Sleep is a phase where both the body and the nervous system can recover, allowing the formation and synthesis of protein to increase in activity. The brain controls sleep and reacts to information received internally and externally, once this information is received, the brain, along with the endocrine system, directs body responses to the nervous system. The reasons for sleep are not very well understood, however some primary devices are known. The balance between wake and sleep is controlled by the tuberomammillary nucleus and the ventrolateral preoptic nucleus (Swanson et al, 2015). These two groups of neurons keep the balance between wakefulness (controlled by the tuberomammillary nucleus) and sleep (controlled by the ventrolateral preoptic nucleus) (Swanson et al, 2015).
Regular sleep contains cycles of rapid eye movements (REM) and non-rapid eye movements (nonREM), both stages are categorised by brain patterns and muscular activity (Swanson et al, 2015). The most significant stage of nonREM is slow wave sleep, this is when arousal is at its lowest and cortical activity is synchronised, potentially to facilitate memory consolidation (Tononi et al, 2006). Several studies show that sleep influences metabolic and endocrine function, most importantly bone health and metabolism. However, this research is limited compared to studies looking at sleep and its influence on cardiovascular health for example.
Bone health can be determined on measurements of bone mineral density (BMD), which is the absolute amount of bone. BMD is usually measured using a dual energy low-dose x-ray absorptiometry test (DEXA scan). BMD can predict the risk of developing a fracture.
Decreased bone health, such as low bone mass affects a large portion of the overall population, 50 million adults in the USA suffer from a form of bone disease. (Swanson et al, 2015). The most common bone disease is osteoporosis, which is regarded as low bone mass and a deterioration of bone tissue, leading to an increased chance of fracture (Sasaki, 2016). The World Health Organisation (WHO) has created definitions for low bone mass and osteoporosis (Catherine Burt Driver, 2017). These definitions are based on T-scores, this compares a person’s bone to a normal healthy 30-year-old bone. There are many associations with osteoporosis that have been discussed in many pieces of literature, which include diet, smoking and exercise. (Torgerson et al, 1998).
A link between sleep and bone health can be made through osteocalcin. It is a non-collagen protein in the bone, which exclusively synthesises for bone. It has been suggested that it improves glucose tolerance through increasing beta-cell proliferation and insulin secretion (Lee et al, 2007). Osteocalcin is secreted by osteoblasts and is involved with metabolic regulation (Lee et al, 2007). As osteocalcin is produced by osteoblasts it is usually used as a marker for bone formation. It is usually found that there is a positive correlation between serum-osteocalcin levels and bone mineral density. Osteocalcin is known to show a circadian rhythm, with levels of osteocalcin reaching its highest point during sleep, which suggests that bone formation is increased during this time. However, sleep disruption, resulting in growth hormone disruption does not change the diurnal pattern of the osteocalcin (Everson, Folley & Toth, 2012).
Bone metabolism monitoring involves measuring enzymes which are released during resorption and formation of bone. This involves the use of bone turnover markers (BTM) which assess the rate of bone formation and resorption within the skeleton (Delmas, 1992). BTMs have a day/night pattern, during the day the markers of bone formation and resorption are at their lowest, whereas they peak during the night, normally during the early morning when SWS should be occurring. C-terminal telopeptide (CTX) is a telopeptide that can be used as a biomarker in the serum to measure the rate of bone turnover. It has been shown by Swanson et al (2015) that CTX decreases with food intake while the amplitude of the diurnal rhythm is diminished during fasting. Therefore, this rhythm could be determined by food intake. However, to a much smaller degree, it has been found that this day/night pattern of BTMs can also be influenced by sex or the reproductive hormone status (Szulc, 2011).
Very little is known about the circadian rhythmicity of the osteocyte. It has been discussed by Santosh et al (2013), that there is a day/night pattern of sclerostin, which is produced in osteocytes. The main function of sclerostin is to stop bone formation. This protein is usually present in young and healthy men, which peaks at around 1AM, however this study did not distinguish whether this peak was related to sleep, or whether this internal circadian rhythm would have occurred without the presence of sleep. Therefore, it is unclear whether this circadian rhythm of bone turnover is related with sleep.
Re-occurring awakenings during sleep is known as sleep fragmentation. This creates a build-up of sleep loss/debt. Even though the duration of sleep required varies from each individual, a study by Belenky et al (2003), suggests that physiological deficits can occur with less than 7 hours sleep. It is also discussed by Mahowald et al (1989), that sleep disturbance and a lack of sleep decreases bone mineral density thus exacerbating osteoarthritis and rheumatoid arthritis. This is because a loss of sleep from sleep fragmentation may lead to hypoxia occurring in the bones, thus becoming weaker.
