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Kidney Med
Kidney Med
Kidney Medicine
2590-0595
Elsevier

S2590-0595(24)00106-7
10.1016/j.xkme.2024.100895
100895
Research Letter
Dipstick Leukocyturia as a Kidney Damage Biomarker in Rural Uganda and Kenya
Mohamed Sahra BA, MS 1
Hsu Chi-Yuan MD, MS 2
Charlebois Edwin D. MPH, PhD 3
Kabami Jane BNS, MPH 4
Atukunda Mucunguzi MBChB, MPH 4
Ayieko James MBChB, PhD 5
Orori Gordon BSc 5
Hickey Matthew D. MD 6
Petersen Maya M.D, PhD 7
Kamya Moses R. MBChB, MMed, MPH, PhD 8
Havlir Diane MD 6
Estrella Michelle M. MD, MHS 2
Muiru Anthony N. MD, MPH anthony.muiru@ucsf.edu
2∗
1 Institute for Global Health Sciences, University of California-San Francisco, San Francisco, CA
2 Department of Medicine, Division of Nephrology, University of California, San Francisco, School of Medicine, San Francisco, CA
3 Department of Medicine, Center for AIDS Prevention Studies, Division of Prevention Science, University of California, San Francisco, San Francisco, CA
4 Infectious Diseases Research Collaboration, Kampala, Uganda
5 Kenya Medical Research Institute, Nairobi, Kenya
6 Department of Medicine, Division of HIV, ID and Global Medicine, University of California, San Francisco, CA
7 Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA
8 Department of Medicine, Makerere University, Kampala, Uganda
∗ Address for Correspondence: Anthony N. Muiru, MD, MPH, University of California-San Francisco, 500 Parnassus Ave, MUE 411, Box 0532, San Francisco, CA 94143-0532. anthony.muiru@ucsf.edu
14 8 2024
10 2024
14 8 2024
6 10 100895© 2024 The Authors
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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pmcTo the Editor:

The presence of sterile leukocyturia may provide diagnostic and prognostic insights into kidney health. For instance, leukocyturia among patients with chronic kidney disease (CKD) of unknown etiology is associated with lower estimated glomerular filtration rate, interstitial nephritis, and tubular atrophy on kidney biopsies.1 We previously reported a high prevalence of leukocyturia in sub-Saharan Africa.2 We now seek to better understand the risk factors associated with leukocyturia.

To estimate kidney disease prevalence in rural East Africa from 2016-2017, we measured serum creatinine and performed urine dipstick urinalysis.2 We interpreted dipstick urinalysis showing leukocyte esterase (≥1+) with nitrate negative results, as leukocyturia not explained by urinary tract infection. We included 3,462 community representative participants from Eastern Uganda, Southwestern Uganda, and Western Kenya.2 A detailed description of the study population and our sampling design can be found in the supplemental section (Item S1).

We applied weighted multivariable log-link Poisson regression models with robust standard errors to estimate the adjusted population-level prevalence ratios for leukocyturia and explored associations with risk factors including geographic region, serum creatinine, dipstick proteinuria, sociodemographic factors, smoking, alcohol, diabetes, hypertension, HIV, use of nonsteroidal anti-inflammatory drugs, and traditional herbal medicines. These factors were chosen based on their previous associations with kidney disease in sub-Saharan Africa and other regions.3, 4, 5, 6 We hypothesized that leukocyturia may indicate kidney damage.

We also evaluated whether certain environmental conditions, such as the mean surface air temperatures and high altitudes, were associated with leukocyturia and whether leukocyturia is associated with prevalent CKD (defined as an estimated glomerular filtration rate of <60 mL/min/1.73m2 or dipstick proteinuria ≥1+).

The baseline characteristics of the sampled participants and the weighted population-level estimates are shown in Table 1 and Table S1 respectively.Table 1 Baseline Characteristics of the Sampled Participants

