Independent relationships between cardinal features of obstructive sleep apnea and glycometabolism: a cross-sectional study

Associations Between Life's Essential 8 and Insulin Resistance Among Nondiabetic Adults

BACKGROUND

Insulin resistance (IR) is closely linked to cardiometabolic diseases. Preventing and improving IR in nondiabetic populations is critically important. We aimed to investigate the relationship between Life's Essential 8 (LE8), the latest tool from the American Heart Association quantifying cardiovascular health, and IR among nondiabetic populations in the United States.

METHODS AND RESULTS

This cross-sectional study used data on 11 246 nondiabetic adults aged ≥20 years from the 2005 to 2018 the National Health and Nutrition Examination Survey. The LE8 score was classified into 2 subscale scores: health factor score and health behavior score. IR was measured by homeostasis model assessment of insulin resistance (HOMA-IR). Weighted logistic and linear regression models analyzed associations among the LE8 score, health behavior score, health factor score, and IR. Restricted cubic spline models assessed dose-response relationships. Adjusted subgroup analyses and inverse probability of treatment weighting method also evaluated the LE8-IR relationship. Of the 11 246 participants, 4860 (43.2%) had IR. The mean LE8 score was 70.07 (95% CI, 69.57-70.58). In fully adjusted models, higher LE8 scores were associated with lower IR odds (odds ratio per 10-unit increase, 0.57 [95% CI, 0.54-0.61]). Nonlinear LE8-IR dose-response was observed. Similar patterns were seen for health behavior and health factor subscores, with stronger IR correlations for health factors. The inverse LE8-IR association was significantly more pronounced among White participants and those with higher education, higher income, and without hypertension, cardiovascular disease, or chronic kidney disease. Significant negative LE8-IR associations persisted after inverse probability of treatment weighting.

CONCLUSIONS

LE8 and subscale scores are negatively associated with IR in a nonlinear relationship. Promoting optimal cardiovascular health adherence may improve IR in nondiabetic populations.

Independent relationship between sleep apnea-specific hypoxic burden and glucolipid metabolism disorder: a cross-sectional study

OBJECTIVES

Obstructive sleep apnea (OSA) is associated with abnormal glucose and lipid metabolism. However, whether there is an independent association between Sleep Apnea-Specific Hypoxic Burden (SASHB) and glycolipid metabolism disorders in patients with OSA is unknown.

METHODS

We enrolled 2,173 participants with suspected OSA from January 2019 to July 2023 in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected from each participant. Multiple linear regression analyses were used to evaluate independent associations between SASHB, AHI, CT90 and glucose as well as lipid profile. Furthermore, logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose and lipid metabolism across various SASHB, AHI, CT90 quartiles.

RESULTS

The SASHB was independently associated with fasting blood glucose (FBG) (β = 0.058, P = 0.016), fasting insulin (FIN) (β = 0.073, P < 0.001), homeostasis model assessment of insulin resistance (HOMA-IR) (β = 0.058, P = 0.011), total cholesterol (TC) (β = 0.100, P < 0.001), total triglycerides (TG) (β = 0.063, P = 0.011), low-density lipoprotein cholesterol (LDL-C) (β = 0.075, P = 0.003), apolipoprotein A-I (apoA-I) (β = 0.051, P = 0.049), apolipoprotein B (apoB) (β = 0.136, P < 0.001), apolipoprotein E (apoE) (β = 0.088, P < 0.001) after adjustments for confounding factors. Furthermore, the ORs for hyperinsulinemia across the higher SASHB quartiles were 1.527, 1.545, and 2.024 respectively, compared with the lowest quartile (P < 0.001 for a linear trend); the ORs for hyper-total cholesterolemia across the higher SASHB quartiles were 1.762, 1.998, and 2.708, compared with the lowest quartile (P < 0.001 for a linear trend) and the ORs for hyper-LDL cholesterolemia across the higher SASHB quartiles were 1.663, 1.695, and 2.316, compared with the lowest quartile (P < 0.001 for a linear trend). Notably, the ORs for hyper-triglyceridemia{1.471, 1.773, 2.099} and abnormal HOMA-IR{1.510, 1.492, 1.937} maintained a consistent trend across the SASHB quartiles.

