# Gut Microbiome in Patients with Obstructive Sleep Apnoea
## 3 Results
### Comparison of the OSA and Control Groups Using Bioinformatics Analysis  
Using the centred log ratio data, we performed the LASSO procedure, which resulted in selecting a small subset of microbes in each taxon (see Supplementary Material for the exact species selected). Using this subset, we constructed correlation heatmaps to investigate structural differences between patients with OSA controls at different taxonomic levels. Spearman correlations were used; clustering on the heatmaps was achieved using the Euclidian distance measure. In general, we did not notice major differences in the correlation structure of the data between the examined groups; however, there were some minor findings, especially at the genus level (Figures 1 and 2). In both figures, a clear positively correlated cluster can be identified, mainly composed of genera of the _Prevotellaceae_ family as well as the _Porphylyromonas_ and _Lachnosporaceae_ genera. However, the correlations, especially with the _Porphylyromonas_ genus, are much clearer in the controls, which may signal a disbalance of flora in the OSA group. \begin{table}
\begin{tabular}{l l l l} \hline  & **OSA (\(u\) = 19)** & **Control (\(u\) = 20)** & \(p\) \\ \hline Age (years) & \(55\pm 12\) & \(43\pm 16\) & 0.01 \\ \hline Gender (males\%) & 53 & 35 & 0.27 \\ \hline BMI (kg/m\({}^{2}\)) & 26.3/25.0–28.1/ & 22.8/20.9–27.1/ & 0.07 \\ \hline Smoking (ever\%) & 32 & 10 & 0.09 \\ \hline Hypertension (\%) & 53 & 35 & 0.27 \\ \hline Cardiovascular disease (\%) & 11 & 5 & 0.52 \\ \hline Arrhythmia (\%) & 21 & 10 & 0.34 \\ \hline Diabetes (\%) & 11 & 0 & 0.14 \\ \hline Dyslipidaemia (\%) & 57 & 25 & 0.04 \\ \hline SBP (mmHg) & 135/118–140/ & 120/120–130/ & 0.39 \\ \hline DBP (mmHg) & 80/70–90/ & 75/70–80/ & 0.06 \\ \hline Fasting blood glucose (mmol/L) & 4.8/4.4–5.3/ & 4.4/4.1–5.1/ & 0.32 \\ \hline CRP (mg/L) & 1.8/1.2–4.1/ & 1.6/0.7–4.7/ & 0.66 \\ \hline Total cholesterol (mmol/L) & \(5.5\pm 1.1\) & \(5.4\pm 1.1\) & 0.76 \\ \hline LDL-C (mmol/L) & \(1.0\pm 1.7\) & \(1.0\pm 1.1\) & 0.86 \\ \hline HDL-C (mmol/L) & 1.2/0.9–1.8/ & 1.7/1.3–2.1/ & 0.04 \\ \hline Triglyceride (mmol/L) & 1.7 \(\pm\) 0.6 & 1.1 \(\pm\) 0.4 & \textless{}0.01 \\ \hline TST (min) & 388/361–423/ & 383/357–412/ & 0.56 \\ \hline SPT (min) & 429/413–450/ & 419/397–435/ & 0.59 \\ \hline Sleep\% & 93/89–97/ & 95/85–99/ & 0.66 \\ \hline AHI (1/h) & 8.8/6.5–12.2/ & 1.8/0.9–2.6/ & \textless{}0.01 \\ \hline ODI (1/h) & 6.9/5.2–9.9/ & 0.7/0.2–1.2/ & \textless{}0.01 \\ \hline TST90\% & 0.7/0.2–2.2/ & 0.0/0.0–0.0/ & \textless{}0.01 \\ \hline ESS & 6.9 \(\pm\) 3.5 & 6.8 \(\pm\) 3.5 & 0.93 \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive analysis of the OSA and control groups. Figure 1: Correlation heatmap of control subjects. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **OSA (\(n\) = 19)** & **Control (\(n\) = 20)** & \(p\) \\ \hline Not on any medications (\%) & 47 & 45 & 0.86 \\ \hline ACE inhibitor (\%) & 37 & 10 & 0.04 \\ \hline Angiotensin Receptor Blocker (\%) & 0 & 5 & 0.32 \\ \hline Beta-blocker (\%) & 16 & 10 & 0.59 \\ \hline Calcium channel blocker (\%) & 10 & 0 & 0.13 \\ \hline Diuretic (\%) & 10 & 5 & 0.52 \\ \hline Clopidogrel (\%) & 10 & 5 & 0.52 \\ \hline Aspirin (\%) & 5 & 0 & 0.30 \\ \hline Statin (\%) & 21 & 5 & 0.13 \\ \hline Antidepressant (\%) & 5 & 5 & 0.97 \\ \hline Benzodiazepepines (\%) & 10 & 5 & 0.52 \\ \hline Allopurinol (\%) & 5 & 0 & 0.30 \\ \hline Antidiabetic (\%) & 5 & 0 & 0.30 \\ \hline L-thyrokine (\%) & 5 & 15 & 0.32 \\ \hline Hormonal replacement therapy (\%) & 0 & 5 & 0.32 \\ \hline Laxatives (\%) & 5 & 5 & 0.97 \\ \hline Antihistamine (\%) & 0 & 15 & 0.08 \\ \hline Proton pump inhibitor (\%) & 15 & 5 & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of medications in each group. There was no difference in Shannon indices, either at phylum (\(p\) = 0.35) or genus (\(p\) = 0.77) level. As a next step, we investigated similarities or dissimilarities between the two groups based on the CLR data. Using PERMANOVA we did not observe any significant results.