Associations between Fat Distribution and Obstructive Sleep Apnea Severity Among Individuals with Type 2 Diabetes: An MRI-based study

Associations between Fat Distribution and Obstructive Sleep Apnea Severity Among Individuals with Type 2 Diabetes: An MRI-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Associations between Fat Distribution and Obstructive Sleep Apnea Severity Among Individuals with Type 2 Diabetes: An MRI-based study Kim Ahtola, Martin Ulander, Jonas Agholme, Farida Amiri, Pontus Henriksson, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7420558/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Study Objectives Obstructive Sleep Apnea (OSA) affects 1 billion people globally, with a male-to-female ratio of 2:1. While obesity is a major risk factor, sex differences in fat distribution may influence OSA risk. This study examined the relationship between visceral (VAT), subcutaneous (ASAT), and total abdominal fat (TAAT) and OSA severity in patients with type 2 diabetes (T2DM). Methods We enrolled 164 T2DM patients (97 males, 67 females) from the EPSONIP-Sleep study, of whom 151 with complete MRI and polygraphy data were analyzed. MRI-measured VAT, ASAT, and TAAT volumes and z-scores (normalized for sex and height) were assessed. OSA severity was evaluated via home respiratory polygraphy. Associations were analyzed using linear and logistic regression, descriptive statistics, and Kruskal-Wallis tests. Results In males, higher VAT, ASAT, and TAAT volumes correlated with OSA severity. Logistic regression identified ASAT (OR = 1.2/L, p = 0.048) and TAAT (OR = 1.1/L, p = 0.023) as predictors of moderate-to-severe OSA; VAT showed a non-significant trend. These associations were not significant after adjusting for BMI. Conclusion Sex differences influence the relationship between fat and OSA severity. In males, increased adiposity was linked to higher OSA severity, largely explained by BMI. No associations were found in females. If replicated, these findings could support adiposity-based risk stratification in men. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Sleep-disordered breathing Central obesity Abdominal adiposity Insulin resistance Polygraphy Sex-specific risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Research in Context What is already known about this subject? Obesity, especially visceral adiposity, is linked to systemic inflammation, T2DM, and OSA, with increasing morbidity and mortality. Visceral fat contributes to airway collapsibility and reduced lung volumes, mechanisms underlying OSA development. OSA and T2DM shares obesity as an overlapping risk factor, thus creating a complex interplay OSA is more common and severe in men, likely due to differences in fat distribution. Men have more visceral fat, a known OSA driver, while women store more subcutaneous fat. These differences justify a sex stratified analysis What is the key question? Does abdominal fat distribution, particularly visceral adiposity, influence OSA severity in individuals with T2DM? Are there sex-specific differences, and can MRI-derived measures provide insights beyond BMI? What are the new findings? ASAT and TAAT volumes were associated with OSA severity in males (ASAT: p = 0.010; TAAT: p = 0.009), with significant sex interactions. VAT also showed an association in linear regression (p= 0.009) but without a sex specific effect. However, these associations were no longer significant after adjusting for BMI, indicating that general adiposity—rather than regional fat distribution—likely explains the observed relationships. No associations were observed in females, suggesting alternative mechanisms like craniofacial anatomy or hormonal factors. Z-scores for VAT (VAT-z) and ASAT (ASAT-z) did not correlate with OSA severity. How might this impact clinical practice in the foreseeable future? Higher overall adiposity may help identify people at greater risk of OSA, especially among men. Incorporating fat distribution measures alongside BMI could enhance clinical evaluations and personalized treatment strategies although this requires further investigation. Introduction Obstructive Sleep Apnea (OSA) is a chronic disorder characterized by repeated upper airway obstruction during sleep, resulting in impaired ventilation and oxygen desaturation. The prevalence of OSA varies depending on diagnostic criteria, with estimates ranging from 9% to 38%[1], affecting nearly 1 billion people globally[2]. Although obesity is a significant risk factor, not all obese individuals develop OSA; conversely, OSA can occur in non-obese populations [3-5]. OSA is approximately twice as prevalent in men compared to women, possibly due to hormonal factors, anatomical differences, and distinct fat distribution patterns – men typically accumulate more visceral fat, while women predominantly store subcutaneous fat[6]. Fat distribution plays a critical role in the pathophysiology of OSA. Fat accumulation in the neck and upper airway contributes to pharyngeal narrowing increasing the risk o fairway collapse during sleep. Additionally, abdominal obesity elevates intra-abdominal pressure, decreases functional lung capacity and promotes upper airway collapsibility during sleep [5] . Visceral adiposity is strongly associated with cardiometabolic risks, such as atherosclerosis and Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), whereas gluteofemoral fat may confer protective metabolic effects [3, 4, 7-10]. Techniques such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and computed tomography (CT) have commonly been employed to assess fat distribution in clinical and epidemiological research[11-13], The link between visceral adiposity and OSA is well-documented, with associations generally more pronounced in men than women [11]. Furthermore, the link between OSA and metabolic liver disease, particularly MASLD, is well-documented [3]. However, studies specifically examining detailed fat distribution in OSA populations, especially among individuals with Type 2 Diabetes Mellitus (T2DM), remain limited Magnetic resonance imaging (MRI) is the gold standard for analyzing fat distribution[12, 14], but its use in OSA research has been. Incorporating MRI-based assessments could enhance our understanding of how specific fat depots influence OSA severity, especially given known sex difference in fat distribution and OSA prevalence [15, 16]. Although previous studies have reported associations between OSA and visceral adiposity[11, 16], no studies have specifically utilized MRI to examine detailed fat distribution in relation to OSA severity among patients with T2DM. This study aims to address this knowledge gap by investigating the relationship between MRI-quantified abdominal fat compartment – particularly visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) – and OSA severity in patients with T2DM Materials and Methods Study Protocol This project is based on EPSONIP-Sleep, a sub-study within EPSONIP (Evaluating the Prevalence and Severity of NAFLD in Primary Care) [17]. EPSONIP is a prospective cohort investigating Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), in patients with T2DM using MRI in Swedish primary care [17]. Research participants were randomly recruited from five primary healthcare centers in Southeast Sweden during their annual check-up appointments, with enrollment taking place between March 2019 and October 2023. Inclusion criteria was T2DM diagnosis and age between 35 to 75 years old. Exclusion criteria was an inability undergo MRI (claustrophobia, pacemaker, metal implants, extreme obesity and/or pregnancy), alcohol dependence, previously diagnosed liver cirrhosis or chronic liver disease (except MASLD). The EPSONIP study, including the sub study EPSONIP-Sleep, was conducted in accordance with the Declaration of Helsinki and approved by the Regional Ethical Board of Östergötland (EPSONIP: 2018/176-31 and 2018/494-32, EPSONIP-Sleep: 2019-03854). All participants provided written informed consent prior to inclusion. The study was registered at clinicaltrials.gov (identifier NCT03864510). All Research participants in the EPSONIP study were invited to participate in EPSONIP-Sleep, with the only exclusion criteria being the presence of an implantable cardioverter defibrillator. Research participants underwent an overnight monitoring of signs