Diminished Sphingolipid Metabolism, a Hallmark of Future Type 2 Diabetes Pathogenesis, Is Linked to Pancreatic β Cell Dysfunction

The synergism of Lactobacillaceae, inulin, polyglucose, and aerobic exercise ameliorates hyperglycemia by modulating the gut microbiota community and the metabolic profiles in db/db mice

This study aimed to assess the impact of Lactobacillaceae (L or H represents a low or high dose), inulin (I), and polydextrose (P) combined with aerobic exercise (A) on the composition of the gut microbiota and metabolic profiles in db/db mice. After a 12-week intervention, LIP, LIPA, and HIPA groups exhibited significant improvements in hyperglycemia, glucose tolerance, insulin resistance, inflammatory response, and short-chain fatty acid (SCFA) and blood lipid levels compared to type 2 diabetes mice (MC). After treatment, the gut microbiota composition shifted favorably in the treatment groups which significantly increased the abundance of beneficial bacteria, such as Bacteroides, Blautia, Akkermansia, and Faecalibaculum, and significantly decreased the abundance of Proteus. Metabolomics analysis showed that compared to the MC group, the contents of 5-hydroxyindoleacetic acid, 3-hydroxysebacic acid, adenosine monophosphate (AMP), xanthine and hypoxanthine were significantly decreased, while 3-ketosphinganine, sphinganine, and sphingosine were significantly increased in the LIP and LIPA groups, respectively. Additionally, LIP and LIPA not only improved sphingolipid metabolism and purine metabolism pathways but also activated AMP-activated protein kinase to promote β-oxidation by increasing the levels of SCFAs. Faecalibaculum, Blautia, Bacteroides, and Akkermansia exhibited positive correlations with sphingosine, 3-ketosphinganine, and sphinganine, and exhibited negative correlations with hypoxanthine, xanthine and AMP. Faecalibaculum, Blautia, Bacteroides, and Akkermansia may have the potential to improve sphingolipid metabolism and purine metabolism pathways. These findings suggest that the synergism of Lactobacillaceae, inulin, polydextrose, and aerobic exercise provides a promising strategy for the prevention and management of type 2 diabetes.

Emerging roles and therapeutic potentials of sphingolipids in pathophysiology: emphasis on fatty acyl heterogeneity

Sphingolipids not only exert structural roles in cellular membranes, but also act as signaling molecules in various physiological and pathological processes. A myriad of studies have shown that abnormal levels of sphingolipids and their metabolic enzymes are associated with a variety of human diseases. Moreover, blood sphingolipids can also be used as biomarkers for disease diagnosis. This review summarizes the biosynthesis, metabolism, and pathological roles of sphingolipids, with emphasis on the biosynthesis of ceramide, the precursor for the biosynthesis of complex sphingolipids with different fatty acyl chains. The possibility of using sphingolipids for disease prediction, diagnosis, and treatment is also discussed. Targeting endogenous ceramides and complex sphingolipids along with their specific fatty acyl chain to promote future drug development will also be discussed.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

Sphingolipids and acylcarnitines are altered in placentas from women with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is the most common medical complication of pregnancy and a severe threat to pregnant people and offspring health. The molecular origins of GDM, and in particular the placental responses, are not fully known. The present study aimed to perform a comprehensive characterisation of the lipid species in placentas from pregnancies complicated with GDM using high-resolution MS lipidomics, with a particular focus on sphingolipids and acylcarnitines in a semi-targeted approach. The results indicated that despite no major disruption in lipid metabolism, placentas from GDM pregnancies showed significant alterations in sphingolipids, mostly lower abundance of total ceramides. Additionally, very long-chain ceramides and sphingomyelins with twenty-four carbons were lower, and glucosylceramides with sixteen carbons were higher in placentas from GDM pregnancies. Semi-targeted lipidomics revealed the strong impact of GDM on the placental acylcarnitine profile, particularly lower contents of medium and long-chain fatty-acyl carnitine species. The lower contents of sphingolipids may affect the secretory function of the placenta, and lower contents of long-chain fatty acylcarnitines is suggestive of mitochondrial dysfunction. These alterations in placental lipid metabolism may have consequences for fetal growth and development.

