Prevalence and predictive factors for heart failure among Sudanese individuals with diabetes: Population based survey

Introduction

Non communicable diseases including metabolic such as diabetes mellitus (DM) and obesity, cardiovascular disorders and cancer represent a major and growing health burden in Sub-Saharan Africa (SSA) including Sudan (1). It was estimated that number of people with diabetes in Africa will increase from 14 million in 2011 to 28 million in 2030 (2-4). Between 2011 and 2030, it was estimated that the number of people with diabetes will increase by 54% (5). The burden of diabetes in urban areas of north Sudan was 19% and 2.5% in urban and rural areas of north Sudan respectively (6,7).

Diabetes control is a real challenge facing health authorities in Sudan since the prevalence of uncontrolled diabetes was reported in (83.8%) and (85%) of Sudanese individuals with type 1 and type 2 diabetes respectively (8,9). Factors associated with poor glycemic control were age (P<0.072) and sex (P<0.039) in individuals with type 1 diabetes, while prolonged duration of diabetes (P=0.03), high plasma triglyceride (P=0.02), low HDL level (P=0.04) and low glomerular filtration rate (GFR) (P=0.01) in type 2 diabetes (8,9). Interestingly, low glomerular filtration rate (GFR) is independent factor with poor diabetes control (9). Concerning Sudanese population the following were established risk factors for development of DM; family history of diabetes, central obesity, obesity, increasing age, and hypertension (6,7). Diabetic complications such as retinopathy, peripheral neuropathy and diabetic foot were observed in 72.6%, 68.2% and 12.7% respectively (10) Furthermore, longer duration of diabetes (P<0.001) and living in urban areas (P<0.004) were identified as the factors significantly associated with these complications.

The increasing prevalence of diabetes in Sudan is in keeping with the emerging global pandemics of metabolic syndrome and obesity on one hand and diabetes and its complications on the other hand (4,11). In addition, non-alcoholic fatty liver diseases (NAFLD), is associated with diabetes and insulin resistance. In Sudan, the prevalence of fatty liver is thought to be around 20% in individuals without diabetes and risk factors were related to obesity and an increase in age. However, among individuals with type 2 diabetes the prevalence was found to be higher around 50.3%. The risk factors were overweight, obesity, central obesity, high triglyceride level and low HDL-c level. Individuals with three components of the metabolic syndrome revealed higher prevalence of NAFLD (12,13).

Heart failure (HF) is considered as the commonest DM related cardiovascular complication (14,15). Type 2 DM and insulin resistance are well documented risk factors for HF (16,17). This is partially explained by the shared common pathogenetic factors of DM and HF. The strong association between DM and HF that reflected in increased incidence of heart failure in diabetic subjects may in part be attributed to structural and functional dysfunction of diabetic myocardium (18). Beside insulin resistance other factors like myocardial fibrosis, microvascular disease, impaired calcium homeostasis and autonomic neuropathy, were factors thought to involved in the association of diabetes and HF (19).

Methods

Study design

The study was conducted in Khartoum which is the capital of Sudan between September and December 2015. This is a hospital based cross-sectional study and enrolled 315 individuals with diabetes.

Data collection

We have used the WHO stepwise approach for collection of data in non-communicable diseases surveillance. The stepwise approach is made of questionnaire (demographic data), physical examination (anthropometric and blood pressure) and biochemical tests. For example demographic and background factors: age, gender, smoking, alcohol intake, diabetes duration and medications, and heart failure was included. We measured height and weight to calculate BMI. Blood tests for measurement of HbA1c, cholesterol and triglycerides levels.

Laboratory measures

The blood samples collected from individuals agreed to participate on the study. These samples were kept in Ethylenediaminetetraacetic acid (EDTA) reagent for HgA_1c and lithium reagent for renal function test (RFT) and lipid profile. Analysis of samples was performed using Cobas c 111 analyzer.

Data analysis

After the data was organised and cleaned, we used the Statistical Package for Social Science SPSS software program [version 21.0 computer program (SPSS, Inc., Chicago, IL, USA)]. Chi-squared test was used to test for significance between variables like age, sex, BMI, blood glucose level, retinopathy, neuropathy, albuminuria, blood pressure and a family history of diabetes mellitus, duration of diabetes, cholesterol, triglyceride and HbA1c. Then we used logistic regression analysis to establish absolute risk factors. P value <0.05 was considered statistically significant.

Ethical approval

The ethical clearance of this study was obtained from the Ethical Committee of the Faculty of Medicine – University of Medical Sciences and Technology, Khartoum, Sudan (IRB No. 00008867).

