{"paper_id":"2c64b811-9c22-4276-b25f-34ec2fa05ffd","body_text":"Progression of Peripheral Blood Mononuclear Cell Mitochondrial Function during the Early Phase of Sepsis in Intensive Care Unit Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Progression of Peripheral Blood Mononuclear Cell Mitochondrial Function during the Early Phase of Sepsis in Intensive Care Unit Patients Arthur Van Zanten, Hanneke Moonen, Rianne Slingerland, Jelle De Jong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7137135/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sepsis is a leading cause of ICU admission and is associated with high rates of multiorgan failure and mortality. Altered mitochondrial function is an essential component of the early sepsis syndrome. However, its progression over time in peripheral blood mononuclear cells (PBMCs), essential mediators of the initial inflammatory response, is thus far unclear. Our purpose was to investigate the progression of mitochondrial respiration in peripheral blood mononuclear cells (PBMCs) in the early phase of sepsis in ICU patients. Methods A single-centre prospective observational cohort study was conducted in sepsis patients and compared with age- and sex-matched controls. We measured mitochondrial function using functional respirometry measurements (Oroboros O2K) in PBMCs thrice during the first week of ICU admission. RT-qPCR was used for semi-quantitative analysis of expression of genes involved in oxidative phosphorylation. Secondary endpoints included the associations between mitochondrial function and (I) sepsis severity and (II) clinical outcomes, including 3-month mortality. Results Basal, ATP-linked, maximal and proton leak associated respiration were increased in sepsis patients (n = 25) compared to matched controls (n = 24) at all time points. This was associated with increased expression of SDBH (complex II) and ATP5F1A (complex V). Increased basal respiration was associated with 3-month mortality (HR 3.794, 95%CI 1.018–14.149, p = 0.047). No differences were observed in other secondary outcomes. Conclusion PBMC mitochondria were shown to have an increased respiratory rate during the first week of sepsis, supported by increased expression of genes involved in oxidative phosphorylation. Moreover, a progressive increase in mitochondrial respiration was negatively associated with 3-month survival. These results suggest that the upregulation of basal respiration may serve as a proxy marker for sepsis severity and outcomes. Health sciences/Diseases/Infectious diseases/Bacterial infection Health sciences/Biomarkers/Prognostic markers Immunoparalysis PBMC respiration critical illness metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Take home message The current findings reveal an unexpected association between basal cell functioning and clinical outcomes in sepsis during the first week of ICU admission. Contrary to existing hypotheses, we demonstrated a substantial increase in mitochondrial respiration parameters in septic patients compared to controls, with an association between a progressive increase in basal respiration and 3-month mortality. Background Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a primary reason for admission to an Intensive Care Unit (ICU) [ 1 ]. Sepsis often contributes to (multi)organ failure and is associated with an average 30-day mortality of up to 35% of septic shock cases, accounting for about 20% of all global deaths in 2017 [ 2 , 3 ]. Sepsis survivors are at an increased risk of post-hospital discharge morbidity, mortality and a markedly reduced quality of life, which may last years after hospital discharge [ 4 , 5 ]. A lack of known therapeutic targets partly explains these poor clinical outcomes. There is increasing evidence for the role of altered mitochondrial function in the pathogenesis of sepsis-associated multiple organ dysfunction syndrome [ 6 – 8 ]. Mitochondrial respiration is the set of metabolic reactions and processes requiring oxygen at one of the final steps of the oxidative phosphorylation system (OXPHOS) in mitochondria to convert the energy stored in macronutrients to ATP [ 9 – 11 ] (Fig. 1 ). Created with Biorender. Mitochondrial respiration can be used as a marker to assess the primary function of mitochondria (Fig. 1 D). A decreased mitochondrial respiration has been demonstrated in various cells in septic ICU patients, including muscle tissue and blood platelets [ 7 – 10 , 12 – 16 ]. However, studies that measured mitochondrial function in peripheral blood mononuclear cells (PBMCs), which play an essential role in the initial (hyper)inflammatory response that hallmarks sepsis, have resulted in conflicting results. Human PBMCs are isolated from peripheral blood and identified as any blood cell with a round nucleus (i.e. lymphocytes, monocytes, natural killer cells and dendritic cells) [ 17 ]. Several studies reported a decreased mitochondrial function in PBMCs during sepsis [ 7 , 18 ], while others contrastingly reported an increased mitochondrial function [ 6 , 19 ]. One study even reported an increased mitochondrial respiration, but concomitantly, an increased mitochondrial uncoupling leading to reduced ATP-linked respiration [ 20 ]. Methodological differences, such as varying control groups and respiration mediums, might explain the inconsistency in the results of these studies. Furthermore, in two out of four studies, a measurement at only one time point was performed in each patient, which does not create insight into the progression of mitochondrial function in PBMCs during ICU stay [ 7 , 18 ]. This limitation is unfortunate since performing multiple measurements during ICU stay may reveal time-dependent effects of sepsis on mitochondrial function in PBMCs and its association with clinical outcomes. The two studies that did perform two measurements per patient, show conflicting results regarding correlation between mitochondrial respiration and clinical outcomes [ 7 , 19 ]. Rationale We set out to fill several knowledge gaps based on previously reported studies. To be able to investigate whether mitochondrial derangements originate from the PBMCs themselves, we opted to resuspend the PBMCs in a standardized medium, not plasma. Secondly, we performed repeated mitochondrial respiration measurements during the first week of ICU stay, as studies assessing the potential time-dependent effects of sepsis on mitochondrial function in PBMCs more than twice are lacking. For mechanistic underpinning, the expression of key genes involved in oxidative phosphorylation was also assessed using RT-qPCR. Lastly, we calculated correlations between clinical outcomes and mitochondrial function changes to reveal potential time-dependent associations between mitochondrial function and clinical outcomes. Materials and methods Study design and setting A prospective, observational single-centre cohort study with an age- and sex-matched control group was conducted at Gelderse Vallei Hospital (Ede, The Netherlands) between January 1, 2018, and January 27, 2023. Due to closure of the research laboratories during the SARS-CoV-2 pandemic, study inclusions were temporarily halted between March 14, 2020, and October 1, 2020. PBMC measurements were performed at Wageningen University and Research (Wageningen, The Netherlands). Study participants Patients (aged ≥ 18 years) admitted to the ICU with sepsis and/or septic shock were eligible for inclusion. According to the Third International Consensus Definitions, sepsis was defined as a new life-threatening organ dysfunction caused by a dysregulated host response to microbiologically confirmed or clinically suspected (supported by laboratory or radiology findings) infection, as identified by an increase in the sequential organ failure assessment (SOFA) score of ≥ 2 points. Septic shock was defined as the need for vasopressors to maintain a mean arterial pressure of ≥ 65 mmHg and serum lactate levels > 2 mmol/L (> 18 mg/dL) in the absence of hypovolaemia [ 1 ]. The control group was recruited from metabolically healthy short-stay hospitalised patients planned to undergo elective surgery for benign non-inflammatory indications, and individuals visiting the outpatient (fracture) clinic, individually matched for age (± 2 years) and sex [ 21 ]. Further exclusion criteria can be found in the Supplemental File . Study objectives The primary study objective was to investigate the progression of mitochondrial respiratory function in PBMCs in septic ICU patients during the first week of ICU admission. Secondary objectives were to investigate the association between mitochondrial respiratory function and (I) sepsis severity and (II) clinical outcomes, including ICU-, hospital and 3-month mortality, length of ICU and hospital stay (LOS) and duration of mechanical ventilation. Data collection This study used PBMCs to measure mitochondrial respiratory function. Sepsis group Arterial blood samples were collected at three time points via an indwelling arterial access: day 1–2 (24-48h), day 3–4 (72-96h) and day 5–6 (120-144h) after ICU admission (indicated with T1, T2 and T3), respectively. Control group The control group underwent blood sampling by venepuncture once during their visit to the outpatient clinic or short-stay hospitalisation prior to surgery. PBMC isolation, washing and counting for high resolution respirometry Blood samples for PBMC isolation to be used for high resolution respirometry were collected in sodium citrate buffered cell preparation tubes containing a ficoll solution and centrifuged at 1000g for 30 minutes at room temperature. Next, PBMCs were resuspended in warm (37°C) 10mL of Hank Balanced Salt Solution (HBSS) without CaCl 2 or MgCl 2 (Thermo Fisher Scientific, Waltham, MA, USA) and centrifuged at 400g for 10 minutes at room temperature. The supernatant was then removed, and this washing step was repeated twice. After washing, the resulting PBMC pellet was resuspended in 1 mL of warm (37°C) Seahorse XF base medium supplemented with 2 mM glutamine and 25 mM glucose. The PBMCs were counted using the Cellometer auto T4, and cell viability was assessed by mixing 10 µL of cells with 10 µL acridine orange and propidium iodide stain. PBMCs were then immediately used for high-resolution respirometry. High-resolution respirometry Two to five million live PBMCs were injected into a chamber of the Oroboros O2K (Oxygraph-2k Oroboros Instruments, Innsbruck, Austria). The chamber volume was set to 2mL and filled with Agilent Seahorse XF Base medium supplemented with 25 mM glucose and two mM glutamate, and the pH was set to 7.4. The temperature within the chamber was set to 37°C and stirring speed to 750 rotations per minute. Oxygen concentration is continuously measured, recorded and used to calculate oxygen flux per one million live PBMCs using DatLab Software 4.3 (Oroboros Instruments, Innsbruck, Austria) (Fig. 1 ). After injection of the PBMCs, the basal respiration was recorded first. Second, the complex V inhibitor, oligomycin, was added (2.5 µM), which induced a state in which respiration is primarily to compensate for proton leakage. Third, carbonyl cyanide m-chlorophenylhydrazone (CCCP) was added repeatedly (20 nM) until maximum mitochondrial respiration was reached. Fourth, the complex I inhibitor rotenone and the complex III inhibitor, antimycin A, were added (0.5 µM and 2.5 µM, respectively) to determine non-mitochondrial respiration. Each step of the function profiling test was recorded after respiration had stabilised. Additionally, three parameters were calculated. ATP-linked respiration was calculated by subtracting leak respiration from basal respiration. Coupling efficiency was calculated by dividing ATP-linked respiration by basal respiration. Spare respiratory capacity was calculated by subtracting basal respiration from the maximal respiration. Measurements were considered to be failed when the blood sample yielded too few cells to perform the primary study measurements, if there was no basal respiration signal or if there was no measurable reaction in respiration to the addition of CCCP or one of the inhibitors. PBMC isolation and lysing for RT-qPCR After the isolation of PBMCs as previously described, PBMCs were washed three times with 10 mL of cold (4°C) HBSS and centrifuged at 400 g for 10 minutes at 4°C. After washing, the PBMCs were resuspended in 300 µL of RLT buffer (Qiagen, Hilden, Germany) with 1% (v/v) ß-mercaptoethanol (BME) (Sigma-Aldrich, St. Louis, MO, USA). Lysed PBMCs were stored at -80°C. Real-time quantitative polymerase chain reaction For gene expression analysis, samples from 10 septic ICU patients and 10 matched controls were used. Samples of other subjects were excluded due to insufficient quality or concentration of RNA in at least one of the three timepoints (T1, T2 or T3). All lysates were homogenized by passing it six times through a blunt 20-gauge needle (0.9 mm diameter). Total RNA was extracted from PBMCs using the RNeasy Mini Kit (Qiagen, Hilden, Germany) in accordance with the manufacturer's instructions and RNA quantity and purity were assessed using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). A total of 500 ng of purified RNA was reverse transcribed into complementary DNA (cDNA) using the iScript™ cDNA Synthesis Kit (BioRad, Hercules, CA, USA), following the manufacturer's instructions. The reaction mixture included 500 ng RNA, 1 µL of iScript reverse transcriptase, 4 µL of 5x iScript reaction mix, and RNase/DNase-free water to a final volume of 20 µL. Reverse transcription was carried out in a C1000 Touch™ Thermal Cycler (BioRad, Hercules, CA, USA) with the following conditions: 25°C for 5 minutes, 42°C for 30 minutes, and 85°C for 5 minutes. Pooled cDNA stock was created by mixing 5 µL from each individual cDNA sample to be used for generating calibration curves. Each cDNA sample was then diluted 1:100 before performing real time quantitative reverse transcription polymerase chain reaction (qRT-PCR). qRT-PCR was performed to assess the expression of genes involved in mitochondrial OXPHOS system ( NDUFB8 , SDHB , UQCRC2 , MTCO2 , and ATP5F1A ). The housekeeping genes RPS15 , CSNK2A2 , and ACTB were used as internal controls. All primers targeted all known isoforms of the respective genes and are listed in Supplemental Table 1. The qRT-PCR reactions were conducted using iQ SYBR Green Supermix (BioRad, Hercules, CA, USA) with an end volume of 25 µL consisting of 12.5 µL of Supermix, 8.5 µL RNase/DNase-free water, 1.0 µL each of forward and reverse primers (10 pmol/µL), and 2 µL of 100x diluted cDNA. Amplification was performed on a C1000 Touch™ Thermal Cycler under the following conditions: 95°C for 3 minutes, 40 cycles of 95°C for 15 seconds and 58°C for 45 seconds, followed by melt-curve analysis. Relative gene expression levels were normalized with the three reference genes and all mean gene expression values of the patients were compared to the controls. Additional data sources Data collection from the electronic medical record systems MetaVision® (iMDsoft, Tel Aviv, Israel) and NeoZIS® (MI Consultancy, Katwijk, The Netherlands) included baseline patient characteristics (including disease severity scores), laboratory values and outcome parameters, such as duration of mechanical ventilation and length of ICU and hospital stay. Study size Japiassú et al. previously studied mitochondrial oxygen consumption in PBMCs of septic ICU patients (n = 20) and critically ill postoperative control patients (n = 18) [ 7 ]. The respiratory control ratio (RCR) was significantly reduced in the septic ICU patient group compared to the control group. The difference in the RCR between day 1 and day 7 was approximately half of the difference between the study and the control group. Assuming the changes in mitochondrial oxygen consumption develop linearly during sepsis, the expected difference (effect size) between the measurements at the three different time points during the first week, is similar to 50% of the observed differences by Japiassú et al. Therefore, to achieve a power of 0.95 with a two-sided significance level of 0.05, 30 subjects per group were needed (as calculated with G*power, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). When three valid measurements from an ICU subject could not be obtained due to withdrawal of consent, death, early ICU discharge or technical problems, patients were not matched with a control subject. Moreover, additional patients were included consecutively until complete measurements were obtained in 30 pairs. Separate measurements from patients who did not have three complete consecutive measurements were excluded from mitochondrial function progression analyses (Figs. 3 and 4 ). Patients with valid measurements at T1 and T3 were included in the cox regression models and analyses comparing survivors vs. non-survivors (Figs. 5 and 6 ). Statistical analyses Data verification was conducted manually. Descriptive statistics were performed