J Bone Miner Metab (2005) 23:157–166 DOI 10.1007/s00774-004-0555-6
© Springer-Verlag Tokyo 2005
ORIGINAL ARTICLE Eirini Babaroutsi · Faidon Magkos · Yannis Manios Labros S. Sidossis
Body mass index, calcium intake, and physical activity affect calcaneal ultrasound in healthy Greek males in an age-dependent and parameter-specific manner
Received: May 24, 2004 / Accepted: September 9, 2004
Abstract Quantitative ultrasound (QUS) is a peripheral bone densitometry technique that is rapidly gaining in popularity for the assessment of skeletal status. This study was carried out to examine the effect of anthropometric, dietary, physical activity, and other lifestyle factors on QUS parameters in healthy Greek males of various ages, including children (n ⫽ 192), adults (n ⫽ 106), and elderly (n ⫽ 86) subjects. Calcaneal QUS measurements were performed with the Sahara device (Hologic), which measures broadband ultrasound attenuation (BUA) and speed of sound (SOS) through the os calcis. A composite parameter, the quantitative ultrasound index (QUI), and an estimate of heel bone mineral density (eBMD) were also derived. BUA correlated positively with height, weight, and body mass index, as well as waist and hip circumferences (P ⬍ 0.001), but this was not the case for SOS. QUI and eBMD were inconsistently related with anthropometric characteristics. Overweight and obese males had significantly higher BUA than normal-weight subjects (P ⬍ 0.05), but similar SOS, QUI, and eBMD; this held true for all age groups. Boys participating in organized physical activities had significantly higher SOS, QUI, and eBMD than those who did not (P ⬍ 0.05), although BUA was similar in the two groups; no differences according to organized physical activity were detected in adults and the elderly. On the other hand, adult men devoting at least some time to nonorganized physical activities had significantly higher QUS values than their non-exercising peers (P ⬍ 0.05); no such effects, however, were seen in children and the elderly. Adult men with calcium intakes above 800 mg/day had significantly higher SOS, QUI, and eBMD than those consuming less calcium (P ⬍ 0.05), and also tended towards higher BUA (P ⫽ 0.079); no such differences were observed
E. Babaroutsi · F. Magkos · Y. Manios · L.S. Sidossis (*) Laboratory of Nutrition and Clinical Dietetics, Department of Nutrition and Dietetics, Harokopio University, 70 El. Venizelou Avenue, 17671 Athens, Greece Tel. ⫹30-210-9549-154; Fax ⫹30-210-9549-141 e-mail:
[email protected]
among children and elderly men. The effects of physical activity and calcium intake on heel QUS persisted even after controlling for body size. Overall, body weight was the sole significant positive determinant of BUA (β ⫽ 0.373; t ⫽ 6.589; P ⬍ 0.001), explaining approximately 14% of the total variance, while age was the sole significant negative determinant of SOS (β ⫽ ⫺0.198; t ⫽ ⫺3.321; P ⫽ 0.001), albeit explaining only less than 4% of the total variance. In conclusion, body size, dietary calcium intake, and physical activity patterns seem to inconsistently and agedependently influence heel QUS among healthy Greek males in a parameter-specific manner. Key words Quantitative ultrasound · Bone · Weight · Exercise · Calcium · Greece
Introduction Osteoporosis is a major cause of morbidity and mortality around the world [1]. Demographic patterns and secular trends look set to ensure that, globally, the magnitude of the problem will increase substantially over the next several decades. Although significant advances have been made in the treatment of osteoporosis, much attention is still focused on preventive strategies [2]. Osteoporotic fractures are widely recognized as a common and important cause of disability among postmenopausal women. In men, osteoporotic fractures are often underestimated and result in tremendous morbidity and cost [3]. One-seventh of all osteoporotic vertebral compression fractures and one-fourth to one-fifth of all hip fractures occur in men. Moreover, the projected number of hip fractures up to the year 2010 is expected to decrease among women but increase in men [4]. Most studies examining bone mass and density in aging men have shown a gradual decline in bone mineral content at cortical sites (3%–4% loss per decade after the age of 40 years) and a greater rate of loss at cancellous sites (7%–12% loss per decade after the age of 30–35 years) [5], suggesting that male individuals are not “immune” to
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osteoporotic risk, even though rates of bone loss are much lower compared to those observed in females [6]. Poor lifestyle behaviors, however, have been shown to accelerate the reduction of bone density in men [7]. There is considerable evidence to suggest that the risk of osteoporosis and its sequelae may be reduced by maximizing the accrual of bone mass in the first few decades of life [2]. It is well known that bone mineral content increases during childhood, reaching a maximum rate of accumulation around puberty; thereafter, it gradually diminishes with age. Failure to achieve peak bone mass during this critical period cannot be compensated for later in life. Bone mass is determined by a number of factors that influence bone gain during growth and bone loss later in life [8]. The most widely used technique to measure bone mineral density (BMD) is dual-energy X-ray absorptiometry (DXA). However, due to increasing demand for bone densitometry services, alternative technologies have been developed in recent years, such as quantitative ultrasound (QUS) [9]. A large number of studies published in the past decade have examined the utility of QUS and its potential role in the field of osteoporosis. Although its long-term precision may fall short compared to spinal DXA or peripheral quantitative computed tomography [10], QUS is useful in clinical practice for fracture risk discrimination [11] and/or prediction [12,13], and conditionally, for the diagnosis of osteoporosis [14] and the monitoring of skeletal status changes [15]. Additional benefits include low cost, simplicity of use, potential portability, and absence of ionizing radiation exposure [16]. Most research on QUS, however, has been conducted mainly in premenopausal and especially in postmenopausal women, while studies in children and male individuals are scarce [17,18]. At the local level, we have previously reported on lifestyle correlates of heel QUS among females [19], but there is currently no relevant information regarding men. The purpose of the present study, therefore, was to investigate the putative role of several lifestyle factors in determining calcaneal QUS in a healthy population of Greek males, comprising children, adults, and elderly subjects, that is, three different age groups that represent different stages of bone mass development during the life cycle.
