Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia

Unveiling risk factors for post-COVID-19 syndrome development in people with type 2 diabetes

Introduction

Post-COVID-19 syndrome (PCS) is a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-associated chronic condition characterized by long-term violations of physical and mental health. People with type 2 diabetes (T2D) are at high risk for severe COVID-19 and PCS.

Aim

The current study aimed to define the predictors of PCS development in people with T2D for further planning of preventive measures and improving patient outcomes.

Materials and methods

The data were collected through the national survey targeting persons with T2D concerning the history of COVID-19 course and signs and symptoms that developed during or after COVID-19 and continued for more than 12 weeks and were not explained by an alternative diagnosis. In total, 469 patients from different regions of Ukraine were enrolled in the study. Among them, 227 patients reported PCS development (main group), while 242 patients did not claim PCS symptoms (comparison group). Stepwise multivariate logistic regression and probabilistic neural network (PNN) models were used to select independent risk factors.

Results

Based on the survey data, 8 independent factors associated with the risk of PCS development in T2D patients were selected: newly diagnosed T2D (OR 4.86; 95% CI 2.55-9.28; p<0.001), female sex (OR 1.29; 95% CI 0.86-1.94; p=0.220), COVID-19 severity (OR 1.35 95% CI 1.05-1.70; p=0.018), myocardial infarction (OR 2.42 95% CI 1.26-4.64; p=0.002) and stroke (OR 3.68 95% CI 1.70-7.96; p=0.001) in anamnesis, HbA1c above 9.2% (OR 2.17 95% CI 1.37-3.43; p=0.001), and the use of insulin analogs (OR 2.28 95% CI 1.31-3.94; p=0.003) vs human insulin (OR 0.67 95% CI 0.39-1.15; p=0.146). Although obesity aggravated COVID-19 severity, it did not impact PCS development. In ROC analysis, the 8-factor multilayer perceptron (MLP) model exhibited better performance (AUC 0.808; 95% CІ 0.770-0.843), allowing the prediction of the risk of PCS development with a sensitivity of 71.4%, specificity of 76%, PPV of 73.6% and NPV of 73.9%.

Conclusions

Patients who were newly diagnosed with T2D, had HbA1c above 9.2%, had previous cardiovascular or cerebrovascular events, and had severe COVID-19 associated with mechanical lung ventilation were at high risk for PCS.

Multi-omics in exploring the pathophysiology of diabetic retinopathy

Diabetic retinopathy (DR) is a leading global cause of vision impairment, with its prevalence increasing alongside the rising rates of diabetes mellitus (DM). Despite the retina's complex structure, the underlying pathology of DR remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) and recent advancements in multi-omics analyses have revolutionized molecular profiling, enabling high-throughput analysis and comprehensive characterization of complex biological systems. This review highlights the significant contributions of scRNA-seq, in conjunction with other multi-omics technologies, to DR research. Integrated scRNA-seq and transcriptomic analyses have revealed novel insights into DR pathogenesis, including alternative transcription start site events, fluctuations in cell populations, altered gene expression profiles, and critical signaling pathways within retinal cells. Furthermore, by integrating scRNA-seq with genetic association studies and multi-omics analyses, researchers have identified novel biomarkers, susceptibility genes, and potential therapeutic targets for DR, emphasizing the importance of specific retinal cell types in disease progression. The integration of scRNA-seq with metabolomics has also been instrumental in identifying specific metabolites and dysregulated pathways associated with DR. It is highly conceivable that the continued synergy between scRNA-seq and other multi-omics approaches will accelerate the discovery of underlying mechanisms and the development of novel therapeutic interventions for DR.

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response

Purpose

Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.

Methods

We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models.

Results

Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%.

Conclusions

This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. It implies that metabolic indicators can effectively predict early disease progression and potential weak responders to anti-VEGF therapy in DME eyes.

Translational Relevance

The identified metabolic indicators may aid in predicting the early progression of DR and optimizing therapeutic strategies for DME.

Analysis of early-pregnancy metabolome in early- and late-onset gestational diabetes reveals distinct associations with maternal overweight

AIMS/HYPOTHESIS

It is not known whether the early-pregnancy metabolome differs in patients with early- vs late-onset gestational diabetes mellitus (GDM) stratified by maternal overweight. The aims of this study were to analyse correlations between early-pregnancy metabolites and maternal glycaemic and anthropometric characteristics, and to identify early-pregnancy metabolomic alterations that characterise lean women (BMI <25 kg/m2) and women with overweight (BMI ≥25 kg/m2) with early-onset GDM (E-GDM) or late-onset GDM (L-GDM).

METHODS

We performed a nested case-control study within the population-based prospective Early Diagnosis of Diabetes in Pregnancy cohort, comprising 210 participants with GDM (126 early-onset, 84 late-onset) and 209 normoglycaemic control participants matched according to maternal age, BMI class and primiparity. Maternal weight, height and waist circumference were measured at 8-14 weeks' gestation. A 2 h 75 g OGTT was performed at 12-16 weeks' gestation (OGTT1), and women with normal results underwent repeat testing at 24-28 weeks' gestation (OGTT2). Comprehensive metabolomic profiling of fasting serum samples, collected at OGTT1, was performed by untargeted ultra-HPLC-MS. Linear models were applied to study correlations between early-pregnancy metabolites and maternal glucose concentrations during OGTT1, fasting insulin, HOMA-IR, BMI and waist circumference. Early-pregnancy metabolomic features for GDM subtypes (participants stratified by maternal overweight and gestational timepoint at GDM onset) were studied using linear and multivariate models. The false discovery rate was controlled using the Benjamini-Hochberg method.

RESULTS

In the total cohort (n=419), the clearest correlation patterns were observed between (1) maternal glucose concentrations and long-chain fatty acids and medium- and long-chain acylcarnitines; (2) maternal BMI and/or waist circumference and long-chain fatty acids, medium- and long-chain acylcarnitines, phospholipids, and aromatic and branched-chain amino acids; and (3) HOMA-IR and/or fasting insulin and L-tyrosine, certain long-chain fatty acids and phospholipids (q<0.001). Univariate analyses of GDM subtypes revealed significant differences (q<0.05) for seven non-glucose metabolites only in overweight women with E-GDM compared with control participants: linolenic acid, oleic acid, docosapentaenoic acid, docosatetraenoic acid and lysophosphatidylcholine 20:4/0:0 abundances were higher, whereas levels of specific phosphatidylcholines (P-16:0/18:2 and 15:0/18:2) were lower. However, multivariate analyses exploring the early-pregnancy metabolome of GDM subtypes showed differential clustering of acylcarnitines and long-chain fatty acids between normal-weight and overweight women with E- and L-GDM.

