Computational Biology and Bioinformatics

| Peer-Reviewed |

Age in Impacting the Occurrence of Chronic Diseases: Case of Recurrently Diagnosed Diseases at Korhogo Regional Hospital in Northern of Cote d’Ivoire

Received: 3 October 2022    Accepted: 26 October 2022    Published: 15 December 2022
Views:       Downloads:

Share This Article

Abstract

Background: Our previous study suggested Korhogo district as strongly influenced by parasitical and infectious diseases and as well high blood pressure (HBP) troubles. In the same study, we shown that recurrently diagnosed diseases at Korhogo Regional Hospital (KRH) clustered in two group, depending on the regular and/or irregular dynamism of their increasing. Diseases with regular increasing dynamism (i.e. hypertension) claiming to be chronic diseases were controlled by patients’ anthropomorphic parameters such as age and weight as opposite to diseases with irregular increasing frequency dynamism dominated by malaria and influenza (infectious and tropical diseases). Basing on these results, we embarked here in assessing the relationship between age anthropomorphic parameter and the occurrence of recurrently diagnosed diseases at KRH. Methods: Patients clinical and anthropomorphic parameters data (i.e. age and weight), collected from 2014 to 2018 at the general medicine division of KRH, were subsequently structured and submitted to a multivariate computational statistical analysis in R programming environment. Results: Our findings showed the strong influence of aging on the occurrence of all recurrent listed and analyzed pathologies recorded at KRH from 2014 to 2018 and in particular, on chronic diseases such as cardiovascular troubles dominated by high blood pressure (HBP), osteo-articular/muscular, metabolic diseases (diabetes) and digestive troubles. Conclusion: Considering as a whole, even if our study supported a high concordance between aging and occurrence of diseases recurrently recorded at KRH, it is noteworthy to underline the significant correlation between aging (age increasing) and chronic diseases occurrence. This trend results accentuated for chronic diseases, i.e. high blood pressure, osteo-articular and muscular diseases for age over 50 years.

DOI 10.11648/j.cbb.20221002.13
Published in Computational Biology and Bioinformatics (Volume 10, Issue 2, December 2022)
Page(s) 68-79
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Recurrently Diagnosed Diseases, Chronic Diseases, Korhogo Regional Hospital (KRH), Aging, Multivariate Analysis, R Fitting Curve

