Amino acids, microbiota-related metabolites, and the risk of incident diabetes among normoglycemic Chinese adults: Findings from the 4C study

Multiomics Picture of Obesity in Young Adults

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

Alterations of the glutamatergic system in diabetes mellitus

Diabetes mellitus (DM) is a chronic disease characterized by elevated blood glucose levels caused by a lack of insulin production (type 1 diabetes) or insulin resistance (type 2 diabetes). It is well known that DM is associated with cognitive deficits and metabolic and neurophysiological changes in the brain. Glutamate is the main excitatory neurotransmitter in the central nervous system that plays a key role in synaptic plasticity, learning, and memory processes. An increasing number of studies have suggested that abnormal activity of the glutamatergic system is implicated in the pathophysiology of DM. Dysfunction of glutamatergic neurotransmission in the central nervous system can provide an important neurobiological substrate for many disorders. Magnetic resonance spectroscopy (MRS) is a non-invasive technique that allows a better understanding of the central nervous system factors by measuring in vivo the concentrations of brain metabolites within the area of interest. Here, we briefly review the MRS studies that have examined glutamate levels in the brain of patients with DM. The present article also summarizes the available data on abnormalities in glutamatergic neurotransmission observed in different animal models of DM. In addition, the role of gut microbiota in the development of glutamatergic alterations in DM is addressed. We speculate that therapeutic strategies targeting the glutamatergic system may be beneficial in the treatment of central nervous system-related changes in diabetic patients.

Targeted Metabolomics Analysis of Serum Amino Acids in T2DM Patients

Purpose

Amino acids are the important metabolites in the body and play a crucial role in biological processes. The purpose of this study is to provide a profile of amino acids change in the serum of T2DM patients and identify potential biomarkers.

Patients and Methods

In this study, we quantitatively determined the serum amino acid profiles of 30 T2DM patients and 30 healthy volunteers. T test and multivariate statistical analysis were used to identify candidate biomarkers with GraphPad Prism 9.5 software and MetaboAnalyst 5.0 on-line platform.

Results

Thirty-four amino acids were quantified, and 19 amino acid levels differed significantly between T2DM and Healthy groups. Screened by the specific screening criteria (VIP>1.0; P<0.05; FC>1.5, or FC<0.67) in MetaboAnalyst 5.0 platform, 8 amino acids were identified as potential biomarkers. Pearson rank correlation test showed 14 differential amino acids were significantly correlated with T2DM-related physiological parameters.

Conclusion

The results of this study provide theoretical basis for the subsequent development of dietary supplements for the prevention or treatment of T2DM and its complications.

Whole Blood-based Transcriptional Risk Score for Nonobese Type 2 Diabetes Predicts Dynamic Changes in Glucose Metabolism

CONTEXT

The performance of peripheral blood transcriptional markers in evaluating risk of type 2 diabetes (T2D) with normal body mass index (BMI) is unknown.

OBJECTIVE

We developed a whole blood-based transcriptional risk score (wb-TRS) for nonobese T2D and assessed its contributions on disease risk and dynamic changes in glucose metabolism.

METHODS

Using a community-based cohort with blood transcriptome data, we developed the wb-TRS in 1105 participants aged ≥40 years who maintained a normal BMI for up to 10 years, and we validated the wb-TRS in an external dataset. Potential biological significance was explored.

RESULTS

The wb-TRS included 144 gene transcripts. Compared to the lowest tertile, wb-TRS in tertile 3 was associated with 8.91-fold (95% CI, 3.53-22.5) higher risk and each 1-unit increment was associated with 2.63-fold (95% CI, 1.87-3.68) higher risk of nonobese T2D. Furthermore, baseline wb-TRS significantly associated with dynamic changes in average, daytime, nighttime, and 24-hour glucose, HbA1c values, and area under the curve of glucose measured by continuous glucose monitoring over 6 months of intervention. The wb-TRS improved the prediction performance for nonobese T2D, combined with fasting glucose, triglycerides, and demographic and anthropometric parameters. Multi-contrast gene set enrichment (Mitch) analysis implicated oxidative phosphorylation, mTORC1 signaling, and cholesterol metabolism involved in nonobese T2D pathogenesis.

CONCLUSION

A whole blood-based nonobese T2D-associated transcriptional risk score was validated to predict dynamic changes in glucose metabolism. These findings suggested several biological pathways involved in the pathogenesis of nonobese T2D.