Results

Sociodemographic variables

The study is a retrospective observational analysis of 315 Sudanese individuals with an established diagnosis of diabetes. Prevalence of heart failure was 12.06% in this sample [95% confidence interval (CI): 8.44–15.61%].

The mean age was 58.7 years [standard deviation (SD) =10.5 years]. Range between 25 and 90 years). The median age was 60 years. The mean age for HF group was 61.2 years, whereas the mean age for those with no HF was 58.4 years. This 2.8 years difference was only weakly statistically significant (t=1.9927, degrees of freedom =58.186, P value =0.051). Male representation in the sample was 184 (59.3%) and only 25 (13.5%) of them has HF while female were 131 (40.7%) and 13 (9.9%) has HF. However, this association between males and increased risk of HF was not statistically significant (X-squared =0.6257, df =1, P value =0.4289).The mean BMI was 26.2 (SD =4.23; range, 14.06–43.00). The median BMI was 26.47. The mean BMI for HF group was 26.4, whereas the mean BMI for those with no HF was 26.1. This 0.3 units difference was not statistically significant (t=0.423, degrees of freedom =53.475, P value =0.674). Out of the total participants with HF, there were 25 with family history of diabetes (Prevalence of HF =12.02%) and 13 without (Prevalence of HF =12.15%). However, this association between positive family history and increased risk of HF was not statistically significant (X-squared =0, df =1, P value =1).

Correlates and unadjusted risk factors for HF

Cholesterol

The mean cholesterol was 162.9 (SD =62.1; range, 4.35–514.6). The median cholesterol was 157.6. The mean cholesterol for HF group was 144.5, whereas the mean cholesterol for those with no HF was 165.4. This 21.9 units difference was statistically significant (t=2.4841, degrees of freedom =58.372, P value =0.01588).

Triglycerides

The mean triglycerides level was 143.9 (SD =91.2; range, 2.12–715.8). The median triglycerides level was 123.3. The mean triglycerides level for HF group was 130.4, whereas the mean triglycerides level for those with no HF was 145.7. This 5.3 units difference was not statistically significant (t=1.097, degrees of freedom =52.124, P value =0.2777).

HDL

The mean HDL level was 37.26 (SD =13.36; range, 2.8–122.2). The median HDL level was 35.13. The mean HDL level for HF group was 36.67, whereas the mean HDL level for those with no HF was 37.34. This 0.6 units difference was not statistically significant (t=0.2634, degrees of freedom =45.223, P value =0.7934).

LDL

The mean LDL level was 115.2 (SD =53.08; range, 2.2–305). The median LDL level was 115.1. The mean LDL level for HF group was 106.0, whereas the mean LDL level for those with no HF was 116.5. This 9.5 units difference was not statistically significant (t=1.1306, degrees of freedom =47.508, P value =0.2639).

HbA1c

The mean HbA1c was 10.01 (SD = 2.2, Range between 4.7 and 17.8). The median HbA1c was 9.9. The mean HbA1c for HF group was 10.52, whereas the mean HbA1c for those with no HF was 9.95. This 0.63 units difference was not statistically significant (w = 3174, P value = 0.1043).

Duration of DM

Out of the total 38 participants with HF, there were 18 (16.5%) who were diabetic for more than 20 years. There were 13 (10.9%) who were diabetic for between 11 and 20 years. There were 5 (9.4%) were living with diabetes for between 6 and 10 years, with 2 (5.8%) from the under 5-year duration group getting the HF complication. This association between the duration of diabetes and increased risk of HF was not statistically significant (X-squared =3.8661, df =3, P value =0.2763).

Hypertension

Out of the total 38 participants with HF, there were 28 with comorbid hypertension diagnosis (prevalence =18.4%) and 10 without (prevalence =6.1%). Clearly, this unadjusted association between comorbid hypertension diagnosis and increased risk of HF was statistically significant (X-squared =10.1894, df =1, P value =0.001412).

Retinopathy

Out of the total 38 participants with HF, there were 20 (9.8%) who developed diabetic retinopathy in both eyes. There were 6 (17.6%) developed diabetic retinopathy in left eye and further 4 (19.0%) who developed diabetic retinopathy in right eye. The rest (n=8, prevalence =15.4%) were free from diabetic retinopathy. However, this association between diabetic retinopathy and increased risk of HF was not statistically significant (X-squared =3.4422, df =3, P value =0.3283).

