Rengasamy Manivel, Moriarity Daniel, Price Rebecca
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.
Transl Psychiatry. 2025 Jan 25;15(1):29. doi: 10.1038/s41398-025-03244-3.
Peripheral inflammatory markers (PIMs), such as C-reactive protein (CRP) or white blood cell count (WBC), have been associated with depression severity in meta-analyses and large cohort studies. However, in typically-sized psychoimmunology studies (N < 200) that explore associations between PIMs and neurobiological/psychosocial constructs related to depression and studies that examine less-studied PIMs (e.g., interferon gamma), significant concerns about reproducibility of results exist. For the well-characterized association between PIMs (CRP/WBC) and depression severity, we examined statistical errors as a function of sample size in a large community cohort (n = 24,550). We further assessed how statistical errors varied as related to analytic decisions (e.g., number of covariates) and characteristics related to study design (e.g., relationships within subgroups of patients). Only large samples (e.g., n = 1000 to n = 10,000) were sufficiently powered to detect PIM-depression associations and minimized overestimation of effect sizes (e.g., effect size inflation), and greater sample sizes were required as more covariates were included in analytic models. Moderately sized samples (n > 500) generally ensured the correct directionality of effect sizes (e.g., low rates of sign reversal). Sample sizes required for 80% power also varied widely depending on study design characteristics (e.g., N = 350 to N = 10,000+). Typically-sized psychoimmunology studies examining PIM-depression associations (N < 200) are likely underpowered and at high risk of overestimation of effect sizes. Study design characteristics also notably influence power and statistical error rates. Use of large sample sizes (e.g., N > 7000) and consideration of analytic decisions (e.g., number/choice of covariates) will maximize reproducibility of psychoimmunology studies related to depression to enhance development of treatments for depression or to help understand pathophysiological mechanisms of depression.
外周炎症标志物（PIMs），如C反应蛋白（CRP）或白细胞计数（WBC），在荟萃分析和大型队列研究中已与抑郁症严重程度相关联。然而，在探索PIMs与抑郁症相关的神经生物学/心理社会结构之间关联的典型规模心理免疫学研究（N < 200）以及研究较少研究的PIMs（如干扰素γ）的研究中，结果的可重复性存在重大问题。对于PIMs（CRP/WBC）与抑郁症严重程度之间已充分表征的关联，我们在一个大型社区队列（n = 24,550）中检查了作为样本量函数的统计误差。我们进一步评估了统计误差如何随分析决策（如协变量数量）以及与研究设计相关的特征（如患者亚组内的关系）而变化。只有大样本（如n = 1000至n = 10,000）才有足够的效能来检测PIMs与抑郁症之间的关联，并将效应大小估计的高估（如效应大小膨胀）降至最低，并且随着分析模型中纳入更多协变量，需要更大的样本量。中等规模样本（n > 500）通常能确保效应大小具有正确的方向性（如符号反转率低）。达到80%效能所需的样本量也因研究设计特征而异（如N = 350至N = 10,000 +）。研究PIMs与抑郁症关联的典型规模心理免疫学研究（N < 200）可能效能不足，且存在效应大小高估的高风险。研究设计特征也显著影响效能和统计错误率。使用大样本量（如N > 7000）并考虑分析决策（如协变量的数量/选择）将使与抑郁症相关的心理免疫学研究的可重复性最大化，以促进抑郁症治疗的发展或帮助理解抑郁症的病理生理机制。
