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Published in final edited form as: J Sleep Res. 2021 Apr 1;30(5):e13327. doi: 10.1111/jsr.13327

Metabolomic profile associated with obstructive sleep apnoea severity in obese pregnant women with gestational diabetes mellitus: A pilot study

Sirimon Reutrakul 1, Hui Chen 2, Naricha Chirakalwasan 3,4, Suranut Charoensri 5, Ekasitt Wanitcharoenkul 5, Somvang Amnakkittikul 5, Sunee Saetung 5, Brian T Layden 1,6, George E Chlipala 7
PMCID: PMC8446288  NIHMSID: NIHMS1678120  PMID: 33792106

Summary

Obstructive sleep apnoea (OSA) is prevalent in obese women with gestational diabetes mellitus (GDM). The present pilot study explored associations between OSA severity and metabolites in women with GDM. A total of 81 obese women with diet-controlled GDM had OSA assessment (median gestational age [GA] 29 weeks). The metabolic profile was assayed from fasting serum samples via liquid chromatography–mass spectrometry (LC-MS) using an untargeted approach. Metabolites were extracted and subjected to an Agilent 1,290 UPLC coupled to an Agilent 6,545 quadrupole time-of-flight (Q-TOF) MS. Data were acquired using electrospray ionisation in positive and negative ion modes. The raw LC-MS data were processed using the OpenMS toolkit to detect and quantify features, and these features were annotated using the Human Metabolite Database. The feature data were compared with OSA status, apnea–hypopnea index (AHI), body mass index (BMI) and GA using “limma” in R. Correlation analyses of the continuous covariates were performed using Kendall’s Tau test. The p values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate correction. A total of 42 women (51.8%) had OSA, with a median AHI of 9.1 events/hr. There were no significant differences in metabolomics profiles between those with and without OSA. However, differential analyses modelling in GA and BMI found 12 features that significantly associated with the AHI. These features could be annotated to oestradiols, lysophospholipids, and fatty acids, with higher levels related to higher AHI. Metabolites including oestradiols and phospholipids may be involved in pathogenesis of OSA in pregnant women with GDM. A targeted approach may help elucidate our understanding of their role in OSA in this population.

Keywords: oestriols, insulin resistance, intermittent hypoxia, lysophosphatidylcholine, pre-eclampsia, sleep fragmentation

1 |. INTRODUCTION

Obstructive sleep apnoea (OSA) is a common sleep disorder. Cardinal features of OSA include recurrent episodes of complete (apnoea) or partial (hypopnoea) obstruction of the upper airway causing intermittent hypoxaemia, sleep fragmentation resulting in increased oxidative stress and systemic inflammation (Reutrakul & Mokhlesi, 2017). OSA has been linked to increased insulin resistance, diabetes mellitus (DM), hypertension, and cardiovascular disease. Prevalence of OSA in the general population, generally higher in men, increases in parallel with the obesity rate (Heinzer et al., 2015). While young females are generally at lower risk for OSA, anatomical and hormonal changes during pregnancy may predispose women to either a new development or worsening of the pre-existing OSA (Johns et al., 2020). Obesity seems to be a risk factor for OSA during pregnancy, similar to the non-pregnant population. For example, a study found that 40% of obese women had OSA when evaluated in the third trimester, compared to only 14% of those who were normal weight (Pien et al., 2014). OSA in pregnancy has been linked to adverse pregnancy outcomes including increased risk of gestational hypertension, pre-eclampsia, gestational DM (GDM), caesarean section, small for gestational age infants, postoperative wound infection, longer hospital stays, and rare medical and surgical complications (Bourjeily et al., 2017; Liu et al., 2018; Luque-Fernandez et al., 2013; Pamidi et al., 2016; Wanitcharoenkul et al., 2017).

Several pathophysiological processes are involved in OSA-related complications. Intermittent hypoxia leads to increased sympathetic neural activity, oxidative stress, systemic inflammation, and activation of hypothalamic–pituitary–adrenal (HPA) axis. These mechanisms predispose individuals to hypertension, DM, and cardiovascular disease (Heinzer et al., 2015; Johns et al., 2020; Reutrakul & Mokhlesi, 2017; Wang et al., 2013). While the mechanistic studies in pregnant population are scarce, small studies showed that sleep disruption was associated with elevated serum tumour necrosis factor-α (TNF-α) (Okun et al., 2007), and that OSA was related to blunted cortisol awakening response (Bublitz et al., 2018). These may contribute to adverse maternal and fetal outcomes.

