Metabolome-Defined Obesity and the Risk of Future Type 2 Diabetes and Mortality

Metabolite profiles of distinct obesity phenotypes integrating impacts of altitude and their association with diet and metabolic disorders in Tibetans

OBJECTIVE

Improved understanding of metabolic obesity phenotypes holds great promise for personalized strategies to combat obesity and its co-morbidities. Such investigation is however lacking in Tibetans with unique living environments and lifestyle in the highlands. Effects of altitude on heterogeneous metabolic obesity phenotypes remain unexplored.

METHODS

We defined metabolic obesity phenotypes i.e., metabolically healthy/unhealthy and obesity/normal weight in Tibetans (n = 1204) living at 2800 m in the suburb or over 4000 m in pastoral areas. 129 lipoprotein parameters and 25 low-molecular-weight metabolites were quantified and their associations with each phenotype were assessed using logistic regression models adjusting for potential confounders. The metabolic BMI (mBMI) was generated using a machine learning strategy and its relationship with prevalence of obesity co-morbidities and dietary exposures were investigated.

RESULTS

Ultrahigh altitude positively associated with the metabolically healthy and non-obese phenotype and had a tendency towards a negative association with metabolically unhealthy phenotype. Phenotype-specific associations were found for 107 metabolites (e.g., lipoprotein subclasses, N-acetyl-glycoproteins, amino acids, fatty acids and lactate, p < 0.05), among which 55 were manipulated by altitude. The mBMI showed consistent yet more pronounced associations with cardiometabolic outcomes than BMI. The ORs for diabetes, prediabetes and hypertriglyceridemia were reduced in individuals residing at ultrahigh altitude compared to those residing at high altitude. The mBMI mediated the negative association between pastoral diet and prevalence of prediabetes, hypertension and hypertriglyceridemia, respectively.

CONCLUSIONS

We found metabolite markers representing distinct obesity phenotypes associated with obesity co-morbidities and the modification effect of altitude, deciphering mechanisms underlying protective effect of ultrahigh altitude and the pastoral diet on metabolic health.

Autonomic and Enteric Profiling May Help Predict Response to Diverse Obesity Therapies

PURPOSE

Changes in autonomic (ANS) and enteric nervous systems (ENS) may be involved in pathogenesis of obesity. We hypothesized that baseline autonomic and enteric parameters may predict outcomes of diverse obesity therapies.

MATERIAL AND METHODS

We studied ANS and ENS physiology in 37 patients (8 male, 29 female, age 45 years, weight 129.7 kg) at 4 centers in patients undergoing medical (9: low-calorie diet) versus invasive (22: 16 sleeve, 6 bypass) and semi-invasive (6: 2 band, 2 high energy stimulation, 2 aspiration) weight loss therapies. Weight loss was reported as percent weight loss from baseline to latest values at 1 year and in some up to 5 years; classified as < or > /= 20% for each group. ANS testing included sympathetic adrenergic function by measuring reflex vasoconstriction and postural adjustment ratio. ENS was measured non-invasively using cutaneous low-resolution electrogastrogram.

RESULTS

Percent weight loss was greater with the invasive (28.5%) than semi-invasive (9.1%) or non-invasive low-calorie diet (4.4%) (p < .001). Percent weight loss at 1 year (and up to 5 years) corresponded to the adrenergic measure of postural adjustment ratio (r = .42, p = .012), total pulse amplitude at rest (r = .56, p < .001), and electrogastrogram standing-to-rest difference (r = .33, p = .056).

CONCLUSION

Baseline autonomic and enteric function measures correspond to percentage with loss in this pilot study using diverse weight loss methods. Autonomic and enteric profiling has potential clinical use for evaluation and treatment of obesity but needed larger controlled trials.

The Food Sources in Western Diets Modulate Obesity Development, Insulin Sensitivity, and the Plasma and Cecal Metabolome in Mice

SCOPE

Dietary constituents modulate development of obesity and type 2 diabetes. The metabolic impact from different food sources in western diets (WD) on obesity development is not fully elucidated. This study aims to identify dietary sources that differentially affect obesity development and the metabolic processes involved.

METHODS AND RESULTS

Mice were fed isocaloric WDs with protein and fat from different food groups, including egg and dairy, terrestrial meat, game meat, marine, vegetarian, and a mixture of all. This study evaluates development of obesity, glucose tolerance, insulin sensitivity, and plasma and cecal metabolome. WD based on marine or vegetarian food sources protects male mice from obesity development and insulin resistance, whereas meat-based diets promote obesity. The intake of different food sources induces marked differences in the lipid-related plasma metabolome, particularly impacting phosphatidylcholines. Fifty-nine lipid-related plasma metabolites are positively associated with adiposity and a distinct cecal metabolome is found in mice fed a marine diet.

