Microbiota-Related Metabolites and the Risk of Type 2 Diabetes

Diabetes and infection: review of the epidemiology, mechanisms and principles of treatment

An association between diabetes and infection has been recognised for many years, with infection being an important cause of death and morbidity in people with diabetes. The COVID-19 pandemic has re-kindled an interest in the complex relationship between diabetes and infection. Some infections occur almost exclusively in people with diabetes, often with high mortality rates without early diagnosis and treatment. However, more commonly, diabetes is a complicating factor in many infections. A reciprocal relationship occurs whereby certain infections and their treatments may also increase the risk of diabetes. People with diabetes have a 1.5- to 4-fold increased risk of infection. The risks are the most pronounced for kidney infection, osteomyelitis and foot infection, but are also increased for pneumonia, influenza, tuberculosis, skin infection and general sepsis. Outcomes from infection are worse in people with diabetes, with the most notable example being a twofold higher rate of death from COVID-19. Hyperglycaemia has deleterious effects on the immune response. Vascular insufficiency and neuropathy, together with altered skin, mucosal and gut microbial colonisation, contribute to the increased risk of infection. Vaccination is important in people with diabetes although the efficacy of certain immunisations may be compromised, particularly in the presence of hyperglycaemia. The principles of treatment largely follow those of the general population with certain notable exceptions.

Exploring the causal association between serum metabolites and erectile dysfunction: a bidirectional Mendelian randomisation study

Erectile dysfunction is a common sexual disorder in men. Some studies have found a strong association between some serum metabolites and erectile dysfunction. To investigate this association further, we used bidirectional Mendelian randomisation to investigate causality and possible biological mechanisms.Firstly, this study screened the statistics of genome-wide association studies of serum metabolites and erectile dysfunction to obtain instrumental variables. Inverse variance weighting was used as the primary method for causal effect analysis of instrumental variables in forward or reverse Mendelian randomisation, and the results obtained by MR-Egger regression and the weighted median method were used as references. Subsequently, the metabolites causally associated with erectile dysfunction were subjected to replication analyses and meta-analyses, and the results of the meta-analyses were analysed by pathway analyses to find influential pathways. In this process, Mendelian randomisation results need to be assessed for stability and reliability using sensitivity analysis.It was found that a total of six serum metabolites were causally associated with erectile dysfunction in a forward Mendelian randomisation study. 1,3,7-trimethyluraten (0.85 (0.73-0.99), P = 0.0368), ergothioneine (0.65 (0.45-0.94), P = 0.0226) and gamma-glutamylglutamate (0.63 (0.46-0.88), P = 0.0059) were protective against the development of erectile dysfunction, whereas 2-hydroxyhippurate (1.10 (1.02-1.19), P = 0.0152), N2,N2-dimethylguanosine (1.57 (1.02-2.40), P = 0.0395) and octanoylcarnitine (1.38 (1.06-1.82), P = 0.0183) were able to induce the development of erectile dysfunction. In addition, metabolic pathway analysis showed that 1,3,7-trimethylurate was able to influence the development of erectile dysfunction via the caffeine metabolism pathway (P = 0.0454). On the other hand, reverse Mendelian randomisation analysis showed that erectile dysfunction reduced serum homocitrulline levels (0.99 (0.97-1.00), P = 0.0360). Sensitivity analyses, including heterogeneity tests and pleiotropy tests, confirmed the reliability of the results.In conclusion, this study demonstrated a bidirectional causal relationship between serum metabolites and erectile dysfunction using bidirectional Mendelian randomisation analysis and replication meta-analysis. On this basis, this study provides a new direction of thinking and strong evidence for the therapeutic application and adjunctive diagnosis of serum metabolites in erectile dysfunction, and provides a certain reference value for subsequent related studies.

Discovery and validation of plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults

AIMS/HYPOTHESIS

Many studies have examined the relationship between plasma metabolites and type 2 diabetes progression, but few have explored saliva and multi-fluid metabolites.

METHODS

We used LC/MS to measure plasma (n=1051) and saliva (n=635) metabolites among Puerto Rican adults from the San Juan Overweight Adults Longitudinal Study. We used elastic net regression to identify plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting baseline HOMA-IR in a training set (n=509) and validated these scores in a testing set (n=340). We used multivariable Cox proportional hazards models to estimate HRs for the association of baseline metabolomic scores predicting insulin resistance with incident type 2 diabetes (n=54) and prediabetes (characterised by impaired glucose tolerance, impaired fasting glucose and/or high HbA1c) (n=130) at 3 years, along with regression from prediabetes to normoglycaemia (n=122), adjusting for traditional diabetes-related risk factors.

RESULTS

Plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance included highly weighted metabolites from fructose, tyrosine, lipid and amino acid metabolism. Each SD increase in the plasma (HR 1.99 [95% CI 1.18, 3.38]; p=0.01) and multi-fluid (1.80 [1.06, 3.07]; p=0.03) metabolomic scores was associated with higher risk of type 2 diabetes. The saliva metabolomic score was associated with incident prediabetes (1.48 [1.17, 1.86]; p=0.001). All three metabolomic scores were significantly associated with lower likelihood of regressing from prediabetes to normoglycaemia in models adjusting for adiposity (HRs 0.72 for plasma, 0.78 for saliva and 0.72 for multi-fluid), but associations were attenuated when adjusting for lipid and glycaemic measures.

CONCLUSIONS/INTERPRETATION

The plasma metabolomic score predicting insulin resistance was more strongly associated with incident type 2 diabetes than the saliva metabolomic score. Only the saliva metabolomic score was associated with incident prediabetes.

Diet Mediate the Impact of Host Habitat on Gut Microbiome and Influence Clinical Indexes by Modulating Gut Microbes and Serum Metabolites

The impact of external factors on the human gut microbiota and how gut microbes contribute to human health is an intriguing question. Here, the gut microbiome of 3,224 individuals (496 with serum metabolome) with 109 variables is studied. Multiple analyses reveal that geographic factors explain the greatest variance of the gut microbiome and the similarity of individuals' gut microbiome is negatively correlated with their geographic distance. Main food components are the most important factors that mediate the impact of host habitats on the gut microbiome. Diet and gut microbes collaboratively contribute to the variation of serum metabolites, and correlate to the increase or decrease of certain clinical indexes. Specifically, systolic blood pressure is lowered by vegetable oil through increasing the abundance of Blautia and reducing the serum level of 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1), but it is reduced by fruit intake through increasing the serum level of Blautia improved threonate. Besides, aging-related clinical indexes are also closely correlated with the variation of gut microbes and serum metabolites. In this study, the linkages of geographic locations, diet, the gut microbiome, serum metabolites, and physiological indexes in a Chinese population are characterized. It is proved again that gut microbes and their metabolites are important media for external factors to affect human health.

