Abstract
Purpose
To investigate the association of positive airway pressure (PAP) therapy and cardiometabolic factors in patients with sleep apnea (SA).
Methods
Patients with cardiometabolic disease from a multicenter cardiometabolic registry from January 2019 to August 2023 were included. Baseline characteristics of patients with SA stratified by PAP use were compared. Logistic regression was used to assess the association of PAP use and cardiometabolic factors (Hemoglobin A1c [HbA1c], systolic and diastolic blood pressure [SBP and SDB], Triglycerides [TG] and body mass index [BMI]) in an unadjusted model (Model 1), after adjusting for age, sex, ethnicity, race, BMI (Model 2), and additionally for cardiac comorbidities, medications, and medication adherence (Model 3).
Results
Of a total of 1575 patients (median age: 65 years [interquartile range 58.0–71.0], 683 (43.4 %) presented with SA, and of those, 447 (65.4 %) reported routine PAP use. Logistic regression analysis showed that PAP use was associated with lower HbA1c (-0.31 %, 95 % CI -0.59 to -0.04, p = 0.025) in Model 2, but not in Model 3 (-0.19 %, 95 % CI -0.46 to 0.08, p = 0.165). In addition, PAP use was associated with lower SBP (-3.99 mmHg, 95 % CI -7.18 to -0.8, p = 0.014) and lower DBP (-2.52 mmHg, 95 % CI -4.45 to -0.6, p = 0.010) in Model 3.
Conclusions
In this multicenter registry of patients with cardiometabolic disease, PAP use was associated with improvement of cardiometabolic key risk factors. This effect was observed for SBP and DBP even after adjusting for medication adherence.
Keywords: Sleep Apnea, Cardiometabolic Risk Factors, Metabolic Syndrome, Positive Airway Pressure
Central Illustration: Graphical abstract presenting the study design and key findings.
1. Introduction
Sleep apnea (SA) is characterized by repeated episodes of apneas or hypopneas caused by collapse of the upper airway, associated with a decrease in oxygen saturation or arousal from sleep [1]. The disorder affects almost 1 billion people globally, with a prevalence in the United States of 25–30 % in men and 9–17 % in women [2,3].
Metabolic syndrome (MS) describes a cluster of risk factors including obesity, dyslipidemia, insulin resistance and hypertension [4]. MS and SA are frequently co-prevalent, and both pathologies share pathophysiological mechanisms of cardiometabolic complications [5]. Association between SA and MS has been investigated in few cross-sectional studies, which revealed both high occurrence of MS in patients with SA (9.1 times higher than in patients without SA) and high prevalence of SA in patients with MS (60.5 %) [6,7]. In a multivariate logistic regression analysis of the US National Health and Nutrition Examination Survey and pooled data from genome-wide association analysis, Tang et al. showed positive relationship of SA with MS and strong association of SA with abdominal obesity, hypertension, hyperglycemia, high triglycerides, and low high density lipoprotein (HDL) [8]. In addition, genetically driven SA was causally associated with a higher risk of MS [8].
As a chronic disorder, SA has a relevant impact on the quality of life and cardiometabolic risk factors (CMRF) of affected patients, often requiring a lifelong therapy [9]. Basic treatment strategies aim to control modifiable risk factors, whereas PAP remains the treatment of choice [10,11]. Despite data from interventional trials on PAP and SA, and randomized controlled trials on PAP and CMRF, there have been few real-world clinical practice studies on CMRF [[12], [13], [14]]. In addition, none of these studies considered the potentially substantial effect of medication adherence on CMRF. Therapeutic success on blood pressure levels or other cardiometabolic parameters depends on the consistency of medication intake. This is known to vary considerably and therefore bias the effects of non-pharmacological treatments [15]. In a real-world study, the adjustment of the study results according to medication adherence can make a significant contribution to reduce methodological limitations.
Despite growing evidence of the association between SA and CMRF, and relative paucity of large real-world data, especially regarding the role and effect of PAP therapy, we chose to leverage an existing multicenter national registry to investigate the effect of PAP on CMRF. The focus was particularly directed towards the group of patients with cardiometabolic diseases, which has so far been underrepresented in the existing literature. We hypothesized that PAP would have significant impact of CMRF even after consideration of confounding variables including medication adherence.
2. Materials and methods
This study involved a retrospective analysis of the Cardiometabolic Center Alliance (CMCA) registry from January 2019 to August 2023. This initiative was designed to treat cardiovascular complications in patients with type 2 diabetes, prediabetes, and other cardiovascular comorbidities. The patient care workflow of the initiative as well as the design of the integrated multicenter registry are described elsewhere [16]. Participation in this study consists of seven medical centers in the United States. Only patients of age 18 and older were included. The study protocol received approval from the institutional review board. The study adhered to the ‘Strengthening the Reporting of Observational studies in Epidemiology’ (STROBE) guidelines [17].
The primary predictor variables were SA diagnosis and PAP use. The primary outcome variables were hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (SDB), low density lipoprotein (LDL), HDL, Triglycerides (TG) and body mass index (BMI). Other variables included in the study were age, sex, ethnicity, race, cardiac comorbidities, medications, and medication adherence. Assessment of investigated variables (SA diagnosis, PAP use, medications, medication adherence, etc.) followed predefined CMCA registry standards, including medical chart review, patient interviews and questionnaires. Assessment of routine PAP device usage was binary (yes/no) and based on patient interview and chart review. Medication adherence was assessed by graduated incremental time intervals (number of days) of missing medication within a month and was based on patient interview: 0–6 days (0–20 %), 7–12 days (21–40 %), 13–18 days (41–60 %), 19–24 days (61–80 %) and 25–30 days (81–100 %).
