- Email: [email protected]
Association between basal metabolic function and bone metabolism in postmenopausal women with type 2 diabetes
Accepted Manuscript Association between basal metabolic function and bone metabolism in postmenopausal women with type 2 diabetes Makiko Ogata, Risa Ide, Miho Takizawa, Mizuho Tanaka, Tamaki Tetsuo, Asako Sato, Naoko Iwasaki, Yasuko Uchigata PII:
S0899-9007(15)00282-8
DOI:
10.1016/j.nut.2015.06.012
Reference:
NUT 9561
To appear in:
Nutrition
Received Date: 12 February 2015 Revised Date:
8 June 2015
Accepted Date: 18 June 2015
Please cite this article as: Ogata M, Ide R, Takizawa M, Tanaka M, Tetsuo T, Sato A, Iwasaki N, Uchigata Y, Association between basal metabolic function and bone metabolism in postmenopausal women with type 2 diabetes, Nutrition (2015), doi: 10.1016/j.nut.2015.06.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Association between basal metabolic function and bone metabolism in postmenopausal women with type 2 diabetes
Asako Sato2), Naoko Iwasaki1), Yasuko Uchigata1)
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Makiko Ogata1), Risa Ide1), Miho Takizawa1), Mizuho Tanaka1), Tamaki Tetsuo1),
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1) Diabetes Center, Tokyo Women’s Medical University, Tokyo, Japan
Corresponding author: Makiko Ogata
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2) Clinical Laboratory, Tokyo Women’s Medical University, Tokyo Japan
8-1, Kawada-cho, Shinjyuku-ku, 162-8666 Tokyo, Japan
Fax: +81-3-3358-1941
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Tel: +81-3-3353-8111 Ext. 27011
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Email: [email protected]
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Abstract Objective: Diabetes is a risk factor for osteoporosis, and glycemic control is critical during osteoporosis treatment in patients with type 2 diabetes (T2D). However, diabetic
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therapies have potentially adverse effects on bone metabolism. In addition, biomarkers for bone metabolism are directly affected by osteoporosis drug therapies. This study examined resting energy expenditure (REE) and respiratory quotient (RQ) as indices of
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bone metabolism in postmenopausal Japanese women with T2D.
Methods: Forty-six postmenopausal Japanese women with T2D were examined.
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Procollagen type 1 N-terminal propeptide (P1NP, a fasting serum bone formation marker) and carboxy-terminal collagen crosslinks-1 (CTX-1, a resorption marker) were evaluated, along with intact parathyroid hormone, 25-hydroxyvitamin D (25[OH]D), urine microalbumin, motor nerve conduction velocity, sensory nerve conduction
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velocity, R-R interval, body composition, REE, RQ, and bone mineral density at the non-dominant distal radius.
Results: The mean T-score was low with high variance (–1.7 ± 1.6), and 18 patients
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(39%) met the criteria for osteoporosis. REE was positively correlated with body mass index (β = 0.517, r2 = 0.250), serum calcium (β = 0.624, r2 = 0.200), HbA1c for the
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previous 6 months (β = 0.395, r2 = 0.137), and the serum P1NP/CTX-1 ratio (β = 0.380, r2 = 0.144). RQ was positively correlated with serum 25[OH]D (β = 0.387, r2 = 0.131).
Conclusions: The basal metabolic rate and diabetic pathophysiology are interrelated with bone turnover.
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Keywords: Diabetes, Menopause, Resting energy expenditure, Bone metabolism, Osteoporosis
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Abbreviations
ALP, alkaline phosphatase; BMI, body mass index; BMD, bone mineral density; Ca, calcium; CTX-1; carboxy-terminal collagen crosslinks-1; CPR, C peptide; DPPIV-I,
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dipeptidyl-peptidase IV inhibitor; HbA1c, glycated hemoglobin A1c, HDL, high-density lipoprotein; LDL, low-density lipoprotein; MCV, Motor nerve conduction velocity;
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OHA, oral hypoglycemic agent; P, phosphate; P1NP, procollagen type 1 N-terminal propeptide; iPTH, intact parathyroid hormone; REE, resting energy expenditure; RQ, respiratory quotient; SCV, sensory nerve conduction velocity; SU, sulfonylurea; T2D,
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type 2 diabetes; 25[OH]D, 25-hydroxyvitamin D.
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Introduction The risk of fracture is particularly high among patients with diabetes [1]. Although the risk in patients with type 1 diabetes (T1D) is attributed to their lower
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maximal bone mass (due to their insulin deficiency), patients with type 2 diabetes (T2D) typically have a high bone mineral density (BMD) [2, 3]. Therefore, the
etiology of osteoporosis is thought to differ between patients with insulin deficiency
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and insulin resistance. Bone metabolism and blood glucose metabolism are considered
closely related [4], and low bone mass is thought to be due to unbalanced bone
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remodeling, which involves both resorption of the bone matrix by osteoclasts and bone formation by osteoblasts [5]. In addition, the finding that fat regulates bone metabolism has been viewed as an indication that bone metabolism might regulates some aspects of energy metabolism via a feedback loop [6]. Moreover, insulin
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resistance in patients with metabolic syndrome is associated with their low resting energy expenditure (REE)/body weight ratio [7], and fracture risk is high in these patients [8, 9]. The metabolic effects, with the subsequent changes in body mass index
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(BMI), can be predicted using the respiratory quotient (RQ), which represents inner respiration, and REE in patients with T2D [10]. Furthermore, REE is more closely
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associated with BMD (compared to BMI) in African American women [11]. Moreover, osteocalcin, leptin (a hormone that is derived from fat) and serotonin (an anorexigenic neurotransmitter in the brain) are closely related with bone remodeling and energy metabolism [6, 12-14]. When taken together, these findings suggest that basal metabolism in patients with T2D may be involved in bone metabolism. In late postmenopausal women, vertebral fracture was well predicted by bone turnover markers, independent of BMD, over a 10-year follow-up period [15]. 4
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Thus, serum biochemical markers of bone turnover should predict the risk of vertebral fracture in patients with T2D [16]. In addition, women with T2D have a particularly high risk of femoral neck fracture, and it has recently been reported that suppression of
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bone turnover increases the fracture risk in postmenopausal women with T2D [17]. It
is also well known that the basal metabolism in postmenopausal women with diabetes is markedly low [18]. Thus, reduced bone metabolism due to a lower basal metabolic
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ratio is a possible fracture risk factor in postmenopausal women with T2D. Furthermore, markers for bone turnover can be used to evaluate the efficacy of a drug
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during osteoporosis treatment, although diabetes may also alter bone metabolism, which may then affect bone turnover markers [19, 20].
In this study, we aimed to examine the relationship between bone metabolism and basal metabolic function in postmenopausal women with T2D, and to test the
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hypothesis that basal metabolism is a useful marker for evaluating bone health.
Materials and Methods
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Patients
We initially recruited 56 postmenopausal Japanese women (>50 years old) with
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T2D, who had attended our clinic for at least 1 year. Ten patients were excluded from the study after a careful examination of their medical histories, based on the intake of dietary supplements in the previous 3 months (e.g., vitamins), or possible latent adult autoimmune diabetes with the presence of autoimmune antibodies, such as antiglutamic acid decarboxylase antibodies. The remaining 46 patients had no signs of complications, were not overtly proteinuric, and exhibited no symptoms of major
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diseases other than hypertension, dyslipidemia, and obesity (BMI ≥30 kg/m2). Written informed consent was obtained from all patients prior to their participation. The study was approved by the ethics committee of the Tokyo Women’s
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Medical University (IRB number: 2396).
Measurements
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To evaluate orthostatic hypotension, blood pressure was measured in the supine
and standing positions within a 3-min interval. Blood samples were collected after a
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10-h overnight fast, and were used for all the tests that were performed in this study. Serum levels of P1NP and CTX-1 were measured at Roche Diagnostics (Tokyo, Japan) in a blinded manner. Serum levels of intact parathyroid hormone (iPTH), 25hydroxyvitamin D (25[OH]D), calcium, and phosphate were evaluated in our hospital
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using aliquots of the same serum samples. General serum tests were also performed to measure aspartate transaminase, alanine transaminase, cholesterol, triglyceride, and creatinine levels. Microalbumin content in the patients’ first morning urine samples
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was used to evaluate complications. Motor nerve conduction velocity (MCV) and sensory nerve conduction velocity (SCV) were calculated using the Neuropack X1
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system (Nihon Kohden, Tokyo, Japan). The R-R interval was calculated as the maximum difference in pulses/min under deep breathing conditions. Body composition was calculated via the impedance method using a body
composition analyzer (Tanita, Tokyo, Japan). REE and RQ were calculated over a 20min period via respiratory gas analysis in a thermoneutral environment using Vmax Spectra indirect calorimetry (Cardinal Health, OH, USA). A diagnosis of retinopathy
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was recorded within the 6 months prior to the patient’s participation in this study. BMD was evaluated using dual-energy X-ray absorptiometry (Hologic, Bedford, MA, USA) at the non-dominant distal radius. The cortex of the radial bone is thinner than
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that of the lumbar bone, which causes the radial bone to be fractured more frequently. Furthermore, bone mineral density in the non-dominant distal radius has been used to screen for osteoporosis [21-23], and it is common for the first fracture to occur in the
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distal radius [21, 24, 25]. Although BMD in the lumbar or femoral neck can increase,
BMD in the distal radius is not affected by poor blood glucose control [26]. Therefore,
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the BMD in the distal radius was considered the most appropriate screening tool for osteoporosis. The T-score described the number of standard deviations by which an individual’s BMD differed from the mean value that is expected in young healthy
Statistical analysis
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individuals at the same point.
