Antibody response to oral biofilm is a biomarker for acute coronary syndrome in periodontal disease

Abstract

Cumulative evidence over the last decades have supported the role of gum infections as a risk for future major cardiovascular events. The precise mechanism connecting coronary artery disease (CAD) with periodontal findings has remained elusive. Here, we employ next generation phage display mimotope-variation analysis (MVA) to identify the features of dysfunctional immune system that associate CAD with periodontitis. We identify a fine molecular description of the antigenic epitope repertoires of CAD and its most severe form - acute coronary syndrome (ACS) by profiling the antibody reactivity in a patient cohort with invasive heart examination and complete clinical oral assessment. Specifically, we identify a strong immune response to an EBV VP26 epitope mimicking multiple antigens of oral biofilm as a biomarker for the no-CAD group. With a 2-step biomarker test, we stratify subjects with periodontitis from healthy controls (balanced accuracy 84%), and then assess the risk for ACS with sensitivity 71–89% and specificity 67–100%, depending on the oral health status. Our findings highlight the importance of resolving the immune mechanisms related to severe heart conditions such as ACS in the background of oral health. Prospective validation of these findings will support incorporation of these non-invasive biomarkers into clinical practice.

Differential patterns of cross-reactive antibody response against SARS-CoV-2 spike protein detected for chronically ill and healthy COVID-19 naïve individuals

“…The identification of core consensus sequences was either performed discriminatorily (see below for details), where selected peptide sets were studied for enrichment compared to control group, or non-discriminatorily (see below for details), where enrichment was identified compared to a random-generated peptide set (hypothesis-free). The decision of whether to compare sample peptides to random-generated reference peptide sets or to peptides from matched controls samples was determined by the clinical question of the study 40 , 45 , 46 with parameters chosen to maximise the number of epitope consensuses in the output. In brief, for each clinical group, 100,000s of distinct peptide antigens were used as input, from which 1000s of distinct core epitopes were identified with SPEXS2 algorithm.…”

“…The generation of distinct consensus epitopes (n = 8088) for CAD cohort is described in detail in 46 . The generation of distinct consensus epitopes (n = 4019) for MI cohort is in 45 .…”

“…The sample data on myocardial infarction (MI), breast cancer (BC) and schizophrenia (SZ) samples are described in detail in Pupina et al 2022 45 . The use of multiple sclerosis (MS) and coronary artery disease (CAD) cohort samples was approved by the regional ethics committees (Dno 83/13/03/01/2013 40 and licence no 106/2007 46 , respectively). Use of samples from type II diabetes (T2D) patients was approved by the Ethics Committee of University Hospital of Liege (permit no 2018/78).…”

“…COVID-19 naïve blood samples were collected prior to SARS-CoV-2 emergence (between April 2010 to Dec 2018) and chosen to represent a cross-section of the general uninfected, unexposed, unvaccinated population. Cardiovascular disease (CAD 46 and MI 45 ) and neuropsychiatric disease (FEP and SZ 45 , 47 , 48 ) samples are matched with control samples (Table 1 ) by the design of the original studies. Type II diabetes (T2D), multiple sclerosis (MS) and breast cancer (BC) sub-cohort patient samples have been analysed against selected controls samples (Table 1 ).…”

Possible association between COVID-19 caused stress and periodontal health - a pilot study

“…The main etiological factor is dental plaque [7]. Clinical manifestations of the disease are determined by the nature of the immune response to microorganisms clustered in biofilm [8]. In addition to oral plaque, the onset and progression of the disease are influenced by other local and systemic factors, such as tobacco consumption, viral infections, and diabetes mellitus [9].…”