Baseline characteristics of patients with SA and PAP use were described using mean and standard deviation and were compared using Student’s t-test or Wilcoxon Rank-Sum test for continuous variables. Categorical variables were summarized by frequency and percent and compared using Chi-square or Fisher's exact tests, as appropriate. The percentage of missing values across the variables were assessed and multiple imputations by chained equation and candidate variables was used to generate imputed datasets. To identify the association of SA/PAP use with metabolic parameters (HbA1c, SBP, DBP, LDL, HDL, and TG), a logistic regression model of metabolic parameters was regressed on SA/PAP use (Model 1), adjusted for age, sex, ethnicity, race, BMI (Model 2) and then additionally adjusted for cardiac comorbidities, medications, and medication adherence (Model 3) on imputed datasets. An additional logistic regression analysis was performed regarding the influence of the type of insurance coverage (commercial/governmental/self-pay) on the primary outcome variables. All statistical analyses were performed using SAS version 9.4 software (SAS Institute, Inc, Cary, NC). Two-sided p < 0.05 were considered statistically significant.
3. Results
A total of 1575 patients with a median age of 65 (interquartile range 58.0–71.0) years were included in the analysis. Of these, 683 (43.4 %) presented a diagnosis of SA. Among those patients with SA, 447 (65.4 %) were PAP users. Patients with SA using PAP therapy were older (66.0 vs. 63.0 years, p = 0.006), more likely male (61.5 % vs. 52.8 %, p = 0.034), more likely white (89.0 % vs. 73.9 %, p < 0.001), and had lower HbA1c (7.2 ± 1.5 vs. 7.6 ± 1.9 %, p = 0.002) and DBP (72.9 ± 11.5 vs. 76.0 ± 12.1 mmHg, p = 0.002) compared to patients with SA without PAP therapy. The percentage of patients taking metformin (Glucophage) was higher in the subgroup of PAP users (54.4 % vs. 42.5 %, p = 0.004) compared to non-PAP users. There were no statistically significant differences between the subgroups regarding other medications or comorbidities. Medication adherence showed a significant difference between the subgroups (p = 0.001). Table 1 shows the comparison of demographic and clinical characteristics between patients with SA and patients without SA. Table 2 shows the comparison of medications, medication adherence, and comorbidities between patients with SA and patients without SA. Table 3 shows the comparison of demographic and clinical characteristics between patients with SA using PAP and patients with SA not using PAP. Table 4 shows the comparison of medications, medication adherence, and comorbidities between patients with SA using PAP and patients with SA not using PAP.
Table 1.
Comparison of demographics and cardiometabolic risk factors between patients with SA and patients without SA.
| Total | SA | No SA | P-value | |
|---|---|---|---|---|
| n = 1575 | N = 683 (43.4 %) | n = 892 (56.6 %) | ||
| Demographics | ||||
| Age, Median (IQR) | 65.0 (58.0, 71.0) | 65.0 (58.0, 71.0) | 65.0 (58.0, 72.0) | 0.553 |
| Sex, n ( %) | 0.490 | |||
| Male | 900 (57.1 %) | 397 (58.1 %) | 503 (56.4 %) | |
| Female | 675 (42.9 %) | 286 (41.9 %) | 389 (43.6 %) | |
| Ethnicity, n ( %) | 0.083 | |||
| Non-Hispanic/Latino | 1453 (92.3 %) | 642 (94.0 %) | 811 (91.0 %) | |
| Hispanic/Latino | 101 (6.4 %) | 35 (5.1 %) | 66 (7.4 %) | |
| Unknown | 20 (1.3 %) | 6 (0.9 %) | 14 (1.6 %) | |
| Race, n ( %) | < 0.001 | |||
| Black | 229 (14.5 %) | 87 (12.7 %) | 142 (15.9 %) | |
| Other | 62 (3.9 %) | 15 (2.2 %) | 47 (5.3 %) | |
| White | 1284 (81.5 %) | 581 (85.1 %) | 703 (78.8 %) | |
| Metabolic Parameters | ||||
| BMI, kg/m2 | 35.5 ± 7.6 | 38.5 ± 7.7 | 33.1 ± 6.6 | < 0.001 |
| HbA1c, % | 7.5 ± 1.7 | 7.4 ± 1.6 | 7.6 ± 1.8 | 0.005 |
| Blood Pressure | ||||
| SBP, mmHg | 129.2 ± 18.0 | 129.3 ± 18.6 | 129.1 ± 17.6 | 0.881 |
| DBP, mmHg | 74.7 ± 11.2 | 73.9 ± 11.7 | 75.5 ± 10.6 | 0.006 |
| Lipid Panel | ||||
| Total cholesterol, mg/dL | 154.1 ± 47.9 | 150.5 ± 45.0 | 156.9 ± 50.0 | 0.010 |
| LDL, mg/dL | 78.2 ± 38.2 | 75.5 ± 35.0 | 80.4 ± 40.5 | 0.018 |
| HDL, mg/dL | 41.4 ± 12.6 | 39.9 ± 11.9 | 42.6 ± 13.0 | < 0.001 |
| Triglycerides, mg/dL | 185.5 ± 139.8 | 189.9 ± 132.7 | 182.0 ± 145.3 | 0.279 |
Demographics, metabolic parameters, blood pressure and lipid panel for all patients, patients with sleep apnea and patients without sleep apnea. Age described using median and interquartile range and compared using Wilcoxon Rank-Sum test. Sex, ethnicity, and race summarized by frequency and percent and compared using Chi-square or Fisher's exact tests. Metabolic parameters, blood pressure and lipid panel described using mean and standard deviation and compared using Student’s t-test. Abbreviations: SA: Sleep apnea. IQR: Interquartile range. BMI: Body mass index. HbA1c: Hemoglobin A1C. SBP: Systolic blood pressure. DBP: Diastolic blood pressure. LDL: Low density lipoprotein. HDL: High density lipoprotein.
Table 2.
Comparison of medications, medication adherence and comorbidities between patients with SA and patients without SA.
| Total | SA | No SA | P-value | |
|---|---|---|---|---|
| n = 1575 | N = 683 (43.4 %) | n = 892 (56.6 %) | ||
| Medications | ||||
| SGLT2 Inhibitor, n ( %) | 467 (30.6 %) | 217 (31.8 %) | 250 (29.7 %) | 0.372 |
| GLP1 Agonists, n ( %) | 382 (25.0 %) | 204 (29.9 %) | 178 (21.1 %) | < 0.001 |
| Sulfonylurea Agents, n ( %) | 260 (17.0 %) | 94 (13.8 %) | 166 (19.7 %) | 0.002 |
| Anti-Coagulant, n ( %) | 288 (18.9 %) | 165 (24.4 %) | 123 (14.6 %) | < 0.001 |
| Statin, n ( %) | 1244 (81.5 %) | 571 (83.6 %) | 673 (79.8 %) | 0.059 |
| PCSK9 Inhibitors, n ( %) | 68 (4.5 %) | 28 (4.1 %) | 40 (4.7 %) | 0.543 |
| Omega-3, n ( %) | 297 (19.5 %) | 148 (21.7 %) | 149 (17.7 %) | 0.050 |
| fenofibrate (Tricor), n ( %) | 91 (6.0 %) | 47 (6.9 %) | 44 (5.2 %) | 0.172 |
| ACE Inhibitors, n ( %) | 490 (32.1 %) | 213 (31.2 %) | 277 (32.9 %) | 0.486 |
| Beta-Blocker, n ( %) | 979 (64.3 %) | 446 (65.5 %) | 533 (63.3 %) | 0.375 |
| ARB, n ( %) | 541 (35.5 %) | 255 (37.3 %) | 286 (33.9 %) | 0.166 |
| ARNI, n ( %) | 91 (6.0 %) | 49 (7.2 %) | 42 (5.0 %) | 0.072 |
| MRA, n ( %) | 240 (15.7 %) | 140 (20.5 %) | 100 (11.9 %) | < 0.001 |
| metformin (Glucophage), n ( %) | 814 (53.3 %) | 343 (50.2 %) | 471 (55.9 %) | 0.027 |
| DPP4 Inhibitors, n ( %) | 101 (6.6 %) | 44 (6.4 %) | 57 (6.8 %) | 0.802 |
| Medication Adherence | ||||
| Missing Medication Per Month, n ( %) | 0.203 | |||
| 0–6 days (0–20 %) | 935 (89.6 %) | 423 (91.2 %) | 512 (88.4 %) | |
| 7–12 days (21–40 %) | 49 (4.7 %) | 22 (4.7 %) | 27 (4.7 %) | |
| 13–18 days (41–60 %) | 26 (2.5 %) | 6 (1.3 %) | 20 (3.5 %) | |
| 19–24 days (61–80 %) | 10 (1.0 %) | 3 (0.6 %) | 7 (1.2 %) | |
| 25–30 days (81–100 %) | 23 (2.2 %) | 10 (2.2 %) | 13 (2.2 %) | |
| Comorbidities | ||||
| Pre-Diabetes, n ( %) | 94 (6.0 %) | 44 (6.4 %) | 50 (5.6 %) | 0.487 |
| Type 2 Diabetes, n ( %) | 1364 (86.6 %) | 599 (87.7 %) | 765 (85.8 %) | 0.262 |
| Hypertension, n ( %) | 1376 (87.4 %) | 631 (92.4 %) | 745 (83.5 %) | < 0.001 |
| NASH, n ( %) | 143 (9.1 %) | 87 (12.7 %) | 56 (6.3 %) | < 0.001 |
| Chronic Kidney Disease, n ( %) | 462 (29.3 %) | 232 (34.0 %) | 230 (25.8 %) | < 0.001 |
| Myocardial Infarction, n ( %) | 361 (22.9 %) | 143 (20.9 %) | 218 (24.4 %) | 0.101 |
| PCI, n ( %) | 438 (27.8 %) | 178 (26.1 %) | 260 (29.1 %) | 0.175 |
| CABG, n ( %) | 237 (15.0 %) | 105 (15.4 %) | 132 (14.8 %) | 0.751 |
| Angina, n ( %) | 107 (6.8 %) | 50 (7.3 %) | 57 (6.4 %) | 0.467 |
| Dyslipidemia, n ( %) | 1338 (85.0 %) | 600 (87.7 %) | 738 (82.7 %) | 0.004 |
| Familial Hypercholesterolemia, n ( %) | 35 (2.2 %) | 17 (2.5 %) | 18 (2.0 %) | 0.529 |
| Heart Failure, n ( %) | 544 (34.5 %) | 299 (43.8 %) | 245 (27.5 %) | < 0.001 |
| Peripheral Artery Disease, n ( %) | 116 (7.4 %) | 47 (6.9 %) | 69 (7.7 %) | 0.520 |
| Cerebral Vascular Disease, n ( %) | 201 (12.8 %) | 101 (14.8 %) | 100 (11.2 %) | 0.034 |
| Atrial Fibrillation, n ( %) | 256 (16.3 %) | 151 (22.1 %) | 105 (11.8 %) | < 0.001 |
| ICD, n ( %) | 78 (5.0 %) | 43 (6.3 %) | 35 (3.9 %) | 0.031 |
| Valve Disease, n ( %) | 176 (11.2 %) | 98 (14.3 %) | 78 (8.7 %) | < 0.001 |
| Cardiac Transplant, n ( %) | 20 (1.3 %) | 15 (2.2 %) | 5 (0.6 %) | 0.004 |
| Kidney Transplant, n ( %) | 16 (1.0 %) | 7 (1.0 %) | 9 (1.0 %) | 0.975 |
Medications, medication adherence profile and comorbidities for all patients, patients with sleep apnea and patients without sleep apnea. Medications and comorbidities summarized by frequency and percentage and compared using Chi-square or Fisher's exact tests. Abbreviations: SA: Sleep apnea. SGLT2: Sodium-glucose cotransporter 2. GLP1: Glucagon-like peptide 1. PCSK9: Proprotein convertase subtilisin/kexin type 9. ACE: Angiotensin-converting enzyme. ARB: Angiotensin receptor blocker. ARNI: Angiotensin receptor-neprilysin inhibitor. MRA: Mineralocorticoid receptor antagonist. DPP4: Dipeptidyl peptidase 4. NASH: Nonalcoholic steatohepatitis. PCI: Percutaneous coronary intervention. CABG: Coronary artery bypass graft. ICD: Implantable cardioverter-defibrillator.
Table 3.
Comparison of demographics and cardiometabolic risk factors between patients with SA using PAP and patients with SA not using PAP.
| SA | SA and no PAP use | SA and PAP use | P-value | |
|---|---|---|---|---|
| N = 683 (43.4 %) | n = 212 (31.0 %) | n = 447 (65.4 %) | ||
| Demographics | ||||
| Age, Median (IQR) | 65.0 (58.0, 71.0) | 63.0 (55.5, 71.0) | 66.0 (60.0, 71.0) | 0.006 |
| Sex, n ( %) | 0.034 | |||
| Male | 397 (58.1 %) | 112 (52.8 %) | 275 (61.5 %) | |
| Female | 286 (41.9 %) | 100 (47.2 %) | 172 (38.5 %) | |
| Ethnicity, n ( %) | 0.357 | |||
| Non-Hispanic/Latino | 642 (94.0 %) | 196 (92.5 %) | 423 (94.6 %) | |
| Hispanic/Latino | 35 (5.1 %) | 15 (7.1 %) | 20 (4.5 %) | |
| Unknown | 6 (0.9 %) | 1 (0.5 %) | 4 (0.9 %) | |
| Race, n ( %) | < 0.001 | |||
| Black | 87 (12.7 %) | 43 (20.3 %) | 41 (9.2 %) | |
| Other | 15 (2.2 %) | 6 (2.8 %) | 8 (1.8 %) | |
| White | 581 (85.1 %) | 163 (76.9 %) | 398 (89.0 %) | |
| Metabolic Parameters | ||||
| BMI, kg/m2 | 38.5 ± 7.7 | 38.0 ± 8.5 | 38.7 ± 7.4 | 0.259 |
| HbA1c, % | 7.4 ± 1.6 | 7.6 ± 1.9 | 7.2 ± 1.5 | 0.002 |
| Blood Pressure | ||||
| SBP, mmHg | 129.3 ± 18.6 | 131.1 ± 19.4 | 128.2 ± 18.2 | 0.064 |
| DBP, mmHg | 73.9 ± 11.7 | 76.0 ± 12.1 | 72.9 ± 11.5 | 0.002 |
| Lipid Panel | ||||
| Total cholesterol, mg/dL | 150.5 ± 45.0 | 151.9 ± 44.4 | 149.5 ± 44.6 | 0.520 |
| LDL, mg/dL | 75.5 ± 35.0 | 78.3 ± 38.0 | 73.8 ± 32.7 | 0.140 |
| HDL, mg/dL | 39.9 ± 11.9 | 40.4 ± 11.8 | 39.5 ± 11.4 | 0.382 |
| Triglycerides, mg/dL | 189.9 ± 132.7 | 176.9 ± 97.9 | 197.7 ± 147.8 | 0.066 |
Demographics, metabolic parameters, blood pressure and lipid panel for patients with sleep apnea, stratified by positive airway pressure use and no positive airway pressure use. Age described using median and interquartile range and compared using Wilcoxon Rank-Sum test. Sex, ethnicity, and race summarized by frequency and percent and compared using Chi-square or Fisher's exact tests. Metabolic parameters, blood pressure and lipid panel described using mean and standard deviation and compared using Student’s t-test. Abbreviations: SA: Sleep apnea. PAP: Positive airway pressure. IQR: Interquartile range. BMI: Body mass index. HbA1c: Hemoglobin A1C. SBP: Systolic blood pressure. DBP: Diastolic blood pressure. LDL: Low density lipoprotein. HDL: High density lipoprotein.
