Fat and bone

Archives of Biochemistry and Biophysics 503 (2010) 20–27

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Review

Fat and bone q Ian R. Reid * Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand

a r t i c l e

i n f o

Article history: Available online 3 July 2010 Keywords: Body composition Lean mass Osteoporosis Insulin Leptin Amylin

a b s t r a c t Body weight is a principal determinant of bone density and fracture risk, and adipose tissue mass is a major contributor to this relationship. In contrast, some recent studies have argued that ‘‘fat mass after adjustment for body weight” actually has a deleterious effect on bone, but these analyses are confounded by the co-linearity between the variables studied, and therefore have produced misleading results. Mechanistically, fat and bone are linked by a multitude of pathways, which ultimately serve the function of providing a skeleton appropriate to the mass of adipose tissue it is carrying. Adiponectin, insulin/amylin/preptin, leptin and adipocytic estrogens are all likely to be involved in this connection. In the clinic, the key issues are that obesity is protective against osteoporosis, but underweight is a major preventable risk factor for fractures. Ó 2010 Elsevier Inc. All rights reserved.

Introduction A consideration of the relationships between adipose tissue and the skeleton involves two quite different sets of data. The first is epidemiological evidence which relates measures of adiposity to measures of skeletal mass/density and facture risk. The second is a consideration of the physiological mechanisms that lead to these relationships. Each of these aspects of this subject will be considered in turn. Epidemiology Fat mass and bone density The relationship between fat mass and bone density emerged from clinical studies carried out in the early 1990s [1–3]. Dual-energy X-ray absorptiometry (DXA)1 had just become available for measurement of axial bone density, and these devices could also assess fat mass and lean mass. Thus, a number of groups undertook studies of the relationships between soft tissue mass and bone density, and found consistently positive relationships, though studies varied according to whether fat mass or lean mass showed the strongest correlation with bone density (reviewed in detail elsewhere [4,5]). Most studies have assessed only total fat mass,

q

Supported by the Health Research Council of New Zealand. * Fax: +64 9 308 2308. E-mail address: [email protected] 1 Abbreviations used: DXA, Dual-energy X-ray absorptiometry; BMD, bone mineral density; BMI, body mass index; GLP-1, glucagon-like peptide-1; GIP, glucosedependent insulinotropic polypeptide. 0003-9861/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.abb.2010.06.027

but Gilsanz has recently suggested that it is subcutaneous fat that is beneficial to bone, and that visceral fat is deleterious [6]. There have now been a large number of essentially similar studies looking at relationships between fat mass and bone density, with an increasing variety of outcomes. Most of that variability in outcomes is attributable to the use of diverse methods for measuring bone density and, most importantly, to differences in the methods of statistical analysis. When assessing relationships between fat mass and bone, it must be remembered that bone mineral content, areal bone density and volumetric bone density are not interchangeable. Bone mineral content and areal bone density are both dependent, to different degrees, on skeletal size, so will correlate with any other variable (such as lean mass) which is also dependent on skeletal size. DXA measures areal bone density (in g/cm2) so is also influenced by bone size, as well as the material density of the bone being assessed. Failure to appreciate this fact will lead to the description of spurious relationships, with inappropriate inferences of causality. The statistical problems relate to inappropriately treating highly inter-correlated variables as being independent. A particular example of this is correcting variables, such as fat mass, for others, such as weight, to which they are closely related. I will use a welldescribed cohort of healthy postmenopausal women from the Auckland Calcium Study [7] to illustrate the relationships between soft tissue mass and bone density, and the results of incautious analyses of these variables.

Auckland Calcium Study Fig. 1 shows some of the relevant relationships from this study. In Fig. 1a, we see a direct linear relationship between body weight

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Fig. 1. Regression relationships between total hip bone mineral density (g/cm2) and indices of soft tissue mass in 1462 normal postmenopausal women from the Auckland Calcium Study. ‘‘r” is the Pearson correlation coefficient. Copyright Reid, used with permission.