Subjects Study population A random sample of 462 individuals, stratified by age, was taken from four randomly selected Greek counties covering from the southern part (Crete) to the northeastern part (Thrace), as well as mainland Greece (Attica and Agrinio). Stratification was also made according to the socioeconomic distribution (urban and rural areas) of the four counties. To be included in the study, subjects had to be free of any health condition known to affect bone metabolism, such as endocrine, renal, or bone diseases, and not to have been
using any kind of medications or dietary supplements (⬎6 months). In this respect, apparently healthy boys (10–15 years), adult men (26–33 years), and elderly men (60–75 years) were recruited. Town blocks and villages throughout the four counties were randomly selected and all individuals satisfying the age criterion of the protocol were included in the study. Identification of eligible participants was made from the respective municipality registries. Subjects were prescreened via telephone and all those who apparently satisfied all the inclusion criteria were visited at their place of residence by a member of the research team. After this initial contact, 78 individuals were excluded or did not agree to participate, yielding a final sample of 384 and a response rate of 83.1%. Prior to enrollment, children’s parents or guardians, as well as the adults and the elderly participants, were fully informed about the objectives and methods of the study and signed a written consent; children provided their verbal assent. Approval to conduct the survey was granted by the Bioethics Committee of Harokopio University, Athens, Greece. Data were collected from October 2001 to September 2002. Anthropometric assessment Subjects were weighed on a digital scale (SECA, Hamburg, Germany), without shoes and in light clothes or underwear. Body weight was recorded to the nearest 0.1 kg. Standing height was measured, without shoes, to the nearest 0.5 cm, using a portable wall-mounted stadiometer (SECA), by the stretch stature method. Body mass index (BMI; kg/m2) was calculated as weight (kg) divided by height (m) squared, and was used for subjects’ classification as normal weight, overweight, or obese. Children were classified according to the previously proposed cutoff points adopted by the International Obesity Task Force [20]. Conventionally accepted BMI cutoff points were used for adults and the elderly, i.e., BMI more than 25 kg/m2 for overweight and more than 30 kg/m2 for obesity [21]. Waist and hip circumferences were also measured, and the waist-to-hip ratio (WHR) was calculated. Dietary assessment Dietary information was collected using a multipass 24-h recall, as described in detail elsewhere [22]. Respondents were asked to recall the type and amount of any food and beverage consumed during the previous day, in chronological order, i.e., from the time they woke up in the morning to the same time the following day. To improve the accuracy of food descriptions, standard household measures (cups, tablespoons, etc.) and picture food models (Dairy Food Council, USA) were used during interviews to define amounts when appropriate. Food intake data were analyzed with Nutritionist V diet analysis software (First DataBank, San Bruno, CA, USA), amended to include traditional Greek recipes. Also, information on processed foods was obtained from food companies and national fast food chains, in order to enter in the Nutritionist V database
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actual weights of processed foods, as well as nutrient content, when available. Physical activity and sedentary activity assessment Assessment of physical activity was made by a 3-day activity interview questionnaire. Respondents reported the time spent on various physical activities during 2 consecutive weekdays and 1 weekend day, either alone or with peers. The questionnaire classified all work, sport, and leisure activities into four categories, on the basis of their average intensity relative to the impact on the cardiovascular system (low to high), and also by subgrouping activities according to their impact on bone mass (low to high) [23]. Questions were designed to determine the frequency (months per year, weeks per month, days per week, as appropriate), duration (hours per session), and intensity (low to high impact on the cardiovascular system) of a given activity. From these data, the amount of time devoted to organized (defined as all activities performed under the supervision of a trainer on a regular weekly time basis, more likely within a sports club setting or fitness center) and nonorganized activities that promote bone mass was estimated [23]. The weekly time