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259 results for “BMI”
Environmental and social factors influencing median income and BMI in the State of Geneva
<p>Hectometric grid (100m x 100m) covering the inhabited areas of the State of Geneva. It contains informations relative to the bmi and median income (GIREC) within the cells, together with a series of environmental and social factors, with which a correlation can be sought.</p>
Evidence for Correlations Between BMI-Associated SNPs and circRNAs
<p><strong>The datasets provided here are part of the study "Evidence for Correlations Between BMI-Associated SNPs and circRNAs" by Rajcsanyi et al.</strong></p> <p><strong>Abstract of the study:</strong></p> <p>Circular RNAs (circRNAs) are regulators of processes like adipogenesis. Their expression can be modulated by SNPs. We analysed links between BMI-associated SNPs and circRNAs. First, we detected an enrichment of BMI-associated SNPs on circRNA genomic loci in comparison to non-significant variants. Analysis of sex-stratified GWAS data revealed that circRNA genomic loci encompassed more genome-wide significant BMI-SNPs in females than in males. To explore if the enrichment is restricted to BMI, we investigated nine additional GWAS studies. We showed an enrichment of trait-associated SNPs in circRNAs for four analysed phenotypes (body height, chronic kidney disease, anorexia nervosa and autism spectrum disorder). To analyse the influence of BMI-affecting SNPs on circRNA levels in vitro, we examined rs4752856 located on hsa_circ_0022025. The analysis of heterozygous individuals revealed an increased level of circRNA derived from the BMI-increasing SNP allele. We conclude that genetic variation may affect the BMI partly through circRNAs.</p> <p><strong>Information regarding the datasets:</strong></p> <p>The data provided represents the analysed as well as generated data throughout the study. For further information about the datasets used, processed and generated, please see the study by Rajcsanyi et al.</p> <p><strong>circRNA datasets:</strong></p> <p>The analysed circRNA datasets were extracted from four circRNA databases (circAtlas v2.0, circBase, CIRCpediaV2 and circVAR) and were further processed to exclude internal duplicates and circRNAs derived from sex chromosomes. These original datasets have been downloaded from the following websites:</p> <p><em>circAtlas v2.0: http://159.226.67.237:8080/new/links.php</em></p> <p><em>circBase: http://www.circbase.org/cgi-bin/downloads.cgi</em></p> <p><em>CIRCpediaV2: http://yang-laboratory.com/circpedia/download</em></p> <p><em>circVAR: http://soft.bioinfo-minzhao.org/circvar/</em></p> <p><strong>GWAS datasets:</strong></p> <p>The original genome-wide association study (GWAS) summary statistics dataset of the BMI GWAS by Yengo et al. (2018) were classified into significant (P < 5*10<sup>-8</sup>) and non-significant (P >= 5*10<sup>-8</sup>) SNPs. A subsequent sensitivty analysis adjusted the P-value threshold of the non-significant SNPs to either P >= 5*10<sup>-5</sup>, P >= 5*10<sup>-6</sup> or 5*10<sup>-7</sup>.<br> Please note that these datasets are not provided in this repository, as these were solely classified and divided based on the SNPs' P-value. The same applied for all additional GWAS data solely divided based on the P-value (GWAS data for Anorexia nervosa, Autism spectrum disorder, etc.). The original and complete summary statistcs data can be obtained in the stated references below for each GWAS. Yet, the datasets generated for an approximation of the linkage disequilibrium based on the GWAS data by Yengo et al. (2018, BMI) are provided in this repository.<br> <br> <em>BMI and body height: Yengo et al. (2018)</em></p> <p><em>BMI sex-stratified: Pulit et al. (2019)</em></p> <p><em>Anorexia nervosa: Watson et al. (2018)</em></p> <p><em>Amyotropic lateral scerlosis: Iacoangeli et al. (2020)</em></p> <p><em>Autism spectrum disorder: Grove et al. (2019)</em></p> <p><em>Chronic kidney disease: Wuttke et al. 2019</em></p> <p><em>Epilepsy: ILAE consortium et al. 2018</em></p> <p><em>Heart Failure: Shah et al. 2020</em></p> <p><em>Pernicious anemia: Glanville et al. 2021</em></p> <p><em>Ulcerative Colitis: de Lange et al. 2017</em></p> <p><strong>Generated data:</strong></p> <p>The unprocessed output data of the study produced by the custom R script is provided here. Please note that the amount of information (rsID, P-value, Beta-value, allele frequency, circRNA_ID, circRNA strand, etc.) can vary between the output files due to differences in the data included in each circRNA and GWAS dataset. These dataset represent the raw and thus unprocessed output data. The results/counts presented in the study were obtained by further processing these output files. Further, the data produced by the SNaPshot assay are provided as well.</p>