The results were expressed as mean ± standard deviation. All analyses were
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performed using SPSS software (version 21.0, SPSS, Chicago, IL, USA). Intergroup comparisons were performed using Student’s t-test (with vs. without a history of minor
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fracture, or low vs. normal 25[OH]D levels). To evaluate the relationship between basal metabolism and bone metabolism, correlation analysis was performed between the various factors. Regression analysis was performed between each pair of variables that exhibited a significant correlation, and all coefficients for determination data were adjusted (r2). The contributions of blood glucose control, basal metabolism, and aging to bone metabolism were evaluated for each relevant biological parameter that had a
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significance of p < 0.1 in the univariate analysis, and these were assessed in 3 multiple regression analysis models. Differences were considered statistically significant at a p-
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value of <0.05.
Results Patient characteristics
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The patients’ clinical information is listed in Table 1; the mean age was 65.0 ± 5.9 years, mean BMI was 24.5 ± 3.6 kg/m2, and mean duration of diabetes was 15.8 ±
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8.7 years. It is notable that none of the patients had been diagnosed with osteoporosis, although 10 patients had a history of bone fracture. Among these patients, 9 had nontraumatic fractures. The patients fractured their arms, lower legs, or toes, and 1 had fractured her lower leg and finger twice. The MCVs in the ulnar and peroneal nerves
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were 55.0 ± 4.5 m/s (normal: 58.2 ± 4.7 m/s [27]) and 46.3 ± 5.0 m/s (normal: 47.2 ± 3.7 m/s), respectively. The SCVs in the ulnar and sural nerves were 51.5 ± 3.7 m/s (normal: 55.0 ± 3.8 m/s) and 48.9 ± 7.7 m/s (normal: 50.8 ± 5.1 m/s), respectively.
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Regarding respiratory load, the mean R-R interval was shorter than the predicted agerelated values in 5 patients, and no significant differences were observed between the
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blood pressures in the decubitus and standing positions for all patients. There was no significant difference in the neurological outcomes for patients with or without a history of fracture.
Bone metabolism The mean BMD and T-score in the tracheal bones were 0.473 ± 0.07 g/cm2 and
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−1.76 ± 1.52, respectively. Eighteen patients (39%) had a T-score of less than −2.5, and were diagnosed with osteoporosis [28]. The serum levels of the bone metabolism markers are listed in Table 1. The mean serum levels of CTX-1 and P1NP were lower
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in our patients, compared to the values that have been reported in normal postmenopausal women (CTX-1: 0.36 ± 0.13 ng/mL vs. 0.56 ± 0.23 ng/mL, P1NP: 37.6 ng/mL vs. 45.05 ng/mL) [20].
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The CTX-1 levels were significantly and negatively correlated with the patients’ initial HbA1c levels (r = −0.397, p = 0.006) and average HbA1c levels for the
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previous 6 months (r = −0.385, p = 0.008) (Fig. 1A); the CTX-1 levels were positively correlated with P1NP levels (r = 0.645, p < 0.001). In addition, the P1NP levels were significantly and negatively correlated with the patients’ initial HbA1c levels (r = −0.41, p = 0.005) and duration of diabetes (r = −0.36, p = 0.01), and were significantly
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and positively correlated with their 25[OH]D levels (r = 0.321, p = 0.03) and CTX-1 levels (r = 0.645, p < 0.0001). The P1NP and CTX-1 levels were strongly correlated with each other (r = 0.645, p < 0.0001), although neither was correlated with BMD
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(P1PN: r = 0.063, p = 0.679; CTX-1: r = –0.16, p = 0.288). The ratio of P1NP to CTX1 (P1NP/CTX-1), which is an indicator of bone turnover, was significantly correlated
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with BMI (r = 0.455, p = 0.001), RQ (r = 0.336, p = 0.02), REE (r = 0.38, p = 0.009), and BMD (r = 0.321, p = 0.029); P1NP/CTX-1 was negatively correlated with iPTH (r = −0.326, p = 0.027). However, P1NP/CTX-1 was not correlated with iPTH after adjusting for REE, BMD, and serum calcium levels. Although the serum calcium and iPTH levels were normal in all patients, the 25[OH]D levels were <20 ng/mL in 23 patients (50%). The characteristics of the
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patients with low vitamin D levels are shown in Table 2. These patients did not habitually walk for more than 30 min twice per week, had a significantly lower C-
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peptide response, and had a significantly increased insulin dependency (p < 0.01).
Basal metabolism and RQ
The patients’ REE was less than the value that was predicted using the
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modified Harris Benedict equation (940.5 ± 155.7 kcal vs. 1,176.7 ± 98.3 kcal; p < 0.0001), and the mean RQ was 0.88 ± 0.10. REE was significantly associated with
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BMI (p < 0.0001), serum calcium (p < 0.0001), and HbA1c at 6 months (p = 0.007). Although REE was not correlated with CTX-1 or P1NP, it was significantly correlated with P1NP/CTX-1 (r = 0.38, p = 0.009) (Fig. 1B). After adjusting for BMI, P1NP/CTX-1, and HbA1c levels at 6 months, multiple regression analysis revealed
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that REE was significantly correlated with serum calcium levels (p = 0.002). Furthermore, RQ was weakly correlated with P1NP/CTX-1 (r = 0.336, p = 0.02) and 25[OH]D levels (r = 0.387, p = 0.008), and was strongly correlated with 25[OH]D
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after adjusting for P1NP/CTX-1 (Fig. 1C).
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Multiple regression analysis models All single correlations were confirmed by linear regression analysis (Table 3),
and the multiple regression analysis models were used to evaluate the relationships between basal metabolism, blood glucose control, bone metabolism, and BMD. The multiple linear regression analysis models that were used to evaluate REE, blood glucose control for the previous 6 months, and age are shown in Table 4. The mean
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HbA1c levels in the previous 6 months were negatively correlated with both CTX-1 and P1NP, after adjusting for REE and age. REE was correlated with the P1NP/CTX-1
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ratio, and BMD was correlated with age.
Discussion
In the present study of postmenopausal women with T2D who were not taking
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supplements, all patients had attended our hospital for at least 1 year, with continuous diabetic treatment for at least 3 years without severe complications. As expected, the
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basal metabolic rate was associated with markers of bone turnover and serum calcium levels. In addition, the 25[OH]D levels were correlated with RQ, which is the index of the internal respiration that is caused by oxidation of carbohydrates, protein, or fat. These results demonstrate that REE and RQ are important parameters to consider
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when evaluating osteoporosis in patients with postmenopausal diabetes. Osteoporosis is defined using a combination of BMD and bone quality. Bone quality is defined using the characteristics of the bone matrix, such as
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microarchitecture, bone turnover, micro-damage accumulation, the degree of calcification, and collagen, which can be clinically assessed by measuring bone
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metabolism using biochemical markers of bone turnover [29, 30-32]. These makers are also associated with an increased risk of osteoporotic fracture in postmenopausal women, independent of BMD [33]. However, BMD is not a definitive predictive marker of bone fracture in patients with T2D [2], although serum biochemical markers of bone turnover (for both formation and resorption) are accurate predictors of osteoporosis or osteopenia in women [16]. Among the many biochemical markers of
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bone turnover, CTX-1 and P1NP are markers for the resorption and formation of collagen, and are expected to reflect the early changes during bone turnover [34]. Our patients’ REE was lower than the predicted value that was calculated
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using the modified Harris Benedict equation. However, the REE in Asian women is
usually lower than that in Caucasian women, when measured using indirect calorimetry, relative to the expenditures that are calculated using prediction equations [35].
