炎症细胞比值与抑郁症状及短期疗效的关系

doi:10.3969/j.issn.1004-583X.2026.10.004

摘要/Abstract

摘要：

目的探讨中性粒细胞/淋巴细胞比值(neutrophil-to-lymphocyte ratio,NLR)与抑郁症状及短期疗效的关系。方法本研究采用两阶段分析框架:第一阶段利用美国国家健康与营养调查2005-2018年全国代表性样本,系统评估NLR与抑郁症状的线性、非线性及症状特异性关系;第二阶段利用单中心真实世界抑郁障碍纵向队列,评价基线NLR与8周症状轨迹、残余症状以及短期疗效的关系,并考察其增量模型性能。结果美国国家健康与营养调查中标准化log(NLR)与9项患者健康问卷总分≥10分的完全调整线性关联差异无统计学意义(OR=1.041,95%CI:0.980~1.106),但存在非线性关联(P非线性=0.007)。医院队列中,基线标准化log(NLR)每升高1个标准差,8周17项汉密尔顿抑郁量表总分平均升高0.567分(95%CI:0.038~1.095,P=0.036)。结论 NLR与当前症状严重度关系有限,但可能提供少量短期预后信息。

关键词: 抑郁症, 中性粒细胞/淋巴细胞比值, 炎症, PHQ-9, HAMD-17

Abstract:

Objective To investigate the associations of the neutrophil-to-lymphocyte ratio (NLR)with depressive symptoms and short-term treatment response. Methods This study employed a two-stage analytical framework. In the first stage, a nationally representative sample from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 was used to systematically evaluate the linear, nonlinear, and symptom-specific associations between NLR and depressive symptoms. In the seesidual symptoms, and short-term treatment response, as well as its incremental model performance. Results In the NHANES population, the fully adjusted linear association between standardized log(NLR) and the 9-item Patient Health Questionnaire (PHQ-9) score ≥10 was not statistically significant (OR=1.041, 95%CI: 0.980-1.106), although a nonlinear association was observed (P_nonlinear=0.007). In the hospital-based cohort, each 1-standard-deviation increase in baseline standardized log(NLR) was associated with a mean increase of 0.567 points in the 8-week 17-item Hamilton depression rating scale(HAMD-17) score (95%CI: 0.038-1.095, P=0.036). Conclusion NLR appears to have a limited association with current symptom severity but may provide modest prognostic information regarding short-term outcomes.

Key words: depressive disorder, neutrophil-to-lymphocyte ratio, inflammation, PHQ-9, HAMD-17

中图分类号:

R749.41

王筱涵, 王栋泽, 韩冰. 炎症细胞比值与抑郁症状及短期疗效的关系[J]. 临床荟萃, 2026, 41(10): 891-900.

Wang Xiaohan, Wang Dongze, Han Bing. Association of inflammatory cell ratios with depressive symptoms and short-term treatment response[J]. Clinical Focus, 2026, 41(10): 891-900.

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链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2026.10.004