Topological Overlap Matrices for DNA Methylation data of Gestational Diabetes Cohort with BMI and Exposure Status
<p>DNA methylation in placenta was measured with the Infinium HumanMethylation450 BeadChip (Illumina, Inc) microarray, in a sample of 28 women, 20 of whom had a gestational diabetes (GD)-affected pregnancy and 8 who did not. We used GD status as our exposure variable, assuming that this has widespread effects on DNA methylation and on its correlation patterns. Our response, Y, is the standardized body mass index (BMI) in the offspring at the age of 5. For the 10,000 most variable probes, we provide 3 topological overlap matrices (TOM), which are used in our analysis (note that each of the following TOM matrices are a 10,000 by 10,000 symmetric matrix with row names and column names corresponding to the CpG probe IDs:</p> <ol> <li>TOM_Methylation_All_10k.rds: based on all 28 subjects, </li> <li>TOM_Methylation_E0_10k.rds: based on the 8 subjects without a GD-affected pregnancy</li> <li>TOM_Methylation_E1_10k.rds: based on the 20 subjects with a GD-affected pregnancy </li> </ol> <p>The BMI (phenotype) and GD status (exposure) are given in the following dataset:</p> <ol> <li>BMI_and_Exposure_Status.rds: 28 x 2 matrix of the phenotype and exposure. each row is a subject.</li> </ol> <p>Using our ECLUST method (preprint available at http://sahirbhatnagar.com/slides/manuscript1_SB_v4.pdf), we derive 77 clusters, and here we provide the 1st principal component of each cluster:</p> <ol> <li>Cluster_Summary_1stPC.rds: 28 x 77 matrix, where each row is a subject, in the same order as the BMI_and_Exposure_Status.rds data</li> <li>Cluster_CpGs_names.rds: a list of length 77, where each element of the list contains the list of CpG probe IDs contained in each of the clusters</li> </ol> <p>To read in the data use the readRDS function, e.g.:</p> <p>TOM_All <- readRDS(file = "TOM_Methylation_All_10k.rds")</p>
Differences in gut microbiome abundances and diversity by physical activity levels and BMI among patients with colorectal cancer
<p>We investigated associations of physical activity, BMI, and combinations of physical activity levels/BMI with gut microbiome diversity and differential abundances among colorectal cancer patients. Pre-surgery stool samples from 179 colorectal cancer patients were used to perform 16S rRNA gene sequencing.</p>
Minimally Invasive Transaxillary Approach: Aortic Valve Replacement in a Patient with BMI >50 kg/m²
<p>This video demonstrates the successful application of the transaxillary MICLAT-S (<em>M</em>inimally <em>I</em>nvasive <em>C</em>ardiac <em>L</em>ateral <em>S</em>urgery) approach for aortic valve replacement (AVR) in a patient with a BMI >50 kg/m². The procedure showcases the feasibility and safety of the MICLAT-S technique in a high-risk, morbidly obese patient, a group traditionally considered challenging for minimally invasive surgery. By sparing the sternum and utilizing a lateral, transaxillary access route, the MICLAT-S method minimizes surgical trauma, reduces the risk of postoperative wound complications, and preserves thoracic stability, even in patients with extreme obesity.</p> <p>Despite the patient’s elevated BMI, the surgical team was able to maintain excellent access to the aortic valve, ensuring a smooth and efficient valve replacement. This video further supports the findings of our study, which demonstrate that the transaxillary concept of MICLAT-S is a viable alternative to traditional sternotomy, offering significant advantages, particularly in obese patients.</p> <p>This description complements the video by tying it to the findings of the paper and underlining the advantages of the transaxillary concept of MICLAT-S technique in this specific patient population.</p>
Forest plot: Associations between BMI at baseline and pCR following NACT – stratification according to ER status.
<p>Forest plot: Associations between BMI at baseline and pCR following NACT – stratification according to ER status.</p>
Behavior Intervention for Weight Loss for Type 2 Diabetes Mellitus Adults With Obesity Problem (BMI of ≥23kg/m2)
ClinicalTrials.gov study NCT05736536. IPD Sharing: NO. Countries: 1. Publications: 1.
EC PK in Women With Normal and Obese BMI
ClinicalTrials.gov study NCT02689804. IPD Sharing: NO. Countries: 1. Publications: 3.
