Determinants of Bone Mineral Density in Young Women on Low Habitual Calcium Intakes
Osteoporosis is one of the main causes of morbidity and mortality in later life. It is characterized by low bone mass which increases the vulnerability to fractures and the associated complications of diminished mobility. Nutrition plays a vital role in bone health during childhood. Nutrition is an important modifiable factor in the development and maintenance of bone mass and to prevent the development of osteoporosis. Aim: To assess the association between bone mineral density BMD and blood and serum calcium, vitamin D, BMI and age in young women with low habitual calcium intake. Methods: women aged 20-39 were recruited basically according to their habitual calcium intake (<750 mg/day) in addition to other criteria. Basic blood and urine samples were collected and analysed. Calcium intake calculated using four-day food diary. The association between different variables (Calcium intake, Vitamin D levels, BMD) was performed using regression and ANOVA tests. Results: There was no significant association observed at lumbar spine and hip sites and nutrients levels (P>0.05) and the observations suggest that there was no observed pattern between the variables which can be further investigated by longer term studies and multiple measurements to reduce the effect of confounders.
Osteoporosis and low bone mass are major health issues encountered by all the developing and non-developing countries. The prevalence increases with age and it is particularly common in postmenopausal women. One in three women and one in twelve men over the age of 50 will suffer an osteoporotic fracture (SIGN, 2003). Osteoporosis is characterized by decreased in bone mass (Geissler and Powers. 2003) and this bone thinning disease is a silent epidemic that affects individuals across all racial and ethnic groups. Postmenopausal bone mineral density is a function of peak bone mass and the rate of subsequent bone loss, which are equally important risk factors for fracture in later life (Winzenberg.2006). Nutrition plays a key role in optimizing the bone health from childhood till ageing. Calcium along with vitamin D plays a key role in shaping the bone health. Various short term and longitudinal studies are carried out to understand nutrient effects on bone health. The results from some study suggest that calcium supplementation may increase bone mass not only by inhibiting the process of remodeling but also by stimulating bone modeling (Dodiuk-Gad et al, 2005).
1.1 Anatomy and physiology
The skeleton of an adult human is composed of 213 bones with specific function or functions of each unique single bone. (Eastell, 2006) The main two classifications of bone are flat bones (like the skull, scapula and the mandible) and the long bones (like tibia, femur and ulna). Long bone bones consist of a shaft, which is the long hollow tubular structure and it is also know as the diaphysis, and a metaphyses which are the cone-shaped ends of the bone. (Eastell, 2006) About 80% of the adult skeleton is a cortical bone and the remaining 20% is a cancellous bone but the distribution is different depending mainly on the site. The shaft of long bones contain more cortical bone as much as 95% in the radius diaphysis, whereas the metaphyses contain more cancellous bone (about 50%)(Eastell, 2006) The main functions of the human skeleton are the structural and the supportive functions as well as protective and metabolic. The axial skeleton which includes skull, vertebral column, thorax and pelvis has both functions of giving the human body its natural appearance and protecting vital organs like brain, spinal cord, heart and lungs. The appendicular skeleton which is the upper and the lower limbs and along with the axial skeleton provides sites for attachments for the muscular system and together help in movement and support. (Underwood, 2000). The other major function is acting as a storage place for some nutrients including some minerals like calcium, phosphorous and magnesium which are incorporated into the bone structure and released in the process of bone resorption. Other minerals stored inside the bone marrow includes iron which is released according to the body needs. (Underwood, 2000). Bone marrow also is one of the most active metabolic and synthetic sites in the body where haemopoiesis (which is the production of red and blood cells and platelets) takes place. Fig 1.1: Blood cells produced in bone (From: The Merck manual of diagnosis and therapy, 2006)
The bone tissue is renewable like many other tissues and controlled by the process of remodeling which is achieved by balancing bone resorption and formation. The osteocytes (The bone cells) control bone remodeling by regulating the action of the osteoclasts and the osteoblasts (Bone breakdown and bone formation cells) (Eastell, 2006)
1.1.1 Bone modeling
Bone modeling is the resorption and formation of bone by the independent action of the osetoclasts and the osteoblasts. Bone modeling is a process that can shape bone on large scale and it is occasionally occur in response to an external factor like injury and physical adaptation to certain type of sport especially in young athletes. In older people bone modeling is usually occur in response to internal organic disorders and as a side effect of medications rather than a normal physiological response or adaptation. (Eastell,2006)
1.1.2 Bone remodeling
Bone remodeling is a continuous process of revitalization of the bone tissue. It is the single normal bone changing process in adult human which is achieved by the collaborative and organized action of a group of cells known as bone remodeling unit (BRU). The main four stages in bone remodeling are: Activation Resorption Reversal Formation Fig. 1.2: Bone remodeling Bone remodeling is important process in maintaining the strength and the integrity of the skeleton to cope with continuous mechanical stress by replacing the exhausted bone. The process is also important in maintain the homeostasis by providing a huge stores of calcium and phosphorous the extracellular fluid and other tissues.
1.2.1 The epidemiology of osteoporosis (From Compston and Rosen, 2006)
The condition is one of the main causes of morbidity and mortality in elderly. It is characterized by low bone mass which increases the vulnerability to fractures and the associated complications of diminished mobility. The most common osteoporotic fractures are the hip, spine and wrist. Statistically, one in three women and one in five men over age 50 will have the condition, and financially, the cost of fractures resulting from low bone mass is estimated at £1.75 billion in the United Kingdom, €25 billion in European Union and 14 billion USD in the United States. Globally, over 200 million postmenopausal women are affected worldwide. In the UK, women who age over 50 have a risk of 40% of developing osteoporosis which is similar to the risk of coronary heart disease (CHD) and the risk of developing the condition is 13% in men over the age of 50. The impact osteoporosis is very intensive that in the UK there are around 60.000 hip fractures 50.000 radial and 40.000 vertebral fractures each year. The incidence of the disease increases with age especially in women after the age of 45 in which the most common fracture site is the wrist and forearm whereas after the age of 65 the main fracture site is the hip and the spine. In men, the risk of osteoporotic fractures increases after the age 75 and by the age of 85 both men and women usually suffer from hip fractures more than other sites.
One of the main factors in the pathogenesis of osteoporosis is the decrease in the bone mineral density (BMD) which in turn is affected by the achievement of peak bone mass at younger age (adolescence) and the rate by which bone is demineralized in the subsequent life. (Compston and Rosen, 2006)
1.3 Relative importance of peak bone mass and rates of bone loss in determining fracture risk of older women
Bone Mineral Density (BMD) is a key recognised predictor of fracture risk. This is evidenced by standards specified by the World health Organisation (WHO) in using absolute BMD for the diagnosis of osteoporosis. These standards stipulate that persons with a BMD value more than 2.5 standard deviations below the average specified for a 25-year-old Caucasian female are defined as having osteoporosis, while those between 1.0 and 2.5 standard deviations below the average are considered to have osteopenia (Shoback et al, 2001). Osteoporosis is a clinical term to imply reduced bone density; a condition associated with high morbidity and mortality. Where the reduction is mainly in the trabecular bone, the commonest complications are crush fractures of vertebrae, which may manifest as reductions in height and “dowager’s humps”. However, if cortical bone is involved, fractures of long bones such as those involving the neck of femur are likely; this is a major cause of death especially among older women (Longmore et al, 2004) Compston and Rosen (2002) outline the pathophysiology of low bone mass as a predisposing factor for osteoporotic fractures. They summarise that BMD is affected by Peak Bone Mass (PBM) and the extent of subsequent bone loss, both of which are regulated at the level of bone remodelling units. The function of such remodelling units in turn is influenced by interactions involving genetic and environmental factors. PBM is achieved between the ages of 20 and 30 years, with remodelling favouring the formation of bone, resulting in a significant increase in bone mass (Compston and Rosen, 2002), which has lifelong implications. While most important factors that interact to regulate PBM are of genetic origin, studies in twins have suggested that this genetic regulation of PBM can be influenced by hormonal and environmental factors such as timely secretion of sex steroids in adequate volumes, supplemental calcium intake and balanced physical activity (Compston and Rosen, 2002). Whereas PBM is achieved by the age of approximately 30 years, bone loss is a process that occurs over a much longer duration. In outlining the relative importance of PBM and rates of bone loss in determining the BMD at any given, Compston and Rosen (2002) acknowledge that chronic loss of bone is a commonly recognised feature in many cases of osteoporosis. However, they emphasise that impairments in the acquisition of PBM are known to be responsible for “60-70% of the variance in bone mass at any age. Hormonal and environmental factors remain the strongest determinants of bone loss after the fourth decade in both men and women, whereas heritable influences, sex hormone status and dietary calcium are the principal regulators of PBM” (Compston and Rosen, 2002). Given below is a list of factors known to interact in the regulation of PBM, and thereby determine the risk of osteoporosis.
