This was a cross-sectional, analytic, and retrospective study over a two-year period from May 1^st, 2023, to April 30^th, 2025, at the diabetic outpatient consultation unit of the Garoua General Hospital. This reference health facility is located in the capital of the North region of Cameroon, with a population of about 2.5 million. It is a first-category hospital open to the public since September 1^st, 2022. Accessible via a well-paved road, it is situated 15 minutes from the city centre. The hospital consists of ten units divided into three groups: administrative services, clinical services, and auxiliary services. The diabetic unit includes a therapeutic education unit, a nutrition unit, an outpatient consultations unit, and a conventional hospitalization unit. Outpatient endocrinology/diabetes consultations have been conducted every Tuesday and Thursday by the same endocrinologist since May 2023. Patients can also receive consultations in nutrition, cardiovascular, ophthalmological workups, and various biological investigations.

We included all type 2 diabetic patients’ files who measured their uricemia during the study period. Incomplete files, patients with gout, and those presenting symptoms of acute gouty crises were excluded.

We conducted a consecutive, non-probabilistic, and exhaustive sampling for this in-hospital study.

Files of patients from diabetic outpatient consultations and those hospitalized in the diabetic unit were retrieved from the archives. A pre-established and validated electronic questionnaire, created with Epi Info software version 7.2.6.0, was completed to allow direct data entry during the survey. The following data were collected:

Case definition: hyperuricemia in the absence of gout (1^st preclinical stage). Musculoskeletal ultrasound and dual-energy computed tomography were not performed to detect urate crystal deposits in tissues.

Prevalence of asymptomatic hyperuricemia: number of hyperuricemia cases out of the total number of diabetic patients enrolled in the study.

Glycaemic control [5] :

Android obesity: abdominal circumference > 94 cm for men and >80 cm for women [7] .

BMI interpretation [8] :

Data collected from Epi Info software version 7.2.6.0 were exported to IBM SPSS software version 23 for statistical analysis. Numbers and frequencies were calculated for all qualitative variables. The mean [standard deviation (SD)] and the median [interquartile range (IQR)] were used to characterize quantitative variables, depending on whether the data distribution was normal or not, respectively. The prevalence [confidence interval at 95% (95% CI)] of asymptomatic hyperuricemia was calculated as the proportion of asymptomatic hyperuricemia cases among the total number of diabetic patients recruited. Patients were divided into two groups based on whether they had asymptomatic hyperuricemia (AHU) or not. We used Student’s t-test or its non-parametric equivalent to compare continuous variables. Proportions were compared using the chi-square test. Logistic regression was used to identify factors associated with asymptomatic hyperuricemia. Explanatory variables with p < 0.05 in univariate analysis were included in the multinomial logistic regression model to identify independent determinants of asymptomatic hyperuricemia. The significance threshold in this multivariate analysis was set at 5%. The power of association was assessed with the adjusted odds ratio (aOR) and its 95% CI.

The study population consisted of 123 men (58.57%) and 87 women (41.43%), with a male-to-female ratio of 1.41. The median age (IQR) of patients was 52 (43-62) years. The majority of patients were under 55 years old (118; 56.19%) and were of Cameroonian nationality (200; 95.24%) ( Table 1 ).

The median (IQR) duration of diabetes in our study population was 5 (2 - 9) years, and the majority presented poor glycemic control (175; 85.78%). The main cardiovascular risk factors associated with diabetes in our study population were a sedentary lifestyle (181; 86.19%), dyslipidemia (123; 58.57%), hypertension (71; 33.81%), and obesity (70; 33.33%) ( Table 2(a) ).

Note: A1c = Glycated Hemoglobin.

Almost all participants consumed purine-rich foods (207; 98.57%), whereas few consumed processed food (14; 6.67%). Twenty-seven (12.87%) patients were on diuretics, mainly loop diuretics (16; 59.26%) ( Table 2(b) ).

According to the American NCEP-ATP III classification and sex, 101 (48.10%) patients in our sample had android obesity, and 64 (30.48%) were overweight ( Table 3 ).

Table 4 summarizes the biological parameters of the study population and their distribution according to the glomerular filtration rate and uricemia levels.

Note: eGFR = Glomerular Filtration Rate.

The prevalence (95% CI) of asymptomatic hyperuricemia in our study population was 40.48% (33.82% - 47.45%).

On bivariate analysis, age ≥ 55 years (p = 0.028), the presence of hypertension (p = 0.014), a past history of obesity (p = 0.047), a past history of renal disease (p = 0.001), and eGFR <60 mL/min/1.73m² (p < 0.001) were significantly associated with asymptomatic hyperuricemia.

On multivariate analysis, independent factors associated [aOR (95% CI), p] with asymptomatic hyperuricemia were a past history of obesity [1.99 (1.03 - 3.83), p = 0.040] and eGFR < 60 mL/min/1.73m² [5.95 (1.51 - 23.38), p = 0.011] ( Table 5 ).