for demographic and clinical data of all patients and the primary outcome. Normality was assessed numerically and graphically. Continuous values were reported as means with standard deviations (SD; parametric data) or medians with interquartile ranges [IQR; non-parametric data]. Discrete data were presented as proportions (%). Differences in baseline characteristics and clinical outcomes between the sepsis and control group were assessed using the independent samples t-test, Wilcoxon rank sum, Wilcoxon signed rank, chi-squared, or Fisher's exact tests, where appropriate. Secondary outcomes were evaluated using uni- and multivariable Cox proportional hazards regression models or ANOVA analyses, where appropriate. Multivariable Cox regression analyses were performed using the Enter and Forward Stepwise Wald methods. Based on literature and clinical relevance, the variables age, sex, body mass index (BMI), acute physiology and chronic health evaluation II (APACHE II), SOFA and modified nutrition risk in critically ill (mNUTRIC) scores were analysed in regression analyses. Variables were dichotomised (using the median) in case of non-linearity, with the outcome parameter assessed by visual inspection of boxplots. Finally, all samples' PBMC lymphocyte-monocyte ratios (LMR) were calculated. Their changes over time and differences between survivors and non-survivors were evaluated. Moreover, correlation with parameters of mitochondrial function was assessed using Kendall’s Tau-b. Multicollinearity was assessed using the variance inflation factor (VIF); a value below two was considered acceptable. Gene expression analysis was performed using relative expression values calculated via the ΔΔCq method. For statistical analysis, the ΔΔCq values were used as input, and consistency with the raw Bio-Rad qPCR output files was manually verified. Fold changes (2 −ΔΔCq ) were calculated and used exclusively for graphical representation of the results. Normality was assessed of both gene expression data and mitochondrial respiration parameters using the Shapiro-Wilk test. When data were not normally distributed, logarithmic transformations were applied in an attempt to normalize the distribution. Data that remained non-normally distributed after transformation were analyzed using a non-parametric one-way ANOVA (Kruskal-Wallis test). For data that were normally distributed after transformation, a parametric one-way ANOVA was performed using the transformed data. When significant differences were detected, Tukey’s post-hoc tests (in case of normally distributed data) or Dunn’s test (in case of not normally distributed data) were applied. IBM SPSS statistics 27 (IBM Corp, Armonk, NY, USA) or Graphpad Prism 10.4.2 (GraphPad Software, Boston, MA, USA) was used for statistical analyses and figures representing statistics. Only two-sided analyses were used. P-values ≤ 0.05 were considered statistically significant. Ethics approval The study was approved by the Medical Ethical Committee of Wageningen University (METC-WUR, which was incorporated in the METC Oost-Nederland in 2021, dossier no. 2021–13011) and the assessment Committee for Scientific Research of ZGV (dossier no. 1801-004). The protocol was registered in the Netherlands Trial Register (number NTR6969) and was made available through the International Clinical Trial Registry Platform (NL5918). Patients were enrolled after the informed consent form was signed by the patient or legal representative. Results To obtain thirty matched pairs with complete data, informed consent was obtained from 46 septic patients and 30 age- and sex-matched controls (Fig. 2 ). The septic patients were predominantly male (n = 33, 72%) and had a mean age of 68 (± 13 SD) years, with a mean BMI of 27 (± 6 SD) kg/m 2 . Pneumonia was found to be the most common cause of sepsis (n = 24, 52%), followed by an abdominal origin (n = 17, 37%). At baseline, patients had the following clinical scores: mNUTRIC 5 [IQR 3–6], SOFA 8 [ 7 – 10 ] and APACHE II 18 [ 14 – 22 ]. The control patients were age- and sex matched and were subsequently predominantly male as well (n = 22, 73%) and had a mean age of 71 (± 15 SD) years, which was not different from the sepsis group (p = 0.3). After study completion, upon reviewing the obtained data, single respirometry measurements in five patients had to be discarded as they did not meet quality standards (T1 n = 1, T2 n = 1, T3 n = 2 and T1-3 n = 1). Measurements were discovered to have failed in four controls (13%) and partially failed (no reaction to CCCP, rendering basal respiration and proton leak useable) in one patient. As the inclusion phase had already been completed, no further inclusions were performed to compensate for these losses. The clinical patient characteristics at the time of blood samplings are shown in Table 1 . Table 1 Patient and control characteristics at time of the blood samplings. Controls (n = 26) Sepsis patients (n = 40 a ) T1 (n = 38) p-value b T2 (n = 35) p-value c T3 (n = 28) p-value d Clinical parameters SOFA score n.a. 6 [ 4 – 8 ] n.a. 4 [ 3 – 8 ] < 0.001* 5 [ 3 – 7 ] < 0.001* Lactate (mmol/L) n.a. 1.2 [0.8–1.7] n.a. 0.9 [0.8–1.2] 0.002* 0.8 [0.6–1.2] < 0.001* Leukocytes (*10^9/L) 6.5 [5.7–8.0] 13.6 [10.0-18.9] < 0.001* 12.7 [9.1–16.3] 0.189 12.2 [9.4–19.1] 0.388 PMBC LMR 4.6 [3.3–5.6] 2.0 [1.1-3.0] 0.010* 2.0 [1.5–3.3] 0.465 2.0 [1.3-3.0] 0.670 CRP (mg/L) 0 249 [148–323] < 0.001* 120 [77–214] < 0.001* 109 [58–204] < 0.001* Ureum (mmol/L) 6.3 [5.5–7.1] 10.7 [7.8–19.5] n = 26 0.002* 11 [5.5–25] n = 20 0.844 10.1 [7.5–15.8] n = 18 0.529 Creatinine (umol/L) 74 [62–87] 84 [60–145] 0.024* 72 [52–117] 0.002* 60 [46–112] < 0.001* Cortisol (nmol/L) 395 [311–472] 724 [546–1568] < 0.001* 676 [418–1005] < 0.01* 592 [431–725] < 0.001* Insulin (mmol/L) 19 [9.4–18.5] 18 [8.8–30] 0.280 21 [12–35] 0.523 11.5 [7.7–31.5] 0.755 Insulin supplementation (IU) n.a. 0 [0–19] n.a. 0 [0–24] 0.449 0 [0–26] 0.234 a. Unless stated otherwise due to missing variables. b. T1 compared to the control group; p-values were calculated using the Wilcoxon signed rank test c. T1 compared to T2, p-values were calculated using the Wilcoxon signed rank test; d. T1 compared to T3, p-values were calculated using the Wilcoxon signed rank test; * p-value < 0.05. [Insert Table 1 here] Study measurements Mitochondrial function over time in septic patients and controls Basal respiration was significantly higher in septic patients compared to controls (2.5 ± 0.3 pmol O 2 /(s*ml)) at timepoints 1 (5.3 ± 1.0 pmol O 2 /(s*ml), p < 0.05), 2 and 3 (4.8 ± 0.6 and 6.1 ± 1.0 pmol O 2 /(s*ml) respectively, both p < 0.01, Fig. 3 A). Proton leak was significantly higher in septic patients compared to controls (1.1 ± 0.1 pmol O 2 /(s*ml)) at timepoints 2 and 3 (1.6 ± 0.2 and 1.7 ± 0.2 pmol O 2 /(s*ml), respectively, both p < 0.05, Fig. 3 B). Maximal respiration was significantly higher in septic patients (12.4 ± 2.1 pmol O 2 /(s*ml)) compared to controls (5.7 ± 0.6 pmol O 2 /(s*ml), p < 0.05, Fig. 3 C) at timepoint 3 only. ATP-linked respiration was significantly higher in septic patients compared to controls (1.4 ± 0.2 pmol O 2 /(s*ml)) at timepoints 2 (3.3 ± 0.5 pmol O 2 /(s*ml), p < 0.05) and 3 (4.4 ± 0.7 pmol O 2 /(s*ml), p < 0.001, Fig. 3 D). No differences were detected in spare respiratory capacity ( Fig. 3 E ) . Expression of oxidative phosphorylation genes over time The expression of genes encoding for subunits in complex II ( SDHB ) and complex V ( ATP5F1A ) was significantly higher in septic patients compared to controls at multiple timepoints of the first week of ICU admission (Fig. 4 ). At T1, SDBH and ATP5F1A expression were 43% (p < 0.05) and 52% (p < 0.01) higher in septic patients compared to controls, respectively. At T2, SDBH was 54% higher in septic patients compared to controls (p < 0.01). At T3, SDBH and ATP5F1A expression was 54% (p < 0.01) and 40% (p < 0.01) higher in septic patients compared to controls, respectively. In genes encoding other subunits no statistically significant differences were detected. Survivors versus non-survivors All-cause 3-month mortality in the sepsis cohort (n = 40) was 35% (n = 14). Five (36%) of the deceased patients died in the ICU, seven (50%) in the ward and two after hospital discharge (14%). Compared to the surviving patients (n = 31), the non-survivors (n = 15) were older (77 (± 10 SD) versus 63 (± 13 SD) years of age, p < 0.001), had higher APACHE II (median 20 [IQR 17–26] versus 15 [ 12 – 20 ], p = 0.008] and mNUTRIC scores (6 [IQR 6–7] versus 4 [ 3 – 5 ], p < 0.001) at ICU admission. No significant differences were found in other baseline characteristics. Regarding biochemical parameters, only significant differences in serum insulin levels between survivors and non-survivors were found (see Table 2 ). Table 2 Survivors (n = 26) a versus all-cause 3-month non-survivors (n = 14) a T1 T2 T3 survivors (n = 26) non-survivors (n = 12) p-value survivors (n = 22) non-survivors (n = 13) p-value survivors (n = 15) non-survivors (n = 13) p-value Clinical parameters SOFA score 6 [ 3 – 8 ] 7 [ 5 – 8 ] 0.6 3 [ 2 – 7 ] 5 [ 4 – 6 ] 0.3 4 [ 2 – 8 ] 5 [ 3 – 5 ] 0.9 Lactate (mmol/L) 1.3 [0.8-1.725] 1.2 [1.0-1.6] 0.9 0.9 [0.8–1.3] 1.0 [0.7–1.3] 0.9 0.8 [0.5–1.2] 0.9 [0.7–1.2] 0.5 Leukocytes (*10^9/L) 14.3 [8.5–19.0] 12.8 [10.5–16.8] 1.0 12.5 [9.1–16.3] 13.2 [8.2–16.6] 1.0 12.1 [9.5–17.7] 14.9 [9.2–22.6] 0.3 PBMC LMR 2 [1.1–3.1] 1.6 [1.0-3.9] n = 9 1.0 2.3 [1.5–3.6] n = 21 1.8 [1.2–2.4] 0.3 2.0 [1.7–3.5] 2.0 [1.1–2.5] 0.2 CRP (mg/L) 252 [169–323] 236 [137–318] 0.63 140 [74–214] 112 [87–280] 0.9 109 [70–168] 97 [35–212] 0.8 Ureum (mmol/L) 10.3 [6.5–18.2] 11.4 [9.1–25.2] 0.3 10.8 [5.1–14.4] 16.4 [8.8–32.6] 0.3 10.8 [6.6–24.7] 9.4 [7.9–14.4] 0.5 Creatinine (umol/L) 84 [57–188] 95 [75–132] 0.9 70 [52–114] 66 [52–124] 0.7 64 [46–137] 53 [44–109] 0.4 Cortisol (nmol/L) 674 [560–1397] 1398 [525–1959] 0.2 549 [418–1005] 697 [583–1062] 0.5 617 [368–761] 601 [506–768] 0.6 Insulin (mmol/L) 20.0 [11.4–36.3] 8.4 [4.6–20.5] 0.014* 20 [12–35] 17.5 [7.9–40.3] 0.8 12.5 [9.1–25.8] 13.0 [5.7–35.8] 1.0 Insulin supplementation (IU) 0 [0-0.75] 9 [0–38] 0.066 0 [0–1] 0 [0–45] 0.3 0 [0-0.75] 0 [0–31] 0.3 Insulin supplementation (IU)** 32 [5–55] 36 [28–54] 0.5 41 [8–95] 45 [38–87] 0.6 50 [13–59] 36 [21–57] 0.8 a. Unless stated otherwise due to missing variables. Definitions: T1, day 1–2 (24-48h); T2, day 3–4 (72-96h); T3, day 5–6 (120-144h) after ICU admission; Abbreviations SOFA, Sequential Organ Failure Assessment; PBMC, peripheral blood mononuclear cell; LMR, lymphocyte-monocyte ratio; CRP, C-reactive protein; IU, international units; ATP-linked respiration = basal respiration minus proton leak; SRC = spare respiratory capacity; maximal respiration minus basal respiratory capacity; coupling efficiency = ATP-linked respiration divided by basal respiration. All values are reported as medians with interquartile ranges. P-values were calculated using the Mann-Whitney U test; * p-value < 0.05. [Insert Table 2 here] In high-resolution respirometry, no mitochondrial respiration parameters were significantly different at T1 between survivors and non-survivors (Fig. 5 ). A trend for a lower mitochondrial proton leak at T1 in survivors (1.14 ± 0.2 pmol O 2 /(s*ml) compared to non-survivors 1.4 ± 0.2 pmol O 2 /(s*ml)) (p = 0.06, Fig. 5 B) was found. To assess potential differences in the progression of mitochondrial respiratory parameters between non-survivors vs. survivors, delta (Δ) values were calculated by subtracting values at timepoint 1 from those at timepoint 3. Significant differences between survivors and non-survivors in ΔBasal respiration (-0.8 ± 0.9 vs. 2.7 ± 0.9 pmol O 2 /(s*ml), respectively), ΔMaximal respiration (-2.3 ± 2.0 vs. 4.8 ± 2.5 pmol O 2 /(s*ml), respectively), ΔATP-linked respiration (-0.5 ± 0.8 vs. 2.3 ± 0.7 pmol O 2 /(s*ml), respectively), and ΔSpare respiratory respiration (-1.4 ± 1.2 vs. 2.5 ± 1.4 pmol O 2 /(s*ml), respectively), were detected (all p < 0.05, Fig. 6 ). In fact, delta values were generally negative for survivors, indicating a decrease in mitochondrial respiration over-time during ICU stay. Conversely, in non-survivors delta values were positive, indicating an increase in mitochondrial respiration over-time during ICU stay (Fig. 6 ). Cox regression The variables age and mNUTRIC were omitted in the final regression models because of their overlap (and visual correlation) with the APACHE II score. Measured mitochondrial respiration parameters were intercorrelated (all p < 0.01); therefore, only basal respiration was used in the final model. None of the mitochondrial respiratory parameters were correlated with the SOFA score. The deltas of these two parameters over time were entered into the final model (ΔSOFA and ΔBasal respiration as calculated by T3 minus T1). In the final Cox regression multivariable model, ΔBasal respiration (≥ 0.07 nmol O 2 /min/10 7 from T1 to T3) was associated with the primary endpoint of 3-month mortality (HR 3.8, 95%CI 1.0-14.1, p = 0.047) (see Table 3 ). The VIF was < 2 for the variables in this final model. Table 3 Univariable and multivariable COX regressions for the association of primary endpoint 3-month mortality, baseline and clinical characteristics and mitochondrial respiratory function (n = 40). Univariable p-value Multivariable p-value HR (95%-CI) HR (95%-CI) A. 3-month mortality (n = 14) Sex (female) 1.060 (0.332–3.385) 0.9 0.707 (0.164–3.051) 0.6 BMI (> 25.7) 1.040 (0.364–2.967) 0.9 1.400 (0.381–5.139) 0.6 APACHE II on admission (> 18) 2.029 (0.703–5.859) 0.2 1.926 (0.509–7.289) 0.3 delta SOFA T3-1 (≥-2) 0.863 (0.303–2.463) 0.8 1.038 (0.218–3.832) 0.9 delta basal T3-1 (> 0.068**) 3.041 (0.886–10.431) 0.08 3.794 (1.018–14.149) 0.047* B. ICU mortality (n = 5) Sex (female) 1.633 (0.182–14.639) 0.7 Difficulties with multivariable regression due to low number of deaths BMI (> 25.7) 1.578 (0.263–9.460) 0.6 APACHE II on admission (> 18) 2.139 (0.356–12.861) 0.4 delta SOFA T3-1 (≥-2)** 0.223 (0.025–1.998) 0.2 delta basal T3-1 (> 0.068**) 118 (0.007–2.121*10 6 ) 0.3 C. Hospital mortality (n = 7) Sex (female) 1.291 (0.349–4.775) 0.7 0.979 (0.183–5.228) 0.9 BMI (> 25.7) 1.028 (0.331–3.192) 1.0 1.554 (0.389–6.207) 0.5 APACHE II on admission (> 18) 1.490 (0.480–4.630) 0.5 1.232 (0.288–5.273) 0.8 delta SOFA T3-1 (≥-2)** 0.61 (0.197–1.959) 0.4 0.916 (0.232–3.627) 0.9 delta basal T3-1 (> 0.068**) 2.112 (0.565–7.901) 0.3 2.341 (0.577–9.494) 0.2 T1, day 1–2 (24-48h); T2, day 3–4 (72-96h); T3, day 5–6 (120-144h) after ICU admission. Delta was calculated as: (mitochondrial parameter at T3 minus T1). Abbreviations: HR; hazard ratio, 95%-CI; 95%-confidence interval, BMI; body mass index, APACHE II; Acute Physiology And Chronic Health Evaluation, SOFA; sequential organ failure assessment, basal; basal respiration (measured in nmol O2/min/10^7). * p-value < 0.05. ** including negative values (= a decrease in SOFA score or basal respiration, respectively, over time). Multivariable Cox regression analyses were performed using the Enter and Forward Stepwise Wald methods. [Insert Table 3 here] Lymphocyte-monocyte ratio The PBMC LMR was lower in sepsis patients than in controls. No change over time (T1-T3) was noted, nor were any differences between survivors and non-survivors (Tables 1 – 2 ). Discussion In this prospective observational study, we found a significant increase in basal, ATP-linked, maximal and proton leak associated respiration in sepsis patients compared to controls within the first week of ICU admission. These findings were corroborated by gene expression analysis, showing an increased expression of genes encoding OXPHOS subunits (complex II and V) in septic patients, consistent with the functional respiration data. These observations contrasted our hypothesis, as we did not demonstrate a decrease in PBMC mitochondrial respiration during sepsis. Moreover, a more significant increase in basal, ATP-linked and maximal respiration and spare respiratory capacity was observed during the first week of ICU stay in non-survivors compared to survivors (p < 0.05), although these respiration parameters were not statistically different between the sepsis and control patients at baseline measurements. This progression of basal respiration was associated with the primary endpoint of 3-month mortality after correction for relevant covariates. Therefore, the current results show that septic patients undergo a progressive upregulation of PBMC mitochondrial respiration, which may be related to adverse outcome of disease. Our findings are consistent with those of Sjövall et al. [ 19 ] and Belikova et al [ 6 ], who also found that basal mitochondrial respiration in PBMCs was significantly increased within the first 48 hours of ICU admission. In addition, Sjövall et al. [ 19 ] demonstrated a progressive increase in basal and maximal respiration during the first week of sepsis patients compared to healthy controls. Strikingly, they observed no differences between surviving and non-surviving patients at any