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Original Studies |
Department of Internal Medicine, Division of Endocrinology and Metabolism, and the Department of Medical Biometry (K.M.), University of Heidelberg, Heidelberg, Germany
Address all correspondence and requests for reprints to: Markus J. Seibel, M.D., Department of Internal Medicine, Division of Endocrinology and Metabolism, Bergheimerstrasse 58, D-69115 Heidelberg, Germany. E-mail: Markus-Seibel{at}krzmail.krz.uni-heidelberg.de
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Abstract |
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To evaluate the contributions of anthropometric, life style, and environmental variables on bone turnover, biochemical markers of bone metabolism were determined in a population-based sample of 580 adults, aged 50–81 yr (297 men and 283 women). Subjects were recruited during 14 consecutive months within the framework of the European Vertebral Osteoporosis Study. Serum total and bone-specific alkaline phosphatase (S-BAP), serum C-terminal propeptide of type I collagen, and serum osteocalcin (S-OC) were measured as bone formation markers. Urinary total pyridinoline and deoxypyridinoline were included as bone resorption indexes.
In females, serum levels of 25-hydroxyvitamin D3 were significantly higher (P < 0.01) in summer (May–September) than in winter (October–April), whereas no significant differences were found in males. In both sexes, no seasonal changes were seen in serum PTH. In males, serum total alkaline phosphatase (P < 0.01), S-BAP (P < 0.001), and S-OC (P < 0.05) were significantly higher in winter than in summer. During the same period, females had higher values of S-BAP (P < 0.05), S-OC (P < 0.01), and urinary pyridinoline and deoxypyridinoline (P < 0.001, respectively). Univariate analyses of the effects of life style habits on markers of bone metabolism revealed that in females, regular alcohol consumption and current smoking led to a suppression of markers of bone turnover, whereas in males, only alcohol intake was associated with such changes. In contrast, physical activity was associated with higher levels of bone formation markers and reduced levels of bone resorption indexes in both sexes. As shown by multivariate regression analyses, seasonal variations accounted for more of the variability in most biomarkers (up to 12%) than any of the other anthropometric or life style factors except age. This effect may be attributed to subclinical vitamin D deficiency during the winter period, which is common in countries of the northern hemisphere. We conclude that seasonal variation contributes significantly to the biological variability of bone turnover and needs consideration when interpreting the results of bone marker measurements.
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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However, all of these dynamic measures are influenced by a number of variables that may hamper their interpretation and, hence, limit their routine clinical application. Thus, analytical (i.e. intra- and interassay) variability in the various assays has been reported to contribute to up to 15% of the variation in bone marker measurements (8). Moreover, both serum osteocalcin (9) and urinary cross-links (10) exhibit diurnal rhythms with amplitudes of up to 30%. Most biomarkers reveal a more or less linear increase with age (3, 11, 12), and in women, menopausal status as well as hormone replacement therapy are known to exert significant effects on bone turnover (12, 13, 14). Furthermore, life style habits such as physical activity, alcohol consumption, or smoking are known to affect bone mineral density (15, 16, 17), and it is likely that these factors also influence bone metabolism.
A number of investigations have shown that the hormonal regulation of calcium metabolism exhibits a certain degree of seasonal variation. Both, serum levels of 25-hydroxyvitamin D3 (25OHD3) (18, 19, 20, 21) and urinary calcium excretion (22, 23) are elevated in late summer and decreased in winter, whereas PTH levels tend to increase during winter (24, 25, 26). Less attention, however, has been paid to the annual variations in biochemical parameters of bone turnover.
Based on a population survey carried out within the framework of the European Vertebral Osteoporosis Study (EVOS), the aim of the present study was 1) to evaluate whether bone turnover, as determined by biochemical markers, varies by season; and 2) to determine the relative contributions of these seasonal changes to the overall variability in biochemical markers.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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As part of the European Vertebral Osteoporosis Study (EVOS), an age- and sex-stratified random sample of a total of 1072 men and women, aged 50–81 yr, was recruited in a southwestern German community by written invitation between December 1991 and March 1993. A total of 996 persons were contactable, and of these, 297 men and 283 women agreed to participate in the study (58%). Among female participants, 5 women were premenopausal and therefore excluded from the statistical analysis. Likewise, subjects receiving current hormonal treatment (sex hormones, T4, or glucocorticosteroids; n = 68) and patients with a history of bilateral ovariectomy (n = 20), breast cancer (n = 4), or prostate cancer (n = 2) were excluded from the study. The present investigation is, therefore, based on data available from 276 men and 200 women.