related to OSA by home respiratory polygraphy (Nox T3, Nox Medical, Iceland). Research participants were trained by research staff on how to set up the device. The device was used to monitor nasal airflow, heart rate, blood oxygen levels, thoracic and abdominal respiratory movements (by respiratory inductance plethysmography), body position, and snoring sound (via sound recording) [18]. Apnea was defined as a ≥90% reduction in airflow for at least 10 seconds, while hypopnea was a ≥30% reduction in airflow for at least 10 seconds, with ≥3% blood oxygen drops (desaturation). The Apnea-Hypopnea Index (AHI; events/h) was the number of apneas + hypopneas divided by estimated sleep time. The oxygen desaturation index (ODI; events/h) was the number of ≥3% desaturations per hour of estimated sleep time. Research participants with an AHI ≥5 events/h were classified as having OSA and were further categorized into groups based on severity: mild (AHI 5-15 events/h), moderate (15-30 events/h), and severe (≥30 events/h), Average nocturnal oxygen saturation was computed as the area under the saturation time curve (trapezoidal rule) divided by total sleep time Measurement of Body Fat Distribution Research participants underwent neck-to-knee imaging using Achieve dStream 1.5 T MR scanners (Philips Healthcare, The Netherlands), providing volumetric data on VAT, ASAT, as well as liver fat fraction (proton density fat fraction; PDFF), and muscle composition. The image data were processed and quantified using AMRA® Researcher (AMRA Medical AB, Linköping, Sweden). AMRA developed the MRI protocol and performs body composition analysis in the UK Biobank, the same methods are employed in this study [14, 19]. MRI-derived z-scores were computed using matched virtual control groups (VCG) . For each participant, ASAT-z and VAT-z were referenced to the distribution of VCG controls matched on sex and BMI within ±1 kg/m²; if <150 matches were available, the BMI window was symmetrically widened in 0.1 kg/m² steps until ≥150 controls were included. VAT and ASAT volumes (L) were normalized by height² (m²) to L/m² before z-score calculation [19]. Inspired by previous studies, such as Tejani et al[13] and Linge et al[19] VAT-z and ASAT-z were dichotomized as high (≥0) or low (<0). Participants were stratified into four groups: high/high, high/low, low/high, and low/low (VAT-z/ASAT-z) to examine how depot patterns relate to OSA severity. In addition to z-scores, we analyzed volumetric total abdominal adipose tissue (TAAT = VAT + ASAT, in liters) as a global burden metric potentially reflecting mechanical load on respiratory function. Statistical Analysis All statistical analyses were performed using R version 4.3.3. (The R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistics were used to summarize the data, reporting means and standard deviations (SD) for continuous variables. Welch’s t-test was employed to compare group means where variance equality could not be assumed. Linear regression was used to examine relationships between continuous variables, while logistic regression was applied to analyze associations with binary outcomes. Statistical significance was set at p < 0.05. The Apnea-Hypopnea Index (AHI) was the primary outcome of interest in all regression analyses, as it is the most widely accepted and clinically relevant measure of OSA severity. To support and contextualize the main findings, we also performed exploratory analyses using additional indices of nocturnal hypoxemia: average oxygen saturation during sleep (AvSat), the proportion of sleep spent with oxygen saturation below 90% (T90), and the Oxygen Desaturation Index (ODI). These supplementary outcomes were analyzed using the same regression framework and are presented in Supplementary Table 2. We conducted an a priori power appraisal. Detecting a small effect (Cohen’s f²=0.02) in simple linear regression at α=0.05 and 80% power would require approximately 395 participants. For medium effects (f²=0.15), approximately 54 participants are needed; for logistic models with medium effects (Cohen’s h =0.5), approximately 63 per group; and for two-sample t-tests with d=0.5, approximately 64 per group We included 151 research participants in the primary complete-case AHI analyses (91 males and 60 females), providing adequate power for medium-sized effects. We acknowledge reduced power for small effects and within-sex strata (particularly in females). Primary models were run unadjusted and adjusted for BMI to assess whether depot measures add information beyond overall adiposity. Results Table 1 Demographics and clinical characteristics of study population. Fat volumes (VAT, ASAT) are expressed in liters (L) All anthropometric measures are expressed in cm or kg as indicated As illustrated in Figure 1, 345 individuals were invited; 164 underwent both MRI and overnight polygraphy. However, 13 lacked usable MRI results, leaving 151 participants with complete MRI and AHI data for the primary analyses. We used complete-case analyses. Sample sizes for secondary outcomes (ODI, average saturation, and T90) differed due to missingness and are reported in Supplementary Table 2. Reasons for exclusion at each step are detailed in Figure 1. The study population consisted of 164 research participants (97 males and 67 females) with a mean age of 63.2 ± 8.4 years (Table 1). The overall BMI was 29.6 ± 4.8 kg/m². Males had larger waist circumferences (106.9 ± 13.6 cm) compared to females (103.5 ± 12.0 cm), while females exhibited larger hip circumferences (109.4 ± 11.7 cm) than males (104.0 ± 8.7 cm). Antihyperglycemic and cardiometabolic medications are summarized in supplementary table 3. Hypertension was present in 107 participants, with 66 males and 41 females diagnosed. Descriptive statistics for BMI, VAT-z, ASAT-z, and TAAT across AHI categories are provided in Supplementary Table 1. Overall, 67.5% met criteria for OSA (AHI ≥5). Moderate-to-severe OSA (AHI ≥15) was present in 31.3% and was more prevalent in males than females (37.4% vs 18.3%; χ² p=0.020). VAT-z and ASAT-z were not associated with AHI in either sex (linear regression: VAT-z β=0.76, p=0.393; ASAT-z β=−0.12, p=0.911). AHI distributions did not differ across VAT-z/ASAT-z strata (Kruskal–Wallis p=0.43; Figure 2). Unadjusted volumetric analyses showed significant associations with AHI in males but not females. In males: ASAT β=1.5 events/h per liter (p=0.010), VAT β=1.3 events/h per liter (p=0.009), TAAT β=0.85 events/h per liter (p=0.009). In females: ASAT β=−0.38 events/h per liter (p=0.325), VAT β=0.80 events/h per liter (p=0.325), TAAT β=−0.10 events /h per liter (p=0.745). Supplementary Figures 1 and 2 illustrate the sex disparity in OSA severity, highlighting higher AHI distribution in males. Figure 3 demonstrates the linear regression analysis results, showing significant positive associations between VAT, ASAT and TAAT with AHI in males but no significant associations in females. Interaction analyses further supported these findings, revealing that the relationships between VAT, ASAT, and TAAT and AHI were significantly modified by sex . Specifically, the association between ASAT and AHI differed by sex (p = 0.007), as did the association for TAAT (p = 0.03) , indicating stronger effects in males. No significant sex interaction was observed for VAT (p = 0.65) . Logistic regression analyses partly supported these findings, identifying ASAT, and TAAT but not VAT as significant predictors of moderate-to-severe OSA in males (Figure 4). ASAT showed an odds ratio of 1.23 per liter (95% CI: 1.01-1.49, p = 0.049, VAT an odds ratio of 1.12 per liter (95% CI: 0.93-1.34, p = 0.22), and TAAT an odds ratio of 1.10 per liter (95% CI: 1.00-1.22, p = 0.02). In females, no significant predictive value was found for any of these measures (p > 0.8), as indicated by the confidence intervals cross 1.0 in Figure 4. Logistic regression analyses were performed using moderate-to-severe OSA (AHI≥15) as the outcome, since this threshold is more likely to reflect clinically relevant disease requiring intervention. When repeating the analyses using any OSA (AHI>5) as outcome, the associations between fat distribution and OSA were weaker and not statistically significant. This supports the use of moderate-to-severe OSA as the more meaningful outcome in this context. In a subsequent analysis (Figure 5), BMI adjustment was added to the logistic regression models in males. Once BMI was considered, the associations with ASAT and TAAT were no longer significant, indicating that overall adiposity largely explains these relationships. The figure demonstrates that in the unadjusted models, ASAT and TAAT lie above the reference line (OR=1.0) with CIs not crossing 1.0, whereas in the BMI-adjusted models, both fat volumes shift closer to the null and become non-significant. These findings suggest that although abdominal fat may initially appear to be an important driver of moderate-to-severe OSA in males, much of this association can be attributed to general adiposity (BMI). As supportive analyses, we examined the associations between VAT, ASAT, and TAAT with T90 and average oxygen saturation. The results closely mirrored those observed with AHI and were likewise attenuated after adjustment for BMI (Supplementary Table 2). Discussion This study provides novel insights into the sex-specific associations between abdominal fat distribution and OSA severity, leveraging the high precision of MRI. While previous studies have relied on anthropometric proxies[12, 13, 16], our approach enabled compartment-specific volume assessment, offering a clearer view of how visceral and subcutaneous fat may influence AHI. To our knowledge, this is among the first studies to use whole-body MRI to quantify fat distribution in relation to OSA severity in individuals with type 2 diabetes. Regression analyses revealed significant associations between unadjusted fat volumes and AHI in males, particularly for ASAT and TAAT. Notably, these associations were not observed in females, highlighting a clear sex disparity in the relationship between fat distribution and OSA severity. Interaction analysis further confirmed that sex significantly modified the associations for ASAT and TAAT, supporting the presence of male-specific effects. VAT also showed a positive trend in males but did not reach statistical significance in logistic regression, and no significant sex interaction was observed for VAT. Logistic models were consistent: in males, ASAT and TAAT predicted moderate-to-severe OSA (AHI ≥15), whereas no depot predicted OSA in females. BMI-adjusted logistic models attenuated these associations in males, rendering the relationships with ASAT and TAAT non-significant. This suggests that the observed associations may be largely mediated by overall adiposity. However, the unadjusted MRI volumes may still retain clinical relevance[20], as they reflect regional fat accumulation that could contribute to airway obstruction independently of BMI. While BMI captures general adiposity, it may obscure mechanical or anatomical effects of localized fat compartments, especially in males[7, 11]. Absolute fat volumes (ASAT, TAAT) were associated with OSA severity in males, whereas depot z-scores (VAT-z, ASAT-z) were not; this aligns with evidence that size-adjusted or standardized fat metrics can understate clinically relevant effects of absolute depot burden and distribution patterns[13, 14, 19, 20] This study underscores sex differences in the prevalence and predictors of OSA (AHI ≥15)[1, 2, 5, 6, 15, 16]. In males, ASAT, VAT, and TAAT volumes were associated with OSA severity, consistent with evidence that abdominal/visceral fat burden relates to pathophysiology beyond general adiposity [7, 8, 10-12, 16]. In females, no significant associations were observed, aligning with epidemiologic data suggesting differing, possibly hormonal, contributors to OSA risk in women [6, 15]. These findings align with prior research indicating that abdominal fat is a key driver of OSA in males. Unadjusted fat volumes, particularly ASAT and TAAT, emerged as the strongest predictors, with sex-stratified analyses and interaction terms further emphasizing that these effects are male-specific. The absence of associations in females highlights the complexity of OSA etiology in women and the need for further investigation into female-specific risk factors. Although MRI provided detailed and precise fat distribution measurements, limitations due to geography may limit the availability in routine clinical settings thereby reducing its practicality. Nevertheless, our findings highlight the value of MRI in uncovering anatomical contributors to OSA that may be missed by traditional measures. This suggests a role for simpler, non-invasive alternatives in primary care. Anthropometric measures such as waist circumference may offer more accessible options for OSA risk stratification, especially in males with central obesity[12, 13, 15]. Despite the strengths of this study, including MRI-based measurements and sex-stratified analyses, certain limitations must be acknowledged. Stratification by sex reduced the number of participants per group, which may have limited statistical power, particularly in females. The cross-sectional design precludes causal inference, leaving open the question of whether fat accumulation leads to OSA or vice versa. Additionally, the neck-to-knee MRI scan did not include upper airway fat, which may also influence OSA severity. Finally, the use of cardiorespiratory polygraphy, while pragmatic, may underestimate OSA severity due to its inability to detect brief hypopneas or accurately distinguish wake from sleep time. We mitigated this by excluding wakeful periods using actigraphy and sleep logs[18]. Future studies should build on these findings by incorporating longitudinal designs, and more diverse populations. Dedicated investigations into female-specific OSA mechanisms such as hormonal factors or craniofacial anatomy are needed. Integrating sex-specific risk models and precision medicine approaches may help develop tailored screening and prevention strategies, especially for high-risk individuals in primary care settings. These efforts will deepen our understanding of the complex interplay between fat distribution and OSA and support more personalized care for patients with type 2 diabetes. Conclusion This study shows clear sex differences in the relationship between abdominal fat and OSA severity; however, associations of ASAT and TAAT with moderate-to-severe OSA in men disappeared after adjusting for BMI, suggesting general adiposity rather than specific fat distribution is most relevant. No associations were observed in women, highlighting the potential importance of sex-specific strategies in OSA screening and management. Exploratory analyses using alternative oxygenation measures (T90, AvSat) supported these findings. Abbreviations T2DM = Type 2 Diabetes Mellitus OSA = Obstructive Sleep Apnea MRI = Magnetic Resonance Imaging BMI = Body Mass Index AHI = Apnea-Hypopnea index VAT = Visceral Adipose Tissue ASAT =Abdominal Subcutaneous Adipose Tissue TAAT = Total Abdominal Adipose Tissue ASAT-z = Abdominal Subcutaneous Adipose Tissue z-Score VAT-z = Visceral Adipose Tissue z-Score MASLD = Metabolic dysfunction-Associated Steatotic Liver Disease T90 = Time under 90% saturation ODI = Oxygen Desaturation Index NAFLD = Non-Alcoholic Fatty Liver disease Declarations Funding Declaration The study has been financially supported by Lions research fund, ALF grants, Region Östergötland, Linköping, Sweden ( RÖ-965260, RÖ-938301 ). PL obtained grants from National Research Council (VR/NT) and County Council of Östergötland ALF. FI and PN are associated clinical fellows of Wallenberg Centre for Molecular Medicine receiving financial support from the Knut and Alice Wallenberg Foundation. Availability of Data and Materials The datasets generated and analyzed during the current study are not publicly available due to the sensitive nature of personal data and the requirement for ethical approval from The Swedish Ethical Review Authority for the data to be legally shared but are available from the corresponding author on reasonable request and with a valid ethical permit. Conflicts of Interest FI has received speaker fees and served on advisory boards for Novo Nordisk, Sanofi, and Boehringer Ingelheim. PN has received consulting fees from Boehringer Ingelheim. MF is an employee and shareholder of AMRA Medical AB. ODL is an employee, shareholder, and board member of AMRA Medical AB. PL is a shareholder of AMRA Medical AB. JA has received speaker fees from AstraZeneca. All other authors declare no competing interests. References Senaratna, C.V., et al., Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Med Rev, 2017. 