Epigenetic Grimage Acceleration and Cognitive Impairment in Bipolar Disorder

Bipolar disorder (BD) has been previously associated with clinical signs of premature aging, including accelerated epigenetic aging in blood and brain, and a steeper age-related decline in cognitive function. However, the clinical drivers and cognitive correlates of epigenetic aging in BD are still unknown. We aimed to investigate the relationship between multiple measures of epigenetic aging acceleration with clinical, functioning, and cognitive outcomes in patients with BD and controls. Blood genome-wide DNA methylation levels were measured in BD patients (n = 153) and matched healthy controls (n = 50) with the Infinium MethylationEPIC BeadChip (Illumina). Epigenetic age estimates were calculated using an online tool, including the recently developed lifespan predictor GrimAge, and analyzed with generalized linear models controlling for demographic variables and blood cell proportions. BD was significantly associated with greater GrimAge acceleration (AgeAccelGrim, β=0.197, p = 0.009), and significant group-dependent interactions were found between AgeAccelGrim and blood cell proportions (CD4+ T-lymphocytes, monocytes, granulocytes, and B-cells). Within patients, higher AgeAccelGrim was associated with worse cognitive function in multiple domains (short-term affective memory (β=-0.078, p = 0.030), short-term non-affective memory (β=-0.088, p = 0.018), inhibition (β=0.064, p = 0.046), and problem solving (β=-0.067, p = 0.034)), age of first diagnosis with any mood disorder (β=-0.076, p = 0.039) or BD (β=-0.102, p = 0.016), as well as with current non-smoking status (β=-0.392, p \u3c 0.001). Overall, our findings support the contribution of epigenetic factors to the aging-related cognitive decline and premature mortality reported in BD patients, with an important driving effect of smoking in this population

Energetic metabolic profile of ewes presenting low body condition score induced to subclinical hypocalcemia in early postpartum

The aim of the present study is to assess plasma concentrations of metabolites related to energy balance in ewes showing low body condition score (BCS) induced to subclinical hypocalcemia in early postpartum. Sixteen crossbred ewes (Ideal x Corriedale) presenting BCS <3 were divided in two groups: 1) control group (n = 9), which received no treatment, and 2) hypocalcemia group (n = 7), which was subjected to twelve hours of induced subclinical hypocalcemia through intravenous Na2EDTA infusion six hours postpartum. Ionized calcium levels were monitored and kept between 0.62 and 0.87 mmol/L. All ewes were subjected to daily blood sampling for five days in order to set the postpartum metabolic profile. There was no interaction between day and treatment in total and ionized calcium concentrations (P>0.05), whereas the mean concentrations of these marks after five days were lower in the hypocalcemia group (P<0.05). However, neither the mean group were different at glucose, non-esterified fatty acids, beta-hydroxybutyrate and insulin (P>0.05) level. These results evidence that, despite the subclinical hypocalcemia induction at early postpartum, ewes presenting low body condition do not change the concentrations of energy balance-related metabolites in the following five days

Deep Learning Analysis of Cardiac MRI in Legacy Datasets:Multi-Ethnic Study of Atherosclerosis

The shape and motion of the heart provide essential clues to understanding the mechanisms of cardiovascular disease. With the advent of large-scale cardiac imaging data, statistical atlases become a powerful tool to provide automated and precise quantification of the status of patient-specific heart geometry with respect to reference populations. The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular MRI in over 5000 participants, and there is now a wealth of follow-up data over 20 years. Building a machine learning based automated analysis is necessary to extract the additional imaging information necessary for expanding original manual analyses. However, machine learning tools trained on MRI datasets with different pulse sequences fail on such legacy datasets. Here, we describe an automated atlas construction pipeline using deep learning methods applied to the legacy cardiac MRI data in MESA. For detection of anatomical cardiac landmark points, a modified VGGNet convolutional neural network architecture was used in conjunction with a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views. A U-Net architecture was used for detection of the endocardial and epicardial boundaries in short axis images. Both network architectures resulted in good segmentation and landmark detection accuracies compared with inter-observer variations. Statistical relationships with common risk factors were similar between atlases derived from automated vs manual annotations. The automated atlas can be employed in future studies to examine the relationships between cardiac morphology and future events

Self-reported Age of Hypertension Onset and Hypertension-Mediated Organ Damage in Middle-Aged Individuals

BackgroundObjectively defined early onset hypertension, based on repeated blood pressure measurements, is a strong risk factor for cardiovascular disease (CVD). We aimed to assess if also self-reported hypertension onset age is associated with hypertension-mediated organ damage (HMOD). Additionally, we evaluated the agreement between self-reported and objectively defined hypertension onset age.MethodsWe studied 2,649 participants (50 4 years at the time of outcome assessment, 57% women) of the Coronary Artery Risk Development in Young Adults (CARDIA) study who underwent measurements for echocardiographic left ventricular hypertrophy (LVH), left ventricular diastolic dysfunction (LVDD), coronary calcification, and albuminuria. We divided the participants into groups according to self-reported hypertension onset age (= 45 years, and no hypertension). We used multivariable-adjusted logistic regression models to assess the relation between self-reported hypertension onset age with the presence of HMOD, with those who did not report hypertension as the referent group.ResultsCompared with individuals without self-reported hypertension, self-reported hypertension onset at = 45 years was only associated with LVDD (OR, 1.81; 95% CI, 1.06-3.08). The agreement between self-reported and objectively defined hypertension onset age groups was 78-79%.ConclusionsOur findings suggest that self-reported hypertension onset age, a pragmatically feasible assessment in clinical practice, is a reasonable method for assessing risk of HMOD and CVD

Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.Comment: 25 pages, 5 figure

Coronary computed tomography angiography compared with single photon emission computed tomography myocardial perfusion imaging as a guide to optimal medical therapy in patients presenting with stable angina: The RESCUE trial

Background The RESCUE (Randomized Evaluation of Patients with Stable Angina Comparing Utilization of Noninvasive Examinations) trial was a randomized, controlled, multicenter, comparative efficacy outcomes trial designed to assess whether initial testing with coronary computed tomographic angiography (CCTA) is noninferior to single photon emission computed tomography (SPECT) myocardial perfusion imaging in directing patients with stable angina to optimal medical therapy alone or optimal medical therapy with revascularization. Methods and Results The end point was first major adverse cardiovascular event (MACE) (cardiac death or myocardial infarction), or revascularization. Noninferiority margin for CCTA was set a priori as a hazard ratio (HR) of 1.3 (95% CI=0, 1.605). One thousand fifty participants from 44 sites were randomized to CCTA (n=518) or SPECT (n=532). Mean follow-up time was 16.2 (SD 7.9) months. There were no cardiac-related deaths. In patients with a negative CCTA there was 1 acute myocardial infarction; in patients with a negative SPECT examination there were 2 acute myocardial infarctions; and for positive CCTA and SPECT, 1 acute myocardial infarction each. Participants in the CCTA arm had a similar rate of MACE or revascularization compared with those in the SPECT myocardial perfusion imaging arm, (HR, 1.03; 95% CI=0.61-1.75)