Factors associated with the risk of the incidence heart failure at 5 years in diabetic patients followed in Goma, Democratic Republic of the Congo

Introduction

The current classification of heart failure (HF) by the American Heart Association (AHA) classifies diabetes mellitus (DM) as stage A HF, which leads to an increased risk of developing stage B HF or asymptomatic left ventricular (LV) dysfunction (1). Data from the literature show that people with diabetes have a much higher risk of developing HF than those without diabetes (2).

Several clinical and experimental studies have shown that DM leads to functional, biochemical and morphological abnormalities of the heart, independently of the concept of myocardial ischemia, and some of these changes occur earlier in the natural history of diabetes (1,3). This association between diabetes and HF has historically been thought to be due to the increase in coronary atherosclerosis in people with diabetes. However, contrary to this hypothesis, the risk of HF is also elevated in people with diabetes without coronary artery disease (4) and in diabetic patients under tight glycemic control (5). These results suggest that factors other than coronary atherosclerosis may contribute to the increased risk of HF in diabetics (3).

Early detection of HF at stage A, B is mandatory in patients with DM to avoid progression from the asymptomatic stage to the symptomatic stage, improve the prognosis and adapt the therapy (6) for example for good control of arterial hypertension in diabetic patients, it is necessary to normalize dyslipidemia, reduce weight, reduce LV hypertrophy (LVH) and introduce sodium-glucose co-transporter inhibitors into the treatment (7).

Brain natriuretic peptides (BNP) are considered very sensitive markers in the diagnosis of HF (8) and are associated with high morbidity and mortality (9). BNP are factors associated with HF independently of other potential risk factors in diabetics (10,11). BNP-based screening identifies patients most at risk for cardiovascular (CV) events and, more specifically, patients with HF (12,13).

The diagnosis of HF is difficult in daily practice because of its subclinical character. To circumvent this approach, it is necessary to measure blood pressure (BP) in all diabetics because relationships between BP, heart rate (HR) and HF have been described in the literature (14). High systolic BP (SBP) is a classic cause of acute HF (14). Hypertension is a potent CV risk factor that induces arterial stiffness in large arteries and LVH and fibrosis, which are responsible for various CV events. Pulse pressure (PP) is a pulsatile component of BP, which reflects the buffer function of large arteries. Brachial PP is influenced by arterial stiffness, which also has a negative influence on renal function; LV function and CV risk (15). High PP is an independent predictor of CV risk, particularly in diabetic and especially elderly patients. To these factors indicated above, sex is added in the prediction of the incidence of HF. Several studies from around the world have shown gender differences in DM and HF (16-18). Diagnosis of DM in women is often made at a later age than men (17). The incidence of HF is twice as high in diabetic men and five times as high in diabetic women compared to their respective non-diabetic counterparts (18).

In the Democratic Republic of Congo (DRC), studies explaining the relationship between sex, age, PP and renal function and the incidence of HF in diabetics are rare. The objective of this study was identify easily measured clinical factors associated with elevated BNP in patients with DM, which may facilitate to prompt screening for HF.

Methods

Study population and design

The study was carried out at the Center of the Association of Diabetics in Congo (ADIC) in Goma, DRC. This site was selected on the basis of a reasoned choice based on the age of the center and the number of diabetic patients seen in one week. This was a cross-sectional and analytical study including all known asymptomatic diabetics in the city of Goma during the period from February 5 to 19, 2023. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was reviewed and approved by the ethics committee of the University of Goma (UNIGOM/CEM/09/2022). Written informed consent was obtained from all the participants and/or their legally acceptable representatives. Non-literate participants were accompanied by a literate peer of their choice. Participants under 18 years of age were accompanied by their parents or guardians. Their informed assents and consent from parents or guardians were requested and signed before the enrolment to the study. Participants had the right to provide consent or not and to withdraw from the study at any time during the interview, without having to provide a reason. Risks to participants in this study were expected to be minimal, as the invasiveness of the ultrasound and pathology examination was minimal.

