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

Investigating the mechanism of cornel iridoid glycosides on type 2 diabetes mellitus using serum and urine metabolites in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cornel iridoid glycosides (CIG) are extracted from Corni fructus, a herbal medicine used in traditional Chinese medicine to treat diabetes. However, the antidiabetic effects of CIG and the underlying metabolic mechanisms require further exploration.

AIM OF THE STUDY

This study aimed to assess the antidiabetic effects and metabolic mechanism of CIG by performing metabolomic analyses of serum and urine samples of rats.

MATERIALS AND METHODS

A rat model of type 2 diabetes mellitus (T2DM) was established by administering a low dose of streptozotocin (30 mg/kg) intraperitoneally after 4 weeks of feeding a high-fat diet. The model was evaluated based on several parameters, including fasting blood glucose (FBG), random blood glucose (RBG), urine volume, liver index, body weight, histopathological sections, and serum biochemical parameters. Subsequently, serum and urine metabolomics were analyzed using ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS). Data were analyzed using unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). Differential metabolites were examined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways to explore the underlying mechanisms.

RESULTS

After 4 weeks of treatment with different doses of CIG, varying degrees of antidiabetic effects were observed, along with reduced liver and pancreatic injury, and improved oxidative stress levels. Compared with the T2DM group, 19 and 23 differential metabolites were detected in the serum and urine of the CIG treatment group, respectively. The key metabolites involved in pathway regulation include taurine, chenodeoxycholic acid, glycocholic acid, and L-tyrosine in the serum and glycine, hippuric acid, phenylacetylglycine, citric acid, and D-glucuronic acid in the urine, which are related to lipid, amino acid, energy, and carbohydrate metabolism.

CONCLUSIONS

This study confirmed the antidiabetic effects of CIG and revealed that CIG effectively controlled metabolic disorders in T2DM rats. This seems to be meaningful for the clinical application of CIG, and can benefit further studies on CIG mechanism.

Dysregulated microRNAs in type 2 diabetes and breast cancer: Potential associated molecular mechanisms

Type 2 diabetes (T2D) is a multifaceted and heterogeneous syndrome associated with complications such as hypertension, coronary artery disease, and notably, breast cancer (BC). The connection between T2D and BC is established through processes that involve insulin resistance, inflammation and other factors. Despite this comprehension the specific cellular and molecular mechanisms linking T2D to BC, especially through microRNAs (miRNAs), remain elusive. miRNAs are regulators of gene expression at the post-transcriptional level and have the function of regulating target genes by modulating various signaling pathways and biological processes. However, the signaling pathways and biological processes regulated by miRNAs that are associated with T2D and BC have not yet been elucidated. This review aims to identify dysregulated miRNAs in both T2D and BC, exploring potential signaling pathways and biological processes that collectively contribute to the development of BC.

Plant foods, healthy plant-based diets, and type 2 diabetes: a review of the evidence

Type 2 diabetes (T2D) is a metabolic chronic disease in which insulin resistance and insufficient insulin production lead to elevated blood glucose levels. The prevalence of T2D is growing worldwide, mainly due to obesity and the adoption of Western diets. Replacing animal foods with healthy plant foods is associated with a lower risk of T2D in prospective studies. In randomized controlled trials, the consumption of healthy plant foods in place of animal foods led to cardiometabolic improvements in patients with T2D or who were at high risk of the disease. Dietary patterns that limit or exclude animal foods and focus on healthy plant foods (eg, fruits, vegetables, whole grains, nuts, legumes), known as healthy, plant-based diets, are consistently associated with a lower risk of T2D in cohort studies. The aim of this review is to examine the differential effects of plant foods and animal foods on T2D risk and to describe the existing literature about the role of healthy, plant-based diets, particularly healthy vegan diets, in T2D prevention and management. The evidence from cohort studies and randomized controlled trials will be reported, in addition to the potential biological mechanisms that seem to be involved.

Baseline phenotypes with preserved β-cell function and high insulin concentrations have the best improvements in glucose tolerance after weight loss: results from the prospective DEXLIFE and EGIR-RISC studies

BACKGROUND

Weight loss and lifestyle intervention improve glucose tolerance delaying the onset of type 2 diabetes (T2D), but individual responses are highly variable. Determining the predictive factors linked to the beneficial effects of weight loss on glucose tolerance could provide tools for individualized prevention plans. Thus, the aim was to investigate the relationship between pre-intervention values of insulin sensitivity and secretion and the improvement in glucose metabolism after weight loss.

METHODS

In the DEXLIFE cohort (373 individuals at high risk of T2D, assigned 3:1 to a 12-week lifestyle intervention or a control arm, Trial Registration: ISRCTN66987085), K-means clustering and logistic regression analysis were performed based on pre-intervention indices of insulin sensitivity, insulin secretion (AUC-I), and glucose-stimulated insulin response (ratio of incremental areas of insulin and glucose, iAUC I/G). The response to the intervention was evaluated in terms of reduction of OGTT-glucose concentration. Clusters' validation was done in the prospective EGIR-RISC cohort (n = 1538).

RESULTS

Four replicable clusters with different glycemic and metabolomic profiles were identified. Individuals had similar weight loss, but improvement in glycemic profile and β-cell function was different among clusters, highly depending on pre-intervention insulin response to OGTT. Pre-intervention high insulin response was associated with the best improvement in AUC-G, while clusters with low AUC-I and iAUC I/G showed no beneficial effect of weight loss on glucose control, as also confirmed by the logistic regression model.

CONCLUSIONS

Individuals with preserved β-cell function and high insulin concentrations at baseline have the best improvement in glucose tolerance after weight loss.

Comparison of Cognitive Intervention Strategies for Individuals With Alzheimer's Disease: A Systematic Review and Network Meta-analysis

Accumulating evidence has shown the effectiveness of cognitive interventions, which can be divided into cognitive training (CT), cognitive stimulation (CS), cognitive rehabilitation (CR), and combined interventions (i.e., cognitive interventions combined with other non-pharmacological interventions such as physical exercise), in individuals with Alzheimer's disease (AD). However, the effectiveness of cognitive interventions varies greatly among studies and more comprehensive studies are required. We aimed to evaluate whether the current evidence shows that cognitive interventions are effective at improving cognition, neuropsychiatric symptoms, depression, quality of life, and basic activities of daily living among individuals with possible or probable AD. Randomized controlled trials of all types of cognitive intervention were identified for inclusion in pairwise and network meta-analyses. There was a moderate and statistically significant post-intervention improvement in global cognition among individuals with AD for all types of cognitive intervention compared to control interventions (39 studies, g = 0.43, 95% CI: 0.28 to 0.58, p < 0.01; Q = 102.27, df = 38, p < 0.01; I2 = 61.97%, τ2 = 0.13). Regarding the specific types of cognitive intervention, combined interventions had the highest surface under the cumulative ranking curve (SUCRA) value (90.7%), followed by CT (67.8%), CS (53.4%), and lastly CR (28.9%). Significant effects of cognitive interventions were also found for working memory, verbal memory, verbal fluency, confrontation naming, attention, neuropsychiatric symptoms, basic activities of daily living, and quality of life.

Gut microbial metabolism is linked to variations in circulating non-high density lipoprotein cholesterol

BACKGROUND

Non-high-density lipoprotein cholesterol (non-HDL-c) was a strong risk factor for incident cardiovascular diseases and proved to be a better target of lipid-lowering therapies. Recently, gut microbiota has been implicated in the regulation of host metabolism. However, its causal role in the variation of non-HDL-c remains unclear.

METHODS

Microbial species and metabolic capacities were assessed with fecal metagenomics, and their associations with non-HDL-c were evaluated by Spearman correlation, followed by LASSO and linear regression adjusted for established cardiovascular risk factors. Moreover, integrative analysis with plasma metabolomics were performed to determine the key molecules linking microbial metabolism and variation of non-HDL-c. Furthermore, bi-directional mendelian randomization analysis was performed to determine the potential causal associations of selected species and metabolites with non-HDL-c.

