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> Physiology > Organism Attribute > Adiposity

Adiposity

Adiposity refers to the state or condition of being overweight or obese.
It is characterized by the accumulation of excess fat tissue in the body, which can lead to various health concerns such as cardiovascular disease, type 2 diabetes, and certain types of cancer.
Adiposity is a complex trait influenced by genetic, environmental, and lifestyle factors.
Researchers studying adiposity may utilize PubCompaore.ai, an AI-driven platform that enhances reproducibility and accuracy in locating protocols from literature, preprints, and patents.
This tool can help identify the best protocols and products for adiposity research needs, supporting advancements in understanding and addressing this important public health issue.

Most cited protocols related to «Adiposity»

1
Adipose Distribution Modeling with VAI
A total of 315 healthy subjects with BMI between 20 and 30 kg/m2 were further selected from the 1,498 primary-care patients to calculate a model of adipose distribution (MOAD). To correct MOAD for fat function, TG (mmol/l) and HDL (mmol/l) levels were introduced in the formula. This was defined as VAI:


assuming VAI = 1 in healthy nonobese subjects with normal adipose distribution and normal TG and HDL levels (supplemental Appendix 2).
Amato M.C., Giordano C., Galia M., Criscimanna A., Vitabile S., Midiri M, & Galluzzo A. (2010). Visceral Adiposity Index: A reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care, 33(4), 920-922.
Publication 2010
Adiposity Healthy Volunteers
2
Mediterranean Diet Adherence and Adiposity
We compared the baseline characteristics of participants according to three categories of adherence to the Mediterranean diet (≤5, 6–9 and ≥10 points of the 14-item questionnaire). We calculated means (SD) or percentages for each variable across the three categories and assessed the statistical significance of the differences among them with one-way ANOVA and chi-squared tests, respectively.
We compared means and 95% confidence intervals (CI) of the three adiposity indexes (BMI, WC and WHtR) across 5 categories of the 14-item score of adherence to the Mediterranean diet (≤5, 6–7, 8, 9, ≥10 points). Comparisons were done separately for women and men. We also calculated the Pearson correlation coefficients (r) between each of the three adiposity indexes and the 14-item score. Because the Pearson coefficient is sensitive to data distribution, we also calculated Spearman rank correlations. We also estimated partial correlations after accounting for total energy intake.
We used multivariable linear regression modelling to compare adjusted differences in the three adiposity indexes between participants with the lowest adherence to the Mediterranean diet (score ≤7, reference category) and those with higher levels of adherence (8–9, or ≥10). We also used the 14-item score as a continuous variable and assessed differences in each of the three indexes for a two-point increment. Comparisons were done separately for women and men and were adjusted for age (continuous), smoking (3 categories), diabetes status, hypertensive status, physical activity (METS-h/day, continuous), educational level (3 categories), marital status, and centre. Additionally, we also adjusted for total energy intake. We also used multivariable logistic regression to estimate odds ratios (OR) for obesity (BMI≥30 kg/m2) or abdominal obesity (defined as WHtR>0.6) for participants with higher levels of adherence to the Mediterranean diet (8–9, or ≥10 points in the 14-item score) versus those with lower adherence (≤7 points, reference category) in separated models for women and men and after adjusting for age, smoking and centre. In another model, we additionally adjusted for diabetes status, hypertensive status, educational level, marital status and physical activity. Finally, we also used multivariable logistic regression to estimate the association between each of the 14 individual items of the score and the odds of obesity (BMI≥30 kg/m2) or abdominal obesity (WHtR>0.6) after adjusting for sex, age, smoking and centre. Additionally, we also adjusted for all the other items in the score. A p value <0.05 was considered as statistically significant. Stata 12.0 was used for all analyses.
Martínez-González M.A., García-Arellano A., Toledo E., Salas-Salvadó J., Buil-Cosiales P., Corella D., Covas M.I., Schröder H., Arós F., Gómez-Gracia E., Fiol M., Ruiz-Gutiérrez V., Lapetra J., Lamuela-Raventos R.M., Serra-Majem L., Pintó X., Muñoz M.A., Wärnberg J., Ros E, & Estruch R. (2012). A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial. PLoS ONE, 7(8), e43134.