References
[1] Fourcade L. Transition épidémiologique et développement: l’essor des maladies non transmissibles est-il une fatalité? Médecine tropicale. 2007; 67: 543-544.
[2] Noel DD, Olefongo D, Lazare T, Wagniman S, Koffi NGBS, Joel KK, Florent KA, Eboulé AER, Guehi ZE and Yangni-Angaté KH. Assessment of recurrently diagnosed diseases dynamism at Korhogo General Hospital in Northern Cote d’Ivoire from 2014 to 2018. International Journal of Medicine and Medical Sciences. 2022; 14 (1), 1-19. DOI: 10.5897/IJMMS2021.1469.
[3] Hossain P, Kawar B, Nahas ME. Obesity and diabetes in the developing world. A growing challenge. N. Engl. J. Med. 2007; 356, 213-215.
[4] Gning SB, Thiam M, Fall F, Ba-Fall K, Mbaye PS and Fourcade L. Le diabète sucré en Afrique subsaharienne. Aspects épidémiologiques, difficultés de prise en charge. Médecine tropicale. 2007; 67 (6), 607-611.
[5] Bove J, Martinez-Vicente M and Vila M. Fighting neurodegeneration with rapamycin: mechanistic insights. Nat. Rev. Neurosci. 2011; 12, 437-452.
[6] Alic N and Partridge L. Death and dessert: nutrient signaling pathways and ageing. Curr. Opin. Cell Biol. 2011; 23, 738-743.
[7] Prasad S, Sung B, Aggarwal BB. Age-associated chronic diseases require age-old medicine: role of chronic inflammation. Prev Med. 2012; 54 Suppl (Suppl): S29-37. doi: 10.1016/j.ypmed.2011.11.011.. PMID: 22178471; PMCID: PMC3340492.
[8] Teresa N and Linda P. Ageing as a Risk Factor for Disease. Current Biology. 2012; 22 (17), 741-752. https://doi.org/10.1016/j.cub.2012.07.024.c.
[9] Dago DN, Lallié HD, Moroh AJL, Niamien CJM, Ouattara H, Dagnogo O, Téhoua L, Touré A, Kouadio KJ, Kimou AF, Djaman AJ. Relationship between Age and Weight Features in Discriminating Hypertensive Patient Population Gender, American Journal of Medicine and Medical Sciences. 2018; 8 (2), 360-367. doi: 10.5923/j.ajmms.20180812.02.
[10] Esposito K, Kastorini CM, Panagiotakos DB, Giugliano D. Prevention of type 2 diabetes by dietary patterns: a systematic review of prospective studies and meta-analysis. Metab Syndr Relat Disord. 2010; 8, 471-476. [PubMed] [Google Scholar].
[11] Noel DD, Nafan D, Hermann-Désiré L, Souleymane S, Tuo Y, Inza JF, Edouard KNG, Florent AK, Adama C. Performance Assessment of the Recurrence of Cardiovascular Pathologies Symptoms in a Hypertensive Population. Journal of Health Science. 2017; 7 (1), 9-19 DOI: 10.5923/j.health.20170701.03.
[12] Dago DN, Diarrassouba N, Touré A, Lallié HD, N’Goran KE, Ouattara H, Kouadio KJ and Coulibaly A. Whole Screening Analysis Discerning Recurrently Diagnosed Diseases in a Northern Locality of Côte d’Ivoire. International Journal of Development Research. 2017; 7 (11), 16598-16604.
[13] Birgé L and Rozenholc Y. How many bins should be put in a regular histogram, ESAIM: Probability and Statistics? 2006; 10, 24-45.
[14] Scott DW. Sturges' rule, WIREs Computational Statistics. 2009; 1, ‎303-306.
[15] Sturges HA. The Choice of a Class Interval. Journal of the American Statistical Association. 1962; 21 (153), 65-66.
[16] Dago DN, Inza JF, Nafan D, Mohamed LB, Jean-Luc AM, Olefongo D, Loukou N, Martial DSY, Souleymane S, Giovanni M. A Quick Computational Statistical Pipeline Developed in R Programming Environment for Agronomic Metric Data Analysis. American Journal of Bioinformatics Research. 2019; 9 (1): 22-44.
[17] Christoph F. Comparing the Logarithmic Transformation and the Box-Cox Transformation for Individual Tree Basal Area Increment Models, Forest Science. 2016; 62 (3), 297-306. https://doi.org/10.5849/forsci.15-135.
[18] Dago DN, Kablan GAJ, Alui KA, Lallié HD, Dagnogo D, Diarrassouba N and Giovanni M. Normality Assessment of Several Quantitative Data Transformation Procedures. Biostat Biom Open Access J. 2021; 10 (3), 555786. DOI: 10.19080/BBOAJ.2021.10.5557786.
[19] Jelihovschi JCFEG and Allaman IB. Conventional Tukey Test. Universidade Estadual de Santa Cruz - UESC, Ilheus, Bahia, Brasil. 2021.
[20] Reinhard H, Kurt H and Stefan T. Rsymphony: Symphony in R. URL http://CRAN.R-project.org/package= Rsymphony. R package version 0.1-17. 2013.
[21] Suzuki R and Hidetoshi S. Pvclust: Hierarchical Clustering with P-Values via Multiscale Bootstrap. 2015.
[22] Murray CJL and Lopez AD. Quantifying disability: data, methods and results. Bull WHO. 1994; 72 (3), 481-494.
[23] Brinks R, Landwehr S, Icks A, Koch M, Giani G. Deriving age-specific incidence from prevalence with an ordinary differential equation. Stat Med. 2012; http://dx.doi.org/10.1002/sim.5651.
[24] Brinks R, Landwehr S, Waldeyer R. Age of onset in chronic diseases: new method and application to dementia in Germany. Popul Health Metrics. 2013; 11, 6. https://doi.org/10.1186/1478-7954-11-6.
