Response to pulmonary arterial hypertension drug therapies in patients with pulmonary arterial hypertension and cardiovascular risk factors.

The age at diagnosis of pulmonary arterial hypertension (PAH) and the prevalence of cardiovascular (CV) risk factors are increasing. We sought to determine whether the response to drug therapy was influenced by CV risk factors in PAH patients. We studied consecutive incident PAH patients (n = 146) between January 1, 2008, and July 15, 2011. Patients were divided into two groups: the PAH-No CV group included patients with no CV risk factors (obesity, systemic hypertension, type 2 diabetes mellitus, permanent atrial fibrillation, mitral and/or aortic valve disease, and coronary artery disease), and the PAH-CV group included patients with at least one. The response to PAH treatment was analyzed in all the patients who received PAH drug therapy. The PAH-No CV group included 43 patients, and the PAH-CV group included 69 patients. Patients in the PAH-No CV group were younger than those in the PAH-CV group (P < 0.0001). In the PAH-No CV group, 16 patients (37%) improved on treatment and 27 (63%) did not improve, compared with 11 (16%) and 58 (84%) in the PAH-CV group, respectively (P = 0.027 after adjustment for age). There was no difference in survival at 30 months (P = 0.218). In conclusion, in addition to older age, CV risk factors may predict a reduced response to PAH drug therapy in patients with PAH

Interference microscopy highlights properties and peculiarities of SAPO STA-7 crystals: Interference microscopy highlights properties and peculiarities ofSAPO STA-7 crystals

In the framework of this study a new generation of SAPO STA-7 crystals has been investigated with the help of Interference Microscopy. The ability of the abovementioned technique to record intracrystalline concentration profiles during uptake/release of guest molecules revealed oddities of the system under study. In other words, these crystals have the tendency to break in the middle, enhancing in this way diffusion. On the other hand, molecules have to confront high surface barriers when they try to diffuse through the other sides of the crystal, where it is not broken

Sleep, major depressive disorder and Alzheimer’s disease: a Mendelian randomisation study

Objective To explore the causal relationships between sleep, major depressive disorder (MDD), and Alzheimer’s disease (AD). Methods We conducted bi-directional two-sample Mendelian randomisation analyses. Genetic associations were obtained from the largest genome-wide association studies currently available in UK Biobank (N=446,118), the Psychiatric Genomics Consortium (N=18,759), and the International Genomics of Alzheimer’s Project (N=63,926). We used the inverse variance weighted Mendelian randomisation method to estimate causal effects, and weighted median and MR-Egger for sensitivity analyses to test for pleiotropic effects. Results We found that higher risk of AD was significantly associated with being a “morning person” (odds ratio (OR)=1.01, P=0.001), shorter sleep duration (self-reported: β=-0.006, P=1.9×10-4; accelerometer-based: β=-0.015, P=6.9×10-5), less likely to report long sleep (β=-0.003, P=7.3×10-7), earlier timing of the least active 5 hours (β=-0.024, P=1.7×10-13), and a smaller number of sleep episodes (β=-0.025, P=5.7×10-14) after adjusting for multiple comparisons. We also found that higher risk of AD was associated with lower risk of insomnia (OR=0.99, P=7×10-13). However, we did not find evidence either that these abnormal sleep patterns were causally related to AD or for a significant causal relationship between MDD and risk of AD. Conclusion We found that AD may causally influence sleep patterns. However, we did not find evidence supporting a causal role of disturbed sleep patterns for AD or evidence for a causal relationship between MDD and AD

Pleiotropic genetic architecture and novel loci for C-reactive protein levels

C-reactive protein is involved in a plethora of pathophysiological conditions. Many genetic loci associated with C-reactive protein are annotated to lipid and glucose metabolism genes supporting common biological pathways between inflammation and metabolic traits. To identify novel pleiotropic loci, we perform multi-trait analysis of genome-wide association studies on C-reactive protein levels along with cardiometabolic traits, followed by a series of in silico analyses including colocalization, phenome-wide association studies and Mendelian randomization. We find 41 novel loci and 19 gene sets associated with C-reactive protein with various pleiotropic effects. Additionally, 41 variants colocalize between C-reactive protein and cardiometabolic risk factors and 12 of them display unexpected discordant effects between the shared traits which are translated into discordant associations with clinical outcomes in subsequent phenome-wide association studies. Our findings provide insights into shared mechanisms underlying inflammation and lipid metabolism, representing potential preventive and therapeutic targets

Circulatory proteins relate cardiovascular disease to cognitive performance: a Mendelian randomisation study