34 : p. 70-81. Benjafield, A.V., et al., Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med, 2019. 7 (8): p. 687-698. Mesarwi, O.A., R. Loomba, and A. Malhotra, Obstructive Sleep Apnea, Hypoxia, and Nonalcoholic Fatty Liver Disease. Am J Respir Crit Care Med, 2019. 199 (7): p. 830-841. Snel, M., et al., Ectopic fat and insulin resistance: pathophysiology and effect of diet and lifestyle interventions. Int J Endocrinol, 2012. 2012 : p. 983814. Prisant, L.M., T.A. Dillard, and A.R. Blanchard, Obstructive sleep apnea syndrome. J Clin Hypertens (Greenwich), 2006. 8 (10): p. 746-50. Franklin, K.A. and E. 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Ejnell, H.F., D.; Grote, L.; Harlid, R.; Hedner, J.; Isacsson, G.; Lindberg, E.; Mifgren, B.; Puzio, J.; Tegelberg Å.; Ulander, M.; Wartenberg, C. . Riktlinjer for utredning av misstankt somnapne hos vuxna . 2018. Linge, J., et al., Skewness in Body fat Distribution Pattern Links to Specific Cardiometabolic Disease Risk Profiles. The Journal of Clinical Endocrinology & Metabolism, 2023. Rubino, F., et al., Definition and diagnostic criteria of clinical obesity. The Lancet Diabetes & Endocrinology, 2025. 13 (3): p. 221-262. Additional Declarations No competing interests reported. Supplementary Files Manuskriptsupplement20augusti2025.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 20 Dec, 2025 Editor invited by journal 04 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 28 Aug, 2025 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. 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University","correspondingAuthor":false,"prefix":"","firstName":"Patrik","middleName":"","lastName":"Nasr","suffix":""},{"id":581145634,"identity":"3d842896-d731-44e8-aae4-cd9275b7200a","order_by":11,"name":"Mikael Forsgren","email":"","orcid":"","institution":"AMRA Medical AB","correspondingAuthor":false,"prefix":"","firstName":"Mikael","middleName":"","lastName":"Forsgren","suffix":""},{"id":581145635,"identity":"042bdd4d-c464-41bd-9a0d-2d573ccff2c5","order_by":12,"name":"Mattias Ekstedt","email":"","orcid":"","institution":"Linköping University","correspondingAuthor":false,"prefix":"","firstName":"Mattias","middleName":"","lastName":"Ekstedt","suffix":""},{"id":581145636,"identity":"c1101e08-6b95-4c6e-8e2b-f37d88c0d931","order_by":13,"name":"Fredrik Iredahl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3OMQrCMBTG8VcCz+XZrhFRrxARRFHwKhWhLgqOHRwyZdLDuDkqBaceQHHyAK6ioOKriOBg6uiQ//S94UcC4HL9YQUNIEGFgHxIT7eqvsghtPoksoG/EQifR0b6Ju9jVE7W+3gSgc9je1nKoSmQ8s6xhfjRoJ2qEWA25qkcG0FKUPqd9IiaJa1iQB6yaF7Es3yPKDi9CI+bkUNk4l3uNkLIZJS9gmV+JcwIFLWNYKOtVUTIo1Mxsm4EThLa2Ig47PR1UK3NeBxNtxYEyeJwnn4nb/pxrfKBy+VyuWw9AO4TO6oup5wRAAAAAElFTkSuQmCC","orcid":"","institution":"Linköping University","correspondingAuthor":true,"prefix":"","firstName":"Fredrik","middleName":"","lastName":"Iredahl","suffix":""}],"badges":[],"createdAt":"2025-08-20 20:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7420558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7420558/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101434172,"identity":"a47c1002-cba6-4ae0-9a5c-de13f2d021ef","added_by":"auto","created_at":"2026-01-29 16:04:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91663,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of EPSONIP-Sleep inclusion. Participant flow: 345 invited → 164 completed MRI + respiratory polygraphy → 151 included in primary AHI analyses. Complete-case analyses were used. Sample sizes for ODI, average saturation, and T90 are reported in Supplementary Table 2. Abbreviations: AvSat, average saturation; T90, time with saturation \u0026lt;90%; AHI, apnea–hypopnea index.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/b7dbbb6fdb6c7220bfaa2688.jpg"},{"id":101434167,"identity":"d344e6ff-5cbd-4f9c-b314-709fdfa7e693","added_by":"auto","created_at":"2026-01-29 16:04:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60851,"visible":true,"origin":"","legend":"\u003cp\u003eAHI across VAT-z/ASAT-z strata (high ≥0 vs low \u0026lt;0). Boxes show median and IQR; whiskers denote 1.5×IQR. Groups did not differ (Kruskal–Wallis p=0.43). (+) z-score ≥0; (−) z-score \u0026lt;0.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/3f66e3866e902e34caaa331d.jpg"},{"id":101751518,"identity":"403aacb7-0071-46b5-b927-34aa439f8ca1","added_by":"auto","created_at":"2026-02-03 10:20:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125681,"visible":true,"origin":"","legend":"\u003cp\u003eLinear associations of VAT (3A), ASAT (3B), and TAAT (3C) with AHI, stratified by sex. In males, higher VAT, ASAT, and TAAT were each associated with higher AHI (e.g., TAAT β=0.85 events/h per liter, p=0.009). In females, associations were not significant (e.g., TAAT β=−0.10 events/h per liter, p=0.745).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/155e723606d15985f33339cd.jpg"},{"id":101751549,"identity":"80978f38-fb0e-4621-b574-da610f77f8a7","added_by":"auto","created_at":"2026-02-03 10:21:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53306,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratios (95% CI) for moderate-to-severe OSA (AHI ≥15) per liter increase in ASAT, TAAT, and VAT, stratified by sex. In males, ASAT (OR 1.23, 95% CI 1.01–1.49) and TAAT (OR 1.10, 95% CI 1.00–1.22) were significant; VAT was not (OR 1.12, 95% CI 0.93–1.34). In females, no associations were significant (all 95% CI cross 1.0).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/87fdc6951a494947448ca150.jpg"},{"id":101751800,"identity":"02892eef-13f4-4f4c-8990-311e8fd18bdb","added_by":"auto","created_at":"2026-02-03 10:23:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61818,"visible":true,"origin":"","legend":"\u003cp\u003eUnadjusted and BMI-adjusted odds ratios (95% CI) for AHI ≥15 per liter of ASAT, TAAT, and VAT in males. Associations significant in unadjusted models attenuated to non-significance after BMI adjustment.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/3818eed11ac546b0a04d35be.jpg"},{"id":102962093,"identity":"06877600-689a-4291-ae13-260dd1e32323","added_by":"auto","created_at":"2026-02-19 04:01:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1360545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/cd042807-71ff-4156-8736-11ede700c111.pdf"},{"id":101434170,"identity":"38fc5544-b98a-452d-9a46-6ffa54adf765","added_by":"auto","created_at":"2026-01-29 16:04:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":409104,"visible":true,"origin":"","legend":"","description":"","filename":"Manuskriptsupplement20augusti2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7420558/v1/f50449b09a1714dcf4939ee6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between Fat Distribution and Obstructive Sleep Apnea Severity Among Individuals with Type 2 Diabetes: An MRI-based study","fulltext":[{"header":"Research in Context","content":"\u003ch4\u003eWhat is already known about this subject?\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003eObesity, especially visceral adiposity, is linked to systemic inflammation, T2DM, and OSA, with increasing morbidity and mortality.\u003c/li\u003e\n \u003cli\u003eVisceral fat contributes to airway collapsibility and reduced lung volumes, mechanisms underlying OSA development.\u003c/li\u003e\n \u003cli\u003eOSA and T2DM shares obesity as an overlapping risk factor, thus creating a complex interplay\u003c/li\u003e\n \u003cli\u003eOSA is more common and severe in men, likely due to differences in fat distribution. Men have more visceral fat, a known OSA driver, while women store more subcutaneous fat. These differences justify a sex stratified analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003eWhat is the key question?\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003eDoes abdominal fat distribution, particularly visceral adiposity, influence OSA severity in individuals with T2DM? Are there sex-specific differences, and can MRI-derived measures provide insights beyond BMI?\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003eWhat are the new findings?\u003c/h4\u003e\n\u003cp\u003eASAT and TAAT volumes were associated with OSA severity in males (ASAT: p = 0.010; TAAT: p = 0.009), with significant sex interactions. VAT also showed an association in linear regression (p= 0.009) but without a sex specific effect.