[25] Malawi HDR. Reversing HIV/AIDS in Malawi. Washington, DC: United Nations Development Programme. [Google Scholar]. 2005.
[26] Boutayeb A. Developing countries and neglected diseases: challenges and perspectives. Int J Eq Health. 2007; doi: 10.1186/1475-9276-6-20. [PMC free article] [PubMed].
[27] Mitchell GF, Lacourcière Y, Ouellet JP, Izzo JL, Neutel J, Kerwin LJ, Block AJ, Pfeffer MA. Determinants of elevated pulse pressure in middle-aged and older subjects with uncomplicated systolic hypertension: the role of proximal aortic diameter and the aortic pressure-flow relationship. Circulation. (2003); 108, 1592-1598. doi: 10.1161/01.CIR.0000093435.04334.1F.
[28] Lipsitz LA. A 91-year-old woman with difficult-to-control hypertension: a clinical review. JAMA. 2013; 310: 1274-1280. doi: 10.1001/jama.2013.277027.
[29] Franklin SS, Gustin W, Wong ND, Larson MG, Weber MA, Kannel WB, Levy D. Hemodynamic patterns of age-related changes in blood pressure. The Framingham Heart Study. Circulation. 1997; 96, 308-315.
[30] Daugherty SL, Masoudi FA, Ellis JL, Ho PM, Schmittdiel JA, Tavel HM, Selby JV, O'Connor PJ, Margolis KL, Magid DJ. Age-dependent gender differences in hypertension management. J Hypertens. 2011; 29 (5), 1005-11. doi: 10.1097/HJH.0b013e3283449512. PMID: 21330934; PMCID: PMC3319751.
[31] Martins D, Nelson K, Pan D, Tareen N, Norris K. The effect of gender on age-related blood pressure changes and the prevalence of isolated systolic hypertension among older adults: data from NHANES III. J Gend Specif Med. 2001; 4, 10-13, 20.
[32] Whelton PK. Blood pressure in adults and the elderly. In: Bulpitt CJ, ed. Handbook of Hypertension. 1985; Vol 6. Amsterdam, Netherlands: Elsevier: 51-69.
[33] Chang AM and Halter JB. Aging and insulin secretion. American Journal of Physiology and Endocrinology Metabolism. 2003; 284 (1), E7-12. doi: 10.1152/ajpendo.00366.2002. PMID: 12485807.
[34] Ketut S, Pande D, Made SS, Tuty KRA. Age is an Important Risk Factor for Type 2 Diabetes Mellitus and Cardiovascular Diseases, Glucose Tolerance, Sureka Chackrewarthy, Intech Open. 2012; DOI: 10.5772/52397.
[35] Giuseppina I, James PB, Theodore JT, Doug C, Dana D, Richard FH, Jean ML, Angela DL, Lenna LL, Elizabeth JMD, Beatriz LR, Debra S. for the SEARCH for Diabetes in Youth Study Group; Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050: Dynamic modeling of incidence, mortality, and population growth. Diabetes Care 1. 2012; 35 (12), 2515-2520. https://doi.org/10.2337/dc12-0669.
[36] Lawrence RC, Felson DT, Helmick CG, Arnold LM, Choi H, Deyo RA, Gabriel S, Hirsch R, Hochberg MC, Hunder GG, Jordan JM, Katz JN, Kremers HM, Wolfe F, National Arthritis Data Workgroup. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. Arthritis Rheum. 2008; 58 (1), 26-35. doi: 10.1002/art.23176. PMID: 18163497; PMCID: PMC3266664.
[37] Loeser RF. Age-related changes in the musculoskeletal system and the development of osteoarthritis. Clin Geriatr Med. 2010; (3), 371-86. doi: 10.1016/j.cger.2010.03.002. PMID: 20699160; PMCID: PMC2920876.
[38] Mitrovic D. Vieillissement articulaire et arthrose [The aging of joints and osteo-arthrosis]. Rev Prat. 1989; 39 (7), 551-4. French. PMID: 2704965.
[39] Green LK and Graham DY. Gastritis in the elderly. Gastroenterol Clin North Am. 1990; 19 (2), 273-92. PMID: 2194946.
[40] Xia HH, Kalantar JS, Talley NJ, Wyatt JM, Adams S, Chueng K, Mitchell HM. Antral-type mucosa in the gastric incisura, body, and fundus (antralization): a link between Helicobacter pylori infection and intestinal metaplasia. Am J Gastroenterol. 2000; 95 (1), 114-21. doi: 10.1111/j.1572-0241.2000.01609.x. PMID: 10638568.
[41] Pimanov SI and Makarenko EV. Chronic gastritis: last decade's achievements and problems Klin Med (Mosk). 2005; 83 (1), 54-8. PMID: 15759493.
[42] Finch C. The Biology of Human Longevity. San Diego, CA: Academic Press. 2007.
[43] Baksi AJ, Treibel TA, Davies JE, Hadjiloizou N, Foale RA, Parker KH, Francis DP, Mayet J, Hughes AD. A meta-analysis of the mechanism of blood pressure change with aging. Journal of the American College of Cardiology. 2009; 54 (22), 2087-2092.
[44] Brooker S, Kolaczinski JH, Gitonga CW, Noor AM, Snow RW. The use of schools for malaria surveillance and programme evaluation in Africa. Malaria Journal. 2009; 8 (1), 1-9.
[45] Anna ME, Jenny H, Abdisalan MN, Robert WS, Feiko OK. Prevalence of malaria infection in pregnant women compared with children for tracking malaria transmission in sub-Saharan Africa: asystematic review and meta-analysis, The Lancet Global Health. 2015; 3 (10), e617-e628. https://doi.org/10.1016/S2214-109X (15) 00049-2.
Cite This Article
  • APA Style

