Neck circumference, metabolic syndrome and obstructive sleep apnea syndrome; evaluation of possible linkage

Application of Machine Learning Techniques for the Diagnosis of Obstructive Sleep Apnea/Hypopnea Syndrome

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a condition linked to severe cardiovascular and neuropsychological consequences, characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to compromised ventilation, hypoxemia, and micro-arousals. Polysomnography (PSG) serves as the gold standard for confirming OSAHS, yet its extended duration, high cost, and limited availability pose significant challenges. In this paper, we employ a range of machine learning techniques, including Neural Networks, Decision Trees, Random Forests, and Extra Trees, for OSAHS diagnosis. This approach aims to achieve a diagnostic process that is not only more accessible but also more efficient. The dataset utilized in this study consists of records from 601 adults assessed between 2014 and 2016 at a specialized sleep medical center in Colombia. This research underscores the efficacy of ensemble methods, specifically Random Forests and Extra Trees, achieving an area under the Receiver Operating Characteristic (ROC) curve of 89.2% and 89.6%, respectively. Additionally, a web application has been devised, integrating the optimal model, empowering qualified medical practitioners to make informed decisions through patient registration, an input of 18 variables, and the utilization of the Random Forests model for OSAHS screening.

Cut-off points of neck and waist circumference as predictors of obstructive sleep apnea in the Colombian population: a comparison with polysomnography

BACKGROUND

Neck circumference (NC) is a useful anthropometric measure for predicting obstructive sleep apnea (OSA). Ethnicity and sex also influence obesity phenotypes. NC cut-offs for defining OSA have not been established for the Latin American population.

OBJECTIVES

To evaluate NC, waist circumference (WC), and body mass index (BMI) as predictors of OSA in the Colombian population and to determine optimal cut-off points.

DESIGN AND SETTING

Diagnostic tests were conducted at the Javeriana University, Bogota.

METHODS

Adults from three cities in Colombia were included. NC, WC, and BMI were measured, and a polysomnogram provided the reference standard. The discrimination capacity and best cut-off points for diagnosing OSA were calculated.

RESULTS

964 patients were included (57.7% men; median age, 58 years) and 43.4% had OSA. The discrimination capacity of NC was similar for men and women (area under curve, AUC 0.63 versus 0.66, P = 0.39) but better for women under 60 years old (AUC 0.69 versus 0.57, P < 0.05). WC had better discrimination capacity for women (AUC 0.69 versus 0.57, P < 0.001). There were no significant differences in BMI. Optimal NC cut-off points were 36.5 cm for women (sensitivity [S]: 71.7%, specificity [E]: 55.3%) and 41 cm for men (S: 56%, E: 62%); and for WC, 97 cm for women (S: 65%, E: 69%) and 99 cm for men (S: 53%, E: 58%).

CONCLUSIONS

NC and WC have moderate discrimination capacities for diagnosing OSA. The cut-off values suggest differences between Latin- and North American as well as Asian populations.

Knowledge, experiences, and perceptions relating to obesity management among primary care physicians in the Lazio Region, Italy

Background

Primary care providers (PCPs) play an essential role in obesity care as they represent the first contact for patients seeking weight loss interventions.

Objective

This study explored the knowledge, experiences, and perceptions of PCPs in the Lazio Region of Italy in the management of obesity.

Design and subjects

We conducted an anonymous survey delivered from March to July 2022 via the newsletter of Rome Provincial Order of Physicians and Dentists and at the annual meeting of the regional section of the Italian Obesity Society.

Approach

The survey consisted of 24 closed-ended questions grouped into 5 sections: sociodemographic and work information; assessment of obesity; management of obesity; connections with regional Centres for Obesity Management; attitudes towards obesity.

Key results

A total of 92 PCPs accessed the survey. Of those, 2.2% were excluded because they did not see any patients with obesity. A total of 68 PCPs (75.6%) had complete questionnaires and were included in this analysis. All participants reported asking their patients about their eating habits, lifestyle, and clinical complications at the first assessment. Body weight and blood pressure were measured by 98.5% of participants and 82% calculate body mass index (BMI), while a small proportion of PCPs analysed body composition and fat distribution. Over 80% prescribed laboratory tests and ECG. Approximately 40% of PCPs did not refer patients for nutritional counselling, and most prescribed a low-calorie diet. Sixty-three percent referred patients to an endocrinologist, 48.5% to a psychotherapist, and a minority to specialists for obesity complications. Twenty-three percent prescribed anti-obesity medications and 46.5% referred patients for bariatric surgery only in severe cases. Ninety-one percent stated that obesity is "a complex and multifactorial disease" and 7.4% considered obesity to be secondary to other conditions.

Conclusions

Despite most PCPs adopt a correct approach to manage patients with obesity, many aspects could be improved to ensure optimal and multidisciplinary management.

Relationships between Heart Chamber Morphology or Function and Respiratory Parameters in Patients with HFrEF and Various Types of Sleep-Disordered Breathing

Sleep-disordered breathing (SDB), i.e., central sleep apnea (CSA) and obstructive sleep apnea (OSA), affects the prognosis of patients with heart failure with reduced ejection fraction (HFrEF). The study assessed the relationships between heart chamber size or function and respiratory parameters in patients with HFrEF and various types of SDB. The 84 participants were patients aged 68.3 ± 8.4 years (80% men) with an average left ventricular ejection fraction (LVEF) of 25.5 ± 6.85% who qualified for cardioverter-defibrillator implantation with or without cardiac resynchronization therapy. SDB, defined by an apnea-hypopnea index (AHI) ≥ five events/hour, was diagnosed in 76 patients (90.5%); SDB was severe in 31 (36.9%), moderate in 26 (31.0%), and mild in 19 (22.6%). CSA was the most common type of SDB (64 patients, 76.2%). A direct proportional relationship existed only in the CSA group between LVEF or stroke volume (SV) and AHI (p = 0.02 and p = 0.07), and between LVEF or SV and the percentage of total sleep time spent with hemoglobin oxygen saturation < 90% (p = 0.06 and p = 0.07). In contrast, the OSA group was the only group in which right ventricle size showed a positive relationship with AHI (for basal linear dimension [RVD1] p = 0.06), mean duration of the respiratory event (for RVD1 p = 0.03, for proximal outflow diameter [RVOT proximal] p = 0.009), and maximum duration of respiratory event (for RVD1 p = 0.049, for RVOT proximal p = 0.006). We concluded that in HFrEF patients, SDB severity is related to LV systolic function and SV only in CSA, whereas RV size correlates primarily with apnea/hypopnea episode duration in OSA.

Sleep disorder and apnea events detection framework with high performance using two-tier learning model design

Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.

Sleep quality and clinical association with sleep disturbance in systemic sclerosis

BACKGROUND

Poor sleep quality is a common and potentially debilitating problem in systemic sclerosis (SSc). To date, no data clarifies the potential factors related to poor sleep quality and the clinical associations with sleep disturbance among Thais with SSc-mainly the diffuse cutaneous SSc (dcSSc) subset. We aimed to evaluate sleep quality and identify the clinical association with sleep disturbance among SSc patients.

METHODS

A cross-sectional study was conducted between May 2021 and September 2021. Adult SSc patients were enrolled at the Scleroderma Clinic, Khon Kaen University, Thailand. All patients had their neck circumference measured, underwent airway evaluation using the Mallampati classification, had sleep quality assessed using the Pittsburgh Sleep Quality Index (PSQI), and the Berlin and Patient Health Questionnaire-9 completed. In addition, the clinical association with poor sleep quality (or sleep disturbance) was investigated using the PSQI.