CONCLUSIONS

We found SASHB was independently associated with hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-triglyceridemia and hyper-LDL cholesterolemia in Chinese Han population. Further prospective studies are needed to confirm that SASHB can be used as a predictor of abnormal glycolipid metabolism disorders in patients with OSA.

TRIAL REGISTRATION

ChiCTR1900025714 { http://www.chictr.org.cn/ }; Prospectively registered on 6 September 2019; China.

Clinical and neuroradiological correlates of sleep in myotonic dystrophy type 1

Abnormalities of sleep are common in myotonic dystrophy type 1 (DM1), but few previous studies have combined polysomnography with detailed clinical measures and brain imaging. In the present study, domiciliary polysomnography, symptom questionnaires and cognitive evaluation were undertaken in 39 DM1-affected individuals. Structural brain MRI was completed in those without contra-indication (n = 32). Polysomnograms were adequate for analysis in 36 participants. Sleep efficiency was reduced, and sleep architecture altered in keeping with previous studies. Twenty participants (56%) had moderate or severe sleep-disordered breathing (apnoea-hypopnoea index [AHI] ≥ 15). In linear modelling, apnoeas were positively associated with increasing age and male sex. AHI ≥ 15 was further associated with greater daytime pCO₂ and self-reported physical impairment, somnolence and fatigue. Percentage REM sleep was inversely associated with cerebral grey matter volume, stage 1 sleep was positively associated with occipital lobe volume and stage 2 sleep with amygdala volume. Hippocampus volume was positively correlated with self-reported fatigue and somnolence. Linear relationships were also observed between measures of sleep architecture and cognitive performance. Findings broadly support the hypothesis that changes in sleep architecture and excessive somnolence in DM1 reflect the primary disease process in the central nervous system.

Age-specific markers of adiposity in patients with obstructive sleep apnea

OBJECTIVES

Adiposity can have varying effects on the individual depending upon its distribution pattern. We assessed age-related distribution of adipose tissue by anthropometric measures and bioelectrical impedance analysis, as well as their association with obstructive sleep apnea (OSA) severity.

METHODS

Participants were 169 elderly (aged ≥ 65 years) and 142 non-elderly (aged < 65 years) referred for overnight polysomnography. The associations between obesity parameters and apnea-hypopnea index (AHI) were determine by univariate and multivariate linear regression analyses. Area under receiver operating characteristic curve (AUC) was used to access the predicting performance of some parameters.

RESULTS

Compared with non-elderly, elderly showed higher conicity index and visceral adiposity (VA)/subcutaneous adiposity (SA), lower body mass index (BMI), neck circumference, waist circumference, hip circumference and SA. Multiple regression analyses revealed that VA and VA/SA were independently associated with AHI in elderly (explained 17.2% of the AHI ^0.5 variability), while BMI and VA/SA were independently associated with AHI in non-elderly (explained 25.9% of the AHI ^0.5 variability), after adjusting for age, sex, cigarette smoking, alcohol drinking and main comorbidities. In elderly, VA over 128 cm² and VA/SA less than 0.41 resulted in sensitivity, specificity and AUC of 0.382, 0.790, 0.580 and 0.176, 0.947, 0.553 in predicting moderate-to-severe OSA, respectively. In non-elderly, BMI over 24.7 kg/m² and VA/SA over 0.54 resulted in sensitivity, specificity and AUC of 0.883, 0.484, 0.704 and 0.550, 0.710, 0.667 in predicting moderate-to-severe OSA, respectively.

