Impact of tongue fat volume on obstructive sleep apnea in non-obese patients 

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Orestes, Gregory S. Hill, Robert Shih, Jacob F. Collen, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4707158/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Evidence suggests excess head and neck adipose tissue contributes to OSA, particularly in obese patients. Surgical treatments are often ineffective in this subset of the population. We sought to investigate the role of tongue fat in the normal and overweight populations. In this prospective cohort study, patients underwent overnight polysomnogram and MRI using a Dixon sequence. Volumetric reconstruction evaluated the size and distribution of tongue fat deposits in subjects with and without sleep apnea. The study included 86 patients; mean age of 42.2 (SD 11.2) years, 16% female. Average BMI 27.5 (SD 2.9), with 18.6% (n=16) normal BMI, 61.6% (n=53) overweight, 19.8% (n=17) obese. Logistic regression lines showed positive associations for BMI and age with AHI. No significant correlation was found between tongue fat volume or fraction and increased AHI nor presence of OSA. Although tongue volume and fat fraction were higher in patients with AHI ≥5, the difference was not statistically significant. This study suggests that tongue fat does not play a significant role in the pathophysiology of OSA in the non-obese (BMI<30) patient population. Therefore, selective treatments targeting tongue fat should focus on obese and morbidly obese patients. Health sciences/Health care/Medical imaging/Magnetic resonance imaging Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Health sciences/Health care/Diagnosis/Body mass index sleep apnea OSA tongue fat MRI Figures Figure 1 Figure 2 Background Obstructive sleep apnea is a chronic medical condition that can lead to excessive daytime sleepiness, cognitive impairment, and an increased risk of motor vehicle accidents. 1,2 The underlying causes of OSA are multifaceted, including anatomic obstruction, inadequate dilation of dilator muscles during sleep, and an oversensitive ventilatory control system. 3 There is a growing body of evidence suggesting that excess adipose tissue within the head and neck contributes to OSA, particularly in obese patients. 4-6 Recent studies have shown that weight loss by lifestyle modification or bariatric surgery in obese patients with OSA patients leads to a reduction in tongue fat volume as observed on MRI, which is associated with a decrease in apnea-hypopnea index, though these studies defined OSA as AHI>10 on polysomnogram. 4,7 While the mechanism through which AHI is reduced remains unclear, it is theorized that reduction of tongue fat may increase the retroglossal airway space or improve the protrusion ability of the tongue. Previous research has suggested that high fat content in muscles may impair their effectiveness. 8 However, a recent study examining the relationship between tongue fat and tongue protrusion found that patients with more severe OSA had larger tongue volumes and greater inspiratory tongue movement, indicating that tongue protrusion ability is not affected by increased tongue fat. 9 Furthermore, one study reported higher levels of tongue fat in obese individuals with OSA compared to obese individuals without OSA, suggesting that the phenotype of OSA in obese patients may differ from non-obese patients. 10 These findings may provide further insight as to why OSA in obese patients is notoriously difficult to treat. 11 Current mainstream hypopharyngeal surgeries include tongue radiofrequency ablation, midline glossectomy, genioglossus advancement, lingual tonsillectomy and hypoid suspension, with hypopharyngeal procedures constituting nearly 35% of sleep apnea inpatient procedures in the US during 2006. 12 Procedures specifically designed to reduce tongue volume, such as tongue radiofrequency, midline glossectomy, and lingual tonsillectomy comprised the majority of these, with tongue base radiofrequency being the most common. 12 However, this procedure is associated with a 96.4% rate of significant postoperative pain. 13 In this study, we aimed to further investigate the role of tongue fat in OSA with our main focus on non-obese patients and hypothesized that there would be a significant difference in tongue fat volume between obese and non-obese patients with OSA. Objective: To assess for an association between tongue fat and AHI in non-obese and mildly obese patients. Study Design & Methods This study was conducted in accordance with the ethical guidelines provided by the Walter Reed National Military Medical Center (WRNMMC) Institutional Review Board (IRB). The study protocol was approved by the IRB (#2018-BOT). Written informed consent was obtained from all participants prior to their inclusion in the study. In this prospective cohort study, patients were recruited immediately after undergoing an overnight type 1 polysomnogram at an American Academy of Sleep Medicine (AASM) accredited lab at a single institution over a 24-month period, in compliance with the approved institutional IRB protocol. The investigators were blinded to whether the patients had OSA. Recruited subjects meeting inclusion criteria then underwent MRI of the face utilizing a two-point Dixon sequence to separate water from fat signal. All MRIs were performed on a 3.0-T scanner within one week of PSG, prior to final interpretation of the patients’ PSG. Image analysis with Amira Software 2020.3.1 (Thermo Fisher Scientific, Waltham, MA) was performed by a neuroradiologist with 15 years of experience in head and neck imaging, who was blinded to patient BMI and PSG results. Eligible participants included adults undergoing a PSG for evaluation of sleep apnea who could obtain an MRI. We excluded those with a history of head and/or neck radiation, history of oropharyngeal cancer, current pregnancy, or those with a relative or absolute contraindication to undergoing MRI. One subject was excluded due to dental braces and severe metallic susceptibility artifact obscuring tongue visualization on face MRI. The study size was determined to be adequate via comparative results by reproducing known, significant associations at the end of the study period. Demographics including age and sex were collected by chart review. Height and weight were measured at the time of PSG, and BMI was calculated from these measurements. BMI was classified according to CDC guidelines, with obesity subdivided into Class 1 (BMI of 30 to < 35), Class 2 (BMI 35 to <40) and Class 3 (BMI of 40 or higher). Outcomes including apnea-hypopnea index (AHI) and OSA were defined in accordance with AASM guidelines. Using a 3.0 Tesla scanner, high-resolution MRI of the face without contrast was performed to image the oral cavity and oropharynx, including an axial T2-weighted Dixon MRI sequence (Figure 1) to separate water from fat signal (in-plane resolution 0.35 x 0.35 mm and slice thickness 3 mm). For each MRI, our neuroradiologist used Amira image analysis software to segment the tongue and to measure total tongue volume (mL) on the in-phase images with multiplanar reformats. Mean signal in the tongue volume on the fat-only images was divided by mean signal in parapharyngeal adipose tissue to yield an estimate of tongue fat fraction (%), which was then multiplied by total tongue volume to yield an estimate of tongue fat volume (mL) for each subject. Multiple logistic regression testing was performed to assess for associations among demographics, BMI, AHI, tongue volume, tongue fat volume, and tongue fat fraction. Volumetric analyses of patients with OSA were compared to their matched controls without. Results are reported in accordance with STROBE guidelines. Results A total of 86 patients met inclusion criteria, with all patients completing both a PSG and MRI. The mean age of this cohort was 42.2 (SD, 11.2) years, 81.4% male (n=70) and 18.6% female (n=16). The average BMI was 27.5 (SD, 2.89), with 18.6% (n=16) normal BMI, 61.6% (n=53) overweight, 19.8% (n=17) obese (Table 1a). All patients had BMI less than 35. Measured variables from PSG data included AHI, from which classification of OSA was determined. Measured variables from Amira image analysis of the face MRIs included tongue volume (mL), mean tongue signal (unitless), mean adipose signal (unitless), tongue fat fraction (%), and tongue fat volume (mL). The