Chronic kidney disease (CKD) is a major public health concern, affecting around 13.4% of people worldwide [1][2]. The progression of CKD is of particular concern in resource-limited countries. This is linked to the concurrent rise in the prevalence of hypertension, diabetes and obesity, an aging population, and the high prevalence of communicable diseases [1][3][4]. In West Africa, the prevalence of CKD is estimated at 16.5%, which ranks among the highest rates worldwide [3]. This may be partly explained by genetic factors, particularly the high prevalence of APOL1 gene variants that predispose individuals to CKD [1].

CKD progresses through five stages of increasing severity. At end-stage renal disease (ESRD), renal replacement therapy becomes necessary for patient survival [2]. However, the rate of progression varies significantly between individuals. Consequently, patients with comparable CKD stages may follow distinct evolutionary trajectories: some maintain stable renal function for prolonged periods, while others experience accelerated decline [5]. Rapid CKD progression is associated with factors such as advanced age, male sex, the presence of proteinuria, and a low baseline estimated glomerular filtration rate (eGFR) [6]-[8]. The progression of CKD is accompanied by an increased prevalence of complications, leading to higher healthcare costs [2]. CKD is responsible for approximately 2.3 million deaths worldwide and is projected to become the fifth leading cause of death by 2040 [1]. Access to renal replacement therapy remains limited in Africa, where only 9% - 16% of eligible patients receive it [9]. Early interventions are crucial in reducing the burden of this disease before it reaches the terminal stage, through targeted prevention and management strategies [4].

Serum creatinine is the most commonly used biomarker for routine assessment of renal function. It is also a predictive factor for CKD progression [10]. Studying serum creatinine trajectories over time provides valuable insight for monitoring CKD progression, with results comparable to those obtained by eGFR trajectory analysis [11].

However, serum creatinine remains an imperfect GFR marker, influenced by physiological factors (muscle mass, dietary protein intake) and analytical factors [12]. Two main assay methods exist: the Jaffe method and the enzymatic method. Regardless of technique, calibration must be standardized, ideally by reference to isotope dilution mass spectrometry (IDMS), the gold standard [12]. The Jaffe method, although less expensive, is subject to interferences (glucose, bilirubin, uric acid, medications) and tends to overestimate serum creatinine, leading to eGFR underestimation compared to the enzymatic method [12][13].

In our study context, serum creatinine assays were performed using the Jaffe method, without IDMS standardization, and in different laboratories. Given this potential bias, we chose to study serum creatinine trajectories directly.

An increase in serum creatinine over time is a key indicator of CKD progression. Identifying subgroups of patients with similar evolutionary profiles is critical for personalizing care and improving prognosis. Latent class analysis (LCA) provides a useful methodological framework for this purpose. This statistical approach enables the identification of homogeneous subgroups that are not directly observable, termed “latent classes”. This approach models diversity in individual trajectories by inferring each individual’s class membership based on their observed evolutionary profile [5][14].

To our knowledge, no previous study has characterized CKD progression trajectories by analyzing serum creatinine in Burkina Faso. We therefore conducted the present study, which aimed to identify serum creatinine trajectories and analyze associated factors among patients attending a nephrology outpatient clinic.

Between January 1, 2014, and December 31, 2017, 123 patients were included at their first consultation for chronic kidney disease (CKD). All 123 patients were seen at the 6- and 12-month follow-up visits. At 18 and 24 months, 74 and 72 patients were seen, respectively. At 30 and 36 months, 57 patients (46.3%) remained in follow-up (at least biannual check-ups), while 66 patients (53.7%) were lost to follow-up.

In this study, we examined serum creatinine trajectories rather than eGFR trajectories. This choice was guided by several factors. First, creatinine was assayed using the Jaffe method, which overestimates creatinine levels compared to enzymatic methods [22]. Second, inter-laboratory variability in creatinine assays is well documented and can substantially affect eGFR accuracy without standardized calibration [12][22]. Using non-standardized values to calculate eGFR would have introduced significant bias, with observed variations reflecting calibration differences rather than true CKD progression. By studying serum creatinine trajectories, we mitigated this potential bias. This approach is supported by Onuigbo et al. [11], who demonstrated that the prognostic relevance of serum creatinine trajectories is comparable to that of GFR trajectories.

However, this methodological choice is not without limitations. Serum creatinine remains an imperfect marker of renal function, influenced by several physiological factors. Therefore, it cannot replace eGFR calculated under standardized conditions, which remains the reference indicator. Consequently, our results are not directly comparable to studies examining eGFR trajectories. Nevertheless, they offer a practical approach for studying CKD progression in settings where assay standardization is not always possible.

We identified three distinct serum creatinine trajectories, consistent with previous studies describing slow, moderate and rapid profiles of CKD progression [5][23][24]. Previous studies have varied in the number of classes identified, with some reporting up to five trajectories based on population characteristics and methodology [5][23][24].

In a study including 1,967 patients with a median age of 58 years, Boucquemont et al. [5] used latent class analysis to identify five distinct trajectories of measured GFR: a first class characterized by rapid progression with a sharp decline in eGFR; a second marked by high baseline eGFR with non-linear improvement; and three classes (3, 4, and 5) showing near-linear progression, with slow, moderate-slow, and severe-slow eGFR decline, respectively. Patients in the rapid progression class (Class 1) were, on average, younger than those in the moderate-decline classes (Classes 3, 4, and 5) [5].