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Furthermore, among women who are 30–60 years old, the predictive equation provides an overestimation of 9.0% (relative to the actual basal metabolic rate) [36]. Therefore,
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although the REE in this study was relatively low, this finding is adequate for our purposes. Nevertheless, it is important to determine the basal metabolism using indirect calorimetry for accurate evaluations in these subjects. Although we observed that the BMD of the distal radius was correlated with BMI, age, fat mass, and P1NP/CTX-1, it
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was not correlated with REE, CTX-1, or P1NP. This is likely explained by the fact that several metabolic factors, such as REE, CTX-1, and P1NP, were correlated with blood glucose control in the previous 6 months.
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REE is closely related to leptin [37], which is an adipokines that is secreted by fat tissue, which controls energy homeostasis [38, 39]. Peripheral leptin acts on the
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skeleton through the leptin receptor of osteoblasts [40]. However, peripheral leptin levels are not correlated with BMD in postmenopausal women [41]. Therefore, central leptin function is thought to influence bone regulation, and also regulates energy expenditure in leptin receptor knockout mouse [42]. In contrast, blockage of leptin signaling in a murine model inhibited bone mass accrual by up-regulating sympathetic activity, independent of any change in appetite or energy expenditure [43]. Serotonin
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has also recently been reported to be a key regulator of bone metabolism through leptin [44-46]. Inhibition of peripheral serotonin synthesis reduces obesity and metabolic dysfunction, and increases REE [47]. Moreover, leptin and serotonin have been reported
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to be dysregulated in diabetes [48-51]. Thus bone, glucose, and energy metabolism must be influenced by each other in the peripheral and neuro-central systems. Furthermore,
REE is a parameter that is influenced directly by all of these factors in this energy-bone
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cascade, and will exhibit values that reflect these factors’ function in this cascade.
In the present study, we demonstrated that the patients’ REE was correlated
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with the ratio of a bone collagen formation marker (P1NP) to a bone collagen resorption marker (CTX-1), which indicates active bone turnover. Our data are the first to demonstrate the direct correlation of bone metabolism with energy expenditure in patients with T2D. In addition, REE was positively correlated with BMI, initial HbA1c
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levels, and HbA1c levels at 6 months. Finally, CTX-1 and P1NP were positively correlated with each other, and both factors were negatively correlated with average HbA1c in the previous 6 months. Taken together, our findings indicate that bone
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turnover is correlated with the patients’ basal metabolic rate, and ultimately with their blood glucose control. These findings also imply that poor glycemic control may lead
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to low bone turnover in patients with well-controlled T2D. Although CTX-1 is negatively correlated with P1NP, and tends to be negatively
correlated with HbA1c [52], P1NP decreases in postmenopausal women with T2D [53]. Interestingly, low bone turnover and bone deficiency in T2D is thought to be caused by elevated glycemic levels, based on a study of bone metabolic markers [54]. In addition, although postmenopausal women exhibit high levels of bone resorption markers [15,
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55], given their high bone turnover, it has recently been reported that suppression of bone turnover increases the fracture risk in postmenopausal women with T2D [17, 54], which is consistent with our results. Furthermore, although bisphosphonate (an inhibitor
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of bone resorption) is typically used to treat osteoporosis, it is less effective for patients with T2D [56]. Moreover, bone formation is reduced by bisphosphonates with bone
resorption in a mouse model of diabetes [57]. Thus, the association of CTX-1 (a bone
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resorption factor) with glycemic control and low bone turnover at the pretreatment stage, as reported in the present study, may explain why bisphosphonate is ineffective in
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patients with osteoporosis and poorly controlled diabetes. In this context, good glycemic control and increased basal metabolism are desirable for patients with postmenopausal diabetes. However, their clinical management is complex, as strict dietary regimens may lead to decreased fat mass, BMD, and REE [58-61], and tight glycemic control with
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aggressive diabetes drug therapies may result in increased fat mass and BMD [62-64] without any increase in REE [65, 66], thereby increasing the risk of developing atherosclerosis [67-69]. Although insulin therapy may improve bone metabolism in
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patients with insulin-requiring diabetes [70], early initiation of insulin therapy decreases patients’ REE [68, 71, 72]. Both fat mass and REE were correlated with the ratio of
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bone formation to bone resorption marker, whereas fat mass was correlated with BMD and was not correlated with HbA1c. However, REE was correlated with HbA1c and was not correlated with BMD. Thus, bone metabolic markers, which indicate bone quality, are considered better markers of osteoporosis, compared to BMD, especially in T2D [15, 16]. Nutritional and bone turnover markers is useful predictors of bone loss in elderly women [73], although these bone metabolic markers are likely altered by drug therapies
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for osteoporosis [74, 75]. Therefore, the levels of REE and RQ are likely useful for evaluating bone metabolism and therapeutic strategies for controlling blood glucose levels in patients with diabetes.
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A correlation between sensory nerve function and bone volume has been reported in a mouse model and in elderly humans [76, 77], and the relationship between diabetic polyneuropathy and non-traumatic fractures has also been reported
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[78]. However, MCV and SCV (as indicators of diabetic neuropathy) were not
correlated with bone turnover markers in the present study. Thus, it appears that the
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correlation between peripheral nerve function and bone turnover may be weak in elderly female patients with diabetes, and this theory is consistent with the findings of a previous report [79]. Alternatively, our screening tests for sensory and autonomic nervous function may not be suitable for evaluating the subtle changes in nervous
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function that occur in diabetic neuropathy, as these tests are commonly used for the general evaluation of diabetic neuropathy. However, the patients’ MCV in the ulnar and peroneal nerves and SCV in the ulnar and sural nerves were below the normal
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ranges for Japanese persons of a similar age, which suggests that they had diabetic neuropathy [27, 80].
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Patients with diabetes are known to have low vitamin D levels, and the
frequency of hypovitaminosis D in our subjects was similar to that in a previous Japanese study [81]. Furthermore, we found that 25[OH]D levels were correlated with RQ, which is an index of internal respiration that indicates the nutrient source of energy [82]. The relationship between 25[OH]D and RQ may be complex, although several speculations can be considered. For example, a low RQ indicates reduced lipid
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oxidation [83] in patients with low vitamin D, although 25[OH]D levels are positively associated with lipid metabolism [84, 85]. In addition, patients with T2D and low vitamin D levels have significantly lower C-peptide levels (compared to patients with
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normal vitamin D levels), and these patients require insulin more often [86]. Thus, the pathogenesis of diabetes reflects decreased insulin secretion and sensitivity. Therefore,
our results indicate that RQ (internal respiration) is correlated with serum vitamin D
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levels, which can predict fracture, and that these levels are also correlated with BMI, insulin sensitivity, and pancreatic β cell dysfunction in the prediabetic state [87].
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The association of REE with bone metabolism in this study may indicate that these factors are regulated via neurotransmitters and adipocyte hormones, although further studies are needed to evaluate this hypothesis.
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Conflict of Interest
The authors have no conflicts of interest to declare.
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Acknowledgements
We thank Prof. T. Matsumoto for his advice, Ms. M. Tomioka for her assistance with the
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data analyses, and Mr. M. Tomoda for de-identifying the patients’ data. This study was partially supported by Grant-in-Aids (22510214) for Scientific Research from MEXT, the National Center for Global Health and Medicine (21A114) from the MHLW of Japan (N.I.), and a research grant from Lilly (M.O.).
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Association of vitamin D with insulin resistance and beta-cell dysfunction in subjects at risk for type 2 diabetes. Diabetes Care. 2010;33:1379-81. [87] Kayaniyil S, Vieth R, Harris SB, Retnakaran R, Knight JA, Gerstein HC, et al.
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Association of 25(OH)D and PTH with metabolic syndrome and its traditional and
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nontraditional components. J Clin Endocrinol Metab. 2011;96:168-75.
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Figure Legend
Fig. 1. Correlation diagram of the relationship between basal metabolism and bone
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metabolism. A: Correlation between serum carboxy-terminal collagen crosslinks-1 (CTX-1) levels and mean glycated hemoglobin (HbA1c) levels for the previous 6 months (y = –0.05x +
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0.71: r2 = 0.140). B: Correlation between resting energy expenditure (REE) and the procollagen type 1 N-terminal propeptide (P1NP) to CTX-1 ratio (y = 0.08x + 35.58: r2
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30.18x + 5.06: r2 = 0.150).
