Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations

Maternal antenatal depression is associated with metabolic alterations that predict birth outcomes, neurodevelopment and mental health of the child

BACKGROUND

Evidence regarding metabolic alterations associated with maternal antenatal depression (AD) is limited, and their role as potential biomarkers improving the prediction of AD and adverse child birth, neurodevelopmental, and mental health outcomes remains unexplored.

METHODS

In a cohort of 331 mother-child dyads, we studied associations between AD (history of medical register diagnoses and/or Center of Epidemiological Studies Depression Scale score during pregnancy≥20) and 95 metabolic measures analyzed three times during pregnancy. We tested whether the AD-related metabolic measures increased variance explained in AD over its risk factors, and in child birth, neurodevelopmental, and mental health outcomes over AD. We replicated the findings in a cohort of 416 mother-child dyads.

RESULTS

Elastic net regression identified 15 metabolic measures that collectively explained 25% (p<0.0001) of variance in AD, including amino and fatty acids, glucose, inflammation, and lipids. These metabolic measures increased the variance explained in AD over its risk factors (32.3%,p<0.0001 vs. 12.6%,p=0.004), and in child gestational age (9.0%,p<0.0001 vs. 0.7%, p=0.34), birth weight(9.0%,p=0.03 vs. 0.7%, p=0.33), developmental milestones at the age of 2.3-5.7 years(21.0%,p=0.002 vs. 11.6%,p<0.001) and any mental or behavioral disorder by the age of 13.1-16.8 years(25.2%,p=0.03 vs. 5.0%,p=0.11) over AD, child sex and age. These findings replicated in the independent cohort.

CONCLUSIONS

AD is associated with alterations in 15 metabolic measures, which collectively improve the prediction of AD over its risk factors, and birth, neurodevelopmental and mental health outcomes of the child over AD. These metabolic measures may become biomarkers identifying at-risk mothers and children for personalized interventions.

Polyunsaturated fatty acids and diabetic microvascular complications: a Mendelian randomization study

Background

Observational studies and clinical trials have implicated polyunsaturated fatty acids (PUFAs) in potentially safeguarding against diabetic microvascular complication. Nonetheless, the causal nature of these relationships remains ambiguous due to conflicting findings across studies. This research employs Mendelian randomization (MR) to assess the causal impact of PUFAs on diabetic microvascular complications.

Methods

We identified instrumental variables for PUFAs, specifically omega-3 and omega-6 fatty acids, using the UK Biobank data. Outcome data regarding diabetic microvascular complications were sourced from the FinnGen Study. Our analysis covered microvascular outcomes in both type 1 and type 2 diabetes, namely diabetic neuropathy (DN), diabetic retinopathy (DR), and diabetic kidney disease (DKD). An inverse MR analysis was conducted to examine the effect of diabetic microvascular complications on PUFAs. Sensitivity analyses were performed to validate the robustness of the results. Finally, a multivariable MR (MVMR) analysis was conducted to determine whether PUFAs have a direct influence on diabetic microvascular complications.

Results

The study indicates that elevated levels of genetically predicted omega-6 fatty acids substantially reduce the risk of DN in type 2 diabetes (odds ratio (OR): 0.62, 95% confidence interval (CI): 0.47-0.82, p = 0.001). A protective effect against DR in type 2 diabetes is also suggested (OR: 0.75, 95% CI: 0.62-0.92, p = 0.005). MVMR analysis confirmed the stability of these results after adjusting for potential confounding factors. No significant effects of omega-6 fatty acids were observed on DKD in type 2 diabetes or on any complications in type 1 diabetes. By contrast, omega-3 fatty acids showed no significant causal links with any of the diabetic microvascular complications assessed.

Conclusions

Our MR analysis reveals a causal link between omega-6 fatty acids and certain diabetic microvascular complications in type 2 diabetes, potentially providing novel insights for further mechanistic and clinical investigations into diabetic microvascular complications.

