Background Hyperphosphatemia is common in maintenance hemodialysis (MHD) and often coexists with other abnormalities of chronic kidney disease–mineral and bone disorder (CKD-MBD). We described predefined biochemical patterns related to hyperphosphatemia and examined their clinical correlates. Methods This single-center retrospective cross-sectional study screened 589 patients receiving hemodialysis between March 2023 to March 2026; 254 patients comprised the final complete-case analytical cohort. Four prespecified threshold-based biochemical patterns were defined using serum phosphorus, parathyroid hormone (PTH), and alkaline phosphatase (ALP). Univariable and fixed multivariable logistic regression were used to examine factors associated with hyperphosphatemia. Restricted cubic splines were used to assess the functional forms of BUN, uric acid, and creatinine. Sensitivity analyses and 1,000 bootstrap resamples were performed to examine the robustness of the adjusted estimates. Results Hyperphosphatemia was present in 182 patients (71.7%). The four patterns comprised 72 (28.3%) non-hyperphosphatemia, 106 (41.7%) HP/PTH−, 61 (24.0%) HP/PTH+/ALP−, and 15 (5.9%) HP/PTH+/ALP+. BUN, uric acid, and creatinine were higher in the hyperphosphatemic groups, whereas Kt/V was similar. After adjustment, creatinine was positively associated with hyperphosphatemia (OR 1.27 per 100 μmol/L, 95% CI 1.07–1.50, P = 0.005). Uric acid showed a nonlinear association (overall P = 0.005, P for nonlinearity=0.004), although estimates at the upper end of its distribution were less precise. The BUN association was borderline (OR 1.33 per 5 mmol/L, 95% CI 1.00–1.78, P = 0.054) and weakened with the stricter phosphorus cutoff. Conclusion Predefined biochemical patterns captured heterogeneity in hyperphosphatemia among patients receiving MHD. Creatinine remained associated with hyperphosphatemia, uric acid showed a nonlinear but imprecise high-range association, and BUN was less consistent. These findings may help frame future studies of phosphate control using routinely available laboratory data.
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Zhang et al. (2026) studied this question.
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