Abstract TP351: Causal Effects of HDL and LDL Cholesterol on Post-Stroke Recovery

Background: It is well-established that elevated low-density lipoprotein cholesterol (LDL-C) levels increase stroke risk, while elevated high-density lipoprotein cholesterol (HDL-C) levels are protective. While treatment of elevated LDL-C levels is a standard of care for secondary stroke prevention, it is unknown if elevated LDL-C and HDL-C levels impact recovery after ischemic stroke. Methods: We conducted a two-sample Mendelian Randomization (MR) study to assess the causal effects of genetically determined LDL-C and HDL-C on post-stroke recovery, as measured by longitudinal NIH Stroke Scale (NIHSS) scores. We utilized the trans-ancestral summary statistics from the Global Lipids Genetic Consortium (GLGC) (n=1,654,960) for instrument selection, the Vitamin Intervention for Stroke Prevention trial (n=1,200) for outcome associations, and the Stress, Stroke Rehabilitation, Genetics study (n=565) for replication. The LDL-C and HDL-C summary statistics were pruned (p < 5 × 10 -30 ; r 2 = 0.01) using the 1K Genomes reference panel. Causal effect was modeled utilizing inverse-variance-weighted (IVW), simple median, and Iterative Mendelian Randomization and Pleiotropy (IMRP) models. MR-Egger models assessed residual pleiotropic effects. Results: A total of 459 genetic variants for HDL-C and 464 for LDL-C were included. For HDL-C, the IVW showed a significant inverse causal effect of genetically predicted HDL-C on post-stroke recovery (β = -0.138, 95% CI: -0.012, -0.264, p = 0.032) with replication (β = -0.438, 95% CI: -0.640, -0.236, p < 0.001). Results from simple median and IMRP models were consistent, and MR-Egger intercepts (p = 0.966; p = 0.704) suggested no residual pleiotropy. In contrast, IVW analysis of LDL-C showed no evidence of a causal effect (β: -0.103; 95% CI: -0.224, 0.018; p = 0.097), which replicated (β = -0.066, 95% CI: -0.408, 0.276, p = 0.705). The simple median and IMRP models had consistent results. MR-Egger intercept (β: -0.011; p = 0.014) suggested the presence of residual pleiotropy Conclusion: Our findings indicate that genetically determined HDL-C levels improve post-stroke recovery, suggesting that available circulating HDL-C levels may enhance neuroplasticity independent of the benefits of diet and exercise. In contrast, we found LDL-C levels lack an effect on post-stroke recovery. These results highlight HDL-C as a candidate for further investigation, while supporting current practices around LDL-C management.

Authors

Institutions

Publication Details

Journal
Stroke
Published
2026-01-29
DOI
https://doi.org/10.1161/str.57.suppl_1.tp351
Primary Topic
Cardiovascular Health and Risk Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract TP351: Causal Effects of HDL and LDL Cholesterol on Post-Stroke Recovery

Chad Aldridge, Chakkarai Sathyaseelan, Alison Holman, Steven Cramer et al.
Stroke
Cardiovascular Health and Risk Factors
article

Abstract TP351: Causal Effects of HDL and LDL Cholesterol on Post-Stroke Recovery

Chad Aldridge, Chakkarai Sathyaseelan, Alison Holman, Steven Cramer, Eric Mao, Bradford Worrall, Muralidharan Sargurupremraj
article en

Abstract

Background: It is well-established that elevated low-density lipoprotein cholesterol (LDL-C) levels increase stroke risk, while elevated high-density lipoprotein cholesterol (HDL-C) levels are protective. While treatment of elevated LDL-C levels is a standard of care for secondary stroke prevention, it is unknown if elevated LDL-C and HDL-C levels impact recovery after ischemic stroke. Methods: We conducted a two-sample Mendelian Randomization (MR) study to assess the causal effects of genetically determined LDL-C and HDL-C on post-stroke recovery, as measured by longitudinal NIH Stroke Scale (NIHSS) scores. We utilized the trans-ancestral summary statistics from the Global Lipids Genetic Consortium (GLGC) (n=1,654,960) for instrument selection, the Vitamin Intervention for Stroke Prevention trial (n=1,200) for outcome associations, and the Stress, Stroke Rehabilitation, Genetics study (n=565) for replication. The LDL-C and HDL-C summary statistics were pruned (p < 5 × 10 -30 ; r 2 = 0.01) using the 1K Genomes reference panel. Causal effect was modeled utilizing inverse-variance-weighted (IVW), simple median, and Iterative Mendelian Randomization and Pleiotropy (IMRP) models. MR-Egger models assessed residual pleiotropic effects. Results: A total of 459 genetic variants for HDL-C and 464 for LDL-C were included. For HDL-C, the IVW showed a significant inverse causal effect of genetically predicted HDL-C on post-stroke recovery (β = -0.138, 95% CI: -0.012, -0.264, p = 0.032) with replication (β = -0.438, 95% CI: -0.640, -0.236, p < 0.001). Results from simple median and IMRP models were consistent, and MR-Egger intercepts (p = 0.966; p = 0.704) suggested no residual pleiotropy. In contrast, IVW analysis of LDL-C showed no evidence of a causal effect (β: -0.103; 95% CI: -0.224, 0.018; p = 0.097), which replicated (β = -0.066, 95% CI: -0.408, 0.276, p = 0.705). The simple median and IMRP models had consistent results. MR-Egger intercept (β: -0.011; p = 0.014) suggested the presence of residual pleiotropy Conclusion: Our findings indicate that genetically determined HDL-C levels improve post-stroke recovery, suggesting that available circulating HDL-C levels may enhance neuroplasticity independent of the benefits of diet and exercise. In contrast, we found LDL-C levels lack an effect on post-stroke recovery. These results highlight HDL-C as a candidate for further investigation, while supporting current practices around LDL-C management.

StrokeVol. 57(Suppl_1)
The University of Texas at San Antonio Health Science Center (US), University of California, Irvine (US), UCLA Health (US), University of Virginia (US)
Good health and well-being
Openalex Percentile: Top 75%
Cardiovascular Health and Risk Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.