point in time. Both their inclusion and mortality rates were lower than in the present study, which may have led the study to be underpowered for differentiation between survivors and non-survivors. However, their article does neither report the original data nor p-values for the comparisons, so this claim cannot be substantiated. In contrast with our findings, Jang et al. (studying mitochondrial respiration of PBMCs in 10 septic patients measured once shortly after presentation to an emergency department), Japiassú et al. (studying mitochondrial respiration of PBMCs in 20 patients during the first 48 hours of septic shock) and Garrabou et al. (studying mitochondrial respiration in 19 septic patients, time of measurement not mentioned) observed a significant reduction of ADP-linked respiration in permeabilized PBMCs of septic patients compared to controls [ 7 , 8 , 18 ]. In addition, in the study of Japiassú et al. , a significant reduction was observed in ADP-linked respiration in non-surviving sepsis patients compared with the postoperative controls without sepsis (5.60 versus 9.89 nmol O 2 /min/10 7 , respectively, p < 0.01). Survivors demonstrated a 2.9x increase in ATP-linked respiration after one week [ 7 ]. Contrastingly, we did not observe a significant change in respiratory function over time in survivors. Instead, we found a significant increase in basal and ATP-linked respiration in non-survivors during the first week of ICU stay. Similarly, studies reporting gene expression data of genes encoding OXPHOS subunits are conflicting. We observed an increased expression of OXPHOS genes ( SDBH and ATP5F1A ) in septic patients compared to controls, a finding that is in line with those of Li et al. , whom also found an increased expression of OXPHOS subunits compared to controls [ 22 ]. Similarly, Sjövall et al. reported increased mtDNA copies in septic patients compared to controls, indirectly indicating a higher number of mitochondria and OXPHOS subunits present in septic patients [ 19 ]. However, Nucci et al. reported a downregulation of genes encoding OXPHOS subunits in samples collected from septic patients during admission [ 23 ]. These contradictory observations may be due to methodological variety. A clear difference between the abovementioned studies is the composition of the control group, which may influence the outcomes of comparisons between sepsis and control groups. In the current study, we chose to include sex- and age-matched controls since those are two factors known to influence mitochondrial respiratory function, which has not been done in other studies besides the study of Garrabou et al. [ 8 ]. Moreover, we selected metabolically healthy age- and sex-matched controls. In contrast, Japiassú et al. included critically ill postoperative ICU patients, whereas Jang et al. chose to use three unmatched control groups of younger, older and infected (but not septic) patients [ 7 , 18 ]. It is intriguing that Sjövall et al. and our study still proved an increase in mitochondrial function parameters in PBMCs of septic patients, eventhough healthy controls were included [ 19 ]. Secondly, exclusion criteria differ between mentioned studies. We excluded many common comorbidities known to affect mitochondrial respiratory function (such as diabetes mellitus and COPD), which allowed us to exclude the potential confounding effect of these comorbidities. Such exclusion criteria were not reported in other studies. Thirdly, the time at which the PBMCs were collected and measured respiration differed between studies. The timing of blood collection is not described by Garrabou et al. [ 8 ]. Jang et al. and Nucci et al. collected blood samples from patients with sepsis or septic shock upon presentation to the emergency department [ 18 ], whereas our measurements and those of Japiassú et al. commenced within 48 hours of ICU admission [ 7 ]. These may be very different (metabolic) time points in a patient's journey. Furthermore, our cohort was slightly older than those of Jang [ 18 ] and Garrabou [ 8 ] and their coworkers (68 vs 63, resp. 64 years of age), and although SOFA scores were similar in all studies, it is unknown whether all patients in the Garrabou and Jang cohorts required ICU admission. Fourthly, the methods of respiration measurement vary in comparison with current literature. The current study resuspended PBMCs in a standardized medium, not plasma. This was similar to Japiassú et al. [ 7 ]. On the contrary, Sjövall and coworkers used the patient's plasma [ 19 ]. However, mitochondrial function in PBMCs is altered by plasma, as was demonstrated by Belikova and co-workers [ 6 ]. Consequently, it is difficult to disentangle the effects of sepsis on plasma content from the effect of sepsis on mitochondria in PBMCs per se. Strikingly, in the current study, decreased mitochondrial respiration and decreased expression of OXPHOS genes was, in fact, not visible. This approach revealed that the mitochondria of PBMCs are not dysfunctional and capable of improving respiratory function. In addition, this suggests that if a worsened respiratory function is observed in PBMCs of septic patients, this is perhaps more likely to originate from potential dysregulating components present in plasma. In addition, in some studies, PBMCs were permeabilized, while we used non-permeabilized PBMCs for respiratory measurements [ 7 , 8 ]. It could be hypothesized that this explains the differences with our results. However, since both Sjövall et al. [ 19 ] and Jang et al. [ 18 ] have performed their measurements in both permeabilized and non-permeabilized PBMCs and found consistent results between those experiments, this is unlikely to explain the contrasting results with our studies. Lastly, it can be hypothesized that the differences in respiratory function of PBMCs belonging to the different groups of our study are caused by a shift in PBMC composition rather than a shift in mitochondrial function per se. In humans, PBMC cell ratios vary across individuals, but typically, lymphocytes are in the range of 70–90%, monocytes from 10 to 20%, while dendritic cells are rare, accounting for only 1–2% [ 17 ]. In our cohort, the PBMC lymphocyte-monocyte ratio (LMR) was lower in sepsis patients than in controls. This lower count is to be expected, as a lower LMR is associated with systemic inflammation [ 24 ]. However, parameters of mitochondrial function did not correlate with the LMR, nor did the LMR change over time, and also not when survivors versus non-survivors were compared (Table 2 ). However, it could still be of interest to measure mitochondrial function in distinct cell populations in future studies and should perhaps be considered when leukocytes are used as bioenergetic biomarkers [ 25 ]. Although we could not identify consistent methodological differences among all the studies mentioned, combining these methodological differences can contribute to the contrasting results. Previous studies, although few, measuring mitochondrial function in muscle tissue of different origins have consistently reported a lower activity of mitochondrial complexes and a lower ATP content, concomitant with an altered expression of genes involved in regulating mitochondrial dynamics [ 15 , 26 , 27 ]. In the current study, mitochondrial function was measured in PBMCs. PBMCs are easily and non-invasively obtained. However, PBMCs are important cells during inflammation and systemic infection and may have a different metabolic response to sepsis compared to other tissues directly involved in multiorgan failure, such as the liver and muscles. Based on the results of the current study and in comparison to studies performed using skeletal muscle biopsies, PBMCs do not necessarily reflect the decrease in mitochondrial function, which is reported elsewhere in the body. Indeed, Jeger et al. reported that results from previous animal and clinical studies investigating mitochondrial function in several tissue types during sepsis are heterogeneous, reporting increased and decreased mitochondrial oxygen consumption [ 28 ]. Although speculative, these differences in mitochondrial functioning between tissues may reflect differences in their role during sepsis. Thus, the increased basal, ATP-linked and maximal respiration together with the increased expression of genes encoding for subunits of OXPHOS complexes (in particular complex II and IV) as found in the current study may reflect an increased ATP demand of PBMCs during human sepsis, as a result of an activation of the immune system to combat the underlying infection. The clinical relevance of this increase is suggested by the higher increase in basal, ATP-linked and maximal respiration respiration and spare respiratory capacity over time (i.e., between T1 and T3) in non-survivors, compared to survivors. Our findings may thus contrast speculation that mitochondrial dysfunction is the root cause of immunoparalysis and could be responsible for the onset of organ dysfunction [ 29 ]. Still, pathophysiological interpretation of this difference between survivors and non-survivors is precarious. The higher increase over time may, partially, be due to a lower basal and ATP-linked respiration in PBMC’s at T1 of non-survivors compared to survivors, although this was not statistically significant. Thus, a delayed up-regulation of PBMC activation in PBMCs of non-survivors compared to survivors, cannot fully be excluded but, in view of the lack of statistical significance, is highly speculative. Still, disregarding possible differences at T1, a higher increase in both respiration parameters over time could reflect differences in the development of the infection, or indicate immune dysfunction. This study does not provide clarity in this respect, especially since immune mechanisms during sepsis are complex, consisting of simultaneous hyperinflammation and immune suppression. Future studies, however, will take these hypotheses into account. Strengths Combining functional measurements of mitochondrial functioning with relevant gene expression patterns, both exhibiting increases in sepsis, provides a more robust and in-depth analysis of the situation. Also, the consecutive measurements at three moments during the first week of ICU admission with fixed intervals are considered a strength of this study, as this enabled us to better understand the progress of mitochondrial respiration during the first week of ICU admission in septic patients. In addition, the extensive list of exclusion criteria based on common comorbidities (e.g., diabetes mellitus and COPD) known to affect metabolism and mitochondrial function is a unique strength of this study, as this allowed us to exclude potential confounding effects of these comorbidities. Limitations The current study is limited by its single-centre design. However, as the samples needed to arrive at the laboratory in a fresh state, the hospital's proximity to a university laboratory equipped with an Oroboros was an essential condition for this study. Secondly, the possible effects of administered medication on mitochondrial function may represent an unaccounted confounder. Thirdly, multivariate Cox regression analyses performed on small numbers found an association between the delta basal respiration and ICU and 3-month mortality, not the severity-of-disease score APACHE II. This may be due to additional confounding factors, which were not accounted for (residual confounding) or multicollinearity, although the VIF was low (< 2). Finally, the recruitment period covered five years. During this period the standard of care may have changed, weakening the in- and external validity of our findings. Future directions Further research is needed to elucidate the role of mitochondria in the sepsis pathophysiology. More extensive multicentre trials are needed to consolidate the current study's findings. It would be interesting to measure mitochondrial respiratory function in various other tissues in parallel to create more insight into the potential role of PBMCs as a proxy marker for mitochondrial respiratory function in other tissues. Conclusion This study demonstrated an increased basal, ATP-linked, maximal and proton leak associated respiration in PBMCs within the first week of ICU admission in sepsis patients compared to their healthy matched controls. Similarly, the expression of genes encoding subunits of complex II and V were increased in septic patients compared to controls. In addition, a progressive increase of mitochondrial respiration in PBMCs during the first week of ICU stay was positively associated with 3-month mortality. Abbreviations ADP Adenine diphosphate ATP Adenine triphosphate APACHE II Acute physiology and chronic health evaluation II BMI Body mass index CCCP Carbonyl cyanide m-chlorophenylhydrazone CI Confidence interval COPD Chronic obstructive pulmonary disease FEV1 First second forced expiration FVC Forced vital capacity ICU Intensive care unit HR Hazard ratio IQR Interquartile range LMR Lymphocyte-monocyte ratio LOS Length of stay mNUTRIC Modified nutrition risk in critically ill score OXPHOS Oxidative phosphorylation system PBMC Peripheral blood mononuclear cells RCR Respiratory control ratio SARS-CoV-2 Severe acute respiratory coronavirus 2 SD Standard deviation SOFA Sequential Organ Failure Assessment score TCA Tricarboxylic acid VIF Variation inflation factor WUR Wageningen University and Research ZGV Ziekenhuis Gelderse Vallei (Gelderse Vallei hospital) Declarations Ethics approval and consent to participate The study was approved by the Medical Ethical Committee of Wageningen University (METC-WUR, which was incorporated in the METC Oost-Nederland in 2021, dossier no. 2021-13011) and the assessment Committee for Scientific Research of ZGV (dossier no. 1801-004). The protocol was registered in the Netherlands Trial Register (number NTR6969) and was made available through the International Clinical Trial Registry Platform (NL5918). Patients were enrolled after signing the informed consent by the patient or legal representative. Consent for publication Not applicable. Availability of data and material The dataset supporting the conclusions of this article is available upon reasonable request from the corresponding author. Competing interests Prof. Dr. Van Zanten reported receiving honoraria for advisory board meetings, lectures, research grants, and travel expenses from Abbott, AOP Pharma, Baxter, Danone-Nutricia, Dutch Medical Food, Fresenius Kabi, GE Healthcare, Medcaptain, Nestle, PAION and Rousselot. The other authors have nothing to declare. Funding This research was funded by the Research Foundation of the Intensive Care of Gelderse Vallei Hospital, Ede, The Netherlands. Any article processing charges will be put forth by Wageningen University & Research. Authors’ contributions HM and RSB contributed equally to data collection, data analysis and interpretation, and writing and revising of the manuscript. AvN, SG, and JdJ contributed to the conception of the study, data collection, analysis, interpretation, and revision of the manuscript. AW contributed to data collection and interpretation. AvZ contributed to the conception of the study, data interpretation and revision of the manuscript. Acknowledgements We owe thanks to the following people for their valuable contributions to data collection and the writing of preliminary reports: Monique Daanje, Rosan van den Boogaard, Demi Vogels, Laura Willemse, Marloes Snijder, Max van den Bergh, Julia Boeré, Susana Mittmann, Panagiotis Vlachogiannis, Marieke Krüger, Rebeka Darmati, Viditha Venkatramu and Lisa Kleverwal. 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Am J Physiol Endocrinol Metab. 2006;291(5):E1044-50. Jeger V, Djafarzadeh S, Jakob SM, Takala J. Mitochondrial function in sepsis. Eur J Clin Invest. 