Figure 4 - uploaded by Stéphane Cullati
Content may be subject to copyright.
Source publication
Life course epidemiology aims to study the effect of exposures on health outcomes across the life course from a social, behavioural, and biological perspective. In this Review, we describe how life course epidemiology changes the way the causes of chronic diseases are understood, with the example of hypertension, breast cancer, and dementia, and ho...
Citations
... Our observations align with, and extend, the concept of the MS continuum [28]. Findings also position MS within the life-course model of disease, providing a useful framework and approach to disease management and prevention of MS [15,29]. Drawing on life-course principles combined with recognized risk factors for MS [29], an early sensitive period for exposure could start at conception, with factors, such as maternal diabetes [30], suboptimal sunlight or low serum vitamin D [31], smoking [32], higher body size/obesity, and adversity (e.g., physical or psychological trauma and social hardships) [33][34][35], increasing susceptibility through into early adulthood. ...
... Findings also position MS within the life-course model of disease, providing a useful framework and approach to disease management and prevention of MS [15,29]. Drawing on life-course principles combined with recognized risk factors for MS [29], an early sensitive period for exposure could start at conception, with factors, such as maternal diabetes [30], suboptimal sunlight or low serum vitamin D [31], smoking [32], higher body size/obesity, and adversity (e.g., physical or psychological trauma and social hardships) [33][34][35], increasing susceptibility through into early adulthood. In turn, several of these exposures increase risk for morbidities, such as psychiatric illness [36], which could contribute to some of the early rises in mental health-related healthcare observed in our study. ...
Objective
Phenotype hospital, physician, and emergency department (ED) visits by diagnoses and specialty up to 29 years pre‐multiple sclerosis (MS) onset versus a matched population without MS.
Methods
We identified people with MS (PwMS) using population‐based administrative data from Ontario, Canada (1991–2020). The first MS/demyelinating diagnostic code defined MS onset (the index date). Annual rates of healthcare use (hospital, physician, ED) by primary diagnosis (chapter‐level) and physician specialty pre‐index were compared between PwMS and up to 5 matched population comparators using overdispersed‐Poisson regression.
Results
Up to 35,018 PwMS and 136,007 population comparators were included. Consistently elevated yearly physician visit rate ratios (RRs) were observed 28 years pre‐index for: mental‐health (RR > 1.29) and ill‐defined signs/symptoms (RR > 1.15), 24 years for: nervous (RR > 1.47), musculoskeletal (RR > 1.21), injury, and respiratory‐related issues (RR > 1.07), and 22 years for digestive‐system (RR > 1.18). The magnitude increased as the index date approached, peaking the year pre‐index for physician, hospital, and ED visit RRs for: nervous‐system (range: 12.06–17.13); ill‐defined signs/symptoms (range: 3.51–5.45), mental‐health (range: 2.13–2.70), musculoskeletal (range: 1.84–2.96), injury (range: 1.58–2.27), digestive‐system (range: 1.49–1.78) and respiratory‐system (range: 1.37–2.06). By specialty, yearly visit RRs for primary care were > 1.08 for 28 years pre‐index, internal medicine exceeded 1.19 for 25 years, and psychiatry and neurology > 1.52 for 24 years pre‐index.
Interpretation
Higher healthcare use was evident for over two decades before the first demyelinating event. Mental‐related, ill‐defined signs/symptoms and primary care visits were consistently elevated the longest (28 years pre‐index), followed by nervous‐system, musculoskeletal, injury, respiratory‐related, and digestive‐system (22–24 years pre‐index). Health‐related phenotypical differences appear early in the MS disease process.
... The pathway model emphasises the sequential ordering of exposures, and the social mobility model focuses on the trajectories of exposures between different life course periods (10,11). To date, no review has considered a life course perspective on this topic. ...
... Further, only one of these studies provides results supporting the accumulation model, finding that higher cumulative SEP was protectively associated with fall occurrences (29). Overall, these results indicate a substantial knowledge gap regarding the impact of life course SEP on falls, which is concerning given the important knowledge that life course research provides to inform public health policies and interventions (10). ...
