Data_Sheet_1_Association of plasma propionate concentration with coronary artery disease in a large cross-sectional study.PDF

BackgroundMicrobiome has been linked to the pathogenesis of coronary artery disease (CAD) but data providing direct evidence for an association of short-chain fatty acids (SCFA) with CAD are lacking. This study aimed to evaluate the role of propionate, the most important SCFA in patients with CAD.MethodsWe performed a cross-sectional study enrolling patients admitted for invasive coronary angiography in two university hospitals in Germany. Patients with known or suspected CAD and risk factors for cardiovascular disease were prospectively recruited. Blood sampling was performed after overnight fasting and before invasive procedures. Measurement of propionate was performed by liquid chromatography.ResultsThe study included 1,253 patients (median [IQR], 67 [58–76] years; 799 men [64%]). A total of 739 had invasively confirmed CAD with at least one coronary artery stenosis ≥50% and 514 had exclusion of CAD. CAD patients had significant lower levels of propionate (median 5.75 μM, IQR, 4.1–7.6) compared to the non-CAD groups 6.53 μM (4.6–8.6, p ConclusionThe study provides large-scale in vivo data for the association of propionate to manifest coronary artery disease, independent of other traditional cardiovascular risk factors.</p

Immune function curve parameters.

<p>Immune function curve parameters.</p

Fitting of immune response data.

<p>The calculated values for the immune response (lines) are plotted against the observed values (plus sign). Note the difference of time scales between the rows.</p

Results of the model fitting for the hypotheses on dominant immune modes of action.

<p>Results of the model fitting for the hypotheses on dominant immune modes of action.</p

Modelled time course of BKV viral load clearance for hypothesis VPε-sLTμ.

<p>The results of the model (Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.e005" target="_blank">3</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.e014" target="_blank">5</a>) under hypothesis VPε-sLTμ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.s002" target="_blank">S2 Table</a>) using the parameters in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.t004" target="_blank">Table 4</a> are plotted: viral load (<i>V</i>(t)) is shown as a black line, the immune responses virus production blockage (<i>ε</i>(t)) and accelerated killing of infected cells (<i>μ</i>(t)) are shown in green and red, respectively. Observed viral load values are shown as black plus signs. Please note the difference of time scales between the rows.</p

Viral load and immune response data of the patients.

<p>For each patient, the time course of viral load (black) and the Elispot read-out for each immunogenic BKV antigen (coloured) are plotted. The change of immunosuppressant therapy is marked as a dashed blue line. This change in immunosuppressant therapy is known to foster the development of an immune response against BKV. On the upper row the patients that had not cleared within 700 days after transplantation are shown, while those that achieved clearance in a shorter time appear in the lower row. Please note the difference of time scales between the rows.</p

Parameter for the viral load clearance model under hypothesis VPε-sLTμ.

<p>Parameter for the viral load clearance model under hypothesis VPε-sLTμ.</p

Schematic representation of the ODE model.

<p>Healthy cells produce other healthy cells (rate proportional to <i>g</i>) and die at rate <i>d</i>. The virus triggers the conversion of healthy cells into infected cells (rate <i>β</i>). Infected cells die at rate <i>d·k</i> and produce the virus at rate <i>p</i>, which is cleared at rate <i>c</i>. The immune system can intervene through three different mechanisms: blocking virus production (<i>ε(t)</i>), enhancing infected cell death (<i>μ(t)</i>) and blocking infection (<i>ν(t)</i>).</p

Prediction of non-protection to the A(H1N1)pdm09 influenza strain as function of the combination of age, NSSN and CD4⁺ T cells after the validation study.

<p>The logistic regression model combining baseline CD4⁺ T cell counts with age and NSSN is validated with a high ROC-AUC = 0.85, significant p-value = 0.0056 and high accuracy of 85% for the same age groups (<31 and >49 years) as in the pilot study (left panels in A and B). However, the addition of the middle age group (31–49 years) in the validation study somewhat reduces the accuracy of the prediction when using age as a linear function (center panels in A and B), because donors with these ages respond rather like the younger donors. Transformation of age to a sigmoid based function (with a midpoint age of 50 years) gives the best prediction with accuracy 85% and a highly significant p-value = 0.0000004 when combining both studies (right panels in A and B). The multi-factorial risk profile for non-protection (HAI<40) to the A(H1N1)pdm09 influenza strain is clearly seen (C) when combining the sero-negative vaccinees from both studies (N = 80). Donors with high baseline CD4⁺ T cell counts (>860 cells/μL) are all protected (p = 0.02 for NSSN = 3), as well as young (<50 years) donors with low CD4⁺ counts but NSSN = 1–2. Non-protection is only observed for old donors with low CD4⁺ counts (20%, 50% and 64% for NSSN = 1, 2 and 3 respectively) and for young donors with low CD4⁺ counts and NSSN = 3 (24%). Lastly, a prediction model (D) for the probability of non-protection to the California H1N1 strain is obtained by simulating the continuous contribution of age (after logistic function transformation from 20 years young in blue to 80 years old in red), NSSN and baseline CD4⁺ T cell counts, where the combined effect of the 3 variables can be clearly seen.</p

Hierarchical network representation of immune cell-subset counts at baseline with respect to A(H1N1)pdm09 protection in the pilot study.

<p>We monitored 36 immune cell subpopulations in A(H1N1)pdm09 sero-negative donors and compared donors who became either sero-protected or not at day 21 after vaccination. We observe a number of cell populations for which the counts are significantly different between protected and non-protected donors, specifically on the CD4⁺ T cell axis. The colors indicate the relative median counts of the groups. Significant differences were determined using the Wilcoxon-Test and indicated with * for p<0.05 and ** for p<0.01.</p