Sleep Apnea and Cardiovascular Risk in Patients with Prediabetes and Type 2 Diabetes

Genetic variations of low-density lipoprotein cholesterol on metabolic disorders in obstructive sleep apnea

BACKGROUND

The study aimed to explore the relationship between low-density lipoprotein cholesterol (LDL-C) genetic variants and obstructive sleep apnea (OSA) and its complications, including cardiovascular diseases (CVD), insulin resistance (IR), and metabolic syndrome (MS).

METHOD

4329 individuals with suspected OSA who underwent a comprehensive assessment of anthropometric, biochemical, and polysomnography (PSG) data, along with 30 LDL-C single nucleotide polymorphisms (SNPs) were enrolled. The 10-year Framingham CVD risk score (FRS), IR and MS were evaluated for each subject. Linear regression and logistic regression were utilized to examine the correlations among these variables.

RESULTS

After the Benjamini-Hochberg correction, linear regression results indicated positive correlations between variants rs3741297 and rs629301 with FRS (β = 0.031, PBH=0.002; β = 0.026, PBH=0.015). Logistic regression revealed that rs3741297 increased MS risk among total subjects [OR = 1.67 (95% CI:1.369-2.038), PBH=1.32 × 10- 5] and increased IR risk in females [OR = 3.475 (95% CI:1.653-7.307), PBH=0.03]. In males, rs2642438 decreased MS risk [OR = 0.81 (95% CI:0.703-0.933), PBH=0.045].

CONCLUSIONS

The rs3741297 variant correlated with susceptibility to CVD, IR, and MS in the OSA population. OSA, CVD, IR and MS share a potentially common genetic background, which may promote precision medicine.

CINICAL TRIAL REGISTRATION

The study protocol was registered with the Chinese Clinical Trial Registry (ChiCTR1900025714).

The benefits of hypoglycemic therapy for patients with obstructive sleep apnea

PURPOSE

Obstructive sleep apnea (OSA) is often associated with glycemic abnormalities. This study is conducted to investigate the effects of hypoglycemic therapy on OSA-related indicators.

METHOD

We systematically searched Web of Science, PubMed, Embase, and the Cochrane Library for articles on OSA patients receiving any hypoglycemic drugs, published until December 25, 2022. Seven original studies were finally included. The proposal was registered with PROSPERO (CRD42022351206).

RESULTS

In summary, in addition to reduced glycosylated hemoglobin A1c (HbA1c), we found that hypoglycemic treatment can lower the apnea-hypopnea index (AHI) by 7.07/h (p = 0.0001). Although long-term treatment (> 12 weeks) achieved a more significant reduction in HbA1c (- 1.57% vs. - 0.30%) compared to short-term treatment (≤ 12 weeks), there was no significant difference between the two in terms of AHI (intergroup p-value = 0.27). We also found that patients using sodium glucose cotransporter 2 inhibitors (SGLT2i) experienced a greater reduction in AHI (- 11.00/h, p < 0.00001). Additionally, hypoglycemic treatment also showed certain improvements in related indicators like Epworth Sleepiness Scale, body mass index, and blood pressure.

CONCLUSIONS

Our results affirm the benefits of hypoglycemic treatment for OSA patients and highlight the notable effect of SGLT2i. Further researches are needed to help doctors gain a comprehensive understanding of the interaction between OSA and glycemic abnormalities.

Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators

Purpose

The purpose of this study is to explore the independent-influencing factors from normal people to prediabetes and from prediabetes to diabetes and use different prediction models to build diabetes prediction models.

Methods

The original data in this retrospective study are collected from the participants who took physical examinations in the Health Management Center of Peking University Shenzhen Hospital. Regression analysis is individually applied between the populations of normal and prediabetes, as well as the populations of prediabetes and diabetes, for feature selection. Afterward,the independent influencing factors mentioned above are used as predictive factors to construct a prediction model.

Results

Selecting physical examination indicators for training different ML models through univariate and multivariate logistic regression, the study finds Age, PRO, TP, and ALT are four independent risk factors for normal people to develop prediabetes, and GLB and HDL.C are two independent protective factors, while logistic regression performs best on the testing set (Acc: 0.76, F-measure: 0.74, AUC: 0.78). We also find Age, Gender, BMI, SBP, U.GLU, PRO, ALT, and TG are independent risk factors for prediabetes people to diabetes, and AST is an independent protective factor, while logistic regression performs best on the testing set (Acc: 0.86, F-measure: 0.84, AUC: 0.74).

Conclusion

The discussion of the clinical relationships between these indicators and diabetes supports the interpretability of our feature selection. Among four prediction models, the logistic regression model achieved the best performance on the testing set.

