| Title: | A high reticulocyte count is a risk factor for the onset of metabolic dysfunction-associated steatotic liver disease: Cross-sectional and prospective studies of data of 310,091 individuals from the UK Biobank |
| Journal: | Frontiers in Pharmacology |
| Published: | 1 Jul 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39011501/ |
| DOI: | https://doi.org/10.3389/fphar.2024.1281095 |
| URL: | http://dx.doi.org/10.3389/fphar.2024.1281095 |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 11754
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Abstract
Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) poses a considerable health risk. Nevertheless, its risk factors are not thoroughly comprehended, and the association between the reticulocyte count and MASLD remains uncertain. This study aimed to explore the relationship between reticulocyte count and MASLD. Methods: A total of 310,091 individuals from the UK Biobank were included in this cross-sectional study, and 7,316 individuals were included in this prospective study. The cross-sectional analysis categorized reticulocyte count into quartiles, considering the sample distribution. Logistic regression models examined the connection between reticulocyte count and MASLD. In the prospective analysis, Cox analysis was utilized to investigate the association. Results: Our study findings indicate a significant association between higher reticulocyte count and an elevated risk of MASLD in both the cross-sectional and prospective analyses. In the cross-sectional analysis, the adjusted odds ratios (ORs) of MASLD increased stepwise over reticulocyte count quartiles (quartile 2: OR 1.22, 95% CI 1.17-1.28, p < 0.001; quartile 3: OR 1.44; 95% CI 1.38-1.51, p < 0.001; quartile 4: OR 1.66, 95% CI 1.59-1.74, p < 0.001). The results of prospective analyses were similar. Conclusion: Increased reticulocyte count was independently associated with a higher risk of MASLD. This discovery offers new insights into the potential of reticulocytes as biomarkers for MASLD.</p>
13 Authors
- Peng-Cheng Ma
- Qi-Mei Li
- Rui-Ning Li
- Chang Hong
- Hao Cui
- Zi-Yong Zhang
- Yan Li
- Lu-Shan Xiao
- Hong Zhu
- Lin Zeng
- Jun Xu
- Wei-Nan Lai
- Li Liu
1 Application
| Application ID | Title |
|---|---|
| 92668 | Development and validation of enhanced prediction models based on polygenic analysis and clinical factors for chronic diseases |
Enabling scientific discoveries that improve human health