FINDINGS

Decreased Eubacterium rectale but increased Clostridium sp CAG_299 were causally linked to a higher level of non-HDL-c. A total of 16 microbial capacities were found to be independently associated with non-HDL-c after correcting for age, sex, demographics, lifestyles and comorbidities, with the strongest association observed for tricarboxylic acid (TCA) cycle. Furthermore, decreased 3-indolepropionic acid and N-methyltryptamine, resulting from suppressed capacities for microbial reductive TCA cycle, functioned as major microbial effectors to the elevation of circulating non-HDL-c.

INTERPRETATION

Overall, our findings provided insight into the causal effects of gut microbes on non-HDL-c and uncovered a novel link between non-HDL-c and microbial metabolism, highlighting the possibility of regulating non-HDL-c by microbiota-modifying interventions.

FUNDING

A full list of funding bodies can be found in the Sources of funding section.

Lipidomic analysis of serum exosomes identifies a novel diagnostic marker for type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) intricately involves disrupted lipid metabolism. Exosomes emerge as carriers of biomarkers for early diagnosis and monitoring. This study aims to identify lipid metabolites in serum exosomes for T2DM diagnosis.

METHODS

Serum samples were collected from newly diagnosed T2DM patients and age and body mass index-matched healthy controls. Exosomes were isolated using exosome isolation reagent, and untargeted/targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify and validate altered lipid metabolites. Receiver operating characteristic curve analysis was used to evaluate the diagnostic value of candidate lipid metabolites.

RESULTS

Serum exosomes were successfully isolated from both groups, with untargeted LC-MS/MS revealing distinct lipid metabolite alterations. Notably, phosphatidylethanolamine (PE) (22:2(13Z,16Z)/14:0) showed stable elevation in T2DM-serum exosomes. Targeted LC-MS/MS confirmed significant increase of PE (22:2(13Z,16Z)/14:0) in T2DM exosomes but not in serum. PE (22:2(13Z,16Z)/14:0) levels not only positively correlated with hemoglobin A1C levels and blood glucose levels, but also effectively distinguished T2DM patients from healthy individuals (area under the curve = 0.9141).

CONCLUSION

Our research sheds light on the importance of serum exosome lipid metabolites in diagnosing T2DM, providing valuable insights into the complex lipid metabolism of diabetes.

A secondary analysis of indices of hepatic and beta cell function following 12 weeks of carbohydrate and energy restriction vs. free-living control in adults with type 2 diabetes

BACKGROUND

Substantial weight loss in people living with type 2 diabetes (T2D) can reduce the need for glucose-lowering medications while concurrently lowering glycemia below the diagnostic threshold for the disease. Furthermore, weight-loss interventions have also been demonstrated to improve aspects of underlying T2D pathophysiology related to ectopic fat in the liver and pancreatic beta-cell function. As such, the purpose of this secondary analysis was to explore the extent to which a low-carbohydrate and energy-restricted (LCER) diet intervention improves markers of beta-cell stress/function, liver fat accumulation, and metabolic related liver function in people with type 2 diabetes.

METHODS

We conducted secondary analyses of blood samples from a larger pragmatic community-based parallel-group randomized controlled trial involving a 12-week pharmacist implemented LCER diet (Pharm-TCR: <50 g carbohydrates; ~850-1100 kcal/day; n = 20) versus treatment-as-usual (TAU; n = 16). Participants were people with T2D, using ≥ 1 glucose-lowering medication, and a body mass index of ≥ 30 kg/m2. Main outcomes were C-peptide to proinsulin ratio, circulating microRNA 375 (miR375), homeostatic model assessment (HOMA) beta-cell function (B), fatty liver index (FLI), hepatic steatosis index (HSI), HOMA insulin resistance (IR), and circulating fetuin-A and fibroblast growth factor 21 (FGF21). Data were analysed using linear regression with baseline as a covariate.

RESULTS

There was no observed change in miR375 (p = 0.42), C-peptide to proinsulin ratio (p = 0.17) or HOMA B (p = 0.15). FLI and HSI were reduced by -25.1 (p < 0.0001) and - 4.9 (p < 0.0001), respectively. HOMA IR was reduced by -46.5% (p = 0.011). FGF21 was reduced by -161.2pg/mL (p = 0.035) with a similar tendency found for fetuin-A (mean difference: -16.7ng/mL; p = 0.11). These improvements in markers of hepatic function were accompanied by reductions in circulating metabolites linked to hepatic insulin resistance (e.g., diacylglycerols, ceramides) in the Pharm TCR group.

CONCLUSIONS

The Pharm-TCR intervention did not improve fasting indices of beta-cell stress; however, markers of liver fat accumulation and and liver function were improved, suggesting that a LCER diet can improve some aspects of the underlying pathophysiology of T2D.

TRIAL REGISTRATION

Clinicaltrials.gov (NCT03181165).

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.

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

AIMS/HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS/INTERPRETATION

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

Association of serum metal levels with type 2 diabetes: A prospective cohort and mediating effects of metabolites analysis in Chinese population

Several studies have suggested an association between exposure to various metals and the onset of type 2 diabetes (T2D). However, the results vary across different studies. We aimed to investigate the associations between serum metal concentrations and the risk of developing T2D among 8734 participants using a prospective cohort study design. We utilized inductively coupled plasmamass spectrometry (ICP-MS) to assess the serum concentrations of 27 metals. Cox regression was applied to calculate the hazard ratios (HRs) for the associations between serum metal concentrations on the risk of developing T2D. Additionally, 196 incident T2D cases and 208 healthy control participants were randomly selected for serum metabolite measurement using an untargeted metabolomics approach to evaluate the mediating role of serum metabolite in the relationship between serum metal concentrations and the risk of developing T2D with a nested casecontrol study design. In the cohort study, after Bonferroni correction, the serum concentrations of zinc (Zn), mercury (Hg), and thallium (Tl) were positively associated with the risk of developing T2D, whereas the serum concentrations of manganese (Mn), molybdenum (Mo), barium (Ba), lutetium (Lu), and lead (Pb) were negatively associated with the risk of developing T2D. After adding these eight metals, the predictive ability increased significantly compared with that of the traditional clinical model (AUC: 0.791 vs. 0.772, P=8.85×10-5). In the nested casecontrol study, a machine learning analysis revealed that the serum concentrations of 14 out of 1579 detected metabolites were associated with the risk of developing T2D. According to generalized linear regression models, 7 of these metabolites were significantly associated with the serum concentrations of the identified metals. The mediation analysis showed that two metabolites (2-methyl-1,2-dihydrophthalazin-1-one and mestranol) mediated 46.81% and 58.70%, respectively, of the association between the serum Pb concentration and the risk of developing T2D. Our study suggested that serum Mn, Zn, Mo, Ba, Lu, Hg, Tl, and Pb were associated with T2D risk. Two metabolites mediated the associations between the serum Pb concentration and the risk of developing T2D.

A Plasma Metabolite Score Related to Psychological Distress and Diabetes Risk: A Nested Case-control Study in US Women

CONTEXT

Psychological distress has been linked to diabetes risk. Few population-based, epidemiologic studies have investigated the potential molecular mechanisms (eg, metabolic dysregulation) underlying this association.

OBJECTIVE

To evaluate the association between a metabolomic signature for psychological distress and diabetes risk.

METHODS

We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses' Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 matched controls. Blood samples were collected between 1989 and 1990 and incident diabetes was diagnosed between 1992 and 2008. Based on our prior work, we calculated a weighted plasma metabolite-based distress score (MDS) comprised of 19 metabolites. We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% confidence intervals (CI) for diabetes risk according to MDS.

