Chronic kidney disease (CKD), also known as chronic renal failure (CRF), is characterized by a significant decrease in kidney function, irrespective of the cause. This results in the kidneys’ diminished ability to filter the body’s blood effectively, a condition that is permanent and irreversible. CKD is a major public health issue, affecting over 10% of the global population, which equates to more than 800 million people. Furthermore, it has emerged as one of the leading causes of death in the twenty-first century [1] . In Africa, the prevalence of CKD is often underestimated due to its silent nature and the fact that most prevalence studies conducted in Africa are suboptimal [2] . A meta-analysis published in 2018 revealed that the overall prevalence of CKD was higher in Sub-Saharan Africa compared to North Africa [3] . In Cameroon, a study by Kaze et al. (2015) found a higher prevalence of chronic kidney disease in rural areas compared to urban areas, with rates of 14.1% and 10.9%, respectively [4] . Furthermore, Aseneh et al. (2020) demonstrated that the prevalence in urban areas varied between 10.0% and 14.2%.

Obesity is a significant risk factor for CKD and end-stage renal disease due to its associated conditions. In fact, the global prevalence of chronic kidney disease is on the rise in correlation with obesity and metabolic disorders [5] . A particular study discovered that being overweight or obese heightens the risk of developing CKD, regardless of the presence of diabetes, hypertension, or cardiovascular disease [6] .

Although the indirect effects of obesity on the kidneys are well understood, primarily through the development of other risk factors for chronic kidney disease, such as diabetes and arterial hypertension, some obese individuals exhibit few or no metabolic complications [7] . These subjects are referred to as “metabolically healthy obese” (MHO). However, the association between the MHO phenotype and chronic kidney disease remains largely unknown, and the studies reviewed present contradictory findings. One study has demonstrated that this phenotype is associated with a lower risk of developing kidney failure [8] , while others have indicated that this phenotype, like its counterpart with metabolic abnormalities, is associated with a high risk of developing kidney failure [5] [9] . In Africa, few studies have evaluated the relationship between the obesity phenotype and chronic kidney disease [10] - [14] , and in Cameroon [3] [15] [16] , most tend to focus on discussing risk factors and the prevalence of chronic renal failure. The aim of this study was therefore to investigate the association between the obesity phenotype and the risk of chronic kidney disease in an adult population in both urban and peri-urban/rural areas of Cameroon.

Table 1 presents the characteristics of the population in different areas. Of the 317 participants enrolled, the MHO phenotype was predominant among our obese individuals, accounting for 62.8% in urban areas and 64.7% in peri-urban/rural areas. Females were more represented in both areas; the prevalence of MHO increased with age in both areas.

Regarding anthropometric parameters, in urban areas, MUO individuals had significantly higher mean waist circumference, weight, visceral fat, and waist/hip circumference ratio than MHO individuals. In peri-urban and rural areas, only the means of waist circumference and weight were significantly higher in MUO

Data were expressed in terms of numbers (n) and frequencies (%), and in terms of mean ± standard deviation. MHO: Metabolically Healthy Obese; MUO: Metabolically Unhealthy Obese; BMI: Body Mass Index; WHR: Waist-to-Hip Ratio; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; TC: Total Cholesterol; HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; TG: Triglycerides; Creat: Creatinemia; AIP: Atherogenic Index of Plasma; GFR: Glomerular Filtration Rate. *P < 0.05 is significant.

individuals compared to MHO. However, the means of SBP, DBP, glycemia, and triglycerides were significantly higher in MUO participants in the two areas. Conversely, the means of HDL-C were significantly higher in MHO individuals compared to MUO individuals. We found no significant difference in the glomerular filtration rate between the two obese subgroups in the different areas. Cardiovascular risk, as determined by the atherogenicity indices (TC/HDL and the logarithm of the TG/HDL ratio), was significantly higher in the MUO group in both urban and peri-urban/rural areas.

Figure 1 displays the prevalence of CKD according to study areas. It reveals that CKD was significantly more prevalent in urban areas than in semi-urban/rural areas, with rates of 28% and 14.6%, respectively (P < 0.05). Figure 2 illustrates the prevalence of CKD as a function of obesity phenotype in the different populations. The prevalence of CKD was higher in MUO individuals in both urban and semi-urban/rural areas, at 33.8% and 15.2%, respectively. Conversely, the prevalence of CKD was higher in MHO individuals in urban areas (24.6%) compared to those in semi-urban/rural areas, but the difference was not statistically significant.

The risk factors for CKD in our study population were: urban area [2.274 (1.268 - 4.080)]; age groups of 40 - 59 years [5.711 (1.68 - 19.33)] and over 60 years [16.87 (4.90 - 58.11)]; high visceral fat [2.33 (1.207 - 4.507)]; and high TC/HDL ratio [2.134 (1.24 - 3.66)]; the differences were significant (P-value < 0.05) ( Table 2 ).

MHO: Metabolically Healthy Obese; MUO: Metabolically Unhealthy Obese; BMI: Body Mass Index; AIP: Atherogenic Index of Plasma; TC/HDL: Ratio Total Cholesterol/High Density Lipoprotein; *P < 0.05.