	No Leukocyturia n = 3,163	Leukocyturia n = 299	Total
N = 3,462	
Region	
 Eastern Uganda	987 (31.2)	164 (54.8)	1,151 (33.2)	
 Southwestern Uganda	732 (23.1)	98 (32.8)	830 (24.0)	
 Western Kenya	1,444 (45.7)	37 (12.4)	1,481 (42.8)	
Sex	
 Female	2,025 (64.0)	228 (76.3)	2,253 (65.1)	
 Male	1,137 (36.0)	71 (23.7)	1,208 (34.9)	
Age categories	
 18-29 y	613 (19.4)	58 (19.4)	671 (19.4)	
 30-44 y	1,270 (40.1)	97 (32.4)	1,367 (39.5)	
 45-59 y	800 (25.3)	89 (29.8)	889 (25.7)	
 ≥60 y	480 (15.2)	55 (18.4)	535 (15.4)	
Education level	
 No formal education	459 (14.8)	82 (27.7)	541 (15.9)	
 Primary school	2,174 (70.0)	172 (58.1)	2,346 (68.9)	
 Secondary school and beyond	474 (15.2)	42 (14.2)	516 (15.2)	
Wealth index/scorea	
 1st quintile	587 (18.8)	71 (24.0)	658 (19.3)	
 2nd quintile	517 (16.6)	56 (18.9)	573 (16.8)	
 3rd quintile	630 (20.2)	53 (17.9)	683 (20.0)	
 4th quintile	685 (22.0)	67 (22.6)	752 (22.0)	
 5th quintile	698 (22.4)	49 (16.6)	747 (21.9)	
Farmer	1,996 (64.2)	231 (78.0)	2,227 (65.4)	
Smoking status	
 Never smoker	2,714 (87.3)	260 (87.5)	2,974 (87.3)	
 Current	192 (6.2)	17 (5.7)	209 (6.1)	
 Past	203 (6.5)	20 (6.7)	223 (6.6)	
Any current alcohol use	291 (10.1)	34 (12.4)	325 (10.3)	
CKDb	240 (7.6)	79 (26.7)	319 (9.2)	
HIV-positive	1,378 (44.2)	143 (48.3)	1,521 (44.6)	
Diabetes mellitus	121 (3.9)	13 (4.4)	134 (3.9)	
Hypertension	596 (19.3)	56 (18.9)	652 (19.2)	
Any NSAID use over the previous 90 days	1,594 (51.3)	128 (43.13)	1,722 (50.5)	
Any traditional medicine use over the previous 90 days	833 (26.8)	89 (30.0)	922 (27.1)	
Abbreviations: NSAID, nonsteroidal anti-inflammatory drugs; CKD, chronic kidney disease.

a Wealth index/score (divided in quintiles) was calculated using principal components analysis based on ownership of livestock and other household items.

b Defined as serum creatinine estimated glomerular filtration rate of <60 mL/min/1.73m2 or proteinuria (urine dipstick ≥1+).

We observed a striking geographic variation in the prevalence of leukocyturia, with notably higher rates in Uganda (11.2% in Eastern and 8.7% in Southwestern Uganda) compared with 1.6% in Kenya.

In our fully adjusted model, residences in Eastern and Southwestern Uganda (compared with Kenya) were strongly associated with leukocyturia (adjusted prevalence ratio [aPR], 7.51; 95% confidence interval [CI], 4.36-12.95 and aPR, 7.78; 95% CI, 4.48-13.51). In addition, female sex (aPR, 1.80; 95% CI, 1.14-2.84), primary school education (aPR, 2.07; 95% CI, 1.04-4.12, compared with secondary school and beyond), and dipstick proteinuria (aPR, 3.58; 95% CI, 2.35-5.45) were associated with leukocyturia. We did not observe any significant associations between HIV, diabetes, or hypertension with leukocyturia (Table 2).Table 2 Unadjusted and Adjusted Association of Potential Risk Factors With Leukocyturia

