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INTRODUCTION

Diabetes mellitus type 2 (T2DM) and cardiovascular disease (CVD) are closely correlated. T2DM is associated with a two to four times increased risk of coronary artery disease and stroke (Go et al., 2014). On the other hand, CVD is the main cause of death and disability among patients with diabetes mellitus (DM). Therefore, reducing CVD burden in diabetes is a major clinical imperative that should be prioritised to reduce premature death, improve quality of life and lessen individual and economic burdens of associated morbidities, decreased work productivity and high cost of medical care (Martín-Timón et al., 2014). Intensive glycaemic control remains a major focus in the treatment of patients with diabetes. However, this therapeutic goal should always be achieved in the context of a comprehensive programme to reduce cardiovascular (CV) risk factors. Various studies have independently confirmed that although reducing hyperglycaemia and intense glycaemic control reduce the onset and progression of microvascular complications, the effect on CV complications remains unclear. Some major studies have even shown an increase in CV risk in aggressive glycaemic control in elderly patients with multiple advanced disease (Inzucchi et al., 2012). UKPDS risk engine software was developed to refine the calculation of the CV risk of diabetic patients (Kavaric et al., 2018; Stevens et al., 2001). The software calculates a 10-year risk of non-fatal and fatal coronary heart disease (CHD), stroke and fatal stroke (Kothari et al., 2002; Stevens et al., 2001). The main advantage of this type of calculation is the fact that it also takes into account variables such as HbA1c and the duration of diabetes (Almeda-Valdes et al., 2010). The aim of this study was to determine glycaemic compensation and CV risk in 10 years in patients with T2DM according to gender.

METHODS
Patients‘ data collection

This study was designed as retrospective analysis of data obtained from documentation of the treated diabetic patients in primary care – the ambulance of diabetologist. The study was approved by institution’s ethics committee. Patients were included according to the following inclusion criteria: 1) T2DM diagnosed at least for 3 years and 2) newly added dipeptidyl peptidase-4 inhibitor (DPP4i) or sodium–glucose co-transporter-2 (SGLT2) inhibitor to the therapy with metformin and/or sulphonylureas and/or insulin.

Data were collected from two visits at the diabetologist’s. Visit 1 (V1) was considered as the visit when DPP4i or SGLT2 inhibitor was added to the therapy. Visit 2 (V2) was 18 months after V1. Patients were divided into five groups according to the type of their treatment (n = number of patients):

Metformin + DPP4i (DPP4i; n = 25)

Metformin + sulphonylurea + DPP4i (SU + DPP4i; n = 47)

Metformin + sulphonylurea + SGLT2 inhibitor (SU + SGLT; n = 37)

Metformin + insulin + DPP4i (INZ + DPP4i; n = 37)

Metformin + insulin + SGLT2 inhibitor (INZ + SGLT; n = 33)

Besides antidiabetic treatment, patients were treated with statins (62%), fibrates (24%) and antihypertensives (53%). The number of patients on antihypertensive as well as hypolipidemic treatment did not change significantly during the study.

The evaluated data were basic demographic and biometric data from the patient’s history, data about comorbidities and diabetic complications, and basic biochemical and physical parameters. To evaluate the patient’s CV risk, UKPDS risk engine (UKPDS, n.d.) was used to calculate the CV risk in 10 years for every patient at both visits. The glycaemic compensation was evaluated according to ADA standards and the therapeutic target was HbA1c <7.0%.

Patients‘ characteristics

There were 179 patients included in our study (56% women, 44% men). The mean age was 62.72 ± 0.68 years (range 36–84). The mean age of women was 63.39 ± 0.87 years (range 39–83) and the mean age of men was 61.85 ± 1.06 years (range 36–84). Six per cent of women and 22% men were smokers. The most frequent diabetic complication was neuropathy (27.4%), and the most frequent comorbidities were dyslipidaemia (78.2%) and arterial hypertension (75%). The representation of men and women in each group according to the type of the therapy was approximately the same (for detailed baseline characteristics of patients, see Tables 1S and 2S).

