Abstract
Background: Poverty is associated with a great number of diseases, but the prevalence of quality alterations of the bone, measured by trabecular bone score and quantitative ultrasounds and its association to osteoporosis and fragility fractures are less well known.
Objective: To evaluate the associations between poverty, bone quality and fragility fractures in a population of Southern European postmenopausal women.
Method: Cross-sectional study was carried out in 762 postmenopausal Caucasian Spanish women. The socio-economic status of the participants was determined after a personal interview, according to the criteria of the Spanish Institute of Statistics. Participants were divided into two socio-economic levels: low (poverty) and medium or high socioeconomic level. The study protocol included a health questionnaire, a complete physical examination, lateral radiograph of the dorsal and lumbar spine and measurement of bone mineral density (BMD) at the lumbar spine (L2-L4) and proximal femur, trabecular bone score (TBS) at the lumbar spine and Quantitative ultrasounds at the heel.
Results: Low socio-economic status was associated with lower values of TBS at the lumbar spine and also lower values of QUS (QUI and BUA) after adjusting by age and BMI. Poverty was also associated with a higher prevalence of all type of fragility fractures: vertebral, hip fracture and some non-vertebral: Colles´, Humerus and tibia but not to ribs fractures.
Conclusions: Poor postmenopausal Caucasian Spanish women have a high prevalence of osteoporosis and osteoporotic fractures. Poverty was associated with older age and higher BMI and lower values of TBS and QUS could reflect a worse bone quality.
Keywords
Poverty, Bone quality, Quantitative ultrasound, Trabecular bone score, Fragility fractures, Postmenopausal, Caucasian Spanish women
Introduction
Osteoporosis is a common bone disease characterized by low bone mass and altered bone microarchitecture, resulting in decreased bone strength with an increased risk of fractures [1]. It has been well documented that dual-energy X-ray absorptiometry (DXA) provides an accurate estimate of BMD [6] because as T-scores decrease, the relative risk for fracture increases [2]. This principle makes the T-score an effective means of identifying those individuals at increased fracture risk and offers a cut point that allows for a diagnosis of osteoporosis [3,4] However, this does not always translate into an accurate estimate of future fracture risk [5].
It is, moreover, now well established that BMD is not the only characteristic of bone that determines its strength and fragility [6] and, therefore, must be considered when deciding upon therapy to prevent new or further osteoporotic fractures. Other factors that influence both bone strength and fracture risk include such bone characteristics as the bone macro-geometry, bone mineralization, and bone turnover, which have been globally referred to as "bone quality"[7-9]. Another key determinant of bone strength is its microarchitecture, the importance of which has been increasingly appreciated in recent years [10]. This acknowledgement has led to the recognition that evaluating bone micro-architecture [11] might significantly enhance the accuracy of bone strength evaluations and, consequently, also of fracture risk [11,12].
Trabecular bone score (TBS) represents a new texture parameter coming from pixel gray-level variations in DXA images at lumbar spine. It has been proposed to reflect bone microarchitecture status and bone quality and cumulating evidences suggest that it may contribute to fracture risk assessment [10,12,13]
Quantitative ultrasound (QUS), as measured by broadband ultrasound attenuation (BUA, in decibels per megahertz) and speed of sound (meters per second), has been suggested to be reflective of both bone density and bone structure [8,14,15]. Many studies have shown that QUS measurements, particularly those measured at the heel, can predict fracture risk as well, and independent of BMD [16-19].
We have previously carried out a study in which we observed that poor women had a greater risk of suffering fragility fractures [20]. Our initial literature search produced no studies measuring bone quality by TBS or QUS, leading to our carrying out this work.
Material and Methods
Subjects
Between January 2022 and January 2023, 762 consecutive first time outpatient postmenopausal Caucasian women at a Metabolic Bone Clinic in the Spanish public health system (Canary Islands; Latitude/Longitude 28 06′ N, 15o 24′W) as outpatients were included in this study. The inclusion criteria were as follows: age between 50 and 90 years, being born and resident in the Canary Islands. Postmenopausal women were defined as those who had had their last menstrual period at least one year previously, in accordance with the clinical definition of the World Health Organization [21].