CONCLUSIONS/INTERPRETATION

GDM subtypes show distinct early-pregnancy metabolomic features that correlate with maternal glycaemic and anthropometric characteristics. The patterns identified suggest early-pregnancy disturbances of maternal lipid metabolism, with most alterations observed in overweight women with E-GDM. Our findings highlight the importance of maternal adiposity as the primary target for prevention and treatment.

Causality of Blood Metabolites on Proliferative Diabetic Retinopathy: Insights From a Genetic Perspective

Background: Our goal was to examine the causal link between blood metabolites, their ratios, and the risk of developing proliferative diabetic retinopathy (PDR) from a genetic insight. Methods: Summary-level data about 1400 blood metabolites and their ratios, as well as PDR, were sourced from prior genome-wide association studies (GWAS). A two-sample univariate and multivariate Mendelian randomization (MR) approach was utilized. Additionally, metabolic pathway analysis and sensitivity analysis were also conducted. Results: After adjusting for multiple tests, four blood metabolites significantly correlated with PDR risk. Two ceramides, including glycosyl-N-palmitoyl-sphingosine (d18:1/16:0) (odds ratio [OR] = 1.12, 95% confidence interval (CI): 1.06-1.17, p < 0.001, false discovery rate (FDR) = 0.005) and glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) (OR = 1.11, 95% CI: 1.06-1.16, p < 0.001, FDR = 0.017), were linked to increased risk. Additionally, 3-methylcytidine (OR = 1.05, 95% CI: 1.03-1.08, p < 0.001, FDR = 0.021) also posed a risk, whereas (N(1)+N(8))-acetylspermidine (OR = 0.91, 95% CI: 0.87-0.94, p < 0.001, FDR = 0.002) appeared protective. Multivariable MR analysis further confirmed a direct, protective effect of (N(1)+N(8))-acetylspermidine on PDR risk (OR = 0.94, 95% CI: 0.89-1.00, p = 0.040). The sensitivity analysis results indicated that evidence for heterogeneity and pleiotropy was absent. Conclusion: These metabolites have the potential to be used as biomarkers and are promising for future research into the mechanisms and drug targets for PDR.

Associations of cardiometabolic index with diabetic statuses and insulin resistance: the mediating role of inflammation-related indicators

BACKGROUND

This study aimed to analyze the associations of cardiometabolic index (CMI) with diabetic statuses and insulin resistance (IR) using data from the National Health and Nutrition Examination Survey (NHANES) and examined the potential mediating role of inflammation in these correlations.

METHODS

This study enrolled 9477 participants across four NHANES cycles from 2011 to 2018. The primary outcomes of the study included the risk of having prediabetes, diabetes and the level of the homeostasis model assessment of IR (HOMA-IR). Other outcomes including the levels of fasting blood glucose (FBG), hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT) results, fasting insulin, the risk of oral hypoglycemic medicine use, insulin use, and retinopathy were also collected and analyzed. Logistic regression model, subgroup analysis, restricted cubic spine (RCS), and Pearson correlation coefficients were conducted to assess the associations of CMI with diabetic statuses and IR. The mediating role of inflammation was evaluated to investigate the potential mechanism of the associations between CMI and diabetic statuses.

RESULTS

Among included participants, the CMI levels in normal participants, prediabetes and diabetes in this study were 0.48, 0.73 and 1.07. After multivariable adjustment, CMI was positively associated with the risk of prediabetes (OR = 1.49, 95% CI = 1.24-1.79), diabetes (OR = 2.14, 95% CI = 1.82-2.50) and the level of HOMA-IR (β = 2.57, 95% CI = 2.14-3.01). Besides, an increased CMI was correlated with higher levels of FBG, HBA1c, OGTT results and fasting insulin as well as the greater risk of oral hypoglycemic medicine use and insulin use. The RCS showed an inverted L-shaped association of CMI with prediabetes and diabetes (P for non-linearity < 0.001). According to Pearson correlation coefficients, higher CMI was linked to higher rises in HOMA-IR (r = 0.224, P < 0.001). Inflammation-related indicators including leukocyte and neutrophil demonstrated significant mediating effects in the associations of CMI with prediabetes (15.5%, 9.8%), diabetes (5.1%, 6.0%) and HOMA-IR (3.3%, 2.6%).

CONCLUSION

CMI was positively associated with the risk of worse diabetic statuses and higher level of IR while the associations may be partially mediated by inflammation-related indicators, suggesting that CMI could be a promising indicator for the prediction of severe diabetes and IR.

Serum metabolome analysis reveals medicinal fungi Phellinus igniarius ameliorated type 2 diabetes mellitus indications in rats via modulation of amino acid and carbohydrate metabolism

Medicinal fungi Phellinus igniarius exhibited hypoglycemic effects; however, the protective mechanisms of P. igniarius on type 2 diabetes are not yet fully understood. Herein, the anti-diabetic effect of P. igniarius was investigated via gas chromatography-mass spectrometry (GC/MS)-based metabolome analysis. The rats were divided into normal group; model group; positive group; and groups treated with low, medium, and high dose of P. igniarius. After the treatments, a significant decrease in blood glucose concentration was observed. The levels of total cholesterol and triglyceride were dramatically decreased, whereas the level of insulin was increased. Multivariate statistical analysis revealed 31 differential endogenous metabolites between model group and normal group. A total of 14, 28, and 31 biomarkers were identified for low, medium, and high dose of P. igniarius treated groups, respectively. Twenty-one of the biomarkers were validated by using standard substances. The linear correlation coefficients ranged from 0.9990 to 1.0000. The methodology exhibited good repeatability, recoveries, and stability. The major intervened metabolic pathways covered glyoxylate and dicarboxylic acid metabolism; alanine, aspartate, and glutamate metabolism; and glycine, serine, and threonine metabolism. Our metabolome analysis has provided insights into the underlying mechanism of P. igniarius on type 2 diabetes.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Multidisciplinary approach combining food metabolomics and epidemiology identifies meglutol as an important bioactive metabolite in tempe, an Indonesian fermented food

This study introduces a multidisciplinary approach to investigate bioactive food metabolites often overlooked due to their low concentrations. We integrated an in-house food metabolite library (n = 494), a human metabolite library (n = 891) from epidemiological studies, and metabolite pharmacological databases to screen for food metabolites with potential bioactivity. We identified six potential metabolites, including meglutol (3-hydroxy-3-methylglutarate), an understudied low-density lipoprotein (LDL)-lowering compound. We further focused on meglutol as a case study to showcase the range of characterizations achievable with this approach. Green pea tempe was identified to contain the highest meglutol concentration (21.8 ± 4.6 mg/100 g). Furthermore, we identified a significant cross-sectional association between plasma meglutol (per 1-standard deviation) and lower LDL cholesterol in two Hispanic adult cohorts (n = 1,628) (β [standard error]: -5.5 (1.6) mg/dl, P = 0.0005). These findings highlight how multidisciplinary metabolomics can serve as a systematic tool for discovering and enhancing bioactive metabolites in food, such as meglutol, with potential applications in personalized dietary approaches for disease prevention.