Integrative physiology of lysine metabolites

Lysine is an essential amino acid that serves as a building block in protein synthesis. Beside this, the metabolic activity of lysine has only recently been unraveled. Lysine metabolism is tissue specific and is linked to several renal, cardiovascular, and endocrinological diseases through human metabolomics datasets. As a free molecule, lysine takes part in the antioxidant response and engages in protein modifications, and its chemistry shapes both proteome and metabolome. In the proteome, it is an acceptor for a plethora of posttranslational modifications. In the metabolome, it can be modified, conjugated, and degraded. Here, we provide an update on integrative physiology of mammalian lysine metabolites such as α-aminoadipic acid, saccharopine, pipecolic acid, and lysine conjugates such as acetyl-lysine, and sugar-lysine conjugates such as advanced glycation end products. We also comment on their emerging associative and mechanistic links to renal disease, hypertension, diabetes, and cancer.

Association Between Body Temperature and In-Hospital Mortality Among Congestive Heart Failure Patients with Diabetes in Intensive Care Unit: A Retrospective Cohort Study

Body temperature (BT) has been utilized to assess patient outcomes across various diseases. However, the impact of BT on mortality in the intensive care unit (ICU) among patients with congestive heart failure (CHF) and diabetes mellitus (DM) remains unclear. We conducted a retrospective cohort study using data from the Medical Information Mart for Intensive Care (MIMIC)-IV data set. The primary outcome assessed was in-hospital mortality rates. BT was treated as a categorical variable in the analyses. The association between BT on ICU admission and in-hospital mortality was examined using multivariable logistic regression models, restricted cubic spline, and subgroup analysis. The cohort comprised 7063 patients with both DM and CHF (3135 females and 3928 males), with an average age of 71.5 ± 12.2 years. Comparative analysis of the reference group (Q4) revealed increased in-hospital mortality in Q6 and Q1 temperature groups, with fully adjusted odds ratios of 2.08 (95% confidence interval [CI]: 1.45-2.96) and 1.95 (95% CI: 1.35-2.79), respectively. Restricted cubic spline analysis demonstrated a U-shaped relationship between temperature on admission and mortality risk (p nonlinearity <0.001), with the nadir of risk observed at 36.8°C. The effect sizes and corresponding CIs below and above the threshold were 0.581 (95% CI: 0.434-0.777) and 1.674 (95% CI: 1.204-2.328), respectively. Stratified analyses further validated the robustness of this correlation. Our study establishes a nonlinear association between BT and in-hospital mortality in patients with both CHF and DM, with optimal suitable BT at 36.8°C. Further research is necessary to confirm this relationship.

Quantitative profiling and diagnostic potential of one-carbon and central metabolism pools in MODY2 and T1DM

BACKGROUND

Maturity-onset diabetes of the young type 2 (MODY2) is a rare genetic disorder characterized as mild fasting hyperglycemia with low risk of vascular complications caused by glucokinase gene mutation. This study aims to investigate metabolites alteration associated with MODY2, exploring possible mechanism underlying characteristic clinical manifestations and low cardiovascular risks of MODY2 and providing serum metabolite biomarkers to facilitating MODY2 diagnosis.

METHODS

Fasting serum samples from MODY2, type 1 diabetes (T1DM) and healthy individuals were collected. By using targeted metabolomics via liquid chromatography-tandem mass spectrometry platform, we quantified the metabolites involved in tricarboxylic acid (TCA) cycle and one-carbon metabolism.

RESULTS

Metabolomic profiling revealed significant difference of intermediates from central metabolism cycle, methionine cycle and several amino acids between MODY2 and T1DM groups. Among these, serum citrate, α-ketoglutaric acid, serine, glycine, glutamine and homocysteine were significantly elevated in MODY2 patients compared with T1DM patients; and compared with healthy subjects, malate and methionine levels were significantly increased in the two groups of diabetic patients. The correlation analysis with clinical indexes showed that α- ketoglutarate, serine, glycine, and glutamine were negatively correlated with blood glucose indicators including fasting blood glucose, HbA1c, and GA, while citrate was positively correlated with C-peptide. And homocysteine displayed positive correlation with HDL and negative with C-reactive protein, which shed light on the mechanism of mild symptoms and low risk of cardiovascular complications in MODY2 patients. A panel of 4 metabolites differentiated MODY2 from T1DM with AUC of 0.924, and a combination of clinical indices and metabolite also gained good diagnostic value with AUC 0.948.

