Abstract
Background
Cardiovascular disease (CVD) remains the leading cause of mortality in individuals with type 2 diabetes mellitus (T2DM), driven by chronic hyperglycaemia, dyslipidaemia, and systemic inflammation. In Nigeria, genetic predispositions, ethnic and environmental factors may further modulate CVD risk. This study aimed to evaluate the association between high-sensitivity C-reactive protein (hsCRP) and CVD risk in Nigerian T2DM patients receiving standard care.
Methods
This cross-sectional hospital-based study was conducted over 13 months. Data on socio-demographic characteristics, medical history, clinical findings, and laboratory parameters were collected using a structured proforma. Serum hsCRP was measured by homogenous immunoassay, while 10-year CVD risk was estimated with the WHO CVD risk assessment chart validated for Western sub-Saharan Africa. Statistical analyses, including binary logistic regression to assess the association between hsCRP and CVD risk, were conducted using SPSS version 25, with significance set at p < 0.05.
Results
Moderate-to-high CVD risk was prevalent in 51.5% of the study population. Longer diabetes duration (AOR = 1.345, 95% CI: 1.222–1.480, p < 0.001), elevated HbA1c (OR = 1.438, 95% CI: 1.061–1.949, p = 0.019), and co-morbid hypertension (OR = 14.498, 95% CI: 2.611–80.515, p = 0.002) were significantly associated with higher CVD risk. Serum hsCRP levels were higher in moderate-to-high-risk individuals (median: 2.42 mg/L, IQR: 2.8; 2.71 mg/L, IQR: 1.8) compared to lower-risk individuals (median: 1.22 mg/L, IQR: 2.5; 1.48 mg/L, IQR: 2.6), p = 0.012. However, it was not an independent predictor of CVD risk after adjusting for confounders (p = 0.369).
Conclusion
There is a high burden of increased CVD risk in this population despite ongoing management, with prolonged diabetes duration, poor glycaemic control and co-morbid hypertension as key predictors. Although hsCRP levels were elevated in higher-risk individuals, its clinical utility as an independent predictor of CVD risk may be limited. These findings emphasize the need to strengthen routine CVD risk assessment, prioritize modifiable risk factors, and optimize glycaemic control to reduce CVD burden in Nigerian T2DM patients.
Keywords: Cardiovascular disease, Type 2 diabetes mellitus, High-sensitivity C-reactive protein, CVD risk assessment, Glycaemic control, Nigeria.
Introduction
Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality globally, with a growing burden in developing countries such as Nigeria [1, 2]. Individuals with type 2 diabetes mellitus (T2DM) face a significantly higher risk of CVD, with chronic hyperglycaemia, dyslipidaemia, hypertension, and systemic inflammation contributing to accelerated atherosclerosis and increased cardiovascular events [3–5]. Identifying reliable biomarkers for cardiovascular risk assessment in this population is essential for improving early detection and preventive strategies.
High-sensitivity C-reactive protein (hsCRP) is a widely studied inflammatory marker that reflects systemic inflammation and has been strongly associated with an increased risk of cardiovascular events like myocardial infarction, stroke, and cardiovascular mortality [6, 7]. In T2DM, chronic low-grade inflammation is a key factor in disease progression, and elevated hsCRP levels have been linked to increased cardiovascular risk independent of traditional risk factors [8]. Although extensive research has established hsCRP as a strong predictor of CVD events in T2DM patients across mostly Caucasian populations [9, 10], data on its significance in Nigerian patients remain limited, particularly given the unique cultural and environmental factors that may influence their inflammatory state.
Several population-specific factors, including genetic predisposition, dietary habits, lifestyle patterns, and environmental exposures modulate T2DM epidemiology [11], and may influence the relationship between hsCRP and CVD risk in Nigerians. Nigeria has one of the largest populations of people living with diabetes in Africa [12], and with the rising prevalence of T2DM and the significant burden of CVD-related complications, understanding this association in a local context is crucial for refining cardiovascular risk assessment strategies. While the WHO CVD risk assessment tool is validated for use in the region, it does not incorporate inflammatory markers such as hsCRP, which may offer additional insights into cardiovascular risk beyond traditional factors. Nonetheless, its adoption in this study reflects its practicality and relevance in Nigeria, where it serves as the only regionally calibrated, accessible, and cost-effective tool for estimating CVD risk.
This study investigates the association between hsCRP and CVD risk in Nigerian T2DM patients. and evaluates the utility of hsCRP as a marker of CVD risk in this population. The primary objective was to determine whether serum hsCRP levels are independently associated with 10-year CVD risk as assessed by the WHO risk charts. Secondary objectives included evaluating the distribution of CVD risk categories among T2DM patients and identifying clinical and biochemical predictors of elevated CVD risk. A better understanding of this relationship could enhance risk stratification, facilitate early intervention, and inform targeted prevention and management strategies. Findings from this study may contribute to refining CVD risk assessment and supporting evidence-based approaches for CVD prevention in Nigerian T2DM patients.
Materials and methods
Study design and setting
This hospital-based cross-sectional study design was employed to assess the association between serum hsCRP and CVD risk status in Nigerian patients with a diagnosis of T2DM accessing standard diabetes care at the endocrinology clinic of the medical out-patient department of Benue State University Teaching Hospital (BSUTH), a tertiary healthcare center in Makurdi, North-central region of Nigeria, from October 2019 to October 2020.
Ethical considerations
This study was approved as part of a larger research project by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi (protocol number BSUTH/CMAC/HREC/101/V.I/47) on 21 st January 2019, and was conducted in accordance with the Declaration of Helsinki [13]. Prior to recruitment and data collection, informed written consent was obtained from all participants. To ensure confidentiality, unique number codes were assigned to each participant, and their clinical information and test results were securely stored in restricted-access areas.
Sample size determination and participant recruitment
The sample size for this study was determined using the prevalence of T2DM from a recent Nigerian study [11]. A minimum sample size of 192 was determined to be necessary for this study using the formula for calculating sample size in cross-sectional studies, a margin of error of 0.05, a confidence interval of 95%, and adjusting for a 10% non-response rate. However, to further enhance the statistical power, account for population variability, the final sample size was set at 200 participants.
Participants were recruited consecutively using a convenience sampling method during routine visits to the endocrinology clinic at BSUTH until the chosen sample size was reached. Trained research personnel identified potential participants through clinic records, conducted initial screening interviews, and provided study details to those meeting the inclusion criteria. Eligible individuals, aged 40–74 years, with a diagnosis of T2DM, and no history of recent infection (past four weeks) or inflammatory diseases were enrolled after providing written informed consent. Patients older than 74 years were excluded to maintain consistency with the validated age range of the WHO CVD risk chart used in this study. Also, patients with type 1 diabetes, gestational diabetes, history of established CVD (previous myocardial infarction, stroke, or coronary revascularization), patients with chronic kidney disease (eGFR < 60 mL/min/1.73 m²) or liver disease, and pregnant or lactating women were excluded, as were those with incomplete clinical or laboratory data essential for the study analysis. Patients on anti-inflammatory medications for acute pain affecting hsCRP levels were also excluded, but those on long-term antiplatelet therapy as part of standard care were not excluded.
Data collection and interpretation
A structured research proforma was used to obtain data on participants’ socio-demographic characteristics, medical history, medication use, and clinical features. Blood pressure (BP) was measured in mmHg using an AccuSure Mercury Sphygmomanometer BP Monitor, with participants seated upright and their arms positioned to align the cuff with chest level. Two readings of both systolic (SBP) and diastolic blood pressure (DBP) were taken at 5-minute intervals, and the average value was recorded in the data collection form for each participant. Anthropometric measurements, including height (m) and weight (kg), were obtained using a SECA weighing balance with height attachment, measured to the nearest decimal with participants wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight (kg)/height (m²) and obesity was defined as BMI ≥ 30 kg/m² [14].