	Leukocyturia	
Unadjusted Prevalence Ratio (95% CI)	P Value	Adjusted Prevalence Ratio (95% CI)	P Value	
Region	
 Eastern Uganda	7.20 (4.51-11.51)	<0.001	7.51 (4.36-12.95)a	<0.001a	
 Southwestern Uganda	5.58 (3.34-9.33)	<0.001	7.78 (4.48-13.51)a	<0.001a	
 Western Kenya	Reference		Reference		
Female	2.22 (1.40-3.52)	0.001	1.80 (1.14-2.84)a	0.01a	
Age	
 18-29 y	Reference		Reference		
 30-44 y	0.68 (0.42-1.10)	0.11	0.70 (0.44-1.12)	0.14	
 45-59 y	1.04 (0.65-1.68)	0.86	1.00 (0.63-1.59)	0.99	
 ≥60 y	1.39 (0.86-2.24)	0.18	1.43 (0.88-2.33)	0.15	
Education level	
 No formal education	3.27 (1.62-6.60)	0.001	1.95 (0.95-3.99)	0.07	
 Primary school	1.73 (0.87-3.42)	0.12	2.07 (1.04-4.12)a	0.04a	
 Secondary school and beyond	Reference		Reference		
Wealth index	
 1st quintile (least wealth)	Reference		Reference		
 2nd quintile (less wealth)	1.04 (0.62-1.72)	0.89	1.02 (0.63-1.67)	0.91	
 3rd quintile (middle wealth)	0.74 (0.41-1.32)	0.30	0.98 (0.54-1.78)	0.95	
 4th quintile (more wealth)	0.96 (0.57-1.61)	0.87	1.24 (0.76-2.03)	0.39	
 5th quintile (most wealth)	0.73 (0.41-1.32)	0.30	1.05 (0.61-1.81)	0.85	
Farmer	1.93 (1.19-3.13)	0.01	1.04 (0.67-1.60)	0.86	
Smoking status	
 Current smoker	0.89 (0.47-1.69)	0.73	0.95 (0.48-1.88)	0.88	
 Past smoker	0.88 (0.47-1.64)	0.69	0.80 (0.43-1.49)	0.48	
Any alcohol use	0.68 (0.37-1.26)	0.22	0.62 (0.32-1.20)	0.16	
Dipstick proteinuria	5.94 (4.08-8.65)	<0.001	3.58 (2.35-5.45)a	<0.001a	
Serum creatinine	0.32 (0.13-0.77)	0.01	1.26 (0.78-2.06)	0.35	
HIV-positive	0.73 (0.55-0.98)	0.04	1.14 (0.82-1.59)	0.43	
Diabetes mellitus	0.94 (0.44-1.99)	0.87	0.69 (0.31-1.52)	0.36	
Hypertension	0.97 (0.64-1.48)	0.90	0.70 (0.46-1.07)	0.10	
Any NSAIDs use over the previous 90 days	0.75 (0.52-1.09)	0.13	1.12 (0.78-1.60)	0.55	
Any traditional medicine use over the previous 90 days	1.11 (0.77-1.60)	0.57	0.86 (0.61-1.24)	0.43	
Note: Adjusted model included geographic region, serum creatinine, dipstick proteinuria, sociodemographic factors, health habits (smoking and alcohol), diabetes, hypertension, HIV, proteinuria, hematuria, use of NSAIDs, and traditional herbal medicines.

Abbreviations: CI, confidence interval; NSAID, nonsteroidal anti-inflammatory drugs.

a Significant values.

We found that the mean surface air temperatures in 2016 were 23.7 °C, 20.2 °C, and 22.1 °C, and the mean altitudes were 1,131 meters, 1,470 meters, and 1,397 meters in Eastern Uganda, Southwestern Uganda, and Kenya, respectively.7,8 However, neither the mean surface air temperatures nor altitudes were associated with leukocyturia (Table S2).

Leukocyturia was associated with CKD in the unadjusted (prevalence ratio, 4.81; 95% CI, 3.27-7.09) and fully adjusted models (aPR, 3.54; 95% CI, 2.20-5.68).

Given the patterns of leukocyturia in our study—highly variable geographic distribution between study communities and association with CKD—we postulate that leukocyturia may represent a marker of kidney damage in this region.

The observed geographical variations in leukocyturia distribution may suggest that these differences are attributed to environmental exposures. However, we could not verify this as neither mean surface air temperatures nor altitudes were found to be associated with leukocyturia.

Differences in leukocyturia prevalence have been noted among farm workers in various job categories, hinting at a potential influence from farming-related environmental factors.1 Despite a larger proportion of Eastern Uganda and Southwestern Uganda participants identifying as farmers compared with those in Western Kenya, farming occupation was not associated with leukocyturia in our study. Further investigations are needed to elucidate the role of environmental exposures in leukocyturia in this region.

Population-based studies on asymptomatic leukocyturia are scarce,9 and our report contributes to the literature by delineating patterns and risk factors for leukocyturia in rural East Africa. However, we acknowledge several limitations. We did not do urine cultures to rule out bacterial infection. We were unable to establish the relationship between leukocyturia and longitudinal loss of kidney function. Additionally, we did not conduct microscopic examinations of urine sediment, and relying solely on dipstick urinalysis limits our ability to make definitive links between our findings and clinically relevant kidney disease.

