Plasmatic Biochemical Variables Associated with Polymorphisms in the Endothelin-1 and Endothelin-1 Receptor a Genes in Hypertensive Patients: Pilot Study

Endothelin-1 (ET-1) is a potent vasoconstrictive peptide, and its activity is mediated by thetype A receptor (EDNRA). This action may play a significant role in the etiology of hypertension.There are different works that shows an association between certain polymorphisms of endothelinaxis and clinical phenotype of hypertension. We describe the genetic variability +138/ex1Short Research ArticleLassen et al.; BJMMR, 11(7): 1-8, 2016; Article no.BJMMR.205202insertion/deletion (I/D) adenosine (A) in the ET-1 gene and polymorphism thymidine/cytosine (T/C)His323His in the EDNRA gene associated at the clinical variability in hypertensive patients.Study Design: Observational, transversal and analytical study.Place and Duration of Study: Hypertension Service at the Internal Medicine Department ofCórdoba Hospital, and Biochemical and Molecular Biology Department in School of Medicine,National University of Cordoba, Argentine. Patients considered hypertensive between April 2009and April 2010.Methodology: Were assessed 136 patients serum lipid profiles, renal and hepatic functions andwere taken Thoracic X-rays, electrocardiograms, and echocardiographs. DNA extracted fromcirculating leukocyte were used to analyze the polymorphisms of genes by PCR-RFLP.Results: For the polymorphisms of Receptor A from Endothelin -1 studied the presence ofcytosine homozygous genotype was less frequent in males (P = .02). For both genders, the samegenotype was associated to low plasma alkaline phosphatase activity and cholesterol levels. Thepresence of thymidine nucleotide allele correlated with plasma alkaline phosphatase activity andcholesterol levels. The Thymidine allele correlated with the degree of cardiovascular compromise(r = 0.54, P= .002). For the genetic variant in the ET-1 gene, the homozygous adenine deletionwas associated to normal plasma levels of glutamate/pyruvate transaminase enzyme activity, uricacid concentration, cholesterol, and Low Density Lipoprotein in hypertensive subjects withoutclinical risk.Conclusion: We observed a gender-specific protective effect for EDNRA gene variations, thesubjects that carried the TT genotype presented more aggressive symptomatology. These resultsshow an association between plasmatic biochemical parameters, the clinical condition, andpolymorphisms in the endothelin axis genes.Fil: Lassen, Oscar. Universidad Nacional de Córdoba. Facultad de Medicina. Hospital Córdoba; ArgentinaFil: Herrera, Jimena María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Dotto, Gladys. Universidad Nacional de Córdoba. Facultad de Medicina. Hospital Córdoba; ArgentinaFil: Ojeda, Silvia. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Garutti, Alicia. Universidad Nacional de Córdoba. Facultad de Medicina. Hospital Córdoba; ArgentinaFil: Bertolotto, Patricia Isolina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Tabares, Sandra. Universidad Nacional de Córdoba. Facultad de Medicina. Cátedra de Bioquímica y Biología Molecular; ArgentinaFil: Sembaj, Adela. Universidad Nacional de Córdoba. Facultad de Medicina. Cátedra de Bioquímica y Biología Molecular; Argentin

Estudio comparativo de características clínicas, bioquímicas y genéticas entre pacientes chagásicos y no chagásicos: asociación entre polimorfismos genéticos y variables marcadoras de riesgo cardiovascular