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Publication 2012
Adiposity Diabetes Mellitus Diet, Mediterranean neuro-oncological ventral antigen 2, human Obesity Woman
3
Evaluating Genetic Influences on Body Composition
Similarly to Shungin et al. (5 (link)), we carried out analysis on the 346 index SNPs and their association with BF% and WHR. We obtained association statistics for the 346 SNPs on BF% and WHR from a GWAS of 443 001 unrelated European-ancestry UK Biobank individuals. We aligned all results to the WHR-increasing allele and used a Bonferroni-corrected P-value (0.05/346 = 1.44 × 10−4) to determine if an SNP was associated with BF% (Fig. 2). To determine whether sex-specific WHRadjBMI index SNPs have an adiposity phenotype, we took the 97 (female-specific) and 8 (male-specific) SNPs and independently compared their effects on WHRadjBMI and BF% in men and women. To identify which sex-dimorphic SNPs were strongly associated with BF% in men and women separately, we used a Bonferroni-corrected P-value of 0.05/105 (4.8 × 10−4) (Supplementary Material, Fig. 7 and Supplementary Material, Table 9). We obtained Pearson’s r correlations using the cor() function in R for each comparison.
Pulit S.L., Stoneman C., Morris A.P., Wood A.R., Glastonbury C.A., Tyrrell J., Yengo L., Ferreira T., Marouli E., Ji Y., Yang J., Jones S., Beaumont R., Croteau-Chonka D.C., Winkler T.W., Hattersley A.T., Loos R.J., Hirschhorn J.N., Visscher P.M., Frayling T.M., Yaghootkar H, & Lindgren C.M. (2018). Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Human Molecular Genetics, 28(1), 166-174.
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Publication 2018
Adiposity Alleles Europeans Females Genome-Wide Association Study Males Phenotype Single Nucleotide Polymorphism Woman
4
Childhood Obesity Determinants: A Global Perspective
Data on participant attributes such as demographic, anthropometric, and lifestyle behaviours will be summarized separately for boys and girls, within and across all study sites, as counts and percentages for categorical variables and means and standard deviations for continuous variables. Given that the primary aim of ISCOLE is to predict obesity as a function of lifestyle behaviours and environmental characteristics, general linear and nonlinear statistical models, including covariate-adjusted models, will be employed to investigate relationships between adiposity and its potential determinants. Multilevel random-effects models that treat schools within site and children within schools as well as schools within countries as random effects will be used for all analyses. Statistical significance will be defined as p < 0.05 with appropriate adjustments for multi-testing.
Katzmarzyk P.T., Barreira T.V., Broyles S.T., Champagne C.M., Chaput J.P., Fogelholm M., Hu G., Johnson W.D., Kuriyan R., Kurpad A., Lambert E.V., Maher C., Maia J., Matsudo V., Olds T., Onywera V., Sarmiento O.L., Standage M., Tremblay M.S., Tudor-Locke C., Zhao P, & Church T.S. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health, 13, 900.
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Publication 2013
Adiposity Boys Child Obesity Woman
5
Gut Microbiome Humanization in Mice
All experiments involving mice were performed using protocols approved by the Washington University Animal Studies Committee. Germ-free adult male C57BL/6J mice were maintained in plastic gnotobiotic isolators under a strict 12 hr light cycle and fed an autoclaved low-fat, polysaccharide-rich chow diet (LF/PP; B&K autoclavable diet 7378000) ad libitum. Humanization was performed by diluting a freshly voided human fecal sample (1g) in 10mL reduced PBS, under anaerobic conditions. The fecal material was then suspended by vortexing, and 0.2mL of the suspension introduced by gavage, into each germ-free recipient. A similar procedure was used for a frozen fecal subsample, which was homogenized with a mortar and pestle while frozen prior to dilution. Mice were subsequently maintained in separate cages in a gnotobiotic isolator, and fed a LF/PP or a high-fat/high-sugar Western diet (Harlan-Teklad TD96132) ad libitum.