RESULTS

After adjusting for sociodemographic factors, family history of diabetes, and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the MDS was 1.00 (reference) for Q1, 1.16 (0.77, 1.73) for Q2, 1.30 (0.88, 1.91) for Q3, 1.99 (1.36, 2.92) for Q4, and 2.47 (1.66, 3.67) for Q5. Each SD increase in MDS was associated with 36% higher diabetes risk (95% CI: 1.21, 1.54; P-trend <.0001). This association was moderately attenuated after additional adjustment for body mass index (comparable OR: 1.17; 95% CI: 1.02, 1.35; P-trend = .02). The MDS explained 17.6% of the association between self-reported psychological distress (defined as presence of depression or anxiety symptoms) and diabetes risk (P = .04).

CONCLUSION

MDS was significantly associated with diabetes risk in women. These results suggest that differences in multiple lipid and amino acid metabolites may underlie the observed association between psychological distress and diabetes risk.

24-h urinary sodium and potassium excretions, plasma metabolomic profiles, and cardiometabolic biomarkers in the United States adults: a cross-sectional study

BACKGROUND

High-sodium and low-potassium intakes are associated with a higher risk of hypertension and cardiovascular disease, but there are limited data on the circulating metabolomics profiles of 24-h urinary sodium and potassium excretions in free-living individuals.

OBJECTIVES

We aimed to characterize the metabolomics signatures of a high-sodium and low-potassium diet in a cross-sectional study.

METHODS

In 1028 healthy older adults from the Women's and Men's Lifestyle Validation Studies, we investigated the association of habitual sodium and potassium intakes measured by 2 to 4 24-h urine samples with plasma metabolites (quantified using liquid chromatography-tandem mass spectrometry) and metabolomic pathways. Our primary exposures were energy-adjusted 24-h urinary sodium excretion, potassium excretion, and sodium-to-potassium ratio, calculated based on energy expenditure derived from the doubly labeled water method. We then assessed the partial correlations of their metabolomics scores, derived from elastic net regressions, with cardiometabolic biomarkers.

RESULTS

Higher sodium excretion was associated with 38 metabolites including higher piperine, phosphatidylethanolamine, and C5:1 carnitine. In pathway analysis, higher sodium excretion was associated with enhanced biotin and propanoate metabolism and enhanced degradation of lysine and branched-chain amino acids (BCAAs). Metabolites associated with higher potassium and lower sodium-to-potassium ratio included quinic acid and proline-betaine. After adjusting for confounding factors, the metabolomics score for sodium-to-potassium ratio positively correlated with fasting insulin (Spearman's rank correlation coefficient ρ = 0.27), C-peptide (ρ = 0.30), and triglyceride (ρ = 0.46), and negatively with adiponectin (ρ = -0.40), and high-density lipoprotein cholesterol (ρ = -0.42).

CONCLUSIONS

We discovered metabolites and metabolomics pathways associated with a high-sodium diet, including metabolites related to biotin, propanoate, lysine, and BCAA pathways. The metabolomics signature for a higher sodium low-potassium diet is associated with multiple components of elevated cardiometabolic risk.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis

OBJECTIVE

This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC).

DESIGN

In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients.

METHODS

The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes.

RESULTS

Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups.

CONCLUSIONS

These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Mild autonomous cortisol secretion: pathophysiology, comorbidities and management approaches

The majority of incidentally discovered adrenal tumours are benign adrenocortical adenomas and the prevalence of adrenocortical adenomas is around 1-7% on cross-sectional abdominal imaging. These can be non-functioning adrenal tumours or they can be associated with autonomous cortisol secretion on a spectrum that ranges from rare clinically overt adrenal Cushing syndrome to the much more prevalent mild autonomous cortisol secretion (MACS) without signs of Cushing syndrome. MACS is diagnosed (based on an abnormal overnight dexamethasone suppression test) in 20-50% of patients with adrenal adenomas. MACS is associated with cardiovascular morbidity, frailty, fragility fractures, decreased quality of life and increased mortality. Management of MACS should be individualized based on patient characteristics and includes adrenalectomy or conservative follow-up with treatment of associated comorbidities. Identifying patients with MACS who are most likely to benefit from adrenalectomy is challenging, as adrenalectomy results in improvement of cardiovascular morbidity in some, but not all, patients with MACS. Of note, diagnosis and management of patients with bilateral MACS is especially challenging. Current gaps in MACS clinical practice include a lack of specific biomarkers diagnostic of MACS-related health outcomes and a paucity of clinical trials demonstrating the efficacy of adrenalectomy on comorbidities associated with MACS. In addition, little evidence exists to demonstrate the efficacy and safety of long-term medical therapy in patients with MACS.

Metabolic and inflammatory perturbation of diabetes associated gut dysbiosis in people living with and without HIV infection

BACKGROUND

Gut dysbiosis has been linked with both HIV infection and diabetes, but its interplay with metabolic and inflammatory responses in diabetes, particularly in the context of HIV infection, remains unclear.

METHODS

We first conducted a cross-sectional association analysis to characterize the gut microbial, circulating metabolite, and immune/inflammatory protein features associated with diabetes in up to 493 women (~ 146 with prevalent diabetes with 69.9% HIV +) of the Women's Interagency HIV Study. Prospective analyses were then conducted to determine associations of identified metabolites with incident diabetes over 12 years of follow-up in 694 participants (391 women from WIHS and 303 men from the Multicenter AIDS Cohort Study; 166 incident cases were recorded) with and without HIV infection. Mediation analyses were conducted to explore whether gut bacteria-diabetes associations are explained by altered metabolites and proteins.

RESULTS

Seven gut bacterial genera were identified to be associated with diabetes (FDR-q <  0.1), with positive associations for Shigella, Escherichia, Megasphaera, and Lactobacillus, and inverse associations for Adlercreutzia, Ruminococcus, and Intestinibacter. Importantly, the associations of most species, especially Adlercreutzia and Ruminococcus, were largely independent of antidiabetic medications use. Meanwhile, 18 proteins and 76 metabolites, including 3 microbially derived metabolites (trimethylamine N-oxide, phenylacetylglutamine (PAGln), imidazolepropionic acid (IMP)), 50 lipids (e.g., diradylglycerols (DGs) and triradylglycerols (TGs)) and 23 non-lipid metabolites, were associated with diabetes (FDR-q <  0.1), with the majority showing positive associations and more than half of them (59/76) associated with incident diabetes. In mediation analyses, several proteins, especially interleukin-18 receptor 1 and osteoprotegerin, IMP and PAGln partially mediate the observed bacterial genera-diabetes associations, particularly for those of Adlercreutzia and Escherichia. Many diabetes-associated metabolites and proteins were altered in HIV, but no effect modification on their associations with diabetes was observed by HIV.

CONCLUSION

Among individuals with and without HIV, multiple gut bacterial genera, blood metabolites, and proinflammatory proteins were associated with diabetes. The observed mediated effects by metabolites and proteins in genera-diabetes associations highlighted the potential involvement of inflammatory and metabolic perturbations in the link between gut dysbiosis and diabetes in the context of HIV infection.

Metabolic profiling of smoking, associations with type 2 diabetes and interaction with genetic susceptibility

BACKGROUND

Smokers are at increased risk of type 2 diabetes (T2D), but the underlying mechanisms are unclear. We investigated if the smoking-T2D association is mediated by alterations in the metabolome and assessed potential interaction with genetic susceptibility to diabetes or insulin resistance.

METHODS

In UK Biobank (n = 93,722), cross-sectional analyses identified 208 metabolites associated with smoking, of which 131 were confirmed in Mendelian Randomization analyses, including glycoprotein acetyls, fatty acids, and lipids. Elastic net regression was applied to create a smoking-related metabolic signature. We estimated hazard ratios (HR) of incident T2D in relation to baseline smoking/metabolic signature and calculated the proportion of the smoking-T2D association mediated by the signature. Additive interaction between the signature and genetic risk scores for T2D (GRS-T2D) and insulin resistance (GRS-IR) on incidence of T2D was assessed as relative excess risk due to interaction (RERI).