Metabolomics, high-sensitivity, high-throughput profiling methods, can help elucidate the molecular signature of OSA and further our understanding of the pathophysiological mechanisms (Lebkuchen et al., 2020; Patti et al., 2012). A few studies have explored the association between metabolites in those with and without OSA, using gas chromatography or liquid chromatography coupled to mass spectrometry (GC-MS or LC-MS) (Ferrarini et al., 2013; Lebkuchen et al., 2018; Xu et al., 2016). Differences in metabolites between the two groups were found, e.g. platelet-activating factors (Ferrarini et al., 2013), lysophospholipids (Ferrarini et al., 2013; Lebkuchen et al., 2018), fatty acid derivatives, and branch chain amino acids (Xu et al., 2016). Another study found that salivary phosphatidylcholine inversely correlated with OSA severity in men, suggesting its role in reducing surface tension and improving airway patency (Kawai et al., 2013). Some of these metabolites helped predict the presence of OSA and could potentially serve as screening tools for OSA detection (Lebkuchen et al., 2018). Further, studies have previously explored biomarkers in several pathways associated with GDM, including amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, and fatty acid metabolism (Chen et al., 2018).

To date, there have been no studies exploring metabolomics profile in pregnant women with OSA. The present pilot study aimed to find associations between metabolites and the presence and severity of OSA in obese pregnant women with diet-controlled GDM. This group was chosen as obesity and GDM were both known to be associated with OSA, where these results provide a first examination of metabolomics for OSA in pregnancy.

2 |. METHODS

This is a secondary analysis of a study of obese pregnant women with diet-controlled GDM who underwent assessment of OSA in the late second to early third trimester as previously reported (Wanitcharoenkul et al., 2017). The data presented in this manuscript were the baseline data, prior to interventions, of a randomised controlled study exploring the effect of OSA treatment on glucose metabolism in women with GDM and OSA (www.ClinicalTrials.gov NCT02108197). In brief, pregnant women with a singleton pregnancy, gestational age (GA) between 24 and 34 weeks and pre-pregnancy body mass index (BMI) of ≥25 kg/m2, with GDM diagnosed during routine prenatal care per the International Association of the Diabetes and Pregnancy Study Groups criteria (Metzger et al., 2010) were included. Exclusion criteria were history of DM, sleep disorders, severe pulmonary, cardiac, renal diseases or steroid use, active neurological or psychiatric disorders, use of medications affecting sleep or glucose metabolism, smoking, alcohol consumption >7 drinks/week or caffeine >400 mg/day, performing shift work, use of opioids/narcotics, α-blockers, clonidine, methyldopa or nitroglycerin. This study was approved by the Ethical Clearance Committee, Faculty of Medicine Ramathibodi Hospital (ID 11–56-39 and 10–60-49). Written informed consent was obtained from all patients.

2.1 |. Demographics and OSA assessment

Age and pre-pregnancy weight were obtained. OSA was assessed using a United States Food and Drug Administration (FDA)-approved portable diagnostic device, Watch-PAT200 (Itamar Medical, Caesarea, Israel), which has been validated against polysomnography (PSG) in pregnancy (O’Brien et al., 2012). The device measures changes in peripheral arterial tone and oxygen saturation, along with a built-in actiography to measure sleep time. The severity of OSA was assessed by peripheral arterial tonometry (PAT) Apnea–Hypopnea Index (AHI), which is automatically generated by the software, using changes in the PAT. OSA is considered present if the AHI is ≥5 events/hr. The device cannot differentiate obstructive from central apnoea events; however, central apnoea is considered uncommon in pregnancy (Bourjeily, Sharkey, et al., 2015).

