Cross-sectional, interventional, and causal investigation of insulin sensitivity using plasma proteomics in diverse populations.

Background: We previously reported significant correlations between a direct measure of insulin sensitivity (IS) and blood levels of proteins measured using the Proximity Extension Assay (PEA) in two European cohorts. However, protein correlations with IS within non-European populations, in response to short-term interventions that improve IS, and any causal associations with IS have not yet been established.

Methods: We measured 1,470 proteins using the PEA in the plasma of 1,015 research participants at Stanford University who underwent one or more direct measures of IS.

Heart failure treatment in the last years of life: A nationwide study of 364 000 individuals.

Aims: In patients with heart failure, treatment patterns in the last years of life have not been assessed at large scale. We aimed to assess whether heart failure treatment patterns up to 5 years prior to death changed over time.

Methods And Results: In a cohort study covering the whole Swedish population, we assessed all heart failure patients who died between 1 July 2007 and 31 December 2020 for evidence-based treatments.

Markers of imminent myocardial infarction.

Myocardial infarction is a leading cause of death globally but is notoriously difficult to predict. We aimed to identify biomarkers of an imminent first myocardial infarction and design relevant prediction models. Here, we constructed a new case-cohort consortium of 2,018 persons without prior cardiovascular disease from six European cohorts, among whom 420 developed a first myocardial infarction within 6 months after the baseline blood draw.

Evaluating regression and probabilistic methods for ECG-based electrolyte prediction.

Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool.

Usefulness of Heart Failure Categories Based on Left Ventricular Ejection Fraction.

Background: Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high.

Methods And Results: We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020.

Heart failure outcomes by left ventricular ejection fraction in a contemporary region-wide patient cohort.

Aims: This study aimed to characterize a contemporary population with subtypes of incident or prevalent heart failure (HF) based on reduced (HFrEF), mildly reduced, or preserved (HFpEF) left ventricular ejection fraction (LVEF) and to assess how outcomes, healthcare, treatments, and healthcare costs vary between each subtype of incident HF.

Methods And Results: Using Swedish data from the CardioRenal and Metabolic disease Heart Failure (CaReMe HF) study, updated to cover a more recent time period, this population-based study characterized patients from Stockholm County, Sweden, with incident HF (patients with a first HF diagnosis between 1 January 2015 and 31 December 2019) or prevalent HF (patients with a first HF diagnosis before 1 January 2020). Patients with incident HF had LVEF measured by echocardiography within ±90 days of their first HF diagnosis, and patients with prevalent HF within 5 years prior to the index date.

Mortality, Health Care Burden, and Treatment of CKD: A Multinational, Observational Study (OPTIMISE-CKD).

Key Points: Newly detected, moderately progressed CKD is associated with high clinical risks and health care costs. Most patients with moderately progressed CKD do not have diabetes and are at the same clinical risk as those with diabetes. Substantial inertia with kidney-protective treatment is observed when moderately progressed CKD is detected.

Plasma Protein Profiling of Incident Cardiovascular Diseases: A Multisample Evaluation.

Background: Proteomic profiling could potentially disclose new pathophysiological pathways for cardiovascular diseases (CVD) and improve prediction at the individual level. We therefore aimed to study the plasma protein profile associated with the incidence of different CVDs.

Methods: Plasma levels of 245 proteins suspected to be linked to CVD or metabolism were measured in 4 Swedish prospective population-based cohorts (SIMPLER [Swedish Infrastructure for Medical Population-Based Life-Course and Environmental Research], ULSAM (Uppsala Longitudinal Study of Adult Men), EpiHealth, and POEM [Prospective Investigation of Obesity, Energy Production, and Metabolism]) comprising 11 869 individuals, free of CVD diagnoses at baseline.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

Aims/hypothesis: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC.

Methods: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay.

Single-cell transcriptome dataset of human and mouse in vitro adipogenesis models.

Adipogenesis is a process in which fat-specific progenitor cells (preadipocytes) differentiate into adipocytes that carry out the key metabolic functions of the adipose tissue, including glucose uptake, energy storage, and adipokine secretion. Several cell lines are routinely used to study the molecular regulation of adipogenesis, in particular the immortalized mouse 3T3-L1 line and the primary human Simpson-Golabi-Behmel syndrome (SGBS) line. However, the cell-to-cell variability of transcriptional changes prior to and during adipogenesis in these models is not well understood.

in vitro .

Adipogenesis is a process in which fat-specific progenitor cells (preadipocytes) differentiate into adipocytes that carry out the key metabolic functions of the adipose tissue, including glucose uptake, energy storage, and adipokine secretion. Several cell lines are routinely used to study the molecular regulation of adipogenesis, in particular the immortalized mouse 3T3-L1 line and the primary human Simpson-Golabi-Behmel syndrome (SGBS) line. However, the cell-to-cell variability of transcriptional changes prior to and during adipogenesis in these models is not well understood.

Fibroblast Growth Factor-23 and Risk of Cardiovascular Diseases: A Mendelian Randomization Study.

Background: Fibroblast growth factor-23 (FGF-23) is associated with a range of cardiovascular and noncardiovascular diseases in conventional epidemiological studies, but substantial residual confounding may exist. Mendelian randomization approaches can help control for such confounding.

Methods: SCALLOP Consortium data of 19,195 participants were used to generate an FGF-23 genetic score.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.

Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches.

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients.