Abstract
Background/Aim: Current evidence suggests that dyslipidemia in patients with diabetes mellitus is associated with multiple microvascular and macrovascular complications. The prolonged failure to achieve glycemic and lipid profile targets increases cardiovascular risk, mortality, and the costs associated with medical care. This study aimed to explore the correlations between dyslipidemia and glycemic control in patients with diabetes, evaluate variations in lipid profiles, examine the impact of dyslipidemia on diabetes-related complications and assess the effectiveness of lipid-lowering treatments.
Patients and Methods: A cohort observational study was conducted on 304 patients hospitalized at the Emergency County Hospital in Oradea, Bihor, located in Northwest Romania, over a two-year period from 2022 to 2023. The study included data on diabetes control (fasting blood glucose, HbA1c), associated conditions including diabetes complications, laboratory analyses-lipid profile, creatinine, urea, albuminuria-anthropometric data, and home medication.
Results: In the group without dyslipidemia, the mean HbA1c value was 8.96±2.81, compared to 9.70±2.80 in the dyslipidemia group. The mean blood glucose level was 207.44±126.09 in the group without dyslipidemia and 257.77±140.68 in the dyslipidemia group (p=0.048). Coronary artery disease was significantly more common in patients with dyslipidemia (58.9%) than in those without (4.6%) (p=0.004). Uric acid levels were higher in patients with dyslipidemia (p=0.048). Lipid-lowering therapy reduced high lipid values, but a significant percentage of patients on treatment still exhibited elevated cholesterol, LDL, and triglyceride levels.
Conclusion: In patients with poorly controlled diabetes, dyslipidemia associates with poorer glycemic control, greater obesity levels, cardiovascular diseases, particularly coronary artery disease and peripheral arterial disease, as well as chronic kidney disease. Despite treatment, the lipid profile continues to remain elevated.
Introduction
Dyslipidemia is defined by abnormal blood lipid levels and is one of the main modifiable risk factors for cardiovascular diseases (CVD), the leading cause of death worldwide. CVD accounts for 17.9 million deaths annually, representing 31% of global mortality, according to WHO (1). Diabetes mellitus, a common metabolic disorder, is marked by hyperglycemia and involves disruptions in glucose, lipid, and protein metabolism. Consequently, dyslipidemia is more prevalent in patients with diabetes due to these pathophysiological pathways (2). Diabetic dyslipidemia is primarily characterized by high triglycerides, low HDL-cholesterol, and elevated LDL-cholesterol, closely linking diabetes to CVD (3-7). Individuals with dyslipidemia have a twofold increased risk of developing CVD compared to those with normal lipid levels (8-10).
Research has identified key risk factors for dyslipidemia in patients with diabetes, including high blood pressure, elevated body mass index, advanced age, limited physical activity, and longer diabetes duration. Early detection and careful lipid management can significantly reduce CVD-related morbidity and mortality in diabetes (11-13). The American Diabetes Association (ADA) advises that individuals with diabetes undergo an annual assessment of CVD risk factors (14).
Studies show that patients with diabetes and poor lipid control face higher risks of myocardial infarction, cerebrovascular events, and peripheral artery disease (15-17). Although lipid-lowering medications are widely used, many patients with diabetes still do not reach recommended lipid targets, leaving them at continued risk (17). Additionally, dyslipidemia in diabetes is linked to the progression of chronic kidney disease (CKD), with higher LDL and triglycerides correlating with worsening kidney function. This interplay between dyslipidemia, CVD, and kidney disease emphasizes the need for an integrated management strategy that targets both glucose and lipid control to reduce these complications and improve overall outcomes (18-21).
In patients with poorly controlled diabetes, lipid abnormalities worsen, making it difficult to reach target lipid levels, despite medications. Guidelines suggest lifestyle changes, glycemic control, and statins therapy to reduce cardiovascular risk (22, 23).
While some studies confirm that managing blood sugar levels can improve lipid profiles, addressing diabetic dyslipidemia requires a comprehensive strategy targeting both blood glucose and lipid abnormalities (23-28). This integrated approach is essential for reducing the risk of cardiovascular complications in individuals with diabetes. Based on this rationale, the research aims to assess the response to lipid-lowering treatment in patients with diabetes.