median AHI was 7.4 (IQR, 12.9), with 41.9% (n=36) without OSA, 29.1% (25) mild OSA, 16.3% (14) moderate OSA, and 10.5% (9) severe OSA. Fisher Exact testing was performed to assess for significant associations of gender and BMI category with OSA, with no significant associations found. Wilcox rank sum test was performed to assess for significant associations of demographic and measured variables with OSA. Whereas tongue volume, mean tongue signal, tongue fat fraction, and tongue fat volume were not associated with OSA (p>0.05) (Figure 2), demographic variables of age and BMI value were associated with OSA (p<0.05, p<0.005, respectively). To further explore these associations, we plotted logistic regression lines comparing age and BMI to normalized AHI values. We found positive, significant correlations for both age (r 2 0.12, p<0.005, SE 0.02) and BMI (r 2 0.14, p<0.001, SE 0.005). Overall, patients with OSA (AHI ≥5, n=48) demonstrated higher average age (45.4 years) and BMI (28.3) compared those without OSA (AHI<5, n=36) who had an average age (38.2 years) and BMI (26.5) in those with AHI <5. Regression lines were plotted to assess for any correlation of our volumetric measurements with BMI or AHI. No significant correlations were found between BMI and tongue volume nor tongue fat fraction. Likewise, no significant correlations were found between AHI and tongue fat fraction nor tongue fat volume. Finally, we used a linear regression model to assess for correlations between tongue fat fraction and normalized AHI values while adjusting for both BMI and age, and did not find a significant correlation. A similar model assessing correlations between tongue fat fraction and the presence or absence of OSA showed a higher tongue fat fraction in patients with OSA, but this was not statistically significant. Discussion Our study suggests that excess tongue fat does not appear to play a significant role in the pathophysiology of OSA in the non-obese or overweight (BMI < 30) patient population. While we also found that tongue fat volume was not associated with mild obesity (BMI 30-34.9), the mildly obese population in this study was small (n = 17). There were no significant correlations between tongue fat volume and AHI nor BMI. We did find significant positive correlations of OSA with age and BMI in our study population. This correlation has been previously described in the literature, but mainly in very obese patients, which is different from the population we studied here. In contrast to previous studies of tongue fat and its relationship to obstructive sleep apnea, our study is unique in that it assesses this relationship in a lower BMI population. No patients in our cohort had a BMI ≥ 35. In fact, only about 20% of our study population was obese compared to roughly 40% of the US adult population. This likely reflects the well described phenomenon of a lower prevalence of obesity in military communities compared to the general population. 14 When analyzed in context with existing literature on tongue fat in patients with OSA, our data suggests that tongue fat is not a significant contributor to the development of OSA in non-obese or overweight patient populations, as these patients did not have a significant difference in tongue fat volume or tongue fat fraction compared to matched controls without OSA. Given that prior studies have demonstrated a correlation between tongue fat volume and OSA in obese patients—both of which improved with weight loss—it is possible that tongue fat plays a significant role of OSA in the obese population, and may represent a distinct phenotype. 4,15 One of these studies using similar MRI volumetric analyses demonstrated that weight loss in obese patients with OSA resulted in significant reductions in parapharyngeal, retropharyngeal, and base of tongue fat, and these reductions were strongly associated with AHI reduction, with base of tongue fat volume reduction showing the strongest association. 4 However, this study defined OSA as AHI > 10, whereas in this study we maintain the AASM definition of AHI ≥ 5. Our findings suggest that such results would not be expected in a non-obese population, though our study does not suggest what other factors may contribute to a non-obese phenotype of OSA. Future studies should explore other structural and/or functional contributors to OSA in a non-obese population. In the obese population, the role of tongue fat as a contributor to OSA is well described. This may help to explain why obese and morbidly obese patients are difficult to treat, as the most commonly used current therapies do not target tongue fat. 16,17 While weight loss appears to be beneficial, weight loss is a difficult goal to achieve, with an estimated median success rate of 15%. 18 A recent small randomized control trial demonstrated a significant reduction in tongue volume and associated reductions in body weight, BMI, and waist circumference in obese women after treatment with semaglutide (Ozempic) versus placebo. 19 Bariatric surgery has also been described to reduce body weight, improve the volume of the velopharyngeal airway, and decrease the volume of the tongue fat and pharyngeal fat. 20 Current surgical therapies for this population are associated with poor surgical outcomes. 11 The most common procedure used to address tongue volume is radiofrequency ablation, which was demonstrated in a systematic review to reduce ESS and RDI by 31% in the first year after surgery, though no differentiation was made by BMI. 21 A recent systematic review and meta-analysis of tongue base surgery complications demonstrated a mean complication rate of 12.79%, with a 4.4% rate for tongue base radiofrequency ablation and 42.42% rate for tongue base ablation. 22 These treatments often require repeated procedures to obtain a favorable outcome and are associated with high rates of postoperative pain. 13,23 Future therapies that selectively target tongue fat in a minimally invasive and practical way may be helpful in this population. A recent study evaluating the safety and feasibility of selective tongue fat reduction with ultrasound-guided base of tongue ice slurry injection demonstrated that the technology was feasible and well tolerated in a preclinical swine model, without histologic evidence of neurovascular damage nor airway compromise. 24 We envision this becoming an in office procedure where patients with OSA and contributory tongue fat can be treated with a simple injection to selectively target and remove tongue fat. Further study into selective therapies for base of tongue fat reduction may prove to play a key role in OSA management for obese patients. Some limitations of this prospective cohort study include a somewhat small sample size, especially for mildly obese patients and lack of patients with BMI > 35. It is possible that a there is a small difference in tongue fat volume does indeed exist between mildly obese and non-obese patients with OSA, but that the power of our study was inadequate to reveal it given our sample size. However, we were able to demonstrate a statistically significant correlation between BMI and AHI along with age and AHI., a well described correlation in the literature. 