Circulating Sphingolipids in Insulin Resistance, Diabetes and Associated Complications

Sphingolipids play an important role in the development of diabetes, both type 1 and type 2 diabetes, as well as in the development of both micro- and macro-vascular complications. Several reviews have been published concerning the role of sphingolipids in diabetes but most of the emphasis has been on the possible mechanisms by which sphingolipids, mainly ceramides, contribute to the development of diabetes. Research on circulating levels of the different classes of sphingolipids in serum and in lipoproteins and their importance as biomarkers to predict not only the development of diabetes but also of its complications has only recently emerged and it is still in its infancy. This review summarizes the previously published literature concerning sphingolipid-mediated mechanisms involved in the development of diabetes and its complications, focusing on how circulating plasma sphingolipid levels and the relative content carried by the different lipoproteins may impact their role as possible biomarkers both in the development of diabetes and mainly in the development of diabetic complications. Further studies in this field may open new therapeutic avenues to prevent or arrest/reduce both the development of diabetes and progression of its complications.

metGWAS 1.0: an R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome-wide association studies

MOTIVATION

The method of genome-wide association studies (GWAS) and metabolomics combined provide an quantitative approach to pinpoint metabolic pathways and genes linked to specific diseases; however, such analyses require both genomics and metabolomics datasets from the same individuals/samples. In most cases, this approach is not feasible due to high costs, lack of technical infrastructure, unavailability of samples, and other factors. Therefore, an unmet need exists for a bioinformatics tool that can identify gene loci-associated polymorphic variants for metabolite alterations seen in disease states using standalone metabolomics.

RESULTS

Here, we developed a bioinformatics tool, metGWAS 1.0, that integrates independent GWAS data from the GWAS database and standalone metabolomics data using a network-based systems biology approach to identify novel disease/trait-specific metabolite-gene associations. The tool was evaluated using standalone metabolomics datasets extracted from two metabolomics-GWAS case studies. It discovered both the observed and novel gene loci with known single nucleotide polymorphisms when compared to the original studies.

AVAILABILITY AND IMPLEMENTATION

The developed metGWAS 1.0 framework is implemented in an R pipeline and available at: https://github.com/saifurbd28/metGWAS-1.0.

Pharmacometabolomic Approach to Investigate the Response to Metformin in Patients with Type 2 Diabetes: A Cross-Sectional Study

Metformin constitutes the foundation therapy in type 2 diabetes (T2D). Despite its multiple beneficial effects and widespread use, there is considerable inter-individual variability in response to metformin. Our objective is to identify metabolic signatures associated with poor and good responses to metformin, which may improve our ability to predict outcomes for metformin treatment. In this cross-sectional study, clinical and metabolic data for 119 patients with type 2 diabetes taking metformin were collected from the Qatar Biobank. Patients were empirically dichotomized according to their HbA1C levels into good and poor responders. Differences in the level of metabolites between these two groups were compared using orthogonal partial least square discriminate analysis (OPLS-DA) and linear models. Good responders showed increased levels of sphingomyelins, acylcholines, and glutathione metabolites. On the other hand, poor responders showed increased levels of metabolites resulting from glucose metabolism and gut microbiota metabolites. The results of this study have the potential to increase our knowledge of patient response variability to metformin and carry significant implications for enabling personalized medicine.

