Relationships Among and Predictive Values of Obesity, Inflammation Markers, and Disease Severity in Pediatric Patients with Obstructive Sleep Apnea Before and After Adenotonsillectomy

Both obstructive sleep apnea (OSA) and obesity are major health issues that contribute to increased systemic inflammation in children. To date, adenotonsillectomy (AT) is still the first-line treatment for childhood OSA. However, the relationships among and predictive values of obesity, inflammation, and OSA severity have not been comprehensively investigated. This prospective study investigated body mass index (BMI), serum inflammatory markers, and OSA severity before and after AT in 60 pediatric patients with OSA. At baseline, differences in levels of interleukin-6, interleukin-9, basic fibroblast growth factor, platelet-derived growth factor-BB, as well as regulated on activation, normal T cell expressed and secreted (RANTES) were significant among the various weight status and OSA severity subgroups. After 3 months postoperatively, the differences in these inflammatory markers diminished along with a decrease in OSA severity while obesity persisted. The rate of surgical cure (defined as postoperative obstructive apnea-hypopnea index < 2.0 and obstructive apnea index < 1.0) was 62%. Multivariate analysis revealed that age, BMI z-score, granulocyte-macrophage colony-stimulating factor, monocyte chemotactic protein-1, and RANTES independently predicted surgical cure. Despite the significant reductions in inflammatory markers and OSA severity after AT, an inter-dependent relationship between obesity and OSA persisted. In addition to age and BMI, several inflammatory markers helped to precisely predict surgical cure.


Participants
We prospectively recruited consecutive children who were referred to the Department of Otorhinolaryngology-Head and Neck Surgery at Linkou Chang Gung Memorial Hospital (Taoyuan, Taiwan) for AT between March 1, 2017 and January 31, 2019. The primary inclusion criteria were: (a) age 5-12 years; (b) obstructive apnea-hypopnea index (OAHI) ≥ 5.0 events/h or OAHI ≥ 1.0 event/h plus at least one OSA-related morbidity (such as elevated blood pressure, daytime sleepiness or learning problems, growth failure and enuresis) [17][18][19]; and (c) agreeing to answer the OSA-18 questionnaire [20] and to undergo blood sampling and/or polysomnography at baseline and after AT. The exclusion criteria were: (a) refusing to participate after instruction; (b) being unable to undergo general anesthesia and AT after preoperative evaluations; and (c) craniofacial, neuromuscular, or chronic inflammatory disorders such as asthma, allergies, eczema, or other atopic/autoimmune disease [21].

Polysomnography
We used standard in-laboratory full-night polysomnography with simultaneous video recording to document OSA parameters at baseline and 3 months after AT. During all nocturnal polysomnography recordings, a family member was required to be present [19]. The AHI was defined as the sum of all obstructive and mixed apneas (≥ 90% decrease in airflow for a duration of ≥ 2 breaths), plus hypopneas (≥ 50% decrease in airflow and either ≥ 3% desaturation or electroencephalographic arousal, for a duration of ≥ 2 breaths), divided by the number of hours of total sleep time [22]. In this study, we further focused on a subgroup of patients with OAHI ≥ 2.0 events/h or obstructive apnea index (OAI) ≥ 1.0 events/h according to a key reference study [23]. Thereafter, the OAHI, OAI, mean oxygen saturation measured by pulse oximetry (SpO 2 ), and minimal SpO 2 were recorded for further comparisons. In the present study, the patients were categorized as having 'severe' (OAHI ≥ 10.0 events/h) or 'non-severe' (OAHI ≥ 2.0 events/h to < 10.0 events/h) OSA [24]. 'Surgical cure' was defined by a reduction in both the OAHI < 2.0 events/h and the OAI < 1.0 event/h (resolution of OSA) after AT [23]. The subjects were further divided into four subgroups based on their weight status and OSA severity: 'non-obese with non-severe OSA' ('nO-nS'), 'non-obese with severe OSA' ('nO-S'), 'obese with non-severe OSA' ('O-nS'), and 'obese with severe OSA' ('O-S'). In this study, 'obesity' was defined as a body mass index (BMI) z-score ≥1.645 [25].