Advances in fecal microbiota transplantation for the treatment of diabetes mellitus

Diabetes mellitus (DM) refers to a group of chronic diseases with global prevalence, characterized by persistent hyperglycemia resulting from various etiologies. DM can harm various organ systems and lead to acute or chronic complications, which severely endanger human well-being. Traditional treatment mainly involves controlling blood sugar levels through replacement therapy with drugs and insulin; however, some patients still find a satisfactory curative effect difficult to achieve. Extensive research has demonstrated a close correlation between enteric dysbacteriosis and the pathogenesis of various types of DM, paving the way for novel therapeutic approaches targeting the gut microbiota to manage DM. Fecal microbiota transplantation (FMT), a method for re-establishing the intestinal microbiome balance, offers new possibilities for treating diabetes. This article provides a comprehensive review of the correlation between DM and the gut microbiota, as well as the current advancements in FMT treatment for DM, using FMT as an illustrative example. This study aims to offer novel perspectives and establish a theoretical foundation for the clinical diagnosis and management of DM.

Serum betaine and dimethylglycine in mid-pregnancy and the risk of gestational diabetes mellitus: a case-control study

PURPOSE

To investigate the associations of choline, betaine, dimethylglycine (DMG), L-carnitine, and Trimethylamine-N-oxide (TMAO) with the risk of Gestational diabetes mellitus (GDM) as well as the markers of glucose homeostasis.

METHODS

We performed a case-control study including 200 diagnosed GDM cases and 200 controls matched by maternal age (±2 years) and gestational age (±2 weeks). Concentrations of serum metabolites were measured by the high-performance liquid chromatography - tandem mass spectrometry (HPLC-MS/MS).

RESULTS

Compared to the control group, GDM group had significantly lower serum betaine concentration and betaine/choline ratio, and higher DMG concentration. Furthermore, decreased betaine concentration and betaine/choline ratio, increased DMG concentration showed significant association with the risk of GDM. In addition, serum betaine concentrations were negatively associated with blood glucose levels at 1-h post-glucose load (OGTT-1h), and both betaine and L-carnitine concentrations were positively associated with 1,5-anhydroglucitol levels. Betaine/choline ratio was negatively associated with OGTT-1h and blood glucose levels at 2-h post-glucose load (OGTT-2h) and serum choline concentrations were negatively associated with fasting blood glucose and positively associated with OGTT-2h.

CONCLUSION

Decreased serum betaine concentrations and betaine/choline ratio, and elevated DMG concentrations could be significant risk factors for GDM. Furthermore, betaine may be associated with blood glucose regulation and short-term glycemic fluctuations.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Evaluation of insulin secretion and insulin sensitivity in pregnant women: Application value of simple indices

The prevalence of gestational diabetes mellitus (GDM) is increasing annually, which poses substantial harm to the health of both mothers and children. Therefore, selection of clinically applicable and easily detectable indicators in the assessment of maternal insulin secretory function and insulin sensitivity in pregnant women undoubtedly holds great importance in evaluating the risk of GDM, guiding the choice of GDM therapy modalities, and improving the ability to provide early warning of adverse pregnancy outcomes. Compared with the classic clamp technique, many simple indices are more suited for use among pregnant women due to the low frequency of blood sampling and simple administration involved. While indices derived from fasting blood glucose and fasting insulin levels are most readily available, they are unable to provide information on the ability of insulin to manage the glucose load during pregnancy. Although the indices derived from the insulin and glucose values at each time point of the oral glucose tolerance test can provide a more comprehensive picture of the insulin sensitivity and insulin secretory function of the body, their application is constrained by the complexity of the procedure and associated high costs. Concomitantly, the findings from different studies are influenced by a variety of confounding factors, such as the gestational age during testing, race, and detection method. Furthermore, insulin secretory function and insulin sensitivity in pregnant women differ from those in non-pregnant women in that they change significantly with prolonged pregnancy; hence, there is an urgent need to develop a pregnancy-specific reference range. This article reviews the progress in the application of simple indices to help clinicians better understand their potential application in detecting GDM.

Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed up after 4 years for progression to DM. Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z-octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.

Comparative analysis of salivary microbiota in diabetic and non-diabetic individuals of North India using metagenomics

Background

Saliva, an oral secretion is considered an essential biological modulator involved in maintaining oral homeostasis. Increased glucose levels in diabetic patients' saliva may have an impact on diversity of microbes. Comparing the salivary microflora of diabetic and non-diabetic cohorts will help in diagnosis and risk assessment of oral health complications. This will provide greater knowledge about the contribution of oral microbes to the development of oral illnesses. The association between salivary microbiota and diabetic state is less explored in the North Indian population, hence current observational study was performed to analyze the salivary microflora of diabetic and non-diabetic individuals using metagenomic analysis.

Materials and methods

This single-center non-randomized observational trial was conducted in Uttar Pradesh, India. Participants were enrolled into either diabetic (n = 68) or non-diabetic groups (n = 68) based on their diabetes status. Following saliva collection, DNA was extracted and metagenomic sequencing was performed.

Results

Phylum Bacteroidetes and Fusobacteria were significantly abundant in diabetic individuals (p < 0.0001), while Proteobacteria was significantly higher among non-diabetic individuals (p < 0.0001). No statistical difference in phylum Actinobacteria and Firmicutes among diabetics and non-diabetics. Veillonella, Prevotella, Porphyromonas, Leptotrichia, Lactobacillus, and Streptococcus were greater in diabetics whereas the abundance of Capnocytophaga and Neisseria was more among non-diabetics (p < 0.05).

Conclusions

The genera Veillonella, Prevotella, Porphyromonas, Leptotrichia, Lactobacillus, and Streptococcus were comparatively over the odds with the diabetics in India. The association between microbiota in diabetic population and risk related to increase in occurrence of caries, gingivitis, and periodontitis in diabetic population prevalence should be investigated.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

INTRODUCTION

Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.

METHODS

A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.

RESULTS

Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.

CONCLUSIONS

Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.

Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke

BACKGROUND

Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke.

METHODS

Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin.

RESULTS

After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes.

CONCLUSIONS

The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.