RESULTS

A total of 88 patients were enrolled. Forty-eight (54.6%) patients experienced poor sleep quality (95%CI 43.6-65.2). Digital ulcers and dyspepsia were associated with poor sleep quality as per a logistic regression (OR 10.73: 95%CI 1.09-106.15 and 4.60: 95%CI 1.01-20.89), respectively. Overall pain-evaluated using the visual analog scale (VAS)-was positively correlated with the PSQI score (Rho 0.2586; p = 0.02).

CONCLUSION

Around half of the SSc patients reported poor sleep quality, and the significantly associated factors were digital ulcers and dyspepsia. The PSQI scores positively correlated with overall pain as evaluated by VAS. With early assessment and treatment of digital ulcers, stomach symptoms, and pain control, sleep problems might be reduced among SSc patients.

International Consensus Statement on Obstructive Sleep Apnea

BACKGROUND

Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA).

METHODS

Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus.

RESULTS

The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated.

CONCLUSION

This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.

Study of the obstructive sleep apnea syndrome in cerebral infarction patients

Introduction

Obstructive Sleep Apnea Syndrome (OSAS) is the most common respiratory disorder during sleep. Many studies have shown an association between obstructive sleep apnea syndrome and stroke, and OSAS has not been adequately considered in Vietnam compared to the actual clinical dangers. This study aims to assess the prevalence and general characteristics of obstructive sleep apnea syndrome in patients with cerebral infarction and investigate the relationship between obstructive sleep apnea syndrome and the severity of cerebral infarction.

Methods

Descriptive cross-sectional study. We identified 56 participants from August 2018 to July 2019. Subacute infarcts were identified by neuroradiologists. For each participant, vascular risk factors, medications, clinical symptoms, and neurological examination were abstracted from the medical record. Patients were taken for history and clinical examination. The patients were divided into two groups according to their AHI (Apnea-Hypopnea Index) (<5 and ≥5).

Results

A total of 56 patients were registered for the study. The mean age is 67.70 ± 11.07. The proportion of men is 53.6%. AHI has a positive correlation with neck circumference (r = 0.4), BMI (r = 0.38), the Epworth Sleepiness Scale (r = 0.61), LDL cholesterol (r = 0.38), the Modified Rankin Scale (r = 0.49), NIHSS (National Institutes of Health Stroke Scale) (r = 0.53), and an inverse correlation with SpO₂ (r = 0.61).

Conclusion

Obstructive sleep apnea Syndrome is a factor in the prognosis of cerebral infarction as well as cardiovascular diseases such as hypertension. Thus, understanding the risk of stroke in people with sleep apnea is necessary and working with a doctor to diagnose and treat sleep apnea is important.

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

Using Machine-Learning to Predict Sleep-Disordered Breathing Diagnosis From Medical Comorbidities and Craniofacial Features

Objectives This paper attempts to use machine-learning (ML) algorithms to predict the presence of sleep-disordered breathing (SDB) in a patient based on their body habitus, craniofacial anatomy, and social history. Materials and methods Data from a group of 69 adult patients who attended a dental clinic for oral surgeries and dental procedures in the last 10 years was used to train machine-learning models to predict whether a subject is likely to have SDB based on input information such as age, gender, smoking history, body mass index (BMI), oropharyngeal airway (Mallampati assessment), forward head posture (FHP), facial skeletal pattern, and sleep quality. Logistic Regression (LR), K-nearest Neighbours (kNN), Support Vector Machine (SVM) and Naïve Bayes (NB) were selected as these are the most frequently used supervised machine-learning models for classification of outcomes. The data was split into two sets for machine training (80% of total records) and the remaining was used for testing (validation). Results Initial analysis of collected data showed overweight BMI (at 25 or above), periorbital hyperchromia (dark circle eyes), nasal deviation, micrognathia, convex facial skeletal pattern (class 2) and Mallampati class 2 or above have positive correlations with SDB. Logistic Regression was found to be the best performer amongst the four models used with an accuracy of 86%, F1 score of 88% and area under the ROC curve (AUC) of 93%. LR also had 100% specificity and 77.8% sensitivity. Support Vector Machine was the second-best performer with an accuracy of 79%, F1 score of 82% and AUC of 93%. k-Nearest Neighbours and Naïve Bayes performed reasonably well with F1 scores of 71% and 67%, respectively. Conclusions This study demonstrated the feasibility of using simple machine-learning models as a credible predictor of sleep-disordered breathing in patients with structural risk factors for sleep apnoea such as craniofacial anomalies, neck posture and soft tissue airway obstruction. By utilising higher-level machine-learning algorithms, it is possible to incorporate a broader range of risk factors, including non-structural features like respiratory diseases, asthma, medication use, and more, into the prediction model.

Excessive Daytime Sleepiness as a Risk Factor for Obstructive Sleep Apnoea among Public Transport Drivers: A Cross-Sectional Study

Context

Excessive daytime sleepiness (EDS) due to obstructive sleep apnoea (OSA) is reported to be a major contributor to many road traffic accidents. Lack of awareness and diagnosis of OSA among public transport workers remains a threat to the society.

Aims

The primary aim of this study was to assess the risk of OSA among transport drivers of south Kerala using modified Berlin questionnaire. The secondary objective included craniofacial assessment of the high-risk patients identified through the questionnaire using lateral cephalogram.

Settings and Design

A cross-sectional study was conducted among 180 transport drivers of south Kerala.

Methods and Material

Modified Berlin questionnaire and limited physical examination [body mass index (kg/m2), neck circumference (cm), waist circumference (cm), hip circumference and waist to hip ratio, blood pressure (mm Hg)] were recorded. The screened subjects were categorized as high-risk snorers and low-risk snorers based on modified Berlin questionnaire. Craniofacial morphological variations of high-risk group were assessed by lateral cephalograms.

Statistical Analysis Used

The descriptive statistics were represented as mean and standard deviation and percentage. Inter-group comparison was performed with independent sample t test.

Results

The study demonstrated 64.4% of subjects were non-snorers and 35.6% were snorers. Furthermore, among the snorers, 46.9% were identified as high-risk snorers, whereas the remaining 53.1% represented low-risk snorers.

Conclusions

The study revealed the concealed risk of OSA among transport drivers could be screened through the questionnaires and demographics assessment. The application of the proposed screening protocol would triage and enhance safety of OSA affected transport drivers.

Relationships of orexigenic and anorexigenic hormones with body fat distribution in patients with obstructive sleep apnea syndrome

PURPOSE

We aimed to examine the relationships of disease activity and risk factors with serum levels of orexigenic and anorexigenic hormones in patients with obstructive sleep apnea syndrome (OSAS).

METHODS

Fasting blood samples were taken for hormonal analysis of all participants, abdominal/neck bioimpedance measurements were recorded, and polysomnography (PSG) analyses were performed. According to the apnea-hypopnea index (AHI), 34 patients with newly diagnosed OSAS and 34 participants without OSAS were compared.

RESULTS

The median body mass index (BMI) measured in the OSAS group was 30.39 kg/m² and AHI was 18.95 and these values were 25.40 kg/m² and 1.55 in the control group. There was a higher level of visceral adiposity and neuropeptide Y (NPY) in the moderate-to-severe OSAS group compared to the mild OSAS and control groups, and in the mild OSAS group compared to the control group (p = 0.001, p < 0.001). A positive correlation between the level of NPY and AHI and BMI (p < 0.001, p = 0.011), and a negative correlation between NPY levels and oxygen saturation (p = 0.001) was found. Oxygen saturation and desaturation rates were correlated with body fat percentage, body fat mass, abdominal adiposity, visceral adiposity, resting metabolic rate, and NPY levels.

CONCLUSIONS

The visceral adiposity ratio and increase in NPY levels are important parameters that increase the severity of OSAS. Considering the negative effects of NPY on vascular endothelium, measurement of basal NPY level before PSG in patients with OSAS is considered a parameter related to disease severity.