2013;43(5):532–42. Nedel W, Deutschendorf C, Portela LVC. Sepsis-induced mitochondrial dysfunction: A narrative review. World J Crit Care Med. 2023;12(3):139–152. doi: 10.5492/wjccm.v12.i3.139 . PMID: 37397587; PMCID: PMC10308342. Additional Declarations There is NO Competing Interest. Supplementary Files MICSupplementalTables.docx Supplemental Material belonging to Progression of peripheral blood mononuclear cell mitochondrial function during the early phase of sepsis in Intensive Care Unit patients Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7137135\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":490433935,\"identity\":\"9bd49a33-b56c-4dc7-ab8f-ce22a0391a52\",\"order_by\":0,\"name\":\"Arthur Van Zanten\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBADOQhVQJxqxgYgYQxhG5CgJbGBaC3y7WePP/i4wy59fv8aA+YCYrQw9uQlNs48k5zbOOONAfMMYrQwM+QYNvO2Mec2SxxLYOYhRgsb/xvD5r9t9elsRGvhkQDawth2OIGHv/kAcVokJN4YzuxtO244Q4L5wGGitMj35xh8+NlWLS/ff7DxMU8FEVqQ7EtgOECSBgYGflI1jIJRMApGwYgBALa5MJLc+pHhAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Ziekenhuis Gelderse Vallei\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Arthur\",\"middleName\":\"Van\",\"lastName\":\"Zanten\",\"suffix\":\"\"},{\"id\":490433936,\"identity\":\"a843687c-7b9f-4a9c-b9d8-374c47b373f7\",\"order_by\":1,\"name\":\"Hanneke Moonen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ziekenhuis Gelderse Vallei\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hanneke\",\"middleName\":\"\",\"lastName\":\"Moonen\",\"suffix\":\"\"},{\"id\":490433937,\"identity\":\"4b47e7af-b1c5-4840-9375-e359deb80df0\",\"order_by\":2,\"name\":\"Rianne Slingerland\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rianne\",\"middleName\":\"\",\"lastName\":\"Slingerland\",\"suffix\":\"\"},{\"id\":490433938,\"identity\":\"752ef678-5aa9-46ac-bebb-f76c62eb0671\",\"order_by\":3,\"name\":\"Jelle De Jong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jelle\",\"middleName\":\"\",\"lastName\":\"De Jong\",\"suffix\":\"\"},{\"id\":490433939,\"identity\":\"6b697ece-efde-416b-8a74-34c507d70548\",\"order_by\":4,\"name\":\"Anais Wiech\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anais\",\"middleName\":\"\",\"lastName\":\"Wiech\",\"suffix\":\"\"},{\"id\":490433940,\"identity\":\"4417fa0c-b1cf-4697-9236-59e6629ee4a9\",\"order_by\":5,\"name\":\"Arie Nieuwenhuizen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Arie\",\"middleName\":\"\",\"lastName\":\"Nieuwenhuizen\",\"suffix\":\"\"},{\"id\":490433941,\"identity\":\"9b751c4b-2d05-441c-b08c-4de14e17e3a6\",\"order_by\":6,\"name\":\"Sander Grefte\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sander\",\"middleName\":\"\",\"lastName\":\"Grefte\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-16 07:40:27\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7137135/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7137135/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":87827926,\"identity\":\"13e90ee3-4780-442b-a567-5bfac0cd1ad1\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 11:59:10\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":209114,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic overview of ATP production in a mitochondrion via the process of the citric acid cycle and oxidative phosphorylation\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/d7a6fc5037487080f69fc042.jpeg\"},{\"id\":87828699,\"identity\":\"c538f18b-9ab6-46bc-bc66-db4c7ff6d5bf\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 12:07:10\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":50013,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStudy flow chart. ICU = intensive care unit. Created with Biorender.com.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/432b6dd68584c5a3adb60fb8.jpeg\"},{\"id\":87827929,\"identity\":\"4cde02c2-244b-4045-86ac-1031c2c2f21e\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 11:59:10\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32981,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProgression of mitochondrial respiratory parameters during the first week of ICU admission in septic patients and controls. T1, day 1-2 (24-48h); T2, day 3-4 (72-96h); T3, day 5-6 (120-144h) after ICU admission. Values represent mean ± SEM. *p \\u0026lt; 0.05, **p \\u0026lt; 0.01, and ***p \\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/1768ac95800eeee1a0c5373c.png\"},{\"id\":87828700,\"identity\":\"0d031bb2-6a99-4000-a881-6d7d9d006a10\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 12:07:10\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":26484,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eExpression of genes encoding subunits of each of the five OXPHOS complexes; (A) \\u003cem\\u003eNDUFB8 \\u003c/em\\u003e(Complex I), (B) \\u003cem\\u003eSDHB \\u003c/em\\u003e(Complex II), (C) \\u003cem\\u003eUQCRC2 \\u003c/em\\u003e(Complex III), (D) \\u003cem\\u003eCOXII \\u003c/em\\u003e(Complex IV) and (E) \\u003cem\\u003eATP5F1A \\u003c/em\\u003e(Complex V). Data were obtained from septic patients and matched controls (n=10 per group). T1, day 1-2 (24-48h); T2, day 3-4 (72-96h); T3, day 5-6 (120-144h) after ICU admission. Values represent mean ± SEM. *p \\u0026lt; 0.05 and **p \\u0026lt; 0.01.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/f7c0235f31221bc7077d8bef.png\"},{\"id\":87827937,\"identity\":\"05f14b7d-8515-4cd6-9aa6-c5078f5cc918\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 11:59:10\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":25258,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of mitochondrial respiratory parameters at T1 (24-48h after ICU admission) of sepsis survivors versus non-survivors. Values represent mean ± SEM.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/0d1cfa33db535f8a1964b714.png\"},{\"id\":87827935,\"identity\":\"b5653e04-2a39-453c-89b3-2e6c80c4cfda\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 11:59:10\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":25307,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of the progression of mitochondrial respiratory parameters during the first week of ICU stay of sepsis survivors and non-survivors. Δ = delta (as calculated by subtracting values at timepoint 1 from those at timepoint 3); T1, day 1-2 (24-48h); T3, day 5-6 (120-144h) after ICU admission. Values represent mean ± SEM. *p \\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/021feec9efa11c4120c7e4b0.png\"},{\"id\":94490142,\"identity\":\"7f5a1957-3288-4ce3-869b-c0c0663922fe\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 17:07:50\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1461074,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/e9874b7d-6fd1-4d83-8c8c-b6b0655432af.pdf\"},{\"id\":87827927,\"identity\":\"9bd1b3e4-5ee8-48d4-a588-104d0f4ab331\",\"added_by\":\"auto\",\"created_at\":\"2025-07-29 11:59:10\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":23351,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplemental Material belonging to Progression of peripheral blood mononuclear cell mitochondrial function during the early phase of sepsis in Intensive Care Unit patients\",\"description\":\"\",\"filename\":\"MICSupplementalTables.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7137135/v1/a5c12fad62bcfb07acf0012b.docx\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Progression of Peripheral Blood Mononuclear Cell Mitochondrial Function during the Early Phase of Sepsis in Intensive Care Unit Patients\",\"fulltext\":[{\"header\":\"Take home message\",\"content\":\"\\u003cp\\u003eThe current findings reveal an unexpected association between basal cell functioning and clinical outcomes in sepsis during the first week of ICU admission. Contrary to existing hypotheses, we demonstrated a substantial increase in mitochondrial respiration parameters in septic patients compared to controls, with an association between a progressive increase in basal respiration and 3-month mortality.\\u003c/p\\u003e\"},{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eSepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a primary reason for admission to an Intensive Care Unit (ICU) [\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Sepsis often contributes to (multi)organ failure and is associated with an average 30-day mortality of up to 35% of septic shock cases, accounting for about 20% of all global deaths in 2017 [\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Sepsis survivors are at an increased risk of post-hospital discharge morbidity, mortality and a markedly reduced quality of life, which may last years after hospital discharge [\\u003cspan class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. A lack of known therapeutic targets partly explains these poor clinical outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003eThere is increasing evidence for the role of altered mitochondrial function in the pathogenesis of sepsis-associated multiple organ dysfunction syndrome [\\u003cspan class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Mitochondrial respiration is the set of metabolic reactions and processes requiring oxygen at one of the final steps of the oxidative phosphorylation system (OXPHOS) in mitochondria to convert the energy stored in macronutrients to ATP [\\u003cspan class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e] (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eCreated with Biorender.\\u003c/p\\u003e\\n\\u003cp\\u003eMitochondrial respiration can be used as a marker to assess the primary function of mitochondria (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD). A decreased mitochondrial respiration has been demonstrated in various cells in septic ICU patients, including muscle tissue and blood platelets [\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. However, studies that measured mitochondrial function in peripheral blood mononuclear cells (PBMCs), which play an essential role in the initial (hyper)inflammatory response that hallmarks sepsis, have resulted in conflicting results. Human PBMCs are isolated from peripheral blood and identified as any blood cell with a round nucleus (i.e. lymphocytes, monocytes, natural killer cells and dendritic cells) [\\u003cspan class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Several studies reported a decreased mitochondrial function in PBMCs during sepsis [\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e], while others contrastingly reported an increased mitochondrial function [\\u003cspan class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. One study even reported an increased mitochondrial respiration, but concomitantly, an increased mitochondrial uncoupling leading to reduced ATP-linked respiration [\\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eMethodological differences, such as varying control groups and respiration mediums, might explain the inconsistency in the results of these studies. Furthermore, in two out of four studies, a measurement at only one time point was performed in each patient, which does not create insight into the progression of mitochondrial function in PBMCs during ICU stay [\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. This limitation is unfortunate since performing multiple measurements during ICU stay may reveal time-dependent effects of sepsis on mitochondrial function in PBMCs and its association with clinical outcomes. The two studies that did perform two measurements per patient, show conflicting results regarding correlation between mitochondrial respiration and clinical outcomes [\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRationale\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe set out to fill several knowledge gaps based on previously reported studies. To be able to investigate whether mitochondrial derangements originate from the PBMCs themselves, we opted to resuspend the PBMCs in a standardized medium, not plasma. Secondly, we performed repeated mitochondrial respiration measurements during the first week of ICU stay, as studies assessing the potential time-dependent effects of sepsis on mitochondrial function in PBMCs more than twice are lacking. For mechanistic underpinning, the expression of key genes involved in oxidative phosphorylation was also assessed using RT-qPCR. Lastly, we calculated correlations between clinical outcomes and mitochondrial function changes to reveal potential time-dependent associations between mitochondrial function and clinical outcomes.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eStudy design and setting\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eA prospective, observational single-centre cohort study with an age- and sex-matched control group was conducted at Gelderse Vallei Hospital (Ede, The Netherlands) between January 1, 2018, and January 27, 2023. Due to closure of the research laboratories during the SARS-CoV-2 pandemic, study inclusions were temporarily halted between March 14, 2020, and October 1, 2020. PBMC measurements were performed at Wageningen University and Research (Wageningen, The Netherlands).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStudy participants\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003ePatients (aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years) admitted to the ICU with sepsis and/or septic shock were eligible for inclusion. According to the Third International Consensus Definitions, sepsis was defined as a new life-threatening organ dysfunction caused by a dysregulated host response to microbiologically confirmed or clinically suspected (supported by laboratory or radiology findings) infection, as identified by an increase in the sequential organ failure assessment (SOFA) score of \\u0026ge;\\u0026thinsp;2 points. Septic shock was defined as the need for vasopressors to maintain a mean arterial pressure of \\u0026ge;\\u0026thinsp;65 mmHg and serum lactate levels\\u0026thinsp;\\u0026gt;\\u0026thinsp;2 mmol/L (\\u0026gt;\\u0026thinsp;18 mg/dL) in the absence of hypovolaemia [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The control group was recruited from metabolically healthy short-stay hospitalised patients planned to undergo elective surgery for benign non-inflammatory indications, and individuals visiting the outpatient (fracture) clinic, individually matched for age (\\u0026plusmn;\\u0026thinsp;2 years) and sex [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Further exclusion criteria can be found in the \\u003cb\\u003eSupplemental File\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStudy objectives\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe primary study objective was to investigate the progression of mitochondrial respiratory function in PBMCs in septic ICU patients during the first week of ICU admission. Secondary objectives were to investigate the association between mitochondrial respiratory function and (I) sepsis severity and (II) clinical outcomes, including ICU-, hospital and 3-month mortality, length of ICU and hospital stay (LOS) and duration of mechanical ventilation.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eData collection\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis study used PBMCs to measure mitochondrial respiratory function.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eSepsis group\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eArterial blood samples were collected at three time points via an indwelling arterial access: day 1\\u0026ndash;2 (24-48h), day 3\\u0026ndash;4 (72-96h) and day 5\\u0026ndash;6 (120-144h) after ICU admission (indicated with T1, T2 and T3), respectively.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eControl group\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe control group underwent blood sampling by venepuncture once during their visit to the outpatient clinic or short-stay hospitalisation prior to surgery.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003ePBMC isolation, washing and counting for high resolution respirometry\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eBlood samples for PBMC isolation to be used for high resolution respirometry were collected in sodium citrate buffered cell preparation tubes containing a ficoll solution and centrifuged at 1000g for 30 minutes at room temperature. Next, PBMCs were resuspended in warm (37\\u0026deg;C) 10mL of Hank Balanced Salt Solution (HBSS) without CaCl\\u003csub\\u003e2\\u003c/sub\\u003e or MgCl\\u003csub\\u003e2\\u003c/sub\\u003e (Thermo Fisher Scientific, Waltham, MA, USA) and centrifuged at 400g for 10 minutes at room temperature. The supernatant was then removed, and this washing step was repeated twice. After washing, the resulting PBMC pellet was resuspended in 1 mL of warm (37\\u0026deg;C) Seahorse XF base medium supplemented with 2 mM glutamine and 25 mM glucose. The PBMCs were counted using the Cellometer auto T4, and cell viability was assessed by mixing 10 \\u0026micro;L of cells with 10 \\u0026micro;L acridine orange and propidium iodide stain. PBMCs were then immediately used for high-resolution respirometry.