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= 0.144). C: Correlation between serum vitamin D levels and respiratory quotient (y =
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Table 1. Patient characteristics (n = 46) 65.0 ± 5.9 24.5 ± 3.6 5 / 41 11 / 35
Ca (mg/dL) P (mg/dL) 25OHD (ng/mL) iPTH (pg/mL) Serum CTX-1 (ng/mL) [normal]
9.2 ± 0.3 3.6 ± 0.4 21.8 ± 7.7 59.5 ± 17.5
7.6 ± 1.1
Serum P1NP (ng/mL) [normal] BMD (g/cm2)
37.6 ± 11.7 [45.1 (20.3–76.3)]** 0.472 ± 0.0730
Mean HbA1c prior for 6 months (%) Family history of bone fracture (+/-) Past history of bone fracture (atraumatic/traumatic/none) Duration of diabetes Insulin/OHA/diet SU/thiazolidine/ DPPIV-I/biguanide Antihypertensive drug (+/-) Lipid lowering agent (statins)
15 / 31
T-score
–1.76 ± 1.52
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Age BMI Tobacco (+/-) Alcohol (+/-) Serum fasting blood sugar (mmol/L) HbA1c (%)
0.36 ± 0.13 [0.56 ± 0.23]*
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8.3 ± 2.7 7.5 ± 1.1
Respiratory quotient
9 / 1 / 36 15.8 ± 8.7 13/30/3
REE / estimated normal average (Cal)
940.5 ± 155.7 / 1,176.7 ± 98.3
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21/1/14/16 24 / 22
0.88 ± 0.098
30 (24) / 16
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MCV ulnar/ Retinopathy (P/S/N) 7 / 10 / 29 peroneal 55.0 ± 4.5 / 46.3 ± 5.0 Urine albumin (+/-) 7 / 39 SCV ulnar/sural 51.5 ± 3.7 / 48.9 ± 7.7 Creatinine (mg/dL) 0.67 ± 0.15 R-R interval (s) 115 ± 5.9 Resting (upright) HDL (mg/dL) 64.8 ± 16.9 systolic BP Triglyceride (mg/dL) 131.7 ± 101.0 (mmHg) 136.3 ± 17.8 (139.0 ± 22.3) Resting (upright) LDL (mg/dL) 123.2 ± 30.2 diastolic BP CPR (ng/mL) 1.6 ± 0.7 (mmHg) 74.6 ± 9.6 (78.4 ± 10.5) Blood pressure values in parentheses indicate the pressure in the upright position. Data for non-diabetic subjects are shown in brackets. *Mean ± standard deviation in normal postmenopausal women. **Mean (5th–95th percentile) in normal postmenopausal women without hormone replacement therapy. BMI: body mass index, Hb1Ac: glycated hemoglobin, OHA: oral hypoglycemic agent, SU: sulfonylurea, DPPIV-I:
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dipeptidyl-peptidase IV inhibitor, HDL: high-density lipoprotein, LDL: low-density lipoprotein, CPR: C-peptide response, Ca: calcium, P: phosphate, 25OHD: 25-hydroxyvitamin D, iPTH: intact parathyroid hormone, CTX-1: carboxy-terminal
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collagen crosslinks-1, P1NP: procollagen type 1 N-terminal propeptide, BMD: bone mass density, REE: resting energy expenditure, MCV: motor nerve velocity, SCV:
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sensory nerve velocity, BP: blood pressure.
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Table 2. Characteristics of patients with low and normal levels of vitamin D
7.5 ± 1.2 0.66 ± 0.1 65.4 ± 17.4 144.6 ± 130.5 122.8 ± 29.8 9.2 ± 0.3 3.6 ± 0.3 1.39 ± 0.6* 0.33 ± 0.11 33.3 ± 7.3 0.474 ± 0.08
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Patients with 25OHD >20 ng/mL, n = 23 65.1 ± 6.9 24.3 ± 3.0 34.2 ± 5.5 37.3 ± 3.2 932.7 ± 168.9
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8.1 ± 2.9 7.6 ± 1.3
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Age BMI Percent body fat Lean body mass (kg) REE (Cal) Fasting blood glucose (mmol/L) HbA1c (%) Average HbA1c over the previous 6 months (%) Serum creatinine (mg/dL) HDL (mg/dL) Triglyceride (mg/dL) LDL (mg/dL) Ca (mg/dL) P (mg/dL) CPR (ng/mL) CTX-1 (ng/mL) PINP BMD total Habit of exercising >30 min twice per week
Patients with 25OHD ≤20 ng/mL, n = 23 64.9 ± 4.9 24.7 ± 4.1 34.9 ± 6.0 37.1 ± 3.0 948.4 ± 144.7
8.4 ± 2.5 7.7 ± 1.0 7.7 ± 1.0 0.66 ± 0.1 62.2 ± 16.3 124.3 ± 56.9 123.2 ± 31.7 9.2 ± 0.4 3.6 ± 0.4 1.98 ± 0.6* 0.40 ±0.14 41.8 ± 13.7 0.471 ± 0.06
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Characteristics
9*
17 *
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*p < 0.05.
BMI: body mass index, REE: resting energy expenditure, Hb1Ac: glycated hemoglobin, HDL: high-density lipoprotein, LDL: low-density lipoprotein, Ca: calcium, P: phosphate, CPR:C peptide, CTX-1: carboxy-terminal collagen crosslinks-1, P1NP: procollagen type 1 N-terminal propeptide, BMD: bone mass density, 25OHD: 25-hydroxyvitamin D.
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Table 3. The results of the regression analysis for basal metabolism and bone metabolism, which was performed for each pair that exhibited a significant correlation. CTX-1
P1NP
P1NP/CTX-1 ratio
p
R2
β
p
R2
β
p
R2
p
R2
Age
0.15
0.34
0.00
–0.03
0.82
–0.02
–0.22
0.15
0.03
–0.43
0.00*
0.16
BMI
–0.26
0.08
0.05
0.02
0.89
–0.02
0.46
0.00*
0.19
0.34
0.021*
0.10
Fat mass
–0.30
0.04*
0.07
–0.01
0.96
–0.02
0.47
0.00*
0.20
0.33
0.027*
0.09
% fat mass
–0.22
0.14
0.03
0.00
1.00
–0.02
0.38
0.01*
0.12
0.25
0.10
0.04
REE
–0.18
0.22
0.01
0.07
0.64
–0.02
0.38
0.01*
0.13
0.24
0.11
0.04
RER
–0.10
0.53
–0.01
0.20
Albumin
0.21
0.16
0.02
0.12
ALP
0.10
0.53
–0.01
HbA1c
–0.40
0.01*
0.14
Mean HbA1c
–0.39
0.01*
0.13
-0.27
iPTH
0.17
0.26
0.01
25OHD
0.18
0.22
0.01
β
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β
BMD
0.02
0.34
0.02*
0.09
0.04
0.78
–0.02
0.44
–0.01
–0.17
0.25
0.01
–0.33
0.02*
0.09
0.37
0.01*
0.12
0.42
0.00*
0.16
0.33
0.02*
0.09
–0.41
0.01*
0.15
0.19
0.20
0.02
0.23
0.13
0.03
0.07
0.05
0.26
0.08
0.05
0.24
0.12
0.03
–0.17
0.25
0.01
–0.33
0.03*
0.09
0.10
0.52
–0.01
0.32
0.03
0.08
0.05
0.76
–0.02
–0.18
0.22
0.01
EP
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0.18
BMI: body mass index, REE: resting energy expenditure, RER: resting exchange ratio,
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ALP: alkaline phosphatase, Hb1Ac: glycated hemoglobin, iPTH: intact parathyroid hormone, 25OHD: 25-hydroxyvitamin D. All coefficients for determination data were adjusted (r2).
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Table 4. Independent effect of objectively measured mean HbA1c levels during the previous 6 months on CTX-1 and P1NP, and the effect of resting energy expenditure on the P1NP/CTX-1 ratio.
β
p
P1NP R2
β
p
P1NP/CTX-1 ratio R2
Model 1
REE
–0.04
0.02* 0.11 0.81
Model 2 Age
0.00
–0.35 0.03* 0.07
0.98 0.09
–0.37
0.03*
REE
–0.24
0.81
0.19
–0.13
0.40 0.06
–0.39 0.02* 0.19
0.23
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Mean HbA1c
0.21
0.13
p
R2
0.40 0.12
SC
–0.37
0.33 0.04*
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Mean HbA1c
β
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CTX-1
–0.10 0.10
0.53 0.11
EP
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0.30 0.04
0.17
0.29
–0.38 0.02* 0.14 0.78
0.32 0.05*
0.12
0.43
carboxy-terminal collagen crosslinks-1, P1NP: procollagen type 1 N-terminal
R2
0.17
0.05
Hb1Ac: glycated hemoglobin, REE: resting energy expenditure, CTX-1:
5
p
0.53
All models included 46 participants.
propeptide, BMD: bone mass density.
β
BMD
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0.8
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0.4 0.2
6.0 8.0 10.0 12.0 Mean HbA1c : 6months (%)
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250 200 150 100 50
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P1NP/CTX-1 ratio
B
0.6
SC
Serum CTX-1 level (mg/dl)
A
EP 50
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C
Serum 25(OH)D (ng/ml)
400 600 800 1000 1200 1400 Resting energy expenditure (Cal)
40 30 20
10 0 0.7
0.8 0.9 1.0 1.1 Respiratory quotient
1.2
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Highlights
Basal metabolic rate and bone metabolism were evaluated in postmenoposal patients with type 2 diabetes Bone metabolism was associated with resting energy expenditure (REE).
