Erectile Dysfunction in Patients with Sleep Apnea – A Nationwide Population-Based Study

<div><p>Increased incidence of erectile dysfunction (ED) has been reported among patients with sleep apnea (SA). However, this association has not been confirmed in a large-scale study. We therefore performed a population-based cohort study using Taiwan National Health Insurance (NHI) database to investigate the association of SA and ED. From the database of one million representative subjects randomly sampled from individuals enrolled in the NHI system in 2010, we identified adult patients having SA and excluded those having a diagnosis of ED prior to SA. From these suspected SA patients, those having SA diagnosis after polysomnography were defined as probable SA patients. The dates of their first SA diagnosis were defined as their index dates. Each SA patient was matched to 30 randomly-selected, age-matched control subjects without any SA diagnosis. The control subjects were assigned index dates as their corresponding SA patients, and were ensured having no ED diagnosis prior to their index dates. Totally, 4,835 male patients with suspected SA (including 1,946 probable SA patients) were matched to 145,050 control subjects (including 58,380 subjects matched to probable SA patients). The incidence rate of ED was significantly higher in probable SA patients as compared with the corresponding control subjects (5.7 vs. 2.3 per 1000 patient-year; adjusted incidence rate ratio = 2.0 [95% CI: 1.8-2.2], <i>p</i><0.0001). The cumulative incidence was also significantly higher in the probable SA patients (<i>p</i><0.0001). In multivariable Cox regression analysis, probable SA remained a significant risk factor for the development of ED after adjusting for age, residency, income level and comorbidities (hazard ratio = 2.0 [95%CI: 1.5-2.7], <i>p</i><0.0001). In line with previous studies, this population-based large-scale study confirmed an increased ED incidence in SA patients in Chinese population. Physicians need to pay attention to the possible underlying SA while treating ED patients.</p></div

Algorithm for identifying the study population.

<p>(Abbreviations: SA = sleep apnea; ED = erectile dysfunction; PSG = polysomnography).</p

Incidence rate of erectile dysfunction after the index date in each group.

<p>The adjusted IRRs were calculated by multivariable analyses adjusting for age, residency, income and the presence of various comorbidities (except for the variable used for stratification).</p><p>*<i>p</i><0.05</p><p>**<i>p</i><0.01</p><p>***<i>p</i><0.0001</p><p>Abbreviation: SA = sleep apnea; CCI = Charlson Comorbidity Index</p><p>N = number of patients; ED = number of patients with erectile dysfunction; PY = total patient-years</p><p>IR = incident rate, as expressed as ED incidence per 1000 patient-years; IRR = incidence rate ratio; CI = confidence interval.</p><p>Incidence rate of erectile dysfunction after the index date in each group.</p

Baseline characteristics of the study population.

<p>Abbreviation: SA = sleep apnea; CCI = Charlson Comorbidity Index.</p><p>Baseline characteristics of the study population.</p

Stratified analyses of the multivariable Cox proportional hazards regression analyses.

<p>The results are presented with adjusted HRs (95% CI) of sleep apnea, which are adjusted for age, residency, income and the presence of various comorbidities (except for the variable used for stratification). *Abbreviations: SA = sleep apnea; CCI = Charlson Comorbidity Index; HR = hazard ratio; CI = confidence interval. †: Due to small sample size, hazard ratio cannot be estimated.</p

The cumulative incidences of erectile dysfunction (ED).

<p>The blue dashed lines and red continuous lines show the cumulative incidence of ED for the control cohort and the sleep apnea (SA) cohort, respectively. (A, C, E) study arm A (suspected SA vs. control A); (B, D, F) study arm B (probable SA vs. control B); (A, B) whole study population; (C, D) subjects ≤ 50 years old; (E, F) subjects >50 years old.</p

Plasminogen Activator Inhibitor-2 Plays a Leading Prognostic Role among Protease Families in Non-Small Cell Lung Cancer

<div><p>Background</p><p>In lung cancer, uPA, its receptor (uPAR), and the inhibitors PAI-1 and PAI-2 of the plasminogen activator family interact with MMP-2 and MMP-9 of the MMP family to promote cancer progression. However, it remains undetermined which of these markers plays the most important role and may be the most useful indicator to stratify the patients by risk.</p><p>Methods</p><p>We determined the individual prognostic value of these 6 markers by analyzing a derivation cohort with 98 non-small cell lung cancer patients by immunohistochemical staining. The correlation between the IHC expression levels of these markers and disease prognosis was investigated, and an immunohistochemical panel for prognostic prediction was subsequently generated through prognostic model analysis. The value of the immunohistochemical panel was then verified by a validation cohort with 91 lung cancer patients.</p><p>Results</p><p>In derivation cohort, PAI-2 is the most powerful prognostic factor (HR = 2.30; <i>P</i> = 0.001), followed by MMP-9 (HR = 2.09; <i>P</i> = 0.019) according to multivariate analysis. When combining PAI-2 and MMP-9, the most unfavorable prognostic group (low PAI-2 and high MMP-9 IHC expression levels) showed a 6.40-fold increased risk of a poor prognosis compared to the most favorable prognostic group (high PAI-2 and low MMP-9 IHC expression levels). PAI-2 and MMP-9 IHC panel could more precisely identify high risk patients in both derivation and validation cohort.</p><p>Conclusions</p><p>We revealed PAI-2 as the most powerful prognostic marker among PA and MMP protease family even after considering their close relationships with each other. By utilizing a combination of PAI-2 and MMP-9, more precise prognostic information than merely using pathological stage alone can be obtained for lung cancer patients.</p></div