A standardized interview was performed to obtain information on medical and reproductive history as well as current and past medication use. A questionnaire (27) was applied to evaluate life style habits, including alcohol intake, smoking, and physical activity. Weight and height were recorded while the subjects were dressed in light clothing without shoes. Obesity was estimated by calculating the body mass index (BMI; kilograms per m2). Spot urine and blood samples were collected between the hours of 0800–1200 h after participants had eaten their usual breakfasts. All participants had routine laboratory evaluations, including the detection of serum creatinine (Cr) and total calcium (both by standardized colorimetric assays). These measurements were performed immediately after phlebotomy. Moreover, serum 25OHD3 (by RIA; Incstar Corp., Stillwater, MA) and serum PTH (S-PTH; by luminescence immunoassay; Ciba Corning Diagnostics, Walpole, MA) were measured by immunoassays. The applied assay for the quantification of 25OHD detects the serum levels of both 25OHD2 and 25OHD3. However, in contrast to the United States, there is no dietary vitamin D fortification in Germany. Therefore, the levels of 25OHD2 detected by the assay are insignificant and contribute less than 1% to the total circulating vitamin D pool. Serum calcium was corrected for whole protein content according to the method of Husdan et al. (28).
The study protocol was approved by the ethics committee of the University of Heidelberg, and written consent was obtained from all participants before inclusion in the study.
Laboratory analyses
To minimize laboratory imprecision, an accurate storage procedure was applied. Blood samples were processed within 3 h after phlebotomy, centrifuged at 1500 x g for 10 min, and stored as aliquots at -80 C until analysis. Urine specimens were protected from light exposure and stored within 3 h of collection at -30 C until analysis. These steps assure that biochemical measurements are not affected by storage (29). All laboratory analyses were performed in random fashion within a total analytical period of 1 month after completion of patient recruitment. For immunoassay analyses, the same charge of the respective assay was used for all measurements.
Biochemical measurements
Serum total alkaline phosphatase (S-TAP) was measured according to the optimized standard method of the Deutsche Gesellschaft für Klinische Chemie (30), using an automated colorimetric assay (BM/Hitachi System 704 analyzer, Boehringer Mannheim, Mannheim, Germany) and para-nitrophenyl phosphate as a substrate. Intra- and interassay coefficients of variation were less than 5%, with a normal range of 60–170 U/L in both sexes. A solid phase, two-site immunoradiometric assay (Tandem-Ostase, Hybritech Europe, Lüttich, Belgium) was applied to determine the serum concentration of S-BAP (1). Intra- and interassay variations ranged between 3.7–6.7% and 7.0–8.1%, respectively. Normal values are 11.6 ± 4.1 µg/L for premenopausal females and 12.4 ± 4.4 µg/L for males. The level of serum carboxyl-terminal propeptide of type I procollagen (S-PICP) was measured by a RIA (Orion Diagnostica, Finland) as described earlier (3). Intra- and interassay coefficients of variation ranged from 2.1–3.2% and 4.0–6.6%, respectively, with reference values of 50–170 µg/L in women and 38–202 µg/L in men. Human intact S-OC was detected with a competitive luminescence immunoassay (LUMItest Osteocalcin, Brahms Diagnostica, Berlin, Germany), using a monoclonal antibody against the 37–49 sequence. Coefficients of variation were less than 10% for intraassay variance and less than 15% for interassay variance. Reference values range between 4–12 µg/L in healthy males and premenopausal females.
Urinary concentrations of total PYD (U-PYD) and DPD (U-DPD) were measured by reverse phase, ion-paired, high performance liquid chromatography (HPLC) as described previously (6). The total cross-link content was determined after complete acid hydrolysis of urine (6 mol/L HCl, 16 h, 107 C). Using a standard isolated from sheep bone (provided by Dr. S. P. Robins, Aberdeen, Scotland), the overall reproducibility of the assay, including the cellulose fractionation step, ranged between 3–12%. Urinary Cr was measured immediately after sample collection in a Beckman II Cr analyzer (Palo Alto, CA) employing the Jaffe-Rate technique. Concentrations of pyridinium cross-links were expressed relative to those of Cr (nmol/mmol Cr). Reference values ranged between 5.9–72.6 nmol/mmol Cr for U-PYD and 2.6–18.5 nmol/mmol Cr for U-DPD in men and premenopausal women.