CONCLUSION

This study demonstrates differences in obesity development between the food groups. Diet specific metabolomic signatures in plasma and cecum associated with adiposity, where a marine based diet modulates the level of plasma and cecal phosphatidylcholines in addition to preventing obesity development.

Molecular Mechanisms behind Obesity and Their Potential Exploitation in Current and Future Therapy

Obesity is a chronic disease caused primarily by the imbalance between the amount of calories supplied to the body and energy expenditure. Not only does it deteriorate the quality of life, but most importantly it increases the risk of cardiovascular diseases and the development of type 2 diabetes mellitus, leading to reduced life expectancy. In this review, we would like to present the molecular pathomechanisms underlying obesity, which constitute the target points for the action of anti-obesity medications. These include the central nervous system, brain-gut-microbiome axis, gastrointestinal motility, and energy expenditure. A significant part of this article is dedicated to incretin-based drugs such as GLP-1 receptor agonists (e.g., liraglutide and semaglutide), as well as the brand new dual GLP-1 and GIP receptor agonist tirzepatide, all of which have become "block-buster" drugs due to their effectiveness in reducing body weight and beneficial effects on the patient's metabolic profile. Finally, this review article highlights newly designed molecules with the potential for future obesity management that are the subject of ongoing clinical trials.

Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations

Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.

A Prospective Study on Lifestyle Factors, Body Mass Index Changes, and Lipitension Risk in Japanese Young and Middle-Aged Women

Background: This study investigates how lifestyle factors and westernization contribute to obesity and examines the influence of body mass index (BMI) changes and lifestyle factors on "lipitension," a significant risk factor for heart disease and metabolic syndrome. Methods: This prospective study focused on women aged 20-64 without pre-existing hypertension and dyslipidemia who underwent regular medical checkups between April 2016 and March 2022. Anthropometric measurements and blood pressure, along with low-density lipoprotein, high-density lipoprotein, and triglycerides levels, were assessed. Results: Over an average 46.5-month follow-up, 11.5% of initially healthy young and middle-aged women developed lipitension. Categorizing participants based on BMI changes revealed stable (63.8%), decreased (12.5%), and increased (23.8%) groups within this 11.5%. Increased BMI is linked with a heightened hazard risk for lipitension. Women with increased BMI who refrained from snacking (aHR [95% confidence interval (CI)] = 2.750 [1.433-5.279]), avoided late-night eating (aHR [95% CI] = 1.346 [1.032-1.754]), and engaged in alcohol consumption (aHR [95% CI] = 2.037 [1.138-3.646]) showed an elevated risk. Conversely, within the decreased BMI group, behaviors like skipping breakfast (aHR [95% CI] = 0.190 [0.047-0.764]), eating quickly (aHR [95% CI] = 0.457 [0.215-0.972]), and not eating late (aHR [95% CI] = 0.665 [0.467-0.948]) were associated to a reduced lipitension. Subgroup analysis for women with BMI <23 revealed specific behaviors influencing lipitension risk in both BMI-increased and BMI-stable groups. Conclusion: Customized interventions, including for women with BMI <23, enhance heart health, mitigating global lifestyle diseases and obesity.

Metabolic Profiling Changes Induced by Fermented Blackberries in High-Fat-Diet-Fed Mice Utilizing Gas Chromatography-Mass Spectrometry Analysis

The aim of this study was to investigate the metabolic changes associated with the anti-obesity effects of fermented blackberry extracts in the liver tissues of high-fat-diet-fed mice using mass spectrometry-based metabolomics analysis. C57BL/6J mice were divided into eight groups: normal-diet-fed mice, high-fat-diet-fed mice, high-fat diet treated with blackberry extract, high-fat-diet mice treated with blackberry fermented by L. plantarum, and high-fat diet with blackberry fermented by L. brevis. After 12 weeks, the high-fat-diet group exhibited a greater increase in liver weight compared to the control group, and among the groups, the group administered with blackberry fermented with L. plantarum showed the most pronounced reduction in liver weight. As the primary organ responsible for amino acid metabolism, the liver is crucial for maintaining amino acid homeostasis. In our study, we observed that the levels of several essential amino acids, including isoleucine and valine, were decreased by the high-fat diet, and were recovered by administration of blackberry extract fermented with L. plantarum. Our results demonstrated the potential of blackberry extract fermented with L. plantarum as a functional material for metabolic disorders by restoring some of the amino acid metabolism disturbances induced by a high-fat diet.