Albuminuria

Out of the total 38 participants with HF, there were 12 with comorbid albuminuria (prevalence =31.6%) and 26 without (prevalence =32.7%). However, this association between comorbid albuminuria and decreased risk of HF was not statistically significant (X-squared =0, df =1, P value =1).

Neuropathy

Out of the total 38 participants with HF, there were 32 with comorbid neuropathy (prevalence =13.9%) and 6 without (prevalence =7.1%). However, this association between comorbid neuropathy and increased risk of HF was not statistically significant (X-squared =2.0202, df =1, P value =0.1552).

Summary of unadjusted risk factors for HF

The statistical associations between different variables showed that hypertension and cholesterol level were found to have significant association with HF with P value of 0.001 and 0.015 respectively (Table 1).

Logistic regression analysis

Utilizing the full logistic regression model that adjusts for all risk factors for HF simultaneously, only the presence of comorbid hypertension was significant statistically. Even after adjusting for all other potential risk factors, comorbid hypertension is associated with an increase in the HF [OR =2.739 (95% CI: 1.167–6.431), P=0.02066]. Notably the unadjusted effect for individual risk factors, utilizing the logistic regression modelling, only comorbid hypertension [OR =3.368 (95% CI: 1.643–7.409), P=0.001467] was significantly associated with HF in the context of diabetes (Table 2).

Discussion

Cardiovascular diseases (CVD) are the most prevalent cause of mortality and morbidity in diabetic populations (20). The burden of HF was estimated to be 26 million patients worldwide, with 1–4% of all hospitalized patients as percentage of total hospital in-patients (21,22). In United States, for example, around 5.7 million adults have HF, almost one tenth of deaths in 2009 attributed to HF and half of people who develop HF die within 5 years of diagnosis (23). The prevalence of CVD in Sudan was reported as 2.5% following the community-based household survey representing all states of Sudan (24). In this study we have shown that the prevalence of heart failure among Sudanese individuals with diabetes was 12.06% (95% CI: 8.44–15.61%). Nichols GA et al revealed almost the same prevalence of HF (11.8%) among subjects registered with type 2 DM at baseline in USA (25). Interestingly, the Framingham Heart Study showed that the frequency of HF was twice as high in men with diabetes and five times higher in women with diabetes compared with control subjects (26). There is high prevalence of prediabetes and diabetes in individuals with heart failure. For instance, prediabetes was reported in more than one-third of patients who are hospitalized for heart failure. The prevalence of diabetes in patients with heart failure was high and this estimated to be between 25% and 40% (27). The cardiovascular mortality is increased by 11% for every 1% increase in HbA1c in individuals with poor glycemic control (28). Both, the high susceptibility of CV risk factors and direct well established pathological effects of diabetes on the CV system make individuals with diabetes at increased risk of developing CV complications such as increased prevalence of MI, revascularization, stroke and HF (20). Unfortunately, optimizing diabetes control will decrease CV events but will not abolish it and this due the complex and multifaceted nature of the relationship linking DM to CVD. While obesity, hypertension (HT) and dyslipidaemia are common risk factors for CVD, still they are prevalent particularly in patients with type 2 DM. According to our results, the unadjusted risk factors for HF were hypertension and cholesterol level (P value of 0.001 and 0.015 respectively). No surprise as the prevalence rates of HT among patients with type 1 DM (T1DM) and type 2 DM (T2DM), are 30% and 60%, respectively (20,25,27). Comparatively, in Sudan HT was found in 39.9% and high cholesterol and triglyceride noted in 59.9%, 32.5% among individuals with diabetes respectively (10). Importantly, hypertension in Sudan was shown to one of the leading cause of heart failure and cardiovascular disease (29,30). Logistic regression analysis showed that only hypertension was absolute risk factor for HF (P=0.001467). High prevalence of hypertension among rural and urban population in Sudan was noted. For example, the prevalence of hypertension in Urban areas in north of Sudan was estimated to be more than 30%, in rural population was 15% to 38% and in Nubia ethnic population in South of Sudan was around 50% (31-34). Furthermore, cholesterol was associated with heart failure only in unadjusted risk analysis, but not with logistic regression analysis. Hypertension can be associated with hyperlipidaemia, diabetic nephropathy, nephrotic syndrome and increase in risk of CVD (4,6). The presence of retinopathy, albuminuria, duration of diabetes and neuropathy in this study were not associated with HF. HF was not significantly associated with age, sex, HbA1c, HDL, triglyceride and low LDL. Many clinical studies showed evidences that supported the impact of glycemic control in improving the CV outcomes in patients with DM (35,36). Tighter controls with tailored individualized patient-centred HbA1c targets are recommended in the recent guidelines in younger patients free of diabetic complications, while remain looser in diabetic patients with established cardiovascular complications (37).