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eHowever, these associations were no longer significant after adjusting for BMI, indicating that general adiposity—rather than regional fat distribution—likely explains the observed relationships.\u003c/li\u003e\n \u003cli\u003eNo associations were observed in females, suggesting alternative mechanisms like craniofacial anatomy or hormonal factors.\u003c/li\u003e\n \u003cli\u003eZ-scores for VAT (VAT-z) and ASAT (ASAT-z) did not correlate with OSA severity.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003eHow might this impact clinical practice in the foreseeable future?\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003eHigher overall adiposity may help identify people at greater risk of OSA, especially among men.\u003c/li\u003e\n \u003cli\u003eIncorporating fat distribution measures alongside BMI could enhance clinical evaluations and personalized treatment strategies although this requires further investigation.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eObstructive Sleep Apnea (OSA) is a chronic disorder characterized by repeated upper airway obstruction during sleep, resulting in impaired ventilation and oxygen desaturation. The prevalence of OSA varies depending on diagnostic criteria, with estimates ranging from 9% to 38%[1], affecting nearly 1 billion people globally[2]. Although obesity is a significant risk factor, not all obese individuals develop OSA; \u0026nbsp;conversely, OSA can occur in non-obese populations [3-5].\u0026nbsp;OSA is approximately twice as prevalent in men compared to women, possibly due to hormonal factors, anatomical differences, and distinct fat distribution patterns – men typically accumulate more visceral fat, while women predominantly store subcutaneous fat[6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFat distribution plays a critical role in the pathophysiology of OSA. Fat accumulation in the neck and upper airway contributes to pharyngeal narrowing increasing the risk o fairway collapse during sleep. Additionally, abdominal obesity elevates intra-abdominal pressure, decreases functional lung capacity and promotes upper airway collapsibility during sleep\u003cstrong\u003e[5]\u003c/strong\u003e. Visceral adiposity is strongly associated with cardiometabolic risks, such as atherosclerosis and Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), whereas gluteofemoral fat may confer protective metabolic effects\u0026nbsp;[3, 4, 7-10].\u003c/p\u003e\n\u003cp\u003eTechniques such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and computed tomography (CT) have commonly been employed to assess fat distribution in clinical and epidemiological research[11-13],\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe link between visceral adiposity and OSA is well-documented, with associations generally more pronounced in men than women [11]. Furthermore, the link between OSA and metabolic liver disease, particularly MASLD, is well-documented [3]. However, studies specifically examining detailed fat distribution in OSA populations, especially among individuals with Type 2 Diabetes Mellitus (T2DM), remain limited\u003c/p\u003e\n\u003cp\u003eMagnetic resonance imaging (MRI) is the gold standard for analyzing fat distribution[12, 14], but its use in OSA research has been. Incorporating MRI-based assessments could enhance our understanding of how specific fat depots influence OSA severity, especially given known sex difference in fat distribution and OSA prevalence [15, 16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough previous studies have reported associations between OSA and visceral adiposity[11, 16], no studies have specifically utilized MRI to examine detailed fat distribution in relation to OSA severity among patients with T2DM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aims to address this knowledge gap by investigating the relationship between MRI-quantified abdominal fat compartment – particularly visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) – and OSA severity in patients with T2DM\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003eStudy Protocol\u003c/h3\u003e\n\u003cp\u003eThis project is based on EPSONIP-Sleep, a sub-study within EPSONIP (Evaluating the Prevalence and Severity of NAFLD in Primary Care) [17]. EPSONIP is a prospective cohort investigating Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), in patients with T2DM using MRI in Swedish primary care [17]. Research participants were randomly recruited from five primary healthcare centers in Southeast Sweden during their annual check-up appointments, with enrollment taking place between March 2019 and October 2023. Inclusion criteria was T2DM diagnosis and age between 35 to 75 years old. Exclusion criteria was an inability undergo MRI (claustrophobia, pacemaker, metal implants, extreme obesity and/or pregnancy), alcohol dependence, previously diagnosed liver cirrhosis or chronic liver disease (except MASLD). The EPSONIP study, including the sub study EPSONIP-Sleep, was conducted in accordance with the Declaration of Helsinki and approved by the Regional Ethical Board of Östergötland\u0026nbsp; (EPSONIP: 2018/176-31 and 2018/494-32, EPSONIP-Sleep:\u0026nbsp;2019-03854). All participants provided written informed consent prior to inclusion. The study was registered at clinicaltrials.gov (identifier NCT03864510).\u003c/p\u003e\n\u003cp\u003eAll Research participants in the EPSONIP study were invited to participate in EPSONIP-Sleep, with the only exclusion criteria being the presence of an implantable cardioverter defibrillator.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch participants underwent an overnight monitoring of signs related to OSA by home respiratory polygraphy (Nox T3, Nox Medical, Iceland). Research participants were trained by research staff on how to set up the device. The device was used to monitor nasal airflow, heart rate, blood oxygen levels, thoracic and abdominal respiratory movements (by respiratory inductance plethysmography), body position, and snoring sound (via sound recording) [18]. Apnea was defined as a ≥90% reduction in airflow for at least 10 seconds, while hypopnea was a ≥30% reduction in airflow for at least 10 seconds, with ≥3% blood oxygen drops (desaturation).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Apnea-Hypopnea Index (AHI; events/h) was the number of apneas + hypopneas divided by estimated sleep time. The oxygen desaturation index (ODI; events/h) was the number of ≥3% desaturations per hour of estimated sleep time.\u003c/p\u003e\n\u003cp\u003eResearch participants with an AHI ≥5 events/h were classified as having OSA and were further categorized into groups based on severity: mild (AHI 5-15 events/h), moderate (15-30 events/h), and severe (≥30 events/h),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAverage nocturnal oxygen saturation was \u0026nbsp;computed as the area under the saturation time curve (trapezoidal rule) divided by total sleep time\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc184290295\"\u003eMeasurement of Body Fat Distribution\u003c/h2\u003e\n\u003cp\u003eResearch participants underwent neck-to-knee imaging using Achieve dStream 1.5 T MR scanners (Philips Healthcare, The Netherlands), providing volumetric data on VAT, ASAT, as well as liver fat fraction (proton density fat fraction; PDFF), and muscle composition. The image data were processed and quantified using AMRA® Researcher (AMRA Medical AB, Linköping, Sweden). AMRA developed the MRI protocol and performs body composition analysis in the UK Biobank, the same methods are employed in this study [14, 19].\u003c/p\u003e\n\u003cp\u003eMRI-derived z-scores were computed using matched \u003cstrong\u003evirtual control groups (VCG)\u003c/strong\u003e. For each participant, ASAT-z and VAT-z were referenced to the distribution of VCG controls matched on sex and BMI within ±1 kg/m²; if \u0026lt;150 matches were available, the BMI window was symmetrically widened in 0.1 kg/m² steps until ≥150 controls were included. VAT and ASAT volumes (L) were normalized by height² (m²) to L/m² before z-score calculation [19].