devoted to sedentary activities, such as television watching and recreational computer usage (TV/PC) was estimated by asking subjects to report the time spent on such activities during 2 consecutive weekdays and 1 weekend day. The 3 days recalled for physical and sedentary activity assessment were those closest to the day of the interview (e.g., Thursday, Friday, and Sunday if the interview was held on Monday), and were appropriately weighed to derive weekly (7-day) estimates. Other information Alcohol drinking and cigarette smoking habits were determined using a questionnaire probing for the frequency and amount of alcohol (all types) consumed during a week (portions/week) and cigarettes smoked during a day (number/day). Skeletal status assessment Evaluation of skeletal status was based on calcaneal QUS measurements, carried out by the Sahara Clinical Bone Sonometer (Hologic, Waltham, MA, USA) [24]. Subjects had both their heels measured twice, according to the scanning protocol provided by the manufacturer, and results presented in this report are the average of these measurements, to account for possible contralateral differences in QUS parameters [25]. For the Sahara device, sound transducers are mounted coaxially on a motorized caliper that enables direct contact with the heel through elastomer pads and an ultrasonic coupling gel. One transducer acts as a transmitter and the other as a receiver. The positions of the foot, ankle, and leg were fixed by a positioning aid that extended from the foot to mid-shin, in order to avoid possible rotations of the heel that could interfere with the
measurements. Broadband ultrasound attenuation (BUA; dB/MHz) and speed of sound (SOS; m/s) were measured at a fixed region in the mid-calcaneus. BUA and SOS were then combined into a single parameter, the quantitative ultrasound index (QUI; %) or stiffness index. An estimate of heel BMD (eBMD; g/cm2) was also derived. Quality control checking was performed daily, by scanning phantoms provided by the manufacturer prior to testing the subjects. The Sahara bone sonometer had its own phantom with previously known values for SOS and BUA for calibration purposes. The short-term reproducibility of the measurements—expressed as the coefficient of variation (CV)—for BUA, SOS, QUI, and eBMD was 2.68%, 0.40%, 2.71%; and 2.84%, respectively, among boys; 2.67%, 0.36%, 2.69%, and 2.86%, respectively, among adult men; and 2.64%, 0.41%, 2.65%, and 2.94%, respectively, among elderly men. More details on QUS measurements, calculations, and standardization procedures are presented elsewhere [26]. Statistical analysis Differences between the three age groups were examined by one-way analysis of variance (ANOVA). For post-hoc comparison of means, Tukey’s honestly significant difference (HSD) test was used. Univariate relationships between QUS parameters and selected lifestyle factors (continuous variables) were examined for each age group separately by computing Pearson’s linear correlation coefficient (r). A partial correlation coefficient (R) was also calculated to identify significant relationships for the sample as whole, after controlling for the confounding effect of age. Stepwise linear regression analysis was performed to identify the most important predictors of BUA and SOS in this population of healthy males, by constructing a set of two dummy variables defining the three different age groups. The importance of each putative predictor to the overall significance of the regression model and to the total explained variance (R2) was evaluated at Pentry ⫽ 0.05. Subjects were also classified into surrogate categories based on several conventionally accepted factors that may influence bone mass (e.g., normal or abnormal BMI, calcium intake above or below 800 mg/day, exercising or not, etc.). Comparison of QUS variables between these categories (within each age group) was made by Student’s independent t-test. Analysis of covariance (ANCOVA) was used to adjust for potential confounding factors. Statistical significance was set at P ⬍ 0.05. All analyses were carried out using SPSS 10.0.5 for Windows (SPSS, Chicago, IL, USA). Values for results are reported as means ⫾ SD.
Results Descriptive characteristics across age groups The descriptive characteristics of the study participants, by age group, are shown in Table 1. Adult and elderly men
160 Table 1. Descriptive characteristics of the study participants (n ⫽ 384) Children (n ⫽ 192)
Adults (n ⫽ 106)
Elderly (n ⫽ 86)
ANOVA P value
Anthropometric Age (years) Height (cm) Weight (kg) Body mass index (kg/m2) Waist circumference (cm) Hip circumference (cm) Waist-to-hip ratio
11.9 ⫾ 1.1a 152.3 ⫾ 9.9a 48.1 ⫾ 12.0a 20.52 ⫾ 3.69a 72.0 ⫾ 9.8a 83.1 ⫾ 9.0a 0.865 ⫾ 0.066a