Heterogeneity in Early Postpartum Metabolic Profiles Among Women with GDM Who Progressed to Type 2 Diabetes During 10-Year Follow-Up: The SWIFT Study

GDM is a strong risk factor for progression to T2D after pregnancy. Although both GDM and T2D exhibit heterogeneity, the link between the distinct heterogeneity of GDM and incident T2D has not been established. Herein, we evaluate early postpartum profiles of women with recent GDM who later developed incident T2D using a soft clustering method, followed by the integration of both clinical phenotypic variables and metabolomics to characterize these heterogeneous clusters/groups clinically and their molecular mechanisms. We identified three clusters based on two indices of glucose homeostasis at 6-9 weeks postpartum - HOMA-IR and HOMA-B among women who developed incident T2D during the 12-year follow-up. The clusters were classified as follows: pancreatic beta-cell dysfunction group (cluster-1), insulin resistant group (cluster-3), and a combination of both phenomena (cluster-2) comprising the majority of T2D. We also identified postnatal blood test parameters to distinguish the three clusters for clinical testing. Moreover, we compared these three clusters in their metabolomics profiles at the early stage of the disease to identify the mechanistic insights. A significantly higher concentration of a metabolite at the early stage of a T2D cluster than other clusters indicates its essentiality for the particular disease character. As such, the early-stage characters of T2D cluster-1 pathology include a higher concentration of sphingolipids, acyl-alkyl phosphatidylcholines, lysophosphatidylcholines, and glycine, indicating their essentiality for pancreatic beta-cell function. In contrast, the early-stage characteristics of T2D cluster-3 pathology include a higher concentration of diacyl phosphatidylcholines, acyl-carnitines, isoleucine, and glutamate, indicating their essentiality for insulin actions. Notably, all these biomolecules are found in the T2D cluster-2 with mediocre concentrations, indicating a true nature of a mixed group. In conclusion, we have deconstructed incident T2D heterogeneity and identified three clusters with their clinical testing procedures and molecular mechanisms. This information will aid in adopting proper interventions using a precision medicine approach.

The roles of dietary lipids and lipidomics in gut-brain axis in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM), one of the main types of Noncommunicable diseases (NCDs), is a systemic inflammatory disease characterized by dysfunctional pancreatic β-cells and/or peripheral insulin resistance, resulting in impaired glucose and lipid metabolism. Genetic, metabolic, multiple lifestyle, and sociodemographic factors are known as related to high T2DM risk. Dietary lipids and lipid metabolism are significant metabolic modulators in T2DM and T2DM-related complications. Besides, accumulated evidence suggests that altered gut microbiota which plays an important role in the metabolic health of the host contributes significantly to T2DM involving impaired or improved glucose and lipid metabolism. At this point, dietary lipids may affect host physiology and health via interaction with the gut microbiota. Besides, increasing evidence in the literature suggests that lipidomics as novel parameters detected with holistic analytical techniques have important roles in the pathogenesis and progression of T2DM, through various mechanisms of action including gut-brain axis modulation. A better understanding of the roles of some nutrients and lipidomics in T2DM through gut microbiota interactions will help develop new strategies for the prevention and treatment of T2DM. However, this issue has not yet been entirely discussed in the literature. The present review provides up-to-date knowledge on the roles of dietary lipids and lipidomics in gut-brain axis in T2DM and some nutritional strategies in T2DM considering lipids- lipidomics and gut microbiota interactions are given.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study

BACKGROUND

Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear.

OBJECTIVES

We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk.

METHODS

In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM).

RESULTS

Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05.

CONCLUSIONS

Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.

Replacement Flame-Retardant 2-Ethylhexyldiphenyl Phosphate (EHDPP) Disrupts Hepatic Lipidome: Evidence from Human 3D Hepatospheroid Cell Culture

The present study aims to evaluate the effects of repeated exposure to 2-ethylhexyldiphenyl phosphate (EHDPP) on human liver cells. In vitro three-dimensional (3D) hepatospheroid cell culture was utilized to explore the potential mechanisms of EHDPP-mediated metabolic disruption through morphological, transcriptional, and biochemical assays. Lipidomics analysis was performed on the individual hepatospheroids to investigate the effects on intracellular lipid profiles, followed by hepatospheroid morphology, growth, functional parameters, and cytotoxicity evaluation. The possible mechanisms were delineated using the gene-level analysis by assessing the expression of key genes encoding for hepatic lipid metabolism. We revealed that exposure to EHDPP at 1 and 10 μM for 7 days alters the lipid profile of human 3D hepatospheroids. Dysregulation in several lipid classes, including sterol lipids (cholesterol esters), sphingolipids (dihydroceramide, hexosylceramide, ceramide, sphingomyelin), glycerolipids (triglycerides), glycerophospholipids, and fatty acyls, was noted along with alteration in genes including ACAT1, ACAT2, CYP27A1, ABCA1, GPAT2, PNPLA2, PGC1α, and Nrf2. Our study brings a novel insight into the metabolic disrupting effects of EHDPP and demonstrates the utility of hepatospheroids as an in vitro cell culture model complemented with omics technology (e.g., lipidomics) for mechanistic toxicity studies.