2.2 |. Metabolomics analysis using LC-MS

Fasting serum samples were obtained after an overnight fast immediately after OSA assessments. Participants fasted after midnight and samples were obtained in the morning (generally between 08:00 and 09:00 hours) in red-top tubes. Samples were centrifuged, serum separated and stored at −80°C until assayed. The metabolic profile was assayed via LC-MS using an untargeted approach. The metabolites from each of the samples were extracted using a mixture of methanol and ethanol (2:1). A 500 μl aliquot of this solvent mixture extract containing internal standards (Met-Arg-Phe-Ala pentapeptide [MRFA] and aminobiphenyl) was added to 50 μl of each sample, followed by vortex for 20 s and rotation for 15 min at 4°C. The samples were then centrifuged at 4°C for 15 min at 10,000 rpm. The supernatant were placed into separate glass vials and dried by nitrogen.

The reconstituted samples in mobile phase A were subjected to MS using an Agilent 1290 Infinity LC System coupled to an Agilent 6545 Accurate mass quadrupole time-of-flight (Q-TOF) with a dual Agilent Jet Stream source. Data were acquired using electrospray ionisation (ESI) in positive and negative ion modes using modified polar reverse phase C18 column (Agilent Poroshell 120 EC, 2.1 × 100 mm, 1.9 μm) using the following gradient: 2%–55%B 0–11 min, 55%–95%B 11–13 min, 95%B 13–17 min (A: 0.1% formic acid in water, B: 0.1% formic acid in acetonitrile). Source parameters were as follows: gas temperature (300°C), drying gas (11 l/min), nebuliser (35 pounds per square inch [psi]/241.3 kPa), sheath gas temperature (350°C), sheath gas flow (12 l/min ), capillary voltage (VCap; 3,000 V), and fragmentor (145 V). Data were collected using a scan speed of 3 MS spectra/s. A Quality Control sample (pooled from all samples) was run every 20 randomised samples with the same conditions as all the samples, indicating stable instrument performance during the whole sample analysis (e.g. low relative standard deviation% value of retention and peak area under the curve, stable mass accuracy as shown in Figures S1–S3; Ancillotti et al. 2019).

2.2.1 |. Basic processing

Feature detection and basic quantitation of raw LC-MS data were performed using the OpenMS toolkit separately for each acquisition mode, i.e. positive and negative (Röst et al., 2016). Briefly, raw LC-MS data were filtered to retain spectra between 46 and 1,020 s. The LC-MS features were then detected using the following peak shape parameters; noise threshold of 1,000 counts, expected full-width at half maximum (fwhm) of 5 s, minimum fwhm of 2 s and maximum fwhm of 30 s; and an isotopic filtering model consistent with non-halogenated metabolites with an allowed mass error of 20 parts per million (ppm). Features were quantified using integrated area of extracted ion chromatograms. The resulting feature table for each sample was then aligned using a POSE clustering approach considering a maximum of 1,000 features with maximum mass-to-charge (m/z) ratio distances of 0.5 and maximum retention time distance fraction of 0.1. The aligned feature tables were combined into a single consensus feature table using a maximum m/z difference of 20 ppm and a maximum retention time distance of 30 s.

The resulting features were annotated using the Human Metabolome Database (HMDB) as a reference (Wishart et al., 2018). Annotation searches were bounded by a maximum difference of 20 ppm using the monoisotopic mass of the feature. The annotation searches also considered the detected charge state and the possible gas phase adducts of [M+H]+, [M+Na]+, [M+NH4]+, [2M+H]+ and [M+2H]2+ for positive mode features and [M-H]-, [M+HCO2]−, [2M-H]− and [M-2H]2− for negative mode features.

2.2.2. Differential analysis

Prior to differential analysis, the consensus feature tables were filtered to remove features that were present in <40% of the samples. Differential abundance statistics (p value) were computed using generalised linear models assuming a Gaussian distribution. The fit of the model was tested using the F test. The feature data were compared with the OSA status, AHI, BMI and GA using “limma” in R. One subject was excluded from the analysis due to being an outlier (AHI >5 SDs of the mean). Nominal p values were adjusted for multiple testing using the false discovery rate (FDR) correction of Benjamini and Hochberg (Benjamini & Hochberg, 1995).

3 |. RESULTS

A total of 81 women underwent OSA assessment at a median (interquartile range [IQR]) gestational age of 29.0 (26.0, 31.8) weeks (Wanitcharoenkul et al., 2017). The mean (SD) age was 31.3 (6.1) years and the median (IQR) pre-pregnancy BMI was 28.8 (26.5, 32.1) kg/m2. The median (IQR) AHI of all women was 5.3 (1.9, 9.5) events/hr. A total of 42 women (51.8%) had OSA, with a median (IQR) AHI of 9.1 (6.3, 11.9) events/hr.

