Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study

Glycine receptor activation promotes pancreatic islet cell proliferation via the PI3K/mTORC1/p70S6K pathway

Glycine and β-alanine activate glycine receptors (GlyRs), with glycine known to enhance insulin secretion from pancreatic islet β cells, primarily through GlyR activation. However, the effects of GlyR activation on β cell proliferation have not been examined. Here, we aim to investigate the potential proliferative effects of glycine and β-alanine on islets. In vitro experiments on mouse and human islets revealed that glycine and β-alanine, via GlyR activation, stimulated the proliferation of β cells and α cells, without affecting insulin or glucagon secretion. Further analysis indicated the involvement of the PI3K/mTORC1/p70S6K signaling pathway in this process. Inhibition of GlyRs and PI3K/mTORC1/p70S6K signaling attenuated proliferative effects of glycine and β-alanine. In vivo and ex vivo studies supported these findings, showing increased β and α cell mass after 12 weeks of oral administration of glycine and β-alanine, with no changes in insulin secretion or glucose homeostasis under normal conditions. However, during an acute insulin resistance induced by insulin receptor antagonist S961, glycine and β-alanine enhanced insulin secretion and reduced blood glucose levels by increasing β cell secretory capacity. These findings demonstrate glycine and β-alanine in vivo and in vitro promote islet cell proliferation via GlyR activation and the PI3K/mTORC1/p70S6K pathway, potentially providing a target to enhance islet capacity.

Metabolic Signature of Insulin Resistance and Risk of Alzheimer's Disease

BACKGROUND

Substantial evidence supports the relationship between peripheral insulin resistance (IR) and the development of Alzheimer's disease (AD)-dementia. However, the mechanisms explaining these associations are only partly understood. We aimed to identify a metabolic signature of IR associated with the progression from mild cognitive impairment (MCI) to AD-dementia.

METHODS

This is a case-control study on 400 MCI subjects, free of type 2 diabetes, within the ACE cohort, including individuals ATN + and ATN-. After a median of 2.1 years of follow-up, 142 subjects converted to AD-dementia. IR was assessed using the homeostasis model assessment for insulin resistance (HOMA-IR). A targeted multiplatform approach profiled over 600 plasma metabolites. Elastic net penalized linear regression with 10-fold cross-validation was employed to select those metabolites associated with HOMA-IR. The prediction ability of the signature was assessed using support vector machine and performance metrics. The metabolic signature was associated with AD-dementia risk using a multivariable Cox regression model. Using counterfactual-based mediation analysis, we investigated the mediation role of the metabolic signature between HOMA-IR and AD-dementia. The metabolic pathways in which the metabolites were involved were identified using MetaboAnalyst.

RESULTS

The metabolic signature comprised 18 metabolites correlated with HOMA-IR. After adjustments by confounders, the signature was associated with increased AD-dementia risk (HR = 1.234; 95% CI = 1.019-1.494; p < .05). The metabolic signature mediated 35% of the total effect of HOMA-IR on AD-dementia risk. Significant metabolic pathways were related to glycerophospholipid and tyrosine metabolism.

CONCLUSIONS

We have identified a blood-based metabolic signature that reflects IR and may enhance our understanding of the biological mechanisms through which IR affects AD-dementia.

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits

Small molecules (SMs) are integral to biological processes, influencing metabolism, homeostasis, and regulatory networks. Despite their importance, a significant knowledge gap exists regarding their downstream effects on biological pathways and gene expression, largely due to differences in scale, variability, and noise between untargeted metabolomics and sequencing-based technologies. To address these challenges, we developed a multi-omics framework comprising a machine learning-based protocol for data processing, a semi-supervised network inference approach, and network-guided analysis of complex traits. The ML protocol harmonized metabolomic, lipidomic, and transcriptomic data through batch correction, principal component analysis, and regression-based adjustments, enabling unbiased and effective integration. Building on this, we proposed a semi-supervised method to construct transcriptome-SM interaction networks (TSI-Nets) by selectively integrating SM profiles into gene-level networks using a meta-analytic approach that accounts for scale differences and missing data across omics layers. Benchmarking against three conventional unsupervised methods demonstrated the superiority of our approach in generating diverse, biologically relevant, and robust networks. While single-omics analyses identified 18 significant genes and 3 significant SMs associated with insulin sensitivity (IS), network-guided analysis revealed novel connections between these markers. The top-ranked module highlighted a cross-talk between fiber-degrading gut microbiota and immune regulatory pathways, inferred by the interaction of the protective SM, N-acetylglycine (NAG), with immune genes (FCER1A, HDC, MS4A2, and CPA3), linked to improved IS and reduced obesity and inflammation. Together, this framework offers a robust and scalable solution for multi-modal network inference and analysis, advancing SM pathway discovery and their implications for human health. Leveraging data from a population of thousands of individuals with extended longevity, the inferred TSI-Nets demonstrate generalizability across diverse conditions and complex traits. These networks are publicly available as a resource for the research community.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

Type 2 diabetes metabolomics score and risk of progression to type 2 diabetes among women with a history of gestational diabetes mellitus

BACKGROUND

Several metabolites are individually related to incident type 2 diabetes (T2D) risk. We prospectively evaluated a novel T2D-metabolite pattern with a risk of progression to T2D among high-risk women with a history of gestational diabetes mellitus (GDM).