6 p.En la Enfermedad de Chagas (EC), la presencia del Trypanosoma cruzi (T.cruzi) es un factor determinante en la evolución hacia el deterioro de la función cardiaca. Para contrarrestar la infección, el huésped activa un conjunto de mecanismos de defensa (inmune e inflamatorio) que persisten en el tiempo. Podríamos atribuir parte de la responsabilidad de la variabilidad en la expresión de síntomas y evolución de la EC a características genéticas de cada individuo que permiten que se pongan en marcha mecanismos moleculares adaptativos del huésped, para sobrellevar la función cardiaca con normalidad y no poner en riesgo la vida [1,2]. Diversos trabajos han observado asociación entre polimorfismos de un solo nucleótido (SNPs) de diferentes factores en diferentes cardiomiopatías [3,4]. En este sentido poco se conoce de lo que sucede con la fisiopatogenia de la Cardiomiopatía Chagásica Cronica. Nos propusimos identificar polimorfismos en el gen de la SOD-Mn (superóxido dismutasa dependiente de Manganeso) en el gen de Endotelina 1- y su receptor A y asociar la variabilidad génica a características clínicas y/o bioquímicas en individuos con y sin infección con T.cruzi.Fil: Lassen, Oscar. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas. Departamento de Semiología UAMI N° 3. Hospital Córdoba. Consultorio de Chagas e hipertensión; Argentina.Fil: Dotto, Gladys. Hospital Córdoba. Laboratorio Central; Argentina.Fil: Ojeda, Silvia. Universidad Nacional de Córdoba. Facultad de Matemáticas, Astronomía y Física. Argentina.Fil: Garutti, Alicia. Hospital Córdoba. Laboratorio Central; Argentina.Fil: Bertolotto, Patricia. Universidad Nacional de Córdoba. Facultad de Matemáticas, Astronomía y Fisca; Argentina.Fil: Tabares, Sandra. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas. Cátedra de Bioquímica y Biología Molecular; Argentina.Fil: Gallerano, Rafael. Universidad Nacional de Córdoba. Escuela de Ciencias Médicas. Departamenteo de Semiología UAMI 3. Hospital Córdoba. Consultorio de Chagas e hipertensión; Argentina.Fil: Sembaj, Adela. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas. Cátedra de Bioquímica y Biología Molecular; Argentina.Bioquímica y Biología Molecula

The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle

Publication history: Accepted - 9 February 2022; Published online - 26 March 2022Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.This paper is the result of the concerted effort of all participants and support from the networks of COST Action FA1302 “METHAGENE: Large-scale methane measurements on individual ruminants for genetic evaluations.” The authors thank all individuals and groups who have directly or indirectly contributed to this work; special thanks are due to the technical and financial support from the COST Action FA1302 of the European Union. In addition, all financial and technical support from all participating countries and research centers involved in this work is greatly acknowledged

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

All At Once You Love Her

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Combining heterogeneous across-country data for prediction of enteric methane from proxies in dairy cattle

Large-scale measurement of enteric methane (CH4) from individual animals is a requisite for estimation of genetic parameters and prediction of breeding values. Direct measurement of individual CH4 emissions is logistically demanding and expensive, and correlated traits (proxies) or models can be used instead as a means to predict emissions. However, most predictive models tend to be specific and are valid mainly within the circumstances under which they were developed. Robust prediction models that work across countries and production environments may be built by combining heterogeneous data from several sources. However, combining heterogeneous individual animal observations on CH4 proxies from several sources is challenging and reports are scant in literature. The main objective of this study was to combine heterogeneous individual animal observations on CH4 proxies to develop robust enteric CH4 prediction models. Data on dairy cattle CH4 emissions and related proxies from 16 herds were made available by 13 research centers across 9 European countries within the Methagene EU COST Action FA1302 consortium on “Large-scale methane measurements on individual ruminants for genetic evaluations”. After a through edition and harmonization, the final dataset comprised 48,804 observations from 2,391 cows. Random Forest (RF) models were used to predict CH4 emissions and to estimate the relative importance of proxies for across-country predictions. Principal component analysis (PCA) was used to detect potential data stratifications. Milk yield, milk fat, DIM, BW, herd and country of origin appeared to be the most relevant proxies in the prediction model. An overall prediction accuracy of 0.81 was estimated from the combined heterogeneous data. This study is a first attempt to develop methods and approaches to combine heterogeneous individual animal data on proxies for CH4 to build robust models for prediction of CH4 emissions across diverse production systems and environments. The methodology outlined here can be extended to combining heterogeneous data, pedigree information and genome-wide dense marker information for estimation of genetic parameters and prediction of breeding values for traits related to dairy system CH4 emissions. Keywords: enteric methane, heterogeneous data, prediction accuracy, methane proxies, random forest, dairy cattle

Combining heterogeneous across-country data for prediction of enteric methane from proxies in dairy cattle

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