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Composición corporal y relación con la densidad mamográfica en mujeres colombianas que asisten a tamizaje en la Clínica Universitaria Colombia

dc.contributor.advisorPerdomo Charry, Oscar Julián
dc.contributor.advisorPedraza Flechas, Ana María
dc.creatorVenegas Torres, Mónica Natalia
dc.creatorUrrego Torres, Diana Andrea
dc.creatorMezamell Macías, Farid
dc.creator.degreeMagíster en Epidemiología
dc.creator.degreeLevelMaestría
dc.creator.degreetypeFull time
dc.date.accessioned2023-07-17T13:13:59Z
dc.date.available2023-07-17T13:13:59Z
dc.date.created2023-07-06
dc.descriptionIntroducción: El objetivo primario de este estudio es determinar la composición corporal de las mujeres colombianas y su relación con la densidad mamográfica. Métodos: Estudio descriptivo transversal con componente analítico. Mediante muestreo no probabilístico se seleccionaron 670 pacientes que cumplían los criterios de inclusión y exclusión. Las variables cualitativas se representan mediante distribución de frecuencias absolutas y proporciones, y las variables cuantitativas con estadísticos de tendencia central y dispersión. Se ajusta un modelo de regresión lineal simple para determinar la relación entre valores autorreportados y medidos de peso y talla, así como la relación entre indicadores de adiposidad con edad, consumo de anticonceptivos y estado menopaúsico. También mediante modelo de regresión lineal simple y múltiple se relaciona la densidad mamográfica con variables clínicas y antropométricas, tomado un valor significativo de p<0,05. Finalmente, se construyó un árbol de decisión para identificar aquellas variables explicativas para ser incluidas en un modelo final de regresión para poder estimar la densidad mamográfica, al que finalmente se calculó la potencia estadística del modelo para valorar posible utilidad en la práctica clínica. Resultados: El promedio de edad fue 57,9 años (DE 6,3). Según el IMC el 43,4% presentaba sobrepeso, seguido de peso normal, obesidad grado I, grado II, grado III y bajo peso, con porcentajes de 29,1%, 16,6%, 6,3%, 1,2% y 0,3%, respectivamente. Las mujeres de mayor edad presentaron mayor porcentaje de masa grasa (p 0,02) y razón cintura/cadera (p 0,01). El 53,4% de las mujeres tenían una densidad mamográfica menor del 25%. Las mujeres premenopáusicas presentaron 14,2% de mayor densidad mamográfica que en las postmenopáusicas. El IMC y la edad presentaron relación lineal inversa con la densidad mamográfica (B= -4.0, valor p 0,000; B=-0,7, valor p 0,000, respectivamente). La edad mantiene una relación lineal inversa con el porcentaje de densidad mamográfica mientras que los anticonceptivos orales mostraron relación lineal directa, ambas estadísticamente significativas. La edad, el IMC, perímetro de cintura y talla del brasier se identificaron como variables susceptibles de ser incluidas en un modelo de predicción de porcentaje de densidad mamográfica. Conclusiones: La mayor proporción de mujeres en este estudio se encontraron en rango de sobrepeso, al igual que con patrones de densidad mamográfica bajos (<50%). Se pudo proponer la inclusión de variables como la edad, el IMC, la talla del brasier y el perímetro de cintura en un modelo final de estimación de densidad mamográfica, estimando una potencia estadística del modelo de 1,00.
