BackgroundThe intermediate metabolites associated with the development of atherosclerotic cardiovascular disease (ASCVD) remain largely unknown. Thus, we conducted a large panel of metabolomics profiling to identify the new candidate metabolites that were associated with 10-year ASCVD risk.MethodsThirty acylcarnitines and twenty amino acids were measured in the fasting plasma of 1,102 randomly selected individuals using a targeted FIA-MS/MS approach. The 10-year ASCVD risk score was calculated based on 2013 ACC/AHA guidelines. Accordingly, the subjects were stratified into four groups: low-risk (n = 620), borderline-risk (n = 110), intermediate-risk (n = 225), and high-risk (n = 147). 10 factors comprising collinear metabolites were extracted from principal component analysis.ResultsC4DC, C8:1, C16OH, citrulline, histidine, alanine, threonine, glycine, glutamine, tryptophan, phenylalanine, glutamic acid, arginine, and aspartic acid were significantly associated with the 10-year ASCVD risk score (p-values ≤ 0.044). The high-risk group had higher odds of factor 1 (12 long-chain acylcarnitines, OR = 1.103), factor 2 (5 medium-chain acylcarnitines, OR = 1.063), factor 3 (methionine, leucine, valine, tryptophan, tyrosine, phenylalanine, OR = 1.074), factor 5 (6 short-chain acylcarnitines, OR = 1.205), factor 6 (5 short-chain acylcarnitines, OR = 1.229), factor 7 (alanine, proline, OR = 1.343), factor 8 (C18:2OH, glutamic acid, aspartic acid, OR = 1.188), and factor 10 (ornithine, citrulline, OR = 1.570) compared to the low-risk ones; the odds of factor 9 (glycine, serine, threonine, OR = 0.741), however, were lower in the high-risk group. “D-glutamine and D-glutamate metabolism”, “phenylalanine, tyrosine, and tryptophan biosynthesis”, and “valine, leucine, and isoleucine biosynthesis” were metabolic pathways having the highest association with borderline/intermediate/high ASCVD events, respectively.ConclusionsAbundant metabolites were found to be associated with ASCVD events in this study. Utilization of this metabolic panel could be a promising strategy for early detection and prevention of ASCVD events.
Introduction: There is a strong correlation between the skeletal muscle mass index (SMI) and handgrip strength as indicators of sarcopenias. Multivariate methods can be exploited statistical power in determining the association between these correlated heritable indicators. Methods: We conducted a multivariate candidate-gene study based on data collected from the ongoing Bushehr Elderly Health (BEH) cohort, which evaluated the prevalence of musculoskeletal disorders in 2772 Iranians over 60 years old with 663377 single nucleotide polymorphisms (SNPs). We chose genetic variants on IL10 (chromosome 1: 206940947, 206945839), a strongly associated gene known to cause muscle diseases, as candidate regions, which included 27 independent SNPs with LD<0.4 (MAF>0.01 and p-valuehwe >0.05). MultiPhen uses a linear combination of genotypes, including SMI and handgrip, to obtain stronger statistical power. To outperform and confirm the MultiPhen results, it combined with a summary statistics level genebased association test, GATES. Results: Among the participants, 1138 men (48%) and 1205 women (52%) aged 69.2±6.35 and 69.56±6.45, were present respectively. 27 SNPs with a maximum MAF of 0.488 and a minimum of 0.0098, p-value hwe=0.3 were selected on Interleukin 10 (IL10). In the joint model MultiPhen test, 3 intronic variants (rs11119603, rs3950619, rs57461190) were associated with IL10 with effect sizes between 0.178 and 0.883 (p-value<0.05). We used the GATES model to assess the multivariate aggregated effect of IL10 on the phenotypes. Using this method, the gene's effect was significant (0.046), showing that it is a risk gene for sarcopenia. Conclusion: This study examined the association of handgrip, SMI, with IL10, as demonstrated in previous studies as risk factors for muscular diseases, using multivariate methods that utilized a joint model to achieve a high level of statistical power.
BACKGROUND Patients undergoing coronary artery bypass graft surgery (CABGS) may fail to adhere to their treatment regimen for many reasons. Among these, one of the most important reasons for nonadherence is the inadequate training of such patients or training using inappropriate methods. OBJECTIVE This study aimed to compare the effect of gamification and teach-back training methods on adherence to a therapeutic regimen in patients after CABGS. METHODS This randomized clinical trial was conducted on 123 patients undergoing CABGS in Tehran, Iran, in 2019. Training was provided to the teach-back group individually. In the gamification group, an app developed for the purpose was installed on each patient’s smartphone, with training given via this device. The control group received usual care, or routine training. Adherence to the therapeutic regimen was assessed using a questionnaire on adherence to a therapeutic regimen (physical activity and dietary regimen) and an adherence scale as a pretest and a 1-month posttest. RESULTS One-way analysis of variance (ANOVA) for comparing the mean scores of teach-back and gamification training methods showed that the mean normalized scores for the dietary regimen (<i>P</i><.001, <i>F</i>=71.80), movement regimen (<i>P</i><.001, <i>F</i>=124.53), and medication regimen (<i>P</i><.001, <i>F</i>=9.66) before and after intervention were significantly different between the teach-back, gamification, and control groups. In addition, the results of the Dunnett test showed that the teach-back and gamification groups were significantly different from the control group in all three treatment regimen methods. There was no statistically significant difference in adherence to the therapeutic regimen between the teach-back and control groups. CONCLUSIONS Based on the results of this study, the use of teach-back and gamification training approaches may be suggested for patients after CABGS to facilitate adherence to the therapeutic regimen. CLINICALTRIAL Iranian Registry of Clinical Trials IRCT20111203008286N8; https://en.irct.ir/trial/41507
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