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eHigh-resolution respirometry\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eTwo to five million live PBMCs were injected into a chamber of the Oroboros O2K (Oxygraph-2k Oroboros Instruments, Innsbruck, Austria). The chamber volume was set to 2mL and filled with Agilent Seahorse XF Base medium supplemented with 25 mM glucose and two mM glutamate, and the pH was set to 7.4. The temperature within the chamber was set to 37\\u0026deg;C and stirring speed to 750 rotations per minute. Oxygen concentration is continuously measured, recorded and used to calculate oxygen flux per one million live PBMCs using DatLab Software 4.3 (Oroboros Instruments, Innsbruck, Austria) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAfter injection of the PBMCs, the basal respiration was recorded first. Second, the complex V inhibitor, oligomycin, was added (2.5 \\u0026micro;M), which induced a state in which respiration is primarily to compensate for proton leakage. Third, carbonyl cyanide m-chlorophenylhydrazone (CCCP) was added repeatedly (20 nM) until maximum mitochondrial respiration was reached. Fourth, the complex I inhibitor rotenone and the complex III inhibitor, antimycin A, were added (0.5 \\u0026micro;M and 2.5 \\u0026micro;M, respectively) to determine non-mitochondrial respiration. Each step of the function profiling test was recorded after respiration had stabilised. Additionally, three parameters were calculated. ATP-linked respiration was calculated by subtracting leak respiration from basal respiration. Coupling efficiency was calculated by dividing ATP-linked respiration by basal respiration. Spare respiratory capacity was calculated by subtracting basal respiration from the maximal respiration. Measurements were considered to be failed when the blood sample yielded too few cells to perform the primary study measurements, if there was no basal respiration signal or if there was no measurable reaction in respiration to the addition of CCCP or one of the inhibitors.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003ePBMC isolation and lysing for RT-qPCR\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eAfter the isolation of PBMCs as previously described, PBMCs were washed three times with 10 mL of cold (4\\u0026deg;C) HBSS and centrifuged at 400 g for 10 minutes at 4\\u0026deg;C. After washing, the PBMCs were resuspended in 300 \\u0026micro;L of RLT buffer (Qiagen, Hilden, Germany) with 1% (v/v) \\u0026szlig;-mercaptoethanol (BME) (Sigma-Aldrich, St. Louis, MO, USA). Lysed PBMCs were stored at -80\\u0026deg;C.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eReal-time quantitative polymerase chain reaction\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eFor gene expression analysis, samples from 10 septic ICU patients and 10 matched controls were used. Samples of other subjects were excluded due to insufficient quality or concentration of RNA in at least one of the three timepoints (T1, T2 or T3). All lysates were homogenized by passing it six times through a blunt 20-gauge needle (0.9 mm diameter). Total RNA was extracted from PBMCs using the RNeasy Mini Kit (Qiagen, Hilden, Germany) in accordance with the manufacturer's instructions and RNA quantity and purity were assessed using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). A total of 500 ng of purified RNA was reverse transcribed into complementary DNA (cDNA) using the iScript\\u0026trade; cDNA Synthesis Kit (BioRad, Hercules, CA, USA), following the manufacturer's instructions. The reaction mixture included 500 ng RNA, 1 \\u0026micro;L of iScript reverse transcriptase, 4 \\u0026micro;L of 5x iScript reaction mix, and RNase/DNase-free water to a final volume of 20 \\u0026micro;L. Reverse transcription was carried out in a C1000 Touch\\u0026trade; Thermal Cycler (BioRad, Hercules, CA, USA) with the following conditions: 25\\u0026deg;C for 5 minutes, 42\\u0026deg;C for 30 minutes, and 85\\u0026deg;C for 5 minutes. Pooled cDNA stock was created by mixing 5 \\u0026micro;L from each individual cDNA sample to be used for generating calibration curves. Each cDNA sample was then diluted 1:100 before performing real time quantitative reverse transcription polymerase chain reaction (qRT-PCR). qRT-PCR was performed to assess the expression of genes involved in mitochondrial OXPHOS system (\\u003cem\\u003eNDUFB8\\u003c/em\\u003e, \\u003cem\\u003eSDHB\\u003c/em\\u003e, \\u003cem\\u003eUQCRC2\\u003c/em\\u003e, \\u003cem\\u003eMTCO2\\u003c/em\\u003e, and \\u003cem\\u003eATP5F1A\\u003c/em\\u003e). The housekeeping genes \\u003cem\\u003eRPS15\\u003c/em\\u003e, \\u003cem\\u003eCSNK2A2\\u003c/em\\u003e, and \\u003cem\\u003eACTB\\u003c/em\\u003e were used as internal controls. All primers targeted all known isoforms of the respective genes and are listed in \\u003cb\\u003eSupplemental Table\\u0026nbsp;1.\\u003c/b\\u003e The qRT-PCR reactions were conducted using iQ SYBR Green Supermix (BioRad, Hercules, CA, USA) with an end volume of 25 \\u0026micro;L consisting of 12.5 \\u0026micro;L of Supermix, 8.5 \\u0026micro;L RNase/DNase-free water, 1.0 \\u0026micro;L each of forward and reverse primers (10 pmol/\\u0026micro;L), and 2 \\u0026micro;L of 100x diluted cDNA. Amplification was performed on a C1000 Touch\\u0026trade; Thermal Cycler under the following conditions: 95\\u0026deg;C for 3 minutes, 40 cycles of 95\\u0026deg;C for 15 seconds and 58\\u0026deg;C for 45 seconds, followed by melt-curve analysis. Relative gene expression levels were normalized with the three reference genes and all mean gene expression values of the patients were compared to the controls.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eAdditional data sources\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eData collection from the electronic medical record systems MetaVision\\u0026reg; (iMDsoft, Tel Aviv, Israel) and NeoZIS\\u0026reg; (MI Consultancy, Katwijk, The Netherlands) included baseline patient characteristics (including disease severity scores), laboratory values and outcome parameters, such as duration of mechanical ventilation and length of ICU and hospital stay.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStudy size\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eJapiass\\u0026uacute; \\u003cem\\u003eet al.\\u003c/em\\u003e previously studied mitochondrial oxygen consumption in PBMCs of septic ICU patients (n\\u0026thinsp;=\\u0026thinsp;20) and critically ill postoperative control patients (n\\u0026thinsp;=\\u0026thinsp;18) [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The respiratory control ratio (RCR) was significantly reduced in the septic ICU patient group compared to the control group. The difference in the RCR between day 1 and day 7 was approximately half of the difference between the study and the control group. Assuming the changes in mitochondrial oxygen consumption develop linearly during sepsis, the expected difference (effect size) between the measurements at the three different time points during the first week, is similar to 50% of the observed differences by Japiass\\u0026uacute; et al. Therefore, to achieve a power of 0.95 with a two-sided significance level of 0.05, 30 subjects per group were needed (as calculated with G*power, Heinrich-Heine-Universit\\u0026auml;t D\\u0026uuml;sseldorf, D\\u0026uuml;sseldorf, Germany).\\u003c/p\\u003e\\u003cp\\u003eWhen three valid measurements from an ICU subject could not be obtained due to withdrawal of consent, death, early ICU discharge or technical problems, patients were not matched with a control subject. Moreover, additional patients were included consecutively until complete measurements were obtained in 30 pairs. Separate measurements from patients who did not have three complete consecutive measurements were excluded from mitochondrial function progression analyses (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Patients with valid measurements at T1 and T3 were included in the cox regression models and analyses comparing survivors vs. non-survivors (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStatistical analyses\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eData verification was conducted manually. Descriptive statistics were performed for demographic and clinical data of all patients and the primary outcome. Normality was assessed numerically and graphically. Continuous values were reported as means with standard deviations (SD; parametric data) or medians with interquartile ranges [IQR; non-parametric data]. Discrete data were presented as proportions (%). Differences in baseline characteristics and clinical outcomes between the sepsis and control group were assessed using the independent samples t-test, Wilcoxon rank sum, Wilcoxon signed rank, chi-squared, or Fisher's exact tests, where appropriate.\\u003c/p\\u003e\\u003cp\\u003eSecondary outcomes were evaluated using uni- and multivariable Cox proportional hazards regression models or ANOVA analyses, where appropriate. Multivariable Cox regression analyses were performed using the Enter and Forward Stepwise Wald methods.\\u003c/p\\u003e\\u003cp\\u003eBased on literature and clinical relevance, the variables age, sex, body mass index (BMI), acute physiology and chronic health evaluation II (APACHE II), SOFA and modified nutrition risk in critically ill (mNUTRIC) scores were analysed in regression analyses. Variables were dichotomised (using the median) in case of non-linearity, with the outcome parameter assessed by visual inspection of boxplots.\\u003c/p\\u003e\\u003cp\\u003eFinally, all samples' PBMC lymphocyte-monocyte ratios (LMR) were calculated. Their changes over time and differences between survivors and non-survivors were evaluated. Moreover, correlation with parameters of mitochondrial function was assessed using Kendall\\u0026rsquo;s Tau-b. Multicollinearity was assessed using the variance inflation factor (VIF); a value below two was considered acceptable.\\u003c/p\\u003e\\u003cp\\u003eGene expression analysis was performed using relative expression values calculated via the ΔΔCq method. For statistical analysis, the ΔΔCq values were used as input, and consistency with the raw Bio-Rad qPCR output files was manually verified. Fold changes (2\\u003csup\\u003e\\u0026minus;ΔΔCq\\u003c/sup\\u003e) were calculated and used exclusively for graphical representation of the results. Normality was assessed of both gene expression data and mitochondrial respiration parameters using the Shapiro-Wilk test. When data were not normally distributed, logarithmic transformations were applied in an attempt to normalize the distribution. Data that remained non-normally distributed after transformation were analyzed using a non-parametric one-way ANOVA (Kruskal-Wallis test). For data that were normally distributed after transformation, a parametric one-way ANOVA was performed using the transformed data. When significant differences were detected, Tukey\\u0026rsquo;s post-hoc tests (in case of normally distributed data) or Dunn\\u0026rsquo;s test (in case of not normally distributed data) were applied.\\u003c/p\\u003e\\u003cp\\u003eIBM SPSS statistics 27 (IBM Corp, Armonk, NY, USA) or Graphpad Prism 10.4.2 (GraphPad Software, Boston, MA, USA) was used for statistical analyses and figures representing statistics. Only two-sided analyses were used. P-values\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05 were considered statistically significant.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u003c/strong\\u003e\\u003cp\\u003e The study was approved by the Medical Ethical Committee of Wageningen University (METC-WUR, which was incorporated in the METC Oost-Nederland in 2021, dossier no. 2021\\u0026ndash;13011) and the assessment Committee for Scientific Research of ZGV (dossier no. 1801-004). The protocol was registered in the Netherlands Trial Register (number NTR6969) and was made available through the International Clinical Trial Registry Platform (NL5918). Patients were enrolled after the informed consent form was signed by the patient or legal representative.\\u003c/p\\u003e\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eTo obtain thirty matched pairs with complete data, informed consent was obtained from 46 septic patients and 30 age- and sex-matched controls (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The septic patients were predominantly male (n\\u0026thinsp;=\\u0026thinsp;33, 72%) and had a mean age of 68 (\\u0026plusmn;\\u0026thinsp;13 SD) years, with a mean BMI of 27 (\\u0026plusmn;\\u0026thinsp;6 SD) kg/m\\u003csup\\u003e2\\u003c/sup\\u003e. Pneumonia was found to be the most common cause of sepsis (n\\u0026thinsp;=\\u0026thinsp;24, 52%), followed by an abdominal origin (n\\u0026thinsp;=\\u0026thinsp;17, 37%). At baseline, patients had the following clinical scores: mNUTRIC 5 [IQR 3\\u0026ndash;6], SOFA 8 [\\u003cspan additionalcitationids=\\\"CR8 CR9\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e] and APACHE II 18 [\\u003cspan additionalcitationids=\\\"CR15 CR16 CR17 CR18 CR19 CR20 CR21\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. The control patients were age- and sex matched and were subsequently predominantly male as well (n\\u0026thinsp;=\\u0026thinsp;22, 73%) and had a mean age of 71 (\\u0026plusmn;\\u0026thinsp;15 SD) years, which was not different from the sepsis group (p\\u0026thinsp;=\\u0026thinsp;0.3).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eAfter study completion, upon reviewing the obtained data, single respirometry measurements in five patients had to be discarded as they did not meet quality standards (T1 n\\u0026thinsp;=\\u0026thinsp;1, T2 n\\u0026thinsp;=\\u0026thinsp;1, T3 n\\u0026thinsp;=\\u0026thinsp;2 and T1-3 n\\u0026thinsp;=\\u0026thinsp;1). Measurements were discovered to have failed in four controls (13%) and partially failed (no reaction to CCCP, rendering basal respiration and proton leak useable) in one patient. As the inclusion phase had already been completed, no further inclusions were performed to compensate for these losses. The clinical patient characteristics at the time of blood samplings are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003ePatient and control characteristics at time of the blood samplings.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"11\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eControls (n\\u0026thinsp;=\\u0026thinsp;26)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"9\\\" nameend=\\\"c11\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eSepsis patients (n\\u0026thinsp;=\\u0026thinsp;40\\u003csup\\u003ea\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eT1 (n\\u0026thinsp;=\\u0026thinsp;38)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ep-value\\u003c/b\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eT2 (n\\u0026thinsp;=\\u0026thinsp;35)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ep-value\\u003c/b\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eT3 (n\\u0026thinsp;=\\u0026thinsp;28)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ep-value\\u003c/b\\u003e\\u003csup\\u003ed\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c11\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eClinical parameters\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSOFA score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en.a.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6 [\\u003cspan additionalcitationids=\\\"CR5 CR6 CR7\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" 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align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.9 [0.8\\u0026ndash;1.2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.002*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.8 [0.6\\u0026ndash;1.2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLeukocytes (*10^9/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.5 [5.7\\u0026ndash;8.0]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.6 [10.0-18.9]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12.7 [9.1\\u0026ndash;16.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.189\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e12.2 [9.4\\u0026ndash;19.1]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.388\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePMBC LMR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.6 [3.3\\u0026ndash;5.6]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.0 [1.1-3.0]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.010*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.0 [1.5\\u0026ndash;3.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.465\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2.0 [1.3-3.0]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.670\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCRP (mg/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e249 [148\\u0026ndash;323]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e120 [77\\u0026ndash;214]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e109 [58\\u0026ndash;204]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUreum (mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.3 [5.5\\u0026ndash;7.1]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10.7 [7.8\\u0026ndash;19.