1.3.1 Factors known to interact in the regulation of PBM and therefore considered risk factors for osteoporosis (Compston and Rosen, 2002).
Hypogonadism (including premature menopause) Glucocorticoid therapy Previous fragility fracture Low body weight Cigarette smoking Excess alcohol consumption Low dietary calcium intake Vitamin D deficiency Late menarche Physical activity High caffeine intake Maternal history of hip fracture Rates of bone loss too are influenced by similar factors. Predominant among these are hormonal influences, especially linked to menopause, where oestrogen deprivation enhances the rate of bone dissolution, and the chronic ‘uncoupling’, or an imbalance, of resorption and formation of bone manifest as age-associated bone loss. Other factors associated with bone loss include calcium or vitamin D deficiency, secondary hyperparathyroidism, therapeutic use of glucocorticoids, immunosuppressant therapy, excess thyroid levels and chronic anticonvulsant therapy (Compston and Rosen, 2002). Consideration of above reveals a significant overlap in the factors that influence PBM and bone loss.
1.3.2 Effects of calcium supplementation in younger women
The recognition of the importance of PBM and rates of bone loss in determining the risk of osteoporosis has led to a combination of preventative measures being identified in an attempt to minimise the mortality and morbidity associated with the condition. These include holistic measures such as fall avoidance and exercise, in addition to therapeutic measures such as high-calcium diet, calcium and vitamin D supplementation and oestrogen replacement therapy for postmenopausal women (Coble, 1995). The rationale for calcium supplementation resides in its physiological action on bone modulation. The physiological effect of calcium, in bone modulation, manifests through the actions of osteoblasts and osteoclasts. Osteoclasts, when stimulated, work towards the resorption of bone mass with an increased release of calcium and a rise in plasma calcium levels. Osteoblasts, in comparison, have a bone-building effect leading to the incorporation calcium into the bone substance, resulting in a lower plasma calcium concentration. A low plasma calcium concentration due to chronic loss, or reduced intake, therefore, results in demineralization of bone substance leading to an increased risk of osteoporosis (Simonsen et al, 2006). The optimal time for calcium supplementation, as means of preventing osteoporosis, has been a focus of much research. Coble (1995) outlined the need for calcium supplementation of all age groups based on the rationale that the daily requirement of calcium for young adults ranged between 1000 and 1500 milligrams, adults required a minimum of 1000 milligrams and postmenopausal women (especially those not receiving oestrogen) approximated 1500 milligrams. A recent issue of the British National Formulary (BNF) expands on this, recommending that calcium supplements should usually be indicated only in circumstances involving a deficiency in dietary intake. The recommendation highlights that calculation of the daily requirement is complex and multifactorial: for instance the requirement varies with age (greater in childhood) and during pregnancy and lactation, due, in both instances, to increased demand. Conversely, the requirement also increases in old age, due, in this instance, to impaired absorption (Joint Formulary Committee, 2008). There is a consensus that research has already established the value of calcium supplementation in reducing osteoporotic fractures, as evidenced by a recent systematic review conducted by Gennari and Martini (2008). However, a majority of these studies have looked at supplementation targeted at postmenopausal women. The systematic review conducted by Gennari and Martini (2008) also looked only at studies involving participants over the age of 50 years. In comparison, is there sufficient evidence to suggest routine calcium supplementation of younger women would prevent later manifestations of osteoporosis? Understandably, the value of supplementation would be significantly influenced by the background levels of calcium intake in communities, which are subject to wide variation. Barger-Lux et al (2005) reporting on a randomised controlled trial which looked at the augmentation of bone density following calcium supplementation of 152 post-adolescent healthy young women consuming a diet moderately low in calcium, concluded that the combined impact of increased dietary intake of calcium and the small quantity of calcium provided in the form of multivitamin tablets together, resulted in a mean increase in the control group (800 mg or 20 mmols per day) which was “possibly at or near the threshold beyond which additional calcium has no further effect on bone accrual” (Barger-Lux et al,2005). Woo et al (2007) reporting on a 2 year study involving 441 women between the ages of 20 and 35, looking at milk supplementation and bone health, also concluded that milk supplementation was not associated with consistent changes in BMD in that age group. It seems reasonable therefore to conclude that the current scientific evidence does not support mandatory calcium supplementation of all young women, as means of reducing the longer term risk of osteoporosis. However, this should not be taken to mean that young women exposed to particularly lower levels of dietary calcium would not benefit from calcium supplementation, as indicated in the BNF quoted above.
1.4 Effect of Nutrition on Bone health:
Nutrition plays a vital role in bone health during childhood. Nutrition is an important modifiable factor in the development and maintenance of bone mass (Vatanparast.2005). The nutrients occur in the foods and their intake can be detected by assessing dietary patterns. Jones et al (2001) reported cross-sectional data that showed a positive link between the consumption of fruits and vegetables and bone mineral density in 10 year old girls. McGartland et al (2004) studied the relation between bone mineral density and fruit and vegetables consumption during adolescence. 324 adolescents participated in the study a usual fruit, vegetable consumption diet history was recorded and using Dual energy X-ray absorptiometry (DXA) bone mineral density was measured. Regression analysis showed that high amounts of fruit had significantly higher heel of Bone Mineral Density (BMD) then the moderate fruit consumers. It also explained that fruits alkaline forming properties mediate the body’s acid-base balance. A study conducted by Vantanparast et al (2005) on 85 boys and 67 girls aged 8-20y using serial 24-h recalls along with anthropometric measurements , physical activity were assessed every 6 months. Bone mineral density was measured using DXA in fall of each year for 7 years. The results concluded that in addition to adequate dietary calcium intake, appropriate intakes of vegetables and fruits have a beneficial effect on BMD. Wachman and Brenstein proposed that dietary intake is related to the development of osteoporosis as foods provide acids and base products to neutralize the acid loads. In any absence it changes the pH hence high consumption of fruits and vegetables should help in counterbalance more acid produced and thereby reducing the burden on skeleton (Tylavsky et al, 2004).
Proteins has both positive and negative effects on calcium balance, it stimulates the production of insulin-like growth factor-1, a factor that promotes osteoblast-mediated bone formation (Dawson-Hughes, 2003). Proteins supplements significantly reduced bone loss in elderly hip-fracture patients. Two studies have examined the impact of dietary protein supplementation in elderly patients with hip fractures (calcium supplements along with protein) both studies concluded that supplementation increased the serum IGF-1 levels and reduced bone loss in the contralateral hips during the year after the fracture (Dawson-Hughes, 2003). Dawson-Hughes et al (2003) examined the association between protein intake, changes in BMD in healthy men and women aged 65, and older in 3 yr randomized controlled trail and the main result of the trail were that supplementation lowered turnover rate by 10 to 15% ,reduced bone loss form spine , hip and total body. Schurch et al (1998) studied protein supplements on 82 patients with recent osteoporotic hip fracture and its effect on the insulin growth factor-1. They were administered with calcium supplementation and one dose of vitamin D. They found that protein supplementation increased the insulin like growth factors and there was decrease in attenuation of femur bone mineral density and hence protein repletion can help in reducing hospital stays.