25 With respect to the BMI of our population, we did not assess the impact of tongue fat in these moderate-severely obese patients. However, the goal of our study was to explore the role of tongue fat as a contributor to OSA in a lower BMI population. Conclusions Tongue fat does not play a significant role in the pathophysiology of OSA in the non-obese (BMI < 30) patient populations. Future studies should explore the role of tongue fat in moderate and severely obese patients (BMI ≥ 35) with OSA, as well as other structural and/or functional contributors to OSA in the non-obese population. Abbreviations OSA = obstructive sleep apnea; WRNNMC = Walter Reed National Military Medical Center; BMI = body mass index; AHI = apnea-hypopnea index; MRI = magnetic resonance imaging; PSG = polysomnogram; AASM = American Academy of Sleep Medicine Declarations Data Availability Statement: The datasets generated during the current study are not publicly available to due HIPAA regulations but are available from the corresponding author on reasonable, HIPAA-compliant request. Acknowledgements Author Contributions : MIO, RS, JFC, KRF, YK, RWT, LG. contributed to the conception and design of the study. MIO, GSH, RS, JFC, KRF, EAM, NLW, YK, RWT, LG contributed to acquisition, accuracy, completeness, statistical analysis of data, and fidelity to the study protocol. MIO, GSH, RS, JFC, KRF, EAM, KW, NLW, YK, RWT, LG. drafted and revised the manuscript. All authors reviewed, revised, and approved the manuscript prior to submission. Other Contributions : There are no additional contributions to note. Additional Information : There is no supplementary data to report. Additional Information: Conflicts of Interest Summary: No relevant relationships by Jacob Collen No relevant relationships by Kenneth Feehs Advisory Committee Member, Scientific Co-Founder with Brixton Biosciences, 9/2019 Ownership Interest by Lilit Garibyan Consultant relationship with P&G, 10/2021 Consulting fee by Lilit Garibyan Advisory Committee Member relationship with Vyome Therapuetics, 7/2020 Consulting fee by Lilit Garibyan Advisory Committee Member relationship with Aegle Research, 1/2021 Consulting fee by Lilit Garibyan Investor relationship with Clarity Cosmetics, 3/2021, Convertible Note by Lilit Garibyan Co-Founder relationship with EyeCool Therapuetics, 4/2021, Equity by Lilit Garibyan No relevant relationships by Yeva Khachatryan No relevant relationships by Michael Orestes No relevant relationships by Robert Shih No relevant relationships by Richard Thomas No relevant relationships by Nora Watson No relevant relationships by Gregory Hill No relevant relationships by Emily Montgomery No relevant relationships by Katelyn Waring The opinions or assertions contained herein are the private ones of the authors and are not to be construed as official or reflecting the views of the Department of Defense, the Uniformed Services University of the Health Sciences or any other agency of the U.S. Government. 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Normal Mild Moderate Severe Overall (n=36) (n=25) (n=14) (n=9) (n=86) Age Mean (SD) 38.2 (10.9) 44.7 (10.1) 41.9 (10.9) 52.4 (9.03) 42.2 (11.2) Median [Min, Max] 38.0 [20.0, 61.0] 42.0 [30.0, 71.0] 42.0 [25.0, 66.0] 55.0 [31.0, 60.0] 42.0 [20.0, 71.0] Gender Female 8 (22.2%) 3 (12.0%) 3 (21.4%) 1 (11.1%) 16 (18.6%) Male 28 (77.8%) 22 (88.0%) 11 (78.6%) 8 (88.9%) 70 (81.4%) BMI Mean (SD) 26.5 (2.64) 28.0 (2.90) 28.1 (3.27) 29.5 (1.67) 27.5 (2.89) Median [Min, Max] 26.5 [19.6, 32.8] 28.2 [22.5, 34.0] 28.4 [22.1, 33.6] 29.2 [27.3, 32.1] 27.2 [19.6, 34.0] BMI Category Normal 9 (25.0%) 4 (16.0%) 2 (14.3%) 0 (0%) 16 (18.6%) Overweight 23 (63.9%) 15 (60.0%) 8 (57.1%) 6 (66.7%) 53 (61.6%) Obese 4 (11.1%) 6 (24.0%) 4 (28.6%) 3 (33.3%) 17 (19.8%) Height Mean (SD) 69.7 (3.28) 70.3 (3.05) 70.3 (3.75) 68.8 (2.54) 69.9 (3.20) Median [Min, Max] 70.0 [63.0, 76.0] 70.0 [65.0, 76.5] 70.0 [62.0, 76.0] 69.0 [65.0, 73.0] 70.0 [62.0, 76.5] Weight Mean (SD) 186 (22.6) 198 (30.3) 199 (26.7) 198 (11.9) 193 (25.6) Median [Min, Max] 186 [129, 231] 203 [135, 265] 209 [150, 235] 199 [186, 220] 191 [129, 265] Tongue Volume Mean (SD) 94,200 (15,200) 98,300 (17,600) 103,000 (14,700) 97,300 (8,300) 96,800 (15,600) Median [Min, Max] 95,400 [56,700, 121,000] 94,700 [61,900, 140,000] 107,000 [75,700, 124,000] 94,700 [88,000, 111,000] 95,700 [56,700, 140,000] Mean Signal Mean (SD) 637 (404) 623 (452) 732 (418) 589 (426) 644 (419) Median [Min, Max] 742 [161, 1330] 303 [121, 1490] 924 [139, 1250] 312 [213, 1140] 734 [121, 1490] Fat Signal Mean (SD) 2,100 (1,200) 1,980 (1,230) 2,340 (1,150) 1,930 (1,210) 2,080 (1,190) Median [Min, Max] 2,700 [464, 3,710] 1,330 [505, 4,070] 2,880 [741, 3,690] 1,110 [737, 3,510] 2,610 [464, 4,070] Fat Fraction Mean (SD) 0.300 (0.0672) 0.303 (0.0869) 0.295 (0.0724) 0.291 (0.0677) 0.301 (0.0733) Median [Min, Max] 0.302 [0.152, 0.493] 0.276 [0.159, 0.491] 0.299 [0.147, 0.384] 0.286 [0.195, 0.423] 0.302 [0.147, 0.493] Fat Volume Mean (SD) 28,500 (9,080) 29,800 (9,870) 30,200 (7,490) 28,300 (6,530) 29,100 (8,640) Median [Min, Max] 27,600 [13,000, 47,900] 29,100 [14,600, 47,200] 32,600 [16,500, 40,600] 28,000 [19,800, 40,100] 28,200 [13,000, 47,900] Table 1b. Characteristics by presence or absence of sleep apnea. Demographics of study population including age and gender, measured values of BMI, height, and weight, and volumetric analyses including tongue volume, fat signal, fat fraction, and fat volume are stratified by the presence or absence of sleep apnea. Values generated by volumetric analyses are unitless. AHI<5 AHI≥5 Overall (n=36) (n=48) (n=86) Age Mean (SD) 38.2 (10.9) 45.4 (10.6) 42.2 (11.2) Median [Min, Max] 38.0 [20.0, 61.0] 43.0 [25.0, 71.0] 42.0 [20.0, 71.0] Gender Female 8 (22.2%) 7 (14.6%) 16 (18.6%) Male 28 (77.8%) 41 (85.4%) 70 (81.4%) BMI Mean (SD) 26.5 (2.64) 28.3 (2.84) 27.5 (2.89) Median [Min, Max] 26.5 [19.6, 32.8] 28.4 [22.1, 34.0] 27.2 [19.6, 34.0] BMI Category Normal 9 (25.0%) 6 (12.5%) 16 (18.6%) Overweight or Obese 27 (75.0%) 42 (87.5%) 70 (81.4%) Height Mean (SD) 69.7 (3.28) 70.0 (3.18) 69.9 (3.20) Median [Min, Max] 70.0 [63.0, 76.0] 70.0 [62.0, 76.5] 70.0 [62.0, 76.5] Weight Mean (SD) 186 (22.6) 198 (26.3) 193 (25.6) Median [Min, Max] 186 [129, 231] 203 [135, 265] 191 [129, 265] Tongue Volume Mean (SD) 94,200 (15,200) 99,600 (15,300) 96,800 (15,600) Median [Min, Max] 95,400 [56,700, 121,000] 96,600 [61,900, 140,000] 95,700 [56,700, 140,000] Mean Signal Mean (SD) 637 (404) 648 (432) 644 (419) Median [Min, Max] 742 [161, 1,330] 720 [121, 1,490] 734 [121, 1,490] Fat Signal Mean (SD) 2,100 (1,200) 2,080 (1,190) 2,080 (1,190) Median [Min, Max] 2,700 [464, 3,710] 2,450 [505, 4,070] 2,610 [464, 4,070] Fat Fraction Mean (SD) 0.300 (0.0672) 0.299 (0.0782) 0.301 (0.0733) Median [Min, Max] 0.302 [0.152, 0.493] 0.293 [0.147, 0.491] 0.302 [0.147, 0.493] Fat Volume Mean (SD) 28,500 (9,080) 29,600 (8,540) 29,100 (8,640) Median [Min, Max] 27,600 [13,000, 47,900] 28,700 [14,600, 47,200] 28,200 [13,000, 47,900] Additional Declarations Competing interest reported. Lilit Garibyan reports a relationship with EyeCool Therapuetics that includes: equity or stocks. Lilit Garibyan reports a relationship with Clarity Cosmetics that includes: equity or stocks. Lilit Garibyan reports a relationship with Aegle Research that includes: consulting or advisory. Lilit Garibyan reports a relationship with Vyome Therapuetics that includes: consulting or advisory. Lilit Garibyan reports a relationship with P&G that includes: consulting or advisory. Lilit Garibyan reports a relationship with Brixton Biosciences that includes: equity or stocks. All the remaining authors declare no conflict of interest. Supplementary Files ScientificReportsSupplementaryMaterial070824.docx Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Oct, 2024 Reviews received at journal 24 Oct, 2024 Reviews received at journal 13 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers invited by journal 12 Sep, 2024 Editor assigned by journal 12 Sep, 2024 Editor invited by journal 12 Jul, 2024 Submission checks completed at journal 10 Jul, 2024 First submitted to journal 08 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4707158","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334304880,"identity":"f4ee4795-ec8a-4f9d-8b7f-7451a321211e","order_by":0,"name":"Michael I. Orestes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3OMUsDMRTA8ScHmV699YXY+gkKKQepQ/GzWITrUle5oRw31aV077e4yTlHoFPg1o6K0MnhoMstVq+KHYqXrg75QwIv5EcC4PP9xwJgzX5znHvNOpzAReYm9DNogOg8gRMyzs6R/lOwfakTglAYs6uSdJKXVr3WMOrm+m+iDBsOFpaAL+OYtDUP+WY6jBDiqJ0go86cQFpUUMx1Q1AJADN2Ef6xP5BwVxX7dCKbj/EaPp1EdLLvV4CKLLiTeqoIQTsIU+JqTcgXTJFdm8FqEz8KlPfRqo2UZsvfZ6NeiMFblczS68vSPPM6ue0uW8hveDJL93Wfz+fzufsCfElZIpU2SaMAAAAASUVORK5CYII=","orcid":"","institution":"Walter Reed National Military Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"I.","lastName":"Orestes","suffix":""},{"id":334304881,"identity":"99d2e1e3-016d-4b86-b7b9-64b55bb6c6de","order_by":1,"name":"Gregory S. Hill","email":"","orcid":"","institution":"Walter Reed National Military Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"S.","lastName":"Hill","suffix":""},{"id":334304882,"identity":"65ed6682-17c2-409a-8b8a-5f61bb0d7edb","order_by":2,"name":"Robert Shih","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Shih","suffix":""},{"id":334304883,"identity":"c5158140-81de-457b-b23e-ecaf53bc6785","order_by":3,"name":"Jacob F. Collen","email":"","orcid":"","institution":"Walter Reed National Military Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"F.","lastName":"Collen","suffix":""},{"id":334304884,"identity":"d69c24f7-583e-4a48-8f91-7451a38ace04","order_by":4,"name":"Kenneth