Stroke Genomics: Current Knowledge, Clinical Applications and Future Possibilities

The pathophysiology of stoke involves many complex pathways and risk factors. Though there are several ongoing studies on stroke, treatment options are limited, and the prevalence of stroke is continuing to increase. Understanding the genomic variants and biological pathways associated with stroke could offer novel therapeutic alternatives in terms of drug targets and receptor modulations for newer treatment methods. It is challenging to identify individual causative mutations in a single gene because many alleles are responsible for minor effects. Therefore, multiple factorial analyses using single nucleotide polymorphisms (SNPs) could be used to gain new insight by identifying potential genetic risk factors. There are many studies, such as Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) which have identified numerous independent loci associated with stroke, which could be instrumental in developing newer drug targets and novel therapies. Additionally, using analytical techniques, such as meta-analysis and Mendelian randomization could help in evaluating stroke risk factors and determining treatment priorities. Combining SNPs into polygenic risk scores and lifestyle risk factors could detect stroke risk at a very young age and help in administering preventive interventions

Stroke Genomics: Current Knowledge, Clinical Applications and Future Possibilities

The pathophysiology of stoke involves many complex pathways and risk factors. Though there are several ongoing studies on stroke, treatment options are limited, and the prevalence of stroke is continuing to increase. Understanding the genomic variants and biological pathways associated with stroke could offer novel therapeutic alternatives in terms of drug targets and receptor modulations for newer treatment methods. It is challenging to identify individual causative mutations in a single gene because many alleles are responsible for minor effects. Therefore, multiple factorial analyses using single nucleotide polymorphisms (SNPs) could be used to gain new insight by identifying potential genetic risk factors. There are many studies, such as Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) which have identified numerous independent loci associated with stroke, which could be instrumental in developing newer drug targets and novel therapies. Additionally, using analytical techniques, such as meta-analysis and Mendelian randomization could help in evaluating stroke risk factors and determining treatment priorities. Combining SNPs into polygenic risk scores and lifestyle risk factors could detect stroke risk at a very young age and help in administering preventive interventions

Association between vitamin D deficiency and hypothyroidism: results from the National Health and Nutrition Examination Survey (NHANES) 2007–2012

Purpose: Many smaller studies have previously shown a significant association between thyroid autoantibody induced hypothyroidism and lower serum vitamin D levels. However, these finding have not been confirmed by large-scale studies. In this study, we evaluated the relationship between hypothyroidism and vitamin D levels using a large population-based data. Methods: For this study, we used National Health and Nutrition Examination Survey (NHANES) during the years 2007–2012. We categorized participants into three clinically relevant categories based on vitamin D levels: optimal, intermediate and deficient. Participants were also split into hypothyroid and hyperthyroid. Weighted multivariable logistic regression analyses were used to calculate the odds of being hypothyroid based on vitamin D status. Results: A total of 7943 participants were included in this study, of which 614 (7.7%) were having hypothyroidism. Nearly 25.6% of hypothyroid patients had vitamin D deficiency, compared to 20.6% among normal controls. Adjusted logistic regression analyses showed that the odds of developing hypothyroidism were significantly higher among patients with intermediate (adjusted odds ratio [aOR], 1.7, 95% CI: 1.5–1.8) and deficient levels of vitamin D (aOR, 1.6, 95% CI: 1.4–1.9). Conclusion: Low vitamin D levels are associated with autoimmune hypothyroidism. Healthcare initiatives such as mass vitamin D deficiency screening among at-risk population could significantly decrease the risk for hypothyroidism in the long-term

Prevalence of Cardiovascular Risk Factors among Cancer Patients in the United States

Background: Cancer and cardiovascular diseases (CVDs) are leading causes of morbidity and mortality. We analyzed national data to examine the prevalence of CVD risk factors among adult cancer survivors in the United States. Methods: Participants included adults ≥18 years of age from the National Health and Nutrition Examination Survey 2001-2002 to 2013-2014. CVD risk factors included hypertension, diabetes, dyslipidemia, obesity, smoking, and physical activity. Prevalence of 1, 2, or ≥3 CVD risk factors was compared between cancer and noncancer participants. All CVD risk factors were adjusted for age and smoking and additionally for sex. Differences in CVD risk factors among cancer and noncancer participants were identified using logistic regression analysis. Results: Among 35,379 eligible participants, 2906 (8.4%) had a history of cancer. The proportion of participants having a single CVD risk factor was lower among cancer survivors compared with noncancer participants (25.8% vs. 33.9%, P \u3c 0.001). The proportions of participants having two CVD risk factors (33.5% vs. 24.6%, P \u3c 0.001) and ≥3 CVD risk factors (27.4% vs. 16.4%, P \u3c 0.001) were higher among cancer survivors. However, these associations lost significance upon adjusting for age. The odds of total hypertension (odds ratio [OR] 1.25, 95% confidence interval [CI]: 1.11-1.40) and total diabetes (OR 1.33, 95% CI: 1.08-1.65) were significantly higher among cancer survivors. Conclusions: Our study showed that adult cancer survivors in the United States had higher levels of CVD risk factors primarily due to age-related factors, in addition to cancer complications. There is a significant need for improved CVD risk assessment and prevention services for cancer survivors

Targeting ROCK2 isoform with its widely used inhibitors for faster post-stroke recovery

