Background: Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data.Method: Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC).Results: The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches.Conclusion: Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

The genetic variations among individuals are one of the notable factors determining disease severity and drug response. Nowadays, COVID-19 pandemic has been adversely affecting many aspects of human life. We used the Tehran Cardio-Metabolic Genetic Study (TCGS) data that is an ongoing genetic study including the whole-genome sequencing of 1200 individuals and chip genotyping of more than 15,000 participants. Here, the effect of ACE2 variations by focusing on the receptor-binding site of SARS-CoV-2 and ACE2 cleavage by TMPRSS2 protease were investigated through simulations study. After analyzing TCGS data, 570 genetic variations on the ACE2 gene, including single nucleotide polymorphisms (SNP) and insertion/deletion (INDEL) were detected. Interestingly, two observed missense variants, K26R and S331F, which only the first one was previously reported, can reduce the receptor affinity for the viral Spike protein. Moreover, our bioinformatics simulation of 3D structures and docking of proteins explains important details of ACE2-Spike and ACE2-TMPRSS2 interactions, especially the critical role of Arg652 of ACE2 for protease function of TMPRSS2 was uncovered. As our results show that the genetic variation of ACE2 can at least influence the affinity of this receptor to its partners, we need to consider the genetic variations on ACE2 as well as other genes in the pathways that contribute to the pathogenesis of COVID-19 for designing efficient drugs and vaccines.

The aim of the present study was to investigate the association between matrix metalloproteinase-9 (MMP-9) expression with BRAF V600E mutation and clinicopathological features, in Iranian papillary thyroid cancer (PTC) patients. In total, 90 participants including 60 PTC patients (15 males and 45 females) and 30 individuals with benign multinodular goiter (MNG) (5 males and 25 females) which were confirmed by surgical pathology, were investigated. MMP-9 was evaluated at both mRNA and protein levels, using SYBR-Green Real-Time PCR and enzyme-linked immune sorbent assay (ELISA), respectively. BRAF V600E mutation was detected by sequencing. Mean age of PTC and MNG patients was 37.6 ± 12.6 and 48.1 ± 13.3 years, respectively (P = 0.001). BRAF V600E mutation was found in 24 of the 60 (40%) PTC cases, with mean tumor size of 1.59 ± 1.20 cm. MMP-9 mRNA levels were elevated in tumoral compared to the adjacent non-tumoral tissues (P = 0.039); moreover, this rise was also observed in PTC patients compared to MNG patients (P = 0.001). The mRNA levels of MMP-9 increased in patients aged≥45 years (P = 0.015), those with lymphovascular invasion (P = 0.003), and higher tumor stages (III and IV) (P = 0.011). The protein level of MMP-9 increased in tumoral compared to adjacent non-tumoral tissues (P < 0.001); this increase was also found in PTC patients compared to MNG participants (P = 0.004). MMP-9 protein level was higher in patients aged≥45 years (P = 0.001), those with lymphovascular invasion (P = 0.036) and higher TNM stages (III and IV) (P = 0.001). Area under the ROC curve (AUC) was 0.70 (95%CI: 0.57–0.83, P = 0.003), with 91.4% sensitivity and 51.9% specificity at the cutoff value of 0.50. The mRNA and protein levels of MMP-9 had no association with BRAF V600E mutation in Iranian PTC patients. These levels were associated with age, TNM stages, and lymphovascular invasion, being defined as malignant factors. Thus, elevated levels of MMP-9 in PTC patients compared to MNG participants illustrated that it can be used as a potential biomarker to differentiate PTC patients from those with MNG.

Background: Traditional observational studies have shown positive associations between c-reactive protein (CRP) and heart failure (HF) risk. However, this association has not been fully elucidated. Therefore, Mendelian randomization was used to examine CRP’s possible etiological roles with HF. Methods: We implemented a two-sample Mendelian randomization framework to examine the causality of the association between CRP and HF based on summary statistics by large-scale genome-wide association studies (GWAS) datasets of European ancestry through inverse-variance weighted, weighted median, MREgger regression, and MR-PRESSO methods. The summary statistics dataset on the association of genetic variants with CRP was used from the published GWAS of European descent in UK Biobank participants (N = 427,367) and the CHARGE consortium (N = 575,531). The GWAS dataset used to identify genetic variants underlying HF from the HERMES consortium includes 977,323 participants (47,309 cases and 930,014 controls). The odds ratio (OR) with 95% confidence intervals (CIs) was employed to examine this association. Results: The results of our IVW indicated that CRP was strongly associated with HF (OR = 4.18, 95% CI = 3.40–5.13, p < 0.001). The Cochran heterogeneity test showed significant heterogeneity among SNPs of CRP (Q = 317.55, p < 0.001; I² = 37.6%), and no considerable pleiotropy was detected for the association of CRP with HF [intercept = 0.003; p = 0.234]. This finding remained consistent using different Mendelian randomization methods and sensitivity analyses. Conclusion: Our MR study did identify convincing evidence to support CRP associated with HF risk. Human genetic data suggest that CRP is a causative factor in HF. Hence, CRP assessment may offer additional prognostic information as an adjuvant to overall risk assessment in HF patients. These findings prompt significant questions about the function of inflammation in the progression of HF. More research into the role of inflammation in HF is needed to guide trials of anti-inflammation management.

Background: Peroxisome proliferator-activated receptor gamma (PPARγ) is a promising therapeutic molecule. Epigenetic mechanisms, including non-coding RNAs, regulate the expression level of the PPARγ gene. Objective: We aimed to examine the PPARγ expression in non-diabetic individuals in four body mass index (BMI) categories and its association with miR-34a and miR-143 expression. Methods: Visceral and subcutaneous adipose tissues (VAT and SAT) samples were collected from patients undergoing bariatric or elective open abdominal surgeries. The subjects (mean age: 42±14.8 years) included 18 normal-weight, 19 overweight, 18 obese, and 19 morbidly obese individuals. The RNAs levels were determined by quantitative real-time PCR. Results: The PPARγ expression was significantly upregulated in both adipose depots of the morbidly obese subjects compared to the normal group. SAT PPARγ level was significantly increased in the obese group compared to the normal-weight group (P<0.01); this increase was also significant in the SAT of morbidly obese subjects compared to the overweight cases (P=0.02). Differences in the regulation of PPARγ expression in both SAT and VAT were significant between the four groups (P0.01). After controlling for the confounding factors, the expression of VAT PPARγ was directly associated with the miR-34a level in the normal-weight group (β=0.311, P=0.010). A negative association was observed between the VAT PPARγ expression and miR-34a expression in obese cases (β = − 0.594, P=0.039). Conclusion: The results also confirmed the regulatory function of microRNAs in the PPARγ expression and adipogenesis.