Type-2 Diabetes Mellitus and the Gut Microbiota: Systematic Review

The gut microbiota is a community situated in the gastrointestinal tract that consists of bacteria thriving and contributing to the functions of our body. It is heavily influenced by what individuals eat, as the quality, amount, and frequency of food consumed can favor and inhibit specific bacteria. Type-2 diabetes mellitus (T2DM) is a common but detrimental condition that arises from excessive hyperglycemia, leading to either insulin resistance or damage to the B-cells that produce insulin in the pancreas. A poor diet high in sugar and fats leads to hyperglycemia, and as this persists, it can lead to the development of T2DM. Both insulin resistance and damage to B-cells are greatly affected by the diet an individual consumes, but is there a more involved relationship between the gut microbiota and T2DM? This paper aimed to evaluate the changes in the gut microbiota in patients with T2DM and the impacts of the changes in gut microbiota. Bacteroides, Proteobacteria, Firmicutes, and Actinobacteria prevailed in patients with T2DM and healthy control, but their abundance varied greatly. There was also a significant decrease in bacteria like Lactobacilli spp.and F. prausnitizii associated with insulin resistance. High levels of BMI in patients with T2DM have also been associated with increased levels of A. muciniphilia, which has been associated with decreased fat metabolism and increased BMI. Metabolites such as butyrates and melatonin have also been identified as influencing the development and progression of T2DM. Testosterone levels have also been greatly influenced by the gut microbiota changes in T2DM, such that males with lower testosterone have a greater abundance of bacteria like Gemella, Lachnospiraceae, and Massiia. Identifying these changes and how they impact the body may lead to a treatment addressing insulin dysfunction and the changes that the altered gut microbiota leads to. Future research should address how treatment methods such as healthy diets, exercise, and anti-diabetics affect the gut microbiota and see if they influence sustained changes and reduced hyperglycemia.

Recent insights of obesity-induced gut and adipose tissue dysbiosis in type 2 diabetes

An imbalance in microbial homeostasis, referred to as dysbiosis, is critically associated with the progression of obesity-induced metabolic disorders including type 2 diabetes (T2D). Alteration in gut microbial diversity and the abundance of pathogenic bacteria disrupt metabolic homeostasis and potentiate chronic inflammation, due to intestinal leakage or release of a diverse range of microbial metabolites. The obesity-associated shifts in gut microbial diversity worsen the triglyceride and cholesterol level that regulates adipogenesis, lipolysis, and fatty acid oxidation. Moreover, an intricate interaction of the gut-brain axis coupled with the altered microbiome profile and microbiome-derived metabolites disrupt bidirectional communication for instigating insulin resistance. Furthermore, a distinct microbial community within visceral adipose tissue is associated with its dysfunction in obese T2D individuals. The specific bacterial signature was found in the mesenteric adipose tissue of T2D patients. Recently, it has been shown that in Crohn's disease, the gut-derived bacterium Clostridium innocuum translocated to the mesenteric adipose tissue and modulates its function by inducing M2 macrophage polarization, increasing adipogenesis, and promoting microbial surveillance. Considering these facts, modulation of microbiota in the gut and adipose tissue could serve as one of the contemporary approaches to manage T2D by using prebiotics, probiotics, or faecal microbial transplantation. Altogether, this review consolidates the current knowledge on gut and adipose tissue dysbiosis and its role in the development and progression of obesity-induced T2D. It emphasizes the significance of the gut microbiota and its metabolites as well as the alteration of adipose tissue microbiome profile for promoting adipose tissue dysfunction, and identifying novel therapeutic strategies, providing valuable insights and directions for future research and potential clinical interventions.

Unlocking the Potential: Amino Acids' Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus

Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), imposes a significant global burden with adverse clinical outcomes and escalating healthcare expenditures. Early identification of biomarkers can facilitate better screening, earlier diagnosis, and the prevention of diabetes. However, current clinical predictors often fail to detect abnormalities during the prediabetic state. Emerging studies have identified specific amino acids as potential biomarkers for predicting the onset and progression of diabetes. Understanding the underlying pathophysiological mechanisms can offer valuable insights into disease prevention and therapeutic interventions. This review provides a comprehensive summary of evidence supporting the use of amino acids and metabolites as clinical biomarkers for insulin resistance and diabetes. We discuss promising combinations of amino acids, including branched-chain amino acids, aromatic amino acids, glycine, asparagine and aspartate, in the prediction of T2DM. Furthermore, we delve into the mechanisms involving various signaling pathways and the metabolism underlying the role of amino acids in disease development. Finally, we highlight the potential of targeting predictive amino acids for preventive and therapeutic interventions, aiming to inspire further clinical investigations and mitigate the progression of T2DM, particularly in the prediabetic stage.

Vitamin D deficiency in early life regulates gut microbiome composition and leads to impaired glucose tolerance in adult and offspring rats

Vitamin D has been found to be involved in glucose metabolism in recent years. Its deficiency is very common, especially in children. Whether vitamin D deficiency in early life affects adult diabetes risk is unknown. In this study, a rat model of early life vitamin D deficiency (F1 Early-VDD) was established by depriving it of vitamin D from the 0 to the 8th week. Further, some rats were switched to normal feeding conditions and sacrificed at the 18th week. Other rats were mated randomly to generate offspring rats (F2 Early-VDD), and F2 rats were fed under normal conditions and sacrificed at the 8th week. Serum 25(OH)D₃ level decreased in F1 Early-VDD at the 8th week and returned to normal at the 18th week. Serum 25(OH)D₃ level in F2 Early-VDD at the 8th week was also lower than that in control rats. Impaired glucose tolerance was observed in F1 Early-VDD at the 8th week and 18th week and also in F2 Early-VDD at the 8th week. The gut microbiota composition in F1 Early-VDD at the 8th week significantly changed. Among the top ten genera with a rich difference, Desulfovibrio, Roseburia, Ruminiclostridium, Lachnoclostridium, A2, GCA-900066575, Peptococcus, Lachnospiraceae_FCS020_ group, and Bilophila increased owing to vitamin D deficiency, whereas Blautia decreased. There were 108 significantly changed metabolites in F1 Early-VDD at the 8th week, of which 63 were enriched in known metabolic pathways. Correlations between gut microbiota and metabolites were analyzed. Blautia was positively related to 2-picolinic acid, whereas Bilophila was negatively related to indoleacetic acid. Moreover, some of the changes in microbiota, metabolites, and enriched metabolic pathways still existed in F1 Early-VDD rats at the 18th week and F2 Early-VDD rats at the 8th week. In conclusion, vitamin D deficiency in early life leads to impaired glucose tolerance in adult and offspring rats. This effect may be partly achieved by regulating gut microbiota and their co-metabolites.