Neck circumference and the burden of metabolic syndrome disease: a population-based sample

BACKGROUND

This study aims to verify the association between neck circumference (NC) and metabolic syndrome and establish NC cut-off points to predict metabolic syndrome.

METHODS

Weight, height, NC, waist circumference, body mass index, fasting plasma glucose, HDL cholesterol, triglycerides and blood pressure were measured in a cross-sectional and population-based study with 966 adults. The association between NC and the burden of metabolic syndrome disease was evaluated by multinomial logistic regression. Receiver operating characteristic curves were used to acquire gender-specific cut-off values and predict metabolic syndrome. The NC is a simple anthropometric measurement, has low evaluation costs, can estimate the subcutaneous fat in the upper body and is related to cardiometabolic risks.

RESULTS

NC is an independent predictor of metabolic syndrome burden with high association to women. The syndrome components stratification indicated that the NC of individuals with one component was lower than those with three or more (P = 0.001). Metabolic syndrome prediction cut-off point was a NC of 39.5 cm for men and 33.3 cm for women.

CONCLUSIONS

Increased NC was associated with higher metabolic syndrome risks. This anthropometric parameter can be used as an additional marker for screening cardiovascular risk diseases.

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

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.

The Predictive Role of Subcutaneous Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea

Simple Summary

Although several methods are used to diagnose obstructive sleep apnoea (OSA), the disorder is still underdiagnosed, leading to public healthcare problems. The main aim of the present study was to analyse the role of artificial intelligence in OSA diagnostics and obstruction localisation and, moreover, the role of subcutaneous adipose tissue in OSA pathophysiology. The significance of the present investigation is that using US in OSA diagnostics and obstruction location, an additional opportunity besides standard procedures (i.e., drug-induced sleep endoscopy or polygraphy) is presented, which is vital due to the high number of undiagnosed cases. Applying the algorithm, including artificial intelligence, the presence of obstructions and its localisation, can be determined with high precision. This can be essential in therapy planning or preoperative patient preparation.

Abstract

Introduction: Our aim was to investigate the applicability of artificial intelligence in predicting obstructive sleep apnoea (OSA) and upper airway obstruction using ultrasound (US) measurements of subcutaneous adipose tissues (SAT) in the regions of the neck, chest and abdomen. Methods: One hundred patients were divided into mild (32), moderately severe-severe (32) OSA and non-OSA (36), according to the results of the polysomnography. These patients were examined using anthropometric measurements and US of SAT and drug-induced sleep endoscopy. Results: Using SAT US and anthropometric parameters, oropharyngeal obstruction could be predicted in 64% and tongue-based obstruction in 72%. In predicting oropharyngeal obstruction, BMI, abdominal and hip circumferences, submental SAT and SAT above the second intercostal space on the left were identified as essential parameters. Furthermore, tongue-based obstruction was predicted mainly by height, SAT measured 2 cm above the umbilicus and submental SAT. The OSA prediction was successful in 97% using the parameters mentioned above. Moreover, other parameters, such as US-based SAT, with SAT measured 2 cm above the umbilicus and both-sided SAT above the second intercostal spaces as the most important ones. Discussion: Based on our results, several categories of OSA can be predicted using artificial intelligence with high precision by using SAT and anthropometric parameters.

Anthropometric Measures for the Prognosis of Obstructive Sleep Apnea in Obese

OBJECTIVE

The objective of this study was to identify, among the different anthropometric indicators, the one that shows higher discriminatory power for the prognosis of Obstructive Sleep Apnea Syndrome (OSAS).

DESIGN

Observational cross-sectional study PARTICIPANTS: Obese individuals elective to bariatric surgery METHODS: A study based on data of 758 patients aged ≥ 21 years old, of both sexes, in the pre-operatory stage of the surgical procedure of gastric bypass. Obstructive sleep apnea and obstructive sleep hypopnea were evaluated and classified through the apnea-hypopnea index, which was obtained through the examination of polysomnography. Variables were divided into two groups: individuals with and without OSAS. As predictors, measures of body mass index (BMI), neck circumference (NC), and waist circumference (WC) were used.

RESULTS

The area under the ROC curve was used to check the sensitivity and specificity. All evaluated anthropometric indicators showed statistical significance. WC: area of 0.62 (CI 95%: 0.58 - 0.67), NC: area of 0,68 (CI 95%: 0.64 - 0.72) and BMI: area of 0.58 (CI 95%: 0.54 - 0.63).

CONCLUSION

The investigated anthropometric indicators performed as good predictors of OSAS. However, NC seems to be the best anthropometric indicator for the prognosis of OSAS in obese individuals when compared to BMI and WC.

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

Background

OSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.

Method

We collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.

Results

There are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ² = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.

Conclusion

The prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.

Clinical usefulness of neck circumference for predicting sarcopenia in patients undergoing peritoneal dialysis

BACKGROUND

Neck circumference (NC), per se, includes muscle mass beyond simple fat mass and mainly subcutaneous fat. We aimed to investigate the clinical usefulness of NC for predicting muscle mass, strength, or sarcopenia in patients undergoing peritoneal dialysis (PD).

METHODS

We retrospectively analyzed the data of patients undergoing PD (n = 199). NC, body compositions, and handgrip strength (HGS) were measured in all patients. Appendicular lean mass (ALM) was evaluated using dual-energy x-ray absorptiometry at the time of dry abdomen. Sarcopenia and metabolic syndrome were diagnosed using the definitions from previous guidelines. We analyzed the data divided by sex.

RESULTS

In male patients, the discrimination performance of BMI or NC for sarcopenia was similar, but BMI was more closely associated with the prediction of metabolic syndrome than NC. Linear and logistic regression analyses showed that, in males, BMI was associated with the ALM index and metabolic syndrome, but NC was associated with sarcopenia and HGS. In addition, the addition of NC in the multivariate model was associated with better predictability of sarcopenia in men. In female patients, the discrimination performance of BMI or NC for sarcopenia or metabolic syndrome was similar.

CONCLUSION

The present study demonstrated that NC is associated with HGS and sarcopenia in male patients undergoing PD. NC may be a useful option for screening for sarcopenia beyond simple BMI and may be helpful in identifying whether further evaluation for a diagnosis of sarcopenia is required.

Sleep and Athletic Performance: Impacts on Physical Performance, Mental Performance, Injury Risk and Recovery, and Mental Health: An Update

Sleep health is an important consideration for athletic performance. Athletes are at high risk of insufficient sleep duration, poor sleep quality, daytime sleepiness and fatigue, suboptimal sleep schedules, irregular sleep schedules, and sleep and circadian disorders. These issues likely have an impact on athletic performance via several domains. Sleep loss and/or poor sleep quality can impair muscular strength, speed, and other aspects of physical performance. Sleep issues can also increase risk of concussions and other injuries and impair recovery after injury. Cognitive performance is also impacted in several domains, including vigilance, learning and memory, decision making, and creativity.

Identifying the Predictors of Short Term Weight Loss Failure after Roux-En-Y Gastric Bypass

INTRODUCTION

Gastric bypass surgery is a gold standard therapy for severe obesity. This study aimed to evaluate anthropometric predictors for short-term excess weight loss (EWL) after Roux-en-Y gastric bypass surgery (RYGB) in a sample of severely obese patients.

MATERIALS AND METHODS

This cohort study was conducted on severely obese candidates for RYGB bariatric surgery in Mashhad, Iran. Indirect calorimetry, anthropometric measurements, and body composition data were collected before, one, and six months after RYGB.