and total hip bone mineral density (BMD), similar to what has been seen in many studies previously. In trying to deduce whether this is primarily a relationship with fat mass or with lean mass, body mass index (BMI) can be substituted for weight. BMI is commonly used as a surrogate for adiposity (see Table 1), and we see almost the same relationship between this variable and bone density as we saw with weight. However, using DXA it is possible to measure total body fat mass and total body lean mass separately, and these two plots are shown in panels c and d of Fig. 1. Their relationship to BMD is not quite as close as that of weight or BMI, but the correlation coefficients are comparable to one another. However, fat mass

and lean mass are related to one another (see Table 1) so it is possible that the relationships shown in these panels are contributed to by co-linearity between fat and lean masses. Multiple regression analysis potentially provides a way of dissecting these relationships. Using this technique, with total hip BMD as the dependent variable and fat mass and lean mass as the independent variables, we find that both are positively related to BMD (p < 0.0001), but that fat mass has a partial R2 value of 0.09, whereas that for lean mass is 0.03, implying that fat mass accounts for three times more of the variance than does lean mass. Formal statistical tests for the influence of multi co-linearity (calculating the variance inflation

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Table 1 Correlations between soft tissue indices and BMD in the Auckland Calcium Study. Lean mass Fat mass Weight % Fat mass % Lean mass BMI L1-4 BMD Total hip BMD

Fat mass

Weight

% Fat mass

0.37 0.65 0.07a

0.92 0.92

0.72

0.02b

0.85

0.73

0.91

0.91 0.25 0.30

0.90 0.35 0.41

0.79 0.15 0.20

0.42 0.28 0.28

% Lean mass

0.80 0.19 0.30

BMI

0.30 0.39

L1-4 BMD

puberty in boys is associated with bone gain, loss of fat mass, and gain in muscle mass. Therefore, men, premenopausal women, and postmenopausal women are quite distinct populations with respect to their bone densities, soft tissue masses, and the regulation of these variables, so should not be combined together in the same cross-sectional analysis. Regression analysis assumes normal distributions of all the variables being studied and the lumping together of such disparate populations violates these assumptions and is likely to produce results which are quite different from those which would emerge from the analysis of individual homogeneous populations on their own.

0.58

All other correlations are significant, p < 0.0001. a p = 0.07. b p = 0.01.

factor) indicate that these analyses are sound. Another way of assessing the relative importance of fat and lean masses without depending upon the complexities and potential pitfalls of multiple regression analysis, is to simply relate BMD to either fat mass or lean mass as a percent of body weight, and this is shown in panels e and f of Fig. 1. It can be seen that BMD is positively related to percent fat and, therefore, inversely related to percent lean, since these two variables are the converse of one another. Some authors have then attempted to use multiple regression analysis to ask how fat mass is related to BMD after correction for body weight. This involves entering both fat mass and body weight into the same multiple regression analysis, and Table 1 shows that this is not likely to yield valid results because of the substantial co-linearity between these variables. Indeed, when body weight is added to the multiple regression analysis of fat and lean mass as referred to above, then the diagnostic tests carried out as part of that analysis indicate that co-linearity invalidates the result. If we ignore this caution, the following equation results:

BMD ¼ 0:4 þ 0:02  Weight  0:01  Fat  0:01  Lean This indicates that both fat mass and lean mass are now inversely related to bone density while weight is positively related to BMD. Weight is the dominant variable in this regression (partial R2 is 0.17 in comparison with 0.04 for fat mass and 0.03 for lean mass). Interpreted literally, this would indicate that increases in either lean or fat mass will have a detrimental effect on bone density whereas increases in body weight will have beneficial effects. This is obviously nonsense and results from inappropriate use of statistics. It is a sad reflection on the current state of journal reviewing, that a number of papers making this error have been published in recent times. It has resulted in statements that obesity is a risk factor for osteoporosis, whereas properly analyzed studies show quite the opposite, as does the epidemiology of fractures (see below). Similar issues may arise with studies of factors released from adipocytes (e.g. leptin) when these are included as ‘independent’ variables along with fat mass itself. Interpretation of such analyses requires great care if these analyses are not to be misleading. Other methodological issues A further issue which needs to be considered in cross-sectional studies of bone and soft tissue, is the effect of pooling disparate groups, such as men and women, or pre- and postmenopausal women. Sex hormones have profound effects on bone, but they also influence and are influenced by soft tissue composition. For instance, the menopausal transition is associated with substantial bone loss, but gain in fat mass. Conversely, the transition through

