Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-17T09:19:45.220Z Has data issue: false hasContentIssue false

Biochemical markers for assessment of calcium economy and bone metabolism: application in clinical trials from pharmaceutical agents to nutritional products

Published online by Cambridge University Press:  14 November 2014

Jean-Philippe Bonjour*
Affiliation:
Division of Bone Disease, University Hospitals and Faculty of Medicine, Geneva, Switzerland
Wendy Kohrt
Affiliation:
Division of Geriatric Medicine, University of Colorado, Denver, CO, USA
Régis Levasseur
Affiliation:
Service de Rhumatologie, CHU Angers, Angers, France
Michelle Warren
Affiliation:
Department of Obstetrics and Gynecology and Medicine, Columbia University Medical Center, New York, NY, USA
Susan Whiting
Affiliation:
College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
Marius Kraenzlin
Affiliation:
Division of Endocrinology, Diabetes and Metabolism, University Hospital, Bale, Switzerland
*
* Corresponding author: Professor Jean-Philippe Bonjour, fax +41 22 382 99 73, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Nutrition plays an important role in osteoporosis prevention and treatment. Substantial progress in both laboratory analyses and clinical use of biochemical markers has modified the strategy of anti-osteoporotic drug development. The present review examines the use of biochemical markers in clinical research aimed at characterising the influence of foods or nutrients on bone metabolism. The two types of markers are: (i) specific hormonal factors related to bone; and (ii) bone turnover markers (BTM) that reflect bone cell metabolism. Of the former, vitamin D metabolites, parathyroid hormone, and insulin-like growth factor-I indicate responses to variations in the supply of bone-related nutrients, such as vitamin D, Ca, inorganic phosphate and protein. Thus modification in bone remodelling, the key process upon which both pharmaceutical agents and nutrients exert their anti-catabolic or anabolic actions, is revealed. Circulating BTM reflect either osteoclastic resorption or osteoblastic formation. Intervention with pharmacological agents showed that early changes in BTM predicted bone loss and subsequent osteoporotic fracture risk. New trials have documented the influence of nutrition on bone-tropic hormonal factors and BTM in adults, including situations of body-weight change, such as anorexia nervosa, and weight loss by obese subjects. In osteoporosis-prevention studies involving dietary manipulation, randomised cross-over trials are best suited to evaluate influences on bone metabolism, and insight into effects on bone metabolism may be gained within a relatively short time when biochemical markers are monitored.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Authors 2014

Introduction

The aim of the present report is to review how biochemical markers can be used to evaluate the potential benefit of foods or nutrients on adult bone health and in primary and secondary osteoporosis prevention. Nutrition and physical activity are key behavioural determinants of the development and progression of several chronic diseases, including osteoporosis. In primary prevention, nutrition combined with moderate physical activity plays an important role in attenuating age-related bone loss. In secondary prevention, adequate nutrition is adjuvant to specific anti-osteoporotic medications, thus enabling their full therapeutic effects.

This review focuses on the clinical application of biochemical analyses that can respond to nutritional interventions aimed at either preserving bone integrity or attenuating bone loss in adults.

Effects of foods and nutrients on Ca and inorganic phosphate (Pi) economy( Reference Rizzoli, Bonjour, Seibel, Robins and Bilezikian 1 ) can be measured using bone-related circulating hormonal markers, such as vitamin D metabolites( Reference Lensmeyer, Binkley, Drezner, Seibel, Robins and Bilezikian 2 ), parathyroid hormone (PTH)( Reference Souberbielle, Friedlander and Cormier 3 , Reference Jüppner, Fulehan, Seibel, Robins and Bilezikian 4 ) and insulin-like growth factor (IGF)-I( Reference Hjortebjerg and Frystyk 5 ), as well as bone turnover markers (BTM) that reflect bone formation and resorption( Reference Kraenzlin, Seibel, Seibel, Robins and Bilezikian 6 Reference Vasikaran, Cooper and Eastell 9 ).

New findings on BTM have improved how they are viewed in the design, follow-up and interpretation of clinical trial outcomes, particularly in postmenopausal women and the elderly( Reference Naylor and Eastell 8 , Reference Vasikaran, Cooper and Eastell 9 ). Indeed, BTM changes can predict the later influence of pharmaceutical agents on bone mineral density (BMD)/content and fragility fractures( Reference Kraenzlin, Seibel, Seibel, Robins and Bilezikian 6 Reference Vasikaran, Cooper and Eastell 9 ). This will be discussed in the context of bone health-related claims as they relate to evidence-based knowledge of nutritional benefits. Examples of clinical trials are presented that document how nutrients influence bone-trophic hormones and biochemical markers of bone remodelling. Finally, appropriate study designs using both biochemical markers and new tools for assessing bone structure and function, as well as biomechanical properties, will be discussed, taking into account distinct adult populations.

Osteoporosis, a major health problem

Osteoporosis is a systemic skeletal disease characterised by low bone mass and micro-architectural deterioration of bone tissue with a consequent increase in bone fragility and susceptibility to fracture that worsens with ageing( 10 ). Osteoporosis causes more than 8·9 million fractures annually worldwide( 10 ). The lifetime risk for wrist, hip or vertebral fracture is estimated to be 30 to 40 % in developed countries. Epidemiological studies in both the USA and UK indicate that one in two women aged 50 years or older will have an osteoporotic fracture in their remaining life; for men it is one in five( Reference Harvey, Dennison, Cooper, Rosen, Compston and Lian 11 ). The most serious osteoporotic fracture, at the proximal femur, typically results when subjects having an intrinsic bone fragility caused by both reduced mineral density and impaired structural design have fallen. These fractures show an exponential age-related increase and have a severe detrimental effect on the health of elderly populations( 10 , Reference Harvey, Dennison, Cooper, Rosen, Compston and Lian 11 ). Fragility fractures in elderly individuals are the long-term pathological consequence of an imbalance between bone resorption and bone formation leading to bone loss with micro-structural alterations of both cortical and trabecular structures( 10 , Reference Harvey, Dennison, Cooper, Rosen, Compston and Lian 11 ).

Therapeutic options and limitations

There are many efficacious pharmaceutical options that have been shown, in large and well-designed randomised controlled trials (RCT), to significantly reduce the risk of fracture in postmenopausal women and the elderly( Reference Hopkins, Goeree and Pullenayegum 12 , Reference Murad, Drake and Mullan 13 ). Nevertheless, insufficient compliance in terms of both adherence and persistence to drug prescription limits their effectiveness for the management of individual patients in the real-world conditions of medical practice( Reference Downey, Foltz and Boccuzzi 14 Reference Schousboe 17 ).

It is relevant to note that in the stepwise drug development phases, virtually all testing of major anti-osteoporotic medications relied on BTM measurement to assess efficacy.

Evidence-based criteria in osteoporosis management

Clinical evidence arises from the analysis of various research approaches with the aim of proposing the most reliable recommendations for practical disease management. The RCT is at the top of the strength-of-evidence hierarchy. These RCT usually measure non-vertebral fractures, and require that thousands of patients be followed for at least 3 years to reach a sufficient statistical power. However, some well-designed RCT testing specific pharmaceutical agents in postmenopausal women have failed to demonstrate efficacy for preventing hip fracture( Reference Hopkins, Goeree and Pullenayegum 12 ). The incidence of hip fracture depends not only on the drug efficacy for improving bone structure and metabolism, but also on extra-skeletal factors, mainly those related to the occurrence and circumstances of falling. This makes hip fracture a stochastic event, largely independent of bone-active medications. Furthermore, RCT for osteoporosis typically provide both treatment and placebo groups with sufficient Ca and vitamin D, which may have independent benefits on bone metabolism and attenuate the potential effect size on hip fracture reduction.

Concept of evidence-based nutrition in bone health and osteoporosis

Clinical research related to the impact of foods and/or nutrients on bone metabolism and structure is hampered by the difficulty to reach a level of evidence( Reference Blumberg, Heaney and Huncharek 18 , Reference Heaney 19 ) similar to that required of clinical trials that test pharmaceutical drugs for osteoporosis management( Reference Guyatt 20 Reference Woolf, DiGuiseppi and Atkins 22 ). It is difficult to prove the efficacy of drugs for preventing fragility fracture using large RCT designed for this purpose (see below), but it is even more challenging to use the RCT approach to evaluate whether foods or nutrients can reduce the risk of fragility fracture( Reference Blumberg, Heaney and Huncharek 18 , Reference Heaney 19 ). There is a major difference in the level of strength evidence required for foods or nutrients as compared with drugs, to evaluate not only benefits but also risks( Reference Biesalski, Aggett and Anton 23 Reference Woodside, Koletzko and Patterson 26 ). As explicitly stated by Heaney & Blumberg, ‘…we cannot get RCT-level evidence from many, and perhaps most, nutritional questions…’( Reference Heaney and Blumberg 25 ). Therapeutic decisions using anti-osteoporotic drugs require evidence based on high-quality RCT that have as their main outcome the reduction of fragility fractures( Reference Guyatt 20 ). Inferences are weakened when studies rely on surrogate endpoints such as BMD or BTM( Reference Guyatt 20 ). Testing the efficacy of a food or nutrient with the reduction of hip fracture risk as the main outcome will probably never be amenable to RCT because it would require that thousands of patients remain compliant to the intervention for at least 3 to 4 years( Reference Blumberg, Heaney and Huncharek 18 , Reference Heaney 19 , Reference Biesalski, Aggett and Anton 23 ). Industry is unlikely to sponsor these large RCT because, in contrast to drugs, marketing the products is not dependent on the results of RCT. However, as compared with foods or nutrients, the risk:benefit ratio is much greater with drugs, as they may cause more severe adverse side effects( Reference Blumberg, Heaney and Huncharek 18 , Reference Heaney 19 , Reference Fenton and Fenton 24 , Reference Heaney and Blumberg 25 ).

In the nutrition field, valuable information could be obtained from RCT that have BTM endpoints reflecting bone metabolism. The change in BTM can be detected at a shorter time than the time for assessing effects on fragility fracture rate. The validity of results obtained from short-term RCT is strengthened by preclinical studies carried out in reliable animal models of human osteoporosis( Reference Bonjour, Ammann and Rizzoli 27 Reference Syed and Melim 31 ). The next sections will deal with the scientific basis underlying these concepts.

Bone-related biomarkers: biochemical analyses and clinical application

Both hormonal factors and biomarkers of bone remodelling can be useful to assess the baseline status and the response to an intervention in the context of prevention or treatment of osteoporosis.

Bone mass can be estimated by measuring BMD, i.e. areal BMD (aBMD) using dual-energy X-ray absorptiometry (DXA). While low aBMD is an important determinant of fracture risk, its measurement does not capture all the risk factors for fracture( Reference Kanis, McCloskey and Johansson 32 ). In fact, up to half of patients with incident fractures have baseline spinal or hip aBMD assessed by DXA above the diagnostic threshold of osteoporosis defined as a T-score of − 2·5 sd or more below the average value of young healthy adults (i.e. WHO definition of osteoporosis)( Reference Schuit, van der Klift and Weel 33 , Reference Sornay-Rendu, Munoz and Garnero 34 ). Using peripheral measurement devices, 82 % of postmenopausal women with fracture had T scores better than − 2·5( Reference Siris, Chen and Abbott 35 ). Furthermore, there is increasing evidence that the decision to use pharmacological intervention for the prevention of fracture should be based on the fracture probability rather than only on the presence of osteoporosis as defined by aBMD T-score( Reference Kanis, McCloskey and Johansson 32 ). Therefore, in selecting patients for treatment, the risk of fracture in individual subjects is now calculated by the use of algorithms which include a number of recognised independent risk factors for fracture in addition to aBMD, such as age, sex, BMI, family history, past history of fracture, secondary causes of osteoporosis such as rheumatoid arthritis, use of medications such as glucocorticoids, smoking and excessive alcohol intake( Reference Kanis, McCloskey and Johansson 32 ). FRAX® (WHO Fracture Risk Assessment Tool) is such a fracture risk calculator for the next 10 years (www.shef.ac.uk/FRAX/).

Bone-related circulating hormonal factors

Hormonal changes to insufficient supply of calcium, vitamin D and protein

The main consequence of vitamin D insufficiency, as expressed by a low circulating level of 25-hydroxyvitamin D (25(OH)D), is a decrease in the intestinal absorption of both Ca and Pi. The reduction in the supply of Ca from the intestinal tract leads to an increase in the production of PTH to, at least partially, compensate for hypocalcaemia by stimulating both tubular Ca reabsorption and osteoclastic bone resorption. The secondary hyperparathyroidism also contributes to the associated hypophosphataemia resulting from the reduced tubular Pi reabsorption.