All patients were informed about the aims of the study and having given written informed consent. Medications likely to influence skeletal metabolism or the interpretation of results (i.e. thiazides, hormonal replacement therapy) were recorded. Women treated with anti-osteoporosis medications including calcium and vitamin D supplements and those with known malabsorption, cancer or other serious diseases were excluded from the study. The participants were referred to by Gynaecology, Endocrinology and Internal Medicine Services after ruling out secondary causes of osteoporosis. The younger women had suffered a bilateral oophorectomy without malignancy. The study was conducted following the Principles of the Helsinki Declaration [22]. The Ethics Committee of the Insular University Hospital of Gran Canaria approved the study.
Methods
A complete physical examination was performed. Height and weight were measured to obtain the body-mass index (BMI) of each subject. The socioeconomic status of the participants was stated after a personal interview, following the criteria of the Spanish Institute of Statistics, who establishes the presence of poverty with an annual income lower than 8,871 Euros in a one member family [23]. Actual calcium intake was calculated using a short food frequency questionnaire [24], previously used in other studies [25,26].
QUS measurements
All subjects underwent calcaneus QUS measurement. This was performed using the Sahara® Clinical sonometer (Hologic, Bedford, MA). The system consists of two unfocused transducers mounted coaxially on a monitor caliper. One transducer acts as the transmitter and the other as a receiver. The transducers are acoustically coupled to the heel using soft rubber pads and an oil-based coupling gel. The Sahara device measures both broadband ultrasound attenuation (BUA) and speed of sound (SOS) at a fixed region of interest in the mid-calcaneus, and the BUA and SOS results are combined to provide an estimate of the quantitative ultrasound index (QUI).
For all QUS measurements, the corresponding T scores were calculated as TS = (QUS - lP)/rP, where QUS represents each QUS measurement and lP and rP represent the mean and standard deviation, respectively, corresponding to the peak bone mass in healthy Spanish women, previously established for the Spanish population by our working group in an evaluation of 1,145 healthy women, aged 20–99 [27].
Quality assurance
Additional quality controls were done every morning for the QUS and DXA devices, according to the guidelines of the manufacturers, to verify the stability of the respective systems. The quality-control data of both devices (QUS and DXA) did not show any shift or drift during the entire study period. Consequently, the devices included in this study were characterized as stable.
We chose the Sahara Calcaneus ultrasonograph because we have used it in several previous multicentre clinical studies with the research group on ultrasound and bone metabolism (GIUMO), having established the normality values for the Spanish population as well as the precision and accuracy of this test [27] and being used in some other studies [48,49].
Determination of bone mineral density and diagnosis of fractures
The BMD was measured in all patients in the lumbar spine (L2–L4) and in the proximal femur (sub-regions: femoral neck, total hip, trochanter and intertrochanter) using dual X-ray absorptiometry with a Hologic® 4500 apparatus. The T-score was obtained using normality values established in the Canarian population [28]. The coefficient of variation (CV) has been established in 1% for lumbar spine and 1.5% for femoral neck [29].
Trabecular bone score
TBS was calculated from the same LS DXA scan as BMD using TBS iNsight Software (version 2.2.0; Medimaps, Geneva, Switzerland). We excluded women with body mass index (BMI) outside the range 15-37 kg/m2 as recommended by the TBS manufacturer [30].
We have chosen the TBS INsight software because it was the one used by the working group to establish normality values in the Spanish population [12].
Fracture diagnosis
To establish the diagnosis of fragility fractures, all the patients were clinically studied and all the fractures. To detect vertebral fractures, X-rays in the lumbar and dorsal spine were carried out in all patients; the diagnosis of vertebral fractures was established after applying the Genant criteria for vertebral deformities [31]. The data on non-vertebral fractures were gathered from information provided by the patients, confirmed by a review of their clinical history, and documented through X-ray or a report from a specialist.