A study investigated causative role of fiber in understanding the role of bone health by supplementing them with wheat bran to 237 older men and women aged between 40 to 80 years. They were randomized to receive the supplementation and using single photon absorptiometry at baseline and yearly during the intervention to monitor the possible effects of fiber supplementation. They found no correlations between bone loss and supplementation (Chen et al, 2004). There is not much research which convinces the role of fiber in relation for change in bone health as the intake of fruits and vegetables determines the effect on the pH balance which indirectly affects the activity of bone health.
Observational studies have shown that increasing calcium intake is associated with a greater gain in bone mass and hence a higher peak bone mass (Rizzoli, 2008). Dawson-Hughes et al (1997) studied the inadequate dietary intake of calcium and vitamin D and prevalence of osteoporosis among older persons for 3 years. The BMD was measured using DXA, blood, and urine were analyzed every six months and they found the significant associations which would help in reducing the bone loss at neck, spine and total body over the three year period and reduce the incidence of fractures. Calcium and Vitamin D suppress bone turnover, slow bones and reduce fracture risk (Jensen et al, 2002). Jensen et al (2002) also carried out a study on long term effects of bone turnover and calcitrophic hormone by supplementation and dietary instruction aimed at increasing calcium intake through foods. 99 healthy postmenopausal women participated in a 3yr, randomized trail. It explained the attenuation over time in suppression of parathyroid hormone and bone turnover might help explain the effects of calcium and vitamin D. Aloia et al (1994) had carried out study to understand whether calcium supplementation with or without hormone replacement therapy helps in preventing postmenopausal bone loss on 118 healthy white women. Random allocation of calcium supplementations were provided along with equine estrogens, progesterone and placebo. The subjects were also receiving vitamin D of 400IU daily. The outcome was measured by total body calcium and photon absorptiometry for bone mineral density. The study recommended that dietary calcium augmentation should be recommended as a strategic option in helping to prevent early postmenopausal bone loss. Increased milk consumption significantly enhances bone mineral acquisition in adolescent girls and could favourably modify attainment of peak bone mass as shown by Cadogan et al (1997) and found that even though Bone turnover was not affected by milk supplementation. Serum concentrations of insulin-like growth factor I increased in the milk group compared with the control group (35% v 25%, P = 0.02). Napoli et al (2007) carried out a study on effects of dietary calcium and compared its effects on estrogen metabolism and bone mineral density. 168 healthy postmenopausal women participated in the study. The outcome was based on women who obtained calcium from diet alone, diet and supplemental calcium and from those who obtained only from supplements. There was wide association between women who obtained calcium from both source with obtained form the supplements, as there is shift in estrogen metabolism toward the active 16alpha hydroxyl metabolic pathway and with greater BMD. Disorders of estrogen metabolism have been implicated in the etiology of certain estrogen dependent condition such as cancers and now osteoporosis (Leelawattana et al, 2000). A study was investigated to determine whether girls with low calcium intake benefited from supplementation as a 18 month randomised trail with a follow up 2years after the withdrawal from the supplements, the result supported the fact that calcium supplementation enhances the bone mineral accretion in teenage girls but it is short lived (Lambert et al, 2008). Similar study was carried out with 94 girls to evaluate the effect of calcium supplementation on bone acquisition using a double randomised, double-blind placebo trail for 18months with calcium supplementation on bone density and bone mass. The study outcomes explained the importance of increasing the dose of calcium helps in improving the bone density and provide protection against future osteoporotic fracture (Lloyd et al, 1993). Bone mineral density is largely genetically determined, this influence is most powerful in the period of rapid skeletal development in childhood, and adolescence.91 teenage girls participated in a two year randomised controlled trail in diary supplementation on dietary patterns, body composition and bone density using self administered questionnaires and supplementation (2years of supplementation and 1 year follow up). They found significant associations at trochanter, femoral and lumbar spine when supplemented with dairy products foods and to a lesser extent at the lumbar spine (Merrilees et al, 2000). Chan et al (1995) carried out a study to understand the effects of calcium supplementation with dairy products on the bone and bone composition on pubertal girls. They found primarily dairy products at provided that dietary calcium intake or above the recommended dietary allowances had an increase rate of bone mineralization.
Alcohol is considered an important risk factor for various bone diseases but at same contradictory reports explains the positive effects of alcohol on bone health. Alcohol decreases the osteobalstic activity, leading to decreased bone formation and defective mineralization (Rico, 1990). Alcohol increases urinary calcium, magnesium and zinc excretion and deficiency in zinc is causative factor for osteoporosis (Rico, 1990). Felson et al (1995) carried out a study on alcohol consumption and its effects on bone mineral density in elderly men and women. In al 1,154 subjects from the Framingham heart study has participated. Bone density and alcohol consumption was assessed every 2 years from the examination. The authors concluded that alcohol intake of at least 7 oz is associated with high bone density in postmenopausal women, related to the estrogen levels augmentation by alcohol and men had higher bone densities than the light drinkers. Tucker et al (2009) with her colleagues had carried a study on effects of beer, wine and liquor intakes on BMD in older men and women. After adjusting the potential confounding factors she found that moderate consumption of alcohol may be beneficial for postmenopausal women however the BMD were low in men. Nutrition play important role in bone mass either directly by modifying bone turnover, or indirectly through hormone secretion. Nutrient, either macro or micro can be determinant of bone health such as calcium, protein, vitamin D and vitamin K. Fibre, phosphate and alcohol also has been studied as a factor that influence calcium bioavailability and absorption.