When associated with a deficiency of protein, as expressed by a low serum level of the anabolic agent IGF-I, osteoblastic bone formation rate no longer is matched to osteoclastic bone resorption rate. Moreover, insufficient dietary protein, in concert with insufficient vitamin D status, results in low intestinal Ca absorption. At the kidney, the reabsorption of Pi and the synthesis of the active metabolite of vitamin D, 1,25-dihydroxyvitamin D, tend also to be depressed. Overall, the insufficient dietary supply of these bone-trophic nutrients leads to an uncoupling between the resorption and the formation rate of the bone mineralised organic matrix that favours resorption. Intermediate and long-term possible consequences are measurable bone loss, by DXA or by high-resolution peripheral computed tomography( Reference Boutroy, Bouxsein and Munoz 36 ) and increased risk of fragility fracture.

In the course of dietary interventions aimed at correcting these nutrient insufficiencies, reliable biochemical assays are available to monitor changes in serum levels of 25(OH)D, PTH and IGF-I. In order to link these hormonal changes to the response of bone metabolism, BTM can concurrently be measured during the dietary intervention trial (see below).

Biochemical determination of bone-trophic hormonal factors: parathyroid hormone

A recommendation has been made to establish reference ranges for serum PTH in vitamin D-replete healthy individuals( Reference Eastell, Arnold and Brandi 37 ), and to measure PTH in postmenopausal osteoporotic women to exclude secondary osteoporosis due to primary hyperparathyroidism( Reference Souberbielle, Friedlander and Cormier 3 , Reference Tannenbaum, Clark and Schwartzman 38 ). Further, secondary hyperparathyroidism resulting from inadequate Ca intake and/or vitamin D deficiency, leading to an increase in bone resorption, is thus an important determinant of hip fractures in the elderly( Reference Lips, Duong and Oleksik 39 Reference Tang, Eslick and Nowson 41 ). It has been shown by several investigators that acute oral Ca load or longer-term Ca supplementation (i.e. fortified dairy products) have favourable effects on bone metabolism by decreasing PTH and bone resorption markers( Reference Tenta, Moschonis and Koutsilieris 42 , Reference Kanellakis, Moschonis and Tenta 43 ).

Biochemical determination of bone-trophic hormonal factors: 25-hydroxyvitamin D

The measurement of serum 25(OH)D is the best assessment of vitamin D status. The level associated with optimal suppression of circulating PTH levels has been used to define criteria for vitamin D insufficiency, because of the inverse relationship between serum 25(OH)D and PTH. However, this relationship may be influenced by age, Ca intake, physical inactivity, renal function, ethnicity and other factors. Furthermore, the use of different assays for 25(OH)D and PTH( Reference Cavalier, Rozet and Gadisseur 44 , Reference Eddington, Hudson and Oliver 45 ) may also influence the apparent threshold of 25(OH)D concentration at which secondary hyperparathyroidism occurs. Conservatively, a 25(OH)D level of 50 nmol/l (20 ng/ml) is considered as vitamin D sufficient, representing a level above which there is no further suppression of PTH, not much further reduction in BTM, and not much further improvement of muscle function( Reference Mason 46 ). The Institute of Medicine also considers that serum levels of vitamin D above 50 nmol/l are the level that is needed for good bone health for practically all individuals, and set a recommended daily intake of 600 IU/d for children and adults and 800 IU for women and men 71 years or older based on this cut-off( Reference Ross, Manson and Abrams 47 ). However, more recently, the clinical practice guidelines of the Endocrine Society defined vitamin D deficiency as 25(OH)D levels below 50 nmol/l (20 ng/ml) and vitamin D sufficiency as 25(OH)D levels greater than 75 nmol/l (30 ng/ml)( Reference Holick, Binkley and Bischoff-Ferrari 48 ). In a cross-sectional analysis (National Health and Nutrition Survey (NHANES) 2003–2006) in the US population, maximum PTH suppression expected to occur when vitamin D is sufficient, corresponding to the zero slope between 25(OH)D and PTH, did not occur until a serum 25(OH)D level of approximately 100–125 nmol/l (40–50 ng/ml)( Reference Ginde, Wolfe and Camargo 49 ). Debate about the optimal vitamin D status continues( Reference Ginde, Wolfe and Camargo 49 Reference Rosen, Abrams and Aloia 51 ). There is evidence that Ca in combination with vitamin D supplementation reduces the risk of fracture( Reference Bischoff-Ferrari, Willett and Wong 52 Reference Warriner and Saag 55 ), bone loss( Reference Macdonald, Wood and Aucott 56 ) and non-vertebral fracture risk( Reference Bischoff-Ferrari, Willett and Wong 52 ); these effects are more efficacious at 800 IU/d (20 μg/d) than 400 IU/d (10 μg/d).

Biochemical determination of bone-trophic hormonal factors: insulin-like growth factor-I

Measurement of serum IGF-I is an important tool with growth hormone (GH) not only in the diagnosis of various stature disorders( Reference Clemmons 57 ), but also as a nutritional marker, particularly of protein intake( Reference Schürch, Rizzoli and Slosman 58 , Reference Thissen, Ketelslegers and Underwood 59 ). IGF-I and GH are regulators of bone homeostasis and are important for the acquisition of bone mass during adolescence, for the achievement of peak bone mass and for the maintenance of skeletal architecture during adult life( Reference Bonjour, Ammann, Chevalley, New and Bonjour 60 , Reference Giustina, Mazziotti and Canalis 61 ). Although GH may act directly on skeletal cells, most of its effects are mediated by IGF-I, which is present in the systemic circulation. Systemic IGF-I is synthesised in the liver under the influence of GH. IGF-I is also produced by a variety of cell types and acts in a paracrine fashion in many tissues. Availability of IGF-I is regulated by IGF binding proteins (IGFBP). Serum IGF-I levels are associated with bone mass. IGF-I levels peak at puberty when bone acquisition is maximal but levels of GH, IGF-I, and some IGFBP that regulate IGF activity decrease with age( Reference Giustina, Mazziotti and Canalis 61 Reference Perrini, Laviola and Carreira 63 ). This decline in GH and IGF-I secretion has been correlated with bone loss in postmenopausal women. A relationship between IGF-I levels and fracture risk has also been reported( Reference Sugimoto, Nishiyama and Kuribayashi 64 , Reference Garnero, Sornay-Rendu and Delmas 65 ). However, it is not entirely clear how IGF-I is involved in the pathogenesis of osteoporosis, as serum IGF-I levels are strongly influenced by catabolic states, undernutrition and advanced age, all factors that increase the risk for fracture.

Dietary proteins influence both the production and action of IGF-I. Protein restriction is associated with reduced IGF-I levels by inducing a resistance to the GH action in the liver, and by an increased IGF-I metabolism. Decreased IGF-I levels are observed in states of undernutrition (i.e. also in anorexia nervosa). On the other hand, increased protein intake can prevent the decrease in IGF-I in hypoenergetic states. Experimental and clinical observational data suggest that IGF-I plays an important role in the pathophysiology of osteoporosis, osteoporotic fractures and its complications. Correcting altered IGF-I in the elderly by protein replenishment may favourably influence not only bone mass, but also muscle mass and strength that are important determinants for the risk of falls. The changes of IGF-I levels in nutrition-related changes in body weight are described below.

Bone turnover markers

Importance of bone remodelling in osteoporosis

Bone remodelling is an important process that continuously renews the cortical and trabecular envelopes throughout life. It repairs micro-damage, maintains mineral homeostasis and ensures mechanical competence by modifying the micro-architecture. Bone remodelling is regulated by a variety of systemic and local factors as well as by nutritional factors (Fig. 1). Bone remodelling follows a sequence of events that includes activation, recruitment of osteoclasts to begin resorption, degradation and removal of bone, reversal, and formation of new bone by osteoblasts (Fig. 1). After this phase, a quiescent or resting period occurs. Remodelling begins with release of cytokines from osteoblast precursors, leading to recruitment of osteoclast precursors, which differentiate in multinucleated osteoclasts and subsequently attach to the bone surface. Resorption of the skeletal matrix releases growth factors such as IGF-I and transforming growth factor-β, which recruit lining cells and early osteoblast precursors that eventually form new bone (Fig. 1). Osteoblasts that do not undergo apoptosis remain on the bone surface as lining cells or become entrapped as osteocytes into the new bone matrix. These osteocytes respond to gravitational forces and can signal to lining cells to initiate remodelling. Antiresorptive and anabolic agents influence bone structure by affecting remodelling. Likewise, supply in Ca, vitamin D and protein influences bone remodelling. The presence of Ca and vitamin D inhibits bone resorption, and protein stimulates bone formation (Fig. 1). Ca, Pi and vitamin D are necessary for adequate mineralisation of the newly formed bone matrix (Fig. 1).

Fig. 1 Bone remodelling cycle. Bone turnover follows a sequence of events that includes activation, recruitment of osteoclasts (Ocl) to begin resorption, degradation and removal of bone, reversal, and formation of new bone by osteoblasts (Ob). After this phase a quiescent or resting period occurs. LC, lining cell; Oc, osteocyte.

In young adulthood, the amount of bone removed by the osteoclasts is equal to the amount of bone formed by the osteoblasts, so that bone mass is maintained. After the menopause and with ageing in both sexes, there is a progressive bone loss due to an imbalance between bone resorption and formation.

There is a substantial body of evidence that accelerated bone turnover is associated with a higher risk of fracture, independent of other parameters such as age or BMD (see below). BTM are not included in the fracture risk calculator (FRAX®), mainly for the reason that their relationships with other established risk factors included in the risk calculator are not clarified and because different markers and different methodologies for their assessment have been used across studies. This has led to the recommendation for the standardisation of BTM measurements in future studies and it is suggested that serum carboxy terminal telopeptide of collagen type I (s-CTX) is used as the reference bone resorption marker and serum procollagen type I N-terminal propeptide (sPINP) as the reference bone formation marker, measured by standardised assays( Reference Vasikaran, Eastell and Bruyere 66 ).

Bone markers reflecting bone formation and bone resorption

The BTM can be divided into two groups: markers of resorption and markers of formation (Fig. 2). The principal, specific markers of bone formation, measured in serum by immunoassays, are bone alkaline phosphatase (BAP), osteocalcin (OC) and PINP. Most assays are now adapted to automated platforms( Reference Lee and Vasikaran 7 ) (Fig. 2). Markers of bone resorption are the pyridinium cross-links (pyridinoline and deoxypyridinoline) and their associated peptides (telopeptides), released during collagen breakdown( Reference Kraenzlin, Seibel, Seibel, Robins and Bilezikian 6 ). Markers of osteoclast number include tartrate-resistant acid phosphatase isoenzyme 5b (TRAP 5b), the osteoclast-specific isoform of total TRAP, and, potentially, cathepsin K (Fig. 2). These enzymes are expressed and released by osteoclasts( Reference Henriksen, Tanko and Qvist 67 ). TRAP 5b is the most commonly used marker of osteoclast number.

Fig. 2 Diagram of the relationship between bone resorption or formation and bone turnover markers. On the resorption side, tartrate-resistant acid phosphatase (TRAP) reflects osteoclast number and activity, whereas pyridium cross-links, carboxy terminal telopeptide (CTX) and N-terminal telopeptide (NTX) are markers of bone matrix resorption. On the formation side, collagen type I propeptides, such as procollagen type I N-terminal propeptide (PINP), bone alkaline phosphatase (BAP) and osteocalcin reflect mainly osteoblastic bone formation. Osteoid corresponds to the non-mineralised bone matrix.

TRAP 5b is predominantly expressed by osteoclasts, and immunoassays to detect this isoform have been developed( Reference Halleen, Hentunen and Karp 68 Reference Kraenzlin, Lau and Liang 70 ). In absolute terms, the response of TRAP 5b to antiresorptive therapy is not as great as the response of s-CTX, but within-subject variability is significantly lower. TRAP 5b has a lower diurnal variability than s-CTX and lower acute effect of feeding, possibly due to a longer circulating half-life, making it less susceptible to short-term influences( Reference Hannon, Clowes and Eagleton 71 ). Furthermore, TRAP 5b is not cleared by the kidney, and thus does not accumulate in renal failure( Reference Halleen, Alatalo and Janckila 69 , Reference Hannon, Clowes and Eagleton 71 ).