A study by Niu et al (2015) examined the relationship between sleep patterns and bone mineral density in Puerto Ricans. A cross-sectional study was performed which included 750 Puerto Rican adults. Bone mineral density was measured by dual-energy X-ray absorptiometry (DEXA). Insomnia symptoms and sleep duration was measured by a questionnaire. Results from this study showed that men who slept for more than 9 hours a day had a significantly lower bone mineral density of the femoral neck compared to men that slept for 8 hours a day. However, this relationship lost significance when adjustments to the data was made for serum inflammation biomarkers. Therefore, this study did not agree with the findings from Mahowald et al (1989), that less sleep would result in lower bone mineral density. Conversely in a study by Fu et al (2011) it was shown that lower and regional bone mineral density in those that sleep less than 7 hours a night compared to those that sleep more than 8 hours a night. In the studies discussed above all the differences in bone health are small. Although these differences could amass when sleep loss happens during a certain time, such as bone modelling (Swanson et al, 2015).
Sleep consists of cycles of rapid eye movement and non-rapid eye movement, with each stage consisting of distinctive brain patterns. One stage of sleep which is very important is slow wave sleep (SWS). During this stage, arousal is at its lowest and cortical activity is copied (Tononi & Cirelli, 2006). SWS is also associated with a decreased sympathetic activity, thus reducing the chance of osteoporosis if SWS occurs. As sleep can affect bone health, those that suffer with sleep disorders may be at high risk from bone disease. The most common sleep disorder is obstructive sleep apnoea (OSA). At least 4% of men of the general population are diagnosed with OSA and its characteristic symptoms (Young, 2009). OSA is regarded as repeated episodes of airway collapse resulting from changes during sleep in the upper-airway dilator muscle tone leading to decreased airflow or an interruption of airflow. The severity of OSA can be determined by the apnoea-hypopnea index (AHI), this is the number of apnoea’s and hypopnea’s during one hour of sleep. Generally, those with a greater AHI are more likely to suffer from daytime sleepiness and morning headaches. OSA can lead to hypoxia in muscle tissue, increased inflammation and can possibly disrupt melatonin secretion which plays an important role in the regulation of the sleep cycles, for example the circadian rhythm (Dubocovich, 2007). The choice of treatment that is generally used by OSA sufferers is continuous positive airway pressure. There has been evidence which suggests a relationship between OSA and bone metabolism disorders. OSA can indirectly affect bone health by risk of fracture through sleepiness or directly affect bone health through different mechanisms that will be discussed throughout this report.
It has been found from a study by Tomiyama et al (2008), that an increased rate of bone turnover occurs in OSA sufferers, as a positive correlation was found between AHI and urine CTX in men. This CTX level then decreased after 3 months of continuous positive airway pressure.
In more recent studies there have been many conflicting results when the relationship of OSA and bone mineral density was examined. Uzkeser et al, (2013) found that 21 men with OSA had a statistically lower bone mineral density at the femoral neck than 26 healthy men. However, in a similar study by Gnessi et al, (2012) found no relationship between OSA and bone mineral density in obese participants that suffer with OSA. This study also found no relationship between the severity of OSA and femoral neck bone mineral density. However, it should be noted that this study did not obtain a control group. Thirdly, a study by Torres et al, (2013), which had similar findings to the study by Gnessi et al, (2012) found no difference in bone mineral density in the femur of mice after being exposed to intermittent hypoxia over a 32-day period, thus mimicking OSA and normal mice. However, it is suggested that this 32-day period of hypoxia exposure was not long enough to show a change in bone mineral density, as humans could be exposed to many years of untreated and undiagnosed OSA.
One of the most recent studies which looked at the relationship between OSA and bone health was Tien et al, (2014), this was a longitudinal study looking at 1377 OSA patients in Taiwan. This study stated that people with OSA are 2.7 times more likely to develop osteoporosis. This was compared to 20655 healthy individuals over a 6 year follow up procedure.
There are many contradicting results when looking at the relationship between OSA and bone health. This may be due to differing study designs and skeletal difference between participants. Furthermore, the damaging effects of OSA on the bone might only be clearly observed in those that are prone to imbalances in bone turnover, this will depend on age, sex and BMI.
One characteristic of OSA is intermittent hypoxia and is suggested as the major factor responsible for mortality (Dewan et al, 2015). Patients with OSA usually suffer from short intermittent high frequency hypoxia, which occurs during sleep, that could last for months. Once bones are exposed to hypoxia, osteogenic differentiation is down-regulated which stimulates osteoclast formation which therefore stimulates bone resorption (Arnett, 2010). It has been shown in animal models with intermittent hypoxia stimulated early deployment of mesenchymal stem cells (MSC) from bone marrow into the blood. This discharge of MSC is a device triggered by tissue lesions which allows the differentiation of MSC within different cells such as osteoblasts. The use of these experimental models which are exposed to intermittent hypoxia could be applied to OSA and other sleep disorders, in which there is an occurrence of cyclical arterial oxygen desaturation during sleep apnoea (Almendros et al, 2012).