dc.description.abstractIntroduction: The primary objective of this study is to determine the body composition of Colombian women and its relationship with mammographic density. Methods: Cross-sectional descriptive study with an analytical component. Using non-probabilistic sampling, 670 patients who met the inclusion and exclusion criteria were selected. Qualitative variables are represented by absolute frequency distribution and proportions, and quantitative variables by central tendency and dispersion statistics. A simple linear regression model was fitted to determine the relationship between self-reported and measured values of weight and height, as well as the relationship between indicators of adiposity with age, contraceptive use, and menopausal status. Also using a simple and multiple linear regression model, mammographic density is related to clinical and anthropometric variables, taking a significant value of p<0.05. Finally, a decision tree was built to identify those explanatory variables to be included in a final regression model to be able to estimate mammographic density, to which the statistical power of the model was finally calculated to assess its possible utility in clinical practice. Results: The mean age was 57.9 years (SD 6.3). According to the BMI, 43.4% were overweight, followed by normal weight, obesity grade I, grade II, grade III and underweight, with percentages of 29.1%, 16.6%, 6.3%, 1.2 % and 0.3%, respectively. Older women had a higher percentage of fat mass (p 0.02) and waist/hip ratio (p 0.01). 53.4% of the women had a mammographic density of less than 25%. Premenopausal women presented 14.2% higher mammographic density than postmenopausal women. BMI and age presented an inverse linear relationship with mammographic density (B= -4.0, p value 0.000; B= -0.7, p value 0.000, respectively). Age maintains an inverse linear relationship with the percentage of mammographic density, while oral contraceptives showed a direct linear relationship, both statistically significant. Age, BMI, waist circumference, and bra size were identified as variables likely to be included in a mammographic density percentage prediction model. Conclusions: The highest proportion of women in this study were in the overweight range, as well as with low mammographic density patterns (<50%). It was possible to propose the inclusion of variables such as age, BMI, bra size, and waist circumference in a final mammographic density estimation model, estimating a statistical power of 1.00 for the model.
dc.format.extent52
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_40169
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/40169
dc.language.isospa
dc.publisherUniversidad del Rosariospa
dc.publisherUniversidad CES. Facultad de Medicina
dc.publisher.departmentEscuela de Medicina y Ciencias de la Saludspa
dc.publisher.programMaestría en Epidemiologíaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
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dc.source.bibliographicCitation1. World Health Organization: WHO. (2021). Cáncer de mama. www.who.int. https://www.who.int/es/news-room/fact-sheets/detail/breast-cancer
dc.source.bibliographicCitation2. Herrera, M. P. (2019). CANCER DE MAMA Y CUELLO UTERINO, COLOMBIA 2018. Vigilancia y análisis del riesgo en salud, Institución Nacional de Salud, Colombia.
dc.source.bibliographicCitation3. Neira, V. P. (2013). Densidad mamaria y riesgo de cáncer mamario. Revista Médica Clínica Las Condes, 24(1), 122–130. https://doi.org/10.1016/s0716-8640(13)70137-8
dc.source.bibliographicCitation4. Dorgan, J. F., Klifa, C., Shepherd, J. A., Egleston, B. L., Kwiterovich, P. O., Himes, J. H., Gabriel, K. P., Van Horn, L., Snetselaar, L., Stevens, V. J., Barton, B. A., Robson, A. M., Lasser, N. L., Deshmukh, S., & Hylton, N. M. (2012). Height, adiposity and body fat distribution and breast density in young women. Breast Cancer Research, 14(4). https://doi.org/10.1186/bcr3228
dc.source.bibliographicCitation5. Elmore, J. G., Carney, P. A., Abraham, L., Barlow, W. E., Egger, J., Fosse, J. S., Cutter, G., Hendrick, R. E., D’Orsi, C. J., Paliwal, P., & Taplin, S. H. (2004). The Association Between Obesity and Screening Mammography Accuracy. Archives of Internal Medicine, 164(10), 1140. https://doi.org/10.1001/archinte.164.10.1140
