Asian Journal of Dairy and Food Research, volume 40 issue 4 (december 2021) : 434-439

​Modelling the Evolution of Serum Cholesterol Level of Broiler Chickens

M. Alam, M. Ohid Ullah, M.S. Islam
1Department of Statistics, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh.
Cite article:- Alam M., Ullah Ohid M., Islam M.S. (2021). ​Modelling the Evolution of Serum Cholesterol Level of Broiler Chickens. Asian Journal of Dairy and Food Research. 40(4): 434-439. doi: 10.18805/ajdfr.DR-234.
Background: With the demand of the growing population, the broiler industry has grown up rapidly over the last few decades and it plays as an affordable source of good quality nutritious animal protein. This broiler industry focuses mainly on optimizing the profit through improving body weight and feed efficiency but the health issues of consumers are not taken into consideration seriously. It is important to know the changing pattern of concentration level of the biochemical parameter (total cholesterol) due to different feeds as well as different ages of chicken. 
Methods: This experimental study through longitudinal data was conducted using repeated measurements from each of seventy randomly selected broilers, partitioned into two groups according to two types of feed, at four-time points. Since measurements from the same subject were taken at four time periods, traditional approach of analysis may not be appropriate as it ignore the correlation between repeated measurements. Therefore, linear mixed model was adopted for the analysis of our obtained dataset.
Result: Linear mixed effect model did not reveal any significant difference of standard and hatcher’s supplied feeds over time on the evolution of total cholesterol level. This might be due to little difference in different compositions of both feeds. However, both exploratory data analysis and modelling confirmed that irrespective of the available feed types, total cholesterol level of broiler serum increased significantly over time (age) which leads to a recommendation for the consumers to eat younger age (lower weight) broiler chicken.

  1. Abubakar A., Mabruok, M.A., Gerie A.B., Dikko A.A., Aliyu S., Yusuf T., Magaji, R.A, Kabir M.A., Adama U.W. (2009). Relation of body mass index with lipid profile and blood pressure in healthy female of lower socioeconomic group, in Kaduna, Northern Nigeria. Asian Journal of Medical Science. 1(3): 94-96.

  2. Ahmad, A, Anjum, A.A, Rabbani, M., Ashraf, K., Awais, M.M, Nawaz, M., Ahmad, N., Asif, A. and Sana, S. (2017). Effects of fermented rice bran on growth performance and bioavailability of phosphorus in broiler chickens. Indian Journal of Animal Research. 53: 361-365.

  3. Anupama, K.R. and Chandrashekara, S. (2014). Mixed effect frameworks in the analysis of longitudinal data. Internet Journal of Clinical Immunology and Rheumatology. 2(1). 

  4. Bangladesh Bureau of Statistics (BBS). (2001). Statistical Year Book of Bangladesh. Bangladesh Bureau of Statistics, Ministry of Planning, Government of People’s Republic of Bangladesh. 

  5. Bhuiyan, A.K.F.H. (2011). Implementation of national livestock development policy (2007) and national poultry development policy (2008): Impact on smallholder livestock rearers. Keynote paper presented at the South Asia Pro Poor Livestock Policy Programme (SAPPLP)-BRAC workshop held at BRAC Centre Inn, Dhaka.

  6. Castelli, W.P. anderson, K., Wilson, P.W. and Levy, D. (1992). Lipids and risk of coronary heart disease: The framingham study. Ann. Epidemiol. 2(1-2): 23-8. 

  7. Cicek, H. and Tandogan, M. (2016). Estimation of optimum slaughter age in broiler chicks. Indian Journal of Animal Research. 50: 621-623.

  8. Dauqan, E.M.A., Abdullah, A. and Sani, H.A. (2011). Natural antioxidants, lipid profile, lipid peroxidation, antioxidant enzymes of different vegetable oils. Advance Journal of Food Science and Technology. 3(4): 308-16.

  9. DeLivera, A.M, Zaloumis, S., Simpson, J.A. (2014). Models for the analysis of repeated continuous outcome measures in clinical trials. Respirology. 19: 155-161.

  10. Egwurugwu, J.N., Nwafor, A., Chinko, B.C., Oluronfemi, O.J., Iwuji, S.C. and Nwankpa, P. (2013). Effects of prolonged exposure to gas flares on the lipid profile of humans in the niger delta region, Nigeria. American Journal of Research Communication. 1(5): 115-45.

