使用代谢学和部分最小方位回归方法识别下尿路症状的可能生物标志物
Florin V Hopland-Nechita1,2, John R Andersen3, Tarja Kvalheim Rajalahti4
1Department of Urology, Førde Central Hospital, Førde, Norway. florin.nechita@icloud.com.
Metabolomics : Official journal of the Metabolomic Society
|September 12, 2023
概括
这项研究使用代谢学探索了下泌尿道症状 (LUTS) 的新生物标志物. 在使用部分最小平方回归时,统计分析没有发现与LUTS的显著代谢物关联,强调了方法的重要性.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
背景情况:
- 下尿道症状 (LUTS) 显著影响生活质量.
- 鉴定LUTS的新生物标志物对于有效的诊断和治疗至关重要.
- 目前的生物标志物发现通常需要强大的统计验证.
研究的目的:
- 调查中度至重度LUTS的潜在的新型血代谢物生物标志物.
- 应用先进的统计方法来识别和验证生物标志物.
- 为了将发现与之前报告的LUTS生物标志物进行比较.
主要方法:
- 分析了82名患者和对照组的队列.
- 使用核磁共振光谱学量化了等离子体代谢物.
- 统计分析包括皮尔森相关性,多重线性回归和部分最小平方回归 (PLS).
主要成果:
- 最初的相关性显示了LUTS与氨酸和氨酸等代谢物之间的关联.
- 多重线性回归确定了氨酸作为潜在的显著生物标志物.
- 然而,部分最小平方回归 (PLS) 分析显示,任何代谢物和LUTS.之间没有显著的关联.
结论:
- 强大的PLS回归方法表明,没有一个研究的代谢物与LUTS有显著的关联.
- 这项研究强调了在生物标志物研究中采用适当的统计方法的重要性.
- 仔细的统计验证是必要的,以确认生物标志物在泌尿病的相关性.
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