Table 4.
Comparison of medications, medication adherence and comorbidities between patients with SA using PAP and patients with SA not using PAP.
| SA | SA and no PAP use | SA and PAP use | P-value | |
|---|---|---|---|---|
| N = 683 (43.4 %) | n = 212 (31.0 %) | n = 447 (65.4 %) | ||
| Medications | ||||
| SGLT2 Inhibitor, n ( %) | 217 (31.8 %) | 75 (35.4 %) | 134 (30.0 %) | 0.164 |
| GLP1 Agonists, n ( %) | 204 (29.9 %) | 73 (34.4 %) | 122 (27.3 %) | 0.060 |
| Sulfonylurea Agents, n ( %) | 94 (13.8 %) | 26 (12.3 %) | 64 (14.3 %) | 0.473 |
| Anti-Coagulant, n ( %) | 165 (24.4 %) | 43 (20.3 %) | 119 (26.6 %) | 0.077 |
| Statin, n ( %) | 571 (83.6 %) | 174 (82.1 %) | 374 (83.7 %) | 0.609 |
| PCSK9 Inhibitors, n ( %) | 28 (4.1 %) | 12 (5.7 %) | 16 (3.6 %) | 0.216 |
| Omega-3, n ( %) | 148 (21.7 %) | 37 (17.5 %) | 106 (23.7 %) | 0.068 |
| fenofibrate (Tricor), n ( %) | 47 (6.9 %) | 11 (5.2 %) | 36 (8.1 %) | 0.181 |
| ACE Inhibitors, n ( %) | 213 (31.2 %) | 59 (27.8 %) | 150 (33.6 %) | 0.140 |
| Beta-Blocker, n ( %) | 446 (65.5 %) | 150 (70.8 %) | 281 (63.1 %) | 0.054 |
| ARB, n ( %) | 255 (37.3 %) | 72 (34.0 %) | 166 (37.1 %) | 0.428 |
| ARNI, n ( %) | 49 (7.2 %) | 19 (9.0 %) | 30 (6.7 %) | 0.303 |
| MRA, n ( %) | 140 (20.5 %) | 43 (20.3 %) | 94 (21.0 %) | 0.825 |
| metformin (Glucophage), n ( %) | 343 (50.2 %) | 90 (42.5 %) | 243 (54.4 %) | 0.004 |
| DPP4 Inhibitors, n ( %) | 44 (6.4 %) | 15 (7.1 %) | 27 (6.0 %) | 0.611 |
| Medication Adherence | ||||
| Missing Medication Per Month, n ( %) | 0.001 | |||
| 0–6 days (0–20 %) | 423 (91.2 %) | 112 (83.6 %) | 301 (94.4 %) | |
| 7–12 days (21–40 %) | 22 (4.7 %) | 12 (9.0 %) | 9 (2.8 %) | |
| 13–18 days (41–60 %) | 6 (1.3 %) | 2 (1.5 %) | 4 (1.3 %) | |
| 19–24 days (61–80 %) | 3 (0.6 %) | 1 (0.7 %) | 2 (0.6 %) | |
| 25–30 days (81–100 %) | 10 (2.2 %) | 7 (5.2 %) | 3 (0.9 %) | |
| Comorbidities | ||||
| Pre-Diabetes, n ( %) | 44 (6.4 %) | 19 (9.0 %) | 25 (5.6 %) | 0.105 |
| Type 2 Diabetes, n ( %) | 599 (87.7 %) | 187 (88.2 %) | 388 (86.8 %) | 0.612 |
| Hypertension, n ( %) | 631 (92.4 %) | 195 (92.0 %) | 412 (92.2 %) | 0.933 |
| NASH, n ( %) | 87 (12.7 %) | 28 (13.2 %) | 59 (13.2 %) | 0.997 |
| Chronic Kidney Disease, n ( %) | 232 (34.0 %) | 70 (33.0 %) | 153 (34.2 %) | 0.759 |
| Myocardial Infarction, n ( %) | 143 (20.9 %) | 43 (20.3 %) | 97 (21.7 %) | 0.677 |
| PCI, n ( %) | 178 (26.1 %) | 58 (27.4 %) | 116 (26.0 %) | 0.701 |
| CABG, n ( %) | 105 (15.4 %) | 41 (19.3 %) | 63 (14.1 %) | 0.084 |
| Angina, n ( %) | 50 (7.3 %) | 15 (7.1 %) | 34 (7.6 %) | 0.808 |
| Dyslipidemia, n ( %) | 600 (87.7 %) | 188 (88.7 %) | 390 (87.2 %) | 0.601 |
| Familial Hypercholesterolemia, n ( %) | 17 (2.5 %) | 7 (3.3 %) | 10 (2.2 %) | 0.420 |
| Heart Failure, n ( %) | 299 (43.8 %) | 86 (40.6 %) | 206 (46.1 %) | 0.182 |
| Peripheral Artery Disease, n ( %) | 47 (6.9 %) | 17 (8.0 %) | 28 (6.3 %) | 0.404 |
| Cerebral Vascular Disease, n ( %) | 101 (14.8 %) | 27 (12.7 %) | 72 (16.1 %) | 0.257 |
| Atrial Fibrillation, n ( %) | 151 (22.1 %) | 42 (19.8 %) | 105 (23.5 %) | 0.289 |
| ICD, n ( %) | 43 (6.3 %) | 16 (7.5 %) | 26 (5.8 %) | 0.395 |
| Valve Disease, n ( %) | 98 (14.3 %) | 28 (13.2 %) | 70 (15.7 %) | 0.408 |
| Cardiac Transplant, n ( %) | 15 (2.2 %) | 3 (1.4 %) | 11 (2.5 %) | 0.565 |
| Kidney Transplant, n ( %) | 7 (1.0 %) | 3 (1.4 %) | 3 (0.7 %) | 0.392 |
Medications, medication adherence profile and comorbidities for patients with sleep apnea, stratified by positive airway pressure use. Medications and comorbidities summarized by frequency and percentage and compared using Chi-square or Fisher's exact tests. Abbreviations: SA: Sleep apnea. PAP: Positive airway pressure. SGLT2: Sodium-glucose cotransporter 2. GLP1: Glucagon-like peptide 1. PCSK9: Proprotein convertase subtilisin/kexin type 9. ACE: Angiotensin-converting enzyme. ARB: Angiotensin receptor blocker. ARNI: Angiotensin receptor-neprilysin inhibitor. MRA: Mineralocorticoid receptor antagonist. DPP4: Dipeptidyl peptidase 4. NASH: Nonalcoholic steatohepatitis. PCI: Percutaneous coronary intervention. CABG: Coronary artery bypass graft. ICD: Implantable cardioverter-defibrillator.