Clinical-Grade Patches as a Medium for Enrichment of Sweat-Extracellular Vesicles and Facilitating Their Metabolic Analysis

Cell-secreted extracellular vesicles (EVs), carrying components such as RNA, DNA, proteins, and metabolites, serve as candidates for developing non-invasive solutions for monitoring health and disease, owing to their capacity to cross various biological barriers and to become integrated into human sweat. However, the evidence for sweat-associated EVs providing clinically relevant information to use in disease diagnostics has not been reported. Developing cost-effective, easy, and reliable methodologies to investigate EVs' molecular load and composition in the sweat may help to validate their relevance in clinical diagnosis. We used clinical-grade dressing patches, with the aim being to accumulate, purify and characterize sweat EVs from healthy participants exposed to transient heat. The skin patch-based protocol described in this paper enables the enrichment of sweat EVs that express EV markers, such as CD63. A targeted metabolomics study of the sweat EVs identified 24 components. These are associated with amino acids, glutamate, glutathione, fatty acids, TCA, and glycolysis pathways. Furthermore, as a proof-of-concept, when comparing the metabolites' levels in sweat EVs isolated from healthy individuals with those of participants with Type 2 diabetes following heat exposure, our findings revealed that the metabolic patterns of sweat EVs may be linked with metabolic changes. Moreover, the concentration of these metabolites may reflect correlations with blood glucose and BMI. Together our data revealed that sweat EVs can be purified using routinely used clinical patches, setting the foundations for larger-scale clinical cohort work. Furthermore, the metabolites identified in sweat EVs also offer a realistic means to identify relevant disease biomarkers. This study thus provides a proof-of-concept towards a novel methodology that will focus on the use of the sweat EVs and their metabolites as a non-invasive approach, in order to monitor wellbeing and changes in diseases.

Characterization of gut microbial and metabolite alterations in faeces of Goto Kakizaki rats using metagenomic and untargeted metabolomic approach

BACKGROUND

In recent years, the incidence of type 2 diabetes (T2DM) has shown a rapid growth trend. Goto Kakizaki (GK) rats are a valuable model for the study of T2DM and share common glucose metabolism features with human T2DM patients. A series of studies have indicated that T2DM is associated with the gut microbiota composition and gut metabolites. We aimed to systematically characterize the faecal gut microbes and metabolites of GK rats and analyse the relationship between glucose and insulin resistance.

AIM

To evaluate the gut microbial and metabolite alterations in GK rat faeces based on metagenomics and untargeted metabolomics.

METHODS

Ten GK rats (model group) and Wistar rats (control group) were observed for 10 wk, and various glucose-related indexes, mainly including weight, fasting blood glucose (FBG) and insulin levels, homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of β cell (HOMA-β) were assessed. The faecal gut microbiota was sequenced by metagenomics, and faecal metabolites were analysed by untargeted metabolomics. Multiple metabolic pathways were evaluated based on the differential metabolites identified, and the correlations between blood glucose and the gut microbiota and metabolites were analysed.

RESULTS

The model group displayed significant differences in weight, FBG and insulin levels, HOMA-IR and HOMA-β indexes (P < 0.05, P < 0.01) and a shift in the gut microbiota structure compared with the control group. The results demonstrated significantly decreased abundances of Prevotella sp. CAG:604 and Lactobacillus murinus (P < 0.05) and a significantly increased abundance of Allobaculum stercoricanis (P < 0.01) in the model group. A correlation analysis indicated that FBG and HOMA-IR were positively correlated with Allobaculum stercoricanis and negatively correlated with Lactobacillus murinus. An orthogonal partial least squares discriminant analysis suggested that the faecal metabolic profiles differed between the model and control groups. Fourteen potential metabolic biomarkers, including glycochenodeoxycholic acid, uric acid, 13(S)-hydroxyoctadecadienoic acid (HODE), N-acetylaspartate, β-sitostenone, sphinganine, 4-pyridoxic acid, and linoleic acid, were identified. Moreover, FBG and HOMA-IR were found to be positively correlated with glutathione, 13(S)-HODE, uric acid, 4-pyridoxic acid and allantoic acid and ne-gatively correlated with 3-α, 7-α, chenodeoxycholic acid glycine conjugate and 26-trihydroxy-5-β-cholestane (P < 0.05, P < 0.01). Allobaculum stercoricanis was positively correlated with linoleic acid and sphinganine (P < 0.01), and 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate was negatively associated with Prevotella sp. CAG:604 (P < 0.01). The metabolic pathways showing the largest differences were arginine biosynthesis; primary bile acid biosynthesis; purine metabolism; linoleic acid metabolism; alanine, aspartate and glutamate metabolism; and nitrogen metabolism.

CONCLUSION

Metagenomics and untargeted metabolomics indicated that disordered compositions of gut microbes and metabolites may be common defects in GK rats.

Gut Microbial Genes and Metabolism for Methionine and Branched-Chain Amino Acids in Diabetic Nephropathy

Diabetic mellitus nephropathy (DMN) is a serious complication of diabetes and a major health concern. Although the pathophysiology of diabetes mellitus (DM) leading to DMN is uncertain, recent evidence suggests the involvement of the gut microbiome. This study aimed to determine the relationships among gut microbial species, genes, and metabolites in DMN through an integrated clinical, taxonomic, genomic, and metabolomic analysis. Whole-metagenome shotgun sequencing and nuclear magnetic resonance metabolomic analyses were performed on stool samples from 15 patients with DMN and 22 healthy controls. Six bacterial species were identified to be significantly elevated in the DMN patients after adjusting for age, sex, body mass index, and estimated glomerular filtration rate (eGFR). Multivariate analysis found 216 microbial genes and 6 metabolites (higher valine, isoleucine, methionine, valerate, and phenylacetate levels in the DMN group and higher acetate levels in the control group) that were differentially present between the DMN and control groups. Integrated analysis of all of these parameters and clinical data using the random-forest model showed that methionine and branched-chain amino acids (BCAAs) were among the most significant features, next to the eGFR and proteinuria, in differentiating the DMN group from the control group. Metabolic pathway gene analysis of BCAAs and methionine also revealed that many genes involved in the biosynthesis of these metabolites were elevated in the six species that were more abundant in the DMN group. The suggested correlation among taxonomic, genetic, and metabolic features of the gut microbiome would expand our understanding of gut microbial involvement in the pathogenesis of DMN and may provide potential therapeutic targets for DMN. IMPORTANCE Whole metagenomic sequencing uncovered specific members of the gut microbiota associated with DMN. The gene families derived from the discovered species are involved in the metabolic pathways of methionine and branched-chain amino acids. Metabolomic analysis using stool samples showed increased methionine and branched-chain amino acids in DMN. These integrative omics results provide evidence of the gut microbiota-associated pathophysiology of DMN, which can be further studied for disease-modulating effects via prebiotics or probiotics.

