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

Area of Science:

• Biochemistry
• Genetics
• Endocrinology

Background:

• Hypophosphatasia (HPP) is a rare metabolic disorder causing defective bone and teeth mineralization.
• It is often underdiagnosed due to challenges in identifying affected individuals.

Purpose of the Study:

• To assess the diagnostic value of alkaline phosphatase (ALP) thresholds for identifying adults with ALPL variants.
• To determine if including ALP substrates (PLP and PEA) enhances HPP diagnosis through a biochemical algorithm.

Main Methods:

• Analyzed 77 subjects with persistent hypophosphatasaemia, divided into those with (+GT) and without (-GT) pathogenic ALPL variants.
• Calculated diagnostic utility of ALP, PLP, and PEA thresholds using ROC curves.
• Developed and validated predictive models using logistic regression and machine learning.

Main Results:

• Optimal thresholds identified: ALP < 25 IU/L, PLP > 180 nmol/L, PEA > 30 µmol/g creatinine.
• A model combining ALP and PLP showed high diagnostic accuracy (93%), sensitivity (100%), and NPV (100%).
• Adding PEA further improved the model's AUC to 0.94, enhancing diagnostic utility.

Conclusions:

• A biochemical predictive model using ALP < 25 IU/L and PLP > 180 nmol/L effectively identifies individuals with ALPL variants.
• This algorithm can facilitate the diagnostic work-up of hypophosphatasia.
• Combining ALP with its substrates significantly improves diagnostic performance for HPP.