FINDINGS

The HR of T2D was 1·73 (95% confidence interval (CI) 1·54 - 1·94) for current versus never smoking, and 38·3% of the excess risk was mediated by the metabolic signature. The metabolic signature and its mediation role were replicated in TwinGene. The metabolic signature was associated with T2D (HR: 1·61, CI 1·46 - 1·77 for values above vs. below median), with evidence of interaction with GRS-T2D (RERI: 0·81, CI: 0·23 - 1·38) and GRS-IR (RERI 0·47, CI: 0·02 - 0·92).

INTERPRETATION

The increased risk of T2D in smokers may be mediated through effects on the metabolome, and the influence of such metabolic alterations on diabetes risk may be amplified in individuals with genetic susceptibility to T2D or insulin resistance.

Association of Gut Microbiota-Related Metabolites and Type 2 Diabetes in Two Puerto Rican Cohorts

(1) Aims: Gut microbiota metabolites may play integral roles in human metabolism and disease progression. However, evidence for associations between metabolites and cardiometabolic risk factors is sparse, especially in high-risk Hispanic populations. We aimed to evaluate the cross-sectional and longitudinal relationships between gut microbiota related metabolites and measures of glycemia, dyslipidemia, adiposity, and incident type 2 diabetes in two Hispanic observational cohorts. (2) Methods: We included data from 670 participants of the Boston Puerto Rican Health Study (BPRHS) and 999 participants of the San Juan Overweight Adult Longitudinal Study (SOALS). Questionnaires and clinical examinations were conducted over 3 years of follow-up for SOALS and 6 years of follow-up for BPRHS. Plasma metabolites, including L-carnitine, betaine, choline, and trimethylamine N-oxide (TMAO), were measured at baseline in both studies. We used multivariable linear models to evaluate the associations between metabolites and cardiometabolic risk factors and multivariable logistic and Poisson regressions to assess associations with prevalent and incident type 2 diabetes, adjusted for potential confounding factors. Cohort-specific analyses were combined using a fixed-effects meta-analysis. (3) Results: Higher plasma betaine was prospectively associated with lower fasting glucose [-0.97 mg/dL (95% CI: -1.59, -0.34), p = 0.002], lower HbA1c [-0.02% (95% CI: -0.04, -0.01), p = 0.01], lower HOMA-IR [-0.14 (95% CI: -0.23, -0.05), p = 0.003], and lower fasting insulin [-0.27 mcU/mL (95% CI: -0.51, -0.03), p = 0.02]. Betaine was also associated with a 22% lower incidence of type 2 diabetes (IRR: 0.78, 95% CI: 0.65, 0.95). L-carnitine was associated with lower fasting glucose [-0.68 mg/dL (95% CI: -1.29, -0.07), p = 0.03] and lower HbA1c at follow-up [-0.03% (95% CI: -0.05, -0.01), p < 0.001], while TMAO was associated with higher fasting glucose [0.83 mg/dL (95% CI: 0.22, 1.44), p = 0.01] and higher triglycerides [3.52 mg/dL (95% CI: 1.83, 5.20), p < 0.0001]. Neither choline nor TMAO were associated with incident type 2 diabetes. (4) Conclusions: Higher plasma betaine showed consistent associations with a lower risk of glycemia, insulinemia, and type 2 diabetes. However, TMAO, a metabolite of betaine, was associated with higher glucose and lipid concentrations. These observations demonstrate the importance of gut microbiota metabolites for human cardiometabolic health.

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Predictive Value of Lysophosphatidylcholine for Determining the Disease Severity and Prognosis of Elderly Patients with Community-Acquired Pneumonia

Purpose

To investigate the clinical value of serum lysophosphatidylcholine (LPC) as a predictive biomarker for determining disease severity and mortality risk in hospitalized elderly patients with community-acquired pneumonia (CAP).

Methods

This prospective, single-center study enrolled 208 elderly patients, including 67 patients with severe CAP (SCAP) and 141 with non-SCAP between November 1st, 2020, and November 30th, 2021 at the Qingdao Municipal Hospital, Shandong Province, China. The demographic and clinical parameters were recorded for all the included patients. Serum LPC levels were measured on day 1 and 6 after admission using ELISA. Propensity score matching (PSM) was used to balance the baseline variables between SCAP and non-SCAP patient groups. Receiver operative characteristic (ROC) curve analysis was used to compare the predictive performances of LPC and other clinical parameters in discriminating between SCAP and non-SCAP patients and determining the 30-day mortality risk of the hospitalized CAP patients. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with SCAP. Cox proportional hazard regression analysis was used to determine if serum LPC was an independent risk factor for the 30-day mortality of CAP patients.

Results

The serum LPC levels at admission were significantly higher in the non-SCAP patients than in the SCAP patients (P = 0.011). Serum LPC level <24.36 ng/mL, and PSI score were independent risk factors for the 30-day mortality in the elderly patients with CAP. The risk of 30-day mortality in the elderly CAP patients with low serum LPC levels (< 24.36ng/mL) was >5-fold higher than in the patients with high serum LPC levels (≥ 24.36ng/mL).

Conclusion

Low serum LPC levels were associated with significantly higher disease severity and 30-day mortality in the elderly patients with CAP. Therefore, serum LPC is a promising predictive biomarker for the early identification of elderly CAP patients with poor prognosis.

Impacts of glutamate, an exercise-responsive metabolite on insulin signaling

AIMS

Disruption of the insulin signaling pathway leads to insulin resistance (IR). IR is characterized by impaired glucose and lipid metabolism. Elevated levels of circulating glutamate are correlated with metabolic indicators and may potentially predict the onset of metabolic diseases. Glutamate receptor antagonists have significantly enhanced insulin sensitivity, and improved glucose and lipid metabolism. Exercise is a well-known strategy to combat IR. The aims of our narrative review are to summarize preclinical and clinical findings to show the correlations between circulating glutamate levels, IR and metabolic diseases, discuss the causal role of excessive glutamate in IR and metabolic disturbance, and present an overview of the exercise-induced alteration in circulating glutamate levels.

MATERIALS AND METHODS

A literature search was conducted to identify studies on glutamate, insulin signaling, and exercise in the PubMed database. The search covered articles published from December 1955 to January 2024, using the search terms of "glutamate", "glutamic acid", "insulin signaling", "insulin resistance", "insulin sensitivity", "exercise", and "physical activity".

KEY FINDINGS

Elevated levels of circulating glutamate are correlated with IR. Excessive glutamate can potentially hinder the insulin signaling pathway through various mechanisms, including the activation of ectopic lipid accumulation, inflammation, and endoplasmic reticulum stress. Glutamate can also modify mitochondrial function through Ca2+ and induce purine degradation mediated by AMP deaminase 2. Exercise has the potential to decrease circulating levels of glutamate, which can be attributed to accelerated glutamate catabolism and enhanced glutamate uptake.

SIGNIFICANCE

Glutamate may act as a mediator in the exercise-induced improvement of insulin sensitivity.

Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study

INTRODUCTION

Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites.

RESEARCH DESIGN AND METHODS

This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin.

RESULTS

Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models.

CONCLUSIONS

Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

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

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

Associations of Lifestyle Patterns with Glucose and Lipid Metabolism in Finnish Adults at Increased Risk of Type 2 Diabetes

SCOPE

Various lifestyle and sociodemographic factors have been associated with risk factors for type 2 diabetes (T2D). However, their combined associations with T2D risk factors have been studied much less.