The limitation of the study can be attributed to the cross-sectional design of the study, and the fact that we could not take into account the temporal relationship between potential risk factors and outcomes. Furthermore, we have not assessed whether tight glycaemic control may reduce the prevalence of heart failure with diabetes or late diagnosis of diabetes may enhance the process of diabetes induce heart failure. Despite these limitations we believe our study is novel and is the first one to report the prevalence of heart failure among Sudanese individuals with diabetes.

Conclusions

The prevalence of HF is estimated to be 12.06% among Sudanese individuals with diabetes. The risk factors are hypertension and high cholesterol. Hypertension was an absolute risk factor for HF. Therefore, further planning and strategies by health authorities in Sudan are needed to prevent, treat and manage hypertension in individuals with diabetes.

Acknowledgments

The authors would like thank their families for their support during the preparation of this manuscript. Financial support and sponsorship from Health Insurance Corporation, Khartoum state (HIKS), Sudan and Patients Helping Fund, Khartoum, Sudan.

Funding: None.

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jphe.2018.04.03). MHA serves as an unpaid editorial board member of Journal of Public Health and Emergency from Aug 2017 to Jul 2019. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by Ethical Committee of the Faculty of Medicine – University of Medical Sciences and Technology, Khartoum, Sudan (IRB No. 00008867). Informed consent was taken from all subjects.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Dalal S, Beunza JJ, Volmink J, et al. Non-communicable diseases in sub-Saharan Africa: what we know now. Int J Epidemiol 2011;40:885-901. [Crossref] [PubMed]
2. International Diabetes Federation. IDF Diabetes Atlas. 5th. Brussels, Belgium: International Diabetes Federation, 2011.
3. Hall V, Thomsen RW, Henriksen O, et al. Diabetes in Sub Saharan Africa 1999-2011: epidemiology and public health implications. A systematic review. BMC Public Health 2011;11:564. [Crossref] [PubMed]
4. van Dieren S, Beulens JW, van der Schouw YT, et al. The global burden of diabetes and its complications: an emerging pandemic. Eur J Cardiovasc Prev Rehabil 2010;17:S3-8. [Crossref] [PubMed]
5. IDF. Diabetes atlas. 4th ed. Brussels: International Diabetes Federation, 2009.
6. Elmadhoun WM, Noor SK, Ibrahim AA, et al. Prevalence of diabetes mellitus and its risk factors in urban communities of north Sudan: Population-based study. J Diabetes 2016;8:839-46. [Crossref] [PubMed]
7. Noor SK, Bushara SO, Sulaiman AA, et al. Undiagnosed diabetes mellitus in rural communities in Sudan: prevalence and risk factors. East Mediterr Health J 2015;21:164-70. [Crossref] [PubMed]
8. Almobarak AO, Noor SK, Elmadhoun WM, et al. Metabolic control targets in Sudanese adults with type 1 diabetes: A population based study. J Family Med Prim Care 2017;6:374-9. [Crossref] [PubMed]
9. Noor SK, Elmadhoun WM, Bushara SO, et al. Glycaemic control in Sudanese individuals with type 2 diabetes: population based study. Diabetes Metab Syndr 2017;11:S147-51. [Crossref] [PubMed]
10. Awadalla H, Noor SK, Elmadhounc WM, et al. Diabetes complications in Sudanese individuals with type 2 diabetes: Overlooked problems in sub-Saharan Africa? Diabetes Metab Syndr 2017;11:S1047-51. [Crossref] [PubMed]
11. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 2012;70:3-21. [Crossref] [PubMed]
12. Almobarak AO, Barakat S, Khalifa MH, et al. Non alcoholic fatty liver disease (NAFLD) in a Sudanese population: what is the prevalence and risk factors? Arab J Gastroenterol 2014;15:12-5. [Crossref] [PubMed]
13. Almobarak AO, Barakat S, Suliman EA, et al. Prevalence of and predictive factors for nonalcoholic fatty liver disease in Sudanese individuals with type 2 diabetes: is metabolic syndrome the culprit? Arab J Gastroenterol 2015;16:54-8. [Crossref] [PubMed]
