Genetic risk score based on obesity-related genes and progression in weight loss after bariatric surgery: a 60-month follow-up study.

BACKGROUND: Obesity is a polygenic multifactorial disease. Recent genome-wide association studies have identified several common loci associated with obesity-related phenotypes. Bariatric surgery (BS) is the most effective long-term treatment for patients with severe obesity. The huge variability in BS outcomes between patients suggests a moderating effect of several factors, including the genetic architecture of the patients. OBJECTIVE: To examine the role of a genetic risk score (GRS) based on 7 polymorphisms in 5 obesity-candidate genes (FTO, MC4R, SIRT1, LEP, and LEPR) on weight loss after BS. SETTING: University hospital in Spain. METHODS: We evaluated a cohort of 104 patients with severe obesity submitted to BS (Roux-en-Y gastric bypass or sleeve gastrectomy) followed up for >60 months (lost to follow-up, 19.23%). A GRS was calculated for each patient, considering the number of carried risk alleles for the analyzed genes. During the postoperative period, the percentage of excess weight loss total weight loss and changes in body mass index were evaluated. Generalized estimating equation models were used for the prospective analysis of the variation of these variables in relation to the GRS. RESULTS: The longitudinal model showed a significant effect of the GRS on the percentage of excess weight loss (P = 1.5 × 10-5), percentage of total weight loss (P = 3.1 × 10-8), and change in body mass index (P = 7.8 × 10-16) over time. Individuals with a low GRS seemed to experience better outcomes at 24 and 60 months after surgery than those with a higher GRS. CONCLUSION: The use of the GRS in considering the polygenic nature of obesity seems to be a useful tool to better understand the outcome of patients with obesity after BS.

Use of polygenic risk scores to assess weight loss after bariatric surgery: a five-year follow-up study.

BACKGROUND: Bariatric surgery is currently the most effective long-term treatment for severe obesity. However, the interindividual variability observed in surgical outcomes suggests a moderating effect of several factors, including individual genetic background. Our goal was to investigate the contribution of the genetic architecture of BMI to the variability in weight loss outcomes after bariatric surgery. METHODS: A total of 106 patients with severe obesity who underwent Roux-en-Y gastric bypass or sleeve gastrectomy were followed up for five years. Changes in body mass index (BMIchange) and percentage of total weight loss (%TWL) were evaluated during the postoperative period. Polygenic risk scores including 50 genetic variants were calculated for each participant to determine their individual genetic risk for high BMI based on a previous GWAS. Generalized estimating equation models were used to study the role of the individual's polygenic score, as well as other factors, on BMIchange and %TWL in the long term after surgery. RESULTS: We found an effect of the polygenic score on %TWL and BMIchange, where subjects with lower scores had better outcomes after surgery than those with higher scores. Furthermore, when analyzing only patients who underwent Roux-en-Y gastric bypass, these results were replicated showing greater weight loss after surgery for those with lower polygenic scores. DISCUSSION: These results indicate that genetic background assessed with polygenic risk scores, along with other individual factors such as sex, age, and preoperative BMI, has an impact on bariatric surgery outcomes and could represent a useful tool for estimating surgical outcomes in advance.

New Distance-Based approach for Genome-Wide Association Studies.

With the rise of genome-wide association studies (GWAS), the analysis of typical GWAS data sets with thousands of single-nucleotide polymorphisms (SNPs) has become crucial in biomedicine research. Here, we propose a new method to identify SNPs related to disease in case-control studies. The method, based on genetic distances between individuals, takes into account the possible population substructure, and avoids the issues of multiple testing. The method provides two ordered lists of SNPs; one with SNPs which minor alleles can be considered risk alleles for the disease, and another one with SNPs which minor alleles can be considered as protective. These two lists provide a useful tool to help the researcher to decide where to focus attention in a first stage.

Influence of the BDNF Val66Met polymorphism on weight loss after bariatric surgery: a 24-month follow-up.