Cox multivariate analysis with false discovery rate correction of PAI-1, PAI-2, uPA, uPAR, MMP-2 and MMP-9 IHC expression levels and pathological stage in derivation cohort with 98 NSCLC cases.

<p>*<i>P</i> value after Benjamini and Hochberg false discovery rate (FDR) procedure.</p><p>PAI-1: plasminogen activator inhibitor-1; PAI-2: plasminogen activator inhibitor-2; uPA: urokinase-type plasminogen activator; uPAR: urokinase-type plasminogen activator receptor; MMP-2: matrix metalloproteinase 2; MMP-9: matrix metalloproteinase 9.</p><p>Cox multivariate analysis with false discovery rate correction of PAI-1, PAI-2, uPA, uPAR, MMP-2 and MMP-9 IHC expression levels and pathological stage in derivation cohort with 98 NSCLC cases.</p

Cox multivariate analysis with false discovery rate correction of PAI-1, PAI-2, uPA, uPAR, MMP-2 and MMP-9 IHC expression levels and pathological stage in derivation cohort with 98 NSCLC cases.

<p>*<i>P</i> value after Benjamini and Hochberg false discovery rate (FDR) procedure.</p><p>PAI-1: plasminogen activator inhibitor-1; PAI-2: plasminogen activator inhibitor-2; uPA: urokinase-type plasminogen activator; uPAR: urokinase-type plasminogen activator receptor; MMP-2: matrix metalloproteinase 2; MMP-9: matrix metalloproteinase 9.</p><p>Cox multivariate analysis with false discovery rate correction of PAI-1, PAI-2, uPA, uPAR, MMP-2 and MMP-9 IHC expression levels and pathological stage in derivation cohort with 98 NSCLC cases.</p

An IHC panel comprising PAI-2 and MMP-9 provides a more precise prognostic predictive power for 98 NSCLC patients in derivation cohort and 91 NSCLC patients in validation cohort.

<p>(A) Kaplan-Meier plots and ROC curve of overall survival and disease-free survival for the combination of PAI-2 and MMP-9 as an IHC panel. Patients with high PAI-2 and low MMP-9 IHC expression levels had the most favorable prognosis, whereas patients with low PAI-2 and high MMP-9 IHC expression levels had the most unfavorable prognosis (<i>P</i> < 0.001 for both OS and DFS). Increase of area under curve (AUC) of ROC was observed after combining PAI-2 and MMP-9 as a panel in disease-free survival. In the heat map clustered by end-point survival status, patients who were dead at the end-point tended to have low PAI-2 and high MMP-9 IHC expression level. (B) Representative images of the usage of the PAI-2 and MMP-9 IHC panel in clinical NSCLC samples. Patient 1 had stage I adenocarcinoma with high PAI-2 and low MMP-9 IHC expression levels and was alive at the final follow up. In contrast, patient 2 had stage IV adenocarcinoma with low PAI-2 and high MMP-9 IHC expression levels and died 2 months after treatment commenced. Patients 3 and 4 were both diagnosed with stage III adenocarcinoma. Patient 3, who had high PAI-2 and low MMP-9 IHC expression levels, was alive and had no tumor recurrence at the final follow up; patient 4, who had low PAI-2 and high MMP-9 IHC expression levels, experienced tumor recurrence within 9 months and died 14 months after treatment commenced. Photographs were taken at a magnification of 400×. Scale bars represent 100 μm. (C) Kaplan-Meier plots and ROC curve of overall survival in validation cohort showed that patients with low PAI-2 IHC expression level (<i>P</i> = 0.002) or high MMP-9 IHC expression level (<i>P</i> = 0.035) had poor overall survival. When stratified by PAI-2 and MMP-9 IHC panel, high risk group patients had low PAI-2 and high MMP-9 IHC expression levels and low risk group patients had high PAI-2 and low MMP-9 IHC expression level (<i>P</i> < 0.001). Increase of area under curve (AUC) of ROC was observed after combining PAI-2 and MMP-9 as a panel. The heat map showed that patients who were dead at the end-point tended to have low PAI-2 and high MMP-9 IHC expression level.</p