... The copyright holder for this preprint this version posted May 13, 2025. ; https://doi.org/10.1101/2025.05.13.25327493 doi: medRxiv preprint the life course, which can inform the timing and targeting of interventions, including the life course periods where fall prevention efforts can be most effective (10). Additionally, studies should also consider the role of intersectionality by examining how social factors such as sex, gender and race/ethnicity may intersect with SEP to shape fall inequalities (157). ...
Background:
Falls among middle- and older-aged adults are a significant public health concern. However, a holistic understanding of how different indicators of socioeconomic position (SEP) are associated with falls is lacking, particularly for SEP across the life course.
Methods:
We systematically searched for observational studies analysing the association between at least one indicator of SEP and one fall outcome. Due to heterogeneity between included studies, results were narratively synthesised.
Results:
After de-duplication, 5,880 search results were screened and 125 studies were included. Only 14 included studies explicitly aimed to study the relationship between SEP and falls, which generally found that higher SEP was associated with lower risks/rates of falls. An additional nine studies also had relevant adjusted models that also largely showed a protective relationship. However, adjusted results were mixed and often lacked statistical significance. The remaining 102 studies only contained unadjusted results of interest, with 50%-100% of results for each SEP indicator showing that low SEP groups experience disproportionately high risks/rates of fall outcomes compared to high SEP groups. Notably, only four studies measured any SEP indicators from a stage of the life course prior to the study period.
Conclusions:
Our findings suggest that falls disproportionately impact low SEP groups and that knowledge gaps exist regarding the relationship between different SEP indicators and falls, particularly for SEP exposures across the life course. Future research on this topic should utilise causal diagrams for appropriate model building and include a wide range of SEP indicators across the life course.
... Therefore, future studies should investigate adolescence and childhood, or even during gestation when fetal programming takes place that lays the ground for downstream reproductive development. It is also possible that exposure to air pollution from gestation to adulthood exerts cumulative effects on ovarian reserve, which cannot be identified by investigating specific time windows (Wagner et al., 2024). ...
... This represents a major limitation in the evidence, as changes in exposure at different life stages may result in varying risks for subsequent health outcomes. 17,18 To address these limitations, we examined the lifelong impacts of SLEs on PPC-MM. We used harmonised data from 24,955 middle-aged and older adults in the US, England, and China, capturing information on their exposure to SLEs in both childhood and adulthood. ...
... For example, stressful life events in adulthood but not in childhood were associated with an increased risk of physical-cognitive and psychological-cognitive multimorbidity. Further research is needed to determine the extent to which upward social mobility or other favourable life changes can mitigate or eliminate the adverse effects of childhood experiences 17,43 and whether, in some cases, childhood adversities might foster resilience, enhancing the ability to cope with challenges in adulthood. 44 To characterise the role of SLEs in each health transition leading to PPC-MM, we further conducted multi-state models to explore the associations between stressful life events across life course and the likelihood of transitions from baseline to different PPC-MM patterns. ...
Background
Stressful life events, such as financial hardship, and death of own child, have been associated with various adverse health outcomes, but their impacts on complex multimorbidities remain unknown. This study examined the association between stressful life events in childhood and adulthood and later-life physical, psychological and cognitive multimorbidities.
Methods
We harmonised and pooled longitudinal data from three nationally representative cohort studies from the Program on Global Ageing, Health and Policy: the US Health and Retirement Study (HRS), the English Longitudinal Study on Ageing (ELSA), and the China Health and Retirement Longitudinal Study (CHARLS), encompassing the years 2011–2020. Participants were middle-aged and older adults free from physical, psychological and cognitive multimorbidities and with information on six stressful life events in childhood and six stressful life events in adulthood. Multimorbidities were measured according to the coexistence of physical, psychological and cognitive conditions. Three lifestyle factors, including physical inactivity, alcohol consumption, and smoking, were treated as potential mediators. We used Cox proportional hazards regression models and multi-state models to estimate the risk of developing or progressing multimorbidities at follow-up in the pooled population and in each study.
