The Prevalence of Left Ventricular Hypertrophy in Obese Children Varies Depending on the Method Utilized to Determine Left Ventricular Mass

Obesity and left ventricular hypertrophy (LVH) have been identified as independent risk factors for cardiovascular events. The definition of LVH depends on the geometric algorithm used to calculate LV mass (LVM) by echocardiography and the method used to normalize LVM for body size. This study evaluates the effect of these methods on the prevalence of LVH in obese children. LVM for 109 obese and 109 age-matched non-obese children was calculated using M-mode or two-dimensional echocardiography (2DE). LVM was then normalized to height 2.7 as indexed LVM (LVMI), to body surface area (BSA), height, and lean body mass (LBM) as LVM Z-scores. LVH was defined as LVMI >95th ‰ using age-specific normal reference values or LVM Z-scores ≥2. The prevalence of LVH by LVMI and LVM Z-scores was compared. There was a correlation between LVM determined by M-mode and by 2DE (R ² = 0.91), although M-mode LVM was greater than 2DE LVM. However, the difference between these values was greater in obese children than in non-obese children. Based on the method of normalization, the prevalence of LVH among obese children was 64 % using LVMI, 15 % using LVM Z-scores for height, 8 % using LVM Z-scores for BSA and 1 % using LVM Z-scores for LBM. Height-based normalization correlates with obesity and hypertension. The methods used to measure and normalize LVM have a profound influence on the diagnosis of LVH in obese children. Further study is needed to determine which method identifies children at risk for cardiovascular morbidity and mortality.     

Fourth Annual Conference of the American Society for Nanomedicine

The 20th conference of the Society on NeuroImmune Pharmacology will be held March 26-29, 2014. It features the latest in research examining the intersection of neuroscience, immunology and pharmacology, relevant for human health and disease. Particular emphases are placed on HIV and other infectious diseases, and abused substances, including illicit drugs and alcohol.     

A randomized feasibility trial of clonidine to reduce perioperative cardiac risk in patients on chronic beta-blockade: the EPIC study

Purpose

Clonidine may help prevent cardiac complications in patients undergoing non-cardiac surgery and receiving chronic beta-blocker therapy. We conducted a multicentre pilot randomized trial to estimate recruitment rates for a full-scale trial and to assess the safety and tolerability of combining clonidine with chronic beta-blockade.

Methods

Patients who were at elevated perioperative cardiac risk, receiving chronic beta-blockade, and scheduled for major non-cardiac surgery were recruited in a blinded (participants, clinicians, outcome assessors) placebo-controlled randomized trial at three Canadian hospitals. Participants were randomized to clonidine (0.2 mg oral tablet one hour before surgery, plus 0.2 mg·day^?1 transdermal patch placed one hour before surgery and removed four days after surgery or hospital discharge, whichever came first) or matching placebo. Feasibility was evaluated based on recruitment rates, with each centre being required to recruit 50 participants within 12-18 months. Additionally, we reviewed study drug withdrawals and safety outcomes, including clinically significant hypotension or bradycardia.

Results

Eighty-two of the 168 participants were randomized to receive clonidine and 86 to receive placebo. The average time to recruit 50 participants at each centre was 14.3 months. Six patients (7%) withdrew from clonidine, while four (5%) withdrew from placebo. Based on qualitative review, there were no major safety concerns related to clonidine. There was a moderate overall rate of cardiac morbidity, with 18 participants (11%) suffering postoperative myocardial infarction.

Conclusion

This pilot randomized trial confirmed the feasibility, safety, and tolerability of a full-scale trial of oral and transdermal clonidine for reducing the risk of cardiac complications during non-cardiac surgery. This trial was registered at www.clinicaltrials.gov: NCT00335582.     

Population genetics of 30 insertion/deletion polymorphisms in the Kuwaiti population

International Journal of Legal Medicine - This study evaluates the forensic utility of the 30 insertion and deletion (indel) markers contained in the Qiagen Investigator® DIPplex kit in the...     

Prenatal depressive symptoms and abnormalities of glucose tolerance during pregnancy among Hispanic women