A further study by Elefteriou et al. (2005) which links the role of sleep and sympathetic nervous system showed that this sympathetic nervous activity could play a crucial role in bone metabolism, which in turn has a connection with sleep, as insufficient sleep is associated with an increase in sympathetic nervous activity (Chouchou et al, 2013). This increased sympathetic activity, beta2-adrenergic activity which can be caused by insufficient sleep is known to cause bone mass loss, potentially resulting in osteoporosis, this is either by bone resorption or a decrease in bone formation. Decreased bone formation is due to the reserve of osteoblastic activity through beta2-adregernic receptors on osteoblasts. (Chouchou et al, 2013).
Leptin is a crucial cytokine which links the relationship between the sympathetic nervous system and bone health. Leptin is a cytokine which is secreted by adipose tissue at levels determined by the amount of body fat, insulin levels and alcohol intake (Mantzoros et al, 1998). This secretion of leptin has a circadian rhythm and is controlled by the wake/sleep cycle, leptin classically reaches its peak level during sleep (Pan & Kastin, 2014). Even though in the past it has been discussed that leptin directly affects bone health (Mantzoros et al, 1998), recent experiments involving cell specific gene deletion have showed that leptin only indirectly affects bone health (Motyl and Rosen, 2012). A study by Fu et al, (2005) discussed how the sympathetic nervous system is the main facilitator of leptin’s inhibition of bone mass accumulation. Leptin uses its antiosteogenic effects by connecting to receptors in the hypothalamus, allowing the release of noradrenaline from the nerve fibres in bone. This then hinders bone formation by fastening on to beta2-adrenergic receptors on the osteoblasts (Hamrick, 2005).
Melatonin is a hormone made by the pineal gland, in the brain. Melatonin is secreted by humans during night and is a very important factor for the regulation of sleep. The frequent increase in melatonin regularly correlates with the rise in sleep, this increase usually occurs 2 hours before a person’s bedtime (Dubocovich, 2007). Immediately before this secretion is the least likely time for sleep, but when this secretion begins the likelihood of sleep increases. This rhythm of melatonin secretion is controlled by the central circadian rhythm generator within the anterior hypothalamus – the suprachiasmatic nucleus (SCN) (Atul Khullar, 2017). The SCN connects with clock genes, these interact with each other to create an auto-regulatory feedback loop, it’s the activation and repression cycle takes one day to complete. (Swanson et al, 2015). Clock genes have been identified in all cells, including bone cells within the osteoblasts and osteoclasts. The repetitive sleep cycle is controlled by a couple of factors, the circadian process which is an internal clock that influences the rhythm of the sleep-wake cycle, and the sleep process, which regulates the amount of sleep gained. The SCN interacts with both processes, excitatory signals from the SCN and melatonin suppression encourage wakefulness during the day in response to the suppression of melatonin reserve of the SCN (Atul Khullar, 2017). This reserved SCN is released during night time and leads to melatonin synthesis which then promotes sleep.
The relationship between melatonin and bone is a little more complex, it has been suggested by Satomura et al (2007) that melatonin receptor expression increases during night time thus might have the same circadian rhythm expression on type I collagens such as osteocalcin, osteopontin and alkaline phosphatase, stimulating the formation of a mineralised matrix within these cells (Sethi et al, 2010). Melatonin also indirectly affects bone metabolism by clearing up the free radicals that are made by osteoclasts during bone resorption. These free radicals are superoxide anions which contribute to degradation during the bone resorption process. Therefore, melatonin protects bone cells from oxidative attacks (Ladizesky et al, 2006).
As well as aiding in bone metabolism, melatonin is believed to speed up repair from bone fractures. A study by Halici et al, (2010) carried out an observation study investigating the effect of melatonin on fracture healing in the tibia of a rat model using histopathological methods. 80 rats were randomly placed into 2 groups, a control group and a melatonin group. Fractures were produced by manual breakage, during recovery the melatonin group were given 30mg/kg/day of melatonin. The findings showed a decrease in malondialdehyde levels and superoxide dismutase activity in the melatonin group in the early stages of healing process. These findings show that melatonin was useful in overturning the effects of free radicals, therefore reducing the time for the fracture-healing process.