Overall, there were 20,223 and 12,851 features detected in the positive and negative mode datasets, respectively. Of these features, 12,881 and 5,508 features could be annotated in each of the datasets. After filtering, there were 2,350 (1,806 annotated) and 2,317 (1,102 annotated) features in the datasets.

There were no significant differences in metabolomics profiles between those with and without OSA, when the feature data were modelled against the OSA status, AHI, BMI and GA using “limma” in R. However, differential analyses modelling of the total AHI along with GA and pre-pregnancy BMI found 12 features that significantly associated with the AHI (Table 1). Some of these features could be annotated to oestradiol/oestriol, lysophospholipids (lysophosphatidylcholine and lysophosphatidylethanolamine), long-chain acylcarnitine (e.g. L-palmitoylcarnitine, oleoylcarnitine) and fatty acids (e.g. methylglutaric acid) with higher levels related to higher AHI (Table 1). Figure 1 illustrates the relationship of a few of these features to the AHI.

TABLE 1.

List of significant features as compared with total AHI

Feature q value (direction) R2 Adduct Putative annotations
169.047640@103.02[] 0.03 (+) 0.178 [M+Na]+ 2-Methylglutaric acid
Adipic acid
Methylglutaric acid
Monomethyl glutaric acid
2,2-Dimethylsuccinic acid
Solerol
(S)-2-Aceto-2-hydroxybutanoic acid
Dimethyl succinate
347.183481@774.94[] 0.03 (−) 0.191 [M+Na]+ Lactapiperanol D
Cibaric acid
370.297372@812.01[] 0.05 (+) 0.141 [2M+NH4]+ (Cyclohexylmethyl)pyrazine
400.342656@847.29[1] 0.05 (+) 0.133 [M+H]+ L-Palmitoylcarnitine
[M+NH4]+ Monoacylglyceride(0:0/20:2(11Z,14Z)/0:0)
Monoacylglyceride(20:2(11Z,14Z)/0:0/0:0)
Persenone B
Lepidiumterpenyl ester
414.321072@802.65[] 0.01 (+) 0.204 [M+H]+ 3-Hydroxy-9-hexadecenoylcarnitine
426.358347@856.73[1] 0.04 (+) 0.148 [M+H]+ Oleoylcarnitine
Vaccenyl carnitine
Elaidic carnitine
11Z-Octadecenylcarnitine
438.298225@836.63[] 0.04 (+) 0.171 [M+H]+ Lysophosphatidylethanolamine(16:1)
[M+NH4]+ Cyclic phosphatidic acid(18:0/0:0)
480.345134@842.60[] 0.04 (+) 0.165 [M+H]+ Lysophosphatidylcholine(P-16:0)
[M+NH4]+ Hydroxysintaxanthin 5,6-epoxide
546.353511@878.63[1] 0.05 (+) 0.128 [M+H]+ Lysophosphatidylcholine(20:3(5Z,8Z,11Z))
Lysophosphatidylcholine(20:3(8Z,11Z,14Z))
[M+Na]+ Lysophosphatidylcholine(18:0)
Lysophosphatidylcholine(0:0/18:0)
550.387287@891.24[] 0.05 (+) 0.136 [M+H]+ Lysophosphatidylcholine(20:1(11Z))
Phosphatidylcholine(18:1(9Z)e/2:0)
569.341371@827.27[] 0.01 (+) 0.204 [2M+H]+ 6,7-Dihydro-4-(hydroxymethyl)-2-(p-hydroxyphenethyl)-7-methyl-5H-2-pyrindinium
594.376938@848.13[1] 0.04 (+) 0.147 [2M+NH4]+ Estriol
2-Hydroxyestradiol
16b-Hydroxyestradiol
17-Epiestriol
16,17-Epiestriol
4-Hydroxyestradiol
O-Geranylvanillin
2-Polyprenyl-3-methyl-6-methoxy-1,4-benzoquinone
4-hydroxystradiol
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FIGURE 1.

