Neural Network Aided Detection of Huntington Disease

Huntington Disease (HD) is a degenerative neurological disease that causes a significant impact on the quality of life of the patient and eventually death. In this paper we present an approach to create a biomarker using as an input DNA CpG methylation data to identify HD patients. DNA CpG methylation is a well-known epigenetic marker for disease state. Technological advances have made it possible to quickly analyze hundreds of thousands of CpGs. This large amount of information might introduce noise as potentially not all DNA CpG methylation levels will be related to the presence of the illness. In this paper, we were able to reduce the number of CpGs considered from hundreds of thousands to 237 using a non-linear approach. It will be shown that using only these 237 CpGs and non-linear techniques such as artificial neural networks makes it possible to accurately differentiate between control and HD patients. An underlying assumption in this paper is that there are no indications suggesting that the process is linear and therefore non-linear techniques, such as artificial neural networks, are a valid tool to analyze this complex disease. The proposed approach is able to accurately distinguish between control and HD patients using DNA CpG methylation data as an input and non-linear forecasting techniques. It should be noted that the dataset analyzed is relatively small. However, the results seem relatively consistent and the analysis can be repeated with larger data-sets as they become available

Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

A nonlinear approach to identifying combinations of CpGs DNA methylation data, as biomarkers for Alzheimer (AD) disease, is presented in this paper. It will be shown that the presented algorithm can substantially reduce the amount of CpGs used while generating forecasts that are more accurate than using all the CpGs available. It is assumed that the process, in principle, can be non-linear; hence, a non-linear approach might be more appropriate. The proposed algorithm selects which CpGs to use as input data in a classification problem that tries to distinguish between patients suffering from AD and healthy control individuals. This type of classification problem is suitable for techniques, such as support vector machines. The algorithm was used both at a single dataset level, as well as using multiple datasets. Developing robust algorithms for multi-datasets is challenging, due to the impact that small differences in laboratory procedures have in the obtained data. The approach that was followed in the paper can be expanded to multiple datasets, allowing for a gradual more granular understanding of the underlying process. A 92% successful classification rate was obtained, using the proposed method, which is a higher value than the result obtained using all the CpGs available. This is likely due to the reduction in the dimensionality of the data obtained by the algorithm that, in turn, helps to reduce the risk of reaching a local minima

An Entropy Approach to Multiple Sclerosis Identification

Multiple sclerosis (MS) is a relatively common neurodegenerative illness that frequently causes a large level of disability in patients. While its cause is not fully understood, it is likely due to a combination of genetic and environmental factors. Diagnosis of multiple sclerosis through a simple clinical examination might be challenging as the evolution of the illness varies significantly from patient to patient, with some patients experiencing long periods of remission. In this regard, having a quick and inexpensive tool to help identify the illness, such as DNA CpG (cytosine-phosphate-guanine) methylation, might be useful. In this paper, a technique is presented, based on the concept of Shannon Entropy, to select CpGs as inputs for non-linear classification algorithms. It will be shown that this approach generates accurate classifications that are a statistically significant improvement over using all the data available or randomly selecting the same number of CpGs. The analysis controlled for factors such as age, gender and smoking status of the patient. This approach managed to reduce the number of CpGs used while at the same time significantly increasing the accuracy

Diagnostic effectiveness of [123I] Ioflupane single photon emission computed tomography (SPECT) in multiple system atrophy

Background: Multiple system atrophy (MSA) is a rapidly progressive neurodegenerative disorder that has no curative treatment. Diagnosis is based on a set of criteria established by Gilman (1998 and 2008) and recently updated by Wenning (2022). We aim to determine the effectiveness of [123I]Ioflupane SPECT in MSA, especially at the initial clinical suspicion. Methods: A cross-sectional study of patients at the initial clinical suspicion of MSA, referred for [123I]Ioflupane SPECT. Results: Overall, 139 patients (68 men, 71 women) were included, 104 being MSA-probable and 35 MSA-possible. MRI was normal in 89.2%, while SPECT was positive in 78.45%. SPECT showed high sensitivity (82.46%) and positive predictive value (86.24), reaching maximum sensitivity in MSA-P (97.26%). Significant differences were found when relating both SPECT assessments in the healthy–sick and inconclusive–sick groups. We also found an association when relating SPECT to the subtype (MSA-C or MSA-P), as well as to the presence of parkinsonian symptoms. Lateralization of striatal involvement was detected (left side). Conclusions: [123I]Ioflupane SPECT is a useful and reliable tool for diagnosing MSA, with good effectiveness and accuracy. Qualitative assessment shows a clear superiority when distinguishing between the healthy–sick categories, as well as between the parkinsonian (MSA-P) and cerebellar (MSA-C) subtypes at initial clinical suspicion