Soft tissue and bone relationships during growth As mentioned above, some parameters of bone density are directly related to skeletal size. This becomes a particular problem when dealing with children, in whom a wide range of skeletal sizes will often be studied in the same cohort. This problem is made all the more acute by the preference of many pediatric bone researchers to use bone mineral content as the index of bone density. This is very closely related to skeletal size, as is total lean mass. Therefore, many of these studies are simply saying that large children have more muscle and more bone, and this is not telling very much about the regulation of bone density. The use of experimental designs which allow separation of effects on linear growth from those on bone density would allow greater insight into optimization of these two potentially independent contributors to pediatric skeletal health. It should not be assumed that the relationships between soft tissue and bone are necessarily the same during growth as they are in adult life, since the biological processes involved are quite different. Indeed, the influence of fat mass on BMD appears to be greater in older rather than younger women [3,8,9] which would be consistent with fat mass exerting an influence on bone turnover throughout the adult years different from the effect it has during adolescence, when most of adult bone mass is laid down. Studies of children are also complicated by the advent of puberty which has a positive effect on bone density in both sexes, but in boys is associated with loss of fat mass. Studies which mix boys and girls, and individuals at different states of pubertal development could result in quite unstable results. Again, homogeneity of the cohorts assessed is likely to be an important consideration in producing biologically meaningful results. Animal studies The above considerations relate to clinical studies. There are now a large number of animal models used to study relationships between bone and adipose tissue, and these carry with them a similar set of problems. There is a diversity of bone mass/density assessment techniques used in animal studies, including histomorphometry, quantitative computed tomography and DXA scanning. These techniques can be applied to axial trabecular bone or to appendicular cortical bone. Many of the contradictions in the animal studies of leptin and bone, for instance, can be traced back to these differences. Thus, an animal with reduced long bone growth will have reduced total body bone mineral content but can still have increased trabecular bone volume in the vertebral body. The studies of leptin effects on bone in animals suggest quite different effects on the different skeletal envelopes, in a way that does not appear to occur in humans. Therefore, the relevance of these findings to human physiology can be questioned. It should be remembered that mice and rats do not show haversian remodeling of cortical bone in the way that humans do. Thus, studies in these species are essentially of growing bone and may not be relevant to what is seen in the remodeling bone of adult humans. Finally, it is possible to subject animals to completely non-physiological interventions, such as treatment with supra-physiological doses of

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drugs, selective targeting of regulatory compounds to a single target organ (e.g. leptin administration to the brain), or selective or generalized knockout of a regulatory factor. Each of these may give vital insights into physiological mechanisms, but interpretation of such studies must always consider how close the experimental intervention is to the physiological situation that obtains in humans, in order to determine its applicability to human physiology.

Fat mass and fractures One of the most consistent findings from epidemiological studies is that low body weight is a risk factor for fracture. This has now been confirmed in a meta-analysis of 60,000 men and women from 12 prospective, population-based cohorts, which showed that total fractures, osteoporotic fractures and hip fractures are all inversely related to BMI in both men and women [10] (Fig. 2). When these results are adjusted for BMD, the relationship is lost for total fractures and osteoporotic fractures, suggesting that the effect of BMI is mediated through its effect on BMD. However, for hip fracture there is still a residual protective effect from high BMI even after adjustment for BMD. Possibly, it is the shock-absorbing effect of adipose tissue over the greater trochanter that provides this extra fracture protection at the hip. Other studies have related fracture risk to fat and lean masses directly. In the Study of Osteoporotic Fractures, both lean mass and fat mass (assessed using bioelectrical impedance) were shown to be related to hip fracture risk [11]. In a large French epidemiological study there was a 40% increase in hip fracture risk for each standard deviation decrease in fat mass, but no effect of lean mass [12]. Similarly, in a study of Chinese men in Hong Kong, the odds ratio for vertebral fracture was 7.0 when the first quartile of fat mass was compared with the fourth quartile, whereas comparisons of the same quartiles for lean mass produced a non-significant odds ratio of 2.2 [13]. Thus, there is unequivocal evidence that BMI is related to fracture risk, and as Table 1 demonstrates, BMI is closely related to fat mass and percent fat mass, so is a valid measure of adiposity. While studies relating fat mass to fracture risk are smaller and less numerous, their results are consistent with the notion that adiposity is protective against fracture. When considering fracture epidemiology in relation to soft tissue mass in children, it is important to remember that children suffer predominantly forearm fractures, and that the risk factors for such fractures in adults are not the same as those for other fractures. Thus, increased body weight has been found to be less protective for forearm than for hip or vertebral fractures in adult cohorts [14,15].

