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Original Research: SLEEP MEDICINE |

Association Between Polysomnographic Measures of Disrupted Sleep and Prothrombotic Factors* FREE TO VIEW

Roland von Känel, MD; José S. Loredo, MD, FCCP; Sonia Ancoli-Israel, PhD; Paul J. Mills, PhD; Loki Natarajan, PhD; Joel E. Dimsdale, MD
Author and Funding Information

*From the Department of General Internal Medicine (Dr. von Känel), University Hospital Berne, Switzerland; and the Departments of Psychiatry (Drs. Mills, Dimsdale, and Ancoli-Israel) and Medicine (Dr. Loredo), and Division of Statistics, Department of Family and Preventive Medicine (Dr. Natarajan), University of California San Diego, La Jolla, CA.

Correspondence to: Joel E. Dimsdale, MD, Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093-0804; e-mail: jdimsdale@ucsd.edu



Chest. 2007;131(3):733-739. doi:10.1378/chest.06-2006
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Background: Subjective sleep disturbances have been associated with increased risk of coronary artery disease (CAD). We hypothesized that disrupted sleep as verified by polysomnography is associated with increased levels of prothrombotic hemostasis factors previously shown to predict CAD risk.

Methods: Full-night polysomnography was performed in 135 unmedicated men and women (mean age ± SD, 36.8 ± 7.8 years) without a history of sleep disorders. Morning fasting plasma levels of von Willebrand Factor (VWF) antigen, soluble tissue factor (sTF) antigen, d-dimer, and plasminogen activator inhibitor (PAI)-1 antigen were determined. Statistical analyses were adjusted for age, gender, ethnicity, body mass index, BP, and smoking history.

Results: Higher total arousal index (ArI) was associated with higher levels of VWF (β = 0.25, p = 0.011, ΔR2 = 0.045), and longer wake after sleep onset was associated with higher levels of sTF (β = 0.23, p = 0.023, ΔR2 = 0.038). More nighttime spent at mean oxygen saturation < 90% (β = 0.20, p = 0.020, ΔR2 = 0.029) and higher apnea-hypopnea index (AHI) [β = 0.19, p = 0.034, ΔR2 = 0.024] were associated with higher PAI-1. There was a trend for a relationship between mean oxygen desaturation < 90% and PAI-1 (p = 0.053), even after controlling for AHI. Total ArI (β = 0.28, p = 0.005, ΔR2 = 0.056) and WASO (β = 0.25, p = 0.017, ΔR2 = 0.042) continued to predict VWF and sTF, respectively, even after controlling for AHI.

Conclusions: Polysomnographically verified sleep disruptions were associated with prothrombotic changes. Measures of sleep fragmentation and sleep efficiency were related to VWF and sTF, respectively. Apnea-related measures were related to PAI-1. Our findings suggest that sleep disruptions, even in a relatively healthy population, are associated with potential markers of prothrombotic cardiovascular risk.

Enhanced blood coagulability plays a paramount role in the initiation and progression of atherosclerosis and clinical manifestation of coronary artery disease (CAD).14 Previous work has suggested that different aspects of disrupted sleep contribute to a prothrombotic state in populations with increased coronary risk, such as patients with obstructive sleep apnea (OSA)57 and elderly caregivers of Alzheimer disease.89 As compared to patients with no OSA, those with OSA have higher plasma levels of several procoagulant molecules such as fibrinogen,1012 activated clotting factor FVII (FVIIa), FXIIa, and thrombin/antithrombin III complexes.13Platelet activity1415 and the fibrinolysis-inhibiting enzyme plasminogen activator inhibitor (PAI)-116have also been reported as higher in sleep apnea patients. In patients with moderate-to-severe OSA, we previously found that higher apnea-hypopnea index (AHI) and lower mean nighttime oxyhemoglobin saturation (Spo2) were both associated with a higher concentration of circulating PAI-1.17 In addition, higher percentage of time spent in stage 2 sleep correlated with higher levels of d-dimer in caregivers of dementia patients; and, moreover, lower sleep efficiency was associated with higher d-dimer in both caregivers and their gender-matched noncaregiving counterparts.18