R. Feehs","email":"","orcid":"","institution":"Wake Forest University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"R.","lastName":"Feehs","suffix":""},{"id":334304885,"identity":"14aab1dd-32ed-4c99-b6ee-fcce81ac5c69","order_by":5,"name":"Emily A. Montgomery","email":"","orcid":"","institution":"Walter Reed National Military Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"A.","lastName":"Montgomery","suffix":""},{"id":334304886,"identity":"98adab98-4f9e-4840-b377-3a4902e73e3c","order_by":6,"name":"Katelyn M. Waring","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Katelyn","middleName":"M.","lastName":"Waring","suffix":""},{"id":334304887,"identity":"c2c7b413-6466-46b1-921d-d8bb261d6a85","order_by":7,"name":"Nora L. Watson","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nora","middleName":"L.","lastName":"Watson","suffix":""},{"id":334304888,"identity":"17d6a7dd-7d5d-4007-bfe8-69c84dbacc5d","order_by":8,"name":"Richard W. Thomas","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"W.","lastName":"Thomas","suffix":""},{"id":334304889,"identity":"1b553167-181c-4b31-b226-059d3999922e","order_by":9,"name":"Lilit Garibyan","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Lilit","middleName":"","lastName":"Garibyan","suffix":""}],"badges":[],"createdAt":"2024-07-08 17:08:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4707158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4707158/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08747-z","type":"published","date":"2025-09-30T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62186376,"identity":"14b7004f-9bbe-4db1-813a-6435179ad30f","added_by":"auto","created_at":"2024-08-10 12:05:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTongue fat tissue volumetric analysis.\u003c/strong\u003eExample screenshot of Amira image analysis software to segment the tongue and to measure total tongue volume (mL) on the in-phase T2-weighted images with multiplanar reformats. Mean signal in the tongue volume on the fat-only images (not shown) was divided by mean signal in parapharyngeal adipose tissue to yield an estimate of tongue fat fraction (%), which was then multiplied by total tongue volume to yield an estimate of tongue fat volume (mL) for each subject. Values are reported in Tables 1a and 1b.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4707158/v1/1adc3d5e55ce616d6f1d854a.png"},{"id":62186375,"identity":"4fb68022-f4c5-4c2e-a77f-ce97e3a85e3d","added_by":"auto","created_at":"2024-08-10 12:05:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTongue fat contribution to AHI in normal weight vs overweight and obese patients. \u003c/strong\u003eBox and whiskers plots of fat fraction (A) and fat volume (B) vs AHI, when stratified into groups of patients of normal weight and overweight + obese patients. No statistically significant difference in tongue fat fraction or fat volume was found between these groups (P\u0026gt;0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4707158/v1/63520577a73ae4cdafcd0432.png"},{"id":92885100,"identity":"961f4e64-1690-4d5a-b174-f0d0539c6432","added_by":"auto","created_at":"2025-10-06 16:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1393011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4707158/v1/aa197a9f-8ba5-4d7a-9201-b82f05444949.pdf"},{"id":62186378,"identity":"c7b4cff2-88e0-4b56-84ce-c9ec0554a572","added_by":"auto","created_at":"2024-08-10 12:05:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":260196,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsSupplementaryMaterial070824.docx","url":"https://assets-eu.researchsquare.com/files/rs-4707158/v1/40085084e82d63ae897d4bde.docx"}],"financialInterests":"Competing interest reported. Lilit Garibyan reports a relationship with EyeCool Therapuetics that includes: equity or stocks. Lilit\nGaribyan reports a relationship with Clarity Cosmetics that includes: equity or stocks. Lilit Garibyan\nreports a relationship with Aegle Research that includes: consulting or advisory. Lilit Garibyan reports a\nrelationship with Vyome Therapuetics that includes: consulting or advisory. Lilit Garibyan reports a\nrelationship with P\u0026G that includes: consulting or advisory. Lilit Garibyan reports a relationship with\nBrixton Biosciences that includes: equity or stocks. All the remaining authors declare no conflict of interest.","formattedTitle":"Impact of tongue fat volume on obstructive sleep apnea in non-obese patients ","fulltext":[{"header":"Background","content":"\u003cp\u003eObstructive sleep apnea is a chronic medical condition that can lead to excessive daytime sleepiness, cognitive impairment, and an increased risk of motor vehicle accidents.\u003csup\u003e1,2\u003c/sup\u003e The underlying causes of OSA are multifaceted, including anatomic obstruction, inadequate dilation of dilator muscles during sleep, and an oversensitive ventilatory control system.\u003csup\u003e3\u003c/sup\u003e There is a growing body of evidence suggesting that excess adipose tissue within the head and neck contributes to OSA, particularly in obese patients.\u003csup\u003e4-6\u003c/sup\u003e Recent studies have shown that weight loss by lifestyle modification or bariatric surgery in obese patients with OSA patients leads to a reduction in tongue fat volume as observed on MRI, which is associated with a decrease in apnea-hypopnea index, though these studies defined OSA as AHI\u0026gt;10 on polysomnogram.\u003csup\u003e4,7\u0026nbsp;\u003c/sup\u003eWhile the mechanism through which AHI is reduced remains unclear, it is theorized that reduction of tongue fat may increase the retroglossal airway space or improve the protrusion ability of the tongue. Previous research has suggested that high fat content in muscles may impair their effectiveness.\u003csup\u003e8\u003c/sup\u003e However, a recent study examining the relationship between tongue fat and tongue protrusion found that patients with more severe OSA had larger tongue volumes and greater inspiratory tongue movement, indicating that tongue protrusion ability is not affected by increased tongue fat.\u003csup\u003e9\u003c/sup\u003e Furthermore, one study reported higher levels of tongue fat in obese individuals with OSA compared to obese individuals without OSA, suggesting that the phenotype of OSA in obese patients may differ from non-obese patients.\u003csup\u003e10\u003c/sup\u003e These findings may provide further insight as to why OSA in obese patients is notoriously difficult to treat.\u003csup\u003e11\u003c/sup\u003e Current mainstream hypopharyngeal surgeries include tongue radiofrequency ablation, midline glossectomy, genioglossus advancement, lingual tonsillectomy and hypoid suspension, with hypopharyngeal procedures constituting nearly 35% of sleep apnea inpatient procedures in the US during 2006.\u003csup\u003e12\u003c/sup\u003e Procedures specifically designed to reduce tongue volume, such as tongue radiofrequency, midline glossectomy, and lingual tonsillectomy comprised the majority of these, with tongue base radiofrequency being the most common.\u003csup\u003e12\u003c/sup\u003e However, this procedure is associated with a 96.4% rate of significant postoperative pain.\u003csup\u003e13\u003c/sup\u003e In this study, we aimed to further investigate the role of tongue fat in OSA with our main focus on non-obese patients and hypothesized that there would be a significant difference in tongue fat volume between obese and non-obese patients with OSA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To assess for an association between tongue fat and AHI in non-obese and mildly obese patients.