Longitudinal associations of an exposome score with serum metabolites from childhood to adolescence

Environmental and lifestyle factors, including air pollution, impaired diet, and low physical activity, have been associated with cardiometabolic risk factors in childhood and adolescence. However, environmental and lifestyle exposures do not exert their physiological effects in isolation. This study investigated associations between an exposome score to measure the impact of multiple exposures, including diet, physical activity, sleep duration, air pollution, and socioeconomic status, and serum metabolites measured using LC-MS and NMR, compared to the individual components of the score. A general population of 504 children aged 6-9 years at baseline was followed up for eight years. Data were analysed with linear mixed-effects models using the R software. The exposome score was associated with 31 metabolites, of which 12 metabolites were not associated with any individual exposure category. These findings highlight the value of a composite score to predict metabolic changes associated with multiple environmental and lifestyle exposures since childhood.

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

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.

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality

CONTEXT

Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed.

OBJECTIVE

To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models.

METHODS

This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics.

RESULTS

In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures).

CONCLUSIONS

The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

Associations of polymetabolic risk of high maternal pre-pregnancy body mass index with pregnancy complications, birth outcomes, and early childhood neurodevelopment: findings from two pregnancy cohorts

BACKGROUND

A substantial proportion of maternal pregnancy complications, adverse birth outcomes and neurodevelopmental delay in children may be attributable to high maternal pre-pregnancy Body Mass Index (BMI). However, BMI alone is insufficient for the identification of all at-risk mothers and children as many women with non-obesity(< 30 kg/m2) or normal weight(18.5-24.99 kg/m2) and their children may suffer from adversities. Evidence suggests that BMI-related metabolic changes during pregnancy may predict adverse mother-child outcomes better than maternal anthropometric BMI.

METHODS

In a cohort of 425 mother-child dyads, we identified maternal BMI-defined metabolome based on associations of 95 metabolic measures measured three times during pregnancy with maternal pre-pregnancy BMI. We then examined whether maternal BMI-defined metabolome performed better than anthropometric BMI in predicting gestational diabetes, hypertensive disorders, gestational weight gain (GWG), Caesarian section delivery, child gestational age and weight at birth, preterm birth, admission to neonatal intensive care unit (NICU), and childhood neurodevelopment. Based on metabolic measures with the highest contributions to BMI-defined metabolome, including inflammatory and glycolysis-related measures, fatty acids, fluid balance, ketone bodies, lipids and amino acids, we created a set of maternal high BMI-related polymetabolic risk scores (PMRSs), and in an independent replication cohort of 489 mother-child dyads tested their performance in predicting the same set of mother-child outcomes in comparison to anthropometric BMI.

RESULTS

BMI-defined metabolome predicted all of the studied mother-child outcomes and improved their prediction over anthropometric BMI, except for gestational hypertension and GWG. BMI-related PMRSs predicted gestational diabetes, preeclampsia, Caesarian section delivery, admission to NICU, lower gestational age at birth, lower cognitive development score of the child, and improved their prediction over anthropometric BMI. BMI-related PMRSs predicted gestational diabetes, preeclampsia, Caesarean section delivery, NICU admission and child's lower gestational age at birth even at the levels of maternal non-obesity and normal weight.

CONCLUSIONS

Maternal BMI-defined metabolome improves the prediction of pregnancy complications, birth outcomes, and neurodevelopment in children over anthropometric BMI. The novel, BMI-related PMRSs generated based on the BMI-defined metabolome have the potential to become biomarkers identifying at-risk mothers and their children for timely targeted interventions even at the level of maternal non-obesity and normal weight.

Metabolomic signature between diabetic and non-diabetic obese patients: A protocol for systematic review

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a chronic and progressive condition defined by hyperglycemia caused by abnormalities in insulin production, insulin receptor sensitivity, or both. Several studies have revealed that higher body mass index (BMI) is associated with increasing risk of developing diabetes. In this study, we perform a protocol for systematic review to explore metabolite biomarkers that could be used to identify T2DM in obese subjects.