Previous studies19did not investigate, however, whether prothrombotic changes occur with disrupted sleep in populations other than those with OSA or stressed elderly. As there is some evidence from large-scale epidemiologic studies20for an association between insomnia complaints and an increased risk of CAD, it is worthwhile to explore these relationships. Demonstrating that objectively determined sleep disruptions are associated with prothrombotic changes in a relatively normal population might provide one possible explanation for the epidemiologic link between subjectively perceived poor sleep quality and CAD.21

An increased plasma level of von Willebrand factor (VWF) and of soluble tissue factor (sTF) both indicate endothelial damage.22VWF exerts its procoagulant function by mediating platelet adhesion to subendothelial structures and by stabilizing FVIII in plasma.23The binding of circulating sTF to FVIIa initiates blood coagulation.24The hypercoagulability marker d-dimer is generated on degradation of fibrin by plasmin and reflects activation of the entire coagulation and fibrinolysis cascades.25PAI-1 inhibits fibrinolytic activity by virtue of inactivating circulating tissue-type plasminogen activator in a tissue-type plasminogen activator/PAI-1 complex.26Metaanalyses have shown that VWF27and d-dimer28are both predictive for future coronary events. Also, increased levels of sTF29 and of PAI-126 may contribute to atherothrombotic events.

We therefore examined if hemostatic activity is adversely affected by polysomnographically verified sleep disruption in a middle-aged relatively healthy sample without a history of sleep disorders. We hypothesized that measures indicating disrupted sleep are associated with higher plasma levels of four hemostasis factors, namely VWF antigen, sTF antigen, d-dimer, and PAI-1 antigen, independent of covariates commonly affecting hemostatic function.

Study Participants

All subjects gave informed written consent on the study protocol approved by the University of California San Diego Institutional Review Board. Participants consisted of employed (≥ 30 h/wk) men and women recruited from the community by advertisement, by word of mouth, or by referral from local medical practitioners to participate in a study on BP, cardiovascular physiology, and sleep as described in more detail elsewhere.3031 Recruitment strategies yielded a total of 385 individuals who contacted us and expressed interest in participating in the study. Of those who contacted us, 153 subjects completed the study. Individuals who did not enroll in the study either were outside of the inclusion criteria or were unable to be excused from work to participate.

Here, we report on 135 subjects who had data available for polysomnography and hemostasis factors. Eligible subjects were required to have a body weight 1.0 to 2.0 times the ideal body weight as determined from Metropolitan Life Insurance tables32 and resting BP < 180/110 mm Hg at screening. Patients receiving antihypertensive drugs underwent drug tapering under close monitoring of BP and were studied after a 3-week washout period. No subjects received any other medication on a regular basis. Some subjects had uncomplicated essential hypertension, and some were subsequently found to have OSA; otherwise, physical examination and ECG findings were normal. All subjects underwent full-night polysomnography at the University of California San Diego General Clinical Research Center Gillin Laboratory of Sleep and Chronobiology. A board-certified physician took a medical history from each participant to define the presence or absence of the following specific exclusion criteria: congestive heart failure; symptomatic obstructive pulmonary, coronary, and cerebrovascular disease; history of life-threatening arrhythmias; cardiomyopathy; history of psychosis; narcolepsy; current alcohol or drug abuse; previous surgery for treatment of OSA; and periodic limb movement index ≥ 15 events/h.

Demographic Characteristics

Subject ethnicity was defined by self identification. Subjects who currently smoked one or more cigarettes per day were termed smokers. Body mass index (BMI) was computed as the ratio of body weight in kilograms divided by the square of height in meters. We computed the average systolic and diastolic BPs based on three seated resting measurements. For analyses, we used mean arterial BP (MAP).

Sleep Recordings

Sleep was studied using a sleep recording system (Heritage model PSG36–2; Grass Technologies; West Warwick, RI). Spo2 was monitored using a pulse oximeter (Biox 3740; Ohmeda; Louisville, CO) and analyzed using software (Profox; Escondido, CA). As many sleep parameters are closely related, we choose a reasonable set of six sleep measures reflecting different domains of sleep.

Wake after sleep onset (WASO) and sleep efficiency percentage were used to describe objective sleep efficiency in general. Sleep efficiency was computed as the ratio of total sleep time to time spent in bed multiplied by 100.

The total arousal index (ArI) and the percentage time of slow wave sleep (SWS) were selected to describe sleep fragmentation and sleep architecture, respectively. An arousal was defined as a shift in EEG frequency to α or θ for ≥ 3 s but < 15 s duration as scored from central, occipital or both of these EEG derivations.33The total ArI was computed by dividing the total number of arousals by the total sleep time. Rechtshaffen and Kales criteria34 were used to manually score sleep stages. Stage 3 and stage 4 sleep were combined as SWS.