Findings
In the 24,955 participants (mean age 63.6 years, standard deviation 10.6), 4284 (17.2%) reported stressful life events in childhood, 6509 (26.1%) in adulthood, and 5364 (21.5%) in both. During a follow-up of 8–9 years, 10,913 (43.7%) participants developed physical, psychological and cognitive multimorbidities. After adjusting for age, sex, study, and education, individuals with both childhood and adulthood stressful life events experienced a 1.71 (vs. none, hazard ratio: 1.71, 95% confidence interval: 1.54–1.90), 1.26 (1.16–1.38), 1.58 (1.22–2.04), and 1.89 (1.69–2.11) times higher risk of physical–psychological multimorbidity, physical–cognitive multimorbidity, psychological–cognitive multimorbidity, and physical–psychological–cognitive multimorbidity respectively. The associations with multimorbidities that included a psychological condition as one component were stronger than those that included only physical or cognitive conditions. Childhood stressful life events were associated with transitions from baseline to physical–psychological and psychological–cognitive multimorbidities, while adulthood and life-course stressful events were associated with all transitions between baseline and multimorbidities (≥2 adulthood events vs. 0 and transition to physical, psychological and cognitive multimorbidity: 1.73, 1.43–2.09). Smoking status, physical inactivity, and alcohol consumption partially mediated the associations, and the strongest mediation effect was observed for alcohol consumption which accounted for 18.2% of the associations between childhood stressful life events and physical–cognitive multimorbidity.
Interpretation
From the studied cohorts middle-aged and older adults with a history of stressful life events in childhood or adulthood were seen to be at increased risk of developing multimorbidities involving psychological, physical and cognitive conditions. These findings emphasise the importance of preventive strategies targeting both social and lifestyle factors throughout the life course.
Funding
10.13039/501100001809Natural Science Foundation of China, 10.13039/501100004835Hundred Talents Program Research Initiation Fund from Zhejiang University, Fundamental Research Funds for the Central Universities.
... Moreover, this association remains consistent across major subgroups and sensitivity analyses, and was not significantly modified by genetic risk and later-life healthy lifestyles. Developments in the life course epidemiology of T2D offer new perspectives for primordial prevention strategies, especially for early-life period [26]. Previous studies have reported that individual early-life risk factors, including low birth weight [19], non-breastfed [6,7], and maternal smoking around birth [8,14], were associated with higher risks of T2D. ...
... Recent studies based on UK Biobank reported that individuals exposed to both maternal smoking and non-breastfed were at higher risks of adultonset T2D [9] and hypertension [29], compared with those exposed to isolated risk factor. Taken together, our findings support the "accumulation of risk" hypothesis in life course epidemiology, which suggests that metabolic system damage increases with the accumulation of various exposures over a lifetime [26]. Thus, targeting multiple T2D-related risk factors early in the life course may represent a more effective intervention strategy to prevent T2D. ...
Background
The combined influence of early life risk factors on the type 2 diabetes (T2D) development is not well-studied, and it is unclear whether these associations can by modified by genetic risk and healthy lifestyles in later life.
Methods
We studied 148,621 participants in the UK Biobank. We calculated early-life risk scores (ERS) by summing the cumulative number of three early-life risk factors: low birth weight, maternal smoking during pregnancy, and non-breastfed as a baby. We estimated polygenic risk scores (PRS) for T2D and calculated participants’ modifiable healthy lifestyle score (MHS) during adulthood.
