Phenotype-genotype interactions on renal function in type 2 diabetes: an analysis using structural equation modelling.
OBJECTIVE
Cardiovascular and renal diseases share common risk factors. We used structural equation modelling (SEM) to evaluate the independent and combined effects of phenotypes and genotypes implicated in cardiovascular diseases on renal function in type 2 diabetes.


METHODS
1,188 type 2 diabetic patients were stratified into high-risk and low-risk groups according to bimodal distributions of the logarithmically transformed (log(e)) urinary albumin:creatinine ratio and plasma creatinine levels. Models for these groups, comprising continuous and non-ranking categorical data, were developed separately to evaluate the inter-relationships among measured variables and latent factors using non-linear SEMs, Bayesian estimation and model selection as assessed by a goodness-of-fit statistic.


RESULTS
Inter-correlated measured variables (obesity, glycaemia, lipid, blood pressure) and variants of the genes encoding endothelial nitric oxide synthase (NOS), beta-adrenergic receptor (ADRB), components of the renin-angiotensin system (RAS) and lipid metabolism were loaded onto their respective latent factors of phenotypes and genotypes. In addition to direct and indirect effects, latent factors of obesity, lipid and BP interacted with latent factors of ADRB and RAS genotypes to influence renal function. Together with variants of the genes encoding peroxisome proliferator-activated receptor gamma, atrial natriuretic peptide, adducin, G protein beta(3) subunit, epithelial sodium channel alpha subunit and matrix metallopeptidase 3, these parameters explained 39-80% of the variance in renal function in the high-risk and low-risk models.


CONCLUSIONS
SEM is a useful tool for confirming and quantifying multiple interactions of biological pathways with genetic determinants. The combined and interactive effects of blood pressure, lipid and obesity on renal function may have therapeutic implications, especially in type 2 diabetic individuals with genetic risk factors.
Song
X Y
XY
Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, People's Republic of China.
Lee
S Y
SY
Ma
R C W
RC
So
W Y
WY
Cai
J H
JH
Tam
C
C
Lam
V
V
Ying
W
W
Ng
M C Y
MC
Chan
J C N
JC
D000368:Aged; D001499:Bayes Theorem; D001794:Blood Pressure; D004247:DNA / Q000235:genetics / Q000302:isolation & purification; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics* / Q000503:physiopathology*; D005260:Female; D005838:Genotype; D006801:Humans; D006973:Hypertension / Q000503:physiopathology; D007668:Kidney / Q000503:physiopathology*; D007677:Kidney Function Tests; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D008957:Models, Genetic; D009765:Obesity / Q000503:physiopathology; D010641:Phenotype; D016133:Polymerase Chain Reaction / Q000379:methods; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors; D012907:Smoking / Q000503:physiopathology
D004247:DNA