Identifying depressive symptoms in patients with type 2 diabetes mellitus: the role of glucose variability and concomitant hypothyroidism

Etemadi, Ardavan; Dabaghi, Parviz; Hosseini, Yasaman; Gholampourdehaki, Mehrzad; Solouki, Sogand; Gholamhosseini, Leila; Eshtiaghi, Radina

doi:10.1007/s13410-023-01177-5

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…These outcomes include increased risk of cardiovascular disease, serious diabetic complications (e.g., vascular dysfunction, chronic kidney disease, etc. ), depression, dementia, and in rare cases, myxedema, an extreme and life-threatening complication of untreated HT [ 15 , 16 , 20 , 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes

Adelson,

Garikipati,

Zhou

et al. 2024

Diagnostics

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Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes

Adelson,

Garikipati,

Zhou

et al. 2024

Diagnostics

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“…Recent studies have shown that the compositional and functional changes of the intestinal microbiota through several mechanisms, including increased intestinal permeability and low-grade endotoxemia, changes in the production of short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs), and disturbances in the metabolism of bile acids play an important role in the development of metabolic syndrome diseases such as cardiovascular, type 2 diabetes, obesity, and intestinal inflammation [6][7][8]. Disturbance in the intestinal microbiome causes more absorption of monosaccharides, an increase in the level of lipogenic enzymes, and the occurrence of insulin resistance [9,10]. Evidence shows that the development of obesity, diabetes, and pre-diabetes are associated with changes in the gut microbiota [11,12].…”

Section: Introductionmentioning

confidence: 99%

Efficacy of high-intensity interval and continuous endurance trainings on cecal microbiota metabolites and inflammatory factors in diabetic rats induced by high-fat diet

Solouki,

Gorgani-Firuzjaee,

Jafary

et al. 2024

PLoS ONE

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Physical exercise is known to modulate the intestinal microbiota composition and control the symptoms of metabolic syndrome. In this research, we intend to investigate and compare the effect of high-intensity interval and continuous endurance trainings (HIIT and CET) on cecal microbiota metabolites and inflammatory factors in diabetic rats. A number of Wistar rats were made diabetic by a high-fat diet and trained under two types of exercise protocols, HIIT and CET. After taking samples from the cecal tissue and serum of rats to reveal the effect of exercise, three microbial species from the Firmicute and Bacteroid phyla, which are the main types of intestinal microbes, and their metabolites include two short-chain fatty acids (SCFAs), butyrate and propionate and also, the inflammatory factors TLR4 and IL6 were analyzed through quantitative polymerase chain reaction (qPCR), high-performance liquid chromatography (HPLC), and Enzyme-linked immunosorbent assay (ELISA) methods. In general, exercise while increasing the representative of Firmicute has caused a relative reduction of Bacteroides and improved the concentration of SCFAs. In this regard, HIIT outperforms CET in up-regulating Akkermansia and Butyrivibrio expression, and butyrate and propionate metabolites concentration. Also, both exercises significantly reduced cecal expression of TLR4 and sera concentration of IL6 compared to the diabetic group, although the reduction rate was higher in the CET group than in HIIT. Our findings suggest that some symptoms of metabolic syndrome such as intestinal dysbiosis and the resulting metabolic disorders are better controlled by HIIT and inflammation by CET. Certainly, more extensive research on other contributing factors could help clarify the results.

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Unveiling the potential of prodrug and drug-conjugate strategies in treatment of diabetes mellitus and its complications

Bhilare,

Shedge,

Tambe

et al. 2024

Med Chem Res

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Identifying depressive symptoms in patients with type 2 diabetes mellitus: the role of glucose variability and concomitant hypothyroidism

Cited by 3 publications

References 40 publications

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes

Efficacy of high-intensity interval and continuous endurance trainings on cecal microbiota metabolites and inflammatory factors in diabetic rats induced by high-fat diet

Unveiling the potential of prodrug and drug-conjugate strategies in treatment of diabetes mellitus and its complications

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