Background. The fracture pathophysiology associated with type 2 diabetes and chronic kidney disease (CKD) is incompletely understood. We examined individual fracture predictors and prediction sets based on different pathophysiological hypotheses, testing whether any of the sets improved prediction beyond that based on traditional osteoporotic risk factors. Methods. Within the CREDENCE cohort with adjudicated fracture outcomes, we assessed the association of individual factors with fracture using Cox regression models. We used the Akaike information criteria (AIC) and Schwartz Bayes Criterion (SBC) to assess six separate variable sets based on hypothesized associations with fracture, namely, traditional osteoporosis, exploratory general population findings, cardiovascular risk, CKD-mineral and bone disorder, diabetic osteodystrophy, and an all-inclusive set containing all variables. Results. Fracture occurred in 135 (3.1%) participants over a median 2.35 [1.88–2.93] years. Independent fracture predictors were older age (hazard ratio [HR] 1.04, confidence interval [CI] 1.01–1.06), female sex (HR 2.49, CI 1.70–3.65), previous fracture (HR 2.30, CI 1.58–3.34), Asian race (HR 1.74, CI 1.09–2.78), vitamin D therapy requirement (HR 2.05, CI 1.31–3.21), HbA1c (HR 1.14, CI 1.00–1.32), prior cardiovascular event (HR 1.60, CI 1.10–2.33), and serum albumin (HR 0.41, CI 0.23–0.74) (lower albumin associated with greater risk). The goodness of fit of the various hypothesis sets was similar (AIC range 1870.92–1849.51, SBC range 1875.60–1948.04). Conclusion. Independent predictors of fracture were identified in the CREDENCE participants with type 2 diabetes and CKD. Fracture prediction was not improved by models built on alternative pathophysiology hypotheses compared with traditional osteoporosis predictors.