dc.source.bibliographicCitation6. World Health Organization: WHO. (2021). Obesidad y sobrepeso. www.who.int. https://www.who.int/es/news-room/fact-sheets/detail/obesity-and-overweight
dc.source.bibliographicCitation7. ENSIN: Encuesta Nacional de Situación Nutricional. (s.f.). Portal ICBF – Instituto Colombiano de Bienestar Familiar ICBF. https://www.icbf.gov.co/bienestar/nutricion/encuesta-nacional-situacion-nutricional#ensin3
dc.source.bibliographicCitation8. Safety, N. a. F. (2016). Report of the commission on ending childhood obesity. www.who.int. https://www.who.int/publications/i/item/9789241510066
dc.source.bibliographicCitation9. Yk, K., Danforth, E., Jensen, Kopelman, P., Lefebvre, P., & Ba, R. (2001). Dose-response issues concerning physical activity and health: an evidence-based symposium. Medicine and Science in Sports and Exercise, 33(Supplement), S351–S358. https://doi.org/10.1097/00005768-200106001-00003
dc.source.bibliographicCitation10. GBD Compare. (n.d.). Institute for Health Metrics and Evaluation. https://vizhub.healthdata.org/gbd-compare/
dc.source.bibliographicCitation11. de, M. (2019). Detecte el cáncer de mama a tiempo. Minsalud.gov.co. https://www.minsalud.gov.co/Paginas/Detecte-el-cancer-de-mama-a-tiempo.aspx
dc.source.bibliographicCitation12. Robles, S. (2001). El cáncer de mama en América Latina y el Caribe: Informar sobre las opciones. https://iris.paho.org/handle/10665.2/3105?locale-attribute=es
dc.source.bibliographicCitation13. Mammographic densities and risk of breast cancer. (1991). PubMed. https://doi.org/10.1002/1097-0142(19910601)67:11
dc.source.bibliographicCitation14. Baquero-Serrano, A., López-Martínez, L., Vera-Campos, S. N., Rosales-Rueda, S., Jaramillo-Botero, N., & Ochoa-Vera, M.E. (2020). Prevalencia de tejido mamario denso en una población en la ciudad de Bucaramanga, Colombia. Revista Colombiana de Cancerología. https://doi.org/10.35509/01239015.94
dc.source.bibliographicCitation15. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249. https://doi.org/10.3322/caac.21660
dc.source.bibliographicCitation16. Cáncer de mama - Estadísticas. (2023,10 may). Cancer.Net. https://www.cancer.net/es/tipos-de-c%C3%A1ncer/c%C3%A1ncer-de-mama/estadisticas
dc.source.bibliographicCitation17. Tan, P. H., Ellis, I. O., Allison, K. H., Brogi, E., Fox, S. B., Lakhani, S. R., Lazar, A. J., Morris, E. A., Sahin, A. A., Salgado, R., Sapino, A., Sasano, H., Schnitt, S. J., Sotiriou, C., Van Diest, P. J., White, V. A., Lokuhetty, D., & Cree, I. A. (2020). The 2019 World Health Organization classification of tumours of the breast. Histopathology, 77(2), 181–185. https://doi.org/10.1111/his.14091
dc.source.bibliographicCitation18. Elson, B. C., Ikeda, D., Andersson, I., & Wattsgård, C. (1992). Fibrosarcoma of the breast: mammographic findings in five cases. American Journal of Roentgenology, 158(5), 993–995. https://doi.org/10.2214/ajr.158.5.1314479
dc.source.bibliographicCitation19. Aibar, L., Santalla, A., Criado, L., Pérez, I. G., Calderon, M. A., Gallo, J. R., & Parra, J. F. (2011). Clasificación radiológica y manejo de las lesiones mamarias. Clínica e Investigación en Ginecología y Obstetricia, 38(4), 141–149. https://doi.org/10.1016/j.gine.2010.10.016
dc.source.bibliographicCitation20. Sarquis, F. (2018). BI-RADS® 5ta Edición. https://www.redalyc.org/journal/3825/382555870012/html/
dc.source.bibliographicCitation21. Ekpo, E. U., Ujong, U. P., Mello-Thoms, C., & McEntee, M. F. (2016). Assessment of Interradiologist Agreement Regarding Mammographic Breast Density Classification Using the Fifth Edition of the BI-RADS Atlas. American Journal of Roentgenology, 206(5), 1119–1123. https://doi.org/10.2214/ajr.15.15049
dc.source.bibliographicCitation22. Winkler, N., Raza, S., Mackesy, M. M., & Birdwell, R. L. (2015). Breast Density: Clinical Implications and Assessment Methods. Radiographics, 35(2), 316–324. https://doi.org/10.1148/rg.352140134
dc.source.bibliographicCitation23. Wolfe, J. N. (1976). Breast patterns as an index of risk for developing breast cancer. American Journal of Roentgenology, 126(6), 1130–1137. https://doi.org/10.2214/ajr.126.6.1130