Predictive value of early changes in bone markers in response to pharmaceutical agents on later effects on bone mineral density/content and fragility fractures: bone turnover markers and the rate of bone loss

There is evidence that bone turnover increases rapidly after menopause and persists for up to 40 years( Reference Garnero, Borel and Delmas 72 , Reference Garnero, Sornay-Rendu and Chapuy 73 ). In postmenopausal women bone turnover rate accounts for >50 % of the variation in bone mass, whereas in premenopausal women it is 0–10 %( Reference Garnero, Hausherr and Chapuy 74 , Reference Henriksen, Leeming and Christiansen 75 ).

Several prospective studies have demonstrated correlations between levels of BTM and rates of bone loss assessed by serial measurement of aBMD or bone mineral content at different skeletal sites over a time period spanning 13 years( Reference Stepan 76 ). These studies support an inverse relationship between BTM and aBMD. This inverse relationship becomes stronger with advancing age and the relationship is significantly stronger for resorption markers than for formation markers, due to the fact that the increase of bone resorption with ageing is greater than the increase in bone formation( Reference Delmas, Eastell and Garnero 77 ). However, this inverse correlation between BTM and aBMD does not seem to apply to all skeletal sites. A significant correlation between the rate of turnover and rate of bone loss at the hip or radius has been demonstrated in postmenopausal women, but not for the bone loss at the spine( Reference Garnero and Delmas 78 , Reference Garnero, Sornay-Rendu and Duboeuf 79 ). Extra-skeletal calcification and/or compression fracture occurring with advancing age can overestimate aBMD measurements at the spine. There is also evidence indicating that bone resorption markers can detect ‘rapid losers’ (>3 % loss in aBMD per year) in a follow-up period of 2–12 years( Reference Delmas 80 Reference Rosen, Chesnut and Mallinak 82 ). At the present time a single measurement of a biochemical marker cannot predict the absolute rate of bone loss in a single individual. However, increased BTM can be regarded as a risk factor for rapid bone loss.

Predictive value of early changes in bone markers in response to pharmaceutical agents on later effects on bone mineral density/content and fragility fractures: bone turnover markers and fracture risk

BTM may be useful in the assessment of fracture risk. Findings of prospective studies have shown that BTM of resorption predict fracture risk in postmenopausal women and in men (Table 1)( Reference Garnero, Hausherr and Chapuy 74 , Reference Garnero, Sornay-Rendu and Claustrat 83 Reference Garnero, Delmas, Seibel, Robins and Bilezikian 89 ). In five prospective studies of postmenopausal women (Epidemiology of Osteoporosis (EPIDOS), Rotterdam, Os de Femmes de Lyon (OFELY), Hawaii Osteoporosis Study (HOS), and Malmö Osteoporosis Prospective Risk Assessment (OPRA)), there were significant associations of baseline levels of urinary or s-CTX, urinary free deoxypyridinoline, or serum TRAP 5b with fracture risk( Reference Garnero, Hausherr and Chapuy 74 , Reference Garnero, Sornay-Rendu and Claustrat 83 Reference Meier, Nguyen and Center 87 ) (Table 1). An increase of these markers above the premenopausal range was associated, after adjustment for aBMD, with a twofold increase in risk for hip, vertebral and non-vertebral fractures, over a follow-up period of 1.8 to 5 years. The results for the relationship between bone formation markers and fracture risk are not consistent. In the French cohort study of elderly women (EPIDOS), no significant relationship between OC or BAP and fracture risk could be demonstrated during a 2-year follow-up. In contrast, in younger healthy postmenopausal women (OFELY and HOS cohorts), a significant association was found between increased BAP serum levels and risk of vertebral as well as non-vertebral fracture( Reference Garnero, Hausherr and Chapuy 74 , Reference Garnero, Sornay-Rendu and Claustrat 83 , Reference Ross, Kress and Parson 84 ).

Table 1 Relationship between increases in bone resorption rate and fracture risk

RR, relative risk; BMD, bone mineral density; EPIDOS, Epidemiology of Osteoporosis; CTX, carboxy terminal telopeptide of collagen type I; OFELY, Os de Femmes de Lyon; HOS, Hawaii Osteoporosis Study; DPD, free deoxypyridinoline; OPRA, Osteoporosis Prospective Risk Assessment; TRAP, tartrate-resistant acid phosphatase; DOES, Dubbo Osteoporosis Epidemiology Study; ICTP, C-terminal telopeptide generated by metalloproteinases.

* RR-BMD =  RR for fracture by 1 sd decrease in BMD.

RR-marker =  RR for fracture by 1 sd increase in marker above the premenopausal normal range.

For the highest quintile.

These results suggest that a combined approach, with both aBMD and indices of bone turnover, could improve fracture prediction in postmenopausal women. In fact, calculating the absolute risk of fracture by combining clinical risk factor (i.e. previous fracture), low aBMD and increased bone turnover (high CTX) resulted in a better hip fracture probability at 10 years than with low aBMD alone( Reference Johnell, Odén and De Laet 90 ). In elderly men high bone resorption was also associated with an increased risk of osteoporotic fracture( Reference Meier, Nguyen and Center 87 ).

Bone turnover markers and treatment monitoring

The main domain for the clinical use of BTM is the monitoring of osteoporosis therapy. The ultimate goal in treating patients with osteoporosis is to reduce their fracture risk. However, the short-term incidence of osteoporotic fractures is low, and the absence of fracture during treatment does not necessarily mean that the treatment is effective. Consequently, serial measurements of changes in aBMD as a surrogate marker of therapeutic efficacy are currently the standard approach to monitor osteoporosis therapy. Yet, changes in aBMD occur slowly and therapeutic effects are usually not detectable before 1–2 or more years of treatment( Reference Delmas 80 , Reference Bonnick and Shulman 91 Reference Roux, Garnero and Thomas 94 ). In contrast, BTM levels change much faster than aBMD in response to therapeutic interventions( Reference Delmas 80 ). Antiresorptive treatment generates a rapid decrease in bone resorption markers after only 2–4 weeks, reaching a plateau after 3 to 6 months of treatment( Reference Delmas 80 , Reference Garnero, Shih and Gineyts 95 ) (Fig. 3(a)). The decrease in bone formation markers, reflecting the physiological coupling of bone formation to bone resorption, is delayed and reaches a plateau after 6–12 months. The magnitude of the decreases varies according to the type and dose of the drug and the marker used to assess the effect (Table 2).

Fig. 3 Schematic representation of changes in bone turnover markers (BTM) at 3–6 months and corresponding increase in bone mineral density (BMD) during treatment of osteoporosis. (a) Bone antiresorptive treatment (i.e. bisphosphonate): a more pronounced decrease in bone resorption marker at 3–6 months is associated with larger increases in BMD. (b) Bone formation-stimulating treatment (i.e. recombinant parathyroid hormone): a greater increase in bone formation marker is associated with a larger increase in BMD. * Mild response in changes in BTM and BMD. † Moderate response in changes in BTM and BMD. ‡ Pronounced response in changes in BTM and BMD. The diagrams show the relationship between early changes (3–6 months) in BTM and changes in BMD after 2 years of either bone resorption inhibition (a) or bone formation stimulation (b). Fig. 3(a) is based on quantitative data obtained by antiresorptive treatment with alendronate, documenting tertile changes at 6 months of the bone resorption marker urinary N-terminal telopeptide (u-NTX) and the changes in BMD as assessed by dual-energy X-ray absorptiometry (DXA) in total hip, trochanter and spine at 3 years in postmenopausal women( Reference Greenspan, Resnick and Parker 98 ). Fig. 3(b) is based on quantitative data obtained by anabolic treatment with parathyroid hormone documenting tertile changes at 3 months of the bone formation marker N-terminal procollagen I propeptide (PINP) and the change in areal and volumetric BMD at 12 months, as assessed in the spine by DXA and quantitative computed tomography in postmenopausal women( Reference Greenspan, Resnick and Parker 98 , Reference Bauer, Garnero and Bilezikian 113 ).

Table 2 Changes from baseline in serum tartrate-resistant acid phosphatase (s-TRAP), carboxy terminal telopeptide of collagen type I (s-CTX) and procollagen type I N-terminal propetide (s-PINP) in response to antiresorptive and bone formation-stimulating treatments*

p.o., Oral; i.v., intravenous; s.c., subcutaneous; PTH, parathyroid hormone.

* Data have been adapted from the references Naylor & Eastell( Reference Naylor and Eastell 8 ), Henriksen et al. ( Reference Henriksen, Tanko and Qvist 67 ) and Bonjour et al. ( Reference Bonjour, Benoit and Pourchaire 127 ).

Evidence from interventional drug trials, where the placebo groups received Ca and vitamin D, as well as in nutritional interventions with fortified food, shows a reduction in bone resorption markers of 20–30 % (as outlined in more detail in the ‘Study designs to test the effects of food products on bone metabolism’ section). The decrease in BTM during antiresorptive treatment is inversely related to the subsequent increase in aBMD, predominantly at the lumbar spine( Reference Delmas 80 , Reference Delmas, Hardy and Garnero 81 ). Several studies of postmenopausal women treated with antiresorptive agents (hormone replacement therapy, raloxifen, bisphosphonates) have indicated that the degree of short-term reduction in BTM (after 3 to 6 months) correlates with the observed long-term increase in aBMD after 1 to 3 years of treatment( Reference Garnero, Sornay-Rendu and Duboeuf 79 , Reference Delmas, Hardy and Garnero 81 , Reference Garnero, Shih and Gineyts 95 Reference Ravn, Hosking and Thompson 99 ). Reduction in fracture risk is only partially explained by the documented changes in aBMD (4–28 %)( Reference Bonnick and Shulman 91 , Reference Cummings, Karpf and Harris 100 Reference Watts, Geusens and Barton 104 ). There is also some evidence that even a modest decrease in bone mass is nevertheless associated with some fracture risk reduction( Reference Chapurlat, Palermo and Ramsay 105 ). There is thus substantial evidence that bone mass or changes thereof does not explain satisfactorily either the skeletal fragility of osteoporosis or the effects of bone active agents( Reference Heaney 106 ). It is therefore possible that changes in other determinants of bone strength, including the rate of bone turnover, may be more predictive of anti-fracture efficacy than changes in aBMD. In fact, several studies confirmed that short-term reductions in bone turnover were associated with a reduction in vertebral and/or non-vertebral fracture risk in women treated with hormone replacement therapy, raloxifene, risedronate and alendronate( Reference Bauer, Black and Garnero 88 , Reference Hochberg, Greenspan and Wasnich 102 , Reference Bjarnason, Sarkar and Duong 107 Reference Reginster, Sarkar and Zegels 109 ).

In contrast to antiresorptive agents, PTH fragments administered intermittently in low doses increase bone remodelling, stimulating bone formation preferentially over bone resorption, and resulting in net gain of bone and reducing fracture risk( Reference Cranney, Papaioannou and Zytaruk 110 Reference Kraenzlin and Meier 112 ). Increases in BTM indicate a skeletal response to PTH. There is evidence that early measurement of bone formation markers (OC, BAP and particularly PINP) correlates positively with the subsequent BMD response to PTH (Fig. 3(b))( Reference Bauer, Garnero and Bilezikian 113 Reference Lane, Sanchez and Genant 116 ). These data suggest that serial BTM measurements could become useful in identifying skeletal responders to an anabolic therapy with PTH. Based on results from clinical studies and improved technical performance on automated platforms, several national and international guidelines recommend these markers in the follow-up of patients treated for postmenopausal osteoporosis( Reference Kanis, McCloskey and Johansson 32 , Reference Bergmann, Body and Boonen 117 , 118 ).

Usefulness of biomarkers of bone turnover in clinical nutrition research

In the process of generating scientific support for claims on foods, BTM were deemed as not sufficiently indicative of bone health to provide evidence for enhanced function or reduced osteoporosis risk( Reference Prentice, Bonjour and Branca 29 ). However, over the last 10 years there have been improvements in analytical performance and new knowledge of how biochemical markers can be of great utility in osteoporosis management. Because BTM measurement can assess response to anti-osteoporotic medications, the time has come to use BTM levels to reflect bone formation and resorption in response to foods and nutrients.