The Hydrophilic Metabolite UMP Alleviates Obesity Traits through a HIF2α-ACER2-Ceramide Signaling Axis

Metabolic abnormalities contribute to the pathogenesis of obesity and its complications. Yet, the understanding of the interactions between critical metabolic pathways that underlie obesity remains to be improved, in part owing to the lack of comprehensive metabolomics studies that reconcile data from both hydrophilic and lipophilic metabolome analyses that can lead to the identification and characterization of key signaling networks. Here, the study conducts a comprehensive metabolomics analysis, surveying lipids and hydrophilic metabolites of the plasma and omental adipose tissue of obese individuals and the plasma and epididymal adipose tissue of mice. Through these approaches, it is found that a significant accumulation of ceramide due to inhibited sphingolipid catabolism, while a significant reduction in the levels of uridine monophosphate (UMP), is critical to pyrimidine biosynthesis. Further, it is found that UMP administration restores sphingolipid homeostasis and can reduce obesity in mice by reversing obesity-induced inhibition of adipocyte hypoxia inducible factor 2a (Hif2α) and its target gene alkaline ceramidase 2 (Acer2), so as to promote ceramide catabolism and alleviate its accumulation within cells. Using adipose tissue Hif2α-specific knockout mice, the study further demonstrates that the presence of UMP can alleviate obesity through a HIF2α-ACER2-ceramide pathway, which can be a new signaling axis for obesity improvement.

Mortality outcomes in diabetic metabolic dysfunction-associated fatty liver disease: non-obese versus obese individuals

The difference in the survival of obese patients and normal-weight/lean patients with diabetic MAFLD remains unclear. Therefore, we aimed to describe the long-term survival of individuals with diabetic MAFLD and overweight/obesity (OT2M), diabetic MAFLD with lean/normal weight (LT2M), MAFLD with overweight/obesity and without T2DM (OM), and MAFLD with lean/normal weight and without T2DM (LM). Using the NHANESIII database, participants with MAFLD were divided into four groups. Hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause, cardiovascular disease (CVD)-related, and cancer-related mortalities for different MAFLD subtypes were evaluated using Cox proportional hazards models. Of the 3539 participants, 1618 participants (42.61%) died during a mean follow-up period of 274.41 ± 2.35 months. LT2M and OT2M had higher risks of all-cause mortality (adjusted HR, 2.14; 95% CI 1.82-2.51; p < 0.0001; adjusted HR, 2.24; 95% CI 1.32-3.81; p = 0.003) and CVD-related mortality (adjusted HR, 3.25; 95% CI 1.72-6.14; p < 0.0001; adjusted HR, 3.36; 95% CI 2.52-4.47; p < 0.0001) than did OM. All-cause and CVD mortality rates in LT2M and OT2M patients were higher than those in OM patients. Patients with concurrent T2DM and MAFLD should be screened, regardless of the presence of obesity.

Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow-Up in the General Population

BACKGROUND

Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality.

METHODS AND RESULTS

We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R2=0.27 and R2=0.39, respectively). Metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low-density lipoprotein. After 23 years of follow-up, metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality.

CONCLUSIONS

Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.

Intake of different types of seafood and meat and risk of type 2 diabetes in women: a prospective study supported by a dietary intervention in mice