\u003c/p\u003e\n\u003cp\u003eInspired by previous studies, such as Tejani et al[13] and Linge et al[19] VAT-z and ASAT-z were dichotomized as high (≥0) or low (\u0026lt;0). Participants were stratified into four groups: high/high, high/low, low/high, and low/low (VAT-z/ASAT-z) to examine how depot patterns relate to OSA severity.\u003c/p\u003e\n\u003cp\u003eIn addition to z-scores, we analyzed volumetric total abdominal adipose tissue (TAAT = VAT + ASAT, in liters) as a global burden metric potentially reflecting mechanical load on respiratory function.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R version 4.3.3. (The R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistics were used to summarize the data, reporting means and standard deviations (SD) for continuous variables. Welch’s t-test was employed to compare group means where variance equality could not be assumed. Linear regression was used to examine relationships between continuous variables, while logistic regression was applied to analyze associations with binary outcomes. Statistical significance was set at p \u0026lt; 0.05. The Apnea-Hypopnea Index (AHI) was the primary outcome of interest in all regression analyses, as it is the most widely accepted and clinically relevant measure of OSA severity.\u003cbr\u003e\u0026nbsp;To support and contextualize the main findings, we also performed exploratory analyses using additional indices of nocturnal hypoxemia: average oxygen saturation during sleep (AvSat), the proportion of sleep spent with oxygen saturation below 90% (T90), and the Oxygen Desaturation Index (ODI). These supplementary outcomes were analyzed using the same regression framework and are presented in Supplementary Table 2.\u003c/p\u003e\n\u003cp\u003eWe conducted an a priori power appraisal. Detecting a\u0026nbsp;\u003cstrong\u003esmall\u0026nbsp;\u003c/strong\u003eeffect (Cohen’s f²=0.02) in simple linear regression at α=0.05 and 80% power would require approximately 395 participants. For\u0026nbsp;\u003cstrong\u003emedium\u003c/strong\u003e effects (f²=0.15), approximately 54 participants are needed; for logistic models with medium effects (Cohen’s h =0.5), approximately 63 per group; and for two-sample t-tests with d=0.5, \u0026nbsp;approximately 64\u0026nbsp;per group\u003c/p\u003e\n\u003cp\u003eWe included 151 research participants in the primary complete-case AHI analyses (91 males and 60 females), providing adequate power for medium-sized effects. We acknowledge reduced power for small effects and within-sex strata (particularly in females). Primary models were run unadjusted and adjusted for BMI to assess whether depot measures add information beyond overall adiposity.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographics and clinical characteristics of study population.\u003c/p\u003e\n\u003cp\u003eFat volumes (VAT, ASAT) are expressed in liters (L) All anthropometric measures are expressed in cm or kg as indicated\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"605\" height=\"542\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 1, 345 individuals were invited; 164 underwent both MRI and overnight polygraphy. However, 13 lacked usable MRI results, leaving 151 participants with complete MRI and AHI data for the primary analyses. We used complete-case analyses. Sample sizes for secondary outcomes (ODI, average saturation, and T90) differed due to missingness and are reported in Supplementary Table 2. Reasons for exclusion at each step are detailed in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe study population consisted of 164 research participants (97 males and 67 females) with a mean age of 63.2 \u0026plusmn; 8.4 years (Table 1). The overall BMI was 29.6 \u0026plusmn; 4.8 kg/m\u0026sup2;. Males had larger waist circumferences (106.9 \u0026plusmn; 13.6 cm) compared to females (103.5 \u0026plusmn; 12.0 cm), while females exhibited larger hip circumferences (109.4 \u0026plusmn; 11.7 cm) than males (104.0 \u0026plusmn; 8.7 cm). Antihyperglycemic and cardiometabolic medications are summarized in supplementary table 3. \u0026nbsp;Hypertension was present in 107 participants, with 66 males and 41 females diagnosed. Descriptive statistics for BMI, VAT-z, ASAT-z, and TAAT across AHI categories are provided in Supplementary Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, 67.5% met criteria for OSA (AHI \u0026ge;5). Moderate-to-severe OSA (AHI \u0026ge;15) was present in 31.3% and was more prevalent in males than females (37.4% vs 18.3%; \u0026chi;\u0026sup2; p=0.020). VAT-z and ASAT-z were not associated with AHI in either sex (linear regression: VAT-z \u0026beta;=0.76, p=0.393; ASAT-z \u0026beta;=\u0026minus;0.12, p=0.911). AHI distributions did not differ across VAT-z/ASAT-z strata (Kruskal\u0026ndash;Wallis p=0.43; Figure 2).\u003c/p\u003e\n\u003cp\u003eUnadjusted volumetric analyses showed significant associations with AHI in males but not females. In males: ASAT \u0026beta;=1.5\u0026nbsp;\u003cstrong\u003eevents/h per liter\u003c/strong\u003e (p=0.010), VAT \u0026beta;=1.3 events/h per liter (p=0.009), TAAT \u0026beta;=0.85 events/h \u0026nbsp;per liter (p=0.009). In females: ASAT \u0026beta;=\u0026minus;0.38 events/h per liter (p=0.325), VAT \u0026beta;=0.80 events/h per liter (p=0.325), TAAT \u0026beta;=\u0026minus;0.10 events /h \u0026nbsp;per liter (p=0.745).\u003c/p\u003e\n\u003cp\u003eSupplementary Figures 1 and 2 illustrate the sex disparity in OSA severity, highlighting higher AHI distribution in males.\u003c/p\u003e\n\u003cp\u003eFigure 3 demonstrates the linear regression analysis results, showing significant positive associations between VAT, ASAT and TAAT with AHI in males but no significant associations in females.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction analyses further supported these findings, revealing that the relationships between VAT, ASAT, and TAAT and AHI were significantly modified by sex\u003c/strong\u003e. \u003cstrong\u003eSpecifically, the association between ASAT and AHI differed by sex (p = 0.007), as did the association for TAAT (p = 0.03)\u003c/strong\u003e, indicating stronger effects in males. \u003cstrong\u003eNo significant sex interaction was observed for VAT (p = 0.65)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses partly supported these findings, identifying ASAT, and TAAT but not VAT as significant predictors of moderate-to-severe OSA in males (Figure 4). ASAT showed an odds ratio of 1.23 per liter\u0026nbsp;(95% CI: 1.01-1.49, p = 0.049, VAT an odds ratio of \u0026nbsp;1.12 per liter (95% CI: 0.93-1.34, p = 0.22), and TAAT an odds ratio of 1.10 per liter (95% CI: 1.00-1.22, p = 0.02). In females, no significant predictive value was found for any of these measures (p \u0026gt; 0.8), as indicated by the confidence intervals cross 1.0 in Figure 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses were performed using moderate-to-severe OSA (AHI\u0026ge;15) as the outcome, since this threshold is more likely to reflect clinically relevant disease requiring intervention. When repeating the analyses using any OSA (AHI\u0026gt;5) as outcome, the associations between fat distribution and OSA were weaker and not statistically significant. This supports the use of moderate-to-severe OSA as the more meaningful outcome in this context.\u003c/p\u003e\n\u003cp\u003eIn a subsequent analysis (Figure 5), BMI adjustment was added to the logistic regression models in males. Once BMI was considered, the associations with ASAT and TAAT were no longer significant, indicating that overall adiposity largely explains these relationships. The figure demonstrates that in the unadjusted models, ASAT and TAAT lie above the reference line (OR=1.0) with CIs not crossing 1.0, whereas in the BMI-adjusted models, both fat volumes shift closer to the null and become non-significant. These findings suggest that although abdominal fat may initially appear to be an important driver of moderate-to-severe OSA in males, much of this association can be attributed to general adiposity (BMI). As supportive analyses, we examined the associations between VAT, ASAT, and TAAT with T90 and average oxygen saturation. The results closely mirrored those observed with AHI and were likewise attenuated after adjustment for BMI (Supplementary Table 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides novel insights into the sex-specific associations between abdominal fat distribution and OSA severity, leveraging the high precision of MRI. While previous studies have relied on anthropometric proxies[12, 13, 16], our approach enabled compartment-specific volume assessment, offering a clearer view of how visceral and subcutaneous fat may influence AHI. To our knowledge, this is among the first studies to use whole-body MRI to quantify fat distribution in relation to OSA severity in individuals with type 2 diabetes.