Abstract

Introduction: Diabetes mellitus is a non-communicable disease whose prevalence is increasing in sub-Saharan Africa, particularly in Benin, where it represents a major public health problem. The main objective of this study was to investigate diabetes mellitus in the general population of Parakou in 2025. Methods: This was a descriptive cross-sectional study with an analytical aim conducted from May 1 to July 31, 2025. The study population consisted of individuals aged at least 18 years, residing in Parakou for at least six months, and who provided informed consent to participate in the study. The dependent variable was diabetes mellitus, while the independent variables included sociodemographic, behavioral, and clinical characteristics. Binary logistic regression was used to identify factors associated with diabetes mellitus. A p-value < 0.05 was considered statistically significant. Results: A total of 600 individuals were included. The mean age of participants was 33.50 ± 11.30 years, with a male predominance (65.33%). The prevalence of diabetes mellitus was 14.17%. Among individuals with diabetes, 69.41% were unaware of their condition. Factors associated with diabetes mellitus were age ≥ 40 years (OR = 2.44 [1.40 - 4.26], p = 0.002), monthly income ≥ 104,000 FCFA (OR = 5.72 [1.36 - 24.05], p = 0.017), overweight (OR = 3.43 [1.54 - 7.64], p = 0.001), obesity (OR = 19.12 [8.62 - 42.37], p = 0.001), increased waist circumference (OR = 3.11 [1.85 - 5.28], p = 0.001), and physical inactivity (OR = 2.78 [1.17 - 6.59], p = 0.020). Conclusion: Diabetes mellitus is common in the population of Parakou. Most individuals with diabetes were unaware of their condition, highlighting the need for screening, particularly among high-risk groups.

Keywords

Diabetes Mellitus, Prevalence, Associated Factors, Parakou

Share and Cite:

1. Introduction

Diabetes mellitus, of which type 2 is the most common form, remains a major global public health problem, with prevalence expected to increase from 10.5% in 2021 to 12.2% by 2045 if no preventive measures are implemented [1]. Developing countries, particularly those in Africa, are the most affected, while still facing major challenges related to communicable diseases, such as malaria, tuberculosis, HIV infection, and more recently COVID-19 and viral hemorrhagic fevers [2]. In Africa, a 149% increase in the number of people with diabetes is expected by 2045, rising from 19 million in 2019 to 26 million in 2045 [3]. This exponential increase is mainly explained by rapid urbanization, sedentary lifestyles, and increased consumption of energy-dense foods. Benin is not spared from this global trend, particularly in its northern metropolitan city of Parakou, where diabetes prevalence increased from 4.6% in 2008 to 7.9% in 2015 [4]. Chronic hyperglycemia leads to progressive dysfunction and long-term failure of several organs, including the eyes, kidneys, nerves, heart, and blood vessels, resulting in macrovascular complications (myocardial infarction, transient ischemic attack, stroke, and limb ischemia) and/or microvascular complications (retinopathy, nephropathy, peripheral neuropathy) [5]. These complications develop insidiously, often before the onset of clinical symptoms. Beyond its clinical consequences, diabetes has a substantial economic impact. Each year, more than 727 billion US dollars, approximately 12% of global health expenditure are devoted to diabetes care worldwide [6]. Diabetes is responsible for more than one million deaths annually and ranks among the top ten causes of mortality worldwide [7]. In developing countries, where resources for diabetes management are limited, the disease is often diagnosed late, with 27% of patients presenting with classic symptoms and 61.53% already having complications at diagnosis [8]. This late diagnosis is partly due to the lack of systematic screening, resulting in at least three out of four people with diabetes being unaware of their condition in 2025 [9]. To reduce the negative impact of diabetes on national economic development, preventive actions and early screening are essential. Updating epidemiological data on diabetes is therefore crucial. Since 2015, no population-based data on diabetes have been available in the Borgou Department, particularly in Parakou. This study was initiated to fill this gap by determining the prevalence of diabetes mellitus and identifying associated factors. It also aimed to screen undiagnosed cases to facilitate early management and prevent complications.

2. Study Setting and Methods

Study design

This was a descriptive and analytical cross-sectional study conducted from May 1 to July 31, 2025.

Study population

The study population consisted of individuals aged at least 18 years who had been residing in Parakou for at least six months and who provided informed consent. Individuals unable to answer questions and pregnant women were excluded.

Sampling

A stratified random sampling technique was used. The sample size was calculated using Schwartz’s formula. Data collection was conducted in the three districts of Parakou. Sixteen neighborhoods were selected: ten in the first district and three each in the second and third districts. This was done in accordance with the number of neighborhoods in each district: the first district includes 23 neighborhoods, the second 10, and the third 9.