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.002*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e11 [5.5\\u0026ndash;25]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.844\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e10.1 [7.5\\u0026ndash;15.8]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.529\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCreatinine (umol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e74 [62\\u0026ndash;87]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e84 [60\\u0026ndash;145]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.024*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e72 [52\\u0026ndash;117]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.002*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e60 [46\\u0026ndash;112]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCortisol (nmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e395 [311\\u0026ndash;472]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e724 [546\\u0026ndash;1568]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e676 [418\\u0026ndash;1005]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e592 [431\\u0026ndash;725]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInsulin (mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19 [9.4\\u0026ndash;18.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18 [8.8\\u0026ndash;30]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.280\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e21 [12\\u0026ndash;35]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.523\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e11.5 [7.7\\u0026ndash;31.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.755\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInsulin supplementation (IU)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en.a.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0 [0\\u0026ndash;19]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003en.a.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0 [0\\u0026ndash;24]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.449\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0 [0\\u0026ndash;26]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.234\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"11\\\"\\u003ea. Unless stated otherwise due to missing variables. b. T1 compared to the control group; p-values were calculated using the Wilcoxon signed rank test c. T1 compared to T2, p-values were calculated using the Wilcoxon signed rank test; d. T1 compared to T3, p-values were calculated using the Wilcoxon signed rank test; * p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e[Insert\\u003c/b\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cb\\u003ehere]\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStudy measurements\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eMitochondrial function over time in septic patients and controls\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eBasal respiration was significantly higher in septic patients compared to controls (2.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml)) at timepoints 1 (5.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.0 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), 2 and 3 (4.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6 and 6.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.0 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml) respectively, both p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). Proton leak was significantly higher in septic patients compared to controls (1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml)) at timepoints 2 and 3 (1.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 and 1.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), respectively, both p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). Maximal respiration was significantly higher in septic patients (12.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml)) compared to controls (5.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC) at timepoint 3 only. ATP-linked respiration was significantly higher in septic patients compared to controls (1.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml)) at timepoints 2 (3.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and 3 (4.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). No differences were detected in spare respiratory capacity \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE\\u003cb\\u003e)\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eExpression of oxidative phosphorylation genes over time\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe expression of genes encoding for subunits in complex II (\\u003cem\\u003eSDHB\\u003c/em\\u003e) and complex V (\\u003cem\\u003eATP5F1A\\u003c/em\\u003e) was significantly higher in septic patients compared to controls at multiple timepoints of the first week of ICU admission (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). At T1, \\u003cem\\u003eSDBH\\u003c/em\\u003e and \\u003cem\\u003eATP5F1A\\u003c/em\\u003e expression were 43% (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and 52% (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) higher in septic patients compared to controls, respectively. At T2, \\u003cem\\u003eSDBH\\u003c/em\\u003e was 54% higher in septic patients compared to controls (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). At T3, \\u003cem\\u003eSDBH\\u003c/em\\u003e and \\u003cem\\u003eATP5F1A\\u003c/em\\u003e expression was 54% (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) and 40% (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) higher in septic patients compared to controls, respectively. In genes encoding other subunits no statistically significant differences were detected.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eSurvivors versus non-survivors\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eAll-cause 3-month mortality in the sepsis cohort (n\\u0026thinsp;=\\u0026thinsp;40) was 35% (n\\u0026thinsp;=\\u0026thinsp;14). Five (36%) of the deceased patients died in the ICU, seven (50%) in the ward and two after hospital discharge (14%). Compared to the surviving patients (n\\u0026thinsp;=\\u0026thinsp;31), the non-survivors (n\\u0026thinsp;=\\u0026thinsp;15) were older (77 (\\u0026plusmn;\\u0026thinsp;10 SD) versus 63 (\\u0026plusmn;\\u0026thinsp;13 SD) years of age, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), had higher APACHE II (median 20 [IQR 17\\u0026ndash;26] versus 15 [\\u003cspan additionalcitationids=\\\"CR13 CR14 CR15 CR16 CR17 CR18 CR19\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], p\\u0026thinsp;=\\u0026thinsp;0.008] and mNUTRIC scores (6 [IQR 6\\u0026ndash;7] versus 4 [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) at ICU admission. No significant differences were found in other baseline characteristics. Regarding biochemical parameters, only significant differences in serum insulin levels between survivors and non-survivors were found (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eSurvivors (n\\u0026thinsp;=\\u0026thinsp;26) \\u003csup\\u003ea\\u003c/sup\\u003e versus all-cause 3-month non-survivors (n\\u0026thinsp;=\\u0026thinsp;14)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"13\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT1\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eT2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eT3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003esurvivors (n\\u0026thinsp;=\\u0026thinsp;26)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003enon-survivors (n\\u0026thinsp;=\\u0026thinsp;12)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003esurvivors (n\\u0026thinsp;=\\u0026thinsp;22)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003enon-survivors (n\\u0026thinsp;=\\u0026thinsp;13)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003esurvivors (n\\u0026thinsp;=\\u0026thinsp;15)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003enon-survivors (n\\u0026thinsp;=\\u0026thinsp;13)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClinical parameters\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSOFA score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6 [\\u003cspan additionalcitationids=\\\"CR4 CR5 CR6 CR7\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7 [\\u003cspan additionalcitationids=\\\"CR6 CR7\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3 [\\u003cspan additionalcitationids=\\\"CR3 CR4 CR5 CR6\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e5 [\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e4 [\\u003cspan additionalcitationids=\\\"CR3 CR4 CR5 CR6 CR7\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e5 [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLactate (mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.3 [0.8-1.725]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.2 [1.0-1.6]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.9 [0.8\\u0026ndash;1.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.0 [0.7\\u0026ndash;1.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.8 [0.5\\u0026ndash;1.2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0.9 [0.7\\u0026ndash;1.2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLeukocytes (*10^9/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14.3 [8.5\\u0026ndash;19.0]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e12.8 [10.5\\u0026ndash;16.8]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12.5 [9.1\\u0026ndash;16.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e13.2 [8.2\\u0026ndash;16.6]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e12.1 [9.5\\u0026ndash;17.7]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e14.9 [9.2\\u0026ndash;22.6]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePBMC LMR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 [1.1\\u0026ndash;3.1]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.6 [1.0-3.9] n\\u0026thinsp;=\\u0026thinsp;9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.3 [1.5\\u0026ndash;3.6] n\\u0026thinsp;=\\u0026thinsp;21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.8 [1.2\\u0026ndash;2.4]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e2.0 [1.7\\u0026ndash;3.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e2.0 [1.1\\u0026ndash;2.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCRP (mg/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e252 [169\\u0026ndash;323]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e236 [137\\u0026ndash;318]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e140 [74\\u0026ndash;214]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e112 [87\\u0026ndash;280]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e109 [70\\u0026ndash;168]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e97 [35\\u0026ndash;212]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUreum (mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10.3 [6.5\\u0026ndash;18.2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11.4 [9.1\\u0026ndash;25.2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e10.8 [5.1\\u0026ndash;14.4]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e16.4 [8.8\\u0026ndash;32.6]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e10.8 [6.6\\u0026ndash;24.7]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e9.4 [7.9\\u0026ndash;14.4]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCreatinine (umol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e84 [57\\u0026ndash;188]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e95 [75\\u0026ndash;132]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e70 [52\\u0026ndash;114]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e66 [52\\u0026ndash;124]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e64 [46\\u0026ndash;137]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e53 [44\\u0026ndash;109]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCortisol (nmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e674 [560\\u0026ndash;1397]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1398 [525\\u0026ndash;1959]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e549 [418\\u0026ndash;1005]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e697 [583\\u0026ndash;1062]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e617 [368\\u0026ndash;761]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e601 [506\\u0026ndash;768]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInsulin (mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e20.0 [11.4\\u0026ndash;36.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.4 [4.6\\u0026ndash;20.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.014*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e20 [12\\u0026ndash;35]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e17.5 [7.9\\u0026ndash;40.3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e12.5 [9.1\\u0026ndash;25.8]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e13.0 [5.7\\u0026ndash;35.8]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInsulin supplementation (IU)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0 [0-0.75]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9 [0\\u0026ndash;38]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.066\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0 [0\\u0026ndash;1]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0 [0\\u0026ndash;45]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0 [0-0.75]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e0 [0\\u0026ndash;31]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInsulin supplementation (IU)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e32 [5\\u0026ndash;55]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e36 [28\\u0026ndash;54]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e41 [8\\u0026ndash;95]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e45 [38\\u0026ndash;87]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e50 [13\\u0026ndash;59]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e36 [21\\u0026ndash;57]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"13\\\"\\u003ea. Unless stated otherwise due to missing variables. Definitions: T1, day 1\\u0026ndash;2 (24-48h); T2, day 3\\u0026ndash;4 (72-96h); T3, day 5\\u0026ndash;6 (120-144h) after ICU admission; Abbreviations SOFA, Sequential Organ Failure Assessment; PBMC, peripheral blood mononuclear cell; LMR, lymphocyte-monocyte ratio; CRP, C-reactive protein; IU, international units; ATP-linked respiration\\u0026thinsp;=\\u0026thinsp;basal respiration minus proton leak; SRC\\u0026thinsp;=\\u0026thinsp;spare respiratory capacity; maximal respiration minus basal respiratory capacity; coupling efficiency\\u0026thinsp;=\\u0026thinsp;ATP-linked respiration divided by basal respiration. All values are reported as medians with interquartile ranges. P-values were calculated using the Mann-Whitney U test; * p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e[Insert\\u003c/b\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e \\u003cb\\u003ehere]\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn high-resolution respirometry, no mitochondrial respiration parameters were significantly different at T1 between survivors and non-survivors (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). A trend for a lower mitochondrial proton leak at T1 in survivors (1.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml) compared to non-survivors 1.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml)) (p\\u0026thinsp;=\\u0026thinsp;0.06, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB) was found.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo assess potential differences in the progression of mitochondrial respiratory parameters between non-survivors vs. survivors, delta (Δ) values were calculated by subtracting values at timepoint 1 from those at timepoint 3. Significant differences between survivors and non-survivors in ΔBasal respiration (-0.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9 vs. 2.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), respectively), ΔMaximal respiration (-2.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0 vs. 4.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), respectively), ΔATP-linked respiration (-0.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.8 vs. 2.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), respectively), and ΔSpare respiratory respiration (-1.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2 vs. 2.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4 pmol O\\u003csub\\u003e2\\u003c/sub\\u003e/(s*ml), respectively), were detected (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). In fact, delta values were generally negative for survivors, indicating a decrease in mitochondrial respiration over-time during ICU stay. Conversely, in non-survivors delta values were positive, indicating an increase in mitochondrial respiration over-time during ICU stay (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eCox regression\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe variables age and mNUTRIC were omitted in the final regression models because of their overlap (and visual correlation) with the APACHE II score. Measured mitochondrial respiration parameters were intercorrelated (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01); therefore, only basal respiration was used in the final model. None of the mitochondrial respiratory parameters were correlated with the SOFA score. The deltas of these two parameters over time were entered into the final model (ΔSOFA and ΔBasal respiration as calculated by T3 minus T1). In the final Cox regression multivariable model, ΔBasal respiration (\\u0026ge;\\u0026thinsp;0.07 nmol O\\u003csub\\u003e2\\u003c/sub\\u003e/min/10\\u003csup\\u003e7\\u003c/sup\\u003e from T1 to T3) was associated with the primary endpoint of 3-month mortality (HR 3.8, 95%CI 1.0-14.1, p\\u0026thinsp;=\\u0026thinsp;0.047) (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The VIF was \\u0026lt;\\u0026thinsp;2 for the variables in this final model.