The relationship between bone and sleep can be looked at both ways. It was stated in a study by Foley et al (2004), that those that suffer with osteoporosis are more likely to have a decreased sleep duration than those that don’t have osteoporosis. Even though this causality cannot be recognised just from this one observation, it does still show an association between osteoporosis suffering and sleep restriction. This could create a self-continuing cycle of decreased sleep quality and diminished bone health.
Previous research has focused primarily on sleep duration and its effect on bone health, however sleep quality may also be a significant factor in the control of bone mineral density, and prior research has indicated that decreased sleep quality influences bone health.
Therefore, in this current study, examination of cross-sectional associations between sleep quality and total/regional bone mineral density and bone mineral content in professional footballers was performed. Due to evidence in previous research it was hypothesised that sleep quality would have a significant effect on bone health, specifically decreased sleep quality would cause lower bone mineral density and content.
36 professional male footballers (25.19 4.9 years, 182.30 6.5cm, 83.20 7.9kg) volunteered to participate in this present study, height, weight and age were recorded as well as BMI, these measurements took place throughout the football season, (post pre-season, in season and post-season). Bone scanning (DEXA and pQCT) also occurred at post pre-season, in season and post-season, some players did not complete all 3 scanning periods due to injury or transfer to another professional club. Players also completed a sleep and dyspnoea questionnaire before each match. Participants were removed from data analysis if they did not complete both sleep questionnaire and bone scanning, thus leaving a total of 23 participants.
This study is a of a cross sectional design, comparing results between subjects. Subjects were place in different groups depending on their average sleep score throughout the year.
Before each match, participants underwent an unmonitored sleep self-assessment. The following parameters were measured: energy levels, leg dyspnoea, sleep quality (all marked out of 5) and overall feeling (marked out of 10) The question ‘How well did you sleep last night’ was used to assess the sleep quality. A score of 1 represented the lowest energy levels/poor quality of sleep dependant on category. Whereas a score of 5 or 10 depending on which category represented high-energy levels/high quality sleep. Participants were put into two groups depending on their self-assessed sleep score. Within the sleep parameter, a score less than 3.75 was believed to be sleep deprived (SleepDep), whereas a score above 3.90 was believed to be sufficient sleep (SleepSuf). This guide was created by the experimenters of this present study as no previous research had analysed sleep quality in a similar manner.
Height and weight of the participant was measured at each scanning date (post-preseason, in season and post season). This was used to calculate their body mass index (BMI). Dual energy X-ray absorptiometry (DEXA) scan (DEXA; GE Lunar-Prodigy; software version 10.0) of total body was performed to measure characteristics of bone. To perform a DEXA scan participants are asked to lay still on their back on an open X-ray table. Following this a large scanning arm is moved over the body with a narrow beam of low-dose X-rays passing through the body. Height and weight (shoeless) was measured by a wall stadiometer (Seca 217, Seca Precision for Health, Hamburg Germany) and scale (Seca 703, Seca Precision for Health, Hamburg Germany). This was all performed by a trained operative to measure bone mineral density (BMD) bone mineral content (BMC) and bone area.
Participants were told before the scan to remove all metal objects and lay in a supine position in minimal clothing on the DEXA bed. The scan was done from head to toe and lasted approximately 10 minutes.
Scans were taken from the dominant leg (the dominant leg was defined as the leg which was most comfortable to kick a ball with). Before the scan started, the scanner needed to be calibrated, this was done by using phantoms of known density in alliance with the manufacturer guidelines. Tibial length was measured to the nearest millimetre. This was determined as the midpoint of the medial malleolus to the medial aspect of the tibial plateau. The leg was then put in the scanner whilst their foot was secured. The leg was aligned with an integral laser and a clamp was also placed on the knee to minimise movement. The participant was also instructed to remain as still as possible during the duration of the scan. Firstly, a reference point locating scout-view scan was performed in the frontal plane, this was to confirm the position of the middle of the distal end plate, this acted as the positioning line.
At 4%, 14%, 38% and 66% from the positioning line, sectional images were obtained with a voxel size set at 0.5mm and a slice thickness of 2.5mm for all measurements. A contour mode was used, which had a threshold of 180mg.cm-3, this was used to separate the soft tissue from the bone. Trabecular bone was analysed, to do this a constant default threshold of 711mg.cm-3 was used to remove cortical bone. The images were analysed, to do this the integral XCT2000L software (version 6.20A) was used.
Images were classed as invalid if any movement artefacts were present, a repeated measure was performed if this was the case. If a second artefact was discovered in the repeated measure image, then the participant was removed from the study, due to the radiation exposure guidelines.