Statistical analysis
The categorical variables were summarized as percentages and the continuous ones as means and standard deviations when the data followed a normal distribution or medians and interquartile range (IQR) when they did not. The percentages were compared using the chi-square test, the means by the t-test and the medians by the Wilcoxon test. For QUS and DXA results including TBS, means were adjusted by for age and BMI and compared using the corresponding F-test. Statistical significance was set at p<0.05. Those variables that showed independent association with poverty were then entered in a multiple logistic regression analysis. A selection was carried out using a retrospective method based on Akaike’s information criterion. Data were analyzed with the SPSS statistical program (19.0, SPSS, Chicago, Il).
Results
(Table 1) shows the baseline characteristics of the population studied. Women were classified according to their socioeconomic status into two groups: Group I (Poverty) was composed of 354 women with low socioeconomic status, whereas 408 women were classified not in poverty (Group II). Women in poverty were older, heavier and shorter than those not in poverty. Because of this, all the comparisons were performed after adjusting by age and BMI. There was a similar distribution of some lifestyles risk factors for osteoporosis. Women in poverty showed a lower consumption of tobacco (20,3% vs. 39,5%) and a lower use of hormonal replacement therapy (HRT) than women not in poverty (11% vs 19,4%). On the other hand, women in poverty showed a higher prevalence of obesity (45,5% vs. 22,3%), type 2 diabetes mellitus (23.7% vs. 8.8%), arterial hypertension (60.2% vs 38.2%), use of hypnotics (76,3% vs 66,2%), and low physical activity (78.1% vs 60.5%). Actual calcium intake and the prevalence of falls were similar in both groups.
|
|
Poverty |
||
|
|
Group I. Yes |
Group II. No |
P value |
|
|
n= 354 |
n= 408 |
|
|
Age (years) |
66.8 ± 9.7 |
64.5 ± 10 |
0.001 |
|
Weight (Kg) |
70.8 ± 14.1 |
65.1 ± 11.6 |
<0.001 |
|
Height (cm) |
154.7 ± 6.8 |
156.9 ± 6.7 |
<0.001 |
|
Body mass index (Kg/m2) |
29.5 ± 5.4 |
26.4 ± 4.5 |
<0.001 |
|
Actual calcium intake (mg/day) |
735.5 ± 355.4 |
742.6 ± 331.8 |
0.770* |
|
Living in rural areas (%) |
30.5 |
14.2 |
<0.001* |
|
Tobacco consumption (%) |
20.3 |
39.5 |
<0.001* |
|
HRT use (%) |
11 |
19.4 |
<0.001* |
|
Thiazide use (%) |
16.4 |
13 |
0.650* |
|
Low physical activity (%) |
78.1 |
60.5 |
0.001* |
|
Obesity (IMC ≥ 30 Kg/m2) (%) |
45.5 |
22.3 |
<0.001** |
|
Diabetes Mellitus (%) |
23.7 |
8.8 |
<0.001* |
|
Hypertension (%) |
60.2 |
38.2 |
<0.001* |
|
Hipnotics use (%) |
76.3 |
66.2 |
0.003* |
|
Falls (%) |
42.7 |
34.3 |
0.096* |
|
*Adjusted by age and body mass index (BMI) |
|||
(Table 2) shows Quantitative ultrasound, DXA and TBS measurements in postmenopausal women with poverty and non-poverty. Comparison was made after adjusting by age and body mass index (BMI). All the parameters showed statistically significant lower values in postmenopausal women with poverty compared to those with non-poverty. This occurred with BUA, QUI (QUS parameters), DXA measurements (at the lumbar spine -L2-L4 and total hip) and finally with TBS measured at the lumbar spine. There were no statistically significant differences in SOS and femoral.