CV risk assessment

CV risk was calculated based on the used UKPDS risk engine software (UKPDS, n.d.) of 10-year risk of non-fatal, fatal CHD and non-fatal, fatal stroke in populations.

Based on the calculated risk, patients were classified into three groups: low-risk (<15%), medium-risk (≥15% and <30%) and high-risk (≥30%) category.

Statistical analysis

All data were collected in MS Excel 2010 and converted to SPSS v.19. Data were subsequently evaluated with descriptive statistics and expressed as mean ± standard error. In the results, the data are presented as absolute value (the number of patients ‘n’) or as a percentage of the whole group. All data were statistically analysed using analysis of variance (ANOVA) with Bonferroni post hoc test, Chi-square and Student’s t-tests. The relationships between variables were identified by using Spearman's correlation and logistic regression. In all analyses, p < 0.05 was considered statistically significant.

RESULTS
Glycaemic compensation

The levels of HbA1c in patients decreased through the whole study period. At the beginning of the study (V1), more than 60% of all patients had HbA1c level higher than 8% (65% of men and 57% of women). After 1.5 years of therapy (V2), HbA1c level higher than 8% was identified in 40% of the patients in both sexes (Fig. 1). The HbA1c levels decreased during the study significantly (V1: 8.7% ± 0.1%, V2: 7.9% ± 0.1%, p < 0.001).

Figure 1

The distribution of patients according to HbA1c levels at the baseline and at the end of the study. V1 – visit 1 (baseline), V2 – visit 2 (the end of the study).

At V1, the HbA1c levels were similar in both men (8.7% ± 0.2%) and women (8.6% ± 0.1%). After 18 months, the HbA1c levels significantly decreased both in men (8.5% ± 0.6%) and women (7.8% ± 0.2%) compared to baseline (p < 0.001). When treatment and gender together were taken into account, we found significant decrease in HbA1c levels in DPP4i and SU + SGLT groups in women compared to INZ + DPP4i and INZ + SGLT (p < 0.05) at V2. In men, we found no significant differences in treatment regiments at V2 (Tables 3S and 4S).

CVD risk according to gender

We found 46% of men at V1 at high risk of non-fatal CHD. In women, it was only 6%, which increased to 10% at V2 (Fig. 2). The average risk of a non-fatal CHD was alarmingly high in men, reaching up to 30% (Table 1). We found a significantly lower risk of non-fatal CHD in women compared to men (p < 0.001) (Fig. 3). We found that men had a 33-fold higher risk of non-fatal CHD than women (odds ratio [OR] 32.78 (CI 7.97–134.79), p < 0.001).

Figure 2

The distribution of patients according to the cardiovascular risk in men and women. V1 – visit 1 (baseline), V2 – visit 2 (the end of the study), CHD – coronary heart disease.

Figure 3

The cardiovascular risk according UKPDS risk engine in men and women. Values represents mean ± standard error of the mean. **p<0.01, ***p<0.001 vs. women, CHD – coronary heart disease.

The cardiovascular risk according to UKPDS risk engine in men and women.

CVD risk Women mean (%) ± SEM (min.–max. %) Men mean (%) ± SEM (min.–max. %) M vs. W p <
V1 CHD 16.58 ± 0.89 (3.10–54.90) 30.61 ± 1.87 (4.10–75.80) 0.001
V2 CHD 17.17 ± 1.03 (1.40–63.60) 30.94 ± 1.75 (5.00–70.90) 0.001
V1 fatal CHD 12.49 ± 0.84 (1.50–49.30) 23.33 ± 1.76 (1.10–66.50) 0.001
V2 fatal CHD 13.38 ± 0.98 (0.50–60.50) 24.05 ± 1.70 (1.50–65.70) 0.001
V1 stroke 9.76 ± 0.88 (1.00–55.80) 15.99 ± 1.94 (0.90–89.20) 0.01
V2 stroke 11.91 ± 1.00 (1.10–58.60) 19.23 ± 2.07 (0.80–92.90) 0.01
V1 fatal stroke 1.71 ± 0.17 (0.20–10.30) 2.98 ± 0.41 (0.10–21.40) 0.01
V2 fatal stroke 2.00 ± 0.19 (0.10–11.30) 3.44 ± 0.38 (0.10–16.80) 0.01

