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Updated: Apr 29, 2026
Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Using hematogram model to predict future metabolic syndrome in elderly: a 4-year longitudinal study
Yu-Hsiang Fu1, Chun-Hsien Hsu, Jiunn-Diann Lin
1Department of Internal Medicine .
Insights
Hematogram components can predict future metabolic syndrome (MetS) in older adults. These models offer a practical approach for early detection in clinical settings.
Area of Science:
- Gerontology
- Cardiovascular Research
- Endocrinology
Background:
- Metabolic syndrome (MetS) is a known predictor of cardiovascular diseases and diabetes.
- Non-traditional risk factors, including hematogram components, are also associated with these adverse outcomes.
- Predictive models for MetS are crucial for early intervention in aging populations.
Purpose of the Study:
- To develop and validate predictive models for the future occurrence of MetS in elderly men and women separately.
- To investigate the utility of hematogram components as predictors of MetS.
- To assess the clinical applicability of hematogram-based MetS prediction.
Main Methods:
- A four-year longitudinal study involving 4539 participants aged over 65 without MetS or related diseases.
- Development of separate predictive models for men and women using hematogram components.
- Statistical analysis including receiver operating characteristic curves and Cox regression.
Main Results:
- 1327 out of 4539 participants developed MetS during the four-year follow-up.
- Predictive models demonstrated significant accuracy (area under the receiver operating curves).
- Cox regression revealed a significant correlation between hematogram models and future MetS (HRs 1.567 for men, 1.738 for women).
Conclusions:
- Hematogram-based models effectively predict future MetS in the elderly population.
- These models offer a practical and convenient tool for daily clinical practice.
- Hematogram analysis provides valuable insights into MetS risk prediction in older adults.
Objectives:
The metabolic syndrome (MetS) is proposed to predict future occurrence of cardiovascular diseases and diabetes. There are some other "non-traditional" risk factors such as hematogram components that are also related to the same endpoints as MetS. In this four-year longitudinal study, we used hematogram components to build models for predicting future occurrence of MetS in older men and women separately.
Methods:
Subjects above 65 years without MetS and related diseases were enrolled. All subjects were followed up until they developed MetS or until up to four years from the day of entry, whichever was earlier.
Results:
Among the 4539 study participants, 1327 developed MetS. Models were built for men and women separately and the areas under the receiver operation curves were significant. The Kaplan-Meier plot showed that the models could predict future MetS. Finally, Cox regression analysis showed that the hematogram model was correlated to future MetS with hazard ratios of 1.567 and 1.738 in men and women, respectively.
Conclusion:
Our hematogram models could significantly predict future MetS in elderly and might be more practical and convenient for daily clinical practice.
