Metabolic Obesity Phenotypes and Risk of Chronic Kidney Disease: A Cross-Sectional Study from Communities in the Center Region of Cameroun ()
1. Introduction
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.
2. Methodology
2.1. Study Design
This cross-sectional study was carried out in three localities in the Centre Region of Cameroon, from March to July 2022 in urban localities (Yaoundé) and from September to October 2022 in peri-urban/rural localities (Obala and Efok). The study involved participants of both sexes, aged between 20 and 75 years. We used a consecutive sampling technique, and all volunteers who met the inclusion criteria were recruited. Physically disabled individuals, those with mental disorders (delirium, mental confusion), seriously ill individuals (extreme thinness, visible breathing difficulties, terminal cancer), breastfeeding and pregnant women, and people suffering from known kidney disease were excluded. A questionnaire was developed for this study and was utilized to collect the following information from each participant: sex, age, marital status, level of education, and place of residence. The sample size was calculated using Magnani’s formula, considering a 95% confidence level, a 5% margin of error, and the prevalence of obesity in Cameroon, which was 9.6% in 2016 [17]. In the end, the study included 317 participants, with 183 from urban areas and 134 from semi-urban/rural areas. The study included men and women aged between 20 and 70 years who had been residing in the study areas for at least 6 months, had provided their informed consent, and had a BMI of ≥27 kg/m2.
2.2. Ethical Considerations
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.
2.3. Physical and Clinical Examination
Anthropometric measurements were carried out by trained personnel using standard procedures. Wearing minimal clothing, subjects were weighed to the nearest 0.01 kg decimal place using an electronic scale (OMRON Body Composition Monitor BF 508). Height was measured to the nearest 0.1 cm decimal place using a portable gauge. The body mass index (BMI) was calculated as weight (kg)/height (m)2. Waist circumference was measured to the nearest 0.1 cm using a non-elastic tape measure at the midpoint between the subcostal and supracostal planes, taken at the end of a normal exhalation. Hip circumference was also measured to the nearest 0.1 cm using a tape measure after asking the participant to empty their pockets.
Body composition was determined using an OMRON Body Composition Monitor (BF 508). Blood pressure was measured at the elbow crease, in participants who had been at rest for at least 10 minutes, using an OMRON Electronic Radial Sphygmomanometer.
After fasting for 10 to 12 hours, approximately 6 ml of venous blood was collected from the participants into dry tubes. The blood was then centrifuged, and the resulting serum was used to perform the following assays: total cholesterol, HDL cholesterol, triglycerides, urea, and creatinine. These assays were conducted spectrophotometrically using kits from the Cypress brand. Low-density lipoprotein cholesterol (LDLc) was estimated using the Friedewald equation [18]. Fasting blood glucose was measured using the glucose oxidase peroxidase method (GOP-POD) with a glucometer and test strips (One-Touch Plus), applied directly to the participant’s fingertip.
2.4. Diagnosis of Cardiometabolic Risk Factors and Determination of Obesity Phenotypes
The metabolic status was determined using the definition of NCEP-ATP III (National Cholesterol Education Program Adult Treatment Panel III). According to this definition, a person was considered metabolically healthy if they had fewer than three of the following risk factors: increased waist circumference (>102 cm for men, >88 cm for women), elevated triglycerides (≥1.50 g/l), low HDL cholesterol (<0.4 g/l in men, <0.5 g/l in women), hypertension (≥130/≥85 mmHg), and impaired fasting glucose (>1.0 g/l) [19].
A BMI threshold of >27 kg/m2 has been used to diagnose obesity. This is because a BMI of ≈27 kg/m2 for both men and women corresponds to a weight 20% higher than desirable. It is associated with an increased risk of hypertension, hypercholesterolemia, and diabetes mellitus, as well as premature death [20]. According to Suwala’s study in 2024, a novel threshold for BMI (27.6 kg/m2) was suggested, which increases the risk of cardiovascular disease by 3.3 - 5.3 times, depending on gender [21]. Colman’s study in 2012 guidelines recommended that only people with a BMI > 27 kg/m2 should be eligible for drug treatment for obesity [20]. Furthermore, a study by Nguedjo et al. in 2022 found that in Cameroon, people with a BMI ≥ 27 kg/m2 were considered obese [22]. Obesity phenotypes were classified into two groups:
Metabolically healthy obese individual (MHO), having fewer than three cardio-metabolic risk criteria.
Metabolically unhealthy obesity (MUO), presenting three or more cardiometabolic risk criteria.
We calculated the atherogenicity indices from the lipid balance parameters: the atherogenic index of plasma (AIP) and the TC/HDL ratio (total cholesterol/high-density lipoprotein). The AIP was calculated as the logarithm of the ratio of triglycerides to HDL (high-density lipoprotein) (log [TG/HDL]). An AIP greater than 0.15 was considered high [23]. This was used to determine cardiovascular risk.
2.5. Diagnosis of Chronic Kidney Disease
The glomerular filtration rate (GFR) was estimated using the CKD epidemiology collaboration (CKD-EPI) formula.