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.

Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.

Dysregulated Metabolic Pathways in Subjects with Obesity and Metabolic Syndrome

Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-matched to subjects with obesity and metabolic syndrome (OBM, n = 21). We measured 1069 serum metabolites and correlated them to clinical features. Results: A total of 83 metabolites, mostly lipids, were significantly different (p < 0.05) between the two groups. Among lipids, 22 sphingomyelins were decreased in OBM compared to OBO. Among non-lipids, quinolinate, kynurenine, and tryptophan were also decreased in OBM compared to OBO. Sphingomyelin is negatively correlated with glucose, HbA1C, insulin, and triglycerides but positively correlated with HDL, LDL, and cholesterol. Differentially enriched pathways include lysine degradation, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, fructose and mannose metabolism, and galactose metabolism. Conclusions: Metabolites and pathways associated with chronic inflammation are differentially expressed in subjects with obesity and metabolic syndrome compared to subjects with obesity but without the clinical features of metabolic syndrome.

Type 2 diabetes disrupts circadian orchestration of lipid metabolism and membrane fluidity in human pancreatic islets

Recent evidence suggests that circadian clocks ensure temporal orchestration of lipid homeostasis and play a role in pathophysiology of metabolic diseases in humans, including type 2 diabetes (T2D). Nevertheless, circadian regulation of lipid metabolism in human pancreatic islets has not been explored. Employing lipidomic analyses, we conducted temporal profiling in human pancreatic islets derived from 10 nondiabetic (ND) and 6 T2D donors. Among 329 detected lipid species across 8 major lipid classes, 5% exhibited circadian rhythmicity in ND human islets synchronized in vitro. Two-time point-based lipidomic analyses in T2D human islets revealed global and temporal alterations in phospho- and sphingolipids. Key enzymes regulating turnover of sphingolipids were rhythmically expressed in ND islets and exhibited altered levels in ND islets bearing disrupted clocks and in T2D islets. Strikingly, cellular membrane fluidity, measured by a Nile Red derivative NR12S, was reduced in plasma membrane of T2D diabetic human islets, in ND donors’ islets with disrupted circadian clockwork, or treated with sphingolipid pathway modulators. Moreover, inhibiting the glycosphingolipid biosynthesis led to strong reduction of insulin secretion triggered by glucose or KCl, whereas inhibiting earlier steps of de novo ceramide synthesis resulted in milder inhibitory effect on insulin secretion by ND islets. Our data suggest that circadian clocks operative in human pancreatic islets are required for temporal orchestration of lipid homeostasis, and that perturbation of temporal regulation of the islet lipid metabolism upon T2D leads to altered insulin secretion and membrane fluidity. These phenotypes were recapitulated in ND islets bearing disrupted clocks.

