Prognostic networks for unraveling the biological mechanisms of Sarcopenia

Cernea, Ana; Fernández-Martínez, Juan Luis; Andrés-Galiana, Enrique J. de; Fernández-Muñiz, Zulima; Bermejo-Millo, Juan Carlos; González-Blanco, Laura; Pérez-Solano, Juan J.; Coto‐Montes, Ana; Caballero, Beatriz

doi:10.1016/j.mad.2019.111129

Cited by 10 publications

(10 citation statements)

References 30 publications

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“…This algorithm has been named holdout sampler [19] and has been used to sample the uncertainty space in various inverse problems [60,61]. It has been also successfully applied in phenotype prediction and drug design [62][63][64][65]. Besides, the predicted pathways are compared with those obtained using other sampling algorithms such as the Fisher's ratio sampler [66] and Random Forest [67].…”

Section: Sampling Defective Pathways In Phenotype Prediction Problemsmentioning

confidence: 99%

Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

Fernández-Martínez

Álvarez-Machancoses

Andrés-Galiana

et al. 2020

IJMS

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We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

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Section: Sampling Defective Pathways In Phenotype Prediction Problemsmentioning

confidence: 99%

Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

Fernández-Martínez

Álvarez-Machancoses

Andrés-Galiana

et al. 2020

IJMS

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“…A study on sarcopenia with similar technical characteristics to our study [20] used four classifiers, Random Forest, SVM, Gradient Boosting, and Logistic regression, with Random Forest as the best classifier, obtained an accuracy of 0.82. The article for the measurement of variables involved in the development of sarcopenia, using forecasting networks based on automatic learning approaches, where the results show an accuracy of 82%, analyzed 114 variables in this study [25].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Cristian

Zúñiga

2020

IJERPH

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The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

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“…As a consequence, the pH of the proximal intestine is increased [29,32], which can affect the gut microbiome [33,34]. The use of PPIs has also been associated with a decreased absorption of vitamin B 12 , magnesium, calcium, iron, vitamin C, β-carotene, and zinc, which is at least in part a direct consequence of their reduced release from the food matrix and (or) effects on the absorption process [29,[35][36][37][38] At the clinical level, PPI use has been reported as a variable of importance, after gait speed, oxidative stress, and depression, in the prediction of muscle mass and function loss in aging (sarcopenia) [39]. Moreover, it has been reported that long term use of PPIs can result in polymyositis, a type of chronic inflammation in the muscles [40].…”

Section: Adverse Effects Of Long Term Use Of Ppismentioning

confidence: 99%

“…To conclude, although generally considered safe, long-term use of PPIs could ultimately lead to deficiencies in several vitamins and minerals [36,37,43]. Furthermore, the changed pH can affect the gut microbiome [33,34], and finally, it can result in the presence of myopathy, which might affect muscle function and mass [39].…”

Section: Adverse Effects Of Long Term Use Of Ppismentioning

confidence: 99%

The Use of Proton Pump Inhibitors May Increase Symptoms of Muscle Function Loss in Patients with Chronic Illnesses

Vinke

Wesselink

Orten-Luiten

et al. 2020

IJMS

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Long-term use of proton pump inhibitors (PPIs) is common in patients with muscle wasting-related chronic diseases. We explored the hypothesis that the use of PPIs may contribute to a reduction in muscle mass and function in these patients. Literature indicates that a PPI-induced reduction in acidity of the gastrointestinal tract can decrease the absorption of, amongst others, magnesium. Low levels of magnesium are associated with impaired muscle function. This unwanted side-effect of PPIs on muscle function has been described in different disease backgrounds. Furthermore, magnesium is necessary for activation of vitamin D. Low vitamin D and magnesium levels together can lead to increased inflammation involved in muscle wasting. In addition, PPI use has been described to alter the microbiota’s composition in the gut, which might lead to increased inflammation. However, PPIs are often provided together with nonsteroidal anti-inflammatory drugs (NSAIDs), which are anti-inflammatory. In the presence of obesity, additional mechanisms could further contribute to muscle alterations. In conclusion, use of PPIs has been reported to contribute to muscle function loss. Whether this will add to the risk factor for development of muscle function loss in patients with chronic disease needs further investigation.

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Prognostic networks for unraveling the biological mechanisms of Sarcopenia

Cited by 10 publications

References 30 publications

Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

The Use of Proton Pump Inhibitors May Increase Symptoms of Muscle Function Loss in Patients with Chronic Illnesses

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