Therefore, the study aimed to explore the correlations between dyslipidemia and glycemic control in patients with diabetes, analyze variations in lipid profile, assess the impact of dyslipidemia on diabetes-related complications, including metabolic and cardiovascular complications, and to evaluate the effectiveness of lipid-lowering treatment.
Patients and Methods
Study design and study site. A retrospective, observational cohort study was conducted on a total of 304 patients, hospitalized at the Emergency County Hospital Oradea, Bihor, in Northwest (NW) Romania, over 2 years, from 2022 to 2023. Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper. Inclusion criteria were: age >18 and patients with type 2 diabetes, compensated, with or without complications. Exclusion criteria were: pregnant females, age <18 years, presence of acute complications of type 2 diabetes (diabetic ketoacidosis, hyperosmolar coma) at the time of admission, patients who do not have diabetes or have other types of diabetes. All patient data were anonymized. The demographic characteristics (age, sex, urban and rural areas of provenance) and the medical history were provided by the patients at admission.
Variables and data sources. In all subjects, data on baseline comorbidities and diabetes complications were collected. Clinical data of patients were extracted from the hospital records. Clinically, blood pressure, ventricular rate, ankle-brachial index, weight, height, and abdominal circumference were measured. Peripheral sensitivity was tested by specialist neurologists to detect diabetic polyneuropathy. The fundoscopic examination was performed by specialist ophthalmologists to assess diabetic retinopathy.
Laboratory tests aimed to determine the level of diabetes control by assessing glycated hemoglobin and fasting blood glucose, lipid profile [triglycerides (Tg), total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C)], renal function (creatinine, urea, uric acid, albuminuria).
The prevalence of associated conditions and baseline therapy were evaluated at the time of admission. The patients’ medical history was reviewed for evidence of cardiovascular involvement (including a history of coronary artery disease, stroke, or peripheral arterial disease) and renal impairment (chronic kidney disease).
Measurements of anthropometric parameters and blood pressure. To determine blood pressure and pulse, patients rested for 5-10 min, without engaging in intense physical activity for 30 min prior or consuming coffee, in a comfortable position with their arm supported at the heart level. An automatic blood pressure monitor was used. For calculating the ankle-brachial index, the preparation of the patient was similar, but blood pressure was measured in both the upper and lower limbs. A Doppler was used to identify the anterior tibial artery in the lower limbs and a blood pressure cuff was applied to the ankle, noting the pressure at which the first sound was detected with the Doppler device. Ankle-brachial index with normal values=0.9-1.4, peripheral artery disease <0.9, arterial calcification >1.4. Body weight was measured to the nearest 0.1 kg using a digital scale. Body mass index (BMI) was calculated using the height and weight measurement (kg/m2). Waist circumference was measured to the nearest 0.1 cm by placing a plastic tape at the midpoint between the lower rib margin and the iliac crest. Abdominal circumference was considered elevated if it exceeded 80 cm for females and 94 cm for males.
Definition of variables. Diabetes mellitus has been defined by a glycated hemoglobin value of over 6.5 mg/dl. Based on BMI, overweight is defined as a BMI between 25 kg/m2 and 29.9 kg/m2, class 1 obesity as a BMI of 30 kg/m2 to 34.9 kg/m2, class 2 obesity as a BMI of 35 kg/m2 to 39.9 kg/m2, and class 3 obesity as a BMI greater than 40 kg/m2. Hypertension is defined as a systolic blood pressure (SBP) ≥140 mmHg and a diastolic blood pressure (DBP) ≥90 mmHg or the presence of anti-hypertensive therapy. The stage of hypertension was determined based on medical history or the American Heart Association (AHA) criteria.
Cut-off values for serum lipid profiles were as follows: high total cholesterol (TC) ≥200 mg/dl, high triglycerides (Tg) ≥150 mg/dl, high low-density lipoprotein cholesterol (LDL-C) ≥100 mg/dl, and low high-density lipoprotein cholesterol (HDL-C) <60 mg/dl for males and <40 mg/dl for females.
