Jean-Philippe Bertocchio

Abnormal enamel mineralization has been reported in genetic conditions associated with hypocalcemia but never with hypercalcemia. In the present study, we report 10 patients from 2 unrelated families in whom hypomineralized enamel co-segregated with hypercalcemia due to a complete deletion or frameshifting premature truncation (p.(Ser18Argfs*2)) of the guanine nucleotide–binding protein (G-protein) subunit α ₁₁ ( GNA11 ) gene. Both GNA11 variants were heterozygous and predicted to cause a loss of function of Gα ₁₁ . Multiproxy imaging analyses of naturally exfoliated primary teeth and clinically extracted permanent teeth from 2 affected patients revealed abnormal enamel mineralization, with constitutive patches of hypomineralization following no set pattern. In parallel, the study of wild-type mouse tooth germs using combined molecular and protein analyses showed Gna11 and Gα ₁₁ protein expression in ameloblasts at the secretion and maturation stages, as well as in odontoblasts, suggesting a role for Gα ₁₁ in tooth formation. Furthermore, the analysis of a mouse model of Familial Hypocalciuric Hypercalcemia 2 (FHH2) revealed delayed onset of enamel mineralization in the continuously growing incisors and hypomineralized enamel in both incisor and molars of heterozygous ( Gna11 Tm1b ^+/− ) mice, with defects that are very similar to those affecting human FHH2 teeth. In conclusion, our study reveals that abnormal enamel mineralization may occur in association with hypercalcemia due to loss-of-function GNA11 mutations, opening a new field of investigation and highlighting the need to include a dentist in the multidisciplinary team in charge of patients with monogenic calcium disorders.

Background: Typically, e-cohorts are susceptible to low participation rates, undermining representativeness. Frequent reminders can be a cost-effective strategy to increase response rates. However, their effectiveness may vary depending on the delivery mode and message content. Objective: This study aimed to evaluate different reminder strategies (ie, emails and/or text messages with standard and/or institutional wording) and compare their effectiveness in increasing the response rate in a population-based e-cohort of healthy adults. Methods: We conducted a 4-arm parallel randomized controlled trial nested within the Le French Gut e-cohort (registration number 2021-A01439-32). In November 2024, we included participants who were enrolled online (SKEZIA platform) but were not yet active participants (ie, eligibility, consent form, and/or personal information questionnaire had not been completed). Computer-generated randomization was stratified by time since enrollment. We sent 3 reminders, 72 hours apart, following 4 experimental designs: group 1, standard emails only; group 2, text messages only; group 3, institutional emails only; and group 4, standard email, followed by a text message and an institutional email. The primary outcome was the 30-day completion rate of the personal information questionnaire. We also measured completion time (ie, time between reminder received and questionnaire fully completed), online login rate, login time, email opening, and click-through rate. Participants were unaware of the trial, and investigators were blinded to group allocation. Results: At the end of the trial, of the 20,487 eligible participants, 19,525 received at least 1 reminder (n=5225 in group 1; n=3858 in group 2, because n=1365 of the initial 5223 had no phone number and could not receive text messages; n=5222 in group 3; n=5220 in group 4). The per-protocol completion rate was 8.4%, with a higher rate in group 4 (9.4% vs 7.4%, 8.4%, and 8.3% for groups 1, 2, and 3, respectively; P <.001). Completion time was faster in group 2 (mean 6.3, SD 4.3 d) compared to groups 3 and 4 (mean 7.0, SD 3.5 and mean 7.2, SD 3.8 d, respectively; P =.003). The online login rate was higher, and the login time was faster in group 4 (login rate: 15.6% vs 12.1%, 14.3%, and 15.3% for groups 1, 2, and 3, respectively; P <.001; and login time: mean 7.1, SD 4.3 d vs mean 6.7, SD 4.3 d, mean 6.2, SD 4.6 d, mean 6.8, SD 3.9 d for groups 1, 2, and 3, respectively; P =.002). For emails, opening rates were similar ( P =.87), but the click-through rate was higher for institutional emails (23.1% vs 19.6% for standard formatting; P <.001). Conclusions: For the first time in a specifically designed randomized controlled trial, we directly compare email-only, text message–only, and a combined reminder strategy in a large e-cohort. While previous studies focused on specific populations, our work examines a general adult population, demonstrating that a mixed delivery mode strategy effectively increases response rate by 27% compared to a single-channel approach. Text messages yielded faster responses, and institutional emails, with formal wording and coordinator’s signature, seemed more appealing than less formal wording. These results provide concrete guidance for researchers seeking to improve recruitment in real-world e-cohorts.