MATERIALS AND RESULTS

This study investigates cross-sectional associations of lifestyle patterns with T2D risk factors among 2925 adults at increased risk participating in the Stop Diabetes study. Lifestyle patterns are determined using principal component analysis (PCA) with several lifestyle and sociodemographic factors. The associations of lifestyle patterns with measures of glucose and lipid metabolism and serum metabolites analyzed by nuclear magnetic resonance (NMR) spectroscopy are studied using linear regression analysis. "Healthy eating" pattern is associated with better glucose and insulin metabolism, more favorable lipoprotein and fatty acid profiles and lower serum concentrations of metabolites related to inflammation, insulin resistance, and T2D. "High socioeconomic status and low physical activity" pattern is associated with increased serum concentrations of branched-chain amino acids, as are "Meat and poultry" and "Sleeping hours" patterns. "Snacks" pattern is associated with lower serum concentrations of ketone bodies.

CONCLUSIONS

Our results show, in large scale primary care setting, that healthy eating is associated with better glucose and lipid metabolism and reveal novel associations of lifestyle patterns with metabolites related to glucose metabolism.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Causal associations between gut microbiota, metabolites and asthma: a two-sample Mendelian randomization study

BACKGROUND

While several traditional observational studies have suggested associations between gut microbiota and asthma, these studies are limited by factors such as participant selection bias, confounders, and reverse causality. Therefore, the causal relationship between gut microbiota and asthma remains uncertain.

METHODS

We performed two-sample bi-directional Mendelian randomization (MR) analysis to investigate the potential causal relationships between gut microbiota and asthma as well as its phenotypes. We also conducted MR analysis to evaluate the causal effect of gut metabolites on asthma. Genetic variants for gut microbiota were obtained from the MiBioGen consortium, GWAS summary statistics for metabolites from the TwinsUK study and KORA study, and GWAS summary statistics for asthma from the FinnGen consortium. The causal associations between gut microbiota, gut metabolites and asthma were examined using inverse variance weighted, maximum likelihood, MR-Egger, weighted median, and weighted model and further validated by MR-Egger intercept test, Cochran's Q test, and "leave-one-out" sensitivity analysis.

RESULTS

We identified nine gut microbes whose genetically predicted relative abundance causally impacted asthma risk. After FDR correction, significant causal relationships were observed for two of these microbes, namely the class Bacilli (OR = 0.84, 95%CI = 0.76-0.94, p = 1.98 × 10-3) and the order Lactobacillales (OR = 0.83, 95%CI = 0.74-0.94, p = 1.92 × 10-3). Additionally, in a reverse MR analysis, we observed a causal effect of genetically predicted asthma risk on the abundance of nine gut microbes, but these associations were no longer significant after FDR correction. No significant causal effect of gut metabolites was found on asthma.

CONCLUSIONS

Our study provides insights into the development mechanism of microbiota-mediated asthma, as well as into the prevention and treatment of asthma through targeting specific gut microbiota.

Combined untargeted and targeted lipidomics approaches reveal potential biomarkers in type 2 diabetes mellitus cynomolgus monkeys

BACKGROUND

The significantly increasing incidence of type 2 diabetes mellitus (T2DM) over the last few decades triggers the demands of T2DM animal models to explore the pathogenesis, prevention, and therapy of the disease. The altered lipid metabolism may play an important role in the pathogenesis and progression of T2DM. However, the characterization of molecular lipid species in fasting serum related to T2DM cynomolgus monkeys is still underrecognized.

METHODS

Untargeted and targeted LC-mass spectrometry (MS)/MS-based lipidomics approaches were applied to characterize and compare the fasting serum lipidomic profiles of T2DM cynomolgus monkeys and the healthy controls.

RESULTS

Multivariate analysis revealed that 196 and 64 lipid molecules differentially expressed in serum samples using untargeted and targeted lipidomics as the comparison between the disease group and healthy group, respectively. Furthermore, the comparative analysis of differential serum lipid metabolites obtained by untargeted and targeted lipidomics approaches, four common serum lipid species (phosphatidylcholine [18:0_22:4], lysophosphatidylcholine [14:0], phosphatidylethanolamine [PE] [16:1_18:2], and PE [18:0_22:4]) were identified as potential biomarkers and all of which were found to be downregulated. By analyzing the metabolic pathway, glycerophospholipid metabolism was associated with the pathogenesis of T2DM cynomolgus monkeys.

CONCLUSION

The study found that four downregulated serum lipid species could serve as novel potential biomarkers of T2DM cynomolgus monkeys. Glycerophospholipid metabolism was filtered out as the potential therapeutic target pathway of T2DM progression. Our results showed that the identified biomarkers may offer a novel tool for tracking disease progression and response to therapeutic interventions.

Metabolomic Insights on Potassium Excretion, Blood Pressure, and Glucose Homeostasis: The African-PREDICT Study

BACKGROUND

Low-potassium intake is associated with a higher risk of type 2 diabetes and hypertension. Both conditions occur more frequently in Black populations, who also consume less potassium-rich foods.

OBJECTIVES

Using metabolomics to identify dysregulated metabolic pathways associated with low-potassium excretion may procure more accurate entry points for nutritional prevention and intervention for type 2 diabetes and hypertension.

METHODS

A total of 440 White and 350 Black adults from the African-PREDICT study (aged 20-30 y) were included. Twenty-four-hour blood pressure (BP) was measured. Potassium, sodium, and fasting glucose concentrations were analyzed in 24-h urine and plasma samples. Liquid chromatography-tandem mass spectrometry-based metabolomics included the analyses of amino acids and acylcarnitines in spot urine samples.

RESULTS

Black participants had lower urinary potassium concentrations than Whites (36.6 compared with 51.1 mmol/d; P < 0.001). In White but not Black adults, urinary potassium correlated positively with 2-aminoadipic acid (2-AAA) (r = 0.176), C3-[propionyl]carnitine (r = 0.137), C4-[butyryl]carnitine (r = 0.169) and C5-[isovaleryl]carnitine (r = 0.167) in unadjusted and 2-AAA (r = 0.158) and C4-carnitine (r = 0.160) in adjusted analyses (all P < 0.05 and q < 0.05). Elevated C0-, C3-, and C5-carnitine in turn were positively associated with systolic BP (Black and White groups), diastolic BP (Black group), and glucose (White group) (all P < 0.05).

CONCLUSIONS

Racial differences are an important consideration when investigating nutrient-metabolite relationships and the role thereof in cardiovascular disease. Only in White adults did urinary potassium associate with 2-AAA and short-chain acylcarnitines. These metabolites were positively related to BP and fasting plasma glucose concentrations. In White adults, the metabolomic profiles related to potassium excretion may contribute to BP regulation and glucose homeostasis. This trial was registered at clinicaltrials.gov as NCT03292094.

Plasma metabolite predictors of metabolic syndrome incidence and reversion

BACKGROUND

Metabolic Syndrome (MetS) is a progressive pathophysiological state defined by a cluster of cardiometabolic traits. However, little is known about metabolites that may be predictors of MetS incidence or reversion. Our objective was to identify plasma metabolites associated with MetS incidence or MetS reversion.

METHODS

The study included 1468 participants without cardiovascular disease (CVD) but at high CVD risk at enrollment from two case-cohort studies nested within the PREvención con DIeta MEDiterránea (PREDIMED) study with baseline metabolomics data. MetS was defined in accordance with the harmonized International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute criteria, which include meeting 3 or more thresholds for waist circumference, triglyceride, HDL cholesterol, blood pressure, and fasting blood glucose. MetS incidence was defined by not having MetS at baseline but meeting the MetS criteria at a follow-up visit. MetS reversion was defined by MetS at baseline but not meeting MetS criteria at a follow-up visit. Plasma metabolome was profiled by LC-MS. Multivariable-adjusted Cox regression models and elastic net regularized regressions were used to assess the association of 385 annotated metabolites with MetS incidence and MetS reversion after adjusting for potential risk factors.