Serum vitamin D was associated with respiratory quotient (RQ) and insulin
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requirement.
S0899-9007(15)00282-8
DOI:
10.1016/j.nut.2015.06.012
Reference:
NUT 9561
To appear in:
Nutrition
Received Date: 12 February 2015 Revised Date:
8 June 2015
Accepted Date: 18 June 2015
Please cite this article as: Ogata M, Ide R, Takizawa M, Tanaka M, Tetsuo T, Sato A, Iwasaki N, Uchigata Y, Association between basal metabolic function and bone metabolism in postmenopausal women with type 2 diabetes, Nutrition (2015), doi: 10.1016/j.nut.2015.06.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Association between basal metabolic function and bone metabolism in postmenopausal women with type 2 diabetes
Asako Sato2), Naoko Iwasaki1), Yasuko Uchigata1)
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Makiko Ogata1), Risa Ide1), Miho Takizawa1), Mizuho Tanaka1), Tamaki Tetsuo1),
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1) Diabetes Center, Tokyo Women’s Medical University, Tokyo, Japan
Corresponding author: Makiko Ogata
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2) Clinical Laboratory, Tokyo Women’s Medical University, Tokyo Japan
8-1, Kawada-cho, Shinjyuku-ku, 162-8666 Tokyo, Japan
Fax: +81-3-3358-1941
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Tel: +81-3-3353-8111 Ext. 27011
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Email: [email protected]
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Abstract Objective: Diabetes is a risk factor for osteoporosis, and glycemic control is critical during osteoporosis treatment in patients with type 2 diabetes (T2D). However, diabetic
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therapies have potentially adverse effects on bone metabolism. In addition, biomarkers for bone metabolism are directly affected by osteoporosis drug therapies. This study examined resting energy expenditure (REE) and respiratory quotient (RQ) as indices of
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bone metabolism in postmenopausal Japanese women with T2D.
Methods: Forty-six postmenopausal Japanese women with T2D were examined.
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Procollagen type 1 N-terminal propeptide (P1NP, a fasting serum bone formation marker) and carboxy-terminal collagen crosslinks-1 (CTX-1, a resorption marker) were evaluated, along with intact parathyroid hormone, 25-hydroxyvitamin D (25[OH]D), urine microalbumin, motor nerve conduction velocity, sensory nerve conduction
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velocity, R-R interval, body composition, REE, RQ, and bone mineral density at the non-dominant distal radius.
Results: The mean T-score was low with high variance (–1.7 ± 1.6), and 18 patients
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(39%) met the criteria for osteoporosis. REE was positively correlated with body mass index (β = 0.517, r2 = 0.250), serum calcium (β = 0.624, r2 = 0.200), HbA1c for the
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previous 6 months (β = 0.395, r2 = 0.137), and the serum P1NP/CTX-1 ratio (β = 0.380, r2 = 0.144). RQ was positively correlated with serum 25[OH]D (β = 0.387, r2 = 0.131).
Conclusions: The basal metabolic rate and diabetic pathophysiology are interrelated with bone turnover.
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Keywords: Diabetes, Menopause, Resting energy expenditure, Bone metabolism, Osteoporosis
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Abbreviations
ALP, alkaline phosphatase; BMI, body mass index; BMD, bone mineral density; Ca, calcium; CTX-1; carboxy-terminal collagen crosslinks-1; CPR, C peptide; DPPIV-I,
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dipeptidyl-peptidase IV inhibitor; HbA1c, glycated hemoglobin A1c, HDL, high-density lipoprotein; LDL, low-density lipoprotein; MCV, Motor nerve conduction velocity;
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OHA, oral hypoglycemic agent; P, phosphate; P1NP, procollagen type 1 N-terminal propeptide; iPTH, intact parathyroid hormone; REE, resting energy expenditure; RQ, respiratory quotient; SCV, sensory nerve conduction velocity; SU, sulfonylurea; T2D,
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type 2 diabetes; 25[OH]D, 25-hydroxyvitamin D.
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Introduction The risk of fracture is particularly high among patients with diabetes [1]. Although the risk in patients with type 1 diabetes (T1D) is attributed to their lower
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maximal bone mass (due to their insulin deficiency), patients with type 2 diabetes (T2D) typically have a high bone mineral density (BMD) [2, 3]. Therefore, the
etiology of osteoporosis is thought to differ between patients with insulin deficiency
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and insulin resistance. Bone metabolism and blood glucose metabolism are considered
closely related [4], and low bone mass is thought to be due to unbalanced bone
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remodeling, which involves both resorption of the bone matrix by osteoclasts and bone formation by osteoblasts [5]. In addition, the finding that fat regulates bone metabolism has been viewed as an indication that bone metabolism might regulates some aspects of energy metabolism via a feedback loop [6]. Moreover, insulin
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resistance in patients with metabolic syndrome is associated with their low resting energy expenditure (REE)/body weight ratio [7], and fracture risk is high in these patients [8, 9]. The metabolic effects, with the subsequent changes in body mass index
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(BMI), can be predicted using the respiratory quotient (RQ), which represents inner respiration, and REE in patients with T2D [10]. Furthermore, REE is more closely
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associated with BMD (compared to BMI) in African American women [11]. Moreover, osteocalcin, leptin (a hormone that is derived from fat) and serotonin (an anorexigenic neurotransmitter in the brain) are closely related with bone remodeling and energy metabolism [6, 12-14]. When taken together, these findings suggest that basal metabolism in patients with T2D may be involved in bone metabolism. In late postmenopausal women, vertebral fracture was well predicted by bone turnover markers, independent of BMD, over a 10-year follow-up period [15]. 4
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Thus, serum biochemical markers of bone turnover should predict the risk of vertebral fracture in patients with T2D [16]. In addition, women with T2D have a particularly high risk of femoral neck fracture, and it has recently been reported that suppression of
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bone turnover increases the fracture risk in postmenopausal women with T2D [17]. It
is also well known that the basal metabolism in postmenopausal women with diabetes is markedly low [18]. Thus, reduced bone metabolism due to a lower basal metabolic
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ratio is a possible fracture risk factor in postmenopausal women with T2D. Furthermore, markers for bone turnover can be used to evaluate the efficacy of a drug
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during osteoporosis treatment, although diabetes may also alter bone metabolism, which may then affect bone turnover markers [19, 20].
In this study, we aimed to examine the relationship between bone metabolism and basal metabolic function in postmenopausal women with T2D, and to test the
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hypothesis that basal metabolism is a useful marker for evaluating bone health.
Materials and Methods
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Patients
We initially recruited 56 postmenopausal Japanese women (>50 years old) with
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T2D, who had attended our clinic for at least 1 year. Ten patients were excluded from the study after a careful examination of their medical histories, based on the intake of dietary supplements in the previous 3 months (e.g., vitamins), or possible latent adult autoimmune diabetes with the presence of autoimmune antibodies, such as antiglutamic acid decarboxylase antibodies. The remaining 46 patients had no signs of complications, were not overtly proteinuric, and exhibited no symptoms of major
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diseases other than hypertension, dyslipidemia, and obesity (BMI ≥30 kg/m2). Written informed consent was obtained from all patients prior to their participation. The study was approved by the ethics committee of the Tokyo Women’s
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Medical University (IRB number: 2396).
Measurements
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To evaluate orthostatic hypotension, blood pressure was measured in the supine
and standing positions within a 3-min interval. Blood samples were collected after a
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10-h overnight fast, and were used for all the tests that were performed in this study. Serum levels of P1NP and CTX-1 were measured at Roche Diagnostics (Tokyo, Japan) in a blinded manner. Serum levels of intact parathyroid hormone (iPTH), 25hydroxyvitamin D (25[OH]D), calcium, and phosphate were evaluated in our hospital
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using aliquots of the same serum samples. General serum tests were also performed to measure aspartate transaminase, alanine transaminase, cholesterol, triglyceride, and creatinine levels. Microalbumin content in the patients’ first morning urine samples
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was used to evaluate complications. Motor nerve conduction velocity (MCV) and sensory nerve conduction velocity (SCV) were calculated using the Neuropack X1
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system (Nihon Kohden, Tokyo, Japan). The R-R interval was calculated as the maximum difference in pulses/min under deep breathing conditions. Body composition was calculated via the impedance method using a body
composition analyzer (Tanita, Tokyo, Japan). REE and RQ were calculated over a 20min period via respiratory gas analysis in a thermoneutral environment using Vmax Spectra indirect calorimetry (Cardinal Health, OH, USA). A diagnosis of retinopathy
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was recorded within the 6 months prior to the patient’s participation in this study. BMD was evaluated using dual-energy X-ray absorptiometry (Hologic, Bedford, MA, USA) at the non-dominant distal radius. The cortex of the radial bone is thinner than
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that of the lumbar bone, which causes the radial bone to be fractured more frequently. Furthermore, bone mineral density in the non-dominant distal radius has been used to screen for osteoporosis [21-23], and it is common for the first fracture to occur in the
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distal radius [21, 24, 25]. Although BMD in the lumbar or femoral neck can increase,
BMD in the distal radius is not affected by poor blood glucose control [26]. Therefore,
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the BMD in the distal radius was considered the most appropriate screening tool for osteoporosis. The T-score described the number of standard deviations by which an individual’s BMD differed from the mean value that is expected in young healthy
Statistical analysis
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individuals at the same point.