Detailed knowledge regarding the associations between intake of different types of seafood and meat and the risk of type 2 diabetes (T2D), and insight into possible mechanisms are warranted. In this study we aimed to evaluate the associations between intake of different types of seafood and meat and the subsequent risk of T2D using the Norwegian Mother, Father, and Child Cohort Study (MoBa), and furthermore, by using a mouse model to gain further insight into possible molecular mechanisms contributing to the associated metabolic changes. Women in MoBa who were free of pharmacologically treated diabetes at baseline (n = 60,777) were prospectively evaluated for incident T2D, identified on the basis of medication usages > 90 days after delivery, ascertained by the Norwegian Prescription Database. Dietary intake was obtained with a validated 255-item food frequency questionnaire which assessed habitual diet during the first 4-5 months of pregnancy. Metabolic phenotypes and plasma metabolome were investigated in female mice fed isocaloric diets with different types of seafood and meat mimicking the dietary intake in the human cohort. During maximum 10-year and mean (SD) 7.2 (1.6) years follow-up time, 681 (1.1%) women developed pharmacologically treated T2D. All statistical models identified a higher risk of T2D with increased shellfish intake, whereas no associations were observed for total seafood, fatty fish, total meat and red meat in the adjusted models. In mice, the shellfish-based western diet induced reduced glucose tolerance and insulin secretion compared to the diet based on lean fish, and we identified a number of metabolites elevated in plasma from shellfish-fed mice that correlated with glucose intolerance. Mice fed a western diet based on meat also exhibited reduced glucose tolerance in comparison to lean fish fed mice, whereas mice fed fatty fish, total seafood or red meat did not differ from lean fish fed mice. We observed a diet-specific metabolic signature in plasma demonstrating five distinct metabolite profiles in mice fed shellfish, fatty fish, total seafood/lean fish, a mixed diet and meat. In conclusion, these findings demonstrate that different types of seafood have different outcome on T2D risk. In women, intake of shellfish was associated with higher risk of T2D. In female mice, a shellfish enriched diet reduced glucose tolerance and altered the abundance of several distinct plasma metabolites correlating with glucose tolerance.

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.

Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease Among Low-Income Black and White Americans

BACKGROUND

Life's essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about the LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals.

METHODS

In a nested case-control study of coronary heart disease (CHD) among 299 pairs of Black and 298 pairs of White low-income Americans from the Southern Community Cohort Study, we estimated LE8 score and applied untargeted plasma metabolomics and elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. The mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 299 pairs of Chinese adults.

RESULTS

Higher LE8 score was associated with lower CHD risk (standardized odds ratio, 0.61 [95% CI, 0.53-0.69]). The MetaSig, consisting of 133 metabolites, showed significant correlation with LE8 score (r=0.61) and inverse association with CHD (odds ratio, 0.57 [0.49-0.65]), robust to adjustment for LE8 score and across participants with different sociodemographic and health status ([odds ratios, 0.42-0.69]; all P<0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort (r=0.49; odds ratio, 0.57 [0.46-0.69]).

CONCLUSIONS

Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective measure of LE8 and its metabolic phenotype relevant to CHD prevention among diverse populations.

Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

INTRODUCTION

Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.

METHODS

A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.

RESULTS

Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.

CONCLUSIONS

Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.

Metabolomic Profiles of Nonobese and Obese Women With Gestational Diabetes

CONTEXT

In non-pregnant population, nonobese individuals with obesity-related metabolome have increased risk for type 2 diabetes and cardiovascular diseases. The risk of these diseases is also increased after gestational diabetes.

OBJECTIVE

This work aimed to examine whether nonobese (body mass index [BMI] < 30) and obese (BMI ≥ 30) women with gestational diabetes mellitus (GDM) and obese non-GDM women differ in metabolomic profiles from nonobese non-GDM controls.

METHODS

Levels of 66 metabolic measures were assessed in early (median 13, IQR 12.4-13.7 gestation weeks), and across early, mid (20, 19.3-23.0), and late (28, 27.0-35.0) pregnancy blood samples in 755 pregnant women from the PREDO and RADIEL studies. The independent replication cohort comprised 490 pregnant women.

RESULTS

Nonobese and obese GDM, and obese non-GDM women differed similarly from the controls across early, mid, and late pregnancy in 13 measures, including very low-density lipoprotein-related measures, and fatty acids. In 6 measures, including fatty acid (FA) ratios, glycolysis-related measures, valine, and 3-hydroxybutyrate, the differences between obese GDM women and controls were more pronounced than the differences between nonobese GDM or obese non-GDM women and controls. In 16 measures, including HDL-related measures, FA ratios, amino acids, and inflammation, differences between obese GDM or obese non-GDM women and controls were more pronounced than the differences between nonobese GDM women and controls. Most differences were evident in early pregnancy, and in the replication cohort were more often in the same direction than would be expected by chance alone.

CONCLUSION

Differences between nonobese and obese GDM, or obese non-GDM women and controls in metabolomic profiles may allow detection of high-risk women for timely targeted preventive interventions.

Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models

BACKGROUND

Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia.

OBJECTIVE

This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index.

STUDY DESIGN

This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate.

RESULTS

First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes.