The study population was consisted of all diabetics who were consulted the ADIC Center and those from other hospitals during the study period; after raising awareness in their WhatsApp groups and on the radio. The sample size was calculated from Fisher’s formula: n ≥ (Z² × (p)(1 − p))/d² where n = sample size, z = 1.96 (confidence coefficient), p = previous prevalence, d = 0.05 (margin of error or range of imprecision reflecting the desired degree of absolute precision). The probability of the risk of HF in diabetics being not yet elucidated in our country, we prefer to take 50% which was the median where the phenomenon is better distributed. So, p=0.5 was the prior prevalence was used in all studies to calculate the sample size. So, the calculated sample size was n ≥ (1.96)² × 0.5 × 0.5/(0.05)² = 384. By incorporating the 10% of non-respondents, we obtain 422 diabetics to be included. During the data collection period in this center, 408 diabetics met the inclusion criteria.

The selection was made on free informed consent and recorded in writing. The inclusion criteria were asymptomatic diabetic status, residence in the city of Goma and age between 18 years minimum and 90 years maximum. The criteria for non-inclusion included diabetic complications including heart disease undergoing treatment, diabetics on dialysis, with serious complications related to DM.

Data collection and procedure

Data collection was done using a form pre-established by our research group. The parameters of interest were retained and collected by way of questioning for the ethnic group, sex, age and by way of clinical and paraclinical examinations for the anthropometric, biochemical and hemodynamic parameters measured by the equipment indicated in following paragraphs.

Anthropometric data were measured for weight and height using the scale coupled with the Health O meter^® brand (9500 West 55^th Street McCook, IL 60525-7110 USA) height rod, model 500KL, SN 5000155271, DATE CODE: 3718, Made in China. A millimeter tape measure was used to measure waist circumference (WC)and hip circumference (HC). Pulmonary artery systolic (PAS) and pulmonary artery diastolic (PAD) arterial blood pressures (BPs) were measured using an OMRON model M2 Basic electronic BP monitor (HEM-7120-E). The electrocardiogram (ECG) was recorded by a Comen brand device (Shenzhen, China), model: CM 1200B, SN 92190522018B, manufactured on May 22, 2019, connected to the electric generator, after explaining the technique to the patient, he lies down on the bed examination in dorsal decubitus, undressed in the thorax and shoes, calm. After the identification of the patient, the application of the gel to the sites, the electrodes are placed on the thorax and the limbs, and then an ECG is printed. The Peguero index was used to search for LVH. The ECGs were interpreted by a single cardiologist for better and uniform results.

Biological data included blood data. The venous blood was collected at the level of the fold of the elbow on dry tubes and ethylenediaminetetraacetic acid (EDTA) tubes for the various analyses, 5 mL of venous blood collected was put on an EDTA tube for the analyzes of glycated hemoglobin, BNP. The dry tubes were used for analyzes such as Creatinine. The packaging of the samples included first an absorbent paper, an appropriate Biohazard brand specimen transport bag, then in the isothermal container containing the ice packs. All the samples were stored in a blood bank type refrigerator; brand XY130 (in China) between 2 to 4 ℃. The dosage of glycated hemoglobin is carried out on whole blood collected on EDTA K2 anticoagulant by nephelometry method on Genrui PA120 Fully-auto Specific Protein Analyzer (Shenzhen, China). SN: 1141030201223, REF 31000003. Place the three reagents including the 20 mL diluent, the 15 mL latex and the 5 mL anti-serum in the machine according to their programmed position in the machine, then you will have to read the Mag card for each kit (which contains the reagent information), introduce the patient’s identity by the patient’s number then save, present the whole blood sample to be well homogenized to the probe of the machine and click on the start button, the machine allows 20 µL to be aspirated of the sample, the machine will automatically start the analysis by pipetting the three different reagents and the result appears on the screen of the machine in 60 seconds in a quantitative way in percentage whose reference value is from 4.2–6.5%.

