Causes, consequences, and treatment of metabolically unhealthy fat distribution.

Obesity, the major cause of the current global epidemic of type 2 diabetes (T2D), induces insulin resistance in peripheral insulin target tissues. Several mechanisms have been identified related to cross-talk between adipose tissue, skeletal muscle and liver. These mechanisms involve both increased free fatty acid release and altered secretion of adipokines from adipose tissue. A major determinant of metabolic health is the ability of subcutaneous adipose tissue (SAT) to store excess fat rather than allowing it to accumulate in ectopic depots including liver (i.e. in nonalcoholic fatty liver disease), muscle and heart, or in epicardial/pericardial and visceral fat depots which promote the metabolic complications of obesity. The ability to recruit and differentiate precursor cells into adipose cells (adipogenesis) in SAT is under genetic regulation and is reduced in high-risk individuals who have first-degree relatives with T2D. Early recruitment of new adipose cells is dependent on the cross-talk between canonical WNT and BMP4 signalling; WNT enhances their undifferentiated and proliferative state whereas BMP4 induces their commitment to the adipogenic lineage. Dysregulation of these signalling pathways is associated with impaired adipogenesis and impaired ability to respond to the need to store excess lipids in SAT. This leads to hypertrophic, dysfunctional and insulin-resistant adipose cells with a reduced content of GLUT4, the major insulin-regulated glucose transporter, which in turn reduces adipose tissue glucose uptake and de novo lipogenesis. We recently identified that reduced GLUT4 and lipogenesis in adipocytes impairs the synthesis of a novel family of lipids secreted by adipose tissue (and potentially other tissues), branched fatty acid esters of hydroxy fatty acids (FAHFAs). FAHFAs have beneficial metabolic effects, including enhancing insulin-stimulated glucose transport and glucose-stimulated GLP1 and insulin secretion, as well as powerful anti-inflammatory effects. FAHFA levels are reduced in subcutaneous adipose tissue in insulin-resistant individuals, and this novel family of lipids may become of future therapeutic use.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/joim.12540

Adipose tissue regulates insulin sensitivity: role of adipogenesis, de novo lipogenesis and novel lipids

The adipose tissue is crucial in regulating insulin sensitivity and risk for diabetes through its lipid storage capacity and thermogenic and endocrine functions. Subcutaneous adipose tissue (SAT) stores excess lipids through expansion of adipocytes (hypertrophic obesity) and/or recruitment of new precursor cells (hyperplastic obesity). Hypertrophic obesity in humans, a characteristic of genetic predisposition for diabetes, is associated with abdominal obesity, ectopic fat accumulation, and the metabolic syndrome (MS), while the ability to recruit new adipocytes prevents this. We review the regulation of adipogenesis, its relation to SAT expandability and the risks of ectopic fat accumulation, and insulin resistance. The actions of GLUT4 in SAT, including a novel family of lipids enhancing insulin sensitivity/secretion, and the function of bone morphogenetic proteins (BMPs) in white and beige/brown adipogenesis in humans are highlighted.

Insulin resistance and impaired adipogenesis.

Background 
Excess adiposity is associated with cardiovascular disease (CVD) risk factors such as hypertension, diabetes mellitus and dyslipidemia. Amongst the various measures of adiposity, the best one to help predict these risk factors remains contentious. A novel index of adiposity, the Body Adiposity Index (BAI) was proposed in 2011, and has not been extensively studied in all populations. Therefore, the purpose of this study is to compare the relationship between Body Mass Index (BMI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR), Waist-to-Height Ratio (WHtR), Body Adiposity Index (BAI) and CVD risk factors in the local adult population.


Methods and Findings 
This is a cross sectional study involving 1,891 subjects (Chinese 59.1% Malay 22.2%, Indian 18.7%), aged 21–74 years, based on an employee health screening (2012) undertaken at a hospital in Singapore. Anthropometric indices and CVD risk factor variables were measured, and Spearman correlation, Receiver Operating Characteristic (ROC) curves and multiple logistic regressions were used. BAI consistently had the lower correlation, area under ROC and odd ratio values when compared with BMI, WC and WHtR, although differences were often small with overlapping 95% confidence intervals. After adjusting for BMI, BAI did not further increase the odds of CVD risk factors, unlike WC and WHtR (for all except hypertension and low high density lipoprotein cholesterol). When subjects with the various CVD risk factors were grouped according to established cut-offs, a BMI of ≥23.0 kg/m2 and/or WHtR ≥0.5 identified the highest proportion for all the CVD risk factors in both genders, even higher than a combination of BMI and WC.


Conclusions 
BAI may function as a measure of overall adiposity but it is unlikely to be better than BMI. A combination of BMI and WHtR could have the best clinical utility in identifying patients with CVD risk factors in an adult population in Singapore.

Comparison of Body Mass Index (BMI), Body Adiposity Index (BAI), Waist Circumference (WC), Waist-To-Hip Ratio (WHR) and Waist-To-Height Ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore.

Indications for Surgery for Obesity and Weight-Related Diseases: Position Statements from the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO)

Background: Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI). Methods and Findings: The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5-22.4 kg/m(2)) with a low WC ( = 35 kg/m(2)) and a high WC (> 102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50-103 per 1,000 person-years in men and 28-74 per 1,000 person-years in women). Conclusions: WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action.

/pdf/long-term-risk-of-incident-type-2-diabetes-and-measures-of-3qgmj36k8t.pdf

Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

L. van der A. Daphne

Author Tools

Chat about Author

Papers

Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study.