CONCLUSION

Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

Comparison of the Effect of Phospholipid Extracts from Salmon and Silver Carp Heads on High-Fat-Diet-Induced Metabolic Syndrome in C57BL/6J Mice

Metabolic syndrome (MetS) is a global health problem, and EPA/DHA-enriched phospholipids (EPA/DHA-PLs) have been found to have positive effects on MetS improvement. Currently, research on EPA/DHA-PL mainly focuses on special and rare seafood, such as phospholipids derived from krill, sea cucumber, squid, and fish roe. However, it has been recently demonstrated that abundant EPA/DHA-PL can also be found in bulk fish and its by-products. Nonetheless, there is still limited research on the biological activities of EPA/DHA-PL derived from these sources. The aim of this study was to investigate the effect of phospholipid extracts from the heads of salmon and silver carp (S-PLE and SC-PLE) on the high-fat-diet-induced MetS in C57/BL mice. After an 8-week intervention, both SC-PLE and S-PLE had a significant ameliorating effect on MetS. Moreover, SC-PLE was more effective than S-PLE in reducing liver inflammation and fasting glucose. Both of the PL extracts were able to regulate the expression of key genes in lipid synthesis, fatty acid β-oxidation, and insulin signaling pathways. Compared with S-PLE, dietary SC-PLE had a greater influence on liver metabolomics. Pathway enrichment analysis showed that the differential metabolites of SC-PLE were mainly involved in arachidonic acid metabolism and glutathione metabolism. The results indicated that the different metabolic regulation methods of S-PLE and SC-PLE could be related to their variant molecular composition in EPA/DHA-PL.

Salivary Metabolomic Signatures and Body Mass Index in Italian Adolescents: A Pilot Study

Context

Obesity surveillance is scarce in adolescents, and little is known on whether salivary metabolomics data, emerging minimally invasive biomarkers, can characterize metabolic patterns associated with overweight or obesity in adolescents.

Objective

This pilot study aims to identify the salivary molecular signatures associated with body mass index (BMI) in Italian adolescents.

Methods

Saliva samples and BMI were collected in a subset of n = 74 young adolescents enrolled in the Public Health Impact of Metal Exposure study (2007-2014). A total of 217 untargeted metabolites were identified using liquid chromatography-high resolution mass spectrometry. Robust linear regression was used to cross-sectionally determine associations between metabolomic signatures and sex-specific BMI-for-age z-scores (z-BMI).

Results

Nearly 35% of the adolescents (median age: 12 years; 51% females) were either obese or overweight. A higher z-BMI was observed in males compared to females (P = .02). One nucleoside (deoxyadenosine) and 2 lipids (18:0-18:2 phosphatidylcholine and dipalmitoyl-phosphoethanolamine) were negatively related to z-BMI (P < .05), whereas 2 benzenoids (3-hydroxyanthranilic acid and a phthalate metabolite) were positively associated with z-BMI (P < .05). In males, several metabolites including deoxyadenosine, as well as deoxycarnitine, hyodeoxycholic acid, N-methylglutamic acid, bisphenol P, and trigonelline were downregulated, while 3 metabolites (3-hydroxyanthranilic acid, theobromine/theophylline/paraxanthine, and alanine) were upregulated in relation to z-BMI (P < .05). In females, deoxyadenosine and dipalmitoyl-phosphoethanolamine were negatively associated with z-BMI while deoxycarnitine and a phthalate metabolite were positively associated (P < .05). A single energy-related pathway was enriched in the identified associations in females (carnitine synthesis, P = .04).

Conclusion

Salivary metabolites involved in nucleotide, lipid, and energy metabolism were primarily altered in relation to BMI in adolescents.

The Associations of Habitual Intake of Sulfur Amino Acids, Proteins and Diet Quality with Plasma Sulfur Amino Acid Concentrations: The Maastricht Study

BACKGROUND

Plasma sulfur amino acids (SAAs), i.e., methionine, total cysteine (tCys), total homocysteine (tHcy), cystathionine, total glutathione (tGSH), and taurine, are potential risk factors for obesity and cardiometabolic disorders. However, except for plasma tHcy, little is known about how dietary intake modifies plasma SAA concentrations.

OBJECTIVE

To investigate whether the intake of SAAs and proteins or diet quality is associated with plasma SAAs.