Efficacy and safety assessment of traditional Chinese patent medicine for dyslipidemia: a systematic review of randomized clinical trials with meta-analysis and trial sequential analysis

Background

The overall prevalence of dyslipidemia continues to increase, which poses a significant risk for coronary artery disease. Some patients with dyslipidemia do not respond to or benefit from conventional lipid-lowering therapy, which warrants the need for alternative and complementary therapies. Chinese patent medicine (CPM) has shown great potential in the treatment of dyslipidemia, but its clinical value needs to be further explored. This study aims to systematically evaluate the efficacy and safety of CPM in treating dyslipidemia.

Methods

This study was registered in INPLASY as INPLASY202330090. The randomized controlled trials included in this study were published in January 2013 to March 2023 and retrieved from the Web of Science, PubMed, Embase, Cochrane Library, SinoMed, China National Knowledge Internet, WanFang, and VIP. The bias risk in the study was independently evaluated by two reviewers using the Cochrane Randomized Trial Bias Risk Tool (RoB 2) Review Manager 5.4 software was used for the overall effect analysis and subgroup analysis of four blood lipids, and the trial sequential analysis (TSA) was conducted to check the results.

Results

A total of 69 studies were included, involving 6,993 participants. The methodological quality was in the middle level. Meta-analysis showed that CPM markedly improved the levels of total cholesterol (TC) [mean difference (MD) =-0.54 mmol/L; 95% confidence interval (CI): -0.71 to -0.37; P<0.001], triglyceride (TG) (MD =-0.43 mmol/L; 95% CI: -0.53 to -0.33; P<0.001), low-density lipoprotein cholesterol (LDL-C) (MD =-0.40 mmol/L; 95% CI: -0.50 to -0.30; P<0.001) and increased levels of high-density lipoprotein cholesterol (HDL-C) (MD =0.23 mmol/L; 95% CI: 0.18 to 0.27; P<0.001), in patients with dyslipidemia. Though CPM did not differ significantly from statins when used alone, it could improve lipid profile better in all cases when used in combination with statins and with drugs used for comorbidities or co-morbidities. Subgroup analysis found that the efficacy of pill formulations was superior to other formulations, and CPM showed better lipid-lowering response in the context of comorbidity. The TSA confirmed the robustness of the analysis of the LDL-C level. No significant difference was observed in the incidence of adverse events between the treatment group and the control group [risk ratio (RR) =0.89; 95% CI: 0.69-1.16; P=0.40].

Conclusions

CPM can yield superior therapeutic effects in ameliorating dyslipidemia without exacerbating adverse effects as an alternative and complementary therapy. In addition, the therapeutic effect can be improved by emphasizing pill formulation and strengthening the standardization of syndromes.

Is systemic inflammation a missing link between cardiometabolic index with mortality? Evidence from a large population-based study

BACKGROUND

This study sought to elucidate the associations of cardiometabolic index (CMI), as a metabolism-related index, with all-cause and cardiovascular mortality among the older population. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), we further explored the potential mediating effect of inflammation within these associations.

METHODS

A cohort of 3029 participants aged over 65 years old, spanning six NHANES cycles from 2005 to 2016, was enrolled and assessed. The primary endpoints of the study included all-cause mortality and cardiovascular mortality utilizing data from National Center for Health Statistics (NCHS). Cox regression model and subgroup analysis were conducted to assess the associations of CMI with all-cause and cardiovascular mortality. The mediating effect of inflammation-related indicators including leukocyte, neutrophil, lymphocyte, systemic immune-inflammation index (SII), neutrophil to lymphocyte ratio (NLR) were evaluated to investigate the potential mechanism of the associations between CMI and mortality through mediation package in R 4.2.2.

RESULTS

The mean CMI among the enrolled participants was 0.74±0.66, with an average age of 73.28±5.50 years. After an average follow-up period of 89.20 months, there were 1,015 instances of all-cause deaths and 348 cardiovascular deaths documented. In the multivariable-adjusted model, CMI was positively related to all-cause mortality (Hazard Ratio (HR)=1.11, 95% CI=1.01-1.21). Mediation analysis indicated that leukocytes and neutrophils mediated 6.6% and 13.9% of the association of CMI with all-cause mortality.

CONCLUSION

Elevated CMI is positively associated with all-cause mortality in the older adults. The association appeared to be partially mediated through inflammatory pathways, indicating that CMI may serve as a valuable indicator for poor prognosis among the older population.

Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach

Purpose

The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia.

Methods

The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls.

Results

Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01384-9.

Association between obesity and the prevalence of dyslipidemia in middle-aged and older people: an observational study

This study aimed to explore the link between various forms of obesity, including body mass index (BMI) and waist circumference (WC), and the risk of dyslipidemia among Chinese residents. We selected the study population through a multi-stage random sampling method from permanent residents aged 35 and older in Ganzhou. Obesity was categorized as non-obesity, general obesity, central obesity, or compound obesity according to established diagnostic criteria. We employed a logistic regression model to assess the relationship between different types of obesity and the risk of dyslipidemia. Additionally, we used the restricted cubic spline model to analyze the association between BMI, WC, and the risk of dyslipidemia. The study included 2030 residents aged 35 or older from Ganzhou, China. The prevalence of dyslipidemia was found to be 39.31%, with an age-standardized prevalence of 36.51%. The highest prevalence of dyslipidemia, 58.79%, was observed among those with compound obesity. After adjusting for confounding factors, we found that the risk of dyslipidemia in those with central and compound obesity was respectively 2.00 (95% CI 1.62-2.46) and 2.86 (95% CI 2.03-4.03) times higher than in the non-obese population. Moreover, the analysis using the restricted cubic spline model indicated a nearly linear association between BMI, WC, and the risk of dyslipidemia. The findings emphasize the significant prevalence of both dyslipidemia and obesity among adults aged 35 and above in Ganzhou, China. Notably, individuals with compound obesity are at a substantially increased risk of dyslipidemia. Therefore, it is crucial to prioritize the use of BMI and WC as screening and preventive measures for related health conditions.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Causal relationships between blood metabolites and diabetic retinopathy: a two-sample Mendelian randomization study

Background

Diabetic retinopathy (DR) is a microvascular complication of diabetes, severely affecting patients' vision and even leading to blindness. The development of DR is influenced by metabolic disturbance and genetic factors, including gene polymorphisms. The research aimed to uncover the causal relationships between blood metabolites and DR.

