In the second trimester, the Ow group was significantly more flexible than the Nw group (0.190 ± 0.016 vs 0.077 ± 0.015, respectively (p = 0.004)). For the third trimester, the Ow and Nw groups did not differ in metabolic flexibility (0.074 ± 0.013 vs 0.087 ± 0.021, respectively) (p = 0.40). The most influential variables for metabolic flexibility during pregnancy were lactate, leptin, β-hydroxybutyrate, glycerol, aromatic amino acids, medium and long chain acylcarnitine's.

CONCLUSIONS

Our findings indicate that metabolic flexibility changes throughout pregnancy, independently of pre-pregnancy BMI. These changes maintain metabolic homeostasis between the mother and foetus, allowing for appropriate adjustments during pregnancy.

Lipid and metabolite correlation networks specific to clinical and biochemical covariate show differences associated with sexual dimorphism in a cohort of nonagenarians

This study defines and estimates the metabolite-lipidic component association networks constructed from an array of 20 metabolites and 114 lipids identified and quantified via NMR spectroscopy in the serum of a cohort of 355 Italian nonagenarians and ultra-nonagenarian. Metabolite-lipid association networks were built for men and women and related to an array of 101 clinical and biochemical parameters, including the presence of diseases, bio-humoral parameters, familiarity diseases, drugs treatments, and risk factors. Different connectivity patterns were observed in lipids, branched chains amino acids, alanine, and ketone bodies, suggesting their association with the sex-related and sex-clinical condition-related intrinsic metabolic changes. Furthermore, our results demonstrate, using a holistic system biology approach, that the characterization of metabolic structures and their dynamic inter-connections is a promising tool to shed light on the dimorphic pathophysiological mechanisms of aging at the molecular level.

Macrophage Responses to Environmental Stimuli During Homeostasis and Disease

Work over the last 40 years has described macrophages as a heterogeneous population that serve as the frontline surveyors of tissue immunity. As a class, macrophages are found in almost every tissue in the body and as distinct populations within discrete microenvironments in any given tissue. During homeostasis, macrophages protect these tissues by clearing invading foreign bodies and/or mounting immune responses. In addition to varying identities regulated by transcriptional programs shaped by their respective environments, macrophage metabolism serves as an additional regulator to temper responses to extracellular stimuli. The area of research known as "immunometabolism" has been established within the last decade, owing to an increase in studies focusing on the crosstalk between altered metabolism and the regulation of cellular immune processes. From this research, macrophages have emerged as a prime focus of immunometabolic studies, although macrophage metabolism and their immune responses have been studied for centuries. During disease, the metabolic profile of the tissue and/or systemic regulators, such as endocrine factors, become increasingly dysregulated. Owing to these changes, macrophage responses can become skewed to promote further pathophysiologic changes. For instance, during diabetes, obesity, and atherosclerosis, macrophages favor a proinflammatory phenotype; whereas in the tumor microenvironment, macrophages elicit an anti-inflammatory response to enhance tumor growth. Herein we have described how macrophages respond to extracellular cues including inflammatory stimuli, nutrient availability, and endocrine factors that occur during and further promote disease progression.

Evaluation of Biological Mechanisms of Eucommiae Folium in Hypertensive Kidney Injury by Integration of Untargeted Metabolomics and Network Pharmacology

Hypertensive kidney injury (Hki) is one of the most common complications of hypertension. Early prevention and treatment of renal injury in patients with hypertension is great significance. The study, which used an integrated ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS) analysis, network pharmacology approach, and plasma metabolomics, aimed to discover the active ingredients and therapeutic mechanisms of Eucommiae folium (Ef) in treating Hki. The chemical components of Ef were analyzed by UPLC-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS), and the "compound-target-disease" network was constructed by screening the closely related drug targets from the drug-target database, then the signaling pathways related to Hki were analyzed. Finally, the enzyme-linked immunosorbent assay (ELISA) and real-time quantitative reverse-transcription polymerase chain reaction were used to test and verify the key targets in the common pathways of metabolomics and network pharmacology. The results indicated that Eucommiae folium might play an excellent role in treating Hki, likely through regulating the vascular endothelial growth factor signaling pathway, hypoxia inducible factor 1 (HIF-1) signaling pathway, and glycerophospholipid metabolism pathway, which were validated by increasing levels of nitric oxide, endothelial nitric oxide synthase and reducing levels of endothelin 1, angiotensin II, renin, cyclic guanosine monophosphate, blood urea nitrogen, and serum creatinine, as well as the reduced gene expression of Ache, Ddah2, Egfr, Lcat, Pla2g2a, Stat3 and Vegfa. The study systematically explored the protective mechanisms of Ef against Hki and also provided the practical treatment strategies of Hki from the Chinese herb.