METHODS

Data from a cross-sectional subset of The Maastricht Study (n = 1145, 50.5% men, 61 interquartile range: [55, 66] y, 22.5% with prediabetes and 34.3% with type 2 diabetes) were investigated. Dietary intake was assessed using a validated food frequency questionnaire. The intake of SAAs (total, methionine, and cysteine) and proteins (total, animal, and plant) was estimated from the Dutch and Danish food composition tables. Diet quality was assessed using the Dutch Healthy Diet Index, the Mediterranean Diet Score, and the Dietary Approaches to Stop Hypertension score. Fasting plasma SAAs were measured by liquid chromatography (LC) tandem mass spectrometry (MS) (LC/MS-MS). Associations were investigated with multiple linear regressions with tertiles of dietary intake measures (main exposures) and z-standardized plasma SAAs (outcomes).

RESULTS

Intake of total SAAs and total proteins was positively associated with plasma tCys and cystathionine. Associations were stronger in women and in those with normal body weight. Higher intake of cysteine and plant proteins was associated with lower plasma tHcy and higher cystathionine. Higher methionine intake was associated with lower plasma tGSH, whereas cysteine intake was positively associated with tGSH. Higher intake of methionine and animal proteins was associated with higher plasma taurine. Better diet quality was consistently related to lower plasma tHcy concentrations, but it was not associated with the other SAAs.

CONCLUSION

Targeted dietary modifications might be effective in modifying plasma concentrations of tCys, tHcy, and cystathionine, which have been associated with obesity and cardiometabolic disorders.

Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases

BACKGROUND

We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes.

METHODS

This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study developed and validated a metBMI prediction model using a ridge regression model among 89,830 participants based on 249 metabolites. Five obesity phenotypes were obtained by metBMI and actual BMI (actBMI): normal weight (NW, metBMI of 18.5-24.9 kg/m²), overweight (OW, metBMI of 25-29.9 kg/m²), obesity (OB, metBMI ≥ 30 kg/m²), overestimated (OE, metBMI-actBMI > 5 kg/m²), and underestimated (UE, metBMI-actBMI < - 5 kg/m²). Additional participants from the Guangzhou Diabetes Eye Study (GDES) were included for validating the hypothesis. Outcomes included all-cause and cardiovascular (CVD)-cause mortality, as well as incident CVD (coronary heart disease, heart failure, myocardial infarction [MI], and stroke) and age-related eye diseases (age-related macular degeneration [AMD], cataracts, glaucoma, and diabetic retinopathy [DR]).

RESULTS

In the UKB, although OE group had lower actBMI than NW group, the OE group had a significantly higher risk of all-cause mortality than those in NW prediction group (HR, 1.68; 95% CI 1.16-2.43). Similarly, the OE group had a 1.7-3.6-fold higher risk than their NW counterparts for cardiovascular mortality, heart failure, myocardial infarction, and coronary heart disease (all P < 0.05). In addition, risk of age-related macular denegation (HR, 1.96; 95% CI 1.02-3.77) was significantly higher in OE group. In the contrast, UE and OB groups showed similar risks of mortality and of cardiovascular and age-related eye diseases (all P > 0.05), though the UE group had significantly higher actBMI than OB group. In the GDES cohort, we further confirmed the potential of metabolic BMI (metBMI) fingerprints for risk stratification of cardiovascular diseases using a different metabolomic approach.

CONCLUSIONS

Gaps of metBMI and actBMI identified novel metabolic subtypes, which exhibit distinctive cardiovascular and ocular risk profiles. The groups carrying obesity-related metabolites were at higher risk of mortality and morbidity than those with normal health metabolites. Metabolomics allowed for leveraging the future of diagnosis and management of 'healthily obese' and 'unhealthily lean' individuals.

Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease among Low-Income Black and White Americans

Background and Aims

Life's Essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals.

Methods

In a nested case-control study of coronary heart disease (CHD) among 598 Black and 596 White low-income Americans, we estimated LE8 score, conducted untargeted plasma metabolites profiling, and used elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. Mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 598 Chinese adults.

Results

Higher LE8 score was associated with lower CHD risk [standardized OR (95% CI)=0.61 (0.53-0.69)]. The identified MetaSig, consisting of 133 metabolites, showed strong correlation with LE8 score ( r =0.61) and inverse association with CHD risk [OR (95% CI)=0.57 (0.49-0.65)], robust to adjustment for LE8 score and across participants with different sociodemographic and health status (ORs: 0.42-0.69; all P <0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%) ( P <0.001). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort [ r =0.49; OR (95% CI)=0.57 (0.46-0.69)].

Conclusions

Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective and comprehensive measure of LE8 score and its metabolic phenotype relevant to CHD prevention among diverse populations.