To define total body fat content, animals were anesthetized with an intraperitoneal injection of ketamine (10 mg/kg body weight) and xylazine (10 mg/kg), and subjected to dual-energy X-ray absorptiometry [DEXA; Lunar PIXImus Mouse, GE Medical Systems; (12 (link))]. Epidydymal fat pad weights were also used as a biomarker of adiposity.
Procedures used for (i) gut microbial community DNA preparation, (ii) sequencing of 16S rRNA gene amplicons, (iii) isolation of C. innocuum strain SB23, (iv) pyrosequencing of total community DNA and the C. innocuum SB23 genome, (v) C. innocuum strain SB23 genome annotation and metabolic reconstruction, (vi) database searches and in silico microbiome metabolic reconstructions, (vii) meta-transcriptomics (RNA-Seq), (viii) qRT-PCR, and (ix) statistical analyses, are described in SM.
Turnbaugh P.J., Ridaura V.K., Faith J.J., Rey F.E., Knight R, & Gordon J.I. (2009). The Effect of Diet on the Human Gut Microbiome: A Metagenomic Analysis in Humanized Gnotobiotic Mice. Science translational medicine, 1(6), 6ra14.
Publication 2009
Adiposity Adult Animals Biological Markers Body Fat Body Weight Carbohydrates Diet, High-Fat Dual-Energy X-Ray Absorptiometry Fat-Restricted Diet Feces Freezing Gene Expression Profiling Genes Genome Homo sapiens Injections, Intraperitoneal isolation Ketamine Males Mice, House Mice, Inbred C57BL Microbial Community Microbiome Pad, Fat Polysaccharides Reconstructive Surgical Procedures RNA, Ribosomal, 16S RNA-Seq Silver Strains Technique, Dilution Therapy, Diet Tube Feeding Xylazine

Most recents protocols related to «Adiposity»

1
Improving Meat Quality through Antioxidant Incorporation
Animal fat is primarily stored in fatty tissue, which can be further subdivided into adipose fat, subcutaneous fat, intermuscular fat, and marbling fat. The fat found within the muscles is known as marbling, and it helps produce a favorable texture (Lawrie & Ledward, 2014 ). Depending on the animal’s fat excretion and the preparation method, a given piece of meat may include varying amounts of intermuscular and depot fat. Saturated fatty acids (SFA) are commonly thought to make up the bulk of animal fat, whereas over 50% of the fatty acids in meat are unsaturated (Lawrie & Ledward, 2014 ). Lipids in meat typically comprise less than half saturated fatty acids (beef 50–52%) and as much as 70% unsaturated fatty acids (Valsta, Tapanainen & Männistö, 2005 (link)).
The grinding, cooking, and storing steps in the processing of meat products expose lipids to the air, which causes them to oxidize quickly and irreversibly. Meat and meat products lose their desirable flavor and texture because of rancidity, turn brown, and create hazardous substances including malondialdehyde and cholesterol oxidation products due to lipid oxidation (Choe et al., 2014 (link)). The addition of various fruit and waste extract may help in retarding lipid oxidation and reducing the fat content of the meat products. In one study, to a more significant extent, persimmon peel extracts prevented lipid oxidation of pork patties while they were being refrigerated (Choe, Kim & Kim, 2017 (link)). In another study, inulin from chicory root was able to reduce the fat at a significant level in pork and chicken meatball (Montoya et al., 2022 (link)). Thus incorporation of fruit and vegetable waste and their extract in meat products may help in slowing down lipid oxidation and rancidity.
Haque A., Ahmad S., Azad Z.R., Adnan M, & Ashraf S.A. (2023). Incorporating dietary fiber from fruit and vegetable waste in meat products: a systematic approach for sustainable meat processing and improving the functional, nutritional and health attributes. PeerJ, 11, e14977.