|
|
Poverty |
||
|
|
Group I. Yes |
Group II. No |
|
|
|
n= 354 |
n= 408 |
P value |
|
QUS |
|||
|
BUA (db/mgHz) |
58.5 ± 17.4 |
60.5 ± 17 |
0.023 |
|
Tscore |
-1.2 ± 1.1 |
-1.1 ± 1.1 |
0.023 |
|
SOS (m/s) |
1509.4 ± 101.8 |
1509.8 ± 117.8 |
0.686 |
|
Tscore |
-1.8 ± 3.3 |
-1.8 ± 3.8 |
0.686 |
|
QUI |
74.3 ± 18 |
77.5 ± 19.3 |
0.018 |
|
Tscore |
-1.6 ± 1 |
-1.4 ± 1 |
0.018 |
|
TBS (g/cm2) |
1.227 ± 0.1 |
1.258 ± 0.1 |
0.014 |
|
Tscore |
-2.7 ± 1.4 |
-2.3 ± 1.9 |
0.014 |
|
DXA |
|||
|
L2L4 (g/cm2) |
0.839 ± 0.17 |
0.846 ± 0.16 |
0.003 |
|
Tscore |
-1.6 ± 1.4 |
-1.5 ± 1.3 |
0.003 |
|
Femoral neck (g/cm2) |
0.666 ± 0.12 |
0.661 ± 0.13 |
0.103 |
|
Tscore |
-1.52 ± 1 |
-1.56 ± 1 |
0.103 |
|
Total hip (g/cm2) |
0.798 ± 0.15 |
0.783 ± 0.14 |
0.030 |
|
Tscore |
-0.36 ± 1.1 |
-0.47 ± 1.1 |
0.030 |
|
TBS: Trabecular Bone Score |
|||
(Table 3) shows the prevalence of fragility fractures. Women with poverty showed a higher prevalence of all fragility fractures (considered all of them together) but no statistically significant differences were found when the different types of fractures were studied individually: vertebral, forearm, humerus, tibia, ribs and hip.
|
|
Poverty |
||
|
|
Group I. Yes |
Group II. No |
|
|
|
n= 354 |
n= 408 |
P value |
|
Fragility fractures Yes (%) |
41.8 |
31.6 |
0.039 |
|
Vertebral fractures Yes (%) |
14.1 |
12 |
0.098 |
|
Colles fracture Yes (%) |
11.6 |
9.6 |
0.120 |
|
Humerus fracture Yes (%) |
4.8 |
5.1 |
0.381 |
|
Tibia fractures Yes (%) |
4.5 |
5.2 |
0.064 |
|
Rib fractures Yes (%) |
1.4 |
2.9 |
0.683 |
|
Hip fracture Yes (%) |
5.1 |
4.2 |
0.067 |
Discussion
Our study shows that poor postmenopausal women have reduced bone quality compared to those who are not poor, estimating bone quality by two different methods: TBS and calcaneal ultrasound. This leads to a higher prevalence of fragility fractures. In a previous study conducted by our working group more than 10 years ago, we published the association between poverty and osteoporosis [20]. However, in that study we were only able to establish the existence of a lower bone mineral density without studying other bone components related to bone strength, such as microarchitecture and bone quality.
Over the past decade, a new way to assess bone microarchitecture has emerged, called the trabecular bone score (TBS) [32-34]. This novel method, which is entirely noninvasive and, in fact, requires no further patient testing, has been repeatedly shown to be a BMD-independent predictor of skeletal strength and fracture risk. It consists of gray-scale textural analysis of anteroposterior LS-DXA images previously obtained for BMD assessments. It also, by assessing the micro-architectural texture of trabecular bone, contributes to the evaluation of bone strength, thereby aiding in the diagnosis of osteoporosis and the prediction of future osteoporotic fractures [10,12,32,35-37]. Besides being non-invasive and efficient for use, since no additional patient visits are required, the fact that TBS utilizes previously obtained DXA images has allowed for the rapid generation of an extensive body of empirical data. In the largest published study assessing TBS and considering 29,407 postmenopausal women in the Canadian province of Manitoba, there were 1,668 incident major osteoporotic fractures, including 439 vertebral fractures and 293 hip fractures over the 5 years of follow-up. BMD at lumbar spine and TBS predicted fractures equally well, and the combination of both performed better than either individually [38]. Moreover, TBS appeared to enhance fracture risk prediction in those women with BMD in the normal or osteopenic range, but not in those women in the osteoporotic BMD range [39]. On the other hand, QUS of the heel has been proposed as a screening tool to evaluate the bone status and risk of osteoporotic fragility fractures. QUS appears to be a valuable method for the assessment of osteoporotic fracture risk [40], but assessing other aspects of bone, apparently more closely related to bone quality [7,15,41,42].