RESULTS

Fifty-four participants (43, 79.6% women and 11, 20.4% men) with a mean age of 39.63 ± 9.66 years participated in this study. The mean total weight and BMI loss within six months were 32.89 ± 20.22 kg and 12.37 ± 7.34 kg/m², respectively. The mean reduction in adipose tissue and fat-free mass was 24.49 kg and 7.46 kg, respectively. The mean resting metabolism rate (RMR) reduction at one and six months after RYGB was 260.49 kcal and 396.07 kcal, respectively. There was a significant difference in mean RMR between the baseline and one and six months after RYGB (p < 0.001). There was no significant gender difference in mean weight and BMI loss percentage at six months post-RYGB (p > 0.05). Baseline skeletal muscle mass (SMM), excess BMI loss (EBMIL) at first month after surgery, and baseline neck circumference (NC) could predict EWL six months after surgery.

CONCLUSION

Reduced RMR shortly after RYGB may be due to FFM reduction. Some anthropometric and their acute changes after RYGB may predict the short-term EWL in RYGB patients.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

Body impedance analyzer and anthropometric indicators; predictors of metabolic syndrome

Aim

Metabolic syndrome is one of the outcomes of a sedentary lifestyle in the modern world. In this study, we want to introduce the predictors of metabolic syndrome using anthropometric indices and Bio-Electrical Impedance Analysis (BIA) test values.

Method

This cross-sectional study was performed on 2284 employees of Tehran University of Medical Sciences in different job categories. Metabolic syndrome was determined according to IDF criteria. Anthropometric dimensions, para-clinical tests, basic information were collected from the participants. Also, the body analysis of the participants was performed using a BIA method.

Result

The prevalence of metabolic syndrome in this study was 23.2% based on IDF criteria, which was 21% and 26.6% in men and women, respectively. The most important factor among the components of IDF criteria was HDL deficiency. In this study, neck circumference, fat mass, visceral fat, muscle mass percentage and waist to height ratio were observed as predictors of metabolic syndrome.

Conclusion

This study realized that there is association between fat mass, fat-free mass, visceral fat and muscle mass which all are some elements of body composition analysis and metabolic syndrome as a major health issue.

Predictive Value of Clinical and Questionnaire Based Screening Tools of Obstructive Sleep Apnea in Patients With Type 2 Diabetes Mellitus

Background

Obstructive sleep apnea (OSA) is a major health problem for people with type 2 diabetes mellitus (DM2) and is associated with poorer glycemic control. Early detection is critical to proper management. In this study, we planned to assess and compare the diagnostic accuracy of various OSA screening tools in patients with DM2.

Methods

In this cross-sectional study, we consecutively recruited 58 patients with DM2, presenting to the endocrinology department of Services Hospital Lahore between February 2020 to March 2021. Along with demographic and anthropometric measurements, including BMI and neck circumference, participants answered the snoring, tiredness, observed apnea, high blood pressure - BMI, age, neck circumference, and gender (STOP-BANG) questionnaire, Berlin questionnaire, and Epworth sleepiness scale. All participants then underwent an overnight, level 3 polysomnography.

Results

The overall prevalence of OSA, diagnosed by overnight polysomnography, was 65.5% in type 2 diabeticDM2 patients. The STOP-BANG questionnaire had the highest sensitivity for mild, moderate, and severe OSA i.e., 84.2%, 90.3%, and 100% respectively. Berlin questionnaire also had 100% sensitivity for severe OSA and was most specific for mild and moderate OSA (70% and 63% respectively) whereas the Epworth sleepiness scale had the highest specificity of 53.3% for severe OSA.

Conclusion

This study shows that OSA is highly prevalent in DM2 patients in Pakistan. The STOP-BANG and Berlin questionnaire proved to be effective screening tools, especially for severe OSA. Results of our study should encourage the routine use of these questionnaires in clinical practice, to help in the early identification of OSA in diabetics.

Neck Circumference as a Predictor of Metabolic Syndrome in Koreans: A Cross-Sectional Study

Metabolic syndrome (MetS) is a complex metabolic disorder and a high-risk condition for type 2 diabetes and cardiovascular disease. Rapid screening of at-risk individuals using accurate and time-saving tools is effective in disease management. Using the Korea National Health and Nutrition Examination Survey (KNHANES) data, we collected data from 2234 participants suitable for the study design, of which 974 (43.6%) were men and 1260 (56.4%) were women. We used receiver operating characteristic (ROC) curve analysis to estimate the optimal sex-specific neck circumference (NC) cut-off point to predict the MetS risk. To analyze the risk of MetS according to the estimated NC, logistic regression analysis was performed to identify the confounding factors. The result of the ROC analysis showed that the optimal neck cut-off points for predicting the risk of MetS were 38.25 cm (AUC: 0.759, 95% CI: 0.729–0.790) in men and 33.65 cm (AUC: 0.811, 95% CI: 0.782–0.840) in women. In the upper NC cut-off point compared to the lower NC cut-off point, NC was associated with an increased MetS risk by 2.014-fold (p = 0.010) in men and 3.650-fold (p < 0.001) in women, after adjustments. The current study supports NC as an effective anthropometric indicator for predicting the risk of MetS. It is suggested that more studies should be conducted to analyze the disease prediction effect of the combined application of anthropometric indicators currently in use and NC.

Developing neck circumference growth reference charts for Pakistani children and adolescents using the lambda-mu-sigma and quantile regression method

OBJECTIVE

Neck circumference (NC) is currently used as an embryonic marker of obesity and its associated risks. But its use in clinical evaluations and other epidemiological purposes requires sex and age-specific standardised cut-offs which are still scarce for the Pakistani paediatric population. We therefore developed sex and age-specific growth reference charts for NC for Pakistani children and adolescents aged 2-18 years.

DESIGN

Cross-sectional multi-ethnic anthropometric survey (MEAS) study.

SETTING

Multan, Lahore, Rawalpindi and Islamabad.

PARTICIPANTS

The dataset of 10 668 healthy Pakistani children and adolescents aged 2-18 years collected in MEAS were used. Information related to age, sex and NC were taken as study variables. The lambda-mu-sigma (LMS) and quantile regression (QR) methods were applied to develop growth reference charts for NC.

RESULTS

The 5th, 10th, 25th, 50th, 75th, 90th and 95th smoothed percentile values of NC were presented. The centile values showed that neck size increased with age in both boys and girls. During 8 and 14 years of age, girls were found to have larger NC than boys. A comparison of NC median (50th) percentile values with references from Iranian and Turkish populations reveals substantially lower NC percentiles in Pakistani children and adolescents compared to their peers in the reference population.

CONCLUSION

The comparative results suggest that the uses of NC references of developed countries are inadequate for Pakistani children. A small variability between empirical centiles and centiles obtained by QR procedure recommends that growth charts should be constructed by QR as an alternative method.

Longer and Deeper Desaturations Are Associated With the Worsening of Mild Sleep Apnea: The Sleep Heart Health Study

Study Objectives

Obesity, older age, and male sex are recognized risk factors for sleep apnea. However, it is unclear whether the severity of hypoxic burden, an essential feature of sleep apnea, is associated with the risk of sleep apnea worsening. Thus, we investigated our hypothesis that the worsening of sleep apnea is expedited in individuals with more severe desaturations.

Methods

The blood oxygen saturation (SpO₂) signals of 805 Sleep Heart Health Study participants with mild sleep apnea [5 ≤ oxygen desaturation index (ODI) < 15] were analyzed at baseline and after a mean follow-up time of 5.2 years. Linear regression analysis, adjusted for relevant covariates, was utilized to study the association between baseline SpO₂-derived parameters and change in sleep apnea severity, determined by a change in ODI. SpO₂-derived parameters, consisting of ODI, desaturation severity (DesSev), desaturation duration (DesDur), average desaturation area (avg. DesArea), and average desaturation duration (avg. DesDur), were standardized to enable comparisons between the parameters.

