Interaction of DIO2 T92A and PPAR γ 2 P12A Polymorphisms in the Modulation of Metabolic Syndrome
Type 2 iodothyronine deiodinase (DIO2) converts thyroid prohormone tetraiodothyronine into the biologically active triiodothyronine hormone, which increases insulin sensitivity at the skeletal muscle level. The DIO2 T92A polymorphism modulates deiodinase activity and has been inconsistently associat...
Gespeichert in:
Veröffentlicht in: | Obesity (Silver Spring, Md.) Md.), 2012-09, Vol.15 (12), p.2889-2895 |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Type 2 iodothyronine deiodinase (DIO2) converts thyroid prohormone tetraiodothyronine into the biologically active triiodothyronine hormone, which increases insulin sensitivity at the skeletal muscle level. The
DIO2
T92A polymorphism modulates deiodinase activity and has been inconsistently associated with insulin resistance. Also, the
P121A
polymorphism of the peroxisome proliferator‐activated receptor (
PPAR
) γ2 gene, which encodes a transcription factor involved in insulin signaling, has been inconsistently associated with insulin resistance. This study was aimed at evaluating the combined effect of
DIO2
T92A and
PPAR
γ
2
P12A polymorphisms on insulin resistance‐related features in 590 non‐diabetic whites. A significant gene‐gene interaction was observed in the modulation of systolic (
p
= 0.01) and diastolic (
p
= 0.02) blood pressure and metabolic syndrome (
p
= 0.02), with carriers of both
DIO2
A92 and
PPAR
γ
2
A12 variants showing the worst phenotype. This latter interaction was also shown by multifactor dimensionality reduction analysis (
p
= 0.0045). A peroxisome proliferator response element in the DIO2 promoter was identified by in silico analysis and confirmed by in vitro gel shift mobility assay, thus providing a biological plausibility for the observed gene‐gene interaction. If confirmed in other populations, comprising several thousand individuals, these data may help identify individuals at risk for insulin resistance‐related abnormalities. |
---|---|
ISSN: | 1930-7381 1930-739X |
DOI: | 10.1038/oby.2007.343 |