Approach the Patient With Obstructive Sleep Apnea and Obesity

Obstructive sleep apnea (OSA) and obesity are highly prevalent and bidirectionally associated. OSA is underrecognized, however, particularly in women. By mechanisms that overlap with those of obesity, OSA increases the risk of developing, or having poor outcomes from, comorbid chronic disorders and impairs quality of life. Using 2 illustrative cases, we discuss the relationships between OSA and obesity with type 2 diabetes, dyslipidemia, cardiovascular disease, cognitive disturbance, mood disorders, lower urinary tract symptoms, sexual function, and reproductive disorders. The differences in OSA between men and women, the phenotypic variability of OSA, and comorbid sleep disorders are highlighted. When the probability of OSA is high due to consistent symptoms, comorbidities, or both, a diagnostic sleep study is advisable. Continuous positive airway pressure or mandibular advancement splints improve symptoms. Benefits for comorbidities are variable depending on nightly duration of use. By contrast, weight loss and optimization of lifestyle behaviors are consistently beneficial.

Possible Molecular Mechanisms of Hypertension Induced by Sleep Apnea Syndrome/Intermittent Hypoxia

Intermittent hypoxia (IH) is a central characteristic of sleep apnea syndrome (SAS), and it subjects cells in the body to repetitive apnea, chronic hypoxia, oxygen desaturation, and hypercapnia. Since SAS is linked to various serious cardiovascular complications, especially hypertension, many studies have been conducted to elucidate the mechanism of hypertension induced by SAS/IH. Hypertension in SAS is associated with numerous cardiovascular disorders. As hypertension is the most common complication of SAS, cell and animal models to study SAS/IH have developed and provided lots of hints for elucidating the molecular mechanisms of hypertension induced by IH. However, the detailed mechanisms are obscure and under investigation. This review outlines the molecular mechanisms of hypertension in IH, which include the regulation systems of reactive oxygen species (ROS) that activate the renin-angiotensin system (RAS) and catecholamine biosynthesis in the sympathetic nervous system, resulting in hypertension. And hypoxia-inducible factors (HIFs), Endotheline 1 (ET-1), and inflammatory factors are also mentioned. In addition, we will discuss the influences of SAS/IH in cardiovascular dysfunction and the relationship of microRNA (miRNA)s to regulate the key molecules in each mechanism, which has become more apparent in recent years. These findings provide insight into the pathogenesis of SAS and help in the development of future treatments.

Sleep duration and mortality in patients with chronic noncommunicable disease: a population-based cohort study

BACKGROUND

Inadequate sleep behaviors may confer a higher risk of premature death, however, evidence in patients with chronic noncommunicable disease (NCD) is scarce. To investigate the relationship between sleep duration and mortality from all-cause and heart diseases in NCD patients from a prospective cohort.

METHODS

Totally, 14,171 participants with at least one NCD, including 8275 with hypertension, 7547 with high cholesterol, 4065 with diabetes, and 5815 with chronic renal failure were enrolled from the National Health and Nutrition Examination Survey during 2005-2014. Cox proportional hazard models were performed to estimate the hazard ratio (HR) for sleep duration and mortality after adjusting for potential confounding factors.

RESULTS

After a median follow-up of 9 years, 2514 all-cause deaths were identified. Compared with sleeping 7-8 h/day, sleeping over 8 h/day was significantly associated with a higher risk of all-cause mortality, where the multivariable-HRs were 1.29 (1.11, 1.50) for hypertension, 1.23 (1.01, 1.51) for high cholesterol, 1.44 (1.13, 1.82) for diabetes, and 1.36 (1.10, 1.68) for chronic renal failure. Similar patterns were observed for heart disease mortality. A nonlinear association was detected between sleep duration and mortality in patients with NCD. Age modified the association in patients with hypertension (P-interaction: 0.036). Trouble sleeping modified the association in patients with diabetes (P-interaction: 0.042).

CONCLUSIONS

Long sleep duration was associated with higher risks of all-cause and heart disease mortality in patients with chronic NCD. Our findings highlight that improving sleep behaviors may decrease the risk of premature deaths and help to NCD tertiary prevention.

The ZJU index is associated with the risk of obstructive sleep apnea syndrome in Chinese middle-aged and older people: a cross-sectional study

BACKGROUND

The ZJU index, a novel calculation that combines body mass index, triglycerides, fasting blood glucose and the ratio of alanine aminotransferase to aspartate aminotransferase, is a closely related measure of obesity and insulin resistance. Studies of the ZJU index in relation to obstructive sleep apnea syndrome (OSAS) have not been reported. This study assessed the correlation between the ZJU values and OSAS risk.

METHODS

A total of 2,130 participants who underwent polysomnographic monitoring were included in the study. The participants' basic information and laboratory biochemical indicators were collected, and the ZJU index was computed. The ZJU index was divided into quartiles. The correlation between the different ZJU index levels and OSAS risk was assessed using logistic regression. Drew a receiver operating characteristic (ROC) relationship curve, with prediction efficacy judged by the area under the curve (AUC), and found the optimum cut-off point for ZJU index to predict OSAS. Relative risks were presented as odds ratios (OR). The range of OR values is expressed in the form of 95% confidence intervals (95% CI).

