A ketogenic diet lowers myocardial fatty acid oxidation but does not affect oxygen consumption: a study in overweight humans.

OBJECTIVE: A ketogenic diet (KD) characterized by very low carbohydrate intake and high fat consumption may simultaneously induce weight loss and be cardioprotective. The "thrifty substrate hypothesis" posits that ketone bodies are more energy efficient compared with other cardiac oxidative substrates such as fatty acids. This work aimed to study whether a KD with presumed increased myocardial ketone body utilization reduces cardiac fatty acid uptake and oxidation, resulting in decreased myocardial oxygen consumption (MVO2 ). METHODS: This randomized controlled crossover trial examined 11 individuals with overweight or obesity on two occasions: (1) after a KD and (2) after a standard diet. Myocardial free fatty acid (FFA) oxidation, uptake, and esterification rate were measured using dynamic [11 C]palmitate positron emission tomography (PET)/computed tomography, whereas MVO2 and myocardial external efficiency (MEE) were measured using dynamic [11 C]acetate PET. RESULTS: The KD increased plasma ß-hydroxybutyrate, reduced myocardial FFA oxidation (p < 0.01) and uptake (p = 0.03), and increased FFA esterification (p = 0.03). No changes were observed in MVO2 (p = 0.2) or MEE (p = 0.87). CONCLUSIONS: A KD significantly reduced myocardial FFA uptake and oxidation, presumably by increasing ketone body oxidation. However, this change in cardiac substrate utilization did not improve MVO2 , speaking against the thrifty substrate hypothesis.

X chromosome dosage and the genetic impact across human tissues.

BACKGROUND: Sex chromosome aneuploidies (SCAs) give rise to a broad range of phenotypic traits and diseases. Previous studies based on peripheral blood samples have suggested the presence of ripple effects, caused by altered X chromosome number, affecting the methylome and transcriptome. Whether these alterations can be connected to disease-specific tissues, and thereby having clinical implication for the phenotype, remains to be elucidated. METHODS: We performed a comprehensive analysis of X chromosome number on the transcriptome and methylome in blood, fat, and muscle tissue from individuals with 45,X, 46,XX, 46,XY, and 47,XXY. RESULTS: X chromosome number affected the transcriptome and methylome globally across all chromosomes in a tissue-specific manner. Furthermore, 45,X and 47,XXY demonstrated a divergent pattern of gene expression and methylation, with overall gene downregulation and hypomethylation in 45,X and gene upregulation and hypermethylation in 47,XXY. In fat and muscle, a pronounced effect of sex was observed. We identified X chromosomal genes with an expression pattern different from what would be expected based on the number of X and Y chromosomes. Our data also indicate a regulatory function of Y chromosomal genes on X chromosomal genes. Fourteen X chromosomal genes were downregulated in 45,X and upregulated in 47,XXY, respectively, in all three tissues (AKAP17A, CD99, DHRSX, EIF2S3, GTPBP6, JPX, KDM6A, PP2R3B, PUDP, SLC25A6, TSIX, XIST, ZBED1, ZFX). These genes may be central in the epigenetic and genomic regulation of sex chromosome aneuploidies. CONCLUSION: We highlight a tissue-specific and complex effect of X chromosome number on the transcriptome and methylome, elucidating both shared and non-shared gene-regulatory mechanism between SCAs.

Effects of transdermal estrogen therapy on satellite cell number and molecular markers for muscle hypertrophy in response to resistance training in early postmenopausal women.

PURPOSE: To investigate the effects of resistance training with or without transdermal estrogen therapy (ET) on satellite cell (SC) number and molecular markers for muscle hypertrophy in early postmenopausal women. METHODS: Using a double-blinded randomized controlled design, we allocated healthy, untrained postmenopausal women to perform 12 weeks of resistance training with placebo (PLC, n = 16) or ET (n = 15). Muscle biopsies obtained before and after the intervention, and two hours after the last training session were analyzed for fiber type, SC number and molecular markers for muscle hypertrophy and degradation (real-time PCR, western blotting). RESULTS: The analysis of SCs per Type I fiber showed a time x treatment interaction caused by a 47% decrease in PLC, and a 26% increase after ET after the training period. Also, SCs per Type II fiber area was lower after the intervention driven by a 57% decrease in PLC. Most molecular markers changed similarly in the two groups. CONCLUSION: A decline in SC per muscle fiber was observed after the 12-week training period in postmenopausal women, which was counteracted when combined with use of transdermal ET. CLINICAL TRIAL REGISTRATION NUMBER: nct03020953.