CONCLUSIONS

VA is strongly associated with OSA severity in elderly, independently of general obesity as per BMI standards, while general adiposity appears to be more strongly associated with OSA severity in non-elderly. Our study supports age-specific approaches should be developed with respect to prediction and treatment of OSA.

Body Fat Indices as Effective Predictors of Insulin Resistance in Obstructive Sleep Apnea: Evidence from a Cross-Sectional and Longitudinal Study : BFI as Predictors of IR in OSA

PURPOSE

Body fat indices serve as predictive markers of insulin resistance (IR) in metabolic diseases. IR is common in obstructive sleep apnea (OSA). However, whether body fat indices have utility as predictors of IR in OSA remain unknown.

MATERIALS AND METHODS

A longitudinal study was conducted in 46 patients undergoing bariatric surgery to explore the relationship between IR and body fat indices. Then, a cross-sectional study was performed to evaluate the relationships between body fat indices and IR, and receiver operating characteristic (ROC) curves were generated. Body indices, homeostasis model assessment index of insulin resistance (HOMA-IR), biological indicators, and polysomnographic variables were collected.

RESULTS

In the longitudinal study, significant relationships were found between remission of IR and changes in visceral adiposity index (VAI) (r = 0.452, P < 0.05) and triglyceride-glucose index (TyG) (r = 0.650, P < 0.01). In the cross-sectional study, lipid accumulation product (LAP) (best cutoff value: 30.16, area under the curve (AUC) = 0.728, P < 0.001) and TyG (best cutoff value: 8.54, AUC = 0.740, P < 0.001) were indicators of IR in normal weight group. In overweight/obese group, body mass index (BMI) (best cutoff value: 27.69 AUC = 0.707, P < 0.001) and waist circumference (WC) (best cutoff value: 97.25, AUC = 0.708, P < 0.001) were markers of IR. TyG showed better ability to predict IR in normal weight females (best cutoff value: 8.39 AUC = 0.813, P < 0.001).

CONCLUSIONS

Body fat indices are predictive markers of IR in patients with OSA.

Association of apnea-hypopnea index during rapid eye movement sleep with insulin resistance in patients with suspected obstructive sleep apnea: a cross-sectional study

Background

Obstructive sleep apnea (OSA) is associated with insulin resistance. However, the association between special stages of OSA [rapid eye movement (REM) sleep] and insulin resistance is not clear. This study was designed to assess the association of the frequency of respiratory events during REM sleep with insulin resistance in adults with suspected OSA.

Methods

In this cross-sectional study, 4,062 adult participants with suspected OSA who underwent polysomnography in our sleep center between 2009 and 2016 were screened. Polysomnographic variables, biochemical indicators, and physical measurements were collected. Logistic regression analyses were conducted to determine the odds ratios (ORs) and 95% confidence intervals (95% CIs) for insulin resistance as assessed by the presence of hyperinsulinemia, the homeostasis model assessment of insulin resistance (HOMA-IR) index, the fasting insulin resistance index (FIRI), and Bennett’s insulin sensitivity index (ISI).

Results

The final analyses included 2,899 adults with suspected OSA. Multivariate adjustments, including the apnea-hypopnea index (AHI) during non-REM sleep (AHI_NREM), were performed. The AHI during REM sleep (AHI_REM) was found to be independently associated with insulin resistance across increasing AHI_REM quartiles. For hyperinsulinemia the ORs (95% CIs) followed the order of 1.340 (1.022, 1.757), 1.210 (0.882, 1.660), and 1.632 (1.103, 2.416). For abnormal HOMA-IR, ORs (95% CIs) were 1.287 (0.998, 1.661), 1.263 (0.933, 1.711), and 1.556 (1.056, 2.293). For abnormal FIRI, ORs (95% CIs) were 1.386 (1.048, 1.835), 1.317 (0.954, 1.818), and 1.888 (1.269, 2.807). For abnormal Bennett’s ISI, ORs (95% CIs) were 1.297 (1.003, 1.678), 1.287 (0.949, 1.747), and 1.663 (1.127, 2.452). All linear trends were statistically significant (P<0.01). Additionally, the results showed that REM sleep duration was independently associated with hyperinsulinemia (OR =0.777, 95% CI: 0.615–0.982; P<0.05).