dc.source.bibliographicCitation24. Ciatto, S., Houssami, N., Apruzzese, A., Bassetti, E., Brancato, B.,F,C., Lamberini, M., Marcelli, G., Pellizzoni, R., Pesce, B., Risso, G. A., Russo, F. P., & Scorsolini, A. (2005). Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. The Breast, 14(4), 269-275. https://doi.org/10.1016/j.breast.2004.12.004
dc.source.bibliographicCitation25. Redondo, A., Comas, M., Macià, F., Ferrer, F., Murta-Nascimento, C., Maristany, M.T., Molins, E., Sala, M.M., & Castells, X. (2012). Inter - and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. British Journal of Radiology, 85(1019), 1465-1470. https://doi.org/10.1259/bjr/21256379
dc.source.bibliographicCitation26. Timmers, J.M.H., Van Doorne-Nagtegaal, H.J., Verbeek, A., Heeten, G. J. D., & , Broeders, M.J.M. (2012). A dedicated BI-RADS training programme: Effect on the inter-observer variation among screening radiologists. European Journal of Radiology, 81(9), 2184-2188. https://doi.org/10.1016/j.ejrad.2011.07.011
dc.source.bibliographicCitation27. Byng, J. W., Yaffe, M. J., Jong, R., Shumak, R., Lockwood, G. W., Tritchler, D., & Boyd, N. F. (1998). Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics, 18(6), 1587–1598. https://doi.org/10.1148/radiographics.18.6.9821201
dc.source.bibliographicCitation28. Crivellé, M. S. (2014). La densidad mamaria. Una aproximación. Revista De Senología Y Patología Mamaria, 27(3), 138–142. https://doi.org/10.1016/j.senol.2014.04.001
dc.source.bibliographicCitation29. ITI. (2020, 13 marzo). dmscan - ITI. https://www.iti.es/dmscan/
dc.source.bibliographicCitation30. Llobet, R., Martín, M., Antón, J., Miranda-García, J., Casals, M. R., Martínez, I. J. M., Ruiz-Perales, F., Pérez-Gómez, B., Salas-Trejo, D., & Perez-Cortes, J. (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine, 116(2), 105–115. https://doi.org/10.1016/j.cmpb.2014.01.021
dc.source.bibliographicCitation31. Torre, L.A., Siegel, R.L., Ward, E.C., & Jemal, A.(2016). Global Cancer Incidence and Mortality Rates and Trends-An Update. Cancer Epidemiology, Biomarkers & Prevention, 25(1), 16-27. https://doi.org/10.1158/1055-9965.epi-15-0578
dc.source.bibliographicCitation32. Boyd, N.F., Dite, G.S., Stone, J., Gunasekara, A., English, D.R., McCredie, M.R.E., Giles, G.G., Tritchler, D., Chiarelli, A. M., Yaffe, M. J., & Hopper, J. L. (2002).Heritability of Mammographic Density, a Risk Factor for Breast Cancer. The New England Journal of Medicine, 347(12), 886-894. https://doi.org/10.1056/nejmoa013390
dc.source.bibliographicCitation33. Sprague, B.L., Gangnon, R.E., Burt, V., Trentham-Dietz, A., Hampton, J.M., Wellman, R.D., Kerlikowske, K. (2014). Prevalence of Mammographically Dense Breasts in the United States. 106. https://doi.org/10.1093/jnci/dju255
dc.source.bibliographicCitation34. Nazari, S., & Mukherjee, P. (2018). An overview of mammographic density and its association with breast cancer. Breast Cancer, 25(3), 259–267. https://doi.org/10.1007/s12282-018-0857-5
dc.source.bibliographicCitation35. Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., Jong, R. A., Hislop, G., Chiarelli, A. M., Minkin, S., & Yaffe, M. J. (2007). Mammographic Density and the Risk and Detection of Breast Cancer. The New England Journal of Medicine, 356(3), 227–236. https://doi.org/10.1056/nejmoa062790
dc.source.bibliographicCitation36. Fletcher, S. W., & Elmore, J. G. (2003). Mammographic Screening for Breast Cancer. The New England Journal of Medicine, 348(17), 1672–1680. https://doi.org/10.1056/nejmcp021804
dc.source.bibliographicCitation37. Kolb, T., Lichy, J., Newhouse, J.H. Comparison of the Performance of Screening Mammography, Physical Examination, and Breast US and Evaluation of Factors that influence Them: An Analysis of 27,825 Patient Evaluations. Radiology, 225(1),165-175. https://doi.org/10.1148/radiol.2251011667
dc.source.bibliographicCitation38. Association between mammographic parenchymal pattern classification and incidence of breast cancer. (1980). Pubmed. https://doi.org/10.1002/1097-0142(19800515)45:10
dc.source.bibliographicCitation39. Breast size and mammographic pattern in relation to breast cancer risk. (1996, February1). PubMed. https://pubmed.ncbi.nlm.nih.gov/8664807/