The percentage of total time in bed spent at Spo2 < 90% and the AHI were selected as measures related to sleep apnea. Apnea was defined as a decrement in airflow ≥ 90% from baseline for ≥ 10 s. Hypopnea was defined as a decrement in airflow ≥ 50% but < 90% from baseline for ≥ 10 s. AHI was defined as the number of apneas plus hypopneas per hour of sleep.

All sleep scorers had interrater reliability indexes (κ) > 0.85 for staging, arousal, and respiratory variables. Due to a technical error, one subject had missing data for percentage of total time in bed spent at Spo2 < 90%.

Hemostasis Measures

An indwelling 22-gauge venous catheter was placed on the dorsum of the hand at 5:00 pm on the night of polysomnography. The morning after, fasting blood samples were drawn at 6:00 am, while the subjects were still resting, into 10-mL plastic tubes containing 3.8% sodium citrate (ratio 9:1). Samples were spun in a refrigerated centrifuge between 4°C and 8°C for 10 min at 3,000g. Obtained plasma was immediately frozen in polypropylene tubes at – 80°C.

Plasma levels of VWF antigen, d-dimer, and PAI-1 antigen (Asserachrom; Diagnostica Stago; Asnières, France), and of sTF antigen (Imubind tissue factor; American Diagnostica; Stamford, CT) were determined by enzyme-linked immunosorbent assay following the instructions of the manufacturer. The lower detection limits of these assays are 2% of VWF, 5 ng/mL of d-dimer, 1.0 ng/mL of PAI-1, and 10 pg/mL of sTF. In all subjects, the values of hemostasis variables were well above the minimally detectable limit. Because of occasional assay problems and sample availability, values were missing for sTF in 12 subjects. Interassay and intraassay coefficients of variation were ≤ 10% for all hemostasis measures.

Statistical Analysis

Data were analyzed using statistical software (SPSS 14.0 for Windows; SPSS; Chicago, IL) and are given as mean ± SD. The level of significance was set at p ≤ 0.05 (two tailed). We also report on results with borderline significance (p < 0.10) to show trends that may help to better illustrate the overall effect of disrupted sleep on hemostasis. We used the Blom transformation to obtain a normal distribution of BMI values, sleep measures, and hemostasis variables. Pearson correlation analysis was used to estimate the univariate relationship between two variables. Hierarchical linear regression analysis, using forced entry, was employed to identify which sleep variables were significantly linked with hemostasis factors, independent of demographic characteristics.

Subject Characteristics

Table 1 provides the demographic and sleep characteristics as well as plasma levels of hemostasis factors of the entire sample of 135 subjects. Nineteen subjects (14%) were found to have an AHI > 15 events/h and were considered to have OSA.

Bivariate Associations Between Subject Characteristics and Hemostasis

Levels of d-dimer correlated with age (r = 0.26, p = 0.002) and were higher in women than in men (341 ± 318 ng/mL vs 223 ± 147 ng/mL, p < 0.001) and in African Americans than in whites (328 ± 315 ng/mL vs 233 ± 155 ng/mL, p = 0.011). PAI-1 correlated with BMI (r = 0.55, p < 0.001) and with MAP (r = 0.23, p = 0.008). VWF was also associated with MAP (r = 0.23, p = 0.010), and sTF was higher in men than in women (241 ± 232 pg/mL vs 172 ± 114 pg/mL, p = 0.001). Hemostasis factors were not significantly different between current smokers and nonsmokers.

Bivariate Associations Between Sleep and Hemostasis

Table 2 shows that all relationships between sleep measures and hemostasis variables were in the expected direction, consistently indicating that more disrupted sleep was associated with higher levels of prothrombotic factors. More precisely, less sleep efficiency, and more WASO were associated with higher levels of VWF, sTF, and PAI-1. Sleep fragmentation and disturbances in sleep architecture (ie, higher total ArI and less SWS) were related to increased PAI-1 and to VWF. Sleep measures related to apnea (ie, more percentage of time slept at Spo2 < 90% and higher AHI) correlated particularly strongly with higher levels of PAI-1 and to a lesser extent with higher levels of sTF. Levels of d-dimer showed no prominent association with sleep measures.