Phase 2a Study of HU6 in Subjects With Elevated Liver Fat and High BMI Volunteers
ClinicalTrials.gov study NCT04874233. IPD Sharing: NO. Countries: 1. Publications: 1.
PEEP as Rescue Therapy for Asthmatics With Elevated BMI
ClinicalTrials.gov study NCT02696980. IPD Sharing: YES. Countries: 1. Publications: 2.
Effect of Head Rotation on Efficacy of Face Mask Ventilation in Anesthetized Obese (BMI ≥ 35) Adults
ClinicalTrials.gov study NCT03876873. IPD Sharing: NO. Countries: 1. Publications: 5.
Safety and Pharmacokinetics of Clindamycin in Pediatric Subjects With BMI ≥ 85th Percentile
ClinicalTrials.gov study NCT01744730. IPD Sharing: Not stated. Countries: 1. Publications: 27.
Misoprostol Dosing in BMI Greater Than 30
ClinicalTrials.gov study NCT05262738. IPD Sharing: NO. Countries: 1. Publications: 23.
Effectiveness and Safety Study of LAP-BAND Treatment in Subjects With BMI >/= 30 kg/m2 and < 40 kg/m2
ClinicalTrials.gov study NCT00570505. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effects Of Kiwifruit Consumption On Sleep Quality, Fatigue And BMI Of Saudi Adults
ClinicalTrials.gov study NCT05953324. IPD Sharing: NO. Countries: 1. Publications: 27.
Preventing Hypotension in Parturients With an Elevated Body Mass Index (BMI)
ClinicalTrials.gov study NCT01481740. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Data from: 8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: a comparison of younger and older Queensland adults
Lifestyle behaviours significantly contribute to high levels of chronic disease in older adults. The aims of the study were to compare the prevalence and the prevalence trends of health behaviours (physical activity, fruit and vegetable consumption, fast food consumption, TV viewing, smoking and alcohol consumption), BMI and a summary health behaviour indicator score in older (65+ years) versus younger adults (18-65 years). The self-report outcomes were assessed through the Queensland Social Survey annually between 2007-2014 (n=12,552). Regression analyses were conducted to compare the proportion of older versus younger adults engaging in health behaviours and of healthy weight in all years combined and examine trends in the proportion of younger and older adults engaging in health behaviours and of healthy weight over time. Older adults were more likely to meet recommended intakes of fruit and vegetable (OR=1.43, 95%CI=1.23-1.67), not consume fast food (OR=2.54, 95%CI=2.25-2.86) and be non-smokers (OR=3.02, 95%CI=2.53-3.60) in comparison to younger adults. Conversely, older adults were less likely to meet the physical activity recommendations (OR=0.86, 95%CI= 0.78-0.95) and watch less than 14 hours of TV per week (OR=0.65, 95%CI=0.58-0.74). Overall, older adults were more likely to report engaging in 3, or at least 4 out of 5 healthy behaviours. The proportion of both older and younger adults meeting the physical activity recommendations (OR=0.97, 95%CI=0.95-0.98 and OR=0.94, 95%CI=0.91-0.97 respectively), watching less than 14 hours of TV per week (OR=0.96, 95%CI=0.94-0.99 and OR=0.94, 95%CI=0.90-0.99 respectively) and who were a healthy weight (OR=0.95, 95%CI=0.92-0.99 and OR=0.96, 95%CI=0.94-0.98 respectively) decreased over time. The proportion of older adults meeting the fruit and vegetable recommendations (OR=0.90, 95%CI=0.84-0.96) and not consuming fast food (OR=0.94, 95%CI=0.88-0.99) decreased over time. Although older adults meet more health behaviours than younger adults, the decreasing prevalence of healthy nutrition behaviours in this age group needs to be addressed.
A Phase II Efficacy Study Comparing 2',3'-Dideoxyinosine (ddI) (BMY-40900) and Zidovudine Therapy of Patients With HIV Infection Who Have Been on Long Term Zidovudine Treatment
ClinicalTrials.gov study NCT00000671. IPD Sharing: Not stated. Countries: 2. Publications: 9.
BMI and Its Relationship to Hypoglycemic Seizures in Children With Insulin-requiring Diabetes
ClinicalTrials.gov study NCT00717483. IPD Sharing: NO. Countries: 1. Publications: 4.
Carbetocin at Elective Cesarean Deliveries: A Dose-finding Study in Women With BMI ≥ 40kg/m2
ClinicalTrials.gov study NCT03672045. IPD Sharing: NO. Countries: 1. Publications: 1.
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