dc.source.bibliographicCitation40. Sala, E., Warren, R. B., McCann, J. J., Duffy, S. W., Day, N. P. J., & Luben, R. (1998). Mammographic parenchymal patterns and mode of detection: implications for the breast screening programme. Journal of Medical Screening, 5(4), 207-212. . https://doi.org/10.1136/jms.5.4.207
dc.source.bibliographicCitation41. McCormack, ., & Silva, I. D. S. (2006). Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis. Cancer Epidemiology, Biomarkers Prevention, 15(6), 115-1169. https://doi.org/10.1158/1055-9965.epi-06-0034
dc.source.bibliographicCitation42. Li, T., Sun, L., Miller, N., Nicklee, T., Woo, J., Hulse-Smith, L., Tsao, M., Khokha, R., Martin, L., Boyd, N. (2005). The Association of Measured Breast Tissue Characteristics With Mammographic Density and Other Risk Factors For Breast Cancer. 14(2),343-349. https://doi.org/10.1158/1055-9965.epi-04-0490
dc.source.bibliographicCitation43. Del Carmen, M.G., Halpern, E.F., Kopans, D.B., Moy, B., Moore, R., Goss, P.E., & Hughes, K. S. (2007). Mammographic Breast Density and Race. American Journal of Roentgenology, 188(4), 1147-1150. https://doi.org/10.2214/ajr.06.0619
dc.source.bibliographicCitation44. Boyd, N.F., Martin, L.J., Sun, L., Guo, H., Chiarelli, A. M., Hislop, G., Yaffe, M. J., & Minkin, S. (2006). Body Size, Mammographic Density, and Breast Cancer Risk. Cancer Epidemiology, Biomarkers & Prevention, 15(11), 2086-2092. https://doi.org/10.1158/1055-9965.epi-06-0345
dc.source.bibliographicCitation45. Wang, Z., Pierson, R.N., & Heymsfield, S. B. (1992). The five-level model: a new approach to organizing body-composition research. The American Journal of Clinical Nutrition, 56(1), 19-28. https://doi.org/10.1093/ajcn/56.1.19
dc.source.bibliographicCitation46. Santana, Porbén S., Alicia, L., Borrás, E. (n.d). Composición corporal. https://www.medigraphic.com/pdfs/actamedica/acm-2003/acm031e.pdf
dc.source.bibliographicCitation47. Heymsfield, S.B., Pietrobelli, A., Wang, Z., Saris, W.H.M.(2005). The end of body composition methodology research? Current Opinion in clinical Nutrition and Metabolic Care, 8(6), 591-594. https://doi.org/10.1097/01.mco.0000171151.43410.a5
dc.source.bibliographicCitation48. Shah, A.S.M., Bilal, R. (2009). Body Composition, its Significance and Models for Assessment. Pakistan Journal of Nutrition. 8(2), 198-202. https://doi.org/10.3923/pjn.2009.198.202
dc.source.bibliographicCitation49. Berral FJ, Escribano A, Berral CJ, Lancho JL. Body composition of top performance athletes determined by a modification of Kerr’s method. Med Sci Sport Exer. 1992;4–6.
dc.source.bibliographicCitation50. Resende, C., J., Junior, Vieira, M. N., Ferriolli, E., Netto, A. S., Da Silva Castro Perdona, G., & Monteiro, J. L. (2011). Body composition measures of obese adolescents by the deuterium oxide dilution method and by bioelectrical impedance. Brazilian Journal of Medical and Biological Research, 44(11), 1164-1170. https://doi.org/10.1590/s0100-879x2011007500122
dc.source.bibliographicCitation51. Kaur, M., Talwar, I. (2011).Body composition and fat distribution among older Jat females: a rural-urban comparison. Homo-journal of Comparative Human Biology, 62(5), 374-385. https://doi.org/10.1016/j.jchb.2010.05.004
dc.source.bibliographicCitation52. Park, H., Park, K., Kim, M., Kim G.S., & Chung, S.(2011). Gender Differences in Relationship between Fat-Free Mass Index and fat mass index among Korean Children Using Body Composition chart. Yonsei Medical Journal, 52(6), 948. https://doi.org/10.3349/ymj.2011.52.6.948
dc.source.bibliographicCitation53. Sant’Anna, M., Priore, S.E., Franceschini, S. (2009). Métodos de avaliação da composição corporal em crianças. Revista Paulista De Pediatria, 27(3), 315-321. https://doi.org/10.1590/s0103-05822009000300013
dc.source.bibliographicCitation54. Carbajal, Á. (2013). Manual de Nutrición y Dietética. https://eprints.ucm.es/id/eprint/22755/1/Manual-nutricion-dietetica-CARBAJAL.pdf
dc.source.bibliographicCitation55. Durnin, J., Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 Years. British Journal of Nutrion, 32(1),77-97. https://doi.org/10.1079/bjn19740060
dc.source.bibliographicCitation56. Pietrobelli, A., Heymsfield S.B., Wang, Z., Gallagher, D.L. (2001). Multi-component body composition models: recent advances and future directions. European Journal of Clinical Nutrition, 55(2), 69-75. https://doi.org/10.1038/sj.ejcn.1601105.