AT
Using the plasma knife technique (PEAK PlasmaBlade; Medtronic Inc, Jacksonville, FL, USA), the principal investigator (L.A.L.) performed all surgeries under general anesthesia with an average hospitalization of 2 days. The detailed surgical techniques have been described elsewhere [27].

Statistical Analysis
Means and standard deviations were used to summarize continuous variables, and numbers with percentages were used to present categorical variables. Student's t-tests and one-way analysis of variance (ANOVA) with post-hoc Tukey's honestly significant difference tests were used to compare continuous variables, and Fisher's exact tests and chi-square tests were used to compare categorical variables in different groups, as appropriate. Pearson correlation test was used to analyze the association between continuous variables whereas Spearman correlation test was used to analyze the association between categorized variables and continuous variables. Paired Student's t-tests were used to compare changes in the variables 3 months postoperatively. Variables with a p-value < 0.05 in the Student's t and ANOVA tests were further dichotomized according to the optimal cut-off value using receiver operating characteristic curves [28]. The dichotomized variables were then assessed using multivariate logistic regression models. All p-values were two-sided, and statistical significance was accepted at p < 0.05. All statistical analyses were performed using SPSS software (version 23; International Business Machines Corp., Armonk, NY, USA).

Patients' Characteristics at Baseline
A total of 60 Taiwanese children from Han ancestry with OSA (14 [23%] girls and 46 [77%] boys) with a mean age of 7.5 ± 2.2 years completed follow-up assessments. The patients' characteristics in the four subgroups categorized by weight status and OSA severity at baseline are shown in Table 1. As expected, there were significant differences in BMI z-scores, OAHI, OAI, mean SpO 2, and minimal SpO 2 ; however, there were no significant differences in age, sex, and OSA-18. The nO-nS subgroup had lower OAHI (compared to nO-S, O-S), lower OAI (compared to nO-S), higher minimal SpO 2 (compared to O-S), and higher minimal SpO 2 (compared to nO-S, O-S). The nO-S subgroup had higher OAHI (compared to nO-nS, nO-S), higher OAI (compared to nO-nS, nO-S), and lower minimal SpO 2 (compared to nO-nS, O-nS). The O-nS subgroup had lower OAHI (compared to nO-S, O-S), lower OAI (compared to nO-S), higher minimal SpO 2 (compared to O-S), and higher minimal SpO 2 (compared to nO-S, O-S). The O-S subgroup had higher OAHI (compared to nO-nS, O-nS), lower minimal SpO 2 (compared to nO-nS, O-nS), and lower minimal SpO 2 (compared to nO-nS, O-nS). Table 2 shows the levels of the inflammatory biomarkers across obesity and OSA severity subgroups at baseline. The average time duration of the sleep study and collection of the markers of inflammation at baseline was 3.9 ± 0.7 weeks. Differences in the levels of IL-6 ( Figure 1d

Associations between Patients' Characteristics and Inflammatory Biomarkers at Baseline
The associations of age with the levels of basic-FGF (r = −0.32, p = 0.01) and interferon-γ (r = −0.29, p = 0.02) were significant. The BMI z-score was significantly related to the level of IL-8 (r = −0.31, p = 0.01). The sex, OAHI, OAI, mean SpO 2 , and minimal SpO 2 were not statistically significantly correlated with the levels of inflammatory biomarkers (data not shown).