Comparison of fecal and blood metabolome reveals inconsistent associations of the gut microbiota with cardiometabolic diseases

Blood metabolome is commonly used in human studies to explore the associations of gut microbiota-derived metabolites with cardiometabolic diseases. Here, in a cohort of 1007 middle-aged and elderly adults with matched fecal metagenomic (149 species and 214 pathways) and paired fecal and blood targeted metabolomics data (132 metabolites), we find disparate associations with taxonomic composition and microbial pathways when using fecal or blood metabolites. For example, we observe that fecal, but not blood butyric acid significantly associates with both gut microbiota and prevalent type 2 diabetes. These findings are replicated in an independent validation cohort involving 103 adults. Our results suggest that caution should be taken when inferring microbiome-cardiometabolic disease associations from either blood or fecal metabolome data.

Kynurenine Pathway in Diabetes Mellitus-Novel Pharmacological Target?

The tryptophan-kynurenine pathway (Trp-KYN) is the major route for tryptophan conversion in the brain and in the periphery. Kynurenines display a wide range of biological actions (which are often contrasting) such as cytotoxic/cytoprotective, oxidant/antioxidant or pro-/anti-inflammatory. The net effect depends on their local concentration, cellular environment, as well as a complex positive and negative feedback loops. The imbalance between beneficial and harmful kynurenines was implicated in the pathogenesis of various neurodegenerative disorders, psychiatric illnesses and metabolic disorders, including diabetes mellitus (DM). Despite available therapies, DM may lead to serious macro- and microvascular complications including cardio- and cerebrovascular disease, peripheral vascular disease, chronic renal disease, diabetic retinopathy, autonomic neuropathy or cognitive impairment. It is well established that low-grade inflammation, which often coincides with DM, can affect the function of KP and, conversely, that kynurenines may modulate the immune response. This review provides a detailed summary of findings concerning the status of the Trp-KYN pathway in DM based on available animal, human and microbiome studies. We highlight the importance of the molecular interplay between the deranged (functionally and qualitatively) conversion of Trp to kynurenines in the development of DM and insulin resistance. The Trp-KYN pathway emerges as a novel target in the search for preventive and therapeutic interventions in DM.

A Single Strain of Lactobacillus (CGMCC 21661) Exhibits Stable Glucose- and Lipid-Lowering Effects by Regulating Gut Microbiota

Type 2 diabetes (T2D) is usually accompanied by obesity and nonalcoholic fatty-liver-related insulin resistance. The link between T2D and dysbiosis has been receiving increasing attention. Probiotics can improve insulin sensitivity by regulating imbalances in microbiota, but efficacy varies based on the probiotic used. This study screened the main strain in the feces of healthy adult mice and found it to be a new Lactobacillus (abbreviated as Lb., named as CGMCC No. 21661) after genetic testing. We designed the most common Bifidobacterium longum subsp. longum (CGMCC1.2186, abbreviated as B. longum. subsp.), fecal microbiota transplantation (FMT), and Lb. CGMCC No. 21661 protocols to explore the best way for modulating dysbiosis to improve T2D. After 6 weeks of gavage in T2D mice, it was found that all three protocols had a therapeutic alleviating effect. Among them, compared with the B. longum. subsp. and FMT, the Lb. CGMCC No. 21661 showed a 1- to 2-fold decrease in blood glucose (11.84 ± 1.29 mmol/L, p < 0.05), the lowest HOMA-IR (p < 0.05), a 1 fold increase in serum glucagon-like peptide-1 (5.84 ± 1.1 pmol/L, p < 0.05), and lowest blood lipids (total cholesterol, 2.21 ± 0.68 mmol/L, p < 0.01; triglycerides, 0.4 ± 0.15 mmol/L, p < 0.01; Low-density lipoprotein cholesterol, 0.53 ± 0.16 mmol/L, p < 0.01). In addition, tissue staining in the Lb. CGMCC No. 21661 showed a 2- to 3-fold reduction in T2D-induced fatty liver (p < 0.0001), a 1- to 2-fold decrease in pancreatic apoptotic cells (p < 0.05), and a significant increase in colonic mucus layer thickness (p < 0.05) compared with the B. longum. subsp. and FMT. The glucose and lipid lowering effects of this Lb. CGMCC No. 21661 indicate that it may provide new ideas for the treatment of diabetes.

Diet-rich in wheat bran modulates tryptophan metabolism and AhR/IL-22 signalling mediated metabolic health and gut dysbacteriosis: A novel prebiotic-like activity of wheat bran

Tryptophan metabolism has shown to involve in pathogenesis of various metabolic diseases. Gut microbiota-orientated diets hold great potentials to improve metabolic health via regulating tryptophan metabolism. The present study showed that the 6-week high fat diet (HFD) disturbed tryptophan metabolism accompanied with gut dysbacteriosis, also influenced the dietary tryptophan induced changes in cecum microbiome and serum metabolome in mice. The colonic expressions of aryl hydrocarbon receptor (AhR) and interleukin-22 (IL-22) were significantly reduced in mice fed on HFD. Notably, a diet- rich in wheat bran effectively inhibited transformation of tryptophan to kynurenine-pathway metabolites, while increased melatonin and microbial catabolites, i.e. indole-3-propionic acid, indole-3-acetaldehyde and 5-hydroxy-indole-3-acetic acid. Such regulatory effects were accompanied with reduced fasting glucose and total triglycerides, and promoted AhR and IL-22 levels in HFD mice. Wheat bran increased the abundance of health promoting bacteria (e.g., Akkermansia and Lactobacillus), which were significantly correlated with tryptophan derived indolic metabolites. Additionally, beneficial modulatory effects of wheat bran on indolic metabolites in associations with gut dysbacteriosis from type 2 diabetes patients were confirmed in vitro fecal fermentation experiment. Our study proves the detrimental effects of HFD induced gut dysbacteriosis on tryptophan metabolism that may influence immune modulation, and provides novel insights in the mechanisms by which wheat bran could induce health benefits.

The gut microbiota as a target to improve health conditions in a confined environment

Confined environments increase psychological stress and lead to health problems such as abnormal mood and rhythm disruption. However, the mechanism by which confined environments impact health has remained unclear. Significant correlations have been reported between psychological stress and changes in gut microbiota. Therefore, we investigated the effect of a confined environment on the composition of the gut microbiota by 16s rDNA high-throughput sequencing, and analyzed the correlation between gut microbiota and health indicators such as uric acid (UA), sleep, and mood. We found that the gut microbiota of the subjects clustered into two enterotypes (Bi and Bla), and that the groups differed significantly. There were notable differences in the abundances of genera such as Bifidobacterium, Dorea, Ruminococcus_torques_group, Ruminococcus_gnavus_group, Klebsiella, and UCG-002 (p < 0.05). A confined environment significantly impacted the subjects' health indicators. We also observed differences in how the subjects of the two enterotypes adapted to the confined environment. The Bi group showed no significant differences in health indicators before and after confinement; however, the Bla group experienced several health problems after confinement, such as increased UA, anxiety, and constipation, and lack of sleep. Redundancy analysis (RDA) showed that UA, RBC, mood, and other health problems were significantly correlated with the structure of the gut microbiota. We concluded that genera such as UCG-002, Ruminococcus, CAG352, and Ruminococcus_torques_group increased vulnerability to confined environments, resulting in abnormal health conditions. We found that the differences in the adaptability of individuals to confined environments were closely related to the composition of their gut microbiota.