METHODS

The longitudinal Nurses' Health Study II cohort enroled 116,429 women in 1989 and collected blood samples from 1996 to 1999. We profiled plasma metabolites in 175 incident T2D cases and 175 age-matched controls, all with a history of GDM before the blood draw. We derived a metabolomics score from 21 metabolites previously associated with incident T2D in the published literature by scoring according to the participants' quintile (1-5 points) of each metabolite. We modelled the T2D metabolomics score categorically in quartiles and continuously per 1 standard deviation (SD) with the risk of incident T2D using conditional logistic regression models adjusting for body mass index at the blood draw, and other established T2D risk factors.

RESULTS

The percentage of women progressing to T2D ranged from 10% in the bottom T2D metabolomics score quartile to 78% in the highest score quartile. Adjusting for established T2D risk factors, women in the highest quartile had more than a 20-fold greater diabetes risk than women in the lowest quartile (odds ratios [OR] = 23.1 [95% CI = 8.6, 62.1]; p for trend<0.001). The continuous T2D metabolomics score was strongly and positively associated with incident T2D (adjusted OR = 2.7 per SD [95% CI = 1.9, 3.7], p < 0.0001).

CONCLUSIONS

A pattern of plasma metabolites among high-risk women is associated with a markedly elevated risk of progression to T2D later in life.

Metabolite profiles of diabetes mellitus and response to intervention in anti-hyperglycemic drugs

Type 2 diabetes mellitus (T2DM) has become a major health problem, threatening the quality of life of nearly 500 million patients worldwide. As a typical multifactorial metabolic disease, T2DM involves the changes and interactions of various metabolic pathways such as carbohydrates, amino acid, and lipids. It has been suggested that metabolites are not only the endpoints of upstream biochemical processes, but also play a critical role as regulators of disease progression. For example, excess free fatty acids can lead to reduced glucose utilization in skeletal muscle and induce insulin resistance; metabolism disorder of branched-chain amino acids contributes to the accumulation of toxic metabolic intermediates, and promotes the dysfunction of β-cell mitochondria, stress signal transduction, and apoptosis. In this paper, we discuss the role of metabolites in the pathogenesis of T2DM and their potential as biomarkers. Finally, we list the effects of anti-hyperglycemic drugs on serum/plasma metabolic profiles.

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.

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Urinary metabolomics of phenolic compounds reveals biomarkers of type-2 diabetes within the PREDIMED trial

BACKGROUND

Phenolic compounds have been associated with protective effects against type-2 diabetes (T2D). We used a metabolomics approach to determine urinary phenolic metabolites associated with T2D and fasting plasma glucose.

METHODS

This case-control study within the PREDIMED trial included 200 participants at high cardiovascular risk, 102 of whom were diagnosed with T2D. A panel of urinary phenolic compounds were analysed using a novel method based on liquid chromatography coupled to mass spectrometry. Multivariate statistics and adjusted logistic regressions were applied to determine the most discriminant compounds and their association with T2D. The relationship between the discriminant phenolic compounds and plasma glucose was assessed using multivariable linear regressions.

RESULTS

A total of 41 phenolic compounds were modeled in the orthogonal projection to latent structures discriminant analysis, and after applying adjusted logistic regressions two were selected as discriminant: dihydrocaffeic acid (OR = 0.22 (CI 95 %: 0.09; 0.52) per 1-SD, p-value = 0.021) and genistein diglucuronide (OR = 0.72 (CI 95%: 0.59; 0.88) per 1-SD, p-value = 0.021). Both metabolites were associated with a lower risk of suffering from T2D, but only dihydrocaffeic acid was inversely associated with plasma glucose (β = -17.12 (95 % CI: -29.92; -4.32) mg/dL per 1-SD, p-value = 0.009).

CONCLUSIONS

A novel method using a metabolomics approach was developed to analyse a panel of urinary phenolic compounds for potential associations with T2D, and two metabolites, dihydrocaffeic acid and genistein diglucuronide, were found to be associated with a lower risk of this condition.

Short-chain fatty acids are key mediators of the favorable effects of the Mediterranean diet on intestinal barrier integrity: data from the randomized controlled LIBRE trial

BACKGROUND

The Mediterranean diet is associated with the prevention of diabetes, cardiovascular disease, and cancer, all of which are linked to intestinal barrier impairment.

OBJECTIVES

Here, we hypothesize that the Mediterranean diet, possibly via the induction of short-chain fatty acids (SCFAs), improves intestinal barrier integrity. Furthermore, we aim to establish novel personalized nutrition advice based on machine learning algorithms.

METHODS

We studied 260 women with intestinal barrier impairment. The women were allocated to follow either a Mediterranean diet or a control diet for 3 mo. Intestinal permeability was assessed by measuring lipopolysaccharide binding protein (LBP) in plasma and zonulin in feces. SCFA concentrations were analyzed in feces. Bi- and multivariate analyses and machine learning algorithms (random forest classification) were conducted.

