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Logo of dmsoDove Medical PressThis ArticleSubscribeSubmit a ManuscriptSearchFollowDovepressDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy
 
Diabetes Metab Syndr Obes. 2012; 5: 395–401.
Published online 2012 November 7. doi:  10.2147/DMSO.S37761
PMCID: PMC3496369

Obstructive sleep apnea and bone mineral density in obese patients

Abstract

Context

Obesity and its co-morbidities may adversely affect bone mineral density (BMD). Obstructive sleep apnea (OSA) is a major complication of obesity. To date, the effects of OSA on BMD in obese patients have been poorly studied.

Objective

To examine whether the severity of OSA independently correlates with BMD in obese patients.

Methods

One hundred and fifteen obese subjects with OSA (Apnea/Hypopnea Index [AHI] ≥5 events per hour) were included in the study. BMD was measured at lumbar spine, total hip, and femoral neck by dual energy X-ray absorptiometry. Body mass index, lean mass, and representative measures of metabolic syndrome (waist circumference, fasting plasma glucose, blood pressure, HDL-cholesterol, triglycerides) and inflammation (ESR, CRP, fibrinogen) were also evaluated.

Results

BMD did not differ among obese individuals regardless of OSA severity. Correlation coefficient analysis for all the covariates showed a lack of association between AHI and BMD that was strongly influenced by age and weight.

Conclusion

Our study does not support an independent association between AHI and BMD in obese patients. Controlled studies involving a greater number of patients are warranted.

Keywords: obesity, polysomnography, metabolic syndrome

Introduction

Obstructive sleep apnea (OSA) is a common disorder frequently associated with obesity.1 This disorder adversely affects multiple organs and systems2 and is characterized by recurrent episodes of partial or complete collapse of the upper airways during sleep, leading to oxygen desaturation and sleep fragmentation. Daytime sleepiness, loud snoring, cardiovascular3,4 and metabolic consequences,5 and other disorders are eventually associated with OSA. Obesity is a predisposing factor for OSA1,6,7 and, in turn, OSA may worsen obesity because of sleep and metabolic derangement.8,9 The final effect of OSA’s action on bone is unknown. Excess adipose tissue may alter bone mineral density (BMD). Bone is a complex tissue, frequently victim to pathophysiologic mechanisms. Because of BMD reduction, bone is predisposed to osteoporosis, a disease characterized by deterioration of microarchitectural integrity of the bone and increased risk of fracture. Obesity and osteoporosis are reciprocally related. Obesity affects bone metabolism through weight loading and production of cytokines and hormones. Conversely, bone-derived factors influence body weight and adipose tissue.10 A common mesenchymal stem cell progenitor derivation of adipocytes and osteoblasts further confirms the relationship between bone and fat.11

Mechanical loading conferred by body weight is traditionally viewed as beneficial to bone health, despite being a risk factor for many other chronic disorders. However, there is no clear consensus regarding the real effect of obesity on BMD12,13 and body mass index (BMI), a non-specific index of obesity, which is often the only measure of body habitus used to evaluate the relationship between obesity and BMD. Recent data strongly support that fat accumulation is detrimental to bone mass.1113 The underlying pathophysiological relationship between obesity and bone is complex and represents an active research area. Obesity-related comorbidities could explain some of the supposed negative effects of excessive fat accumulation on bone.

While obesity-related comorbidities like low grade systemic inflammation,14,15 metabolic syndrome,16 insulin resistance, diabetes,17 and oxidative stress18,19 are recognized modifiers of bone metabolism, few studies have evaluated the relationship between OSA and BMD, and it has not been proven that there is one. The relationship between OSA, a potential risk factor for bone mass loss through hypoxia and inflammatory oxidative stress,18,19 and bone is poorly studied. In vitro studies have shown that hypoxia induces osteoclast differentiation and bone resorption.20 Hypoxia in rats suppresses the functions of osteoblasts by inhibiting their growth, differentiation, and bone-forming capacity.21 A report based on a human study provided the evidence of an elevation of the urinary bone resorption marker C-terminal cross-linking telopeptide of type I collagen (CTX) in 50 male OSA patients, suggesting a positive association between OSA and the excretion of CTX, independent of age or BMI.22 Furthermore, the authors observed a significant decrease of the urinary excretion of CTX following 3 months’ of continuous positive airway pressure. Although these findings suggest a bone mass reduction in subjects suffering from OSA, a clinical limit of the study is the lack of BMD assessment. A significant difference between 21 male OSA patients and a control group in relation to lumbar BMD has been recently reported.23 However, the small number and the non-homogeneous weight range of the patients are weak points in the study. Thus, since the balance between osteoclast and osteoblast is disrupted in favor of osteoclast in cases of hypoxia,2427 OSA may be a risk factor for reduced bone mass.28

