Abstract
Introduction
Chronic kidney disease (CKD) is closely linked to high blood pressure (HBP), which is its leading cause in developing countries. Hypertension affects 1.2 billion people worldwide. However, a significant portion of individuals with HBP are undiagnosed, and their kidney function is even less known. The objective of this study was to determine the prevalence and associated factors of CKD among three sub-groups of blood pressure status (normotensive, diagnosed hypertension, and undiagnosed hypertension) individuals.
Patients and Methods
We conducted a cross-sectional study in the general population of three northern regions in Senegal using a two-level cluster sampling method. The sample was constituted with a precision of 5% and a power of 80%, with an additional 10% attrition margin. Individuals aged 18–80 years were included in the study after consent. Pregnant women, hospitalized persons within the past 3 months, patients with general or urinary symptoms within the past 7 days and individuals undergoing renal replacement therapy were excluded. Investigators collected clinical and biological data at participants’ homes using a modified version of the WHO’s STEPwise questionnaire. Samples were collected for biochemical analysis (serum creatinine, lipid profile, and blood sugar). Estimated glomerular filtration rate was calculated using the CKD-EPI 2021 formula.
Results
A total of 2,441 participants were included in the study with a mean age of 45.4 ± 16.0 years and a sex ratio M/F of 0.4. The overall prevalence of HBP and CKD were, respectively, 52.0% and 17.8%. Three out of every five hypertensive patients were undiagnosed. CKD was more frequent among known hypertensive patients (30.5%) compared to individuals with undiagnosed hypertension (19.1%) and normotensive individuals (10.9%). Multivariate analysis showed that CKD was associated with older age and female sex.
Conclusion
Undiagnosed hypertension is common among populations in northern Senegal. A high prevalence of CKD was found among both diagnosed and undiagnosed individuals with hypertension. Extending strategies for early detection and management in the general population could help prevent or reduce morbidity and mortality associated with CKD.
Keywords: Epidemiology, Hypertension, Chronic kidney disease, Senegal
Introduction
Chronic kidney disease (CKD) is a major public health problem with over 800 million affected individuals worldwide [1]. It is closely linked to high blood pressure (HBP), which can be both the cause and/or consequence of CKD [2]. With diabetes, HBP is by far the two leading causes of CKD worldwide. HBP remains a heavy health burden. It is estimated that 1.2 billion people worldwide have hypertension and HBP causes over 7 million deaths worldwide annually [3, 4]. Unfortunately, many studies reported a low hypertension diagnosis proportion in adults meeting the criteria for hypertension [5]. In fact, the insidious and long-term asymptomatic nature of the disease leads to a high rate of undiagnosed HBP [6]. Indeed, a prevalence of 30% of undiagnosed HBP has been reported in sub-Saharan Africa [7]. Undiagnosed HBP exposes individuals to a higher risk of cardiovascular and renal complications [8]. A prevalence of 22% of CKD has been reported in the USA among patients with undiagnosed HBP [9]. Early detection and management of hypertension is a key to preventing such end-stage organ damage. To our knowledge, no study has been conducted in Senegal to determine the prevalence and associated factors of CKD among undiagnosed and diagnosed hypertensive individuals. In this context, we conducted this study with the objective of determining the prevalence and the associated factors of CKD among three sub-groups of blood pressure status (normotensive, diagnosed hypertension, and undiagnosed hypertension) in the northern region of Senegal.
Patients and Methods
We conducted a population-based cross-sectional study in the three regions of the northern area of Senegal (Louga, Matam, and Saint-Louis) from 1 March to 31 April in 2018. Individuals aged 18–80 years of living in these regions for at least 3 months and consenting to participate in the study were included. Considering a precision of 5% and a power of 80%, the required sample size was 2,219, and we added a 10% attrition margin, resulting in a total sample of 2,441 participants. A two-level cluster sampling method was used to constitute a representative sample of adults in the study area. We initially randomly selected 10 municipalities in each of the three regions and then randomly selected a number of households proportional to the population size of each municipality from the database of the National Agency of Statistics and Demography. Data were collected during a field visit using a questionnaire. In each randomly selected household, 5 participants were included. Pregnant women, hospitalized persons within the past 3 months, patients with general or urinary symptoms within the past 7 days, and individuals receiving renal replacement therapy were not included.