Specimens were collected from each participant for biochemical analysis of lipid profiles, glycaemic control, and inflammatory markers. Dyslipidaemia was defined by the presence of one or more of the following lipid abnormalities; total cholesterol ≥ 5.17 mmol/L (≥ 200 mg/dL), LDL cholesterol ≥ 2.6 mmol/L (≥ 100 mg/dL), triglycerides ≥ 1.7 mmol/L (≥ 150 mg/dL), or HDL cholesterol < 1 mmol/L (< 40 mg/dL) for men and < 1.3 mmol/L (< 50 mg/dL) for women [15]. Glycaemic control in T2DM patients was categorized as well-controlled if glycated haemoglobin (HbA1c) was < 7% and poorly controlled if HbA1c was ≥ 7% over the long term [15]. For short-term control, fasting blood glucose (FBG) levels < 7.0 mmol/L were considered well-controlled, while values exceeding this threshold indicated poor control. High-sensitivity C-reactive protein (hsCRP) levels > 3 mg/L were used to identify participants with high risk of systemic inflammation [16, 17].
The updated World Health Organization (WHO) CVD risk assessment laboratory-based chart for individuals with diabetes in Western sub-Saharan Africa was used to assess participants’ cardiovascular risk. This tool estimates the 10-year probability of experiencing a fatal or non-fatal CVD event based on key clinical and demographic factors, including age, sex, systolic blood pressure, smoking status, diabetes status, and total cholesterol levels. Participants were categorized into very low (< 5%), low (5%–<10%), moderate (10%–<20%), and high (≥ 20%) cardiovascular risk groups according to WHO-defined thresholds. This stratification provided a structured approach for examining the relationship between hsCRP and CVD risk in T2DM patients, particularly within a resource-constrained healthcare setting [18].
Sample collection and assays
Blood samples were drawn from participants using aseptic techniques after an overnight fast of at least 8 h into five-millilitre vacutainers. To ensure accuracy and reliability, samples for FPG were collected into a fluoride oxalate container and HbA1c into an EDTA container, while samples for lipid profile and hsCRP were collected into lithium heparin containers. The samples were centrifuged at 5000 revolutions per minute (rpm) for 5 min after collection and the plasma/serum samples were aliquoted in plain cryovial tubes.
Glucose, HbA1c, and lipid profile (total cholesterol, HDL-cholesterol, and triglyceride) were measured immediately in the fluoride oxalate plasma, EDTA sample whole blood, and lithium heparin plasma, respectively while LDL-cholesterol and non-HDL-cholesterol levels were calculated. Aliquots of heparinised plasma for hsCRP were stored at −250C for up to three months before testing. All assays were performed using the Cobas c311® automated random-access analyzer for clinical chemistry and homogenous immunology assay (HIA) (Roche Diagnostics, Mannheim, Germany).
Statistical analysis
Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 25 (IBM Corp., Armonk, NY, USA) and Microsoft Excel. Demographic and clinical characteristics of the study population were summarized using descriptive statistics. Continuous variables were assessed for normality using the Shapiro-Wilk test and expressed as mean ± standard deviation (SD) if normally distributed or as median (interquartile range, IQR) if non-normally distributed. Categorical variables were presented as frequencies and percentages. Analysis of variances (ANOVA) was used to compare normally distributed continuous variables across various categories of CVD risk states in T2DM patients while the Chi-square test (or Fisher’s exact test) was used for the categorical variables.
Binary logistic regression analysis was conducted to identify predictors of moderate-to-high (≥ 10%) 10-year cardiovascular disease (CVD) risk among T2DM patients, based on the WHO risk classification. The dependent variable was dichotomized into low risk (< 10%) and moderate-to-high risk (≥ 10%). Independent variables included hsCRP, fasting plasma glucose (FPG), triglycerides, HbA1c, duration of diabetes, presence of co-morbid hypertension, use of anti-hypertensive therapy, and use of lipid-lowering drugs while adjusting for BMI, sex, ethnicity and occupation. Age, blood pressure, and total cholesterol were excluded from the model as they are integral to the WHO risk score. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported to describe the strength and direction of associations. Model performance was assessed using the likelihood ratio chi-square test, Hosmer-Lemeshow goodness-of-fit test, and Nagelkerke R². All statistical analyses were two-tailed, and a p-value < 0.05 was considered statistically significant.