RESULTS

Particularly in the intervention group, adherence to the Mediterranean diet increased, whereas plasma LBP and fecal zonulin concentrations decreased (all q < 0.001 for the intervention group, all q < 0.1 for control group). In the intervention group, fecal SCFA concentrations increased (propionate + 19%; butyrate + 44%; both q < 0.001). Multivariate analyses showed that adherence to the Mediterranean diet was associated with SCFA concentrations (all q < 0.001) and inversely associated with LBP and zonulin concentrations (all q < 0.02). Mediation analyses identified propionate and butyrate as the key mechanistic link between diet and intestinal permeability integrity. Accordingly, using baseline SCFA data, we could predict the effect of the Mediterranean diet on intestinal permeability using a machine learning algorithm (receiver operating characteristic AUC: 0.78-0.96).

CONCLUSIONS

Our data suggest that SCFAs are key mediators for the relation between diet and gut health. Assessment of SCFAs may form a basis for personalized nutrition in future clinical care. These results need to be verified in larger studies powered for this purpose, comprising different study populations. The trial was registered at clinicaltrials.gov as NCT02087592 and NCT02516540.

The Effect of Dietary Carbohydrate and Fat Manipulation on the Metabolome and Markers of Glucose and Insulin Metabolism: A Randomised Parallel Trial

High carbohydrate, lower fat (HCLF) diets are recommended to reduce cardiometabolic disease (CMD) but low carbohydrate high fat (LCHF) diets can be just as effective. The effect of LCHF on novel insulin resistance biomarkers and the metabolome has not been fully explored. The aim of this study was to investigate the impact of an ad libitum 8-week LCHF diet compared with a HCLF diet on CMD markers, the metabolome, and insulin resistance markers. n = 16 adults were randomly assigned to either LCHF (n = 8, <50 g CHO p/day) or HCLF diet (n = 8) for 8 weeks. At weeks 0, 4 and 8, participants provided fasted blood samples, measures of body composition, blood pressure and dietary intake. Samples were analysed for markers of cardiometabolic disease and underwent non-targeted metabolomic profiling. Both a LCHF and HCLF diet significantly (p < 0.01) improved fasting insulin, HOMA IR, rQUICKI and leptin/adiponectin ratio (p < 0.05) levels. Metabolomic profiling detected 3489 metabolites with 78 metabolites being differentially regulated, for example, an upregulation in lipid metabolites following the LCHF diet may indicate an increase in lipid transport and oxidation, improving insulin sensitivity. In conclusion, both diets may reduce type 2 diabetes risk albeit, a LCHF diet may enhance insulin sensitivity by increasing lipid oxidation.

Presurgical blood metabolites and risk of postsurgical pelvic pain in young patients with endometriosis

OBJECTIVE

To identify metabolites in presurgical blood associated with risk of persistent postsurgical pelvic pain 1 year after endometriosis surgery in adolescent and young adult patients.

DESIGN

Prospective observational study within the Women's Health Study: From Adolescence to Adulthood, a US-based longitudinal cohort of adolescents and women enrolled from 2012-2018.

SETTING

Two tertiary care hospitals.

PATIENT(S)

Laparoscopically confirmed endometriosis patients (n = 180) with blood collected before their endometriosis surgery. Of these, 77 patients additionally provided blood samples 5 weeks to 6 months after their surgery. We measured plasma metabolites using liquid chromatography tandem mass spectrometry, and a total of 390 known metabolites were included in our analysis.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Persistent postsurgical pelvic pain, defined as severe, life-impacting pelvic pain 1 year after endometriosis surgery.

RESULT(S)

Most patients (>95%) were at stage I/II of the revised American Society for Reproductive Medicine classification. Their average age at diagnosis was 18.7 years, with 36% reporting persistent postsurgical pelvic pain. Of the 21 metabolites in presurgical blood that were associated with risk of persistent postsurgical pelvic pain, 19 metabolites, which were mainly lipid metabolites, were associated with increased risk. Only 2 metabolites-pregnenolone sulfate (odds ratio = 0.64, 95% confidence interval = 0.44-0.92) and fucose (odds ratio = 0.69, 95% confidence interval = 0.47-0.97)-were associated with decreased risk. Metabolite set enrichment analysis revealed that higher levels of lysophosphatidylethanolamines (false discovery rate = 0.01) and lysophosphatidylcholines (false discovery rate = 0.01) in presurgical blood were associated with increased risk of persistent postsurgical pelvic pain.

CONCLUSION(S)

Our results suggest that dysregulation of multiple groups of lipid metabolites may play a role in the persistence of pelvic pain postsurgery among young endometriosis patients.

LC-MS-Based Lipidomic Analysis of Serum Samples from Patients with Type 2 Diabetes Mellitus (T2DM)

Background

With the development of social economy, type 2 diabetes mellitus (T2DM) is becoming a severe health problem globally.

Methods

To systematically understand the lipid metabolism in T2DM, we applied untargeted lipidomics to the serum of T2DM patients and control group using ultrahigh-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (MS).

Results

Over two thousand molecular features were detected by our approach, of which 222 lipid species in positive ion mode and 145 species in negative were reliably identified based on precise molecular weights and MS/MS patterns. Multivariate analysis was adopted to differentiate T2DM patients and the control group using principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA). The dysregulated lipid species were found and their significance in pathophysiology was discussed. Correlation analysis of selected lipids and important clinical variables was performed and addressed.