RESULTS

Of the 603 participants without baseline MetS, 298 developed MetS over the median 4.8-year follow-up. Of the 865 participants with baseline MetS, 285 experienced MetS reversion. A total of 103 and 88 individual metabolites were associated with MetS incidence and MetS reversion, respectively, after adjusting for confounders and false discovery rate correction. A metabolomic signature comprised of 77 metabolites was robustly associated with MetS incidence (HR: 1.56 (95 % CI: 1.33-1.83)), and a metabolomic signature of 83 metabolites associated with MetS reversion (HR: 1.44 (95 % CI: 1.25-1.67)), both p < 0.001. The MetS incidence and reversion signatures included several lipids (mainly glycerolipids and glycerophospholipids) and branched-chain amino acids.

CONCLUSION

We identified unique metabolomic signatures, primarily comprised of lipids (including glycolipids and glycerophospholipids) and branched-chain amino acids robustly associated with MetS incidence; and several amino acids and glycerophospholipids associated with MetS reversion. These signatures provide novel insights on potential distinct mechanisms underlying the conditions leading to the incidence or reversion of MetS.

Proteomic and Metabolomic Signatures of Diet Quality in Young Adults

The assessment of "omics" signatures may contribute to personalized medicine and precision nutrition. However, the existing literature is still limited in the homogeneity of participants' characteristics and in limited assessments of integrated omics layers. Our objective was to use post-prandial metabolomics and fasting proteomics to identify biological pathways and functions associated with diet quality in a population of primarily Hispanic young adults. We conducted protein and metabolite-wide association studies and functional pathway analyses to assess the relationships between a priori diet indices, Healthy Eating Index-2015 (HEI) and Dietary Approaches to Stop Hypertension (DASH) diets, and proteins (n = 346) and untargeted metabolites (n = 23,173), using data from the MetaAIR study (n = 154, 61% Hispanic). Analyses were performed for each diet quality index separately, adjusting for demographics and BMI. Five proteins (ACY1, ADH4, AGXT, GSTA1, F7) and six metabolites (undecylenic acid, betaine, hyodeoxycholic acid, stearidonic acid, iprovalicarb, pyracarbolid) were associated with both diets (p < 0.05), though none were significant after adjustment for multiple comparisons. Overlapping proteins are involved in lipid and amino acid metabolism and in hemostasis, while overlapping metabolites include amino acid derivatives, bile acids, fatty acids, and pesticides. Enriched biological pathways were involved in macronutrient metabolism, immune function, and oxidative stress. These findings in young Hispanic adults contribute to efforts to develop precision nutrition and medicine for diverse populations.

Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

BACKGROUND

Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD.

METHODS

The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models.

RESULTS

Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period.

CONCLUSIONS

This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease.

TRIAL REGISTRATION

ISRCTN35739639.

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.

Circulating sphingolipids and relationship to cardiac remodelling before and following a low-energy diet in asymptomatic Type 2 Diabetes

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is a heterogenous multi-system syndrome with limited efficacious treatment options. The prevalence of Type 2 diabetes (T2D) continues to rise and predisposes patients to HFpEF, and HFpEF remains one of the biggest challenges in cardiovascular medicine today. Novel therapeutic targets are required to meet this important clinical need. Deep phenotyping studies including -OMIC analyses can provide important pathogenic information to aid the identification of such targets. The aims of this study were to determine; 1) the impact of a low-energy diet on plasma sphingolipid/ceramide profiles in people with T2D compared to healthy controls and, 2) if the change in sphingolipid/ceramide profile is associated with reverse cardiovascular remodelling.

METHODS

Post-hoc analysis of a randomised controlled trial (NCT02590822) including adults with T2D with no cardiovascular disease who completed a 12-week low-energy (∼810 kcal/day) meal-replacement plan (MRP) and matched healthy controls (HC). Echocardiography, cardiac MRI and a fasting blood for lipidomics were undertaken pre/post-intervention. Candidate biomarkers were identified from case-control comparison (fold change > 1.5 and statistical significance p < 0.05) and their response to the MRP reported. Association between change in biomarkers and change indices of cardiac remodelling were explored.

RESULTS

Twenty-four people with T2D (15 males, age 51.1 ± 5.7 years), and 25 HC (15 male, 48.3 ± 6.6 years) were included. Subjects with T2D had increased left ventricular (LV) mass:volume ratio (0.84 ± 0.13 vs. 0.70 ± 0.08, p < 0.001), increased systolic function but impaired diastolic function compared to HC. Twelve long-chain polyunsaturated sphingolipids, including four ceramides, were downregulated in subjects with T2D at baseline. Three sphingomyelin species and all ceramides were inversely associated with LV mass:volume. There was a significant increase in all species and shift towards HC following the MRP, however, none of these changes were associated with reverse cardiac remodelling.

CONCLUSION

The lack of association between change in sphingolipids/ceramides and reverse cardiac remodelling following the MRP casts doubt on a causative role of sphingolipids/ceramides in the progression of heart failure in T2D.

TRIAL REGISTRATION

NCT02590822.

The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT)

AIMS/HYPOTHESIS

High-throughput metabolomics technologies in a variety of study designs have demonstrated a consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be reversed with weight loss and diabetes remission has been weakly investigated. We aimed to characterise the metabolomic consequences of a weight-loss intervention in individuals with type 2 diabetes.

METHODS

We analysed 574 fasted serum samples collected within an existing RCT (the Diabetes Remission Clinical Trial [DiRECT]) (N=298). In the trial, participating primary care practices were randomly assigned (1:1) to provide either a weight management programme (intervention) or best-practice care by guidelines (control) treatment to individuals with type 2 diabetes. Here, metabolomics analysis was performed on samples collected at baseline and 12 months using both untargeted MS and targeted 1H-NMR spectroscopy. Multivariable regression models were fitted to evaluate the effect of the intervention on metabolite levels.

RESULTS

Decreases in branched-chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention (Holm-corrected p<0.05). In individuals who lost more than 9 kg between baseline and 12 months, those who achieved diabetes remission saw greater reductions in glucose, fructose and mannose, compared with those who did not achieve remission.

CONCLUSIONS/INTERPRETATION

We have characterised the metabolomic effects of an integrated weight management programme previously shown to deliver weight loss and diabetes remission. A large proportion of the metabolome appears to be modifiable. Patterns of change were largely and strikingly opposite to perturbances previously documented with the development of type 2 diabetes.

DATA AVAILABILITY

The data used for analysis are available on a research data repository ( https://researchdata.gla.ac.uk/ ) with access given to researchers subject to appropriate data sharing agreements. Metabolite data preparation, data pre-processing, statistical analyses and figure generation were performed in R Studio v.1.0.143 using R v.4.0.2. The R code for this study has been made publicly available on GitHub at: https://github.com/lauracorbin/metabolomics_of_direct .

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease

BACKGROUND

Olive oil consumption has been inversely associated with the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, the impact of olive oil consumption on plasma metabolites remains poorly understood. This study aims to identify plasma metabolites related to total and specific types of olive oil consumption, and to assess the prospective associations of the identified multi-metabolite profiles with the risk of T2D and CVD.

METHODS

The discovery population included 1837 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) trial with available metabolomics data at baseline. Olive oil consumption was determined through food-frequency questionnaires (FFQ) and adjusted for total energy. A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation sample. Plasma metabolomics analyses were performed using LC-MS. Cross-sectional associations between 385 known candidate metabolites and olive oil consumption were assessed using elastic net regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite-weighted models and FFQ-derived olive oil consumption in each pair of training-validation data sets within the discovery sample. We further estimated the prospective associations of the identified plasma multi-metabolite profile with incident T2D and CVD using multivariable Cox regression models.

RESULTS

We identified a metabolomic signature for the consumption of total olive oil (with 74 metabolites), VOO (with 78 metabolites), and COO (with 17 metabolites), including several lipids, acylcarnitines, and amino acids. 10-CV Pearson correlation coefficients between total olive oil consumption derived from FFQs and the multi-metabolite profile were 0.40 (95% CI 0.37, 0.44) and 0.27 (95% CI 0.22, 0.31) for the discovery and validation sample, respectively. We identified several overlapping and distinct metabolites according to the type of olive oil consumed. The baseline metabolite profiles of total and extra virgin olive oil were inversely associated with CVD incidence (HR per 1SD: 0.79; 95% CI 0.67, 0.92 for total olive oil and 0.70; 0.59, 0.83 for extra virgin olive oil) after adjustment for confounders. However, no significant associations were observed between these metabolite profiles and T2D incidence.