Influence of foods or nutrients on bone turnover markers

Several studies have examined the response of BTM levels and related hormonal factors to varying food or nutrient consumption in postmenopausal women or in elderly individuals. These studies illustrate how BTM can be important in establishing food and nutrient health claims.

Postmenopausal women

In a 12-month intervention involving postmenopausal women (mean age 60–62 years), the consumption of Ca- and vitamin D-fortified milk and yogurt favourably influenced vitamin D status and bone metabolism, as expressed by changes in serum 25(OH)D, PTH, CTX and IGF-I, as compared with a control group maintaining their usual diet or a group receiving equivalent Ca as lactate–gluconate and bicarbonate salts( Reference Manios, Moschonis and Trovas 119 ) (Fig. 4). A prospective cross-over trial in postmenopausal women (mean age 59 years) found that supplementation with non-fortified milk for 6 weeks induced a decrease in serum PTH and BTM, including CTX, and a non-significant increase in IGF-I( Reference Bonjour, Brandolini-Bunlon and Boirie 120 ). In a randomised study of postmenopausal women (mean age 57 years), consumption of a vitamin D- and Ca-fortified soft white cheese significantly reduced the sensitive biochemical marker of bone resorption TRAP 5b and increased serum IGF-I levels( Reference Bonjour, Benoit and Rousseau 121 ).

Fig. 4 Changes (%) in serum biochemical indexes at 5 months (a) and in bone mineral density (BMD) at 12 months (b) in response to fortified dairy products (▓) for 12 months as compared with controls (░) in postmenopausal women. Data are adapted from Manios et al. ( Reference Manios, Moschonis and Trovas 119 ). PTH, parathyroid hormone; CTX, carboxy terminal telopeptide of collagen type I; IGF-I, insulin-like growth factor-I. P= Probability level for treatment ×  time interaction effect as compared with the control group.

Elderly individuals

Favourable effects of milk, fortified or not with vitamin D or Ca, on bone turnover in elderly individuals have been documented in several intervention studies( Reference Tenta, Moschonis and Koutsilieris 42 , Reference Bonjour, Brandolini-Bunlon and Boirie 120 , Reference Heaney, McCarron and Dawson-Hughes 122 Reference Manios, Moschonis and Panagiotakos 124 ).

Interventions with dairy products such as yogurts( Reference Heaney, Rafferty and Dowell 125 , Reference Bonjour, Benoit and Payen 126 ) or soft white cheese( Reference Bonjour, Benoit and Payen 126 Reference Bonjour, Benoit and Pourchaire 128 ) have shown reductions in bone turnover. In institutionalised elderly individuals with vitamin D deficiency and secondary hyperparathyroidism, an 8-week consumption of vitamin D- and Ca-fortified yogurts saw increases in serum 25(OH)D, associated with a reduction in the BTM CTX and TRAP 5b, and a normalisation of previously high serum PTH( Reference Bonjour, Benoit and Payen 126 ). Elderly patients hospitalised for an osteoporotic fracture of the proximal femur often present with signs of undernutrition, particularly protein malnutrition( Reference Bonjour, Schurch and Rizzoli 129 ). In a randomised study, protein repletion by the daily consumption of a 20 g casein-enriched food v. an isoenergetic non-protein supplement led to a significant elevation in serum IGF-I by the end of the 6-month dietary intervention( Reference Schürch, Rizzoli and Slosman 58 ). The serum IGF-I response to 20 g/d protein repletion in patients with a recent hip fracture was observable by 7 d, with no further rise in serum IGF-I( Reference Chevalley, Hoffmeyer and Bonjour 130 ). Thus, in hip fracture patients, the long-term effects of various protein preparations on IGF-I can be predicted from changes observed as early as 1 week following protein supplementation( Reference Chevalley, Hoffmeyer and Bonjour 130 ).

Bone turnover markers in clinical research on eating disorders

Several nutrition-related disorders can lead to bone loss and increased risk of osteoporotic fracture. Their pathophysiology and clinical expression in relation to the application of biochemical markers are relevant to the present review.

Anorexia nervosa

In young women with anorexia nervosa, aBMD is reduced at several skeletal sites( Reference Grinspoon, Thomas and Pitts 131 ). Body weight, but not oestrogen use, is a significant predictor of aBMD in these patients. Together with low endogenous oestrogen levels and Ca deficiency, a low protein intake very probably also contributes to the bone deficit observed in anorexia nervosa. However, low Ca intake has not been consistently reported and 25(OH)D serum levels are usually normal( Reference Warren 132 ). The BTM of bone formation, serum OC and BAP, are significantly reduced in adolescents whereas in adults with anorexia nervosa, the markers of bone resorption are elevated while those of formation are suppressed( Reference Soyka, Misra and Frenchman 133 , Reference Dominguez, Goodman and Sen Gupta 134 ). The serum level of IGF-I is low in anorexia nervosa( Reference Soyka, Grinspoon and Levitsky 135 , Reference Hotta, Fukuda and Sato 136 ), an observation quite compatible with insufficient protein intake( Reference Soyka, Grinspoon and Levitsky 135 ) although low protein intake has not been consistently recorded. Nutritional rehabilitation reversed the uncoupling of bone remodelling( Reference Caillot-Augusseau, Lafage-Proust and Margaillan 137 ) and led to a significant increase in BTM of formation followed by increases in aBMD in as little as 3 months( Reference Dominguez, Goodman and Sen Gupta 134 ). This reversibility occurs before oestrogen levels rise and menses return, suggesting that nutritional factors are of paramount importance( Reference Warren 132 , Reference Dominguez, Goodman and Sen Gupta 134 ).

The serum level of IGF-I was reported to increase rapidly after the onset of intravenous hyperalimentation, followed by a progressive elevation in serum osteocalcin( Reference Hotta, Fukuda and Sato 136 ). IGF-I level changes were dependent on variations in the nutritional state( Reference Soyka, Misra and Frenchman 133 ). Increased GH levels accompanied by decreased IGF-I are seen in anorexia nervosa, suggesting an acquired resistance to GH that reversed with refeeding( Reference Warren 132 ). The administration of recombinant IGF-I alone has only a small bone-sparing effect( Reference Grinspoon, Thomas and Miller 138 ). This is not surprising, since the anabolic effects of IGI-I on bone formation are tempered by poor nutrition, particularly by insufficient protein intake( Reference Bourrin, Ammann and Bonjour 139 ).

Intensive exercise

In female athletes or ballet dancers, intensive exercise in the absence of appropriate nutrition can lead to hypothalamic dysfunction with delayed menarche and disruption of menstrual cyclicity, bone loss and increase risk of fracture( Reference Warren and Perlroth 140 Reference Warren, Brooks-Gunn and Fox 142 ). Nutritional restriction, more common when leanness confers advantages for performance, can play an important role in the disturbance of the female reproductive system when combined with intense physical activity. Insufficient energy intake with respect to energy expenditure impairs the secretion of gonadotropin-releasing hormone and thereby leads to a state of hypo-oestrogenism. This hormonal disturbance is reversed by nutritional rehabilitation leading to an increase in both luteinising hormone and follicle-stimulating hormone. These changes occur well before oestrogen rises and menses return, suggesting the dominant effect of nutritional factors( Reference Warren 132 ). However, the relative contribution of insufficient protein intake with low IGF-I remains to be assessed since it is often associated with reduced energy intake. The consequence on bone metabolism of acute experimental energy restriction has been investigated in young exercising female volunteers( Reference Ihle and Loucks 143 ). This study indicated that severe reduction in energy availability rapidly induces a negative uncoupling between bone formation and bone resorption( Reference Ihle and Loucks 143 ). The resulting bone loss may become irreversible with persistence of such a negative uncoupled remodelling, explaining the increased risk of fragility fractures in anorexia nervosa( Reference Warren 132 , Reference Misra and Klibanski 144 ) as well as in certain athletes and ballet dancers( Reference Warren and Perlroth 140 , Reference Warren, Brooks-Gunn and Fox 142 ).

Induced weight loss in overweight/obese subjects

Reduction in energy balance induced by energy restriction is often associated with bone loss that varies according to sex, age, ovarian function and initial fat mass (for a recent review, see Shapses & Sukumar( Reference Shapses and Sukumar 145 )). The degree of bone loss also depends upon the kind of diet accompanying the energy restriction. Bone mineral loss during body-weight reduction may not be fully recovered with weight regained, at least in postmenopausal women( Reference Villalon, Gozansky and Van Pelt 146 ). Hence, it is important to understand the underlying pathophysiological mechanisms and thereby define the best strategies to prevent bone loss during energy restriction in overweight/obese individuals. Several studies have used bone health biomarkers, particularly with the objective to identify the key implicated nutrients.

Ca intake typically decreases with energy-restriction diets. In this condition, the reduced Ca intake is associated with elevation in both serum PTH and biochemical markers of bone remodelling( Reference Jensen, Kollerup and Quaade 147 , Reference Ricci, Heymsfield and Pierson 148 ). Consumption of Ca salt supplements can suppress the rise in both serum PTH and BTM( Reference Jensen, Kollerup and Quaade 147 , Reference Ricci, Chowdhury and Heymsfield 149 Reference Riedt, Schlussel and von Thun 151 ).

Dairy products may prevent bone loss during energy restriction not only by providing Ca but also by supplying proteins. Weight-reducing diets that are high in dairy products can suppress the rise in bone turnover and prevent bone loss( Reference Bowen, Noakes and Clifton 152 , Reference Thorpe, Jacobson and Layman 153 ). In postmenopausal women, a randomised 12-month controlled study showed that higher dietary protein intake (86 v. 60 g/d) during weight reduction increased serum IGF-I and attenuated loss of aBMD at several skeletal sites and volumetric BMD at the distal tibia( Reference Sukumar, Ambia-Sobhan and Zurfluh 154 ). The additional consumed proteins were from various food sources including dairy products, with a whey supplement providing 6 g protein/d( Reference Sukumar, Ambia-Sobhan and Zurfluh 154 ). Combining dairy foods with physical exercise( Reference Ryan, Nicklas and Dennis 155 , Reference Silverman, Nicklas and Ryan 156 ) might be appropriate to prevent bone loss during weight loss in overweight/obese human adults( Reference Josse and Phillips 157 ). In a recent study, hypo-energetic diets higher in dairy Ca and protein combined with daily exercise favourably affected bone health biomarkers as follows: decrease in PTH; increase in bone formation markers (OC and PINP) with no change in bone resorption markers (CTX and N-terminal telopeptide (NTX)); increment in osteoprotegerin:receptor activator of NFκB ligand (RANKL) ratio( Reference Josse and Phillips 157 ).

Study designs to test the effects of food products on bone metabolism

In order to provide information on medical practice, the needed evidence-based criteria differ for foods and nutrients as compared with drugs. The development of drugs requires a very high level of rigour, more for safety concerns than for providing evidence of efficacy( Reference Blumberg, Heaney and Huncharek 18 , Reference Fenton and Fenton 24 , Reference Heaney and Blumberg 25 ). The present review essentially considers the rationale for using BTM in clinical research on foods or nutrients that influence bone mineral economy and/or metabolism.

Issues with randomised controlled trials in osteoporosis

An RCT is the clinical study design that best permits strong causal inference between a tested drug or nutrient and a given outcome( Reference Blumberg, Heaney and Huncharek 18 , Reference Guyatt 20 ). Previous reviews or position papers have emphasised the distinct differences in the evidence that can be obtained by using RCT in the context of drug testing, as compared with the development of nutrient requirement or dietary guidelines( Reference Blumberg, Heaney and Huncharek 18 , Reference Biesalski, Aggett and Anton 23 , Reference Fenton and Fenton 24 , Reference Heaney, Marcus, Feldman and Nelson 158 , Reference Heaney, Marcus, Feldman and Nelson 159 ). In the osteoporosis context, in order to demonstrate the efficacy of a new medication, regulatory agencies usually require RCT of 3 years’ duration, with fracture as the primary endpoint. In the nutrition field, all types of research are useful in setting dietary recommendations: observational studies, to document prevalence and generate hypotheses; preclinical experiments to identify physiological and biochemical pathways; qualitative studies to understand the human condition; and, finally, RCT, to establish a relationship of causality( Reference Blumberg, Heaney and Huncharek 18 , Reference Fenton and Fenton 24 , Reference Heaney and Blumberg 25 , Reference Mann 160 ).