Conclusions

This study unveils several new lipids and pathways considerably involved in T2DM and provides novel insights into understanding the pathogenesis underlying T2DM.

Impact of Different Durations of Fasting on Intestinal Autophagy and Serum Metabolome in Broiler Chicken

Simple Summary

Fasting is usually used before metabolizable energy assessment in poultry. Recently, fasting-induced autophagy has been of concern because of the beneficial function of autophagy. In this study, we found that the intestinal autophagy gene Atg7 has a good quadratic fitting with fasting duration. We found that the serum metabolism pathways involved in glycerophospholipid, phenylalanine, GnRH signaling pathways, glycosylphosphatidylinositol anchor biosynthesis, autophagy, and ferroptosis changed with fasting. Furthermore, we found a correlation between intestinal autophagy and serum metabolite PE (18:3(9Z,12Z,15Z)/P-18:0).

Abstract

Fasting-induced autophagy in the intestine is beneficial for body health. This study was designed to explore the relationship between the host metabolism and intestinal autophagy. Broilers were randomly assigned into 48 cages. At 0 (CT), 12 (FH12), 24 (FH24), 36 (FH36), 48(FH48), and 72 h (FH72) before 09:00 a.m. on day 25, eight cages of birds were randomly allotted to each fasting time point using completely random design, and their food was removed. At 09:00 a.m. on day 25, the blood and jejunum were sampled for serum metabolome and autophagy gene analyses, respectively. The results showed that the autophagy gene Atg7 has a good quadratic fit with fasting duration (R² = 0.432, p < 0.001). Serum phosphatidylethanolamine (PE) and lyso-PE were decreased in the birds that were fasted for 24 h or longer. Conversely, the serum phosphatidylcholine (PC) and lyso-PC were increased in the birds that were fasted for 36 h or longer. Metabolism pathway analysis showed that the serum glycerophospholipid, phenylalanine, and GnRH signaling pathways were downregulated with the extended fasting duration. The serum metabolites involved in glycosylphosphatidylinositol anchor biosynthesis, autophagy, and ferroptosis were upregulated in all of the fasted groups. Correlation analysis showed that serum PE (18:3(9Z,12Z,15Z)/P-18:0) was a potential biomarker for intestinal autophagy. Our findings provide a potential biomarker related to intestinal autophagy.

Serum Metabolites Responding in a Dose-Dependent Manner to the Intake of a High-Fat Meal in Normal Weight Healthy Men Are Associated with Obesity

Although the composition of the human blood metabolome is influenced both by the health status of the organism and its dietary behavior, the interaction between these two factors has been poorly characterized. This study makes use of a previously published randomized controlled crossover acute intervention to investigate whether the blood metabolome of 15 healthy normal weight (NW) and 17 obese (OB) men having ingested three doses (500, 1000, 1500 kcal) of a high-fat (HF) meal can be used to identify metabolites differentiating these two groups. Among the 1024 features showing a postprandial response, measured between 0 h and 6 h, in the NW group, 135 were dose-dependent. Among these 135 features, 52 had fasting values that were significantly different between NW and OB men, and, strikingly, they were all significantly higher in OB men. A subset of the 52 features was identified as amino acids (e.g., branched-chain amino acids) and amino acid derivatives. As the fasting concentration of most of these metabolites has already been associated with metabolic dysfunction, we propose that challenging normal weight healthy subjects with increasing caloric doses of test meals might allow for the identification of new fasting markers associated with obesity.

Effects of Mediterranean Diet on plasma metabolites and their relationship with insulin resistance and gut microbiota composition in a crossover randomized clinical trial

BACKGROUND & AIMS

The Mediterranean Diet (MedDiet) may decrease the cardiometabolic risk through modulation of metabolic pathways. Furthermore, the interplay between MedDiet, metabolites and microbial metabolism may improve our understanding on the metabolic effects of this diet. We aimed to evaluate the effect of the MedDiet compared to nuts supplementation on circulating metabolites and their relationship with cardiometabolic health. We further examined whether changes in the metabolomic profiles were associated with changes in gut microbiota composition in a multi-omics integrative approach.

METHODS

Forty-four adults with Metabolic Syndrome (MetS), (aged 37-65) participated in a randomized controlled, crossover 2-months dietary-intervention trial with a 1-month wash-out period, consuming a MedDiet or a non MedDiet plus nuts (50 g/day). Nutritional data were collected at the beginning and the end of each intervention period using 3-day dietary records, as well as fasting blood and fecal samples. Plasma metabolites (m = 378) were profiled using targeted metabolomics. Associations of these metabolites with the interventions were assessed with elastic net regression analyses. Gut microbiota composition was assessed by 16S rRNA sequencing. A sparse least regression analysis combined with a canonical correlation analysis was conducted between the plasma selected metabolites and genera in order to identify the relevant dual-omics signatures discriminating the dietary interventions.

RESULTS

Changes in 65 circulating metabolites were significantly associated with the MedDiet (mainly lipids, acylcarnitines, amino acids, steroids and TCA intermediates). Importantly, these changes were associated with decreases in glucose, insulin and HOMA-IR. The network analysis identified two main clusters of genera with an opposite behaviour towards selected metabolites, mainly PC species, ChoE(20:5), TGs and medium/long-chain acylcarnitines.