Background and objectives: Mechanistic research suggests synergistic effects of cardiovascular disease (CVD) and dementia pathologies on cognitive decline. Interventions targeting proteins relevant to shared mechanisms underlying CVD and dementia could also be used for the prevention of cognitive impairment. Methods: We applied Mendelian randomisation (MR) and colocalization analysis to investigate the causal relationships of 90 CVD-related proteins measured by the Olink CVD I panel with cognitive traits. Genetic instruments for circulatory protein concentrations were obtained using a meta-analysis of genome-wide association studies (GWAS) from the SCALLOP consortium (N = 17,747) based on three sets of criteria: 1) protein quantitative trait loci (pQTL); 2) cis-pQTL (pQTL within ±500 kb from the coding gene); and 3) brain-specific cis-expression QTL (cis-eQTL) which accounts for coding gene expression based on GTEx8. Genetic associations of cognitive performance were obtained from GWAS for either: 1) general cognitive function constructed using Principal Component Analysis (N = 300,486); or, 2) g Factor constructed using genomic structural equation modelling (N = 11,263–331,679). Findings for candidate causal proteins were replicated using a separate protein GWAS in Icelanders (N = 35,559). Results: A higher concentration of genetically predicted circulatory myeloperoxidase (MPO) was nominally associated with better cognitive performance (p < 0.05) using different selection criteria for genetic instruments. Particularly, brain-specific cis-eQTL predicted MPO, which accounts for protein-coding gene expression in brain tissues, was associated with general cognitive function (βWald = 0.22, PWald = 2.4 × 10−4). The posterior probability for colocalization (PP.H4) of MPO pQTL with the g Factor was 0.577. Findings for MPO were replicated using the Icelandic GWAS. Although we did not find evidence for colocalization, we found that higher genetically predicted concentrations of cathepsin D and CD40 were associated with better cognitive performance and a higher genetically predicted concentration of CSF-1 was associated with poorer cognitive performance. Conclusion: We conclude that these proteins are involved in shared pathways between CVD and those for cognitive reserve or affecting cognitive decline, suggesting therapeutic targets able to reduce genetic risks conferred by cardiovascular disease

Associations of genetically predicted vitamin B12 status across the pohenome

Variation in vitamin B12 levels has been associated with a range of diseases across the life-course, the causal nature of which remains elusive. We aimed to interrogate genetically predicted vitamin B12 status in relation to a plethora of clinical outcomes available in the UK Biobank. Genome-wide association study (GWAS) summary data obtained from a Danish and Icelandic cohort of 45,576 individuals were used to identify 8 genetic variants associated with vitamin B12 levels, serving as genetic instruments for vitamin B12 status in subsequent analyses. We conducted a Mendelian randomisation (MR)-phenome-wide association study (PheWAS) of vitamin B12 status with 945 distinct phenotypes in 439,738 individuals from the UK Biobank using these 8 genetic instruments to proxy alterations in vitamin B12 status. We used external GWAS summary statistics for replication of significant findings. Correction for multiple testing was taken into consideration using a 5% false discovery rate (FDR) threshold. MR analysis identified an association between higher genetically predicted vitamin B12 status and lower risk of vitamin B deficiency (including all B vitamin deficiencies), serving as a positive control outcome. We further identified associations between higher genetically predicted vitamin B12 status and a reduced risk of megaloblastic anaemia (OR = 0.35, 95% CI: 0.20–0.50) and pernicious anaemia (0.29, 0.19–0.45), which was supported in replication analyses. Our study highlights that higher genetically predicted vitamin B12 status is potentially protective of risk of vitamin B12 deficiency associated with pernicious anaemia diagnosis, and reduces risk of megaloblastic anaemia. The potential use of genetically predicted vitamin B12 status in disease diagnosis, progression and management remains to be investigated

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Direct and indirect effect of the COVID-19 pandemic on patients with cardiomyopathy

Objectives: (i) To evaluate the prevalence and hospitalisation rate of COVID-19 infections amongst patients with dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) in the Royal Brompton & Harefield Hospital Cardiovascular Research Centre (RBHH CRC) Biobank. (ii) To evaluate the indirect impact of the pandemic on patients with cardiomyopathy through the Heart Hive COVID-19 study. (iii) To assess the impact of the pandemic on national cardiomyopathy-related hospital admissions. Methods: (i) 1,236 patients (703 DCM, 533 HCM) in the RBHH CRC Biobank were assessed for COVID-19 infections and hospitalisations; ii) 207 subjects (131 cardiomyopathy, 76 without heart disease) in the Heart Hive COVID-19 study completed online surveys evaluating physical health, psychological wellbeing, and behavioural adaptations during the pandemic; (iii) 11,447 cardiomyopathy-related hospital admissions across NHS England were studied from NHS Digital Hospital Episode Statistics over 2019-2020. Results: A comparable proportion of patients with cardiomyopathy in the RBHH CRC Biobank had tested positive for COVID-19 compared with the UK population (1.1% vs 1.6%, p=0.14), but a higher proportion of those infected were hospitalised (53.8% vs 16.5%, p=0.002). In the Heart Hive COVID-19 study, more patients with cardiomyopathy felt their physical health had deteriorated due to the pandemic than subjects without heart disease (32.3% vs 13.2%, p=0.004) despite only 4.6% of the cardiomyopathy cohort reporting COVID-19 symptoms. A 17.9% year-on-year reduction in national cardiomyopathy-related hospital admissions was observed in 2020. Conclusion: Patients with cardiomyopathy had similar reported rates of testing positive for COVID-19 to the background population, but those with test-proven infection were hospitalised more frequently. Deterioration in physical health amongst patients could not be explained by COVID-19 symptoms, inferring a significant contribution of the indirect consequences of the pandemic

Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S