METHODS

The protocol of this review was registered in PROSPERO (CRD42023405518). Three databases, EMBASE, PubMed, and Web of Science were selected to collect potential literature from their inceptions to July December 2023. Data for collection will include title, authors, study subjects, publication date, sample size, detection and analytical platforms, participant characteristics, biological samples, confounding factors, methods of statistical analysis, the frequency and directions of changes in potential metabolic biomarkers, and major findings. Pathway analysis of differential metabolites will be performed with MetaboAnalyst 5.0 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Human Metabolome Database.

RESULTS

The results of this systematic review will be published in a peer-reviewed journal.

CONCLUSION

This systematic review will summarize the potential biomarkers and metabolic pathways to provide a new reference for the prevention and treatment of T2DM in obese subjects.

Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts

AIMS

To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c.

METHODS

We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720).

RESULTS

The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012).

CONCLUSIONS

Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.

High-density lipoprotein subclasses and cardiovascular disease and mortality in type 2 diabetes: analysis from the Hong Kong Diabetes Biobank

OBJECTIVE

High-density lipoproteins (HDL) comprise particles of different size, density and composition and their vasoprotective functions may differ. Diabetes modifies the composition and function of HDL. We assessed associations of HDL size-based subclasses with incident cardiovascular disease (CVD) and mortality and their prognostic utility.

RESEARCH DESIGN AND METHODS

HDL subclasses by nuclear magnetic resonance spectroscopy were determined in sera from 1991 fasted adults with type 2 diabetes (T2D) consecutively recruited from March 2014 to February 2015 in Hong Kong. HDL was divided into small, medium, large and very large subclasses. Associations (per SD increment) with outcomes were evaluated using multivariate Cox proportional hazards models. C-statistic, integrated discrimination index (IDI), and categorial and continuous net reclassification improvement (NRI) were used to assess predictive value.

RESULTS

Over median (IQR) 5.2 (5.0-5.4) years, 125 participants developed incident CVD and 90 participants died. Small HDL particles (HDL-P) were inversely associated with incident CVD [hazard ratio (HR) 0.65 (95% CI 0.52, 0.81)] and all-cause mortality [0.47 (0.38, 0.59)] (false discovery rate < 0.05). Very large HDL-P were positively associated with all-cause mortality [1.75 (1.19, 2.58)]. Small HDL-P improved prediction of mortality [C-statistic 0.034 (0.013, 0.055), IDI 0.052 (0.014, 0.103), categorical NRI 0.156 (0.006, 0.252), and continuous NRI 0.571 (0.246, 0.851)] and CVD [IDI 0.017 (0.003, 0.038) and continuous NRI 0.282 (0.088, 0.486)] over the RECODe model.

CONCLUSION

Small HDL-P were inversely associated with incident CVD and all-cause mortality and improved risk stratification for adverse outcomes in people with T2D. HDL-P may be used as markers for residual risk in people with T2D.

Current Studies on Molecular Mechanisms of Insulin Resistance

Diabetes is a metabolic disease that raises the risk of microvascular and neurological disorders. Insensitivity to insulin is a characteristic of type II diabetes, which accounts for 85-90 percent of all diabetic patients. The fundamental molecular factor of insulin resistance may be impaired cell signal transduction mediated by the insulin receptor (IR). Several cell-signaling proteins, including IR, insulin receptor substrate (IRS), and phosphatidylinositol 3-kinase (PI3K), have been recognized as being important in the impaired insulin signaling pathway since they are associated with a large number of proteins that are strictly regulated and interact with other signaling pathways. Many studies have found a correlation between IR alternative splicing, IRS gene polymorphism, the complicated regulatory function of IRS serine/threonine phosphorylation, and the negative regulatory role of p85 in insulin resistance and diabetes mellitus. This review brings up-to-date knowledge of the roles of signaling proteins in insulin resistance in order to aid in the discovery of prospective targets for insulin resistance treatment.