Result
Among the study participants, 13.0% (n = 26) had a very low CVD risk (< 5%), 35.5% (n = 71) had a low risk (5–<10%), 31.5% (n = 63) had a moderate risk (10–<20%), and 20.0% (n = 40) were classified as high risk (≥ 20%) (Fig. 1). Notably, more than half of the study population (51.5%) belonged to the moderate-to-high CVD risk groups while 48.5% were low-risk.
Fig. 1.
Distribution of participants based on their WHO 10-year CDV risk categories
Table 1 presents the socio-demographic, clinical, and biochemical characteristics of participants based on their 10-year CVD risk status. Participants in the high-risk category were significantly older (60.5 ± 3.4 vs. 55.7 ± 8.8 vs. 50.0 ± 7.0 vs. 40.2 ± 4.1 years, p < 0.001) and had a longer duration of diabetes (11.4 ± 5.5 vs. 9.2 ± 5.7 vs. 5.6 ± 4.6 vs. 1.5 ± 1.7 years, p < 0.001) compared to those in the moderate-, low-, and very low-risk groups, respectively. Occupation was significantly associated with CVD risk, with retired participants more frequently in the high-risk group (25.0% vs. 33.3% vs. 9.9% vs. 3.8%, p = 0.037). Ethnicity also showed a significant relationship with CVD risk, as the Tiv ethnic group had the highest representation in the moderate- and high-risk categories (57.1% and 45.0%, respectively, p = 0.022). Systolic (141.0 ± 8.7 vs. 132.2 ± 8.5 vs. 120.1 ± 6.7 vs. 116.5 ± 8.5 mmHg; p < 0.001) and diastolic blood pressure (81.0 ± 6.3 vs. 79.1 ± 6.7 vs. 75.5 ± 6.0 vs. 73.9 ± 5.0 mmHg, p < 0.001) were significantly higher in the high-risk groups, with a similar trend observed among participants with co-morbid hypertension (28.2% vs. 40.5% vs. 26.7% vs. 4.6%; p < 0.001).
Table 1.
Socio-demographic, clinical, and biochemical characteristics of study participants stratified by 10-Year CVD risk status
| Characteristics | Very Low CVD Risk (N = 26) n (%) or Mean ± SD or Median (IQR) |
Low CVD Risk (N = 71) n (%) or Mean ± SD or Median (IQR) |
Moderate CVD Risk (N = 63) n (%) or Mean ± SD or Median (IQR) |
High CVD Risk (N = 40) n (%) or Mean ± SD or Median (IQR) |
p-values |
|---|---|---|---|---|---|
| Age (years) | 40.2 ± 4.1 | 50.0 ± 7.0 | 55.7 ± 8.8 | 60.5 ± 3.4 | < 0.001*a |
| Sex | |||||
| Female | 18 (69.2) | 40 (56.3) | 30 (47.6) | 21 (52.5) | 0.303b |
| Male | 8 (30.8) | 31 (43.7) | 33 (52.4) | 19 (47.5) | |
| Ethnicity/Tribe | |||||
| Idoma | 8 (30.8) | 15 (21.1) | 6 (9.5) | 10 (25.0) | 0.022*b |
| Igbo | 9 (34.6) | 12 (16.9) | 9 (14.3) | 5 (12.5) | |
| Igede | 0 (0.0) | 0 (0.0) | 6 (9.5) | 0 (0.0) | |
| Tiv | 7 (26.9) | 31 (43.7) | 36 (57.1) | 18 (45.0) | |
| Others | 2 (7.7) | 13 (18.3) | 6 (9.5) | 7 (17.5) | |
| Occupation | |||||
| Civil Servant | 14 (53.8) | 27 (38.0) | 23 (36.5) | 9 (22.5) | 0.037*b |
| Farming | 1 (3.8) | 4 (5.6) | 5 (7.9) | 1 (2.5) | |
| Housewife | 1 (3.8) | 7 (9.9) | 5 (7.9) | 4 (10.0) | |