Very-Long-Chain Unsaturated Sphingolipids Mediate Oleate-Induced Rat β-Cell Proliferation

Fatty acid (FA) signaling contributes to β-cell mass expansion in response to nutrient excess, but the underlying mechanisms are poorly understood. In the presence of elevated glucose, FA metabolism is shifted toward synthesis of complex lipids, including sphingolipids. Here, we tested the hypothesis that sphingolipids are involved in the β-cell proliferative response to FA. Isolated rat islets were exposed to FA and 16.7 mmol/L glucose for 48-72 h, and the contribution of the de novo sphingolipid synthesis pathway was tested using the serine palmitoyltransferase inhibitor myriocin, the sphingosine kinase (SphK) inhibitor SKI II, or knockdown of SphK, fatty acid elongase 1 (ELOVL1) and acyl-CoA-binding protein (ACBP). Rats were infused with glucose and the lipid emulsion ClinOleic and received SKI II by gavage. β-Cell proliferation was assessed by immunochemistry or flow cytometry. Sphingolipids were analyzed by liquid chromatography-tandem mass spectrometry. Among the FAs tested, only oleate increased β-cell proliferation. Myriocin, SKI II, and SphK knockdown all decreased oleate-induced β-cell proliferation. Oleate exposure did not increase the total amount of sphingolipids but led to a specific rise in 24:1 species. Knockdown of ACBP or ELOVL1 inhibited oleate-induced β-cell proliferation. We conclude that unsaturated very-long-chain sphingolipids produced from the available C24:1 acyl-CoA pool mediate oleate-induced β-cell proliferation in rats.

Roux-en-Y gastric bypass and sleeve gastrectomy induce substantial and persistent changes in microbial communities and metabolic pathways

Bariatric surgery induces significant microbial and metabolomic changes, however, links between microbial and metabolic pathways have not been fully elucidated. The objective of this study was to conduct a comprehensive investigation of the microbial, metabolomic, and inflammatory changes that occur following Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). A prospective clinical trial was conducted with participants undergoing RYGB, SG, and non-operative controls (CTRL). Clinical parameters, blood samples, and fecal samples were collected pre-intervention and at 3 and 9 months. A multi-omics approach was used to perform integrated microbial-metabolomic analysis to identify functional pathways in which weight loss and metabolic changes occur after surgery. RYGB led to profound microbial changes over time that included reductions in alpha-diversity, increased Proteobacteria and Verrucomicrobiota, decreased Firmicutes, and numerous changes at the genera level. These changes were associated with a reduction in inflammation and significant weight loss. A reduction in Romboutsia genera correlated strongly with weight loss and integrated microbial-metabolomic analysis revealed the importance of Romboutsia. Its obliteration correlated with improved weight loss and insulin resistance, possibly through decreases in glycerophospholipids. In contrast, SG was associated with no changes in alpha-diversity, and only a small number of changes in microbial genera. A cluster of Firmicutes genera including Butyriciccocus, Eubacterium ventriosum, and Monoglobus was decreased, which correlated with decreased weight, insulin resistance, and systemic inflammation. This work represents comprehensive analyses of microbial-metabolomic changes that occur following bariatric surgery and identifies several pathways that are associated with beneficial metabolic effects of surgery.

Adaptive Changes in Glucose Homeostasis and Islet Function During Pregnancy: A Targeted Metabolomics Study in Mice

OBJECTIVE

Pregnancy is a dynamic state involving multiple metabolic adaptions in various tissues including the endocrine pancreas. However, a detailed characterization of the maternal islet metabolome in relation to islet function and the ambient circulating metabolome during pregnancy has not been established.

METHODS

A timed-pregnancy mouse model was studied, and age-matched non-pregnant mice were used as controls. Targeted metabolomics was applied to fasting plasma and purified islets during each trimester of pregnancy. Glucose homeostasis and islet function was assessed. Bioinformatic analyses were performed to reveal the metabolic adaptive changes in plasma and islets, and to identify key metabolic pathways associated with pregnancy.

RESULTS

Fasting glucose and insulin were found to be significantly lower in pregnant mice compared to non-pregnant controls, throughout the gestational period. Additionally, pregnant mice had superior glucose excursions and greater insulin response to an oral glucose tolerance test. Interestingly, both alpha and beta cell proliferation were significantly enhanced in early to mid-pregnancy, leading to significantly increased islet size seen in mid to late gestation. When comparing the plasma metabolome of pregnant and non-pregnant mice, phospholipid and fatty acid metabolism pathways were found to be upregulated throughout pregnancy, whereas amino acid metabolism initially decreased in early through mid pregnancy, but then increased in late pregnancy. Conversely, in islets, amino acid metabolism was consistently enriched throughout pregnancy, with glycerophospholid and fatty acid metabolism was only upregulated in late pregnancy. Specific amino acids (glutamate, valine) and lipids (acyl-alkyl-PC, diacyl-PC, and sphingomyelin) were found to be significantly differentially expressed in islets of the pregnant mice compared to controls, which was possibly linked to enhanced insulin secretion and islet proliferation.