\u003c/p\u003e\n\u003cp\u003eRegression analyses revealed significant associations between unadjusted fat volumes and AHI in males, particularly for ASAT and TAAT. Notably, these associations were not observed in females, highlighting a clear sex disparity in the relationship between fat distribution and OSA severity. Interaction analysis further confirmed that sex significantly modified the associations for ASAT and TAAT, supporting the presence of male-specific effects. VAT also showed a positive trend in males but did not reach statistical significance in logistic regression, and no significant sex interaction was observed for VAT.\u003c/p\u003e\n\u003cp\u003eLogistic models were consistent: in males, ASAT and TAAT predicted moderate-to-severe OSA (AHI ≥15), whereas no depot predicted OSA in females.\u003c/p\u003e\n\u003cp\u003eBMI-adjusted logistic models attenuated these associations in males, rendering the relationships with ASAT and TAAT non-significant. This suggests that the observed associations may be largely mediated by overall adiposity. However, the unadjusted MRI volumes may still retain clinical relevance[20], as they reflect regional fat accumulation that could contribute to airway obstruction independently of BMI. While BMI captures general adiposity, it may obscure mechanical or anatomical effects of localized fat compartments, especially in males[7, 11].\u003c/p\u003e\n\u003cp\u003eAbsolute fat volumes (ASAT, TAAT) were associated with OSA severity in males, whereas depot z-scores (VAT-z, ASAT-z) were not; this aligns with evidence that size-adjusted or standardized fat metrics can understate clinically relevant effects of absolute depot burden and distribution patterns[13, 14, 19, 20]\u003c/p\u003e\n\u003cp\u003eThis study underscores sex differences in the prevalence and predictors of OSA (AHI ≥15)[1, 2, 5, 6, 15, 16]. In males, ASAT, VAT, and TAAT volumes were associated with OSA severity, consistent with evidence that abdominal/visceral fat burden relates to pathophysiology beyond general adiposity [7, 8, 10-12, 16]. In females, no significant associations were observed, aligning with epidemiologic data suggesting differing, possibly hormonal, contributors to OSA risk in women [6, 15].\u003c/p\u003e\n\u003cp\u003eThese findings align with prior research indicating that abdominal fat is a key driver of OSA in males. Unadjusted fat volumes, particularly ASAT and TAAT, emerged as the strongest predictors, with sex-stratified analyses and interaction terms further emphasizing that these effects are male-specific. The absence of associations in females highlights the complexity of OSA etiology in women and the need for further investigation into female-specific risk factors.\u003c/p\u003e\n\u003cp\u003eAlthough MRI provided detailed and precise fat distribution measurements, limitations due to geography may limit the availability in routine clinical settings thereby reducing its practicality. Nevertheless, our findings highlight the value of MRI in uncovering anatomical contributors to OSA that may be missed by traditional measures. This suggests a role for simpler, non-invasive alternatives in primary care. Anthropometric measures such as waist circumference may offer more accessible options for OSA risk stratification, especially in males with central obesity[12, 13, 15].\u003c/p\u003e\n\u003cp\u003eDespite the strengths of this study, including MRI-based measurements and sex-stratified analyses, certain limitations must be acknowledged. Stratification by sex reduced the number of participants per group, which may have limited statistical power, particularly in females. The cross-sectional design precludes causal inference, leaving open the question of whether fat accumulation leads to OSA or vice versa. Additionally, the neck-to-knee MRI scan did not include upper airway fat, which may also influence OSA severity. Finally, the use of cardiorespiratory polygraphy, while pragmatic, may underestimate OSA severity due to its inability to detect brief hypopneas or accurately distinguish wake from sleep time. We mitigated this by excluding wakeful periods using actigraphy and sleep logs[18].\u003c/p\u003e\n\u003cp\u003eFuture studies should build on these findings by incorporating longitudinal designs,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;and more diverse populations. Dedicated investigations into female-specific OSA mechanisms such as hormonal factors or craniofacial anatomy are needed. Integrating sex-specific risk models and precision medicine approaches may help develop tailored screening and prevention strategies, especially for high-risk individuals in primary care settings. These efforts will deepen our understanding of the complex interplay between fat distribution and OSA and support more personalized care for patients with type 2 diabetes.\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThis study shows clear sex differences in the relationship between abdominal fat and OSA severity; however, associations of ASAT and TAAT with moderate-to-severe OSA in men disappeared after adjusting for BMI, suggesting general adiposity rather than specific fat distribution is most relevant. No associations were observed in women, highlighting the potential importance of sex-specific strategies in OSA screening and management. Exploratory analyses using alternative oxygenation measures (T90, AvSat) supported these findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eT2DM = Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eOSA = Obstructive Sleep Apnea\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRI = Magnetic Resonance Imaging\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI = Body Mass Index\u003c/p\u003e\n\u003cp\u003eAHI = Apnea-Hypopnea index\u003c/p\u003e\n\u003cp\u003eVAT = Visceral Adipose Tissue\u003c/p\u003e\n\u003cp\u003eASAT =Abdominal Subcutaneous Adipose Tissue\u003c/p\u003e\n\u003cp\u003eTAAT = Total Abdominal Adipose Tissue\u003c/p\u003e\n\u003cp\u003eASAT-z = Abdominal Subcutaneous Adipose Tissue z-Score\u003c/p\u003e\n\u003cp\u003eVAT-z = Visceral Adipose Tissue z-Score\u003c/p\u003e\n\u003cp\u003eMASLD = Metabolic dysfunction-Associated Steatotic Liver Disease\u003c/p\u003e\n\u003cp\u003eT90 = Time under 90% saturation\u003c/p\u003e\n\u003cp\u003eODI = Oxygen Desaturation Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNAFLD = Non-Alcoholic Fatty Liver disease\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThe study has been financially supported by Lions research fund, ALF grants, Region Östergötland, Linköping, Sweden (\u003cstrong\u003eRÖ-965260, RÖ-938301\u003c/strong\u003e). PL obtained grants from National Research Council (VR/NT) and County Council of Östergötland ALF.\u003c/p\u003e\n\u003cp\u003eFI and PN are associated clinical fellows of Wallenberg Centre for Molecular Medicine receiving financial support from the Knut and Alice Wallenberg Foundation.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to the sensitive nature of personal data and the requirement for ethical approval from The Swedish Ethical Review Authority for the data to be legally shared but are available from the corresponding author on reasonable request and with a valid ethical permit.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFI has received speaker fees and served on advisory boards for Novo Nordisk, Sanofi, and Boehringer Ingelheim. PN has received consulting fees from Boehringer Ingelheim. MF is an employee and shareholder of AMRA Medical AB. ODL is an employee, shareholder, and board member of AMRA Medical AB. PL is a shareholder of AMRA Medical AB. JA has received speaker fees from AstraZeneca. All other authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSenaratna, C.V., et al., \u003cem\u003ePrevalence of obstructive sleep apnea in the general population: A systematic review.\u003c/em\u003e Sleep Med Rev, 2017. \u003cstrong\u003e34\u003c/strong\u003e: p. 70-81.