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Evidence of a causal relationship between adiponectin levels and insulin sensitivity: a Mendelian randomization study.

Abstract
The adipocyte-secreted protein adiponectin is associated with insulin sensitivity in observational studies. We aimed to evaluate whether this relationship is causal using a Mendelian randomization approach. In a sample of Swedish men aged 71 years (n = 942) from the Uppsala Longitudinal Study of Adult Men (ULSAM), insulin sensitivity (M/I ratio) was measured by the euglycemic insulin clamp. We used three genetic variants in the ADIPOQ locus as instrumental variables (IVs) to estimate the potential causal effect of adiponectin on insulin sensitivity and compared these with results from conventional linear regression. The three ADIPOQ variants, rs17300539, rs3774261, and rs6444175, were strongly associated with serum adiponectin levels (all P ≤ 5.3 × 10(-9)) and were also significantly associated with M/I ratio in the expected direction (all P ≤ 0.022). IV analysis confirmed that genetically determined adiponectin increased insulin sensitivity (β = 0.47-0.81, all P ≤ 0.014) comparable with observational estimates (β = 0.50, all P(difference) ≥ 0.136). Adjustment for BMI and waist circumference partly explained the association of both genetically determined and observed adiponectin levels with insulin sensitivity. The observed association between higher adiponectin levels and increased insulin sensitivity is likely to represent a causal relationship partly mediated by reduced adiposity.
AuthorsHe Gao, Tove Fall, Rob M van Dam, Allan Flyvbjerg, Björn Zethelius, Erik Ingelsson, Sara Hägg
JournalDiabetes (Diabetes) Vol. 62 Issue 4 Pg. 1338-44 (Apr 2013) ISSN: 1939-327X [Electronic] United States
PMID23274890 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Chemical References
  • Adiponectin
  • Biomarkers
Topics
  • Adiponectin (blood, genetics, metabolism)
  • Adiposity (genetics, physiology)
  • Aged
  • Biomarkers
  • Gene Expression Regulation
  • Genetic Predisposition to Disease
  • Genetic Variation
  • Humans
  • Insulin Resistance (genetics, physiology)
  • Linear Models
  • Male
  • Mendelian Randomization Analysis

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