For renal function, high creatinine >1.1 mg/dl, high urea >20 mg/dl, high uric acid >6 mg/dl for females and >7 mg/dl for males, and high albuminuria >30 mg/dl may indicate kidney disease. Staging of chronic kidney disease (CKD) was performed according to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, based on the estimated glomerular filtration rate (eGFR) and the presence of kidney damage (albuminuria).
Ethical approval. This study was approved by the Ethical Council (IRB No. 33435 from 6 October 2022) and the Ethical Committee (IRB No. 33445 from 7 October 2022) of the Bihor County Emergency Hospital. The research was conducted in compliance with the Declaration of the World Medical Association of Helsinki.
Statistical analyses. Data were analyzed using IBM SPSS Statistics (version 20; IBM Corp., Armonk, NY, USA). The comparison of the results between samples was performed using independent-samples t-tests and ANOVA test. Pearson’s correlation coefficient was calculated to assess the correlation between dyslipidemia and other parameters. The level of significance was set at 0.05 (p -value ≤0.05).
Results
Cohort description. This study conducted a comprehensive evaluation of 304 patients with uncontrolled type 2 diabetes, considering clinical, paraclinical, and biological variables to assess health status. The baseline characteristics of study participants, including associated pathologies and medications, are shown in Table I.
Cohort description.
Hematologic analysis revealed significant variability in glycosylated hemoglobin (HbA1c) levels, with a mean of 9.62±2.8 and a range from 4.3 to 18.9. The average blood glucose was 252.14. The BMI had a mean of 30.93, indicating a high prevalence of obesity. The average waist circumference was 109.02 cm, reflecting abdominal fat distribution.
Comorbidities were prevalent among the study participants. Overall, 68.8% were diagnosed with cardiovascular diseases, including 63.5% with coronary artery disease, 18.1% with cerebrovascular diseases, and 23% with peripheral arterial disease. Hypertension classification was as follows: 12.2% without hypertension, 9.2% with grade 1 hypertension, 73.7% with grade 2 hypertension, and 4.9% with grade 3 hypertension.
The mean creatinine levels were 1.09±0.59 mg/dl, with values ranging from 0.40 to 7.7 mg/dl. The estimated glomerular filtration rate (eGFR) had a mean of 73.76±26.14, indicating a mildly reduced renal function. Additionally, the mean uric acid level was 5.81±1.97 mg/dl, and the mean urea level was 30.30 mg/dl.
The lipid profile analysis showed a mean total cholesterol of 166.03, ranging from 70 to 347. The mean HDL-C was 38.49, LDL-C mg/dl was 94.70 mg/dl, and Tg were 168.69 mg/dl.
The clinical impact of dyslipidemia in patients with uncontrolled type 2 diabetes. Among the total participants in the study, 150 male patients (49.3%) and 120 female patients (39.5%) were diagnosed with dyslipidemia (Table II). In contrast, 11 male patients (3.6%) and 23 female patients (7.6%) did not present this condition. These findings indicate a higher prevalence of dyslipidemia among male patients compared to females.
Demographic study based on the presence or absence of dyslipidemia.
The average age of participants with dyslipidemia was 66.75 years, with a standard deviation of 10.98, reflecting relatively low variability around the mean. In contrast, the participants without dyslipidemia, had a higher mean age of 67.15 years, with a standard deviation of 11.98. This finding showed that, although the average age between the two groups was similar, there was a minimal difference in age distribution.
Dyslipidemia was more prevalent in rural areas, with 150 individuals (49.3%) diagnosed in rural settings compared to 120 individuals (39.5%) in urban settings. Among those without dyslipidemia, 20 individuals (6.6%) were from rural areas, while 14 individuals (4.6%) were from urban areas.
Systolic blood pressure (SBP) was assessed for both groups (Table III). The mean SBP showed little variation, with a value of 142.09 mmHg in the group without dyslipidemia, and a similar mean of 142.93 mmHg in the group with dyslipidemia. Patients with dyslipidemia may have slightly higher diastolic blood pressure (DBP) values compared to those without dyslipidemia. The mean diastolic blood pressure in the group without dyslipidemia was 77.38 mmHg, whereas in the dyslipidemia group, it was 79.62 mmHg. Additionally, the mean ventricular workload was slightly increased for patients with dyslipidemia, averaging 80.08, compared to an average of 77.12 for those without dyslipidemia.
