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Observational Study
. 2022 Jul 7:13:911744.
doi: 10.3389/fimmu.2022.911744. eCollection 2022.

The Neuroimmune Response to Surgery - An Exploratory Study of Trauma-Induced Changes in Innate Immunity and Heart Rate Variability

Affiliations
Observational Study

The Neuroimmune Response to Surgery - An Exploratory Study of Trauma-Induced Changes in Innate Immunity and Heart Rate Variability

Malin Hildenborg et al. Front Immunol. .

Abstract

Surgery triggers a systemic inflammatory response that ultimately impacts the brain and associates with long-term cognitive impairment. Adequate regulation of this immune surge is pivotal for a successful surgical recovery. We explored the temporal immune response in a surgical cohort and its associations with neuroimmune regulatory pathways and cognition, in keeping with the growing body of evidence pointing towards the brain as a regulator of peripheral inflammation. Brain-to-immune communication acts through cellular, humoral and neural pathways. In this context, the vagal nerve and the cholinergic anti-inflammatory pathway (CAP) have been shown to modify peripheral immune cell activity in both acute and chronic inflammatory conditions. However, the relevance of neuroimmune regulatory mechanisms following a surgical trauma is not yet elucidated. Twenty-five male patients undergoing elective laparoscopic abdominal surgery were included in this observational prospective study. Serial blood samples with extensive immune characterization, assessments of heart rate variability (HRV) and cognitive tests were performed before surgery and continuing up to 6 months post-surgery. Temporal immune responses revealed biphasic reaction patterns with most pronounced changes at 5 hours after skin incision and 14 days following surgery. Estimations of cardiac vagal nerve activity through HRV recordings revealed great individual variations depending on the pre-operative HRV baseline. A principal component analysis displayed distinct differences in systemic inflammatory biomarker trajectories primarily based on pre-operative HRV, with potiential consequences for long-term surgical outcomes. In conclusion, individual pre-operative HRV generates differential response patterns that associate with distinct inflammatory trajectories following surgery. Long-term surgical outcomes need to be examined further in larger studies with mixed gender cohorts.

Keywords: heart rate variability (HRV); inflammation; innate immunity; neuroimmune alterations; perioperative neurocognitive disorders (PND); surgery.

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Conflict of interest statement

HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, Annexon, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Pinteon Therapeutics, Red Abbey Labs, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). TS, affiliated with Karolinska Institutet, conducts HRV analysis through his company Nicollier-Schlegel SARL, Switzerland. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Clinical trial profile. CBC, complete blood count; LPS, lipopolysaccharide.
Figure 2
Figure 2
Derivation of Heart Rate Variability parameters. RRV, R-to-R variability; SDNN, standard deviation of normal-normal heart beats; rMSSD, root mean square of successive differences; LF, low frequency; HF, high frequency.
Figure 3
Figure 3
Temporal HRV trajectories. Mean values, SD. For the indexed parameter IIQTVI and the ratio LF : HF, a greater value implies less variability. * P-values ≤0.05, Tukey´s multiple comparison test.
Figure 4
Figure 4
Temporal HRV subgroup trajectories. Mean values, SD. Dotted line is level at start of ‘response time’ (24hr.). * Bonferroni’s multiple comparison test, ** Group x time effect (mixed effect model). For the variable IIQTVI – the more negative the value, the stronger the variability. For all timepoints, see Supplementary Figure 2 .
Figure 5
Figure 5
Principal component 2. The second PC showed a significant group-time interaction assessed by a mixed-effects model (P = 0.013 < 0.05/3 = 0.017, i.e. after Bonferroni correction).
Figure 6
Figure 6
Analyses of TNF-α. (A) TNF-α release ex vivo following LPS stimulation divided by systemic WBC at same timepoint. (B) TNF-α release ex vivo following LPS stimulation without adjustment for WBC. (C) Systemic TNF-α in circulation (serum) expressed as normalized units (NPX) on a log scale. * P-values ≤0.05 Tukey´s multiple comparison test, not all significant differences between timepoints are outlined. LPS, lipopolysaccharide; WBC, white blood cell count; NPX, normalized protein units.
Figure 7
Figure 7
Systemic cytokines, alarmins (serum), cellular systemic trajectories and ex vivo LPS challenge-induced TNF-α release. For Ex vivo LPS challenge, TNF-α (pg/ml) is divided by WBC (x10^9) in order to describe the ‘reactivity per white blood cell’ to endotoxin. *P values ≤0.05 from the preceding timepoint or interval when present (Tukey´s multiple comparison test). Not all significant changes between timepoints are outlined.

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