| Retired | 1 (3.8) | 7 (9.9) | 21 (33.3) | 10 (25.0) | |
| Trading | 8 (30.8) | 26 (36.6) | 9 (14.3) | 15 (37.5) | |
| BMI (kg/m²) | 25.52 (6.1) | 27.34 (8.5) | 28.03 (6.8) | 26.81 (8.5) | 0.173c |
| Blood Pressure | |||||
| Systolic BP (mmHg) | 116.5 ± 8.5 | 120.1 ± 6.7 | 132.2 ± 8.5 | 141.0 ± 8.7 | < 0.001*a |
| Diastolic BP (mmHg) | 73.9 ± 5.0 | 75.5 ± 6.0 | 79.1 ± 6.7 | 81.0 ± 6.3 | < 0.001*a |
| Glycaemic Control | |||||
| FBG (mmol/L) | 5.95 (2.4) | 7.00 (3.4) | 7.40 (4.2) | 7.00 (3.6) | 0.019*c |
| HbA1c (%) | 6.00 (1.2) | 7.00 (2.8) | 7.50 (2.5) | 7.45 (3.1) | 0.021*c |
| Lipid Profile | |||||
| Triglycerides (mg/dL) | 80.00 (34.0) | 110.00 (73.0) | 95.00 (60.0) | 113.00 (83.5) | < 0.001* |
| Total Cholesterol (mg/dL) | 189.8 ± 45.7 | 184.4 ± 43.0 | 169.5 ± 38.6 | 202.2 ± 71.6 | 0.011*a |
| HDL-Cholesterol (mg/dL) | 53.0 ± 10.4 | 49.9 ± 13.6 | 46.8 ± 12.2 | 50.3 ± 14.7 | 0.179a |
| LDL-Cholesterol (mg/dL) | 116.6 (62.9) | 100.6 (54.8) | 105.0 (56.4) | 122.3 (85.1) | 0.233c |
| Non-HDL-Cholesterol (mg/dL) | 137.5 (61.3) | 131.0 (56.0) | 122.0 (59.0) | 150.0 (110.3) | 0.190c |
| hsCRP (mg/L) | 1.22 (2.5) | 1.48 (2.6) | 2.42 (2.8) | 2.71 (1.8) | 0.012*c |
| Duration of Diabetes (years) | 1.5 ± 1.7 | 5.6 ± 4.6 | 9.2 ± 5.7 | 11.4 ± 5.5 | < 0.001*a |
| Co-morbid Hypertension | |||||
| No | 20 (29.0%) | 36 (52.2%) | 10 (14.5%) | 3 (4.3%) | < 0.001*b |
| Yes | 6 (4.6%) | 35 (26.7%) | 53 (40.5%) | 37 (28.2%) | |
| Anti-Lipidemic Drug Therapy | |||||
| No | 10 (38.5) | 9 (12.7) | 3 (4.8) | 1 (2.5) | < 0.001*b |
| Yes | 16 (61.5) | 62 (87.3) | 60 (95.2) | 39 (97.5) | |
| Anti-Hypertensive Drug Use | |||||
| No | 21 (80.8) | 40 (56.3) | 15 (23.8) | 7 (17.5) | < 0.001*b |
| Yes | 5 (19.2) | 31 (43.7) | 48 (76.2) | 33 (82.5) | |
*p-values statistically significant at < 0.05; Statistical tests: aANOVA, bchi square, ckruskal-Wallis
SD Standard Deviation, IQR Interquartile range
Glycaemic control worsened with increasing CVD risk, as fasting blood glucose levels were highest in the moderate- and high-risk groups (7.4 IQR 4.2 vs. 7.0 IQR 3.6 vs. 7.0 IQR 3.4 vs. 5.9 IQR 2.4 mmol/L, p = 0.019), and HbA1c levels followed a similar pattern (7.5 IQR 2.5 vs. 7.4 IQR 3.1 vs. 7.0 IQR 2.8 vs. 6.0 IQR 1.2%, p = 0.021). Among lipid parameters, triglyceride levels were significantly higher in the high-risk group (113.00 IQR 83.5 vs. 95.00 IQR 60.0 vs. 110.00 IQR 73.0 vs. 80.00 IQR 34.0 mg/dL, p < 0.001). Total cholesterol levels also showed a significant difference across groups (202.2 ± 71.6 vs. 169.5 ± 38.6 vs. 184.4 ± 43.0 vs. 189.8 ± 45.7 mg/dL, p = 0.011). Inflammatory burden, assessed using hsCRP, was highest in the high-risk category (2.7 IQR 1.8 vs. 2.4 IQR 2.8 vs. 1.5 IQR 2.6 vs. 1.2 IQR 2.5 mg/L, p = 0.012). Antihypertensive drug use was more frequent in higher CVD risk categories (82.5% vs. 76.2% vs. 43.7% vs. 19.2%, p < 0.001), and lipid-lowering therapy was also more common in the high-risk group (97.5% vs. 95.2% vs. 87.3% vs. 61.5%, p < 0.001). Other variables, including LDL-C and non-HDL-C levels, sex distribution, and BMI did not show statistically significant differences across risk categories.