Results

In the group consisting of both men and women, avg. DesDur (β = 1.594, p = 0.001), avg. DesArea (β = 1.316, p = 0.004), DesDur (β = 0.998, p = 0.028), and DesSev (β = 0.928, p = 0.040) were significantly associated with sleep apnea worsening, whereas ODI was not (β = -0.029, p = 0.950). In sex-stratified analysis, avg. DesDur (β = 1.987, p = 0.003), avg. DesArea (β = 1.502, p = 0.024), and DesDur (β = 1.374, p = 0.033) were significantly associated with sleep apnea worsening in men.

Conclusion

Longer and deeper desaturations are more likely to expose a patient to the worsening of sleep apnea. This information could be useful in the planning of follow-up monitoring or lifestyle counseling in the early stage of the disease.

Bioelectrical Impedance Analysis and Manual Measurements of Neck Circumference Are Interchangeable, and Declining Neck Circumference Is Related to Presarcopenia

Purpose

Preventive medicine is important in an aging society. Presarcopenia is the preliminary stage of sarcopenia. Recent advances in bioelectrical impedance analysis (BIA) devices have enabled automatic estimation of neck circumference (NC). However, the agreement between and interchangeability of NC measured manually and that calculated with BIA have not been evaluated. We performed these analyses in the context of health checkups and investigated their associations with presarcopenia.

Methods

We enrolled 318 participants who underwent anthropometric measurements, including NC measured manually and by BIA; assessment of physical function; and blood testing. We used Bland-Altman analysis to calculate the agreement between and interchangeability of NC measurements by BIA and by the manual method. We then statistically compared normal participants and those with presarcopenia. Using multivariable analysis, we subsequently investigated significant risk factors for presarcopenia. We defined presarcopenia according to the appendicular skeletal muscle index (aSMI; the ratio of arm and leg skeletal muscle mass to height²).

Results

Bland-Altman analysis showed that bias (BIA-manual) was negative overall (−1.07), for male participants (−1.23), and for female participants (−0.96). This finding suggests that BIA measurement is an underestimate in comparison with manual measurement. NC measurement by BIA was found to be interchangeable with that by manual methods, inasmuch as the percentage error was less than 5% overall (4.38%), for male participants (3.81%), and for female participants (4.58%). Univariable analysis revealed that NC was significantly smaller in the participants with presarcopenia than in those without. Multivariable analysis, adjusted for confounding factors, revealed that a decrease in NC was significantly correlated with presarcopenia.

Conclusions

BIA measurements of NC are interchangeable within about 95% with manual measurements. The decrease in NC measured by BIA was significantly associated with presarcopenia in both genders. NC measurement can be used for early detection of presarcopenia.

Neck circumference in Latin America and the Caribbean: A systematic review and meta-analysis

Background: High neck circumference (NC) is associated with high burden diseases in Latin American and the Caribbean (LAC). NC complements established anthropometric measurements for early identification of cardio-metabolic and other illnesses. However, evidence about NC has not been systematically studied in LAC. We aimed to estimate the mean NC and the prevalence of high NC in LAC.

Methods: We conducted a systematic review in MEDLINE, Embase, Global Health and LILACS. Search results were screened and studied by two reviewers independently. To assess risk of bias of individual studies, we used the Hoy et al. scale and the Newcastle-Ottawa scale. We conducted a random-effects meta-analysis.

Results: In total, 182 abstracts were screened, 96 manuscripts were reviewed and 85 studies (n= 51,978) were summarized. From all the summarized studies, 14 were conducted in a sample of the general population, 23 were conducted with captive populations and 49 studies were conducted with patients. The pooled mean NC in the general population was 35.69 cm (95% IC: 34.85cm-36.53cm; I²: 99.6%). In our patient populations, the pooled mean NC in the obesity group was 42.56cm (95% CI 41.70cm-43.42cm; I²: 92.40%). Across all studied populations, there were several definitions of high NC; thus, prevalence estimates were not comparable. The prevalence of high NC ranged between 37.00% and 57.69% in the general population. The methodology to measure NC was not consistently reported.

Conclusions: Mean NC in LAC appears to be in the range of estimates from other world regions. Inconsistent methods and definitions hamper cross-country comparisons and time trend analyses. There is a need for consistent and comparable definitions of NC so that it can be incorporated as a standard anthropometric indicator in surveys and epidemiological studies.

Analyzing Neck Circumference as an Indicator of CPAP Treatment Response in Obstructive Sleep Apnea with Network Medicine

We explored the relationship between obstructive sleep apnea (OSA) patients’ anthropometric measures and the CPAP treatment response. To that end, we processed three non-overlapping cohorts (D1, D2, D3) with 1046 patients from four sleep laboratories in Western Romania, including 145 subjects (D1) with one-night CPAP therapy. Using D1 data, we created a CPAP-response network of patients, and found neck circumference (NC) as the most significant qualitative indicator for apnea–hypopnea index (AHI) improvement. We also investigated a quantitative NC cutoff value for OSA screening on cohorts D2 (OSA-diagnosed) and D3 (control), using the area under the curve. As such, we confirmed the correlation between NC and AHI (ρ=0.35, p<0.001) and showed that 71% of diagnosed male subjects had bigger NC values than subjects with no OSA (area under the curve is 0.71, with 95% CI 0.63–0.79, p<0.001); the optimal NC cutoff is 41 cm, with a sensitivity of 0.8099, a specificity of 0.5185, positive predicted value (PPV) = 0.9588, negative predicted value (NPV) = 0.1647, and positive likelihood ratio (LR+) = 1.68. Our NC =41 cm threshold classified the D1 patients’ CPAP responses—measured as the difference in AHI prior to and after the one-night use of CPAP—with a sensitivity of 0.913 and a specificity of 0.859.

Brain-derived neurotrophic factor in relation to central obesity in children with sleep disordered breathing

INTRODUCTION

Sleep disordered breathing (SDB) represents common comorbidities of childhood obesity leading to interrupted sleep and sleep deprivation. Sleep deprivation alters secretion of brain-derived neurotrophic factor (BDNF), which is an appetite regulator. However, little is known about the relation between BDNF and central obesity in children with SDB. The aim of the study was to evaluate BDNF level and anthropometric indices in relation to SDB in children with obesity.

MATERIAL AND METHODS

A prospective case-control study was conducted on 30 children with obesity (BMI > 95^th percentile) and 30 healthy lean children (BMI 5^th-85^th percentile). Polysomnographic, anthropometric data and BDNF serum level were obtained from all included children. Serum level of BDNF and anthropometric indices of obesity were assessed in relation to SDB in children with obesity. Regression analysis was done to determine predictors for SDB in children with obesity.

RESULTS

In comparison to healthy controls, anthropometric indices of central obesity were significantly higher while BDNF was significantly lower in obese children, especially those with SDB. Respiratory disturbance index has a significant positive correlation with anthropometric indices of central obesity and a significant negative correlation with BDNF level. Central obesity and decreased BDNF were associated with 2-fold increased risk for SDB. Waist circumference/height ratio and neck circumference/height ratio have 89.5%, 75% sensitivity and 81.23%, 84.62% specificity at a cutoff point > 0.62, > 0.24 respectively for prediction of SDB in children with obesity.

CONCLUSIONS

Central obesity and decreased BDNF represent independent predictors for SDB in children with obesity. Anthropometric indices adjusted to height are a simple screening tool for SDB in obese children.

Neck circumference as reliable predictor of mechanical ventilation support in adult inpatients with COVID-19: A multicentric prospective evaluation

AIMS

COVID-19 is especially severe for elderly subjects with cardiometabolic and respiratory comorbidities. Neck circumference (NC) has been shown to be strongly related to cardiometabolic and respiratory illnesses even after adjustment for body mass index (BMI). We performed a prospective study to investigate the potential of NC to predict the need for invasive mechanical ventilation (IMV) in adult COVID-19 inpatients.