https://www.lchc.cn/CN/Y2026/V41/I10/891

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参考文献 32

[1]	Qin K, Wei Q. Global and regional burden of major depressive disorder and associated risk factors: Analysis from the Global Burden of Disease Study, 1990-2021[J]. Eur Arch Psychiatry Clin Neurosci, 2026, 276(5):2509-2520. doi:10.1007/s00406-026-02265-3.
[2]	Yan G, Zhang Y, Wang S, et al. Global, regional, and national temporal trend in burden of major depressive disorder from 1990 to 2019: An analysis of the Global Burden of Disease Study[J]. Psychiatry Res, 2024, 337:115958. doi:10.1016/j.psychres.2024.115958.
[3]	Santomauro DF, Vos T, Whiteford HA, et al. Service coverage for major depressive disorder: Estimated rates of minimally adequate treatment for 204 countries and territories in 2021[J]. Lancet Psychiatry, 2024, 11(12):1012-1021. doi:10.1016/S2215-0366(24)00317-1. pmid: 39572105
[4]	Marx W, Penninx BWJH, Solmi M, et al. Major depressive disorder[J]. Nat Rev Dis Primers, 2023, 9(1):44. doi:10.1038/s41572-023-00454-1. pmid: 37620370
[5]	Cui L, Li S, Wang S, et al. Major depressive disorder: Hypothesis, mechanism, prevention and treatment[J]. Signal Transduct Target Ther, 2024, 9(1):30. doi:10.1038/s41392-024-01738-y.
[6]	Réus GZ, Manosso LM, Quevedo J, et al. Major depressive disorder as a neuro-immune disorder: Origin, mechanisms, and therapeutic opportunities[J]. Neurosci Biobehav Rev, 2023, 155:105425. doi:10.1016/j.neubiorev.2023.105425.
[7]	Kuring JK, Mathias JL, Ward L, et al. Inflammatory markers in persons with clinically-significant depression, anxiety or PTSD: A systematic review and meta-analysis[J]. J Psychiatr Res, 2023, 168:279-292. doi:10.1016/j.jpsychires.2023.10.009. pmid: 37931509
[8]	Ang SH, Ho RC, McIntyre RS, et al. The clinical utility of biomarkers in diagnosing major depressive disorder in adults: A systematic review of literature from 2013 to 2023[J]. Psychiatry Investig, 2025, 22(4):341-356. doi:10.30773/pi.2024.0152.
[9]	Zhou Y, Jiang X, Hu Y, et al. The relationship between neutrophil-to-lymphocyte ratio and the prevalence and clinical outcomes of depressive disorders: A systematic review and meta-analysis[J]. Harv Rev Psychiatry, 2025, 33(6):289-304. doi:10.1097/HRP.0000000000000444.
[10]	Ninla-Aesong P, Kietdumrongwong P, Neupane SP, et al. Relative value of novel systemic immune-inflammatory indices and classical hematological parameters in predicting depression, suicide attempts and treatment response[J]. Sci Rep, 2024, 14(1):19018. doi:10.1038/s41598-024-70097-z. pmid: 39152198
[11]	Paolini M, Harrington Y, Raffaelli L, et al. Neutrophil to lymphocyte ratio and antidepressant treatment response in patients with major depressive disorder: Effect of sex and hippocampal volume[J]. Eur Neuropsychopharmacol, 2023, 76:52-60. doi:10.1016/j.euroneuro.2023.07.010. pmid: 37544076
[12]	Koparal B, Temizkan HN, Aksu MH, et al. Higher neutrophil-lymphocyte ratio predicts poor response to repetitive transcranial magnetic stimulation (rTMS) in treatment resistant depression[J]. Eur Arch Psychiatry Clin Neurosci, 2025, 275(8):2547-2555. doi:10.1007/s00406-025-01977-2.
[13]	Zheng Y, Yin K, Li L, et al. Association between immune-inflammation-based prognostic index and depression: An exploratory cross-sectional analysis of NHANES data[J]. J Affect Disord, 2024, 362:75-85. doi:10.1016/j.jad.2024.06.103.
[14]	Liu D, He C, Luo X. Relationship between systemic immune-inflammation index and depression among individuals with and without cardiovascular disease[J]. J Cardiothorac Surg, 2025, 20(1):15. doi:10.1186/s13019-024-03314-5. pmid: 39755660