Circulating miRNAs as predictive biomarkers of type 2 diabetes mellitus development in coronary heart disease patients fromt he CORDIOPREV study

Circulating microRNAs (miRNAs) have been proposed as type 2 diabetes biomarkers, and they may be a more sensitive way to predict development of the disease than the currently used tools. Our aim was to identify whether circulating miRNAs, added to clinical and biochemical markers, yielded better potential for predicting type 2 diabetes. The study included 462 non-diabetic patients at baseline in the CORDIOPREV study. After a median follow-up of 60 months, 107 of them developed type 2 diabetes. Plasma levels of 24 miRNAs were measured at baseline by qRT-PCR, and other strong biomarkers to predict diabetes were determined. The ROC analysis identified 9 miRNAs, which, added to HbA1c, have a greater predictive value in early diagnosis of type 2 diabetes (AUC = 0.8342) than HbA1c alone (AUC = 0.6950). The miRNA and HbA1cbased model did not improve when the FINDRISC was included (AUC = 0.8293). Cox regression analyses showed that patients with low miR-103, miR-28-3p, miR-29a, and miR-9 and high miR-30a-5p and miR-150 circulating levels have a higher risk of disease (HR = 11.27; 95% CI = 2.61–48.65). Our results suggest that circulating miRNAs could potentially be used as a new tool for predicting the development of type 2 diabetes in clinical practice

Development of a High-Throughput Calcium Mobilization Assay for CCR6 Receptor Coupled to Hydrolase Activity Readout

CCR6 is a chemokine receptor highly implicated in inflammatory diseases and could be a potential therapeutic target; however, no therapeutic agents targeting CCR6 have progressed into clinical evaluation. Development of a high-throughput screening assay for CCR6 should facilitate the identification of novel compounds against CCR6. To develop a cell-based assay, RBL-2H3 cells were transfected with plasmids encoding β-hexosaminidase and CCR6. Intracellular calcium mobilization of transfected cells was measured with a fluorescent substrate using the activity of released hexosaminidase as readout of the assay. This stable, transfected cell showed a specific signal to the background ratio of 19.1 with low variability of the signal along the time. The assay was validated and optimized for high-throughput screening. The cell-based calcium mobilization assay responded to the specific CCR6 ligand, CCL20, in a dose-dependent manner with an EC50 value of 10.72 nM. Furthermore, the assay was deemed robust and reproducible with a Z’ factor of 0.63 and a signal window of 7.75. We have established a cell-based high-throughput calcium mobilization assay for CCR6 receptor. This assay monitors calcium mobilization, due to CCR6h activation by CCL20, using hexosaminidase activity as readout. This assay was proved to be robust, easy to automate and could be used as method for screening of CCR6 modulators

Extra-Virgin Olive Oil Modifies the Changes Induced in Non-Nervous Organs and Tissues by Experimental Autoimmune Encephalomyelitis Models

This study reveals the existence of oxidative stress (reactive oxygen species (ROS)) in non-nervous organs and tissues in multiple sclerosis (MS) by means of a model of experimental autoimmune encephalomyelitis (EAE) in rats. This model reproduces a similar situation to MS, as well as its relationship with intestinal microbiota starting from the changes in bacterial lipopolysaccharide levels (LPS) in the outer wall of the gram-negative bacteria. Finally, the administration of extra-virgin olive oil (EVOO), hydroxytirosol (HT), and oleic acid (OA) exert beneficial effects. Twenty-five Dark Agouti two-month-old male rats, weighing around 190 g, were distributed into the following groups: Control, EAE (experimental autoimmune encephalomyelitis group), EAE + EVOO, EAE + HT, and EAE + OA. The glutathione redox system with the EAE was measured in heart, kidney, liver, and small and large intestines. The LPS and the correlation with oxidative stress in the small and large intestines were also investigated. The results showed that (1) the oxidative damage in the EAE model affects non-nervous organs and tissues; (2) The LPS is related to inflammatory phenomena and oxidative stress in the intestinal tissue and in other organs; (3) The administration of EVOO, HT, and OA reduces the LPS levels at the same time as minimizing the oxidative damage; (4) EVOO, HT, and OA improve the disease’s clinical score; and (5) on balance, EVOO offers a better neuroprotective effect

Evolution of Metabolic Phenotypes of Obesity in Coronary Patients after 5 Years of Dietary Intervention: From the CORDIOPREV Study