4%: total cross sectional area (CSA, mm2) and trabecular BMD (mg.cm-3). 14% and 38%: CSA, (mm2), cortical CSA (mm2), cortical BMD (mg.cm-3), cortical thickness (mm), periosteal circumference (mm) and stress strain index (SSI, mm-3). 66%: total CSA, (mm2) and cortical BMD (mg.cm-3). These measures were all analysed.
Results are presented as means standard deviation (SD) for continuous variables. The characteristics of the participants, as well as DEXA, pQCT and anthropometric measurements were compared between participants with and without sleep deprivation using an independent samples T-test. The relationship between bone mineral density measurements, bone mineral content measurements, trabecular density, trabecular surface area, cortical density, cortical surface area, cortical thickness, T-scores and anthropometric measurements were estimated using Pearson correlation coefficient. Data were analysed using the IBM statistical analysis software package (SPSS statistics, Chicago, IL, USA). All reported P values had statistical significance threshold set at P < 0.05.
The SleepDep group comprised of 12 male participants, 26 4.89 years, 178.83 7.48cm, 79.55 9.14kg. Their average sleep score over the 3 testing periods was 3.63 0.23. The SleepSuf group comprised of 11 male participants, 26.27 3.9 years, 186.26 4.9cm, 87.36 6.45kg. The average sleep score for the SleepSuf group over the 3 testing periods was 4.05 0.23. Of the total population, 52% suffered from sleep deprivation and 48% of the total population had sufficient sleep according to this present studies sleep parameters. Participant mean characteristics for continuous variables (age, height, weight, BMI) and sleep scores for the whole population and for the separate groups (SleepDep and SleepSuf) are summarised in Table 1. Comparisons between participants with sleep deprivation and sufficient sleep in the total sample revealed no significant difference in age, weight, height and BMI (P>0.05).
Table 1 – Characteristics for continuous variables for the total sample (n = 23) and for SleepDep (n = 12) and SleepSuf (n = 11) separately (mean standard deviation).
|Total Sample (n =23)||SleepDep (n=12)||SleepSuf (n=11)||P|
|Age||25.19 4.9yrs||26.08 4.9yrs||26.27 3.9yrs||ns|
|BMI||25 1.7 kg/m2||24.84 2.1 kg/m2||25.15 1.3 kg/m2||ns|
|Height||182.30 6.5cm||178.83 7.5cm||186.26 4.9cm||ns|
|Weight||83.20 7.9kg||79.55 9.1kg||87.36 6.5 kg||ns|
|Sleep||3.84 0.3||3.64 0.2||4.05 0.2||ns|
P values in the table refer to independent samples T-test differences between subjects with and without sleep deprivation. *P < 0.05. BMI, body mass index.
Looking at the DEXA scan of the whole-body, values of whole body BMD and BMC were analysed. Results for the whole population and for the SleepDep and SleepSuf groups are shown in table 2. The mean value of BMD of SleepDep and SleepSuf were compared, the SleepDep group had a total of 1.39 0.11 mg/cm3 whereas the SleepSuf group had a 7.9% larger whole-body BMD of 1.50 0.1 mg/cm3, however this difference was not deemed statistically significant. Whole body BMC was also compared, this showed that the SleepDep group had a whole-body BMC of 3617.42 442.05 whereas the SleepSuf group had a much larger BMC of 4208.45 444.57, however this difference was also deemed to be statistically insignificant.
Table 2 – The whole-body BMD and BMC for the total sample and for SleepDep and SleepSuf separately (mean standard deviation). *P < 0.05. BMI, body mass index.
|Total Sample (n = 23)||SleepDep (n = 12)||SleepSuf (n = 11)||P|
|Total BMD||1.44 0.1||1.39 0.1||1.50 0.1||ns|
|Total BMC||3900.08 527.9||3617.42 442.1||4208.45 444.6||ns|
P values in the table refer to independent samples T-test difference between subjects with and without sleep deprivation. *P < 0.05. BMD, bone mineral density; BMC, bone mineral content.
When comparing the individual sleep scores of each participant with the individual BMC and BMD it is shown that sleep and BMC has a weak positive correlation (r = 0.202) (shown in figure 1.) whereas sleep and BMD has a very weak and positive correlation r = 0.090) However both correlations were statistically insignificant.
Figure 1 – Comparingindividual sleep scores for all participant’s vs their bone mineral content and bone mineral density. Correlations are shown by trend lines.
Regional BMC and BMD were also analysed at the spine, pelvis, ribs and trunk, shown in table 3. Trunk BMD was 8.8% higher in the SleepSuf group compared to the SleepDep group (1.35 0.1 vs 1.25 0.1). On a similar note, trunk BMC was 13.5% higher in the SleepSuf group compared to the SleepDep group (1236.48 232.31 vs 1085.08 164.7), however all regional BMD and BMC data was statistically insignificant. Average T-scores for both groups were also compared (SleepDep = 1.92 1.1, SleepSuf = 3.08 0.9), this is a difference of 60% between the 2 groups, however this difference was also deemed to be statistically insignificant.