V1 – beginning of the study, V2 – the end of the study, CHD – coronary heart disease, CVD – cardiovascular disease, SEM – standard error of mean

When comparing the fatal CHD, at V1, almost 73% of women were at low risk. On the other hand, only 32% of men belonged to the low-risk group of fatal CHD at V1, and this number decreased to 29% after 18 months (Fig. 2). The mean risk of a fatal CHD was significantly higher in men compared to women (M: 24.05 ± 1.70 vs. W: 13.38 ± 0.98, p < 0.001; Table 1). The relationship between gender and the risk of a fatal CHD was moderate (ρ = 0.414, p < 0.01), and we found that the risk of fatal CHD was 38-fold higher in men compared to women (OR 37.56 (Cl 10.75–131.25), p < 0.01).

The risk of non-fatal stroke was much lower in the studied group of patients than the risk of CHD or fatal CHD. However, even with these values, we observed a higher risk in men than in women. Up to 83% of women were in the low-risk group, and this number decreased to 74% after 18 months of treatment. In contrast, only 66% of men at V1 belonged to the low-risk group, and this decreased to 51% after 18 months. The number of men at high risk increased from 13% to 16% (Fig. 2). The mean risk of non-fatal stroke was significantly higher in men than in women (p < 0.01, Table 1). The relationship between gender and the risk of non-fatal stroke was weak (ρ = 0.201, p < 0.01). The risk of a fatal stroke was negligible in our group of patients in both sexes at both the beginning and end of the study. All patients were in the low-risk group of fatal stroke, except for 1% of men in the medium-risk group (Fig. 2).

When treatment and gender together were taken into account, we found similar tendency. In men, there was significantly higher risk of CVD compared to women in fatal CHD in patients treated by DPP4i, in CHD and fatal CHD in patients treated by all other groups but DPP4i. When evaluating the risk of stroke and fatal stroke, we found no significant differences between genders in any of the treated groups (Table 5S).