Statistical analysis
Descriptive statistics and simple and partial Pearson
correlations were obtained using the Statistical Analysis System
software package (SAS Institute, Cary, NC). Univariate analyses are
presented as the mean ± SD or as z-scores. The
z-scores are expressed as the SD of the mean of the total
male or female population according to the following equation:
z = (x - mean)/SD. The
significance of group differences was tested using Student’s
t test for parametric data. To correct for multiple testing,
the significance level was adjusted according to the Bonferroni
correction (significance level/number of tests). To test for seasonal
differences, participants were subdivided into groups including male or
female subjects recruited from October 1 to April 30 (i.e.
winter) and from May 1 to September 30 (i.e. summer). In the
next step, a stochastic function was applied to model the seasonal
rhythm of the studied parameters. This analysis was based on data
obtained from 456 participants seen between January 1, 1992 and
December 31, 1992. Due to differences in the number and characteristics
of the subjects when using monthly intervals, representative and
comparable intervals of a randomly distributed data set were computed
according to the method of Sturges (31). The following equation was
used: 
= (Dmax - Dmin)/(1 + 3.322 x
lg n), in which is the interval width, Dmax is the end
of the analysis period (December 31, 1992), Dmin is the
beginning of the analysis period (January 1, 1992), and n is the number
of data points. The equation 1 + 3.322 x lg n gives the number of
intervals. To eliminate extreme values within intervals, r statistics
according to Dixon were performed (32). After computing mean values
(given as the percent change from the annual mean of each marker of
bone turnover) and weighing for the number of data points in each
interval, graphs were constructed using least square curve fitting by
means of a polynomial regression (33).
Life style variables were classified into the following groups: 1) alcohol consumption: group I, no alcohol (strict nonuser); group II, two or fewer times per week (moderate user); and group III, more than two times per week (regular user); 2) smoking habits: group I, no nicotin (nonsmoker); group II, previous nicotin use (past smoker); and group III, current nicotin use (current smoker); 3) physical activity: group I, no regular exercise; group II, 2 h or less of exercise/week; group III, more than 2 h of exercise/week. Fisher’s exact test was used to test for differences in the distribution of participants within categories.
Sex-specific stepwise multivariate regression analyses were performed to evaluate the contributions of seasonal variations, age, BMI, life style factors, and, in women, menopausal status to the overall variability in bone metabolic markers. The inclusion of anthropometric and life style variables in the final model was based on the results of a stepwise regression search, in which the probability for entry into the final equation was set at P = 0.15. The statistical significance of a predictor variable was defined as P < 0.05 based on two-tailed tests.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The anthropometric, biochemical, and life style characteristics of
the study population are summarized in Table 1
. As expected, males were significantly
heavier and taller than females (P < 0.001), but there
was no significant difference with respect to mean BMIs. Anthropometric
data did not differ between the groups when stratified by season
(winter vs. summer; Table 1).
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The distribution of participants in each life style category (see
Materials and Methods) did not differ significantly between
seasons, except for physical activity in men, who were less active in
winter than in summer. However, women reported significantly less
physical activity than men, stated less alcohol intake, and were
predominantly nonsmokers. Notably, there was an approximately equal
number of nondrinkers in the groups of men and women (Table 1
).
Influence of season on biochemical markers of bone turnover (univariate analysis)
Results are summarized in Table 2.
In females, all indexes of bone turnover were higher in winter than in
summer. This difference was statistically significant for S-BAP
(P < 0.05), S-OC (P < 0.01), U-PYD
(P < 0.001), and U-DPD (P < 0.001).
In males, mean serum levels of all bone formation markers (except
S-PICP) were significantly higher in winter than in summer (Table 2).
Less pronounced results were found for the bone resorption indexes.
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, both S-BAP and U-DPD followed an
annual rhythm, with highest values during the winter months and lowest
mean levels in summer. All other bone metabolic markers revealed
similar, but somewhat less pronounced, fluctuations. Compared to the
changes in markers of bone metabolism, S-PTH values followed a similar
pattern (in females only), whereas 25OHD levels revealed an inverse
function, with highest levels in late summer and a nadir in winter in
both sexes (Fig. 2). However, changes in
both S-PTH and 25OHD occurred later than those observed in the bone
biomarkers.
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Influence of alcohol intake on biochemical markers of bone metabolism (univariate analysis)
In females, lowest serum and urinary levels of all biomarkers were found in subjects with regular alcohol consumption (more than twice per week). Results were statistically significant for S-TAP and S-OC (regular vs. moderate alcohol consumption, P < 0.05). However, highest values were seen in the group of strict nonusers, except for S-OC (data not shown).
Men reporting alcohol consumption of more than two times per week (regular user) showed significantly reduced serum levels of S-TAP, S-BAP, and S-PICP (P < 0.05) compared to men reporting moderate alcohol consumption (two times or less per week). In contrast, no differences between these groups could be observed for the bone resorption markers. Strict nonusers had similar mean values for all bone turnover indexes than regular users (data not shown). Due to the uncertainty in the detection of former alcohol addicts, strict nonusers (24 men and 20 woman) were excluded from the multivariate analyses (see below).