Statistical analysis of blood pressure and ventricular workload parameters.
Regarding the presence of hypertension, only 5 individuals (1.6%) without dyslipidemia did not have hypertension, while 32 individuals (10.5%) with dyslipidemia did not present with hypertension. In the hypertensive group, 29 individuals (9.5%) without dyslipidemia and 238 individuals (78.3%) with dyslipidemia were diagnosed with this condition. These data suggest a significantly higher prevalence of hypertension among patients with dyslipidemia.
Analysis of these cardiovascular parameters in relation to dyslipidemia indicates a trend towards increased diastolic blood pressure, mean arterial pressure, and ventricular workload in patients with dyslipidemia (Figure 1). Furthermore, the prevalence of hypertension is significantly higher among patients with dyslipidemia.
Graphical representation of clinical parameters based on dyslipidemia: (A) Systolic blood pressure mmHg (Sbp, mmHg), (B) Diastolic blood pressure (Dbp, mmHg), and (C) Heart rate (HR, bpm).
HbA1c is a crucial marker for monitoring long-term glycemic control. In the group without dyslipidemia, the mean HbA1c value was 8.96±2.81 compared to 9.70±2.80 in the dyslipidemia group (p=0.151). The mean blood glucose level was 207.44±126.09 in the group without dyslipidemia and 257.77±140.68 in the dyslipidemia group (p=0.048). These results suggest that patients with dyslipidemia tend to have higher blood glucose levels.
Analysis of the presence of peripheral neuropathy (PNP) revealed that 15 participants (4.9%) without dyslipidemia and 138 participants (45.4%) with dyslipidemia were not diagnosed with PNP. Conversely, 19 participants (6.2%) without dyslipidemia and 132 participants (43.4%) with dyslipidemia were diagnosed with PNP (F=0.588, p=0.444). In the case of diabetic retinopathy (RET), 27 participants (8.9%) without dyslipidemia and 201 participants (66.1%) with dyslipidemia were not diagnosed with this condition. Additionally, 7 participants (2.3%) without dyslipidemia and 69 participants (22.7%) with dyslipidemia were diagnosed with diabetic retinopathy (p=0.530).
These data indicate that, although there are differences in mean HbA1c and blood glucose values between patients with and without dyslipidemia, only the difference in blood glucose levels reaches statistical significance. The presence of peripheral neuropathy and diabetic retinopathy did not show significant differences between groups. These findings suggest that dyslipidemia may be associated with poorer glycemic control but does not significantly influence the presence of diabetic complications such as PNP and RET.
The relationship between the presence of dyslipidemia and various weight parameters, including obesity, BMI, weight status, and abdominal circumference was examined. Regarding obesity, 21 participants (6.9%) without dyslipidemia and 128 participants (42.1%) with dyslipidemia were not diagnosed with obesity. In contrast, 13 participants (4.3%) without dyslipidemia and 142 participants (46.7%) with dyslipidemia were diagnosed with obesity (p=0.115).
BMI showed different mean values between the two groups. In the group without dyslipidemia, the mean BMI was 29.53±5.76, while in the group with dyslipidemia, the mean BMI was 31.11±5.69 (p=0.129). The mean waist circumference in the group without dyslipidemia was 103.65±19.58 cm compared to 109.70±20.68 cm in the dyslipidemia group (p=0.107).
The analysis of weight parameters in relation to dyslipidemia did not reveal statistically significant differences between the two groups. However, the data indicates a trend of increasing BMI and abdominal circumference values in patients with dyslipidemia. Additionally, there is a higher prevalence of overweight and various degrees of obesity among patients with dyslipidemia.
Table IV provides a detailed statistical analysis of the relationship between dyslipidemia and various conditions, aiming to better understand how dyslipidemia may influence or coexist with other medical conditions. The presence of cardiovascular diseases is more frequent in the dyslipidemia group (62.5%) compared to the group without dyslipidemia (6.2%) (p=0.086). Coronary artery disease is significantly more common in patients with dyslipidemia (58.9%) compared to those without (4.6%) (p=0.004). For cerebrovascular diseases, the analysis does not reveal significant differences, indicating a similar distribution between patients with and without dyslipidemia (p=0.689). Peripheral artery disease is more frequently observed in the dyslipidemia group (21.7%) compared to the group without (1.3%) (p=0.099).