30.1 178.0 84.3 26.59 91.2 102.7 0.888
⫾ 2.7b ⫾ 5.8b ⫾ 10.3b ⫾ 2.80b ⫾ 6.9b ⫾ 6.3b ⫾ 0.049a
70.3 ⫾ 4.1c 165.6 ⫾ 5.9c 80.8 ⫾ 10.3b 29.48 ⫾ 3.62c 101.6 ⫾ 15.0c 101.9 ⫾ 10.1b 1.119 ⫾ 0.036b
⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.001
Dietary Energy (kcal/day) Protein (g/day) Protein (g/1000 kcal per day) Carbohydrate (g/day) Carbohydrate (g/1000 kcal per day) Fat (g/day) Fat (g/1000 kcal per day) Calcium (mg/day) Calcium (mg/1000 kcal per day) Alcohol (portions/week)*
2168 ⫾ 765a 79.2 ⫾ 31.1a 37.3 ⫾ 10.3 236.7 ⫾ 98.6a 109.1 ⫾ 23.3 103.3 ⫾ 45.2a 47.3 ⫾ 10.2 1039 ⫾ 523a 487 ⫾ 188 –
2617 99.9 38.2 260.4 101.6 119.3 45.2 1216 457 5.85
⫾ 997b ⫾ 45.9b ⫾ 8.7 ⫾ 102.0a ⫾ 25.7 ⫾ 53.3b ⫾ 10.0 ⫾ 720b ⫾ 208 ⫾ 6.75a
1728 ⫾ 574c 64.6 ⫾ 24.0c 38.2 ⫾ 11.3 169.8 ⫾ 55.1b 102.1 ⫾ 29.6 83.8 ⫾ 43.5c 47.2 ⫾ 13.0 849 ⫾ 489c 519 ⫾ 276 9.56 ⫾ 17.8b
⬍0.001 ⬍0.001 0.717 ⬍0.001 0.025 ⬍0.001 0.282 ⬍0.001 0.163 NA
Lifestyle OAPBM (h/week) NOAPBM (h/week) TAPBM (h/week) TV/PC (h/week) Smoking (cigarettes/day)*
3.04 ⫾ 4.10a 6.73 ⫾ 6.42a 9.77 ⫾ 9.12a 18.37 ⫾ 10.27a –
1.57 ⫾ 2.95b 3.21 ⫾ 8.84b 4.78 ⫾ 9.95b 16.44 ⫾ 13.93a 11.03 ⫾ 16.49a
0.36 ⫾ 1.57c 2.01 ⫾ 5.54b 2.38 ⫾ 6.04b 23.99 ⫾ 16.38b 5.77 ⫾ 11.82b
⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 NA
76.94 ⫾ 18.66b 1548.5 ⫾ 31.9b 95.40 ⫾ 20.46 0.534 ⫾ 0.147a,b
⬍0.001 0.018 0.083 0.018
Heel ultrasound BUA (dB/MHz) SOS (m/s) QUI (%) eBMD (g/cm2)
65.77 ⫾ 12.99a 1559.5 ⫾ 21.2a 95.34 ⫾ 12.76 0.532 ⫾ 0.087a
79.64 1557.2 100.10 0.570
⫾ 18.57b ⫾ 30.2a,b ⫾ 19.50 ⫾ 0.131b
Values are shown as means ⫾ SD OAPBM, organized activity promoting bone mass; NOAPBM, nonorganized activity promoting bone mass; TAPBM, total activity promoting bone mass; TV/PC, time devoted to television watching and recreational computer usage; NA, not available a,b,c Age groups not sharing the same letter are significantly different from each other at P ⬍ 0.05, by one-way ANOVA and Tukey HSD post-hoc test * Compared between adults and the elderly by Student’s independent t-test
were heavier and had greater hip circumference than boys, while waist circumference and BMI increased progressively with advancing age (i.e., children ⬍ adults ⬍ elderly; P ⬍ 0.05). Body weight tended to be higher in adult men compared to elderly men (P ⫽ 0.076). The WHR was higher in the elderly compared to adult men or boys (P ⬍ 0.05), but was not different between the latter two groups. Total energy (in kcal/day), protein (in g/day), fat (in g/ day), and calcium (in mg/day) intakes followed the pattern: adults ⬎ children ⬎ elderly (P ⬍ 0.05), while carbohydrate intake (in g/day) was similar in children and adults, but higher in both than in the elderly (P ⬍ 0.05). However, differences in macronutrient and calcium intakes were abolished when values were normalized against total energy intake (Table 1). Still, boys tended to consume more carbohydrates per 1000 kcal per day than adults (P ⫽ 0.055) or the elderly (P ⫽ 0.090). Elderly individuals smoked fewer cigarettes (P ⫽ 0.014) and drank more alcohol (P ⫽ 0.049) than adult men. None of the boys reported regular use of alcohol or tobacco. Among adult men, 51.9% were nonsmokers and 17.9% were non-drinkers, while the respective proportions among the elderly were 74.4% and 45.3%. With respect to physical activity, boys were found to devote significantly more of their weekly time to exercise
than adult or elderly men; this difference was apparent in all physical activity indices examined, i.e., whether organized, nonorganized, or total (Table 1). There were no differences between adults and the elderly in nonorganized or total indices, probably because of the large inter-individual variability observed. On the other hand, elderly men devoted significantly more of their weekly time to sedentary activities (TV/PC) than adults or children (P ⬍ 0.001). Regarding QUS parameters, BUA was higher in adult and elderly men compared to boys (P ⬍ 0.001). Boys had higher SOS than the elderly and lower eBMD than adult men (Table 1). Further, adults tended to have higher SOS (P ⫽ 0.092) and eBMD (P ⫽ 0.080) than elderly individuals. No significant age group differences were identified for QUI, although adults tended to have higher values than boys (P ⫽ 0.096).
Effects of anthropometric characteristics on QUS parameters Significant positive partial correlations between height, BMI, waist and hip circumferences, and BUA were observed, as well as between weight and all QUS parameters
161 Table 2. Relationships between selected lifestyle factors and QUS parameters for all subjects (n ⫽ 384) BUA
Height Weight Body mass index Waist circumference Hip circumference Waist-to-hip ratio Energy Protein Protein per 1000 kcal Carbohydrate Carbohydrate per 1000 kcal Fat Fat per 1000 kcal Calcium Calcium per 1000 kcal Alcohola OAPBM NOAPBM TAPBM TV/PC Smokinga