The poor postmenopausal women in our study are older, heavier and shorter (and consequently higher BMI) than those who are not poor. They also have more detrimental lifestyles, as they are more sedentary and also have a higher prevalence of a number of diseases such as hypertension, obesity, type 2 diabetes mellitus, which has been reported elsewhere [43-46]. Some of these diseases, in turn, have been described as possible risk factors for the appearance of osteoporosis or fragility fractures [1,4].
Table 2 shows the densitometric values of both BMD and TBS as well as the ultrasonographic parameters. It can be seen that poor women have lower values for BMD in both the lumbar spine and the proximal femoral extremity, as well as for TBS and QUS parameters, with the exception of SOS, which, curiously, did not show statistically significant differences between poor and non-poor women. It was therefore predictable to find a higher prevalence of fragility fractures among poor postmenopausal women compared to non-poor women, but this only occurred when we grouped all fragility fractures together (p=0.039) but was not observed individually for each of the different fracture types, probably due to the limited number of fractures collected when separated by group.
A similar 2020 study to ours was conducted by Olmos et al. in Cantabria, in the Camargo cohort. In this study, they included 1,450 postmenopausal women aged 44-94 years and found that 11% of the women had a degraded microarchitecture. They found, as we did, an association between TBS and QUS, although it was weak [47].
A limitation of this study is that it does not determine what has come first, the poverty of these women or their poor health condition, making it so they can’t work to provide for themselves. Future studies should examine this issue
There are two more limitations to this study: Firstly, it is a cross-sectional study in which only prevalent osteoporotic fractures or metabolic syndrome prevalence was analyzed; thus, prospective studies are necessary to confirm the results. Secondly, the study was conducted in a relatively small sample, not randomly selected from the Canarian population. However, we consider that our sample was representative of the population, because it was composed of subjects who agreed to participate in several epidemiological studies and BMD values obtained at different age groups did not differ from reference values published for the Canarian and Spanish population [28,48,49].
In conclusion, in this study we have been able to observe that among postmenopausal women with poverty a higher prevalence of all type of fragility fractures: vertebral, distal radius, humerus, tibia and hip fractures. Compared to non-poor postmenopausal women, these poor patients, in addition to lower bone mineral density values at both the lumbar spine and proximal femoral extremity, have lower TBS and QUS values, which have been linked as indicators of bone microarchitecture, in the case of TBS, and empirically of bone quality, in the case of QUS.
Conflict of Interest
The authors declare no conflict of interest.
Contributors
Marlene García Quintana, Ma Jesús Gómez de Tejada-Romero and Manuel Sosa-Henríquez were involved in design of the general concept, development of the study, acquisition of data, analysis and interpretation of data, drafting the article and revising it content, discussion. Pino Quintana-Montesdeoca was involved in the statistical study, analysis and interpretation of data, drafting the article and revising it content, discussion. Diego Hernández-Hernández was involved in the design of the general concept, analysis and interpretation of the data, drafting the article and revising its content and discussion.
Funding
We have received no funding for the study: “Association of Poverty with Higher Risk of Fragility Fractures in Postmenopausal Caucasian Spanish Women”.
Ethical Approval
The study “Association of Poverty with Higher Risk of Fragility Fractures in Postmenopausal Caucasian Spanish Women” was approved”.
References
2. Cummings SR, Bates D, Black DM. Clinical use of bone densitometry: scientific review. JAMA. 2002 Oct 16;288(15):1889-97.
3. Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ 3rd, Khaltaev N. A reference standard for the description of osteoporosis. Bone. 2008 Mar;42(3):467-75.
4. Siris ES, Adler R, Bilezikian J, Bolognese M, Dawson-Hughes B, Favus MJ, et al. The clinical diagnosis of osteoporosis: a position statement from the National Bone Health Alliance Working Group. Osteoporos Int. 2014 May;25(5):1439-43.
5. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy. Osteoporosis prevention, diagnosis, and therapy. JAMA. 2001 Feb 14;285(6):785-95.
6. Heaney RP. BMD: the problem. Osteoporos Int. 2005 Sep;16(9):1013-5.
7. Wallach S, Feinblatt JD, Carstens JH Jr, Avioli LV. The bone "quality" problem. Calcif Tissue Int. 1992 Sep;51(3):169-72.
8. Hans D, Fuerst T, Lang T, Majumdar S, Lu Y, Genant HK, et al. How can we measure bone quality? Baillieres Clin Rheumatol. 1997 Aug;11(3):495-515.
9. Wu C, Glüer C, Lu Y, Fuerst T, Hans D, Genant HK. Ultrasound characterization of bone demineralization. Calcif Tissue Int. 1998 Feb;62(2):133-9.
10. Harvey NC, Glüer CC, Binkley N, McCloskey EV, Brandi ML, Cooper C, et al. Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice. Bone. 2015 Sep;78:216-24.
11. Hans D, Barthe N, Boutroy S, Pothuaud L, Winzenrieth R, Krieg MA. Correlations between trabecular bone score, measured using anteroposterior dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J Clin Densitom. 2011 Jul-Sep;14(3):302-12.
12. Del Rio LM, Winzenrieth R, Cormier C, Di Gregorio S. Is bone microarchitecture status of the lumbar spine assessed by TBS related to femoral neck fracture? A Spanish case-control study. Osteoporos Int. 2013 Mar;24(3):991-8.
13. Hans D, Šteňová E, Lamy O. The Trabecular Bone Score (TBS) Complements DXA and the FRAX as a Fracture Risk Assessment Tool in Routine Clinical Practice. Curr Osteoporos Rep. 2017 Dec;15(6):521-31.
14. Töyräs J, Kröger H, Jurvelin JS. Bone properties as estimated by mineral density, ultrasound attenuation, and velocity. Bone. 1999 Dec;25(6):725-31.
15. Nicholson PH, Müller R, Lowet G, Cheng XG, Hildebrand T, Rüegsegger P, et al. Do quantitative ultrasound measurements reflect structure independently of density in human vertebral cancellous bone? Bone. 1998 Nov;23(5):425-31.
16. Schneider J, Bundschuh B, Späth C, Landkammer C, Müller H, Sommer U, et al. Discrimination of patients with and without vertebral fractures as measured by ultrasound and DXA osteodensitometry. Calcif Tissue Int. 2004 Mar;74(3):246-54.
17. Moayyeri A, Kaptoge S, Dalzell N, Bingham S, Luben RN, Wareham NJ, et al. Is QUS or DXA better for predicting the 10-year absolute risk of fracture? J Bone Miner Res. 2009 Jul;24(7):1319-25.
18. Chen JS, March LM, Cumming RG, Cameron ID, Simpson JM, Lord SR, et al. Role of quantitative ultrasound to predict fracture among institutionalised older people with a history of fracture. Osteoporos Int. 2009 Jan;20(1):105-12.
19. Chen JS, Seibel MJ, Zochling J, March L, Cameron ID, Cumming RG, et al. Calcaneal ultrasound but not bone turnover predicts fractures in vitamin D deficient frail elderly at high risk of falls. Calcif Tissue Int. 2006 Jul;79(1):37-42.
20. Navarro MC, Sosa M, Saavedra P, Lainez P, Marrero M, Torres M, Medina CD. Poverty is a risk factor for osteoporotic fractures. Osteoporos Int. 2009 Mar;20(3):393-8.
