Cécile Proust-Lima

BACKGROUND: Alzheimer's disease (AD) shows considerable heterogeneity in cognitive decline. Executive function (EF), a core cognitive domain, may contribute to this heterogeneity and modulate AD progression. METHODS: We analyzed the trajectories of four EF‐related tests in 649 incident cases of AD from the Three‐City cohort, using multivariate latent class mixed modeling. We linked latent EF patterns with lifestyle at study entry, brain magnetic resonance imaging, clinical markers, and prognosis. RESULTS: The three identified EF patterns, Steep EF Decline, Low EF, and High EF, showed differences in lifestyle‐related factors at baseline, white matter hyperintensity burden, hippocampal volume, and clinical progression. Steep EF Decline predicted worse outcomes and higher mortality. High EF indicated slower decline and better prognosis. DISCUSSION: EF trajectories diverge substantially before AD diagnosis, with implications for prognosis. Preserved EF appears protective, while early or steep EF decline correlates with worse evolution. Lifestyle and vascular factors contribute significantly, highlighting potential intervention targets for cognitive resilience in AD.

Background: Others have examined heterogeneity in Alzheimer's disease (AD); however, few have used longitudinal data while accounting for variation in disease stage. We used latent classes to model heterogeneity in the trajectories of three cognitive domains (memory, language, and executive functioning) starting at AD dementia diagnosis. Objective: Our aim was to describe the patterns of heterogeneity in cognitive decline across cognitive domains during the course of AD and to contextualize our findings by assessing associations with demographic factors and neuropathological measures. Methods: We used cognitive data from the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study in a multi-dimensional joint latent class mixed model, which allowed us to estimate cognitive trajectories that varied across cognitive domains and latent classes. We accounted for the uncertainty in latent class assignment and corrected for multiple hypotheses when assessing the association of the latent classes with demographic and neuropathological variables. Results: We identified five latent classes differentiated by level of impairment (high to low) and rate of decline (slow to fast). Within each latent class, the pattern of decline did not differ substantially across cognitive domains. Classes were associated with APOE genotype, sex, race, education, and neuritic plaque and neurofibrillary tangle burden. Conclusions: Our results highlight global differences in the level of cognitive impairment at diagnosis and the rate of decline rather than differences between domains of cognition. Examination of patterns in the global rate of cognitive decline may improve understanding of heterogeneity in AD.

The role of lifestyle and health-related factors in dementia risk has been established. However, how a combination of modifiable risk factors, as reflected by the LIfestyle for BRAin health (LIBRA) index, contributes to cognitive resilience to genetic susceptibility to dementia (CRgen) remains unclear. We selected 6774 Three-City study participants without dementia at baseline (mean age = 74 years) and with ≥2 cognitive measures over time. Genetic risk was defined through ApoE-ε4 carriage alone (ε4-carriers) or combined with high AD-specific genetic risk scores beyond ApoE (ε4-carriers/GRS-high). To define CRgen, we modeled and compared the cognitive slopes of at-risk individuals to their demographically similar peers without genetic risk; at-risk individuals with the most preserved cognition were defined as resilient (ε4-carriers [n = 237]; ε4-carrier/GRS-high [n = 319]), and the least preserved as non-resilient (ε4-carriers [n = 866]; ε4-carrier/GRS-high [n = 1249]). Lower LIBRA risk scores at baseline, denoting healthier lifestyle and reduced dementia risk, were linearly associated with greater odds of CRgen, both in ε4-carriers (odds ratio [OR] = 1.11;95%CI: 1.05, 1.18) and ε4-carriers/GRS-high (OR = 1.16;95%CI: 1.11, 1.22). When examining the LIBRA components, cognitive activity and coronary heart diseases history showed the strongest independent associations with CRgen (all P ≤ 0.01). Genetically susceptible older adults can develop cognitive resilience, which may be promoted by lifestyle modifications and health management simultaneously.