Ethical consideration in randomised controlled trials and sample size

The demonstration that a drug is efficacious for reducing the burden of fragility fractures necessitates the design of RCT with very large sample sizes. It is especially the case when, for ethical reasons, the new therapy is tested in so-called ‘active comparator trials’, i.e. when the recently developed medication is compared with an approved drug with convincing antifracture efficacy in patients at high risk of fragility fractures( Reference Bouxsein and Delmas 161 ). Moreover, a placebo-controlled trial can neither be considered ethically acceptable when the new drug is tested in patients at low risk of fractures. This study design requires a much larger number of subjects, as compared with previous RCT conducted in patients at high risk of fragility fractures( Reference Bouxsein and Delmas 161 ). Thus, proper design and execution of long-duration large RCT aimed at demonstrating the antifracture efficacy of a drug impose requirements that are difficult to meet( Reference Heaney, Marcus, Feldman and Nelson 158 ). This difficulty also encompasses both the narrow response due to the placebo effect and the non-random loss of enrolled subjects that convert a randomised trial into a cohort study( Reference Heaney, Marcus, Feldman and Nelson 158 ). It can be expected to be magnified in long-duration RCT aimed at testing the antifracture efficacy of foods or nutrients in osteoporotic patients( Reference Blumberg, Heaney and Huncharek 18 , Reference Heaney, Marcus, Feldman and Nelson 158 ).

Surrogate endpoints for antifracture efficacy of new drugs

Therefore, the development of surrogate endpoints for antifracture efficacy of new drugs has been considered not only for foods and nutrients( Reference Heaney, Marcus, Feldman and Nelson 158 , Reference Heaney, Marcus, Feldman and Nelson 159 ), but also for drugs( Reference Bouxsein and Delmas 161 ). Taking into account not only the large sample size but also the very long duration of conducting RCT with fragility fracture as the primary outcome, it is worth considering the measurement of fracture-related data that can be obtainable within a few weeks or months after the onset of the intervention. In clinical investigation, it is recognised that the early bone remodelling response is a good predictor of fragility fracture( Reference Heaney, Marcus, Feldman and Nelson 158 , Reference Heaney, Marcus, Feldman and Nelson 159 ). Effective pharmaceutical agents, whether acting via anticatabolic or anabolic mechanisms, very promptly influence bone remodelling, as biochemically expressed by measuring BTM changes( Reference Heaney, Marcus, Feldman and Nelson 158 ). These pharmoco-kinetic and -dynamic characteristics, that combine an early response to intervention with a reliable predictability of fragility fractures, make BTM tools better suited to assess the effects of foods or nutrients in clinical research on osteoporosis than the delayed changes in DXA-measured aBMD or bone mineral content. Furthermore, the effect size is usually larger with BTM after a few months than the changes in BMD/bone mineral content that can be observed after 1, or more frequently, 2 years of intervention.

Short-duration randomised controlled trials in nutrition clinical research

In adulthood, the effects of foods on BTM can be assessed in appropriately designed RCT. Examples of such studies are described in the ‘Study designs to test the effects of food products on bone metabolism’ section. The short duration of these RCT limits the number of non-compliant participants. Furthermore, one may also expect that few subjects will be completely lost, i.e. without outcome measurement. This makes the ‘intention-to-treat’ data analysis more valuable for comparison with the ‘per protocol’ analysis that will only include treatment-adhering participants, and thereby provides an estimate of the effect size, some sort of ‘dose–response’, of the intervention.

A physiologically meaningful set of biochemical variables can be selected and assessed at several time points. For instance, when nutritional products are evaluated for their capacity to improve vitamin D status and bone metabolism, changes in serum 25(OH)D, PTH, bone formation and resorption markers should provide information on the dynamic interrelationships between these variables. Measuring the circulating level of IGF-I can assess the response to an increase in protein intake. These short-term RCT studies should provide not only qualitative information on the response but also quantitative data on both the magnitude and the pharmacokinetics of the effects.

The advantage of cross-over design in nutritional investigation

A powerful methodology is the cross-over design, also called ‘n of 1 RCT’( Reference Montori and Guyatt 162 , Reference Dawson and Trapp 163 ). Herein there are two intervention periods, separated by a ‘washout’ period, the duration of which depends upon how long the effect of the intervention lingers after its discontinuation. For some anti-osteoporotic agents such as bisphosphonate compounds, the effect can last months or years. For foods and nutrients acting on bone, the effects may be ‘washed out’ much more rapidly, i.e. within a few days or weeks, with the exception of large doses of vitamin D. Within a trial, each participant acts as his or her own control, and permits comparison between and within groups( Reference Mills, Chan and Wu 164 ). This design eliminates any genetic influence and reduces bias associated with imbalance in known and unknown confounding variables, as compared with randomised parallel-group controlled trials enrolling two unrelated groups of subjects( Reference Mills, Chan and Wu 164 ).

Comparative effectiveness research

An important recent trend in medical research is to apply ‘comparative effectiveness research’ (CER)( Reference Iglehart 165 ). CER is a marked departure from the efficacy standard that any new intervention should outperform placebo or randomised controls. There is particular emphasis placed on the leading question regarding clinical effectiveness of care. A CER study is aimed at determining the value of an intervention over another. It answers two relevant questions: (1) how effective is the intervention, if enrolled subjects follow it as prescribed?; (2) how likely is it that the enrolled participants follow it as prescribed?( Reference Kaats, Preuss and Leckie 166 ). In a classically designed study, the answers to these two questions would be given by comparing the results computed by ‘intention to treat’ with those analysed ‘per protocol’. CER is a recommended priority for nutrition research( Reference Iglehart 165 ). Although CER will pose challenges for medical and nutritional industries, it will present numerous opportunities to improve the information base supporting nutritional products, especially when they are integrated into other medical interventions( Reference Kaats, Preuss and Leckie 166 ). Recent CER nutrition studies have been reported on the bone effects of several vitamin D and Ca supplement regimens( Reference Kaats, Preuss and Croft 167 , Reference Michalek, Preuss and Croft 168 ).

Conclusions

Osteoporosis is a major health problem for an increasing number of adults as they age. Nutrition plays a substantial role in the primary and secondary prevention of osteoporosis. Several nutrients exert reproducible beneficial effects on bone metabolism, reducing the acceleration of bone loss that occurs after the menopause and in the elderly. The supply in vitamin D, Ca, Pi and protein affects levels of bone-trophic hormones as well as resorption of the bony organic matrix and its subsequent formation and mineral deposition. Some foods and nutrients positively influence bone metabolism by either reducing bone resorption or increasing bone formation, or both. Measurement of several biochemical components in blood facilitate the delineation of the impact of nutrients and food factors on bone-trophic hormones such as vitamin D metabolites, PTH and IGF-I, and on BTM that reflect bone resorption and/or matrix formation and mineralisation. Cross-over randomised trials appear to be the optimal way to evaluate the influence of nutrients and food factors on bone metabolism. Using such a study design, which is considered near the top of the hierarchy of evidence for nutritional studies, biologically significant insight can be gained into the potential influence of food products on bone metabolism and osteoporosis prevention within a relatively short time.

Acknowledgements

The authors are grateful to Flore Dontot, scientific collaborator of Yoplait France, for her administrative contribution by facilitating the coordination of the six co-authors.

Yoplait France had no role in the design, analysis or writing of this article.

J.-P. B., S. W. and M. K. were in charge of drafting the manuscript; W. K., R. L. and M. W. revised the first drafts. M. K. and J.-P. B. designed the figures. The manuscript was English proofed by S. W. All authors critically reviewed the manuscript and approved the final version. J.-P. B. took the responsibility of the final version.

J.-P. B. is clinical consultant for Yoplait France. The remaining authors have no conflict of interest regarding the content of the present review.

