Song X Y, Lee S Y, Ma R C W, So W Y, Cai J H, Tam C, Lam V, Ying W, Ng M C Y, Chan J C N
Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, People's Republic of China.
Diabetologia. 2009 Aug;52(8):1543-53. doi: 10.1007/s00125-009-1400-1. Epub 2009 May 29.
AIMS/HYPOTHESIS: 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.
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.
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/INTERPRETATION: 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.
目的/假设：心血管疾病和肾脏疾病有共同的风险因素。我们使用结构方程模型（SEM）来评估2型糖尿病患者中与心血管疾病相关的表型和基因型对肾功能的独立和联合作用。
根据对数转换（log(e)）的尿白蛋白：肌酐比值和血浆肌酐水平的双峰分布，将1188例2型糖尿病患者分为高危组和低危组。分别建立包含连续和非排序分类数据的这些组的模型，使用非线性结构方程模型、贝叶斯估计和通过拟合优度统计评估的模型选择来评估测量变量和潜在因素之间的相互关系。
相互关联的测量变量（肥胖、血糖、血脂、血压）以及编码内皮型一氧化氮合酶（NOS）、β-肾上腺素能受体（ADRB）、肾素-血管紧张素系统（RAS）成分和脂质代谢的基因变体被加载到它们各自的表型和基因型潜在因素上。除了直接和间接影响外，肥胖、血脂和血压的潜在因素与ADRB和RAS基因型的潜在因素相互作用以影响肾功能。这些参数与编码过氧化物酶体增殖物激活受体γ、心房利钠肽、内收蛋白、G蛋白β(3)亚基、上皮钠通道α亚基和基质金属蛋白酶3的基因变体一起，在高危和低危模型中解释了肾功能变异的39%-至80%。
结论/解读：结构方程模型是一种用于确认和量化生物途径与遗传决定因素之间多种相互作用的有用工具。血压、血脂和肥胖对肾功能的联合和交互作用可能具有治疗意义，特别是在具有遗传风险因素的2型糖尿病个体中。