CONCLUSIONS

This study reveals a panel of plasma metabolites linked to the consumption of total and specific types of olive oil. The metabolite profiles of total olive oil consumption and extra virgin olive oil were associated with a decreased risk of incident CVD in a high cardiovascular-risk Mediterranean population, though no associations were observed with T2D incidence.

TRIAL REGISTRATION

The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).

Lipidomics Profiling of Metformin-Induced Changes in Obesity and Type 2 Diabetes Mellitus: Insights and Biomarker Potential

Metformin is the first-line oral medication for treating type 2 diabetes mellitus (T2DM). In the current study, an untargeted lipidomic analytical approach was used to investigate the alterations in the serum lipidome of a cohort of 89 participants, including healthy lean controls and obese diabetic patients, and to examine the alterations associated with metformin administration. A total of 115 lipid molecules were significantly dysregulated (64 up-regulated and 51 down-regulated) in the obese compared to lean controls. However, the levels of 224 lipid molecules were significantly dysregulated (125 up-regulated and 99 down-regulated) in obese diabetic patients compared to the obese group. Metformin administration in obese diabetic patients was associated with significant dysregulation of 54 lipid molecule levels (20 up-regulated and 34 down-regulated). Levels of six molecules belonging to five lipid subclasses were simultaneously dysregulated by the effects of obesity, T2DM, and metformin. These include two putatively annotated triacylglycerols (TGs), one plasmenyl phosphatidylcholine (PC), one phosphatidylglycerol (PGs), one sterol lipid (ST), and one Mannosyl-phosphoinositol ceramide (MIPC). This study provides new insights into our understanding of the lipidomics alterations associated with obesity, T2DM, and metformin and offers a new platform for potential biomarkers for the progression of diabetes and treatment response in obese patients.

Branched chain amino acids harbor distinct and often opposing effects on health and disease

BACKGROUND

The branched chain amino acids (BCAA) leucine, isoleucine, and valine are essential nutrients that have been associated with diabetes, cancers, and cardiovascular diseases. Observational studies suggest that BCAAs exert homogeneous phenotypic effects, but these findings are inconsistent with results from experimental human and animal studies.

METHODS

Hypothesizing that inconsistencies between observational and experimental BCAA studies reflect bias from shared lifestyle and genetic factors in observational studies, we used data from the UK Biobank and applied multivariable Mendelian randomization causal inference methods designed to address these biases.

RESULTS

In n = 97,469 participants of European ancestry (mean age = 56.7 years; 54.1% female), we estimate distinct and often opposing total causal effects for each BCAA. For example, of the 117 phenotypes with evidence of a statistically significant total causal effect for at least one BCAA, almost half (44%, n = 52) are associated with only one BCAA. These 52 associations include total causal effects of valine on diabetic eye disease [odds ratio = 1.51, 95% confidence interval (CI) = 1.31, 1.76], valine on albuminuria (odds ratio = 1.14, 95% CI = 1.08, 1.20), and isoleucine on angina (odds ratio = 1.17, 95% CI = 1.31, 1.76).

CONCLUSIONS

Our results suggest that the observational literature provides a flawed picture of BCAA phenotypic effects that is inconsistent with experimental studies and could mislead efforts developing novel therapeutics. More broadly, these findings motivate the development and application of causal inference approaches that enable 'omics studies conducted in observational settings to account for the biasing effects of shared genetic and lifestyle factors.

Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features

AIMS

To identify metabolite and lipid biomarkers of diabetes in the Indian subpopulation in newly diagnosed diabetic and long-term diabetic individuals. To utilize the global polar metabolomic and lipidomic profiles to predict the susceptibility of an individual to diabetes using machine learning algorithms.

MATERIALS AND METHODS

87 individuals, including healthy, newly diabetic, and long-term diabetics on medication, were included in the study. Post consent, their serum was used to isolate polar metabolome and lipidome. NMR and LCMS were used to identify the polar metabolites and lipids, respectively. Statistical analysis was done to determine significantly altered molecules. NMR and LCMS comprehensive data were utilized to generate diabetic models using machine learning algorithms. 10 more individuals (pre-diabetic) were recruited, and their polar metabolomic and lipidomic profiles were generated. Pre-diabetic metabolic profiles were then utilized to predict the diabetic status of the metabolome and lipidome beyond glucose levels.

RESULTS

Mannose, Betaine, Xanthine, Triglyceride (38:1), Sphingomyelin (d63:7), and Phosphatidic acid (37:2) are some of the top key biomarkers of diabetes. The predictive model generated showed the receiver operating characteristic area under the curve (ROC-AUC) as 1 on both test and validation data indicating excellent accuracy. This model then predicted the diabetic closeness of the metabolism of pre-diabetic individuals based on probability scores.

CONCLUSION

Polar metabolic and lipid profile of diabetic individuals is very different from that of healthy individuals. Lipid profile alters before the polar metabolic profile in diabetes-susceptible individuals. Without regard to glucose, the diabetic closeness of the metabolism of any individual can be determined.

Untargeted metabolomics reveals dynamic changes in metabolic profiles of rat supraspinatus tendon at three different time points after diabetes induction

Objective

To investigate the dynamic changes of metabolite composition in rat supraspinatus tendons at different stages of diabetes by untargeted metabolomics analysis.

Methods

A total of 80 Sprague-Dawley rats were randomly divided into normal (NG, n = 20) and type 2 diabetes mellitus groups (T2DM, n = 60) and subdivided into three groups according to the duration of diabetes: T2DM-4w, T2DM-12w, and T2DM-24w groups; the duration was calculated from the time point of T2DM rat model establishment. The three comparison groups were set up in this study, T2DM-4w group vs. NG, T2DM-12w group vs. T2DM-4w group, and T2DM-24w group vs. T2DM-12w group. The metabolite profiles of supraspinatus tendon were obtained using tandem mass spectrometry. Metabolomics multivariate statistics were used for metabolic data analysis and differential metabolite (DEM) determination. The intersection of the three comparison groups' DEMs was defined as key metabolites that changed consistently in the supraspinatus tendon after diabetes induction; then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed.

Results

T2DM-4w group vs. NG, T2DM-12w group vs. T2DM-4w group, and T2DM-24w group vs. T2DM-12w group detected 94 (86 up-regulated and 8 down-regulated), 36 (13 up-regulated and 23 down-regulated) and 86 (24 up-regulated and 62 down-regulated) DEMs, respectively. Seven key metabolites of sustained changes in the supraspinatus tendon following induction of diabetes include D-Lactic acid, xanthine, O-acetyl-L-carnitine, isoleucylproline, propoxycarbazone, uric acid, and cytidine, which are the first identified biomarkers of the supraspinatus tendon as it progresses through the course of diabetes. The results of KEGG pathway enrichment analysis showed that the main pathway of supraspinatus metabolism affected by diabetes (p < 0.05) was purine metabolism. The results of the KEGG metabolic pathway vs. DEMs correlation network graph revealed that uric acid and xanthine play a role in more metabolic pathways.

Conclusion

Untargeted metabolomics revealed the dynamic changes of metabolite composition in rat supraspinatus tendons at different stages of diabetes, and the newly discovered seven metabolites, especially uric acid and xanthine, may provide novel research to elucidate the mechanism of diabetes-induced tendinopathy.

Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial

Background

In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework.

Methods

Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated.

Results

Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines).

Conclusion

The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health.

Clinical trial registration

isrctn.com, identifier ISRCTN15305840.

