School of Basic Medicine, Changsha Medical University, Changsha, China.
The Hunan Provincial Key Laboratory of the TCM Agricultural Biogenomics, Changsha Medical University, Changsha, China.
Front Endocrinol (Lausanne). 2024 Jan 8;14:1321576. doi: 10.3389/fendo.2023.1321576. eCollection 2023.
BACKGROUND: Previous observational studies have investigated the association between endocrine and metabolic factors and idiopathic pulmonary fibrosis (IPF), yet have produced inconsistent results. Therefore, it is imperative to employ the Mendelian randomization (MR) analysis method to conduct a more comprehensive investigation into the impact of endocrine and metabolic factors on IPF. METHODS: The instrumental variables (IVs) for 53 endocrine and metabolic factors were sourced from publicly accessible genome-wide association study (GWAS) databases, with GWAS summary statistics pertaining to IPF employed as the dependent variables. Causal inference analysis encompassed the utilization of three methods: inverse-variance weighted (IVW), weighted median (WM), and MR-Egger. Sensitivity analysis incorporated the implementation of MR-PRESSO and leave-one-out techniques to identify potential pleiotropy and outliers. The presence of horizontal pleiotropy and heterogeneity was evaluated through the MR-Egger intercept and Cochran's Q statistic, respectively. RESULTS: The IVW method results reveal correlations between 11 traits and IPF. After correcting for multiple comparisons, seven traits remain statistically significant. These factors include: "Weight" (OR= 1.44; 95% CI: 1.16, 1.78; =8.71×10), "Body mass index (BMI)" (OR= 1.35; 95% CI: 1.13, 1.62; =1×10), "Whole body fat mass" (OR= 1.40; 95% CI: 1.14, 1.74; =1.72×10), "Waist circumference (WC)" (OR= 1.54; 95% CI: 1.16, 2.05; =3.08×10), "Trunk fat mass (TFM)" (OR=1.35; 95% CI: 1.10,1.65; =3.45×10), "Body fat percentage (BFP)" (OR= 1.55; 95% CI: 1.15,2.08; =3.86×10), "Apoliprotein B (ApoB)" (OR= 0.78; 95% CI: 0.65,0.93; =5.47×10). Additionally, the sensitivity analysis results confirmed the reliability of the MR results. CONCLUSION: The present study identified causal relationships between seven traits and IPF. Specifically, ApoB exhibited a negative impact on IPF, while the remaining six factors demonstrated a positive impact. These findings offer novel insights into the underlying etiopathological mechanisms associated with IPF.
背景：先前的观察性研究已经探讨了内分泌和代谢因素与特发性肺纤维化（IPF）之间的关联，但结果并不一致。因此，采用孟德尔随机化（MR）分析方法更全面地研究内分泌和代谢因素对 IPF 的影响至关重要。
方法：从公开可得的全基因组关联研究（GWAS）数据库中获取 53 种内分泌和代谢因素的工具变量（IVs），并将与 IPF 相关的 GWAS 汇总统计数据作为因变量。因果推断分析包括使用三种方法：逆方差加权（IVW）、加权中位数（WM）和 MR-Egger。敏感性分析采用 MR-PRESSO 和逐一剔除技术来识别潜在的平行性和异常值。通过 MR-Egger 截距和 Cochran's Q 统计量评估水平平行性和异质性的存在。
结果：IVW 方法的结果显示 11 个特征与 IPF 之间存在相关性。经过多次比较校正后，仍有 7 个特征具有统计学意义。这些因素包括：“体重”（OR=1.44；95%CI：1.16，1.78；=8.71×10）、“体重指数（BMI）”（OR=1.35；95%CI：1.13，1.62；=1×10）、“全身脂肪量”（OR=1.40；95%CI：1.14，1.74；=1.72×10）、“腰围（WC）”（OR=1.54；95%CI：1.16，2.05；=3.08×10）、“躯干脂肪量（TFM）”（OR=1.35；95%CI：1.10，1.65；=3.45×10）、“体脂肪百分比（BFP）”（OR=1.55；95%CI：1.15，2.08；=3.86×10）、“载脂蛋白 B（ApoB）”（OR=0.78；95%CI：0.65，0.93；=5.47×10）。此外，敏感性分析结果证实了 MR 结果的可靠性。
结论：本研究确定了七种特征与 IPF 之间的因果关系。具体而言，ApoB 对 IPF 产生负面影响，而其余六种因素则表现出积极影响。这些发现为 IPF 相关的潜在病理生理机制提供了新的见解。
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