Methods

The two-sample mendelian randomization (MR) analysis was employed to estimate the causality of blood metabolites on DR. The genetic variables for exposure were obtained from the genome-wide association study (GWAS) dataset of 486 blood metabolites, while the genetic predictors for outcomes including all-stage DR (All DR), non-proliferative DR (NPDR) and proliferative DR (PDR) were derived from the FinnGen database. The primary analysis employed inverse variance weighted (IVW) method, and supplementary analyses were performed using MR-Egger, weighted median (WM), simple mode and weighted mode methods. Additionally, MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were also conducted to guarantee the accuracy and robustness of the results. Subsequently, we replicated the MR analysis using three additional datasets from the FinnGen database and conducted a meta-analysis to determine blood metabolites associated with DR. Finally, reverse MR analysis and metabolic pathway analysis were performed.

Results

The study identified 13 blood metabolites associated with All DR, 9 blood metabolites associated with NPDR and 12 blood metabolites associated with PDR. In summary, a total of 21 blood metabolites were identified as having potential causal relationships with DR. Additionally, we identified 4 metabolic pathways that are related to DR.

Conclusion

The research revealed a number of blood metabolites and metabolic pathways that are causally associated with DR, which holds significant importance for screening and prevention of DR. However, it is noteworthy that these causal relationships should be validated in larger cohorts and experiments.

Probiotic co-supplementation with absorbent smectite for pancreatic beta-cell function in type 2 diabetes: a secondary-data analysis of a randomized double-blind controlled trials

Introduction

There is growing evidence from animal and clinical studies suggesting probiotics can positively affect type 2 diabetes (T2D). In a previous randomized clinical study, we found that administering a live multistrain probiotic and absorbent smectite once a day for eight weeks to patients with T2D could reduce chronic systemic inflammatory state, insulin resistance, waist circumference and improve the glycemic profile. However, there is a lack of evidence supporting the efficacy of probiotic co-supplementation with absorbent smectite on pancreatic β-cell function in T2D.

Aim

This secondary analysis aimed to assess the effectiveness of an alive multistrain probiotic co-supplementation with absorbent smectite vs placebo on β-cell function in T2D patients.

Material and methods

We performed a secondary analysis on a previously published randomized controlled trial (NCT04293731, NCT03614039) involving 46 patients with T2D. The main inclusion criteria were the presence of β-cell dysfunction (%B<60%) and insulin therapy alone or combined with oral anti-diabetic drugs. The primary outcome was assessing β-cell function as change C-peptide and %B.

Results

We observed only a tendency for improving β-cell function (44.22 ± 12.80 vs 55.69 ± 25.75; р=0.094). The effectiveness of the therapy probiotic-smectite group was confirmed by fasting glycemia decreased by 14% (p=0.019), HbA1c - 5% (p=0.007), HOMA-2 - 17% (p=0.003) and increase of insulin sensitivity by 23% (p=0.005). Analysis of the cytokine profile showed that statistical differences after treatment were in the concentration of both pro-inflammatory cytokines: IL-1β (22.83 ± 9.04 vs 19.03 ± 5.57; p=0.045) and TNF-α (31.25 ± 11.32 vs 26.23 ± 10.13; p=0.041).

Conclusion

Adding a live multistrain probiotic and absorbent smectite supplement slightly improved β-cell function and reduced glycemic-related parameters in patients with T2D. This suggests that adjusting the gut microbiota could be a promising treatment for diabetes and warrants further investigation through more extensive studies.

Characterizing the metabolic divide: distinctive metabolites differentiating CAD-T2DM from CAD patients

OBJECTIVE

To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk.

METHOD

Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites.

RESULTS

Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR < 0.05) and validation (P < 0.05) cohorts, respectively, that were associated with T2DM risk in univariate logistic regression. Further multivariate regression analyses identified 22 characteristic metabolites consistently associated with T2DM risk in both cohorts. Notably, pipecolinic acid and L-pipecolic acid, lysine derivatives, exhibited negative association with CAD-T2DM and influenced cellular glucose metabolism in hepatocytes. Transcriptomic insights shed light on potential metabolic action sites of these metabolites.

CONCLUSIONS

This research underscores the metabolic disparities between CAD and CAD-T2DM patients, spotlighting the protective attributes of pipecolinic acid and L-pipecolic acid. The comprehensive metabolomic and transcriptomic findings provide novel insights into the mechanism research, prophylaxis and treatment of comorbidity of CAD and T2DM.

Association between Gut Microbiota Compositions with MicrovascularComplications in Individuals with Diabetes: A Systematic Review

BACKGROUND

Diabetes is one of the chronic and very complex diseases that can lead to microvascular complications. Recent evidence demonstrates that dysbiosis of the microbiota composition might result in low-grade, local, and systemic inflammation, which contributes directly to the development of diabetes mellitus and its microvascular consequences.

OBJECTIVE

The aim of this systematic review was to investigate the association between diabetes microvascular complications, including retinopathy, neuropathy, nephropathy, and gut microbiota composition.

METHODS

A systematic search was carried out in PubMed, Scopus, and ISI Web of Science from database inception to March 2023. Screening, data extraction, and quality assessment were performed by two independent authors. The Newcastle-Ottawa Quality Assessment Scale was used for quality assessment.

RESULTS

About 19 articles were selected from 590 retrieved articles. Among the included studies, nephropathy has been studied more than other complications of diabetes, showing that the composition of the healthy microbiota is changed, and large quantities of uremic solutes that cause kidney injury are produced by gut microbes. Phyla, including Fusobacteria and Proteobacteria, accounted for the majority of the variation in gut microbiota between Type 2 diabetic patients with and without neuropathy. In cases with retinopathy, an increase in pathogenic and proinflammatory bacteria was observed.

CONCLUSION

Our results revealed that increases in Bacteroidetes, Proteobacteria and Fusobacteria may be associated with the pathogenesis of diabetic nephropathy, neuropathy, and retinopathy. In view of the detrimental role of intestinal dysbiosis in the development of diabetes-related complications, gut microbiota assessment may be used as a biomarker in the future and interventions that modulate the composition of microbiota in individuals with diabetes can be used to prevent and control these complications.