Cardiovascular risk of metabolically healthy obesity in two european populations: Prevention potential from a metabolomic study

BACKGROUND

A new definition of metabolically healthy obesity (MHO) has recently been proposed to stratify the heterogeneous mortality risk of obesity. Metabolomic profiling provides clues to metabolic alterations beyond clinical definition. We aimed to evaluate the association between MHO and cardiovascular events and assess its metabolomic pattern.

METHODS

This prospective study included Europeans from two population-based studies, the FLEMENGHO and the Hortega study. A total of 2339 participants with follow-up were analyzed, including 2218 with metabolomic profiling. Metabolic health was developed from the third National Health and Nutrition Examination Survey and the UK biobank cohorts and defined as systolic blood pressure < 130 mmHg, no antihypertensive drugs, waist-to-hip ratio < 0.95 for women or 1.03 for men, and the absence of diabetes. BMI categories included normal weight, overweight, and obesity (BMI < 25, 25-30, ≥ 30 kg/m²). Participants were classified into six subgroups according to BMI category and metabolic healthy status. Outcomes were fatal and nonfatal composited cardiovascular events.

RESULTS

Of 2339 participants, the mean age was 51 years, 1161 (49.6%) were women, 434 (18.6%) had obesity, 117 (5.0%) were classified as MHO, and both cohorts had similar characteristics. Over a median of 9.2-year (3.7-13.0) follow-up, 245 cardiovascular events occurred. Compared to those with metabolically healthy normal weight, individuals with metabolic unhealthy status had a higher risk of cardiovascular events, regardless of BMI category (adjusted HR: 3.30 [95% CI: 1.73-6.28] for normal weight, 2.50 [95% CI: 1.34-4.66] for overweight, and 3.42 [95% CI: 1.81-6.44] for obesity), whereas those with MHO were not at increased risk of cardiovascular events (HR: 1.11 [95% CI: 0.36-3.45]). Factor analysis identified a metabolomic factor mainly associated with glucose regulation, which was associated with cardiovascular events (HR: 1.22 [95% CI: 1.10-1.36]). Individuals with MHO tended to present a higher metabolomic factor score than those with metabolically healthy normal weight (0.175 vs. -0.057, P = 0.019), and the score was comparable to metabolically unhealthy obesity (0.175 vs. -0.080, P = 0.91).

CONCLUSIONS

Individuals with MHO may not present higher short-term cardiovascular risk but tend to have a metabolomic pattern associated with higher cardiovascular risk, emphasizing a need for early intervention.

Metabolically Healthy Obesity: Are Interventions Useful?

PURPOSE OF REVIEW

This review aims to detail the current global research state of metabolically healthy obesogenesis with regard to metabolic factors, disease prevalence, comparisons to unhealthy obesity, and targeted interventions to reverse or delay progression from metabolically healthy to unhealthy obesity.

RECENT FINDINGS

As a long-term condition with increased risk of cardiovascular, metabolic, and all-cause mortality risks, obesity threatens public health on a national level. The recent discovery of metabolically healthy obesity (MHO), a transitional condition during which obese persons carry comparatively lower health risks, has added to confusion about the true effect of visceral fat and subsequent long-term health risks. In this context, the evaluation of fat loss interventions, such as bariatric surgery, lifestyle changes (diet/exercise), and hormonal therapies require re-evaluation in light of evidence that progression to high-risk stages of obesity relies on metabolic status and that strategies to protect the metabolism may be useful in the prevention of metabolically unhealthy obesity. Typical calorie-based exercise and diet interventions have failed to reduce the prevalence of unhealthy obesity. Holistic lifestyle, psychological, hormonal, and pharmacological interventions for MHO, on the other hand, may at least prevent progression to metabolically unhealthy obesity.

Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic

BACKGROUND/OBJECTIVE

This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.

SUBJECTS/METHODS

Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.

RESULTS

The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.

CONCLUSIONS

Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Associations between plasma sulfur amino acids and specific fat depots in two independent cohorts: CODAM and The Maastricht Study

PURPOSE

Sulfur amino acids (SAAs) have been associated with obesity and obesity-related metabolic diseases. We investigated whether plasma SAAs (methionine, total cysteine (tCys), total homocysteine, cystathionine and total glutathione) are related to specific fat depots.