FIGURE 1

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Relationship between example metabolites and the apnea–hypopnea index (AHI). Top left: Putative annotation: Lysophosphatidylethanolamine(16:1). Top right: Putative annotation: Lysophosphatidylcholine(P-16:0). Middle left: Putative annotations: Lysophosphatidylcholine (20:3(5Z,8Z,11Z)), lysophosphatidylcholine (20:3(8Z,11Z,14Z)), lysophosphatidylcholine(18:0), lysophosphatidylcholine(0:0/18:0). Middle right: Putative annotations: Lysophosphatidylcholine(20:1(11Z)), phosphatidylcholine(18:1(9Z)e/2:0). Bottom: Putative annotations: Oestriol, 2-hydroxyestradiol, 16beta-hydroxyestradiol, 17-epiestriol, 16,17-epiestriol, 4-hydroxyestradiol, O-geranylvanillin, 2-polyprenyl-3-methyl-6-methoxy-1, 4-benzoquinone, 4-hydroxyestradiol

4 |. DISCUSSION

To our knowledge, the present study is the first exploring metabolomics changes in OSA during pregnancy. In the present pilot study of obese women with diet-controlled GDM, untargeted metabolomics profiles in the late second trimester did not demonstrate differences between those with and without OSA. However, we found that there were metabolites significantly associated with OSA severity. These metabolites annotated to oestradiol/oestriol, lysophospholipids, long-chain acylcarnitine and fatty acids, providing preliminary results of pathophysiological pathways involved in OSA during pregnancy.

Only a few studies have explored the changes in metabolomics profiling in adult patients with OSA (Ferrarini et al., 2013; Lebkuchen et al., 2018; Xu et al., 2016) and one study was performed in a paediatric population (Xu et al., 2018). Our present results are in agreement with these data, which revealed alterations in phospholipids and fatty acid metabolisms (Ferrarini et al., 2013; Lebkuchen et al., 2018; Xu et al.,,2016, 2018). A study by Lebkuchen et al., in 37 adult men with OSA and 16 controls using LC-MS and lipidomic analysis, found elevated glycerophosphocholines, lysophosphocholines, and glycerophosphoethanolamines in those with OSA compared to controls (Lebkuchen et al., 2018). Another study that compared participants with PSG-confirmed OSA (n = 60), simple snorers (n = 30) and normal subjects (n = 30), found 18 metabolites that differed significantly between those with OSA and simple snorers (Xu et al., 2016). Prominently, it was observed that fatty acids and phospholipid biosynthesis pathways were influenced in those with OSA (Xu et al., 2016). Ferrarini et al. utilised LC-MS in 33 patients with sleep apnoea and hypopnoea syndrome and found that fatty acids levels were higher in those with more severe sleep apnoea, while phosphatidylcholines and lysophosphatidylcholines were lower than those with less severe sleep apnoea (Ferrarini et al., 2013). Untargeted urine metabolomics analysis was performed in 30 paediatric patients with OSA and 30 control subjects, and revealed 57 metabolites that distinguished the two groups, including those involved in amino acid and fatty acid metabolism (Xu et al., 2018). These metabolomics alterations could be direct results of intermittent hypoxia, which is a cardinal feature of OSA. Animal experiments support some of these findings, e.g. obese mice exposed to chronic intermittent hypoxia were found to have upregulation of multiple genes controlling fatty acid and phospholipid synthesis (Li et al., 2005). Besides intermittent hypoxia, OSA is often associated with insufficient sleep and sleep fragmentation (Reutrakul & Mokhlesi, 2017), and these may additionally contribute to changes in metabolomics profile seen in our present study. In a nested case–control study of coronary heart disease in the Women’s Health Initiative (n = 1,956), poor self-reported sleep quality was associated with multiple lipid-derived metabolites including elevated levels of phosphoethanolamine (Huang et al., 2019). Experimental total and partial sleep deprivation in mice and human have also been shown to result in elevated levels of acylcarnitine (Davies et al., 2014; Shigiyama et al., 2018; van den Berg et al., 2016), and phospholipids and phosphatidylcholines (Chua et al., 2015; Davies et al., 2014). Further, irregular sleep was found to be significantly associated with elevated levels of acylcarnitines, phosphatidylcholines, and lysophosphatidylcholines (Papandreou et al., 2019). Collectively, these data, along with our present results, support that OSA and sleep disturbances are associated with alterations in metabolomics features, especially those involved in phospholipid metabolism.

