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> Disorders > Disease or Syndrome > Obesity

Obesity

Obesity is a complex, multifactorial condition characterized by excessive accumulation of body fat, often resulting from a combination of genetic, environmental, and behavioral factors.
This medical condition is associated with an increased risk of various health problems, including type 2 diabetes, cardiovascular disease, certain types of cancer, and respiratory issues.
Effective management of obesity requires a comprehensive, tailored approach that may involve dietary modifications, increased physical activity, behavioral interventions, and in some cases, pharmacological or surgical treatments.
Ongoing research aims to futher elucidate the underlying mechanisms of obesity and develop more efficatious, personalized strategies for prevention and treatment.

Most cited protocols related to «Obesity»

1
Microbiome Data Analysis with Hypothesis Testing
Although useful for exploring and summarizing microbiome data, many of the graphics and ordination methods discussed here are not formal tests of any particular hypothesis. The most common framework for testing in microbiome studies is the comparisons of samples from different categories (e.g. healthy and obese; control and treated; different environments). Standard test statistics include the t-test, the paired permutation t-test, and ANOVA type tests based on F or pseudo-F statistics. However, microbiome data have two particularities. First, the raw abundance counts are never normally distributed, so the preferred methods are nonparametric. Second, there is contiguous information available about the relationships between OTUs, as well as for variables measured on the samples, so testing is sometimes more elaborate than a two-sample test. The hypergeometric test, also known as Fisher's exact test, is used in cases when we have a test statistic for each of the different OTUs. The goal is to confirm that a certain property of these significant OTUs is overrepresented compared to the general population of OTUs, often called “the universe”. For instance in Holmes et al [65] (link) and Nelson et al [68] several phyla were shown to be significantly over-abundant in IBS rats as compared to healthy controls using this hypergeometric test.
An organizing principle in many nonparametric testing protocols is that the repetition of an analysis multiple times enables the user to control for multiple testing, or to evaluate the quality of estimators or the optimal values of tuning parameters. Modern confirmatory analyses currently depend on these repeated analyses under various data perturbation schemes, of which resampling, permutations, and Monte Carlo simulations are the most common. For instance the bootstrap uses many thousands of analyses of resampled data to address problems such as statistical stability or bias estimation [69] , and can even provide confidence regions [69] for nonstandard parameters, such as phylogenetic trees [70] . Repeating analyses on permuted data can allow for control of the probability of encountering 1 or more false positives (falsely rejected nulls) among your group of simultaneous hypotheses, also called the Family Wise Error Rate (FWER). For instance, Westfall and Young's permutation-based minP procedure controls the FWER [71] and is implemented within the multtest package [72] . The phyloseq package interfaces with minP in multtest through a wrapper function, called mt. In the following example code we use the mt wrapper to control the FWER while simultaneously testing whether each OTU correlates with the “Enterotypes” classification of the samples. Note that we first remove samples that were not assigned an enterotype by the original authors (Table 1).
McMurdie P.J, & Holmes S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217.
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Publication 2013
Microbiome neuro-oncological ventral antigen 2, human Obesity Rattus norvegicus
2
Defining Implementation, Context, and Setting in Research
Implementation, context, and setting are concepts that are widely used and yet have inconsistent definitions and usage in the literature; thus, we present working definitions for each. Implementation is the constellation of processes intended to get an intervention into use within an organization [13 (link)]; it is the means by which an intervention is assimilated into an organization. Implementation is the critical gateway between an organizational decision to adopt an intervention and the routine use of that intervention; the transition period during which targeted stakeholders become increasingly skillful, consistent, and committed in their use of an intervention [14 (link)].
Implementation, by its very nature, is a social process that is intertwined with the context in which it takes place [15 ]. Context consists of a constellation of active interacting variables and is not just a backdrop for implementation [16 ]. For implementation research, 'context' is the set of circumstances or unique factors that surround a particular implementation effort. Examples of contextual factors include a provider's perception of the evidence supporting the use of a clinical reminder for obesity, local and national policies about how to integrate that reminder into a local electronic medical record, and characteristics of the individuals involved in the implementation effort. The theories underpinning the intervention and implementation [17 (link)] also contribute to context. In this paper, we use the term context to connote this broad scope of circumstances and characteristics. The 'setting' includes the environmental characteristics in which implementation occurs. Most implementation theories in the literature use the term context both to refer to broad context, as described above, and also the specific setting.