Multivariate Associations Between Sleep and Hemostasis

Since d-dimer was not significantly linked with sleep in the bivariate analyses, we did not use d-dimer in multivariate analyses. For the remaining hemostasis outcome variables (VWF, sTF, PAI-1), we ran two separate hierarchical linear regression analyses to determine the extent to which sleep variables predict hemostatic factors independently of demographic variables.

For the first analysis, demographic characteristics were forced into the equation in the first step: gender (0 = male, 1 = female), ethnicity (0 = white, 1 = black), age, BMI, MAP, and smoking status (0 = not currently smoking, 1 = current smoker). In the second step, sleep measures entered the model using forced entry. A regression equation was computed for each sleep variable separately.

For the second analysis, we controlled in step 1 for the above demographic variables plus AHI to investigate whether sleep variables would predict hemostasis factors independently of sleep apnea severity. Again, in step 2, sleep variables were entered using forced entry to compute an equation for each of the sleep variables separately

Tables 345 show results of multivariate analyses using VWF, sTF, and PAI-1 as dependent variables. Demographic variables explained a significant amount of the variance in sTF and PAI-1 but not in VWF. In general, analyses suggested that measures of more disrupted sleep predicted higher levels of VWF, sTF, and PAI-1 independently of gender, ethnicity, age, BMI, MAP, and smoking status. In both analyses (ie, with and without controlling for AHI), indexes of poor sleep efficiency predicted sTF and indexes of sleep fragmentation and disturbed sleep architecture predicted VWF, suggesting that apnea severity did not account for these relationships. In contrast, the association between PAI-1 and mean nighttime spent at Spo2 < 90% became of borderline significance when controlling for AHI; this suggests that apnea partially accounted for the relationship between more oxygen desaturation and higher PAI-1. PAI-1 was also predicted with borderline significance by longer WASO and higher total ArI when controlling for AHI.

We additionally reran the multivariate analyses solely on the 116 nonapneic subjects controlling for all demographic factors but not for AHI. We found the same general findings as when we ran the entire sample controlling for AHI, suggesting that the 19 subjects with OSA (AHI > 15 events/h) were not driving the results in the entire sample (data not shown).

The aim of this study was to examine the notion that disrupted sleep may confer a prothrombotic state. We found that poor general sleep efficiency (longer WASO and lower sleep efficiency) predicted sTF. Measures that relate to perturbed sleep architecture and increased sleep fragmentation (less percentage of SWS and more arousals) were related to higher VWF. Measures reflecting apnea severity (higher AHI and more nighttime spent with Spo2 < 90%), were related to PAI-1. Different types of sleep disruption may therefore relate to different hemostatic factors, each representing a particular step in the hemostatic cascade. When controlling for AHI, sleep measures predicting VWF and sTF retained their significance level, whereas oxygen desaturation became of marginal significance in predicting PAI-1. However, WASO and total ArI now showed borderline significance as predictors of PAI-1. This suggests that sleep disruptions caused by apneas may predominantly affect PAI-1 but do not affect other hemostasis factors.

The association between the apnea-related sleep disturbances and PAI-1 is in line with previous research in OSA patients1617 and corroborates the notion that the increased coronary risk in OSA might be partially relate to a prothrombotic state.19 The present study extends these findings in that we found evidence that apnea and desaturation during sleep might exert antifibrinolytic properties even in patients whose sleep disruption may not yet “qualify” for a diagnosis of sleep apnea. In clinical terms, our data suggest that even subclinical apneics show hypercoagulability.

In the current study, we found a marginally significant relationship between higher arousal index and d-dimer that was accounted for by age and gender. We previously found an independent association between higher d-dimer levels and poor sleep quality in the elderly, and higher d-dimer levels and higher stage 2 sleep in distressed dementia caregivers.18 We speculate that the effects of disrupted sleep on d-dimer could be of greater importance in elderly populations.