Results
A total of 7,408 incident T2D were identified. ERS showed a positive dose-response association with T2D risk. Compared with participants with 0 ERS, those with 3 ERS had the highest risk of developing T2D (hazard ratio [HR]: 1.93; 95% confidence interval [CI]: 1.65, 2.26). This association was not modified by T2D-PRS or MHS. In the joint exposure analyses, compared with participants with the lowest risk exposure (i.e., lowest ERS combined with lowest T2D-PRS/healthy lifestyle in later life), we observed highest risk of T2D among individuals with the highest ERS combined with the highest tertile of T2D-PRS (HR = 6.67, 95% CI: 5.43, 8.20) or an unhealthy lifestyle in later life (HR = 4.99, 95% CI: 3.54, 7.02), respectively.
Conclusions
Early-life risk factors are associated with a higher risk of T2D in a dose-response manner, regardless of genetic risk or later-life healthy lifestyle. Therefore, identifying early-life modifiable risk factors is helpful to develop strategies of T2D prevention.
... Addressing health inequities requires a multi-sectoral approach that includes policies for economic empowerment, improved healthcare infrastructure, and targeted social interventions. 34 Reducing disparities in access to nutritious food, safe housing, and quality education can significantly enhance women's longterm health and well-being. ...
Over the years, discourse on women’s health has skewed towards reproductive health, particularly on matters relating to maternal and child health, contraception, and pregnancy-related care. In spite of the relevance of these aspects, such a narrow perspective overlooks the broader spectrum of health concerns that affect women across different life stages, hence the need to refocus women’s health through the lens of the life-course perspective. The life-course perspective is a framework for understanding human development that centers on time and space. From adolescence to old age, women encounter a wide array of health challenges and experiences, including non-communicable diseases (NCDs), mental health disorders, musculoskeletal conditions, and the long-term consequences of early-life exposures. Addressing these issues requires a paradigm shift toward a more comprehensive and inclusive approach that recognizes the lifelong nature of women’s health needs. Rather than treating health issues in isolation, the life-course perspective considers how early-life exposures, social determinants, and lifestyle factors influence health trajectories over the life spectrum. For women, this means recognizing that adolescent health behaviors affect midlife disease risk, menopause has implications for cardiovascular and bone health, and older age brings unique challenges such as frailty and cognitive decline. This model underscores the importance of preventive care, early interventions, and tailored health services at every stage of life. Consequently, this editorial takes a life-course approach to highlight the dominant health and health-related realities of women, segmented into three cardinal phases: emerging adulthood, adulthood, and late adulthood. It concludes by drawing governments and the global community’s attention to the need to focus healthcare systems on universal, gender-sensitive healthcare policies that guarantee accessible, affordable, and high-quality services tailored to women’s needs at every stage of life. Policies and programs that support women at every stage of life must take center stage in the quest to create a future where all women, regardless of age or background, can achieve optimal health and well-being.
... 55 In life course epidemiology, this would be called a sensitive period model, with stronger, more permanent effects being assumed if exposure occurs during, for example, preconception and early life (figure 1). 56 An accumulation model focuses on the accumulated exposure and duration, assuming no differences in risk depending Open access on the timing (age) of exposure. 56 Yet, we cannot assess this appropriately using the currently available Swedish data as the Prescribed Drug Registry was only established in July 2005, and no earlier information is available (PPIs available since 1988). ...
... 56 An accumulation model focuses on the accumulated exposure and duration, assuming no differences in risk depending Open access on the timing (age) of exposure. 56 Yet, we cannot assess this appropriately using the currently available Swedish data as the Prescribed Drug Registry was only established in July 2005, and no earlier information is available (PPIs available since 1988). 56 The other two main life course models (pathway model and social mobility model) do not seem applicable for this research question. ...
... 56 Yet, we cannot assess this appropriately using the currently available Swedish data as the Prescribed Drug Registry was only established in July 2005, and no earlier information is available (PPIs available since 1988). 56 The other two main life course models (pathway model and social mobility model) do not seem applicable for this research question. 56 We applied two graphical approaches to assess age period cohort effects: 55 first, we examined age trends in gastric cancer plotted by year of observation (per 10 years). ...