BACKGROUND: Bariatric surgery is currently the most effective long-term treatment for severe obesity. However, interindividual variation in surgery outcome has been observed, and research suggests a moderating effect of several factors including baseline co-morbidities (e.g., type 2 diabetes [T2D] and genetic factors). No data are currently available on the interaction between T2D and variants in brain derived neurotrophic factor (BDNF) and its effect on weight loss after surgery. OBJECTIVES: To examine the role of the BDNF Val66Met polymorphism (rs6265) and the influence of T2D and their interaction on weight loss after bariatric surgery in a cohort of patients with severe obesity. SETTING: University hospital in Spain. METHODS: The present study evaluated a cohort of 158 patients with obesity submitted to bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy) followed up for 24 months (loss to follow-up: 0%). During the postoperative period, percentage of excess body mass index loss (%EBMIL), percentage of excess weight loss (%EWL), and total weight loss (%TWL) were evaluated. RESULTS: Longitudinal analyses showed a suggestive effect of BDNF genotype on the %EWL (P = .056) and indicated that individuals carrying the methionine (Met) allele may experience a better outcome after bariatric surgery than those with the valine/valine (Val/Val) genotype. We found a negative effect of a T2D diagnosis at baseline on %EBMIL (P = .004). Additionally, we found an interaction between BDNF genotype and T2D on %EWL and %EBMIL (P = .027 and P = .0004, respectively), whereby individuals with the Met allele without T2D displayed a greater %EWL and greater %EBMIL at 12 months and 24 months than their counterparts with T2D or patients with the Val/Val genotype with or without T2D. CONCLUSION: Our data showed an association between the Met variant and greater weight loss after bariatric surgery in patients without T2D. The presence of T2D seems to counteract this positive effect.

Longitudinal changes in telomere length in a cohort of obese patients submitted to bariatric surgery: a 2-year follow-up.

BACKGROUND: Telomere length (TL) is one biomarker of cell aging used to explore the effects of the environment on age-related pathologies. Obesity and high body mass index have been identified as a risk factors for shortened TL. OBJECTIVE: To evaluate TL in different subtypes of obese patients, and to examine changes in TL in relation to weight loss after bariatric surgery. SETTING: University Hospital in Spain. METHODS: A cohort of 94 patients submitted to bariatric surgery were followed-up during 24 months (t24m: lost to follow-up = 0%). All patients were evaluated before surgery (t0) and during the postoperative period (t6m, t12m, and t24m) for body mass index and metabolic variables. We assessed TL at each timepoint using quantitative polymerase chain reactions and the telomere sequence to single-copy gene sequence ratio method. RESULTS: Patients with class III obesity showed significantly shorter TL at baseline than those patients with class II obesity (P = .027). No differences in TL were found between patients with or without type 2 diabetes or metabolic syndrome. Longitudinal analysis did not show an effect of time, type of surgery, age, or sex on TL. However, a generalized estimating equation model showed that TL was shorter amongst class III obesity patients across the time course (P = .008). Comparison between patients with obesity class II and class III showed differences in TL at t6m (adjusted P = .024), whereby class II patients had longer TL. However, no difference was observed at the other evaluated times. CONCLUSION: Obesity severity may have negative effects on TL independently of type 2 diabetes or metabolic syndrome. Although TL is significantly longer in class II obesity patients relative to class III 6 months after bariatric surgery. This difference is not apparent after 24 months.

ORdensity: user-friendly R package to identify differentially expressed genes.

BACKGROUND: Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and may be key elements for a disease. With the increasing volume of data generated by modern biomedical studies, software is required for effective identification of differentially expressed genes. Here, we describe an R package, called ORdensity, that implements a recent methodology (Irigoien and Arenas, 2018) developed in order to identify differentially expressed genes. The benefits of parallel implementation are discussed. RESULTS: ORdensity gives the user the list of genes identified as differentially expressed genes in an easy and comprehensible way. The experimentation carried out in an off-the-self computer with the parallel execution enabled shows an improvement in run-time. This implementation may also lead to an important use of memory load. Results previously obtained with simulated and real data indicated that the procedure implemented in the package is robust and suitable for differentially expressed genes identification. CONCLUSIONS: The new package, ORdensity, offers a friendly and easy way to identify differentially expressed genes, which is very useful for users not familiar with programming. AVAILABILITY: https://github.com/rsait/ORdensity.

Role of the FKBP5 polymorphism rs1360780, age, sex, and type of surgery in weight loss after bariatric surgery: a follow-up study.