Our data indicate that apnea may not be the only “toxic” component of disturbed sleep. Other markers of disturbed sleep are also associated with enhanced coagulability and thus potentially higher risk of cardiovascular disease, even in nonapneics. We found that poorer sleep efficiency and greater sleep fragmentation, not related to OSA, was predictive of sTF and VWF. Others20 have reported that perceived troubles with falling asleep and with waking up during the night are associated with CAD risk. Difficulties initiating sleep have also previously been shown to predict CAD mortality in middle-aged men at 12-year follow-up.35 Based on our findings, it is possible that higher levels of sTF and VWF may be involved in the enhanced risk for CAD reported in nonapneic subjects with difficulty falling and staying asleep.20,35 VWF and sTF are viewed as endothelial activation markers that may indicate endothelial cell damage at a very early stage.2224,27,29 Accordingly, poor sleep quality and disturbed sleep architecture could contribute to atherosclerosis by eliciting increases in VWF and sTF that will clinically manifest many years later in individuals who do not have OSA. Our observations are consistent with a report21 suggesting that perturbations in sleep architecture might contribute to CAD risk in nonapneics.

Our study was not designed to elucidate the mechanistic underpinnings of hemostatic changes in relation to sleep disruptions. However, in OSA patients the antifibrinolytic activity reflected by elevations of PAI-1 have primarily been attributed to surges in sympathetic nervous system activity associated with the apneic events.19 Animal research36suggests that catecholamines initiate messenger RNA expression of PAI-1 in cardiovascular cells. This hypothesis is indirectly supported by the finding that nocturnal treatment with continuous positive airway pressure, which reduces catecholamine activity in OSA,37 also decreased PAI-1 levels in a small sample of apneic subjects.17 Theoretically, sympathetic surges, caused by nonapneic sleep disruptions, could also elicit hypercoagulability in particularly susceptible nonapneics.21 Indeed, catecholamine infusion provokes reliable changes in procoagulant factors in healthy individuals, including VWF.38 We probed for this reasoning in a subsample of 76 subjects also controlling for their 24-h norepinephrine excretion in regression analyses. However, this proxy measure of sympathetic nerve activity did not substantially affect the relationship between sleep disruptions and prothrombotic changes (data not shown). Clearly, more mechanistically oriented studies are needed to definitely pinpoint the molecular mechanisms involved in the prothrombotic changes related to sleep disruptions.

The study was carried out in a highly standardized research environment of a General Clinical Research Center. All subjects were reasonably healthy and unmedicated to mitigate possible influences of diseases and drugs on hemostasis. Moreover, the ample sample size allowed us to control for common demographic confounders of hemostatic function.19 We acknowledge that some results were of borderline significance; therefore, our interpretations need to be interpreted with caution. We conducted a post hoc type II error analysis and found that the sample size would have to have been roughly doubled in order to have perceived as significant relationships between some hemostasis variables and some sleep measures (eg, SWS). This may suggest that some of the relationships between sleep variables and prothrombotic factors may be relatively weak in determining cardiovascular risk. Nonetheless, we believed that reporting these borderline significant findings helps to broaden the knowledge of the effects of objective measures of sleep disruption on hemostasis. Altogether, our findings support our hypothesis that disrupted sleep predicts prothrombotic activity, independent of demographic variables.

In summary, we found that polysomnographic objective measures of sleep disruption in nonapneic subjects were associated with, and predictive of, increased levels of VWF and sTF: two markers of a prothrombotic state that have been associated with a higher risk of CAD. We also found further evidence for an association between apnea-related sleep disruptions and a heightened antifibrinolytic state (higher levels of PAI-1) even in subjects at subclinical stages of sleep apnea. In conclusion, our findings suggest that sleep disruptions, even in a relatively healthy population, are associated with a prothrombotic state that might contribute to CAD.

Abbreviations: AHI = apnea-hypopnea index; ArI = arousal index; BMI = body mass index; CAD = coronary artery disease; MAP = mean arterial BP; OSA = obstructive sleep apnea; PAI = plasminogen activator inhibitor; Spo2 = oxyhemoglobin saturation; sTF = soluble tissue factor; SWS = slow wave sleep; VWF = von Willebrand factor; WASO = wake after sleep onset

This research was supported in part by National Institutes of Health grants HL44915, AG08415, M01 RR00827, HL36005, and K23 HL04056–01.

The authors have no financial or other potential conflicts of interest to disclose.

Table Graphic Jump Location
Table 1. Characteristics of 135 Subjects Studied*
* 

Data are presented as mean ± SD or %.

 

Total sleep time/total time in bed.

 

n = 134.

§ 

n = 122.

Table Graphic Jump Location
Table 2. Bivariate Relationships Between Sleep and Hemostasis Measures in 135 Subjects
* 

n = 122.

