Journal of Translational Internal Medicine· 2026Q1
Urinary amino acids patterns enlighten new predictive models for extensive renal proximal tubular injuries and kidney function
- 0citations
- Q1SCImago
- 2026year
Short summary
Machine learning models using urinary amino acid profiles, particularly citrulline and threonine, achieved high accuracy (AUC 0.81-0.82) in diagnosing extensive proximal tubular dysfunction, outperforming traditional indicators.
AI-generated from the title and abstract; the full text is not read.
Key points
- Urinary amino acid profiles were mapped for specific causes of proximal tubular (PT) injuries.
- Machine learning models using 21 urinary AAs achieved AUCs of 0.81 (XGBoost) and 0.82 (RF) for diagnosing renal Fanconi syndrome.
- A simplified model with four AAs (citrulline, phenylalanine, asparagine, aspartate) reached an AUC of 0.88 for diagnosing PT dysfunction.
- Urinary proline levels were independently correlated with reduced eGFR (< 60 mL/min/1.73 m²).
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Background and Objectives The kidney plays a crucial role in amino acids (AAs) retention and excretion through AA transporters located in the proximal tubules. The impairment of these transporters results in aminoaciduria, one of the features of extensive proximal tubular (PT) dysfunction, namely renal Fanconi syndrome (FS). This study aimed to explore the clinical application of urinary AAs excretion profile and investigate their associations with renal function and other PT dysfunction. Methods We enrolled 157 patients with PT dysfunction and detected urinary AAs levels by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Machine learning models were used to assess diagnostic values of urinary AAs in renal FS and renal function decline, with randomized search to optimize parameters and evaluate algorithm performance. Results The patients enrolled had a median age of 47 (35-60) years and a median eGFR of 92.8 (50.0-115.7) mL/min/1.73 m 2 , with primary Sjӧgren’s syndrome as the most common identified primary cause. We mapped etiology-specific urinary AAs excretion profiles. The most susceptible urinary AAs were citrulline (Cit) and threonine (Thr) transported by SLC6A19 (B 0 AT1), while arginine (Arg), aspartate (Asp) and histidine (His) were the least affected. Diagnostic models of renal FS achieved area under curve (AUC) values of 0.81 (XGBoost) and 0.82 (RF) based on all 21 urinary AAs, outperforming traditional clinical indicators. An optimization model based on four urinary AAs (Cit, phenylalanine [Phe], asparagine [Asn] and Asp) showed similar diagnostic performance (AUC 0.88). The urinary proline (Pro) level was independently correlated with eGFR < 60 mL/min/1.73 m 2 (OR = 2.44, 95% CI: 1.53-3.91, P < 0.001). We found significant associations between abnormal urinary AAs excretion and hypouricemia or hypokalemia, particularly in the male patients. Conclusions We mapped the etiology-specific urinary AAs excretion atlases in patients with PT injuries, and established a fine and simplified machine-learning model based on only four urinary AAs to diagnose extensive PT dysfunction noninvasively and conveniently. Significant associations of aminoaciduria with the other PT dysfunction showed gender differences.
The authors' abstract, as published at the source. Journal of Translational Internal Medicine, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Biochemistry (Biochemistry, Genetics and Molecular Biology)
BiochemistryBiochemistry, Genetics and Molecular Biology