CONCLUSION

Beta cell proliferation and function are elevated during pregnancy, and this is coupled to the enrichment of islet metabolites and metabolic pathways primarily associated with amino acid and glycerophospholipid metabolism. This study provides insight into metabolic adaptive changes in glucose homeostasis and islet function seen during pregnancy, which will provide a molecular rationale to further explore the regulation of maternal metabolism to avoid the onset of pregnancy disorders, including gestational diabetes.

Exploring biological basis of Syndrome differentiation in coronary heart disease patients with two distinct Syndromes by integrated multi-omics and network pharmacology strategy

Background

Traditional Chinese Medicine (TCM) is distinguished by Syndrome differentiation, which prescribes various formulae for different Syndromes of same disease. This study aims to investigate the underlying mechanism.

Methods

Using a strategy which integrated proteomics, metabolomics study for clinic samples and network pharmacology for six classic TCM formulae, we systemically explored the biological basis of TCM Syndrome differentiation for two typical Syndromes of CHD: Cold Congealing and Qi Stagnation (CCQS), and Qi Stagnation and Blood Stasis (QSBS).

Results

Our study revealed that CHD patients with CCQS Syndrome were characterized with alteration in pantothenate and CoA biosynthesis, while more extensively altered pathways including D-glutamine and D-glutamate metabolism; alanine, aspartate and glutamate metabolism, and glyoxylate and dicarboxylate metabolism, were present in QSBS patients. Furthermore, our results suggested that the down-expressed PON1 and ADIPOQ might be potential biomarkers for CCQS Syndrome, while the down-expressed APOE and APOA1 for QSBS Syndrome in CHD patients. In addition, network pharmacology and integrated analysis indicated possible comorbidity differences between the two Syndromes, that is, CCQS or QSBS Syndrome was strongly linked to diabetes or ischemic stroke, respectively, which is consistent with the complication disparity between the enrolled patients with two different Syndromes. These results confirmed our assumption that the molecules and biological processes regulated by the Syndrome-specific formulae could be associated with dysfunctional objects caused by the Syndrome of the disease.

Conclusion

This study provided evidence-based strategy for exploring the biological basis of Syndrome differentiation in TCM, which sheds light on the translation of TCM theory in the practice of precision medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-021-00521-3.

1. Our work was based on clinical samples rather than pure data analysis or animal models.

2. We conducted multiple omics studies. Especially, as for metabolomics study, we performed both untargeted and targeted metabolomics experiments.

3. We performed network pharmacological study to cross-validated the results of multi-omics study. Although the data sources of network pharmacology were completely unrelated with our omics data, they came to the same conclusion about the difference of the two Syndromes.

4. In the network pharmacological study, we made efforts to collect and screen high-quality data. We collected data from multiple TCM databases and conducted drug likeness screening. Especially, we added quality markers of each herb, whose pharmacological relevance had been validated. To enhance the reliability of targets, for each Syndrome, we only studied common targets of 3 different TCM formulae prescribed for this Syndrome.

Associations of network-derived metabolite clusters with prevalent type 2 diabetes among adults of Puerto Rican descent

INTRODUCTION

We investigated whether network analysis revealed clusters of coregulated metabolites associated with prevalent type 2 diabetes (T2D) among Puerto Rican adults.