BACKGROUND: Emerging evidence suggests that the FK506 binding protein 51 (FKBP5/FKBP51), encoded by the FKBP5 gene, influences weight and metabolic regulation. The T allele of a functional polymorphism in FKBP5 (rs1360780), has been associated with the expression of FKBP51 and weight loss after bariatric surgery. OBJECTIVE: To examine the role of the FKBP5 rs1360780 polymorphism in relation to age, sex, and type of surgery in weight loss after bariatric surgery in patients with severe obesity. SETTING: University Hospital in Spain METHODS: A cohort of 151 obese patients submitted to Roux-en-Y gastric bypass (62.3%) and sleeve gastrectomy (37.7%) were followed-up during 24-months (t24m; loss to follow-up: 0%). During the postoperative period body mass index (BMI) and percentage of excess and total weight loss were evaluated. RESULTS: The BMI analysis showed an effect of the interaction FKBP5 genotype by sex (P = .0004) and a tendency to the interaction genotype by surgery (P = .048), so that men carrying the T allele had higher BMI at t24m than those without the T allele, and T-allele carriers that underwent sleeve gastrectomy had higher BMI at t24m than the noncarriers. Additionally, we found an interaction between FKBP5 and age for the percentage of excess weight loss and BMI (P = .0005 and P = 1.5e-7, respectively), whereby individuals >48 years with the T allele displayed significant differences for the analyzed variables at t24m compared with the homozygotes for the alternate C allele showing lower weight loss. CONCLUSION: FKBP5 rs1360780 genotype has specific effects on weight loss outcomes after bariatric surgery depending on sex, age, and type of surgery, suggesting worse results in older males carrying the T allele who have undergone sleeve gastrectomy.

Identification of differentially expressed genes by means of outlier detection.

BACKGROUND: An important issue in microarray data is to select, from thousands of genes, a small number of informative differentially expressed (DE) genes which may be key elements for a disease. If each gene is analyzed individually, there is a big number of hypotheses to test and a multiple comparison correction method must be used. Consequently, the resulting cut-off value may be too small. Moreover, an important issue is the selection's replicability of the DE genes. We present a new method, called ORdensity, to obtain a reproducible selection of DE genes. It takes into account the relation between all genes and it is not a gene-by-gene approach, unlike the usually applied techniques to DE gene selection. RESULTS: The proposed method returns three measures, related to the concepts of outlier and density of false positives in a neighbourhood, which allow us to identify the DE genes with high classification accuracy. To assess the performance of ORdensity, we used simulated microarray data and four real microarray cancer data sets. The results indicated that the method correctly detects the DE genes; it is competitive with other well accepted methods; the list of DE genes that it obtains is useful for the correct classification or diagnosis of new future samples and, in general, it is more stable than other procedures. CONCLUSIONS: ORdensity is a new method for identifying DE genes that avoids some of the shortcomings of the individual gene identification and it is stable when the original sample is changed by subsamples.

Diagnosis using clinical/pathological and molecular information.

In diagnosis and classification diseases multiple outcomes, both molecular and clinical/pathological are routinely gathered on patients. In recent years, many approaches have been suggested for integrating gene expression (continuous data) with clinical/pathological data (usually categorical and ordinal data). This new area of research integrates both clinical and genomic data in order to improve our knowledge about diseases, and to capture the information which is lost in independent clinical or genomic studies. The related metric scaling distance is a not well-known, but very valuable distance to integrate clinical/pathological and molecular information. In this article, we present the use of the related metric scaling distance in biomedical research. We describe how this distance works, and we also explain why it may sometimes be preferred. We discuss the choice of the related metric scaling distance and compare it with other proximity measures to include both clinical and genetic information. Furthermore, we comment the choice of the related metric scaling distance when classical clustering or discriminant analysis based on distances are performed and compare the results with more complex cluster or discriminant procedures specially constructed for integrating clinical and molecular information. The use of the related metric scaling distance is illustrated on simulated experimental and four real data sets, a heart disease, and three cancer studies. The results present the flexibility and availability of this distance which gives competitive results.

Evaluation of Aminoglycoside and Non-Aminoglycoside Compounds for Stop-Codon Readthrough Therapy in Four Lysosomal Storage Diseases.

Nonsense mutations are quite prevalent in inherited diseases. Readthrough drugs could provide a therapeutic option for any disease caused by this type of mutation. Geneticin (G418) and gentamicin were among the first to be described. Novel compounds have been generated, but only a few have shown improved results. PTC124 is the only compound to have reached clinical trials. Here we first investigated the readthrough effects of gentamicin on fibroblasts from one patient with Sanfilippo B, one with Sanfilippo C, and one with Maroteaux-Lamy. We found that ARSB activity (Maroteaux-Lamy case) resulted in an increase of 2-3 folds and that the amount of this enzyme within the lysosomes was also increased, after treatment. Since the other two cases (Sanfilippo B and Sanfilippo C) did not respond to gentamicin, the treatments were extended with the use of geneticin and five non-aminoglycoside (PTC124, RTC13, RTC14, BZ6 and BZ16) readthrough compounds (RTCs). No recovery was observed at the enzyme activity level. However, mRNA recovery was observed in both cases, nearly a two-fold increase for Sanfilippo B fibroblasts with G418 and around 1.5 fold increase for Sanfilippo C cells with RTC14 and PTC124. Afterwards, some of the products were assessed through in vitro analyses for seven mutations in genes responsible for those diseases and, also, for Niemann-Pick A/B. Using the coupled transcription/translation system (TNT), the best results were obtained for SMPD1 mutations with G418, reaching a 35% recovery at 0.25 µg/ml, for the p.W168X mutation. The use of COS cells transfected with mutant cDNAs gave positive results for most of the mutations with some of the drugs, although to a different extent. The higher enzyme activity recovery, of around two-fold increase, was found for gentamicin on the ARSB p.W146X mutation. Our results are promising and consistent with those of other groups. Further studies of novel compounds are necessary to find those with more consistent efficacy and fewer toxic effects.