\u003c/li\u003e\n\u003cli\u003eBenjafield, A.V., et al., \u003cem\u003eEstimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis.\u003c/em\u003e Lancet Respir Med, 2019. \u003cstrong\u003e7\u003c/strong\u003e(8): p. 687-698.\u003c/li\u003e\n\u003cli\u003eMesarwi, O.A., R. Loomba, and A. Malhotra, \u003cem\u003eObstructive Sleep Apnea, Hypoxia, and Nonalcoholic Fatty Liver Disease.\u003c/em\u003e Am J Respir Crit Care Med, 2019. \u003cstrong\u003e199\u003c/strong\u003e(7): p. 830-841.\u003c/li\u003e\n\u003cli\u003eSnel, M., et al., \u003cem\u003eEctopic fat and insulin resistance: pathophysiology and effect of diet and lifestyle interventions.\u003c/em\u003e Int J Endocrinol, 2012. \u003cstrong\u003e2012\u003c/strong\u003e: p. 983814.\u003c/li\u003e\n\u003cli\u003ePrisant, L.M., T.A. Dillard, and A.R. Blanchard, \u003cem\u003eObstructive sleep apnea syndrome.\u003c/em\u003e J Clin Hypertens (Greenwich), 2006. \u003cstrong\u003e8\u003c/strong\u003e(10): p. 746-50.\u003c/li\u003e\n\u003cli\u003eFranklin, K.A. and E. Lindberg, \u003cem\u003eObstructive sleep apnea is a common disorder in the population-a review on the epidemiology of sleep apnea.\u003c/em\u003e J Thorac Dis, 2015. \u003cstrong\u003e7\u003c/strong\u003e(8): p. 1311-22.\u003c/li\u003e\n\u003cli\u003eTchernof, A. and J.-P. Despr\u0026eacute;s, \u003cem\u003ePathophysiology of Human Visceral Obesity: An Update.\u003c/em\u003e Physiological Reviews, 2013. \u003cstrong\u003e93\u003c/strong\u003e(1): p. 359-404.\u003c/li\u003e\n\u003cli\u003eSmith, U., \u003cem\u003eAbdominal obesity: a marker of ectopic fat accumulation.\u003c/em\u003e J Clin Invest, 2015. \u003cstrong\u003e125\u003c/strong\u003e(5): p. 1790-2.\u003c/li\u003e\n\u003cli\u003eAhmed, B., R. Sultana, and M.W. Greene, \u003cem\u003eAdipose tissue and insulin resistance in obese.\u003c/em\u003e Biomedicine \u0026amp; Pharmacotherapy, 2021. \u003cstrong\u003e137\u003c/strong\u003e: p. 111315.\u003c/li\u003e\n\u003cli\u003eGoossens, G.H., \u003cem\u003eThe Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function.\u003c/em\u003e Obes Facts, 2017. \u003cstrong\u003e10\u003c/strong\u003e(3): p. 207-215.\u003c/li\u003e\n\u003cli\u003eHarada, Y., et al., \u003cem\u003eDifferences in Associations between Visceral Fat Accumulation and Obstructive Sleep Apnea by Sex.\u003c/em\u003e Annals of the American Thoracic Society, 2014. \u003cstrong\u003e11\u003c/strong\u003e(3): p. 383-391.\u003c/li\u003e\n\u003cli\u003eBorga, M., et al., \u003cem\u003eAdvanced body composition assessment: from body mass index to body composition profiling.\u003c/em\u003e J Investig Med, 2018. \u003cstrong\u003e66\u003c/strong\u003e(5): p. 1-9.\u003c/li\u003e\n\u003cli\u003eTejani, S., et al., \u003cem\u003eCardiometabolic Health Outcomes Associated With Discordant Visceral and Liver Fat Phenotypes: Insights From the Dallas Heart Study and UK Biobank.\u003c/em\u003e Mayo Clinic Proceedings, 2022. \u003cstrong\u003e97\u003c/strong\u003e(2): p. 225-237.\u003c/li\u003e\n\u003cli\u003eLinge, J., et al., \u003cem\u003eBody Composition Profiling in the UK Biobank Imaging Study.\u003c/em\u003e Obesity (Silver Spring), 2018. \u003cstrong\u003e26\u003c/strong\u003e(11): p. 1785-1795.\u003c/li\u003e\n\u003cli\u003eHuang, T., et al., \u003cem\u003eSex differences in the associations of obstructive sleep apnoea with epidemiological factors.\u003c/em\u003e European Respiratory Journal, 2018. \u003cstrong\u003e51\u003c/strong\u003e(3): p. 1702421.\u003c/li\u003e\n\u003cli\u003eDeng, H., et al., \u003cem\u003eAssociation of adiposity with risk of obstructive sleep apnea: a population-based study.\u003c/em\u003e BMC Public Health, 2023. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 1835.\u003c/li\u003e\n\u003cli\u003eNasr, P., et al., \u003cem\u003eEvaluating the prevalence and severity of NAFLD in primary care: the EPSONIP study protocol.\u003c/em\u003e BMC Gastroenterol, 2021. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 180.\u003c/li\u003e\n\u003cli\u003eEjnell, H.F., D.; Grote, L.; Harlid, R.; Hedner, J.; Isacsson, G.; Lindberg, E.; Mifgren, B.; Puzio, J.; Tegelberg \u0026Aring;.; Ulander, M.; Wartenberg, C. . \u003cem\u003eRiktlinjer for utredning av misstankt somnapne hos vuxna\u003c/em\u003e. 2018.\u003c/li\u003e\n\u003cli\u003eLinge, J., et al., \u003cem\u003eSkewness in Body fat Distribution Pattern Links to Specific Cardiometabolic Disease Risk Profiles.\u003c/em\u003e The Journal of Clinical Endocrinology \u0026amp; Metabolism, 2023.\u003c/li\u003e\n\u003cli\u003eRubino, F., et al., \u003cem\u003eDefinition and diagnostic criteria of clinical obesity.\u003c/em\u003e The Lancet Diabetes \u0026amp; Endocrinology, 2025. \u003cstrong\u003e13\u003c/strong\u003e(3): p. 221-262.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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-disordered breathing, Central obesity, Abdominal adiposity, Insulin resistance, Polygraphy, Sex-specific risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7420558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7420558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudy Objectives\u003c/p\u003e\n\u003cp\u003eObstructive Sleep Apnea (OSA) affects 1 billion people globally, with a male-to-female ratio of 2:1. While obesity is a major risk factor, sex differences in fat distribution may influence OSA risk. This study examined the relationship between visceral (VAT), subcutaneous (ASAT), and total abdominal fat (TAAT) and OSA severity in patients with type 2 diabetes (T2DM).\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eWe enrolled 164 T2DM patients (97 males, 67 females) from the EPSONIP-Sleep study, of whom 151 with complete MRI and polygraphy data were analyzed. MRI-measured VAT, ASAT, and TAAT volumes and z-scores (normalized for sex and height) were assessed. OSA severity was evaluated via home respiratory polygraphy. Associations were analyzed using linear and logistic regression, descriptive statistics, and Kruskal-Wallis tests.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eIn males, higher VAT, ASAT, and TAAT volumes correlated with OSA severity. Logistic regression identified ASAT (OR = 1.2/L, p = 0.048) and TAAT (OR = 1.1/L, p = 0.023) as predictors of moderate-to-severe OSA; VAT showed a non-significant trend. These associations were not significant after adjusting for BMI.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eSex differences influence the relationship between fat and OSA severity. In males, increased adiposity was linked to higher OSA severity, largely explained by BMI. No associations were found in females. If replicated, these findings could support adiposity-based risk stratification in men.\u003c/p\u003e","manuscriptTitle":"Associations between Fat Distribution and Obstructive Sleep Apnea Severity Among Individuals with Type 2 Diabetes: An MRI-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 16:04:04","doi":"10.21203/rs.3.rs-7420558/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T16:27:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T07:36:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16094164631798052898207385358224068707","date":"2026-03-29T15:42:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T21:16:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193730789287179278734175610655496229834","date":"2026-03-18T21:43:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T10:56:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-20T17:18:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T08:35:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T04:19:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-28T17:52:40+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":"bd004ccf-2638-4b93-8101-69b83975f3f4","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61823050,"name":"Health sciences/Diseases"},{"id":61823051,"name":"Health sciences/Endocrinology"},{"id":61823052,"name":"Health sciences/Health care"},{"id":61823053,"name":"Health sciences/Medical research"},{"id":61823054,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-20T11:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 16:04:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7420558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7420558","identity":"rs-7420558","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}