Biomarkers of systemic inflammation as evaluation criteria for the body’s adaptive capabilities under whole‑body cold exposure

Introduction. The complex of unfavorable natural and climatic factors in the North, creating a stressful living environment, induces a pronounced tension in the immune responses that maintain homeostasis. Determining the type of adaptation reaction and the level of reactivity makes it possible to diagnose the functional state of the body and predict its response patterns to environmental factors.

Objective. Determination of systemic inflammation biomarker levels, calculated based on complete blood count data, for assessing the stress and failure of the adaptation process.

Materials and methods. The study examined 178 practically healthy volunteers (136 women and 42 men; mean age 31.17 ± 0.85 years). The assessment of the body’s adaptation state was carried out by analyzing leukograms. The types of non-specific adaptation reactions, levels of body reactivity, and adaptation state were determined according to Garkavi’s method. According to this method, all subjects were divided into the following groups: “physiological norm” (group 1) — 68 women and 23 men, mean age 31.59 ± 1.29 years; “adaptation stress” (group 2) — 50 women and 10 men, mean age 31.21 ± 1.15 years; “adaptation failure” (group 3) — 18 women and 9 men, mean age 28.17 ± 2.51 years. General short-term cold exposure was modeled once in a climatic chamber (USHZ-25N, Russia), in which the subjects, dressed in cotton clothing, remained under continuous video observation for 5 min at an ambient air temperature of –25 °C. Hematological studies were performed using an XS-500i analyzer. The content of catecholamines, cortisol, PPARγC1α, HIF‑1α, SIRT3, and BPI was determined by ELISA. Lymphocyte apoptosis and necrosis were assessed by double staining with Annexin V and propidium iodide (AnV/PI). The quantitative content of ATP was determined using the luciferin‑luciferase method. The following systemic inflammation indices were calculated: systemic immuneinflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI). The results were processed using StatTech v.4.9.2 and Statistica 6.0 software.

Results. In the “physiological norm” group, an increase in leukocyte counts, enhanced cell migration activity, and improved mitochondrial energy supply for their functional activity indicated high non-specific resistance and the absence of a stress effect from this type of cold exposure. Under “adaptation stress”, the fastest cardiovascular responses aimed at maintaining temperature homeostasis were recorded, accompanied by increased catecholamine levels, enhanced leukocyte recirculation and migration, allowing the body to respond rapidly to short‑term stress factors. In the “adaptation failure” group, the baseline tension in the activity of regulatory systems, even after a single short‑term cold exposure, did not lead to the activation of effective thermoregulation mechanisms; leukocyte migration activity was slowed, significantly increasing the risk of maladaptive and pro‑inflammatory reactions. It was established that SIRI, SII, and AISI are significant risk criteria for adaptation strain at SIRI ≥ 0.97, SII ≥ 447.43, AISI ≥ 216.24, and for adaptation failure at index values of ≥1.23, ≥449.76, and ≥239.58, respectively.

Conclusions. “Adaptation stress” and “adaptation failure” are characterized by changes in cellular and humoral parameters of peripheral blood even before the clinical manifestation of the disease, and these changes may be exacerbated by additional exposure to negative factors, even if their impact is short‑term. Baseline systemic inflammation indices (SIRI, SII, AISI) are significant predictors for assessing both “adaptation stress” and “adaptation failure”. The use of these indices, calculated from complete blood count results, will make it possible to assess the presence of an initial imbalance in the body’s adaptation mechanisms, which may subsequently be aggravated by exposure to stress factors.

