Stanley Durrleman

The prevalence of Parkinson disease (PD), multiple sclerosis (MS), and motor neuron diseases (MNDs) is rising globally. However, it is unclear to what degree this is related to an increase in incidence or to improved survival after diagnosis. We performed 2 nationwide, population-based, retrospective cohort studies, including all individuals living in Sweden between 2001 and 2016 and living in France between 2009 and 2022, respectively. Pooled mixed-effects regression models, with country as a random effect, were used to determine temporal trends in prevalence, crude and age-standardized and sex-standardized incidence, and age and life expectancy at diagnosis. Annualized prevalence of PD, MS, and MNDs increased significantly between 2003 and 2022 in the pooled model (PD: prevalence ratio [PR] per year = 1.014, p < 0.001; MS: PR = 1.029, p < 0.001; MND: PR = 1.028, p < 0.001). While the crude incidence of both PD and MS remained nearly stable over time (PD: incidence rate ratio [IRR] per year = 0.998, p < 0.001; MS: IRR = 0.992, p < 0.001), the standardized incidence showed a more marked decrease for PD (IRR = 0.986, p < 0.001) while remaining almost unchanged for MS (IRR = 0.995, p < 0.001). For MNDs, both the crude and standardized incidence increased over time (crude IRR = 1.018, p < 0.001; standardized IRR = 1.008, p < 0.001). Life expectancy at diagnosis of PD increased between 2003 and 2013 (+0.95 months per calendar year, p < 0.001) and then decreased between 2013 and 2022 (−1.20 months, p = 0.002), while it increased significantly over the entire study period for MS (+2.35 months, p < 0.001) and MNDs (+0.34 months, p = 0.01). These findings indicate that the rising MS prevalence is largely survival-driven and the rising MND prevalence reflects a true increase in incidence, whereas PD prevalence grows modestly, largely independent of incidence. Depending on the mechanism that drives prevalence, whether increased incidence reflecting changing risk factor exposures, improved survival due to therapeutic advances, or demographic aging of the population, inferences about underlying causes differ substantially between PD, MS, and MNDs, with direct implications for health care planning and etiologic research.

Prediction-powered inference (PPI) and its subsequent development called PPI++ provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates. The proposed method would empower clinical trials by providing untreated digital twins of the treated patients while remaining statistically valid. The potential implications of this new estimator of the treatment effect in a two-arm randomized clinical trial (RCT) are manifold. First, it leads to an overall reduction of the sample size required to reach the same power as a standard RCT. Secondly, it advocates for an imbalance of controls and treated patients, requiring fewer controls to achieve the same power. Finally, this technique directly transfers any disease prediction model trained on large cohorts to practical and scientifically valid use. In this paper, we demonstrate the theoretical properties of this estimator and illustrate them through simulations. We show that it is asymptotically unbiased for the Average Treatment Effect and derive an explicit formula for its variance. An application to an Alzheimer's disease clinical trial showcases the potential to reduce the sample size.

A multiclass Neuroharmony model was developed and evaluated against other approaches for harmonizing volumetric data in a clinical setting using a large, multicenter brain MRI dataset of individuals undergoing neuroradiologic assessment of Alzheimer disease.

Background: Previous studies reported on the existence of atrophy-based Alzheimer’s disease (AD) subtypes that associate with distinct clinical symptoms. However, the consistency of AD atrophy subtypes across approaches remains uncertain. This large-scale study aims to assess subtype concordance in individuals using two methods of data-driven subtyping. Methods: We included n = 10,011 patients across the clinical spectrum from 10 AD cohorts across Europe, regional volumes using Freesurfer v7.1.1. To characterise atrophy heterogeneity in the AD continuum, we introduced a hybrid two-step approach called Snowphlake (Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs) to identify subtypes and sequence of atrophy-events within each subtype. We compared our results with SuStaIn (Subtype and Stage Inference) which jointly estimates both, and was trained and validated similarly. The training dataset included Aβ+ participants ( n = 1,195), and a control group of Aβ-cognitively unimpaired participants ( n = 1,692). We validated model staging within each subtype, in a held-out clinical-validation dataset ( n = 6,362) comprising patients across the clinical spectrum irrespective of Aβ biomarker status and an independent external dataset ( n = 762). Furthermore, we validated the clinical significance of the detected subtypes, in a subset of Aβ+ validation datasets with n = 1,796 in the held-out sample and n = 159 in the external dataset. Lastly, we performed concordance analysis to assess the consistency between the methods. Results: In the AD dementia (AD-D) training data, Snowphlake identified four subtypes: diffuse cortical atrophy (21.1%, age 67.5 ± 9.3), parieto-temporal atrophy (19.8%, age 60.9 ± 7.9), frontal atrophy (24.8%, age 67.6 ± 8.8) and subcortical atrophy (25.1%, 68.3 ± 8.2). The subtypes assigned in Aβ+ validation datasets were associated with alterations in specific cognitive domains (Cohen’s f: [0.15 - 0.33]), while staging correlated with Mini-Mental State Examination (MMSE) scores (R: [-0.51 to - 0.28]) in the validation datasets. SuStaIn also identified four subtypes: typical (55.7%, age 66.7 ± 7.8), limbic-predominant (24.2%, age 72.2 ± 6.6), hippocampal-sparing (14.6%, age 62.8 ± 6.9), and subcortical (0.8%, age 68.2 ± 7.6). The subtypes assigned in Aβ+ validation datasets using SuStaIn were also associated with alterations in specific cognitive domains (Cohen’s f : [0.17 - 0.34]), while staging correlated with MMSE scores in the validation datasets (R: [-0.54 to - 0.26]). However, we observed low concordance between Snowphlake and SuStaIn, with 39.7% of AD-D patients consistently grouped in concordant subtypes by both the methods. Conclusion: In this multi-cohort study, both Snowphlake and SuStaIn identified four subtypes that were associated with different symptom profiles and atrophy-severity measures that were associated with global cognition. The low concordance between Snowphlake and SuStaIn suggests that heterogeneity may rather be a spectrum than discretised by subtypes.