Data Collection
Teams of investigators collected data during field visits conducted between 7 a.m. and 12 p.m. using a modified version of the WHO STEPwise questionnaire (online suppl. Table S1; for all online suppl. material, see https://doi.org/10.1159/000542567). For each participant, socio-demographic parameters (age, sex, and education level) and personal and family medical history (especially hypertension, diabetes, dyslipidemia, stroke, heart and kidney diseases) were collected. Anthropometric parameters were measured using standardized methods with calibrated devices. Obesity was defined using the thresholds from the International Diabetes Foundation (IDF) [10]. Blood pressure (BP) was measured 3 times at 5-min intervals using a semi-automatic sphygmomanometer and the average of the last two measurements was recorded. Hypertension was defined as a systolic BP ≥140 mm Hg and/or a diastolic BP ≥90 mm Hg and/or use of antihypertensive medication [3]. Known HBP was defined as a participant who was known to have hypertension at the inclusion and unknown HBP as a participant without a history of hypertension who has HBP at the inclusion. Blood samples were also collected once for biochemical analysis. Fasting blood glucose (FBG) was measured using the glucose oxidase method. Diabetes was defined as FBG ≥126 mg/dL, prescription of hypoglycemic agents despite FBG levels or any self-reported history of diabetes [10]. Serum total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides were measured using colorimetric methods. Metabolic syndrome was defined using the IDF criteria [10]. Serum creatinine was measured with the Jaffe kinetic method and glomerular filtration rate (GFR) was estimated using the CKD-EPI 2021 equation [11]. CKD was defined as an estimated GFR <60 mL/min/1.73 m2. The KDIGO classification of CKD according to the GFR was used to classify the participants [12].
Statistical Analysis
Total and undiagnosed prevalence rates of hypertension and CKD were calculated and 95% CIs were estimated. Patient’s characteristics were presented according to, respectively, their renal status, hypertension status, and diabetes status, respectively. Description and comparison of the participants’ characteristics was conducted using chi-square test, t-test and Fisher’s exact test with a significance threshold at p < 0.05. Characteristics associated with CKD were examined using separate logistic regression models, and odds ratios (ORs) and 95% CIs were estimated. Candidate covariates were selected based on prior literature and associations found on descriptive analysis, including sex; age; education; smoking status; history of hypertension, diabetes, and dyslipidemia; use of any antihypertensive treatment; BMI; height/waist ratio; and hypertension, diabetes, dyslipidemia, and metabolic syndrome. Analyses were conducted using R software 4.2.2.
Results
A total of 2,441 participants were included (Fig. 1). The mean age of the participants was 45.4 ± 16.0 years with a sex ratio M:F of 0.40. Five hundred and sixty-six participants (23.2%) were aged between 30 and 39 years. About 38.9% of participants lived in rural settings. The overall prevalence of HBP and CKD was 52.0 and 17.8%, respectively. Undiagnosed HBP represented 55.2% of hypertensive patients. About three quarters (70.6%) of CKD patients were in the hypertensive group. Table 1 shows the characteristics of included patients. HBP was more common in patients with lower education levels (p < 0.001) and those who consume alcohol (p = 0.08) and tobacco (p = 0.02).
Fig. 1.
Flow diagram of included participants. Known HBP, participant known as hypertensive at the inclusion; unknown HBP, participant without history of hypertension who has HBP at the inclusion; no HBP, participant without hypertensive history who has normal blood pressure at the inclusion.
Table 1.