The results were expressed as mean ± standard deviation. All analyses were
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performed using SPSS software (version 21.0, SPSS, Chicago, IL, USA). Intergroup comparisons were performed using Student’s t-test (with vs. without a history of minor
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fracture, or low vs. normal 25[OH]D levels). To evaluate the relationship between basal metabolism and bone metabolism, correlation analysis was performed between the various factors. Regression analysis was performed between each pair of variables that exhibited a significant correlation, and all coefficients for determination data were adjusted (r2). The contributions of blood glucose control, basal metabolism, and aging to bone metabolism were evaluated for each relevant biological parameter that had a
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significance of p < 0.1 in the univariate analysis, and these were assessed in 3 multiple regression analysis models. Differences were considered statistically significant at a p-
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value of <0.05.
Results Patient characteristics
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The patients’ clinical information is listed in Table 1; the mean age was 65.0 ± 5.9 years, mean BMI was 24.5 ± 3.6 kg/m2, and mean duration of diabetes was 15.8 ±
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8.7 years. It is notable that none of the patients had been diagnosed with osteoporosis, although 10 patients had a history of bone fracture. Among these patients, 9 had nontraumatic fractures. The patients fractured their arms, lower legs, or toes, and 1 had fractured her lower leg and finger twice. The MCVs in the ulnar and peroneal nerves
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were 55.0 ± 4.5 m/s (normal: 58.2 ± 4.7 m/s [27]) and 46.3 ± 5.0 m/s (normal: 47.2 ± 3.7 m/s), respectively. The SCVs in the ulnar and sural nerves were 51.5 ± 3.7 m/s (normal: 55.0 ± 3.8 m/s) and 48.9 ± 7.7 m/s (normal: 50.8 ± 5.1 m/s), respectively.
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Regarding respiratory load, the mean R-R interval was shorter than the predicted agerelated values in 5 patients, and no significant differences were observed between the
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blood pressures in the decubitus and standing positions for all patients. There was no significant difference in the neurological outcomes for patients with or without a history of fracture.
Bone metabolism The mean BMD and T-score in the tracheal bones were 0.473 ± 0.07 g/cm2 and
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−1.76 ± 1.52, respectively. Eighteen patients (39%) had a T-score of less than −2.5, and were diagnosed with osteoporosis [28]. The serum levels of the bone metabolism markers are listed in Table 1. The mean serum levels of CTX-1 and P1NP were lower
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in our patients, compared to the values that have been reported in normal postmenopausal women (CTX-1: 0.36 ± 0.13 ng/mL vs. 0.56 ± 0.23 ng/mL, P1NP: 37.6 ng/mL vs. 45.05 ng/mL) [20].
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The CTX-1 levels were significantly and negatively correlated with the patients’ initial HbA1c levels (r = −0.397, p = 0.006) and average HbA1c levels for the
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previous 6 months (r = −0.385, p = 0.008) (Fig. 1A); the CTX-1 levels were positively correlated with P1NP levels (r = 0.645, p < 0.001). In addition, the P1NP levels were significantly and negatively correlated with the patients’ initial HbA1c levels (r = −0.41, p = 0.005) and duration of diabetes (r = −0.36, p = 0.01), and were significantly
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and positively correlated with their 25[OH]D levels (r = 0.321, p = 0.03) and CTX-1 levels (r = 0.645, p < 0.0001). The P1NP and CTX-1 levels were strongly correlated with each other (r = 0.645, p < 0.0001), although neither was correlated with BMD
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(P1PN: r = 0.063, p = 0.679; CTX-1: r = –0.16, p = 0.288). The ratio of P1NP to CTX1 (P1NP/CTX-1), which is an indicator of bone turnover, was significantly correlated
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with BMI (r = 0.455, p = 0.001), RQ (r = 0.336, p = 0.02), REE (r = 0.38, p = 0.009), and BMD (r = 0.321, p = 0.029); P1NP/CTX-1 was negatively correlated with iPTH (r = −0.326, p = 0.027). However, P1NP/CTX-1 was not correlated with iPTH after adjusting for REE, BMD, and serum calcium levels. Although the serum calcium and iPTH levels were normal in all patients, the 25[OH]D levels were <20 ng/mL in 23 patients (50%). The characteristics of the
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patients with low vitamin D levels are shown in Table 2. These patients did not habitually walk for more than 30 min twice per week, had a significantly lower C-
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peptide response, and had a significantly increased insulin dependency (p < 0.01).
Basal metabolism and RQ
The patients’ REE was less than the value that was predicted using the
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modified Harris Benedict equation (940.5 ± 155.7 kcal vs. 1,176.7 ± 98.3 kcal; p < 0.0001), and the mean RQ was 0.88 ± 0.10. REE was significantly associated with
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BMI (p < 0.0001), serum calcium (p < 0.0001), and HbA1c at 6 months (p = 0.007). Although REE was not correlated with CTX-1 or P1NP, it was significantly correlated with P1NP/CTX-1 (r = 0.38, p = 0.009) (Fig. 1B). After adjusting for BMI, P1NP/CTX-1, and HbA1c levels at 6 months, multiple regression analysis revealed
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that REE was significantly correlated with serum calcium levels (p = 0.002). Furthermore, RQ was weakly correlated with P1NP/CTX-1 (r = 0.336, p = 0.02) and 25[OH]D levels (r = 0.387, p = 0.008), and was strongly correlated with 25[OH]D
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after adjusting for P1NP/CTX-1 (Fig. 1C).
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Multiple regression analysis models All single correlations were confirmed by linear regression analysis (Table 3),
and the multiple regression analysis models were used to evaluate the relationships between basal metabolism, blood glucose control, bone metabolism, and BMD. The multiple linear regression analysis models that were used to evaluate REE, blood glucose control for the previous 6 months, and age are shown in Table 4. The mean
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HbA1c levels in the previous 6 months were negatively correlated with both CTX-1 and P1NP, after adjusting for REE and age. REE was correlated with the P1NP/CTX-1
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ratio, and BMD was correlated with age.
Discussion
In the present study of postmenopausal women with T2D who were not taking
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supplements, all patients had attended our hospital for at least 1 year, with continuous diabetic treatment for at least 3 years without severe complications. As expected, the
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basal metabolic rate was associated with markers of bone turnover and serum calcium levels. In addition, the 25[OH]D levels were correlated with RQ, which is the index of the internal respiration that is caused by oxidation of carbohydrates, protein, or fat. These results demonstrate that REE and RQ are important parameters to consider
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when evaluating osteoporosis in patients with postmenopausal diabetes. Osteoporosis is defined using a combination of BMD and bone quality. Bone quality is defined using the characteristics of the bone matrix, such as
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microarchitecture, bone turnover, micro-damage accumulation, the degree of calcification, and collagen, which can be clinically assessed by measuring bone
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metabolism using biochemical markers of bone turnover [29, 30-32]. These makers are also associated with an increased risk of osteoporotic fracture in postmenopausal women, independent of BMD [33]. However, BMD is not a definitive predictive marker of bone fracture in patients with T2D [2], although serum biochemical markers of bone turnover (for both formation and resorption) are accurate predictors of osteoporosis or osteopenia in women [16]. Among the many biochemical markers of
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bone turnover, CTX-1 and P1NP are markers for the resorption and formation of collagen, and are expected to reflect the early changes during bone turnover [34]. Our patients’ REE was lower than the predicted value that was calculated
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using the modified Harris Benedict equation. However, the REE in Asian women is
usually lower than that in Caucasian women, when measured using indirect calorimetry, relative to the expenditures that are calculated using prediction equations [35].
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Furthermore, among women who are 30–60 years old, the predictive equation provides an overestimation of 9.0% (relative to the actual basal metabolic rate) [36]. Therefore,
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although the REE in this study was relatively low, this finding is adequate for our purposes. Nevertheless, it is important to determine the basal metabolism using indirect calorimetry for accurate evaluations in these subjects. Although we observed that the BMD of the distal radius was correlated with BMI, age, fat mass, and P1NP/CTX-1, it
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was not correlated with REE, CTX-1, or P1NP. This is likely explained by the fact that several metabolic factors, such as REE, CTX-1, and P1NP, were correlated with blood glucose control in the previous 6 months.