CONCLUSION

Following a MedDiet, rather than consuming nuts in the context of a non-MedDiet was associated with a specific plasma metabolomic profile, which was also related to metabolic improvements in adults with MetS. The identified correlated network between specific bacteria and metabolites suggests interplay between diet, circulating metabolites and gut microbiota. The trial was registered in the ISRCTN with identifier ISRCTN88780852, https://doi.org/10.1186/ISRCTN88780852.

Disparities in cardio metabolic risk between Black and White women with polycystic ovary syndrome: a systematic review and meta-analysis

OBJECTIVE

We conducted a systematic review and meta-analysis to summarize and quantitatively pool evidence on cardiometabolic health disparities between Black and White women with polycystic ovary syndrome in the United States in response to the call for further delineation of these disparities in the international evidence-based guideline for the assessment and management of polycystic ovary syndrome.

DATA SOURCES

Databases of MEDLINE, Web of Science, and Scopus were searched initially through March 05, 2020, and confirmed on September 11, 2020.

STUDY ELIGIBILITY CRITERIA

Observational studies documenting cardiometabolic risk profile (glucoregulatory, lipid profile, anthropometric, and blood pressure status) in Black and White women with polycystic ovary syndrome were included. Studies on children (<17 years old) and pregnant or menopausal-aged women (>50 years) were excluded. The primary outcome was fasting glucose. Furthermore, data on major cardiovascular events (stroke, coronary heart disease, heart failure) and mortality rate (cardiovascular death, total mortality) were evaluated.

METHODS

Data were pooled by random-effects models and expressed as mean differences and 95% confidence intervals. Studies were weighted based on the inverse of the variance. Heterogeneity was evaluated by Cochran Q and I2 statistics. Study methodologic quality was assessed by the Newcastle-Ottawa scale.

RESULTS

A total of 11 studies (N=2851 [652 Black and 2199 White]) evaluated cardiometabolic risk profile and all had high quality (Newcastle-Ottawa scale score of ≥8). No studies reported on cardiovascular events and mortality rate. Black women had comparable fasting glucose (-0.61 [-1.69 to 2.92] mg/dL; I2=62.5%), yet exhibited increased fasting insulin (6.76 [4.97-8.56] μIU/mL; I2=59.0%); homeostatic model assessment of insulin resistance (1.47 [0.86-2.08]; I2=83.2%); systolic blood pressure (3.32 [0.34-6.30] mm Hg; I2=52.0%); and decreased triglyceride (-32.56 [-54.69 to -10.42] mg/dL; I2=68.0%) compared with White women (all, P≤.03). Groups exhibited comparable total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and diastolic blood pressure (all, P≥.06).

CONCLUSIONS

Black women with polycystic ovary syndrome have a greater tendency for an adverse cardiometabolic risk profile (increased insulin, homeostatic model assessment of insulin resistance, and systolic blood pressure) despite lower triglycerides than White women. Our observations support the consideration of these disparities for diagnostic, monitoring, and management practices in Black women and for future guideline recommendations. Given the heterogeneity among studies, future research should address the relative contributions of biologic, environmental, socioeconomic, and healthcare factors to the observed disparities. Furthermore, longitudinal research is required to address patient-pressing complications, including cardiovascular events and mortality rate in Black women with polycystic ovary syndrome as a high-risk yet understudied population.

Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes

The number of people affected by Type 2 diabetes mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. Given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs. 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than 5 years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM. We confirmed the causality of such association for leucine by 2-sample Mendelian randomisation (MR) based on independent data. Our MR approach further identified new metabolites potentially playing a causal role on T2D, including betaine, lysine and mannose. Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. In addition, our study revealed a reverse causal effect of metabolites such as glutamic acid and alanine. Collectively, these findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers.

Plasma acylcarnitines and risk of lower-extremity functional impairment in older adults: a nested case-control study

Elevated concentrations of acylcarnitines have been associated with higher risk of obesity, type 2 diabetes and cardiovascular disease. The aim of the present study was to assess the association between L-carnitine and acylcarnitine profiles, and 2-year risk of incident lower-extremity functional impairment (LEFI). This case–control study is nested in the Seniors-ENRICA cohort of community-dwelling older adults, which included 43 incident cases of LEFI and 86 age- and sex- matched controls. LEFI was assessed with the Short Physical Performance Battery. Plasma L-carnitine and 28 acylcarnitine species were measured. After adjusting for potential confounders, medium-chain acylcarnitines levels were associated with 2-year incidence of LEFI [odds ratio per 1-SD increase: 1.69; 95% confidence interval: 1.08, 2.64; p = 0.02]. Similar results were observed for long-chain acylcarnitines [odds ratio per 1-SD increase: 1.70; 95% confidence interval: 1.03, 2.80; p = 0.04]. Stratified analyses showed a stronger association between medium- and long-chain acylcarnitines and incidence of LEFI among those with body mass index and energy intake below the median value. In conclusion, higher plasma concentrations of medium- and long-chain acylcarnitines were associated with higher risk of LEFI. Given the role of these molecules on mitochondrial transport of fatty acids, our results suggest that bioenergetics dysbalance contributes to LEFI.