GFR = 141 × min(Scr/κ, 1)α × max(Scr/κ, 1)−1.209 × 0.993Age × 1.018 [if female] × 1.159 [if black].
Scr is serum creatinine (mg/dl).
κ is 0.7 for females and 0.9 for males.
α is −0.329 for females and −0.411 for males.
min indicates the minimum of Scr/κ or 1, and max indicates the maximum of Scr/κ or 1 [24].
We used online software to automatically calculate it. A normal GFR ranges between 90 - 120 ml/min/1.73m2. According to the KDIGO guidelines, a GFR less than 60 ml/min/1.73m2 is considered pathological.
2.6. Statistical Analysis
We analyzed the data using SPSS (Statistical Package for Social Sciences) Version 25. We checked the data for extremes, outliers, and missing values, making corrections where necessary. Categorical variables were presented as frequency (N) and percentage (%), and quantitative variables as mean ± standard deviation (M ± SD). We used the chi-square test to compare the characteristics of participants in urban and peri-urban/rural areas and the Student’s t-test to compare the means of participants in the two areas and between the two phenotypes. We performed binary logistic regression analyses to assess the risk factors predictive of CKD in our population. We ran two models: Model 1 (unadjusted) and Model 2 (adjusted for the covariates sex, waist circumference, triglycerides, hypertension, high-density lipoprotein (HDL), blood glucose, and BMI). The significance level was P < 0.05 for all tests performed.
3. Results
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
Table 1. Characteristics of participants (urban and peri-urban/rural areas) according to metabolic status.
Parameter |
Urban area |
Peri-urban/rural area |
MHO N = 115 (62.8) |
MUO N = 68 (37.2) |
P-value |
MHO N = 86 (64.7) |
MUO N = 47 (35.3) |
P-value |
Gender |
Female |
99 (66.9) |
49 (33.1) |
0.294 |
71 (62.8) |
42 (37.2) |
0.020* |
Male |
16 (45.7) |
19 (54.3) |
15 (75) |
5 (25) |
Age (years) |
20 - 39 |
34 (75.6) |
11 (24.4) |
0.084 |
20 (76.9) |
6 (23.1) |
0.205 |
40 - 59 |
57 (61.3) |
36 (38.7) |
44 (64.7) |
24 (35.3) |
>60 |
24 (53.3) |
21 (46.7) |
21(55.3) |
17 (44.7) |
Marital status |
Married |
71 (61.7) |
44 (38.3) |
0.027* |
39 (60.9) |
25 (39.1) |
0.267 |
Unmarried |
52 (77.6) |
15 (22.4) |
47(70.1) |
20 (29.9) |
Level study |
Not at school |
2 (40) |
3 (60) |
0.533 |
15 (60) |
10 (40) |
0.124 |
Primary |
20 (60.6) |
13 (39.4) |
26 (53.1) |
23 (46.9) |
Secondary |
40 (58.8) |
28 (41.2) |
37 (77.1) |
11 (22.9) |
University |
52 (69.3) |
23 (30.7) |
7 (77.8) |
2 (22.2) |
Waist circumference (cm) |
99 ± 12.46 |
107.27 ± 10.49 |
0.000* |
96.57 ± 11.34 |
100.93 ± 8.7 |
0.024* |
Weigh (kg) |
87.34 ± 13.1 |
95.58 ± 15.28 |
0.000* |
83.42 ± 14.02 |
86.79 ± 11.80 |
0.046 |
BMI (kg/m2) |
33.57 ± 5.31 |
35.55 ± 5.85 |
0.020* |
31.25 ± 3.66 |
32.52 ± 4.42 |
0.080 |
Body fat (%) |
43.62 ± 8.53 |
43.80 ± 8.94 |
0.896 |
41.57 ± 6.9 |
43.86 ± 6.66 |
0.075 |
Visceral fat |
11.06 ± 3.3 |
13.44 ± 3.78 |
0.000* |
10.49 ± 3.65 |
11.37 ± 2.76 |
0.160 |
WHR |
0.85 ± 0.10 |
0.91 ± 0.10 |
0.000* |
0.86 ± 0.91 |
0.87 ± 0.64 |
0.508 |
SBP (mmHg) |
126.08 ± 18.83 |
140.04 ± 21.49 |
0.000* |
130.50 ± 23.47 |
141.91 ± 21.65 |
0.007* |
DBP (mmHg) |
79.65 ± 12.44 |
89.38 ± 13.22 |
0.000* |
81.65 ± 12.84 |
87.89 ± 11.67 |
0.007* |
Glycémia (mg/dl) |
90.27 ± 10.1 |
100.08 ± 34.4 |
0.005* |
87.65 ± 20.24 |
100.97 ± 45.02 |
0.021* |
TC (g/l) |
2.44 ± 1.11 |
2.69 ± 1.09 |
0.135 |
1.87 ± 0.80 |
1.87 ± 0.51 |
0.981 |
HDL (g/l) |
0.55 ± 0.19 |
0.5 ± 0.21 |
0.150 |
0.54 ± 0.17 |
0.45 ± 0.15 |
0.006* |
LDL (g/l) |
1.68 ± 1.07 |
1.82 ± 1.09 |
0.396 |
1.11 ± 0.76 |
1.15 ± 0.47 |
0.762 |
TG (g/l) |
0.96 ± 0.45 |
1.76 ± 1.12 |
0.000* |
1.24 ± 0.65 |
1.60 ± 0.59 |
0.002* |
TC/HDL |
4.76 ± 2.1 |
5.91 ± 2.97 |
0.003* |
3.71 ± 1.67 |
4.51 ± 1.98 |
0.015* |
AIP |
0.21 ± 0.27 |
0.49 ± 0.28 |
0.000* |
0.32 ± 0.25 |
0.53 ± 0.20 |
0.000* |
Creat (mg/l) |
10.84 ± 2.17 |
11.54 ± 2.03 |
0.033* |
10.13 ± 2.32 |
9.98 ± 2.51 |
0.725 |
Urea (g/l) |
0.24 ± 0.10 |
0.27 ± 0.13 |
0.139 |
0.29 ± 0.14 |
0.32 ± 0.13 |
0.282 |
GFR (ml/min/1.72m2) |
77.89 ± 22.05 |
72.87 ± 20.71 |
0.131 |
90.28 ± 30.01 |
89.57 ± 25.95 |
0.882 |
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.