DISCUSSION

CVD remains the leading cause of morbidity and mortality of diabetic patients. With numerous new antidiabetic agents used in the therapy, there is a pressing need to define their potential CVD effects. For years, it was hypothesised that glucose-lowering drugs (using HbA1c as a surrogate marker) might improve CVD outcomes (Control Group et al., 2009). However, this glucocentric approach was proved incorrect because firstly, some glucose-lowering drugs decreased HbA1c levels but worsened CVD outcomes, and secondly, the results of the post-trial follow-up of the UKPDS, and of a meta-analysis of large glucose-lowering outcome trials, suggested an approximately 15% CV risk reduction per 1% decrement in HbA1c (Holman et al., 2008). Good glycaemic management yields substantial and enduring reduction in onset and progression of microvascular complications. This benefit has been seen mostly in studies using metformin, sulphonylureas and insulin, but it is supported by more recent studies with other medication classes. The greatest absolute risk reduction comes from improving poor glycaemic control, and a more modest reduction results from near normalisation of glycaemia. The impact of glucose control on macrovascular complications is less certain. Because the benefits of intensive glycaemic control are slow to see, people living longer can benefit more from intensive glucose control. A reasonable target for HbA1c for most adults is around 7% or less (ADA, 2021). In our study, we saw that both sexes respond similarly to newly initiated therapy, and thus, gender has no significant effect on improving glycaemic control in DPP4i and SGLT2i therapy. The mean HbA1c values at the individual controls were comparable between men and women. However, we found significant decrease in DPP4i and SU + SGLT groups in women compared to INZ + DPP4i and INZ + SGLT opposite to men, where we found no significant differences in treatment regimens. It is important that glycaemic targets should be individualised according to patient goals and preferences, risk of adverse effects of therapy and patient characteristics, including frailty and comorbid conditions (Davies et al., 2018). Our data showed that even though the glycaemic compensation was satisfying and HbA1c was lowered, CV risk increased in time and most of our patients were classified as patients with medium risk or high risk for having non-fatal or fatal CHD. Several trials (SAVOR-TIMI 53, EXAMINE, TECOS) tried to specify the CV effects of DPP4is and they revealed neutral CV effects. In an analysis comparing DPP4i versus placebo or active treatments in patients with T2DM and reporting at least one event among all-cause and CVD mortality, myocardial infarction, stroke and new onset of heart failure, results showed that DPP4is do not affect all-cause and CVD mortality and stroke in diabetic patients; the reduction in myocardial infarction observed with short-term treatment does not persist in the long term. Long-term use of DPP4i in diabetic patients is associated with increased risk of heart failure (Savarese et al., 2015). On the contrary, SGLT2 inhibitors showed possible cardioprotective effects (Rabizadeh et al., 2019; Xu & Rajaratnam, 2017). It is known that both receptors (SGLT1 and SGLT2) play a significant role in CV events. Stressful conditions such as ischaemia, diabetes and heart failure increase the number of both SGLT1 and SGLT2 receptors in the heart. Inhibition of SGLT1 receptors protects the cardiac tissue by reducing oxidative stress, increasing eNOS activity and reducing cardiac fibrosis. Inhibition of SGLT2 receptors increases natriuresis and metabolic conversion and improves calcium utilisation and cardiac fibrosis (Scheen, 2018). The CV benefits of SGLT receptor inhibition, therefore, go far beyond glycaemic control (Li, 2019). SGLT2 inhibitors reduce all-cause mortality and admission to hospital for heart failure (Palmer et al., 2021), as well as the progression of renal disease (Zelniker et al., 2019). In EMPA-REG OUTCOME, heart failure hospitalisation was reduced by 35%, with a rapid separation in the survival curves, suggesting acute benefit of the drug. The reduction in heart failure events was particularly clinically relevant, as drugs from other classes of glucose-lowering drugs with very different mechanisms had previously been found to be associated with an increase in hospitalisations for heart failure (Zinman et al., 2015). These positive effects of SGLT2 inhibitors result from their haemodynamic, metabolic and hormonal actions (Kashiwagi & Maegawa, 2017). SGLT2 inhibitors have also been associated with a significant reduction in albuminuria progression through blood pressure lowering, reduction of intraglomerular pressure and hyperfiltration, modification of inflammatory processes, reduction of ischaemia-related renal injury and increases in glucagon levels (Davidson, 2019). The beneficial effects of SGLT2 inhibitors on renal function result from the lowering of blood glucose that results in decrease of glucotoxicity in the kidney and other organs, leading to reduction of renal growth, inflammation and injury (Vallon & Thomson, 2017). However, the use of SGLT2 is contraindicated in patients with severe chronic kidney disease or in patients who require dialysis (European Medicines Agency, n.d.). In the meta-analysis, Zheng et al. (2018) found the use of SGLT2 inhibitors or glucagon-like peptide-1 (GLP-1) agonists associated with lower mortality than DPP4is. Use of DPP4i was not associated with lower mortality compared to placebo or no treatment. Similarly, Fei et al. (2019) described SGLT2 inhibitors to be superior in reduction of CV and all-cause deaths, hospitalisation for heart failure and renal events, among the new antidiabetic drug classes. They found no beneficial CV effects of DPP4is. Because of all these effects, SGLT2 inhibitors should be the preferred treatment for T2DM (American Diabetes Association, 2018; Fei et al., 2019); however, a higher risk of ketoacidosis was described after SGLT2 inhibitor administration (Fadini et al., 2017). Patients with insulin-treated diabetes who develop nausea, vomiting and malaise during treatment with SGLT2 inhibitors should be promptly evaluated for the possible coexistence of ketoacidosis (Kashiwagi & Maegawa, 2017).

