Atlantic mammal traits: a dataset of morphological traits of mammals in the atlantic forest of south America

Measures of traits are the basis of functional biological diversity. Numerous works consider mean species-level measures of traits while ignoring individual variance within species. However, there is a large amount of variation within species and it is increasingly apparent that it is important to consider trait variation not only between species, but also within species. Mammals are an interesting group for investigating trait-based approaches because they play diverse and important ecological functions (e.g., pollination, seed dispersal, predation, grazing) that are correlated with functional traits. Here we compile a data set comprising morphological and life history information of 279 mammal species from 39,850 individuals of 388 populations ranging from −5.83 to −29.75 decimal degrees of latitude and −34.82 to −56.73 decimal degrees of longitude in the Atlantic forest of South America. We present trait information from 16,840 individuals of 181 species of non-volant mammals (Rodentia, Didelphimorphia, Carnivora, Primates, Cingulata, Artiodactyla, Pilosa, Lagomorpha, Perissodactyla) and from 23,010 individuals of 98 species of volant mammals (Chiroptera). The traits reported include body mass, age, sex, reproductive stage, as well as the geographic coordinates of sampling for all taxa. Moreover, we gathered information on forearm length for bats and body length and tail length for rodents and marsupials. No copyright restrictions are associated with the use of this data set. Please cite this data paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data.Fil: Gonçalves, Fernando. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Bovendorp, Ricardo S.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Beca, Gabrielle. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Bello, Carolina. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Costa Pereira, Raul. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Muylaert, Renata L.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Rodarte, Raisa R.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Villar, Nacho. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Souza, Rafael. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Graipel, Maurício E.. Universidade Federal de Santa Catarina; BrasilFil: Cherem, Jorge J.. Caipora Cooperativa, Florianopolis; BrasilFil: Faria, Deborah. Universidade Estadual de Santa Cruz; BrasilFil: Baumgarten, Julio. Universidade Estadual de Santa Cruz; BrasilFil: Alvarez, Martín R.. Universidade Estadual de Santa Cruz; BrasilFil: Vieira, Emerson M.. Universidade do Brasília; BrasilFil: Cáceres, Nilton. Universidade Federal de Santa María. Santa María; BrasilFil: Pardini, Renata. Universidade de Sao Paulo; BrasilFil: Leite, Yuri L. R.. Universidade Federal do Espírito Santo; BrasilFil: Costa, Leonora Pires. Universidade Federal do Espírito Santo; BrasilFil: Mello, Marco Aurelio Ribeiro. Universidade Federal de Minas Gerais; BrasilFil: Fischer, Erich. Universidade Federal do Mato Grosso do Sul; BrasilFil: Passos, Fernando C.. Universidade Federal do Paraná; BrasilFil: Varzinczak, Luiz H.. Universidade Federal do Paraná; BrasilFil: Prevedello, Jayme A.. Universidade do Estado de Rio do Janeiro; BrasilFil: Cruz-Neto, Ariovaldo P.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Carvalho, Fernando. Universidade do Extremo Sul Catarinense; BrasilFil: Reis Percequillo, Alexandre. Universidade de Sao Paulo; BrasilFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Duarte, José M. B.. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil. Fundación Oswaldo Cruz; BrasilFil: Bernard, Enrico. Universidade Federal de Pernambuco; BrasilFil: Agostini, Ilaria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Lamattina, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Ministerio de Salud de la Nación; ArgentinaFil: Vanderhoeven, Ezequiel Andres. Ministerio de Salud de la Nación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentin

Relationships between Parental Education and Overweight with Childhood Overweight and Physical Activity in 9-11 Year Old Children: Results from a 12-Country Study

Background: Globally, the high prevalence of overweight and low levels of physical activity among children has serious implications for morbidity and premature mortality in adulthood. Various parental factors are associated with childhood overweight and physical activity. The objective of this paper was to investigate relationships between parental education or overweight, and (i) child overweight, (ii) child physical activity, and (iii) explore household coexistence of overweight, in a large international sample. Methods: Data were collected from 4752 children (9-11 years) as part of the International Study of Childhood Obesity, Lifestyle and the Environment in 12 countries around the world. Physical activity of participating children was assessed by accelerometry, and body weight directly measured. Questionnaires were used to collect parents' education level, weight, and height. Results: Maternal and paternal overweight were positively associated with child overweight. Higher household coexistence of parent-child overweight was observed among overweight children compared to the total sample. There was a positive relationship between maternal education and child overweight in Colombia 1.90 (1.23-2.94) [odds ratio (confidence interval)] and Kenya 4.80 (2.21-10.43), and a negative relationship between paternal education and child overweight in Brazil 0.55 (0.33-0.92) and the USA 0.54 (0.33-0.88). Maternal education was negatively associated with children meeting physical activity guidelines in Colombia 0.53 (0.33-0.85), Kenya 0.35 (0.19-0.63), and Portugal 0.54 (0.31-0.96). Conclusions: Results are aligned with previous studies showing positive associations between parental and child overweight in all countries, and positive relationships between parental education and child overweight or negative associations between parental education and child physical activity in lower economic status countries. Relationships between maternal and paternal education and child weight status and physical activity appear to be related to the developmental stage of different countries. Given these varied relationships, it is crucial to further explore familial factors when investigating child overweight and physical activity

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p