Participants in this study were stratified into low (< 10%) and moderate-to-high (≥ 10%) 10-year CVD risk categories, and a binary logistic regression analysis was performed to identify predictors of elevated risk in the population (Table 2). The model included the following independent variables: hsCRP, FPG, triglycerides, HbA1c, duration of diabetes, ethnicity, occupation, presence of co-morbid hypertension, use of anti-hypertensive therapy, and use of lipid-lowering drugs. The model significantly improved upon the intercept-only model (χ² = 102.074, p < 0.001) and demonstrated good fit, as indicated by the Hosmer-Lemeshow test (χ² = 5.857, p = 0.663). It accounted for 53.6% of the variance in CVD risk status (Nagelkerke R² = 0.536) and correctly classified 79.0% of cases. Among the predictors, HbA1c (OR = 1.438, 95% CI: 1.061–1.949, p = 0.019), duration of diabetes (OR = 1.345, 95% CI: 1.222–1.480, p < 0.001), and co-morbid hypertension (OR = 14.498, 95% CI: 2.611–80.515, p = 0.002) were significantly associated with increased odds of moderate-to-high CVD risk. In contrast, hsCRP, FPG, triglycerides, ethnicity, occupation, anti-hypertensive therapy, and lipid-lowering drug use were not significant independent predictors.
Table 2.
Binary logistic regression analysis of factors predicting Moderate-to-High 10-Year CVD risk
| Predictor | B | SE | AOR | 95% CI for OR | p-value |
|---|---|---|---|---|---|
| Constant | −3.733 | 1.555 | 0.024 | – | 0.016 |
| hsCRP | 0.110 | 0.123 | 1.116 | 0.896–1.419 | 0.369 |
| HbA1c | 0.363 | 0.155 | 1.438 | 1.061–1.949 | 0.019* |
| Duration of diabetes | 0.296 | 0.049 | 1.345 | 1.222–1.480 | < 0.001* |
| Co-morbid hypertension | 2.674 | 0.875 | 14.498 | 2.611–80.515 | 0.002* |
*Model statistics: χ² = 102.074, p < 0.001; Nagelkerke R² = 0.536; Overall classification accuracy = 79.0%; Hosmer-Lemeshow test: p = 0.663; *p < 0.05 indicates statistical significance
Discussion
The prevalence of moderate-to-high 10-year CVD risk in this study was high, suggesting that there is a substantial burden of the risk of cardiovascular events among Nigerian T2DM patients receiving standard care. Although hsCRP levels were elevated in individuals with higher risk, it was not an independent predictor of CVD risk after adjusting for other variables. Instead, longer diabetes duration, poor glycaemic control, and the presence of co-morbid hypertension emerged as stronger predictors suggesting that traditional risk factors continue to play a dominant role in cardiovascular risk stratification in this population.