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eUnivariable and multivariable COX regressions for the association of primary endpoint 3-month mortality, baseline and clinical characteristics and mitochondrial respiratory function (n\\u0026thinsp;=\\u0026thinsp;40).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnivariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMultivariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHR (95%-CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eHR (95%-CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eA. 3-month mortality (n\\u0026thinsp;=\\u0026thinsp;14)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex (female)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.060 (0.332\\u0026ndash;3.385)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.707 (0.164\\u0026ndash;3.051)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI (\\u0026gt;\\u0026thinsp;25.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.040 (0.364\\u0026ndash;2.967)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.400 (0.381\\u0026ndash;5.139)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPACHE II on admission (\\u0026gt;\\u0026thinsp;18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.029 (0.703\\u0026ndash;5.859)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.926 (0.509\\u0026ndash;7.289)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003edelta SOFA T3-1 (\\u0026ge;-2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.863 (0.303\\u0026ndash;2.463)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.038 (0.218\\u0026ndash;3.832)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003edelta basal T3-1 (\\u0026gt;\\u0026thinsp;0.068**)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.041 (0.886\\u0026ndash;10.431)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.794 (1.018\\u0026ndash;14.149)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.047*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eB. ICU mortality (n\\u0026thinsp;=\\u0026thinsp;5)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex (female)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.633 (0.182\\u0026ndash;14.639)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDifficulties with multivariable regression due to low number of deaths\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI (\\u0026gt;\\u0026thinsp;25.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.578 (0.263\\u0026ndash;9.460)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPACHE II on admission (\\u0026gt;\\u0026thinsp;18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.139 (0.356\\u0026ndash;12.861)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003edelta SOFA T3-1 (\\u0026ge;-2)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.223 (0.025\\u0026ndash;1.998)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003edelta basal T3-1 (\\u0026gt;\\u0026thinsp;0.068**)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e118 (0.007\\u0026ndash;2.121*10\\u003csup\\u003e6\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eC. Hospital mortality (n\\u0026thinsp;=\\u0026thinsp;7)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex (female)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.291 (0.349\\u0026ndash;4.775)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.979 (0.183\\u0026ndash;5.228)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI (\\u0026gt;\\u0026thinsp;25.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.028 (0.331\\u0026ndash;3.192)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.554 (0.389\\u0026ndash;6.207)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPACHE II on admission (\\u0026gt;\\u0026thinsp;18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.490 (0.480\\u0026ndash;4.630)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.232 (0.288\\u0026ndash;5.273)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003edelta SOFA T3-1 (\\u0026ge;-2)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.61 (0.197\\u0026ndash;1.959)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.916 (0.232\\u0026ndash;3.627)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003edelta basal T3-1 (\\u0026gt;\\u0026thinsp;0.068**)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.112 (0.565\\u0026ndash;7.901)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.341 (0.577\\u0026ndash;9.494)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eT1, day 1\\u0026ndash;2 (24-48h); T2, day 3\\u0026ndash;4 (72-96h); T3, day 5\\u0026ndash;6 (120-144h) after ICU admission. Delta was calculated as: (mitochondrial parameter at T3 minus T1). Abbreviations: HR; hazard ratio, 95%-CI; 95%-confidence interval, BMI; body mass index, APACHE II; Acute Physiology And Chronic Health Evaluation, SOFA; sequential organ failure assessment, basal; basal respiration (measured in nmol O2/min/10^7). * p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. ** including negative values (=\\u0026thinsp;a decrease in SOFA score or basal respiration, respectively, over time). Multivariable Cox regression analyses were performed using the Enter and Forward Stepwise Wald methods.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e[Insert\\u003c/b\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e \\u003cb\\u003ehere]\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eLymphocyte-monocyte ratio\\u003c/span\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe PBMC LMR was lower in sepsis patients than in controls. No change over time (T1-T3) was noted, nor were any differences between survivors and non-survivors (Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this prospective observational study, we found a significant increase in basal, ATP-linked, maximal and proton leak associated respiration in sepsis patients compared to controls within the first week of ICU admission. These findings were corroborated by gene expression analysis, showing an increased expression of genes encoding OXPHOS subunits (complex II and V) in septic patients, consistent with the functional respiration data. These observations contrasted our hypothesis, as we did not demonstrate a decrease in PBMC mitochondrial respiration during sepsis. Moreover, a more significant increase in basal, ATP-linked and maximal respiration and spare respiratory capacity was observed during the first week of ICU stay in non-survivors compared to survivors (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), although these respiration parameters were not statistically different between the sepsis and control patients at baseline measurements. This progression of basal respiration was associated with the primary endpoint of 3-month mortality after correction for relevant covariates. Therefore, the current results show that septic patients undergo a progressive upregulation of PBMC mitochondrial respiration, which may be related to adverse outcome of disease.\\u003c/p\\u003e\\u003cp\\u003eOur findings are consistent with those of Sj\\u0026ouml;vall \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e] and Belikova \\u003cem\\u003eet al\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], who also found that basal mitochondrial respiration in PBMCs was significantly increased within the first 48 hours of ICU admission. In addition, Sj\\u0026ouml;vall \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e] demonstrated a progressive increase in basal and maximal respiration during the first week of sepsis patients compared to healthy controls. Strikingly, they observed no differences between surviving and non-surviving patients at any point in time. Both their inclusion and mortality rates were lower than in the present study, which may have led the study to be underpowered for differentiation between survivors and non-survivors. However, their article does neither report the original data nor p-values for the comparisons, so this claim cannot be substantiated.\\u003c/p\\u003e\\u003cp\\u003eIn contrast with our findings, Jang \\u003cem\\u003eet al.\\u003c/em\\u003e (studying mitochondrial respiration of PBMCs in 10 septic patients measured once shortly after presentation to an emergency department), Japiass\\u0026uacute; \\u003cem\\u003eet al.\\u003c/em\\u003e (studying mitochondrial respiration of PBMCs in 20 patients during the first 48 hours of septic shock) and Garrabou \\u003cem\\u003eet al.\\u003c/em\\u003e (studying mitochondrial respiration in 19 septic patients, time of measurement not mentioned) observed a significant reduction of ADP-linked respiration in permeabilized PBMCs of septic patients compared to controls [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. In addition, in the study of Japiass\\u0026uacute; \\u003cem\\u003eet al.\\u003c/em\\u003e, a significant reduction was observed in ADP-linked respiration in non-surviving sepsis patients compared with the postoperative controls without sepsis (5.60 versus 9.89 nmol O\\u003csub\\u003e2\\u003c/sub\\u003e/min/10\\u003csup\\u003e7\\u003c/sup\\u003e, respectively, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). Survivors demonstrated a 2.9x increase in ATP-linked respiration after one week [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Contrastingly, we did not observe a significant change in respiratory function over time in survivors. Instead, we found a significant increase in basal and ATP-linked respiration in non-survivors during the first week of ICU stay.\\u003c/p\\u003e\\u003cp\\u003eSimilarly, studies reporting gene expression data of genes encoding OXPHOS subunits are conflicting. We observed an increased expression of OXPHOS genes (\\u003cem\\u003eSDBH\\u003c/em\\u003e and \\u003cem\\u003eATP5F1A\\u003c/em\\u003e) in septic patients compared to controls, a finding that is in line with those of Li \\u003cem\\u003eet al.\\u003c/em\\u003e, whom also found an increased expression of OXPHOS subunits compared to controls [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Similarly, Sj\\u0026ouml;vall \\u003cem\\u003eet al.\\u003c/em\\u003e reported increased mtDNA copies in septic patients compared to controls, indirectly indicating a higher number of mitochondria and OXPHOS subunits present in septic patients [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. However, Nucci \\u003cem\\u003eet al.\\u003c/em\\u003e reported a downregulation of genes encoding OXPHOS subunits in samples collected from septic patients during admission [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThese contradictory observations may be due to methodological variety. A clear difference between the abovementioned studies is the composition of the control group, which may influence the outcomes of comparisons between sepsis and control groups. In the current study, we chose to include sex- and age-matched controls since those are two factors known to influence mitochondrial respiratory function, which has not been done in other studies besides the study of Garrabou \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Moreover, we selected metabolically healthy age- and sex-matched controls. In contrast, Japiass\\u0026uacute; \\u003cem\\u003eet al.\\u003c/em\\u003e included critically ill postoperative ICU patients, whereas Jang \\u003cem\\u003eet al.\\u003c/em\\u003e chose to use three unmatched control groups of younger, older and infected (but not septic) patients [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. It is intriguing that Sj\\u0026ouml;vall \\u003cem\\u003eet al.\\u003c/em\\u003e and our study still proved an increase in mitochondrial function parameters in PBMCs of septic patients, eventhough healthy controls were included [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eSecondly, exclusion criteria differ between mentioned studies. We excluded many common comorbidities known to affect mitochondrial respiratory function (such as diabetes mellitus and COPD), which allowed us to exclude the potential confounding effect of these comorbidities. Such exclusion criteria were not reported in other studies.\\u003c/p\\u003e\\u003cp\\u003eThirdly, the time at which the PBMCs were collected and measured respiration differed between studies. The timing of blood collection is not described by Garrabou \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Jang \\u003cem\\u003eet al.\\u003c/em\\u003e and Nucci \\u003cem\\u003eet al.\\u003c/em\\u003e collected blood samples from patients with sepsis or septic shock upon presentation to the emergency department [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e], whereas our measurements and those of Japiass\\u0026uacute; \\u003cem\\u003eet al.\\u003c/em\\u003e commenced within 48 hours of ICU admission [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. These may be very different (metabolic) time points in a patient's journey. Furthermore, our cohort was slightly older than those of Jang [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e] and Garrabou [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e] and their coworkers (68 vs 63, resp. 64 years of age), and although SOFA scores were similar in all studies, it is unknown whether all patients in the Garrabou and Jang cohorts required ICU admission.\\u003c/p\\u003e\\u003cp\\u003eFourthly, the methods of respiration measurement vary in comparison with current literature. The current study resuspended PBMCs in a standardized medium, not plasma. This was similar to Japiass\\u0026uacute; \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. On the contrary, Sj\\u0026ouml;vall and coworkers used the patient's plasma [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. However, mitochondrial function in PBMCs is altered by plasma, as was demonstrated by Belikova and co-workers [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Consequently, it is difficult to disentangle the effects of sepsis on plasma content from the effect of sepsis on mitochondria in PBMCs per se. Strikingly, in the current study, decreased mitochondrial respiration and decreased expression of OXPHOS genes was, in fact, not visible. This approach revealed that the mitochondria of PBMCs are not dysfunctional and capable of improving respiratory function. In addition, this suggests that if a worsened respiratory function is observed in PBMCs of septic patients, this is perhaps more likely to originate from potential dysregulating components present in plasma. In addition, in some studies, PBMCs were permeabilized, while we used non-permeabilized PBMCs for respiratory measurements [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. It could be hypothesized that this explains the differences with our results. However, since both Sj\\u0026ouml;vall \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e] and Jang \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e] have performed their measurements in both permeabilized and non-permeabilized PBMCs and found consistent results between those experiments, this is unlikely to explain the contrasting results with our studies.\\u003c/p\\u003e\\u003cp\\u003eLastly, it can be hypothesized that the differences in respiratory function of PBMCs belonging to the different groups of our study are caused by a shift in PBMC composition rather than a shift in mitochondrial function per se. In humans, PBMC cell ratios vary across individuals, but typically, lymphocytes are in the range of 70\\u0026ndash;90%, monocytes from 10 to 20%, while dendritic cells are rare, accounting for only 1\\u0026ndash;2% [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. In our cohort, the PBMC lymphocyte-monocyte ratio (LMR) was lower in sepsis patients than in controls. This lower count is to be expected, as a lower LMR is associated with systemic inflammation [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. However, parameters of mitochondrial function did not correlate with the LMR, nor did the LMR change over time, and also not when survivors versus non-survivors were compared (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). However, it could still be of interest to measure mitochondrial function in distinct cell populations in future studies and should perhaps be considered when leukocytes are used as bioenergetic biomarkers [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAlthough we could not identify consistent methodological differences among all the studies mentioned, combining these methodological differences can contribute to the contrasting results.