    Dago Dougba Noel, Daramcoum Wentoin Alimata Marie-Pierre, Dagnogo Olefongo, Kouadio Kouassi Joel, Kimou Adjiman Florent. (2022). Age in Impacting the Occurrence of Chronic Diseases: Case of Recurrently Diagnosed Diseases at Korhogo Regional Hospital in Northern of Cote d’Ivoire. Computational Biology and Bioinformatics, 10(2), 68-79. https://doi.org/10.11648/j.cbb.20221002.13

    Copy | Download

    ACS Style

    Dago Dougba Noel; Daramcoum Wentoin Alimata Marie-Pierre; Dagnogo Olefongo; Kouadio Kouassi Joel; Kimou Adjiman Florent. Age in Impacting the Occurrence of Chronic Diseases: Case of Recurrently Diagnosed Diseases at Korhogo Regional Hospital in Northern of Cote d’Ivoire. Comput. Biol. Bioinform. 2022, 10(2), 68-79. doi: 10.11648/j.cbb.20221002.13

    Copy | Download

    AMA Style

    Dago Dougba Noel, Daramcoum Wentoin Alimata Marie-Pierre, Dagnogo Olefongo, Kouadio Kouassi Joel, Kimou Adjiman Florent. Age in Impacting the Occurrence of Chronic Diseases: Case of Recurrently Diagnosed Diseases at Korhogo Regional Hospital in Northern of Cote d’Ivoire. Comput Biol Bioinform. 2022;10(2):68-79. doi: 10.11648/j.cbb.20221002.13