Association of glyphosate exposure with multiple adverse outcomes and potential mediators

Glyphosate (GLY) is a widely used herbicide with potential adverse effects on public health. However, the current epidemiological evidence is limited. This study aimed to investigate the potential associations between exposure to GLY and multiple health outcomes. The data on urine GLY concentration and nine health outcomes, including type 2 diabetes mellitus (T2DM), hypertension, cardiovascular disease (CVD), obesity, chronic kidney disease (CKD), hepatic steatosis, cancers, chronic obstructive pulmonary disease (COPD), and neurodegenerative diseases (NGDs), were extracted from NHANES (2013-2016). The associations between GLY exposure and each health outcome were estimated using reverse-scale Cox regression and logistic regression. Furthermore, mediation analysis was conducted to identify potential mediators in the significant associations. The dose-response relationships between GLY exposure with health outcomes and potential mediators were analyzed using restricted cubic spline (RCS) regression. The findings of the study revealed that individuals with higher urinary concentrations of GLY had a higher likelihood of having T2DM, hypertension, CVD and obesity (p < 0.001, p = 0.005, p < 0.001 and p = 0.005, respectively). In the reverse-scale Cox regression, a notable association was solely discerned between exposure to GLY and the risk of T2DM (adjusted HR = 1.22, 95% CI: 1.10, 1.36). Consistent outcomes were also obtained via logistic regression analysis, wherein the adjusted OR and 95% CI for T2DM were determined to be 1.30 (1.12, 1.52). Moreover, the present investigation identified serum high-density lipoprotein cholesterol (HDL) as a mediator in this association, with a mediating effect of 7.14% (p = 0.040). This mediating effect was further substantiated by RCS regression, wherein significant dose-response associations were observed between GLY exposure and an increased risk of T2DM (p = 0.002) and reduced levels of HDL (p = 0.001). Collectively, these findings imply an association between GLY exposure and an increased risk of T2DM in the general adult population.

Longitudinal metabolomics integrated with machine learning identifies novel biomarkers of gestational diabetes mellitus

BACKGROUND

Evidence from longitudinal studies is crucial to enhance our understanding of the role of metabolites in the progression of gestational diabetes mellitus (GDM). Herein, a longitudinal untargeted metabolomic study was conducted to reveal the metabolomic profiles and biomarkers associated with the progression of GDM, and characterize the changing patterns of metabolites.

METHODS

We collected serum samples at three trimesters from 30 patients with GDM and 30 healthy Chinese pregnant women with pre-pregnancy BMI, age, and parity matched, and untargeted metabolomic analysis was performed, followed by machine learning approaches that integrated bootstrap and LASSO. Cluster analysis was conducted to elucidate the patterns of metabolite changes. Pathway analyses were conducted to gain insights into the underlying pathways involved.

RESULTS

A total of 32 metabolites, mainly belonging to amino acid and its derivatives, were significantly associated with GDM across three trimesters, and were clustered into three distinct patterns. Metabolites belonging to phosphatidylcholines, lysophosphatidylcholines, lysophosphatidic acids, and lysophosphatidylethanolamines were consistently upregulated, and 2,3-Dihydroxypropyl dihydrogen phosphate was downregulated in GDM group. Amino acid-related, glycerophospholipid, and vitamin B6 metabolism were enriched in multiple trimesters. The levels of allantoic acid, which was positively correlated with blood glucose, was consistently higher in GDM patients and exhibited good discriminatory ability for GDM in the early and mid-pregnancy.

CONCLUSION

We identified and characterized distinct patterns of metabolites associated with GDM throughout pregnancy, and found that allantoic acid was a potential biomarker for early diagnosis of GDM.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Association of remnant cholesterol with hypertension, type 2 diabetes, and their coexistence: the mediating role of inflammation-related indicators

PURPOSE

Cholesterol metabolism is a risk factor for cardiovascular disease, and recent studies have shown that cholesterol metabolism poses a residual risk of cardiovascular disease even when conventional lipid risk factors are in the optimal range. The association between remnant cholesterol (RC) and cardiovascular disease has been demonstrated; however, its association with hypertension, type 2 diabetes mellitus (T2DM), and the concomitance of the two diseases requires further study. This study aimed to evaluate the association of RC with hypertension, T2DM, and both in a large sample of the U.S. population, and to further explore the potential mechanisms involved.

METHODS

This cross-sectional study used data from the 2005-2018 cycles of the National Health and Nutrition Examination Survey (N = 17,749). Univariable and multivariable logistic regression analyses were performed to explore the relationships of RC with hypertension, T2DM, and both comorbidities. A restricted cubic spline regression model was used to reveal the dose effect. Mediation analyses were performed to explore the potential mediating roles of inflammation-related indicators in these associations.

RESULTS

Of the 17,749 participants included (mean [SD] age: 41.57 [0.23] years; women: 8983 (50.6%), men: 8766 (49.4%)), the prevalence of hypertension, T2DM, and their co-occurrence was 32.6%, 16.1%, and 11.0%, respectively. Higher RC concentrations were associated with an increased risk of hypertension, T2DM, and their co-occurrence (adjusted odds ratios for per unit increase in RC were 1.068, 2.259, and 2.362, and 95% confidence intervals were 1.063-1.073, 1.797-2.838, and 1.834-3.041, respectively), with a linear dose-response relationship. Even when conventional lipids were present at normal levels, positive associations were observed. Inflammation-related indicators (leukocytes, lymphocytes, monocytes, and neutrophils) partially mediated these associations. Among these, leukocytes had the greatest mediating effect (10.8%, 14.5%, and 14.0%, respectively).

CONCLUSION

The results of this study provide evidence that RC is associated with the risk of hypertension, T2DM, and their co-occurrence, possibly mediated by an inflammatory response.

Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study

BACKGROUND

T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D.

METHODS

An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites.

RESULTS

A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) - a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate - a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D.

CONCLUSIONS

This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.

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.

Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and ¹H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.

