Phenotype-genotype interactions on renal function in type 2 diabetes: an analysis using structural equation modelling

Joint analysis of multivariate failure time data with latent variables

We propose a joint modeling approach to investigate the observed and latent risk factors of the multivariate failure times of interest. The proposed model comprises two parts. The first part is a distribution-free confirmatory factor analysis model that characterizes the latent factors by correlated multiple observed variables. The second part is a multivariate additive hazards model that assesses the observed and latent risk factors of the failure times. A hybrid procedure that combines the borrow-strength estimation approach and the asymptotically distribution-free generalized least square method is developed to estimate the model parameters. The asymptotic properties of the proposed estimators are derived. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a study concerning the risk factors of multiple diabetic complications is provided.

Time-varying coefficient additive hazards model with latent variables

This study considers a time-varying coefficient additive hazards model with latent variables to examine potential observed and latent risk factors for survival of interest. The model consists of two parts: confirmatory factor analysis to measure each latent factor through multiple observable variables and a varying coefficient additive hazards model to examine the time-varying effects of the observed and latent risk factors on the hazard function. A hybrid estimation procedure that combines the expectation-maximum algorithm and corrected estimating equation method is developed to estimate the unknown parameters and nonparametric cumulative hazard functions. The consistency and asymptotic normality of the proposed estimators are established, and the pointwise confidence intervals and general confidence bands of the nonparametric functions are constructed accordingly. A satisfactory performance of the proposed method is demonstrated through simulation studies. An application to a study of chronic kidney disease for Chinese type 2 diabetes patients is presented.

Mixture proportional hazards cure model with latent variables

A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.

Regression analysis of current status data with latent variables

Current status data occur in many fields including demographical, epidemiological, financial, medical, and sociological studies. We consider the regression analysis of current status data with latent variables. The proposed model consists of a factor analytic model for characterizing latent variables through their multiple surrogates and an additive hazard model for examining potential covariate effects on the hazards of interest in the presence of current status data. We develop a borrow-strength estimation procedure that incorporates the expectation-maximization algorithm and correlated estimating equations. The consistency and asymptotic normality of the proposed estimators are established. A simulation study is conducted to evaluate the finite sample performance of the proposed method. A real-life study on the chronic kidney disease of type 2 diabetic patients is presented.

Joint analysis of mixed types of outcomes with latent variables

We propose a joint modeling approach to investigating the observed and latent risk factors of mixed types of outcomes. The proposed model comprises three parts. The first part is an exploratory factor analysis model that summarizes latent factors through multiple observed variables. The second part is a proportional hazards model that examines the observed and latent risk factors of multivariate time-to-event outcomes. The third part is a linear regression model that investigates the determinants of a continuous outcome. We develop a Bayesian approach coupled with MCMC methods to determine the number of latent factors, the association between latent and observed variables, and the important risk factors of different types of outcomes. A modified stochastic search item selection algorithm, which introduces normal-mixture-inverse gamma priors to factor loadings and regression coefficients, is developed for simultaneous model selection and parameter estimation. The proposed method is subjected to simulation studies for empirical performance assessment and then applied to a study concerning the risk factors of type 2 diabetes and the associated complications.

Bayesian proportional hazards model with latent variables

We consider a joint modeling approach that incorporates latent variables into a proportional hazards model to examine the observed and latent risk factors of the failure time of interest. An exploratory factor analysis model is used to characterize the latent risk factors through multiple observed variables. In commonly used confirmatory factor analysis, the number of latent variables and their observed indicators are specified prior to analysis. By contrast, the exploratory factor analysis model allows such information to be fully determined by the data. A Bayesian approach coupled with efficient sampling methods is developed to conduct statistical inference, and the performance of the proposed methodology is confirmed through simulations. The model is applied to a study on the risk factors of chronic kidney disease for patients with type 2 diabetes.

Analysis of proportional mean residual life model with latent variables

End-stage renal disease (ESRD) is one of the most serious diabetes complications. Numerous studies have been devoted to revealing the risk factors of the onset time of ESRD. In this article, we propose a proportional mean residual life (MRL) model with latent variables to assess the effects of observed and latent risk factors on the MRL function of ESRD in a cohort of Chinese type 2 diabetic patients. The proposed model generalizes the conventional proportional MRL model to accommodate the latent risk factor that cannot be measured by a single observed variable. We employ a factor analysis model to characterize the latent risk factors via multiple observed variables. We develop a borrow-strength estimation procedure, which incorporates the expectation-maximization algorithm and an extended estimating equation approach. The asymptotic properties of the proposed estimators are established. Simulation shows that the performance of the proposed methodology is satisfactory. The application to the study of type 2 diabetes reveals insights into the prevention of ESRD. Copyright © 2016 John Wiley & Sons, Ltd.

