Tissue and Circulating MicroRNAs 378 and 142 as Biomarkers of Obesity and Its Treatment Response

Matveev, Georgy A.; Khromova, Natalya V.; Zasypkin, German G.; Kononova, Yulia A.; Vasilyeva, Elena Yu.; Babenko, Alina Yu.; Shlyakhto, Evgeny V.

doi:10.3390/ijms241713426

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…In addition, obesity has been identified as an independent risk factor for developing HCC [64]. There is some evidence linking miRNAs to weight control [65]. The deletion of mir-33 in mice severely affects feeding behavior, with an abnormally high food intake leading to obesity and insulin resistance [66].…”

Section: Mirnas and Obesitymentioning

confidence: 99%

MicroRNAs and Nonalcoholic Steatohepatitis: A Review

Morishita,

Oura,

Tadokoro

et al. 2023

IJMS

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Non-alcoholic fatty liver disease (NAFLD) is a clinicopathologic syndrome caused by fat deposition in hepatocytes. Patients with nonalcoholic steatohepatitis (NASH), an advanced form of NAFLD with severe fibrosis, are at high risk for liver-related complications, including hepatocellular carcinoma (HCC). However, the mechanism of progression from simple fat deposition to NASH is complex, and previous reports have linked NAFLD to gut microbiota, bile acids, immunity, adipokines, oxidative stress, and genetic or epigenetic factors. NASH-related liver injury involves multiple cell types, and intercellular signaling is thought to be mediated by extracellular vesicles. MicroRNAs (miRNAs) are short, noncoding RNAs that play important roles as post-transcriptional regulators of gene expression and have been implicated in the pathogenesis of various diseases. Recently, many reports have implicated microRNAs in the pathogenesis of NALFD/NASH, suggesting that exosomal miRNAs are potential non-invasive and sensitive biomarkers and that the microRNAs involved in the mechanism of the progression of NASH may be potential therapeutic target molecules. We are interested in which miRNAs are involved in the pathogenesis of NASH and which are potential target molecules for therapy. We summarize targeted miRNAs associated with the etiology and progression of NASH and discuss each miRNA in terms of its pathophysiology, potential therapeutic applications, and efficacy as a NASH biomarker.

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Section: Mirnas and Obesitymentioning

confidence: 99%

MicroRNAs and Nonalcoholic Steatohepatitis: A Review

Morishita,

Oura,

Tadokoro

et al. 2023

IJMS

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Brown and Beige Adipose Tissue: One or Different Targets for Treatment of Obesity and Obesity-Related Metabolic Disorders?

Kononova,

Tuchina,

Babenko

2024

IJMS

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The failure of the fight against obesity makes us turn to new goals in its treatment. Now, brown adipose tissue has attracted attention as a promising target for the treatment of obesity and associated metabolic disorders such as insulin resistance, dyslipidemia, and glucose tolerance disorders. Meanwhile, the expansion of our knowledge has led to awareness about two rather different subtypes: classic brown and beige (inducible brown) adipose tissue. These subtypes have different origin, differences in the expression of individual genes but also a lot in common. Both tissues are thermogenic, which means that, by increasing energy consumption, they can improve their balance with excess intake. Both tissues are activated in response to specific inducers (cold, beta-adrenergic receptor activation, certain food and drugs), but beige adipose tissue transdifferentiates back into white adipose tissue after the cessation of inducing action, while classic brown adipose tissue persists, but its activity decreases. In this review, we attempted to understand whether there are differences in the effects of different groups of thermogenesis-affecting drugs on these tissues. The analysis showed that this area of research is rather sparse and requires close attention in further studies.

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Model for Predicting the Effect of Sibutramine Therapy in Obesity

Danilov,

Matveev,

Babenko

et al. 2024

JPM

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Background: The development of models predicting response to weight loss therapy using sibutramine is found in only a few cases. The objective of the work is to develop a data-driven method of personalized recommendation for obesity treatment that would predict the response to sibutramine based on the current set of patient parameters. Methods: The decision system is built on the XGBoost classification algorithm along with recursive feature selection and Shapley data valuation. Using the results of clinical trials, it was trained to estimate the probability of overcoming a weight loss threshold. The model was evaluated by the accuracy metric using the Leave-One-Out cross-validation. Results: The model for predicting response to sibutramine treatment over 3 months has an accuracy of 71%. The model for predicting outcomes at the sixth month visit based on results at 3 months has an accuracy of 80%. Conclusions: Although our developed prediction model may not exhibit high precision compared to certain benchmarks, it significantly outperforms random chance or models relying only on BMI parameters. Our model used the available range of laboratory tests, which makes it possible to use this model for routine clinical use and help doctors decide whether to prescribe sibutramine.

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Tissue and Circulating MicroRNAs 378 and 142 as Biomarkers of Obesity and Its Treatment Response

Cited by 3 publications

References 45 publications

MicroRNAs and Nonalcoholic Steatohepatitis: A Review

MicroRNAs and Nonalcoholic Steatohepatitis: A Review

Brown and Beige Adipose Tissue: One or Different Targets for Treatment of Obesity and Obesity-Related Metabolic Disorders?

Model for Predicting the Effect of Sibutramine Therapy in Obesity

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