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Publication 2023
Adiposity Animals Beef Cardiac Arrest Chickens Cholesterol Cichorium intybus Dietary Fiber Diospyros Fatty Acids Fatty Acids, Unsaturated Flavor Enhancers Fruit Hazardous Substances Inulin Lipids Malondialdehyde Meat Meat Products Muscle Tissue Plant Roots Pork Saturated Fatty Acid Subcutaneous Fat Vegetables
2
Establishing European Pediatric Fitness Norms
A systematic review of existing datasets including fitness tests in children and adolescents was previously performed by Tomkinson et al and details of the search have been published.40 (link) These data were included in the FitBack dataset, with Monte Carlo simulation used to produce pseudo data (from reported means and SDs) when raw data were unavailable. In addition to this, the authors of the FitBack network conducted a centralised narrative search based on fitness terms to identify new datasets not included in the Tomkinson et al review.40 (link) For inclusion, valid data on sex, age and at least one of the ALPHA fitness tests (high-priority version) was required. In the previous study by Tomkinson et al, the age range was 9–17 year old, whereas in this study, we widened the age demographic to include subjects aged 6–18 years old. It is important to note that our search strategy was focused on fitness, and specific searches on adiposity, BMI or waist circumference were not conducted for pragmatic reasons (eg, the very large number of studies including these key words). Therefore, it is possible that we missed relevant anthropometry-specific datasets. This, together with the fact that other organisations are comprehensively monitoring paediatric obesity, is the reason why we primarily focused on CRF and muscular strength, and reported results for anthropometric measures (body height, body mass, BMI and waist circumference) as online supplemental material.
The FitBack network involved numerous experienced researchers working in paediatric fitness across Europe, which helped to identify unpublished fitness datasets that were pooled with gathered data. Moreover, large datasets from existing surveillance systems in Europe such as SLOfit,41 (link) NETFIT42 (link) and Fitescola43 (link) were also included. Further, we excluded older datasets if a more recent and more representative dataset was available for certain countries. The ambition was to use the most recent available data for each country, which in some cases was a single large dataset, while in others was the accumulation of several studies or datasets covering different geographical regions within a country. Sources used for generating the reference values are available on the FitBack website (www.fitbackeurope.eu/en-us/fitness-map/sources) as well as in online supplemental table 1.
Ortega F.B., Leskošek B., Blagus R., Gil-Cosano J.J., Mäestu J., Tomkinson G.R., Ruiz J.R., Mäestu E., Starc G., Milanovic I., Tammelin T.H., Sorić M., Scheuer C., Carraro A., Kaj M., Csányi T., Sardinha L.B., Lenoir M., Emeljanovas A., Mieziene B., Sidossis L.S., Pihu M., Lovecchio N., Konstabel K., Tambalis K.D., Štefan L., Drenowatz C., Rubín L., Gontarev S., Castro-Piñero J., Vanhelst J., O’Keeffe B., Veiga O.L., Gisladottir T., Sandercock G., Misigoj-Durakovic M., Niessner C., Riso E.M., Popovic S., Kuu S., Chinapaw M., Clavel I., Labayen I., Dobosz J., Colella D., Kriemler S., Salaj S., Noriega M.J., Bös K., Sánchez-López M., Lakka T.A., Tabacchi G., Novak D., Ahrens W., Wedderkopp N, & Jurak G. (2023). European fitness landscape for children and adolescents: updated reference values, fitness maps and country rankings based on nearly 8 million test results from 34 countries gathered by the FitBack network. British Journal of Sports Medicine, 57(5), 299-310.
Publication 2023
Adiposity Adolescent Body Height Child Human Body Muscle Strength One-Alpha Pediatric Obesity Waist Circumference
3
Community-Driven Obesity Prevention Capacity Assessment
The CAPITOL Project is a community-based “systems thinking” initiative based in the NW with an intentional focus on assessing obesity prevention capacity (OPC), engaging the community in developing OPC, and evaluating OPC development efforts. The central tenet of this work acknowledges the sustained multidisciplinary collaborations required to manage lifestyle risk factors, including nutrition, physical inactivity, smoking, and mental health, which can either be a cause or a consequence of excess adiposity. While NW Tasmania represents an expansive land mass, for reasons of practicality, the CAPITOL project operations are limited to 3 sentinel sites (Burnie, Circular Head, and Devonport). Sentinel sites are communities from which in-depth data can be collected, which has a high degree of transferability [26 ].