Background: Traditional health care systems have evolved to increasingly recognize patients’ perspectives as key to improving the quality of care, especially in oncology. Hence, patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) can enhance patient–health care provider communication while facilitating individualized care. This tailored approach improves patient outcomes and highlights the need for research methods to account for variability in patient experiences across diverse sociodemographic and clinical backgrounds while proposing artificial intelligence (AI) to automate and accelerate the identification of PROMs and PREMs. Objective: This study aimed to estimate the proportion of clinical studies including PROMs or PREMs, using either a traditional expert-based identification method or an AI-enriched approach. Methods: In a retrospective cross-sectional study using the ClinicalTrial.gov database, we focused on oncology studies between 2012 and 2022. Two methods were used to identify PROMs and PREMs: (1) a traditional expert-based method, where an algorithm identified PROMs and PREMs from a list of 346 oncology-specific PROMs and PREMs (extracted from the PROQOLID database, Mapi Research Trust) and/or 11 PROM- and PREM-specific terms, and (2) an AI-enriched method using a bidirectional encoder representations from transformers model, trained on 2399 outcomes labeled by experts. To evaluate algorithm performance, results were compared with expert decisions. Studies were classified by PROM/PREM use, analyzed using logistic regression to identify drivers, and a named entity recognition model identified frequently used measures. Results: A total of 24,491 studies were included. According to the traditional expert-based algorithm, 7549 (31%) studies used at least one PROM or PREM, as compared to 8029 (33%) studies identified by the AI-enriched algorithm, increasing from 2012 to 2022 (P<.001). With 90% (95% CI 88%-92%) accuracy, the AI-enriched algorithm outperformed (P<.001) the expert-based algorithm (84%, 95% CI 82%-86%) in identifying PROMs and PREMs. Breast and digestive cancers accounted for nearly 50% of all oncology studies using PROMs and PREMs, with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 being the most frequently used. As expected, trials in phases 2 to 4 more frequently included PROMs and PREMs than preclinical or early phase 1 studies (odds ratio [OR] 1.8, 95% CI 1.1-2.8 for phase 2; OR 3.6, 95% CI 2.3-5.8 for phase 3; and OR 2.6, 95% CI 1.6-4.4 for phase 4). In observational studies, cross-sectional and prospective studies incorporated more PROMs and PREMs than retrospective studies (OR 4.6, 95% CI 3.3-6.4 and OR 3.2, 95% CI 2.5-4.1, respectively). Conclusions: Our study shows that an AI-enriched algorithm outperforms traditional expert methods in identifying PROMs and PREMs in oncology. Combining expert-labeled data with AI enables scalable, automated trial monitoring, supports efficient research, informs stakeholders, enhances patient-centered decisions, and can be extended to other diseases and databases.

La ciclosporine est un traitement immunosuppresseur très utilisé : elle inhibe l'activation des lymphocytes T via la calcineurine. Sa néphrotoxicité limite son utilisation : la ciclosporine induit une augmentation de la vasoconstriction ainsi qu'une augmentation de la réponse des cellules musculaires lisses vasculaires (CMLV) aux agents vasoactifs. Le récepteur minéralocorticoïde (RM), au-delà de ses effets sur la réabsorption sodée, agit sur le tonus vasculaire en modulant la réponse des cellules (endothéliales et musculaires lisses) vasculaires aux agents vasoactifs. Notre hypothèse était que le RM pouvait participer à l'action vasoconstrictrice de la ciclosporine ; son inactivation pourrait limiter la néphrotoxicité de la ciclosporine. Deux modèles de souris ont été invalidés génétiquement pour le RM : dans les cellules endothéliales et les CMLV (KO-RM CMLV). Seules les souris KO-RM CMLV étaient protégées contre la néphrotoxicité de la ciclosporine. Ces effets impliquent une action sur le tonus vasculaire rénal. L'antagonisme pharmacologique du RM (par le canrénoate) administré per os confère la même protection. En revanche, la néphrotoxicité induite par le tacrolimus (une autre anticalcineurine) n'est pas prévenue par l'antagonisme du RM. Utiliser un antagoniste sélectif du RM (l'éplérénone) pourrait prévenir la néphrotoxicité de la ciclosporine. Nous avons prouvé sa bonne tolérance en association à la ciclosporine chez les patients transplantés et insuffisants rénaux chroniques. Une kaliémie supérieure à 4,35mmol/L à l'initiation indique un sur-risque de développer une hyperkaliémie. L'efficacité reste à démontrer au cours d'un essai prospectif et randomisé.

Context: Chronic hypoparathyroidism (cHP) is a rare endocrine disorder characterized by hypocalcemia secondary to the insufficient secretion of the parathyroid hormone. Despite the good observance of oral treatment with calcium salts and active vitamin D analogs, and even after adding intermittent subcutaneous injections of teriparatide (IST), some patients are still uncontrolled. Previous studies have suggested that continuous subcutaneous administration of teriparatide (CSAT) may be an option in such cases. Objective: The aim of this study was to assess the evolution of biological parameters with CSAT in comparison to previous treatment. Design: Retrospective observational study including all patients who received CSAT from 3 tertiary care centers of the Epi-Hypo study. Methods: Blood calcium, phosphate, bone remodeling markers, urine calcium, and the evolution of oral treatment were captured from electronic medical records. Quality of life (QoL) was assessed using the Hypoparathyroid Patient Questionnaire (HPQ28). Results: Twenty patients were included, 85% were women, and 75% had post-surgery cHP. Total calcium concentration was significantly higher with CSAT than with IST for 73% patients. HPQ28 showed an improvement in three out of the five patients for whom it was assessed. Osteocalcin increased significantly with IST (P = .003), but no difference was observed after initiating CSAT. Conclusion: CSAT may be an option for patients with uncontrolled cHP. Further studies need to investigate bone consequences and better characterize the improvement of QoL among these patients.