RESEARCH DESIGN AND METHODS

We used liquid chromatography-mass spectrometry to measure fasting plasma metabolites (>600) among participants aged 40-75 years in the Boston Puerto Rican Health Study (BPRHS; discovery) and San Juan Overweight Adult Longitudinal Study (SOALS; replication), with (n=357; n=77) and without (n=322; n=934) T2D, respectively. Among BPRHS participants, we used unsupervised partial correlation network-based methods to identify and calculate metabolite cluster scores. Logistic regression was used to assess cross-sectional associations between metabolite clusters and prevalent T2D at the baseline blood draw in the BPRHS, and significant associations were replicated in SOALS. Inverse-variance weighted random-effect meta-analysis was used to combine cohort-specific estimates.

RESULTS

Six metabolite clusters were significantly associated with prevalent T2D in the BPRHS and replicated in SOALS (false discovery rate (FDR) <0.05). In a meta-analysis of the two cohorts, the OR and 95% CI (per 1 SD increase in cluster score) for prevalent T2D were as follows for clusters characterized primarily by glucose transport (0.21 (0.16 to 0.30); FDR <0.0001), sphingolipids (0.40 (0.29 to 0.53); FDR <0.0001), acyl cholines (0.35 (0.22 to 0.56); FDR <0.0001), sugar metabolism (2.28 (1.68 to 3.09); FDR <0.0001), branched-chain and aromatic amino acids (2.22 (1.60 to 3.08); FDR <0.0001), and fatty acid biosynthesis (1.54 (1.29 to 1.85); FDR <0.0001). Three additional clusters characterized by amino acid metabolism, cell membrane components, and aromatic amino acid metabolism displayed significant associations with prevalent T2D in the BPRHS, but these associations were not replicated in SOALS.

CONCLUSIONS

Among Puerto Rican adults, we identified several known and novel metabolite clusters that associated with prevalent T2D.

Clinical and metabolomic predictors of regression to normoglycemia in a population at intermediate cardiometabolic risk

Background

Impaired fasting glucose (IFG) is a prevalent and potentially reversible intermediate stage leading to type 2 diabetes that increases risk for cardiometabolic complications. The identification of clinical and molecular factors associated with the reversal, or regression, from IFG to a normoglycemia state would enable more efficient cardiovascular risk reduction strategies. The aim of this study was to identify clinical and biological predictors of regression to normoglycemia in a non-European population characterized by high rates of type 2 diabetes.

Methods

We conducted a prospective, population-based study among 9637 Mexican individuals using clinical features and plasma metabolites. Among them, 491 subjects were classified as IFG, defined as fasting glucose between 100 and 125 mg/dL at baseline. Regression to normoglycemia was defined by fasting glucose less than 100 mg/dL in the follow-up visit. Plasma metabolites were profiled by Nuclear Magnetic Resonance. Multivariable cox regression models were used to examine the associations of clinical and metabolomic factors with regression to normoglycemia. We assessed the predictive capability of models that included clinical factors alone and models that included clinical factors and prioritized metabolites.

Results

During a median follow-up period of 2.5 years, 22.6% of participants (n = 111) regressed to normoglycemia, and 29.5% progressed to type 2 diabetes (n = 145). The multivariate adjusted relative risk of regression to normoglycemia was 1.10 (95% confidence interval [CI] 1.25 to 1.32) per 10 years of age increase, 0.94 (95% CI 0.91–0.98) per 1 SD increase in BMI, and 0.91 (95% CI 0.88–0.95) per 1 SD increase in fasting glucose. A model including information from age, fasting glucose, and BMI showed a good prediction of regression to normoglycemia (AUC = 0.73 (95% CI 0.66–0.78). The improvement after adding information from prioritized metabolites (TG in large HDL, albumin, and citrate) was non-significant (AUC = 0.74 (95% CI 0.68–0.80), p value = 0.485).

Conclusion

In individuals with IFG, information from three clinical variables easily obtained in the clinical setting showed a good prediction of regression to normoglycemia beyond metabolomic features. Our findings can serve to inform and design future cardiovascular prevention strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01246-1.

Integration of AI and traditional medicine in drug discovery

AI integration in plant-based traditional medicine could be used to overcome drug discovery challenges.