In our study, the prevalence (95% CI) of AHU was 40.48% [33.82 - 47.45], similar to results found by Choukem et al. at 38.1% in a population of 438 patients with T2DM in Douala, Cameroon [9] . Some studies in sub-Saharan Africa with smaller sample sizes revealed lower prevalence rates: 29.4% in Bouaké, Côte d’Ivoire, and 28.7% in Cotonou, Benin [10] [11] . These authors studied hyperuricemia in general, including gout and systemic hyperuricemia. The difference in prevalence could be explained by a greater proportion of patients presenting with symptomatic hyperuricemia, usually managed in rheumatology outpatient consultations rather than diabetic consultations.

The prevalence of AHU was 45.50% in men versus 33.30% in women, with no statistically significant difference in our study (p = 0.076). This aligns with the masculine predominance in our sample and the results of Choukem et al. in Douala (40.40% in men versus 35.20% in women) [9] .

Patients aged 55 years and above had a high prevalence of AHU (48.90%) and hypertension (52.10%), with a statistically significant difference (p = 0.014). It has been shown that the loss of physiological vasodilation with advancing age positively correlates with the onset of cardiovascular diseases and their risk factors, such as hypertension, hyperuricemia, and diabetes mellitus [12] .

Our study identified a statistically significant relationship between GFR <60 mL/min/1.73m² and AHU [aOR 5.95 (CI at 39%: 1.51 - 23.38), p = 0.011]. This relationship was also highlighted by Choukem et al. in Douala, Cameroon (OR 5.6; CI at 95%: 1.4 - 5.9) [9] ; Wanvoegbe et al. in Cotonou, Benin (p = 0.026) [11] ; and Aksas et al. in Bab el Oued, Algeria (p < 0.001) [13] . Notably, 82.1% of T2DM patients with eGFR < 60 mL/min/1.73m² had AHU compared to 17.9% who did not. Insulin resistance and hyperinsulinism, the physiological bases of type 2 diabetes, restrain cellular utilization of glucose and limit the proximal tubular reabsorption of uric acid, leading to hyperuricemia [13] . The accumulation of uric acid in the kidneys can result in the formation of urate stones and renal lesions. Several authors report a high prevalence of kidney stones in patients with T2DM [14] [15] . Additionally, hyperuricemia appears to be a more detrimental factor in renal progression when diabetes mellitus is present, creating a vicious cycle in the metabolic and renal continuum.

The second determinant of AHU [aOR (CI at 95%), p] identified in our study was obesity with a BMI ≥ 30 kg/m² [1.99 (1.03 - 3.83), p = 0.040]. These findings are similar to those of sub-Saharan authors like Choukem et al. (OR 2.9; CI at 95%: 2.3 - 13.7) [9] and Wanvoegbe et al. (p = 0.04) [11] . Obesity, as a precursor of insulin resistance and hyperinsulinism, results in hyperuricemia by increasing renal reabsorption of uric acid. In the metabolic and renal continuum, hyperuricemia is linked to metabolic syndrome (android obesity, dyslipidemia, diabetes mellitus, and hypertension), a claim dating back to the 19^th century through cellular cultures and animal models [1] .

Hyperuricemia has a U-shaped relationship with mortality in both men and women. This mortality is linked to increased cardiovascular and renal mortality, as proven by epidemiological studies [1] . On the pathophysiological level, hyperuricemia stimulates the production of reactive oxygen species and inhibits nitric oxide. This leads to endothelial dysfunction, resulting in platelet adhesion, increased LDL cholesterol oxidation, and consequently atherosclerosis, contributing to cardiovascular diseases [13] .

Lifestyle changes, regular physical activity, and weight loss form the basis for managing AHU and associated comorbidities to reduce global cardiovascular risk [9] . Initiating hypouricemic therapy in AHU remains controversial due to a lack of strong epidemiological evidence, despite clear benefits in AHU associated with renal failure [16] [17] .

The high prevalence of AHU and key determinants such as obesity and eGFR < 60 mL/min/1.73m² highlight the need for integrating systematic screening strategies and proactive management of AHU in care programs for T2DM patients. Public health policies must encourage awareness programs addressing cardiovascular risks associated with AHU. Improving diagnostic capabilities by reducing the costs of screening tests, such as plasma uric acid measurement, musculoskeletal ultrasound, and dual-energy computed tomography, could be crucial in preventing complications associated with AHU. In daily clinical practice, the results call for an integrated approach to better control modifiable risk factors, such as obesity, and monitor renal function in diabetic patients to reduce overall cardiovascular risk in this vulnerable population.

This study has several limitations that should be acknowledged. Specifically, the lack of musculoskeletal ultrasound and dual-energy computed tomography to identify tissue deposition of urate crystals limited the characterization of preclinical stages, despite their availability in our healthcare system. Additionally, purine intake is prevalent in Northern Cameroon due to the development of livestock farming in this area. However, quantifying purine intake poses a limitation due to the near-daily consumption of red meat by participants. Furthermore, a longitudinal assessment of serum creatinine levels over a three-month period would have provided a more robust evaluation of renal function impairment in this at-risk population.