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Mechanisms The array of pathways linking adipose tissue with bone is becoming steadily more complex, and there is even evidence now that the relationships may operate in both directions. The factors mediating these relationships can be classified in a variety of ways. Some factors, such as those secreted from the adipocyte or the pancreatic beta cell, are directly influenced by fat mass. Some of these factors, and a number of others, are influenced acutely by food ingestion. Over time, both of these influences are likely to impact on bone mass. Bone-active hormones influenced by adiposity Adipokines Leptin is the adipokine that has been most studied in relation to bone. The signaling form of the leptin receptor has been shown to be expressed in both osteoblasts and chondrocytes [16,17]. Leptin increases proliferation and differentiation of osteoblasts [17–19] and promotes bone nodule formation [20,21]. It also increases chondrocyte growth [17,22], accounting for the short limbs of animals with impaired leptin signaling. Leptin also regulates osteoclast development [17,23], at least in part through changes in production of RANK, RANK-ligand, and osteoprotegerin [23,24]. Thus its combined effects on bone formation and resorption increase skeletal mass so that obese individuals will achieve the stronger skeleton needed to support their greater soft tissue mass. Leptin also has the potential to affect bone through its actions on the central nervous system. Infusion of leptin into the third ventricle causes bone loss in leptin-deficient and wild-type mice through inhibition of bone formation [25] and stimulation of bone resorption [26]. Blockade of the sympathetic nervous system abrogates these effects, which appear to be mediated by b-adrenoreceptors on osteoblasts [27]. These effects will interact with the direct effects already described, and the balance of direct and indirect effects is likely to differ in different physiological and experimental situations. Because leptin is produced in bone marrow adipocytes, chondrocytes and cells of the osteoblast lineage [28], its local effects on bone might be expected to be dominant. Also, when it is administered centrally, it leads to a dramatic reduction in appetite with profound weight loss, resulting in reduced serum levels of leptin, and increases in ghrelin [29–31]. Thus, much of the bone loss observed with central administration of leptin might simply reflect the profoundly catabolic state of these animals, a suggestion supported by the finding that bone loss can be produced by caloric restriction alone [32]. Both central administration of leptin and weight loss cause reductions in circulating levels of

Fig. 2. Meta-analysis of the effect of body mass index on fracture risk, with and without adjustment for bone mineral density. From De Laet et al. [10], used with permission.

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insulin, which will also have secondary effects on bone. The potential interactions of these effects are depicted in Fig. 3. Sympathetic nervous system effects on bone are well recognized but may be relatively unimportant in most contexts, since b-adrenergic blockade does not consistently influence bone density, rates of bone loss, or fracture [33]. In a prospective randomized controlled trial in normal women, we did not find that propranolol stimulated bone formation [34], as would have been expected if sympathetic tone was an important regulator of this function in human physiology. Studies of leptin deficiency and systemic leptin administration in animals support these general conclusions. Leptin deficiency is associated with reduced linear growth, reduced cortical bone mass, increased trabecular bone of the spine but reduced trabecular bone in the femora, where huge adipocytes occupy much of the marrow space [35]. Systemic administration of leptin in animals (intact or leptin-deficient) usually results in an improvement in bone formation, skeletal mass or strength [16,17,24,36,37]. In leptin-deficient animals, leptin replacement reverses the adipocyte phenotype and increases total body bone mineral content by >30% [36]. However, systemic administration of high doses of leptin may have negative effects on bone, similar to its central administration [38], because these doses are associated with weight loss and decreases in serum IGF1 levels. Assessments of the effects of leptin administration in humans are scanty. Farooqi et al. [39] provided leptin replacement to a 9year old leptin-deficient girl, and observed weight loss accompanied by bone gain. Welt et al treated eight women suffering from hypothalamic amenorrhea with leptin for up to three months. Thus resulted in increases in estradiol, thyroid hormones, IGF1, insulinlike growth factor-binding protein-3, bone alkaline phosphatase, and osteocalcin, demonstrating the many indirect mechanisms by which this hormone can impact on the skeleton [40]. Both these studies indicate that the skeletal effects of systemic leptin in humans are positive. Ultimately, this dominance is attested to by the consistent positive relationship between fat mass and bone density – if the central effects of leptin were dominant, there would be an inverse relationship. Adiponectin is now emerging as a very important adipokine which circulates in much higher concentrations than other adipocyte products, in the range 0.5–30 lg/mL [41]. It is a 28 kD protein with close homology to compliment factor C1q and collagens VIII and X [42], and circulates as trimers, hexamers and high-molecular weight oligomers [43,44]. Plasma concentrations of adiponectin are inversely related to visceral fat mass and BMI [41]. Adiponectin regulates energy homeostasis, glucose and lipid metabolism and