The aim of this study is to prospectively examine the association between maternal depressive symptoms in early pregnancy and risk of abnormal glucose tolerance (AGT) and impaired glucose tolerance (IGT) in mid-pregnancy. We evaluated this association among 934 participants in Proyecto Buena Salud, a prospective cohort study of Hispanic (predominantly Puerto Rican) women in Western Massachusetts. Depressive symptoms were assessed in early pregnancy using the 10-item Edinburgh Postnatal Depression Scale. Scores ≥13 indicated at least probable minor depression and scores ≥15 indicated probable major depression. AGT and IGT were diagnosed using American Diabetes Association criteria. In early pregnancy, 247 (26.5 %) participants experienced at least minor depression and 163 (17.4 %) experienced major depression. A total of 123 (13.2 %) were classified with AGT and 56 (6.0 %) were classified with IGT. In fully-adjusted models, the odds ratio for AGT associated with minor depression was 1.20 (95 % CI 0.77–1.89) and for major depression was 1.34 (95 % CI 0.81–2.23). The odds ratio for IGT associated with minor depression was 1.22 (95 % CI 0.62–2.40) and for major depression was 1.53 (95 % CI 0.73–3.22). We did not observe an association with continuous screening glucose measures. Findings in this prospective cohort of Hispanic women did not indicate a statistically significant association between minor or major depression in early pregnancy and AGT or screening glucose values in mid-pregnancy. Due to the small number of cases of IGT, our ability to evaluate the association between depression and IGT risk was constrained.     

The Biology of Veganism: Plasma Metabolomics Analysis Reveals Distinct Profiles of Vegans and Non-Vegetarians in the Adventist Health Study-2 Cohort

It is unclear how vegetarian dietary patterns influence plasma metabolites involved in biological processes regulating chronic diseases. We sought to identify plasma metabolic profiles distinguishing vegans (avoiding meat, eggs, dairy) from non-vegetarians (consuming ≥28 g/day red meat) of the Adventist Health Study-2 cohort using global metabolomics profiling with ultra-performance liquid chromatography mass spectrometry (UPLC-MS/MS). Differences in abundance of metabolites or biochemical subclasses were analyzed using linear regression models, adjusting for surrogate and confounding variables, with cross-validation to simulate results from an independent sample. Random forest was used as a learning tool for classification, and principal component analysis was used to identify clusters of related metabolites. Differences in covariate-adjusted metabolite abundance were identified in over 60% of metabolites (586/930), after adjustment for false discovery. The vast majority of differentially abundant metabolites or metabolite subclasses showed lower abundance in vegans, including xanthine, histidine, branched fatty acids, acetylated peptides, ceramides, and long-chain acylcarnitines, among others. Many of these metabolite subclasses have roles in insulin dysregulation, cardiometabolic phenotypes, and inflammation. Analysis of metabolic profiles in vegans and non-vegetarians revealed vast differences in these two dietary groups, reflecting differences in consumption of animal and plant products. These metabolites serve as biomarkers of food intake, many with potential pathophysiological consequences for cardiometabolic diseases.     

An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease–related patterns

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer''s Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure–based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes (TREM2, SORL1, and EXOC3L4) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1, two known Alzheimer''s disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.

High-throughput DNA sequencing of diverse humans has identified millions of genetic variants, the vast majority of which are exceptionally rare. A survey of ∼60,000 individuals from the Exome Aggregation Consortium (ExAC) found that out of ∼7 million variants, 99% have a frequency <1% and 54% are singletons (Taliun et al. 2021). Similarly, in the Alzheimer''s Disease Sequencing Project (ADSP) whole-exome sequencing (WES) of ∼10,000 individuals, 97% of identified variants have a minor allele frequency <1%, and 23% are singletons (Butkiewicz et al. 2018). However, the effect of most rare variants on diseases of interest remains unknown because of insufficient statistical power to detect the associations between these variants and phenotypes.We hypothesized that rare missense variants contribute to common diseases by disrupting the protein function and are likely to form clustered or dispersed patterns within protein structures when examined in population-based studies. Therefore, incorporating spatial context will improve rare variant association tests. Prior studies have shown that missense variants show nonrandom patterns in protein structures, such as cancer-associated hotspot regions with a high density of missense somatic mutations (Tokheim et al. 2016). Our group (Sivley et al. 2018) also found that germline causal missense variants for Mendelian diseases show nonrandom patterns in three-dimensional (3D) space. These patterns include clusters that likely reflect disruption of a key functional region and dispersions that likely reflect depletion of variants within a sensitive protein core.To test this hypothesis within sequencing studies of disease traits, we developed a kernel function to quantify genetic similarity among individuals by using protein structure information. When two individuals have different missense variants distal in genomic coordinates but close in 3D protein structure, these individuals will be assigned a high genetic similarity through our kernel function. When applied over an entire data set, our kernel function captures differences in the spatial patterns of rare missense variants among cases and controls or over continuous traits. Using a statistical framework similar to SKAT (Wu et al. 2011), we test the association of rare variants with quantitative and dichotomous phenotypes using this structure-based kernel. We call this approach protein optimized kernel evaluation of missense nucleotides (POKEMON). We validated that POKEMON can identify trait associations with spatial patterns formed by missense variants both in simulation studies and real-world data.