1.4.5 Regulation of blood calcium
Absorption of calcium in the small intestine and calcium excretion in the urine by the renal system is regulated by three components which also control the release of calcium from bones. These three components are parathyroid hormone (PTH), vitamin D3 (1,25 dihydroxycholecalciferol) and calcitonin which in turn are regulated by feedback mechanism of the plasma calcium to keep its concentration at 2.5 mmol/l (Underwood, 2000) When plasma concentrations of calcium are low, PTH are released from the parathyroid gland to raise calcium level by acting on bone leading to increased bone resorption and also acting on kidneys leading to decreased calcium excretion as well as the conversion of vitamin D to its active form to increase intestinal absorption. In the contrary, when there is an elevation in the plasma calcium levels suppress the release of PTH allowing the action of calcitonin which inhibits the action of the osteoclasts and subsequently preventing bone resorption. Calcitonin also inhibits calcium reabsorption in the renal tubules and calcium absorption in the small intestine. (Underwood, 2000)
1.4.6 Vitamin D
Vitamin D is a fat soluble vitamin which has different metabolic forms including D2 (ergocalciferol) and D3 (Cholecalciferol). As a fat soluble vitamin, vitamin D can be found in some foods with high fat content like cod liver and cod liver oil, milk and milk products, some fortified foods like breakfast cereals and margarine. However, the vitamin can be synthesised within the human body in amounts proportionate to sunlight exposure as well as some other factors. The endogenous synthesised vitamin is superior to that obtained from dietary sources as deficiency is usually accompanied with inadequate exposure to sunlight (Geissler and Powers, 2005). The inadequate exposure is attributed to factors like climate, environment, lifestyle, culture, and religion as well as aging and immobility associated with it. (Geissler and Powers, 2005) In addition to its main role in maintaining plasma concentrations of calcium and phosphorous, vitamin D has been proved to have function in cellular immunity and maintaining cell integrity, also in regulating thyroid and parathyroid hormone and insulin secretion. In a review by O’Donnell et al 2008, it was concluded that most of the studies found fortified foods can improve Vitamin D status in adult. However, O’Donnell et al 2008 also found that the size of these trails is troublingly small which may affect the strength of the supplementation supporting argument. Fig 1.4: The regulation of calcium homeostasis (From Quarles, 2008) According to Macdonald et al (2008) the role of vitamin D in the prevention of rickets is acknowledged but the amount required to prevent fracture and to maintain bone strength is still imprecise and the recommendation was to accurately measure the precise amount of the vitamin for optimal health
1.5 Acid – Base Balance:
Bone is a very large ion exchange buffer. Acid-induced changes in bone mineral are consistent with a role for bone as a proton buffer. Metabolic acidosis stimulated physiochemical mineral dissolution and subsequently cell mediated bone resorption (Krieger et al, 2004). The net endogenous acid produced from the diets measured as net acid secretion, which metabolized from acid residues such as proteins and cereal grains and alkaline residues from fruits and vegetables. An excess acid load is buffered by bone and in the process; calcium is released (Jajoo et al, 2006). Various studies have observed that greater intakes of fruits and vegetables are associated with greater bone mineral density (BMD) and lower urinary excretion of markers of bone resorption (Jajoo et al, 2006). Green and Kleeman (1991) reported that 80% of total body carbonate is in the hydration shell, the water surrounding the bone, as 80% of citrate and 35% sodium that serves the excess acid. Prynne et al (2003) carried a study on 16-18year old boys and girls on dietary acid-base balance and intake of nutrients affecting the bone health. The mean intakes of foods and selected nutrients such as calcium, phosphorous, protein, magnesium, vitamins C and K were calculated. The acid base balance were calculated by using Remer ratio (Net acid excretion) and Frassetto (protein/potassium ratio). Although the results were not consistent to provide accurate answer as there bone cannot be considered in isolation from the other components of the diet. The principal factors contributing to acid load include sulphur from the catabolism of sulphur amino acids (methionine and cysteine), which are highest in animal protein, nuts and cereals; phosphorus, which is mainly supplied by meat and dairy products and chloride (Prynne et al, 2003). Bushinsky (1996) and Arnett and Sakhaee (1996) have documented that osteoclasts and osteoblasts respond independently to small changes in pH in the culture media in which they are growing. A small drop in pH causes a tremendous burst in bone resorption (citied in Pyrnee et al, 2003).To understand the urine acidifying properties of food constituents a four period, double cross over study ( diet intervention) focused on acid load modifying the effect of calcium metabolism in humans was carried out by Buclin et al (2001). Eight healthy volunteers underwent a four day metabolic preparation using two type of diets one including acid-ash forming nutrients and one providing base forming nutrients both have similar contents of calcium , phosphate , sodium and protein. The study results showed that acid forming diet increased urinary calcium excretion by 74% when compared with the base forming diet. These explain the importance of maintaining the acid–base balance in diets to protect against osteoporosis.
1.6 Bone turnover Biomarkers
Bone is a complex tissue and undergoes complex process of renewal and remodelling involving many different factor and substances. Measurement of circulating levels of such substances should provide a picture of the status of the bone at any given time (Hale et al, 2007). A number of serum and urine biomarkers have been measured for bone turn over including total alkaline phosphatase, bone specific alkaline phosphatase, procollagen type 1 N-terminal propeptide (PINP), amino – and carboxy-terminal telopeptides of type 1 collagen, and hydrox proline (Hale et al, 2007). Serum PINP levels have been measured to assess bone turnover in postmenopausal women (Hale et al. 2007). PINP has been used in order to track the effects of bone antiresorptive and anabolic agents (Hale et al, 2007). It is also considered as a serum biomarker correlating to bone formation. Biochemical markers of the bone turnover are indicative of the overall bone metabolism and provide different information hence in testing biochemical markers, it is important to consider that there is a high degree of variability associated with measurement of the markers (urine markers). Testing for biochemical markers of bone turnover could potentially be used to identify slow and fast bone loss at menopause considering the caustaive factor for osteoporosis and bone related diseases. A longitudinal, prospective population based study was carried out by Ivaska et al (2005) in quest to understand the effects of bone turnover markers in 113 elderly women by using the serum and urine samples from 85 women who sustained a fracture and 28 controls with similar trauma but no fracture and bone turnover was assessed by seven different bone turnover markers reflecting various stages of bone metabolism. They found that bone turnover markers within few hours after fracture were not altered from pre-injury levels however bone formation and resorption markers were increased and it was elevated up to a year after fracture. A study carried out by Scariano et al (2001) to investigate the day to day variability and critical differences in serum levels of N-telopeptide, PINP and alkaline phosphatase in healthy women found that circulating levels of bone turnover markers were stable over the period of months, allowing for the determination of signifiant changes in skeletal metabolism of women. Several factor besides bone mineral mass have been related to the risk of hip fracture. High rated of bone resorption may be associated with disruption of trabecular network as well as with an increased rate of bone loss (VanDaele et al, 1996). They investigated that urinary pyridinium crosslinks are markers of bone resorption. They found that urinary pyridinium crosslinks can be used as prediction of hip fractures, which then leads to increased bone fragility. A study confirmed that markers of bone formation and resorption can be used clinically to predict future in earl postmenopausal women. A group of researchers carried out a study to understand the sensitivity and specificity of indexes on 236 menopausal women randomized to hormone replacement therapy or calcium supplementation. The markers selected were osteocalcin, bone alkaline phosphatase, N-telopeptide and urinary free deoxypyridinoline. They were measured at baselines, 3, 6 and 12 months after treatment. All four markers showed time-dependent decreases in women receiving HRT (P < 0.001) and no change in women receiving CS alone. Early postmenopausal women there are different responses of biochemical markers to HRT and CS (Rosen et al, 1997).
1.7 Dual Energy X-ray Absorptiometry DXA
The best method to measure bone status is by using a dual energy x-ray absorptiometry (DXA) method, which measures bone mineral density directly. It is also used to measure total body composition and fat content. DXA is expressed as T-score which equal to or less than -2.5 is indicative of osteoporosis. Dual-energy x-ray absorptiometry (DXA) is recognized as the reference method to measure bone mineral density (BMD) accurately and reproducibly (Blake et al, 2007). Although it is not considered as best measurement for true bone mineral density calculation but it is accurate measure of bone mineral content. A study was carried out to compare various techniques suitable for assessing the bone mineral density using the dual X-ray laser and DXA for total hip and lumbar spine BMD measurements in 164 women aged 40-83 years. They concluded that for detecting osteoporosis or osteopenia dual X-ray laser was quite effective in terms of specificity when compared to DXA although the results were contradictory and the biochemical markers prove more reliable tool for assessing the mineral content of bone (Yumru et al, 2009). Mazess et al (1990) to calculate the BMD carried out a study and soft tissue composition of the total body using DXA on 12 young adults on five occasions found that DXA proves to provide precise results with a low radiation exposure and can be considered as a tool for assaying BMD for other prospective studies. Arabi et al (2007) carried out a study investigating the ability of dual X-ray abosrptiometry method to indentify patients with osteoporotic fractures in 460 subjects (73yrs) with vertebral fractures (VF), when the lumbar spine (LS), hip or both sites are measured. The study provided with an array on hip BMD as the best skeletal site needed to detect subjects with osteoporosis and showed the strong relationship with prevalent vertebral fractures in elderly subjects. DXA also have several practical limitations that includes it provides the areal measurement of BMD (g/cm2) rather than the volumetric measurement. Areal measurements does not account bone thickness and body size. Despite of limitations it is considered as standard method for assessment of BMD relates to the emerging consensus on its use for the diagnosis of osteoporosis followed by low cost and broad availability when compared to the other biomarkers such as serum and urine samples, which require cost and time for most epidemiological studies.