SOS
QUI
eBMD
R
P Value
R
P Value
R
P Value
R
P Value
0.2574 0.3280 0.2654 0.2332 0.2590 0.0356 0.1053 0.0767 ⫺0.0203 0.0953 ⫺0.0101 0.0700 ⫺0.0330 0.0153 ⫺0.0725 ⫺0.0933 ⫺0.0354 ⫺0.0719 ⫺0.0678 ⫺0.0785 ⫺0.0183
⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.548 0.075 0.195 0.732 0.107 0.864 0.237 0.577 0.797 0.222 0.229 0.540 0.214 0.241 0.175 0.814
⫺0.0144 ⫺0.0035 ⫺0.0080 ⫺0.0086 ⫺0.0710 0.0602 0.0821 0.0452 ⫺0.0294 0.1077 0.0198 0.0631 ⫺0.0018 0.0752 0.0004 ⫺0.1242 0.0566 0.0401 0.0535 ⫺0.0461 ⫺0.0078
0.804 0.952 0.890 0.884 0.230 0.308 0.165 0.446 0.620 0.068 0.738 0.286 0.976 0.205 0.994 0.109 0.327 0.489 0.355 0.426 0.920
0.0955 0.1309 0.1023 0.0888 0.0592 0.0530 0.0947 0.0589 ⫺0.0279 0.1075 0.0085 0.0681 ⫺0.0145 0.0531 ⫺0.0302 ⫺0.1153 0.0222 ⫺0.0033 0.0070 ⫺0.0616 ⫺0.0119
0.098 0.023 0.076 0.133 0.317 0.370 0.109 0.320 0.637 0.069 0.885 0.250 0.806 0.371 0.611 0.137 0.701 0.954 0.904 0.288 0.878
0.0956 0.1306 0.1018 0.0883 0.0588 0.0528 0.0939 0.0581 ⫺0.0282 0.1071 0.0091 0.0670 ⫺0.0151 0.0530 ⫺0.0298 ⫺0.1152 0.0221 ⫺0.0034 0.0069 ⫺0.0623 ⫺0.0120
0.097 0.023 0.077 0.135 0.320 0.372 0.113 0.327 0.634 0.070 0.878 0.258 0.799 0.372 0.616 0.137 0.702 0.953 0.905 0.282 0.877
Partial correlation coefficients (R) and exact P values are shown, after controlling for age OAPBM, organized activity promoting bone mass; NOAPBM, non-organized activity promoting bone mass; TAPBM, total activity promoting bone mass; TV/PC, time devoted to television watching and recreational computer usage a n ⫽ 192 (adults and elderly men)
Table 3. Effects of body mass index on QUS parameters Children
BUA (dB/MHz) SOS (m/s) QUI (%) eBMD (g/cm2)
Adults
Normal (n ⫽ 125)
Abnormal (n ⫽ 67)
63.26 ⫾ 11.47 1560.8 ⫾ 20.6 94.85 ⫾ 12.16 0.531 ⫾ 0.082
70.40 1557.0 96.25 0.538
⫾ 14.43* ⫾ 22.2 ⫾ 13.89 ⫾ 0.097
Elderly
Normal (n ⫽ 29)
Abnormal (n ⫽ 77)
74.86 ⫾ 18.23 1555.4 ⫾ 33.0 98.21 ⫾ 20.46 0.559 ⫾ 0.138
81.68 1557.9 100.81 0.575
⫾ 18.73* ⫾ 29.3 ⫾ 19.24 ⫾ 0.130
Normal (n ⫽ 25) 71.72 1547.6 95.01 0.531
⫾ 15.65 ⫾ 32.5 ⫾ 20.94 ⫾ 0.151
Abnormal (n ⫽ 61) 77.85 1555.5 98.62 0.562
⫾ 19.09* ⫾ 26.9 ⫾ 16.90 ⫾ 0.110
* P ⬍ 0.05 vs the “normal” group Values are shown as means ⫾ SD Within-age group comparisons were made were by Student’s independent t-test. The “normal” group includes normal-weight individuals, whereas the “abnormal” group includes overweight and obese ones
except for SOS; QUI and eBMD also tended to relate positively with height and BMI (Table 2). These relationships held true for all age groups, as revealed by Pearson’s linear correlation analysis (data not shown). The only deviation was noted in children, in whom BUA was associated positively with BMI (r ⫽ 0.215; P ⫽ 0.013) and waist circumference (r ⫽ 0.210; P ⫽ 0.021), and also tended to be positively associated with weight (r ⫽ 0.153; P ⫽ 0.078) and hip circumference (r ⫽ 0.177; P ⫽ 0.051), whereas SOS was negatively related to weight (r ⫽ ⫺0.219; P ⫽ 0.011) and hip circumference (r ⫽ ⫺0.230, P ⫽ 0.011), and also tended to be negatively related to height (r ⫽ ⫺0.168, P ⫽ 0.053) and BMI (r ⫽ ⫺0.164; P ⫽ 0.059). To gain a further insight into the effect of body size on skeletal status, QUS values were also compared between
individuals with normal or abnormal BMI (the latter including those who were overweight and obese). As shown in Table 3, overweight and obese subjects had significantly higher BUA than normal-weight ones, but had similar SOS, QUI, and eBMD; this held true for all age groups. Although overweight and obese elderly men also appeared to have increased SOS, QUI, and eBMD compared to their normalweight peers, at no instance did these differences reach significance (Table 3). Similar results were obtained when the effect of BMI was examined for the sample as a whole (n ⫽ 384), after controlling for age by one-way ANCOVA. Overweight and obese males had significantly higher BUA than normal-weight subjects (75.8 ⫾ 17.5 vs 68.4 ⫾ 21.9 dB/ MHz, respectively; P ⫽ 0.001), but similar SOS (P ⫽ 0.673), QUI (P ⫽ 0.273), and eBMD (P ⫽ 0.276).
162 Table 4. Effects of calcium intake on QUS parameters Children
BUA (dB/MHz) SOS (m/s) QUI (%) eBMD (g/cm2)
Adults
⬍800 mg/day (n ⫽ 68)
ⱖ800 mg/day (n ⫽ 124)
67.41 ⫾ 14.76 1558.8 ⫾ 21.3 95.76 ⫾ 13.71 0.541 ⫾ 0.091
65.05 1560.1 95.30 0.528
⫾ 11.88 ⫾ 21.1 ⫾ 12.24 ⫾ 0.083
Elderly
⬍800 mg/day (n ⫽ 35) 75.67 1546.7 94.16 0.530
⫾ 20.24 ⫾ 28.7 ⫾ 19.68 ⫾ 0.125
ⱖ800 mg/day (n ⫽ 71) 81.38 1560.3 102.05 0.588
⫾ 17.46 ⫾ 28.7* ⫾ 18.44* ⫾ 0.131*
⬍800 mg/day (n ⫽ 38) 77.34 1545.8 94.54 0.538
⫾ 19.23 ⫾ 33.5 ⫾ 21.30 ⫾ 0.170
ⱖ800 mg/day (n ⫽ 48) 76.53 1550.5 96.08 0.535
⫾ 18.66 ⫾ 31.2 ⫾ 20.02 ⫾ 0.126