Plasma Metabolomic Profiles of Glycemic Index, Glycemic Load, and Carbohydrate Quality Index in the PREDIMED Study

BACKGROUND

The quality of carbohydrate consumed, assessed by the glycemic index (GI), glycemic load (GL), or carbohydrate quality index (CQI), affects the postprandial glycemic and insulinemic responses, which have been implicated in the etiology of several chronic diseases. However, it is unclear whether plasma metabolites involved in different biological pathways could provide functional insights into the role of carbohydrate quality indices in health.

OBJECTIVES

We aimed to identify plasma metabolomic profiles associated with dietary GI, GL, and CQI.

METHODS

The present study is a cross-sectional analysis of 1833 participants with overweight/obesity (mean age = 67 y) from 2 case-cohort studies nested within the PREDIMED (Prevención con Dieta Mediterránea) trial. Data extracted from validated FFQs were used to estimate the GI, GL, and CQI. Plasma concentrations of 385 metabolites were profiled with LC coupled to MS and associations of these metabolites with those indices were assessed with elastic net regression analyses.

RESULTS

A total of 58, 18, and 57 metabolites were selected for GI, GL, and CQI, respectively. Choline, cotinine, γ-butyrobetaine, and 36:3 phosphatidylserine plasmalogen were positively associated with GI and GL, whereas they were negatively associated with CQI. Fructose-glucose-galactose was negatively and positively associated with GI/GL and CQI, respectively. Consistent associations of 21 metabolites with both GI and CQI were found but in opposite directions. Negative associations of kynurenic acid, 22:1 sphingomyelin, and 38:6 phosphatidylethanolamine, as well as positive associations of 32:1 phosphatidylcholine with GI and GL were also observed. Pearson correlation coefficients between GI, GL, and CQI and the metabolomic profiles were 0.30, 0.22, and 0.27, respectively.

CONCLUSIONS

The GI, GL, and CQI were associated with specific metabolomic profiles in a Mediterranean population at high cardiovascular disease risk. Our findings may help in understanding the role of dietary carbohydrate indices in the development of cardiometabolic disorders. This trial was registered at isrctn.com as ISRCTN35739639.

Plasma Metabolomics Reveals Metabolic Profiling For Diabetic Retinopathy and Disease Progression

BACKGROUNDS

Diabetic retinopathy (DR), the main retinal vascular complication of DM, is the leading cause of visual impairment and blindness among working-age people worldwide. The aim of this study was to investigate the difference of plasma metabolic profiles in patients with DR to better understand the mechanism of this disease and disease progression.

METHODS

We used ultrahigh-performance liquid Q-Exactive mass spectrometry and multivariate statistical analyses to conduct a comprehensive analysis of plasma metabolites in a population with DR and proliferative DR (PDR). A risk score based on the level of the selected metabolite was established and evaluated using the least absolute shrinkage and selection operator regularization logistic regression (LASSO-LR) based machine learning model.

RESULTS

22 differentially expressed metabolites which belonged to different metabolic pathway were identified and confirmed to be associated with the occurrence of DR. A risk score based on the level of the selected metabolite pseudouridine was established and evaluated to strongly associated with the occurrence of DR. Four circulating plasma metabolites (pseudouridine, glutamate, leucylleucine and N-acetyltryptophan) were identified to be differentially expressed between patients with PDR and other patients, and a risk score formula based on these plasma metabolites was developed and assessed to be significantly related to PDR.

CONCLUSIONS

Our work highlights the possible use of the risk score assessment based on the plasma metabolites not only reveal in the early diagnosis of DR and PDR but also assist in enhancing current therapeutic strategies in the clinic.

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

Gestational diabetes mellitus (GDM) is the top risk factor for future type 2 diabetes (T2D) development. Ethnicity profoundly influences who will transition from GDM to T2D, with high risk observed in Hispanic women. To better understand this risk, a nested 1:1 pair-matched, Hispanic-specific, case-control design was applied to a prospective cohort with GDM history. Women who were non-diabetic 6-9 weeks postpartum (baseline) were monitored for the development of T2D. Metabolomics were performed on baseline plasma to identify metabolic pathways associated with T2D risk. Notably, diminished sphingolipid metabolism was highly associated with future T2D. Defects in sphingolipid metabolism were further implicated by integrating metabolomics and genome-wide association data, which identified two significantly enriched T2D-linked genes, CERS2 and CERS4. Follow-up experiments in mice and cells demonstrated that inhibiting sphingolipid metabolism impaired pancreatic β cell function. These data suggest early postpartum alterations in sphingolipid biosynthesis contribute to β cell dysfunction and T2D risk.

Serum sphingolipids and incident diabetes in a US population with high diabetes burden: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

BACKGROUND

Genetic or pharmacological inhibition of de novo sphingolipid synthases prevented diabetes in animal studies.

OBJECTIVES

We sought to evaluate prospective associations of serum sphingolipids with incident diabetes in a population-based cohort.