dc.source.bibliographicCitation57. Moreira, O.C., De Oliveira, R.a.R., De Oliveira, C.M.F., Doimo L.A., Amorim P.R.D.S., Laterza, M. C., Monteiro, W.D., & Marins, J.C.B(2014).Risk factors for cardiovascular disease in prodessors from a public university. Investigación y Educación En Enfermeria, 32(2), 280-290. https://doi.org/10.17533/udea.iee.v32n2a11.
dc.source.bibliographicCitation58. Welborn, T.A., Dhaliwal S. (2007). Preferred clinical measures of central obesity for predicting mortality. European Journal of Clinical Nutrition, 61(12), 1373-1379. https://doi.org/10.1038/sj.ejcn.1602656
dc.source.bibliographicCitation59. World Health Organization. (1998). Obesity : preventing and managing the global epidemic : report of a WHO Consultation on Obesity, Geneva, 3-5 June 1997. https://apps.who.int/iris/handle/10665/63854
dc.source.bibliographicCitation60. Ramírez-Vélez, R., Suaréz-Ortegón, M.F.,& De Plata, A.C.A. (2011b). Asociación entre adiposidad y factores de riesgo cardiovascular en infantes pre-púberes. Endocrinologia y Nutricion, 58(9), 457-463. https://doi.org/10.1016/j.endonu.2011.06.008
dc.source.bibliographicCitation61. Jiménez, E.G. (2013). Composición corporal: estudio y utilidad clínica. Endocrinologia y Nutricion, 60(2), 69-75. https://doi.org/10.1016/j.endou.2012.04.003.
dc.source.bibliographicCitation62. Amaral, T., Restivo, M.T., Guerra R., Marques, E.A., De Fátima Chousal, M., & Mota, J. (2010).Accuracy of a digital skinfold system for measuring skinfold thickness and estimating body fat. British Journal of Nutrition, 105(3), 478-484. https://doi.org/10.1017/s0007114510003727
dc.source.bibliographicCitation63. Haskell, W.L., Lee, I., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., Macera, C. A., Heath, G. W., Thompson, P. M., & Bauman, A. (2007). Physical Activity and Public Health. Medicine and Science in Sports and Ecercise, 29(8), 1423-1434. https://doi.org/10.1249/mss.0b013e3180616b27
dc.source.bibliographicCitation64. Noncommunicable diseases country profiles (2018). World Health Organization. https://apps.who.int/iris/handle/10665/274512
dc.source.bibliographicCitation65. Safety, N. a. F. (2011). Waist circumference and waist-hip ratio: report of a WHO expert consultation. https://www.who.int/publications/i/item/9789241501491
dc.source.bibliographicCitation66. Lear, S. A., James, P. T., Ko, G. T., & Kumanyika, S. K. (2009). Appropriateness of waist circumference and waist-to-hip ratio cutoffs for different ethnic groups. European Journal of Clinical Nutrition, 64(1), 42–61. https://doi.org/10.1038/ejcn.2009.70
dc.source.bibliographicCitation67. Ayvaz, G. (2011,May 23). Methods for Body Composition Analysis in Adults. https://benthamopen.com/ABSTRACT/TOOBEST-3-62
dc.source.bibliographicCitation68. GB REMOTE DISPLAY VERSION COLUMN MOUNTED VERSION BODY COMPOSITION ANALYZER BC-420MA, instruction manual. (n.d.). http://www.tanita.co.th/images/media/Manual_Eng_BC_420_P.pdf
dc.source.bibliographicCitation69. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. (1995).PubMed. https://pubmed.ncbi.nlm.nih.gov/8594834/
