OBJECTIVEEnd-stage renal disease (ESRD) commonly manifests with disrupted calcium balance, leading to renal osteodystrophy. We posited that variations in the genetic makeup of vitamin D and calcium-sensing receptors, specifically single nucleotide polymorphisms (SNPs), could affect calcium homeostasis. This study aimed to identify the genetic predictors related to vitamin D and calcium-sensing receptors on calcium metabolism using machine learning algorithm analysis in ESRD.PATIENTS AND METHODSWe conducted a cross-sectional analysis on adults with ESRD. We gathered comprehensive demographic data and medical history. Blood samples were collected to measure SNPs, and a panel of calcium metabolism biomarkers associated with the calcium-sensing receptor and vitamin D receptor. The biomarkers included calcium, phosphate, vitamin D, parathyroid hormone (PTH), sclerostin, procollagen type 1 alpha 1, osteocalcin, and bone-specific alkaline phosphatase. We utilized machine learning algorithms to pinpoint genetic markers predictive of vitamin D deficiency.RESULTSWe found a notable decrease in serum procollagen type 1 alpha 1 levels among individuals with the CC of rs10190 (related to the calcium-sensing receptor) compared to those with the TT genotype and in those with the TT of rs739837 (pertaining to the vitamin D receptor) compared to the GG genotype. Similarly, the TT genotype of rs10190 was associated with significantly lower serum phosphate levels compared to CC and CT genotypes. Additionally, a lower serum PTH level was noted in individuals with the CT of rs1802757 (calcium-sensing receptor) compared to those with the CC genotype. Our machine learning analysis identified rs2221266 and rs1042636 as the most significant SNPs linked to vitamin D deficiency, demonstrating considerable predictive accuracy.CONCLUSIONSOur findings indicate that specific single nucleotide polymorphisms in the vitamin D and calcium-sensing receptors significantly influence calcium metabolism biomarkers in ESRD patients. Assessing the clinical implications of these genetic variations is crucial for advancing personalized medicine in renal care.