Background: Structural MRI plays a pivotal role in the radiological workup for assessing neurodegeneration. Scanner-related differences hinder quantitative neuroradiological assessment of Alzheimer’s disease (QNAD). This study aims to train a machine-learning model to harmonize brain volumetric data of patients not encountered during model training. Method: Neuroharmony is a recently developed method that uses image quality metrics (IQM) as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between AD-pathology and IQM during harmonization, we developed a multi-class extension of Neuroharmony. We performed cross-validation experiments to benchmark performance against existing approaches using data from 20,864 participants comprising cognitively unimpaired (CU) and impaired (CI) individuals, spanning 11 cohorts and 43 scanners. Evaluation metrics assessed ability to remove scanner-related variations in brain volumes (biomarker concordance), while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results: For each strategy, biomarker concordances between scanners were significantly better (p < 10 ⁻⁶ ) compared to pre-harmonized data. The proposed multi-class model achieved significantly higher concordance than the Neuroharmony model trained on CU individuals (CI: p < 10 ⁻⁶ , CU: p = 0.02) and preserved disease-related signal better than the Neuroharmony model trained on all individuals without our proposed extension (ΔAUC= −0.09). The biomarker concordance was better in scanners seen during training (concordance > 97%) than unseen (concordance < 79%), independent of cognitive status. Conclusion: In a large-scale multi-center dataset, our proposed multi-class Neuroharmony model outperformed other strategies available for harmonizing brain-volumetric data in a clinical setting. This paves the way for enabling QNAD in the future.

Objective: Previous studies have reported a possible prodrome in multiple sclerosis (MS) defined by non-specific symptoms including mood disorder or genito-urinary symptoms and increased health care use detected several years before diagnosis. This study aimed to evaluate agnostically the associations between diseases and symptoms diagnosed in primary care and the risk of multiple sclerosis (MS) relative to controls and two other autoimmune inflammatory diseases with similar population characteristics, namely lupus and Crohn’s disease. Methods: A case-control study was conducted using electronic health records from the Health Improvement Network database in the UK and France. We agnostically assessed the associations between 113 diseases and symptoms in the five years before and after diagnosis in patients with subsequent diagnosis of MS. Individuals with a diagnosis of MS were compared to individuals without MS, and individuals with two other auto-immune diseases, Crohn’s disease and lupus. Results: The study population consisted of patients with MS (n= 20,174), patients without MS (n=54,790), patients with Crohn’s disease (n=30,477) or patients with lupus (n=7,337). Twelve ICD-10 codes were significantly positively associated with the risk of MS compared to controls without MS. After considering ICD-10 codes suggestive of neurological symptoms as the first diagnosis of MS, five ICD-10 codes remained significantly associated with MS: depression (UK OR 1.22 [95%CI 1.11-1.34]), sexual dysfunction (1.47 [1.11-1.95]), constipation (1.5 [1.27-1.78]), cystitis (1.21 [1.05-1.39]), and urinary tract infections of unspecified site (1.38 [1.18-1.61]). However, none of these conditions was selectively associated with MS in comparisons with both lupus and Crohn’s disease. All five ICD-10 codes identified were still associated with MS during the five years after diagnosis. Conclusion: We identified 5 health conditions associated with subsequent MS diagnosis, which may be considered not only prodromal but also early-stage symptoms. However, these health conditions overlap with prodrome of two other autoimmune diseases, hence they lack specificity to MS.

Background: Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers.Objective: To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD).Methods: Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow‐up duration = 5.1; 95% confidence interval, 1.1–8.1] years). Disease course map was estimated by using eight clinical markers (motor and non‐motor symptoms) and four imaging markers (dopaminergic denervation).Results: PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep—all before diagnosis—and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non‐motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non‐motor symptoms and later changes in motor symptoms.Conclusions: Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.

Objective: The purpose of this study was to simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies (DLB), Parkinson's disease (PD), and Alzheimer's disease (AD) compared with controls without neurodegenerative disorders.Methods: Using the longitudinal THIN database in the United Kingdom, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease‐specific effects. We replicated the main findings in the UK Biobank.Results: We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB, and 20,214 healthy controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis, including sleep disorders, falls, psychiatric symptoms, and autonomic dysfunctions. When comparing patients with DLB with patients with PD and patients with AD patients, falls, psychiatric symptoms, and autonomic dysfunction were all more strongly associated with DLB in the 5 years preceding the first neurodegenerative diagnosis. The use of statins was lower in patients who developed PD and higher in patients who developed DLB compared to patients with AD. In patients with PD, the use of statins was associated with the development of dementia in the 5 years following PD diagnosis.Interpretation: Prediagnostic presentations of falls, psychiatric symptoms, and autonomic dysfunctions were more strongly associated with DLB than PD and AD. This study also suggests that although several associations with medications are similar in neurodegenerative disorders, statin usage is negatively associated with PD but positively with DLB and AD as well as development of dementia in PD. ANN NEUROL 2023;94:259–270