Estrogen modulates metabolic risk profile after resistance training in early postmenopausal women: a randomized controlled trial.

OBJECTIVE: Women experience an unhealthy change in metabolic risk profile at menopause. The purpose of the present study was to determine effects of resistance training with or without transdermal estrogen therapy (ET) on adipose tissue mass and metabolic risk profile in early postmenopausal women. METHODS: A double-blinded randomized controlled trial, where healthy, untrained postmenopausal women were allocated to supervised resistance training with placebo (PLC, n = 16) or transdermal ET (n = 15) for 12 weeks. Endpoints with prespecified hypotheses were the change in total fat mass (FM) (main endpoint) and the change in visceral FM (secondary endpoint) from before to after the intervention. Additionally, prespecified endpoints of body composition, metabolic health-related blood markers, fat%, fat cell size, and lipogenic markers in subcutaneous adipose tissue (SAT) from abdominal and femoral region were explored. RESULTS: Compared with the ET group, the PLC group experienced a greater reduction (time × treatment interaction P < 0.05) in total FM (PLC vs ET: -5.6% vs -1.1%) and visceral FM (-18.6% vs -6.8%), and femoral SAT (-5.6% vs 1.0%), but not abdominal SAT mass (-8.5% vs -2.8%, P = 0.15).The ET group improved their metabolic blood profile by reduced low-density lipoprotein, glucose and hemoglobin A1c compared with PLC (time × treatment interaction P < 0.05). The intervention induced changes in lipolytic markers of abdominal SAT, whereas no changes were detected in femoral SAT. CONCLUSION: Use of transdermal ET reduced adipose tissue loss, but improved metabolic blood markers when combined with 12 weeks of progressive resistance training in early postmenopausal women.

Ischemic heart failure mortality is not predicted by cardiac insulin resistance but by diabetes per se and coronary flow reserve: A retrospective dynamic cardiac 18F-FDG PET study.

BACKGROUND & AIMS: The connection between peripheral insulin resistance (IR) and coronary artery disease is well-established. Both are major risk factors for the development of ischemic cardiomyopathy potentially leading to heart failure (HF). Whether cardiac IR also impacts overall survival and morbidity is still debated. We therefore aimed to test if cardiac IR predicts mortality and major cardiovascular events (MACE) in patients with HF scheduled for cardiac viability testing before revascularization. METHODS: This retrospective study included 131 patients with a clinical diagnosis of ischemic HF (114 (87%) male, 33 (25%) with diabetes) referred to a viability Rubidium-82 (perfusion) and dynamic 18F-Fluorodeoxyglucose (metabolism) positron emission tomography combined with computed tomography prior to a potential revascularization procedure. Cardiac IR was assessed by myocardial glucose uptake (MGU) in a remote (non-scarred) area of the left ventricle during a hyperinsulinemic-euglycemic clamp (1mIE/kg/min). RESULTS: MGU correlated with skeletal muscle glucose uptake (p < 0.001) and whole-body glucose uptake (M-value) (p < 0.001), whereas no association was observed for individuals with diabetes. MGU did not predict the risk of death or MACE. However, both overt diabetes and reduced coronary flow reserve predicted overall survival. CONCLUSION: Even though diabetes and related small-vessel disease is associated with increased mortality, cardiac IR per se does not predict cardiovascular morbidity and mortality.

Explainable artificial intelligence model to predict acute critical illness from electronic health records.