Characteristics of patients according to hypertension status
| Characteristics | N | Normotensive (N = 1,171) | Unknown HBP (N = 702) | Known HBP (N = 568) | Total (N = 2,441) | p value1 |
|---|---|---|---|---|---|---|
| Age2, years | 2,437 | 38.0±13.8 | 48.7±14.9 | 56.6±13.5 | 45.4±16.0 | <0.001 |
| Age categories3 | 2,437 | <0.001 | ||||
| 18–29 | 324 (28%) | 60 (8.5%) | 7 (1.2%) | 391 (16%) | ||
| 30–39 | 360 (31%) | 142 (20%) | 64 (11%) | 566 (23%) | ||
| 40–49 | 236 (20%) | 164 (23%) | 90 (16%) | 490 (20%) | ||
| 50–59 | 161 (14%) | 163 (23%) | 162 (29%) | 486 (20%) | ||
| 60+ | 88 (7.5%) | 173 (25%) | 243 (43%) | 504 (21%) | ||
| Sex3 | 2,441 | <0.001 | ||||
| Male | 359 (31%) | 228 (32%) | 105 (18%) | 692 (28%) | ||
| Female | 812 (69%) | 474 (68%) | 463 (82%) | 1,749 (72%) | ||
| School education3 | 2,436 | <0.001 | ||||
| Never attended school | 463 (40%) | 365 (52%) | 340 (60%) | 1,168 (48%) | ||
| Primary school | 368 (32%) | 176 (25%) | 129 (23%) | 673 (28%) | ||
| Secondary school | 177 (15%) | 102 (15%) | 67 (12%) | 346 (14%) | ||
| University | 160 (14%) | 57 (8.1%) | 32 (5.6%) | 249 (10%) | ||
| Smoke3 | 2,441 | 61 (5.2%) | 32 (4.6%) | 13 (2.3%) | 106 (4.3%) | 0.019 |
| Alcohol3 | 2,436 | 15 (1.3%) | 8 (1.1%) | 1 (0.2%) | 24 (1.0%) | 0.080 |
| Physical activity3 | 2,441 | <0.001 | ||||
| No | 722 (62%) | 518 (74%) | 454 (80%) | 1,694 (69%) | ||
| Yes | 449 (38%) | 184 (26%) | 114 (20%) | 747 (31%) | ||
| Hypertension under treatment3 | 2,429 | <0.001 | ||||
| No | 1,165 (100%) | 696 (100%) | 140 (25%) | 2,001 (82%) | ||
| Yes irregularly | 0 (0%) | 0 (0%) | 245 (43%) | 245 (10%) | ||
| Yes regularly | 0 (0%) | 0 (0%) | 183 (32%) | 183 (8.0%) | ||
| History of stroke3 | 2,349 | <0.001 | ||||
| No | 1,127 (99%) | 667 (99%) | 519 (96%) | 2,313 (98%) | ||
| Yes | 7 (0.6%) | 6 (0.9%) | 23 (4.2%) | 36 (1.5%) | ||
| Diabetes3 | 2,441 | 37 (3.2%) | 47 (6.7%) | 60 (11%) | 144 (5.9%) | <0.001 |
| BMI categories3 | 2,432 | <0.001 | ||||
| Underweight | 142 (12%) | 57 (8.2%) | 30 (5.3%) | 229 (9.4%) | ||
| Normal | 665 (57%) | 305 (44%) | 185 (33%) | 1,155 (47%) | ||
| Overweight | 238 (20%) | 198 (28%) | 154 (27%) | 590 (24%) | ||
| Obesity | 124 (11%) | 139 (20%) | 195 (35%) | 458 (19%) | ||
| Dyslipidemia3 | 2,434 | 634 (54%) | 449 (64%) | 375 (66%) | 1,458 (60%) | <0.001 |
| Metabolic syndrome3 | 2,434 | <0.001 | ||||
| Yes | 59 (5.0%) | 60 (8.6%) | 67 (12%) | 186 (7.6%) | ||
| No | 1,110 (95%) | 640 (91%) | 498 (88%) | 2,248 (92%) | ||
| eGFR categories3 | 2,438 | |||||
| No CKD | 1,043 (89.1%) | 568 (80.9%) | 395 (69.5%) | 2,006 (82.2%) | ||
| Stage IIIA | 107 (9.1%) | 112 (16.0%) | 131 (23.1%) | 350 (14.3%) | ||
| Stage IIIb | 15 (1.3%) | 21 (3.0%) | 37 (6.5%) | 73 (3.0%) | ||
| Stage IV and V | 6 (0.5%) | 1 (0.1%) | 5 (0.9%) | 12 (0.5%) | ||
| Estimated GFR2 | 2,438 | 84.7±21.2 | 76.2±18.3 | 69.8±18.1 | 78.8±20.6 | <0.001 |
No CKD = eGFR ≥60 mL/min/1.73 m2.
CKD stages were based upon eGFR.
