Acta Diabetol (2003) 40:S23–S28 DOI 10.1007/s00592-003-0021-2
© Springer-Verlag 2003
E.I. Mohamed • U. Tarantino • L. Promenzio • A. De Lorenzo
Predicting bone mineral density of postmenopausal healthy and cirrhotic Italian women using age and body mass index
Abstract The objective of the present report was to develop mathematical prediction formulae for the lumbar spine, pelvis and total bone mineral density (BMD) based on the osteoporosis risk factors age and BMI in healthy and cirrhotic postmenopausal women. The study population comprised 20 postmenopausal cirrhotic women (late PM cirrhotic women), 20 postmenopausal healthy women matched for age and BMI (late PM healthy women), and 19 younger postmenopausal healthy women matched for BMI (early PM healthy women). Segmental and total bone mineral content and BMD, total bone-free lean body mass and total fat mass were measured for all women using dual X-ray absorptiometry (DXA). The prediction formulae for late PM cirrhotic women had higher cumulative correlation coefficients (r=0.71, p=0.05 for spine BMD, r=0.84, p=0.013 for pelvis BMD, and r=0.89, p=0.004 for total BMD) than those for early PM healthy women (r=0.64, p=0.015 for spine BMD,
E.I. Mohamed () Department of Neurosciences Building F-Sud, 1st Floor, Room 116 University of Tor Vergata Via Montpellier 1, I-00133 Rome, Italy E-mail:
[email protected] E.I. Mohamed • A. De Lorenzo • U. Tarantino • L. Promenzio Divisions of Human Nutrition and Orthopaedics and Traumatology Faculty of Medicine and Surgery University of Tor Vergata, Rome, Italy E.I. Mohamed Department of Biophysics, Medical Research Institute University of Alexandria, Egypt A. De Lorenzo Scientific Institute “S. Lucia”, Rome, Italy
r=0.69, p=0.002 for pelvis BMD, and r=0.62, p=0.022 for total BMD) and late PM healthy women (r=0.29, p=NS for spine BMD, r=0.39, p=NS for pelvis BMD, and r=0.54, p=NS for total BMD). The mathematical formulae based on the variables age and BMI were capable of predicting lumbar spine BMD, pelvis BMD, and total BMD by DXA for the three groups of postmenopausal women. Key words Bone mineral density • Osteoporosis • Postmenopause • Hepatic cirrhosis • Dual X-ray absorptiometry • Prediction • Age • BMI
Introduction Bone is a living tissue that undergoes a continuous cycle of formation and resorption [1], both of which are affected by the impact of mechanical loading on the skeleton, circulating hormones, and local humoral factors [2]. Osteoporosis is a metabolic bone disease characterized by low bone mass and microarchitectural deterioration of bony tissue, with consequent enhanced bone fragility and increased risk of fracture [3]. It has been shown that with increasing age, women lose 30–50% of trabecular bone mass and 25–30% of cortical bone mass and that the greatest loss occurs around the menopausal stage [4]. The main factors that determine postmenopausal bone loss are genetics, mechanical loading, nutrition, and hormones [5]. Liver cirrhosis, regardless of its aetiology, has also been shown to cause bone loss, and the severity of metabolic osteopathy worsens as liver function does, with bone resorption representing the underlying mechanism [6, 7]. The single most important predictor of bone strength and osteoporosis-related fractures of the lumbar spine and proximal femur is bone mineral density (BMD) [8], which is usually measured using dual X-ray absorptiometry (DXA) and expressed as area density (g/cm2) [9]. Although BMD
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measurements compared with normal reference values are diagnostically important, they do not offer sufficient prognostic information. However, detecting the dynamics of bone loss may be more relevant to prognosis [10]. The greater the rate of annual bone loss, the greater the risk of osteoporosis and fractures. Thus, it is important to determine this phenomenon as early as possible, identifying subjects at risk and prescribing a suitable therapy. Studies to model DXA measurements of the lumbar spine have shown that the biochemical markers could not be used as predictors for rate of bone loss in postmenopausal women [11, 12]. The objectives of the present study were to evaluate the effect of menopause and the combined effect of menopause and liver cirrhosis on segmental and total BMD and to develop mathematical prediction formulae for lumbar spine BMD, pelvis BMD, and total BMD based on anthropometric variables in healthy and cirrhotic postmenopausal women.
