| Title |
Fat-free mass and glucose homeostasis: is greater fat-free mass an independent predictor of insulin resistance?
|
|---|---|
| Published in |
Aging Clinical and Experimental Research, July 2018
|
| DOI | 10.1007/s40520-018-0993-y |
| Pubmed ID | |
| Authors |
Ahmed Ghachem, Jean-Christophe Lagacé, Martin Brochu, Isabelle J. Dionne |
| Abstract |
A greater fat-free mass (FFM) is purported to be associated with protective effects on insulin resistance (IR). However, recent studies suggested negative associations between FFM and IR. (1) To explore the direction of the association between FFM and IR in a large heterogeneous sample after controlling for confounding factors. (2) To determine cut off values of FFM associated with an increased risk of IR. Outcome variables were measured in 7044 individuals (48.6% women, 20-79 years; NHANES, 1999-2006): body composition [fat mass (FM), FFM and appendicular FFM (aFFM); DXA], FFM index [FFMI: FFM/height (kg/m2)], appendicular FFMI [aFFM/height (kg/m2)] and insulin resistance (HOMA-IR). Multivariate regression analyses were performed to determine the independent predictors of HOMA-IR in younger (20-49 years) and older (50-79 years) men and women. ROC analyses were used to determine FFM cut-offs to identify a higher risk of insulin resistance (HOMA-IR > 75th percentile). aFFMI was an independent predictor of IR in younger (men: β = 0.21; women: β = 0.31; all p ≤ 0.001) and older (men: β = 0.11; women: β = 0.37; all p ≤ 0.001) individuals. Thresholds for aFFMI at which the risk of IR was significantly increased were 8.96 and 8.39 kg/m2 in younger and older men, and 7.22 and 6.64 kg/m2 in younger and older women, respectively. Independently of age, a greater aFFMI was an independent predictor of IR. These results suggest revisiting how we envision the link between FFM and IR and explore potential mechanisms. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 15% |
| Other | 3 | 9% |
| Student > Master | 3 | 9% |
| Researcher | 3 | 9% |
| Student > Postgraduate | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 13 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 21% |
| Nursing and Health Professions | 3 | 9% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Psychology | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 15 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 May 2019.
All research outputs
#16,053,755
of 25,385,509 outputs
Outputs from Aging Clinical and Experimental Research
#1,175
of 1,868 outputs
Outputs of similar age
#196,905
of 339,365 outputs
Outputs of similar age from Aging Clinical and Experimental Research
#21
of 29 outputs
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