MATERIALS AND METHODS

We prospectively and consecutively enrolled COVID-19 adult patients admitted to dedicated medical wards of two Italian hospitals from 25 March to 7 April 2020. On admission, clinical, biochemical and anthropometric data, including BMI and NC were collected. As primary outcome measure, the maximum respiratory support received was evaluated. Follow-up time was 30 days from hospital admission.

RESULTS

We enrolled 132 subjects (55.0-75.8 years, 32% female). During the study period, 26 (19.7%) patients underwent IMV. In multivariable logistic regression analyses, after adjusting for age, sex, diabetes, hypertension and COPD, NC resulted independently and significantly associated with IMV risk (adjusted OR 1.260-per 1 cm increase 95% CI:1.120-1.417; P < .001), with a stronger association in the subgroup with BMI ≤30 Kg/m² (adjusted OR 1.526; 95% CI:1.243-1.874; P < .001). NC showed a good discrimination power in predicting patients requiring IMV (AUC 0.783; 95% CI:0.684-0.882; P < .001). In particular, NC > 40.5 cm (>37.5 for females and >42.5 for males) showed a higher and earlier IMV risk compared to subjects with lower NC (Log-rank test: P < .001).

CONCLUSIONS

NC is an easy to measure parameter able to predict the need for IMV in adult COVID-19 inpatients.

Interplay between rotational work shift and high altitude-related chronic intermittent hypobaric hypoxia on cardiovascular health and sleep quality in Chilean miners

Mining activities expose workers to diverse working conditions, rotational shifts and high altitude-related hypobaric hypoxia. Separately, each condition has been reported having a negative impact on miners' health risk; however, the combination of both stressors has been poorly explored. The present study aimed to analyse the effects of exposure to rotational work shift (RWS) alone or in combination with high altitude-related chronic intermittent hypobaric hypoxia (CIHH) on cardiometabolic, physical activity and sleep quality related markers in copper miners from Los Pelambres mine in Chile. One hundred and eleven male miners working in RWS with or without CIHH were included. Anthropometrics measures, sleep quality assessment, physical activity level (PAL) and handgrip strength were evaluated. Exposure to CIHH exacerbated the detrimental effects of RWS as miners exposed to the combination of RWS and CIHH where more obese and had a wider neck circumference, reduced PAL at work and worsened sleep quality. Practitioner summary: The purpose was to assess cardiometabolic health and sleep quality markers associated with the combined effects of rotational shift work and high altitude-related intermittent hypobaric hypoxia in miners. Findings showed a wider neck circumference, lower physical activity level and higher prevalence of poor sleep quality in exposed miners. Abbreviations: ANOVA: analysis of variance; BM: body mass; BMI: body mass index; CI: confidence intervals; CIHH: chronic intermittent hypobaric hypoxia; CV: cardiovascular; CVR: cardiovascular risk; HA: high altitude; HACE: high-altitude cerebral edema; HGS: handgrip strength; IPAQ-SF: International Physical Activity Questionnaire - Short Form; LSD: Fisher's least standardized difference; MANCOVA: multivariate general lineal model; MET: metabolic equivalent; PAL: physical activity level; PSQI: Pittsburg sleep quality index; RWS: rotational work shift; WHR: waist-to-hip ratio.

Sleep and Athletic Performance: Impacts on Physical Performance, Mental Performance, Injury Risk and Recovery, and Mental Health

Research has characterized the sleep of elite athletes and attempted to identify factors associated with athletic performance, cognition, health, and mental well-being. Sleep is a fundamental component of performance optimization among elite athletes, yet only recently embraced by sport organizations as an important part of training and recovery. Sleep plays a crucial role in physical and cognitive performance and is an important factor in reducing risk of injury. This article aims to highlight the prevalence of poor sleep, describe its impacts, and address the issue of sport culture surrounding healthy sleep.

Predictors of difficult airway in the obese are closely related to safe apnea time!

Background and Aims:

We aimed to redefine the preoperative factors that may challenge the airway and safe apnea time (SAT) in the obese.

Material and Methods:

We analyzed 834 patients with body mass index (BMI) >35 kg/m² for their difficult airway score (DASc). DASc is a consolidation of measures of difficult airway like mask ventilation, difficult intubation, change of device, and number of personnel required. DASc varied from “0” no difficulty to “12” serious difficulty and DASc ≥6 was considered difficult. Preoperative parameters – neck circumference (NC), BMI, STOPBANG score, Mallampati score, obstructive sleep apnea grade, and waist circumference– were assessed.

Results:

Receiver operating characteristic curve was used to identify risk factors for obese patients at DASc ≥6. The Youden index (for the best threshold, with highest sensitivity and specificity) was BMI 45 kg/m² and NC 44.5 cm. Their absence had an 81% negative predictive value to include a difficult airway, while their presence had a positive predictive value of 55%. This further has sensitivity of 66% and specificity of 73%. The mean SAT (256 ± 6 s) was inversely related to DASc (P < 0.001).

Conclusion:

This study demonstrates that BMI and NC have a strong association with difficult airway in obese patients and are inversely related to SAT. Amongst these NC is the single most important predictor of difficult airway in obese and should be used as a screening tool.

Gender Differences in the Clinical and Polysomnographic Characteristics Among Australian Aboriginal Patients with Obstructive Sleep Apnea

BACKGROUND

In this study, we assessed the clinical and polysomnographic (PSG) characteristics according to gender among Australian Aboriginal men and woman diagnosed to have obstructive sleep apnea (OSA).

METHODS

In this retrospective study, all adult Aboriginal patients over 18 years of age diagnosed to have OSA with an apnea hypopnea index (AHI) > 5/hour over a 5 year period were included.

RESULTS

Of the 337 patients (168 females and 169 males), who underwent a diagnostic PSG, 297 (88%) were diagnosed to have OSA (AHI) >5/hour), 154/297 males (52%) and 143/297 females (48%). Amongst male and female patients with OSA, 63% and 37% were in the severe spectrum (AHI>30/hour). The male cohort had higher stage N1 NREM sleep (P<0.001), reduced N3 NREM sleep (P<0.001), higher AHI severity (P<0.001), higher NREM AHI (P<0.001), and high arousal index (P<0.005). REM sleep-related AHI was higher among female patients with all severity of OSA, along with severe oxygen desaturation during REM sleep. Among patients with severe OSA, the female cohort were younger (age 46 years vs 49 years, P=0.030) and had higher BMI with all severity of OSA, while males had larger neck circumference compared to females. Hypertension increased the odds of severe OSA versus the combined odds of mild and moderate OSA for both genders.

CONCLUSION

This study highlights some important differences in the way sleep apnea manifests in Australian Aboriginal males and females and further studies are warranted to explore avenues to look for a physiological basis for these observations and targeted interventions.

Examination of the Relationship Between Metabolic Syndrome and Obstructive Sleep Apnea in Iranian Patients with Type 2 Diabetes: A Case-Control Study

INTRODUCTION

Obstructive sleep apnea (OSA) is a common risk factor for metabolic syndrome (MS) that increases the chance of cardiovascular disease, stroke, and mortality. Many studies have been conducted on this matter, but the results are still conflicting. The aim of the present study was to examine the relationship between metabolic syndrome (MS) and obstructive sleep apnea (OSA) in Iranian patients with type 2 diabetes (T2D).