METHODS

We included 2010 participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) aged 18-74 y who were free of diabetes and other major chronic diseases at baseline (2008-2011). Metabolomic profiling of fasting serum was performed using a global, untargeted approach. A total of 43 sphingolipids were quantified and, considering subclasses and chemical structures of individual species, 6 sphingolipid scores were constructed. Diabetes status was assessed using standard procedures including blood tests. Multivariable survey Poisson regressions were applied to estimate RR and 95% CI of incident diabetes associated with individual sphingolipids or sphingolipid scores.

RESULTS

There were 224 incident cases of diabetes identified during, on average, 6 y of follow-up. After adjustment for socioeconomic and lifestyle factors, a ceramide score (RR Q4 versus Q1 = 2.40; 95% CI: 1.24, 4.65; P-trend = 0.003) and a score of sphingomyelins with fully saturated sphingoid-fatty acid pairs (RR Q4 versus Q1 = 3.15; 95% CI: 1.75, 5.67; P-trend <0.001) both were positively associated with risk of diabetes, whereas scores of glycosylceramides, lactosylceramides, or other unsaturated sphingomyelins (even if having an SFA base) were not associated with risk of diabetes. After additional adjustment for numerous traditional risk factors (especially triglycerides), both associations were attenuated and only the saturated-sphingomyelin score remained associated with risk of diabetes (RR Q4 versus Q1 = 1.98; 95% CI: 1.09, 3.59; P-trend = 0.031).

CONCLUSIONS

Our findings suggest that a cluster of saturated sphingomyelins may be associated with elevated risk of diabetes beyond traditional risk factors, which needs to be verified in other population studies. This study was registered at clinicaltrials.gov as NCT02060344.

Metabolomics and Proteomics in Type 2 Diabetes

The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.

Blood Metabolite Signatures of Metabolic Syndrome in Two Cross-Cultural Older Adult Cohorts

Metabolic syndrome (MetS) affects an increasing number of older adults worldwide. Cross-cultural comparisons can provide insight into how factors, including genetic, environmental, and lifestyle, may influence MetS prevalence. Metabolomics, which measures the biochemical products of cell processes, can be used to enhance a mechanistic understanding of how biological factors influence metabolic outcomes. In this study we examined associations between serum metabolite concentrations, representing a range of biochemical pathways and metabolic syndrome in two older adult cohorts: The Tsuruoka Metabolomics Cohort Study (TMCS) from Japan (n = 104) and the Baltimore Longitudinal Study of Aging (BLSA) from the United States (n = 146). We used logistic regression to model associations between MetS and metabolite concentrations. We found that metabolites from the phosphatidylcholines-acyl-alkyl, sphingomyelin, and hexose classes were significantly associated with MetS and risk factor outcomes in both cohorts. In BLSA, metabolites across all classes were uniquely associated with all outcomes. In TMCS, metabolites from the amino acid, biogenic amines, and free fatty acid classes were uniquely associated with MetS, and metabolites from the sphingomyelin class were uniquely associated with elevated triglycerides. The metabolites and metabolite classes we identified may be relevant for future studies exploring disease mechanisms and identifying novel precision therapy targets for individualized medicine.

Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes

While the risk factors for Type 2 diabetes (T2DM) are known, early predictive markers of transition from normal to a prediabetes state are unidentified. We studied the basal metabolism and metabolic response to a mixed-meal challenge in 110 healthy subjects in the age group of 18 to 40 years (Male:Female = 1:1); grouped into first degree relatives of patients with T2DM (n = 30), those with a body mass index >23 kg/m² but <30 kg/m² (n = 30), those with prediabetes (n = 20) and normal controls (n = 30). We performed an untargeted metabolomics analysis of plasma and related that with clinical and biochemical parameters, markers of inflammation, and insulin sensitivity. Similar to prediabetes subjects, overweight subjects had insulin resistance and significantly elevated levels of C-peptide, adiponectin and glucagon and lower level of ghrelin. Metabolites such as MG(22:2(13Z, 16Z)/0:0/0:0) and LysoPC (15:0) were reduced in overweight and prediabetes subjects. Insulin sensitivity was significantly lower in men. Fasting levels of uric acid, xanthine, and glycochenodeoxycholic-3-glucuronide were elevated in men. However, both lysophospholipids and antioxidant defense metabolites were higher in women. Impaired postprandial metabolism and insulin sensitivity in overweight normoglycemic young adults indicates a risk of developing hyperglycemia. Our results also indicate a higher risk of diabetes in young men.

Acyl ethanolamides in Diabetes and Diabetic Nephropathy: Novel targets from untargeted plasma metabolomic profiles of South Asian Indian men

The pathophysiology of diabetic nephropathy (DN) in type 2 diabetes (T2D) patients is minimally understood. We compared untargeted high-resolution accurate mass (HRAM) orbitrap-based plasma metabolomic profiles of 31 T2D-DN (with estimated glomerular filtration rate ≤80 mL/min/1.73 m²), 29 T2D and 30 normal glucose tolerance (NGT) Indian men. Of the 939 plasma metabolites that were differentially abundant amongst the NGT, T2D and T2D-DN (ANOVA, False Discovery Rate – FDR adjusted p-value < 0.05), 48 were associated with T2D irrespective of the renal function of the subjects. Acyl ethanolamides and acetylcholine were decreased while monoacylglycerols (MAGs) and cortisol were elevated in both T2D and T2D-DN. Sixteen metabolites, including amino acid metabolites Imidazolelactate and N-Acetylornithine, changed significantly between NGT, T2D and T2D-DN. 192 metabolites were specifically dysregulated in T2D-DN (ratio ≥2 or ≤0.5 between T2D-DN and T2D, similar abundance in NGT and T2D). These included increased levels of multiple acylcarnitine and amino acid metabolites. We observed a significant dysregulation of amino acid and fatty acid metabolism in South Asian Indian male T2D-DN subjects. Unique to this study, we report a reduction in acyl ethanolamide levels in both T2D and T2D-DN males. Those with dysregulation in acyl ethanolamides, which are endogenous agonists of GPR119, are likely to exhibit improved glycemic control with GPR119 agonists.