2.0 Aim of the study:
To assess the association between BMD and blood and serum calcium, vitamin D and age in young women with low habitual calcium intake. The Aim of the study was to understand the effects of low calcium intake and vitamin D on the bone mineral density (BMD) of the young women subjects. The rationale of the study was also to understand whether low dietary calcium intake might show an affect on the serum calcium level (transiently). Hence, we set our further aim as to investigate whether adaption to the low calcium intake needs a higher dose of Vitamin D or serum calcium does not have any pathway linked with dietary calcium and these change can be considered as transient state causing low serum calcium
To examine the association between low calcium intake (from food frequency questionnaire FFQ and Four days diary) and the bone mineral density at lumbar spine (BMD_LS Total) and hip sites (BMD_HIP Total) To examine the association between blood and urine calcium with bone mineral density at lumbar spine (BMD_LS Total) and hip sites (BMD_HIP Total) To examine the relation between blood and urine calcium with Vitamin D levels To examine the relation between blood and urine calcium with calcium intake as calculated by 4 days diary. To find out if there is a difference in blood and urine calcium and BMD (When you compare subjects according to their vitamin D levels, that is >50 is normal and <50 is low vitamin D status To find out if there is a change in BMD according to age. To find out if there is a change in BMD according to BMI
3.0 Materials and methods
3.1 Study design
This main study was a randomized, double blind; placebo controlled, with 4 parallel groups, single-centre study of calcium fortified ice cream. Study measurements were made over a 28 day period. Two levels of participation; all subjects followed the standard protocol to investigate the long term (28 days) effects of fortified ice cream. In this part the relationship and the association the determinants of the BMD in subjects with low habitual calcium intake will be statistically tested. The baseline data include information obtained from the subjects by the lifestyle questionnaire and food frequency questionnaire (FFQ) as well as baseline laboratory and clinical data which include blood and urine tests and BMD using DXA. Age: This was in the range from 20-39 years. Subjects were divided into two subgroups to test the age effect on BMD, group 1 aged from 20-29 and group 2 aged from 30-39. Body mass index (BMI) was calculated at the first visit. FFQ to estimate the dietary calcium intake which should be below 750mg/day. BMD for the hip and spine was measured using DXA at the baseline. Blood samples at the baseline which estimate among other variables the blood levels of vitamin D and calcium. Urine samples to estimate urinary calcium over a period of 24 hours. Four days food diaries to calculate the average calcium intake during the time of the study.
Subjects were recruited from lists held at the osteoporosis clinical trials unit containing details of subjects that have expressed an interest in participation in clinical studies run by the unit. Subjects were also recruited using advertising material such as poster and email distribution lists placed around hospitals, college and universities and other local places visited by young women of this age range i.e. sports facilities, local shops etc in Sheffield. All women interested in the study were contacted by telephone, when eligibly checked against the inclusion/exclusion criteria. Suitable subjects were sent the information sheet by post and an appointment for screening were arranged.
3.3 Study volunteers:
The data was obtained from the subjects recruited for the main study which evaluate the effect of calcium fortified ice-cream on bone health biomarkers.
3.3.1 Inclusion Criteria:
All subjects were females aged between 20 and 39 who were able and willing to participate in the study and they provided written informed consent. Subjects have a habitual calcium intake of less than 750 mg/day as assessed by dietary questionnaire and all of them were willing and able to consume study specific ice cream, one daily for 28 days. The subjects also have: BMI between 18 and 30 Not taking or have in the past 3 months before the study taken any calcium supplementation Regular menstrual cycle (11-12 periods per year) (subjects can use oral contraception but this must be continuous use for more than 2 years). Vitamin D level of > 50 nmol/l (subjects with levels < 50 nmol/l were supplemented and re-tested 4-6 weeks later for inclusion in the study)
3.3.2 Exclusion Criteria
Subjects were not admitted to the study if they showed evidence of any of the following: Use of some the following medications which affect bone in the last 6 months: Bisphosphonates (at any dose within 2 years). Use of any fluoride with the exception use for oral hygiene. Strontium. Teriparatide. Other bone agents (i.e., SERM, isoflavones, HRT, calcitonin, etc). Steroids. Have a history of ongoing conditions or diseases known to cause abnormalities of calcium metabolism or skeletal health (secondary osteoporosis). Malabsorption syndromes (e.g. Celiac, Crohns disease, Colitis). Hyperthyroidism as manifested by TSH outside the lower limit of the normal range. Hyperparathyroidism. Hypocalcemia or hypercalcemia. Osteomalacia. Paget’s disease. Diabetes. Have any history of cancer within the past 5 years excluding skin cancer (non-melanomas). Have suffered a recent fracture within the last 12 months. Have evidence of a clinically significant organic disease which could prevent the subject from completing the study including diabetes. Abuse alcohol or use illicit drugs or who consumed more than 4 servings of any alcoholic beverage one day prior to the visit or have more than 21 units per week (i.e., subjects who might be binge drinkers). Have serum calcium less than 2.2 mmol/l. Have markedly abnormal clinical laboratory parameters that are assessed as clinically significant by the investigator. Have participated in another clinical trial involving active therapy 3 months prior to randomization. Are currently trying to conceive are currently pregnant or currently breast feeding. Subjects who exercise excessively, i.e. more than 4 strenuous sessions per week. Use of Depo-provera in the last 1 year.
3.4 Food Frequency Questionnaire:
The FFQ used in the study was a semi-quantitative with the usual food stuff that considered as a good source of dietary calcium and consumed by the local population The calcium content in each food item was pre-calculated as per 100g using McCance and Widdiowson’s food composition tables (1992). The portion size and its calcium content was then calculated using Ministry of Agriculture, Fisheries and Food guide for food portion sizes (1993). In the appendix II the FFQ is attached showing the amounts and the frequency of items consumed either per day or per week then the average daily intake is calculated and subjects with low intake (>750mg/day) are recruited if they fit other criteria in lifestyle questionnaire (Appendix I)
3.5 Statistical analysis:
The software SPSS (version 15) was used in the analysis. The baseline results (e.g. age, weight, calcium intake, etc) were expressed as range as well as Mean ± SD. The association between different variables was performed using regression and ANOVA tests.
4.0 Results and Analysis
The aim of the current research analysis is to study the relation between the low calcium intake, vitamin D levels and the Bone Mineral Density (BMD) in female subjects aged between 20-39 years. In order to facilitate this research objective following questions were framed Study the relationship or association between low calcium intake (from food frequency questionnaire FFQ and 4 days diary) and the bone mineral density at lumbar spine (BMD_LS Total) and hip sites (BMD_HIP Total), with BMD as the dependent variable. Study the relationship among blood calcium, urine calcium and BMD on both sites, with BMD as the dependent variable. Study the relationship among blood calcium and urine calcium and Vitamin D levels, with vitamin D levels as independent variable. Study the relationship among blood calcium, urine calcium and calcium intake as calculated by 4 days diary, with calcium intake as independent variable. Study the relationship between BMD at both the sites and BMI.
