Metabolite Triplet in Serum Improves the Diagnostic Accuracy of Prediabetes and Diabetes Screening

2-Hydroxyisobutyric acid targeted binding to MT-ND3 boosts mitochondrial respiratory chain homeostasis in hippocampus to rescue diabetic cognitive impairment

BACKGROUND

The prevalence of diabetic cognitive impairment (DCI) is significant, some studies have shown that it is related to mitochondrial respiratory chain homeostasis, but the specific mechanism is not clear. 2-hydroxyisobutyric acid (2-HIBA) is a novel short-chain fatty acid with potential applications in the treatment of metabolic diseases because it can regulate mitochondrial disorders. Our aim was to explore a novel mechanism of action for 2-HIBA in the treatment of DCI in mitochondrial respiratory chain homeostasis.

METHODS

Metabolic substances and differentially active metabolic pathways in the serum of diseased mice were identified based on multi-omics analysis. The nanoLC-Obitrap-MS technology was utilized to detect the content of selected small molecules with differential metabolic activity in the hippocampus and mitochondria of mice to evaluate their permeability through the blood-brain barrier (BBB) and outer mitochondrial membrane. A combination of behavioral, proteomic, and molecular biology approaches was used to explore specific regulatory mechanisms and identify potential pharmacological targets. Additionally, using techniques such as protein thermal shift, drug affinity responsive target stability (DARTS), hydrolase stability, and surface plasmon resonance (SPR) experiments, we demonstrated the direct binding effects of small molecule metabolites with protein targets.

RESULTS

2-HIBA was found to directly ameliorate cognitive dysfunction in db/db mice by penetrating the blood-brain barrier and reversing the decrease in the protein content of NADH dehydrogenase 3 (MT-ND3) in the hippocampus through direct binding to ND3. This action helps maintain the stability of NAD+/NADH and regulate the mitochondrial respiratory chain balance. Furthermore, a combined medication plant agonist of 2-HIBA can enhance the expression of MT-ND3, thereby improving cognitive dysfunction in mice.

CONCLUSION

MT-ND3 is a crucial target for improving diabetic cognitive dysfunction, and 2-HIBA can directly bind to the MT-ND3 protein to alleviate the functional impairment of the mitochondrial respiratory chain in mice to treat DCI.

Serum metabolomics signature of maternally inherited diabetes and deafness by gas chromatography-time of flight mass spectrometry

AIMS/INTRODUCTION

The aim of this study was to identify a metabolic signature of MIDD as compared to healthy controls and other types of diabetes.

METHODS

We performed a comprehensive serum metabolomic analysis using gas chromatography-time of flight mass spectrometry (GC-TOFMS) in participants diagnosed with MIDD (n = 14), latent autoimmune diabetes in adults (LADA) (n = 14), type 2 diabetes mellitus (n = 14), and healthy controls (n = 14). Each group was matched for gender and age.

RESULTS

There were significant metabolic differences among MIDD and other diabetic and control groups. Compared with control, MIDD patients had high levels of carbohydrates (glucose, galactose, mannose, sorbose, and maltose), fatty acids (2-Hydroxybutyric acid, eicosapentaenoic acid, and octadecanoic acid), and other metabolites (alanine, threonic acid, cholesterol, lactic acid, and gluconic acid), but low level of threonine. Compared with LADA, MIDD patients had high levels of threonic acid and some amino acids (alanine, tryptophan, histidine, proline, glutamine, and creatine) but low levels of serine. Compared with type 2 diabetes mellitus, MIDD patients had high levels of citrulline, creatine, 3-Amino-2-piperidone, but low levels of ornithine, fatty acids (arachidonic acid and octadecanoic acid), and intermediates of the tricarboxylic acid cycle (malic acid and succinic acid).

CONCLUSIONS

Our study identified a specific metabolic profile related to glycolysis and the tricarboxylic acid cycle in MIDD that differs from healthy controls and other types of diabetes. This unique metabolic signature provides new perspectives for understanding the pathophysiology and underlying mechanisms of MIDD.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

The effect of probiotic administration on metabolomics and glucose metabolism in CF patients

BACKGROUND AND OBJECTIVES

Cystic fibrosis (CF)-related diabetes (CFRD) affects 50% of CF adults. Gut microbial imbalance (dysbiosis) aggravates their inflammatory response and contributes to insulin resistance (IR). We hypothesized that probiotics may improve glucose tolerance by correcting dysbiosis.

METHODS

A single-center prospective pilot study assessing the effect of Vivomixx® probiotic (450 billion/sachet) on clinical status, spirometry, lung clearance index (LCI), and quality of life (QOL) questionnaires; inflammatory parameters (urine and stool metabolomics, blood cytokines); and glucose metabolism (oral glucose tolerance test [OGTT]), continuous glucose monitoring [CGM], and homeostasis model assessment of IR (HOMA-IR) in CF patients.

RESULTS

Twenty-three CF patients (six CFRD), mean age 17.7 ± 8.2 years. After 4 months of probiotic administration, urinary cysteine (p = 0.018), lactulose (p = 0.028), arabinose (p = 0.036), mannitol (p = 0.041), and indole 3-lactate (p = 0.046) significantly increased, while 3-methylhistidine (p = 0.046) and N-acetyl glutamine (p = 0.047) decreased. Stool 2-Hydroxyisobutyrate (p = 0.022) and 3-methyl-2-oxovalerate (p = 0.034) decreased. Principal component analysis, based on urine metabolites, found significant partitions between subjects at the end of treatment compared to baseline (p = 0.004). After 2 months of probiotics, the digestive symptoms domain of Cystic Fibrosis Questionnaire-Revised improved (p = 0.007). In the nondiabetic patients, a slight decrease in HOMA-IR, from 2.28 to 1.86, was observed. There was no significant change in spirometry results, LCI, blood cytokines and CGM.

CONCLUSIONS

Changes in urine and stool metabolic profiles, following the administration of probiotics, may suggest a positive effect on glucose metabolism in CF. Larger long-term studies are needed to confirm our findings. Understanding the interplay between dysbiosis, inflammation, and glucose metabolism may help preventing CFRD.

Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC-MS/MS technique

Diabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC-MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.

Quantification of Glucose, fructose and 1,5-Anhydroglucitol in plasma of diabetic patients by ultra performance liquid chromatography tandem mass spectrometry

Type 2 diabetes mellitus (T2DM), a worldwide disease that affects the quality of human life and social development. Glucose, fructose and 1,5-anhydroglucitol are closely related to diabetes mellitus. However, few methods have been reported to achieve these three carbohydrates in the blood simultaneously. In this study, a UPLC-MS/MS method allowing to quantify glucose, fructose, and 1,5-anhydroglucitol simultaneously in human plasma was developed. The analysis was performed by UPLC-MS/MS system with HILIC column. This new method provided satisfactory results in terms of calibration curves with good linearity (R² > 0.99) over 3 order of magnitude range, precision (coefficient of variation of intra-day and inter-day: 0.72-10.23% and 2.21-13.8%), accuracy (results of intra-day and inter-day: 97-113%, 100-107%), matrix effects (87-109%), recovery (93-119%), carry-over (0.004-0.014%), as well as stability (0.04-6.9%) within the acceptance criteria. The reproducible, precise and accurate method with suitable dynamic ranges was successfully applied to the analysis of glucose, fructose and 1,5-anhydroglucitol in T2DM under different pathophysiological conditions.