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as early warning scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on electronic health records (EHR) trained artificial intelligence (AI) systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. Here, we present an explainable AI early warning score (xAI-EWS) system for early detection of acute critical illness. xAI-EWS potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far, the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units. Furthermore, we will circumvent several important limitations that have been found in the literature: (1) Model evaluations neglect the clinical consequences of a decision to start, or not start, an intervention for sepsis. (2) Models are evaluated shortly before sepsis onset without considering interventions already initiated. (3) Machine learning models are built on a restricted set of clinical parameters, which are not necessarily measured in all departments. (4) Model performance is limited by current knowledge of sepsis, as feature interactions and time dependencies are hard-coded into the model. METHODS: In this study, we present a model to overcome these shortcomings using a deep learning approach on a diverse multicenter data set. We used retrospective data from multiple Danish hospitals over a seven-year period. Our sepsis detection system is constructed as a combination of a convolutional neural network and a long short-term memory network. We assess model quality by standard concepts of accuracy as well as clinical usefulness, and we suggest a retrospective assessment of interventions by looking at intravenous antibiotics and blood cultures preceding the prediction time. RESULTS: Results show performance ranging from AUROC 0.856 (3 h before sepsis onset) to AUROC 0.756 (24 h before sepsis onset). Evaluating the clinical utility of the model, we find that a large proportion of septic patients did not receive antibiotic treatment or blood culture at the time of the sepsis prediction, and the model could, therefore, facilitate such interventions at an earlier point in time. CONCLUSION: We present a deep learning system for early detection of sepsis that can learn characteristics of the key factors and interactions from the raw event sequence data itself, without relying on a labor-intensive feature extraction work. Our system outperforms baseline models, such as gradient boosting, which rely on specific data elements and therefore suffer from many missing values in our dataset.

Myocardial Viability Testing by Positron Emission Tomography: Basic Concepts, Mini-Review of the Literature and Experience From a Tertiary PET Center.

Ischemic heart disease ranges in severity from slightly reduced myocardial perfusion with preserved contractile function to chronic occlusion of coronary arteries with myocardial cells replaced by acontractile scar tissue-ischemic heart failure (iHF). Progression towards scar tissue is thought to involve a period in which the myocardial cells are acontractile but still viable despite severely reduced perfusion. This state of reduced myocardial function that can be reversed by revascularization is termed "hibernation." The concept of hibernating myocardium in iHF has prompted an increasing amount of requests for preoperative patient workup, but while the concept of viability is widely agreed upon, no consensus on clinical testing of hibernation has been established. Therefore, a variety of imaging methods have been used to assess hibernation including morphology based (MRI and ultrasound), perfusion based (MRI, SPECT, or PET) and/or methods to assess myocardial metabolism (PET). Regrettably, the heterogeneous body of literature on the subject has resulted in few robust prospective clinical trials designed to assess the impact of preoperative viability testing prior to revascularization. However, the PARR-2 trial and sub-studies has indicated that >5% hibernating myocardium favors revascularization over optimized medical therapy. In this paper, we review the basic concepts and current evidence for using PET to assess myocardial hibernation and discuss the various methodologies used to process the perfusion/metabolism PET images. Finally, we present our experience in conducting PET viability testing in a tertiary referral center.

Transdermal Estrogen Therapy Improves Gains in Skeletal Muscle Mass After 12 Weeks of Resistance Training in Early Postmenopausal Women.

CONTEXT: Women show an accelerated loss of muscle mass around menopause, possibly related to the decline in estrogen. Furthermore, the anabolic response to resistance exercise seems to be hampered in postmenopausal women. OBJECTIVE: We aimed to test the hypothesis that transdermal estrogen therapy (ET) amplifies the skeletal muscle response to resistance training in early postmenopausal women. DESIGN: A double-blinded randomized controlled study. SETTING: Department of Public Health, Aarhus University, Denmark. PARTICIPANTS: Thirty-one healthy, untrained postmenopausal women no more than 5 years past menopause. INTERVENTIONS: Supervised resistance training with placebo (PLC, n = 16) or transdermal ET (n = 15) for 12 weeks. MAIN OUTCOME MEASURES: The primary outcome parameter was a cross-sectional area of quadriceps femoris measured by magnetic resonance imaging, and secondary parameters were fat-free mass (dual-energy X-ray absorptiometry), muscle strength, and functional tests. RESULTS: The increase in muscle cross-sectional area was significantly greater in the ET group (7.9%) compared with the PLC group (3.9%) (p < 0.05). Similarly, the increase in whole-body fat-free mass was greater in the ET group (5.5%) than in the PLC group (2.9%) (p < 0.05). Handgrip strength increased in ET (p < 0.05) but did not change in the PLC group. Muscle strength parameters, jumping height, and finger strength were all improved after the training period with no difference between groups. CONCLUSION: The use of transdermal ET enhanced the increase in muscle mass in response to 12 weeks of progressive resistance training in early postmenopausal women.