The metabolomic profiling of total fat and fat distribution in a multi-cohort study of women and men

Currently studies aiming for the comprehensive metabolomics profiling of measured total fat (%) as well as fat distribution in both sexes are lacking. In this work, bioimpedance analysis was applied to measure total fat (%) and fat distribution (trunk to leg ratio). Liquid chromatography-mass spectrometry-based untargeted metabolomics was employed to profile the metabolic signatures of total fat (%) and fat distribution in 3447 participants from three Swedish cohorts (EpiHealth, POEM and PIVUS) using a discovery-replication cross-sectional study design. Total fat (%) and fat distribution were associated with 387 and 120 metabolites in the replication cohort, respectively. Enriched metabolic pathways for both total fat (%) and fat distribution included protein synthesis, branched-chain amino acids biosynthesis and metabolism, glycerophospholipid metabolism and sphingolipid metabolism. Four metabolites were mainly related to fat distribution: glutarylcarnitine (C5-DC), 6-bromotryptophan, 1-stearoyl-2-oleoyl-GPI (18:0/18:1) and pseudouridine. Five metabolites showed different associations with fat distribution in men and women: quinolinate, (12Z)-9,10-dihydroxyoctadec-12-enoate (9,10-DiHOME), two sphingomyelins and metabolonic lactone sulfate. To conclude, total fat (%) and fat distribution were associated with a large number of metabolites, but only a few were exclusively associated with fat distribution and of those metabolites some were associated with sex*fat distribution. Whether these metabolites mediate the undesirable effects of obesity on health outcomes remains to be further investigated.

Altered adolescents obesity metabolism is associated with hypertension: a UPLC-MS-based untargeted metabolomics study

Objective

This study aimed to explore the relationship between the plasma metabolites of adolescent obesity and hypertension and whether metabolite alterations had a mediating effort between adolescent obesity and hypertension.

Methods

We applied untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to detect the plasma metabolomic profiles of 105 adolescents. All participants were selected randomly based on a previous cross-sectional study. An orthogonal partial least squares- discriminant analysis (OPLS-DA), followed by univariate statistics and enrichment analysis, was used to identify differential metabolites. Using logistic regression for variable selection, an obesity-related metabolite score (OMS, OMS=∑k=1nβnmetabolite n) was constructed from the metabolites identified, and hypertension risk was estimated.

Results

In our study, based on P< 0.05, variable importance in projection (VIP) > 1.0, and impact value > 0.1, we identified a total of 12 differential metabolites. Significantly altered metabolic pathways were the sphingolipid metabolism, purine metabolism, pyrimidine metabolism, phospholipid metabolism, steroid hormone biosynthesis, tryptophan, tyrosine, and phenylalanine biosynthesis. The logistic regression selection resulted in a four-metabolite score (thymidine, sphingomyelin (SM) d40:1, 4-hydroxyestradiol, and L-lysinamide), which was positively associated with hypertension risk (odds ratio: 7.79; 95% confidence interval: 2.13, 28.47; for the quintile 4 compared with quartile 1 of OMS) after multivariable adjustment.

Conclusions

The OMS constructed from four differential metabolites was used to predict the risk of hypertension in adolescents. These findings could provide sensitive biomarkers for the early recognition of hypertension in adolescents with obesity.

Age at diagnosis, diabetes duration and the risk of cardiovascular disease in patients with diabetes mellitus: a cross-sectional study

Background

The purpose of the study was to evaluate characteristics and risk of cardiovascular disease (CVD) according to age at diagnosis and disease duration among adults with diabetes mellitus (DM).

Methods

The association between age at diagnosis, diabetes duration and CVD were examined in 1,765 patients with DM. High risk of estimated ten-year atherosclerotic cardiovascular disease (ASCVD) was performed by the Prediction for ASCVD Risk in China (China-PAR) project. Data were compared with analysis of variance and χ2 test, respectively. Multiple logistic regression was used to determine the risk factors of CVD.

Results

The mean age at diagnosis (± standard deviation) was 52.91 ± 10.25 years and diabetes duration was 8.06 ± 5.66 years. Subjects were divided into early-onset DM group (≤43 years), late-onset DM group (44 to 59 years), elderly-onset DM group (≥60 years) according to age at diagnosis. Diabetes duration was classified by 5 years. Both early-onset and longest diabetes duration (>15 years) had prominent hyperglycaemia. Diabetes duration was associated with the risk of ischemic stroke (odds ratio (OR), 1.091) and coronary artery disease (OR, 1.080). Early-onset group (OR, 2.323), and late-onset group (OR, 5.199), and hypertension (OR, 2.729) were associated with the risk of ischemic stroke. Late-onset group (OR, 5.001), disease duration (OR, 1.080), and hypertension (OR, 2.015) and hyperlipidemia (OR, 1.527) might increase the risk of coronary artery disease. Aged over 65 (OR, 10.192), central obesity (OR, 1.992), hypertension (OR, 18.816), cardiovascular drugs (OR, 5.184), antihypertensive drugs (OR, 2.780), and participants with disease duration >15 years (OR, 1.976) were associated with the high risk of estimated ten-year ASCVD in participants with DM.

Conclusion

Age at diagnosis, diabetes duration, hypertension and hyperlipidemia were independent risks of CVD. Longest (>15 years) diabetes duration increased the high risk of ten-year ASCVD prediction among Chinese patients with DM. It's urgent to emphasize the importance of age at diagnosis and diabetes duration to improve primary complication of diabetes.

Synergetic effects of concurrent chronic exposure to a mixture of OCPs and high-fat diets on type 2 diabetes and beneficial effects of caloric restriction in female zebrafish

This study aimed to investigate the relationship among chronic exposure to a low concentration of organochlorine pesticides (OCPs), high-fat diet (HFD)-induced obesity, and caloric restriction in type 2 diabetes (T2D). Thus, female zebrafish were divided into four groups and treated for 12 weeks as follows: (i) negative control, (ii) HFD (obesity) control, (iii) obesity + a mixture of OCPs (OP), and (iv) obesity + a mixture of OCPs + caloric restriction (OPR). We then assessed T2D-related effects via hematological analysis, histopathology, mitochondrial evaluation, and multiomics analyses. The OP group showed a significant increase in glucose levels, whereas the OPR group maintained glucose at nonsignificant levels. Multiomics analyses revealed that the exacerbated metabolic effects in the OP group were associated with molecular alterations in oxidative stress, inflammation, nucleotide metabolism, and glucose/lipid homeostasis. These alterations were histologically verified by the increased numbers of hypertrophic adipocytes and inflammatory cells observed. Caloric restriction activated pathways related to antioxidant response, mitochondrial fatty acid oxidation, and energy metabolism in zebrafish, leading to preserved glucose homeostasis. In conclusion, this study identified molecular mechanisms underlying the synergistic effect of concurrent exposure to a mixture of OCPs and HFD as well as shed light on the beneficial effect of regular caloric restriction in T2D development.