Damschroder L.J., Aron D.C., Keith R.E., Kirsh S.R., Alexander J.A, & Lowery J.C. (2009). Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science : IS, 4, 50.
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Publication 2009
Obesity
3
Polygene Analysis of Obesity Risk
We assessed the cumulative effects of the 97 GWS loci on mean BMI and on their ability to predict obesity (BMI ≥ 30 kg m−2) using the c statistic from logistic regression models in the Health and Retirement Study17 , a longitudinal study of 26,000 European Americans 50 years or older. The variance explained (VarExp) by each SNP was calculated using the effect allele frequency (f) and beta (β) from the meta-analyses using the formula VarExp = β2(1 − f)2f.
For polygene analyses, the approximate conditional analysis from GCTA19 (link),20 (link), was used to select SNPs using a range of P value thresholds (that is, 5 × 10−8, 5 × 10−7, …, 5 × 10−3) based on summary data from the European sex-combined meta-analysis excluding TwinGene and QIMR studies. We performed a within-family prediction analysis using full-sib pairs selected from independent families (1,622 pairs from the QIMR cohort and 2,758 pairs from the TwinGene cohort) and then SNPs at each threshold were used to calculate the percentage of phenotypic variance explained and predict risk (Extended Data Figs 2 and 3). We then confirmed the results from population-based prediction and estimation analyses in an independent sample of unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies (Extended Data Fig. 3 and Fig. 1c). The SNP-derived predictor was calculated using the profile scoring approach implemented in PLINK and estimation analyses were performed using the all-SNP estimation approach implemented in GCTA.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
Europeans Obesity Phenotype Prognosis
4
Gut Microbiome Analysis of Twins and Mothers
To illustrate the application of these ideas to a real data set we reanalysed a study of the gut microbiomes of twins and their mothers [27] (link). These comprised faecal samples from 154 different individuals characterised by family and body mass index – ‘Lean’, ‘Obese’ and ‘Overweight’. Each individual was sampled at two time points approximately two months apart. The V2 hypervariable region of the 16S rRNA gene was amplified by PCR and then sequenced using 454. We reanalysed this data set filtering the reads, denoising and removing chimeras using the AmpliconNoise pipeline [10] (link), [11] . Denoised reads were then classified to the genus level using the RDP stand-alone classifier [5] (link). This gave a total of 570,851 reads split over 278 samples since of the 308 possible some failed to possess any reads following filtering. The size of individual samples varied from just 53 to 10,585 with a median of 1,599. A total of 129 different genera were observed with a genera diversity per sample that varied from just 12 to 50 with a median of 28. One extra category ‘Unknown’ was used for those reads that failed to be classified with greater than 50% bootstrap certainty. We will refer to this as the ‘Twins’ data set.
Holmes I., Harris K, & Quince C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE, 7(2), e30126.
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Publication 2012
Chimera Feces Gastrointestinal Microbiome Index, Body Mass Mothers Obesity Ribosomal RNA Genes Twins
5
Evaluating Reliability of Behavior Change Frameworks
The framework was used independently by RW and SM to classify the 24 components of the 2010 English government tobacco control strategy [22 ] and the 21 components of the 2006 NICE obesity guidance [23 ]. The level of inter-rater agreement was computed and any differences resolved through discussion. The areas of tobacco control and obesity reduction were chosen because these are among the most important in public health and ones where health professional behaviour has consistently been found to fall short of that recommended by evidence-based guidelines [24 (link)-26 (link)]. In addition, these documents cover a wide spectrum of behaviour change approaches. Following reliability testing and discussion of any disagreements, a 'gold standard' was established.
Next, reliability of use by practitioners was assessed by asking two policy experts (the Department of Health Policy Lead for implementation of the 2010 English government tobacco control strategy and a tobacco researcher) to independently classify the 24 components of the strategy (see Additional file 1 for coding materials). Their coding data were compared with the 'gold standard.'
Michie S., van Stralen M.M, & West R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science : IS, 6, 42.
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Publication 2011
Gold Obesity Tobacco Products

Most recents protocols related to «Obesity»

1
Oxyntomodulin Derivatives for Obesity
Not available on PMC !

Example 3

In order to measure in vivo therapeutic activity of oxyntomodulin derivatives, changes in food intake by administration of oxyntomodulin derivatives were examined in ob/ob mouse using native oxyntomodulin as a control.