Statistical analysis of metabolic control, diabetes complications, weight status, and associated conditions.
Regarding renal function, although mean creatinine levels are higher in patients with dyslipidemia, the differences are not statistically significant (p=0.094). The Estimated Glomerular Filtration Rate (eGFR) is lower in patients with dyslipidemia (p=0.040). Uric acid levels are higher in patients with dyslipidemia (p=0.048).
The detailed statistical analysis revealed several significant associations between dyslipidemia and various conditions. In particular, coronary artery disease, glomerular filtration rate, and uric acid levels showed significant differences between patients with and without dyslipidemia (Figure 2).
Graphical representation of clinical and paraclinical parameters according to dyslipidemia, (A) coronary diseases (%), (B) glomerular filtration rate (eRFG ml/min), (C) uric acid level (mg/dl).
The lipid profile of patients was examined based on the presence or absence of dyslipidemia. There were no significant differences in mean total cholesterol levels between the groups with and without dyslipidemia (p=0.386). Mean HDL cholesterol levels were significantly lower in the dyslipidemia group (37.00±12.49 mg/dl) compared to the non-dyslipidemia group (50.38±9.84 mg/dl) (F=36.207, p=0.000). There were no significant differences in mean LDL-C levels between the groups with and without dyslipidemia (p=0.418). Mean triglyceride levels were significantly higher in the dyslipidemia group (179.04±113.58 mg/dl) compared to the non-dyslipidemia group (86.50±35.14 mg/dl) (p=0.000).
Additionally, to more accurately assess the atherogenic profile and overall cardiovascular risk, the non-HDL-C fraction, Tg/HDL-C ratio, and Tg/LDL-C ratio were calculated (Table V). A detailed analysis of the lipid profile revealed that patients with dyslipidemia exhibit significant changes in HDL cholesterol levels, Tg, and non-HDL-C compared to those without dyslipidemia.
Lipid profile analysis and associated paraclinical parameters.
Considering that a significant proportion of patients with dyslipidemia were undergoing lipid-lowering treatment, which reduces atherogenic lipids, the laboratory values of the lipid profiles may appear normal or even low. Therefore, we found it useful to conduct a statistical analysis of dyslipidemia based on medication. Table VI shows the distribution of patients according to their cholesterol, HDL, LDL, and Tg values, divided into three categories: without dyslipidemia, dyslipidemia without treatment, and dyslipidemia with treatment.
Lipid profile analysis based on lipid-lowering therapy.
Cholesterol is normal in 25.7% and high in 4.9% of untreated dyslipidemia patients. In the treatment group, 43.4% have normal values, while 14.8% have high cholesterol. LDL-C shows normal values in 18.1% and high values in 12.5% of untreated patients. In the treatment group, 34.5% have normal LDL, but 23.7% have high LDL. HDL-C shows normal values in 5.9% of untreated patients, while 24.7% have low values. For treated patients, 39.4% have normal values, and 18.8% have low values.
Tg in untreated patients are normal in 18.8% of cases, but 11.9% have high values. In the treatment group, 28.11% have normal values, but 30.4% have elevated Tg. Table VI highlights that treatment reduces elevated lipid values, but a significant percentage of patients on treatment still exhibit elevated cholesterol, LDL, and triglyceride levels.
In detailed research regarding the use of various pharmacological treatments in patients with and without dyslipidemia, notable and significant differences were observed for certain medications. Among patients with dyslipidemia, regarding diabetes therapy, patients received treatment with one or more of the following: metformin (25.7%), DPP-4 inhibitors (4.6%), SGLT2 inhibitors (12.8%), GLP-1 analogs (9.5%), basal insulin (77.3%), rapid insulin (36.2%), sulfonylureas (5.9%), and pioglitazone (1%).
An important aspect of the study was the use of statins. A total of 177 patients (58.2%) with dyslipidemia received statins. Hypolipidemic treatment with fibrates was administered to 63 patients (20.7%) and with fatty acids to 11 patients (3.6%). Table VII provides data regarding the rest of the associated pathology treatments-hypouricemic medication, antihypertensives, peripheral vasodilators, antiplatelets, and anticoagulants.