Recovery after ischemic stroke is slow and highly variable. Activated ROCK (Rho-associated coiled-coil kinase) pathway hampers recovery of impaired neurons. Though inhibiting ROCK pathway has shown therapeutic effects in vitro, the selectivity of most of the ROCK inhibitors is still not investigated. Present study aims to investigate the binding affinity in silico of nine widely used ROCK inhibitors with brainspecific ROCK2 isoform. Three-dimensional structures of ROCK2 and eight drugs were taken from Protein Data Bank and PubChem Chemical Compound Database, respectively, whereas, FSD-C10 structure was generated based on Xin et al., 2015. In docking, ROCK2 was set to be rigid and drugs were free to rotate. All simulations were carried out using AutoDock 4.2. This study demonstrated strong complexation between all ligands and ROCK2. All ROCK inhibitors, except FSD-C10, were able to bind to ROCK2 more strongly [Binding constant (Ka) between 2.6 – 36.7 × 105 M−1] than fasudil (Ka = 2.5 × 105 M−1). SLx-2119 (KD-025) had the highest binding constant (Ka = 36.7 × 105 M−1) thus succeeding as a better ROCK2 specific inhibitor. Selectivity of ROCK inhibitors (in silico) towards ROCK2 can be an indicative measure to estimate therapeutic benefits or adverse effects prior to in vitro study

Burden of maternal and fetal outcomes among pregnant cancer survivors during delivery hospitalizations in the United States

Existing studies on pregnancy-related outcomes among cancer survivors are limited by sample size or specificity of the cancer type. This study estimated the burden of adverse maternal and fetal outcomes among pregnant cancer survivors using a national database. This study was a retrospective analysis of National Inpatient Sample collected during 2010–2014. Multivariate regression models were used to calculate odds ratios for maternal and fetal outcomes. The study included a weighted sample of 64,506 pregnant cancer survivors and 18,687,217 pregnant women without cancer. Pregnant cancer survivors had significantly higher odds for death during delivery hospitalization, compared to pregnant women without cancer (58 versus 5 deaths per 100,000 pregnancies). They also had higher odds of severe maternal morbidity (aOR 2.00 [95% CI 1.66–2.41]), cesarean section (aOR 1.27 [95% CI 1.19–1.37]), labor induction (aOR 1.17 [95% CI 1.07–1.29]), pre-eclampsia (aOR 1.18 [95% CI 1.02–1.36]), preterm labor (aOR 1.55 [95% CI 1.36–1.76]), chorioamnionitis (aOR 1.45 [95% CI 1.15–1.82]), postpartum infection (aOR 1.68 [95% CI 1.21–2.33]), venous thromboembolism (aOR 3.62 [95% CI 2.69–4.88]), and decreased fetal movements (aOR 1.67 [95% CI 1.13–2.46]). This study showed that pregnancy among cancer survivors constitutes a high-risk condition requiring advanced care and collective efforts from multiple subspecialties

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Prevalence and Inpatient Hospital Outcomes of Malignancy-Related Ascites in the United States

Objective: Malignancy-related ascites (MRA) is the terminal stage of many advanced cancers, and the treatment is mainly palliative. This study looked for epidemiology and inpatient hospital outcomes of patients with MRA in the United States using a national database. Methods: The current study was a cross-sectional analysis of 2015 National Inpatient Sample data and consisted of patients ≥18 years with MRA. Descriptive statistics were used for understanding demographics, clinical characteristics, and MRA hospitalization costs. Multivariate regression models were used to identify predictors of length of hospital stay and in-hospital mortality. Results: There were 123 410 MRA hospitalizations in 2015. The median length of stay was 4.7 days (interquartile range [IQR]: 2.5-8.6 days), median cost of hospitalization was US$43 543 (IQR: US$23 485-US$82 248), and in-hospital mortality rate was 8.8% (n = 10 855). Multivariate analyses showed that male sex, black race, and admission to medium and large hospitals were associated with increased hospital length of stay. Factors associated with higher in-hospital mortality rates included male sex; Asian or Pacific Islander race; beneficiaries of private insurance, Medicaid, and self-pay; patients residing in large central and small metro counties; nonelective admission type; and rural and urban nonteaching hospitals. Conclusions: Our study showed that many demographic, socioeconomic, health care, and geographic factors were associated with hospital length of stay and in-hospital mortality and may suggest disparities in quality of care. These factors could be targeted for preventing unplanned hospitalization, decreasing hospital length of stay, and lowering in-hospital mortality for this population

Performance of a cardiac lipid panel compared to four prognostic scores in chronic heart failure

The cardiac lipid panel (CLP) is a novel panel of metabolomic biomarkers that has previously shown to improve the diagnostic and prognostic value for CHF patients. Several prognostic scores have been developed for cardiovascular disease risk, but their use is limited to specific populations and precision is still inadequate. We compared a risk score using the CLP plus NT-proBNP to four commonly used risk scores: The Seattle Heart Failure Model (SHFM), Framingham risk score (FRS), Barcelona bio-HF (BCN Bio-HF) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. We included 280 elderly CHF patients from the Cardiac Insufficiency Bisoprolol Study in Elderly trial. Cox Regression and hierarchical cluster analysis was performed. Integrated area under the curves (IAUC) was used as criterium for comparison. The mean (SD) follow-up period was 81 (33) months, and 95 (34%) subjects met the primary endpoint. The IAUC for FRS was 0.53, SHFM 0.61, BCN Bio-HF 0.72, MAGGIC 0.68, and CLP 0.78. Subjects were partitioned into three risk clusters: low, moderate, high with the CLP score showing the best ability to group patients into their respective risk cluster. A risk score composed of a novel panel of metabolite biomarkers plus NT-proBNP outperformed other common prognostic scores in predicting 10-year cardiovascular death in elderly ambulatory CHF patients. This approach could improve the clinical risk assessment of CHF patients