The threshold used for the interpretation of the results during the study was that indicated by the manufacturer and found in the reagent kits. According to the manufacturer, the normal BNP value should be less than 100 pg/mL. order to guarantee the accuracy of the results, the CIMAK laboratory carried out a commercial internal quality control (freeze-dried serum to be reconstituted). These checks were carried out and validated each morning, and adapted to regulatory requirements. ELITROL I (normal references) and ELITROL II (pathological references) control sera were used. Continuous Quality Improvement (CQI) results were interpreted taking into account Westgard rules and Levey-Jennings charts. Appropriate corrective measures were taken whenever the values fell outside the defined limits. Depending on the case, these measurements concerned the IQC serum and/or the automaton and/or the reagents and/or the calibration.

The following definitions were used in this work:

Hypertension was defined, for some studies, by BP taken in the office, including systolic ≥140 mmHg and/or diastolic ≥90 mmHg and/or the presence of a personal history of hypertension. DM was defined by the following criteria: fasting blood glucose ≥126 mg/dL (7.0 mmol/L) and/or a personal history of known DM and/or a glycated hemoglobin level ≥6.5%. The pathological value of BNP was superior of 50 pg/mL (19). The incidence of HF at 5 years in diabetics was defined with BNP ≥50 pg/mL (19).

Statistical analyzes

After encoding and validation, the data were entered into a computer using Epi-Info^TM software, version 7.1.2.0. Continuous variables were expressed as mean ± standard deviation, median [interquartile range (IQR)] and categorical variables as percentages. The normality of the distribution of continuous variables was tested by the Kolmogorov-Smirnov test. The comparison of means in independent samples was performed using the Student’s t-test for normally distributed variables and that of medians was performed by the Man Whitney U test for non-Gaussian data. To compare proportions, the Pearson Chi-squared test or Fisher’s exact test was used. Spearman mixed linear regression was used to search for the correlation between BNP and the independent variables [age, sex, DM duration, SBP, diastolic blood pressure (DBP), mean blood pressure (MBP), PP, HR, BMI, fasting glucose and estimated glomerular filtration rate (eGFR)]. Spearman’s rho was calculated to assess this correlation. The logistic regression test was used to search for factors associated with the incidence of HF risk within 5 years. The stepwise method was used in the search for associated factors. Only statistically significant variables (P<0.05) in bivariate analysis were introduced into the final model. The odds ratio (OR) and adjusted OR (aOR) were determined to assess the degree of association between the independent variables and the incidence of HF risk at 5 years. The component of BP indicating the diagnosis closest to the incidence of HF was determined as the area under the curve (AUC) by performing the receiver operating characteristic (ROC) curve. The component with a value under the curve closest to 1 was the most determinant of HF. All analyses were performed with SPSS for Windows version 25 software. A P value <0.05 was considered statistically significant.

Results

Figure 1 illustrates the flow of participants in the present study where, out of 450 diabetics who responded to the call launched at Center ADIC, 20 were not included in the study for the following reasons: not being a resident of Goma, followed for HF and presenting with visible pregnancy. Of the 430 diabetics included in the study, 22 were excluded for not having carried out an ECG and the entire assessment. At the end of the study, the sample analyzed is 408 (Figure 1).

Figure 1 Participant flowchart. HF, heart failure; ECG, electrocardiogram.

Table 1 compares the general characteristics of the study population between the types of diabetes (type 1 vs. type 2). For all of the asymptomatic diabetics, a significant difference between the two groups was observed for age, BP components (SBP, DBP, PP and MBP), oral antidiabetics and insulin intake; and eGFR (Table 1).