Santos et al. [23] investigated CKD progression in 378 patients with a mean age of 75.4 years. They identified four eGFR trajectories: slow decline; gradual decline, early rapid decline, and rapid decline. Women, diabetic subjects, those with significant proteinuria, and those hospitalized in the year prior to inclusion were more likely to belong to the rapid decline class. In a cohort of patients with stage 4 CKD, Xie et al. [24] identified three evolutionary trajectories: a slow-decline class comprising 72% of patients (Class 1), a rapid-decline class (Class 2), and a class with initial stable eGFR followed by rapid decline (Class 3).

Age is a well-established risk factor for CKD, with approximately 44% of individuals over 65 affected [7][25][26]. Aging is associated with a higher prevalence of renal risk factors including diabetes, hypertension, and cardiovascular disease [26][27]. Physiological renal decline begins around age 30, with average eGFR loss of 0.9 mL/min/year [25]. Consequently, healthy individuals may exhibit an eGFR of less than 60 ml/min/1.73m² after the age of 60 due to this natural process [26]. However, factors such as hypertension, smoking, dyslipidemia, cardiovascular disease and environmental exposures can accelerate this physiological decline [28][29].

Our multivariate analysis revealed an unexpected relationship between age and chronic kidney disease (CKD) progression, with older patients being more likely to have a stable trajectory. This could be due to a subgroup of older individuals with few comorbidities or slowly progressive nephropathies, who maintain stable renal function. This could reflect either normal physiological decline or indolent disease.

In our cohort, patients in the rapid progression class had a longer follow-up duration, likely due to the onset of complications requiring closer monitoring [5].

Although CKD is generally more prevalent in women, its progression to end-stage renal disease is typically faster in men, attributed to the pro-inflammatory effects of testosterone and a higher prevalence of hypertension and smoking [30][31]. However, in resource-limited settings, women face barriers to healthcare access due to economic and socio-cultural factors, worsening their renal and overall prognosis [31]. This may explain the higher proportion of women in the rapid progression group in our study compared to the stable and moderate progression groups.

Anemia, present in 65.9% of patients at baseline and 71.9% at 36 months, is a marker of worsening CKD. Its prevalence increases with disease progression, rising from 8.4% at stage 1 to 53.4% at stage 5 [32]. It is associated with a two-fold faster eGFR decline [33] and was a strong independent predictor of progression in our study.

Several studies have demonstrated an association between NSAIDs use and CKD risk. Chronic NSAIDs use was specifically associated with moderate progression (class 3), consistent with its link to slowly progressive tubulointerstitial nephropathy. This nephrotoxic effect is cumulative, resulting from episodes of acute kidney injury with incomplete recovery due to the inhibition of prostaglandins involved in renal autoregulation during hypoperfusion [34][35].

Healthcare access disparities exist based on residential area. Patients in rural areas often face poverty, lower education levels, exposure to environmental toxins, and lack of specialized healthcare infrastructure. This leads to inadequate comorbidity management and delayed CKD diagnosis [36]. CKD prevalence is generally higher in rural than urban areas [37][38]. However, socioeconomic inequalities also exist in urban areas where poor populations may live in unsanitary conditions, without access to clean water, exposed to environmental threats and facing healthcare access difficulties [39]. In Pakistan, rural residents had lower risk of moderate to severe CKD (stages 3 and 4) than urban residents [40]. This suggests that inequities in healthcare access (economic, infrastructural, or geographic), rather than the residential area per se, underlie the increased CKD prevalence and faster progression observed in some rural populations.

Urogenital schistosomiasis is a neglected tropical disease that is endemic in sub-Saharan Africa and is responsible for chronic tubulointerstitial and glomerular nephropathies [41][42]. However, a history of urogenital schistosomiasis is associated with a lower probability of belonging to progressive trajectories (Classes 1 and 3). Nevertheless, this result should be treated with prudence, as it was observed in only four patients.

Study limitations

This study has several limitations. The retrospective design and modest sample size (n = 123) restrict the study's generalizability. Serum creatinine was measured only every six months, which could have resulted in underestimation of fluctuations. Behavioral factors such as treatment adherence and lifestyle were not assessed. Furthermore, the latent class analysis was not adjusted for baseline kidney disease severity (creatinine, eGFR or CKD stage). Therefore, it cannot be excluded that the identified trajectories partly reflect differences in baseline severity rather than purely longitudinal changes. Further research involving larger sample sizes is needed to test the robustness of these trajectories when adjusted for baseline kidney function. Finally, the scarcity of CKD progression studies in developing countries limits comparative analysis.

Perspectives

These preliminary results provide a foundation for developing a decision-support tool to predict CKD evolution in patients in Burkina Faso. Such modeling would enable personalized care, facilitating the early identification of patients at high risk of rapid progression and enabling targeted intervention. While we acknowledge the methodological limitations of this preliminary work, we believe it provides a basis for future research.

CKD progresses towards end-stage renal disease at variable rates. Our study identified three CKD progression trajectories in patients attending nephrology outpatient clinics. The results show that anemia is a strong predictor of progression, while semi-urban residence and NSAID use are associated with moderate progression trajectories. The relationship with age was complex, highlighting the role of comorbidities. Further studies on larger populations are needed to develop a decision-making tool that will ensure personalized, optimal care for CKD patients in Burkina Faso.

Table S1. Clinical and biological parameters during follow-up.

BMI: body mass index; CKD: chronic kidney disease; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; SBP: systolic blood pressure. Values are presented as mean ± standard deviation (sd) or number (percentage).