 

Pearson estimate, p ≤ 0.05.

 

Pearson estimate, p ≤ 0.01.

§ 

Pearson estimate, p ≤ 0.001.

 

Pearson estimate, p ≤ 0.10.

 

Total sleep time/total time in bed.

# 

n = 134.

Table Graphic Jump Location
Table 3. Associations Between Subject Characteristics and VWF Antigen (n = 135)
* 

All demographic factors: age, gender, ethnicity, BMI, MAP, smoking history.

 

Model accounted for 6.5% of variance in VWF (F=1.48; degrees of freedom [df] = 6,128; p = 0.19).

 

All demographic factors (age, gender, ethnicity, BMI, MAP, smoking history) and AHI.

§ 

Model accounted for 8.5% of variance in VWF (F = 1.69; df = 7,127; p = 0.12).

Table Graphic Jump Location
Table 4. Associations Between Subject Characteristics and sTF Antigen (n = 122)
* 

All demographic factors: age, gender, ethnicity, BMI, MAP, smoking history. See Table 3 for expansion of abbreviation.

 

Model accounted for 14.4% of variance in sTF (F = 3.22; df = 6,115; p = 0.006).

 

All demographic factors (age, gender, ethnicity, BMI, MAP, smoking history), and AHI.

§ 

Model accounted for 14.8% of variance in sTF (F = 2.82; df = 7,114; p = 0.010).

Table Graphic Jump Location
Table 5. Associations Between Subject Characteristics and PAI-1 Antigen (n = 135)

All demographic factors: age, gender, ethnicity, BMI, MAP, smoking history. See Table 3 for expansion of abbreviation.

 

Model accounted for 31.4% of variance in PAI-1 (F = 9.76; df = 6,128; p < 0.001).

 

All demographic factors (age, gender, ethnicity, BMI, MAP, smoking history), and AHI.

§ 

Model accounted for 33.8% of variance in PAI-1 (F = 9.26; df = 7,127; p < 0.001).

 

n = 134.

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Figures

Tables

Table Graphic Jump Location
Table 1. Characteristics of 135 Subjects Studied*
* 

Data are presented as mean ± SD or %.

 

Total sleep time/total time in bed.

 

n = 134.

§ 

n = 122.

Table Graphic Jump Location
Table 2. Bivariate Relationships Between Sleep and Hemostasis Measures in 135 Subjects
* 

n = 122.

 

Pearson estimate, p ≤ 0.05.

 

Pearson estimate, p ≤ 0.01.

§ 

Pearson estimate, p ≤ 0.001.

 

Pearson estimate, p ≤ 0.10.

 

Total sleep time/total time in bed.

# 

n = 134.

Table Graphic Jump Location
Table 3. Associations Between Subject Characteristics and VWF Antigen (n = 135)
* 

All demographic factors: age, gender, ethnicity, BMI, MAP, smoking history.

 

Model accounted for 6.5% of variance in VWF (F=1.48; degrees of freedom [df] = 6,128; p = 0.19).

 

All demographic factors (age, gender, ethnicity, BMI, MAP, smoking history) and AHI.

§ 

Model accounted for 8.5% of variance in VWF (F = 1.69; df = 7,127; p = 0.12).

Table Graphic Jump Location
Table 4. Associations Between Subject Characteristics and sTF Antigen (n = 122)
* 

All demographic factors: age, gender, ethnicity, BMI, MAP, smoking history. See Table 3 for expansion of abbreviation.

 

Model accounted for 14.4% of variance in sTF (F = 3.22; df = 6,115; p = 0.006).

 

All demographic factors (age, gender, ethnicity, BMI, MAP, smoking history), and AHI.

§ 

Model accounted for 14.8% of variance in sTF (F = 2.82; df = 7,114; p = 0.010).

Table Graphic Jump Location
Table 5. Associations Between Subject Characteristics and PAI-1 Antigen (n = 135)

All demographic factors: age, gender, ethnicity, BMI, MAP, smoking history. See Table 3 for expansion of abbreviation.

 

Model accounted for 31.4% of variance in PAI-1 (F = 9.76; df = 6,128; p < 0.001).

 

All demographic factors (age, gender, ethnicity, BMI, MAP, smoking history), and AHI.

§ 

Model accounted for 33.8% of variance in PAI-1 (F = 9.26; df = 7,127; p < 0.001).

 

n = 134.

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