The cumulative frequency of PP was presented in Figure 2 and it is noted that the PP values were concentrated between 45 and 65 mmHg. The average values noted were 53.3±18.4 mmHg for all diabetics, 43.5±15.2 mmHg for type 1 diabetics and 57.3±18.1 mmHg for DM type 2 (Figure 2).

Figure 2 PP distribution of diabetics. PP, pulse pressure.

Incidence of HF

Of a total of 408 diabetic patients examined, 120 presented a risk of HF at 5 years, a frequency of 29.4% (Figure 3).

Figure 3 Frequency of incidence of HF. HF, heart failure.

By comparing the subjects with incidence of HF to those without incidence of HF, it can be seen that the patients with incidence of IC included a greater proportion of patients aged over 40 years, women, subjects with SBP ≥140 mmHg, PP ≥65 mmHg and eGFR <60 mL/min/1.73 m² (Table 2).

Analysis of the ROC curve showed that PP was the most important component of BP associated with a high risk of subsequent HF [AUC =0.873; 95% confidence interval (CI): 0.829–0.916] compared to SBP (AUC =0.831; 95% CI: 0.777–0.885), DBP (AUC =0.619; 95% CI: 0.539–0.699) and MBP (AUC =0.742; 95% CI: 0.674–0.811) (Figure 4 and Table 3).

Figure 4 Comparison of ROC curves of BP components associated with high risk of HF incidence in the study population. SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; PP, pulse pressure; ROC, receiver operating characteristic; BP, blood pressure; HF, heart failure.

In a simple linear regression analysis (Table 4) age, SBP, PP and BMI were positively associated with variation in BNP, while gender and eGFR were negatively associated with BNP (Table 4).

The determinants of HF incidence in univariate and multivariate analysis appear in Table 5. In univariate logistic regression analysis, age ≥40 years, female gender, SBP ≥140 mmHg, PP ≥65 mmHg, BMI ≥25 kg/m² and eGFR <60 mL/min/1.73 m² emerged as determinants of HF incidence. After adjusting for all these variables in a multiple logistic regression, age ≥40 years (aOR: 2.02, 95% CI: 1.03–3.04) (P=0.02), female gender (aOR: 2.00, 95% CI: 1.24–3.25) (P=0.005), PP ≥65 mmHg (aOR: 2.63, 95% CI: 1.83–3.99) (P<0.001) and eGFR <60 mmHg (aOR: 1.99, 95% CI: 1.09–3.00) (P=0.04) were the independent risk factors associated with the incidence of HF in diabetics (Table 5).

Discussion

The objective of this study is to identify the factors associated with the incidence of HF followed in Goma. This study shows that the presence of DM increases the incidence of HF. The incidence of HF was determined from BNP. In previous studies, BNP has been proven to be a more sensitive tool for screening for subclinical CV disease in the general population (20). Thus, BNP may be elevated early in the disease process, allowing rapid detection of disease before symptoms appear (21). In the Framingham Offspring study of 3,346 asymptomatic middle-aged subjects, Wang et al. found that BNP independently predicted HF, stroke or transient ischemic attack, and atrial fibrillation, even after adjusting for traditional risk factors (12). In patients with DM, BNP is a well-established biomarker for the diagnosis and prognosis of CV events (22).

Thus, starting from BNP ≥50 pg/mL, we found 120 diabetics with BNP ≥50 pg/mL, i.e., an incidence frequency of HF of 29.4%. Several studies have reported this high incidence of HF in diabetics (19,23). DM is known to be a strong risk factor for atherosclerosis, thus causing “diabetic cardiomyopathy” which leads to CI independent of other CV risk factors (23). Other possible mechanisms by which DM leads to HF include hyperinsulinemia (24), endothelial dysfunction (25), metabolic disturbance (26), changes in calcium homeostasis (27) and dysregulation of the autonomic nervous system (28).