BMI, body mass index; CKD, chronic kidney disease; GFR, glomerular filtration rate; known HBP, participant known as hypertensive at the inclusion; unknown HBP, participant without history of hypertension who has HBP at the inclusion; normotensive, participant without hypertensive history who has normal blood pressure at the inclusion.
1Kruskal-Wallis rank sum test; Pearson’s chi-squared test; Fisher’s exact test.
2Mean ± SD; n (%).
3 n (%).
Also, overweight, dyslipidemia, and metabolic syndrome were more frequent in participants with diagnosed HBP compared to normotensives or those with undiagnosed hypertension. The proportion of CKD was significantly different between sub-groups of patients (Fig. 2). Indeed, only 10.9% of normotensive individuals had CKD, while the prevalence rose to 19.1% and 30.5%, respectively, in patients with unknown HBP and known HBP. A significantly lower eGFR was observed in patients with known hypertension.
Fig. 2.
Prevalence of CKD according to blood pressure status. HBP, high blood pressure; CKD, chronic kidney disease; known HBP, participant known as hypertensive at the inclusion; unknown HBP, participant without history of hypertension who have HBP at the inclusion; no HBP, participant without hypertensive history who have normal blood pressure at the inclusion. No CKD = eGFR ≥60 mL/min/1.73 m2. Light grey, CKD; dark grey, no CKD.
Out of a total of 568 patients with known hypertension, 75.3% were receiving treatment. However, only 32.2% of them were regularly taking their medications (Table 2). The overall prevalence of untreated hypertension in the entire population was 66.3%.
Table 2.
Prevalence of CKD stages according to hypertension treatment status
| Characteristics | Not known HBP (N = 1,861) | Known HBP | Total (N = 2,429) | ||
|---|---|---|---|---|---|
| not treated HBP (N = 140) | regularly treated HBP (N = 183) | irregularly treated HBP (N = 245) | |||
| eGFR categories1 | |||||
| No CKD | 85.9% (84%–88%) | 72.9% (65%–80%) | 67.8% (60%–74%) | 69.0% (63%–75%) | 82.4% (81%–84%) |
| Stage IIIa | 11.8% (10%–13%) | 22.8% (16%–31%) | 23.5% (18%−30%) | 22.8% (18%−29%) | 14.3% (13%–16%) |
| Stage IIIb | 1.9% (1.3%–2.6%) | 4.3% (1.8%–9.5%) | 8.2% (4.8%–13.0%) | 6.4% (3.9%–11.0%) | 2.9% (2.4%–3.8%) |
| Stage IV and V | 0.4% (0.2%–0.8%) | 0% (0.0%–3.3%) | 0.5% (0.0%–3.5%) | 1.6% (0.5%–4.4%) | 0.4% (0.3%–0.9%) |
No CKD = eGFR ≥60 mL/min/1.73 m2.
CKD stages were based upon eGFR.
HBP, high blood pressure; CKD, chronic kidney disease; known HBP, participant known as hypertensive at the inclusion; not known HBP, participant without history of hypertension who have HBP at the inclusion.
1% (95% confidence interval).
In the univariate analysis, CKD was significantly associated with female sex (OR 1.36 [95% CI: 1.07–1.74]), with age with the odds ratio (OR) increasing with age group and education level with a lower OR in participants who attended university (OR 0.53 [95% CI: 0.34–0.76]). The presence of cardiovascular history such as hypertension (OR 2.61 [95% CI: 2.10–3.26]) and dyslipidemia (OR 1.63 [95% CI: 1.31–2.04]) were also significantly associated with CKD. In the multivariate analysis, CKD was significantly higher in patients with age >60 years (OR 28.5 [95% CI: 13.7–69.8]) and female sex (OR 1.52 [95% CI: 1.13–2.06]) (Table 3).
Table 3.