Associations of forest negative air ions exposure with cardiac autonomic nervous function and the related metabolic linkages: A repeated-measure panel study

Forest environment has many health benefits, and negative air ions (NAI) is one of the major forest environmental factors. Many studies have explored the effect of forest environment on cardiac autonomic nervous function, while forest NAI in the among function and the underlying mechanism still remain unclear. To explore the associations and molecular linkages between short-term exposure to forest NAI and heart rate variability (HRV), a repeated-measure panel study was conducted among 31 healthy adults. Participants were randomly selected to stay in a forest park for 3 days and 2 nights. Individual exposures including NAI were monitored simultaneously and HRV indices were measured repeatedly at the follow-up period. Urine samples were collected for non-targeted metabolomics analysis. Mixed-effect models were adopted to evaluate associations among NAI, HRV indices and metabolites. The median of NAI concentration was 68.11 (138.20) cm^-3 during the study period. Short-term exposure to forest NAI was associated with the ameliorative HRV indices, especially the excitatory parasympathetic nerve. For instance, per interquartile range increase of 5-min moving average of NAI was associated with 9.99 % (95%CI: 8.95 %, 11.03 %) increase of power in high frequency. Eight metabolites were associated with NAI exposure. The down-regulated tyrosine metabolism was firstly observed, followed by other amino acid metabolic alterations. The NAI-related metabolic changes reflect the reduction of inflammation and oxidative stress. HRV indices were associated with 25 metabolites, mainly including arginine, proline and histidine metabolism. Short-term exposure to forest NAI is beneficial to HRV, especially to the parasympathetic nerve activity, by successively disturbing different metabolic pathways which mainly reflect the increased anti-inflammation and the reduced inflammation. The results will provide epidemiological evidences for developing forest therapy and improving cardiac autonomic nervous function.

Interplay between dietary intake, gut microbiota, and metabolic profile in obese adolescents: Sex-dependent differential patterns

BACKGROUND & AIMS

The interplay among dietary intake, gut microbiota, gut metabolites and circulating metabolites in adolescents is barely known, not to mention sex-dependent pattern. We aimed to explore unique profiles of gut bacterial, gut metabolites and circulating metabolites from both genders of adolescents due to BMI and eating pattern.

METHODS

Clinical indices, fecal gut microbiota, fecal and plasma metabolites, and diet intake information were collected in case-control sample matched for normal and obesity in girls (normal = 12, obesity = 12) and boys (normal = 20, obesity = 20), respectively. 16S rRNA gene sequencing and untargeted metabolomics was performed to analysis the signature of gut microbiota and metabolites. Unique profiles of girls associated with BMI and eating pattern was revealed by Spearman's correlations analysis, co-occurrence network analysis, Kruskal-Wallis test, and Wilcoxon rank-sum test.

RESULTS

Gender difference was found between normal and obese adolescents in gut microbiota, fecal metabolites, and plasma metabolites. The Parabacteroides were only decreased in obese girls. And the characteristic of obese girls' and boys' cases in fecal and plasma was xanthine and glutamine, ornithine and LCA, respectively. Soy products intake was negatively associated with Parabacteroides. The predicted model has a higher accuracy based on the combined markers in obesity boys (AUC = 0.97) and girls (AUC = 0.97), respectively.

CONCLUSIONS

Reduced abundance of Phascolarctobacterium and Parabacteroides, as well as the increased fecal xanthine and ornithine, may provide a novel biomarker signature in obesity girls and boys. Soy products intake was positively and negatively associated with Romboutsia and Parabacteroides abundance, respectively. And the combined markers facilitate the accuracy of predicting obesity in girls and boys in advance.

Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

BACKGROUND

Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population.

METHODS

Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated.

FINDINGS

During the follow-up period (8.3 ± 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value.

INTERPRETATION

Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model.

FUNDING

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2019M3E5D1A02070863 and 2022R1C1C1005458). This work was also supported by the 2020 Research Fund (1.200098.01) of UNIST (Ulsan National Institute of Science & Technology).

Indolepropionic Acid, a Gut Bacteria-Produced Tryptophan Metabolite and the Risk of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease

An intricate relationship between gut microbiota, diet, and the human body has recently been extensively investigated. Gut microbiota and gut-derived metabolites, especially, tryptophan derivatives, modulate metabolic and immune functions in health and disease. One of the tryptophan derivatives, indolepropionic acid (IPA), is increasingly being studied as a marker for the onset and development of metabolic disorders, including type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). The IPA levels heavily depend on the diet, particularly dietary fiber, and show huge variations among individuals. We suggest that these variations could partially be explained using genetic variants known to be associated with specific diseases such as T2D. In this narrative review, we elaborate on the beneficial effects of IPA in the mitigation of T2D and NAFLD, and further study the putative interactions between IPA and well-known genetic variants (TCF7L2, FTO, and PPARG), known to be associated with the risk of T2D. We have investigated the long-term preventive value of IPA in the development of T2D in the Finnish prediabetic population and the correlation of IPA with phytosterols in obese individuals from an ongoing Kuopio obesity surgery study. The diversity in IPA-linked mechanisms affecting glucose metabolism and liver fibrosis makes it a unique small metabolite and a promising candidate for the reversal or management of metabolic disorders, mainly T2D and NAFLD.