Bayesian analysis of transformation latent variable models with multivariate censored data

Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.

Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso

Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the structural equation is formulated using the additive nonparametric functions of observed and latent variables. We propose the use of basis expansions to approximate the unknown functions. By introducing adaptive penalties to the groups of basis expansions, the nonlinear, linear, or non-existent effects of observed and latent variables in the structural equation can be automatically detected. A simulation study demonstrates that the proposed method performs satisfactorily. This paper presents an application of revealing the observed and latent risk factors of diabetic kidney disease.

Genetic variance in nitric oxide synthase and endothelin genes among children with and without endothelial dysfunction

Background

The presence of endothelial dysfunction (ED) constitutes an early risk factor for cardiovascular disease (CVD) in children. Nitric oxide (NO) and endothelin (EDN) are generated in endothelial cells and are critical regulators of vascular function, with ED resulting from an imbalance between these two molecules. We hypothesized that genetic variants in NO synthase and EDN isoforms and its receptors (EDNRA and EDNRB) may account for a proportion of the risk for ED in developing children.

Methods

Consecutive children (ages 5–10 years) were prospectively recruited from the community. Time to peak post-occlusive reperfusion (Tmax) was considered as the indicator of either normal endothelial function (NEF; Tmax < 45 sec) or ED (Tmax ≥ 45 sec). Lipid profiles, high sensitivity C-reactive protein (hsCRP), fasting glucose and insulin were assayed using ELISA. Genomic DNA from peripheral blood was extracted and genotyped for NOS1 (209 SNPs), NOS2 (122 SNPs), NOS3 (50 SNPs), EDN1 (43 SNPs), EDN2 (48 SNPs), EDN3 (14 SNPs), EDNRA (27 SNPs), and EDNRB (23 SNPs) using a custom SNPs array. Linkage disequilibrium was analyzed using Haploview version 4.2 software.

Results

The relative frequencies of SNPs were evaluated in 122 children, 84 with NEF and 38 with ED. The frequencies of NOS1 (11 SNPs), and EDN1 (2 SNPs) were differentially distributed between NEF vs. ED, and no significant differences emerged for all other genes. Significant SNPs for NOS1 and EDN1 SNPs were further validated with RT-PCR.

Conclusions

Genetic variants in the NOS1 and EDN1 genes appear to account for important components of the variance in endothelial function, particularly when concurrent risk factors such as obesity exist. Thus, analysis of genotype-phenotype interactions in children at risk for ED will be critical for more accurate formulation of categorical CVD risk estimates.

Predictive role of multilocus genetic polymorphisms in cardiovascular disease and inflammation-related genes on chronic kidney disease in Type 2 diabetes--an 8-year prospective cohort analysis of 1163 patients

BACKGROUND

Chinese diabetic patients are at greater risk of developing chronic kidney disease (CKD) than Caucasian counterparts. In this hypothesis-generating study, we examined the independent and joint effects of multiple genetic variants on CKD in a prospective Chinese cohort of Type 2 diabetic patients.

METHODS

Seventy-seven single-nucleotide polymorphisms (SNPs) of 54 candidate genes for cardiorenal diseases and inflammation were genotyped in 1163 patients with no past history of CKD at baseline. CKD was defined as the first estimated glomerular filtration rate <60 mL/min/1.73 m(2) or the first hospitalization with a diagnosis of renal disease.

RESULTS

In Cox-regression analysis, 15 SNPs of 13 genes were associated with incident CKD. After correction for multiple comparisons, 6 SNPs including PON1 55Met, PON2 311Cys CETP-629C, ITGA2 873A, LTA 26Asn and LTA 252Gly remained independently associated with CKD, with respective hazard ratios (95% confidence interval):2.6 (1.4-4.8, P = 0.002), 1.5 (1.2-1.9, P = 0.003), 1.4 (1.1-1.7, P = 0.001), 2.2 (1.3-3.7, P = 0.002), 1.6 (1.1-2.2, P = 0.008) and 1.5 (1.1-2.1, P = 0.019). Analysis of joint effect of the six SNPs showed stepwise increase in risk of CKD with the accumulation of risk alleles and weighted genetic risk score (P(trend) = 8.9 × 10(-7) and 4.0 × 10(-5), respectively).