\u003c/p\u003e"},{"header":"Study Design \u0026 Methods","content":"\u003cp\u003eThis study was conducted in accordance with the ethical guidelines provided by the Walter Reed National Military Medical Center (WRNMMC) Institutional Review Board (IRB). The study protocol was approved by the IRB (#2018-BOT). Written informed consent was obtained from all participants prior to their inclusion in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this prospective cohort study, patients were recruited immediately after undergoing an overnight type 1 polysomnogram at an American Academy of Sleep Medicine (AASM) accredited lab at a single institution over a 24-month period, in compliance with the approved institutional IRB protocol. The investigators were blinded to whether the patients had OSA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecruited subjects meeting inclusion criteria then underwent MRI of the face utilizing a two-point Dixon sequence to separate water from fat signal. All MRIs were performed on a 3.0-T scanner within one week of PSG, prior to final interpretation of the patients’ PSG. Image analysis with Amira Software 2020.3.1 (Thermo Fisher Scientific, Waltham, MA) was performed by a neuroradiologist with 15 years of experience in head and neck imaging, who was blinded to patient BMI and PSG results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEligible participants included adults undergoing a PSG for evaluation of sleep apnea who could obtain an MRI. We excluded those with a history of head and/or neck radiation, history of oropharyngeal cancer, current pregnancy, or those with a relative or absolute contraindication to undergoing MRI. One subject was excluded due to dental braces and severe metallic susceptibility artifact obscuring tongue visualization on face MRI. The study size was determined to be adequate via comparative results by reproducing known, significant associations at the end of the study period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographics including age and sex were collected by chart review. Height and weight were measured at the time of PSG, and BMI was calculated from these measurements. BMI was classified according to CDC guidelines, with obesity subdivided into Class 1 (BMI of 30 to \u0026lt; 35), Class 2 (BMI 35 to \u0026lt;40) and Class 3 (BMI of 40 or higher). Outcomes including apnea-hypopnea index (AHI) and OSA were defined in accordance with AASM guidelines. Using a 3.0 Tesla scanner, high-resolution MRI of the face without contrast was performed to image the oral cavity and oropharynx, including an axial T2-weighted Dixon MRI sequence (Figure 1) to separate water from fat signal (in-plane resolution 0.35 x 0.35 mm and slice thickness 3 mm). For each MRI, our neuroradiologist used Amira image analysis software to segment the tongue and to measure total tongue volume (mL) on the in-phase images with multiplanar reformats. Mean signal in the tongue volume on the fat-only images was divided by mean signal in parapharyngeal adipose tissue to yield an estimate of tongue fat fraction (%), which was then multiplied by total tongue volume to yield an estimate of tongue fat volume (mL) for each subject.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiple logistic regression testing was performed to assess for associations among demographics, BMI, AHI, tongue volume, tongue fat volume, and tongue fat fraction. Volumetric analyses of patients with OSA were compared to their matched controls without. Results are reported in accordance with STROBE guidelines.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 86 patients met inclusion criteria, with all patients completing both a PSG and MRI. The mean age of this cohort was 42.2 (SD, 11.2) years, 81.4% male (n=70) and 18.6% female (n=16). The average BMI was 27.5 (SD, 2.89), with 18.6% (n=16) normal BMI, 61.6% (n=53) overweight, 19.8% (n=17) obese (Table 1a). All patients had BMI less than 35.\u003c/p\u003e\n\u003cp\u003eMeasured variables from PSG data included AHI, from which classification of OSA was determined. Measured variables from Amira image analysis of the face MRIs included tongue volume (mL), mean tongue signal (unitless), mean adipose signal (unitless), tongue fat fraction (%), and tongue fat volume (mL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median AHI was 7.4 (IQR, 12.9), with 41.9% (n=36) without OSA, 29.1% (25) mild OSA, 16.3% (14) moderate OSA, and 10.5% (9) severe OSA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFisher Exact testing was performed to assess for significant associations of gender and BMI category with OSA, with no significant associations found.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWilcox rank sum test was performed to assess for significant associations of demographic and measured variables with OSA. Whereas tongue volume, mean tongue signal, tongue fat fraction, and tongue fat volume were not associated with OSA (p\u0026gt;0.05) (Figure 2), demographic variables of age and BMI value were associated with OSA (p\u0026lt;0.05, p\u0026lt;0.005, respectively). To further explore these associations, we plotted logistic regression lines comparing age and BMI to normalized AHI values. We found positive, significant correlations for both age (r\u003csup\u003e2\u003c/sup\u003e 0.12, p\u0026lt;0.005, SE 0.02) and BMI (r\u003csup\u003e2\u003c/sup\u003e 0.14, p\u0026lt;0.001, SE 0.005). Overall, patients with OSA (AHI ≥5, n=48) demonstrated higher average age (45.4 years) and BMI (28.3) compared those without OSA (AHI\u0026lt;5, n=36) who had an average age (38.2 years) and BMI (26.5) in those with AHI \u0026lt;5. Regression lines were plotted to assess for any correlation of our volumetric measurements with BMI or AHI. No significant correlations were found between BMI and tongue volume nor tongue fat fraction. Likewise, no significant correlations were found between AHI and tongue fat fraction nor tongue fat volume. Finally, we used a linear regression model to assess for correlations between tongue fat fraction and normalized AHI values while adjusting for both BMI and age, and did not find a significant correlation. A similar model assessing correlations between tongue fat fraction and the presence or absence of OSA showed a higher tongue fat fraction in patients with OSA, but this was not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study suggests that excess tongue fat does not appear to play a significant role in the pathophysiology of OSA in the non-obese or overweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30) patient population. While we also found that tongue fat volume was not associated with mild obesity (BMI 30-34.9), the mildly obese population in this study was small (n\u0026thinsp;=\u0026thinsp;17). There were no significant correlations between tongue fat volume and AHI nor BMI. We did find significant positive correlations of OSA with age and BMI in our study population. This correlation has been previously described in the literature, but mainly in very obese patients, which is different from the population we studied here.\u003c/p\u003e \u003cp\u003eIn contrast to previous studies of tongue fat and its relationship to obstructive sleep apnea, our study is unique in that it assesses this relationship in a lower BMI population. No patients in our cohort had a BMI\u0026thinsp;\u0026ge;\u0026thinsp;35. In fact, only about 20% of our study population was obese compared to roughly 40% of the US adult population. This likely reflects the well described phenomenon of a lower prevalence of obesity in military communities compared to the general population.\u003csup\u003e14\u003c/sup\u003e When analyzed in context with existing literature on tongue fat in patients with OSA, our data suggests that tongue fat is not a significant contributor to the development of OSA in non-obese or overweight patient populations, as these patients did not have a significant difference in tongue fat volume or tongue fat fraction compared to matched controls without OSA. Given that prior studies have demonstrated a correlation between tongue fat volume and OSA in obese patients\u0026mdash;both of which improved with weight loss\u0026mdash;it is possible that tongue fat plays a significant role of OSA in the obese population, and may represent a distinct phenotype.\u003csup\u003e4,15\u003c/sup\u003e One of these studies using similar MRI volumetric analyses demonstrated that weight loss in obese patients with OSA resulted in significant reductions in parapharyngeal, retropharyngeal, and base of tongue fat, and these reductions were strongly associated with AHI reduction, with base of tongue fat volume reduction showing the strongest association.\u003csup\u003e4\u003c/sup\u003e However, this study defined OSA as AHI\u0026thinsp;\u0026gt;\u0026thinsp;10, whereas in this study we maintain the AASM definition of AHI\u0026thinsp;\u0026ge;\u0026thinsp;5. Our findings suggest that such results would not be expected in a non-obese population, though our study does not suggest what other factors may contribute to a non-obese phenotype of OSA. Future studies should explore other structural and/or functional contributors to OSA in a non-obese population.