Role of the Gut Microbiome in Diabetes and Cardiovascular Diseases Including Restoration and Targeting Approaches- A Review

Metabolic diseases, including cardiovascular diseases (CVD) and diabetes, have become the leading cause of morbidity and mortality worldwide. Gut microbiota appears to play a vital role in human disease and health, according to recent scientific reports. The gut microbiota plays an important role in sustaining host physiology and homeostasis by creating a cross-talk between the host and microbiome via metabolites obtained from the host's diet. Drug developers and clinicians rely heavily on therapies that target the microbiota in the management of metabolic diseases, and the gut microbiota is considered the biggest immune organ in the human body. They are highly associated with intestinal immunity and systemic metabolic disorders like CVD and diabetes and are reflected as potential therapeutic targets for the management of metabolic diseases. This review discusses the mechanism and interrelation between the gut microbiome and metabolic disorders. It also highlights the role of the gut microbiome and microbially derived metabolites in the pathophysiological effects related to CVD and diabetes. It also spotlights the reasons that lead to alterations of microbiota composition and the prominence of gut microbiota restoration and targeting approaches as effective treatment strategies in diabetes and CVD. Future research should focus onunderstanding the functional level of some specific microbial pathways that help maintain physiological homeostasis, multi-omics, and develop novel therapeutic strategies that intervene with the gut microbiome for the prevention of CVD and diabetes that contribute to a patient's well-being.

Role of Micronutrients and Gut Microbiota-Derived Metabolites in COVID-19 Recovery

A balanced and varied diet provides diverse beneficial effects on health, such as adequate micronutrient availability and a gut microbiome in homeostasis. Besides their participation in biochemical processes as cofactors and coenzymes, vitamins and minerals have an immunoregulatory function; meanwhile, gut microbiota and its metabolites coordinate directly and indirectly the cell response through the interaction with the host receptors. Malnourishment is a crucial risk factor for several pathologies, and its involvement during the Coronavirus Disease 2019 pandemic has been reported. This pandemic has caused a significant decline in the worldwide population, especially those with chronic diseases, reduced physical activity, and elder age. Diet and gut microbiota composition are probable causes for this susceptibility, and its supplementation can play a role in reestablishing microbial homeostasis and improving immunity response against Coronavirus Disease 2019 infection and recovery. This study reviews the role of micronutrients and microbiomes in the risk of infection, the severity of disease, and the Coronavirus Disease 2019 sequelae.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling.

RESULTS

In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction.

CONCLUSIONS

This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.

Role of gut microbe-derived metabolites in cardiometabolic diseases: Systems based approach

BACKGROUND

The gut microbiome influences host physiology and cardiometabolic diseases by interacting directly with intestinal cells or by producing molecules that enter the host circulation. Given the large number of microbial species present in the gut and the numerous factors that influence gut bacterial composition, it has been challenging to understand the underlying biological mechanisms that modulate risk of cardiometabolic disease.

SCOPE OF THE REVIEW

Here we discuss a systems-based approach that involves simultaneously examining individuals in populations for gut microbiome composition, molecular traits using "omics" technologies, such as circulating metabolites quantified by mass spectrometry, and clinical traits. We summarize findings from landmark studies using this approach and discuss future applications.

MAJOR CONCLUSIONS

Population-based integrative approaches have identified a large number of microbe-derived or microbe-modified metabolites that are associated with cardiometabolic traits. The knowledge gained from these studies provide new opportunities for understanding the mechanisms involved in gut microbiome-host interactions and may have potentially important implications for developing novel therapeutic approaches.

Global landscape of 2-hydroxyisobutyrylation in human pancreatic cancer

As a new type of post-translational modification (PTM), lysine 2-hydroxyisobutyrylation (K_hib) was firstly identified in histones and functioned as a regulator of transactivation in mammals. However, the role of K_hib proteins remains to be investigated. Here, we firstly identified 10,367 K_hib sites on 2,325 modified proteins in seven patients with pancreatic cancer by applying liquid chromatography with tandem mass spectrometry (LC-MS/MS) qualitative proteomics techniques. Among them, 27 K_hib-modified sites were identified in histones. Bioinformatics analysis revealed that the K_hib-modified proteins were mainly distributed in the cytoplasm and enhanced in metabolic pathways, including glycolysis/gluconeogenesis, the tricarboxylic acid cycle (TCA cycle), and fatty acid degradation. In an overlapping comparison of lysine 2-hydroxyisobutyrylation, succinylation, and acetylation in humans, 105 proteins with 80 sites were modified by all three PTMs, suggesting there may be a complex network among the different modified proteins and sites. Furthermore, MG149, which was identified as a Tip60 inhibitor, significantly decreased the total Khib modification level in pancreatic cancer (PC) and strongly suppressed PC’s proliferation, migration, and invasion ability. Overall, our study is the first profiling of lysine 2-hydroxyisobutyrylome and provides a new database for better investigating K_hib in PC.

Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

Small molecules in the big picture of gut microbiome-host cross-talk

Research on the gut microbiome and related diseases is rapidly growing with the development of sequencing technologies. An increasing number of studies offer new perspectives on disease development or treatment. Among these, the mechanisms of gut microbial metabolite-mediated effects merit better understanding. In this review, we first summarize the shifts in gut microbial metabolites within complex diseases, in which metabolites have correlational and occasionally causal effects on diseases and discuss the reported mechanisms. We further investigate the interactions between gut microbes and drugs, providing insights for precision medication as well as limitations of current research. Finally, we provide new research directions and research strategies for the development of drugs from gut microbial metabolites. FUNDING STATEMENT: None.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies

OBJECTIVE

Tryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.

METHOD

We analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.

RESULTS

Tryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals.

CONCLUSION

Higher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

Serum Metabolomics of Incident Diabetes and Glycemic Changes in a Population With High Diabetes Burden: The Hispanic Community Health Study/Study of Latinos

Metabolomic signatures of incident diabetes remain largely unclear for the U.S. Hispanic/Latino population, a group with high diabetes burden. We evaluated the associations of 624 known serum metabolites (measured by a global, untargeted approach) with incident diabetes in a subsample (n = 2,010) of the Hispanic Community Health Study/Study of Latinos without diabetes and cardiovascular disease at baseline (2008-2011). Based on the significant metabolites associated with incident diabetes, metabolite modules were detected using topological network analysis, and their associations with incident diabetes and longitudinal changes in cardiometabolic traits were further examined. There were 224 incident cases of diabetes after an average 6 years of follow-up. After adjustment for sociodemographic, behavioral, and clinical factors, 134 metabolites were associated with incident diabetes (false discovery rate-adjusted P < 0.05). We identified 10 metabolite modules, including modules comprising previously reported diabetes-related metabolites (e.g., sphingolipids, phospholipids, branched-chain and aromatic amino acids, glycine), and 2 reflecting potentially novel metabolite groups (e.g., threonate, N-methylproline, oxalate, and tartarate in a plant food metabolite module and androstenediol sulfates in an androgenic steroid metabolite module). The plant food metabolite module and its components were associated with higher diet quality (especially higher intakes of healthy plant-based foods), lower risk of diabetes, and favorable longitudinal changes in HOMA for insulin resistance. The androgenic steroid module and its component metabolites decreased with increasing age and were associated with a higher risk of diabetes and greater increases in 2-h glucose over time. We replicated the associations of both modules with incident diabetes in a U.S. cohort of non-Hispanic Black and White adults (n = 1,754). Among U.S. Hispanic/Latino adults, we identified metabolites across various biological pathways, including those reflecting androgenic steroids and plant-derived foods, associated with incident diabetes and changes in glycemic traits, highlighting the importance of hormones and dietary intake in the pathogenesis of diabetes.