After adjusting for covariates, the risk factors for CKD in urban areas were age greater than 50 years (OR: 7.54; 95% CI = 3.09 - 7.45), high TC/HDL ratio (OR: 2.06; 95% CI = 0.97 - 4.41), and high AIP (OR: 2.94; 95% CI = 1.16 - 7.45). MUO phenotype (OR: 2.12; 95% CI = 0.92 - 7.72) and visceral fat (OR: 2.70; 95% CI = 0.95 - 7.66) also had a higher risk of CKD, but the P-value was not significant. The risk factors in semi-urban/rural areas were high TC/HDL ratio (OR: 4.73; 95% CI = 1.33 - 16.86) and high visceral fat (OR: 4.94; 95% CI = 1.33 - 16.86) ( Table 3 ).

Model I: Unadjusted; Model II: Adjusted for sex, waist circumference, triglycerides, hypertension, High-Density Lipoprotein (HDL), blood glucose, and body mass index. *P < 0.05. Ratio CT/HDL: Ratio Total Cholesterol/High-Density Lipoprotein; AIP: Atherogenic Index of Plasma; WHR: Waist-to-Hip Ratio.

The aim of this study was to investigate the association between obesity phenotypes and the risk of chronic kidney disease in an adult population in urban and peri-urban/rural areas in the Center region of Cameroon. The main finding of our study is that the MUO phenotype was associated with a higher prevalence of CKD; this prevalence was higher in urban areas. Our study showed a high prevalence of the metabolically healthy obesity (MHO) phenotype, with a prevalence of 62.8% in urban areas and 64.7% in peri-urban/rural areas. The prevalence of metabolically healthy obese individuals is consistent with previous studies, which found a prevalence ranging from 6% to 75% [25] . A study conducted by Mbanya et al. in 2015 showed that approximately 85.8% of overweight or obese Cameroonians had a healthy profile, with a significantly higher proportion in rural areas in terms of standard cardio-metabolic risk factors [26] .

We found a high prevalence of CKD in urban areas (28%) compared with peri-urban/rural areas, where the prevalence was 14.6%. Our results contradict those obtained by Kaze et al. in 2015, who studied urban and rural populations in West Cameroon and found a higher prevalence of chronic kidney disease in rural areas than in urban areas (14.1% and 10.9%, respectively) [4] . The high prevalence we observed in urban areas may be due to our population consisting entirely of overweight individuals, as obesity is associated with the development and progression of chronic kidney disease. Furthermore, the prevalence of chronic kidney disease seems to correlate with the increase in adiposity in Sub-Saharan Africa [12] .

In the present study, 24.6% of MHO subjects were suffering from CKD in urban areas, compared with 14.3% in peri-urban/rural areas. This difference in prevalence may be due to urbanization. Various studies conducted in low- and middle-income countries have shown that rapid urbanization is at the root of an increased prevalence of metabolic diseases, which coincide with an increase in the prevalence of CKD [27] . This adds to a growing body of evidence indicating that the MHO phenotype is not a benign state [5] [9] . Several studies have suggested that overweight and obese patients are not protected from the risk of CKD by healthy metabolic profiles [5] [28] [29] . Obesity itself could be harmful to renal function, and there could be other mechanisms directly linking obesity to renal damage, independently of metabolic risk factors [30] .

The results of our study showed that living in an urban area was a predictive risk factor for the development of CKD, which is consistent with studies conducted in Ghana [31] and Congo [32] . The age groups of 40 - 59 years [5.711 (1.68 - 19.33)] and more than 60 years [16.87 (4.90 - 58.11)], high visceral fat [2.33 (1.207 - 4.507)], and a high arterogenicity index [2.134 (1.24 - 3.66)] were also risk factors for the occurrence of CKD in our population. Indeed, the glomerular filtration rate (GFR) decreases with age, and the prevalence of CKD is higher in the elderly [33] .

After adjusting for waist circumference, triglycerides, hypertension, HDL, glycaemia, sex, and BMI, age greater than 50 years and a high atherogenic index (AIP and TC/HDL ratio) were identified as risk factors for the onset of CKD in urban areas. The glomerular hyperfiltration characteristic of a high BMI generally begins at a young age. Therefore, it is possible that the progressive loss of renal function becomes more evident in individuals who have been exposed to the effects of obesity for a longer period, which could explain the increase in renal failure with age [34] . It should also be noted that ageing itself leads to an increase in glomerular permeability, a decrease in individual glomerular volume, glomerular sclerosis, and a decrease in the number of nephrons [35] .