    Copy | Download

  • @article{10.11648/j.cbb.20221002.13,
      author = {Dago Dougba Noel and Daramcoum Wentoin Alimata Marie-Pierre and Dagnogo Olefongo and Kouadio Kouassi Joel and Kimou Adjiman Florent},
      title = {Age in Impacting the Occurrence of Chronic Diseases: Case of Recurrently Diagnosed Diseases at Korhogo Regional Hospital in Northern of Cote d’Ivoire},
      journal = {Computational Biology and Bioinformatics},
      volume = {10},
      number = {2},
      pages = {68-79},
      doi = {10.11648/j.cbb.20221002.13},
      url = {https://doi.org/10.11648/j.cbb.20221002.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20221002.13},
      abstract = {Background: Our previous study suggested Korhogo district as strongly influenced by parasitical and infectious diseases and as well high blood pressure (HBP) troubles. In the same study, we shown that recurrently diagnosed diseases at Korhogo Regional Hospital (KRH) clustered in two group, depending on the regular and/or irregular dynamism of their increasing. Diseases with regular increasing dynamism (i.e. hypertension) claiming to be chronic diseases were controlled by patients’ anthropomorphic parameters such as age and weight as opposite to diseases with irregular increasing frequency dynamism dominated by malaria and influenza (infectious and tropical diseases). Basing on these results, we embarked here in assessing the relationship between age anthropomorphic parameter and the occurrence of recurrently diagnosed diseases at KRH. Methods: Patients clinical and anthropomorphic parameters data (i.e. age and weight), collected from 2014 to 2018 at the general medicine division of KRH, were subsequently structured and submitted to a multivariate computational statistical analysis in R programming environment. Results: Our findings showed the strong influence of aging on the occurrence of all recurrent listed and analyzed pathologies recorded at KRH from 2014 to 2018 and in particular, on chronic diseases such as cardiovascular troubles dominated by high blood pressure (HBP), osteo-articular/muscular, metabolic diseases (diabetes) and digestive troubles. Conclusion: Considering as a whole, even if our study supported a high concordance between aging and occurrence of diseases recurrently recorded at KRH, it is noteworthy to underline the significant correlation between aging (age increasing) and chronic diseases occurrence. This trend results accentuated for chronic diseases, i.e. high blood pressure, osteo-articular and muscular diseases for age over 50 years.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Age in Impacting the Occurrence of Chronic Diseases: Case of Recurrently Diagnosed Diseases at Korhogo Regional Hospital in Northern of Cote d’Ivoire
    AU  - Dago Dougba Noel
    AU  - Daramcoum Wentoin Alimata Marie-Pierre
    AU  - Dagnogo Olefongo
    AU  - Kouadio Kouassi Joel
    AU  - Kimou Adjiman Florent
    Y1  - 2022/12/15
    PY  - 2022
    N1  - https://doi.org/10.11648/j.cbb.20221002.13
    DO  - 10.11648/j.cbb.20221002.13
    T2  - Computational Biology and Bioinformatics
    JF  - Computational Biology and Bioinformatics
    JO  - Computational Biology and Bioinformatics
    SP  - 68
    EP  - 79
    PB  - Science Publishing Group
    SN  - 2330-8281
    UR  - https://doi.org/10.11648/j.cbb.20221002.13
    AB  - Background: Our previous study suggested Korhogo district as strongly influenced by parasitical and infectious diseases and as well high blood pressure (HBP) troubles. In the same study, we shown that recurrently diagnosed diseases at Korhogo Regional Hospital (KRH) clustered in two group, depending on the regular and/or irregular dynamism of their increasing. Diseases with regular increasing dynamism (i.e. hypertension) claiming to be chronic diseases were controlled by patients’ anthropomorphic parameters such as age and weight as opposite to diseases with irregular increasing frequency dynamism dominated by malaria and influenza (infectious and tropical diseases). Basing on these results, we embarked here in assessing the relationship between age anthropomorphic parameter and the occurrence of recurrently diagnosed diseases at KRH. Methods: Patients clinical and anthropomorphic parameters data (i.e. age and weight), collected from 2014 to 2018 at the general medicine division of KRH, were subsequently structured and submitted to a multivariate computational statistical analysis in R programming environment. Results: Our findings showed the strong influence of aging on the occurrence of all recurrent listed and analyzed pathologies recorded at KRH from 2014 to 2018 and in particular, on chronic diseases such as cardiovascular troubles dominated by high blood pressure (HBP), osteo-articular/muscular, metabolic diseases (diabetes) and digestive troubles. Conclusion: Considering as a whole, even if our study supported a high concordance between aging and occurrence of diseases recurrently recorded at KRH, it is noteworthy to underline the significant correlation between aging (age increasing) and chronic diseases occurrence. This trend results accentuated for chronic diseases, i.e. high blood pressure, osteo-articular and muscular diseases for age over 50 years.
    VL  - 10
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Department of Biochemistry and Genetic, Genetic Research Unit, Peleforo Gon Coulibaly University, Korhogo, Cote d’Ivoire

  • Department of Biochemistry and Genetic, Genetic Research Unit, Peleforo Gon Coulibaly University, Korhogo, Cote d’Ivoire

  • Biology and Health Laboratory, Biosciences Research and Training Unit, Felix Houphouet Boigny University, Abidjan, Cote d’Ivoire

  • Division of Cardiology and General Medicine, Regional Hospital Center, Korhogo, Cote d’Ivoire

  • Division of Cardiology and General Medicine, Regional Hospital Center, Korhogo, Cote d’Ivoire

  • Sections