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REE is closely related to leptin [37], which is an adipokines that is secreted by fat tissue, which controls energy homeostasis [38, 39]. Peripheral leptin acts on the
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skeleton through the leptin receptor of osteoblasts [40]. However, peripheral leptin levels are not correlated with BMD in postmenopausal women [41]. Therefore, central leptin function is thought to influence bone regulation, and also regulates energy expenditure in leptin receptor knockout mouse [42]. In contrast, blockage of leptin signaling in a murine model inhibited bone mass accrual by up-regulating sympathetic activity, independent of any change in appetite or energy expenditure [43]. Serotonin
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has also recently been reported to be a key regulator of bone metabolism through leptin [44-46]. Inhibition of peripheral serotonin synthesis reduces obesity and metabolic dysfunction, and increases REE [47]. Moreover, leptin and serotonin have been reported
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to be dysregulated in diabetes [48-51]. Thus bone, glucose, and energy metabolism must be influenced by each other in the peripheral and neuro-central systems. Furthermore,
REE is a parameter that is influenced directly by all of these factors in this energy-bone
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cascade, and will exhibit values that reflect these factors’ function in this cascade.
In the present study, we demonstrated that the patients’ REE was correlated
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with the ratio of a bone collagen formation marker (P1NP) to a bone collagen resorption marker (CTX-1), which indicates active bone turnover. Our data are the first to demonstrate the direct correlation of bone metabolism with energy expenditure in patients with T2D. In addition, REE was positively correlated with BMI, initial HbA1c
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levels, and HbA1c levels at 6 months. Finally, CTX-1 and P1NP were positively correlated with each other, and both factors were negatively correlated with average HbA1c in the previous 6 months. Taken together, our findings indicate that bone
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turnover is correlated with the patients’ basal metabolic rate, and ultimately with their blood glucose control. These findings also imply that poor glycemic control may lead
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to low bone turnover in patients with well-controlled T2D. Although CTX-1 is negatively correlated with P1NP, and tends to be negatively
correlated with HbA1c [52], P1NP decreases in postmenopausal women with T2D [53]. Interestingly, low bone turnover and bone deficiency in T2D is thought to be caused by elevated glycemic levels, based on a study of bone metabolic markers [54]. In addition, although postmenopausal women exhibit high levels of bone resorption markers [15,
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55], given their high bone turnover, it has recently been reported that suppression of bone turnover increases the fracture risk in postmenopausal women with T2D [17, 54], which is consistent with our results. Furthermore, although bisphosphonate (an inhibitor
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of bone resorption) is typically used to treat osteoporosis, it is less effective for patients with T2D [56]. Moreover, bone formation is reduced by bisphosphonates with bone
resorption in a mouse model of diabetes [57]. Thus, the association of CTX-1 (a bone
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resorption factor) with glycemic control and low bone turnover at the pretreatment stage, as reported in the present study, may explain why bisphosphonate is ineffective in
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patients with osteoporosis and poorly controlled diabetes. In this context, good glycemic control and increased basal metabolism are desirable for patients with postmenopausal diabetes. However, their clinical management is complex, as strict dietary regimens may lead to decreased fat mass, BMD, and REE [58-61], and tight glycemic control with
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aggressive diabetes drug therapies may result in increased fat mass and BMD [62-64] without any increase in REE [65, 66], thereby increasing the risk of developing atherosclerosis [67-69]. Although insulin therapy may improve bone metabolism in
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patients with insulin-requiring diabetes [70], early initiation of insulin therapy decreases patients’ REE [68, 71, 72]. Both fat mass and REE were correlated with the ratio of
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bone formation to bone resorption marker, whereas fat mass was correlated with BMD and was not correlated with HbA1c. However, REE was correlated with HbA1c and was not correlated with BMD. Thus, bone metabolic markers, which indicate bone quality, are considered better markers of osteoporosis, compared to BMD, especially in T2D [15, 16]. Nutritional and bone turnover markers is useful predictors of bone loss in elderly women [73], although these bone metabolic markers are likely altered by drug therapies
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for osteoporosis [74, 75]. Therefore, the levels of REE and RQ are likely useful for evaluating bone metabolism and therapeutic strategies for controlling blood glucose levels in patients with diabetes.
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A correlation between sensory nerve function and bone volume has been reported in a mouse model and in elderly humans [76, 77], and the relationship between diabetic polyneuropathy and non-traumatic fractures has also been reported
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[78]. However, MCV and SCV (as indicators of diabetic neuropathy) were not
correlated with bone turnover markers in the present study. Thus, it appears that the
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correlation between peripheral nerve function and bone turnover may be weak in elderly female patients with diabetes, and this theory is consistent with the findings of a previous report [79]. Alternatively, our screening tests for sensory and autonomic nervous function may not be suitable for evaluating the subtle changes in nervous
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function that occur in diabetic neuropathy, as these tests are commonly used for the general evaluation of diabetic neuropathy. However, the patients’ MCV in the ulnar and peroneal nerves and SCV in the ulnar and sural nerves were below the normal
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ranges for Japanese persons of a similar age, which suggests that they had diabetic neuropathy [27, 80].
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Patients with diabetes are known to have low vitamin D levels, and the
frequency of hypovitaminosis D in our subjects was similar to that in a previous Japanese study [81]. Furthermore, we found that 25[OH]D levels were correlated with RQ, which is an index of internal respiration that indicates the nutrient source of energy [82]. The relationship between 25[OH]D and RQ may be complex, although several speculations can be considered. For example, a low RQ indicates reduced lipid
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oxidation [83] in patients with low vitamin D, although 25[OH]D levels are positively associated with lipid metabolism [84, 85]. In addition, patients with T2D and low vitamin D levels have significantly lower C-peptide levels (compared to patients with
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normal vitamin D levels), and these patients require insulin more often [86]. Thus, the pathogenesis of diabetes reflects decreased insulin secretion and sensitivity. Therefore,
our results indicate that RQ (internal respiration) is correlated with serum vitamin D
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levels, which can predict fracture, and that these levels are also correlated with BMI, insulin sensitivity, and pancreatic β cell dysfunction in the prediabetic state [87].
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The association of REE with bone metabolism in this study may indicate that these factors are regulated via neurotransmitters and adipocyte hormones, although further studies are needed to evaluate this hypothesis.
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Conflict of Interest
The authors have no conflicts of interest to declare.
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Acknowledgements
We thank Prof. T. Matsumoto for his advice, Ms. M. Tomioka for her assistance with the
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data analyses, and Mr. M. Tomoda for de-identifying the patients’ data. This study was partially supported by Grant-in-Aids (22510214) for Scientific Research from MEXT, the National Center for Global Health and Medicine (21A114) from the MHLW of Japan (N.I.), and a research grant from Lilly (M.O.).
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Association of vitamin D with insulin resistance and beta-cell dysfunction in subjects at risk for type 2 diabetes. Diabetes Care. 2010;33:1379-81. [87] Kayaniyil S, Vieth R, Harris SB, Retnakaran R, Knight JA, Gerstein HC, et al.
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Association of 25(OH)D and PTH with metabolic syndrome and its traditional and
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nontraditional components. J Clin Endocrinol Metab. 2011;96:168-75.
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Figure Legend
Fig. 1. Correlation diagram of the relationship between basal metabolism and bone
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metabolism. A: Correlation between serum carboxy-terminal collagen crosslinks-1 (CTX-1) levels and mean glycated hemoglobin (HbA1c) levels for the previous 6 months (y = –0.05x +
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0.71: r2 = 0.140). B: Correlation between resting energy expenditure (REE) and the procollagen type 1 N-terminal propeptide (P1NP) to CTX-1 ratio (y = 0.08x + 35.58: r2
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30.18x + 5.06: r2 = 0.150).