A comparison of associations of body mass index and dual-energy x-ray absorptiometry measured percentage fat and total fat with global serum metabolites in young women

OBJECTIVE

Body mass index (BMI) does not directly measure adiposity, whereas dual-energy x-ray absorptiometry (DXA) provides valid direct estimates of adiposity. Therefore, this study evaluated usefulness of BMI as a measure of adiposity in serum metabolomics studies.

METHODS

A cross-sectional analysis was conducted of 202 women aged 25 to 29 years in the Dietary Intervention Study in Children Follow-Up Study. Heights and weights were measured, and body composition was quantified using clinical DXA protocols. Serum metabolomic profiling was performed by liquid chromatography-tandem mass spectrometry. Partial correlations of BMI, percentage fat (%FAT), and total fat (TOTFAT) with log transformed serum metabolites were calculated.

RESULTS

There was significant overlap in the 93 metabolites that correlated with BMI, %FAT, and/or TOTFAT; 9 differently correlated with BMI and %FAT, whereas 15 differently correlated with BMI and TOTFAT. Even for these metabolites, absolute differences were modest. Metabolite set enrichment analysis identified diacylglycerol and sphingolipid metabolism as overrepresented among metabolites significantly correlated with all three measures of adiposity.

CONCLUSIONS

BMI can be a good proxy for DXA measured %FAT and TOTFAT in descriptive metabolomic studies of healthy, young White women. Larger studies in more diverse populations are needed to endorse more generalized conclusions.

Untargeted metabolomic profiling identifies serum metabolites associated with type 2 diabetes in a cross-sectional study of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

Type 2 diabetes (T2D) is a complex chronic disease with substantial phenotypic heterogeneity affecting millions of individuals. Yet, its relevant metabolites and etiological pathways are not fully understood. The aim of this study is to assess a broad spectrum of metabolites related to T2D in a large population-based cohort. We conducted a metabolomic analysis of 4,281 male participants within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The serum metabolomic analysis was performed using an LC-MS/GC-MS platform. Associations between 1,413 metabolites and T2D were examined using linear regression, controlling for important baseline risk factors. Standardized β-coefficients and standard errors (SEs) were computed to estimate the difference in metabolite concentrations. We identified 74 metabolites that were significantly associated with T2D based on the Bonferroni-corrected threshold (P < 3.5 × 10-5). The strongest signals associated with T2D were of carbohydrates origin, including glucose, 1,5-anhydroglucitol (1,5-AG), and mannose (β = 0.34, -0.91, and 0.41, respectively; all P < 10-75). We found several chemical class pathways that were significantly associated with T2D, including carbohydrates (P = 1.3 × 10-11), amino acids (P = 2.7 × 10-6), energy (P = 1.5 × 10-4), and xenobiotics (P = 1.2 × 10-3). The strongest subpathway associations were seen for fructose-mannose-galactose metabolism, glycolysis-gluconeogenesis-pyruvate metabolism, fatty acid metabolism (acyl choline), and leucine-isoleucine-valine metabolism (all P < 10-8). Our findings identified various metabolites and candidate chemical class pathways that can be characterized by glycolysis and gluconeogenesis metabolism, fructose-mannose-galactose metabolism, branched-chain amino acids, diacylglycerol, acyl cholines, fatty acid oxidation, and mitochondrial dysfunction.NEW & NOTEWORTHY These metabolomic patterns may provide new additional evidence and potential insights relevant to the molecular basis of insulin resistance and the etiology of T2D.

A Randomized Trial on Resveratrol Supplement Affecting Lipid Profile and Other Metabolic Markers in Subjects with Dyslipidemia

Resveratrol is a polyphenol with a well-established beneficial effect on dyslipidemia and hyperuricemia in preclinical experiments. Nonetheless, its efficacy and dose-response relationship in clinical trials remains unclear. This study examined whether resveratrol supplement improves the serum lipid profile and other metabolic markers in a dose-response manner in individuals with dyslipidemia. A total of 168 subjects were randomly assigned to placebo (n = 43) and resveratrol treatment groups of 100 mg/d (n = 41), 300 mg/d (n = 43), and 600 mg/d (n = 41). Anthropometric and biochemical parameters were analyzed at baseline and 4 and 8 weeks. Resveratrol supplementation for 8 weeks did not significantly change the lipid profile compared with the placebo. However, a significant decrease of serum uric acid was observed at 8 weeks in 300 mg/d (-23.60 ± 61.53 μmol/L, p < 0.05) and 600 mg/d resveratrol groups (-24.37 ± 64.24 μmol/L, p < 0.01) compared to placebo (8.19 ± 44.60 μmol/L). Furthermore, xanthine oxidase (XO) activity decreased significantly in the 600 mg/d resveratrol group (-0.09 ± 0.29 U/mL, p < 0.05) compared with placebo (0.03 ± 0.20 U/mL) after 8 weeks. The reduction of uric acid and XO activity exhibited a dose-response relationship (p for trend, <0.05). Furthermore, a marked correlation was found between the changes in uric acid and XO activity in the resveratrol groups (r = 0.254, p < 0.01). Resveratrol (10 μmol/L) treatment to HepG2 cells significantly reduced the uric acid levels and intracellular XO activity. Nevertheless, we failed to detect significant differences in glucose, insulin, or oxidative stress biomarkers between the resveratrol groups and placebo. In conclusion, resveratrol supplementation for 8 weeks had no significant effect on lipid profile but decreased uric acid in a dose-response manner, possibly due to XO inhibition in subjects with dyslipidemia. The trial was registered on ClinicalTrials.gov (NCT04886297).

Untargeted Metabolome Analysis of Alcohol-Exposed Pregnancies Reveals Metabolite Differences That Are Associated with Infant Birth Outcomes

Prenatal alcohol exposure can produce offspring growth deficits and is a leading cause of neurodevelopmental disability. We used untargeted metabolomics to generate mechanistic insight into how alcohol impairs fetal development. In the Western Cape Province of South Africa, 52 women between gestational weeks 5-36 (mean 18.5 ± 6.5) were recruited, and they provided a finger-prick fasting bloodspot that underwent mass spectrometry. Metabolomic data were analyzed using partial least squares-discriminant analyses (PLS-DA) to identify metabolites that correlated with alcohol exposure and infant birth outcomes. Women who consumed alcohol in the past seven days were distinguished by a metabolite profile that included reduced sphingomyelins, cholesterol, and pregnenolones, and elevated fatty acids, acyl and amino acyl carnitines, and androsterones. Using PLS-DA, 25 of the top 30 metabolites differentiating maternal groups were reduced by alcohol with medium-chain free fatty acids and oxidized sugar derivatives having the greatest influence. A separate ortho-PLS-DA analysis identified a common set of 13 metabolites that were associated with infant length, weight, and head circumference. These included monoacylglycerols, glycerol-3-phosphate, and unidentified metabolites, and most of their associations were negative, implying they represent processes having adverse consequences for fetal development.