ICV Leptin via SNS

Satiety ↓ Insulin Resistance Weight Loss

↓ Peripheral Leptin

↓ Peripheral Insulin, Amylin, Preptin

↓Osteoblast Activity ↑ Osteoclast Activity ↓Bone Mass Fig. 3. Possible mechanisms for central leptin effects on bone, via reduced peripheral insulin and leptin levels. SNS = sympathetic nervous system. Copyright Reid, used with permission.

inflammatory pathways [45]. Serum adiponectin levels correlate with insulin sensitivity [46–48], low levels being found in subjects with coronary artery disease and type 2 diabetes mellitus [49,50]. There is a small but contradictory literature regarding adiponectin’s effects on bone in laboratory studies. The receptors for adiponectin, AdipoR1 and AdipoR2, have been identified on both osteoblasts and osteoclasts [51,52], and it increases osteoblast proliferation and differentiation while inhibiting osteoclastogenesis in vitro [53–55]. Thus, adiponectin would be expected to increase in bone mass in vivo but studies in transgenic mice have been inconsistent [51,53]. However, in 14-week old adiponectin-knockout mice, trabecular bone volume is increased by 30% [56] suggesting that indirect effects are more powerful than direct effects of this adipokine on bone. These indirect effects might include modulation of insulin sensitivity and growth factor binding. The magnitude of the bone changes in the knockout mice indicates that adiponectin is likely to be a significant contributor to the fat–bone relationship. This is supported by clinical studies which show consistent inverse relationships between circulating adiponectin concentrations and BMD [57–59]. Resistin modestly increases the proliferation of osteoblasts in both cell and organ culture systems and increases the formation and activity of osteoclasts [60,61]. Whether these counter-balancing effects lead to any change in bone mass is not known. The adipocyte has long been recognized as an estrogen-producing cell, particularly in postmenopausal women. Thus, early postmenopausal women who lose bone rapidly have lower levels of both estrone and estradiol than ‘‘slow losers”, and this may be contributed to by their lower fat mass [62]. Our own work has confirmed a relationship between circulating estrone levels and bone density, but has shown this to be both independent of the effects of fat mass and substantially weaker [8]. This implies that estrogen is not the only pathway by which fat influences bone density, a suggestion supported by the finding of a fat–bone relationship in premenopausal women, in whom the adipocyte is a relatively minor source of estrogens. As noted above, there is some evidence that subcutaneous and visceral fat have different associations with bone mass. The reasons for this are uncertain but there is evidence that secretion of adiponectin and leptin is less from visceral adipocytes, and that aromatase activity (and therefore estrogen production) is also lower [6]. In contrast, the bone resorbing cytokines TNFa_and IL-6 circulate in higher concentrations in subjects with visceral adiposity [63]. The products of visceral adipocytes can also directly impact on the liver via the portal circulation, which might also contribute to their differential effects on bone metabolism. Beta cell hormones Insulin, and the factors co-secreted with it, amylin and preptin, circulate in increased concentrations in obesity. These peptides are directly stimulatory to osteoblast growth, and amylin also has a potent calcitonin-like effect, inhibiting osteoclastic bone resorption. Insulin may also increase bone density indirectly. Hyperinsulinemic women display increased androgen and estrogen production in the ovary, and insulin directly inhibits production of sex hormone binding globulin in the liver. Thus, insulin increases free concentrations of both androgens and estrogens, which will increase bone mass. Hyperinsulinemia is also associated with increased circulating levels of saturated fatty acids, which are able to directly inhibit osteoclastogenesis [64]. The interaction of these activities is shown in Fig. 4. As a result, high bone density is a very consistent finding across a wide range of hyperinsulinemic states, including obesity, polycystic ovary syndrome [65] and congenital generalized lipodystrophy [66]. The latter is particularly significant because it represents a dissociation of fat mass and insulin levels. These findings are consistent with the literature