Table 3 – Regional BMD and BMC for total sample and for SleepDep and SleepSuf separately (mean standard deviation). Regional sections analysed were trunk, ribs, pelvis and spine.
|Total Sample (n = 23)||SleepDep (n = 12)||SleepSuf (n = 11)||P|
|Trunk BMD||1.29 0.1||1.24 0.1||1.35 0.1||ns|
|Ribs BMD||1.02 0.1||0.98 0.1||1.07 0.1||ns|
|Pelvis BMD||1.48 0.1||1.42 0.1||1.55 0.2||ns|
|Spine BMD||1.36 0.1||1.31 0.1||1.42 0.1||ns|
|Trunk BMC||1157.49 209.9||1085.08 164.7||1236.48 232.3||ns|
|Ribs BMC||332.37 70.2||297.12 57.1||350.58 74.6||ns|
|Pelvis BMC||575.5 100.4||532.87 77.39||611.09 113.7||ns|
|Spine BMC||259.61 51.4||235.88 41.9||274.86 58.3||ns|
P values in the table refer to independent samples T-test difference between subjects with and without sleep deprivation. *P < 0.05. BMD, bone mineral density; BMC, bone mineral content.
pQCT scan data was also analysed. Analysis was performed for the trabecular bone density, trabecular bone surface area, cortical density at 14% from the positioning line, cortical density at 38% from the positioning line, cortical surface area at 14% from the positioning line, cortical surface area at 38% from the positioning line, cortical thickness 14% from the positioning line and cortical thickness 38% from the positioning line. All this data is shown on table 4. Only cortical thickness at 38% from the positioning line is deemed a significant difference (P = 0.039) between SleepSuf and SleepDep (7.25 0.42 vs 6.71 0.77), showing that the SleepSuf group had an 8.04% thicker cortical bone, (as shown in figure 2.) All other analysed pQCT scan data did not differ statistically.
Table 4 – pQCT scan data for total sample and for SleepDep and SleepSuf separately (mean standard deviation).
|Total Sample (n = 23)||SleepDep (n = 12)||SleepSuf (n = 11)||P|
|TD||297.41 34.5||294.64 40.8||300.43 27.7||ns|
|TSA||637.45 80.3||610.06 82.2||667.33 69.7||ns|
|CD14||1110.24 18.4||1110.36 16.8||1110.11 20.7||ns|
|CD38||1143.05 22||1140.57 24.9||1145.76 19.1||ns|
|CSA14||251.46 25.3||243.26 22||260.4 26.6||ns|
|CSA38||421.98 53.9||392.92 52.4||453.68 35.32||ns|
|CT14||3.28 0.4||3.27 0.4||3.29 0.5||ns|
|CT38||6.97 0.7||6.71 0.8*||7.25 0.4*||0.039|
P values in the table refer to independent samples T-test difference between subjects with and without sleep deprivation. *P < 0.05. TD, trabecular density; TSA, trabecular surface area; CD14, cortical density at 14% from positioning line; CD38, cortical density at 38% from positioning line; CSA14, cortical surface area at 14% from positioning line; CSA38, cortical surface area at 38% from positioning line; CT14, cortical thickness at 14% from positioning line; CT38, cortical thickness at 38% from positioning line.
Figure 2. Comparing the average cortical thickness at 38% from the positioning line for SleepSuf and SleepDep. * – denotes statistical significance.
Following this a Pearson’s correlation was performed between the participant’s individual sleep scores and cortical thickness at 38%, (shown in figure 3.). This showed a moderate positive correlation between them (r = 0.343), however this correlation was statistically insignificant (P = 0.109). A Pearson’s correlation was also performed between the cortical thickness at 38% and total BMD and regional BMD (neck, ribs, pelvis and spine). It was found that there was a significant correlation between cortical thickness and spine BMD (r = 0.432) as well as a significant correlation between cortical thickness and total BMD (r = 0.441).
Figure 3. Comparing the individual sleep results and their cortical thickness at 38% from positioning line. The correlation coefficient was specified through Pearson’s correlation (r = 0.343).
In this present study, it was observed that sleep deprivation did not significantly affect bone health, in terms of overall BMD and BMC as well as regional BMD and BMC in the neck, ribs, pelvis and spine. Additionally, trabecular bone density and surface area, as well as cortical density at 14% and 38%, cortical surface area at 14% and 38% and cortical thickness at 14% were all deemed to not be affected by sleep deprivation, although the reason for this may be because of the limited number of participants (n = 23). However, this present study did show that sleep deprivation does significantly affect cortical thickness at 38% from the positioning line.