4.1 Baseline characteristics:
Number of subjects enrolled in the study was 82 and the mean age was 29.9±6.04 years (age ranged between 20.13 and 39.87 years). 51.2% of the subjects were aged below 29 years and 48.8% between 30 and 39 years. The BMI for the total subjects was 24.02±2.7 The mean calcium intake as assessed by the FFQ was 482.52±153.67 mg/day with a range between 113 and 740 mg/day. In the four days food diary the mean calcium intake was higher as the accuracy different between the two methods. The mean calcium intake as calculated from the diaries was 749.65±228.576 mg/day with a range of 334 to 1415 mg/day. The average total BMD at lumbar spine was 1.05±0.1 and 0.98± 0.11 g/cm3 at the hip area. The mean vitamin D level was 70.58±28.08 but it ranged from 20.9 to 139.0 and subjects with very low vitamin D status (below 50?g/dl) were given a supplement before the main study supplements were dispensed. The main baseline characteristics of the study group are shown below
Study the relationship or association between low calcium intake (from food frequency questionnaire FFQ and 4 days diary) and the bone mineral density at lumbar spine (BMD_LS Total) and hip sites (BMD_HIP Total), with BMD as the dependent variable. The relationship between low calcium intake and the Bone Mineral Density can be analyzed using the regression analysis for each of the BMD sites. In order to estimate the regression model we first carried out the curve estimation for each of the independent variables (FFQ and 4 days diary dataset) with BMD at each sites as the dependent variable. BMD at lumbar spine v/s calcium intake (4 Day Diary) The ANOVA test results presented in Table 1 shows the significance test of the linear and the Quadratic models for calcium intake (4 Day Diary) as independent and BMD at lumbar spine as the dependent variable. The results of ANOVA test indicate that none of the models, liner as well as quadratic, fits the observed variation between the BMD at lumbar spine and calcium intake. However, to see the strength of relation predicted by the models we carried out further analysis and calculated the model summary, presented in Table 2. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R-square, coefficient of determination shows that only 0.1% of the observed variation in BMD is explained by the linear model and 3.0% in case of quadratic model. The scatter plot showing the variation between the BMD at lumbar spine and calcium intake presented in Figure 1 show that there is no significant variation of BMD at lumbar spine with the calcium intake (4 Day dairy) Figure 1 Scatter plot diagram showing variation between the BMD at lumbar spine and calcium intake BMD at lumbar spine v/s calcium intake (FFQ) The ANOVA test results presented in Table 3 shows the significance test of the linear and the quadratic models for calcium intake (FFQ) as independent and BMD at lumbar spine as the dependent variable. The results of ANOVA test indicate that none of the models, liner as well as quadratic, fits the observed variation between the BMD at lumbar spine and calcium intake. However, to see the strength of relation predicted by the models we carried out further analysis and calculated the model summary, presented in Table 4. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R-square, coefficient of determination shows that only 0.5% of the observed variation in BMD is explained by the linear model and 0.5% in case of quadratic model. The scatter plot showing the variation between the BMD at lumbar spine and calcium intake presented in Figure 2 show that there is no significant variation of BMD at lumbar spine with the calcium intake (FFQ). Figure 2 Scatter plot diagram showing variation between the BMD at lumbar spine and calcium intake BMD at hips site v/s calcium intake (FFQ) The ANOVA test results presented in Table 5 shows the significance test of the linear and the quadratic models for calcium intake (FFQ) as independent and BMD at Hips site as the dependent variable. The results of ANOVA test indicate that none of the models, liner as well as quadratic, fits the observed variation between the BMD at hips site and calcium intake. However, to see the strength of relation predicted by the models we carried out further analysis and calculated the model summary, presented in Table 6. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R-square, coefficient of determination shows that only 0.4% of the observed variation in BMD is explained by the linear model and 1.5% in case of quadratic model. The scatter plot showing the variation between the BMD at hips site and calcium intake presented in Figure 3 show that there is no significant variation of BMD at hips site with the calcium intake. Figure 3 Scatter plot diagram showing variation between the BMD at hips site and calcium intake BMD at hips site v/s calcium intake (4 Day Diary) The ANOVA test results presented in Table 7 shows the significance test of the linear and the quadratic models for calcium intake (4Day Diary) as independent and BMD at Hips site as the dependent variable. Table 7 ANOVA test results for BMD at hips site v/s calcium intake (4Day Diary) The results of ANOVA test indicate that none of the models, liner as well as quadratic, fits the observed variation between the BMD at hips site and calcium intake. However, to see the strength of relation predicted by the models we carried out further analysis and calculated the model summary, presented in Table 8. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R-square, coefficient of determination shows that only 0.0% of the observed variation in BMD is explained by the linear model and 0.1% in case of quadratic model. The scatter plot showing the variation between the BMD at hips site and calcium intake presented in Figure 4 show that there is no significant variation of the BMD at hips site with the calcium intake. Figure 4 Scatter plot diagram showing variation between the BMD at hips site and calcium intake
The ANOVA test results and the scatter plots discussed above confirm that there is not significant relation between the Calcium intake and the BMD. Further, the significance value suggests that the observed pattern is random with no significant correlation between the variables.
Study the relationship among blood calcium, urinary calcium and BMD on both sites, with BMD as the dependent variable. The relationship between the urinary and blood calcium with the BMD can be tested using the regression analysis. In order to carry out the regression analysis we first check the curve estimates for each of the (blood calcium independent variables with the dependent variable. BMD v/s urinary calcium The ANOVA test results presented in Table 9 shows the significance test of the linear and the quadratic models for urinary calcium as independent and total BMD as the dependent variable. The results of ANOVA test indicate that none of the models, liner as well as quadratic, fits the observed variation between the total BMD and urinary calcium. However, to see the strength of relation predicted by the models we carried out further analysis and calculated the model summary, presented in Table 10. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R-square, coefficient of determination shows that only 0.3% of the observed variation in BMD is explained by the linear model and 0.3% in case of quadratic model. The scatter plot showing the variation between the total BMD and urinary calcium presented in Figure 5 show that there is no significant variation of total BMD with urinary calcium. Figure 5 Scatter plot diagram showing variation between the total BMD and urinary calcium BMD v/s blood calcium The ANOVA test results presented in Table 11 shows the significance test of the linear and the quadratic models for blood calcium as independent and total BMD as the dependent variable. The results of ANOVA test indicate that none of the models, linear as well as quadratic, fits the observed variation between the total BMD and blood calcium. However, to see the strength of relation predicted by the models we carried out further analysis and calculated the model summary, presented in Table 12. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R-square, coefficient of determination shows that only 0.0% of the observed variation in BMD is explained by the linear model and 0.1% in case of quadratic model. The scatter plot showing the variation between the total BMD and blood calcium presented in Figure 6 show that there is no significant variation of total BMD with blood calcium. Figure 6 Scatter plot diagram showing variation between the total BMD and blood calcium
The results of ANOVA test and the scatter plot indicate that there is no significant association between the urinary calcium and BMD and blood calcium and BMD. Further, the significance test results confirm that the observed patter between these variables is random and there is no significant correlation between the variables.
Study the relationship among blood calcium and urine calcium and Vitamin D levels (low and normal); with vitamin D levels as independent variable.
Urinary calcium v/s vitamin D levels
The relationship between the urinary calcium and the vitamin D levels (normal and low) was analyzed using the independent samples t-test. The two vitamin D groups, normal group (>50) and low group (<50), are the independent groups and the urinary calcium is the dependent group. The descriptive statistics presented in Table 13 indicate that the urinary calcium in case of normal vitamin D level (M= 3.533, SD=1.7881) is higher than the urinary calcium in case of low vitamin D level (M=2.790, SD=1.2900). However, in order to confirm that this difference is statistically significant we carried out further analysis. The t-test statistics presented in Table 14 indicate that the observed difference in the urinary calcium for the low vitamin D level and normal vitamin D level is not significantly different, t(45) = -2.012, p >0.05. Thus, the two groups have same level of urinary calcium and the observed difference between the two groups is only due to change and there is no significance associated with the same.
Blood calcium v/s Vitamin D groups
The relationship between the blood calcium and the vitamin D levels is analyzed using the independent samples t-test. The two vitamin D groups, normal group (>50) and low group (<50), are the independent groups and the blood calcium is the dependent group. The descriptive statistics presented in Table 15 indicate that the blood calcium in case of low vitamin D level (M= 2.2760, SD=0.06451) is slightly higher than the blood calcium in case of normal vitamin D level (M=2.790, SD=1.2900). However, in order to confirm that this difference is statistically significant we carried out further analysis. The t-test statistics presented in Table 16 indicate that the observed difference in the blood calcium for the low vitamin D level and normal vitamin D level is not significantly different, t(78) = .985, p >0.05. Thus, the blood calcium level in the two vitamin D groups is not different and the observed different is only due to change and has no statistical significance.
The results of t-test suggest that there is no difference in the blood calcium and the urinary calcium in the two vitamin D groups. Further, the significance values suggest that the observed difference of blood calcium and the urinary calcium in the two Vitamin D groups is only due to chance and has no statistical significance associated with the same.