Objectives
Since proton pump inhibitors (PPI) have been introduced, many concerns were raised regarding potential gastric carcinogenicity. We aim to summarise and weigh the epidemiological evidence and address possible causality.
Design
Systematic literature review, evidence synthesis and life-course assessment.
Data sources
PubMed, Web of Science and Cochrane database (from inception up to October 2024), and back- and forward citation tracking (Web of Science).
Eligibility criteria
Original studies and quantitative evidence syntheses assessing the association between PPIs and gastric cancer in humans, without language restrictions.
Data extraction and synthesis
Study design, definitions (and participant numbers) of PPI use and gastric cancer, study characteristics (setting, period, follow-up, lag-time), age and sex distribution presented in tables and evidence mapping.
Results
We identified 33 original studies, 21 meta-analyses, three umbrella meta-analyses, one individual patient data meta-analysis and a Markov model (2006–2023). PPIs were consistently associated with an increased gastric cancer risk with 20/21 meta-analyses reporting pooled relative risks between 1.3 and 2.9. Available trials were underpowered. Reverse causation/protopathic bias, residual confounding (by indication) and lag time seem the largest methodological challenges, as well as disentangling the effects of Helicobacter pylori and its’ eradication. Insufficient data are available on age and sex-specific risks, with no studies specifically addressing PPIs in young populations. We hypothesise a sensitive-period exposure model, in which PPI use during pregnancy and early life may be particularly damaging regarding long-term cancer risk. An exploration of Swedish cancer incidence data suggests potential cohort effects as overall gastric cancer risk decreased over time (1970–2022). The risk has increased in young (<40 years) men since the early 2000s, ~10 years after the introduction of Helicobacter pylori eradication and PPIs.
Conclusion
Although for older individuals with valid indications, the gastric cancer risk related to PPI use may be limited, we do argue for a more rational and evidence-supported use of PPIs in young populations.
... Understanding the complexity of mechanisms and establishing causality across the life course is crucial to developing preventive and management strategies for chronic diseases [1]. Conceiving life as continuums, causal effects research across lifespan aims to study how physical and social exposures throughout all life stages affect health outcomes in later life, either individually, cumulatively, or interactively [2,3]. ...
... Can findings from a generation born 50 years ago be applied to the current generation? Additionally, measuring exposures across the life course is a major source of bias, often relying on incomplete or poor-quality data [1]. Thus, it is essential to design a reliable, efficient, and generalizable automated system to explore the potential mechanisms and trajectories of a given pair of exposure and adverse outcome in life cycle. ...
... If one article asserts that "A affects B" and another that "B affects C", then "A affects C" is a natural hypothesis. This A-B-C model is quite similar to the pathway model (also known as chain-of-risk model) in life course epidemiology, which focuses on the sequential link between multiple exposures, namely risk A increases the likelihood of risk B and then risk B contributes to outcome C [1]. Bristol University has developed two A-B-C model-based tools, i.e., Text Mining for Mechanism Prioritisation (TeMMPo) and Mining Enriched Literature Objects to Derive Intermediates (MELODI) [12,13], to identify the set of potential intermediate mechanisms. ...
Large language models (LLMs) enhanced with Graph Retrieval-Augmented Generation (GRAG) are promising for life-course epidemiology, which typically depends on costly and incomplete cohort data. Inspired by the epidemiological pathway model, we introduce EpiPathAI, which combines literature-derived causal knowledge graphs with LLMs to mine bridging variables and synthesize potential mechanisms between gestational diabetes and dementia. We test four GRAG strategies on GPT-4 and evaluate the identified mediators with clinical experts and three other LLM reviewers. The knowledge graph identifies 118 bridging variables, including coronary heart disease and chronic kidney disease, previously validated in our data-driven approach through the UK Biobank. EpiPathAI has identified additional clinically meaningful mediators, including high-level low-density lipoprotein (9.8% of effect, 95% CI: 3.7%-23.2%), and depression, which is a reasonable but statistically non-significant mediator in UK Biobank. EpiPathAI serves as a knowledge-driven mechanism mining agent that complements the data-driven approach, providing a compelling foundation for investigating other mediating pathways in future longitudinal cohort studies.