The Involvement of Peripheral and Brain Insulin Resistance in Late Onset Alzheimer's Dementia

Nowadays, Alzheimer's disease (AD) is a severe sociological and clinical problem. Since it was first described, there has been a constant increase in its incidence and, for now, there are no effective treatments since current approved medications have only shown short-term symptomatic benefits. Therefore, it is imperative to increase efforts in the search for molecules and non-pharmacological strategies that are capable of slowing or stopping the progress of the disease and, ideally, to reverse it. The amyloid cascade hypothesis based on the fundamental role of amyloid has been the central hypothesis in the last 30 years. However, since amyloid-directed treatments have shown no relevant beneficial results other theories have been postulated to explain the origin of the pathology. The brain is a highly metabolically active energy-consuming tissue in the human body. It has an almost complete dependence on the metabolism of glucose and uses most of its energy for synaptic transmission. Thus, alterations on the utilization or availability of glucose may be cause for the appearance of neurodegenerative pathologies like AD. In this review article, the hypothesis known as Type 3 Diabetes (T3D) will be evaluated by summarizing some of the data that has been reported in recent years. According to published research, the adherence over time to low saturated fatty acids diets in the context of the Mediterranean diet would reduce the inflammatory levels in brain, with a decrease in the pro-inflammatory glial activation and mitochondrial oxidative stress. In this situation, the insulin receptor pathway would be able to fine tune the mitochondrial biogenesis in neuronal cells, regulation the adenosine triphosphate/adenosine diphosphate intracellular balance, and becoming a key factor involved in the preservation of the synaptic connexions and neuronal plasticity. In addition, new targets and strategies for the treatment of AD will be considered in this review for their potential as new pharmacological or non-pharmacological approaches.

The Intervention Effect of Traditional Chinese Medicine on the Intestinal Flora and Its Metabolites in Glycolipid Metabolic Disorders

Metabolic syndrome (MS), which includes metabolic disorders such as protein disorder, glucose disorder, lipid disorder, and carbohydrate disorder, has been growing rapidly around the world. Glycolipid disorders are a main type of metabolic syndrome and are characterized by abdominal obesity and abnormal metabolic disorders of lipid, glucose, and carbohydrate utilization, which can cause cardiovascular and cerebrovascular diseases. Glycolipid disorders are closely related to intestinal flora and its metabolites. However, studies about the biological mechanisms of the intestinal flora and its metabolites with glycolipid disorders have not been clear. When glycolipid disorders are treated with drugs, a challenging problem is side effects. Traditional Chinese medicine (TCM) and dietary supplements have fewer side effects to treat it. Numerous basic and clinical studies have confirmed that TCM decoctions, Chinese medicine monomers, or compounds can treat glycolipid disorders and reduce the incidence of cardiovascular disease. In this study, we reviewed the relationship between the intestinal flora and its metabolites in glycolipid metabolic disorders and the effect of TCM in treating glycolipid metabolic disorders through the intestinal flora and its metabolites. This review provides new perspectives and strategies for future glycolipid disorders research and treatment.

NMR-Based Metabolomic Approach Tracks Potential Serum Biomarkers of Disease Progression in Patients with Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia associated with alterations in carbohydrate, lipid, and protein metabolism. The prognosis of T2DM patients is highly dependent on the development of complications, and therefore the identification of biomarkers of T2DM progression, with minimally invasive techniques, is a huge need. In the present study, we applied a ¹H-Nuclear Magnetic Resonance (¹H-NMR)-based metabolomic approach coupled with multivariate data analysis to identify serum metabolite profiles associated with T2DM development and progression. To perform this, we compared the serum metabolome of non-diabetic subjects, treatment-naïve non-complicated T2DM patients, and T2DM patients with complications in insulin monotherapy. Our analysis revealed a significant reduction of alanine, glutamine, glutamate, leucine, lysine, methionine, tyrosine, and phenylalanine in T2DM patients with respect to non-diabetic subjects. Moreover, isoleucine, leucine, lysine, tyrosine, and valine levels distinguished complicated patients from patients without complications. Overall, the metabolic pathway analysis suggested that branched-chain amino acid (BCAA) metabolism is significantly compromised in T2DM patients with complications, while perturbation in the metabolism of gluconeogenic amino acids other than BCAAs characterizes both early and advanced T2DM stages. In conclusion, we identified a metabolic serum signature associated with T2DM stages. These data could be integrated with clinical characteristics to build a composite T2DM/complications risk score to be validated in a prospective cohort.