We have previously confirmed a paradoxical mineralizing enthesopathy as a hallmark of X-linked hypophosphatemia. X-linked hypophosphatemia is the most common of the phosphate-wasting disorders mediated by elevated fibroblast growth factor 23 (FGF23) and occurs as a consequence of inactivating mutations of the PHEX gene product. Despite childhood management of the disease, these complications of tendon and ligament insertion sites account for a great deal of the disease's morbidity into adulthood. It is unclear whether the enthesopathy occurs in other forms of renal phosphate-wasting disorders attributable to high FGF23 levels. Here we describe two patients with autosomal recessive hypophosphatemic rickets due to the Met1Val mutation in dentin matrix acidic phosphoprotein 1 (DMP1). In addition to the biochemical and skeletal features of long-standing rickets with elevated FGF23 levels, these individuals exhibited severe, debilitating, generalized mineralized enthesopathy. These data suggest that enthesophytes are a feature common to FGF23-mediated phosphate-wasting disorders. To address this possibility, we examined a murine model of FGF23 overexpression using a transgene encoding the secreted form of human FGF23 (R176Q) cDNA (FGF23-TG mice). We report that FGF23-TG mice display a similar mineralizing enthesopathy of the Achilles and plantar facial insertions. In addition, we examined the impact of standard therapy for phosphate-wasting disorders on enthesophyte progression. We report that fibrochondrocyte hyperplasia persisted in Hyp mice treated with oral phosphate and calcitriol. In addition, treatment had the untoward effect of further exacerbating the mineralization of fibrochondrocytes that define the bone spur of the Achilles insertion. These studies support the need for newer interventions targeted at limiting the actions of FGF23 and minimizing both the toxicities and potential morbidities associated with standard therapy.
Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinical trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy. Specifically, a multi-headed multilayer perceptron is used to estimate the conditional average treatment effect (CATE) using baseline clinical and imaging features, and patients predicted to be most responsive are preferentially randomized into a trial. Leveraging data from six randomized clinical trials (n = 3,830), we first pre-trained the model on the subset of relapsing-remitting MS patients (n = 2,520), then fine-tuned it on a subset of primary progressive MS (PPMS) patients (n = 695). In a separate held-out test set of PPMS patients randomized to anti-CD20 antibodies or placebo (n = 297), the average treatment effect was larger for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p = 0.0218) and 30% (HR, 0.361; 95% CI, 0.165-0.79; p = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p = 0.179) for the entire group. The same model could also identify responders to laquinimod in another held-out test set of PPMS patients (n = 318). Finally, we show that using this model for predictive enrichment results in important increases in power.
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