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Fat Mass Insulin Resistance

β−Cell Hypersecretion

insulin

SHBG

amylin

preptin

Ovarian E production

Osteoblast activity

Osteoclast activity

Free sex hormones

Bone Mass Fig. 4. Summary of the principal mechanisms by which the hyperinsulinemia associated with obesity contributes to increased bone mass. An additional mechanism, not shown, is via increases in free fatty acid levels resulting from hyperinsulinemia which inhibit osteoclastogenesis. Copyright Reid, used with permission.

showing lower bone densities in insulinopenic diabetes and increased facture risk in these subjects [67]. In clinical studies, circulating insulin levels, both fasting and following a glucose load, are related to bone density. We have show this in normal postmenopausal women and found the effect to be partly independent from that of adiposity [68]. Similar effects have been demonstrated in both men and women in the Rotterdam study, again partly independent of BMI [69]. The San Antonio Heart Study has produced comparable findings in women [70]. Abrahamsen et al. [71] studied these relationships in men, and found that insulin sensitivity (measured from an intravenous glucose tolerance test) was inversely related to bone density independently of weight and fat mass. In addition, they found that the dependence of bone density on fat mass was lost when insulin sensitivity was entered into the multiple regression analysis, suggesting that this relationship was mediated through insulin sensitivity. Recently, the Tromso study has found that non-vertebral fracture risk progressively declines with increasing insulin resistance (inferred from the severity of the metabolic syndrome), being reduced by 50% in those with greatest insulin resistance [72]. Integration of effects of fat mass and hormones In the 20 years since the inter-dependence of fat mass and bone mass became apparent, many factors that might mediate the connection have been identified. The problem has now become to determine the relative importance of these factors and the interrelationships between them. In order to partially address this question, we have recently accessed a large database of non-diabetic women which assessed body composition and serum levels of leptin, adiponectin and insulin [73,74]. In premenopausal women, bone density was consistently, positively related to fat mass, lean mass and serum leptin, and inversely related to serum adiponectin. The correlations with insulin tended to be positive but were only significant at the hip. The osteoblast marker, osteocalcin, was inversely related to fat mass and to leptin, but deoxypyridinoline, a marker of bone resorption, was only significantly related (inversely) to adiponectin. Stepwise

multiple regression to determine which variables were the principal determinants of bone density, showed variability from one skeletal site to another. Lean mass was consistently positively related to BMD but fat mass, leptin, adiponectin and insulin were present variably and, to some extent, interchangeably, as a second independent determinant of BMD (Table 2). One possible reason for the consistent presence of lean mass in all these equations is that there were no other closely related variables in the database to compete with it. In postmenopausal women, soft tissue correlations with BMD were similar. However, the inverse relationship between adiponectin and BMD was much stronger and more consistent. Osteocalcin was again inversely related to fat mass, but also positively related to adiponectin. The stepwise regression analysis in postmenopausal women showed adiponectin and lean mass to be the principal players. The ability of adiponectin to displace the other fatrelated variables in postmenopausal women, is in marked contrast to what is seen in premenopausal women. The bone turnover indices (osteocalcin and DPD) were related to both adiponectin and fat mass, though lean mass, as in the premenopausal women, had no influence on either. This dataset confirms the important effects of fat and lean masses on BMD in both pre- and postmenopausal women. However, it shows an even balance of the effects of the various fat-reTable 2 Determinants of BMD and serum osteocalcin in normal women.

BMD Osteocalcin

Premenopausal

Postmenopausal

Lean Fat/insulin/adiponectin () Leptin ()

Lean Adiponectin () Fat () Adiponectin

Summary of results from stepwise multiple regression in 453 premenopausal women and 215 postmenopausal women examining the dependence of BMD at various sites and serum osteocalcin on soft tissue-related variables. () indicates an inverse relationship, and ‘‘/” indicates that these variables were interchangeable in the regression (see Ref. [73] for original data).