1. Dobrodeeva LK. Immunological zoning. Syktyvkar: KNC UrO RAN; 2001 (In Russ.).

2. Garkavi LH, Tolmachev GN, Mikhailov NYu, Esipov YuV, Benia FM, Zverintseva MM, et al. Adaptation reactions and levels of reactivity as effective diagnostic indices of prenosological states. Science in South Russia. 2007;3(1):61–6 (In Russ.). EDN: KVYTWD

3. Zhiemuratova GK, Mambetkarimov GA. Features of the adaptation status of children in the Aral region. Children’s Medicine of the North-West. 2021;9(1):148 (In Russ.). EDN: WHCDGU

4. Gordeeva RV, Filimonov SN, Kuzmenko OV, Kireeva LN, Martynova EA, Mitichkina TV. Adaptive therapy in early rehabilitation of the patients with industrial injuries as an indicator of the effectiveness of course programs. Russian Journal of Occupational Health and Industrial Ecology. 2020;60(6):409– 14 (In Russ.). https://doi.org/10.31089/1026-9428-2020-60-6-409-414

5. Pustovoit VI, Nikonov RV, Samoylov AS, Klyuchnikov MS, Nazaryan SE, Petrova MS. Main cytological and biochemical parameters of blood in the development of a non-specific adaptive reaction with athletes participating in extreme sports. Resort Medicine. 2020;2:85–91 (In Russ.). https://doi.org/10.51871/2304-0343_2021_2_85

6. Petushkov MN, Papin MA. Comparative characteristics of the types of non-specific adaptive reactions of the organism in highly qualified athletes in different training periods. Herald of Tver State University. Series: Biology and Ecology. 2022; 4(68):7–14 (In Russ.). https://doi.org/10.26456/vtbio275

7. Xiu J, Lin X, Chen Q, Yu P, Lu J, Yang Y, et al. The aggregate index of systemic inflammation (AISI): a novel predictor for hypertension. Frontiers in Cardiovascular Medicine. 2023;10:1163900. https://doi.org/10.3389/fcvm.2023.1163900

8. Zinellu A, Paliogiannis P, Mangoni AA. Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 2023;12(14):4584. https://doi.org/10.3390/jcm12144584

9. Su Z, Cao L, Chen H, Zhang P, Wu C, Lu J, et al. Obesity indicators mediate the association between the aggregate index of systemic inflammation (AISI) and type 2 diabetes mellitus (T2DM). Lipids in Health and Disease. 2025;24(1):176. https://doi.org/10.1186/s12944-025-02589-4

10. Tang J, Li TX, Deng L, Huang XC, He PY, Zhao H. Inflammatory indices AISI and SIRI in atherosclerosis risk stratification: validation across community and intensive care populations. Annals of Medicine. 2025;57(1):2530792. https://doi.org/10.1080/07853890.2025.2530792

11. Zhang K, Ma X, Zhou X, Qiu G, Zhang C. Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999–2018. Frontiers in Public Health. 2025;13:1559603. https://doi.org/10.3389/fpubh.2025.1559603

12. Ghobadi H, Mohammadshahi J, Javaheri N, Fouladi N, Mirzazadeh Y, Aslani MR. Role of leukocytes and systemic inflammation indexes (NLR, PLR, MLP, dNLR, NLPR, AISI, SIR-I, and SII) on admission predicts in-hospital mortality in non-elderly and elderly COVID-19 patients. Frontiers in Medicine. 2022;9:2434. https://doi.org/10.3389/fmed.2022.916453

13. Zinellu A, Paliogiannis P, Mangoni AA. Aggregate index of systemic inflammation (AISI), disease severity, and mortality in COVID-19: a systematic review and meta-analysis. Journal of Clinical Medicine. 2023;12:4584. https://doi.org/10.3390/jcm12144584

14. Fois AG, Paliogiannis P, Scano V, Cau S, Babudieri S, Perra R, et al. The systemic inflammation index on admission predicts in-hospital mortality in COVID-19 patients. Molecules. 2020;25:5725. https://doi.org/10.3390/molecules25235725

15. Frantsiyants EM, Bandovkina VA, Dzhavadov DA, Kozel YuYu, Kovalenko VA, Shashkina LY, et al. The opportunities of the systemic immune inflammation index usage for the antitumor treatment in adolescents with Hodgkin’s lymphoma effect assessment. Modern Problems of Science and Education. 2025;2:14 (In Russ.). https://doi.org/10.17513/spno.33981

