Caterina Gregorio

Preserving independence in older adults is a public health priority. Lifestyle interventions can prevent physical disability, but whether their efficacy is influenced by multimorbidity profiles is unclear. In this post hoc analysis of the SPRINTT randomized trial ( NCT02582138 ), we investigated whether the efficacy of a multicomponent intervention (physical activity with technological support and nutritional counseling) differed across multimorbidity patterns. We included 1,199 community-dwelling adults aged ≥70 years with frailty, sarcopenia and two or more chronic conditions. Latent class analysis identified four multimorbidity patterns: unspecific (55%), psychiatric (24%), cardiometabolic (13%) and respiratory (7%). The intervention reduced the overall risk of mobility disability or death (hazard ratio (HR) 0.78, 95% confidence interval (CI) 0.66–0.92). In subgroup analyses, a significant benefit was observed in participants with a psychiatric multimorbidity pattern (HR 0.65, 95% CI 0.47–0.92). Bayesian partial pooling analyses also supported a beneficial effect in the unspecific pattern (HR 0.80, 95% credible interval 0.69–0.93), while estimates for the cardiometabolic and respiratory patterns were imprecise and not statistically significant. The intervention increased the event-free probability at 24 and 36 months and extended the time free from mobility disability or death by 2.09 months overall. Gains were 3.09 months in the psychiatric pattern and 1.99 months in the unspecific pattern, with no meaningful differences observed in the cardiometabolic and respiratory patterns. These findings suggest that the composition of multimorbidity may influence responsiveness to lifestyle interventions and that stratification based on disease patterns could improve the targeting and effectiveness of preventive strategies in older adults.

Background: Gait speed (GS) has been suggested as a predictor of incident dementia in old age. However, the mechanisms underlying this body–mind connection remain unclear, and it is still unknown whether trajectories of GS decline differ according to levels of blood biomarkers related to Alzheimer's disease ( AD ). This study aims to investigate the association between levels of seven blood biomarkers related to AD and long‐term GS changes in dementia‐free older adults living in the community. Methods: The present study included 1665 community‐dwelling adults ≥ 60 years, followed for 15 years, drawn from the Swedish National study on Aging and Care in Kungsholmen (SNAC‐K). GS (m·s ⁻¹ ) was assessed at baseline and at five subsequent follow‐up time points. Blood biomarkers, including serum amyloid‐ β 42 to amyloid‐ β 40 ratio (A β 42/40), phosphorylated Tau181 ( p ‐Tau181) and Tau217 ( p ‐Tau217), total Tau ( t ‐Tau), neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP), were collected from peripheral venous blood samples at baseline. Linear mixed‐effects models were implemented to investigate the association between blood biomarkers and GS changes over time. Results: After adjusting for potential confounders, participants in the highest quartile of p ‐Tau181 (Model I; 4th quartile β : −0.008, 95% CI: −0.013; −0.003), p ‐Tau217 (Model I; 4th quartile β : −0.010, 95% CI: −0.015; −0.005), NfL (Model I; 4th quartile β : −0.013, 95% CI: −0.019; −0.006) and GFAP (Model I; 4th quartile β : −0.007, 95% CI: −0.013; −0.002) exhibited a steeper decline in GS over time, as compared with those in the lowest quartile. Notably, participants in these highest quartiles developed mobility limitations (GS < 0.8 m·s ⁻¹ ), on average, 2.3 to 6.0 years earlier. Of note, only the association between NfL and GS changes over time persisted after further adjusting for time‐varying global cognition. Conclusions: Higher blood biomarker levels of AD are associated with accelerated trajectories of GS decline and earlier onset of mobility limitations. Consequently, a faster decline in GS may reflect underlying neurodegeneration and indicate changes in brain health.

Multimorbidity in older adults is common, heterogeneous, and highly dynamic, and it is strongly associated with disability and increased healthcare utilization. However, existing approaches to studying multimorbidity trajectories are largely descriptive or rely on discrete-time models, which struggle to handle irregular observation intervals and right-censoring. We developed a continuous-time hidden multistate modeling framework to capture transitions among latent multimorbidity patterns while accounting for interval censoring and misclassification. A simulation study compared alternative model specifications under varying sample sizes and follow-up schemes, and the best-performing specification was applied to longitudinal data from the Swedish National study on Aging and Care–Kungsholmen (SNAC-K), including 2716 multimorbid participants followed for up to 18 years. Simulation results showed that hidden multistate models substantially reduced bias in transition hazard estimates compared to non-hidden models, with fully time-inhomogeneous models outperforming piecewise approximations. Application to SNAC-K confirmed the feasibility and practical utility of this framework, enabling identification of risk factors for accelerated progression toward complex multimorbidity and revealing a gradient of mortality risk across patterns. Continuous-time hidden multistate models provide a robust alternative to traditional approaches, supporting individualized predictions and informing targeted interventions and secondary prevention strategies for multimorbidity in aging populations.