Background: Endocrine science remains underrepresented in European Union research programmes despite the fundamental role of hormone health in human well-being. Analysis of the CORDIS database reveals a persistent gap between the societal impact of endocrine disorders and their research prioritization. At national funding level, endocrine societies report limited or little attention of national research funding towards endocrinology. The EndoCompass project—a joint initiative between the European Society of Endocrinology and the European Society of Paediatric Endocrinology—aimed to identify and promote strategic research priorities in endocrine science to address critical hormone-related health challenges. Methods: Research priorities were established through comprehensive analysis of the EU CORDIS database covering the Horizon 2020 framework period (2014-2020). Expert consultation in bone and calcium endocrinology was conducted to identify key research priorities, followed by broader stakeholder engagement including society members and patient advocacy groups. Results: Research priorities encompass primary hyperparathyroidism, chronic hypoparathyroidism, and rare calcium–phosphate disorders. Key areas include bone as endocrine organ, bone mass acquisition, secondary osteoporosis, and skeletal dysplasias. Special emphasis is placed on cellular senescence, sex-specific differences in disease manifestation, and development of novel diagnostic tools for bone quality assessment. Conclusions: This component of the EndoCompass project provides an evidence-based roadmap for strategic research investment. This framework identifies crucial investigation areas into metabolic bone disease pathophysiology, prevention, and treatment strategies, ultimately aimed at reducing the burden of these disorders on individuals and society. The findings support the broader EndoCompass objective of aligning research funding with areas of highest potential impact in endocrine health.

BACKGROUND Traditional health care systems have evolved to increasingly recognize patients’ perspectives as key to improving the quality of care, especially in oncology. Hence, patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) can enhance patient–health care provider communication while facilitating individualized care. This tailored approach improves patient outcomes and highlights the need for research methods to account for variability in patient experiences across diverse sociodemographic and clinical backgrounds while proposing artificial intelligence (AI) to automate and accelerate the identification of PROMs and PREMs. OBJECTIVE This study aimed to estimate the proportion of clinical studies including PROMs or PREMs, using either a traditional expert-based identification method or an AI-enriched approach. METHODS In a retrospective cross-sectional study using the ClinicalTrial.gov database, we focused on oncology studies between 2012 and 2022. Two methods were used to identify PROMs and PREMs: (1) a traditional expert-based method, where an algorithm identified PROMs and PREMs from a list of 346 oncology-specific PROMs and PREMs (extracted from the PROQOLID database, Mapi Research Trust) and/or 11 PROM- and PREM-specific terms, and (2) an AI-enriched method using a bidirectional encoder representations from transformers model, trained on 2399 outcomes labeled by experts. To evaluate algorithm performance, results were compared with expert decisions. Studies were classified by PROM/PREM use, analyzed using logistic regression to identify drivers, and a named entity recognition model identified frequently used measures. RESULTS A total of 24,491 studies were included. According to the traditional expert-based algorithm, 7549 (31%) studies used at least one PROM or PREM, as compared to 8029 (33%) studies identified by the AI-enriched algorithm, increasing from 2012 to 2022 (P<.001). With 90% (95% CI 88%-92%) accuracy, the AI-enriched algorithm outperformed (P<.001) the expert-based algorithm (84%, 95% CI 82%-86%) in identifying PROMs and PREMs. Breast and digestive cancers accounted for nearly 50% of all oncology studies using PROMs and PREMs, with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 being the most frequently used. As expected, trials in phases 2 to 4 more frequently included PROMs and PREMs than preclinical or early phase 1 studies (odds ratio [OR] 1.8, 95% CI 1.1-2.8 for phase 2; OR 3.6, 95% CI 2.3-5.8 for phase 3; and OR 2.6, 95% CI 1.6-4.4 for phase 4). In observational studies, cross-sectional and prospective studies incorporated more PROMs and PREMs than retrospective studies (OR 4.6, 95% CI 3.3-6.4 and OR 3.2, 95% CI 2.5-4.1, respectively). CONCLUSIONS Our study shows that an AI-enriched algorithm outperforms traditional expert methods in identifying PROMs and PREMs in oncology. Combining expert-labeled data with AI enables scalable, automated trial monitoring, supports efficient research, informs stakeholders, enhances patient-centered decisions, and can be extended to other diseases and databases.