The absence of robust health care and research infrastructure in the region10 prevents us from making further connections between leukocyturia and advanced CKD (for example, kidney failure treated with dialysis). We lacked information on endemic infections such as tuberculosis, which might have provided additional insights into the etiology of leukocyturia. Nevertheless, the presence of leukocyturia in the context of infection can indicate genitourinary involvement and potentially contribute to subsequent kidney disease.

In conclusion, we observed that sterile leukocyturia has a striking geographic variation—being highly prevalent in rural Uganda but not in Kenya and is associated with CKD. Leukocyturia may represent a valuable marker of kidney damage in this region, and future studies should further characterize leukocyturia in these communities to determine its clinical significance.

Supplementary Material

Supplementary File (PDF)

Item S1; Table S1 and S2.

Article Information

Authors’ Contributions

Research idea and study design: SM, C-YH, EDC, JK, MA, JA, MP, DH, MME, and ANM; data acquisition: C-YH, EDC, JK, GO, MA, JA, MP, DH, MME, and ANM; data analysis/interpretation: SM, C-YH, EDC, JK, MA, JA, GO, MDP, MP, DH, MME, and ANM; statistical analysis: SM, C-YH, ANM; supervision or mentorship: C-YH, EDC, JK, GO, MA, JA, MP, DH, MME, and ANM. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Support

Dr Muiru was supported by the 10.13039/100005595 University of California , San Francisco, Dean’s Diversity award, NIH T32DK007219-41S1, R01DK114014-01A1S1 diversity supplements, and K23DK119562. Dr Hsu was supported by NIH K24 DK92291. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Financial Disclosure

The authors declare that they have no relevant financial interests.

Peer Review

Received January 29, 2024. Evaluated by 1 external peer reviewer, with direct editorial input from the Statistical Editor and the Editor-in-Chief. Accepted in revised form June 11, 2024.

Supplementary File (PDF)

Item S1: Description of Study Population and Sampling Design

Table S1: Population Characteristics of Adults in Rural East Africa Based on Weighted SEARCH-CKD Participants

Table S2: Exploratory Analysis: Unadjusted and Adjusted Association of Environmental Risk Factors With Leukocyturia
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References

1 Petropoulos Z.E. Laws R.L. Amador J.J. Kidney function, self-reported symptoms, and urine findings in Nicaraguan sugarcane workers Kidney360 1 10 2020 1042 1051 10.34067/KID.0003392020 35368783
2 Muiru A.N. Charlebois E.D. Balzer L.B. The epidemiology of chronic kidney disease (CKD) in rural East Africa: a population-based study PloS One 15 3 2020 e0229649 10.1371/journal.pone.0229649
3 Kalyesubula R. Wearne N. Semitala F.C. Bowa K. HIV-associated renal and genitourinary comorbidities in Africa J Acquir Immune Defic Syndr 67 suppl 1 2014 S68 S78 10.1097/QAI.0000000000000259 25117962
4 Sandler D.P. Burr F.R. Weinberg C.R. Nonsteroidal anti-inflammatory drugs and the risk for chronic renal disease Ann Intern Med 115 3 1991 165 172 10.7326/0003-4819-115-3-165 2058870
5 Jha V. Herbal medicines and chronic kidney disease Nephrology (Carlton) 15 suppl 2 2010 10 17 10.1111/j.1440-1797.2010.01305.x 20586941
6 Hsu C.Y. Iribarren C. McCulloch C.E. Darbinian J. Go A.S. Risk factors for end-stage renal disease: 25-year follow-up Arch Intern Med 169 4 2009 342 350 10.1001/archinternmed.2008.605 19237717
7 Topographic Map https://en-gb.topographic-map.com/
8 The World Bank Climate change knowledge portal https://climateknowledgeportal.worldbank.org/
9 Alwall N. Lohi A. A population study on renal and urinary tract diseases. I. Introduction: selection of 3 998 persons for screening and nephrological examination (55.7 percent), including a control group (16.1 percent) Acta Med Scand 194 6 1973 525 528 10.1111/j.0954-6820.1973.tb19485.x 4773453
10 Kalyesubula R. Brewster U. Kansiime G. Global dialysis perspective: Uganda Kidney360 3 5 2022 933 936 10.34067/KID.0007002021 36128482