Amino Acids Metabolism in Retinopathy: From Clinical and Basic Research Perspective

Retinopathy, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinopathy of prematurity (ROP), are the leading cause of blindness among seniors, working-age populations, and children. However, the pathophysiology of retinopathy remains unclear. Accumulating studies demonstrate that amino acid metabolism is associated with retinopathy. This study discusses the characterization of amino acids in DR, AMD, and ROP by metabolomics from clinical and basic research perspectives. The features of amino acids in retinopathy were summarized using a comparative approach based on existing high-throughput metabolomics studies from PubMed. Besides taking up a large proportion, amino acids appear in both human and animal, intraocular and peripheral samples. Among them, some metabolites differ significantly in all three types of retinopathy, including glutamine, glutamate, alanine, and others. Studies on the mechanisms behind retinal cell death caused by glutamate accumulation are on the verge of making some progress. To develop potential therapeutics, it is imperative to understand amino acid-induced retinal functional alterations and the underlying mechanisms. This review delineates the significance of amino acid metabolism in retinopathy and provides possible direction to discover therapeutic targets for retinopathy.

Metabolomics in Diabetic Retinopathy: From Potential Biomarkers to Molecular Basis of Oxidative Stress

Diabetic retinopathy (DR), the leading cause of blindness in working-age adults, is one of the most common complications of diabetes mellitus (DM) featured by metabolic disorders. With the global prevalence of diabetes, the incidence of DR is expected to increase. Prompt detection and the targeting of anti-oxidative stress intervention could effectively reduce visual impairment caused by DR. However, the diagnosis and treatment of DR is often delayed due to the absence of obvious signs of retina imaging. Research progress supports that metabolomics is a powerful tool to discover potential diagnostic biomarkers and therapeutic targets for the causes of oxidative stress through profiling metabolites in diseases, which provides great opportunities for DR with metabolic heterogeneity. Thus, this review summarizes the latest advances in metabolomics in DR, as well as potential diagnostic biomarkers, and predicts molecular targets through the integration of genome-wide association studies (GWAS) with metabolomics. Metabolomics provides potential biomarkers, molecular targets and therapeutic strategies for controlling the progress of DR, especially the interventions at early stages and precise treatments based on individual patient variations.

Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

The role and mechanisms of gut microbiota in diabetic nephropathy, diabetic retinopathy and cardiovascular diseases

Type 2 diabetes mellitus (T2DM) and T2DM-related complications [such as retinopathy, nephropathy, and cardiovascular diseases (CVDs)] are the most prevalent metabolic diseases. Intriguingly, overwhelming findings have shown a strong association of the gut microbiome with the etiology of these diseases, including the role of aberrant gut bacterial metabolites, increased intestinal permeability, and pathogenic immune function affecting host metabolism. Thus, deciphering the specific microbiota, metabolites, and the related mechanisms to T2DM-related complications by combined analyses of metagenomics and metabolomics data can lead to an innovative strategy for the treatment of these diseases. Accordingly, this review highlights the advanced knowledge about the characteristics of the gut microbiota in T2DM-related complications and how it can be associated with the pathogenesis of these diseases. Also, recent studies providing a new perspective on microbiota-targeted therapies are included.

Novel Plasma Metabolomic Markers Associated with Diabetes Progression in Older Puerto Ricans

We assessed longitudinal associations between plasma metabolites, their network-derived clusters, and type 2 diabetes (T2D) progression in Puerto Rican adults, a high-risk Hispanic subgroup with established health disparities. We used data from 1221 participants free of T2D and aged 40-75 years at baseline in the Boston Puerto Rican Health and San Juan Overweight Adult Longitudinal Studies. We used multivariable Poisson regression models to examine associations between baseline concentrations of metabolites and incident T2D and prediabetes. Cohort-specific estimates were combined using inverse-variance weighted fixed-effects meta-analyses. A cluster of 13 metabolites of branched chain amino acids (BCAA), and aromatic amino acid metabolism (pooled IRR = 1.87, 95% CI: 1.28; 2.73), and a cell membrane component metabolite cluster (pooled IRR = 1.54, 95% CI: 1.04; 2.27) were associated with a higher risk of incident T2D. When the metabolites were tested individually, in combined analysis, 5 metabolites involved in BCAA metabolism were associated with incident T2D. These findings highlight potential prognostic biomarkers to identify Puerto Rican adults who may be at high risk for diabetes. Future studies should examine whether diet and lifestyle can modify the associations between these metabolites and progression to T2D.

Metabolic Profiling of Obesity With and Without the Metabolic Syndrome: A Multisample Evaluation

CONTEXT

There is a dispute whether obesity without major metabolic derangements may represent a benign condition or not.

OBJECTIVE

We aimed to compare the plasma metabolome in obese subjects without metabolic syndrome (MetS) with normal-weight subjects without MetS and with obese subjects with MetS.

METHODS

This was a cross-sectional study at 2 academic centers in Sweden. Individuals from 3 population-based samples (EpiHealth, n = 2342, SCAPIS-Uppsala, n = 4985, and SCAPIS-Malmö, n = 3978) were divided into groups according to their body mass index (BMI) and presence/absence of MetS (National Cholesterol Education Program [NCEP]/consensus criteria). In total, 791 annotated endogenous metabolites were measured by ultra-performance liquid chromatography-tandem mass spectrometry.

RESULTS

We observed major differences in metabolite profiles (427 metabolites) between obese (BMI ≥ 30 kg/m2) and normal-weight (BMI < 25 kg/m2) subjects without MetS after adjustment for major lifestyle factors. Pathway enrichment analysis highlighted branch-chained and aromatic amino acid synthesis/metabolism, aminoacyl-tRNA biosynthesis, and sphingolipid metabolism. The same pathways, and similar metabolites, were also highlighted when obese subjects with and without MetS were compared despite adjustment for BMI and waist circumference, or when the metabolites were related to BMI and number of MetS components in a continuous fashion. Similar metabolites and pathways were also related to insulin sensitivity (Matsuda index) in a separate study (POEM, n = 501).

CONCLUSION

Our data suggest a graded derangement of the circulating metabolite profile from lean to obese to MetS, in particular for metabolites involved in amino acid synthesis/metabolism and sphingolipid metabolism. Insulin resistance is a plausible mediator of this gradual metabolic deterioration.