Study the relationship among blood calcium, urine calcium and calcium intake as calculated by 4 days diary, with calcium intake as independent variable. Blood calcium v/s calcium intake (4 Day Dairy) To measure the variation of blood calcium with the calcium intake (4 day dairy) we carried out the regression analysis, with the blood calcium as the dependent variable and the calcium intake (4 days dairy) as the independent variable. The linear and quadratic curve estimates for the variation of the blood calcium with the calcium intakes are analyzed using the ANOVA test results presented in Table 17. The ANOVA test results indicate that neither the linear curve-fitting model nor the quadratic curve-fitting model is able to represent the observed datasets significantly. However, to see the strength of relation predicted by the model we carried out further analysis and calculated the model summary, presented in Table 18. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R Square, the coefficient of determination, show that in case of linear model only 1.0% of the observed variation in BMD is explained by the model and in case of quadratic model only 1.7% of the observed variation in BMD is explained by the model. The scatter plot showing the variation of the blood calcium with the calcium intake is presented in Figure 7. It clearly shows that there is no significant variation of blood calcium with the calcium intake. Figure 7 Scatter plot diagram showing variation of blood calcium with the calcium intake
Urinary calcium v/s calcium intake
To measure the variation of urinary calcium with the calcium intake (4 day dairy) we carried out the regression analysis, with the urinary calcium as the dependent variable and the calcium intake (4 days dairy) as the independent variable. The linear and quadratic curve estimates for the variation of the urinary calcium with the calcium intakes are analyzed using the ANOVA test results presented in Table 19. The ANOVA test results indicate that neither the linear curve-fitting model nor the quadratic curve-fitting models are able to represent the observed datasets significantly. However, to see the strength of relation predicted by the model we carried out further analysis and calculated the model summary, presented in Table 20. The small value of R, multiple correlation coefficient indicates moderate relationship between the model-predicted values and the observed values. Further, the R Square, the coefficient of determination, show that in case of linear model only 0.7% of the observed variation in BMD is explained by the model and in case of quadratic model only 3.7% of the observed variation in BMD is explained by the model. The scatter plot showing the variation of the urinary calcium with the calcium intake is presented in Figure 8. It clearly shows that there is no significant variation of urinary calcium with the calcium intake. Figure 8 Scatter plot diagram showing variation of urinary calcium with the calcium intake
The results of ANOVA test and the scatter plot diagrams presented above suggest that there is no relationship between the blood and urinary calcium with the calcium intake (4 Day Dairy). Further, the significance test confirms that the observed pattern is just by change and there is no statistical significance associated. Analysis 5 Study the relationship between BMD at both the sites and BMI BMD at hips v/s BMI To analyze if there is any significant change in the BMD at hips with BMI we first carryout a curve estimation (linear and quadratic) to see which of these models will fit the dataset. The results of the curve estimation along with the F-test statistics are presented in Table 21. In addition, Figure 9 represents the scatter plot diagram showing variation of BMD at hips with BMI, along with the two curve estimates. Scatter plot diagram showing variation of BMD at hips with BMI The results of the curve estimate show that the linear model represents the dataset more significantly. Moreover, observing the scatter plot will reveal that the BMD at hips increases with the increase in the BMI. The model coefficients are presented in Table 22 show that for every unit increase in the BMI the BMD at hips increases by 0.009 units. The strength of the model is can be predicted by looking at the model summary table presented in Table 23. The small value of R, multiple correlation coefficient indicates weak relationship between the model-predicted values and the observed values. Further, the R Square, the coefficient of determination, shows that only 5.3% of the observed variation in BMD is explained by the model.
BMD at lumbar spine with BMI
To analyze if there is any significant change in the BMD at lumbar spine with BMI we first carryout a curve estimation (linear and quadratic) to see which of these models will fit the dataset. The results of the curve estimation along with the F-test statistics are presented in Table 24. In addition, Figure 10 represents the scatter plot diagram showing variation of BMD at lumbar spine with BMI, along with the two curve estimates.
Scatter plot diagram showing variation of BMD at lumbar spine with BMI
The results of the curve estimate show that both the linear model and the quadratic model are not able to predict the observed variation significantly. Moreover, observing the scatter plot will reveal that the BMD at lumbar spine increases with the increase in the BMI. The strength of the models is can be predicted by looking at the model summary table presented in Table 25. The small value of R, multiple correlation coefficient indicates weak relationship between the model-predicted values and the observed values. Further, the R Square, the coefficient of determination, shows that only 1.6% of the observed variation in BMD is explained by the model in case of Liner model and 2.0% in case of Quadratic.
The results of the ANOVA test and the scatter plot indicates that there is no strong correlation between the variables. The significance test show that only BMD at hips site vary linearly with the BMI, but with a very low strength. Thus we can safely say that there is only a slight variation of BMD at hips site with BMI and that there is no variation of BMD at hips site with BMI.
In this project, two methods were used to assess the calcium intake food frequency questionnaire FFQ and four-day food dairy. The change in the marker was calculated at baseline scaled ratio (BSR). The results are expressed in terms of association between the variables of the interest, such as bone mineral density (BMD) is dependent variable throughout the analysis. The results of the association between low calcium intake and the bone mineral density are shown in figures (1, 2). There was no significant association observed at lumbar spine and hip sites using both methods. These observations suggest that there was no observed pattern between the variables. Various studies investigated the relation between diet and bone mineral density in pre-menopausal women between aged 30-39years using same methods of evaluation. They found no relations between lumbar spine or distal forearm bone density and calcium intake. It also revealed that higher calcium intake was associated with a higher hip bone density compared with low lifetime calcium intake (Nieves et al, 1995). Jorde et al (2000) and his colleagues explained the relation between the low calcium intake , parathyroid hormone , and blood pressure explained the role of calcium in association with high levels of serum PTH, which however affects the bone mineral content and the density in the lumbar spine along with increased blood pressure. Other study carried out on short-term calcium supplementation on peak bone mass in adolescent girls for 3.5 years showed that calcium supplementation for one year in postmenarcheal girls with low calcium intakes may provide sustained effects on bone mineral density (Dodiuk-Gad et al, 2005). No significant associations were found when blood, urine calcium was assessed with bone mineral density at different sites. The scatter plots of the variables are shown in the diagram (3, 4). Hitz et al, (2007) investigated the risk of osteoporosis at hip, forearm, shoulder and spine using DXA and blood biochemical markers were assessed. In all 122 patients were assessed for bone turnover, no significant change was observed at hip BMD. There was reduced bone turnover and significantly increased BMD in patients younger than 70 years, and decrease bone loss in older. There was significant importance given to physical performance to understand the risk of osteoporosis. Effects of dietary calcium and supplementation was carried out on estrogen metabolism to understand the effects on bone mineral density .they found a considerable shift in estrogen metabolism when the source of calcium was from diet and with the greater BMD which produce favourable effects on bone health (Napoli et al, 2007). We also tried to carry out the analysis on the blood calcium and urine calcium levels in association with vitamin D and found no significant associations as shown in the scatter plots. Vitamin D is more important in development, maintenance of bone and calcium absorption and bone mineralization. Dawson-Hughes et al (2000) carried out a study on calcium supplementation and vitamin D to reduce the bone loss and prevent fractures on bone mineral density. They found that improvements in BMD observed in subjects in supplementation group was lost when discontinued followed by increased in level of serum parathyroid levels and biochemical markers. Hence, the study explained the long lasting use of supplementation for elderly individuals to reduce the fracture risk. No association observed when four-day food dairy and food frequency questionnaire were analysed with blood calcium and urine calcium levels as shown in the diagram. The relationship between diet and bone loss is controversial. Epidemiological studies have reported weak correlations between diet and bone mass. In 1994 Ramsdale et al carried out a study on effects of dietary calcium intake relating to bone mineral density in premenopausal women. Bone density was measured at spine, femur and radius using DXA and food frequency questionnaire was used to assess the intake of dietary calcium. They found correlations between calcium intake and bone density at all three femoral sites and all were independent of body mass. However, they also explained that food frequency methods compare quite well with the weighed methods. It is more important to carry out the repeat measurements and validation of the food frequency questionnaire should be adopted for better results. No correlations were observed when bone mineral density was analysed with body mass index at various sites as shown in the scatter plots (5, 6). However, BMD at hip site varied linearly with BMI, but the overall effect was significantly low to be considered. Body weight is known to be positively associated with the bone mineral density. These was evaluated the association between weight and bone mass in elderly men and women subjects of Framingham study. Various sites such as proximal femur bone and radius bone mass were assessed along with subjects being weighed repeatedly for 40 years. They found that effect of weight and of weight change on bone mineral density was less in men than in women. The results suggested the there is effect of weight in bone mineral density in both the sexes , supporting the fact due to adipose tissue production of estrogen in women after menopause (Felson et al, 1993). Edelstein and his colleague had carried out a cross sectional study to understand the relation between bone mineral density and eight measures of body size such as total weight , body mass index etc. The hip and lumbar spine was measured using DXA. After adjusting for the parameters, they found that total weight played a major role in change in bone mineral density. Tóth et al (2005) carried out a study to examine the association between body mass index and bone mineral density. They divided the subjects according to their BMI. Using DXA, sites were assessed. They found a strong positive association between BMI and femur neck BMD. They found the risk factors for proximal femur osteoporosis are higher in obese and overweight cases. Hence making body mass index as a marker is to be considered in osteoporosis.