Patients' Characteristics After AT
The mean follow-up period after AT was 4.8 ± 2.0 months. Overall, the mean BMI z-score increased from 0.58 ± 2.05 to 0.85 ± 1.5 (p = 0.08), the mean OSA-18 score reduced from 80.7 ± 15.6 to 51.8 ± 13.8 (p = 0.001), and the mean OAHI and OAI significantly reduced from 11.5 ± 13.6 to 2.4 ± 3.1 (p <0.001) and from 2.6 ± 6.0 to 0.3 ± 0.7 (p = 0.01), respectively. The rate of surgical cure was 62% (37/60). The mean SpO 2 and minimal SpO 2 significantly increased from 97.2 ± 1.4% to 97.7 ± 0.8 (p = 0.01) and from 87.9 ± 70.7 to 90.9 ± 3.6 (p = 0.001), respectively. Table 3 shows the postoperative characteristics of the patients across subgroups. The difference in the BMI z-score and OAHI across the four subgroups remained significant (Figure 1a). Postoperative OAHI was significantly lower in the nO-nS subgroup than in the O-nS subgroup (Figure 1b). The rates of surgical cure, in descending order, were 82% for the nO-S subgroup, 75% for the nO-nS subgroup, 46% for the O-S subgroup, and 36% for the O-nS subgroup; the difference in surgical cure rate was significant. Postoperative OSA-18 score, OAI (Figure 1c), mean SpO 2 , and minimal SpO 2 were equivalent.  Furthermore, the OSA-18 score, OAHI, OAI, mean SpO 2 , and minimal SpO 2 significantly improved after AT in the nO-nS subgroup. In the nO-S subgroup, the BMI z-score and minimal SpO 2 significantly increased whereas the OSA-18 score and OAHI significantly reduced after AT. Of note, only the OSA-18 score significantly reduced after AT in the O-nS subgroup. Otherwise, the OSA-18 score, OAHI, OAI, mean SpO 2 , and minimal SpO 2 significantly improved after AT in the O-S subgroup. There were no significant differences in the postoperative levels of inflammatory biomarkers across subgroups after AT (Table 4
Mixed model-2 combined the clinical model and two of the statistically significant inflammatory biomarkers; the best predictive model included age < 7.0 years, BMI z-score < 1.44, MCP-1 < 51.2 pg/mL, and RANTES > 15435.5 pg/mL to independently predict surgical cure. Accordingly, the mixed model-2 achieved an AUC of 0.91 (p < 0.001) with a sensitivity of 92% and specificity of 78% (≥3 predictors) (Figure 2d).

