Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees

Identifying factors associated with of blood pressure using Structural Equation Modeling: evidence from a large Kurdish cohort study in Iran

BACKGROUND

Identifying the risk factors leading to hypertension can help explain why some populations are at a greater risk for developing hypertension than others. The present study seeks to identify the association between the risk factors of hypertension in 35- to 65-year-old participants in western Iran.

METHODS

This cross-sectional study was conducted on 9705 adults from baseline data of Ravansar Non-Communicable Disease (RaNCD) cohort study, in the west region of Iran. Each of the latent variables were confirmed by confirmatory factor analysis. Using Structural Equation Modeling (SEM), we assessed the direct and indirect effects of factors associated with blood pressure.

RESULTS

Socioeconomic status (SES), physical activity, mean of serum lipids, obesity, diabetes and family history of hypertension had a diverse impact on the blood pressure, directly and (or) indirectly. The standardized total effect of SES, physical activity, mean of serum lipids, and obesity were -0.09 vs. -0.14, -0.04 vs. -0.04, 0.13 vs. 0.13 and 0.24 vs. 0.15 in men and women, respectively. Diabetes had a direct relationship with the blood pressure in women (0.03).

CONCLUSION

With regard to control of high blood pressure, public health interventions must target obesity, lifestyle and other risk related to nutritional status such as hyperlipidemia and hyperglycemia in Iranian population and among those with higher SES.

Periodontal Inflamed Surface Area Is Associated With Increased Gestational Blood Pressure and Uric Acid Levels Among Pregnant Women From Rural North China

Background

Periodontal disease has been associated with gestational complications and both conditions have a high prevalence in rural populations from developing regions. A cross-sectional study was carried out to explore the relationship between periodontal inflamed surface area (PISA), blood pressure (BP), and, serum uric acid levels (UA) in a group of rural North Chinese pregnant women in the third trimester of pregnancy.

Methods

Three hundred and thirty-five rural women aged 20–34 years, with normal body mass index (BMI) were examined in a cross-sectional study during their third trimester of gestation. Exclusion criteria were history of pregnancy complications, multiple pregnancy, smoking habits, diabetes, hypertension or any known infectious disease. Socio-demographic variables, including age and socioeconomic status (SES), systolic blood pressure (SBP) and diastolic blood pressure (DBP) readings, serum UA levels, and PISA values were recorded. A structural equation model was implemented with two constructed latent variables including “Dem” (comprising of age and SES category to represent unobserved demographic variables) and, “BP” (comprising of SBP and DBP to account for measurement error and lack of multiple BP readings). The model accounted for co-variance of BP and UA, and implemented simultaneous regressions for BP and UA as outcomes, upon Dem and PISA values as exogenous variables.

Results

The median PISA score was 1,081.7 (IQR = 835.01), reflecting high levels of periodontal inflammation in the sample. SEM showed a significant association of PISA with BP (estimate = 0.011, 95% CI = 0.009–0.012 p < 0.001) and UA (estimate = 0.001, 95% CI = 0.001–0.001, p < 0.001).

Conclusion

Higher PISA values were significantly associated with higher blood pressure and uric acid levels among rural pregnant women in a cross-sectional sample from a center in North China after accounting for a latent demographic construct derived from age and SES.

Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry

The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes occurring during inpatient clinical care. Here we study the genetic underpinnings of the rapid response to phenylephrine, an α1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. We quantified this response by extracting blood pressure (BP) measurements 5 min before and after the administration of phenylephrine. Based on this derived phenotype, we show that systematic differences exist between self-reported ancestry groups: European-Americans (EA; n = 1387) have a significantly higher systolic response to phenylephrine than African-Americans (AA; n = 1217) and Hispanic/Latinos (HA; n = 1713) (31.3% increase, p value < 6e-08 and 22.9% increase, p value < 5e-05 respectively), after adjusting for genetic ancestry, demographics, and relevant clinical covariates. We performed a genome-wide association study to investigate genetic factors underlying individual differences in this derived phenotype. We discovered genome-wide significant association signals in loci and genes previously associated with BP measured in ambulatory settings, and a general enrichment of association in these genes. Finally, we discovered two low frequency variants, present at ~1% in EAs and AAs, respectively, where patients carrying one copy of these variants show no phenylephrine response. This work demonstrates our ability to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from dense clinical and physiological measures captured for managing patients during surgery. We identify genetic variants underlying non response to phenylephrine, with implications for preemptive pharmacogenomic screening to improve safety during surgery.

Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes

Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses. To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases. In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes-subscapular skin fold thickness, body mass index, and waist circumference-as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of-fit measures (χ2 = 536.52, NFI = 0.997, CFI = 0.998, GFI = 0.995, AGFI = 0.993, RMSEA = 0.012).

Modeling of the Weight Status and Risk of Nonalcoholic Fatty Liver Disease in Elderly Individuals: The Potential Impact of the Disulfide Bond-Forming Oxidoreductase A-Like Protein (DsbA-L) Polymorphism on the Weight Status

Nonalcoholic fatty liver disease (NAFLD) is closely associated with obesity. Disulfide bond‐forming oxidoreductase A‐like protein (DsbA‐L) is known to be a key molecule in protection against obesity and obesity‐induced inflammation. In the present study, we used a modeling and simulation approach in an attempt to develop body mass index (BMI) and BMI‐based NAFLD prediction models incorporating the DsbA‐L polymorphism to predict the BMI and NAFLD in 341 elderly subjects. A nonlinear mixed‐effect model best represented the sigmoidal relationship between the BMI and the logit function of the probability of NAFLD prevalence. The final models for BMI and NAFLD showed that DsbA‐L rs1917760 polymorphism, age, and gender were associated with the BMI, whereas gender, patatin‐like phospholipase 3 rs738409 polymorphism, HbA1c, and high‐density and low‐density lipoprotein cholesterol levels were associated with the risk of NAFLD. This information may aid in the genetic‐based prevention of obesity and NAFLD in the general elderly population.

Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19

For Genetic Analysis Workshop 19, 2 extensive data sets were provided, including whole genome and whole exome sequence data, gene expression data, and longitudinal blood pressure outcomes, together with nongenetic covariates. These data sets gave researchers the chance to investigate different aspects of more complex relationships within the data, and the contributions in our working group focused on statistical methods for the joint analysis of multiple phenotypes, which is part of the research field of data integration. The analysis of data from different sources poses challenges to researchers but provides the opportunity to model the real-life situation more realistically.Our 4 contributions all used the provided real data to identify genetic predictors for blood pressure. In the contributions, novel multivariate rare variant tests, copula models, structural equation models and a sparse matrix representation variable selection approach were applied. Each of these statistical models can be used to investigate specific hypothesized relationships, which are described together with their biological assumptions.The results showed that all methods are ready for application on a genome-wide scale and can be used or extended to include multiple omics data sets. The results provide potentially interesting genetic targets for future investigation and replication. Furthermore, all contributions demonstrated that the analysis of complex data sets could benefit from modeling correlated phenotypes jointly as well as by adding further bioinformatics information.