PURPOSE: Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS: We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS: Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION: Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Multimorbidity in older adults is common, heterogeneous, and highly dynamic, and it is strongly associated with disability and increased healthcare utilization. However, existing approaches to studying multimorbidity trajectories are largely descriptive or rely on discrete-time models, which struggle to handle irregular observation intervals and right-censoring. We developed a continuous-time hidden multistate modeling framework to capture transitions among latent multimorbidity patterns while accounting for interval censoring and misclassification. A simulation study compared alternative model specifications under varying sample sizes and follow-up schemes, and the best-performing specification was applied to longitudinal data from the Swedish National study on Aging and Care-Kungsholmen (SNAC-K), including 2,716 multimorbid participants followed for up to 18 years. Simulation results showed that hidden multistate models substantially reduced bias in transition hazard estimates compared to non-hidden models, with fully time-inhomogeneous models outperforming piecewise approximations. Application to SNAC-K confirmed the feasibility and practical utility of this framework, enabling identification of risk factors for accelerated progression toward complex multimorbidity and revealing a gradient of mortality risk across patterns. Continuous-time hidden multistate models provide a robust alternative to traditional approaches, supporting individualized predictions and informing targeted interventions and secondary prevention strategies for multimorbidity in aging populations.

Blood biomarkers of Alzheimer’s disease (AD) are promising for dementia prediction, but their association with progression across intermediate stages of cognitive decline in the general population remains unclear. We followed 2148 dementia-free individuals from a Swedish population-based cohort for up to 16 years. Associations between baseline AD blood biomarkers and transitions between normal cognition, mild cognitive impairment (MCI), and dementia were examined. Lower amyloid-β42/40 ratio and higher phosphorylated-tau181 (p-tau181), p-tau217, total-tau, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were associated with faster progression from MCI to all-cause and AD dementia, with the strongest associations for NfL and p-tau217. Elevated NfL and GFAP were linked to reduced MCI reversion to normal cognition, whereas no biomarker was associated with MCI development from normal cognition. These findings show robust group-level associations and indicate that AD blood biomarkers may help stratify dementia risk at the MCI stage in the community.

Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long‐term care of subjects affected by chronic illnesses, such as potassium in heart failure patients. Particularly in the presence of comorbidities or pharmacological treatments, sudden crises can cause potassium to undergo very abrupt yet transient changes. In the context of the monitoring of potassium, there is a need for a dynamic model that can be used in clinical practice to assess the risk of death related to an observed patient's potassium trajectory. We considered different landmark survival approaches, starting from the simple approach considering the most recent measurement. We then propose a novel method based on wavelet filtering and landmarking to retrieve the prognostic role of past short‐term potassium shifts. We argue that while taking into account the smooth changes in the biomarker, short‐term changes cannot be overlooked. State‐of‐the‐art dynamic survival models are prone to give more importance to the smooth component of the potassium profiles. However, our findings suggest that it is essential to also take into account recent potassium instability to capture all the relevant prognostic information. The data used comes from over 2000 subjects, with a total of over 80,000 repeated potassium measurements collected through administrative health records. The proposed wavelet landmark method revealed the prognostic role of past short‐term changes in potassium. We also performed a simulation study to assess how and when to apply the proposed wavelet‐mixed landmark model.

PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation being a crucial question. Here, we aimed to develop and validate a decision support system to define the optimal timing of HSCT for patients with MDS on the basis of clinical and genomic information as provided by the Molecular International Prognostic Scoring System (IPSS-M). PATIENTS AND METHODS: We studied a retrospective population of 7,118 patients, stratified into training and validation cohorts. A decision strategy was built to estimate the average survival over an 8-year time horizon (restricted mean survival time [RMST]) for each combination of clinical and genomic covariates and to determine the optimal transplantation policy by comparing different strategies. RESULTS: Under an IPSS-M based policy, patients with either low and moderate-low risk benefited from a delayed transplantation policy, whereas in those belonging to moderately high-, high- and very high-risk categories, immediate transplantation was associated with a prolonged life expectancy (RMST). Modeling decision analysis on IPSS-M versus conventional Revised IPSS (IPSS-R) changed the transplantation policy in a significant proportion of patients (15% of patient candidate to be immediately transplanted under an IPSS-R–based policy would benefit from a delayed strategy by IPSS-M, whereas 19% of candidates to delayed transplantation by IPSS-R would benefit from immediate HSCT by IPSS-M), resulting in a significant gain-in-life expectancy under an IPSS-M–based policy ( P = .001). CONCLUSION: These results provide evidence for the clinical relevance of including genomic features into the transplantation decision making process, allowing personalizing the hazards and effectiveness of HSCT in patients with MDS.

PURPOSE: Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner. METHODS: Retrospective real-world data are leveraged to emulate a target trial for treatment timing using multistate modeling and microsimulation. This case study focuses on myelodysplastic syndromes, serving as a prototype for rare cancers characterized by a heterogeneous clinical course and complex genomic background. A cohort of 7,118 patients treated according to conventional available treatments/evidence across Europe and United States is analyzed. The primary clinical objective is to determine the ideal timing for HSCT, the only curative option for these patients. RESULTS: This analysis enabled us to identify the most appropriate time frames for HSCT on the basis of each patient's unique profile, defined by a combination relevant patients' characteristics. CONCLUSION: The developed methodology offers a structured framework to address a relevant clinical issue in the field of hematology. It makes several valuable contributions: (1) novel insights into how to develop decision models to identify the most favorable HSCT timing, (2) evidence to inform clinical decisions in a real-world context, and (3) the incorporation of complex information into decision making. This framework can be applied to provide medical insights for clinical issues that cannot be adequately addressed through randomized clinical trials.