The declining number of patients with low risk of CHD, fatal CHD and stroke after 18 months of therapy in our study suggests that despite reduced HbA1c levels, CV risk increases. We also found that the risk of developing both non-fatal and fatal CHD strongly depends on gender. Men had more than a 30-fold higher risk of non-fatal and fatal CHD than women. Our results are in contrast to those of Wang et al. (2019), which suggested that women with DM had an extremely higher risk for CHD, stroke, cardiac death and all-cause mortality, compared to men with DM. Patient’s sex affects not only the pathophysiology of diabetes and diabetes-associated CV complications, but also the response to drugs (Campesi et al., 2017; Kautzky-Willer & Harreiter, 2017). For example, the use of SGLT2 inhibitors, a higher risk for urinary tract infections and related genital infections have been reported among women (Johnsson et al., 2013). Clinical guidelines for the management of T2DM recommend individual therapy considering age, duration of disease, presence of complication and risk of hypoglycaemia. However, at present, the patient’s sex has no impact on clinical decisions (Kautzky-Willer et al., 2016; Kautzky-Willer & Harreiter, 2017). Regarding a potential sex-related therapeutic approach, women with diabetes reach their target values for HbA1c, blood pressure and low-density lipoprotein cholesterol less often than men (Kautzky-Willer et al., 2019). The reasons of different impact of diabetes between genders are not completely clear (Seghieri et al., 2017).

In our study, patients using insulin therapy had higher CV risk compared to patients who did not have insulin therapy. This finding corresponds to other studies that proved that exogenous insulin is associated with increased CV risk (Bell, 2015; Herman et al., 2017) and that insulin therapy may increase this risk (Bittencourt & Hajjar, 2015; Currie & Johnson, 2012). This effect could be because of hyperinsulinisation due to different magnitude or timing of exogenously administered insulin (Herman et al., 2017). It was assessed, as well, that the intensive glycaemic control by either sulphonylureas or insulin substantially decreases the risk of microvascular, but not macrovascular disease in patients with type 2 diabetes (UKPDS group, 1998). However, the risks of insulin treatment may outweigh the benefits for many patients with T2DM (Herman et al., 2017; Riddle et al., 2010), and thus, long-term maintenance of glycaemic control only on oral antidiabetics could represent a significant benefit to the CV system.

The risk of having a stroke was not as common in patients in our cohort as the risk of having a CV event. One CV risk study from China took into account the gender of diabetic patients and found this trend only in men with prediabetes, whereas in well-compensated type 2 diabetes, the difference was the opposite and women were at higher risk than men (Yang et al., 2019). The risk of a fatal stroke was negligible in our cohort. Finally, from the point of view of UKPDS risk engine calculations, the women in our group turned out to be significantly less at risk and less prone to developing a CV event (CHD, fatal CHD) or stroke during the entire follow-up. In conclusion, despite good glycaemic compensation of enrolled patients, their CV risk increased with the accumulation of other risk factors. Therefore, in addition to the emphasis on improving the parameters of diabetes, more attention should be paid to improving the overall health of the patient. It is also interesting to note that insulin therapy may represent an increase in CV risk, and long-term maintenance of glycaemic compensation only on oral antidiabetics may represent a significant benefit for the CV system in type 2 diabetics. Our monitoring confirms that a comprehensive view of patient therapy is necessary and, in addition to taking into account the CV status of diabetics, it is also necessary to focus more on gender metabolism differences and adapt therapy to the different metabolic requirements of men and women.

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