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= 0.144). C: Correlation between serum vitamin D levels and respiratory quotient (y =
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Table 1. Patient characteristics (n = 46) 65.0 ± 5.9 24.5 ± 3.6 5 / 41 11 / 35
Ca (mg/dL) P (mg/dL) 25OHD (ng/mL) iPTH (pg/mL) Serum CTX-1 (ng/mL) [normal]
9.2 ± 0.3 3.6 ± 0.4 21.8 ± 7.7 59.5 ± 17.5
7.6 ± 1.1
Serum P1NP (ng/mL) [normal] BMD (g/cm2)
37.6 ± 11.7 [45.1 (20.3–76.3)]** 0.472 ± 0.0730
Mean HbA1c prior for 6 months (%) Family history of bone fracture (+/-) Past history of bone fracture (atraumatic/traumatic/none) Duration of diabetes Insulin/OHA/diet SU/thiazolidine/ DPPIV-I/biguanide Antihypertensive drug (+/-) Lipid lowering agent (statins)
15 / 31
T-score
–1.76 ± 1.52
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Age BMI Tobacco (+/-) Alcohol (+/-) Serum fasting blood sugar (mmol/L) HbA1c (%)
0.36 ± 0.13 [0.56 ± 0.23]*
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8.3 ± 2.7 7.5 ± 1.1
Respiratory quotient
9 / 1 / 36 15.8 ± 8.7 13/30/3
REE / estimated normal average (Cal)
940.5 ± 155.7 / 1,176.7 ± 98.3
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21/1/14/16 24 / 22
0.88 ± 0.098
30 (24) / 16
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MCV ulnar/ Retinopathy (P/S/N) 7 / 10 / 29 peroneal 55.0 ± 4.5 / 46.3 ± 5.0 Urine albumin (+/-) 7 / 39 SCV ulnar/sural 51.5 ± 3.7 / 48.9 ± 7.7 Creatinine (mg/dL) 0.67 ± 0.15 R-R interval (s) 115 ± 5.9 Resting (upright) HDL (mg/dL) 64.8 ± 16.9 systolic BP Triglyceride (mg/dL) 131.7 ± 101.0 (mmHg) 136.3 ± 17.8 (139.0 ± 22.3) Resting (upright) LDL (mg/dL) 123.2 ± 30.2 diastolic BP CPR (ng/mL) 1.6 ± 0.7 (mmHg) 74.6 ± 9.6 (78.4 ± 10.5) Blood pressure values in parentheses indicate the pressure in the upright position. Data for non-diabetic subjects are shown in brackets. *Mean ± standard deviation in normal postmenopausal women. **Mean (5th–95th percentile) in normal postmenopausal women without hormone replacement therapy. BMI: body mass index, Hb1Ac: glycated hemoglobin, OHA: oral hypoglycemic agent, SU: sulfonylurea, DPPIV-I:
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dipeptidyl-peptidase IV inhibitor, HDL: high-density lipoprotein, LDL: low-density lipoprotein, CPR: C-peptide response, Ca: calcium, P: phosphate, 25OHD: 25-hydroxyvitamin D, iPTH: intact parathyroid hormone, CTX-1: carboxy-terminal
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collagen crosslinks-1, P1NP: procollagen type 1 N-terminal propeptide, BMD: bone mass density, REE: resting energy expenditure, MCV: motor nerve velocity, SCV:
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sensory nerve velocity, BP: blood pressure.
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Table 2. Characteristics of patients with low and normal levels of vitamin D
7.5 ± 1.2 0.66 ± 0.1 65.4 ± 17.4 144.6 ± 130.5 122.8 ± 29.8 9.2 ± 0.3 3.6 ± 0.3 1.39 ± 0.6* 0.33 ± 0.11 33.3 ± 7.3 0.474 ± 0.08
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Patients with 25OHD >20 ng/mL, n = 23 65.1 ± 6.9 24.3 ± 3.0 34.2 ± 5.5 37.3 ± 3.2 932.7 ± 168.9
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8.1 ± 2.9 7.6 ± 1.3
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Age BMI Percent body fat Lean body mass (kg) REE (Cal) Fasting blood glucose (mmol/L) HbA1c (%) Average HbA1c over the previous 6 months (%) Serum creatinine (mg/dL) HDL (mg/dL) Triglyceride (mg/dL) LDL (mg/dL) Ca (mg/dL) P (mg/dL) CPR (ng/mL) CTX-1 (ng/mL) PINP BMD total Habit of exercising >30 min twice per week
Patients with 25OHD ≤20 ng/mL, n = 23 64.9 ± 4.9 24.7 ± 4.1 34.9 ± 6.0 37.1 ± 3.0 948.4 ± 144.7
8.4 ± 2.5 7.7 ± 1.0 7.7 ± 1.0 0.66 ± 0.1 62.2 ± 16.3 124.3 ± 56.9 123.2 ± 31.7 9.2 ± 0.4 3.6 ± 0.4 1.98 ± 0.6* 0.40 ±0.14 41.8 ± 13.7 0.471 ± 0.06
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Characteristics
9*
17 *
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*p < 0.05.
BMI: body mass index, REE: resting energy expenditure, Hb1Ac: glycated hemoglobin, HDL: high-density lipoprotein, LDL: low-density lipoprotein, Ca: calcium, P: phosphate, CPR:C peptide, CTX-1: carboxy-terminal collagen crosslinks-1, P1NP: procollagen type 1 N-terminal propeptide, BMD: bone mass density, 25OHD: 25-hydroxyvitamin D.
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Table 3. The results of the regression analysis for basal metabolism and bone metabolism, which was performed for each pair that exhibited a significant correlation. CTX-1
P1NP
P1NP/CTX-1 ratio
p
R2
β
p
R2
β
p
R2
p
R2
Age
0.15
0.34
0.00
–0.03
0.82
–0.02
–0.22
0.15
0.03
–0.43
0.00*
0.16
BMI
–0.26
0.08
0.05
0.02
0.89
–0.02
0.46
0.00*
0.19
0.34
0.021*
0.10
Fat mass
–0.30
0.04*
0.07
–0.01
0.96
–0.02
0.47
0.00*
0.20
0.33
0.027*
0.09
% fat mass
–0.22
0.14
0.03
0.00
1.00
–0.02
0.38
0.01*
0.12
0.25
0.10
0.04
REE
–0.18
0.22
0.01
0.07
0.64
–0.02
0.38
0.01*
0.13
0.24
0.11
0.04
RER
–0.10
0.53
–0.01
0.20
Albumin
0.21
0.16
0.02
0.12
ALP
0.10
0.53
–0.01
HbA1c
–0.40
0.01*
0.14
Mean HbA1c
–0.39
0.01*
0.13
-0.27
iPTH
0.17
0.26
0.01
25OHD
0.18
0.22
0.01
β
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β
BMD
0.02
0.34
0.02*
0.09
0.04
0.78
–0.02
0.44
–0.01
–0.17
0.25
0.01
–0.33
0.02*
0.09
0.37
0.01*
0.12
0.42
0.00*
0.16
0.33
0.02*
0.09
–0.41
0.01*
0.15
0.19
0.20
0.02
0.23
0.13
0.03
0.07
0.05
0.26
0.08
0.05
0.24
0.12
0.03
–0.17
0.25
0.01
–0.33
0.03*
0.09
0.10
0.52
–0.01
0.32
0.03
0.08
0.05
0.76
–0.02
–0.18
0.22
0.01
EP
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0.18
BMI: body mass index, REE: resting energy expenditure, RER: resting exchange ratio,
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ALP: alkaline phosphatase, Hb1Ac: glycated hemoglobin, iPTH: intact parathyroid hormone, 25OHD: 25-hydroxyvitamin D. All coefficients for determination data were adjusted (r2).
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Table 4. Independent effect of objectively measured mean HbA1c levels during the previous 6 months on CTX-1 and P1NP, and the effect of resting energy expenditure on the P1NP/CTX-1 ratio.
β
p
P1NP R2
β
p
P1NP/CTX-1 ratio R2
Model 1
REE
–0.04
0.02* 0.11 0.81
Model 2 Age
0.00
–0.35 0.03* 0.07
0.98 0.09
–0.37
0.03*
REE
–0.24
0.81
0.19
–0.13
0.40 0.06
–0.39 0.02* 0.19
0.23
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Mean HbA1c
0.21
0.13
p
R2
0.40 0.12
SC
–0.37
0.33 0.04*
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Mean HbA1c
β
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CTX-1
–0.10 0.10
0.53 0.11
EP
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0.30 0.04
0.17
0.29
–0.38 0.02* 0.14 0.78
0.32 0.05*
0.12
0.43
carboxy-terminal collagen crosslinks-1, P1NP: procollagen type 1 N-terminal
R2
0.17
0.05
Hb1Ac: glycated hemoglobin, REE: resting energy expenditure, CTX-1:
5
p
0.53
All models included 46 participants.
propeptide, BMD: bone mass density.
β
BMD
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0.8
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0.4 0.2
6.0 8.0 10.0 12.0 Mean HbA1c : 6months (%)
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250 200 150 100 50
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P1NP/CTX-1 ratio
B
0.6
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Serum CTX-1 level (mg/dl)
A
EP 50
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C
Serum 25(OH)D (ng/ml)
400 600 800 1000 1200 1400 Resting energy expenditure (Cal)
40 30 20
10 0 0.7
0.8 0.9 1.0 1.1 Respiratory quotient
1.2
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Highlights
Basal metabolic rate and bone metabolism were evaluated in postmenoposal patients with type 2 diabetes Bone metabolism was associated with resting energy expenditure (REE).
Serum vitamin D was associated with respiratory quotient (RQ) and insulin
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requirement.