Logistic regression analysis showed that among patients with SA, PAP use was associated with lower HbA1c (−0.31 %, 95 % CI −0.59 to −0.04, p = 0.025) in Model 2, but not in Model 3 (−0.19 %, 95 % CI −0.46 to 0.08, p = 0.165). In addition, PAP use was associated with lower SBP (−3.99 mmHg, 95 % CI −7.18 to −0.8, p = 0.014) and lower DBP (−2.52 mmHg, 95 % CI −4.45 to −0.6, p = 0.010) in Model 3. Significant reduction of SBP and DBP were also observed in Model 2. Higher TG (23.02 mg/dL, 95 % CI 1.03 to 45.01, p = 0.040) were associated with PAP use compared to non-PAP use in Model 2, but not in Model 3 (20.18 mg/dL, 95 % CI −0.86 to 41.21, p = 0.060). Table 5 shows results of the different regression models for PAP use.
Table 5.
Regression analysis for PAP use compared to non-PAP use among patients with SA.
| Model 1 |
Model 2 |
Model 3 |
||||
|---|---|---|---|---|---|---|
| Regression Coefficient | P-value | Regression Coefficient | P-value | Regression Coefficient | P-value | |
| HbA1c, % | −0.43 (−0.7, −0.16) | 0.002 | −0.31 (−0.59, −0.04) | 0.025 | −0.19 (−0.46, 0.08) | 0.165 |
| SBP, mmHg | −2.97 (−6.08, 0.14) | 0.061 | −3.4 (−6.58, −0.21) | 0.037 | −3.99 (−7.18, −0.8) | 0.014 |
| DBP, mmHg | −3.09 (−5.03, −1.14) | 0.002 | −2.35 (−4.29, −0.42) | 0.017 | −2.52 (−4.45, −0.6) | 0.010 |
| LDL, mg/dL | −5.2 (−11.1, 0.71) | 0.085 | −1.42 (−7.27, 4.42) | 0.633 | −1.28(−6.93, 4.37) | 0.657 |
| HDL, mg/dL | −0.92 (−2.85, 1.01) | 0.350 | 0.08 (−1.77, 1.94) | 0.930 | 0.32 (−1.54, 2.17) | 0.737 |
| TG, mg/dL | 20.61 (−1.48, 42.71) | 0.067 | 23.02 (1.03, 45.01) | 0.040 | 20.18(−0.86, 41.21) | 0.060 |
Regression analysis of Hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglyceride (TG) for patients with sleep apnea (SA) using positive airway pressure (PAP) therapy compared to patients with SA not using PAP therapy in Model 1 (no adjustments), Model 2 (adjusted for age, sex, ethnicity, race, body mass index) and Model 3 (adjusted for age, sex, ethnicity, race, body mass index, cardiac comorbidities, medications, and medication adherence). Regression coefficient with 95 % confidence interval.
Logistic regression analysis showed no statistical differences on the primary outcome variables between patients with commercial, governmental or self-pay insurance type (HbA1c: p = 0.797, SBP: p = 0.551, DBP: p = 0.939, HDL: p = 0.957, LDL: p = 0.314, TG: p = 0.744).
4. Discussion
Using a large multicenter registry, we found a high percentage of patients with SA among cardiometabolic patients, and differences in CMRF between patients with SA using PAP therapy and patients with SA not using PAP therapy. PAP use was associated with significantly lower levels of HbA1c after adjusting for demographics, and significantly lower levels of both SBP and DBP when even additionally adjusted for comorbidities, medications, and medication adherence.
Sleep apnea is a multifactorial disorder. Upper airway narrowing is associated with BMI, which can also be observed with the increasing number of patients with SA alongside the rise in obesity rates [3,18]. In addition to obesity, the most common cause of SA in adults is male sex and advancing age [19]. Adipose tissue inflammation caused by intermittent hypoxia is common in both SA and obesity and is shown to be the main pathway for various changes in glucose and lipid metabolism [20]. In obesity, hypertrophy of adipocytes leads to adipose tissue inflammation, which results in secretion of proinflammatory adipokines (tumor necrosis factor alpha, interleukin-6, resistin) causing insulin resistance and metabolic dysfunction [21]. Intermittent hypoxia – as observed in SA – is also a potent proinflammatory stimulus for adipose tissue, inducing similar pathological mechanisms [20]. Despite parallel and related pathways of SA and MS, causality cannot be assumed, as clinical evidence relies mainly on cross-sectional studies [5].