* P ⬍ 0.05 vs the “⬍800 mg/day” group Values are shown as means ⫾ SD Within-age group comparisons were made by Student’s independent t-test
Effects of dietary factors on QUS parameters No significant partial correlations were detected between any of the dietary intake parameters and QUS values (Table 2). A trend for energy intake to be positively associated with BUA was noted, whereas daily carbohydrate intake (in grams) also tended to correlate positively with all QUS indices (Table 2). Calcium consumption, whether in absolute terms or when normalized against total energy intake, was not associated with QUS (Table 2). When Pearson’s linear correlation analysis was performed within each age group, a significant positive relationship between energy intake and SOS was detected in boys (r ⫽ 0.177; P ⫽ 0.041), as well between calcium intake and BUA in adult men (r ⫽ 0.229; P ⫽ 0.039). Further, in children, SOS tended to relate positively with carbohydrate (r ⫽ 0.147; P ⫽ 0.091) and fat (r ⫽ 0.159; P ⫽ 0.066) intakes, and QUI tended to do so with energy (r ⫽ 0.147; P ⫽ 0.091) and fat (r ⫽ 0.158; P ⫽ 0.068) intakes. Among adult men, calcium intake tended to positively correlate with SOS (r ⫽ 0.185; P ⫽ 0.099). QUS indices were also compared between individuals who consumed less than 800 mg of calcium per day or 800 mg or more per day (Table 4). It appeared that adult men with calcium intakes of 800 mg/day or more had higher SOS (P ⫽ 0.044), QUI (P ⫽ 0.048), and eBMD (P ⫽ 0.049) than those with intakes of less than 800 mg/day, while they also tended towards higher BUA (P ⫽ 0.079); no such differences in QUS parameters, however, were observed among children and the elderly (Table 4). Controlling for body weight did not affect these results, as SOS (P ⫽ 0.032), QUI (P ⫽ 0.039), and eBMD (P ⫽ 0.040) were still higher in adult men with calcium intakes of 800 mg/day or more compared to those with intakes below 800 mg/day, and a similar trend was noted for BUA (P ⫽ 0.060). Again, no significant effects of calcium intake on QUS indices were seen in boys and elderly men. Similar results were obtained when BMI was used as a covariate, instead of body weight, in the ANCOVA models (data not shown). Effects of physical activity on QUS parameters There were no significant partial correlations between any of the activity indexes examined and QUS (Table 2).
Within-age group linear correlation analysis revealed that the time spent on organized activities that promote bone mass was significantly and positively associated with SOS in children (r ⫽ 0.171; P ⫽ 0.048) and also tended to be positively associated with QUI (r ⫽ 0.129; P ⫽ 0.099) and eBMD (r ⫽ 0.120; P ⫽ 0.097). On the other hand, in adult men, a significant positive relationship was detected between the time spent on non-organized physical activities and SOS (r ⫽ 0.344; P ⬍ 0.001); also, positive trends were found for time spent on nonorganized physical activities and BUA, QUI, and eBMD (at P values between 0.05 and 0.1). No significant linear correlations between physical activity and QUS were observed in the elderly. Differences in QUS indices were further examined between individuals who reported spending no time at all on physical activities that promote bone mass (non-exercisers), and those who reported spending at least some of their weekly time on such activities (exercisers). With respect to the total physical activity time, no significant differences could be detected between exercisers and nonexercisers in any age group (Table 5), albeit the former generally tended towards higher QUS measurements than the latter (at P values between 0.05 and 0.15). Interestingly, when the same kind of analysis was performed for organized and nonorganized activities separately, it appeared that children who participated in organized physical activities had significantly higher SOS (P ⫽ 0.020), QUI (P ⫽ 0.024), and eBMD (P ⫽ 0.027) than those who did not participate in such activities; no such differences were observed in adults and elderly men (Table 5). In contrast, regarding nonorganized activities, differences arose among adults, but not in children or the elderly. Adult men devoting at least some of their weekly time to nonorganized physical activity had significantly higher BUA, SOS, QUI, and eBMD than their non-exercising peers (P ⬍ 0.05; Table 5). There were no such differences, however, among children and elderly men, although the elderly who exercised tended towards higher QUS measurements than those who did not (Table 5), at P values between 0.1 and 0.15. Adjusting for body weight or BMI did not affect these results. Further, no significant differences in QUS variables were observed between individuals spending more or less time than 1, 2, or 3 h/day on TV/PC, in any age group (data not shown).