After analyzing different linear regression, logistic regression and ROC curve models, four factors still remain independently associated with the incidence of HF. Among these factors, we encountered PP ≥65 mmHg, age ≥40 years regardless of the type of diabetes, female sex and eGFR <60 mL/min/1.73 m². Among the factors identified, PP had a greater correlation (aOR =2.63, Spearman’s rho =0.290). By comparing the different components of BP, it was also noted that PP was more powerful than PAS, PAS and MBP in predicting the incidence of HF. High PP reflects aortic stiffness, these mechanisms lead to diastolic dysfunction. Albertini et al. showed similar results between PP and HF (29). Our results are also consistent with literature data showing that PP was an independent marker of HF (30). PP and age are closely linked to aortic stiffness, which induces a decrease in renal function, in particular by the remodeling of the large and small renal arteries, which induces pathological effects on renal blood flow resulting in a cardio-cardiac syndrome renal (15,31). The relationship between the incidence of HF and age observed in our study has been described by several authors (19,32). However, the mechanisms by which age and renal function influence HF could be explained through atherosclerosis of the arteries (33).

There are several possible explanations for the increased risk of HF associated with diabetes in women compared to men. First, subclinical LV contractile dysfunction due to myocardial deformation is observed in diabetic women (34). Second, an acceleration of the loss of ß cells from the islets of Langerhans has been observed in diabetic women (35). Third, a difference in the distribution of fat with predominance of visceral fat in women, which leads to an increase in blood volume, systemic pro-atherogenic inflammation thus increasing stroke volume, wall stress LV, myocardial damage and fibrosis, concentric LV remodeling and cardiac dysfunction (36). In women before menopause, estrogens improve the lipid profile, reduce inflammation, increase the production of nitric oxide (NO), maintain the integrity of the endothelium, inhibit epithelial cell apoptosis via the maintenance of endothelial progenitor cells, exert effects on the central nervous system leading to the modulation of food intake and energy expenditure, increase insulin sensitivity and survival of β cells in the islets of Langerhans and the protection of intestinal microbiota (37,38). Therefore, estrogen withdrawal in women results in increased myocardial apoptosis and replacement fibrosis together leading to myocardial contractility disorder, as a consequence LV systolic dysfunction (39).

Although the present study has contributed important data to our understanding of the incidence of HF in diabetics, some limitations cannot, however, be excluded. The present study pooled point-in-time (cross-sectional) data, excluding any conclusions about causal relationships between HF incidence and independent variables. It is therefore important to take this variable into account in the analyses. This is not a longitudinal follow-up of individuals, but participation over a well-defined period. Multivariate model analysis allows for adjustments by many variables, but other important factors may not be observed.

Conclusions

The present study showed that despite its cross-sectional nature, BNP is a good marker for predicting HF in diabetics. The frequency of HF is higher in the population of asymptomatic diabetics in Goma. PP, female gender, advanced age and impaired renal function are the independent factors of HF incidence. All of these results plead for the prevention of HF in any elderly diabetic, female and presenting a very high PP in our environment.

Acknowledgments

The authors would like to express their deepest gratitude to the Administrative Authorities of ADIC Center for their invaluable assistance. We are deeply indebted to all the participants who make this survey possible through their informed consent. We would like to particularly thank all our promoters for their commitment for the success of the present survey.

Funding: None.

Footnote

Data Sharing Statement: Available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-26/dss

Peer Review File: Available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-26/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-26/coif). The 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 reviewed and approved by the ethics committee of the University of Goma (UNIGOM/CEM/09/2022). Written informed consent was obtained from all the participants and/or their legally acceptable representatives. Non-literate participants were accompanied by a literate peer of their choice. Participants under 18 years of age were accompanied by their parents or guardians. Their informed assents and consent from parents or guardians were requested and signed before the enrolment to the study. Participants had the right to provide consent or not and to withdraw from the study at any time during the interview, without having to provide a reason. Risks to participants in this study were expected to be minimal, as the invasiveness of the ultrasound and pathology examination was minimal.

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/.

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