\u003c/p\u003e \u003cp\u003eIn the obese population, the role of tongue fat as a contributor to OSA is well described. This may help to explain why obese and morbidly obese patients are difficult to treat, as the most commonly used current therapies do not target tongue fat.\u003csup\u003e16,17\u003c/sup\u003e While weight loss appears to be beneficial, weight loss is a difficult goal to achieve, with an estimated median success rate of 15%.\u003csup\u003e18\u003c/sup\u003e A recent small randomized control trial demonstrated a significant reduction in tongue volume and associated reductions in body weight, BMI, and waist circumference in obese women after treatment with semaglutide (Ozempic) versus placebo.\u003csup\u003e19\u003c/sup\u003e Bariatric surgery has also been described to reduce body weight, improve the volume of the velopharyngeal airway, and decrease the volume of the tongue fat and pharyngeal fat.\u003csup\u003e20\u003c/sup\u003e Current surgical therapies for this population are associated with poor surgical outcomes.\u003csup\u003e11\u003c/sup\u003e The most common procedure used to address tongue volume is radiofrequency ablation, which was demonstrated in a systematic review to reduce ESS and RDI by 31% in the first year after surgery, though no differentiation was made by BMI.\u003csup\u003e21\u003c/sup\u003e A recent systematic review and meta-analysis of tongue base surgery complications demonstrated a mean complication rate of 12.79%, with a 4.4% rate for tongue base radiofrequency ablation and 42.42% rate for tongue base ablation.\u003csup\u003e22\u003c/sup\u003e These treatments often require repeated procedures to obtain a favorable outcome and are associated with high rates of postoperative pain.\u003csup\u003e13,23\u003c/sup\u003e Future therapies that selectively target tongue fat in a minimally invasive and practical way may be helpful in this population. A recent study evaluating the safety and feasibility of selective tongue fat reduction with ultrasound-guided base of tongue ice slurry injection demonstrated that the technology was feasible and well tolerated in a preclinical swine model, without histologic evidence of neurovascular damage nor airway compromise.\u003csup\u003e24\u003c/sup\u003e We envision this becoming an in office procedure where patients with OSA and contributory tongue fat can be treated with a simple injection to selectively target and remove tongue fat. Further study into selective therapies for base of tongue fat reduction may prove to play a key role in OSA management for obese patients. Some limitations of this prospective cohort study include a somewhat small sample size, especially for mildly obese patients and lack of patients with BMI\u0026thinsp;\u0026gt;\u0026thinsp;35. It is possible that a there is a small difference in tongue fat volume does indeed exist between mildly obese and non-obese patients with OSA, but that the power of our study was inadequate to reveal it given our sample size. However, we were able to demonstrate a statistically significant correlation between BMI and AHI along with age and AHI., a well described correlation in the literature.\u003csup\u003e25\u003c/sup\u003e With respect to the BMI of our population, we did not assess the impact of tongue fat in these moderate-severely obese patients. However, the goal of our study was to explore the role of tongue fat as a contributor to OSA in a lower BMI population.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTongue fat does not play a significant role in the pathophysiology of OSA in the non-obese (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30) patient populations. Future studies should explore the role of tongue fat in moderate and severely obese patients (BMI\u0026thinsp;\u0026ge;\u0026thinsp;35) with OSA, as well as other structural and/or functional contributors to OSA in the non-obese population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOSA = obstructive sleep apnea; WRNNMC = Walter Reed National Military Medical Center; BMI = body mass index; AHI = apnea-hypopnea index; MRI = magnetic resonance imaging; PSG = polysomnogram; AASM = American Academy of Sleep Medicine\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are not publicly available to due HIPAA regulations but are available from the corresponding author on reasonable, HIPAA-compliant request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e: MIO, RS, JFC, KRF, YK, RWT, LG. contributed to the conception and design of\u003c/p\u003e\n\u003cp\u003ethe study. MIO, GSH, RS, JFC, KRF, EAM, NLW, YK, RWT, LG contributed to acquisition, accuracy,\u003c/p\u003e\n\u003cp\u003ecompleteness, statistical analysis of data, and fidelity to the study protocol. MIO, GSH, RS, JFC, KRF,\u003c/p\u003e\n\u003cp\u003eEAM, KW, NLW, YK, RWT, LG. drafted and revised the manuscript. All authors reviewed, revised, and\u003c/p\u003e\n\u003cp\u003eapproved the manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOther Contributions\u003c/em\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThere are no additional contributions to note.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdditional Information\u003c/em\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThere is no supplementary data to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflicts of Interest Summary:\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Jacob Collen\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Kenneth Feehs\u003c/p\u003e\n\u003cp\u003eAdvisory Committee Member, Scientific Co-Founder with Brixton Biosciences, 9/2019 Ownership\u003c/p\u003e\n\u003cp\u003eInterest by Lilit Garibyan\u003c/p\u003e\n\u003cp\u003eConsultant relationship with P\u0026amp;G, 10/2021 Consulting fee by Lilit Garibyan\u003c/p\u003e\n\u003cp\u003eAdvisory Committee Member relationship with Vyome Therapuetics, 7/2020 Consulting fee by Lilit\u003c/p\u003e\n\u003cp\u003eGaribyan\u003c/p\u003e\n\u003cp\u003eAdvisory Committee Member relationship with Aegle Research, 1/2021 Consulting fee by Lilit Garibyan\u003c/p\u003e\n\u003cp\u003eInvestor relationship with Clarity Cosmetics, 3/2021, Convertible Note by Lilit Garibyan\u003c/p\u003e\n\u003cp\u003eCo-Founder relationship with EyeCool Therapuetics, 4/2021, Equity by Lilit Garibyan\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Yeva Khachatryan\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Michael Orestes\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Robert Shih\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Richard Thomas\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Nora Watson\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Gregory Hill\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Emily Montgomery\u003c/p\u003e\n\u003cp\u003eNo relevant relationships by Katelyn Waring\u003c/p\u003e\n\u003cp\u003eThe opinions or assertions contained herein are the private ones of the authors and are not to be construed\u003c/p\u003e\n\u003cp\u003eas official or reflecting the views of the Department of Defense, the Uniformed Services University of the\u003c/p\u003e\n\u003cp\u003eHealth Sciences or any other agency of the U.S. Government.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMarshall NS, Wong KK, Liu PY, Cullen SR, Knuiman MW, Grunstein RR. 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Laryngoscope Investigative Otolaryngology. 2022;1‐ 6. doi:10.1002/lio2.9026.\u003c/li\u003e\n \u003cli\u003eMitra, A. K., Bhuiyan, A. R., \u0026amp; Jones, E. A. Association and Risk Factors for Obstructive Sleep Apnea and Cardiovascular Diseases: A Systematic Review. Diseases (Basel, Switzerland). 2021; \u003cstrong\u003e9(4),\u003c/strong\u003e 88. https://doi.org/10.3390/diseases9040088\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 1a. Characteristics by OSA severity.\u0026nbsp;\u003c/strong\u003eDemographics\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eof study population including age and gender, measured values of BMI, height, and weight, and volumetric analyses including tongue volume, fat signal, fat fraction, and fat volume are stratified by OSA severity. Values generated by volumetric analyses are unitless.