It was originally speculated that sleep deprivation would be associated with lower BMD due to the occurrence of hypoxia in bones previously researched in sleep deprived individuals (Mahowald et al, 1989). Exposure to hypoxia should lead to osteogenic differentiation, which should stimulate the formation of osteoclasts, which in turn stimulates bone resorption (Arnett, 2010). However, the findings of this present study were not consistent with hypoxia occurring on the bones as there were no observed statistical difference in total BMD, regional BMD, or trabecular and cortical BMD. Another reason as to why BMD may decrease with sleep deprivation is decreased levels of melatonin which occur with a lack of sleep. Melatonin usually protects bone cells from oxidative attacks, however this cannot happen if there is a decreased level of melatonin in the body.
Few studies have examined the association between BMD and sleep in adults, and these studies have returned diverse results (Niu et al, 2015). In one study by Specker et al (2006), they found that there was no significant difference in cortical BMD for men with short sleep duration (<6.5 hours/night) compared to men with sleep duration >6.5 hours/night. There was also no difference in regional BMD in the spine and pelvis between sleep deprived and sleep sufficient men which agrees with the results of this present study. Additionally, similar results were gathered from a study by Chen et al (2014) in which there was no association between long or short sleep duration and risk of osteoporosis in men. Conversely, in a study by Niu et al (2015), it was found that men with >9hours/day of sleep had a lower regional BMD in the neck compared with men that had 8hours/day of sleep, thus contrasting with the previous literature and theories discussed earlier in this report as well as the hypothesis that less sleep/poor sleep quality would lead to lower BMD. For example, growth hormone is necessary for increasing BMD, whilst shorter sleep is associated with a decrease in growth hormone production, therefore sufficient sleep is needed to maintain a high BMD. However, previous studies have discussed the possible mechanisms which explain why extended sleep duration may negatively affect BMD. A study by Kobayashi et al (2011), explained that reduced daily mechanical loading from less activity as well as less exposure to light, thus having a lower level of light-induced oestrogen creates a higher probability of developing osteoporosis compared to shorter sleepers. Furthermore, is has been discussed that excessive amounts of sleep may reduce biomechanical forces due to decreased activity. This would mean that there is less need for bone mass, thus leading to the removal of bone tissue (Riggs & Melton, 1995). It must be considered that all the studies discussed above used sleep duration as their dependent variable, whereas this present study used a self-reported sleep quality as the dependant variable, therefore differences in results are expected.
The only significant result gained from this present study was the difference in cortical thickness at 38% from the positioning line in the tibia between the SleepDep and the SleepSuf groups, with a moderately positive correlation between cortical thickness and sleep score. This result could be deemed important when looking at the link between sleep and bone diseases such as osteoporosis. It has been recognised that weight bearing exercise increases BMD and cortical thickness (Nilsson et al, 2010), following from this it has been shown that the cortical bone has a large influence on fracture risk (Ritzel et al, 1997). Therefore, it is believed that the assessment of the cortical bone on a weight bearing long bone such as the tibia is a perfect predictor for osteoporosis and fracture risk (Sadat-Ali et al, 2015). The relationship between cortical bone thickness and BMD has been discussed previously in research, in a study by Patterson et al (2016), cortical thickness positively correlated with DEXA measurements of BMD in the pelvis and femur. This observation was similar to the study performed by Mather et al (2013), in which the average cortical bone thickness measurements strongly correlated with DEXA femur measurements and moderately correlated with DEXA lumbar spine measurements. It was concluded in the study by Mather et al (2013), that average cortical bone thickness measurements are correlated with DEXA scans for BMD. Furthermore, it was specified that cortical bone thickness measurements provide a relevant and sensitive method for ruling out osteoporosis. These results from Mather et al (2013), are comparable to the moderate correlation (r = 0.432) in this present study between cortical thickness and spine BMD. Moreover, this difference in cortical thickness was also consistent with a study performed by Kuriyama et al 2017, in which they looked at the association between loss of bone mass due to short sleep and leptin-sympathetic nervous system activity. Results showed that there was significant difference between a short sleep group and a normal sleep group in cortical bone thickness. Kuriyama concluded that this was owed to the promotion of bone resorption and sympathetic nervous activity. They also found that leptin levels and cortical bone thickness were related, therefore suggesting that cortical bone thickness could be regulated by the leptin-sympathetic nervous system.
