Understanding the development of sarcopenic obesity

Gross, Danae C.; Cheever, C. Ray; Batsis, John A.

doi:10.1080/17446651.2023.2267672

Cited by 4 publications

(2 citation statements)

References 191 publications

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“…The prevalence of obesity and cancer is steadily rising in numerous countries globally, posing a significant risk to human well-being [1,2]. Obesity induces alterations in the physiological and hormonal milieu of the body, thereby fostering the development of various diseases, such as diabetes and cardiovascular disorders [3]. Research has established a positive correlation between obesity and heightened susceptibility to a minimum of 13 distinct forms of cancer, including esophageal adenocarcinoma, colon cancer, endometrial cancer, postmenopausal breast cancer, kidney cancer, and hematopoietic cancers [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Mayuri,

Varalakshmi,

Tharaheswari

et al. 2024

BioMedInformatics

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The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies.

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Section: Introductionmentioning

confidence: 99%

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Mayuri,

Varalakshmi,

Tharaheswari

et al. 2024

BioMedInformatics

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“…Therefore, there is a clear need for new studies using the standardized definition of SO in different populations, especially with varying degrees of obesity, to consider the differences and similarities between the instruments proposed. This information may be useful for the implementation of SO screening and diagnosis in clinical practice ( 14 ). Thus, we aimed to estimate the prevalence rates of SO and their level of agreement using different instruments proposed by the ESPEN/EASO Consensus, in a sample of hospitalized older adults with severe obesity seeking an in-hospital multidisciplinary body weight reduction program.…”

Section: Introductionmentioning

confidence: 99%

Analysis of sarcopenic obesity prevalence and diagnostic agreement according to the 2022 ESPEN and EASO Consensus in hospitalized older adults with severe obesity

Danielewicz,

Marra,

Tringali

et al. 2024

Front. Endocrinol.

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BackgroundSarcopenic obesity (SO) is a clinical disorder characterized by increased adiposity and decreased muscle mass and function, commonly observed in older adults. However, most of the studies that investigated SO prevalence rates were not based on current standardized diagnostic methods. Thus, this study aims to estimate the prevalence rates of SO and their level of agreement using different instruments proposed by the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) Consensus, in a sample of hospitalized older adults with severe obesity.MethodsA cross-sectional study with 90 older adults (≥ 60 years) with severe obesity (body mass index ≥ 35 kg/m/²) seeking an in-hospital multidisciplinary body weight reduction program. Skeletal muscle function was assessed using the five-repetition Sit-Stand test (5-SSt) and Handgrip Strength (HGS). Body composition was evaluated by high percentages of fat mass (FM), low appendicular lean mass (ALM/W), and skeletal muscle mass (SMM/W), adjusted to body weight. The stage of SO was assessed on the presence of at least one comorbidity and specific cut-offs were adopted for each step. All analyses were performed according to gender and age range.ResultsThe prevalence rates of SO in the total sample were 23.3%, 25.5%, 31.1%, and 40.0% considering altered values of 5-SSt+FM+ALM/W, HGS+FM+ALM/W, 5-SSt+FMSSM/W, and HGS+FM+SSM/W, respectively. Higher prevalence rates were observed among female and old elderly subgroups, regardless of the diagnostic combination. There were weak agreements between the muscle function tests (5-SSt versus HGS) using both muscle mass indexes in the total sample and all subgroups. Moderate agreements were observed between muscle mass indexes (SMM/W versus ALM/W) in the total sample, male and younger older adults (using 5-SSt), and strong agreements for men and younger older adults (using HGS).ConclusionThe discrepancies observed between the prevalence rates and their levels of agreement reinforce the need for new studies in similar populations aiming for better standardization of SO assessment.

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Exploring the therapeutic mechanisms of millet in obesity through molecular docking, pharmacokinetics, and dynamic simulation

Lakhani,

Hamid,

Gupta

et al. 2024

Front. Nutr.

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Obesity, a prevalent global health concern, is characterized by excessive fat accumulation, which confers significant nutritional and health risks, including a shortened lifespan and diminished wellbeing. Central to the regulation of energy balance and food intake is the fat mass and obesity-associated (FTO) protein, which modulates the interplay between caloric consumption and energy expenditure. Given its pivotal role in obesity regulation, the identification of effective inhibitors targeting the FTO protein is imperative for developing therapeutic interventions. Currently available anti-obesity drugs are often plagued by undesirable side effects. In contrast, natural plant-derived bioactive compounds are gaining prominence in the pharmaceutical industry due to their efficacy and lower incidence of adverse effects. Little Millet, a traditional cereal known for its rich nutritional profile and high satiety index, was investigated in this study using molecular docking and dynamics simulation approach for its potential as an anti-obesity agent. Our research demonstrates that four bioactive compounds from Little Millet exhibit superior binding energies ranging from 7.22 to 8.83 kcal/mol, compared to the standard anti-obesity drug, orlistat, which has a binding energy of 5.96 kcal/mol. These compounds fulfilled all drug-like criteria, including the Lipinski, Ghose, Veber, Egan, and Muegge rules, and exhibited favorable profiles in terms of distribution, metabolism, and prolonged half-life without toxicity. Conversely, orlistat was associated with hepatotoxicity, a reduced half-life, and multiple violations of drug-likeness parameters, undermining its efficacy. Molecular dynamics simulations and Gibbs free energy assessments revealed that the four identified compounds maintain stable interactions with key residues in the FTO protein’s active site. We propose further validation through extensive In vitro, In vivo, and clinical studies to ascertain the therapeutic potential of these compounds in combating obesity.

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Understanding the development of sarcopenic obesity

Cited by 4 publications

References 191 publications

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Analysis of sarcopenic obesity prevalence and diagnostic agreement according to the 2022 ESPEN and EASO Consensus in hospitalized older adults with severe obesity

Exploring the therapeutic mechanisms of millet in obesity through molecular docking, pharmacokinetics, and dynamic simulation

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