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lated variables in premenopausal women whereas there is a dominance of adiponectin in postmenopausal women. This implies an interaction between estrogen and adiponectin. Whether the relationships between BMD and adiponectin in postmenopausal women represent direct effects of adiponectin on bone, or whether adiponectin is simply acting as a surrogate for a cluster of fat-related effects is unknown. Relevant to this, is the recent suggestion that adiponectin levels may reflect the biological effects of ambient insulin levels [74]. With respect to the bone marker data, it is important to note that turnover is influenced by fat-related variables in both preand postmenopausal women. This suggests that fat mass is related to bone metabolism, whereas lean mass is not. Thus, the two soft tissue compartments could interact with the skeleton through quite different mechanisms. The common origins of osteoblasts, myocytes, and other connective tissue cells, together with their response to similar growth regulatory signals and to loading, probably underlie the bone-lean correlation, which is strongest in children and young adults. However, fat tissue regulates bone turnover in both premenopausal and postmenopausal life, so its influence on bone mass is likely to increase over time, particularly in the absence of other major players such as estrogen. This would account for the frequent observation that fat mass is a more significant correlate of bone density later in life, particularly in women. Bone-active hormones influenced by feeding The advent of sensitive markers of bone resorption has made apparent that osteoclast activity is acutely responsive to food ingestion, such that these assessments are always made in the fasting state to eliminate problems caused by eating. It is now also recognized that osteoblast activity is also influenced by short-term changes in nutrition. The study of Ihle and Loucks [75] has shown that a 30% energy restriction over a period of five days will reduce markers of osteoblast activity by 10%, and a 70% energy restriction over this period reduces osteoblast markers by up to 30%, with bone resorption increasing by about the same amount. These changes in bone turnover are accompanied by reductions in estradiol, insulin, IGF1 and thyroid hormones. Other hormonal intermediaries are likely to be involved in signaling eating activity to bone. Glucose ingestion increases calcitonin secretion [76] and decreases parathyroid hormone [77,78]. Amylin is co-secreted with insulin, and potently inhibits bone resorption [79], so might contribute to these effects. Fat or protein ingestion also reduce bone resorption in humans [80–82]. This could be mediated by changes in parathyroid hormone, amylin and calcitonin, but there are other potential players since these nutrients stimulate secretion of the incretin hormones, glucagon-like peptide-1 (GLP-1) and glucosedependent insulinotropic polypeptide (GIP), which act to enhance postprandial insulin secretion. GLP-1 and GIP do not acutely influence bone resorption [81] but GIP stimulates osteoblast proliferation, can attenuate post-ovarectomy bone loss [83,84], and the GIP-receptor knockout mouse shows decreased bone size, mass, and formation rate [85]. Parenteral administration of the related peptide, GLP-2, produces a dose-dependent reduction in serum C-terminal telopeptide of type I collagen [81], and GLP-2 administration over a 4-month period decreased bone resorption and increased bone density in postmenopausal women [86]. Consistent with these findings, deletion of GLP-2 receptor leads to marked skeletal deficits in growing mice [87]. Ghrelin is a further candidate hormone that might mediate feeding effects on bone. Other mechanistic possibilities Recently, the fat–bone connection has assumed a new complexity with the suggestion that bone might regulate intermediary metabolism. Thus, Ferron et al. [88] have shown that the administration of uncarboxylated osteocalcin to mice for 28 days increases