14. Garcia MJ, McNamara PM, Gordon T, et al. Morbidity and mortality in diabetics in the Framingham population: sixteen year follow up study. Diabetes 1974;23:105-11. [Crossref] [PubMed]
15. Kolovou G, Marvaki A, Bilianou H. One more look at guidelines for primary and secondary prevention of cardiovascular disease in women. Arch Med Sci 2011;7:747-55. [Crossref] [PubMed]
16. Ingelsson E, Sundstrom J, Arnlov J, et al. Insulin resistance and risk of congestive heart failure. JAMA 2005;294:334-41. [Crossref] [PubMed]
17. From AM, Leibson CL, Bursi F, et al. Diabetes in heart failure: prevalence and impact on outcome in the population. Am J Med 2006;119:591-9. [Crossref] [PubMed]
18. Cowie MR, Mosterd A, Wood DA, et al. The epidemiology of HF. Eur Heart J 1997;18:208-25. [Crossref] [PubMed]
19. Kasznicki J, Drzewoski J. Heart failure in the diabetic population - pathophysiology, diagnosis and management. Arch Med Sci 2014;10:546-56. [Crossref] [PubMed]
20. Matheus AS, Tannus LR, Cobas RA, et al. Impact of diabetes on cardiovascular disease: an update. Int J Hypertens 2013;2013:653789.
21. Ambrosy AP, Fonarow GC, Butler J, et al. The Global Health and Economic Burden of Hospitalizations for Heart Failure. Lessons Learned From Hospitalized Heart Failure Registries. J Am Coll Cardiol 2014;63:1123-33. [Crossref] [PubMed]
22. Cowie MR et al. Improving care for patients with acute heart failure. 2014. Oxford PharmaGenesis. ISBN 978-1-903539-12-5. Available online: http://www.oxfordhealthpolicyforum.org/reports/acute-heart-failure/improving-care-for patients-with-acute-heart-failure
23. Writing Group Members. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation 2016;133:e38-360. [Crossref] [PubMed]
24. The state of heart disease in Sudan (PDF Download Available). Available online: https://www.researchgate.net/publication/51609349_The_state_of_heart_disease_in_Sudan [accessed Sep 20, 2017].
25. Nichols GA, Hiller TA, Erbey JR, et al. Congestive heart failure in type 2 diabetes: prevalence, incidence, and risk factors. Diabetes Care 2001;24:1614-9. [Crossref] [PubMed]
26. Kannel WB, McGee DL. Diabetes and cardiovascular disease: the Framingham study. JAMA 1979;241:2035-8. [Crossref] [PubMed]
27. Matsue Y, Suzuki M, Nakamura R, et al. Prevalence and prognostic implications of pre-diabetic state in patients with heart failure. Circ J 2011;75:2833-9. [Crossref] [PubMed]
28. Iribarren C, Karter AJ, Go AS, et al. Glycaemic control and heart failure among adult patients with diabetes. Circulation 2001;103:2668-73. [Crossref] [PubMed]
29. Halim AM, Jacques JE. Rheumatic heart disease in Sudan. Br Heart J 1961;23:383-6. [Crossref] [PubMed]
30. Khalil S, El-Samani F, Dafalla G. Patterns of cardiovascular disease in Sudan: hospital load and recent Trends. Sudan Med J 1984;20:25-38.
31. Bushara SO, Noor SK, Ibraheem AA, et al. Prevalence of and risk factors for hypertension among urban communities of North Sudan: Detecting a silent killer. J Family Med Prim Care 2016;5:605-10. [Crossref] [PubMed]
32. Bushara SO, Noor SK, Elmadhoun WM, et al. Undiagnosed hypertension in a rural community in Sudan and association with some features of the metabolic syndrome: how serious is the situation? Ren Fail 2015;37:1022-6. [Crossref] [PubMed]
33. Balla SA, Abdalla AA, Elmukashfi TA, et al. Hypertension among rural population in four states: Sudan 2012. Glob J Health Sci 2014;6:206-12. [Crossref] [PubMed]
34. Noor SK, Elsugud NA, Bushara SO, et al. High prevalence of hypertension among an ethnic group in Sudan: implications for prevention. Ren Fail 2016;38:352-6. [Crossref] [PubMed]
35. King P, Peacock I, Donnelly R. The UK prospective diabetes study (UKPDS): clinical and therapeutic implications for type 2 diabetes. Br J Clin Pharmacol 1999;48:643-8. [Crossref] [PubMed]
36. Duckworth WC, McCarren M, Abraira C. Glucose control and cardiovascular complications: the VA Diabetes Trial. Diabetes Care 2001;24:942-5. [Crossref] [PubMed]
37. American Diabetes Association. 8. Pharmacologic approaches to glycemic treatment. Diabetes Care 2017;40:S64-74. [Crossref] [PubMed]