Cardiovascular diseases are the leading cause of mortality in individuals with diabetes, occurring often as a consequence of a high burden of macrovascular and microvascular complications. The increased incidence of CVD in T2DM is primarily due to chronic hyperglycaemia and insulin resistance, which trigger endothelial dysfunction, oxidative stress, and systemic inflammation. These processes accelerate atherosclerosis, increasing the risk cardiovascular events.[19, 20] The high prevalence of moderate-to-high 10-year CVD risk among T2DM patients in this study was consistent with previous reports in Nigeria reflecting a persistence of this trend despite ongoing management and other factors like variations in study settings and population characteristics [21–24]. This highlights the urgent need for early intervention strategies, including intensified glycaemic control, lipid management, and blood pressure optimization, to mitigate cardiovascular complications. In a resource-constrained healthcare setting, implementing affordable strategies for the early detection and prevention of cardiovascular risk in T2DM patients is crucial.
In line with previous studies, hyperglycaemia, dyslipidaemia, hypertension, and systemic inflammation were metabolic factors in T2DM contributing to atherosclerosis and cardiovascular events [25–27]. However, beyond these well-established metabolic risk factors, genetic and environmental influences, particularly ethnicity and occupation, showed significant associations with CVD risk in the bivariate analyses. For instance, the Tiv ethnic group had the highest representation in the moderate-to-high-risk categories, suggesting a possible genetic predisposition or shared environmental and lifestyle factors contributing to increased cardiovascular risk. Additionally, retired participants were more frequently in the high-risk group, indicating that age-related factors and socioeconomic status, such as reduced income and limited access to healthcare, may play a role in worsening CVD risk profiles. However, these associations did not persist after adjustment for other predictors. These findings highlight the possibility of a complex interplay between genetic, metabolic, and environmental determinants of cardiovascular risk in Nigerian T2DM patients.
A notable finding in this study is that while hsCRP levels were significantly higher in the moderate and high-risk categories, they were not a significant predictor of cardiovascular disease risk, suggesting that systemic inflammation, though relevant, may not serve as an independent marker of CVD risk as was also the case in a previous study [8]. Although genetic, lifestyle, or environmental factors may influence inflammatory responses [28], the attenuation of hsCRP’s predictive value in this study was more likely due to the widespread use of antihypertensive therapy, particularly among participants with co-morbid hypertension who are typically targeted for aggressive management. Several of these drugs have demonstrated anti-inflammatory effects that could lower hsCRP levels [29]. Additionally, aspirins, commonly prescribed for these patients due to their elevated cardiovascular risk, has well-documented anti-inflammatory and antithrombotic properties [30]. The combined effects of these medications may have blunted the expected association between hsCRP and CVD risk in this study. Future studies should stratify patients based on specific antihypertensive and lipid lowering drugs used to better clarify the role of hsCRP in CVD risk assessment in this setting.
The findings in this study suggest that incorporating hsCRP into CVD risk prediction models may not significantly enhance risk stratification in the study population. This aligns with research indicating that traditional risk factors like hypertension and glycaemic control remain central to CVD risk assessment in this population. For instance, a study among Ghanaian migrants found that established risk algorithms effectively predicted CVD risk without the addition of inflammatory markers such as hsCRP [31]. Furthermore, while systemic inflammation plays a role in CVD pathogenesis, its predictive value varies across different ethnic groups, underlining the need for population-specific validation of biomarkers [32]. Therefore, while hsCRP has been associated with cardiovascular events in various populations, its utility as an independent predictor in Nigerian T2DM patients accessing standard care appears limited, hence the importance of focusing on traditional modifiable risk factors in this demographic.
