Laser induced breakdown spectroscopy with machine learning reveals lithium-induced electrolyte imbalance in the kidneys

• Published in: Journal of pharmaceutical and biomedical analysis Vol. 194; p. 113805
• Main Authors: Ahmed, Irfan, Khan, Muhammad Shehzad, Paidi, Santosh, Liu, Zhenhui, Zhang, Chi, Liu, Yuanchao, Baloch, Gulsher Ali, Law, Alan W.L., Zhang, Yanpeng, Barman, Ishan, Lau, Condon
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 05.02.2021
• Subjects: Electrolytes; Kidney; Lithium; Machine learning; Renal; Spectroscopy; Spectroscopy; Lithium; Electrolytes; Renal; Kidney; Machine learning
• ISSN: 0731-7085; 1873-264X; 1873-264X
• DOI: 10.1016/j.jpba.2020.113805

[Display omitted] •The impact of lithium medication on kidney electrolyte balance is studied using laser induced breakdown spectroscopy (LIBS).•LIBS with machine learning (PCA, RadViz and random forest) links systemic and in situ kidney measures of renal function.•The results show systemic lithium-induced renal dysfunction likely related to kidney electrolyte imbalance.•A classifier predicts electrolyte levels, which cannot be easily measured in vivo, using invasive blood test measures.•LIBS with machine learning has potential to improve clinical management of renal side-effects from lithium medication. Lithium is a major psychiatric medication, especially as long-term maintenance medication for Bipolar Disorder. Despite its effectiveness, lithium has side-effects, such as on renal function. In this study, lithium was administered to adult rats. This animal model of renal function was validated by measuring blood lithium, urea nitrogen (BUN), and thyroxine (T4) using inductively-coupled plasma mass spectrometry and enzyme-linked immunosorbent assay. The kidneys were analyzed by laser induced breakdown spectroscopy (LIBS) with 1064 nm ablation and 300–900 nm detection. Principal components analysis (PCA), radial visualization, and random forest classification were performed on the LIBS spectra for multi-element prediction and classification. Lithium at 0.34 mmol/L was detected in the blood of lithium treated subjects only. BUN was increased (6.6 vs. 5.3 mmol/L) and T4 decreased (58.12 vs. 51.4 mmol/L) in the blood of lithium subjects compared with controls, indicating renal abnormalities. LIBS detected lithium at 2.3 mmol/kg in the kidneys of lithium subjects only. Calcium was also observed to be reduced in lithium subjects, compared with controls. Subsequent PCA observed a change in the balance of sodium and potassium in the kidneys. These are key electrolytes in the body. Importantly, partial least squares regression showed that standard clinical measurements, such as the blood tests, can be used to predict kidney electrolyte measurements, which typically cannot be performed in humans. Overall, lithium accumulates in the kidneys and adversely affects renal function. The effects are likely related to electrolyte imbalance. LIBS with machine learning analysis has potential to improve clinical management of renal side-effects in patients on lithium medication.