Influence of smoking habits on biochemical markers of bone metabolism (univariate analysis)
In females, current smoking was associated with significantly reduced levels of S-BAP, S-PICP, U-PYD, and U-DPD (P < 0.05, respectively) compared to nonsmokers. Females with a history of previous smoking had similar mean values for all bone markers than females who had never smoked. In males, no such pattern was observed (data not shown).
Influence of physical activity on biochemical markers of bone metabolism (univariate analysis)
In males, U-PYD and U-DPD as well as S-OC (as a marker of
bone formation) tended to be higher in groups reporting little or no
physical activity. These results were statistically significant for
S-OC (no regular exercise vs. >2 h/week, P
< 0.05). In contrast, S-BAP and S-PICP were elevated in the group
reporting regular exercise of more than 2 h/week; however, this
difference was not statistically significant. Similar results were
found in females, with significantly increased mean values of U-PYD and
U-DPD in the group reporting no physical activity compared to those in
the group performing moderate exercise (
2 h/week; P
< 0.05 and P < 0.01, respectively). However, no
significant changes could be established for the bone formation markers
(data not shown).
Correlations between laboratory parameters
In both sexes, positive correlations among all biochemical markers of bone metabolism could be established. Strongest correlations were found for S-TAP vs. S-BAP (r = 0.66; P < 0.001) and for U-PYD vs. U-DPD (r = 0.86; P < 0.001) in females. Most metabolic bone markers showed a weak, but consistently inverse, correlation with serum levels of 25OHD3 (except for urinary cross-links in males), ranging from r = -0.06 (not significant, U-PYD vs. 25OHD in females) to r = -0.18 (not significant, S-BAP vs. 25OHD in females). In contrast, S-PTH correlated positively with all biomarkers of bone turnover, ranging from r = 0.08 (not significant, S-PICP vs. S-PTH in males) to r = 0.38 (P < 0.001, S-OC vs. S-PTH in females). When the dataset was reanalyzed according to season (i.e. winter vs. summer), males showed stronger correlations between S-PTH and most biomarkers in summer. In contrast, the inverse correlations between 25OHD and most indexes of bone turnover remained essentially unchanged.
Multivariate regression analyses
Results of the stepwise multivariate regression analyses are
presented in Table 3 (males) and
4 (females). The inclusion of
anthropometric and life style variables in the final model was based on
the results of a stepwise regression search, in which the probability
for entry into the final equation was set at P = 0.15.
Presented in the table are only those variables that reached
statistical significance (P < 0.05). In males, bone
resorption markers were significantly influenced by age (7.6% for
U-PYD and 2.7% for U-DPD), but not by season or any of the life style
factors. Seasonal effects, i.e. the comparison between
winter (October 1 to April 30) and summer (May 1 to September 30)
explained 12.0% of the variability in S-BAP and 2.0% of the
variability in S-OC as markers of bone formation. Alcohol consumption
(moderate vs. regular) explained 3.5% of the variability in
S-BAP. In females, seasonal differences were significant predictor
variables for all bone turnover markers except S-TAP, explaining up to
11.7% of the variability. For S-TAP, U-PYD, and U-DPD, age was a
significant predictor variable (up to 9.7%). Current smoking explained
2.9% of the variability in S-BAP, and BMI explained 6.5% of the
variability in S-OC.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Our results clearly demonstrate that bone turnover, as assessed by specific biochemical markers, is accelerated during winter, with more pronounced findings in females. Furthermore, the seasonal increase in bone metabolism appears to coincide with a significant reduction in serum values of 25OHD3 in females. Also, univariate analyses of life style habits revealed that regular alcohol consumption (in both sexes) and current smoking (in females only) are associated with reduced levels of bone turnover. In contrast, physical activity in both sexes seems to be associated with higher levels of bone formation markers and reduced levels of bone resorption markers. However, for most of the biomarkers included in the present study, seasonal variations accounted for more of the observed variability than any of the other factors except age.