CONCLUSION

In this research, we characterized the metabolite profiles of TCA cycle and one-carbon metabolism in MODY2 and T1DM and identified promising diagnostic biomarkers for MODY2. This study may provide novel insights into the pathogenesis and clinical manifestations of MODY2.

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.

Unlocking the Potential: Amino Acids' Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus

Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), imposes a significant global burden with adverse clinical outcomes and escalating healthcare expenditures. Early identification of biomarkers can facilitate better screening, earlier diagnosis, and the prevention of diabetes. However, current clinical predictors often fail to detect abnormalities during the prediabetic state. Emerging studies have identified specific amino acids as potential biomarkers for predicting the onset and progression of diabetes. Understanding the underlying pathophysiological mechanisms can offer valuable insights into disease prevention and therapeutic interventions. This review provides a comprehensive summary of evidence supporting the use of amino acids and metabolites as clinical biomarkers for insulin resistance and diabetes. We discuss promising combinations of amino acids, including branched-chain amino acids, aromatic amino acids, glycine, asparagine and aspartate, in the prediction of T2DM. Furthermore, we delve into the mechanisms involving various signaling pathways and the metabolism underlying the role of amino acids in disease development. Finally, we highlight the potential of targeting predictive amino acids for preventive and therapeutic interventions, aiming to inspire further clinical investigations and mitigate the progression of T2DM, particularly in the prediabetic stage.

Association of alpha-aminoadipic acid with cardiometabolic risk factors in healthy and high-risk individuals

Introduction

Plasma levels of the metabolite alpha-aminoadipic acid (2-AAA) have been associated with risk of type 2 diabetes (T2D) and atherosclerosis. However, little is known about the relationship of 2-AAA to other cardiometabolic risk markers in pre-disease states, or in the setting of comorbid disease.

Methods

We measured circulating 2-AAA using two methods in 1) a sample of 261 healthy individuals (2-AAA Study), and 2) in a sample of 134 persons comprising 110 individuals with treated HIV, with or without T2D, a population at high risk of metabolic disease and cardiovascular events despite suppression of circulating virus, and 24 individuals with T2D without HIV (HATIM Study). We examined associations between plasma 2-AAA and markers of cardiometabolic health within each cohort.

Results and discussion

We observed differences in 2-AAA by sex and race in both cohorts, with higher levels observed in men compared with women, and in Asian compared with Black or white individuals (P<0.05). There was no significant difference in 2-AAA by HIV status within individuals with T2D in the HATIM Study. We confirmed associations between 2-AAA and dyslipidemia in both cohorts, where high 2-AAA associated with low HDL cholesterol (P<0.001) and high triglycerides (P<0.05). As expected, within the cohort of people with HIV, 2-AAA was higher in the setting of T2D compared to pre-diabetes or normoglycemia (P<0.001). 2-AAA was positively associated with body mass index (BMI) in the 2-AAA Study, and with waist circumference and measures of visceral fat volume in HATIM (all P<0.05). Further, 2-AAA associated with increased liver fat in persons with HIV (P<0.001). Our study confirms 2-AAA as a marker of cardiometabolic risk in both healthy individuals and those at high cardiometabolic risk, reveals relationships with adiposity and hepatic steatosis, and highlights important differences by sex and race. Further studies are warranted to establish molecular mechanisms linking 2-AAA to disease in other high-risk populations.

Association between contralateral adrenal and hypothalamus-pituitary-adrenal axis in benign adrenocortical tumors

Context

Adrenal incidentaloma (AI) is commonly discovered on cross-sectional imaging. Mild autonomous cortisol secretion is the most common functional disorder detected in AI.

Objective

To delineate the association between radiological characteristics of benign adrenocortical tumors and hypothalamus-pituitary-adrenal (HPA) axis.

Methods

In the study, 494 patients diagnosed with benign unilateral adrenocortical tumors were included. Mild autonomous cortisol secretion (MACS) was diagnosed when cortisol after 1mg-dexamethasone suppression test (1-mg DST) was in the range of 1.8-5ug/dl. Non-functional adrenocortical tumor (NFAT) was diagnosed as cortisol following 1-mg DST less than 1.8ug/dL. We performed Logistics regression and causal mediation analyses, looking for associations between radiological characteristics and the HPA axis.