CONCLUSIONS

In Type 2 diabetes, there are independent and joint effects of multiple genetic variants on risk of CKD. Risk associations with PON1, PON2, CETP, ITGA2 and LTA genetic polymorphisms underline the importance of lipid metabolism, haemostasis and inflammation in the development of CKD in patients with Type 2 diabetes.

The Complexity of Vascular and Non-Vascular Complications of Diabetes: The Hong Kong Diabetes Registry

Diabetes is a complex disease characterized by chronic hyperglycemia and multiple phenotypes. In 1995, we used a doctor-nurse-clerk team and structured protocol to establish the Hong Kong Diabetes Registry in a quality improvement program. By 2009, we had accrued 2616 clinical events in 9588 Chinese type 2 diabetic patients with a follow-up duration of 6 years. The detailed phenotypes at enrollment and follow-up medications have allowed us to develop a series of risk equations to predict multiple endpoints with high sensitivity and specificity. In this prospective database, we were able to validate findings from clinical trials in real practice, confirm close links between cardiovascular and renal disease, and demonstrate the emerging importance of cancer as a leading cause of death. In addition to serving as a tool for risk stratification and quality assurance, ongoing data analysis of the registry also reveals secular changes in disease patterns and identifies unmet needs.

Predictive role of polymorphisms in interleukin-5 receptor alpha-subunit, lipoprotein lipase, integrin A2 and nitric oxide synthase genes on ischemic stroke in type 2 diabetes--an 8-year prospective cohort analysis of 1327 Chinese patients

OBJECTIVE

Ischemic stroke is prevalent in type 2 diabetes and may be due to metabolic, vascular and inflammatory factors. Genetic variants implicated in these pathways may have joint effects on stroke risk. In this proof-of-concept study, we examined gene-gene interactions on risk of incident ischemic stroke in an 8-year prospective cohort of Chinese type 2 diabetic patients.

METHODS

Seventy-seven single nucleotide polymorphisms (SNPs) of 53 candidate genes for cardiovascular disease and inflammation were genotyped in 1327 patients with no past history of ischemic stroke. The association of SNPs with stroke was tested using Cox proportional hazard regression analysis. Permutation procedure was performed to control for multiple statistical comparisons.

RESULTS

Genetic variants including A/A of IL5RA (interleukin-5 alpha subunit) -5091G>A, X/X of LPL (lipoprotein lipase) S447X, A/A of ITGA2 (integrin A2) G873A and T/T or G/T of NOS3 (endothelial nitric oxide synthase) G894T showed significant correlations with incident ischemic stroke. The hazard ratios (HR) increased with number of genetic risk factors reaching an adjusted HR (confidence interval) of 3.68 (1.78-7.62, P=4.4×10(-4)) in those with ≥2 genetic risk factors compared to those without.

CONCLUSION

Polymorphisms in IL5RA, LPL, ITGA2 and NOS3 genes were independently associated with ischemic stroke in Chinese diabetic population.

Life-course analysis of a fat mass and obesity-associated (FTO) gene variant and body mass index in the Northern Finland Birth Cohort 1966 using structural equation modeling

The association between variation in the fat mass and obesity-associated (FTO) gene and adulthood body mass index (BMI; weight (kg)/height (m)²) is well-replicated. More thorough analyses utilizing phenotypic data over the life course may deepen our understanding of the development of BMI and thus help in the prevention of obesity. The authors used a structural equation modeling approach to explore the network of variables associated with BMI from the prenatal period to age 31 years (1965–1997) in 4,435 subjects from the Northern Finland Birth Cohort 1966. The use of structural equation modeling permitted the easy inclusion of variables with missing values in the analyses without separate imputation steps, as well as differentiation between direct and indirect effects. There was an association between the FTO single nucleotide polymorphism rs9939609 and BMI at age 31 years that persisted after controlling for several relevant factors during the life course. The total effect of the FTO variant on adult BMI was mostly composed of the direct effect, but a notable part was also arising indirectly via its effects on earlier BMI development. In addition to well-established genetic determinants, many life-course factors such as physical activity, in spite of not showing mediation or interaction, had a strong independent effect on BMI.