Trabecular bone density and surface area was also analysed during this study, however there was no significant difference between trabecular BMD in SleepSuf and SleepDep. Trabecular bone accounts for 15% of total bone mass, sites that contain relatively high amounts of trabecular bone are common for osteoporotic fractures. Bone strength and vulnerability to fracture can rely on trabecular connectivity and arrangement. Thus, the focus on trabecular BMD is very important. In a study by Specker et al (2007), they performed a cross sectional analysis on healthy adults and compared the BMD between sleep deficient and adequate sleep individuals. In this study, they found no difference between the two groups for trabecular BMD, which is consistent with the results from this present study.
T-scores were also analysed between the SleepSuf and SleepDep. SleepSuf had a 60% higher T-score compared to SleepDep, however this difference was deemed to be insignificant, most likely due to the limited number of participants. A T-score is the comparison or a person’s bone mineral density and a typical healthy 30-year-old adult. A score of 0 indicates BMD is equal to the norm, a T-score between -1 and +1 is considered healthy, whereas any score below -1 could lead to low bone mass or osteoporosis. As can be seen from the results of this study, SleepSuf and SleepDep are well above the average T-score, this could be due to a couple of reasons. Firstly, the participants used in this study are not average people, they are professional athletes, therefore it is assumed that their bone mineral density is greater than the average person. A study by Wittich et al (1998) researched whether professional soccer players have a markedly greater skeletal mineral content, density and size than age and BMI matched controls. They found that these professional footballers develop significant increment of BMC due to increased bone size and density, thus agreeing with this present study. Secondly, the average age of the participants was 25.17 years, therefore most these players are younger than the 30-year-old bone that is compared during a T-score, therefore the results of the professional footballers T-scores were expected.
As well as decreased BMD and BMC, poor sleep quality can be associated with cognitive deficiency, falls, metabolic dysfunction, vitamin D deficiency, insulin resistance and depression (Paudel et al, 2008). This means that those with poor sleep quality may have multiple diseases or disorders. Thus, those that suffer with sleep disturbances or poor sleep quality are a very challenging population to research. Furthermore, it is very difficult to identify the exact pathophysiological mechanism that is responsible for the skeletal differences from those that suffer from decreased sleep quality/duration.
Strengths of this study includes the measurement of BMD and BMC at a variety of bone sites, this creates a relatively larger sample size, which was necessary due to a limited number of participants. Secondly this is one of very few studies to focus entirely on sleep quality, rather than sleep quantity/ duration, thus this study is looking at an aspect of sleep that has not been extensively researched, because of this a ‘sleep score’ parameter, marking sleep quality from 1 to 5 was introduced which has never seen before in this subject area. The score 3.9 was chosen as the separating value between the sleep deprivation group and the sufficient sleep group.
This study also had several limitations. Firstly, the sample size was limited, which in turn would have been a major cause in detecting little-to-zero effects on BMD, BMC and cortical density and surface area. Secondly, assessment of sleep quality was based on self-report, which could lead to misclassification based on biased estimates. Previous studies which used objectively confirmed information on sleep quality would most likely provide more reliable evidence, concerning the effect of sleep quality on bone health. Thirdly, this study was limited to a cross-sectional design, therefore causality or temporal relationship may not have been recognised. Lastly, even though BMI was included, several other important variables which could confound the association between sleep and BMD were not included, such as alcohol use, depression and plasma 25-hydroxyvitamin D concentration (Niu et al, 2015).
There are many considerations that should be noted when performing future research. Firstly, many factors that control and affect sleep may decrease over time, due to age, living conditions or health reasons. These factors include melatonin levels, leptin levels, total sleep duration and quality, and slow wave sleep (Swanson et al, 2015). Additionally, sleep patterns and circadian rhythms may change throughout life, therefore the amount of sleep-induced changes in bone will more than likely change a lot over a single lifetime. Also, bone itself is often measured over many months and years, therefore during this time sleep patterns will ultimately change, this then becomes difficult to match precise sleep patterns to skeletal changes. This all makes it very difficult to make conclusions for this subject area due to the complexity of monitoring and evaluating sleep quality, sleep duration and sleep disturbances. For example, sleep quality and duration may need to be self-assessed, which creates uncertainties through biased estimations. Additionally, sleep disturbances may affect bone differently depending on the bone site, or stage of life. All these factors need to be considered for research in the future.
To conclude, this study does not support the hypothesis that sleep deprivation (through lower sleep quality) is associated to lower BMD in professional footballers. However, these results must be interpreted with care due to the very small sample size and cross-sectional design of the study. This is because differences in BMD, BMC, T-score, and cortical surface area and thickness were shown, however it is believed that the small sample size of 23 participants is the reason as to why very little statistical difference was found. Future studies with larger sample size and an objective view of sleep quality and duration are needed before a definitive conclusion regarding the quality and duration of sleep and bone health can be reached.
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