Increased resting energy expenditure and insulin resistance in male patients with moderate-to severe obstructive sleep apnoea

Modelling metabolic performance in paediatric obstructive sleep disordered breathing: A case-control study

Paediatric obstructive sleep disordered breathing (OSDB) has a considerable impact on cardiovascular physiology, but the consequences on children's basal metabolism and response to exercise are far from being known. The objective was to propose model estimations for paediatric OSDB metabolism at rest and during exercise. A retrospective case-control analysis of data from children submitted to otorhinolaryngology surgery was performed. The heart rate (HR) was measured, while oxygen consumption (VO2) and energy expenditure (EE) at rest and during exercise were obtained using predictive equations. The results for the patients with OSDB were compared with controls. A total of 1256 children were included. A total of 449 (35.7%) had OSDB. The patients with OSDB showed a significantly higher resting heart rate (94.55 ± 15.061 bpm in OSDB vs. 92.41 ± 15.332 bpm in no-OSDB, p = 0.041). The children with OSDB showed a higher VO2 at rest (13.49 ± 6.02 mL min-1kg-1 in OSDB vs. 11.55 ± 6.83 mL min-1kg-1 in no-OSDB, p = 0.004) and a higher EE at rest (67.5 ± 30.10 cal min-1kg-1 in OSDB vs. 57.8 + 34.15 cal min-1kg-1 in no-OSDB, p = 0.004). At maximal exercise, patients with OSDB showed a lower VO2max (33.25 ± 5.82 mL min-1kg-1 in OSDB vs. 34.28 ± 6.71 in no-OSDB, p = 0.008) and a lower EE (166.3 ± 29.11 cal min-1kg-1 in OSDB vs. 171.4 ± 33.53 cal min-1kg-1 in no-OSDB, p = 0.008). The VO2/EE increment with exercise (Δ VO2 and Δ EE) was lower in OSDB for all exercise intensities (p = 0.009). This model unveils the effect of paediatric OSDB on resting and exercise metabolism. Our findings support the higher basal metabolic rates, poorer fitness performance, and cardiovascular impairment found in children with OSDB.

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.

Sleep Apnea, Obesity, and Disturbed Glucose Homeostasis: Epidemiologic Evidence, Biologic Insights, and Therapeutic Strategies

PURPOSE OF REVIEW

Obstructive sleep apnea (OSA), obesity, and disturbed glucose homeostasis are usually considered distinct clinical condition, although they are tightly related to each other. The aim of our manuscript is to provide an overview of the current evidence on OSA, obesity, and disturbed glucose homeostasis providing epidemiologic evidence, biological insights, and therapeutic strategies.

RECENT FINDINGS

The mechanisms hypothesized to be involved in this complex interplay are the following: (1) "direct weight-dependent" mechanisms, according to which fat excess compromises respiratory mechanics, and (2) "indirect weight-dependent" mechanisms such as hyperglycemia, insulin resistance and secondary hyperinsulinemia, leptin resistance and other hormonal dysregulations frequently found in subjects with obesity, type 2 diabetes, and/or sleep disorders. Moreover, the treatment of each of these clinical conditions, through weight loss induced by diet or bariatric surgery, the use of anti-obesity or antidiabetic drugs, and continuous positive airway pressure (CPAP), seems to positively influence the others. These recent data suggest not only that there are multiple connections among these diseases but also that treating one of them may result in an improvement of the others.

Obstructive sleep apnea in obese minipigs

Objective:

Obesity has reached epidemic proportions and is a strong risk factor for obstructive sleep apnea (OSA). However, the underlying mechanisms are poorly understood and current treatment strategies for OSA and obesity have critical limitations. Thus, establishment of an obesity-related large animal model with spontaneous OSA is imperative.

Materials and methods:

Natural and sedated sleep were monitored and characterized in 4 obese (body mass index - BMI>48) and 3 non-obese (BMI<40) minipigs. These minipigs were instrumented with the BioRadio system under sedation for the wireless recording of respiratory airflow, snoring, abdominal and chest respiratory movements, electroencephalogram, electrooclulogram, electromyogram, and oxygen saturation. After instrumentation, the minipigs were placed in a dark room with a remote night-vision camera for monitoring all behaviors. Wakefulness and different sleep stages were classified, and episodes of apneas and/or hypopneas were identified during natural and/or sedated sleep.

Results:

No hypopnea episodes were observed in two of the non-obese minipigs, but one non-obese minipig had 5 hypopnea events. Heavy snoring and 27-58 apnea and/or hypopnea episodes were identified in all 4 obese minipigs. Most of these episodes occurred in the rapid eye movement stage during natural sleep and/or sedated sleep in Yucatan minipigs.

Conclusions:

Obese minipigs can experience naturally occurring OSA, thus are an ideal large animal model for obese-related OSA studies.

The Bidirectional Relationship Between Obstructive Sleep Apnea and Metabolic Disease

Obstructive sleep apnea (OSA) is a common sleep disorder, effecting 17% of the total population and 40-70% of the obese population (1, 2). Multiple studies have identified OSA as a critical risk factor for the development of obesity, diabetes, and cardiovascular diseases (3-5). Moreover, emerging evidence indicates that metabolic disorders can exacerbate OSA, creating a bidirectional relationship between OSA and metabolic physiology. In this review, we explore the relationship between glycemic control, insulin, and leptin as both contributing factors and products of OSA. We conclude that while insulin and leptin action may contribute to the development of OSA, further research is required to determine the mechanistic actions and relative contributions independent of body weight. In addition to increasing our understanding of the etiology, further research into the physiological mechanisms underlying OSA can lead to the development of improved treatment options for individuals with OSA.

Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients

The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m²) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of -89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/-19.17 and 66%/-21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.