PATIENTS AND METHODS

This matched case-control study was conducted with 190 patients with T2D in Sanandaj, Iran. The data were selected using the demographic questionnaire, clinical and anthropometric measures, the Berlin Questionnaire (BQ), and the National Cholesterol Education Program's Adult Treatment Panel III (NCEP-ATP III). Participants were divided into two groups of high risk of OSA (experimental) and low risk of OSA (control). The data were analyzed using Stata, version 14.

RESULTS

The frequency of MS was higher in the OSA group than the control group (81.1% vs 70.5%), but the group difference was not statistically significant (p=0.127). The results of crude and age-sex adjusted logistic regression analysis revealed no significant association between OSA and the other variables under study (P>0.05). Sensitivity analysis and external adjustment for BMI showed no significant relationship between OSA and the other variables under study (P=0.319).

CONCLUSION

In the present study, no significant association was found between metabolic syndrome (MS) and obstructive sleep apnea (OSA) in patients with T2D; therefore, more studies should be conducted on this subject.

A Novel Decision Making Procedure during Wakefulness for Screening Obstructive Sleep Apnea using Anthropometric Information and Tracheal Breathing Sounds

Obstructive sleep apnea (OSA) is an underdiagnosed common disorder. Undiagnosed OSA, in particular, increases the perioperative morbidity and mortality risks for OSA patients undergoing surgery requiring full anesthesia. OSA screening using the gold standard, Polysomnography (PSG), is expensive and time-consuming. This study offers an objective and accurate tool for screening OSA during wakefulness by a few minutes of breathing sounds recording. Our proposed algorithm (AWakeOSA) extracts an optimized set (3-4) of breathing sound features specific to each anthropometric feature (i.e. age, sex, etc.) for each subject. These personalized group (e.g. age) classification features are then used to determine OSA severity in the test subject for that anthropomorphic parameter. Each of the anthropomorphic parameter classifications is weighted and summed to produce a final OSA severity classification. The tracheal breathing sounds of 199 individuals (109 with apnea/hypopnea index (AHI) < 15 as non-OSA and 90 with AHI ≥ 15 as moderate/severe-OSA) were recorded during wakefulness in the supine position. The sound features sensitive to OSA were extracted from a training set (n = 100). The rest were used as a blind test dataset. Using Random-Forest classification, the training dataset was shuffled 1200-6000 times to avoid any training bias. This routine resulted in 81.4%, 80.9%, and 82.1% classification accuracy, sensitivity, and specificity, respectively, on the blind-test dataset which was similar to the results for the out-of-bag-validation applied to the training dataset. These results provide a proof of concept for AWakeOSA algorithm as an accurate, reliable and quick OSA screening tool that can be done in less than 10 minutes during wakefulness.

Declining neck circumference is an anthropometric marker related to frailty in middle-aged and elderly women

Objectives: Neck circumference (NC) has been associated with cardiovascular disease and metabolic syndrome. However, the association between NC and frailty remains unknown. We aimed to determine the relationship between frailty and NC in middle-aged and elderly women.Methods: Frailty was diagnosed based on the Japanese version of the Cardiovascular Health Study criteria. Of women who underwent health checkup, 295 women with the following measurements were targeted: four trunk circumferences and appendicular skeletal muscle index (aSMI) measured using bioelectrical impedance analysis; albumin, total cholesterol, triglycerides, and C-reactive protein levels measured using a blood test; and physical function measured using back muscle strength and timed up-and-go test (TUG). Normal and frailty group comparisons were conducted using a statistical method.Results: Significant differences were observed between the two groups in terms of age, body mass index, all trunk circumferences, aSMI, back muscle strength, and TUG. Logistic regression analysis showed that NC was more related with frailty among the four trunk circumferences. In multiple regression analysis, declining NC was significantly associated with frailty.Conclusion: In middle-aged and elderly women, NC has a significant association with frailty. Declining NC was shown to be an anthropometric marker of frailty, and may be a frailty risk factor.

Waist, neck circumferences, waist-to-hip ratio: Which is the best cardiometabolic risk marker in women with severe obesity? The SOON cohort

A centralized deposit of adiposity increases the risk of cardiometabolic diseases. Several anthropometric markers can be used to characterize fat distribution. In the case of severe obesity, several markers, such as hip and waist circumference, are prone to measurement error. Conversely, neck circumference is easy to obtain. The aim was to determine the best surrogate marker of obesity-related cardiometabolic diseases from: body mass index (BMI), waist, hip and neck circumferences and waist-to-hip ratio.

The association of neck circumference with incident congestive heart failure and coronary heart disease mortality in a community-based population with or without sleep-disordered breathing

Background

Neck circumference (NC), representing upper body subcutaneous adipose tissue, may be correlated with increased risk of overweight/obesity, obstructive sleep apnoea, and metabolic and cardiovascular disease. However, the relationship between NC and the incidence of congestive heart failure (CHF) or mortality due to coronary heart disease (CHD) in a community-based population with and without sleep-disordered breathing (SDB) has not yet been clarified.

Methods

We performed a prospective study using the Sleep Heart Health Study (SHHS) cohort. Cox proportional hazards regression models were used to estimate the association of different levels of NC with CHF incidence or CHD mortality in 2234 individuals with SDB and 2199 without SDB, respectively.

Results

After adjusting for age, sex, and body mass index (BMI), NC was significantly associated with CHF when comparing the highest NC quartile group with the lowest (hazard ratio, HR, 2.265, 95% confidence interval, CI, 1.074–4.777) in the non-SDB population. This association diminished after further adjustment for other risk factors, but remained statistically significant, with an adjusted HR of 1.082 (95% CI 1.003–1.166) per unit increase in NC. Additionally, after adjustment for age, sex, and BMI, NC was also shown to be remarkably associated with CHD mortality (HR 1.141, 95% CI 1.014–1.282) per unit increase in NC in the non-SDB population but not in the SDB population. After adjustment for all the covariates, there was a significant association between NC and CHD death in those without SDB, with an adjusted HR of 1.134 (95% CI 1.001–1.284) per unit increase in NC.

Conclusions

NC may correlate with CHF incidence and CHD mortality in population without SDB. NC measurement may help risk stratification for cardiovascular diseases.

Trial registration

NCT00005275, January 1994.

Electronic supplementary material

The online version of this article (10.1186/s12872-018-0846-9) contains supplementary material, which is available to authorized users.

Correlations between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

PURPOSE

The body mass index (BMI), an estimate of body fat, provides a rather imprecise indication of risk for obstructive sleep apnea (OSA). We examined whether other measures, including waist and neck circumference, provide improved indicators of risk in treatment-naïve OSA subjects.

METHODS

We studied 59 OSA subjects [age, 48.8±10.0 years; BMI, 31.9±6.6 kg/m²; apnea-hypopnea-index (AHI), 38.5±23.0 events/hour; sleep efficiency index (SEI, n=52), 78.6±14.4%; lowest oxygen saturation (SaO₂ nadir), 79.5±8.0%; systolic blood pressure (BP), 127.4±15.7 mmHg; diastolic BP, 80.1±9.1 mmHg; 43 male), and determined waist and neck circumferences (waist, 107.4±15.3 cm; neck, 41.8±4.7 cm), daytime sleepiness [Epworth sleepiness scale (ESS), 8.7±4.6], sleep quality [Pittsburgh sleep quality index (PSQI), 8.5±4.1], depression levels [Beck depression inventory II (BDI-II), 6.6±5.7), and anxiety levels [Beck anxiety inventory (BAI), 6.2±7.2]. We used partial correlation procedures (covariates, age and gender) to examine associations between BMI, waist, and neck circumferences vs. AHI, sleep, and neuropsychological variables.