METHODS

We examined cross-sectional subsets from the CODAM cohort (n = 470, 61.3% men, median [IQR]: 67 [61, 71] years) and The Maastricht Study (DMS; n = 371, 53.4% men, 63 [55, 68] years), enriched with (pre)diabetic individuals. SAAs were measured in fasting EDTA plasma with LC-MS/MS. Outcomes comprised BMI, skinfolds, waist circumference (WC), dual-energy X-ray absorptiometry (DXA, DMS), body composition, abdominal subcutaneous and visceral adipose tissues (CODAM: ultrasound, DMS: MRI) and liver fat (estimated, in CODAM, or MRI-derived, in DMS, liver fat percentage and fatty liver disease). Associations were examined with linear or logistic regressions adjusted for relevant confounders with z-standardized primary exposures and outcomes.

RESULTS

Methionine was associated with all measures of liver fat, e.g., fatty liver disease [CODAM: OR = 1.49 (95% CI 1.19, 1.88); DMS: OR = 1.51 (1.09, 2.14)], but not with other fat depots. tCys was associated with overall obesity, e.g., BMI [CODAM: β = 0.19 (0.09, 0.28); DMS: β = 0.24 (0.14, 0.34)]; peripheral adiposity, e.g., biceps and triceps skinfolds [CODAM: β = 0.15 (0.08, 0.23); DMS: β = 0.20 (0.12, 0.29)]; and central adiposity, e.g., WC [CODAM: β = 0.16 (0.08, 0.25); DMS: β = 0.17 (0.08, 0.27)]. Associations of tCys with VAT and liver fat were inconsistent. Other SAAs were not associated with body fat.

CONCLUSION

Plasma concentrations of methionine and tCys showed distinct associations with different fat depots, with similar strengths in the two cohorts.

Current Knowledge on the Pathophysiology of Lean/Normal-Weight Type 2 Diabetes

Since early times, being overweight and obesity have been associated with impaired glucose metabolism and type 2 diabetes (T2D). Similarly, a less frequent adult-onset diabetes in low body mass index (BMI) people has been known for many decades. This form is mainly found in developing countries, whereby the largest increase in diabetes incidence is expected in coming years. The number of non-obese patients with T2D is also on the rise among non-white ethnic minorities living in high-income Western countries due to growing migratory flows. A great deal of energy has been spent on understanding the mechanisms that bind obesity to T2D. Conversely, the pathophysiologic features and factors driving the risk of T2D development in non-obese people are still much debated. To reduce the global burden of diabetes, we need to understand why not all obese people develop T2D and not all those with T2D are obese. Moreover, through both an effective prevention and the implementation of an individualized clinical management in all people with diabetes, it is hoped that this will help to reduce this global burden. The purpose of this review is to take stock of current knowledge about the pathophysiology of diabetes not associated to obesity and to highlight which aspects are worthy of future studies.

Plasma metabolomic signatures of obesity and risk of type 2 diabetes

OBJECTIVE

The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D.

METHODS

In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk.

RESULTS

A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001).

CONCLUSIONS

Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.

Gut microbiota induces DNA methylation via SCFAs predisposing obesity-prone individuals to diabetes

Obesity-prone (OP) individuals have a significant predisposition to obesity and diabetes. Previously, we have found that OP individuals, despite being normal in weight and BMI, have already exhibited diabetes-related DNA methylation signatures. However, the underlying mechanisms remain obscure. Here we determined the effects of gut microbiota on DNA methylation and investigated the underlying mechanism from microbial-derived short-chain fatty acids (SCFAs). Diabetes-related DNA methylation loci were screened and validated in a new OP cohort. Moreover, the OP group was revealed to have distinct gut microbiota compositions, and fecal microbiota transplantation (FMT) demonstrated the role of gut microbiota in inducing diabetes-related DNA methylations and glucolipid disorders. UPLC-ESI-MS/MS analysis indicated a significantly lower level of total fecal SCFAs in the OP group. The gut microbiota from OP subjects yielded markedly decreased total SCFAs, while notably enriched propionate. Additionally, propionate was also identified by variable importance in projection (VIP) score as the most symbolic SCFAs of the OP group. Further cellular experiments verified that propionate could induce hypermethylation at locus cg26345888 and subsequently inhibit the expression of the target gene DAB1, which was crucially associated with clinical vitamin D deficiency and thus may affect the development and progression of diabetes. In conclusion, our study revealed that gut microbiota-derived propionate induces specific DNA methylation, thus predisposing OP individuals to diabetes. The findings partially illuminate the mechanisms of diabetes susceptibility in OP populations, implying gut microbiota and SCFAs may serve as promising targets both for clinical treatment and medication development of diabetes.