The current health profile of NW Tasmania, combined with its predominantly rural, geographically dispersed and socioeconomically disadvantaged population, is a hotbed for the proliferation of lifestyle-related disease. By virtue, the region also has a high demand/need for health promotion [27 ,28 ]. Nonetheless, the existing preventive health system in the region is underdeveloped and underresourced, which compounds existing health issues and stymies the progress of well-intentioned preventive efforts [29 (link)]. In addition to the lack of resourcing, the predominant narrative in the region has traditionally been a deficit-based view of health. To help circumvent these challenges, the holistic systems design of the CAPITOL Project is focused on asset-based community development alongside a primary prevention focus (ie, keeping healthy people healthy) with heightened attention on critical life stages (ie, mothers, infants, children, adolescents, and so on).
Jayasinghe S., Holloway T.P., Soward R., Patterson K.A., Ahuja K.D., Dalton L., Murray S., Hughes R., Byrne N.M, & Hills A.P. (2023). “An Ounce of Prevention is Worth a Pound of Cure”: Proposal for a Social Prescribing Strategy for Obesity Prevention and Improvement in Health and Well-being. JMIR Research Protocols, 12, e41280.
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Publication 2023
Adiposity Adolescent Attention Child Disadvantaged Populations Head Health Promotion Infant Mental Health Mothers Obesity Preventive Health Services Primary Prevention
4
Multi-tissue Transcriptome Analysis of Bariatric Samples
Biopsies from liver (106 samples), jejunum (105 samples), mesenteric adipose fat (104 samples) and subcutaneous adipose fat (105 samples) were collected at the time of the bariatric surgery, as previously described [32 (link)]. RNA was extracted from biopsies using TriPure Isolation Reagent (Roche, Basel, Switzerland) and Lysing Matrix D, 2 mL tubes (MP Biomedical, Irvine, CA, USAs) in a FastPrep®-24 Instrument (MP Biomedical, Irvine, CA, USAs) with homogenization for 20 seconds at 4.0 m/sec, with repeated bursts until no tissue was visible; homogenates were kept on ice for 5 minutes between homogenization bursts if multiple cycles were needed. RNA was purified with chloroform (Merck, Darmstadt, Germany) in phase lock gel tubes (5PRIME) with centrifugations at 4°C, and further purified and concentrated using the RNeasy MinElute kit (Qiagen, Venlo, The Netherlands). The quality of RNA was analysed on a BioAnalyzer instrument (Agilent), with quantification on Nanodrop (Thermo Fisher Scientific, Waltham, MA, USA). Due to degradation of the RNA, libraries for RNAseq sequencing were prepared by rRNA depletion; library preparation and sequencing were performed at Novogene (Nanjing, China) on an HiSeq instrument (Illumina Inc., San Diego, CA, USA) with 150 bp paired-end reads and 10G data/sample. The average read count per sample from liver and jejunum tissues were 42 ± 15 million. For mesenteric and subcutaneous fat, the average read count per sample were 43.2 ± 20 million.
The extracted fastq files were analyzed with nf-core/rnaseq [42 (link)], a bioinformatics analysis pipeline used for RNA sequencing data. The workflow processed raw data from FastQ inputs (FastQC, TrimGalore!), aligned the reads (STAR) with Homo sapiens GRCh38 as reference genome, generates gene counts (featureCounts, StringTie) and performed extensive quality-control on the results (RSeqQC, dupRadar, Preseq, edgeR, multiQC). The pipeline was built using Nextflow.