The significant association of prolonged diabetes duration and elevated HbA1c with higher CVD risk in this study emphasizes the role of chronic hyperglycaemia in driving vascular complications. Prolonged exposure to elevated glucose levels accelerates atherosclerosis increasing the risk of cardiovascular events, and similar findings have been reported in other populations, where longer diabetes duration and poor glycaemic control were strong predictors of CVD risk[33–36]. In contrast, triglycerides did not independently predict CVD risk, aligning with studies suggesting that while dyslipidaemia is a known risk factor, its independent contribution may be less pronounced in certain populations [37]. These findings reinforce the need for stringent glycaemic control and early diabetes management in CVD risk stratification.
There are certain limitations of this study that should be taken into account when interpreting and applying its findings. Firstly, its cross-sectional design precludes the establishment of causal relationships between hsCRP levels and CVD risk in T2DM patients. Secondly, the focus on patients accessing care at a single tertiary healthcare center may limit the generalizability of the findings, especially to broader Nigerian populations, including those in rural areas or with limited access to healthcare. Additionally, the WHO CVD risk assessment tool used in this study does not incorporate non-traditional risk factors, including genetic predisposition and lifestyle factors such as dietary habits and physical activity, which may influence CVD risk. Future studies employing prospective cohort designs, multi-center recruitment, and more comprehensive risk assessment models integrating genetic and lifestyle factors are recommended to enhance causal inferences and generalizability. Nevertheless, this study provides valuable data on the association between hsCRP and CVD risk in Nigerian T2DM patients, contributing to the existing knowledge base and informing future research directions.
Conclusion
This study highlights the high burden of increased CVD risk in Nigerian T2DM patients despite ongoing management. Although traditional risk factors such as prolonged diabetes duration, poor glycaemic control, systemic inflammation, hypertension, and dyslipidaemia remain central to CVD risk assessment, hsCRP may not be a reliable predictor in patients receiving standard care. The findings emphasize the importance of prioritizing modifiable risk factors, initiating early intervention strategies and recognizing the potential influence of genetic and environmental determinants. Future studies incorporating broader population samples and prospective designs are needed to refine CVD risk stratification in Nigerian T2DM patients.
Acknowledgements
We acknowledge the management of Benue State University Teaching Hospital (BSUTH) and the Department of Medicine for granting access to both patients admitted to the wards and those attending the Medical Outpatient Department.
Abbreviations
- BMI
Body Mass Index
- BP
Blood Pressure
- BSUTH
Benue State University Teaching Hospital
- CVD
Cardiovascular Disease
- DBP
Diastolic Blood Pressure
- eGFR
Estimated Glomerular Filtration Rate
- FBG
Fasting Blood Glucose
- HbA1c
Glycated Hemoglobin
- HDL
C–High–Density Lipoprotein Cholesterol
- hsCRP
High–Sensitivity C–Reactive Protein
- IQR
Interquartile Range
- LDL
C–Low–Density Lipoprotein Cholesterol
- OR
Odds Ratio
- rpm
Revolutions Per Minute
- SBP
Systolic Blood Pressure
- SD
Standard Deviation
- SPSS
Statistical Package for the Social Sciences
- T2DM
Type 2 Diabetes Mellitus
- WHO
World Health Organization
Authors’ contributions
All authors collaborated on this research project. BB and JCA conceived and designed the study, and were involved in data acquisition, analysis, and interpretation. JAM, INM and BKM contributed significantly to the data acquisition, analysis, and interpretation. All authors participated in drafting and critically revising the manuscript. They collectively approved the final version for publication and accepted responsibility for all aspects of the work.
Funding
This research did not receive any dedicated funding from a public, commercial, or not-for-profit agency.
Data availability
The datasets from this study will be available upon reasonable request to the corresponding author. This is because the dataset includes additional data that are not relevant to this study and may require exclusion.
Declarations
Consent for publication
Not Applicable in this study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets from this study will be available upon reasonable request to the corresponding author. This is because the dataset includes additional data that are not relevant to this study and may require exclusion.