Here, in a cohort of obese patients, we assessed whether OSA associates with changes of BMD after adjustments for relevant covariates such as age, sex, weight, lean mass, and metabolic and inflammatory indices.

Patients and methods

Study population

The study population was recruited from the Department of Otolaryngology, Audiology and Phonation, Sapienza University of Rome from June 2010 to June 2011. Inclusion criteria were as follows: BMI 30–40 kg/m2, age 30–65 years, apnea/hypopnea index (AHI) ≥5 events per hour (events/h). Patients were excluded from the study if affected by hypothyroidism, acromegaly, acute illness, cardiovascular disease, heart failure, lung disease, any other respiratory disorder, uncontrolled hypertension, craniofacial abnormalities, smoking attitude, or if they were using continuous positive airway pressure. Further exclusion criteria were diseases that affect bone metabolism, such as hyperthyroidism, primary hyperparathyroidism, Cushing syndrome, hypogonadism, chronic renal disease, and malignancy. Patients on glucocorticoid, calcium, vitamin D, parathormone, calcitonin, bisphosphonates, strontium ranelate, hypnotics, proton pump inhibitors, and hormone replacement therapy were also excluded. A total of 209 patients were screened and 115 patients (56 male and 59 female) were enrolled in the study. Participants were stratified into three groups according to their AHI: mild OSA (AHI 5–14 events/h), moderate OSA (AHI 15–29 events/h), and severe OSA (AHI ≥ 30 events/h). Weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively. Body mass index was expressed as weight (kilograms)/height (meters2). Waist circumference (WC) was measured just above the bony landmark of the iliac crest after gentle expiration, and expressed in centimeters. Blood pressure (BP) was measured in the morning, after an overnight fast, by a mercury sphygmomanometer (Riva-Rocci System, ERKA, Chemnitz, Germany). The definition of metabolic syndrome was based on the published third report of the National Cholesterol Education Program’s Adult Treatment Panel (ATPIII) criteria. Dyslipidemia was defined as serum triglycerides ≥ 150 mg/dL and/or high density lipoprotein-cholesterol (HDL-C) ≤40 mg/dL in men and ≤50 mg/dL in women; hypertension as either systolic BP ≥ 130 mmHg and/or diastolic BP ≥ 85 mmHg; and hyperglycemia as fasting plasma glucose (FPG) ≥ 110 mg/dL. Patients with a previous diagnosis of diabetes mellitus, dyslipidemia, or hypertension were treated with metformin as a glucose-lowering drug, fenofibrate as lipid-lowering treatment, and ace inhibitors or sartans as BP-lowering agents. All subjects were enrolled after written consent and approval by the local institution ethics committee.

Assays

Blood samples were obtained in the morning (between 08:00 and 09:00) by venipuncture after an 8-hour fast. The following measurements were performed: HDL-C, triglycerides (Dimension Dade Behring Analyzer and reagent cartridges from Dade Behring SpA Milano, Italy), plasma glucose (hexokinase method, Aeroset Abbott, Abbott Park, IL, USA), erythrocyte sedimentation rate (ESR; Westergreen method), C-reactive protein (CRP; latex immunoturbidimetric assay; Architech C 8000; Abbott Diagnostics and Laboratories, Abbott Park, IL, USA), and fibrinogen (Clauss method, BCT Dade Behring).

Bone and lean mass evaluation

BMD and lean mass were measured by Dual Energy X-ray Absorptiometry (DXA) using a DXA scan (Hologic Inc, Bedford, MA, USA, QDR 4500W, S/N 47168) by a single experienced technician, according to the manufacturer’s instructions, with the subjects in supine position. Specific delimiters for regional analysis were determined by standard software (Hologic Inc, QDR 4500W S/N 47168 VER. 11.2). Before each scanning session, the densitometer was calibrated according to standard procedures. BMD was assessed at the lumbar spine (L1–L4), at the total hip, and at the femoral neck, and expressed in g/cm2. At each site, patients with a T score ≥ −1 were included in the normal group, patients with a T score between <−1 and −2.5 were included in the osteopenia group, and patients with a T score < −2.5 were included in the osteoporosis group, as defined by the World Health Organization.29 Total lean body mass was expressed both in kg and percentage.

