Effect of upper airway fat on tongue dilation during inspiration in awake people with obstructive sleep apnea

Lakner

et al. 2022

Life

This study aimed to analyse the thickness of the adipose tissue (AT) around the upper airways with anthropometric parameters in the prediction and pathogenesis of OSA and obstruction of the upper airways using artificial intelligence. One hundred patients were enrolled in this prospective investigation, who were divided into control (non-OSA) and mild, moderately severe, and severe OSA according to polysomnography. All participants underwent drug-induced sleep endoscopy, anthropometric measurements, and neck MRI. The statistical analyses were based on artificial intelligence. The midsagittal SAT, the parapharyngeal fat, and the midsagittal tongue fat were significantly correlated with BMI; however, no correlation with AHI was observed. Upper-airway obstruction was correctly categorised in 80% in the case of the soft palate, including parapharyngeal AT, sex, and neck circumference parameters. Oropharyngeal obstruction was correctly predicted in 77% using BMI, parapharyngeal AT, and abdominal circumferences, while tongue-based obstruction was correctly predicted in 79% using BMI. OSA could be predicted with 99% precision using anthropometric parameters and AT values from the MRI. Age, neck circumference, midsagittal and parapharyngeal tongue fat values, and BMI were the most vital parameters in the prediction. Basic anthropometric parameters and AT values based on MRI are helpful in predicting OSA and obstruction location using artificial intelligence.

Section: Discussionmentioning

confidence: 56%

The Predictive Role of the Upper-Airway Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea

Lakner

et al. 2022

Life

“…Patients with CHF often present with obstructive sleep apnea syndrome (OSAS) [54]. We know that one of the main causes of OSAS is morphological and functional alterations of the tongue, in particular, an increase in posterior volume and an alteration of the intervention of the autonomic nervous system [55,56]. Lingual dysfunction in CHF and OSAS raises the likelihood of systemic metabolic disorders, with the risk of further cardiovascular sequelae, the onset of metabolic syndrome, and cognitive impairment [56].…”

Section: Mri: Magnetic Resonance Imagingmentioning

confidence: 99%

A Missing Voice: The Lingual Complex and Osteopathic Manual Medicine in the Context of Five Osteopathic Models

Bordoni¹,

Escher²

2021

Cureus

The five osteopathic models recognized by the American Association of Colleges of Osteopathic Medicine guide clinicians in the evaluation and therapeutic choice which must be the most appropriate concerning the patient's needs. Skeletal muscles represent an important interpretation, such as screening and treatment, on which these models are based. A muscle district that is not considered by the usual osteopathic practice is the tongue. The lingual complex has numerous functions, both local and systemic; it can adapt negatively in the presence of pathology, just as it can influence the body system in a non-physiological manner if it is a source of dysfunctions. This paper, the first of its kind in the panorama of scientific literature, briefly reviews the anatomy and neurophysiology of the tongue, trying to highlight the logic and the need to insert this muscle in the context of the five osteopathic models. The clinician's goal is to restore the patient's homeostasis, and we believe that this task is more concrete if the patient is approached after understanding all the contractile districts, including the tongue.

“…This technique has been widely used to assess the fat content in the liver. 1,2 In the past decade, there has been increased interest in MRI quantification of the fat fraction (FF) of the tongue and airway muscles, [3][4][5][6][7] because these FFs may help to elucidate the mechanisms of obstructive sleep apnea (OSA).…”

mentioning

confidence: 99%

“…No significant differences were found between the FFs in the patients with OSA and controls. 3 To understand and clarify the conflicting observations, we will examine the FF estimation methods used in the earlier studies and seek a more accurate method to measure FFs.…”

mentioning

confidence: 99%

“…The 2-point DIXON method 8 has been mainly used to measure the FFs in the tongue and surrounding tissues. [3][4][5][6][7] Two images at in-phase and out-of-phase are typically acquired using a GRE sequence, and then FF can be estimated from these 2 images. 8 The method is semiquantitative and is susceptible to B 0 field inhomogeneities and R2* confounding factors.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Fat Fractions in the Tongue, Soft Palate, Pharyngeal Wall, and Parapharyngeal Fat Pad by the GOOSE and DIXON Methods

et al. 2022

Objective: The 2-point DIXON method is widely used to assess fat fractions (FFs) in magnetic resonance images (MRIs) of the tongue, pharyngeal wall, and surrounding tissues in patients with obstructive sleep apnea (OSA). However, the method is semiquantitative and is susceptible to B 0 field inhomogeneities and R2* confounding factors. Using the method, although several studies have shown that patients with OSA have increased fat deposition around the pharyngeal cavity, conflicting findings was also reported in 1 study. This discrepancy necessitates that we examine the FF estimation method used in the earlier studies and seek a more accurate method to measure FFs. Materials and Methods: We examined the advantages of using the GOOSE (globally optimal surface estimation) method to replace the 2-point DIXON method for quantifying fat in the tongue and surrounding tissues on MRIs. We first used phantoms with known FFs (true FFs) to validate the GOOSE method and examine the errors in the DIXON method. Then, we compared the 2 methods in the tongue, soft palate, pharyngeal wall, and parapharyngeal fat pad of 63 healthy participants to further assess the errors caused by the DIXON method. Six participants were excluded from the comparison of the tongue FFs because of technical failures. Paired Student t tests were performed on FFs to detect significant differences between the 2 methods. All measures were obtained using 3 T Siemens MRI scanners. Results: In the phantoms, the FFs measured by GOOSE agreed with the true FF, with only a 1.2% mean absolute error. However, the same measure by DIXON had a 10.5% mean absolute error. The FFs obtained by DIXON were significantly lower than those obtained by GOOSE ( P < 0.0001) in the human participants. We found strong correlations between GOOSE and DIXON in the tongue (R 2 = 0.90), soft palate (R 2 = 0.66), and parapharyngeal fat pad (R 2 = 0.88), but the correlation was weaker in the posterior pharyngeal walls (R 2 = 0.32) in participants. Conclusions: The widely used 2-point DIXON underestimated FFs, relative to GOOSE, in phantom measurements and tissues studied in vivo. Thus, an advanced method, such as GOOSE, that uses multiecho complex data is preferred for estimating FF.