Mediation analysis aims to decipher the underlying causal mechanisms between an exposure, an outcome, and intermediate variables called mediators. Initially developed for fixed-time mediator and outcome, it has been extended to the framework of longitudinal data by discretizing the assessment times of mediator and outcome. Yet, processes in play in longitudinal studies are usually defined in continuous time and measured at irregular and subject-specific visits. This is the case in dementia research when cerebral and cognitive changes measured at planned visits in cohorts are of interest. We thus propose a methodology to estimate the causal mechanisms between a time-fixed exposure ($X$), a mediator process ($\mathcal {M}_t$), and an outcome process ($\mathcal {Y}_t$) both measured repeatedly over time in the presence of a time-dependent confounding process ($\mathcal {L}_t$). We consider 2 types of causal estimands, the natural effects and path-specific effects. We provide identifiability assumptions, and we employ a multivariate mixed model based on differential equations for their estimation. The performances of the method are assessed in simulations, and the method is illustrated in 2 real-world examples motivated by the 3C cerebral aging study to assess (1) the effect of educational level on functional dependency through depressive symptomatology and cognitive functioning and (2) the effect of a genetic factor on cognitive functioning potentially mediated by vascular brain lesions and confounded by neurodegeneration.

Background: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods: Using a coordinated analytic approach in 10 cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of apolipoprotein-E (APOE) ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and nonlinear time specifications for both timescales. Results: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower, and the average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. The current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multicohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.

Epidemiologic studies often evaluate the association between an exposure and an event risk. When time-varying, exposure updates usually occur at discrete visits although changes are in continuous time and survival models require values to be constantly known. Moreover, exposures are likely measured with error, and their observation truncated at the event time. We aimed to quantify in a Cox regression the bias in the association resulting from intermittent measurements of an error-prone exposure. Using simulations under various scenarios, we compared five methods: last observation carried-forward (LOCF), classical two-stage regression-calibration using measurements up to the event (RC) or also after (PE-RC), multiple imputation (MI) and joint modeling of the exposure and the event (JM). The LOCF, and to a lesser extent the classical RC, showed substantial bias in almost all 43 scenarios. The RC bias was avoided when considering post-event information. The MI performed relatively well, as did the JM. Illustrations exploring the association of Body Mass Index and Executive Functioning with dementia risk showed consistent conclusions. Accounting for measurement error and discrete updates is critical when studying time-varying exposures. MI and JM techniques may be applied in this context, while classical RC should be avoided due to the informative truncation.

Analyzing longitudinal data in health studies is challenging due to sparse and error‐prone measurements, strong within‐individual correlation, missing data and various trajectory shapes. While mixed‐effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non‐parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error‐prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well‐suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case‐control study nested in a population‐based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis.

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan—another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA screening domains are participation profiles over time, missing data, and univariate and multivariate descriptions, and longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan that are other elements of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

Health-related quality of life (Hr-QoL) scales provide crucial information on neurodegenerative disease progression, help improving patient care, and constitute a meaningful endpoint for therapeutic research. However, Hr-QoL progression is usually poorly documented, as for multiple system atrophy (MSA), a rare and rapidly progressing alpha-synucleinopathy. This work aimed to describe Hr-QoL progression during the natural course of MSA, explore disparities between patients, and identify informative items using a four-step statistical strategy.We leveraged the data of the French MSA cohort comprising annual assessments with the MSA-QoL questionnaire for more than 500 patients over up to 11 years. The four-step strategy (1) determined the subdimensions of Hr-QoL in MSA; (2) modelled the subdimension trajectories over time, accounting for the risk of death; (3) mapped the sequence of item impairments with disease stages; and (4) identified the most informative items specific to each disease stage.Among the 536 patients included, 50% were women and they were aged on average 65.1 years old at entry. Among them, 63.1% died during the follow-up. Four dimensions were identified. In addition to the original motor, nonmotor, and emotional domains, an oropharyngeal component was highlighted. While the motor and oropharyngeal domains deteriorated rapidly, the nonmotor and emotional aspects were already slightly to moderately impaired at cohort entry and deteriorated slowly over the course of the disease. Impairments were associated with sex, diagnosis subtype, and delay since symptom onset. Except for the emotional domain, each dimension was driven by key identified items.Hr-QoL is a multidimensional concept that deteriorates progressively over the course of MSA and brings essential knowledge for improving patient care. As exemplified with MSA, the thorough description of Hr-QoL using the 4-step original analysis can provide new perspectives on neurodegenerative diseases' management to ultimately deliver better support focused on the patient's perspective.