  11. Fitzmaurice, G.M., Ravichandran, C. (2008). A primer in longitudinal data analysis. Circulation. 118: 2005-2010.

  12. Gibbons, R.D. and Hedeker, D. (2000). Applications of mixed-effects models in biostatistics. Sankhya: The Indian Journal of Statistics. 62: 70-103.

  13. Goldstein, H., Browne, W. and Rasbash, J. (2002). Multilevel modeling of medical data. Statist. Med. 21: 3291-315. 

  14. Hart, C., Ecob, R. and Smith, G.D. (1997). People, places and coronary heart disease risk factors: A multilevel analysis of the Scotish Heart Health Study. Arch. Soc. Sci Med. 45: 893-02.

  15. Hedeker, D., Gibbons, R.D. and Waternaux, C. (1999). Sample size estimation for longitudinal designs with attrition: Comparing time-related contrasts between two groups. Journal of Educational and Behavioral Statistics. 24: 70- 93. 

  16. Malden, C.N., Richard, E.A. and Leslic, E.C. (1997). Poultry Production. 12th Edn. Library of Congress Cataloging in Publication Data.

  17. Malik, R., Pirzado, Z.A., Ahmed, S. and Sajid, M. (1995). Study of lipid profile, blood pressure and blood glucose in rural population. Pak. J. Med. Res. 34: 152-55.

  18. Miah, M.Y., Chowdhury, S.D., Bhuiyan, A.K.F.H. (2016). Effect of different dietary levels of energy on the growth performance and meat yield of indigenous chicken reared in confinement under the rural condition of Bangladesh. International Journal of Animal Resources. 1(1): 53-60. 

  19. NCEP. (2002). Expert Panel on Detection, Evaluation and Treatment of High Cholesterol in Adults (Adult Treatment Panel 111). Third Report of the National Cholesterol Education Program, Circulation. 

  20. Patrick, S. and Vetter, T.R. (2018). Repeated measures designs and analysis of longitudinal data: If at first you do not succeed-try, try again. Anesthesia and Analgesia. 127(2): 569-575, doi: 10.1213/ANE.0000000000003511.

  21. Prasad, R., Rose, M.K., Virmani, M., Garg, S.L. and Puri, J.P. (2009). Lipid profile of chicken (Gallus domesticus) in response to dietary supplementation of garlic (Allium sativum). International Journal of Poultry Science. 8(3): 270-76. 

  22. Rosário, M.F., Silva, M.A.N., Coelho, A.A.D. and Savino, V.J.M. (2007). Estimating and predicting feed conversion in broiler chickens by modeling covariance structure. International Journal of Poultry Science. 6(7): 508-14.

  23. Shahid, A., Zuberi, S.J. and Hasnain, N. (1985). Lipid pattern in healthy subjects. Pak. J. Med Res. 24: 33-7.

  24. Shakila, F., Bhuiyan, A.K.F.H., Ali, M.Y. and Joy, Z.F. (2017). Breeding for the improvement of indigenous chickens of Bangladesh: Performance of foundation stock. Asian J. Med. Biol. Res. 3: 80-7. 

  25. Simons, P.C.M. (2009). Commercial Egg and Poultry Meat Production and Consumption Trade Worldwide. Proceedings of the 6th International Poultry Show and Seminar. The World’s Poultry Science Association-Bangladesh Branch, Dhaka, Bangladesh.

  26. Tikate, K., Wade, M., Ranade, A.S., Patodkar, V.R., Dhaygude, V.S. and Bhalerao, S.M. (2021). Influence of dietary multiple phase feeding on growth performance of commercial broiler chicken. Indian Journal of Animal Research. 55: 66-70.

  27. Vartiainen, E., Pekkanen, J., Koskinen, S., Jousilahti, P., Salomma, V. and Puska, P. (1998). Do changes in cardiovascular risk factors explain the increasing socioeconomic difference in mortality from Ischaemic Heart in Finland? Journal of Epidemiol Community Health. 52: 416-19.

  28. Veith, J.W. (1998). Diet and Health. Scientific perspectives CRC Press, Scientific Publishers Stuttgart.

  29. Wirsenius, S., Azar, C. and Berndes, G. (2010). How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030? Agric. Sys. 103: 621-38.

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