dc.source.bibliographicCitation70. Montes, B. (2021). Concordancia entre antropometría y bioimpedancia para la estimación del porcentaje graso en adultos (18-59 Años) de la Universidad de Caldas Manizales. https://repositorio.ucaldas.edu.co/bitstream/handle/ucaldas/17231/Bibiana_HurtadoMontes_2021.pdf?sequence=1
dc.source.bibliographicCitation71. World. (2020). The Challenge of Obesity in the WHO European Region and the Strategies for Response. Who.int. https://doi.org/9789289014083
dc.source.bibliographicCitation72. Popkin, B.M. (2004) The nutrition transition: An Overview of World Patterns of Change. Nutrion Reviews, 62, S140-S143. https://doi.org/10.1111/j.1753-4887.2004.tb00084.x
dc.source.bibliographicCitation73. Acosta, K.(2013). Revista de Economía del Rosario. Revista de Economía del Rosario. https://revistas.urosario.edu.co/index.php/economia/article/view/3330
dc.source.bibliographicCitation74. Cutler, D.M., Glaeser, E.L,& Shapiro, J.M. (2003). Why Have Americans Become More Obese? Journal of Economic Perspectives,17(3):93-118. https://doi.org/10.1257/089533003769204371
dc.source.bibliographicCitation75. Chou, S., Grossman, M.,, Saffer, H. (2004). An economic analysis of adult obesity: results from the Behavioral Risk Factor Surveillance System. Journal of Health Economics, 23(3), 565-587. https://doi.org/10.1257/089533001016/j.jhealeco.2003.10.003
dc.source.bibliographicCitation76. ENSIN: (2010) Encuesta Nacional de Situación Nutricional. (n.d.). Portal ICBF - Instituto Colombiano De Bienestar Familiar ICBF. https://www.icbf.gov.co/bienestar/nutricion/encuesta-nacional-situacion-nutricional#ensin2010
dc.source.bibliographicCitation77. Ramírez, L.F.G.&. N.F. (2008). Diferencias en los índices de masa corporal en Colombia en 2005: una aplicación de los indicadores de desigualdad. ideas.repec.org. https://ideas.repec.org/p/col/000092/004693.html
dc.source.bibliographicCitation78. Ramírez, L.F.G.&. N.F. (2009b). Body mass index as a standard of living measure: A different interpretation for the case of Colombia. ideas.repec.org. https://ideas.repec.org/p/col/000092/005218.html
dc.source.bibliographicCitation79. Soguel, L., Durocher, F., Tchernof, A., & Diorio, C. (2017b). Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations. European Journal of Cancer Prevention, 26(6), 511-520. https://doin.org/10.1097/cej.0000000000000310
dc.source.bibliographicCitation80. Sala, E., Warren, R.B., McCann, J.J., Duffy, S.W., Luben, R., & Day, N.P.J.(1999c). High-risk mammographic parenchymal patterns and anthropometric measures: A case-control study. British Journal of Cancer, 81(7),1257-1261. https://doi.org/10.1038/sj.bjc.6690838
dc.source.bibliographicCitation81. Masala, G., Ambrogetti, D., Assedi, M., Giorgi, D., Del Turco, M.R., Palli, D. (2006). Dietary and lifestyle determinants of mammographic breast density. A longitudinal study in a Mediterranean population. International Journal of Cancer, 118(7), 1782-1789. https://doi.org/10.1002/ijc.21558
dc.source.bibliographicCitation82. Rice, M.S., Bertrand, K.A., Lajous, M., Tamimi, R.M., Torres-Mejía, G., Biessy, C., Lopez-Ridaura, R., & Romieu, I. (2013). Body size throughout the life course and mammographic density in Mexican women. Breast Cancer Research and Treatment, 138(2), 601-610. https://doi.org/10.1007/s10549-013-2463-8.
