Towards clinical application of GlycA and GlycB for early detection of inflammation associated with (pre)diabetes and cardiovascular disease: recent evidence and updates

Cardiometabolic diseases are associated with low-grade inflammation early in life and persists into old age. The long latency period presents opportunities for early detection, lifestyle modification and intervention. However, the performance of conventional biomarker assays to detect low-grade inflammation has been variable, particularly for early-stage cardiometabolic disorder including prediabetes and subclinical atherosclerotic vascular inflammation. During the last decade, the application of nuclear magnetic resonance (NMR) spectroscopy for metabolic profiling of biofluids in translational and epidemiological research has advanced to a stage approaching clinical application. Proton (1H)-NMR profiling induces no destructible physical changes to specimens, and generates quantitative signals from deconvoluted spectra that are highly repeatable and reproducible. Apart from quantitative analysis of amino acids, lipids/lipoproteins, metabolic intermediates and small proteins, 1H-NMR technology is unique in being able to detect composite signals of acute-phase and low-grade inflammation indicated by glycosylated acetyls (GlycA) and N-acetylneuraminic acid (sialic acid) moieties (GlycB). Different from conventional immunoassays that target epitopes and are susceptible to conformational variation in protein structure and binding, GlycA and GlycB signals are stable over time, and maybe complementary as well as superior to high-sensitivity C-reactive protein and other inflammatory cytokines. Here we review the physicochemical principles behind 1H-NMR profiling of GlycA and GlycB, and the available evidence supporting their potential clinical application for the prediction of incident (pre)diabetes, cardiovascular disease, and adverse outcomes.

Magnitude of COVID-19 deaths relative to other leading causes of death: a global analysis

OBJECTIVES

To quantify the burden of death that COVID-19 contributes relative to the top three causes of death for all countries.

DESIGN

We performed uncertainty analyses and created contour plots for COVID-19 mortality to place the number of COVID-19 deaths in context relative to the top three causes of death in each country, across a plausible range of values for two key parameters: case fatality rate and magnitude of under-reporting.

SETTING

All countries that have reported COVID-19 cases to the WHO and are included in the Global Burden of Disease Study by the Institute of Health Metrics and Evaluation.

MAIN OUTCOMES AND MEASURES

Monthly number of deaths caused by COVID-19 and monthly number of deaths caused by the top three causes of death for every country.

RESULTS

For countries that were particularly hard hit during the outbreak in 2020, most combinations of model parameters resulted in COVID-19 ranking within the top three causes of death. For countries not as hard hit on a per-capita basis, such as China and India, COVID-19 did not rank higher than the third leading cause of death at any combination of the model parameters within the given ranges. Up-to-date ranking of COVID-19 deaths relative to the top three causes of death for all countries globally is provided in an interactive online application.

CONCLUSIONS

Estimating the country-level burden of death that COVID-19 contributes relative to the top three causes of death is feasible through contour graphs, even when the actual number of deaths or cases is unknown. This method can help convey importance by placing the magnitude of COVID-related deaths in context relative to more familiar causes of death by communicating when COVID-related deaths rank among the top three causes of death.