Specifically, obese diabetic ob/ob mice, commonly used to test the efficacies of therapeutic agents for obesity and diabetes, were fasted for 16 hours, and administered with 1 or mg/kg of oxyntomodulin, or 0.02, 0.1, 1 or 10 mg/kg of the oxyntomodulin derivative of SEQ ID NO: 2. Then, food intake was examined for 2 hours (FIG. 1). FIG. 1 is a graph showing changes in food intake according to administration dose of oxyntomodulin or oxyntomodulin derivative. As shown in FIG. 1, administration of 1 mg/kg of oxyntomodulin derivative showed more excellent inhibitory effects on food intake than administration of 10 mg/kg of oxyntomodulin.

Taken together, the oxyntomodulin derivatives of the present invention have much higher anti-obesity effects than the wild-type oxyntomodulin, even though administered at a lower dose, indicating improvement in the problems of the wild-type oxyntomodulin that shows lower anti-obesity effects and should be administered at a high dose three times a day.

US11872283B2. Conjugate comprising oxyntomodulin and an immunoglobulin fragment, and use thereof (2024-01-16). HANMI SCIENCE CO., LTD [KR]. Inventors: Sung Youb Jung [KR], Dae Jin Kim [KR], Sung Hee Park [KR], Young Eun Woo [KR], In Young Choi [KR], Se Chang Kwon [KR].
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Patent 2024
derivatives Diabetes Mellitus Eating Mice, Obese Mus Obesity Oxyntomodulin Psychological Inhibition Therapeutics
2
Obesity and Adipocyte ACAT Expression
Not available on PMC !

Example 1

To examine the function of ACATs in obesity, the expression patterns of ACAT1 and ACAT2 genes, and their gene products during adipogenesis of murine 3T3-L1 preadipocytes in vitro were examined. ACAT1 mRNA level was markedly increased in adipocytes from 2 days after initiation of adipogenesis (i.e., D2) as judged by real-time PCR assay (FIG. 1). However, ACAT2 mRNA level was similar between preadipocytes (D0) and mature adipocytes (D6) while a temporal reduction of ACAT2 level was observed at D2 (FIG. 1). In addition, white adipose tissue (WAT) isolated from high fat diet-induced obese mice displayed elevated mRNA level of ACAT1 and reduced mRNA level of ACAT2 when compared with those in lean mice as judged by real-time PCR assay. Leptin level was measured in WAT from lean and obese mice to ensure the development of obesity (FIG. 2). In addition, brown adipose tissue (BAT) from obese mice exhibited elevated levels of both ACAT1 and ACAT2. Uncoupling protein-1 (UCP-1) level was measured in BAT from lean and obese mice as a BAT-specific marker protein (FIG. 2). However, liver from lean and obese mice exhibited similar levels of ACAT1 and ACAT2 (FIG. 2).