Conclusions

AHI_REM was independently associated with hyperinsulinemia and an abnormal HOMA-IR, FIRI, and Bennett’s ISI in adults with suspected OSA. Additionally, REM sleep duration was independently associated with hyperinsulinemia.

Effect of Interaction Between Slow Wave Sleep and Obstructive Sleep Apnea on Insulin Resistance: A Large-Scale Study

OBJECTIVE

Slow-wave sleep (SWS) and obstructive sleep apnea (OSA) have attracted recent research attention. However, their joint effects on insulin resistance (IR) remain unclear. This study explored whether SWS influences the relationship between OSA and IR.

METHODS

We enrolled potential participants in our sleep center from 2007 to 2019. We collected demographic and clinical characteristics and determined IR status. SWS was derived from polysomnography data. Logistic regression analysis was used to reveal the associations between SWS and IR.

RESULTS

In all, 6966 participants (5709 OSA and 1257 primary snoring [PS] subjects) were enrolled. Less SWS increased the risk of IR in OSA patients but not in PS patients. OSA patients with SWS <6.5% were more likely to have IR than were those with SWS >21.3%. OSA was an independent risk factor for IR after adjusting for potential confounding factors. In stratified analyses according to the percentage of SWS, OSA patients with SWS <6.5% had an odds ratio for IR of 2.461 (95% CI, 2.018-3.002) compared to the PS group after adjusting for potential confounders.

CONCLUSION

Less SWS is associated with higher odds for IR in OSA patients but not in PS patients. OSA is independently associated with IR. In addition, OSA combined with an extreme lack of SWS has a more harmful effect on the status of IR than OSA itself.

Interaction between obstructive sleep apnea and short sleep duration on insulin resistance: a large-scale study : OSA, short sleep duration and insulin resistance

Objectives

Both short sleep duration and obstructive sleep apnea (OSA) seem to be associated with insulin resistance. We aimed to explore whether short sleep duration modifies the relationship between OSA and insulin resistance.

Methods

Participants were consecutively enrolled from our sleep center during the period from 2007 to 2017. The index of homeostasis model assessment insulin resistance (HOMA-IR) was calculated from insulin and glucose. Sleep duration was determined by standard polysomnography. The associations between sleep duration and insulin resistance were estimated by logistic regression analyses.

Results

A total of 5447 participants (4507 OSA and 940 primary snorers) were included in the study. OSA was independently correlated with insulin resistance after adjusting for all potential confounders (OR, 1.319; 95% CI, 1.088–1.599), but not short sleep duration. In stratified analysis by sleep duration, compared with primary snorers, in the OSA group only extremely short sleep duration (< 5 h) was significantly associated with insulin resistance after adjusting for all covariates (OR, 2.229; 95% CI, 1.283–3.874). Rapid eye movement predominant OSA was significantly associated with insulin resistance (OR = 1.355, 95% CI: 1.019–1.802) after adjustment for confounding factors including age, sex and body mass index.

Conclusions

OSA, but not short sleep duration, was independently associated with insulin resistance. It is worth noting that OSA combined with extremely short sleep duration showed a greater detrimental effect than OSA itself with regard to insulin resistance.

The use of visceral adiposity variables in the prediction of obstructive sleep apnea: evidence from a large cross-sectional study

PURPOSE

The purposes of this study were to evaluate the ability of visceral adiposity variables [the lipid accumulation product (LAP), the visceral adiposity index (VAI), and the triglyceride-glucose index (TyG)] in predicting obstructive sleep apnea hypopnea syndrome (OSAHS) and to determine the effect of sex on the prediction.