21. WHO Scientific Group on Research on the Menopause. Research on the menopause : report of a WHO scientific group 1981. p. 110. Available from: http://www.who.int/iris/handle/10665/41526
22. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013 Nov 27;310(20):2191-4.
23. National Institute of Statistics. (INE). Products and Services / Publications / Publications for free download / Women and Men in Spain / Wages, income, social cohesion (updated on 11 October 2019) / 2.8 Población en riesgo de pobreza relativa según situación laboral y en las personas con trabajo. 2019. Available from: https://www.ine.es/ss/Satellite?c=INESeccion_ C&p=1254735110672&pagename=ProductosYServicios %2FPYSLayout&cid=1259925455948&L=1. Consulted on February 23th, 2020.
24. Wilson P, Horwath C. Validation of a short food frequency questionnaire for assessment of dietary calcium intake in women. Eur J Clin Nutr. 1996 Apr;50(4):220-8.
25. Navarro Rodríguez MC, Saavedra Santana P, de Pablos Velasco P, Sablón González N, de Miguel Ruiz E, Castro Medina R, et al. Estilo de vida, nivel socioeconómico y morbilidad en mujeres posmenopáusicas con obesidad de grados II y III [Lifestyle, socioeconomic status and morbidity in postmenopausal women with grade II and III obesity]. Endocrinol Nutr. 2009 May;56(5):227-32. Spanish.
26. Marco MG, Pescador AM, Rodríguez EG, Santana SG, Padilla EG, Santana PS, et al. Factores relacionados con insuficiencia de vitamina D en estudiantes de Medicina de Gran Canaria. Rev Osteoporos Metab Min. 2010;2(2):11-8.
27. Sosa M, Saavedra P, Muñoz-Torres M, Alegre J, Gómez C, González-Macías J, et al. GIUMO Study Group. Quantitative ultrasound calcaneus measurements: normative data and precision in the spanish population. Osteoporos Int. 2002;13(6):487-92.
28. Sosa M, Hernández D, Estévez S, Rodríguez M, Limiñana JM, Saavedra P, et al. The range of bone mineral density in healthy Canarian women by dual X-ray absorptiometry radiography and quantitative computer tomography. J Clin Densitom. 1998 Winter;1(4):385-93.
29. Diaz Curiel M, Carrasco de la Peña JL, Honorato Perez J, Perez Cano R, Rapado A, Ruiz Martinez I. Study of bone mineral density in lumbar spine and femoral neck in a Spanish population. Multicentre Research Project on Osteoporosis. Osteoporos Int. 1997;7(1):59-64.
30. Silva BC, Broy SB, Boutroy S, Schousboe JT, Shepherd JA, Leslie WD. Fracture Risk Prediction by Non-BMD DXA Measures: the 2015 ISCD Official Positions Part 2: Trabecular Bone Score. J Clin Densitom. 2015 Jul-Sep;18(3):309-30.
31. Genant HK, Wu CY, van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993 Sep;8(9):1137-48.
32. Martineau P, Leslie WD. Trabecular bone score (TBS): Method and applications. Bone. 2017 Nov;104:66-72.
33. Bousson V, Bergot C, Sutter B, Levitz P, Cortet B; Scientific Committee of the Groupe de Recherche et d’Information sur les Ostéoporoses. Trabecular bone score (TBS): available knowledge, clinical relevance, and future prospects. Osteoporos Int. 2012 May;23(5):1489-501.
34. Silva BC, Leslie WD, Resch H, Lamy O, Lesnyak O, Binkley N, et al. Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res. 2014 Mar;29(3):518-30.
35. Martineau P, Silva BC, Leslie WD. Utility of trabecular bone score in the evaluation of osteoporosis. Curr Opin Endocrinol Diabetes Obes. 2017 Dec;24(6):402-10.