16. Kassirskiy N A, Alekseyev GA. Clinical Gematology. Moscow: Meditsina; 1970 (In Russ.).

17. Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158–67. https://doi.org/10.1002/cncr.30057

18. Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clinical Cancer Research. 2014;20:6212–22. https://doi.org/10.1158/1078-0432.CCR-14-0442

19. Bai X, Cheng L, Wang H, Deng Y, Tong X, Wen W, et al. The aggregate index of systemic inflammation (AISI) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in congestive heart failure patients: results from NHANES 1999–2018. Scientific Reports. 2025;15(1):18282. https://doi.org/10.1038/s41598-025-01196-8

20. Valle I, Alvarez-Barrientos A, Arza E, Lamas S, Monsalve M. PGC-1α regulates the mitochondrial antioxidant defense system in vascular endothelial cells. Cardiovascular Research. 2005;66(3):562–73. https://doi.org/10.1016/j.cardiores.2005.01.026

21. Goto T. A review of the studies on food-derived factors which regulate energy metabolism via the modulation of lipid-sensing nuclear receptors. Bioscience, Biotechnology & Biochemistry. 2019;83(4):579–88. https://doi.org/10.1080/09168451.2018.1559025

22. Jamwal S, Blackburn JK, Elsworth JD. PPARgamma/ PGC1alpha signaling as a potential therapeutic target for mitochondrial biogenesis in neurodegenerative disorders. Pharmacology & Therapy. 2021;219:107705. https://doi.org/10.1016/j.pharmthera.2020.107705

23. Gowans JL. The lymphocyte — a disgraceful gap in medical knowledge. Immunology Today. 1996;17(6):288–91. https://doi.org/10.1016/0167-5699(96)80547-0

24. Galluzzi L, Vitale I, Aaronson SA, Abrams JM, Adam D, Agostiniset P, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death & Differentiation. 2018;25(3):486–541. https://doi.org/10.1038/s41418-017-0012-4

25. Feng M, Zheng C, Li X, Chen B, Li W. Reversal of lipopolysaccharide-induced cardiomyocyte apoptosis via alpha7nAChR by dexmedetomidine. Cellular and Molecular Biology. 2024;70(5):127–31. https://doi.org/10.14715/cmb/2024.70.5.17

26. Xu Y, Surman DR, Diggs L, Xi S, Gao S, Gurusamyet D, et al. Bile acid-induced «Minority MOMP» promotes esophageal carcinogenesis while maintaining apoptotic resistance via Mcl-1. Oncogene. 2020;39(4):877–90. https://doi.org/10.1038/s41388-019-1029-6

27. Tang HM, Talbot CC, Fung MC, Tang HL. Transcriptomic study of anastasis for reversal of ethanol-induced apoptosis in mouse primary liver cells. Scientific Data. 2022;9(1):418. https://doi.org/10.1038/s41597-022-01470-8

28. Shvarts VA, Talibova SM, Sokolskaya MA, Ispiryan AYu, Shvarts EN, Petrosyan AD, et al. Association of novel biomarkers of systemic inflammation with atherosclerosis and its severity. Russian Journal of Cardiology. 2024;29(8):67–74 (In Russ.). https://doi.org/10.15829/1560-4071-2024-6025

29. Yang YL, Wu CH, Hsu PF, Chen SC, Huang SS, Chanet WL, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. European Journal of Clinical Investigation. 2020;50(5):e13230. https://doi.org/10.1111/eci.13230

30. Li Q, Ma X, Shao Q, Yang Z, Wang Y, Gao F, et al. Prognostic impact of Multiple lymphocyte-based inflammatory indices in acute coronary syndrome patients. Frontiers in Cardiovascular Medicine. 2022;9:811790. https://doi.org/10.3389/fcvm.2022.811790