The high prevalence of SA in cardiometabolic patients in this study reflects the existing literature, ranging from 60.5 % to 66 % in patients with MS [7,22]. Likewise, the positive effect of PAP therapy on HbaA1c levels in respective patients aligns with existing evidence. A meta-analysis of eleven RCTs including 964 patients showed that PAP therapy improved HbA1c levels (−0.24 %, 95 % CI −0.43 to −0.06, p = 0.001) in patients with SA and type 2 diabetes compared to inactive control groups [23]. The overall reduction of HbA1c in this meta-analysis is comparable to the results of our study. Another recent meta-analysis of 22 studies investigating the effect of different SA treatments on MS showed that PAP therapy reduced the prevalence of MS in patients with SA in both RCTs (risk reduction = 0.82, 95 % CI 0.75 to 0.90. p < 0.01 in RCTs) and single-arm studies (risk reduction = 0.73, 95 % CI 0.63 to 0.84, p < 0.01) [24]. Giampa et al. investigated the effect of PAP in patients with MS and SA in a prospective placebo-controlled trial, including 50 patients in each study group [25]. The trial resulted in a higher rate of MS reversibility after PAP therapy (18 % vs. 4 %, OR 5.27, 95 % CI, 1.27 to 35.86, p = 0.04) compared to the placebo group, although most patients retained the MS diagnosis. Recently, Choudhary et al. presented short-term results of a prospective study investigating the effect of new onset PAP therapy on 79 patients with MS, showing significant improvement of HbA1c and HDL after three months PAP therapy [26]. In addition, 17.7 % of the patients no longer met MS definitions [26]. In additional regard to the impact of PAP use on blood pressure in patients with SA, a meta-analysis of 112 placebo-controlled trials with 572 patients in total showed a net decrease of 1.69 mmHg in 24-hour mean blood pressure (MBP, 95 % CI −2.69 to −0.69) and a greater treatment-related MPB reduction among patients with a more severe SA degree [27]. The more recent AASM systematic review confirms this observation, with significant reduction in nighttime SBP (−4.2 mmHg, 95 % CI −6.0 to −2.5) and DBP (−2.3 mmHg, 95 % CI −3.7 to −0.9), daytime SBP (−2.8 mmHg, 95 % CI −4.3 to −1.2) and DBP (−2.0 mmHg, 95 % CI −3.0 to −0.9), as well as 24-hour SBP (−1.5 mmHg, 95 % CI −2.3 to −0.7) and DBP (−1.6 mmHg, 95 % CI −2.2 to −0.9) [28]. The reduction of SBP and DBP in our study is comparable to this systematic review.
Although observational studies showed an increased risk for adverse cardiovascular events in patients not using PAP, the effect is not certain [29]. The AASM meta-analysis from 2019 showed no impact on cardiovascular events or mortality [28]. Furthermore, randomized controlled trials investigating this effect did not reveal significant reduction in cardiovascular events [[30], [31], [32]]. However, short follow-up periods and low PAP adherence were seen as limitations, as in the SAVE trial as largest investigation regarding this subject with a follow-up of 3.7 years and a PAP adherence of 3.5 h [30]. An individual participant data meta-analysis of the relevant RCTs by Sanchez de la Torre et al. revealed that an adequate adherence to PAP (≥4 h/day) was associated with a reduced recurrence risk of major adverse cardiac or cerebrovascular events with a significant hazard ratio of 0.69, suggesting that treatment adherence is a key factor in secondary cardiovascular prevention in patients with SA [33]. In addition, the impact of PAP on several risk factors of adverse cardiovascular outcomes, such as coronary heart disease, heart failure and pulmonary hypertension has been shown [[34], [35], [36]]. This suggests a reduction in cardiovascular events in the long term and should be investigated further in future studies.
In our study, PAP use led to statistically significant HbA1c reduction in Model 2, but not in Model 3. The attenuated effect after additionally adjusting for medications and medication adherence could be due to respective optimized anti-diabetic therapy, as the described PAP effect could be mitigated by better control of long-term glucose levels. Nevertheless, medications and medication adherence were not considered (and respectively statistically corrected for) in other studies investigating the effect of PAP on CMRF, which underlines the relevance of the (statistically significant) HbA1c reduction in Model 2. Our study also observed that TG were higher in patients with SA and PAP therapy when adjusting for demographics, but not when adjusting additionally for comorbidities and medication. In a systematic review and meta-analysis of 31 RCTs PAP therapy for patients with SA was associated with reduction of total cholesterol, but no reduction of TG, LDL and HDL [37]. In contrast, another meta-analysis showed a reduction of TG with PAP therapy in patients with SA [24]. Although we could not identify the specific reasons for the findings in Model 2, the TG elevation is not statistically significant after adjusting for medications and medication adherence, which also could be explained by respective optimized medical therapy. Furthermore, triglyceride levels are dependent on factors such as genetics, fitness level, lifestyle, diet, fasting, etc., which may also weaken further interpretation [38,39]. Our findings are noteworthy as it involves a large cohort of real-world patients from multiple sites and add to the existing limited literature that PAP therapy leads to reduction in blood pressure despite adjustment for all risk factors, including medication adherence. Although blood pressure treatment effects are shown to be proportional to the intensity of blood pressure reduction, even modest reduction leads to considerable outcome improvement [40]. In a meta-analysis, Canoy et al. showed that for each 5-mmHg SBP reduction, the risk of developing cardiovascular events fell by 10 % (hazard ratio 0.90, 95 % CI 0.88 to 0.92) [40].