References

1 Rizzoli, R & Bonjour, JP (2006) Physiology of calcium and phosphate homeostasis. In Dynamics of Bone and Cartilage Metabolism: Principles and Clinical Applications, 2nd ed., pp. 345360 [Seibel, MJ, Robins, SP and Bilezikian, JP, editors]. San Diego: Academic Press.CrossRefGoogle Scholar
2 Lensmeyer, GL, Binkley, NC & Drezner, MK (2006) New horizons for assessment of vitamin D status in man. In Dynamics of Bone and Cartilage Metabolism: Principles and Clinical Applications, 2nd ed., pp. 513527 [Seibel, M, Robins, SP and Bilezikian, JP, editors]. San Diego: Academic Press.CrossRefGoogle Scholar
3 Souberbielle, JC, Friedlander, G & Cormier, C (2006) Practical considerations in PTH testing. Clin Chim Acta 366, 8189.Google Scholar
4 Jüppner, H & Fulehan, GE-H (2006) Measurement of parathyroid hormone. In Dynamics of Bone and Cartilage Metabolism: Principles and Clinical Applications, 2nd ed., pp. 507512 [Seibel, M, Robins, SP and Bilezikian, J.P., editors]. San Diego: Academic Press.CrossRefGoogle Scholar
5 Hjortebjerg, R & Frystyk, J (2013) Determination of IGFs and their binding proteins. Best Pract Res Clin Endocrinol Metab 27, 771781.Google Scholar
6 Kraenzlin, ME & Seibel, MJ (2006) Measurements of biochemical markers of bone resorption. In Dynamic Bone and Cartilage Metabolism: Principles and Clinical Applications, 2nd ed., pp. 541563 [Seibel, MJ, Robins, SP and Bilezikian, J.P., editors]. San Diego: Academic Press.Google Scholar
7 Lee, J & Vasikaran, S (2012) Current recommendations for laboratory testing and use of bone turnover markers in management of osteoporosis. Ann Lab Med 32, 105112.CrossRefGoogle ScholarPubMed
8 Naylor, K & Eastell, R (2012) Bone turnover markers: use in osteoporosis. Nat Rev Rheumatol 8, 379389.Google Scholar
9 Vasikaran, S, Cooper, C, Eastell, R, et al. (2011) International Osteoporosis Foundation and International Federation of Clinical Chemistry and Laboratory Medicine position on bone marker standards in osteoporosis. Clin Chem Lab Med 49, 12711274.CrossRefGoogle Scholar
10 World Health Organization (2007) Assessment of Osteoporosis at the Primary Health Care Level. Report of a WHO Scientific Group. Geneva: WHO.Google Scholar
11 Harvey, N, Dennison, E & Cooper, C (2008) Epidemiology of osteoporotic fractures. In Primer of the Metabolic Bone Diseases and Disorders of Mineral Metabolism, 7th ed., pp. 198203 [Rosen, CJ, Compston, JE and Lian, J.B., editors]. Washington, DC: The American Society of Bone and Mineral Metabolism.Google Scholar
12 Hopkins, RB, Goeree, R, Pullenayegum, E, et al. (2011) The relative efficacy of nine osteoporosis medications for reducing the rate of fractures in post-menopausal women. BMC Musculoskelet Disord 12, 209.CrossRefGoogle ScholarPubMed
13 Murad, MH, Drake, MT, Mullan, RJ, et al. (2012) Clinical review. Comparative effectiveness of drug treatments to prevent fragility fractures: a systematic review and network meta-analysis. J Clin Endocrinol Metab 97, 18711880.CrossRefGoogle ScholarPubMed
14 Downey, TW, Foltz, SH, Boccuzzi, SJ, et al. (2006) Adherence and persistence associated with the pharmacologic treatment of osteoporosis in a managed care setting. South Med J 99, 570575.Google Scholar
15 Rossini, M, Bianchi, G, Di Munno, O, et al. (2006) Determinants of adherence to osteoporosis treatment in clinical practice. Osteoporos Int 17, 914921.Google Scholar
16 Silverman, S & Gold, DT (2010) Compliance and persistence with osteoporosis medications: a critical review of the literature. Rev Endocr Metab Disord 11, 275280.Google Scholar
17 Schousboe, JT (2013) Adherence with medications used to treat osteoporosis: behavioral insights. Curr Osteoporos Rep 11, 2129.CrossRefGoogle ScholarPubMed
18 Blumberg, J, Heaney, RP, Huncharek, M, et al. (2010) Evidence-based criteria in the nutritional context. Nutr Rev 68, 478484.Google Scholar
19 Heaney, RP (2008) Nutrients, endpoints, and the problem of proof. J Nutr 138, 15911595.CrossRefGoogle ScholarPubMed
20 Guyatt, GH (1998) Evidence-based management of patients with osteoporosis. J Clin Densitom 1, 395402.CrossRefGoogle ScholarPubMed
21 Harris, RP, Helfand, M, Woolf, SH, et al. (2001) Current methods of the US Preventive Services Task Force: a review of the process. Am J Prev Med 20, 2135.CrossRefGoogle ScholarPubMed
22 Woolf, SH, DiGuiseppi, CG, Atkins, D, et al. (1996) Developing evidence-based clinical practice guidelines: lessons learned by the US Preventive Services Task Force. Annu Rev Public Health 17, 511538.Google Scholar
23 Biesalski, HK, Aggett, PJ, Anton, R, et al. (2011) 26th Hohenheim Consensus Conference, September 11, 2010. Scientific substantiation of health claims: evidence-based nutrition. Nutrition 27, S1S20.Google Scholar
24 Fenton, TR & Fenton, CJ (2011) Nutrition science mustn't accept a lower level of evidence. Nutr Rev 69, 413414.CrossRefGoogle Scholar
25 Heaney, RP & Blumberg, J (2011) Author reply: nutrition science mustn't accept a lower level of evidence. Nutr Rev 69, 415416.Google Scholar
26 Woodside, JV, Koletzko, BV, Patterson, CC, et al. (2013) Scientific standards for human intervention trials evaluating health benefits of foods, and their application to infants, children and adolescents. World Rev Nutr Diet 108, 1831.CrossRefGoogle ScholarPubMed
27 Bonjour, JP, Ammann, P & Rizzoli, R (1999) Importance of preclinical studies in the development of drugs for treatment of osteoporosis: a review related to the 1998 WHO guidelines. Osteoporos Int 9, 379393.Google Scholar
28 Bruyere, O, Rizzoli, R, Coxam, V, et al. (2012) Assessment of health claims in the field of bone: a view of the Group for the Respect of Ethics and Excellence in Science (GREES). Osteoporos Int 23, 193199.Google Scholar
29 Prentice, A, Bonjour, JP, Branca, F, et al. (2003) PASSCLAIM – bone health and osteoporosis. Eur J Nutr 42, Suppl. 1, I28I49.Google Scholar
30 Sibai, T, Morgan, EF & Einhorn, TA (2011) Anabolic agents and bone quality. Clin Orthop Relat Res 469, 22152224.CrossRefGoogle ScholarPubMed
31 Syed, FA & Melim, T (2011) Rodent models of aging bone: an update. Curr Osteoporos Rep 9, 219228.CrossRefGoogle ScholarPubMed
32 Kanis, JA, McCloskey, EV, Johansson, H, et al. (2013) European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 24, 2357.CrossRefGoogle ScholarPubMed
33 Schuit, SCE, van der Klift, M, Weel, AEAM, et al. (2004) Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone 34, 195202.Google Scholar
34 Sornay-Rendu, E, Munoz, F, Garnero, P, et al. (2005) Identification of osteopenic women at high risk of fracture: the OFELY study. J Bone Miner Res 20, 18131819.Google Scholar
35 Siris, ES, Chen, YT, Abbott, TA, et al. (2004) Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med 164, 11081112.Google Scholar
36 Boutroy, S, Bouxsein, ML, Munoz, F, et al. (2005) In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab 90, 65086515.Google Scholar
37 Eastell, R, Arnold, A, Brandi, ML, et al. (2009) Diagnosis of asymptomatic primary hyperparathyroidism: proceedings of the third international workshop. J Clin Endocrinol Metab 94, 340350.Google Scholar
38 Tannenbaum, C, Clark, J, Schwartzman, K, et al. (2002) Yield of laboratory testing to identify secondary contributors to osteoporosis in otherwise healthy women. J Clin Endocrinol Metab 87, 44314437.CrossRefGoogle ScholarPubMed
39 Lips, P, Duong, T, Oleksik, A, et al. (2001) A global study of vitamin D status and parathyroid function in postmenopausal women with osteoporosis: baseline data from the multiple outcomes of raloxifene evaluation clinical trial. J Clin Endocrinol Metab 86, 12121221.Google Scholar
40 Boonen, S, Bischoff-Ferrari, HA, Cooper, C, et al. (2006) Addressing the musculoskeletal components of fracture risk with calcium and vitamin D: a review of the evidence. Calcif Tissue Int 78, 257270.Google Scholar
41 Tang, BM, Eslick, GD, Nowson, C, et al. (2007) Use of calcium or calcium in combination with vitamin D supplementation to prevent fractures and bone loss in people aged 50 years and older: a meta-analysis. Lancet 370, 657666.CrossRefGoogle ScholarPubMed
42 Tenta, R, Moschonis, G, Koutsilieris, M, et al. (2011) Calcium and vitamin D supplementation through fortified dairy products counterbalances seasonal variations of bone metabolism indices: the Postmenopausal Health Study. Eur J Nutr 50, 341349.Google Scholar
43 Kanellakis, S, Moschonis, G, Tenta, R, et al. (2012) Changes in parameters of bone metabolism in postmenopausal women following a 12-month intervention period using dairy products enriched with calcium, vitamin D, and phylloquinone (vitamin K1) or menaquinone-7 (vitamin K2): the Postmenopausal Health Study II. Calcif Tissue Int 90, 251262.CrossRefGoogle ScholarPubMed
44 Cavalier, E, Rozet, E, Gadisseur, R, et al. (2010) Measurement uncertainty of 25-OH vitamin D determination with different commercially available kits: impact on the clinical cut offs. Osteoporos Int 21, 10471051.Google Scholar
45 Eddington, H, Hudson, JE, Oliver, RL, et al. (2013) Variability in parathyroid hormone assays confounds clinical practice in chronic kidney disease patients. Ann Clin Biochem 51, 228236.CrossRefGoogle ScholarPubMed
46 Mason, RS (2010) Vitamin D: a hormone for all seasons. Climacteric 14, 197203.Google Scholar
47 Ross, AC, Manson, JE, Abrams, SA, et al. (2011) The 2011 report on dietary reference intakes for calcium and vitamin D from the Institute of Medicine: what clinicians need to know. J Clin Endocrinol Metab 96, 5358.CrossRefGoogle ScholarPubMed
48 Holick, MF, Binkley, NC, Bischoff-Ferrari, HA, et al. (2011) Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab 96, 19111930.Google Scholar
49 Ginde, AA, Wolfe, P, Camargo, CA Jr, et al. (2012) Defining vitamin D status by secondary hyperparathyroidism in the U.S. population. J Endocrinol Invest 35, 4248.Google Scholar
50 Holick, MF, Binkley, NC, Bischoff-Ferrari, HA, et al. (2012) Guidelines for preventing and treating vitamin D deficiency and insufficiency revisited. J Clin Endocrinol Metab 97, 11531158.CrossRefGoogle ScholarPubMed
51 Rosen, CJ, Abrams, SA, Aloia, JF, et al. (2012) IOM committee members respond to Endocrine Society vitamin D guideline. J Clin Endocrinol Metab 97, 11461152.CrossRefGoogle ScholarPubMed
52 Bischoff-Ferrari, HA, Willett, WC, Wong, JB, et al. (2005) Fracture prevention with vitamin D supplementation: a meta-analysis of randomized controlled trials. JAMA 293, 22572264.Google Scholar
53 Boonen, S, Lips, P, Bouillon, R, et al. (2007) Need for additional calcium to reduce the risk of hip fracture with vitamin D supplementation: evidence from a comparative metaanalysis of randomized controlled trials. J Clin Endocrinol Metab 92, 14151423.Google Scholar
54 Bischoff-Ferrari, HA (2010) Vitamin D and fracture prevention. Endocrinol Metab Clin North Am 39, 347353.Google Scholar
55 Warriner, AH & Saag, KG (2013) Osteoporosis diagnosis and medical treatment. Orthop Clinics North Am 44, 125135.Google Scholar
56 Macdonald, HM, Wood, AD, Aucott, LS, et al. (2013) Hip bone loss is attenuated with 1000 IU but not 400 IU daily vitamin D3: a 1-year double-blind RCT in postmenopausal women. J Bone Miner Res 28, 22022213.Google Scholar
57 Clemmons, DR (2011) Consensus statement on the standardization and evaluation of growth hormone and insulin-like growth factor assays. Clin Chem 57, 555559.Google Scholar
58 Schürch, MA, Rizzoli, R, Slosman, D, et al. (1998) Protein supplements increase serum insulin-like growth factor-I levels and attenuate proximal femur bone loss in patients with recent hip fracture. A randomized, double-blind, placebo-controlled trial. Ann Intern Med 128, 801809.Google Scholar
59 Thissen, JP, Ketelslegers, JM & Underwood, LE (1994) Nutritional regulation of the insulin-like growth factors. Endocr Rev 15, 80101.Google Scholar
60 Bonjour, JP, Ammann, P, Chevalley, T, et al (2003) Nutritional aspects of bone growth: an overview. In Nutritional Aspects of Bone Health, pp. 111127 [New, S and Bonjour, JP, editors]. Cambridge, UK: The Royal Society of Chemistry.Google Scholar
61 Giustina, A, Mazziotti, G & Canalis, E (2008) Growth hormone, insulin-like growth factors, and the skeleton. Endocr Rev 29, 535559.Google Scholar
62 Bonjour, JP, Schürch, MA, Chevalley, T, et al. (1997) Protein intake, IGF-1 and osteoporosis. Osteoporos Int 7, Suppl. 3, S36S42.CrossRefGoogle ScholarPubMed
63 Perrini, S, Laviola, L, Carreira, MC, et al. (2010) The GH/IGF1 axis and signaling pathways in the muscle and bone: mechanisms underlying age-related skeletal muscle wasting and osteoporosis. J Endocrinol 205, 201210.Google Scholar
64 Sugimoto, T, Nishiyama, K, Kuribayashi, F, et al. (1997) Serum levels of insulin-like growth factor (IGF) I, IGF-binding protein (IGFBP)-2, and IGFBP-3 in osteoporotic patients with and without spinal fractures. J Bone Miner Res 12, 12721279.CrossRefGoogle ScholarPubMed
65 Garnero, P, Sornay-Rendu, E & Delmas, PD (2000) Low serum IGF-1 and occurrence of osteoporotic fractures in postmenopausal women. Lancet 355, 898899.Google Scholar
66 Vasikaran, S, Eastell, R, Bruyere, O, et al. (2011) Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards. Osteoporos Int 22, 391420.Google Scholar
67 Henriksen, K, Tanko, LB, Qvist, P, et al. (2007) Assessment of osteoclast number and function: application in the development of new and improved treatment modalities for bone diseases. Osteoporos Int 18, 681685.CrossRefGoogle ScholarPubMed
68 Halleen, J, Hentunen, TA, Karp, M, et al. (1998) Characterization of serum tartrate-resistant acid phosphatase and development of a direct two-site immunoassay. J Bone Miner Res 13, 683687.Google Scholar
69 Halleen, J, Alatalo, SL, Janckila, AJ, et al. (2001) Serum tartrate-resistant acid phosphatase 5b is a specific and sensitive marker of bone resorption. Clin Chem 47, 597600.CrossRefGoogle ScholarPubMed
70 Kraenzlin, ME, Lau, KHW, Liang, L, et al. (1990) Development of an immunoassay for human serum osteoclastic tartrate-resistant acid phosphatase. J Clin Endocrinol Metab 71, 442451.Google Scholar
71 Hannon, R, Clowes, J, Eagleton, AC, et al. (2004) Clinical performance of immunoreative tartrate resistent acid phosphatase isoform 5b as a marker of bone resorption. Bone 34, 187194.Google Scholar
72 Garnero, P, Borel, O & Delmas, P (2001) Evaluation of a fully automated serum assay for C-terminal cross-linking teleopeptide of type I collagen in osteoporosis. Clin Chem 47, 694702.CrossRefGoogle Scholar
73 Garnero, P, Sornay-Rendu, E, Chapuy, MC, et al. (1996) Increased bone turnover in late postmenopausal women is a major determinant of osteoporosis. J Bone Miner Res 11, 337349.Google Scholar
74 Garnero, P, Hausherr, E, Chapuy, MC, et al. (1996) Markers of bone resorption predict hip fracture in elderly women: the EPIDOS Prospective Study. J Bone Miner Res 11, 15311538.Google Scholar
75 Henriksen, K, Leeming, DJ, Christiansen, C, et al. (2011) Use of bone turnover markers in clinical osteoporosis assessment in women: current issues and future options. Womens Health (Lond Engl) 7, 689698.Google Scholar
76 Stepan, J (2000) Prediction of bone loss in postmenopausal women. Osteoporos Int 11, Suppl. 6, 4554.CrossRefGoogle ScholarPubMed
77 Delmas, P, Eastell, R, Garnero, P, et al. (2000) The use of biochemical markers of bone turnover in osteoporosis. Committee of Scientific Advisors of the International Osteoporosis Foundation. Osteoporos Int 11, Suppl. 6, S2S17.CrossRefGoogle ScholarPubMed
78 Garnero, P & Delmas, PD (2004) Contribution of bone mineral density and bone turnover markers to the estimation of risk of osteoporotic fracture in postmenopausal women. J Musculoskelet Neuronal Interact 4, 5063.Google Scholar
79 Garnero, P, Sornay-Rendu, E, Duboeuf, F, et al. (1999) Markers of bone turnover predict postmenopausal forearm bone loss over 4 years: the OFELY study. J Bone Miner Res 14, 16141621.CrossRefGoogle ScholarPubMed
80 Delmas, P (2000) Markers of bone turnover for monitoring treatment of osteoporosis with antiresorptive drugs. Osteoporos Int 11, Suppl. 6, S66S76.Google Scholar
81 Delmas, PD, Hardy, P, Garnero, P, et al. (2000) Monitoring individual response to hormone replacement therapy with bone markers. Bone 26, 553560.CrossRefGoogle ScholarPubMed
82 Rosen, C, Chesnut, CH & Mallinak, NJ (1997) The predictive value of biochemical markers of bone turnover for bone mineral density in early postmenopausal women treated with hormone replacement or calcium supplementation. J Clin Endocrinol Metab 82, 19041910.Google Scholar
83 Garnero, P, Sornay-Rendu, E, Claustrat, B, et al. (2000) Biochemical markers of bone turnover, endogenous hormones and risk of fractures in postemenopausal women: the OFELY study. J Bone Miner Res 15, 15261536.Google Scholar
84 Ross, PD, Kress, BC, Parson, RE, et al. (2000) Serum bone alkaline phosphatase and calcaneus bone density predict fractures: a prospective study. Osteoporos Int 11, 7682.Google Scholar
85 van Daele, P, Seibel, M, Burger, H, et al. (1996) Case-control analysis of bone resorption markers, disability and hip fracture: the Rotterdam Study. Br Med J 312, 482483.Google Scholar