Discussion
At baseline, differences in BMI z-scores, OAHI, OAI, mean SpO 2, and minimal SpO 2 across subgroups were a result of patient allocation and thus within expectation. There were more boys than girls in every group of children with OSA in this study, which was consistent with previous reports [29]. With regards to inflammatory biomarkers, there were significant differences in IL-6, IL-9, basic-FGF, PDGF-BB, and RANTES across subgroups. IL-6 is a myokine produced and released from muscle fibers in response to exercise, and it has been shown to have extensive anti-inflammatory functions [30][31][32][33][34][35]. However, IL-6 has also been shown to stimulate inflammatory processes as a pro-inflammatory cytokine in a wide range of diseases including cancers and metabolic, cardiovascular, neurologic, and autoimmune diseases. In the present study, the O-S subgroup had the significantly highest level of IL-6. This could be explained by previous reports of a positive association between IL-6 level and the presence of OSA [36] and also between IL-6 level and obesity and insulin resistance [37]. There was no significant difference between the nO-S and O-nS subgroups, possibly as a result of risk factors in the two groups counteracting each other. In addition, the lack of differences between the nO-nS and nO-S subgroups may have been due to the small sample size of the nO-S subgroup (n = 11).
IL-9 is a cytokine secreted by CD4+ helper cells and stimulates cell proliferation and prevents apoptosis [38]. It is encoded by the human IL-9 gene, a candidate gene for asthma [39]. Also, some studies discover IL-9 as a determining factor in the pathogenesis of airway hyper-responsiveness [38]. The literature reports bronchial asthma as an important bidirectional contributing factor for pediatric OSA [40][41][42]. If we regarded IL-9 as somehow a surrogate indicator of airway hyper-responsiveness, then the baseline differences across subgroups were interesting. When comparing subgroups with the same OSA severity, non-obese patients had higher levels of IL-9. We speculated that this was reflection of a greater contribution from airway hyper-responsiveness to OSA in non-obese patients, while obesity contributed less to OSA in this subgroup. The difference was only trendy but not significant between some subgroups though, probably again due to the lack of a bigger sample size.
Basic-FGF, also known as FGF2, is a growth factor and signaling protein in the family of FGFs. FGFs are involved in biological processes including cellular proliferation, survival, migration, and differentiation. Of the family members, basic-FGF is believed to have effects on angiogenesis and adipogenesis [43]. Interestingly, a previous study in Japan reported a negative association between serum basic-FGF levels and BMI [44], while another study in China reported the opposite findings [45]. The observations in our study were consistent with the study from Japan, however, this negative association was observed only in non-severe subgroups and not the severe subgroups. A possible explanation is that in patients with severe OSA, disease severity confounded the effects of obesity and disease. Another explanation may be that an association was not demonstrated due to the small sample size.
PDGF is one of the earliest identified growth hormones. It is primarily synthesized, stored, and released by platelets, but also has other origins including smooth muscle cells, activated macrophages, and endothelial cells. PDGF-BB is one of the five isoforms of PDGF, comprised of two B subunits (PDGFB). PDGF-BB represents one of the strongest mitogens and chemokines in pulmonary arterial smooth muscle cells and adventitial fibroblasts. It plays a crucial role in vascular development and remodeling [46]. A study reported that the serum level of PDGF-BB was higher in OSA patients, with a positive association with AHI and percentage of time spent when oxygen saturation is lower than 90%, as well as a negative association with an average saturation of blood oxygen and lowest saturation of blood oxygen [47]. In our study, other than an expected upward trend (not significant) with higher disease severity, PDGF-BB level was highest in the nO-S and lowest in the O-nS. Patients who were not obese but severe in OSA, tended to have more profound consequences regarding local airway responses.
RANTES, also known as chemokine ligand 5 (CCL5), is classified as a chemokine and is chemotactic for T cells, eosinophils, and basophils, recruiting leukocytes into inflammatory sites [48]. Other than its widely known role in human immunodeficiency virus infection, RANTES has been shown to play a role in inflammation in the liver [49] and upper airway [50]. RANTES has also been reported to be crucially involved in intermittent hypoxia (IH)-induced pre-atherosclerotic remodeling. A previous study observed that systemic inflammation induced by IH, a main component of OSA, was associated with early and predominant RANTES/CCL5 alterations in mice, thereby contributing to IH-induced pre-atherosclerotic remodeling [51]. Another study reported an independent positive association between RANTES level and AHI in adults [52].
The patterns of differences across subgroups on PDGF-BB and RANTES displayed interesting and amazing similarities. While obesity and inflammation are the two most important factors in OSA patients, their degree of contribution may differ in adults and children. It is well known that obesity does not play as important a role in the development of OSA co-morbidities in children as it does in adults and that there are other attributable mechanisms. Based on our observations from the study, we speculate that for pediatric patients exhibiting similar OSA severity but with less contribution from obesity, it is possible that their local and systemic inflammatory burdens are even greater than those in their obese peers. This is a very important finding because it suggests how subgroups of pediatric OSA patients may possess different pathogenesis and physiological responses of the diseases, and therefore in need of treatments with different priorities and strategies.