Figure 1. Prevalence of chronic kidney disease in different areas.
Figure 2. Prevalence of chronic kidney disease as a function of metabolic status in the different zones.
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).
Table 2. Risk factors associated with the onset of renal failure in the general population.
Parameters |
OR (95% CI) |
P-value |
Zone |
peri-urban |
1 |
0.006* |
Urban |
2.274 (1.268 - 4.080) |
Sex |
Male |
1 |
0.063 |
Female |
2.227 (0.959 - 5.17) |
Age (years) |
20 - 39 |
1 |
|
40 - 59 |
5.711 (1.687 - 19.33) |
0.005* |
>60 |
16.87 (4.90 - 58.11) |
0.000* |
Metabolic status |
MHO |
1 |
0.214 |
MUO |
1.41 (0.82 - 2.42) |
Visceral fat |
Normal |
1 |
0.012* |
High |
2.33 (1.207 - 4.507) |
Waist circumference (cm) |
Normal |
1 |
0.498 |
High |
1.272 (0.635 - 2.549) |
BMI (kg/m2) |
<30 |
1 |
0.835 |
>30 |
0.941 (0.529 - 1.672) |
Blood glucose (g/l) |
Normal |
1 |
0.054 |
High |
1.935 (0.990 - 3.78) |
Triglycerides (g/l) |
Normal |
1 |
0.280 |
High |
0.722 (0.400 - 3.66) |
Blood pressure (mmHg) |
Normal |
1 |
0.280 |
High |
1.345 (0.785 - 2.305) |
HDL |
Normal |
1 |
0.679 |
Low |
0.888 (0.505 - 1.560) |
Ratio TC/HDL |
Normal |
1 |
0.006* |
High |
2.134 (1.245 - 3.660) |
AIP |
Normal |
1 |
0.551 |
High |
1.215 (0.640 - 2.308) |
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).
Table 3. Factors associated with the onset of renal failure in the different zones.
Parameters |
Urban area |
Peri-urban/rural areas |
OR (95% CI) |
OR (95% CI) |
Model I |
Model II |
Model I |
Model II |
Metabolic status |
MHO |
1 |
1 |
1 |
1 |
MUO |
1.57 (0.81 - 3.03) |
2.12 (0.92 - 7.72) |
1.07 (0.39 - 2.95) |
0.42 (0.067 - 2.65) |
Visceral fat |
Normal |
1 |
1 |
1 |
1 |
High |
1.83 (0.81 - 4.14) |
2.70 (0.95 - 7.66) |
2.78 (0.86 - 8.93) |
4.94 (1.14 - 21.34)* |
Ratio TC/HDL |
Normal |
1 |
1 |
1 |
1 |
High |
1.63 (0.85 - 3.12) |
2.06 (0.97 - 4.41)* |
2.76 (1.01 - 7.51) |
4.73 (1.33 - 16.86)* |
AIP |
Normal |
1 |
1 |
1 |
1 |
High |
1.42 (0.67 - 3.013) |
2.94 (1.16 - 7.45)* |
1.185 (0.31 - 4.45) |
2.09 (0.40 - 10.71) |
Age (years) |
<50 |
1 |
1 |
1 |
1 |
≥50 |
5.66 (2.61 - 12.27) |
7.54 (3.09 - 18.4)* |
NA |
NA |
WHR |
Normal |
1 |
1 |
1 |
1 |
High |
0.92 (0.48 - 1.76) |
1.12 (0.54 - 2.34) |
2.20 (0.74 - 6.54) |
2.66 (0.72 - 9.78) |
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.
4. Discussion
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.
5. Conclusion
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.
Acknowledgements
The authors appreciate the cooperation of the patients in this study.
Ethics Approval and Consent to Participate
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.
Availability of Data and Materials
All data generated or analyzed during this study are included in this published article.
Funding
This research received specific funding from the Public Investment Budget of IMPM/MINRESI.
Authors’ Contributions
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.