Gut microbiota-derived metabolites as key actors in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is one of the most risk factors threatening human health. Although genetic and environmental factors contribute to the development of T2DM, gut microbiota has also been found to be involved. Gut microbiota-derived metabolites are a key factor in host-microbe crosstalk, and have been revealed to play a central role in the physiology and physiopathology of T2DM. In this review, we provide a timely and comprehensive summary of the microbial metabolites that are protective or causative for T2DM, including some amino acids-derived metabolites, short-chain fatty acids, trimethylamine N-oxide, and bile acids. The mechanisms by which metabolites affect T2DM have been elaborated. Knowing more about these processes will increase our understanding of the causal relationship between gut microbiota and T2DM. Moreover, some frontier therapies that target gut microbes and their metabolites to improve T2DM, including dietary intervention, fecal microbiota transplantation, probiotics, prebiotics or synbiotics intervention, and drugging microbial metabolism, have been critically discussed. This review may provide novel insights for the development of targeted and personalized treatments for T2DM based on gut microbial metabolites. More high-quality clinical trials are needed to accelerate the clinical translation of gut-targeted therapies for T2DM.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Gut Microbiome Signature Are Correlated With Bone Mineral Density Alterations in the Chinese Elders

Objective

Osteoporosis (OP), clinically featured with a low bone mineral density (BMD) and high risk of bone fracture, has become a major risk factor of disability and death in the elders, especially in postmenopausal women. The gut microbiome (GM) is thought to be implicated in bone metabolism. Herein, we clarified the composition signature and gene functional profile of GM in older people with normal and low BMD.

Design and Methods

A total of 455 participants underwent the BMD measurement and biochemical detection. GM analysis was further performed on 113 cases of postmenopausal women and men aged over 50, including both 16S rRNA and metagenomic sequencing.

Results

Generally, the BMD value was significantly lower in the older age groups, especially in the postmenopausal women. Consistently, we observed obvious vitamin D deficiency or insufficiency in females (compared to the male, P < 0.0001). The results from 16S rRNA sequencing revealed higher numbers of OTUs and diversity indexes in females than in males. The abundance in composition of Firmicutes and Clostridiales were correlated with the BMD values in females. LEfSe analysis discovered several enriched bacteria taxons in OP and normal control (NC) subgroups. A positive correlation between the number of genes and BMD values was observed in females based on metagenomic sequencing analysis. Furthermore, we identified the connecting modules among the GM composition – gene functional signature – BMD value/T score in both females and males.

Conclusions

This study provides evidences upon which to understand the mechanisms of the effects of GM on bone health, consequently revealing the physiology status and potential diagnostic/therapeutic targets based on GM for OP and postmenopausal osteoporosis (PMOP). Besides, the status of vitamin D deficiency or insufficiency need to be concerned and improved in the Chinese people.

Identification of diagnostic serum biomarkers for Hunner-type interstitial cystitis

OBJECTIVES

Diagnosis of Hunner-type interstitial cystitis (HIC) relies on the ability to identify Hunner lesions endoscopically, which can lead to storage symptom misdiagnosis. Here, we examined serum biomarkers for HIC and verified their utility.

METHODS

Based on the previous definition of the Japanese guidelines, which did not distinguish HIC and non-HIC diseases, we searched for serum biomarkers in 25 patients with interstitial cystitis (IC) and 25 control participants using metabolomics during 2013-2014. In 2019, we conducted a validation study in HIC and control groups. Serum samples were analyzed using liquid chromatography-tandem mass spectrometry, and candidate biomarker concentrations were compared between the groups using Mann-Whitney test.

RESULTS

Metabolomics targeted 678 metabolites and revealed that the levels of 14 lysolipids, seven γ-glutamyl amino acids, and two monoacylglycerols were significantly different between the IC and control groups. The following metabolites were selected from each metabolite category as candidates: 1-linoleoylglycerophosphocholine (1-linoleloyl-GPC [18:2]), γ-glutamylisoleucine (γ-Glu-Ile), and 1-arachidonylglycerol (1-AG). The serum concentrations of 1-linoleoyl-GPC (18:2) in the HIC and control groups were 27 920 ± 6261 and 40 360 ± 1514 ng/mL (P = 0.0003), respectively. The serum concentrations of γ-Glu-Ile and 1-AG were not significantly different between the groups. When the cut-off value of 1-linoleoyl-GPC (18:2) was set at 28 400 ng/mL, the sensitivity and specificity were 68% and 84%, respectively.

CONCLUSIONS

Serum 1-linoleoyl-GPC (18:2) is a candidate diagnostic biomarker for HIC. Additional studies on whether this biomarker can distinguish HIC from other diseases with high urination frequency are required for its clinical use.

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Background

Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Methods

Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes.

Results

GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset.

Conclusion

Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

16S rRNA gene sequencing analysis of gut microbiome in a mini-pig diabetes model

BACKGROUND

Currently, increasing attention is being paid to the important role of intestinal microbiome in diabetes. However, few studies have evaluated the characteristics of gut microbiome in diabetic miniature pigs, despite it being a good model animal for assessing diabetes.

METHODS

In this study, a mini-pig diabetes model (DM) was established by 9-month high-fat diet (HFD) combined with low-dose streptozotocin, while the animals fed standard chow diet constituted the control group. 16S ribosomal RNA (rRNA) gene sequencing was performed to assess the characteristics of the intestinal microbiome in diabetic mini-pigs.

RESULTS

The results showed that microbial structure in diabetic mini-pigs was altered, reflected by increases in levels of Coprococcus_3 and Clostridium_sensu_stricto_1, which were positively correlated with diabetes, and decreases in levels of the bacteria Rikenellaceae, Clostridiales_vadinBB60_group, and Bacteroidales_RF16_group, which were inversely correlated with blood glucose and insulin resistance. Moreover, PICRUSt-predicted pathways related to the glycolysis and Entner-Doudoroff superpathway, enterobactin biosynthesis, and the l-tryptophan biosynthesis were significantly elevated in the DM group.

CONCLUSION

These results reveal the composition and predictive functions of the intestinal microbiome in the mini-pig diabetes model, further verifying the relationship between HFD, gut microbiome, and diabetes, and providing novel insights into the application of the mini-pig diabetes model in gut microbiome research.

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.