To our knowledge, very few studies exist on seasonal variations in bone turnover, and to date, there is still some controversy over this issue (34, 35, 36, 37). In good keeping with our findings, Thomsen et al. (37) found a significant seasonal variation in serum osteocalcin, with an annual amplitude of 23%. In this report, peak values were seen in February, and the nadir occurred in July. Although in our study the seasonal variability was most pronounced for S-BAP, an almost identical rhythm could be established for S-OC. In contrast, Overgaard and co-workers, using serum total alkaline phosphatase and urinary hydroxyproline as indexes of bone metabolism, reported a lack of seasonal variation (20). However, both total alkaline phosphatase and urinary hydroxyproline are less specific indexes of bone turnover. Our results seem to confirm this lack in specificity, in that seasonal changes in S-TAP were marginal, whereas levels of S-BAP varied greatly by season. This observation supports the idea that the bone-specific isoenzyme is a more reliable marker of bone formation than S-TAP (1).
As reported previously (18, 19, 20, 21), serum levels of 25OHD3 are significantly lower in winter than in summer, and a significant number of participants in the present study had subnormal serum 25OHD3 values (<10 µg/L) in winter (23.6%). The observed acceleration of bone turnover during the winter period may, therefore, at least in part be due to subclinical vitamin D deficiency and secondary hyperparathyroidism (26, 38). However, the corresponding increase in S-PTH was not statistically significant, and correlations among 25OHD, S-PTH, and biochemical parameters of bone metabolism were weak in both genders. Moreover, the winter-related increase in some biomarkers seemed to chronologically precede the changes in serum levels of 25OHD3 and S-PTH. These observations and the overall less pronounced results seen in males clearly demonstrate the limits of a cross-sectional study design and the necessity for a standardized longitudinal approach. Also, other factors, such as reduced mobility in winter, may account for part of the observed seasonal variations in bone turnover. Lower physical outdoor activities may lead to a net increase in bone resorption, as proposed by Krølner (17) and again suggested by the present data. However, as shown by multivariate analyses, our study did not support the idea that physical activity contributes independently to the variability in bone metabolism.
Analytical assay precision may explain up to 15% of the variability of marker measurements, which clearly is in the range of the observed seasonal changes. In addition, day to day and other biological factors may contribute to numerical variations in marker levels (8). However, almost all of the parameters employed in this study followed a similar seasonal pattern, and analyses of samples were performed randomly within a total analytical period of 4 weeks. If analytical precision would be the major determinant of the observed variability, a concordant pattern such as the one found in this study should be highly unlikely. In fact, the observed seasonal rhythms may have well been attenuated by the technical variability of the assays.
We and others have previously shown that most biomarkers of bone turnover change with age (3, 11, 12). Multivariate analyses of the present data revealed a significant and independent contribution of age on serum and urinary levels of most of the applied markers. However, sex-specific differences are obvious, as shown in Results.
In both sexes, regular alcohol consumption was associated with
decreased bone turnover. Results were more pronounced for markers of
bone formation. This is in keeping with previous reports (15),
describing an uncoupling of bone metabolism in alcohol users, with a
greater decrease in bone formation than in bone resorption.
Interestingly, S-TAP levels were significantly decreased in regular
alcohol users in the absence of liver enzyme (
glutamyl
transpeptidase and glutamate oxalacetate transaminase) elevation in
both sexes. These findings indicate that the suppression of bone
turnover, rather then liver toxicity, may be the major determinant of
serum S-TAP levels in these subjects. The small effect of alcohol
intake on bone metabolism, as evident from the applied multivariate
regression model, may be due in part to the epidemiological study
design.
Regular exercise led to changes in bone turnover compatible with decreased bone resorption and increased bone formation, as shown by univariate analyses. These findings are in keeping with a number of recent reports indicating a protective effect of physical activity on the maintenance of bone mass (16, 17). Furthermore, Barengolts et al. (39) showed that in 9-month-old intact and ovariectomized rats, regular exercise resulted in the formation of fewer bone-remodeling sites but in a higher activity of individual osteoblasts. These results may explain why in our study, S-BAP (as a specific cellular index of osteoblast activity) was elevated in the physically active groups, whereas in contrast, serum levels of OC (as a matrix protein) were decreased.
In summary, biochemical and hormonal markers of bone metabolism vary significantly by season, suggesting an acceleration of bone turnover and possible subclinical vitamin D deficiency in winter. As high bone turnover has been shown to be associated with rapid bone loss (40), it is conceivable that repeated seasonal acceleration of bone metabolism may over the years contribute to the development of low bone mass. If this is the case, a sufficient vitamin D supplementation in winter would be of benefit for the maintenance of bone mass in northern populations (25). Our observations, therefore, may add to the increasing evidence concerning the role of vitamin D in the prevention of the development of osteoporosis (24, 25, 26). In any case, seasonal variations need to be considered when interpreting laboratory measurements of biochemical markers of bone metabolism.
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Acknowledgments |
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Footnotes |
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Received April 30, 1997.
Revised September 10, 1997.
Accepted September 17, 1997.
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