Analysis of paraclinical parameters by pharmacological treatment.
Discussion
The study investigated the impact of dyslipidemia on patients with uncontrolled type 2 diabetes. Clinically, factors such as weight status, complications, and associated pathologies were monitored, while renal function, lipid profile, and glycemic control were assessed through laboratory assessments.
Given that the majority of patients with dyslipidemia were receiving lipid-lowering treatment, which aims to reduce or even normalize lipid profile values, it is understandable that precisely determining the impact is more challenging. However, certain aspects remain evident even in this context.
In this study, dyslipidemia was found to be roughly equal in both sexes, with a higher prevalence among men, particularly in patients from rural areas. Dyslipidemia, both with and without treatment, was associated with poorer glycemic control, as evidenced by higher blood glucose levels and glycated hemoglobin values far from therapeutic targets.
Studies conducted in many countries like China, Rwanda, Indonesia, established a correlation between blood glucose levels and serum lipids as well as lipid ratios (29-31). Higher blood glucose levels were linked to increases in Tg and LDL-C levels and with elevated Tg/HDL-C and LDL-C/HDL-C ratios. Other studies found notable correlations between HbA1c and dyslipidemia, specifically with serum Tg and total cholesterol (32-34). A Korean study demonstrated a significant correlation between dyslipidemia and higher glycated hemoglobin levels in patients aged 40 and above (35). A study conducted in Indonesia demonstrated that there is no statistically significant relationship between HbA1c and triglyceride levels in patients with coronary stenosis and type 2 diabetes mellitus. However, a correlation was found between the degree of stenosis and HDL-C levels (36). In contrast, a study conducted in China found that HbA1c is a predictor factor for severe coronary heart disease (37).
The present study established an association between dyslipidemia and poor glycemic control. However, no link was shown between dyslipidemia and diabetes-related complications, such as peripheral neuropathy and diabetic retinopathy.
It is recognized that elevated plasma lipid levels are linked to an increased risk of various peripheral neuropathies, including axonal distal polyneuropathy, sensory impairments such as vision and hearing loss, motor nerve damage, and dysfunctions of the sympathetic nervous system (38). Additionally, lipid components such as cholesterol, Tg, and lipoproteins are implicated in the pathogenesis of these neuropathies, potentially contributing through mechanisms involving oxidative stress, inflammation, and vascular effects on peripheral nerve function (39). Metabolic syndrome accelerates neuropathy progression in individuals with type 1 and type 2 diabetes, with a notably stronger association in patients with type 2 diabetes (40).
A Danish study showed that achieving therapeutic targets for HDL, LDL, and non-HDL cholesterol did not reveal a clear link between dyslipidemia and diabetic polyneuropathy. However, the presence of hypertriglyceridemia was associated with an increased risk of developing diabetic polyneuropathy, regardless of statin treatment (41). These findings align with the results of the current study, indicating that diabetic polyneuropathy does not have a direct link to the lipid profile at any given time. Instead, it correlates more with prolonged glycemic imbalance, which may have previously been associated with increased lipid profile values.
Regarding diabetic retinopathy, it appears that dyslipidemia is implicated in the development of this complication primarily in the early stages (42, 43). However, the severity of diabetic retinopathy does not correlate with lipid profile values (42). Some studies suggest that while dyslipidemia may influence the onset of diabetic retinopathy, its progression and severity are more closely associated with other factors, such as glycemic control and duration of diabetes. Research indicates that maintaining optimal glycemic control can significantly reduce the risk of diabetic retinopathy, regardless of lipid levels (44). In contrast, the rapid reduction of blood glucose levels is associated with the worsening of diabetic retinopathy (45).
The prevalence of hypertension is higher among patients with dyslipidemia. Considering the baseline antihypertensive therapy, the average systolic blood pressure values do not show significant differences. However, patients with dyslipidemia exhibit higher diastolic blood pressure and heart rates.
