When is the right time to change therapy? An observational study of the time to response to immunosuppressive drugs in systemic lupus erythematosus.

OBJECTIVES: To analyse the response to immunosuppressants (IS) in extrarenal flares of SLE to determine the most appropriate timing during follow-up for a change in therapeutic strategy. METHODS: Observational cohort study including a total of 81 patients with SLE with extrarenal flares requiring a change in IS over the period 2015-2022. Baseline clinical variables were described, and follow-up data at 1, 3, 6 and 12 months time-points were collected. RESULTS: Among patients flaring that achieved lupus low disease activity state (LLDAS5) at 12 months of follow-up, we identified two subgroups ('late responders' and 'early responders'), which showed no significant differences in demographic characteristics, baseline clinical data, cumulative dosage of glucocorticoids or type of IS. Cox model analysis revealed a significant association of a change in IS (p=0.019) and achieving LLDAS5. Contingency table analysis indicated a significant relationship (p=0.004) between IS change at 6 months and individuals achieving LLDAS5 and remission at 12 months. CONCLUSIONS: Our findings suggest that clinical improvement of extrarenal flares typically occurs within 6 months of initiating IS. This timeframe could represent an appropriate timing to evaluate the response in a treat-to-target approach in SLE.

Anabolic treatment for osteoporosis and fragility fracture risk: one year in review 2024.

Osteoporosis is a skeletal disease characterised by reduced bone mass and deterioration of bone microarchitecture, underlying a higher risk of fragility fractures. Several options are available for its treatment, including both anti-resorptive and anabolic agents. The present review discusses and summarises the most recent literature on anabolic treatment, with a focus on abaloparatide, and on the assessment of fragility fracture risk, with a focus on trabecular bone score. Finally, we provide a discussion on the effects of different antiosteoporotic medications in terms of fragility fracture risk reduction.

Which extra-renal flare is 'difficult to treat' in systemic lupus erythematosus? A one-year longitudinal study comparing traditional and machine learning approaches.

OBJECTIVES: To describe phenotypes and outcomes of extra-renal flares in SLE, to identify clusters of extra-renal flares based on baseline features, and to develop a machine learning (ML) tool capable of predicting 'difficult to treat' (D2T) flares. METHODS: Extra-renal flares that occurred in our cohort over the last five years with at least one year of follow-up were included. Baseline clinical variables were described and flares assigned to clusters. Attainment of remission and low disease activity state (LLDAS) at 12 months were compared. Flares were then considered 'D2T' in case of non-attainment of LLDAS at 6 and 12 months. Baseline features were used to train a ML model able to predict future D2T-flares, at admission. Traditional approaches were then compared with informatic techniques. RESULTS: Among 420 SLE patients of the cohort, 114 flares occurred between 2015 and 2021; 79 extra-renal flares, predominantly mucocutaneous (24.1%) and musculoskeletal (45.6%), were considered. After 12 months, 79.4% and 49.4% were in LLDAS and in remission, respectively, while 17 flares were classified as D2T (21.5%); D2T flares received a higher cumulative and daily dose of glucocorticoids. Among the clusters, cluster 'D' (mild-moderate flares with mucocutaneous manifestations in patients with history of skin involvement) was associated with the lowest rate of remission. Among clinical data, not being on LLDAS at 3 months was the unique independent predictor of D2T flares. CONCLUSIONS: Our clusterization well separates extra-renal flares according to their baseline features and may propose a new identification standard. D2T flares, especially refractory skin manifestations, are frequent in SLE and represent an unmet need in the management of the disease as they are associated with higher glucocorticoid (GC) dosage and risk of damage accrual. Our ML model could help in the early identification of D2T flares, flagging them to elevate the attention threshold at admission.

LRP5 high bone mass (Worth-type autosomal dominant endosteal hyperostosis): case report and historical review of the literature.

PURPOSE: LRP5 high bone mass (HBM) is an autosomal dominant endosteal hyperostosis caused by mutations of the low-density lipoprotein receptor-related protein 5 (LRP5) gene. Alternative names included "autosomal dominant osteosclerosis" and "Worth disease." The aim of the paper is to provide an historical overview of a disorder whose literature is complicated and confusing due to the past use of several denominations and lack of reviews. METHODS: We collected case reports of HBM with evidence of autosomal dominant transmission preceding the identification of the LRP5 mutations in 2002 (Worth-type endosteal hyperostosis) and cases of LRP5 HBM confirmed by genetic analysis since 2002. The prevalence of relevant clinical and laboratory findings was estimated. We described an affected woman with neurological manifestations. RESULTS: A 44-year-old Caucasian woman with torus palatinus complained of headache, hypo-/anosmia, and complete mixed deafness. Dual-energy X-ray absorptiometry (DEXA) scan revealed elevated bone mass. The A242T mutation of the LRP5 gene was detected. Including the present case, 155 patients have been reported to date. Neurological involvement and increased serum alkaline phosphatase (ALP) were present in 19.4% and 3.7% of cases, respectively. Facial changes and torus palatinus were observed in 61% and 41% of cases, respectively. CONCLUSIONS: We present the only historical review on Worth-type endosteal hyperostosis, now known as LRP5 HBM. Neurological manifestations, previously considered absent in the disease, affect 19.4% of the patients. Genetic analysis and appropriate denomination of LRP5 HBM are fundamental for diagnosis and to mitigate the confusion that has long characterized this disease.

Osteoporosis and gut microbiota, radiofrequency echographic multispectrometry and machine learning: one year in review 2023.

Osteoporosis is a prevalent bone disease with a relevant burden of mortality and comorbidity, especially due to fragility fractures occurring as a result of reduced bone mineral density. In this review we provide a critical digest of the most recent literature regarding the relationship between gut microbiota and osteoporosis, and discuss the role of radiofrequency echographic multi-spectrometry (REMS) and machine learning in the diagnostic work-up and prevention of osteoporosis.