Polysomnography

Polysomnography was performed overnight, between 10:00 pm and 6:00 am (Somnoscreen™ PSG, Somnomedics GmbH Nonnengarten, 8 D-97270 Kist, Germany). Recordings included electroencephalogram, electrooculogram, electromyogram, electrocardiogram, thermistors for nasal and oral airflow, thoracic and abdominal impedance belts for respiratory effort, pulse oximetry for oxyhemoglobin level, a tracheal microphone for snoring, and sensors to assess position changes during sleep. Polysomnography records were scored according to standard criteria.30 The degree of OSA was determined by AHI, defined as the total number of obstructive apneas (cessation of airflow for at least 10 seconds) and hypopneas (decrease of the airflow signal amplitude by at least 50% associated with oxyhemoglobin desaturation by at least 4% or by arousal) per hour of sleep.

Statistical analysis

All of the statistical analyses were performed using STATISTICA software version 6.1 (Stat Soft, Inc, Tulsa, Oklahoma). Values were reported as medians with interquartile ranges (25th–75th percentiles), and because of asymmetry in the distribution of some parameters, they were compared using the Kruskal–Wallis one-way analysis of variance test for non-parametric continuous variables and Dunn’s multiple-comparison test. Spearman’s correlation analyses were conducted to examine linear associations between variables. Differences were considered statistically significant when P < 0.05.

Results

The demographic, polysomnographic, anthropometric, metabolic, inflammatory, and BMD characteristics of the patients are shown in Table 1. The patients were stratified into three groups according to their AHI. There were no statistical differences in age, height, weight, BP, and BMI between groups. Except for the HDL-C values, significantly lower in the group with severe OSA, no significant differences in metabolic syndrome parameters were observed. Fibrinogen was significantly higher in the severe OSA group than in the moderate and mild OSA groups; CRP values were significantly lower in the moderate OSA group compared with both the mild and severe OSA groups; serum ESR levels were not statistically different between groups.

Table 1
Descriptive characteristics of subjects with varying severities of obstructive sleep apnea (OSA)

Among the 115 patients, 55.65% had normal BMD (n = 64), 40% had osteopenia (n = 46), and 4.35% had osteoporosis (n = 5). In the group with mild OSA, 15/50 patients were osteopenic (30%) and 3/50 patients were osteoporotic (6%); in the group with moderate OSA, 17/33 patients were osteopenic (51.52%) and 0/33 patients were osteoporotic (0%); in the group with severe OSA, 14/32 patients were osteopenic (43.75%) and 2/32 patients were osteoporotic (6.25%). The BMD values measured at the lumbar spine, total hip, and femoral neck levels were not significantly different among groups. There were no statistically different BMD values between men and women (data not shown).

The sub-cohort of menopausal women (32 patients) showed a lower BMD than the pre-menopausal women (27 patients) at all sites analyzed [L2–L4 BMD, 0.971 (0.89–1.07) g/cm2 vs 1.09 (1.05–1.11) g/cm2, P < 0.05; hip BMD, 0.956 (0.85–1.08) g/cm2 vs 1.03 (0.95–1.1) g/cm2, P < 0.05; femoral neck BMD, 0.77 (0.639–0.907) g/cm2 vs 0.88 (0.8–0.95) g/cm2, P < 0.05].

However, independent from the menopause status, no significant differences in BMD were found when the patients were distributed in the mild, moderate, and severe OSA groups, given that the increasing severity of OSA was not accompanied by a reduction in BMD (Table 1).

Lean body mass was significantly higher in men than in women, both in kg [men 65.23 (59.38–70.29) vs women 51.52 (48.19–56.53); P < 0.001] and in percentage [men 63.48 (57.56–68.38) vs women 54.06 (49.66–58.56); P < 0.001]. Moreover, we observed a higher lean mass in severe OSA groups compared to the group with mild OSA (Table 1).