METHODS

A total of 5539 subjects admitted to the sleep center for suspected OSAHS were consecutively recruited from 2007 to 2016. Anthropometric measurements, biological indicators, Epworth sleepiness scale score, and polysomnographic variables were collected. Prediction models for diagnosing OSAHS were established in the test group by logistic regression and verified in the validation group by receiver operating characteristic (ROC) curves.

RESULTS

A total of 4703 patients were included in total. LAP and TyG were of moderate diagnostic accuracy for OSAHS, with the diagnostic efficiency differing between men and women. A prediction model was developed that combined visceral adiposity indicators with waist circumstance and the lowest SpO₂. The sensitivity of those indicators were both 84% in men and women, respectively, and their specificity were both 90%. In addition, the model was confirmed in the validation group with a sensitivity and specificity of 83% and 85% in men and 85% and 84% in women.

CONCLUSIONS

LAP and TyG were of moderate efficiency in screening for OSAHS. The prediction model provides a simple and practical screening tool for OSAHS.

Obstructive sleep apnea and comorbidities: a dangerous liaison

Obstructive sleep apnea (OSA) is a highly prevalent disease, and is traditionally associated with increased cardiovascular risk. The role of comorbidities in OSA patients has emerged recently, and new conditions significantly associated with OSA are increasingly reported. A high comorbidity burden worsens prognosis, but some data suggest that CPAP might be protective especially in patients with comorbidities. Aim of this narrative review is to provide an update on recent studies, with special attention to cardiovascular and cerebrovascular comorbidities, the metabolic syndrome and type 2 diabetes, asthma, COPD and cancer. Better phenotypic characterization of OSA patients, including comorbidities, will help to provide better individualized care. The unsatisfactory adherence to CPAP in patients without daytime sleepiness should prompt clinicians to examine the overall risk profile of each patient in order to identify subjects at high risk for worse prognosis and provide the optimal treatment not only for OSA, but also for comorbidities.

The Relationship between Simple Snoring and Metabolic Syndrome: A Cross-Sectional Study

PURPOSE

This cross-sectional study was performed to assess the relationship between simple snoring and metabolic syndrome (MetS).

METHODS

A total of 5635 participants including 300 healthy volunteers without snoring allegedly were initially included from 2007 to 2016. Polysomnographic variables, anthropometric measurements, and biochemical indicators were collected. The polynomial linear trend test was used to assess the linear trend across snoring intensity for metabolic score, and logistic regression was used to evaluate the odds ratios (ORs) for MetS after controlling for age, sex, obesity, smoking status, and alcohol consumption.

RESULTS

The final study population consisted of 866 participants. Simple snorers showed more severe metabolic disorders and higher prevalence of MetS than nonsnorers. A significant linear trend was observed between snoring intensity and metabolic score. Simple snoring was significantly associated with increased odds for MetS among all participants (OR = 2.328, 95% CI: 1.340-4.045) and female participants (OR = 2.382, 95% CI: 1.136-4.994) after multivariable adjustment. With regard to MetS components, simple snoring was significantly associated with increased odds for hypertension (OR = 1.730, 95% CI: 1.130-2.650), abdominal obesity (OR = 1.810, 95% CI: 1.063-3.083), and hyper-triglycerides (TG) (OR = 1.814, 95% CI: 1.097-2.998) among all participants, with hypertension (OR = 3.493, 95% CI: 1.748-6.979) among males and with abdominal obesity (OR = 2.306, 95% CI: 1.245-4.270) and hyper-TG (OR = 2.803, 95% CI: 1.146-6.856) among females after multivariable adjustment.

CONCLUSIONS

After excluding the influence of repeated apnea and hypoxia, simple snoring was still significantly associated with MetS, especially in women. Furthermore, the associations were more obvious for hypertension among males and for abdominal obesity and hyper-TG among females. In addition to OSA, simple snoring also should be valued.