Evidence map›Paper›PMID 39435249›Full record

ArticleCureus2024

Serum Uric Acid and Microalbuminuria: Predictors of Renal Dysfunction in Type 2 Diabetes Patients in South-Western Uganda.

Simon Peter Rugera, Jazira Tumusiime, Hope Mudondo, Georgina Naruhura, Ritah Kiconco, Charles Nkubi Bagenda

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Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Simon Peter RugeraDepartment of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, UGA.
Jazira TumusiimeDepartment of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, UGA.
Hope MudondoDepartment of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, UGA.
Georgina NaruhuraDepartment of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, UGA.
Ritah KiconcoDepartment of Biochemistry, Soroti University, Soroti, UGA.
Charles Nkubi BagendaDepartment of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, UGA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by insulin resistance and high blood glucose levels, which has become a global pandemic in recent decades and is associated with several health complications, including renal dysfunction. Serum uric acid levels are associated with kidney damage and have been linked to various health conditions. Urine microalbumin is a sensitive marker of kidney damage and is commonly used to monitor renal dysfunction in diabetes. The study aimed to compare the predictive value of serum uric acid and urine microalbumin in detecting kidney damage among T2D patients. Method This secondary data analysis used a cross-sectional dataset of 140 diabetic patients from Mbarara Regional Referral Hospital (MRRH) in Mbarara, Uganda. The main outcome was renal dysfunction, defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m². Key variables included serum uric acid, urinary microalbumin, and various demographic and clinical factors. Data were analyzed using logistic regression and receiver operating characteristic (ROC) curve analysis to evaluate predictive performance. Ethics approval was obtained from the Mbarara University Research Ethics Committee. Results This study involved 140 participants with a median age of 53 years (interquartile range (IQR) 44-60.5), predominantly females (95, 67.9%), primarily educated (76, 54.3%), and mostly married (104, 74.3%). Participants with renal dysfunction were older (median age 61 years, IQR 52-69) compared to those without (median age 49, IQR 40-56), with significant differences in urinary microalbumin and serum uric acid levels (p <0.05). Renal dysfunction prevalence was 33.6% (95% CI: 26.2-41.9), higher in participants with diabetes duration ≥5 years, microalbuminuria, certain marital statuses, and higher diastolic blood pressure. Microalbuminuria (adjusted odds ratio (aOR) 4.71, 95% CI: 1.27-17.50, P = 0.021) and serum uric acid (aOR 1.01, 95% CI: 1.0002-1.0153, P = 0.045) were significantly associated with renal dysfunction. Other associated factors included age, female gender, and diastolic hypertension. Both biomarkers had significant predictive power for renal dysfunction (area under the curve (AUC) 0.62 and 0.65, respectively). Conclusion This study confirms the high prevalence of renal dysfunction among T2D patients, with a finding of 33.6%. The significant association between microalbuminuria and renal dysfunction, as well as the predictive capacity of serum uric acid and urinary microalbumin, highlight the importance of these biomarkers in identifying individuals at risk of kidney complications.

Indexed as

biomarkermicroalbuminuriarenal dysfunctiontype 2 diabetesuric acid

Identifiers

PMID39435249
PMCPMC11492550

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.