Results

Of 494 patients, 352 (71.3%) with NFAT and 142 (28.7%) with MACS were included. Patients with MACS had a higher tumor diameter, thinner contralateral adrenal gland, and lower plasma ACTH and serum DHEAS than those with NFAT. ACTH (OR 0.978, 0.962-0.993) and tumor diameter (OR 1.857, 95%CI, 1.357-2.540) were independent factors associated with decreased serum DHEAS (all P<0.05). ACTH was also associated with decreased contralateral adrenal diameter significantly (OR 0.973, 95%CI, 0.957-0.988, P=0.001). Causal mediation analysis showed ACTH mediated the effect significantly for the association between 1-mg DST results and DHEAS level (Pmediation<0.001, proportion=22.3%). Meanwhile, we found ACTH mediated 39.7% of the effects of 1-mg DST on contralateral adrenal diameter (Pmediation=0.012).

Conclusions

Patients with MACS had thinner contralateral adrenal glands and disturbed HPA axes compared with NFAT. ACTH may partially be involved in mediating the mild autonomous cortisol secretion to DHEAS and the contralateral adrenal gland.

Association of alpha-aminoadipic acid (2-AAA) with cardiometabolic risk factors in healthy and high-risk individuals

Plasma levels of the metabolite alpha-aminoadipic acid (2-AAA) have been associated with risk of type 2 diabetes (T2D) and atherosclerosis. However, little is known about the relationship of 2-AAA to other cardiometabolic risk markers in pre-disease states, or in the setting of comorbid disease. We measured circulating 2-AAA using two methods in 1) a sample of 261 healthy individuals (2-AAA Study), and 2) in a sample of 134 persons comprising 110 individuals with treated HIV, with or without T2D, a population at high risk of metabolic disease and cardiovascular events despite suppression of circulating virus, and 24 individuals with T2D without HIV (HATIM Study). We examined associations between plasma 2-AAA and markers of cardiometabolic health within each cohort. We observed differences in 2-AAA by sex and race in both cohorts, with higher levels observed in men compared with women, and in Asian compared with Black or white individuals (P<0.05). There was no significant difference in 2-AAA by HIV status within individuals with T2D in the HATIM Study. We confirmed associations between 2-AAA and dyslipidemia in both cohorts where high 2-AAA associated with low HDL cholesterol (P<0.001) and high triglycerides (P<0.05). As expected, within the cohort of people with HIV, 2-AAA was higher in the setting of T2D compared to pre-diabetes or normoglycemia (P<0.001). 2-AAA was positively associated with body mass index (BMI) in the 2-AAA Study, and with waist circumference and measures of visceral fat volume in HATIM (all P<0.05). Further, 2-AAA associated with increased liver fat in persons with HIV (P<0.001). Our study confirms 2-AAA as a marker of cardiometabolic risk in both healthy individuals and those at high cardiometabolic risk, reveals relationships with adiposity and hepatic steatosis, and highlights important differences by sex and race. Further studies are warranted to establish molecular mechanisms linking 2-AAA to disease in other high-risk populations.

Protocol for analysis of liquid chromatography-mass spectrometry metabolomics data using R to understand how metabolites affect disease

Liquid-chromatography-mass-spectrometry-based metabolomics is widely used in prospective case-control studies for disease prediction. Given the large amount of clinical and metabolomics data involved, data integration and analyses are crucial to provide an accurate understanding of the disease. We provide a comprehensive analysis approach to explore associations among clinical risk factors, metabolites, and disease. We describe steps for performing Spearman correlation, conditional logistic regression, casual mediation, and variance partitioning to investigate the potential effects of metabolites on disease. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).1.

Accurately quantified plasma free glycine concentration as a biomarker in patients with acute ischemic stroke

We developed a hollow fiber centrifugal ultrafiltration (HFCF-UF) method to study the change of plasma levels of free glycine (Gly) in patients with acute ischemic stroke (AIS). Twenty-four patients with AIS confirmed by diffusion-weighted imaging (DWI) were enrolled. During the study period, the patients did not receive any supplemental amino acids therapy that could affect the obtained results. Our results showed that although AIS patients adopted different methods of treatment (thrombolytic and non-thrombolytic), the clinical NIHSS score of AIS showed a downward trend whereas Gly concentration showed increased trend. Moreover, plasma free Gly concentration was positively correlated with ASPECTS score. The correlation between Gly levels and infarct volume showed a statistical significance. That is to say, higher Gly level predicted smaller infarct size. Thus, the change of free Gly level in plasma could be considered as a potential biomarker of AIS.