... 3 Consequently, prioritizing prevention and management policies for the four main types of chronic diseases is imperative, necessitating global collaboration and a coordinated multidisciplinary approach. 4 To address this escalating burden, the WHO has recommended a life course strategy for preventing and controlling chronic diseases, 5,6 which encompasses comprehensive management through screening, prevention, treatment, rehabilitation, and long-term follow-up care. 7,8 In light of these challenges, there is a significant opportunity for integrating emerging technologies to improve the quality of care. ...
Precision management of chronic diseases is crucial for improving patient quality of life and alleviating global health burdens. Advancements at the intersection of medicine and engineering, particularly through artificial intelligence (AI), have driven significant progress in precision care. From the perspective of the full life span management of chronic diseases, we focus on medicine-engineering crossover for monitoring chronic diseases, developing and implementing precision care plans, and evaluating care outcomes. Through an in-depth discussion, we address key issues such as AI’s potential to enable precision care and the challenges associated with its implementation, including data accuracy, privacy concerns, and clinical adoption. Emphasizing the importance of nurses embracing new technologies and interdisciplinary collaboration, this paper highlights how technological innovation can improve chronic disease management, particularly by enhancing care efficiency and personalizing health interventions. We aim to support the development of integrated healthcare solutions that improve patient outcomes in chronic disease management.
... These results have several implications. First, findings place MS within the life course model of chronic disease, 14 complementing, but greatly extending the MS disease continuum concept. 15 This has public health implications, indicating the need for a life course approach to prevent MS. ...
Objective
Elevated healthcare use before multiple sclerosis (MS) onset suggests earlier opportunity to identify MS. Yet their timing and sociodemographic effects are unclear. We examined rates of healthcare use (and by age/sex) for >two decades pre‐MS onset.
Methods
We identified people with MS (PwMS) using administrative data from Canada (Ontario) and Sweden (1991–2020) (“administrative” cohort), and the Swedish MS Registry (“clinical” cohort). The first MS/demyelinating diagnostic code (administrative) or symptom onset (clinical) defined MS onset. We compared annual rates of healthcare use (hospital, physician, and emergency‐room [ED]) pre‐onset between PwMS and up to five matched population controls using negative binomial regression, and by age/sex.
Results
The administrative cohort = 35,018/136,007 PwMS/controls (Ontario), and 10,269/51,297 (Sweden). Rates of healthcare use were higher for PwMS than controls up to 28 (of 29) years (Ontario) and up to 15 (of 19) years (Sweden) pre‐onset. Annual healthcare use rose steadily as onset approached, particularly escalating 7 years pre‐onset in Ontario (e.g., hospital visit rate ratios [RRs] exceeded 1.30), and 6 years in Sweden (physician visit RRs > 1.10). RRs peaked the year pre‐onset (ED visits [Ontario] = 3.04; 95% CI: 2.94–3.13, physician visits [Sweden] = 2.51; 95% CI: 2.44–2.59). In the year pre‐onset, RRs were disproportionately higher for males (ED RRs [Ontario] = 3.30; 95% CI: 3.13–3.48 vs. females = 2.90; 95% CI: 2.79–3.02), and dropped steadily by age (physician visit RRs [Sweden] = 2.61/2.27/1.97/1.72 for 50/40/30/20‐year‐olds). The smaller clinical cohort (7604/37,974 PwMS/controls) exhibited similar patterns, albeit more modest, with RRs elevated up to 5 years pre‐onset (physician visit RR [year‐5] = 1.08; 95% CI: 1.02–1.14; RR [year‐1] = 1.39;1.33–1.46).
Interpretation
Higher healthcare use was evident decades before MS onset, escalating 6–7 years pre‐onset, peaking the year before, being disproportionately higher for males and older PwMS.