Table 2 shows a linear correlation between the variables. Spearman’s coefficients showed a lack of association between AHI and BMI, body weight, WC, FPG, CRP, triglycerides, and BMD at all bone sites. Indeed, lumbar spine, total hip, and femoral neck BMD were positively correlated to weight (P ≤ 0.001) and inversely correlated to age (P ≤ 0.001). Moreover, there was a correlation between total hip and lumbar BMD and lean mass, but not between femoral neck BMD and lean mass.

Table 2
Spearman’s correlation coefficient analysis for all the covariates

A significant association of AHI with ESR and fibrinogen was found; ESR correlated with weight, BMI, WC, diastolic blood pressure, and CRP; while fibrinogen associated with weight, WC, CRP, and ESR. CRP did not associate with any of the covariates except BMI. Finally, the lean mass showed a significant positive correlation with AHI, weight, WC, and fibrinogen, and a negative correlation with HDL-C and age.

Discussion

We explored the correlation between AHI and BMD in obese men and women with OSA. Our data indicate that, in obese patients, there is no relationship between OSA and BMD was strongly affected by age but not by OSA. After stratification of women on the basis of OSA severity, AHI was not relevant to bone loss regardless of menopause status. On the whole, these results suggest that OSA does not lead to a severity-related reduction of BMD in obese patients.

According to previous studies,31,32 we found that weight is an independent contributor to BMD; conversely, BMI, a surrogate index of obesity, was not a good predictor for BMD. A possible explanation for this discrepancy may reside in the complexity of body composition. Several authors examined the relationships between fat or lean mass and BMD.33,34 In particular, a consistent positive relationship between lean mass and BMD has been reported.3335 We found a positive correlation between lean mass and BMD at hip level and in the lumbar spine. The lean mass–hip BMD association was not unexpected since a recent study showed a strong relationship between lean mass, hip geometry, and BMD.36 Interestingly, we observed higher lean mass values in moderate and severe OSA groups and a correlation between lean mass and AHI. A previous study to determine the predictive value of regional fat distribution for OSA severity showed an association of lean mass of the trunk and AHI.37 It is currently unknown whether this association has a pathophysiological meaning. However, these findings suggest that DEXA-quantified fat and lean mass distribution in different body regions might be more informative than anthropometric measurements, such as WC or BMI, in examining potential causal relationships and predictive values between body composition and OSA severity.

Our data confirm the relationship between OSA and chronic inflammation. However, despite a pro-inflammatory state frequently representing the common denominator of OSA14,18,21 and osteopenia and osteoporosis,38 we did not find a correlation between inflammatory indices and BMD. This finding is difficult to explain. There are various molecular mediators between inflammation and bone. A possible relationship between inflammatory markers different from the markers measured here, such as pro-inflammatory cytokines and BMD, cannot be completely ruled out.

Some intrinsic limitations of the present study must be highlighted. We did not measure the vitamin D concentration in our patients. The implications of vitamin D in bone metabolism are well known, and in obesity, a vitamin D deficit could be associated with an increased degree of inflammation. 39 Nonetheless, a lack of association between vitamin D and BMD has been reported.40 Although ESR, CRP, and fibrinogen are major and reliable indices of inflammatory status in clinical practice, TNF-alfa,41 IL-6,42 and other cytokines involved in bone loss and OSA should be analyzed. Finally, although our aim was to compare groups of patients with various degrees of OSA, the lack of a control population and the relatively small sample size remain weak points.

In conclusion, this cross-sectional study of obese OSA patients shows that AHI is not associated with bone density independent from weight, lean mass, metabolic syndrome, and inflammation, and OSA is not as important as age, weight, and lean mass on bone. These results are not in line with previous evidence of correlation between AHI and lumbar spine osteoporosis in men.23 Therefore, the controversy regarding the effect of obesity on BMD is not explainable by the increased severity of OSA. These preliminary data, taken together, deserve further characterization and focus on larger cohorts of obese OSA patients, particularly in prospective studies. Elucidation of this relationship will generate research interest in the coming years.

Acknowledgment

This study was funded by the Sapienza University of Rome, Italy. The funding source had no role in study design; collection, analysis, and interpretation of data; or the writing of the report and the decision to submit the paper for publication.

Footnotes

Disclosure

The authors declare that there is no conflict of interest in this work.

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