Differential gene expression analysis for five SOM defined cluster participants has been performed for liver, jejunum, subcutaneous adipose and mesenteric adipose tissues, respectively, in R (version 3.6.3) and RStudio (version 1.2.5033) with DESeq2 [43 (link)] package. The statistical analysis method for calculating differential expression rates was the LRT test (log-ratio test). After FDR correction (FDR 5%) with multiple hypothesis testing with IHW [44 ] package, we analyzed genes with P<0.05 by DEGreport’s [45 ] degPatterns function, so as to identify subgroups of co-expressed genes across the SOM clusters and assign a z score to each metabotype. For these differentially significant co-expressed genes we performed gene enrichment with Enrichr platform [46 (link)] using KEGG(Kyoto encyclopedia of genes and genomes) metabolic pathways [47 (link)]. Adjustment for the confounding factors of age and gender was conducted via the built-in function of DESeq2.
Lappa D., Meijnikman A.S., Krautkramer K.A., Olsson L.M., Aydin Ö., Van Rijswijk A.S., Acherman Y.I., De Brauw M.L., Tremaroli V., Olofsson L.E., Lundqvist A., Hjorth S.A., Ji B., Gerdes V.E., Groen A.K., Schwartz T.W., Nieuwdorp M., Bäckhed F, & Nielsen J. (2023). Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery. PLOS ONE, 18(3), e0279335.
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Publication 2023
Adiposity Bariatric Surgery Biopsy Centrifugation Chloroform DNA Library Gender Gene Clusters Gene Expression Profiling Genes Genome Genome, Human isolation Jejunum Liver Mesentery Neoplasm Metastasis Ribosomal RNA RNA Degradation Subcutaneous Fat Tissue, Adipose Tissues
5
Evaluating Digital Meditation on Stress, Cravings, and Adiposity
We aimed to test the effects of a digital meditation intervention vs. an active or wait list control, on subjective measures of perceived stress, food cravings, and adiposity in a sample of employees at a large university with overweight and obesity who reported mild to moderate stress (NCT03945214). We randomized participants to 8-weeks of a digital meditation intervention (using the commercially available application, Headspace), a healthy eating intervention (active control), a digital meditation + healthy eating intervention, or a waitlist control condition. We asked all participants to complete questionnaires and anthropomorphic measurements at an in-person clinic visit at baseline and week 8. Adherence to the digital meditation intervention was tracked remotely by Headspace.
Radin R.M., Epel E.S., Mason A.E., Vaccaro J., Fromer E., Guan J, & Prather A.A. (2023). Impact of digital meditation on work stress and health outcomes among adults with overweight: A randomized controlled trial. PLOS ONE, 18(3), e0280808.
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Publication 2023
Adiposity Clinic Visits Food Meditation Obesity Training Programs

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More about "Adiposity"

Adiposity, also known as obesity or excess body fat, is a complex condition characterized by the accumulation of excess adipose (fat) tissue in the body.
This state of being overweight can lead to a range of health concerns, including cardiovascular disease, type 2 diabetes, and certain types of cancer.
Researchers studying adiposity may utilize various tools and techniques to better understand this condition and its underlying factors.
PubCompaore.ai, an AI-driven platform, can enhance the reproducibility and accuracy of locating protocols from literature, preprints, and patents.
This tool can help researchers identify the best protocols and products for their adiposity research needs, supporting advancements in this important public health issue.
In addition to PubCompaore.ai, researchers may also employ other techniques and technologies, such as TRIzol reagent for RNA extraction, 2100 Bioanalyzer for RNA analysis, MRNA-Seq sample preparation kits for transcriptome profiling, RNA 6000 Nano LabChip Kit for RNA quality assessment, PEA POD for body composition analysis, HiSeq 4000 for high-throughput sequencing, Poly-T oligo attached magnetic beads for mRNA isolation, Lunar iDXA and Lunar Prodigy for bone density and body composition measurements, and QDR 4500A for dual-energy X-ray absorptiometry (DEXA) scans.
By utilizing a combination of these tools and techniques, researchers can gain valuable insights into the complex factors that contribute to adiposity, ultimately supporting the development of more effective strategies for preventing and managing this important public health issue.