WSO - January 2024 - 51

Cai et al.
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Introduction
Frailty refers to a state of increased vulnerability to stressors
due to decreased physiological reserves across multiple
systems and thus leads to adverse clinical outcomes, including
hospitalizations, complications, disability, shorter survival
time, poorer quality of life, and other health problems.1
In recent years, the role of frailty in stroke has received
much attention. A recent meta-analysis with 48,009 participants
reported the prevalence of frailty in stroke patients as
22%. The prevalence of frailty was twofold in patients with
stroke compared to those without stroke.2 Frailty might
indicate dynamic imbalance and impaired physiological
resilience to stroke events, resulting in the deterioration of
the functional ability of frail persons rapidly, increasing the
risk of poor outcomes. Notably, observational studies have
suggested that frailty may affect stroke disease trajectory
and prognosis.3-7 However, the causal link has not been
fully established.
Mendelian randomization (MR) technique applies
genetic variants as instruments to make a causal inference.
This analytical approach leverages the random assortment
of genetic variants at conception to minimize bias caused
by confounding factors and reverse causation. Recently,
two MR studies provide evidence of a causal relationship
between frailty and increased risk of any stroke,8,9 while no
causal link between frailty and risk of ischemic stroke was
detected.9 However, both previous MR studies focused on
stroke risk. The causal role of frailty in poststroke outcomes
is still largely unknown. Considering the predicted rise in
the prevalence of frailty associated with changing demographics
over the coming decades, establishing understanding
of how frailty influences neurological recovery after
stroke has substantial implications, as it may be possible to
attenuate or reverse frailty trajectories by multifaceted
intervention programs to protect against unfavorable prognosis
after stroke.10 Here, we designed a two-sample MR to
explore whether genetic liability to frailty may be related to
poor outcomes at 3 months after ischemic stroke.
Materials and methods
All summary-level genomic data adopted for this work are
publicly available. All data sources were approved by relevant
institutional review boards from original studies, and
all participants were given informed consent. This study
was reported according to the STROBE-MR statement.11
Date source and single-nucleotide
polymorphism selection
The data sources employed in the present study are detailed
in Supplementary Table 1. We selected associated singlenucleotide
polymorphisms (SNPs) for frailty, measured by
frailty index (FI), in the large genome-wide association
study (GWAS) meta-analysis in the UK Biobank and
Swedish TwinGene, which included 175,226 individuals of
European descent.12 FI, which is widely accepted as one
operationalization of frailty,13 was derived based on the
accumulation of a number of health deficits during the life
course using self-reported questionnaire. The 49 or 44 selfreported
components including physiological and mental
health variables were used to calculate the FIs for UK
Biobank and Swedish TwinGene, respectively. Both FIs are
constructed using the Rockwood deficit accumulation
model according to the standard procedure14 and have been
validated elsewhere.15,16 Details of the phenotype measure
are described in Supplementary Tables 2 and 3.
Comparability of the two sets of items has been previously
validated.12 Specifically, 29 of the 49 items used in UK
Biobank have approximate counterparts in TwinGene
(Supplementary Table 4). Besides, the UK Biobank's subset
of 29 items correlate well with the full set of 49 items
(r2 = 0.85, p < 0.001).12
We selected independent SNPs (r2 < 0.001) that
achieved genome-wide significance (p < 5 × 10−8) upon
adjustment for age, gender, and 10 principal components
as instrumental variables. For each index SNP, we calculated
F statistic and R2 value, which represented the
strength and variance explained by an individual genetic
instrument, respectively.
Outcome data sources
Participants were of European ancestry only to minimize
potential bias from population stratification, except for the
body mass index (BMI) dataset with a small portion of
people of non-European ancestry. The GISCOME (Genetic
of Ischemic Stroke Functional Outcome) GWAS metaanalysis
was used to obtain genetic association estimates
for functional outcome after ischemic stroke (6021
patients).17 The GISCOME network included 12 studies
from the United States, Europe, and Australia. Functional
outcome was measured by modified Rankin Scale (mRS)
evaluated as close as possible to 90 days poststroke. In most
studies, mRS assessment was done by trained assessors at
face-to-face or telephone follow-up. An mRS of 0-2
denoted good functional outcome (3741 patients), while
mRS of 3-6 indicated poor functional outcome (2280
patients). The primary model was adjusted for age, sex, five
principal components, and baseline stroke severity assessed
by the National Institutes of Health Stroke Scale (NIHSS)
at 0-10 days after stroke onset. Additionally, model without
adjustment for baseline NIHSS was considered for sensitivity
analysis.
We carefully reviewed the original cohorts in the exposure
and outcome dataset, and found that sample overlap
was negligible. When index SNPs for the exposure were
not available in the outcome dataset, we replaced them
with proxy SNPs (r2 > 0.8) defined using 1000 genomes
International Journal of Stroke, 19(1)
WSO - January 2024

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