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"685\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.893428063943162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.893428063943162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.426287744227352%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.893428063943162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.893428063943162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e38.2 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e44.7 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e41.9 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e52.4 (9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e42.2 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e38.0 [20.0, 61.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e42.0 [30.0, 71.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e42.0 [25.0, 66.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e55.0 [31.0, 60.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e42.0 [20.0, 71.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e8 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e3 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e3 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e1 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e16 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e22 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e11 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e8 (88.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e70 (81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e26.5 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28.0 (2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e28.1 (3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e29.5 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e27.5 (2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e26.5 [19.6, 32.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28.2 [22.5, 34.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e28.4 [22.1, 33.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e29.2 [27.3, 32.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e27.2 [19.6, 34.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e9 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e4 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e2 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e16 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e23 (63.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e15 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e8 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e6 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e53 (61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e4 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e6 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e4 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e3 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e17 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e69.7 (3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e70.3 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e70.3 (3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e68.8 (2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e69.9 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e70.0 [63.0, 76.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e70.0 [65.0, 76.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e70.0 [62.0, 76.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e69.0 [65.0, 73.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e70.0 [62.0, 76.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e186 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e198 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e199 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e198 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e193 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e186 [129, 231]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e203 [135, 265]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e209 [150, 235]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e199 [186, 220]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e191 [129, 265]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTongue Volume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e94,200 (15,200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e98,300 (17,600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e103,000 (14,700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e97,300 (8,300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e96,800 (15,600)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e95,400 [56,700, 121,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e94,700 [61,900, 140,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e107,000 [75,700, 124,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e94,700 [88,000, 111,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e95,700 [56,700, 140,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Signal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e637 (404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e623 (452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e732 (418)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e589 (426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e644 (419)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e742 [161, 1330]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e303 [121, 1490]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e924 [139, 1250]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e312 [213, 1140]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e734 [121, 1490]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat Signal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e2,100 (1,200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e1,980 (1,230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e2,340 (1,150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e1,930 (1,210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e2,080 (1,190)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e2,700 [464, 3,710]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e1,330 [505, 4,070]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e2,880 [741, 3,690]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e1,110 [737, 3,510]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e2,610 [464, 4,070]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat Fraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.300 (0.0672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.303 (0.0869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e0.295 (0.0724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.291 (0.0677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.301 (0.0733)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.302 [0.152, 0.493]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.276 [0.159, 0.491]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e0.299 [0.147, 0.384]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.286 [0.195, 0.423]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e0.302 [0.147, 0.493]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat Volume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28,500 (9,080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e29,800 (9,870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e30,200 (7,490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28,300 (6,530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e29,100 (8,640)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81021897810219%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e27,600 [13,000, 47,900]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e29,100 [14,600, 47,200]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.78832116788321%\"\u003e\n \u003cp\u003e32,600 [16,500, 40,600]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28,000 [19,800, 40,100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.35036496350365%\"\u003e\n \u003cp\u003e28,200 [13,000, 47,900]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1b. Characteristics by presence or absence of sleep apnea.\u0026nbsp;\u003c/strong\u003eDemographics\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eof study population including age and gender, measured values of BMI, height, and weight, and volumetric analyses including tongue volume, fat signal, fat fraction, and fat volume are stratified by the presence or absence of sleep apnea. Values generated by volumetric analyses are unitless.