Obstructive sleep apnoea is known to increase cardiovascular risks and insulin resistance, and changes in metabolomics profiling in OSA may play a role in this relationship. For example, elevated levels of lysophosphatidylcholine could be a result of increased activity of an enzyme lipoprotein-associated phospholipase A2 (Lp-PLA2), which is involved in fatty acid metabolism (Kheirandish-Gozal et al., 2017). Lp-PLA2 is recognised as a marker of inflammation and cardiovascular disease that may play a role in the formation of atheromatous plaque (Bhatti et al., 2010). Lp-PLA2 activity was found to be elevated in children with OSA, which was linked to endothelial dysfunction (Kheirandish-Gozal et al., 2017). Further, increased Lp-PLA2 and lysophosphatidylcholine during pregnancy has been linked to pregnancy-induced hypertension and pre-eclampsia (Balcı Ekmekçi et al., 2015; Okumura et al., 1999; Schott et al., 2012), conditions known to be associated with OSA. Alterations in metabolomics profiling in the present study, exclusively conducted in women with GDM, could also be related to abnormal glucose metabolism. This was supported by the findings that an increase in certain lysophosphatidylcholines, especially LPC 18:0, early in pregnancy was a predictor of GDM development (Liu et al., 2020). Taken together, disturbances in phospholipid metabolism could potentially explain the link between OSA and pregnancy-related complications including pre-eclampsia and GDM.

In the present study, it was found that increasing severity of OSA was related to higher levels of oestradiol/oestriol. Oestrogen can induce mucosal oedema and potentially worsen nasal congestion and snoring during pregnancy (Ayyar et al. 2018). However, a study in pregnant women found that serum oestriol levels were lower in those with OSA than controls (Bourjeily, Butterfield, et al., 2015). The authors suggested that this was indicative of lower fetoplacental wellbeing in pregnant women with OSA (Bourjeily, Butterfield, et al., 2015). In non-pregnant women, lower serum oestradiol level was also found to be associated with more severe OSA (Netzer et al., 2003). This seemingly conflicting evidence from ours could potentially be a result of interactions between changes in oestriol in OSA versus GDM. For example, a large cohort study in California of >100,000 women found that increased levels of second trimester unconjugated oestriol was associated with GDM (Snyder et al., 2020). Similarly, another study in Korea of 1,553 women also found that serum unconjugated oestriol >95th percentile during the first trimester was significantly associated with GDM risks (Hur et al., 2017). Thus, the pathophysiological role of oestriol, if any, in women with OSA and GDM warrants further exploration.

Our present study has the strengths of being performed exclusively in women with GDM, with a relatively large sample size. The limitations include the use of untargeted approach only, and further investigations with a targeted approach will likely yield more specific information. While metabolites were found to be associated with OSA severity, the differences between OSA and non-OSA were not found, possibly due to the mild degree of OSA typically found in pregnancy (Facco et al., 2017). In addition, we only included obese participants based on their pre-pregnancy BMI. Further studies should also focus on the changes in metabolites and OSA-related pregnancy complications to better understand the potential pathophysiological links. How OSA treatment, generally with continuous positive airway pressure, changes these metabolites and how they are linked to clinical outcomes should also be explored.

In summary, untargeted metabolomics profiling during pregnancy in women with GDM demonstrated increased oestriol levels and alterations in phospholipids metabolism, especially an increase in lysophosphatidylcholine, and OSA severity. This could potentially explain the link between OSA and pregnancy-related complications. Further targeted approaches in larger samples are needed to confirm these findings.

Supplementary Material

fS1-S3

ACKNOWLEDGEMENT

This study was funded by the Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand, and the Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA. Bioinformatics analysis in the project described was performed by the UIC Research Informatics Core, supported in part by NCATS through Grant UL1TR002003.

Funding information Mahidol University; University of Illinois at Chicago; NCATS, Grant/Award Number: UL1TR002003

Footnotes

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section.

DATA AVAILABILITY STATEMENT

Data are available upon request

CONFLICT OF INTEREST

SR received speaker fee from Becton Dickinson, outside the submitted work. All other authors declared no conflict of interest.

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