Lipids & metabolic liver disease
Associations of cumulative exposure and dynamic trajectories of the combined atherogenic and frailty index with incident cardiometabolic multimorbidity: a longitudinal analysis based on the China Health and Retirement Longitudinal Study (CHARLS)
Main finding · Incident CMM by baseline AIPFI quartileResult — go to the resultAdjusted hazard ratio 2.46 95% CI 1.77–3.43Authors' conclusion
Both baseline and longitudinal changes in the AIPFI were independently associated with the risk of CMM. The incorporation of longitudinal monitoring of the AIPFI into routine health evaluations may enhance population-level CMM risk prediction and support more effective prevention strategies.
Study context
- Population
- Participants of the China Health and Retirement Longitudinal Study (CHARLS)4,483 in longitudinal analysis
- Participants
- 7,995 participants
- Exposure
- Higher vs lower atherogenic index of plasma-frailty index
- Outcome
- Incident cardiometabolic multimorbidity (CMM)
- Follow-up
- Median 9.0 years
Methodological detailslongitudinal analysis, China Health and Retirement Longitudinal Study (CHARLS), K-means clustering of longitudinal trajectories
- Longitudinal analysis
- China Health and Retirement Longitudinal Study (CHARLS)
- K-means clustering of longitudinal trajectories
Main finding
Incident CMM by baseline AIPFI quartile · median 9.0 years
Adjusted hazard ratio
2.46
95% CI 1.77–3.43
As reported in the abstract
- Incident cardiometabolic multimorbidity (CMMs) overall
- 747 (9.3%)
Observational data: associations, not effects.
Further findings
Incident CMM by longitudinal atherogenic index of plasma-frailty index (AIPFI) trajectory (n = 4,483)
| HR (95% CI) | ||
|---|---|---|
| Cluster 2 (persistently high and increasing AIPFI) | 1.54 (1.19–2.01) | |
| 0.512Hazard ratio (log scale) |
Incident CMM by cumulative AIPFI
- HR for elevated cumulative AIPFI
- 1.01 (95% CI 1.01-1.01)
RCS: baseline and cumulative AIPFI vs CMM risk
- Significant positive nonlinear relationship (all P < 0.05, all P for nonlinear < 0.05)
SHAP predictors
- Hypertension, heart disease and AIPFI were the most influential predictors
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
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