86 Gerdhem, P, Ivaska, KK, Alatalo, SL, et al. (2004) Biochemical markers of bone metabolism and prediction of fracture in elderly women. J Bone Miner Res 19, 386393.Google Scholar
87 Meier, C, Nguyen, TV, Center, JR, et al. (2005) Bone resorption and osteoporotic fractures in elderly men: the Dubbo Osteoporosis Epidemiology Study. J Bone Miner Res 20, 579587.CrossRefGoogle ScholarPubMed
88 Bauer, DC, Black, DM, Garnero, P, et al. (2004) Change in bone turnover and hip, non-spine, and vertebral fracture in alendronate-treated women: the fracture intervention trial. J Bone Miner Res 19, 12501258.Google Scholar
89 Garnero, P & Delmas, PD (2006) Laboratory assessment of osteoporosis. In Dynamics of Bone and Cartilage Metabolism: Principles and Clinical Applications, 2nd ed., pp. 611628 [Seibel, M, Robins, S and Bilezikian, J, editors]. San Diego: Academic Press.Google Scholar
90 Johnell, O, Odén, A, De Laet, C, et al. (2002) Biochemical indices of bone turnover and the assessment of fracture probability. Osteoporos Int 13, 523526.CrossRefGoogle ScholarPubMed
91 Bonnick, SL & Shulman, L (2006) Monitoring osteoporosis therapy: bone mineral density, bone turnover markers, or both? Am J Med 119, S25S31.Google Scholar
92 Briot, K & Roux, C (2005) What is the role of DXA, QUS and bone markers in fracture prediction, treatment allocation and monitoring? Best Pract Res Clin Rheumatol 19, 951964.CrossRefGoogle ScholarPubMed
93 Miller, PD (2005) Bone density and markers of bone turnover in predicting fracture risk and how changes in these measures predict fracture risk reduction. Curr Osteoporos Rep 3, 103110.Google Scholar
94 Roux, C, Garnero, P, Thomas, T, et al. (2005) Recommendations for monitoring antiresorptive therapies in postmenopausal osteoporosis. Joint Bone Spine 72, 2631.Google Scholar
95 Garnero, P, Shih, WJ, Gineyts, E, et al. (1994) Comparison of new biochemical markers of bone turnover in late postmenopausal osteoporotic women in response to alendronate treatment. J Clin Endocrinol Metab 79, 16931700.Google Scholar
96 Delmas, PD (2000) How does antiresorptive therapy decrease the risk of fracture in women with osteoporosis? Bone 27, 13.CrossRefGoogle ScholarPubMed
97 Rosen, C, Chesnut, CHIII & Mallinak, NJS (1997) The predictive value of biochemical markers of bone turnover for bone mineral density in early postmenopausal women treated with hormone replacement or calcium supplementation. J Clin Endocrinol Metab 82, 19041910.Google ScholarPubMed
98 Greenspan, SL, Resnick, NM & Parker, RA (2005) Early changes in biochemical markers of bone turnover are associated with long-term changes in bone mineral density in elderly women on alendronate, hormone replacement therapy, or combination therapy: a three-year, double-blind, placebo-controlled, randomized clinical trial. J Clin Endocrinol Metab 90, 27622767.CrossRefGoogle ScholarPubMed
99 Ravn, P, Hosking, D, Thompson, D, et al. (1999) Monitoring of alendronate treatment and prediction of effect on bone mass by biochemical markers in the early postmenopausal intervention cohort study. J Clin Endocrinol Metab 84, 23632368.Google Scholar
100 Cummings, SR, Karpf, DB, Harris, F, et al. (2002) Improvement in spine bone density and reduction in risk of vertebral fractures during treatment with antiresorptive drugs. Am J Med 112, 281289.Google Scholar
101 Hochberg, M (2000) Preventing fractures in postmenopausal women with osteoporosis: a review of recent controlled trials of antiresorptive agents. Drugs Aging 17, 317330.Google Scholar
102 Hochberg, MC, Greenspan, S, Wasnich, RD, et al. (2002) Changes in bone density and turnover explain the reductions in incidence of nonvertebral fractures that occur during treatment with antiresorptive agents. J Clin Endocrinol Metab 87, 15861592.Google Scholar
103 Wasnich, RD & Miller, P (2000) Antifracture efficacy of antiresorptive agents are related to changes in bone density. J Clin Endocrinol Metab 85, 231236.Google Scholar
104 Watts, NB, Geusens, P, Barton, IP, et al. (2005) Relationship between changes in BMD and nonvertebral fracture incidence associated with risedronate: reduction in risk of nonvertebral fracture is not related to change in BMD. J Bone Miner Res 20, 20972104.Google Scholar
105 Chapurlat, RD, Palermo, L, Ramsay, P, et al. (2005) Risk of fracture among women who lose bone density during treatment with alendronate. The Fracture Intervention Trial. Osteoporos Int 16, 842848.Google Scholar
106 Heaney, RP (2003) Is the paradigm shifting? Bone 33, 457465.Google Scholar
107 Bjarnason, NH, Sarkar, S, Duong, T, et al. (2001) Six and twelve month changes in bone turnover are related to reduction in vertebral fracture risk during 3 years of raloxifene treatment in postmenopausal osteoporosis. Osteoporos Int 12, 922930.Google Scholar
108 Eastell, R, Barton, I, Hannon, RA, et al. (2003) Relationship of early changes in bone resorption to the reduction in fracture risk with risedronate. J Bone Miner Res 18, 10511056.Google Scholar
109 Reginster, JY, Sarkar, S, Zegels, B, et al. (2004) Reduction in PINP, a marker of bone metabolism, with raloxifene treatment and its relationship with vertebral fracture risk. Bone 34, 344351.Google Scholar
110 Cranney, A, Papaioannou, A, Zytaruk, N, et al. (2006) Parathyroid hormone for the treatment of osteoporosis: a systematic review. CMAJ 175, 5259.Google Scholar
111 Hodsman, A, Papaioannou, A & Cranney, A (2006) Clinical practice guidelines for the use of parathyroid hormone in the treatment of osteoporosis. CMAJ 175, 48.Google Scholar
112 Kraenzlin, ME & Meier, C (2011) Parathyroid hormone analogues in the treatment of osteoporosis. Nat Rev Endocrinol 7, 647656.Google Scholar
113 Bauer, DC, Garnero, P, Bilezikian, JP, et al. (2006) Short-term changes in bone turnover markers and bone mineral density response to parathyroid hormone in postmenopausal women with osteoporosis. J Clin Endocrinol Metab 91, 13701375.CrossRefGoogle ScholarPubMed
114 Chen, P, Satterwhite, JH, Licata, AA, et al. (2005) Early changes in biochemical markers of bone formation predict BMD response to teriparatide in postmenopausal women with osteoporosis. J Bone Miner Res 20, 962970.Google Scholar
115 Eastell, R, Krege, JH, Chen, P, et al. (2006) Development of an algorithm for using PINP to monitor treatment of patients with teriparatide. Curr Med Res Opin 22, 6166.Google Scholar
116 Lane, NE, Sanchez, S, Genant, HK, et al. (2000) Short-term increases in bone turnover markers predict parathyroid hormone-induced spinal bone mineral density gains in postmenopausal women with glucocorticoid-induced osteoporosis. Osteoporos Int 11, 434442.Google Scholar
117 Bergmann, P, Body, JJ, Boonen, S, et al. (2009) Evidence-based guidelines for the use of biochemical markers of bone turnover in the selection and monitoring of bisphosphonate treatment in osteoporosis: a consensus document of the Belgian Bone Club. Int J Clin Pract 63, 1926.Google Scholar
118 Swiss Association against Osteoporosis (2010) Osteoporosis: prevention, diagnosis and treatment. Recommendation 2010. http://www.SVGO.ch.Google Scholar
119 Manios, Y, Moschonis, G, Trovas, G, et al. (2007) Changes in biochemical indexes of bone metabolism and bone mineral density after a 12-mo dietary intervention program: the Postmenopausal Health Study. Am J Clin Nutr 86, 781789.CrossRefGoogle ScholarPubMed
120 Bonjour, JP, Brandolini-Bunlon, M, Boirie, Y, et al. (2008) Inhibition of bone turnover by milk intake in postmenopausal women. Br J Nutr 100, 866874.Google Scholar
121 Bonjour, JP, Benoit, V, Rousseau, B, et al. (2012) Consumption of vitamin D- and calcium-fortified soft white cheese lowers the biochemical marker of bone resorption TRAP 5b in postmenopausal women at moderate risk of osteoporosis fracture. J Nutr 142, 698703.Google Scholar
122 Heaney, RP, McCarron, DA, Dawson-Hughes, B, et al. (1999) Dietary changes favorably affect bone remodeling in older adults. J Am Diet Assoc 99, 12281233.Google Scholar
123 Palacios, S, Castelo-Branco, C, Cifuentes, I, et al. (2005) Changes in bone turnover markers after calcium-enriched milk supplementation in healthy postmenopausal women: a randomized, double-blind, prospective clinical trial. Menopause 12, 6368.Google Scholar
124 Manios, Y, Moschonis, G, Panagiotakos, DB, et al. (2009) Changes in biochemical indices of bone metabolism in post-menopausal women following a dietary intervention with fortified dairy products. J Hum Nutr Diet 22, 156165.Google Scholar
125 Heaney, RP, Rafferty, K & Dowell, MS (2002) Effect of yogurt on a urinary marker of bone resorption in postmenopausal women. J Am Diet Assoc 102, 16721674.Google Scholar
126 Bonjour, JP, Benoit, V, Payen, F, et al. (2013) Consumption of yogurts fortified in vitamin D and calcium reduces serum parathyroid hormone and markers of bone resorption: a double-blind randomized controlled trial in institutionalized elderly women. J Clin Endocrinol Metab 98, 29152921.Google Scholar
127 Bonjour, JP, Benoit, V, Pourchaire, O, et al. (2009) Inhibition of markers of bone resorption by consumption of vitamin D and calcium-fortified soft plain cheese by institutionalised elderly women. Br J Nutr 102, 962966.Google Scholar
128 Bonjour, JP, Benoit, V, Pourchaire, O, et al. (2011) Nutritional approach for inhibiting bone resorption in institutionalized elderly women with vitamin D insufficiency and high prevalence of fracture. J Nutr Health Aging 15, 404409.Google Scholar
129 Bonjour, JP, Schurch, MA & Rizzoli, R (1996) Nutritional aspects of hip fractures. Bone 18, 139S144S.CrossRefGoogle ScholarPubMed
130 Chevalley, T, Hoffmeyer, P, Bonjour, JP, et al. (2010) Early serum IGF-I response to oral protein supplements in elderly women with a recent hip fracture. Clin Nutr 29, 7883.Google Scholar
131 Grinspoon, S, Thomas, E, Pitts, S, et al. (2000) Prevalence and predictive factors for regional osteopenia in women with anorexia nervosa. Ann Intern Med 133, 790794.Google Scholar
132 Warren, MP (2011) Endocrine manifestations of eating disorders. J Clin Endocrinol Metab 96, 333343.Google Scholar
133 Soyka, LA, Misra, M, Frenchman, A, et al. (2002) Abnormal bone mineral accrual in adolescent girls with anorexia nervosa. J Clin Endocrinol Metab 87, 41774185.Google Scholar
134 Dominguez, J, Goodman, L, Sen Gupta, S, et al. (2007) Treatment of anorexia nervosa is associated with increases in bone mineral density, and recovery is a biphasic process involving both nutrition and return of menses. Am J Clin Nutr 86, 9299.CrossRefGoogle ScholarPubMed
135 Soyka, LA, Grinspoon, S, Levitsky, LL, et al. (1999) The effects of anorexia nervosa on bone metabolism in female adolescents. J Clin Endocrinol Metab 84, 44894496.Google Scholar
136 Hotta, M, Fukuda, I, Sato, K, et al. (2000) The relationship between bone turnover and body weight, serum insulin-like growth factor (IGF) I, and serum IGF-binding protein levels in patients with anorexia nervosa. J Clin Endocrinol Metab 85, 200206.Google Scholar
137 Caillot-Augusseau, A, Lafage-Proust, MH, Margaillan, P, et al. (2000) Weight gain reverses bone turnover and restores circadian variation of bone resorption in anorexic patients. Clin Endocrinol (Oxf) 52, 113121.Google Scholar
138 Grinspoon, S, Thomas, L, Miller, K, et al. (2002) Effects of recombinant human IGF-I and oral contraceptive administration on bone density in anorexia nervosa. J Clin Endocrinol Metab 87, 28832891.Google Scholar
139 Bourrin, S, Ammann, P, Bonjour, JP, et al. (2000) Dietary protein restriction lowers plasma insulin-like growth factor I (IGF-I), impairs cortical bone formation, and induces osteoblastic resistance to IGF-I in adult female rats. Endocrinology 141, 31493155.Google Scholar
140 Warren, MP & Perlroth, NE (2001) The effects of intense exercise on the female reproductive system. J Endocrinol 170, 311.Google Scholar
141 Gremion, G, Rizzoli, R, Slosman, D, et al. (2001) Oligo-amenorrheic long-distance runners may lose more bone in spine than in femur. Med Sci Sports Exerc 33, 1521.Google Scholar
142 Warren, MP, Brooks-Gunn, J, Fox, RP, et al. (2002) Osteopenia in exercise-associated amenorrhea using ballet dancers as a model: a longitudinal study. J Clin Endocrinol Metab 87, 31623168.Google Scholar
143 Ihle, R & Loucks, AB (2004) Dose-response relationships between energy availability and bone turnover in young exercising women. J Bone Miner Res 19, 12311240.CrossRefGoogle ScholarPubMed
144 Misra, M & Klibanski, A (2011) The neuroendocrine basis of anorexia nervosa and its impact on bone metabolism. Neuroendocrinology 93, 6573.Google Scholar
145 Shapses, SA & Sukumar, D (2012) Bone metabolism in obesity and weight loss. Annu Rev Nutr 32, 287309.Google Scholar
146 Villalon, KL, Gozansky, WS, Van Pelt, RE, et al. (2011) A losing battle: weight regain does not restore weight loss-induced bone loss in postmenopausal women. Obesity (Silver Spring) 19, 23452350.CrossRefGoogle Scholar
147 Jensen, LB, Kollerup, G, Quaade, F, et al. (2001) Bone minerals changes in obese women during a moderate weight loss with and without calcium supplementation. J Bone Miner Res 16, 141147.Google Scholar
148 Ricci, TA, Heymsfield, SB, Pierson, RN Jr, et al. (2001) Moderate energy restriction increases bone resorption in obese postmenopausal women. Am J Clin Nutr 73, 347352.Google Scholar
149 Ricci, TA, Chowdhury, HA, Heymsfield, SB, et al. (1998) Calcium supplementation suppresses bone turnover during weight reduction in postmenopausal women. J Bone Miner Res 13, 10451050.Google Scholar
150 Shapses, SA, Von Thun, NL, Heymsfield, SB, et al. (2001) Bone turnover and density in obese premenopausal women during moderate weight loss and calcium supplementation. J Bone Miner Res 16, 13291336.Google Scholar
151 Riedt, CS, Schlussel, Y, von Thun, N, et al. (2007) Premenopausal overweight women do not lose bone during moderate weight loss with adequate or higher calcium intake. Am J Clin Nutr 85, 972980.Google Scholar
152 Bowen, J, Noakes, M & Clifton, PM (2004) A high dairy protein, high-calcium diet minimizes bone turnover in overweight adults during weight loss. J Nutr 134, 568573.Google Scholar
153 Thorpe, MP, Jacobson, EH, Layman, DK, et al. (2008) A diet high in protein, dairy, and calcium attenuates bone loss over twelve months of weight loss and maintenance relative to a conventional high-carbohydrate diet in adults. J Nutr 138, 10961100.Google Scholar
154 Sukumar, D, Ambia-Sobhan, H, Zurfluh, R, et al. (2011) Areal and volumetric bone mineral density and geometry at two levels of protein intake during caloric restriction: a randomized, controlled trial. J Bone Miner Res 26, 13391348.Google Scholar
155 Ryan, AS, Nicklas, BJ & Dennis, KE (1998) Aerobic exercise maintains regional bone mineral density during weight loss in postmenopausal women. J Appl Physiol 84, 13051310.Google Scholar
156 Silverman, NE, Nicklas, BJ & Ryan, AS (2009) Addition of aerobic exercise to a weight loss program increases BMD, with an associated reduction in inflammation in overweight postmenopausal women. Calcif Tissue Int 84, 257265.Google Scholar
157 Josse, AR & Phillips, SM (2012) Impact of milk consumption and resistance training on body composition of female athletes. Med Sport Sci 59, 94103.Google Scholar
158 Heaney, RP, et al (2008) Design considerations for clinical investigations of osteoporosis. In Osteoporosis, 3rd ed., pp. 15981620 [Marcus, M, Feldman, D and Nelson, DA, editors]. Amsterdam: Elsevier, Inc.Google Scholar
159 Heaney, RP, et al (2008) Nutrition and risk for osteoprosis. In Osteoporosis, pp. 799836 [Marcus, M, Feldman, D and Nelson, DA, editors]. Amsterdam: Elsevier, Inc.Google Scholar
160 Mann, JI (2010) Evidence-based nutrition: does it differ from evidence-based medicine? Ann Med 42, 475486.Google Scholar
161 Bouxsein, ML & Delmas, PD (2008) Considerations for development of surrogate endpoints for antifracture efficacy of new treatments in osteoporosis: a perspective. J Bone Miner Res 23, 11551167.CrossRefGoogle ScholarPubMed
162 Montori, VM & Guyatt, GH (2001) What is evidence-based medicine and why should it be practiced? Respir Care 46, 12011214.Google Scholar
163 Dawson, B & Trapp, RG (2001) Basic and Clinical Biostatistics, 3rd ed.. New York: Lange Medical Books/McGraw-Hill.Google Scholar
164 Mills, EJ, Chan, AW, Wu, P, et al. (2009) Design, analysis, and presentation of crossover trials. Trials 10, 27.Google Scholar
165 Iglehart, JK (2009) Prioritizing comparative-effectiveness research – IOM recommendations. N Engl J Med 361, 325328.Google Scholar
166 Kaats, GR, Preuss, HG & Leckie, RB (2009) Comparative effectiveness research (CER): opportunities and challenges for the nutritional industry. J Am Coll Nutr 28, 234237.Google Scholar
167 Kaats, GR, Preuss, HG, Croft, HA, et al. (2011) A comparative effectiveness study of bone density changes in women over 40 following three bone health plans containing variations of the same novel plant-sourced calcium. Int J Med Sci 8, 180191.Google Scholar
168 Michalek, JE, Preuss, HG, Croft, HA, et al. (2011) Changes in total body bone mineral density following a common bone health plan with two versions of a unique bone health supplement: a comparative effectiveness research study. Nutr J 10, 32.Google Scholar
Figure 0