In our study, PAP users were more likely to be white and had lower baseline HbA1c. This observation leads to the assumption of a selection bias in terms of better outcomes of PAP therapy in patients with better access to healthcare. According to the available information on the insurance type of the individual patients, we showed that this did not influence the effect on CMRF. Nevertheless, the information is limited due to the rather broad categorization (commercial, governmental, self-pay) in the registry.
The strengths of this study include the utilization of a large sample size from multiple centers. To our knowledge, this is the largest multicenter cross-sectional study analyzing PAP effect on cardiometabolic factors. In addition, data collection and analysis within the registry follows predefined and regulated standards that improve data quality and reduce respective observational biases. Moreover, this study is based on a dedicated cardiometabolic patient population, highlighting the effect of PAP therapy for this group more specifically. In addition, this study reports medication adherence and includes this important clinical information in regression analysis. As medication adherence is known to vary significantly, this method allows the influence of PAP use to be differentiated more precisely after adjusting the comparison groups on the basis of the expected medication effect [15]. A recent evaluation of the association between PAP therapy adherence and medication adherence revealed that patients who were adherent with medications used PAP more hours per night (5.4 vs. 4.6, p < 0.001) and were more likely to have regular PAP use compared to patients non-adherent with medications [41]. Limitations include the unavailability of objective sleep study data for SA risk assessment, which could affect outcomes in SA therapy and differences in metabolic parameters by not considering different SA risk levels among affected patients. Furthermore, data regarding PAP adherence is unavailable. The duration of nightly PAP usage has a proportional effect on the therapy success, and adherence is frequently suboptimal [23,42]. The binary assessment of routine PAP device usage in this study could lead to misclassification, assuming that a patient with very low nightly PAP usage could have reported as PAP user in the registry. Hence, the corresponding effect on metabolic risk factors in this study could be biased. Besides, it should be stated that the cross-sectional study type does not allow for interpretation of causal relationship. Nonetheless, according to the current research environment on this topic, this study is useful to provide preliminary evidence for planning future studies.
5. Conclusions
In addition to be the most effective treatment option for patients with SA, PAP therapy was associated with improved HbA1c and with improved SBP and DBP in cardiometabolic patients. The effect on blood pressure was even present after adjusting for (e.g., antihypertensive) medication and medication adherence. The results of this study from a large multicenter registry support the scientific landscape on this topic and provide a basis for prospective studies to eventually optimize individual treatment of patients with SA and MS. Such studies should be based on randomized trials with the aim of answering the yet uncertain effect of a long-term reduction in mortality and cardiovascular events through PAP with adequate adherence.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the Saint Luke’s Mid America Heart Institute and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
CRediT authorship contribution statement
Daniel Körfer: Conceptualization, Investigation, Methodology, Data curation, Writing – original draft. Wendy Cardenas: Conceptualization, Methodology, Writing – review & editing, Data curation, Investigation. Lisa Davis: Methodology, Formal analysis, Investigation, Data curation, Writing – review & editing, Conceptualization. Shachi Patel: Visualization, Resources, Conceptualization, Validation, Data curation, Writing – review & editing, Formal analysis, Software. Mikhail N. Kosiborod: Project administration, Data curation, Resources, Writing – review & editing, Formal analysis, Conceptualization. Melissa Magwire: Project administration, Conceptualization, Writing – review & editing, Data curation, Methodology. Daniel Aistrope: Resources, Formal analysis, Investigation, Writing – review & editing, Data curation, Conceptualization. Jonathan Fialkow: Writing – review & editing, Data curation, Methodology, Project administration, Conceptualization, Resources. Adedapo IIuyomade: Formal analysis, Conceptualization, Methodology, Data curation, Writing – review & editing. Harneet K. Walia: Writing – original draft, Methodology, Conceptualization, Data curation, Supervision, Investigation, Validation, Project administration, Formal analysis.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Harneet K. Walia reports a relationship with ResMed Inc. that includes: board membership. Harneet K. Walia reports a relationship with Eli Lilly and Company that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
I confirm I have included a data availability statement in my main manuscript file. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Contributor Information
Daniel Körfer, Email: daniel.korfer@baptisthealth.net, daniel.koerfer@med.uni-heidelberg.de.
Wendy Cardenas, Email: wendyCa@baptisthealth.net.
Lisa Davis, Email: lisadav@baptisthealth.net.
Shachi Patel, Email: shapatel1@saint-lukes.org.
Mikhail N. Kosiborod, Email: mkosiborod@saint-lukes.org.
Melissa Magwire, Email: mmagwire@saint-lukes.org.
Daniel Aistrope, Email: daistrope@saint-lukes.org.
Jonathan Fialkow, Email: jonathanFi@baptisthealth.net.
Adedapo IIuyomade, Email: adedapo.iluyomade@baptisthealth.net.
Harneet K. Walia, Email: harneetw@baptisthealth.net.
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