\\u003c/p\\u003e\\u003cp\\u003ePrevious studies, although few, measuring mitochondrial function in muscle tissue of different origins have consistently reported a lower activity of mitochondrial complexes and a lower ATP content, concomitant with an altered expression of genes involved in regulating mitochondrial dynamics [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. In the current study, mitochondrial function was measured in PBMCs. PBMCs are easily and non-invasively obtained. However, PBMCs are important cells during inflammation and systemic infection and may have a different metabolic response to sepsis compared to other tissues directly involved in multiorgan failure, such as the liver and muscles. Based on the results of the current study and in comparison to studies performed using skeletal muscle biopsies, PBMCs do not necessarily reflect the decrease in mitochondrial function, which is reported elsewhere in the body. Indeed, Jeger \\u003cem\\u003eet al.\\u003c/em\\u003e reported that results from previous animal and clinical studies investigating mitochondrial function in several tissue types during sepsis are heterogeneous, reporting increased and decreased mitochondrial oxygen consumption [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Although speculative, these differences in mitochondrial functioning between tissues may reflect differences in their role during sepsis.\\u003c/p\\u003e\\u003cp\\u003eThus, the increased basal, ATP-linked and maximal respiration together with the increased expression of genes encoding for subunits of OXPHOS complexes (in particular complex II and IV) as found in the current study may reflect an increased ATP demand of PBMCs during human sepsis, as a result of an activation of the immune system to combat the underlying infection. The clinical relevance of this increase is suggested by the higher increase in basal, ATP-linked and maximal respiration respiration and spare respiratory capacity over time (i.e., between T1 and T3) in non-survivors, compared to survivors. Our findings may thus contrast speculation that mitochondrial dysfunction is the root cause of immunoparalysis and could be responsible for the onset of organ dysfunction [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Still, pathophysiological interpretation of this difference between survivors and non-survivors is precarious. The higher increase over time may, partially, be due to a lower basal and ATP-linked respiration in PBMC\\u0026rsquo;s at T1 of non-survivors compared to survivors, although this was not statistically significant. Thus, a delayed up-regulation of PBMC activation in PBMCs of non-survivors compared to survivors, cannot fully be excluded but, in view of the lack of statistical significance, is highly speculative. Still, disregarding possible differences at T1, a higher increase in both respiration parameters over time could reflect differences in the development of the infection, or indicate immune dysfunction. This study does not provide clarity in this respect, especially since immune mechanisms during sepsis are complex, consisting of simultaneous hyperinflammation and immune suppression. Future studies, however, will take these hypotheses into account.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStrengths\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eCombining functional measurements of mitochondrial functioning with relevant gene expression patterns, both exhibiting increases in sepsis, provides a more robust and in-depth analysis of the situation. Also, the consecutive measurements at three moments during the first week of ICU admission with fixed intervals are considered a strength of this study, as this enabled us to better understand the progress of mitochondrial respiration during the first week of ICU admission in septic patients. In addition, the extensive list of exclusion criteria based on common comorbidities (e.g., diabetes mellitus and COPD) known to affect metabolism and mitochondrial function is a unique strength of this study, as this allowed us to exclude potential confounding effects of these comorbidities.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLimitations\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe current study is limited by its single-centre design. However, as the samples needed to arrive at the laboratory in a fresh state, the hospital's proximity to a university laboratory equipped with an Oroboros was an essential condition for this study. Secondly, the possible effects of administered medication on mitochondrial function may represent an unaccounted confounder. Thirdly, multivariate Cox regression analyses performed on small numbers found an association between the delta basal respiration and ICU and 3-month mortality, not the severity-of-disease score APACHE II. This may be due to additional confounding factors, which were not accounted for (residual confounding) or multicollinearity, although the VIF was low (\\u0026lt;\\u0026thinsp;2). Finally, the recruitment period covered five years. During this period the standard of care may have changed, weakening the in- and external validity of our findings.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFuture directions\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eFurther research is needed to elucidate the role of mitochondria in the sepsis pathophysiology. More extensive multicentre trials are needed to consolidate the current study's findings. It would be interesting to measure mitochondrial respiratory function in various other tissues in parallel to create more insight into the potential role of PBMCs as a proxy marker for mitochondrial respiratory function in other tissues.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study demonstrated an increased basal, ATP-linked, maximal and proton leak associated respiration in PBMCs within the first week of ICU admission in sepsis patients compared to their healthy matched controls. Similarly, the expression of genes encoding subunits of complex II and V were increased in septic patients compared to controls. In addition, a progressive increase of mitochondrial respiration in PBMCs during the first week of ICU stay was positively associated with 3-month mortality.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eADP Adenine diphosphate\\u003c/p\\u003e\\u003cp\\u003eATP Adenine triphosphate\\u003c/p\\u003e\\u003cp\\u003eAPACHE II Acute physiology and chronic health evaluation II\\u003c/p\\u003e\\u003cp\\u003eBMI Body mass index\\u003c/p\\u003e\\u003cp\\u003eCCCP Carbonyl cyanide m-chlorophenylhydrazone\\u003c/p\\u003e\\u003cp\\u003eCI Confidence interval\\u003c/p\\u003e\\u003cp\\u003eCOPD Chronic obstructive pulmonary disease\\u003c/p\\u003e\\u003cp\\u003eFEV1 First second forced expiration\\u003c/p\\u003e\\u003cp\\u003eFVC Forced vital capacity\\u003c/p\\u003e\\u003cp\\u003eICU Intensive care unit\\u003c/p\\u003e\\u003cp\\u003eHR Hazard ratio\\u003c/p\\u003e\\u003cp\\u003eIQR Interquartile range\\u003c/p\\u003e\\u003cp\\u003eLMR Lymphocyte-monocyte ratio\\u003c/p\\u003e\\u003cp\\u003eLOS Length of stay\\u003c/p\\u003e\\u003cp\\u003emNUTRIC Modified nutrition risk in critically ill score\\u003c/p\\u003e\\u003cp\\u003eOXPHOS Oxidative phosphorylation system\\u003c/p\\u003e\\u003cp\\u003ePBMC Peripheral blood mononuclear cells\\u003c/p\\u003e\\u003cp\\u003eRCR Respiratory control ratio\\u003c/p\\u003e\\u003cp\\u003eSARS-CoV-2 Severe acute respiratory coronavirus 2\\u003c/p\\u003e\\u003cp\\u003eSD Standard deviation\\u003c/p\\u003e\\u003cp\\u003eSOFA Sequential Organ Failure Assessment score\\u003c/p\\u003e\\u003cp\\u003eTCA Tricarboxylic acid\\u003c/p\\u003e\\u003cp\\u003eVIF Variation inflation factor\\u003c/p\\u003e\\u003cp\\u003eWUR Wageningen University and Research\\u003c/p\\u003e\\u003cp\\u003eZGV Ziekenhuis Gelderse Vallei (Gelderse Vallei hospital)\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was approved by the Medical Ethical Committee of Wageningen University (METC-WUR, which was incorporated in the METC Oost-Nederland in 2021, dossier no. 2021-13011) and the assessment Committee for Scientific Research of ZGV (dossier no. 1801-004). The protocol was registered in the Netherlands Trial Register (number NTR6969) and was made available through the International Clinical Trial Registry Platform (NL5918).\\u003c/p\\u003e\\n\\u003cp\\u003ePatients were enrolled after signing the informed consent by the patient or legal representative.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and material\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe dataset supporting the conclusions of this article is available upon reasonable request from the corresponding author.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eProf. Dr. Van Zanten reported receiving honoraria for advisory board meetings, lectures, research grants, and travel expenses from Abbott, AOP Pharma, Baxter, Danone-Nutricia, Dutch Medical Food, Fresenius Kabi, GE Healthcare, Medcaptain, Nestle, PAION and Rousselot. The other authors have nothing to declare.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was funded by the Research Foundation of the Intensive Care of Gelderse Vallei Hospital, Ede, The Netherlands. Any article processing charges will be put forth by Wageningen University \\u0026amp; Research.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHM and RSB contributed equally to data collection, data analysis and interpretation, and writing and revising of the manuscript. AvN, SG, and JdJ contributed to the conception of the study, data collection, analysis, interpretation, and revision of the manuscript. AW contributed to data collection and interpretation. AvZ contributed to the conception of the study, data interpretation and revision of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe owe thanks to the following people for their valuable contributions to data collection and the writing of preliminary reports: Monique Daanje, Rosan van den Boogaard, Demi Vogels, Laura Willemse, Marloes Snijder, Max van den Bergh, Julia Boer\\u0026eacute;, Susana Mittmann, Panagiotis Vlachogiannis, Marieke Kr\\u0026uuml;ger, Rebeka Darmati, Viditha Venkatramu and Lisa Kleverwal. We thank all participants of the study, as well as the MSc students and research nurses who aided in data collection.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\\u0026ndash;10.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBauer M, Gerlach H, Vogelmann T, Preissing F, Stiefel J, Adam D. Mortality in sepsis and septic shock in Europe, North America and Australia between 2009 and 2019- results from a systematic review and meta-analysis. Crit Care. 2020;24(1):239.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990\\u0026ndash;2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200\\u0026ndash;211.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eYende S, Austin S, Rhodes A, Finfer S, Opal S, Thompson T, et al. Long-Term Quality of Life Among Survivors of Severe Sepsis: Analyses of Two International Trials. 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Shock. 2019;51(5):580\\u0026ndash;584.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSj\\u0026ouml;vall F, Morota S, Persson J, Hansson MJ, Elm\\u0026eacute;r E. Patients with sepsis exhibit increased mitochondrial respiratory capacity in peripheral blood immune cells. Crit Care. 2013;17(4):R152.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eClere-Jehl R, Helms J, Kassem M, Le Borgne P, Delabranche X, Charles AL, Geny B, Meziani F, Bilbault P. Septic Shock Alters Mitochondrial Respiration of Lymphoid Cell-Lines and Human Peripheral Blood Mononuclear Cells: The Role of Plasma. Shock. 2019;51(1):97\\u0026ndash;104.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSilaidos C, Pilatus U, Grewal R, Matura S, Lienerth B, Pantel J, et al. Sex-associated differences in mitochondrial function in human peripheral blood mononuclear cells (PBMCs) and brain. Biol Sex Differ. 2018;9(1):34.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLi S, Li X, Jiang S, Wang C, Hu Y. Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches. BMC Med Genomics. 2024;17(1):120. doi: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12920-024-01891-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12920-024-01891-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PMID: 38702721; PMCID: PMC11067249.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNucci LA, Santos SS, Brunialti MK, Sharma NK, Machado FR, Assun\\u0026ccedil;\\u0026atilde;o M, de Azevedo LC, Salomao R. Expression of genes belonging to the interacting TLR cascades, NADPH-oxidase and mitochondrial oxidative phosphorylation in septic patients. 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Eur J Clin Invest. 2013;43(5):532\\u0026ndash;42.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNedel W, Deutschendorf C, Portela LVC. Sepsis-induced mitochondrial dysfunction: A narrative review. World J Crit Care Med. 2023;12(3):139\\u0026ndash;152. doi: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.5492/wjccm.v12.i3.139\\u003c/span\\u003e\\u003cspan address=\\\"10.5492/wjccm.v12.i3.139\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PMID: 37397587; PMCID: PMC10308342.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Immunoparalysis, PBMC, respiration, critical illness, metabolism\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7137135/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7137135/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eSepsis is a leading cause of ICU admission and is associated with high rates of multiorgan failure and mortality. Altered mitochondrial function is an essential component of the early sepsis syndrome. However, its progression over time in peripheral blood mononuclear cells (PBMCs), essential mediators of the initial inflammatory response, is thus far unclear. Our purpose was to investigate the progression of mitochondrial respiration in peripheral blood mononuclear cells (PBMCs) in the early phase of sepsis in ICU patients.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eA single-centre prospective observational cohort study was conducted in sepsis patients and compared with age- and sex-matched controls. We measured mitochondrial function using functional respirometry measurements (Oroboros O2K) in PBMCs thrice during the first week of ICU admission. RT-qPCR was used for semi-quantitative analysis of expression of genes involved in oxidative phosphorylation. Secondary endpoints included the associations between mitochondrial function and (I) sepsis severity and (II) clinical outcomes, including 3-month mortality.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eBasal, ATP-linked, maximal and proton leak associated respiration were increased in sepsis patients (n\\u0026thinsp;=\\u0026thinsp;25) compared to matched controls (n\\u0026thinsp;=\\u0026thinsp;24) at all time points. This was associated with increased expression of \\u003cem\\u003eSDBH\\u003c/em\\u003e (complex II) and \\u003cem\\u003eATP5F1A\\u003c/em\\u003e (complex V). Increased basal respiration was associated with 3-month mortality (HR 3.794, 95%CI 1.018\\u0026ndash;14.149, p\\u0026thinsp;=\\u0026thinsp;0.047). No differences were observed in other secondary outcomes.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003ePBMC mitochondria were shown to have an increased respiratory rate during the first week of sepsis, supported by increased expression of genes involved in oxidative phosphorylation. Moreover, a progressive increase in mitochondrial respiration was negatively associated with 3-month survival. These results suggest that the upregulation of basal respiration may serve as a proxy marker for sepsis severity and outcomes.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Progression of Peripheral Blood Mononuclear Cell Mitochondrial Function during the Early Phase of Sepsis in Intensive Care Unit Patients\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-29 11:59:05\",\"doi\":\"10.21203/rs.3.rs-7137135/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"25d43b28-619d-4e9e-97bc-f5d6646e0cca\",\"owner\":[],\"postedDate\":\"July 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":52074968,\"name\":\"Health sciences/Diseases/Infectious diseases/Bacterial infection\"},{\"id\":52074969,\"name\":\"Health sciences/Biomarkers/Prognostic markers\"}],\"tags\":[],\"updatedAt\":\"2025-12-17T15:08:46+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-29 11:59:05\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7137135\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7137135\",\"identity\":\"rs-7137135\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}