Risk factors associated with CKD at multivariate analysis
| Characteristics | Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p value | OR1 | 95% CI1 | p value | |
| Age categories | 2,348 | <0.001 | <0.001 | ||||
| 18–29 | — | — | — | — | |||
| 30–39 | 4.18 | 1.97, 10.3 | 3.77 | 1.77, 9.35 | |||
| 40–49 | 10.4 | 5.08, 25.0 | 8.65 | 4.16, 21.1 | |||
| 50–59 | 16.6 | 8.22, 39.8 | 13.6 | 6.54, 33.1 | |||
| 60+ | 35.0 | 17.5, 83.2 | 28.5 | 13.7, 69.8 | |||
| Sex | 2,348 | 0.011 | 0.006 | ||||
| Male | — | — | — | — | |||
| Female | 1.36 | 1.07, 1.74 | 1.52 | 1.13, 2.06 | |||
| School education | 2,348 | <0.001 | 0.40 | ||||
| Never attended school | — | — | — | — | |||
| Primary school | 0.49 | 0.38, 0.64 | 0.83 | 0.62, 1.11 | |||
| Secondary school | 0.59 | 0.42, 0.81 | 1.03 | 0.71, 1.46 | |||
| University | 0.52 | 0.34, 0.76 | 1.21 | 0.76, 1.88 | |||
| Smoke | 2,348 | 0.30 | 0.65 | ||||
| No | — | — | — | — | |||
| Yes | 0.75 | 0.41, 1.27 | 1.16 | 0.60, 2.12 | |||
| History of hypertension | 2,348 | <0.001 | 0.42 | ||||
| No | — | — | — | — | |||
| Yes | 2.69 | 2.15, 3.35 | 1.20 | 0.77, 1.83 | |||
| Under hypertension treatment | 2,348 | <0.001 | 0.97 | ||||
| No | — | — | — | — | |||
| Yes irregularly | 2.58 | 1.91, 3.46 | 1.32 | 0.63, 1.68 | |||
| Yes regularly | 2.61 | 1.87, 3.61 | 1.07 | 0.65, 1.76 | |||
| Hypertension | 2,438 | ||||||
| No | — | — | <0.001 | — | — | 0.47 | |
| Known | 1.92 | 1.47, 2.49 | 1.06 | 0.79, 1.41 | |||
| Unknown | 3.56 | 2.76, 4.60 | 1.33 | 0.84, 2.08 | |||
| Diabetes | 2,348 | 0.17 | 0.054 | ||||
| No | — | — | — | — | |||
| Yes | 1.34 | 0.88, 2.00 | 0.64 | 0.40, 1.01 | |||
| Dyslipidemia | 2,348 | <0.001 | 0.093 | ||||
| No | — | — | — | — | |||
| Yes | 1.63 | 1.31, 2.04 | 1.23 | 0.97, 1.57 | |||
| BMI | 2,348 | <0.001 | 0.10 | ||||
| Underweight | — | — | — | — | |||
| Normal | 1.63 | 1.07, 2.60 | 1.70 | 1.07, 2.79 | |||
| Overweight | 1.87 | 1.19, 3.03 | 1.36 | 0.83, 2.29 | |||
| Obesity | 2.52 | 1.60, 4.10 | 1.48 | 0.89, 2.52 | |||
| Metabolic syndrome | 2,348 | 0.007 | 0.54 | ||||
| No | — | — | — | — | |||
| Yes | 1.65 | 1.15, 2.32 | 1.14 | 0.75, 1.69 | |||
CI, confidence interval; BMI, body mass index.
1OR, Odds Ratio.
Discussion
The present study included the majority of young adults with a mean age of the participants of 45.4 years and a sex ratio of M:F 0.40. Reported mean age and sex ratio M:F in the same area were 47.9 years ± 16.9 and 0.52, respectively [13]. It revealed a prevalence of 52.0% of HBP among the participants. Previous studies in the same area reported a lower prevalence indicating an increase of HBP burden in the population [13]. Moreover, our results showed that 28.7% of participants had an undiagnosed HBP before the survey. Huguet et al. [14] reported a higher proportion (37.3%) in the USA. A recent systematic review in sub-Saharan Africa reveals a similar percentage of undiagnosed HBP of 30% [7]. In China, the reported prevalence of undiagnosed HBP was 28.8% [15]. In the European population, a prevalence of 21.4% of undiagnosed HBP was reported in the UK [16].
In the present study, the high proportion of undiagnosed hypertension could be attributed to late presentation seen in a chronic condition with insidious progression. Due to poor access to care, patients usually seek healthcare professionals only in late stages [17]. Additionally, the low education level of these populations may partly explain the lack of awareness about the disease. Indeed, among participants who had university level education, 28.9% of HBP cases were undiagnosed versus 42.3% in those who did not go to school (p < 0.001).