At follow-up at least 3 months after AT, weight status for the overall group and its difference across subgroups remained similar to those at baseline. Disease severity-related parameters improved significantly for the overall group, and differences across subgroups largely diminished. The result suggested that AT seemed to have a profound effect on disease severity, but not weight status. Also, the surgical cure rate tended to be better for the non-obese and more severe patients. As for biomarkers, 19 of the 27 inflammatory markers were significantly reduced after AT, implying an alleviation in inflammatory burdens. The difference across subgroups no longer existed, but the improvement tended to be less significant in the O-nS subgroup. Moreover, residual OSA severities of the O-nS and O-S subgroup were similar (Figure 1a), indicating that there was an inter-dependent relationship between obesity and OSA in obese pediatric patients with OSA.
Based on the literature, the short-term use of continuous positive airway pressure therapy did not seem to improve inflammatory or oxidative biomarkers in OSA patients. Neither did physical activities [53,54]. Our study demonstrated that AT as a procedure to correct adenotonsillar hypertrophy, improved disease severity as well as local and systemic inflammation, without an obvious improvement in obesity. These findings indicated some degree of inflammation caused by adenotonsillar hypertrophy and concomitant OSA, which was supported by a recent study [55]. However, despite being an effective and first-line treatment, the disease resolution rate of AT remained suboptimal [15,16]. The main reason for this is, OSA is a complicated chronic condition with multiple pathogenic factors and co-morbidities. Patients with different phenotypes may differ not only in clinical presentations, but also in pathogenic factors, genetic backgrounds, and disease consequences. This means that the most proper treatment for each patient subgroup may differ.
Based on literature reviews and several of our findings discussed above, we postulate that, given similar disease severity, non-obese pediatric OSA patients, compared to their obese peers, may be stronger in other underlying pathogenic factors, which co-existed with stronger systemic inflammation, as a mediator or a result. These other pathogenic factors could be, for instance, local anatomical structures, central respiratory drives, the ability to utilize oxygen, or susceptibility of hypoxia. As AT is more able to correct these other pathogenic factors than to reduce weight, non-obese pediatric OSA patients are more likely to benefit from their obese peers, in terms of achieving an alleviation in both disease severity and systemic inflammation. This hypothesis is preliminary, yet important and considerable, since it could offer a direction to a better understanding of which and how pediatric OSA patients would benefit from AT. Further investigations would be of great interest.
Last but not least, our study aimed to provide a tool that allowed doctors to be more selective and predictive of their surgical treatments for pediatric OSA patients prior to the procedure. We discovered a few parameters significantly related to surgical cure. Clinical or inflammatory parameters alone did not work as satisfactorily as the mixed models did. The best predictive models included age < 7.0 years, BMI z-score < 1.44, GM-CSF < 0.2 pg/mL, MCP-1 < 51.2 pg/mL, and RANTES > 15,435.5 pg/mL. Previous studies have reported that age [16,56], BMI z-score [16,56], and AHI [16,28,56,57] are predictors of post-AT AHI, in addition to asthma [16], OSA-18 [28], and snoring sound energy [28]. However, GM-CSF, MCP-1, and RANTES as a surgical outcome predictor have never been reported. RANTES, as discussed formerly in the article, is somehow linked to underlying pathogenetic factors, and thus not surprisingly linked to the response of surgical treatment. On the other hand, GM-CSF and MCP-1, which are not significantly associated with weight status or disease severity (data not shown) in our study, are two surprising discoveries concerning outcome prediction. To our best knowledge, there has not been a previous report on relating these two parameters to treatment response. Further studies are warranted to further investigate its clinical application on surgical success prediction.
The main contributions of this study include: (a) reviewing differences in a wide range of inflammatory markers across patient subgroups with different weight status and disease severity, (b) understanding improvements in symptoms and inflammatory burdens across patient subgroups after AT, and (c) developing a predictive model for surgical cure with reasonable performance. The limitations of this study are: (a) selection bias might exist concerning a predominance of boys in our study population, a single Han race of subjects, and a lack of normal control group, which might limit the generalizability on the study; and (b) only short-term outcomes, but not long-term outcomes were assessed in this study. Future research should investigate the long-term effects of obesity and inflammatory biomarkers on OSA severity in studies with a larger sample size to minimize patient bias.

Conclusions
In summary, pediatric OSA patients with different disease severity and weight status demonstrated different degrees of inflammation. Levels of IL-6 were higher in the obese and severe OSA patients. Levels of IL-9 were higher in non-obese patients than obese patients with the same OSA severity. Basic-FGF was negatively associated with BMI in non-severe patients. PDGF-BB and RANTES had very similar patterns: higher in the non-obese patients and tended to increase with disease severity. The overall group benefited from AT in terms of disease severity-related indices and most inflammatory biomarkers. The surgical cure rate tended to be better in the non-obese and more severe patients. We hypothesize that the young pediatric patients with a higher disease severity benefited most from the procedure because AT corrected the underlying causes of disease more than in the other subgroups. However, a combination of age, BMI z-score, MCP-1, and RANTES, or a combination of age, GM-CSF, MCP-1, RANTES could more accurately predict the resolution of OSA after AT. Further studies on different pathophysiological mechanisms for different phenotypes are of interest in the future and have great clinical application value for developing precise treatments for pediatric OSA patients.