their insulin sensitivity and decreases their fat mass. The significance of this to normal human physiology and disease states remains to be established. It has also been suggested that marrow adipocytes might directly impact on bone cell function [89]. Adipocytes and osteoblasts are derived from the same pool of pluripotent mesenchymal stem cells, the fate of which is determined by local and systemic influences. There is a growing body of evidence for a reciprocal relationship between marrow adiposity and bone formation, and PPARc agonists, such as rosiglitazone, drive this balance in the direction of the adipocyte, resulting in bone loss [90]. Some fatty acids can have a similar effect in vitro [91]. Also, inflammatory cytokines (TNFa_and IL-6) from marrow adipocytes could directly promote bone resorption [92]. At present, it remains uncertain whether an increase in marrow fat alters bone metabolism or simply occurs as an epiphenomenon in response to a decline in bone formation. Conclusions Fat and bone are linked by a multitude of pathways, which ultimately serve the function of providing a skeleton appropriate to the mass of adipose tissue it is carrying. Adiponectin, insulin/amylin/preptin, leptin and adipocytic estrogens are all likely to be involved in this connection. In the clinic, the key issues are that obesity is protective against osteoporosis, but underweight is a major preventable risk factor for fractures. References [1] C. Ribot, F. Tremollieres, J.M. Pouilles, M. Bonneu, F. Germain, J.P. Louvet, Bone 8 (1987) 327–331. [2] I.R. Reid, R. Ames, M.C. Evans, S. Sharpe, G. Gamble, J.T. France, T.M.T. Lim, T.F. Cundy, J. Clin. Endocrinol. Metab. 75 (1992) 45–51. [3] I.R. Reid, L.D. Plank, M.C. Evans, J. Clin. Endocrinol. Metab. 75 (1992) 779–782. [4] I.R. Reid, Bone 31 (2002) 547–555. [5] I.R. Reid, Osteoporos. Int. 19 (2008) 595–606. [6] V. Gilsanz, J. Chalfant, A.O. Mo, D.C. Lee, F.J. Dorey, S.D. Mittelman, J. Clin. Endocrinol. Metab. 94 (2009) 3387–3393. [7] I.R. Reid, B. Mason, A. Horne, R. Ames, H.E. Reid, U. Bava, M.J. Bolland, G.D. Gamble, Am. J. Med. 119 (2006) 777–785. [8] I.R. Reid, R. Ames, M.C. Evans, S. Sharpe, G. Gamble, J.T. France, T.M. Lim, T.F. Cundy, J. Clin. Endocrinol. Metab. 75 (1992) 45–51. [9] I.R. Reid, M. Legge, J.P. Stapleton, M.C. Evans, A.B. Grey, J. Clin. Endocrinol. Metab. 80 (1995) 1764–1768. [10] C. De Laet, J.A. Kanis, A. Oden, H. Johanson, O. Johnell, P. Delmas, J.A. Eisman, H. Kroger, S. Fujiwara, P. Garnero, E.V. McCloskey, D. Mellstrom, L.J. Melton, P.J. Meunier, H.A.P. Pols, J. Reeve, A. Silman, A. Tenenhouse, Osteoporos. Int. 16 (2005) 1330–1338. [11] K.E. Ensrud, R.C. Lipschutz, J.A. Cauley, D. Seeley, M.C. Nevitt, J. Scott, E.S. Orwoll, H.K. Genant, S.R. Cummings, Am. J. Med. 103 (1997) 274–280. [12] A.M. Schott, C. Cormier, D. Hans, F. Favier, E. Hausherr, P. Dargent-Molina, P.D. Delmas, C. Ribot, J.L. Sebert, G. Breart, P.J. Meunier, Osteoporos. Int. 8 (1998) 247–254. [13] E.M.C. Lau, Y.H. Chan, M. Chan, J. Woo, J. Griffith, H.H.L. Chan, P.C. Leung, Calcif. Tissue Int. 66 (2000) 47–52. [14] A.R. Williams, N.S. Weiss, C.L. Ure, J. Ballard, J.R. Daling, Obstet. Gynecol. 60 (1982) 695–699. [15] T.J. Beck, M.A. Petit, G.L. Wu, M.S. LeBoff, J.A. Cauley, Z. Chen, J. Bone Miner. Res. 24 (2009) 1369–1379. [16] C.M. Steppan, D.T. Crawford, K.L. Chidsey-Frink, H.Z. Ke, A.G. Swick, Regul. Pept. 92 (2000) 73–78. [17] J. Cornish, K.E. Callon, U. Bava, C. Lin, D. Naot, B.L. Hill, A.B. Grey, N. Broom, D.E. Myers, G.C. Nicholson, I.R. Reid, J. Endocrinol. 175 (2002) 405–415. [18] T. Thomas, F. Gori, S. Khosla, M.D. Jensen, B. Burguera, B.L. Riggs, Endocrinology 140 (1999) 1630–1638. [19] J.O. Gordeladze, C.A. Drevon, U. Syversen, J.E. Reseland, J. Cell. Biochem. 85 (2002) 825–836. [20] J.E. Reseland, U. Syversen, I. Bakke, G. Qvigstad, L.G. Eide, O. Hjertner, J.O. Gordeladze, C.A. Drevon, J. Bone Miner. Res. 16 (2001) 1426–1433. [21] U.T. Iwaniec, C.C. Shearon, R.P. Heaney, D.M. Cullen, J.A. Yee, Bone 23 (5 Suppl.) (1998) S212. [22] G. Maor, M. Rochwerger, Y. Segev, M. Phillip, J. Bone Miner. Res. 17 (2002) 1034–1043. [23] W.R. Holloway, F.M. Collier, C.J. Aitken, D.E. Myers, J.M. Hodge, M. Malakellis, T.J. Gough, G.R. Collier, G.C. Nicholson, J. Bone Miner. Res. 17 (2002) 200–209.

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