Subjects and methods Subjects The study population consisted of one study group and two control groups. The study group comprised 20 women with liver cirrhosis who had a mean age (± SD) of 62.70±6.88 years. This group is referred to as the late PM (postmenopausal) cirrhotic group. We recruited these women from the 2nd Gastroenterology Division of the University of La Sapienza (Rome, Italy), where they had been hospitalized for diagnosis, therapy, or both. The diagnosis of liver cirrhosis was based on histological, clinical, and laboratory variables [13]. The control groups consisted of 20 healthy women matched with the study group for age and BMI (referred to as the late PM healthy group) and 19 healthy women (mean age of 53.84±2.97 years) matched for BMI yet younger than the other women (referred to as the early PM healthy group). These women were selected from among participants of a menopause monitoring programme at the Human Nutrition Division of the University of Tor Vergata (Rome, Italy). All participants had gone through a natural menopause (early PM healthy, 1–10 years previously and late PM healthy and late PM cirrhotic, 11–20 years previously). None of the participating women had received hormone replacement therapy in the 5 years prior to initiation of the study protocol. Early and late PM healthy women had initiated a calcium supplementation treatment (Caltrate D, Whitehall Italia SpA, Milan, Italy) following the DXA measurements. All study participants provided signed informed consent prior to their inclusion in the study. The university’s ethics committee approved the study protocol.
Measurements We measured anthropometric and body composition variables for all participants. Specifically, body weight (kg; participants
E.I. Mohamed et al.: Predicting bone mineral density clothed in underwear, bare feet) was measured using a sensitive digital scale (to the nearest 0.01 kg; Body Master, Rowenta, Germany). Height (m) was measured using a stadiometer. BMI was expressed as weight/height2 (kg/m2). Circumferences for waist (W, cm) and hip (H, cm) were measured using a tape measure and the waist-to-hip ratio (W/H) was calculated. Skinfold thickness was measured using a Holtain caliper (Bryberian, UK). A sum of four skinfolds (Sum SF, mm) was calculated (i.e. biceps, triceps, subscapular, and supra-iliac skinfolds). Total bone-free lean body mass (BF-LBM, kg), total fat mass (FM, kg), and total and segmental (i.e. arms, ribs, spine, trunk, pelvis and legs) BMD were measured using DXA total body scan (Lunar DPX, Lunar Radiation Corp., Madison, WI, USA) [14, 15].
Statistical analysis Statistical analysis was carried out using the StatView statistical package (SAS Institute Inc., Cary, NC, USA). One-way analysis of variance (ANOVA) and Scheffe’s post-hoc test of significance were applied to compare different variables among the three groups of women. The significance level was defined as ≤0.05. Simple and partial correlation coefficients (r) were calculated to test the interrelationships between variables (i.e. anthropometric and body composition) and lumbar spine BMD, pelvis BMD, and total BMD in bivariate linear regression models. Multiple linear regression analysis was used to model the association between lumbar spine BMD, pelvis BMD, and total BMD and anthropometric variables (i.e. age, weight, height, and BMI) in various powers and interactions. The regression coefficient, standard error of estimation, and significance level were determined for independent variables added simultaneously. Errors generated by the prediction equations were calculated using the weighed sum of squared errors as χ2=Σni=1[(BMDi(Est)-BMDi(Obs))/σi]2, where BMDi(Est) and BMDi(Obs) are the estimated and observed BMD, respectively, and σi is the standard error of BMDi(Est).
Results The anthropometric and body composition variables of all study groups are presented in Table 1. The late PM cirrhotic women had a significantly higher W/H (1.14±0.09) than early PM healthy women (0.88±0.09, p