Background: Obesity phenotypes with different metabolic status have been described previously. We analyzed metabolic phenotypes in obese coronary patients during a 5-year follow-up, and examined the factors influencing this evolution. Methods: The CORDIOPREV study is a randomized, long-term secondary prevention study with two healthy diets: Mediterranean and low-fat. All obese patients were classified as either metabolically healthy obese (MHO) or metabolically unhealthy obese (MUO). We evaluated the changes in the metabolic phenotypes and related variables after 5 years of dietary intervention. Results: Initially, 562 out of the 1002 CORDIOPREV patients were obese. After 5 years, 476 obese patients maintained their clinical and dietary visits; 71.8% of MHO patients changed to unhealthy phenotypes (MHO-Progressors), whereas the MHO patients who maintained healthy phenotypes (MHO-Non-Progressors) lost more in terms of their body mass index (BMI) and had a lower fatty liver index (FLI-score) (p < 0.05). Most of the MUO (92%) patients maintained unhealthy phenotypes (MUO-Non-Responders), but 8% became metabolically healthy (MUO-Responders) after a significant decrease in their BMI and FLI-score, with improvement in all metabolic criteria. No differences were found among dietary groups. Conclusions: A greater loss of weight and liver fat is associated with a lower progression of the MHO phenotype to unhealthy phenotypes. Likewise, a marked improvement in these parameters is associated with regression from MUO to healthy phenotypes

Common Variants in 22 Genes Regulate Response to Metformin Intervention in Children with Obesity: A Pharmacogenetic Study of a Randomized Controlled Trial

Metformin is a first-line oral antidiabetic agent that has shown additional effects in treating obesity and metabolic syndrome. Inter-individual variability in metformin response could be partially explained by the genetic component. Here, we aimed to test whether common genetic variants can predict the response to metformin intervention in obese children. The study was a multicenter and double-blind randomized controlled trial that was stratified according to sex and pubertal status in 160 children with obesity. Children were randomly assigned to receive either metformin (1g/d) or placebo for six months after meeting the defined inclusion criteria. We conducted a post hoc genotyping study in 124 individuals (59 placebo, 65 treated) comprising finally 231 genetic variants in candidate genes. We provide evidence for 28 common variants as promising pharmacogenetics regulators of metformin response in terms of a wide range of anthropometric and biochemical outcomes, including body mass index (BMI) Z-score, and glucose, lipid, and inflammatory traits. Although no association remained statistically significant after multiple-test correction, our findings support previously reported variants in metformin transporters or targets as well as identify novel and promising loci, such as the ADYC3 and the BDNF genes, with plausible biological relation to the metformin’s action mechanism. Trial Registration: Registered on the European Clinical Trials Database (EudraCT, ID: 2010-023061-21) on 14 November 2011 (URL: https://www.clinicaltrialsregister.eu/ctr-search/trial/2010-023061-21/ES).This research was funded by the Spanish Ministry of Health, Social and Equality, General Department for Pharmacy and Health Products (codes and beneficiaries: EC10-243, Ramón Cañete, Reina Sofía Hospital, Córdoba; EC10-056, Ángel Gil, University of Granada and Virgen de las Nieves University Hospital, Granada; EC10-281, Rosaura Leis, Clinic University Hospital of Santiago, Santiago de Compostela; and EC10-227, Gloria Bueno, Lozano Blesa University Clinical Hospital, Zaragoza

Evaluation of the gut microbiota after metformin intervention in children with obesity: A metagenomic study of a randomized controlled trial

Background: Metformin, a first-line oral antidiabetic agent that has shown promising results in terms of treating childhood and adolescent obesity, might influence the composition of the gut microbiota. We aimed to evaluate whether the gut microbiota of non-diabetic children with obesity changes after a metformin intervention. Methods: The study was a multicenter and double-blind randomized controlled trial in 160 children with obesity. Children were randomly assigned to receive either metformin (1 g/day) or placebo for 6 months in combination with healthy lifestyle recommendations in both groups. Then, we conducted a metagenomic analysis in a subsample obtained from 33 children (15 metformin, 18 placebo). A linear mixed-effects model (LMM) was used to determine the abundance changes from baseline to six months according to treatment. To analyze the data by clusters, a principal component analysis was performed to understand whether lifestyle habits have a different influence on the microbiota depending on the treatment group. Results: Actinobacteria abundance was higher after placebo treatment compared with metformin. However, the interaction time x treatment just showed a trend to be significant (4.6% to 8.1% after placebo vs. 3.8 % to 2.6 % after metformin treatment, p = 0.055). At genus level, only the abundance of Bacillus was significantly higher after the placebo intervention compared with metformin (2.5% to 5.7% after placebo vs. 1.5 % to 0.8 % after metformin treatment, p = 0.044). Furthermore, different ensembles formed by Firmicutes, Bacteroidetes, and Verrucomicrobia were found according to the interventions under a similar food consumptionSpanish Ministry of Health, Social and Equality, General Department for Pharmacy and Health ProductsInstituto de Salud Carlos III-Fondo de Investigación Sanitaria (FONDOS FEDER), Redes temáticas de investigación cooperativa RETIC Red SAMID RD12/0026/001