Fig. 1 Bone remodelling cycle. Bone turnover follows a sequence of events that includes activation, recruitment of osteoclasts (Ocl) to begin resorption, degradation and removal of bone, reversal, and formation of new bone by osteoblasts (Ob). After this phase a quiescent or resting period occurs. LC, lining cell; Oc, osteocyte.

Figure 1

Fig. 2 Diagram of the relationship between bone resorption or formation and bone turnover markers. On the resorption side, tartrate-resistant acid phosphatase (TRAP) reflects osteoclast number and activity, whereas pyridium cross-links, carboxy terminal telopeptide (CTX) and N-terminal telopeptide (NTX) are markers of bone matrix resorption. On the formation side, collagen type I propeptides, such as procollagen type I N-terminal propeptide (PINP), bone alkaline phosphatase (BAP) and osteocalcin reflect mainly osteoblastic bone formation. Osteoid corresponds to the non-mineralised bone matrix.

Figure 2

Table 1 Relationship between increases in bone resorption rate and fracture risk

Figure 3

Fig. 3 Schematic representation of changes in bone turnover markers (BTM) at 3–6 months and corresponding increase in bone mineral density (BMD) during treatment of osteoporosis. (a) Bone antiresorptive treatment (i.e. bisphosphonate): a more pronounced decrease in bone resorption marker at 3–6 months is associated with larger increases in BMD. (b) Bone formation-stimulating treatment (i.e. recombinant parathyroid hormone): a greater increase in bone formation marker is associated with a larger increase in BMD. * Mild response in changes in BTM and BMD. † Moderate response in changes in BTM and BMD. ‡ Pronounced response in changes in BTM and BMD. The diagrams show the relationship between early changes (3–6 months) in BTM and changes in BMD after 2 years of either bone resorption inhibition (a) or bone formation stimulation (b). Fig. 3(a) is based on quantitative data obtained by antiresorptive treatment with alendronate, documenting tertile changes at 6 months of the bone resorption marker urinary N-terminal telopeptide (u-NTX) and the changes in BMD as assessed by dual-energy X-ray absorptiometry (DXA) in total hip, trochanter and spine at 3 years in postmenopausal women(98). Fig. 3(b) is based on quantitative data obtained by anabolic treatment with parathyroid hormone documenting tertile changes at 3 months of the bone formation marker N-terminal procollagen I propeptide (PINP) and the change in areal and volumetric BMD at 12 months, as assessed in the spine by DXA and quantitative computed tomography in postmenopausal women(98,113).

Figure 4

Table 2 Changes from baseline in serum tartrate-resistant acid phosphatase (s-TRAP), carboxy terminal telopeptide of collagen type I (s-CTX) and procollagen type I N-terminal propetide (s-PINP) in response to antiresorptive and bone formation-stimulating treatments*

Figure 5

Fig. 4 Changes (%) in serum biochemical indexes at 5 months (a) and in bone mineral density (BMD) at 12 months (b) in response to fortified dairy products (▓) for 12 months as compared with controls (░) in postmenopausal women. Data are adapted from Manios et al.(119). PTH, parathyroid hormone; CTX, carboxy terminal telopeptide of collagen type I; IGF-I, insulin-like growth factor-I. P= Probability level for treatment ×  time interaction effect as compared with the control group.