Title : Frailty and the dynamic progression trajectories of stroke-dementia comorbidity: Insights from multi-state models and genetic analyses
Abstract:
Importance: Stroke and dementia frequently co-occur, with frailty serving as a critical risk factor for both conditions. However, the relationship between frailty and the progression trajectories of stroke-dementia comorbidity has not been definitively established.
Objective: To determine whether there are associations between frailty and the progression trajectories of stroke-dementia comorbidity.
Design, setting, and participants: This study integrated both observational and genetic analyses. First, this prospective, longitudinal cohort study used data from the UK Biobank and CLHLS to test for associations. Second, Mendelian randomization (MR) analyses were conducted by using 14 frailty-related genetic variants to test for genetic associations. The baseline assessment of UK Biobank was between 2006 and 2010 and follow-up until March 31, 2021, for England and Scotland, and February 28, 2018, for Wales. The CLHLS cohort was initially established on January 13, 1998, with seven subsequent follow-up interviews conducted in 2000, 2002, 2005, 2008–09, 2011–12, 2014, and 2017–18. Participants with baseline dementia, stroke, or Parkinson’s disease, those with missing data on key variables, and diagnosed with stroke and dementia on the same day were excluded. The final analysis included 459,924 and 20,653 participants from UKB and CLHLS, respectively. Statistical analysis was performed between March 2024 and July 2024.
Exposures: Frailty was assessed using the frailty index (FI) and categorized as robust (FI ≤ 0.10), prefrail (0.10 < FI ≤ 0.25), or frail (FI > 0.25).
Main outcomes and measures: The outcomes of interest were stroke, dementia, stroke-dementia comorbidity, and mortality. Theses outcomes were ascertained via self-reported data, International Statistical Classification of Diseases, Tenth Revision, codes or official death registries. Multi-state models and MR were used to assess associations between FI categories and stroke-dementia comorbidity.
Results: In the UK Biobank cohort, 13409, 3984, and 996 participants developed incident stroke, dementia, and their comorbidity over a median 12.5-year follow-up, while in the CLHLS cohort, 1670, 253, and 97 participants developed these conditions over a median 4.1-year follow-up. In comparison to robust, frail group significantly elevated the risk of transitioning from enrollment to stroke [HR (95%CI): 2.32 (2.19-2.45) in UK Biobank; 1.36 (1.15-1.60) in CLHLS], from enrollment to dementia [2.56 (2.31-2.83); 1.65 (1.14-2.38)], from enrollment to mortality [2.32 (2.23-2.42); 1.67 (1.58-1.76)], from stroke to stroke-dementia comorbidity [1.59 (1.23-2.05); 3.58 (1.86-6.87)], and from stroke to mortality [1.25 (1.11-1.40); 1.32 (1.06-1.65)] in both cohorts. MR analyses revealed that genetically predicted frailty index was causally associated with higher risks of stroke, dementia, and stroke-dementia comorbidity. Conclusions and relevance: Our findings suggested that frailty played an important role in the dynamic transitions of stroke-dementia comorbidity, offering important insights for the clinical management and public health strategies
Audience Takeaway Notes:
- The audience will learn about the significant association between frailty and the progression of stroke-dementia comorbidity, allowing healthcare professionals to better identify at-risk populations.
- This research will aid clinicians in implementing frailty assessments in routine evaluations, facilitating early intervention strategies for stroke-dementia comorbidity management.
- Other faculty can leverage these findings to enhance their research on geriatric care or cognitive health, and incorporate this information into curricula related to aging, neurology, and public health.
- The study offers actionable insights for designing screening protocols that integrate frailty assessments, potentially streamlining workflows in clinical settings.
- By identifying key risk factors like hypertension and diabetes, this research can inform the design of targeted interventions and care plans, improving patient outcomes.