Our results are consistent with other studies that showed that increased serum levels of TC, LDL-C, non-HDL-C were linked to a higher risk of hypertension (46-48). Elevated resting heart rate is associated with high Tg and TC (49). In the present study, patients with dyslipidemia had, on average, a higher BMI and abdominal circumference. Additionally, the prevalence of overweight and obesity, to varying degrees, was greater.
Multiple studies indicate a link between abdominal obesity and cardiovascular diseases, which is partly mediated by altered dyslipidemia metabolism. A consistent direct association between abdominal obesity and dyslipidemia was observed, regardless of sex, age, body mass index, blood pressure, or smoking status (50, 51). The results of our research showed that cardiovascular diseases, particularly coronary artery disease and peripheral artery disease, are more commonly found in patients with dyslipidemia.
The American Heart Association (AHA) highlights a strong connection between dyslipidemia and CVD, particularly focusing on how dyslipidemia contributes to increased risks of conditions such as coronary artery disease and peripheral artery disease. AHA identifies diabetes as a major risk factor for CVD, particularly in conjunction with dyslipidemia, highlighting how both conditions can exacerbate the risk of heart-related issues (52). American Stroke Association (ASA) recognize dyslipidemia as a significant risk factor for stroke. Dyslipidemia, specifically elevated LDL-C, is closely associated with an increased risk of ischemic stroke, particularly in cases related to large artery atherosclerosis (53). Regarding renal function, patients with dyslipidemia typically exhibit higher average creatinine levels, lower glomerular filtration rates, and elevated uric acid levels.
Dyslipidemia, frequently observed in the current research, is a significant risk factor for the development of CVD) Distinct quantitative and qualitative alterations occur at various stages of renal impairment and correlate with the decline GFR. In patients with non-dialysis-dependent chronic kidney disease (CKD), there is typically a reduction in HDL levels, normal or low TC and LDL-C, alongside elevated triglyceride levels (54-56). As CKD progresses, dyslipidemia tends to worsen because of down regulation of lipoprotein lipase and the LDL-receptor, and delayed catabolism of triglyceride rich lipoproteins (57).
The lipid profiles of patients vary due to pharmacological treatment, which alters these values. Despite lipid-lowering therapy, therapeutic targets are often not achieved. Despite medical treatment, more than half of the patients exhibit elevated levels of LDL-C. Additionally, the number of patients with high triglyceride levels despite treatment exceeds those with normal triglyceride levels under therapy.
The novelty of this study on dyslipidemia lies in the inclusion criteria of the patients: those with type 2 diabetes, poorly controlled therapeutically, accompanied by numerous complications and comorbidities.
Study limitations. Since the data were collected from a single emergency hospital, the hospitalized patients had significant glycemic imbalances, which may restrict the generalizability of the findings to the broader population. Furthermore, the study relies on a retrospective analysis of patients, which means that certain factors, such as the history of the lipid profile before the initiation of lipid-lowering therapy or the evolution under treatment, may be missing.
Conclusion
In patients with poorly controlled diabetes, dyslipidemia-whether treated or untreated- associates with poorer glycemic control and higher prevalence of hypertension, elevated diastolic blood pressure and heart rate, as well as greater obesity levels and abdominal circumference. Additionally, in these patients, dyslipidemia positively correlates with associated conditions such as cardiovascular diseases, particularly coronary artery disease and peripheral arterial disease, as well as chronic kidney disease. Despite many patients being on lipid-lowering therapy, their lipid profile values often fail to meet therapeutic targets.
Footnotes
Authors’ Contributions
Conceptualization and design of the study, L.P. and R.B.; methodology, A.C.; software, A.V.P.; validation, O.L.P.., M.M.M., A.C. and A.V.P.; formal analysis, L.P.; investigation, R.B.; resources, R.B.; data curation, L.R.; writing—original draft preparation, A.C.; writing—review and editing, M.M.M; visualization, A.V.P.; supervision, M.M.M; project administration, R.B.; funding acquisition, O.L.P. All Authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
The Authors declare that there are no conflicts of interest in relation to this study.
Funding
This research was funded by University of Oradea, Bihor County, Romania.
- Received February 19, 2025.
- Revision received March 22, 2025.
- Accepted March 26, 2025.
- Copyright © 2025 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
References
In this issue
Jump to section
Related Articles
Cited By...
- No citing articles found.