US11872198B2. Compositions and methods for regulating body weight and metabolic syndromes (2024-01-16). Purdue Research Foundation [US]. Inventors: Kee-Hong Kim [US], Yuyan Zhu [US].
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Patent 2024
3T3-L1 Cells Adipocytes Adipogenesis a protein, mouse Biological Assay Brown Adipose Tissue Uncoupling Protein Brown Fat CES1 protein, human Diet, High-Fat Genes Leptin Liver Mice, Obese Mus Obesity Proteins Real-Time Polymerase Chain Reaction RNA, Messenger White Adipose Tissue
3
Menopause and Obesity in Chinese Women
This cross-sectional study analyzed the clinical data of 2514 Chinese women between the ages of 45 and 55 years, and within ten years after menopause, that were evaluated at the Health Examination Center, Huadong Sanatorium between June 2021 and December 2021. Menopause was defined as at least one year since the last menstrual period. The study subjects were divided into lean group (n=1194) with BMI < 23 kg/m2 and obese group (n=1320) with BMI ≥ 23 kg/m2. All the participants underwent comprehensive anthropometric measurements, abdominal ultrasonography, and fasting blood tests. The exclusion criteria included incomplete medical records, surgical menopause, hormone replacement therapy, and severe medical diseases such as cancer or organ failure. This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics and Research Committee of Huadong Sanatorium (Approval No. ECHS2023-01). The requirement for written informed consent was waived because of the retrospective nature of the study and the data analysis was anonymous and confidential.
Lu Y., Ge L., Yang H., He Y, & Wang Y. (2023). Chinese Visceral Adipose Index Shows Superior Diagnostic Performance in Predicting the Risk of Metabolic Dysfunction Associated Fatty Liver Disease in Early Postmenopausal Chinese Women. Diabetes, Metabolic Syndrome and Obesity, 16, 607-617.
Publication 2023
Abdomen Chinese Hematologic Tests Malignant Neoplasms Menopause Menstruation Obesity Operative Surgical Procedures Therapy, Hormone Replacement Ultrasonography Woman
4
Obesity Indices and MAFLD Risk
The statistical analyses were performed using the SPSS 25.0 software and the STATA 16.0 software packages. Continuous variables were described as means ± standard deviation or medians with interquartile ranges, depending on the normality test. Categorical variables were represented as numbers (n) with percentages (%). The baseline characteristics and the biochemical parameters were compared using the Student’s t-test for normally distributed continuous variables, Mann–Whitney U-test for non-normally distributed continuous variables and the chi-square test for the categorical variables. Multivariable logistic regression analysis was performed to determine the relationship between obesity indices (per SD increase) and MAFLD. Adjusted odds ratios (ORs) were presented with 95% confidence intervals (CIs). Restricted cubic spline regression analysis was performed to estimate the association between multivariable-adjusted obesity indices and MAFLD in the lean and overweight/obese subjects and the knots were placed at the 5th, 35th, 65th, and 95th percentiles. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were used to compare the diagnostic values of the obesity indices for predicting the risk of MAFLD. A two-tailed P-value <0.05 was considered statistically significant.
Lu Y., Ge L., Yang H., He Y, & Wang Y. (2023). Chinese Visceral Adipose Index Shows Superior Diagnostic Performance in Predicting the Risk of Metabolic Dysfunction Associated Fatty Liver Disease in Early Postmenopausal Chinese Women. Diabetes, Metabolic Syndrome and Obesity, 16, 607-617.
Publication 2023
Cuboid Bone Diagnosis Obesity Student
5
Peripheral Artery Disease Obesity Indicators
Data entry and management were performed using Epidata software, version 3.1 (Epidata Association, Odense, Denmark). All statistical analyses were conducted with SPSS 22.0 (IBM, Armonk, NY, USA) and R language software (version 4.1.1). Continuous variables were expressed as the mean ± standard deviation, and categorical variables as frequencies (percentages). The chi-square test was used to compare categorical variables. The linear tendency was evaluated among several groups using trend test. The independent-sample t-test and one-way analysis of variance (ANOVA) were used to compare continuous variables among two or more groups. Multiple logistic regression analysis was used to assess the independence of the associations between obesity indicators and various abnormalities of peripheral arteries, and the odds ratio (OR) and 95% confidence interval (95% CI) was calculated. We also explored the nonlinear relationship between BMI and the risk of ABI ≤ 0.9 using a restricted cubic spline model by multivariable adjustment with three knots (at the 10th, 50th, and 90th percentiles). P < 0.05, which is two-sided, was considered significant.
Wang Y., Guo X., Zhang Y., Zhang R, & Li J. (2023). Different associations of general and abdominal obesity with upper and lower extremity artery disease among a community population in China. Nutrition & Metabolism, 20, 14.
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Publication 2023
Arteries Congenital Abnormality Cuboid Bone Obesity

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More about "Obesity"

Obesity is a complex health condition characterized by excessive accumulation of body fat, often resulting from a combination of genetic, environmental, and behavioral factors.
This medical condition, also known as corpulence or overweight, is associated with an increased risk of various comorbidities, including type 2 diabetes, cardiovascular disease, certain types of cancer, and respiratory issues.
Effective management of obesity requires a comprehensive, personalized approach that may involve dietary modifications, increased physical activity, behavioral interventions, and in some cases, pharmacological or surgical treatments.
Ongoing research using advanced tools like SAS version 9.4, D12492, and C57BL/6J mice aims to further elucidate the underlying mechanisms of obesity and develop more efficacious, individualized strategies for prevention and treatment.
Leveraging AI-powered platforms like PubCompare.ai can help expedite obesity research by enabling researchers to easily locate the best protocols from literature, pre-prints, and patents using advanced comparison tools.
These AI-driven insights can help identify the most effective strategies and products for obesity studies, ultimately unlocking new breakthroughs and advancing the field of obesity management.