Biochemical algorithm to identify individuals with ALPL variants among subjects with persistent hypophosphatasaemia

• Published in: Orphanet journal of rare diseases Vol. 17; no. 1; pp. 98 - 11
• Main Authors: Tornero, C., Navarro-Compán, V., Buño, A., Heath, K. E., Díaz-Almirón, M., Balsa, A., Tenorio, J. A., Quer, J., Aguado, P.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 03.03.2022; BioMed Central Ltd; BMC
• Subjects: Adult; Alkaline phosphatase; Alkaline Phosphatase - genetics; ALPL; Biological markers; Bone and Bones; Diagnosis; Electrolytes; Evaluation; Health aspects; Human Genetics; Humans; Hypophosphatasaemia; Hypophosphatasia; Hypophosphatasia - diagnosis; Hypophosphatasia - epidemiology; Hypophosphatasia - genetics; Machine Learning; Medicine; Medicine & Public Health; Metabolic bone diseases; Pharmacology/Toxicology; Pyridoxal Phosphate; Rare bone diseases and skeletal dysplasias; Risk factors; Spain; Alkaline phosphatase; Metabolic bone diseases; Hypophosphatasia; Hypophosphatasaemia; ALPL
• ISSN: 1750-1172; 1750-1172
• DOI: 10.1186/s13023-022-02253-5

Background Hypophosphatasia (HPP) is a rare and underdiagnosed condition characterized by deficient bone and teeth mineralization. The aim of this study was first, to evaluate the diagnostic utility of employing alkaline phosphatase (ALP) threshold levels to identify adults with variants in ALPL among individuals with persistently low ALP levels and second, to determine the value of also including its substrates (serum pyridoxal-5′-phosphate—PLP—and urinary phosphoetanolamine-PEA) for this purpose in order to create a biochemical algorithm that could facilitate the diagnostic work-up of HPP. Results The study population comprised 77 subjects with persistent hypophosphatasaemia. They were divided into two groups according to the presence (+GT) or absence (−GT) of pathogenic ALPL variants: 40 +GT and 37 −GT. Diagnostic utility measures were calculated for different ALP thresholds and Receiver Operating Characteristic (ROC) curves were employed to determine PLP and PEA optimal cut-off levels to predict the presence of variants. The optimal threshold for ALP was 25 IU/L; for PLP, 180 nmol/L and for PEA, 30 µmol/g creatinine. Biochemical predictive models were assessed using binary logistic regression analysis and bootstrapping machine learning technique and results were then validated. For ALP < 25 UI/L (model 1), the area under curve (AUC) and the 95% confidence intervals (CI) was 0.68 (95% CI 0.63–0.72) and it improved to 0.87 (95% CI 0.8–0.9), when PEA or PLP threshold levels were added (models 2 and 3), reaching 0.94 (0.91–0.97) when both substrates were included (model 4). The internal validation showed that the addition of serum PLP threshold levels to the model just including ALP improved significantly sensitivity (S) and negative predictive value (NPV) − 100%, respectively- with an accuracy (AC) of 93% in comparison to the inclusion of urinary PEA (S: 71%; NPV 75% and AC: 79%) and similar diagnostic utility measures as those observed in model 3 were detected when both substrates were added. Conclusions In this study, we propose a biochemical predictive model based on the threshold levels of the main biochemical markers of HPP (ALP < 25 IU/L and PLP > 180 nmol/L) that when combined, seem to be very useful to identify individuals with ALPL variants.