163 Table 5. Effects of physical activity on QUS parameters Children
Adults
No
Yes
TAPBM n BUA (dB/MHz) SOS (m/s) QUI (%) eBMD (g/cm2)
40 65.07 ⫾ 12.69 1558.9 ⫾ 21.5 94.83 ⫾ 12.76 0.526 ⫾ 0.083
152 70.28 1563.0 98.63 0.561
⫾ 14.40 ⫾ 18.8 ⫾ 12.61 ⫾ 0.100
63 77.72 1554.9 98.35 0.551
OAPBM n BUA (dB/MHz) SOS (m/s) QUI (%) eBMD (g/cm2)
79 64.90 ⫾ 13.95 1551.1 ⫾ 20.5 92.66 ⫾ 13.08 0.527 ⫾ 0.094
113 66.98 1559.7 97.12 0.569
⫾ 12.31 ⫾ 21.7* ⫾ 12.61* ⫾ 0.081*
NOAPBM n BUA (dB/MHz) SOS (m/s) QUI (%) eBMD (g/cm2)
42 65.29 ⫾ 12.59 1558.9 ⫾ 21.2 94.90 ⫾ 12.58 0.536 ⫾ 0.082
150 67.51 1561.6 96.94 0.545
⫾ 14.45 ⫾ 21.1 ⫾ 13.49 ⫾ 0.098
Elderly
No
Yes
No
Yes
⫾ 19.32 ⫾ 29.9 ⫾ 19.68 ⫾ 0.126
43 82.46 ⫾ 17.29 1560.6 ⫾ 30.7 102.66 ⫾ 19.20 0.594 ⫾ 0.135
65 75.84 1545.2 93.57 0.527
⫾ 18.07 ⫾ 30.4 ⫾ 19.62 ⫾ 0.148
21 80.93 ⫾ 20.78 1560.3 ⫾ 35.3 101.92 ⫾ 22.69 0.559 ⫾ 0.145
76 79.24 1557.0 99.84 0.564
⫾ 19.65 ⫾ 31.0 ⫾ 20.30 ⫾ 0.135
30 80.68 ⫾ 15.78 1557.8 ⫾ 28.7 100.78 ⫾ 17.63 0.573 ⫾ 0.124
71 77.00 1548.1 95.30 0.534
⫾ 18.78 ⫾ 32.6 ⫾ 20.81 ⫾ 0.149
15 76.11 ⫾ 18.79 1552.8 ⫾ 22.0 96.86 ⫾ 16.66 0.539 ⫾ 0.126
73 77.32 1554.4 97.98 0.548
⫾ 18.65 ⫾ 28.8 ⫾ 18.97 ⫾ 0.122
33 84.74 ⫾ 17.65* 1563.4 ⫾ 32.7* 104.77 ⫾ 20.16* 0.610 ⫾ 0.140*
69 76.15 1545.7 93.91 0.529
⫾ 18.07 ⫾ 30.4 ⫾ 19.61 ⫾ 0.147
17 80.05 ⫾ 21.20 1559.4 ⫾ 36.4 101.19 ⫾ 23.29 0.554 ⫾ 0.148
* P ⬍ 0.05 vs the “no” group Values are shown as means ⫾ SD TAPBM, total activity promoting bone mass; OAPBM, organized activity promoting bone mass; NOAPBM, nonorganized activity promoting bone mass Within-age group comparisons were made by Student’s independent t-test. The “no” group includes individuals who reported spending no time at all on activities that promote bone mass (total, organized, or nonorganized), whereas the “yes” group includes those who reported spending at least some of their weekly time on such activities
Effects of other factors on QUS parameters Alcohol drinking and cigarette smoking did not correlate with QUS indices in adults and the elderly, whether these age groups were examined separately (using Pearson’s linear correlation analysis) or combined (using partial correlation analysis; Table 2). Still, among the elderly, alcohol intake tended to correlate negatively with SOS (r ⫽ ⫺0.223; P ⫽ 0.056) and QUI (r ⫽ ⫺0.207; P ⫽ 0.080). When QUS indices were compared between smokers and nonsmokers and between alcohol drinkers and nondrinkers, no significant differences could be detected in either adults or the elderly, albeit in the latter age group, BUA tended to be lower in drinkers (71.08 ⫾ 16.55 vs 78.75 ⫾ 20.31 dB/MHz in nondrinkers; P ⫽ 0.096) and in smokers (70.50 ⫾ 22.17 vs 79.33 ⫾ 16.79 dB/MHz in nonsmokers; P ⫽ 0.070). Determinants of BUA and SOS Stepwise linear regression analysis was performed, using BUA and SOS as the dependent variables; the independent ones included all the anthropometric, dietary, lifestyle, and physical activity information, as well as a set of two dummy variables defining the three different age groups. Body weight was a positive determinant of BUA (β ⫽ 0.373; t ⫽ 6.589; P ⬍ 0.001), explaining approximately 14% of the total variance, while none of the remaining independent variables had an additional significant contribution to the regression model (F ⫽ 43.42; P ⬍ 0.001; R2 ⫽ 0.139; Standard Error of the Estimate (SEE) ⫽ 16.25 dB/MHz): BUA (dB/MHz) ⫽ 51.452 (⫾3.415) ⫹ 0.319 (⫾0.048) ⫻ weight (kg). On the
other hand, age was the sole significant negative determinant of SOS (β ⫽ ⫺0.198, t ⫽ ⫺3.321; P ⫽ 0.001), but the proportion of the total variance explained was much smaller (F ⫽ 11.03; P ⫽ 0.001; R2 ⫽ 0.039, SEE ⫽ 26.78 m/s): SOS (m/s) ⫽ 1563.205 (⫾2.749) ⫺ 0.227 (⫾0.068) ⫻ age (years).