Hypertension is a major cardiovascular risk factor that exposes individuals to multiple complications including CKD. Our data showed an overall CKD prevalence of 17.8% that is higher than the 4.9% previously reported by Seck et al. [13] in the same region. Part of the explanation could be related to the formula used. Indeed, they used the MDRD formula which can overestimate the eGFR and we used the CKD-EPI 2021 formula. Also, CKD was more frequent in patients with known HBP (30.5%) than in patients with unknown HBP (19.1%) and normotensive individuals (10.9%). Several studies have reported a comparable prevalence in both diagnosed and undiagnosed hypertensive patients. In the USA, a national survey reported a CKD prevalence of 22.0% and 27.5% among undiagnosed and diagnosed hypertensive individuals, respectively [9]. In Africa, reported data showed very high CKD prevalence among hypertensive patients of 45.5% in South Africa and 50.8% in Burkina Faso [18, 19]. The higher prevalence of CKD observed among individuals with known HBP could partly be explained by the fact that they are significantly older and are at a more advanced stage of the disease and that the symptoms linked to complications are even one of the circumstances in which their hypertension is discovered compared to undiagnosed HBP participants. International guidelines suggest at least annual measurement of renal function in hypertensive patients and more frequently in the presence of complications [20]. However, cases finding strategies for CKD screening may miss many patients in African countries like Senegal because of the high proportion of undiagnosed HBP. A recent multinational study demonstrated that in Senegalese population, mass screening for CKD was associated with the identification of additional 59 per 1,000 person beyond those identified by targeting only high-risk patients with diabetes or HBP [21]. Late diagnosis of patients with CKD is a worldwide problem and even in high-income countries, between 61.6% and 95.5% of stage 3 CKD cases are undiagnosed [22].
Our finding of a higher prevalence of CKD in regularly treated HBP patients compared to irregularly and not treated HBP patients could be partly related to the fact that complications such as CKD are often circumstances of discovery of hypertension. These patients presenting immediately with CKD could be the more symptomatic and therefore most likely to adhere to their treatment. Similarly, these patients may be more likely to receive more detailed advice and closer monitoring from their physician.
Apart from HBP, other risk factors associated with CKD at multivariate analysis in our population were age, diabetes, and dyslipidemia. These risk factors had been extensively described in the literature and are commonly targeted by public health actions aimed at reducing CKD prevalence [23].
Despite its interesting findings, the present study might be limited by cross-sectional design with one serum creatinine analysis and the under-representation of men who were absent during visits because they were at their workplaces. The absence of previous serum creatinine or a second measurement 3 months apart and other markers of kidney damage (albuminuria, urinary sediment abnormalities, morphological abnormalities, etc.) may contribute in making it difficult to differentiate CKD from possible acute kidney injury.
Conclusion
Our study showed a significant proportion of undiagnosed hypertensive individuals in the northern region of Senegal. Those participants, like known hypertensive subjects, exhibited a high prevalence of CKD. Public health programs to promote data monitoring and surveillance systems to raise awareness and community-based screening strategies are urgently needed for earlier diagnosis of HBP and CKD. Also, fair access to affordable treatment that helps control hypertension is strategies to help reduce the burden of CKD in populations living in low-income settings.
Statement of Ethics
The study protocol was approved by the Internal Review Board of Cheikh Anta Diop University of Dakar for ethical issues under the number n°0021/CER/UCAD. A form of informed consent was signed by participants to agree to data collection. All participants were personally informed of their screening results, and those with abnormal values were referred to a specialist for further investigation and treatment.
Conflict of Interest Statement
The authors have no conflict of interest to disclose for this article.
Funding Sources
No funding was received for conducting this study.
Author Contributions
Study design: Sidy Mohamed Seck, Amadou Diop Dia, Bamba Gaye, and Mor Diaw. Data collection: Sidy Mohamed Seck and Amadou Diop Dia. Statistical analysis: Audrey Geoffroy. Data interpretation and manuscript preparation: Modou Ndongo and Sidy Mohamed Seck. Final revision of the manuscript: Modou Ndongo, Amadou Diop Dia, Audrey Geoffroy, Mor Diaw, Awa Ba Diop, Bamba Gaye, and Sidy Mohamed Seck. The manuscript has been read and approved by all authors.
Funding Statement
No funding was received for conducting this study.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