Subjects for this study were recruited for a randomized placebo controlled, double, parallel intervention trail and hence our results may therefore have subject to bias. Various biomarkers in blood and urine were selected for short and long-term measures. To measure the status of bone DXA were used as gold standard method. The mean nutrient intakes and calcium intakes were recorded in form four day food dairy and food frequency questionnaire and were closely monitored. Day to day variations in nutrient intake would lead to errors of between 10 to 20% for each individual subject (Earnshaw et al, 1997). Studies of Ca supplementation in postmenopausal women, typically of one to two years duration, have shown that calcium cannot prevent bone loss but can reduce the rate of bone loss to some extent (Cashman, 2002).There are several factors associated with absorption of calcium in the body. Dietary fat, fiber, serum 1, 25 dihydroxyviatmin D2, and alcohol consumption are independent determinants of calcium absorption (Wolf et al, 2000) some studies also suggested that diet low in fats had 19% lower mean fractional calcium absorption values than with women who consumed diets with highest fat in relation to fiber (Wolf et al, 2000). Dietary protein has been recorded to play role in calcium balance. Protein plays important structural component of bone, which accounts most important part of the skeletal matrix. Various studies support the fact that dietary protein could influence the production of insulin-like growth factor-1, which has positive effects on the skeleton (Promislow et al, 2002). Studies conducted on effects of nitrogen, phosphorous and caffeine explained that higher nitrogen intakes are associated with proportionately higher levels of urinary calcium. Caffeine intake was also associated with higher levels of urinary calcium, which explained the possible shift of calcium and its effect on bone mineral density in premenopausal women (Heaney.1982). The use of dual energy X-ray absorptiometry is good method , as scan times are short , radiation dose is low and the information produced at the sites are accurate (Ramsdale et al, 1994). The results produced in our results were low which could not help us in producing a grounded result. Dietary factors were not related to BMD at either the lumbar and hip sites, this was closely attributed, as population diet has no effect on bone mass (Earnshaw et al, 1997). There was a wide range in age, which would have caused a wide variation in the outcomes.
7.0 Limitations and further suggestions:
Dietary calcium uptake in the human intestine is affected by many other dietary components which may increase or decrease calcium absorption. Some of the compounds that decrease calcium absorption are phytate and oxalate which bind to the calcium and inhibit its absorption. These compounds can be found in many food items including tea, coffee, potato and beans and there intake along with calcium containing food (e.g. tea with milk) affect the actual amount of calcium absorbed. In this manner, the FFQ used in such studies should make an account of those effects on total calcium intake. Dietary fibre has a major rule in calcium absorption especially in people with marginal intake and should be put in account as well. Although most of the dietary assessment methods depend on estimating the crude intake, manual calculation of the dietary calcium intake in both FFQ and the Four-day food dairy is not a very accurate procedure to calculate nutrients intake and can be affected by the researcher bias which may worsen the estimate. The use of software is also useful in calculating the calcium content of different food items rather than using an average or an estimate. A longer term supplementation trail may give better results especially when increasing the number of subjects recruited to decrease the error and variation between and within the subjects. The use of medications and supplementations is very common in the study age group (e.g. contraceptives, multivitamins and weight control medications) and including them as variables (rather than excluding criteria) may give interesting results. Educating the subjects about the use of scales to weigh the food in the food dairies and unifying the measurement units may give better estimates as well. In further studies, measurement of the bone turnover biomarkers after the cessation of the supplementation may also may also give a view of the changes and the difference between before, during and after supplementation levels.
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Appendix I: Lifestyle Questionnaire Appendix II: Food Frequency Questionnaire Subject Initials………………….. Subject DoB ……………………………. Food Frequency Questionnaire 1) What type of milk do you use: Whole “ Semi-skimmed “ Skimmed “ Dried “ How much milk do you use each day? – Tea/Coffee with milk: milky “ or average “. Number of cups…5….each day – Milky Drinks (cups)……………….each day – Milk on breakfast cereal (portions)………1…..each day – Milky puddings/custard (medium bowl)…3……..each week 2) Cream: single “ or double “. Number of tablespoons……2…week…each day 3) Yoghurt: plain “ flavoured “ diet “ or Greek “ (Pots)…1………each week 4) Ice-cream: (any type) scoops……2-3…………each week 5) Cheese: – Hard cheese: (portions)………. …………per week – Cheddar: (portion size: small “ medium “ large “) (Portions)……1…../week – Cheshire: (portion)……………………….per week – Soft cheese: (triangle-type) ………………….per week – Cottage cheese: (tablespoon)…….…….per week – Fromage frais: small pots “ large pots “ Number of pots………2….per week – Macaroni – cheese: (portion)…………each week – Lasagne: (portion)……………….…..…each week – Pizzas: small “ medium “ large “ (slice)……1……each week 6) Eggs: number……….each week 7) Quiche: small “ medium “ large “ (slices)………1……..each week 8) Baked Products a) Bread white or brown (slice) …………4………..each day b) Bread wholemeal or granary (slice) ………….each day c) Biscuits (number)……………………………….each week STH 14784 Ice-cream Study Questionnaire V1, 14.01.08 Subject Initials………………….. Subject DoB ……………………………. d) Croissant (number)…………………1…………………each week e) Scones (number)………………………………………..each week f) Buns and cakes (number and slices) ……2b………….each week g) Pie and crumble (portions)…………………….…….each week 9) Cereals (bowl)………….1.…each day 10) Fish (not tuna) a) White fish (Fillets) small “ medium “ large “ (Fillet)…………….per week b) Salmon (tinned or fresh) (portion) …………….per week c) Sardines (portion) ……………………per week d) Prawns (portion)…………..………………….per week e) Pilchards (can) …………….…………………..per week 11) Dark Green Leafy Vegetables a) Spinach (portion)………………….each week b) Broccoli (portion) ………………….each week c) Other…………. ……….each day or ………..each week 12) Beans and pulses (e.g. kidney beans, lentils etc) (Tablespoons)………………..………….each week 13) Fruit Juices (e.g. orange, apple, tomato) Glass/small carton (number)……………….each day 14) Chocolate (plain or white) – Plain chocolate Squares/bars (number)……………0.5……..…each week – White chocolate Squares/bars (number)…………………….each week 15) Water -Tap water ………………….glasses/day – Bottled water brand …………highland spring……………..size…………………….and number of bottles/day………….0.5l………… – Mineral water brand………………………..size ………………..and number of bottles/day……………………. Thank you for completing this questionnaire STH 14784 Ice-cream Study Questionnaire V1, 14.01.08