Discussion The aim of the present study was to determine whether, as well as the extent to which calcaneal QUS variables were affected by lifestyle factors in a sample of healthy Greek males, including individuals from three age groups that represent different stages of bone mass development during the life cycle. The small number of available studies in men [27–30] makes every new observation an important contribution to our knowledge of bone status in men. Although the incidence of osteoporosis in men is lower than that in women, mainly due to mens’ higher peak bone mass, shorter life expectancy, and absence of a distinct menopause-equivalent with associated marked acceleration of bone loss [31], still, osteoporotic fractures in men are often underestimated and result in tremendous morbidity and cost [3]. Further, while the number of hip fractures is projected to decrease among women by the year 2010, the reverse (i.e., an increase) is forecast for men [4]. Body weight is believed to exert a beneficial effect on bone tissue in terms of osteoporotic risk. The skeleton responds to mechanical loading with the stimulation of osteoblast activity, resulting in increased bone mass and density, and BMD at different sites of the skeleton has been
164
reported to correlate positively with body weight and BMI [32]. Subjects in the present study had generally increased body weight, as reflected by their high mean BMI values. Overweight and obese Greek males were found to have significantly higher BUA than normal-weight individuals, but similar SOS, QUI, and eBMD; this held true for all age groups (Table 3). This finding is in line with our previous observation in Greek females [19], suggesting a universal, age- and sex-independent positive effect of increased body weight on BUA, but not SOS. Furthermore, body weight was found to be the sole positive determinant of calcaneal BUA, and this was regardless of age. Our results are consistent with those from other studies demonstrating a strong association between body weight and/or BMI and BUA [29,30], although not all investigations support this relationship [27,33]. On the other hand, SOS appeared to be less responsive to increased body weight and/or BMI and, in fact, it was negatively related to indexes of body size in boys, but not among adult or elderly men. This implies that age, which emerged as the most significant determinant of SOS among our subjects, may mediate the effects of body size on SOS. The hypothesis that the relationship between age and body size to heel QUS may be parameter-specific is also supported by a recent study in Japanese adolescents, where a positive and sex-independent association between body size and BUA was reported, but there was no association between body size and SOS [17]. Physical activity is also thought to have a positive impact on bone mass and density. During weight-bearing exercise, the site-specific bone strain or deformation resulting from external loading is believed to act as an osteogenic stimulus, provided that the minimum effective strain in this area is exceeded [34,35]. Under these circumstances, the stimulus leads to a change in trabecular orientation and density to reduce future stress from comparable loading sustained by the bone cells [36]. The os calcis has a central position in supporting the weight of the body, and is approximately 90% trabecular by volume at the region measured by QUS. As ground reaction forces are maximal at the calcaneus and attenuate as they propagate upwards [37], the os calcis may be considered the most appropriate skeletal site for evaluating the effects of integrated physical activity on bone status. In the present study, individuals who reported spending at least some of their weekly time on physical activities that promote bone mass tended towards higher QUS measurements than nonexercisers, although none of these differences achieved statistical significance. When the analysis was performed separately for organized and nonorganized activities, however, an age- and parameter-specific effect of exercise was revealed (Table 5). Organized physical activity had a significant positive influence on SOS, QUI, and eBMD (but not BUA) in children, while nonorganized activity was found to positively affect all QUS indices among adult men, and similar trends were noted in the elderly (Table 5). Presumably, therefore, our results indicate that organized physical activity may be more important for bone mass development during childhood, highlighting the need for participation in structured physical activity programs early in life, whereas nonorganized physical activity may be
more important for bone mass maintenance (adults) or bone loss delay (elderly) later in life, reflecting the need for preserving a physically active lifestyle as age advances. These findings are consistent with previous studies reporting positive effects of weight-bearing exercise on QUS parameters in male subjects of various ages [33,38]. It is also noteworthy that, contrary to our results in men, physical activity was shown previously to be a much more important determinant of heel QUS in healthy Greek females, and this was regardless of age [19]. Although a sex-dependent response to exercise cannot be readily ruled out, we speculate that this discrepancy between males and females may be related to the fact that females [19] were less active than males; hence, positive effects resulting from increased physical activity would be easier to manifest and detect. Still, collectively, these observations suggest that women may draw greater benefits from an increase in physical activity than men, exactly because they are generally less active [39]. With respect to dietary correlates of QUS, we failed to detect any significant partial relationships, although there were strong trends for absolute carbohydrate intake (in grams per day) to positively influence all QUS parameters (Table 2). This is at variance with the observations by Paakkunainen et al. [40], as well as with our previous finding in Greek females [19], where negative associations between carbohydrate intake and QUS were observed. The reasons for this apparent sex difference, i.e., the negative effect of a high-carbohydrate diet on skeletal status in women [19], but not in men (present study), are not clear at present; hence, further research is warranted. Calcium intake, on the other hand, was found to age-dependently affect QUS indices among Greek males (Table 4). Adult men with calcium intakes of 800 mg/day or more had significantly higher SOS, QUI, and eBMD than those consuming less calcium than 800 mg/day, and those with the higher calcium intakes also tended towards higher BUA. This positive effect of calcium intake, however, was not apparent in children or the elderly (Table 4), possibly due to the large interindividual variability in daily calcium intake, or due to the fact that our study subjects consumed adequate amounts of dietary calcium on average (Table 1). In this respect, our results in males are similar to our previous observations in Greek females, in whom dietary calcium intake was generally adequate and no significant effects on heel QUS could be identified [19]. Although calcium is considered to be the most important nutrient for bone health throughout the lifespan [41], yet, previous studies using QUS have yielded variable results, with a positive association found in some [42,43], but not in others [44]. Cigarette smoking and alcohol drinking were not found to influence bone status in Greek adult and elderly men. These observations may seem to be at variance with results from previous research [29,46–48], but are probably related to the low mean values for alcohol and cigarette consumption that were recorded in the present study (Table 1), and to the generally high prevalence of nonsmokers and nondrinkers in this sample of healthy men, in line with our findings in Greek women [19]. It should be pointed out that
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mean values for alcohol drinking and cigarette smoking for our subjects were considerably lower than those reported in other population-based studies [42,47–52]. In conclusion, BMI, calcium intake, and the time devoted to bone mass-promoting physical activities appear to inconsistently and age-dependently affect heel QUS among healthy Greek males, in a parameter-specific manner. On the whole, however, our observations should be interpreted in light of the relatively small sample size, and the fact that participants in the present study were healthy individuals, not suffering from any kind of disease. Whether the same results would be obtained in a sample representative of the entire population is unknown at present. The above points notwithstanding, our findings suggest that the importance of anthropometric and lifestyle factors in determining calcaneal QUS in males may vary depending on the exact acoustic parameter of interest, as well as on the stage of bone mass development during the life cycle. Acknowledgments This study was supported by Friesland Hellas. The authors thank Maria Bletsa, Maria Rammata, and Anastasia Doulgeri, dietitians; Silia Sidossi, research assistant; and Antigoni Tsiafitsa, technician, for their valuable help in data collection and processing.
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