36. Shevroja E, Lamy O, Kohlmeier L, Koromani F, Rivadeneira F, Hans D. Use of Trabecular Bone Score (TBS) as a Complementary Approach to Dual-energy X-ray Absorptiometry (DXA) for Fracture Risk Assessment in Clinical Practice. J Clin Densitom. 2017 Jul-Sep;20(3):334-45.
37. Liu J, Curtis EM, Cooper C, Harvey NC. State of the art in osteoporosis risk assessment and treatment. J Endocrinol Invest. 2019 Oct;42(10):1149-64.
38. Leslie WD, Anderson WA, Metge CJ, Manness LJ; Maximizing Osteoporosis Management in Manitoba Steering Committee. Clinical risk factors for fracture in postmenopausal Canadian women: a population-based prevalence study. Bone. 2007 Apr;40(4):991-6.
39. Boutroy S, Hans D, Sornay-Rendu E, Vilayphiou N, Winzenrieth R, Chapurlat R. Trabecular bone score improves fracture risk prediction in non-osteoporotic women: the OFELY study. Osteoporos Int. 2013 Jan;24(1):77-85.
40. Glüer CC, Cummings SR, Bauer DC, Stone K, Pressman A, Mathur A, et al. Osteoporosis: association of recent fractures with quantitative US findings. Radiology. 1996 Jun;199(3):725-32.
41. Brandenburger GH. Clinical determination of bone quality: is ultrasound an answer? Calcif Tissue Int. 1993;53 Suppl 1:S151-6.
42. Raum K, Grimal Q, Varga P, Barkmann R, Glüer CC, Laugier P. Ultrasound to assess bone quality. Curr Osteoporos Rep. 2014 Jun;12(2):154-62.
43. Gómez-de-Tejada Romero MJ, Navarro Rodríguez MD, Saavedra Santana P, Quesada Gómez JM, Jódar Gimeno E, Sosa Henríquez M. Prevalence of osteoporosis, vertebral fractures and hypovitaminosis D in postmenopausal women living in a rural environment. Maturitas. 2014 Mar;77(3):282-6.
44. Navarro MC, Sosa M, Saavedra P, Gil-Antullano SP, Castro R, Bonet M, et al. Anthropometric and gynaecological history according to the socioeconomic status of postmenopausal women: poverty and the menopause. Menopause Int. 2010 Mar;16(1):12-7.
45. Tanumihardjo SA, Anderson C, Kaufer-Horwitz M, Bode L, Emenaker NJ, Haqq AM, et al. Poverty, obesity, and malnutrition: an international perspective recognizing the paradox. J Am Diet Assoc. 2007 Nov;107(11):1966-72.
46. Wamala SP, Lynch J, Kaplan GA. Women's exposure to early and later life socioeconomic disadvantage and coronary heart disease risk: the Stockholm Female Coronary Risk Study. Int J Epidemiol. 2001 Apr;30(2):275-84.
47. Olmos JM, Hernández JL, Pariente E, Martínez J, Valero C, González-Macías J. Trabecular bone score and bone quantitative ultrasound in Spanish postmenopausal women. The Camargo Cohort Study. Maturitas. 2020 Feb;132:24-29.
48. Sosa M, Saavedra P, Muñoz-Torres M, Alegre J, Gómez C, González-Macías J, et al. GIUMO Study Group. Quantitative ultrasound calcaneus measurements: normative data and precision in the spanish population. Osteoporos Int. 2002;13(6):487-92.
49. Sosa Henríquez M, Saavedra Santana P, Alegre López J, Gómez Alonso C, González Macías J, Guañabens Gay N, et al. Grupo de investigación en ultrasonidos y metabolismo mineral (GIUMO). Prevalencia de osteoporosis en la población española por ultrasonografía de calcáneo en función del criterio diagnóstico utilizado. Datos del estudio GIUMO [Calcaneous ultrasonography as measurement of osteoporosis prevalence in the general population in relation to the diagnostic criterion utilized. Data of the study GIUMO]. Rev Clin Esp. 2003 Jul;203(7):329-33. Spanish.