Original Article

Linear Mixed Effect Model with Application to Random Blood Sugar Data

Abstract

Background & aim: Diabetes mellitus is a common, chronic, metabolic syndrome characterized by hyperglycemia as a cardinal biochemical feature. Type-1 diabetes is a continuing hormonal deficiency disorder that has significant short-term impacts on health and lifestyle and is associated with major long-term complications like heart failure, kidney, hypertension, eye damage, etc. which reduced life expectancy. The main objective of this study was to assess the risk factor that increase prevalence of type-1 diabetes mellitus and to determine their relationship with outcome of type-1 diabetes mellitus over time.
Methods & materials: To address this objective linear mixed effect model was applied using the random blood sugar of 970 diabetic patient children during treatment period of 3 years at Hiwot Fana hospital which have been implemented in statistical packages STATA, SAS and R.
Result: This study found that the mean progression of random blood sugar level of diabetic children was decreased over time after they starts their follow up and medications. The linear distribution also accounts 92 % variability of the data was explained by the covariates which were included in the study. The variable age, residence, family history, nutrition status, early diet, body mass index, electrolytes and renal function test had significant effect on the change of sugar level (p < 0.05).
Conclusion: The cumulative incidence of type-1 diabetes mellitus disease was increased due to presence of co-infections and decreased with pharmacological diabetes treatment. The linear mixed effects model fitted was appropriate for the estimation of sugar levels based on the risk factor variables for type-1 diabetes mellitus patient children. ABBREVIATIONS:RBS = Random blood sugar; FH = Family history; UM = under malnutrition; OM = Over malnutrition; RFT= Renal function test; NS=Nutritional status

1. Gale E. The rise of childhood type-1 diabetes in the 20th century. Diabetes. Med Klin. 2002, 51:3353–61
2. Karvonen M. Incidence and trends of childhood type-1 diabetes worldwide 1990-1999. Diabet. Med. 2006, 23:857–866.
3. Silva A, Amanda F. Treatment of type 2 diabetes in youth. Diabetes Care. 2011, 34:S177– S183.
4. Patterson C, Dahlquist G, Gyurus E, Green A, Soltesz G. Incidence trends for childhood type-1 diabetes in europe during 1989–2003 and predicted new cases 2005–2020: A multi-center prospective registration study. The Lancet. 20069, 373:2027–2033.
5. Pundziute L, yckå A, Dahlquist G, Nyström L. The incidence of type-1 diabetes has not increased but shifted to a younger age at diagnosis in the 0-34 years group in sweden 1983-1998. Diabetologia. 2002, 45:783–791.
6. Kalk W, Huddle K., Raal F. The age of onset and sex distribution of insulin- dependent diabetes mellitus in Africans in south Africa. Postgrad Med J. 1993, 69:552–556.
7. Palmer J, Fleming G, Greenbaum C. Cpeptide is the appropriate outcome measure for type-1 diabetes clinical trials to preserve beta-cell function: report of anada workshop, 21–22 October 2001. Diabetes. 2004, 57:1934.
8. Control T, Group C. Effect of intensive therapy on residual beta-cell function in patients with type 1 diabetes in the diabetes control and complications trial. A randomized, controlled trial. Annals of Intern Med. 1998, 128:517–523
9. Tonol I, Heyman E, Roelands, B. Type-1 diabetes-associated cognitive decline: a metaanalysis and update of the current literature. Diabetes Journal. 2014, 6:499–513
10. Gaudieri P, Chen R, Greer T, Holmes C. Cognitive function in children with type-1 diabetes: a meta-analysis. Diabetes Care. 20058, 31:1892–1897.
11. Chynna S, William A, Stephen G, Umesh M, Melissa C. Insulin secretion in type. American Diabetes Association. 2004, 52: 426–433.
12. Crowder M, Hand D. Analysis of repeated measures. Chapman and Hall.
13. Cheng J, Endwards L, Komro, K. Real longitudinal data analysis for real people: Building a good enough mixed model statistics in medicine. Statistics in Medicine. 2010, 29:504– 520.
14. Gillespie K, Gale E. Diabetes and gender. Diabetologia, 2001, 44:3–15.
15. Miller L, Willis J, Pearce J, Barnett R, Darlow B, Scott R. Urban-rural variation in childhood type-1 diabetes incidence in canter bury, new zealand, 1980-2004. Health place. 2011, 17:248– 256.
16. Harrison T, Hindorff L, Kim H, Wines R, Bowen D, McGrath B, Edwards K . Family history of diabetes as a potential public health tool. Am J Prev Med. 2003, 24:152–159.
17. Verbeke G, Molenberghs G. Linear mixed models for longitudinal data. New York,NY 10010, USA Verlag New,175 Fifth Avenue. 2000, 8:568.
18. Cooper D, Thompson R. A note on the estimation of the parameters of the autoregressive-moving average process. Biometrika. 1997, 64:625–628.
19. Fitzmaurice G, Davidian M, Molenberghs G. Longitudinal data analysis, handbooks of modern statistical methods. Chapman and Hall /CRC. 2009, 10:625–628.
20. Tyler T, Blader S. Identity and cooperative behavior in groups. Group processes and intergroup relations. 2001, 4:207–226
21. Schiel R, Hoffmann A, Müller U. Quality of care of patients with diabetes mellitus living in a rural area of germany. Medizinische Klinik. 1999, 94:127—132.
Files
IssueVol 4 No 4 (2018) QRcode
SectionOriginal Article(s)
Keywords
Linear mixed model Random blood sugar Type-1 Diabetes

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Zeru M. Linear Mixed Effect Model with Application to Random Blood Sugar Data. JBE. 2019;4(4):244-251.