Towards application of one-class classification methods to medical data.

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques--Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

The depth problem: identifying the most representative units in a data group.

This paper presents a solution to the problem of how to identify the units in groups or clusters that have the greatest degree of centrality and best characterize each group. This problem frequently arises in the classification of data such as types of tumor, gene expression profiles or general biomedical data. It is particularly important in the common context that many units do not properly belong to any cluster. Furthermore, in gene expression data classification, good identification of the most central units in a cluster enables recognition of the most important samples in a particular pathological process. We propose a new depth function that allows us to identify central units. As our approach is based on a measure of distance or dissimilarity between any pair of units, it can be applied to any kind of multivariate data (continuous, binary or multiattribute data). Therefore, it is very valuable in many biomedical applications, which usually involve noncontinuous data, such as clinical, pathological, or biological data sources. We validate the approach using artificial examples and apply it to empirical data. The results show the good performance of our statistical approach.

ICGE: an R package for detecting relevant clusters and atypical units in gene expression.

BACKGROUND: Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample...) belongs to one of these previously identified clusters or to a new group. RESULTS: ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. CONCLUSIONS: We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Chitosan/albumin/CaCO3 as mimics for membrane bioreactor fouling: genesis of structural mineralized-EPS-building blocks and cake layer compressibility.

Membrane bioreactor biofouling is usually described as an extracellular matrix in which biopolymers, inorganic salts and active microbes co-exist. For that reason, biomineralization (BM) models can be useful to describe the spatial organization and environmental constraints within the referred scenario. BM arguments were utilized as background in order to (1) evaluate CaCO(3) influence on flux decline; pore blocking and cake layer properties (resistance, permeability and compressibility) in a wide range of Chitosan/Bovine serum albumin (BSA) mixtures during step-pressure runs and, (2) perform membrane autopsies in order to explore the genesis of mineralized extracellular building blocks (MEBB) during cake layer build up. Using low molecular weight chitosan (LC) and BSA, 2 L of 5 LC/BSA mixtures (0.25-1.85 ratio) were pumped to an external ultra filtration (UF) membrane (23.5cm(2), hydrophobic, piezoelectric, 100kDa as molecular weight cut-off). Eight different pressure steps (40±7 to 540±21kPa) were applied. Each pressure step was held for 900 s. CaCO(3) was added to LC/BSA mixtures at 0.5, 1.5 and 3mM in order to create MEBB during the filtration tests. Membrane autopsies were performed after the filtration tests using thermo gravimetric, scanning microscopy and specific membrane mass (mgcm(-2)) analyses. Biopolymer-CaCO(3) step-pressure filtration created compressible cake layers (with inner voids). The formation of an internal skeleton of MEBB may contribute to irreversible fouling consolidation. A hypothesis for MEBB genesis and development was set forth.

Microarray time course experiments: finding profiles.

Time course studies with microarray techniques and experimental replicates are very useful in biomedical research. We present, in replicate experiments, an alternative approach to select and cluster genes according to a new measure for association between genes. First, the procedure normalizes and standardizes the expression profile of each gene, and then, identifies scaling parameters that will further minimize the distance between replicates of the same gene. Then, the procedure filters out genes with a flat profile, detects differences between replicates, and separates genes without significant differences from the rest. For this last group of genes, we define a mean profile for each gene and use it to compute the distance between two genes. Next, a hierarchical clustering procedure is proposed, a statistic is computed for each cluster to determine its compactness, and the total number of classes is determined. For the rest of the genes, those with significant differences between replicates, the procedure detects where the differences between replicates lie, and assigns each gene to the best fitting previously identified profile or defines a new profile. We illustrate this new procedure using simulated data and a representative data set arising from a microarray experiment with replication, and report interesting results.