Most obese individuals are prone to lipid disorders such as dyslipidemia. Atherogenic indices, which are assessed by various lipid profile ratios including the atherogenic index of plasma (AIP) and the TC/HDL ratio, serve as indicators of abnormal lipid metabolism and are identified as significant contributors to atherosclerosis and cardiovascular disease (CVD) [36] . Our findings suggest that a high TC/HDL ratio or a high AIP escalates the risk of chronic kidney disease (CKD) in obese individuals residing in urban areas. Similar results have been found by other researchers, notably Huang et al. and Li et al., who conducted their studies on a population of Chinese adults [36] [37] . The pathogenic mechanism underlying the increased risk of kidney problems associated with AIP is unclear. However, there are several possible explanations. First, AIP can serve as an indirect indicator of the presence of smaller LDL-C particles, which are associated with an increased risk of atherogenicity [38] . Secondly, a decrease in HDL-C levels leads to a reduction in reverse cholesterol transport and an accumulation of lipids in the glomeruli. Consequently, the accumulation of foam cells results in glomerulosclerosis and the progression of renal dysfunction [39] . Thirdly, elevated AIP is associated with insulin resistance, and the resulting hyperinsulinemia appears to play a role in renal function by inducing glomerular hyperfiltration and increased vascular permeability [40] . Hyperfiltration can be a sign of early kidney damage and can lead to further progression of the disease, particularly in conditions like diabetes and obesity.

The risk factors for the development of CKD in peri-urban/rural areas include a high TC/HDL ratio (OR: 4.73; 95% CI = 1.33 - 16.86) and high levels of visceral fat (OR: 4.94; 95% CI = 1.33 - 16.86). In this area also, we found that, although the P-value was not significant, the odds of CKD in individuals with a high waist-to-hip ratio (WHR) increased from 2.20 in the unadjusted model to 2.66 in the adjusted model; prior studies have shown that a higher WHR, indicating a greater proportion of visceral fat, is associated with an increased risk of developing CKD [41] . It is important to note that, in obese individuals, visceral adipose tissue plays a significant role in the development of metabolic complications. This is because it is much more metabolically and hormonally active than its subcutaneous counterpart. Additionally, it has pro-inflammatory properties and is subject to lipolysis [42] . An increase in visceral adipose tissue leads to hyperfiltration and hyperperfusion of the renal glomeruli, which can result in glomerular hypertrophy, proteinuria, and the development of CKD [43] . Our findings align with those obtained by Lee and colleagues, who demonstrated that visceral adipose tissue is a risk factor for the progression of CKD in Korean adults [44] .

Although not significant, the present study also indicated that in urban areas, after adjusting for potential confounders, the odds of CKD in the MUO phenotype increased (2.12 times higher) compared to the MHO phenotypes, while these odds decreased in peri-urban/rural areas. The same factor can both increase and decrease the odds in two areas; in this case, this may be explained by the fact that rapid urbanization is often associated with the adoption of Western diets, lifestyle changes, and the consumption of highly processed foods that contribute to decreased physical activity and an increased risk of metabolic and cardiovascular diseases [45] . In addition, the risk of CKD increases as the number of metabolic syndrome components a person has. A study by Chen et al. shows that individuals with metabolic syndrome (MetS) had a 2.6-fold increased risk of developing incident chronic kidney disease (CKD), defined as an estimated glomerular filtration rate (eGFR) of less than 60 ml/min, compared to those without MetS. The risk of CKD increased with the number of MetS components, rising from 1.89 in individuals with one component to 5.85 in adults with all five. Adults with MetS had twice the chance of developing microalbuminuria compared to adults without. The risk of microalbuminuria increased gradually with the number of MetS components [46] .

There are also several limitations of this analysis, including the use of single measurements of creatinine; however, as subjects were not acutely ill at the time of study evaluation, these values are likely consistent with chronic kidney function. A second limitation is the lack of albuminuria data, an important risk factor for the development of kidney disease that may also be associated with obesity. Third, the cross-sectional design, which prevents causal inference, the use of a single GFR measurement to diagnose CKD, and the non-random sampling method represent additional limitations.

In conclusion, our study examined the association between obesity phenotypes and the risk of chronic kidney disease (CKD) in an adult population living in urban, peri-urban/rural areas of Cameroon. Our results suggest that although the MUO phenotype is associated with a higher risk of CKD, the MHO phenotype is not a harmless condition, a substantial prevalence of CKD was also observed among individuals with this phenotype. Therefore, relying solely on standard metabolic markers may underestimate the true risk. Additional factors such as visceral adiposity and atherogenic indices should be integrated into the assessment of metabolic health to better capture the complexity of risk in obese populations.

The authors appreciate the cooperation of the patients in this study.

This work received approval from the Centre Regional Ethics Committee for Human Health Research under the reference number CE N˚0885/GRERSHC/2022. The administrative authorities overseeing the selected study settings also granted their approval. Participants who agreed to partake in the study signed a free and informed consent form. The study adhered to the Declaration of Helsinki on human medical ethics.

All data generated or analyzed during this study are included in this published article.

This research received specific funding from the Public Investment Budget of IMPM/MINRESI.

CSMB, GMN, MMKS and HTD designed the study plan; GMN, MMKS, and HTD planned the work; HTD, BRTT, ACA, and CSMB collected the data; CSMB and BRTT analyzed the data; CSMB wrote the manuscript; HTD, BRTT, ACA, MMKS, and GMN read and revised the article. All authors read and approved of the final manuscript.