RESULTS

The number of patients diagnosed with OSAS increased progressively with increasing ZJU index (T1: 9.4%; T2: 20.6%; T3: 28.3%; T4: 41.7%; P < 0.001). The additional confounders were adjusted by the logistic regression models, the study revealed an independent correlation between ZJU index and OSAS. (P < 0.001). The OSAS risk was notably higher at the highest ZJU index levels. (OR = 2.046 [95% CI: 1.057 to 3.964]). The ROC curve for the ZJU index showed an AUC of 0.64 (P < 0.001) for males and 0.75 (P < 0.001) for females, with a specificity of 64% and 55% and a sensitivity of 60% and 92% for males and females, respectively, with the optimum cut-off values of 36.568 and 34.722, respectively.

CONCLUSION

A high ZJU index was significantly associated with an increasing risk of OSAS. The ZJU is expected to be a meaningful index for detecting OSAS in the general population.

From normal population to prediabetes and diabetes: study of influencing factors and prediction models

Objective

The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes.

Materials and methods

The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman's correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model).

Results

This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set.

Conclusion

Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.

Dietary Nutrient Intake and Sleep

Various hormones and neuropeptides implicated in energy metabolism also regulate sleep cycles and wakefulness and promote adequate and restorative sleep [...].

The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results

INTRODUCTION

Obstructive sleep apnea (OSA) is frequently associated with a chronic inflammatory state and cardiovascular/metabolic complications. The aim of this study was to evaluate the influence of certain comorbidities on a panel of 45 chemokines and cytokines in OSA patients with special regard to their possible association with cardiovascular diseases.

MATERIAL AND METHODS

This cross-sectional study was performed on 61 newly diagnosed OSA patients. For the measurement of the plasma concentration of chemokines and cytokines, the magnetic bead-based multiplex assay for the Luminex^® platform was used.

RESULTS

In the patients with concomitant COPD, there were increased levels of pro-inflammatory cytokines (CCL11, CD-40 ligand) and decreased anti-inflammatory cytokine (IL-10), while in diabetes, there were increased levels of pro-inflammatory cytokines (IL-6, TRIAL). Obesity was associated with increased levels of both pro-inflammatory (IL-13) and anti-inflammatory (IL-1RA) cytokines. Hypertension was associated with increased levels of both pro-inflammatory (CCL3) and anti-inflammatory (IL-10) cytokines. Increased daytime pCO₂, low mean nocturnal SaO₂, and the oxygen desaturation index were associated with increased levels of pro-inflammatory cytokines (CXCL1, PDGF-AB, TNF-α, and IL-15).

CONCLUSIONS

In OSA patients with concomitant diabetes and COPD, elevated levels of certain pro-inflammatory and decreased levels of certain anti-inflammatory cytokines may favor the persistence of a chronic inflammatory state with further consequences. Nocturnal hypoxemia, frequent episodes of desaturation, and increased daytime pCO₂ are factors contributing to the chronic inflammatory state in OSA patients.

Mendelian randomization reveals no associations of genetically-predicted obstructive sleep apnea with the risk of type 2 diabetes, nonalcoholic fatty liver disease, and coronary heart disease

BACKGROUND

Obstructive sleep apnea (OSA) has been reported to affect cardiometabolic diseases. However, whether such association is causal is still unknown. Here, we attempt to explore the effect of OSA on type 2 diabetes (T2D), nonalcoholic fatty liver disease (NAFLD) and coronary heart disease (CHD).

METHODS

Genetic variants associated with OSA were requested from a published genome-wide association study (GWAS) and those qualified ones were selected as instrumental variables (IV). Then, the IV-outcome associations were acquired from T2D, NAFLD and CHD GWAS consortia separately. The Mendelian randomization (MR) was designed to estimate the associations of genetically-predicted OSA on T2D, NAFLD and CHD respectively, using the inverse-variance weighted (IVW) method. We applied the Bonferroni method to adjust the p-value. Besides, MR-Egger regression and weighted median methods were adopted as a supplement to IVW. The Cochran's Q value was used to evaluate heterogeneity and the MR-Egger intercept was utilized to assess horizontal pleiotropy, together with MR-PRESSO. The leave-one-out sensitivity analysis was carried out as well.

RESULTS

No MR estimate reached the Bonferroni threshold (p < 0.017). Although the odds ratio of T2D was 3.58 (95% confidence interval (CI) [1.06, 12.11], IVW-p-value = 0.040) using 4 SNPs, such causal association turned insignificant after the removal of SNP rs9937053 located in FTO [OR = 1.30 [0.68, 2.50], IVW p = 0.432]. Besides, we did not find that the predisposition to OSA was associated with CHD [OR = 1.16 [0.70, 1.91], IVW p = 0.560] using 4 SNPs.

CONCLUSION

This MR study reveals that genetic liability to OSA might not be associated with the risk of T2D after the removal of obesity-related instruments. Besides, no causal association was observed between NAFLD and CHD. Further studies should be carried out to verify our findings.