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAHI\u0026lt;5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAHI\u0026ge;5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e38.2 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e45.4 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e42.2 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e38.0 [20.0, 61.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e43.0 [25.0, 71.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e42.0 [20.0, 71.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e8 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e7 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e16 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e28 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e41 (85.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e70 (81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e26.5 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e28.3 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e27.5 (2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e26.5 [19.6, 32.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e28.4 [22.1, 34.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e27.2 [19.6, 34.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e9 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e6 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e16 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eOverweight or Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e27 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e42 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e70 (81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e69.7 (3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e70.0 (3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e69.9 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e70.0 [63.0, 76.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e70.0 [62.0, 76.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e70.0 [62.0, 76.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e186 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e198 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e193 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e186 [129, 231]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e203 [135, 265]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e191 [129, 265]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTongue Volume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e94,200 (15,200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e99,600 (15,300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e96,800 (15,600)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e95,400 [56,700, 121,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e96,600 [61,900, 140,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e95,700 [56,700, 140,000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Signal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e637 (404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e648 (432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e644 (419)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e742 [161, 1,330]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e720 [121, 1,490]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e734 [121, 1,490]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat Signal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e2,100 (1,200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e2,080 (1,190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e2,080 (1,190)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e2,700 [464, 3,710]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e2,450 [505, 4,070]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e2,610 [464, 4,070]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat Fraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e0.300 (0.0672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e0.299 (0.0782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e0.301 (0.0733)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e0.302 [0.152, 0.493]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e0.293 [0.147, 0.491]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e0.302 [0.147, 0.493]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat Volume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e28,500 (9,080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e29,600 (8,540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e29,100 (8,640)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.38801261829653%\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e27,600 [13,000, 47,900]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e28,700 [14,600, 47,200]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.870662460567825%\"\u003e\n \u003cp\u003e28,200 [13,000, 47,900]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sleep apnea, OSA, tongue fat, MRI","lastPublishedDoi":"10.21203/rs.3.rs-4707158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4707158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEvidence suggests excess head and neck adipose tissue contributes to OSA, particularly in obese patients. Surgical treatments are often ineffective in this subset of the population. We sought to investigate the role of tongue fat in the normal and overweight populations. In this prospective cohort study, patients underwent overnight polysomnogram and MRI using a Dixon sequence. Volumetric reconstruction evaluated the size and distribution of tongue fat deposits in subjects with and without sleep apnea. The study included 86 patients; mean age of 42.2 (SD 11.2) years, 16% female. Average BMI 27.5 (SD 2.9), with 18.6% (n=16) normal BMI, 61.6% (n=53) overweight, 19.8% (n=17) obese. Logistic regression lines showed positive associations for BMI and age with AHI. No significant correlation was found between tongue fat volume or fraction and increased AHI nor presence of OSA. Although tongue volume and fat fraction were higher in patients with AHI ≥5, the difference was not statistically significant. This study suggests that tongue fat does not play a significant role in the pathophysiology of OSA in the non-obese (BMI\u0026lt;30) patient population. Therefore, selective treatments targeting tongue fat should focus on obese and morbidly obese patients.\u003c/p\u003e","manuscriptTitle":"Impact of tongue fat volume on obstructive sleep apnea in non-obese patients ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 12:05:41","doi":"10.21203/rs.3.rs-4707158/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-30T05:44:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-24T10:39:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-13T15:41:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193116401851351023344308771508899180675","date":"2024-10-08T15:36:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337500734843767278065373531995865657404","date":"2024-09-30T18:17:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-12T13:19:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-12T13:13:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-12T19:05:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-11T03:04:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-08T17:07:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"447a334d-ec2b-42d2-8617-07ba062721a2","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35402953,"name":"Health sciences/Health care/Medical imaging/Magnetic resonance imaging"},{"id":35402954,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"},{"id":35402955,"name":"Health sciences/Health care/Diagnosis/Body mass index"}],"tags":[],"updatedAt":"2025-10-06T16:13:29+00:00","versionOfRecord":{"articleIdentity":"rs-4707158","link":"https://doi.org/10.1038/s41598-025-08747-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-30 15:58:12","publishedOnDateReadable":"September 30th, 2025"},"versionCreatedAt":"2024-08-10 12:05:41","video":"","vorDoi":"10.1038/s41598-025-08747-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-08747-z","workflowStages":[]},"version":"v1","identity":"rs-4707158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4707158","identity":"rs-4707158","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}