[15]	Duan J, Chen J, Xiang Z. The U-shape relationship between the aggregate index of systemic inflammation and depression in American adults: A cross-sectional study[J]. J Affect Disord, 2025, 380:270-278. doi:10.1016/j.jad.2025.03.139.
[16]	Zhou K, Xie Y, Xie X, et al. U-shaped association between systemic immune-inflammatory index and depression in U.S. adults with hypertension[J]. J Affect Disord, 2025, 386:119437. doi:10.1016/j.jad.2025.119437.
[17]	von Elm E, Altman DG, Egger M, et al. The strengthening the reporting of observational studies in epidemiology statement: Guidelines for reporting observational studies[J]. PLoS Med, 2007, 4(10):e296. doi:10.1371/journal.pmed.0040296.
[18]	Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure[J]. J Gen Intern Med, 2001, 16(9):606-613. doi:10.1046/j.1525-1497.2001.016009606.x. pmid: 11556941
[19]	Inker LA, Eneanya ND, Coresh J, et al. New creatinine- and cystatin C-based equations to estimate GFR without race[J]. N Engl J Med, 2021, 385(19):1737-1749. doi:10.1056/NEJMoa2102953.
[20]	Hamilton M. A rating scale for depression[J]. J Neurol Neurosurg Psychiatry, 1960, 23(1):56-62. doi:10.1136/jnnp.23.1.56.
[21]	Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing[J]. J R Stat Soc Series B, 1995, 57(1):289-300. doi:10.1111/j.2517-6161.1995.tb02031.x.
[22]	Durrleman S, Simon R. Flexible regression models with cubic splines[J]. Stat Med, 1989, 8(5):551-561. doi:10.1002/sim.4780080504. pmid: 2657958
[23]	White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice[J]. Stat Med, 2011, 30(4):377-399. doi:10.1002/sim.4067. pmid: 21225900
[24]	Zeileis A. Econometric computing with HC and HAC covariance matrix estimators[J]. J Stat Softw, 2004, 11(10):1-17. doi:10.18637/jss.v011.i10.
[25]	Bates D, Mächler M, Bolker B, et al. Fitting linear mixed-effects models using lme4[J]. J Stat Softw, 2015, 67(1):1-48. doi:10.18637/jss.v067.i01.
[26]	Meng F, Yan X, Qi J, et al. Association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and monocyte to lymphocyte ratio with depression: A cross-sectional analysis of the NHANES data[J]. J Affect Disord, 2022, 315:168-173. doi:10.1016/j.jad.2022.08.004.
[27]	Canli D. Evaluation of systemic immune inflammation index and neutrophil-to-lymphocyte ratio in schizophrenia, bipolar disorder and depression[J]. Bratisl Lek Listy, 2024, 125(8):472-476. doi:10.4149/BLL_2024_73. pmid: 38989747
[28]	Schumacher A, Tu E, Albaum C, et al. The systemic immune-inflammation index (SII) as a biomarker for depression in a community sample of adolescents[J]. Compr Psychoneuroendocrinol, 2025, 23:100302. doi:10.1016/j.cpnec.2025.100302.
[29]	Baxter L, Utulu N, Utulu F, et al. An umbrella review of inflammatory biomarkers and their relationship to treatment response in MDD[J]. NeurosciBiobehav Rev, 2026, 188:106806. doi:10.1016/j.neubiorev.2026.106806.
[30]	Chun HW, Park YE, Kim JH, et al. Peripheral inflammatory biomarkers as predictors of antidepressant treatment response in major depressive disorder: An updated systematic review and meta-analysis[J]. Psychopharmacol Bull, 2026, 56(4Suppl 1): 88-107. doi:10.64719/pb.20776.
[31]	Pastuszak M, Cubała WJ, Kwa'sny A, et al. The search for consistency in residual symptoms in major depressive disorder: A narrative review[J]. J Pers Med, 2024, 14(8):828. doi:10.3390/jpm14080828.
[32]	Křenek P, Hořínková J, Bartečků E. Peripheral inflammatory markers in subtypes and core features of depression: A systematized review[J]. Psychopathology, 2023, 56(5):403-416. doi:10.1159/000528907.