Variables

The dependent variable was diabetes mellitus, defined as fasting blood glucose ≥ 1.26 g/L confirmed the following day, or known diabetes under treatment. Individuals with fasting blood glucose between 1.10 and 1.26 g/L were classified as prediabetic. Independent variables included sociodemographic, behavioral, and clinical characteristics. Monthly income was defined based on the minimum interprofessional guaranteed wage (SMIG), which was 52,000 FCFA in Benin in 2025. Physical inactivity was defined as failure to perform at least 30 minutes of moderate-intensity physical activity at least five times per week. Nighttime sleep duration between 6 and 8 hours was considered normal. Undernutrition was defined as a body mass index < 18.5 kg/m². Overweight was defined as a body mass index ≥ 25 kg/m². Waist circumference ≥ 94 cm in men and ≥ 80 cm in women was considered elevated.

Data analysis

Data were entered using EPIDATA software and analyzed with EPI INFO (version 3.7.2.1). Pearson’s chi-square test or Fisher’s exact test was used as appropriate to assess associations between the dependent and independent variables. Binary logistic regression was used to identify factors independently associated with diabetes mellitus. A p-value < 0.05 was considered statistically significant.

Ethical considerations

Informed consent and confidentiality were ensured. Authorization was obtained from local administrative authorities. The study was approved by the Local Ethics Committee for Biomedical Research (CLERB) of the University of Parakou (Reference No. 971/2024/CLERB-UP/P/SP/R/SA).

3. Results

General characteristics of the study population

A total of 600 individuals were included. The mean age was 33.50 ± 11.30 years, with a male predominance (65.33%). Monthly income was below two times the SMIG in 89% of participants. Alcohol consumption, tobacco exposure, and physical inactivity were reported by 58.83%, 77.50%, and 90.50% of participants, respectively. Normal nighttime sleep duration was observed in 78%. Overweight and increased waist circumference were present in 45.67% and 29.83%, respectively. The prevalence of hypertension was 31%. Knowledge of diabetes was low in 8.83% of participants (Table 1).

Prevalence of diabetes mellitus

Among all participants, 85 had diabetes mellitus, including 59 newly diagnosed cases, corresponding to a prevalence of 14.17%. Undiagnosed diabetes accounted for 69.41% of cases. Prediabetes was found in 28.17% of participants (Figure 1).

Factors associated with diabetes mellitus

In bivariate analysis, factors associated with diabetes mellitus included age ≥ 40 years, monthly income ≥ 2 SMIG, marital status (married), abnormal sleep duration, overweight, abdominal obesity, physical inactivity, and hypertension (Table 2). In multivariate analysis, independent factors associated with diabetes mellitus were age ≥ 40 years, monthly income ≥ 2 SMIG, overweight, obesity, increased waist circumference, and physical inactivity (Table 3).

Figure 1. Distribution of participants according to the prevalence of diabetes mellitus, prediabetes, and absence of diabetes mellitus, Parakou, 2025 (N = 600).

Table 1. General characteristics of the study population (n = 600).

Table 2. Summary of factors associated with diabetes mellitus (bivariate analysis).

Table 3. Factors associated with diabetes mellitus (multivariate analysis).

4. Discussion

This study, conducted in Parakou, Benin, is the first population-based investigation of diabetes mellitus in the city since 2015. The prevalence of diabetes mellitus was 14.17%, higher than the 9.7% reported by Djrolo et al. in 2015 in the same city [4]. This increase reflects the rapid rise in diabetes prevalence associated with lifestyle changes. Similar prevalences were reported in Guinea (12.5%) [10] and Ethiopia (12.4%) [11]. Higher prevalences have been observed in Saudi Arabia (28%) [12], Thailand (29.6%), and among indigenous populations in French Guiana (40.3%) [13]. Lower prevalences were reported in the Democratic Republic of Congo (6.2%) [14] and Côte d’Ivoire (7%) [15]. A high proportion of individuals with diabetes were unaware of their condition (69.41%), consistent with findings from Bohicon, Benin, where only 3.5% of diabetic workers were aware of their status [16]. Prediabetes affected 20.17% of participants, representing a population at high risk of progression to diabetes if preventive measures are not implemented. The factors independently associated with diabetes mellitus older age, higher socioeconomic status, overweight, obesity, increased waist circumference, and physical inactivity are consistent with findings from other studies. Aging is associated with decreased insulin sensitivity and β-cell dysfunction [17] [18]. Higher socioeconomic status may promote sedentary lifestyles and consumption of refined foods, increasing obesity risk [3]. Visceral adiposity contributes to insulin resistance, a key mechanism in type 2 diabetes [19]. Physical inactivity reduces energy expenditure and promotes fat accumulation and insulin resistance [20]. The use of stratified random sampling and a representative sample size enhanced the validity of the findings. Logistic regression allowed identification of independent associated factors. Although capillary blood glucose is a validated screening method, the inclusion of glycated hemoglobin measurement could have improved diagnostic accuracy. Despite this limitation, the results remain representative of the population of Parakou.

5. Conclusion

The prevalence of diabetes mellitus in Parakou is increasing. Most individuals with diabetes were unaware of their condition, and a substantial proportion of prediabetes was observed. Early screening and management are essential, particularly among high-risk groups. Preventive strategies, including a diet low in sugar, fat, and refined products, as well as regular physical activity, should be promoted among the non-diabetic population.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References