RESULTS

BMI, waist, and neck circumferences were significantly correlated with SaO₂ nadir (BMI; r=-0.423, p=0.001; waist; r=-0.457, p<0.001; neck; r=-0.263, p=0.048), AHI (BMI; r=0.349, p=0.008; waist; r=0.459, p<0.001; neck; r=0.276, p=0.038), and systolic BP (BMI; r=0.354, p=0.007; waist; r=0.321, p=0.015; neck; r=0.388, p=0.003). SEI was significantly correlated with waist circumference (r=0.28, p=0.049), but higher with BMI (r=0.291, p=0.04).

CONCLUSIONS

No other significant waist or neck correlations emerged. This study suggests that waist and neck measures correlate better than BMI with select disease severity (SaO₂ nadir and AHI) in OSA subjects. The findings offer an easily-measured, ancillary means to assess OSA risk.

Neck Circumference-Height Ratio as a Predictor of Sleep Related Breathing Disorder in Children and Adults

STUDY OBJECTIVES

While neck circumference (NC) is a useful predictor of obstructive sleep apnea (OSA) in adults, childhood OSA is difficult to predict clinically. We utilized the neck circumference-height ratio (NHR) to normalize NC in growing children. Our study aimed to determine if (1) NC is a reproducible clinical measurement; (2) NHR predicts OSA in children; (3) this metric translates to adults.

METHODS

For this retrospective study, paired NC measurements (from clinic and sleep laboratory) in 100 consecutive adult subjects were used to confirm inter-observer reproducibility. Polysomnographic (PSG) and anthropometric data from children aged 5-18 years presenting consecutively between July 2007 and February 2012 was obtained. Children with genetic syndromes, severe neurological disorders, craniofacial abnormalities, tracheostomy, past adenotonsillectomy, in-hospital PSG or sleep efficiency < 80% were excluded. Data were analyzed using χ(2) test and logistic and linear regression models. These analyses were also applied to 99 adult patients with similar exclusion criteria.

RESULTS

Adult NC measurement had inter-observer correlation of 0.85 (N = 100). Among children, after correcting for BMI-Z scores, NHR conferred additional predictive value, in both logistic regression and linear models, for both apnea-hypopnea index (AHI) > 2 and > 5 (N = 507). In children, for NHR > 0.25, the odds ratio of AHI > 2 was 3.47. In adults, for NHR > 0.25, the odds ratio of AHI > 5 was 18.

CONCLUSIONS

NHR can be included as a simple screening tool for OSA in children and adults, which along with other predictors, may improve the ability of clinicians to triage children and adults at risk for OSA for further evaluation with PSG.

Facial phenotype in obstructive sleep apnea-hypopnea syndrome: a systematic review and meta-analysis

This systematic review and meta-analysis explores the association between facial phenotype and obstructive sleep apnea-hypopnea syndrome in adults. A comprehensive electronic (Medline via Ovid, Scopus, and Embase) database and reference search were undertaken in relation to imaging modalities for surface craniofacial assessments in subjects with sleep apnea. The outcome measures were surface facial dimensions, morphology and profile. The quality of studies was assessed and a meta-analysis conducted. The studies were weighted using the inverse variance method, and the random effects model was used to analyse data. This systematic review identified eight case-control studies. In five studies (906 participants), adults with sleep apnea showed increased weighted mean differences in neck circumference by 1.26 mm (P = 0.0001) with extensive heterogeneity between studies (I² = 93%). Only two studies (467 participants) shared the following outcomes: mandible length, lower facial height, mandible width and anterior mandible height parameters. The pooled results demonstrated obstructive sleep apnea syndrome was associated with larger parameters than controls. In conclusion, the surface facial assessment was able to demonstrate some characteristic morphological features, facilitating a meta-analysis, in adults with obstructive sleep apnea-hypopnea syndrome. The strength of these findings, however, was limited by the heterogeneity of the studies precluding the identification of a clear phenotype.

Neck Circumference May Be a Better Alternative to Standard Anthropometric Measures

This paper evaluates neck circumference as a metabolic risk marker. Overweight/obese, nondiabetic Hispanics, 40-65 years old, who are free of major cardiovascular diseases, were recruited for the San Juan Overweight Adults Longitudinal Study (SOALS). Baseline exams were completed by 1,206 participants. Partial correlation coefficients (r) and logistic models adjusted for age, gender, smoking status, and physical activity were computed. Neck circumference was significantly correlated with waist circumference (r = 0.64), BMI (r = 0.66), and body fat % (r = 0.45). Neck circumference, highest (compared to lowest) tertile, had higher association with prediabetes: multivariable OR = 2.30 (95% CI: 1.71-3.06) compared to waist circumference OR = 1.97 (95% CI: 1.48-2.66) and other anthropometric measures. Neck circumference showed higher associations with HOMA, low HDL-C, and triglycerides, multivariable OR = 8.42 (95% CI: 5.43-13.06), 2.41 (95% CI: 1.80-3.21), and 1.52 (95% CI: 1.14-2.03), but weaker associations with hs-CRP and hypertension, OR = 3.61 (95% CI: 2.66-4.90) and OR = 2.58 (95% CI: 1.90-3.49), compared to waist circumference. AIC for model fit was generally similar for neck or waist circumference. Neck circumference showed similar or better associations with metabolic factors and is more practicable than waist circumference. Hence, neck circumference may be a better alternative to waist circumference.

Does airway surgery lower serum lipid levels in obstructive sleep apnea patients? A retrospective case review

Background

Obstructive sleep apnea (OSA) is tightly linked to increased cardiovascular disease. Surgery is an important method to treat OSA, but its effect on serum lipid levels in OSA patients is unknown. We aimed to evaluate the effect of upper airway surgery on lipid profiles.

Material/Methods

We performed a retrospective review of 113 adult patients with OSA who underwent surgery (nasal or uvulopalatopharyngoplasty [UPPP]) at a major, urban, academic hospital in Beijing from 2012 to 2013 who had preoperative and postoperative serum lipid profiles.

Results

Serum TC (4.86±0.74 to 4.69±0.71) and LP(a) (median 18.50 to 10.90) all decreased significantly post-operatively (P<0.01, 0.01, respectively), with no changes in serum HDL, LDL, or TG (P>0.05, all). For UPPP patients (n=51), serum TC, HDL and LP(a) improved (P=0.01, 0.01,<0.01, respectively). For nasal patients (n=62), only the serum LP(a) decreased (P<0.01). In patients with normal serum lipids at baseline, only serum LP(a) decreased (P<0.01). In contrast, in patients with isolated hypertriglyceridemia, the serum HDL, TG and LP(a) showed significant improvements (P=0.02, 0.03, <0.01, respectively). In patients with isolated hypercholesterolemia, the serum LP(a) decreased significantly (P=0.01), with a similar trend for serum TC (P=0.06). In patients with mixed hyperlipidemia, the serum TC and LDL also decreased (P=0.02, 0.03, respectively).

Conclusions

Surgery may improve blood lipid levels in patients with OSA, especially in patients with preoperative dyslipidemia, potentially yielding a major benefit in metabolism and cardiovascular sequelae. Prospective studies should examine this potential metabolic effect of airway surgery for OSA.

Obstructive sleep apnea syndrome: An important piece in the puzzle of cardiovascular risk factors

The obstructive sleep apnea syndrome (OSA) is a clinical entity characterized by recurring episodes of apnea and/or hypopnea during sleep, due to a total or partial collapse, respectively, of the upper airway. This collapse originates a set of pathophysiological changes that determine the appearance of several cardiovascular complications. OSA contributes for the development of hypertension, heart failure, arrhythmias and coronary heart disease. Nowadays it is recognized to be an important public health problem, taking into account not just its repercussions but also its prevalence, since the main risk factor for the disease is obesity, a growing problem worldwide, both in developed and developing countries. The present review summarizes the current knowledge about OSA, as regards its definition, pathophysiology, clinical manifestations, diagnosis, cardiovascular effects and treatment.