特征	总体	PHQ-9总分<10分	PHQ-9总分≥10分
年龄(岁)	47.5(0.2)	47.6(0.3)	46.7(0.4)
女性[例(%)]	14 303(50.6)	12 736(49.5)	1 567(64.2)
高中以上教育[例(%)]	15 424(62.0)	14 394(63.0)	1 030(48.8)
已婚或有伴侣[例(%)]	17 237(64.2)	16 114(65.4)	1 123(48.6)
家庭收入与贫困线比值	3.08(1.53~5.00)	3.21(1.64~5.00)	1.65(0.90~3.21)
BMI(kg/m²)	29.1(0.1)	29.0(0.1)	30.7(0.2)
当前吸烟[例(%)]	5 951(20.2)	5 030(18.7)	921(39.3)
糖尿病[例(%)]	3 678(9.5)	3 197(9.0)	481(14.9)
高血压[例(%)]	12 312(37.9)	11 043(37.2)	1 269(47.3)
NLR	1.96(1.50~2.59)	1.95(1.50~2.58)	2.06(1.50~2.76)
SII	474.2(343.6~662.4)	471.7(342.5~656.0)	505.7(359.7~736.0)

特征	总体	PHQ-9总分<10分	PHQ-9总分≥10分
年龄(岁)	47.5(0.2)	47.6(0.3)	46.7(0.4)
女性[例(%)]	14 303(50.6)	12 736(49.5)	1 567(64.2)
高中以上教育[例(%)]	15 424(62.0)	14 394(63.0)	1 030(48.8)
已婚或有伴侣[例(%)]	17 237(64.2)	16 114(65.4)	1 123(48.6)
家庭收入与贫困线比值	3.08(1.53~5.00)	3.21(1.64~5.00)	1.65(0.90~3.21)
BMI(kg/m²)	29.1(0.1)	29.0(0.1)	30.7(0.2)
当前吸烟[例(%)]	5 951(20.2)	5 030(18.7)	921(39.3)
糖尿病[例(%)]	3 678(9.5)	3 197(9.0)	481(14.9)
高血压[例(%)]	12 312(37.9)	11 043(37.2)	1 269(47.3)
NLR	1.96(1.50~2.59)	1.95(1.50~2.58)	2.06(1.50~2.76)
SII	474.2(343.6~662.4)	471.7(342.5~656.0)	505.7(359.7~736.0)

分析	效应值	95%CI	P值	例数
完全调整线性模型,OR/1 SD	1.041	0.980~1.106	0.192	28 663
多重插补,OR/1 SD	1.036	0.979~1.096	0.221	32 196
P1~P99样条:总体关联	-	-	0.002	28 104
P1~P99样条:非线性	-	-	0.007	28 104
炎症排除样条:非线性	-	-	0.045	24 517
替代结点样条:非线性	-	-	0.012	28 104
增加饮酒状态,OR/1 SD	1.040	0.978~1.106	0.208	28 612
2007-2018年增加体力活动,OR/1 SD	1.036	0.969~1.107	0.294	24 949
排除急性/炎症相关情况,OR/1 SD	1.053	0.968~1.145	0.229	24 517

分析	效应值	95%CI	P值	例数
完全调整线性模型,OR/1 SD	1.041	0.980~1.106	0.192	28 663
多重插补,OR/1 SD	1.036	0.979~1.096	0.221	32 196
P1~P99样条:总体关联	-	-	0.002	28 104
P1~P99样条:非线性	-	-	0.007	28 104
炎症排除样条:非线性	-	-	0.045	24 517
替代结点样条:非线性	-	-	0.012	28 104
增加饮酒状态,OR/1 SD	1.040	0.978~1.106	0.208	28 612
2007-2018年增加体力活动,OR/1 SD	1.036	0.969~1.107	0.294	24 949
排除急性/炎症相关情况,OR/1 SD	1.053	0.968~1.145	0.229	24 517

指标	观察值
基线样本	320
2周/4周/8周HAMD-17可用	316/310/300
年龄(岁)	42.7±12.7
女性[例(%)]	221(69.1)
住院[例(%)]	216(67.5)
BMI(kg/m²)	25.1±3.9
复发性抑郁[例(%)]	168(52.5)
治疗抵抗[例(%)]	85(26.6)
焦虑共病[例(%)]	123(38.4)
精神病性症状[例(%)]	23(7.2)
NLR	1.99(1.31~2.94)
基线HAMD-17总分	24.38±5.21
8周HAMD-17总分	10.37±4.54
8周反应/缓解[例(%)]	222/300(74.0)/89/300(29.7)
炎症排除队列/重复CBC	273/169