AGE IMPACT (AGE related disease among auto-IMmune and inflammatory disease PAtients: an observational CohorT study)

Objectif(s) de la recherche et intérêt pour la santé publique

Finalité de l'étude

Objectifs poursuivis

Domaines médicaux investigués

Bénéfices attendus

Chronic inflammation has been identified as a significant contributor to the development of various age-related diseases, with growing evidence linking it to both neurodegeneration and cardiovascular diseases(1–3). Recent research underscores the pivotal role of inflammatory processes in conditions such as dementia and age-related macular degeneration (AMD)(3–5). Inflammatory signaling pathways are implicated in the degeneration of neurons and retinal tissues, with the overactivation of the complement system emerging as a key factor in tissue damage. This persistent inflammatory state is a hallmark of autoimmune and inflammatory diseases, characterized by low-grade chronic inflammation that impacts multiple organ systems. Patients with autoimmune disorders face an elevated risk of cardiovascular events, a connection well-documented in the medical literature (6), and recently a causal signal for neutrophils and other immune cells were reported for atherosclerotic cardiovascular disease(6) However, the potential impact of autoimmune conditions on the development of dementia and AMD has received relatively little attention. Studies highlighted the involvement of immune cells, including neutrophils, lymphocytes and monocytes, in the pathology of dementia, further emphasize the role of inflammation in neurodegenerative processes(3,7).
Despite these findings, the precise mechanisms linking autoimmune diseases to age-related degenerative conditions remain poorly understood(8–11). While some studies have explored the association between autoimmune diseases and the risk of developing dementia or AMD, the research in this area has primarily focused on cardiovascular implications, leaving a significant gap in our understanding of the broader effects of chronic inflammation. Addressing this gap is crucial, as a more comprehensive evaluation of the inflammatory burden in autoimmune diseases may provide novel insights into the pathogenesis of neurodegenerative disorders.
To bridge this knowledge gap, our study leverages data from the French National Health Data System (SNDS), which offers extensive coverage of the French population over a prolonged period. By analyzing this robust dataset, we aim to determine whether patients with autoimmune diseases exhibit a higher incidence of dementia and AMD compared to the general population. Our approach involves comparing autoimmune patients with a carefully matched control group, accounting for key demographic factors such as age, sex, geographic location, and socioeconomic status. Additionally, we will assess the impact of specific autoimmune diseases and the role of chronic inflammation in mediating these associations.

This is a retrospective observational cohort study using SNDS data from 2005 to 2023.
• Primary Outcome: Incidence of an age-related disease, defined by the diagnosis of dementia (each dementia diagnosis at a time and all forms combined) and/or age-related macular degeneration (AMD).
• Exposure: The presence of an autoimmune disease, identified via ICD-10 codes in the PMSI data.
• Exclusion: Patients with a diagnosis of dementia or AMD prior to the diagnosis of an autoimmune disease will be excluded.
3.2. Study Population
Autoimmune Cohort
The main cohort will include patients diagnosed with at least one of the following conditions (identified using specific ICD-10 codes in the PMSI):
• Ankylosing spondylitis
• Rheumatoid arthritis
• Polymyalgia rheumatica
• Sjögren’s syndrome
• Systemic lupus erythematosus
• Systemic sclerosis
• Vasculitis (Horton’s disease, Takayasu disease, Behçet’s disease, ANCA-associated vasculitis, others vasculitis)
• Inflammatory bowel disease
Patients will be identified from 2005 to 2024, with the date of first diagnosis of the autoimmune disease serving as the index date.
Control Population
A control group free of any autoimmune or inflammatory disease will be drawn from the SNDS. Controls will be selected using a stratified random sampling procedure based on the year of follow-up initiation to match 4 controls per autoimmune patient on the following criteria:
• Year of birth
• Sex
• Department of residence

OBJECTIFS DE LA RECHERCHE :
Primary Objective:
To evaluate whether the presence of an autoimmune disease is associated with risk of developing age-related diseases, specifically:
• The incidence of dementia (including Alzheimer’s disease, vascular dementia, frontotemporal dementia, dementia with Lewy bodies, and other forms)
• The incidence of age-related macular degeneration (AMD)
Secondary Objectives:
• To compare the incidence of these age-related diseases in patients with autoimmune diseases versus a control population extracted from the SNDS, matched on age, sex, and department (4:1 matching).
• To explore, within the autoimmune cohort, the impact of potential confounding factors (comorbidities, treatments, frequency of hospitalizations, etc.) on the occurrence of age-related diseases.

METHODES UTILISEES :
Data will be extracted from the SNDS, which includes:
• PMSI: For identifying hospitalizations and diagnoses related to autoimmune diseases, dementia, and AMD, as comorbidities codes
• DCIR: For medication usage and prescription timelines
• Reference Tables (IR_BEN_R, IR_IMB_R, IR_PHA_R): For demographic information and follow-up quality
The variables to be extracted include:
• Identification of Autoimmune Diseases: Specific ICD-10 codes for each autoimmune condition as detailed in Table 1.
• Primary Outcome Variables: Specific ICD-10 codes for each outcome as detailed in Table 1.
o Dementia – All types
o Alzheimer’s disease
o Vascular dementia
o Unspecified dementia
o Frontotemporal dementia
o Dementia in Parkinson’s disease
o Dementia with Lewy bodies
o Dementia in other diseases
o Parkinson’s disease
o AMD
• Confounding Variables:
o Demographics (age, sex, department, social deprivation index)
o Hospitalization frequency
o Comorbidities (derived from ALD records, treatment deliveries and hospital discharge diagnoses)
o Medication treatments that might influence outcomes (e.g., anti-inflammatory, cardiovascular treatments)
Statistical Analyses
All statistical analyses will be performed using SAS software version 9.4 and R version 4.1.0 or later. Descriptive, univariate, and multivariate analyses will be conducted. All statistical tests will be two-sided, and a p-value < 0.05 will be considered statistically significant.
Descriptive Analyses
The demographic and clinical characteristics of patients will be described by calculating frequencies and percentages for categorical variables, and means, medians, interquartile ranges, and standard deviations for continuous variables.
Comparative Analyses
For qualitative (categorical) variables, Pearson’s chi-square test or Fisher’s exact test will be used. For quantitative (continuous) variables, either Student’s t-test or the Wilcoxon test will be applied. If data are not normally distributed, non-parametric tests will be used. The comparative analyses will focus on:
1. Demographic and Clinical Characteristics
Comparing the demographic and clinical characteristics among patients with and without autoimmune disease
2. Incidence of the Primary and Secondary Outcomes
Comparing the incidence of the primary outcome and secondary outcomes between patients with one of the above autoimmune conditions and a matched control population without autoimmune or inflammatory disease (matched 4:1 on birth year, sex, hospitalization frequency in the two years before follow-up, residential department, and the French Deprivation Index), using hazard ratios via a Cox model Potential confounding variables included in the final model will be selected based on their clinical relevance and possible impact on the occurrence of both the primary and secondary endpoints. Sensitivity analyses will be conducted to account for the competing risk of death in relation to the occurrence of the primary and secondary endpoints, using a Fine-Gray model.