Plasma metabolomics supports non-fasted sampling for metabolic profiling across a spectrum of glucose tolerance in the Nile rat model for type 2 diabetes

Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms. The Nile rat (Arvicanthis niloticus) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and has a wide range of overt diabetes onset and diabetes progression patterns in both sexes, better mimicking the heterogeneous diabetic phenotype in humans. While fasted blood glucose has historically been used to monitor diabetic progression, postprandial blood glucose is more sensitive to the initial stages of diabetes. However, there is a long-held assumption that ad libitum feeding in rodent models leads to increased variance, thus masking diabetes-related metabolic changes in the plasma. Here we compared repeatability within triplicates of non-fasted or fasted plasma samples and assessed metabolic changes relevant to glucose tolerance in fasted and non-fasted plasma of 8-10-week-old male Nile rats. We used liquid chromatography-mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. We found that, compared to fasted metabolites, non-fasted plasma metabolites are not only more strongly associated with glucose tolerance on the basis of unsupervised clustering and elastic net regression model, but also have a lower replicate variance. Between the two sampling groups, we detected 66 non-fasted metabolites and 32 fasted metabolites that were associated with glucose tolerance using a combined approach with multivariable elastic net and individual metabolite linear models. Further, to test if metabolite replicate variance is affected by age and sex, we measured non-fasted replicate variance in a cohort of mature 30-week-old male and female Nile rats. Our results support using non-fasted plasma metabolomics to study glucose tolerance in Nile rats across the progression of diabetes.

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.

Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data

Introduction

The aim of this study was to cluster patients with chronic complications of type 2 diabetes mellitus (T2DM) by cluster analysis in Dalian, China, and examine the variance in risk of different chronic complications and metabolic levels among the various subclusters.

Methods

2267 hospitalized patients were included in the K-means cluster analysis based on 11 variables [Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Glucose, Triglycerides (TG), Total Cholesterol (TC), Uric Acid (UA), microalbuminuria (mAlb), Insulin, Insulin Sensitivity Index (ISI) and Homa Insulin-Resistance (Homa-IR)]. The risk of various chronic complications of T2DM in different subclusters was analyzed by multivariate logistic regression, and the Kruskal-Wallis H test and the Nemenyi test examined the differences in metabolites among different subclusters.

Results

Four subclusters were identified by clustering analysis, and each subcluster had significant features and was labeled with a different level of risk. Cluster 1 contained 1112 inpatients (49.05%), labeled as "Low-Risk"; cluster 2 included 859 (37.89%) inpatients, the label characteristics as "Medium-Low-Risk"; cluster 3 included 134 (5.91%) inpatients, labeled "Medium-Risk"; cluster 4 included 162 (7.15%) inpatients, and the label feature was "High-Risk". Additionally, in different subclusters, the proportion of patients with multiple chronic complications was different, and the risk of the same chronic complication also had significant differences. Compared to the "Low-Risk" cluster, the other three clusters exhibit a higher risk of microangiopathy. After additional adjustment for 20 covariates, the odds ratios (ORs) and 95% confidence intervals (95%CI) of the "Medium-Low-Risk" cluster, the "Medium-Risk" cluster, and the"High-Risk" cluster are 1.369 (1.042, 1.799), 2.188 (1.496, 3.201), and 9.644 (5.851, 15.896) (all p<0.05). Representatively, the "High-Risk" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (p<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (p<0.05)] after 20 variables adjusted. Four metabolites with statistically significant distribution differences when compared with other subclusters [Threonine (Thr), Tyrosine (Tyr), Glutaryl carnitine (C5DC), and Butyryl carnitine (C4)].

Conclusion

Patients with chronic complications of T2DM had significant clustering characteristics, and the risk of target organ damage in different subclusters was significantly different, as were the levels of metabolites. Which may become a new idea for the prevention and treatment of chronic complications of T2DM.

Spatio-temporal disparities of Clonorchis sinensis infection in animal hosts in China: a systematic review and meta-analysis

BACKGROUND

Clonorchis sinensis, one of the most important food-borne zoonotic trematodes, remains prevalent in China. Understanding its infection status in animals is crucial for controlling human clonorchiasis. Here we conducted a systematic review and meta-analysis to focus on the spatio-temporal disparities of C. sinensis infection in animals in China.

METHODS

Data on C. sinensis prevalence in snails, the second intermediate hosts, or animal reservoirs in China were extracted from electronic databases including PubMed, Embase, Web of Science, Chinese Wanfang database, CNKI, VIP, and China Biomedical Literature database. A random-effects meta-analysis model was utilized to estimate the pooled prevalence in each of the above animal hosts. Subgroup analysis and multivariable meta-regression were performed to explore potential sources of heterogeneity across studies and compare the temporal disparity of infection rates between high and low epidemic areas. Scatter plots were used to depict the biogeographical characteristics of regions reporting C. sinensis infection in animals.

RESULTS

The overall pooled prevalence of C. sinensis was 0.9% (95% CI: 0.6-1.2%) in snails, 14.2% (12.7-15.7%) in the second intermediate host, and 14.3% (11.4-17.6%) in animal reservoirs. Prevalence in low epidemic areas (with human prevalence < 1%) decreased from 0.6% (0.2-1.2%) before 1990 to 0.0% (0.0-3.6%) after 2010 in snails (P = 0.0499), from 20.3% (15.6-25.3%) to 8.8% (5.6-12.6%) in the second intermediate hosts (P = 0.0002), and from 18.3% (12.7-24.7%) to 4.7% (1.0-10.4%) in animal reservoirs. However, no similar decrease in prevalence was observed in high epidemic areas (with human prevalence ≥ 1.0%). C. sinensis infections were predominantly reported in areas with altitudes below 2346 m and annual cumulative precipitation above 345 mm and were mostly concentrated in eastern China.

CONCLUSIONS

There are spatio-temporal disparities in the animal infections of C. sinensis in different areas of China. Animal infections are primarily concentrated in regions with low altitude and high precipitation. The results suggest that implementing One Health-based comprehensive measures targeting both humans and animals, especially in high epidemic areas, is essential for successful eradication of C. sinensis in China.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Association of choline and betaine with the risk of cardiovascular disease and all-cause mortality: Meta-analysis

BACKGROUND

This study aimed to systematically evaluate the role of circulating levels of choline and betaine in the risk of cardiovascular disease (CVD) and all-cause mortality by comprehensively reviewing observational studies.

METHODS

This study was conducted according to PRISMA 2020 statement. Six electronic databases, including PubMed, Embase and China National Knowledge Infrastructure (CNKI), were searched for cohort studies and derivative research design types (nested case-control and case-cohort studies) from the date of inception to March 2022. We pooled relative risk (RR) and 95% confidence interval (CI) of the highest versus lowest category and per SD of circulating choline and betaine concentrations in relation to the risk of CVD and all-cause mortality.

RESULTS

In the meta-analysis, 17 studies with a total of 33,009 participants were included. Random-effects model results showed that highest versus lowest quantile of circulating choline concentrations were associated with the risk of CVD (RR = 1.29, 95% CI: 1.04-1.61) and all-cause mortality (RR = 1.62, 95% CI: 1.12-2.36). We also observed the risk of CVD were increased 13% (5%-22%) with per SD increment. Furthermore, highest versus lowest quantile of circulating betaine concentrations were not associated with the risk of CVD (RR = 1.07, 95% CI: 0.92-1.24) and all-cause mortality (RR = 1.39, 95% CI: 0.96-2.01). However, the risk of CVD was increased 14% (5%-23%) with per SD increment.

CONCLUSIONS

Higher levels of circulating choline were associated with a higher risk of CVD and all-cause mortality.

Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes

BACKGROUND

A healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk.

METHODS

In a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models.

RESULTS

The metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38-0.77 for dichotomous HL, and 0.22, 0.11-0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42-0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31-1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile.

CONCLUSIONS

In an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD.

TRIAL REGISTRATION

The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).