Early Life Antibiotic Exposure and Weight Development in Children

OBJECTIVE: To examine the timing, frequency, and type of antibiotic exposure during the first 10 years of life in association with (over)weight across this period in a cohort of 979 children. STUDY DESIGN: Within the Child, Parents and Health: Lifestyle and Genetic Constitution Birth Cohort Study, antibiotic exposure record was obtained from general practitioners. Anthropometric outcomes (age- and sex-standardized body mass index, weight and height z-scores, and overweight) were measured repeatedly at 7 time points during the first 10 years of life. Generalized estimating equations method was used for statistical analysis. RESULTS: After adjusting for confounding factors, children exposed to one course of antibiotics compared with none in the first 6 months of life had increased weight- (adjusted generalized estimating equations estimates [adjβ] 0.24; 95% CI 0.03-0.44) and height (adjβ 0.23; 95% CI 0.0002-0.46) z-scores; exposure to ≥2 courses during the second year of life was associated with both increased weight (adjβ 0.34; 95% CI 0.07-0.60), and height z-scores (adjβ 0.29; 95% CI -0.003 to 0.59). Exposure later in life was not associated with anthropometric outcomes. Associations with weight z-scores were mainly driven by exposure to broad- (≥2 courses: adjβ 0.11; 95% CI 0.003-0.22) and narrow-spectrum β-lactams (1 course: adjβ 0.18; 95% CI 0.005-0.35) during the follow-up period. Specific antibiotic used was not associated with body mass index z-scores and overweight. CONCLUSIONS: Repeated exposure to antibiotics early in life, especially β-lactam agents, is associated with increased weight and height. If causality of obesity can be established in future studies, this further highlights the need for restrictive antibiotic use and avoidance of prescriptions when there is minimal clinical benefit

Altered Cigarette Smoke-Induced Lung Inflammation Due to Ablation of Grx1

Glutaredoxins (Grx) are redox enzymes that remove glutathione bound to protein thiols, know as S-glutathionylation (PSSG). PSSG is a reservoir of GSH and can affect the function of proteins. It inhibits the NF-κB pathway and LPS aspiration in Grx1 KO mice with decreased inflammatory cytokine levels. In this study we investigated whether absence of Grx1 similarly repressed cigarette smoke-induced inflammation in an exposure model in mice. Cigarette smoke exposure for four weeks decreased lung PSSG levels, but increased PSSG in lavaged cells and lavage fluid (BALF). Grx1 KO mice had increased levels of PSSG in lung tissue, BALF and BAL cells in response to smoke compared to wt mice. Importantly, levels of multiple inflammatory mediators in the BALF were decreased in Grx1 KO animals following cigarette smoke exposure compared to wt mice, as were levels of neutrophils, dendritic cells and lymphocytes. On the other hand, macrophage numbers were higher in Grx1 KO mice in response to smoke. Although cigarette smoke in vivo caused inverse effects in inflammatory and resident cells with respect to PSSG, primary macrophages and epithelial cells cultured from Grx1 KO mice both produced less KC compared to cells isolated from WT mice after smoke extract exposure. In this manuscript, we provide evidence that Grx1 has an important role in regulating cigarette smoke-induced lung inflammation which seems to diverge from its effects on total PSSG. Secondly, these data expose the differential effect of cigarette smoke on PSSG in inflammatory versus resident lung cells

Large Epidemiologic Studies of Gout: Challenges in Diagnosis and Diagnostic Criteria

Large epidemiologic studies of gout can improve insight into the etiology, pathology, impact, and management of the disease. Identification of monosodium urate monohydrate crystals is considered the gold standard for diagnosis, but its application is often not possible in large studies. Therefore, under such circumstances, several proxy approaches are used to classify patients as having gout, including ICD coding in several types of databases or questionnaires that are usually based on the existing classification criteria. However, agreement among these methods is disappointing. Moreover, studies use the terms acute, recurrent, and chronic gout in different ways and without clear definitions. Better definitions of the different manifestations and stages of gout may provide better insight into the natural course and burden of disease and can be the basis for valid approaches to correctly classifying patients within large epidemiologic studies

A Comparative Study on the WCRF International/University of Bristol Methodology for Systematic Reviews of Mechanisms Underpinning Exposure-Cancer Associations

The World Cancer Research Fund (WCRF) International and the University of Bristol have developed a novel framework for providing an overview of mechanistic pathways and conducting a systematic literature review of the biologically plausible mechanisms underlying exposure-cancer associations. Two teams independently applied the two-stage framework on mechanisms underpinning the association between body fatness and breast cancer to test the framework feasibility and reproducibility as part of a WCRF-commissioned validation study. In stage I, a "hypothesis-free" approach was used to provide an overview of potential intermediate mechanisms between body fatness and breast cancer. Dissimilar rankings of potential mechanisms were observed between the two teams due to different applications of the framework. In stage II, a systematic review was conducted on the insulin-like growth factor 1 receptor (IGF1R) chosen as an intermediate mechanism. Although the studies included differed, both teams found inconclusive evidence for the body fatness-IGF1R association and modest evidence linking IGF1R to breast cancer, and therefore concluded that there is currently weak evidence for IGF1R as mechanism linking body fatness to breast cancer. The framework is a good starting point for conducting systematic reviews by integrating evidence from mechanistic studies on exposure-cancer associations. On the basis of our experience, we provide recommendations for future users. (C) 2017 AACR

Subcutaneous Adipose Tissue and Systemic Inflammation Are Associated With Peripheral but Not Hepatic Insulin Resistance in Humans

Obesity-related insulin resistance (IR) may develop in multiple organs, representing different etiologies towards cardiometabolic diseases. We identified abdominal subcutaneous adipose tissue (ScAT) transcriptome profiles in relation to liver or muscle IR by means of RNA sequencing in overweight/obese participants of the DiOGenes cohort (n=368). Tissue-specific IR phenotypes were derived from a 5-point oral glucose tolerance test. Hepatic and muscle IR were characterized by distinct abdominal ScAT transcriptome profiles. Genes related to extracellular remodeling were upregulated in individuals with primarily hepatic IR, whilst genes related to inflammation were upregulated in individuals with primarily muscle IR. In line with this, in two independent cohorts, CODAM (n=325) and the Maastricht Study (n=685), an increased systemic low-grade inflammation profile was specifically related to muscle IR, but not to liver IR. We propose that increased ScAT inflammatory gene expression may translate into an increased systemic inflammatory profile, linking ScAT inflammation to the muscle IR phenotype. These distinct IR phenotypes may provide leads for more personalized prevention of cardiometabolic diseases. DiOGenes was registered at clinicaltrials.gov as NCT00390637

Metabolomics Profile in Depression:A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls

Background: Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons. Methods: Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses. Results: Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q <.05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms. Conclusions: This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p