Beta2-adrenergic receptor and UCP3 variants modulate the relationship between age and type 2 diabetes mellitus

It is widely accepted that Type 2 Diabetes Mellitus (T2DM) and other complex diseases are the product of complex interplay between genetic susceptibility and environmental causes. To cope with such a complexity, all the statistical and conceptual strategies available should be used. The working hypothesis of this study was that two well-known T2DM risk factors could have diverse effect in individuals carrying different genotypes. In particular, our effort was to investigate if a well-defined group of genes, involved in peripheral energy expenditure, could modify the impact of two environmental factors like age and obesity on the risk to develop diabetes. To achieve this aim we exploited a multianalytical approach also using dimensionality reduction strategy and conservative significance correction strategies.

We collected clinical data and characterised five genetic variants and 2 environmental factors of 342 ambulatory T2DM patients and 305 unrelated non-diabetic controls. To take in account the role of one of the major co-morbidity conditions we stratified the whole sample according to the presence of obesity, over and above the 30 Kg/m2 BMI threshold.

By monofactorial analyses the ADRB2-27 Glu27 homozygotes had a lower frequency of diabetes when compared with Gln27 carriers (Odds Ratio (OR) 0.56, 95% Confidence Interval (CI) 0.36 - 0.91). This difference was even more marked in the obese subsample. Multifactor Dimensionality Reduction method in the non-obese subsample showed an interaction among age, ADRB2-16 and UCP3 polymorphisms. In individuals that were UCP3 T-carriers and ADRB2-16 Arg-carriers the OR increased from 1 in the youngest to 10.84 (95% CI 4.54-25.85) in the oldest. On the contrary, in the ADRB2-16 GlyGly and UCP3 CC double homozygote subjects, the OR for the disease was 1.10 (95% CI 0.53-2.27) in the youngest and 1.61 (95% CI 0.55-4.71) in the oldest.

Although our results should be confirmed by further studies, our data suggests that, when properly evaluated, it is possible to identify genetic factors that could influence the effect of common risk factors.