dc.source.bibliographicCitation83. Oppong, B., Dash, C., O’Neill, S. c., Li, Y, Makambi, K. H., Pien, E., Makariou, E., Coleman, T., & Adams-Campbell, L. L. (2017). Breast density in multiethnic women presenting for screening mammography. Breast Journal. https://doi.org/10.1111/tbj.12941
dc.source.bibliographicCitation84. Quandt, Z., Flom, J.D., Tehranifar, P., Reynolds, D., Terry, M.B., & McDonald, J.A. (2005). The association of alcohol consumption with mammographic density in a multiethnic urban population. BMC Cancer, 15(1). https://doi.org/10.1186/s12885-015-1094-3
dc.source.bibliographicCitation85. Riza, E., Remoundos, D., Bakali, E., Karadedou-Zafiriadou, E., Linos, D., Linos, A. (2008). Anthropometric characteristics and mammographic parenchymal patterns in post-menopausal women: a population-based study in Northern Greece, 20(2), 181-191. https://doi.org/10.1007/s10552-008-9232-8
dc.source.bibliographicCitation86. Pereira, A. I., Garmendia, M,L,, Uauy, R., Neira, P., Lopez-Arana, S., Malkov, S., & Shepherd, J.A.(2017). Determinants of volumetric breast density in Chilean premenopausal women. Breast Cancer Research and Treatment, 162(2), 343-352. https://doi.org/10.1007/s10549-017-4126-7
dc.source.bibliographicCitation87. Alimujiang, A., Imm, K.R., Appleton, C.M., Colditz, G.A., Berkey, C.S., & Toriola, A.T (2008). Adiposity at Age 10 and Mammographic Density among Premenopausal Women. Cancer Prevention Research, 11(5),287-294. https://doi.org/10.1158/1940-6207.capr-17-0309
dc.source.bibliographicCitation88. Warren, R. B., Thompson, D. J., Del Frate, C., Cordell, M., Highnam, R., Tromans, C., Warsi, I., Ding, J. L., Sala, E., Estrella, F., Solomonides, A., Odeh, M., McClatchey, R., Bazzocchi, M., Amendolia, S. R., & Brady, M. P. (2007). A comparison of some anthropometric parameters between an Italian and a UK population: “proof of principle” of a European project using MammoGrid. Clinical Radiology, 62(11),1052-1060. https://doi.org/10.1016/j.crad.2007.04.002
dc.source.bibliographicCitation89. Boyd, N. F., Lockwood, G. W., Byng, J. W., Little, L. R., Yaffe, M. J., & Tritchler, D. (1998). The relationship of anthropometric measures to radiological features of the breast in premenopausal women. British Journal of Cancer, 78(9), 1233-1238. https://doi.org/10.1038/bjc.1998.660
dc.source.bibliographicCitation90. Hosseini, A., Khoury, A., Varghese, F., Carter, J., Wong, J., & Mukhtar, R.A. (2019). Changes in mammographic density following bariatric surgery. Surgery for Obesity and Related Diseases, 15(6), 964-968. https://doi.org/10.1016/j.soard.2019.03.037
dc.source.bibliographicCitation91. Spencer, E.A., Appleby, P.N., Davey, G., & Key, T.J. (2002). Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutrition, 5(4), 561-561-565. https://doi.org/10.1079/phn2001422
dc.source.bibliographicCitation92. Spicer, D.V., Ursin, G., Parisky, Y.R., Pearce, J.M., Shoupe. D, Pike, A., & Pike, -M.C. (1994). Changes in Mammographic Densities induced by a Hormonal Contraceptive Designed to Reduce Breast Cancer Risk. Journal of the Natlional Cancer Institute, 86(6), 431-436. https://doi.org/10.1093/jnci/86.6.431
dc.source.bibliographicCitation93. Heller, S. L., Young Lin, L. L., Melsaether, A. N., Moy, L., & Gao, Y. (2018). Hormonal effects on breast density, fibroglandular tissue, and background parenchymal enhancement. Radiographics: a review publication of the Radiological Society of North America, Inc, 38(4), 983–996. https://doi.org/10.1148/rg.2018180035.
dc.source.bibliographicCitation94. Winkler NS, Raza S, Mackesy M, Birdwell RL (2015). Breast density: clinical implications and assessment methods. Radiographics;35(2):316–24. https://pubmed.ncbi.nlm.nih.gov/25763719/
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectComposición Corporal, Adiposidad, Densidad de la Mama.
dc.subject.keywordBody Composition, Adiposity, Breast Density.
dc.titleComposición corporal y relación con la densidad mamográfica en mujeres colombianas que asisten a tamizaje en la Clínica Universitaria Colombia
dc.title.TranslatedTitleBody composition and relationship with mammographic density in Colombian women attending screening at the Clínica Universitaria Colombia
dc.typemasterThesis
dc.type.documentTesis
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTesis de maestría
local.department.reportEscuela de Medicina y Ciencias de la Salud
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