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. 2024 May 29;13(11):3200.
doi: 10.3390/jcm13113200.

An Exercise Immune Fitness Test to Unravel Disease Mechanisms-A Proof-of-Concept Heart Failure Study

Affiliations

An Exercise Immune Fitness Test to Unravel Disease Mechanisms-A Proof-of-Concept Heart Failure Study

Galyna Bondar et al. J Clin Med. .

Abstract

Background: Cardiorespiratory fitness positively correlates with longevity and immune health. Regular exercise may provide health benefits by reducing systemic inflammation. In chronic disease conditions, such as chronic heart failure and chronic fatigue syndrome, mechanistic links have been postulated between inflammation, muscle weakness, frailty, catabolic/anabolic imbalance, and aberrant chronic activation of immunity with monocyte upregulation. We hypothesize that (1) temporal changes in transcriptome profiles of peripheral blood mononuclear cells during strenuous acute bouts of exercise using cardiopulmonary exercise testing are present in adult subjects, (2) these temporal dynamic changes are different between healthy persons and heart failure patients and correlate with clinical exercise-parameters and (3) they portend prognostic information. Methods: In total, 16 Heart Failure (HF) patients and 4 healthy volunteers (HV) were included in our proof-of-concept study. All participants underwent upright bicycle cardiopulmonary exercise testing. Blood samples were collected at three time points (TP) (TP1: 30 min before, TP2: peak exercise, TP3: 1 h after peak exercise). We divided 20 participants into 3 clinically relevant groups of cardiorespiratory fitness, defined by peak VO2: HV (n = 4, VO2 ≥ 22 mL/kg/min), mild HF (HF1) (n = 7, 14 < VO2 < 22 mL/kg/min), and severe HF (HF2) (n = 9, VO2 ≤ 14 mL/kg/min). Results: Based on the statistical analysis with 20-100% restriction, FDR correction (p-value 0.05) and 2.0-fold change across the three time points (TP1, TP2, TP3) criteria, we obtained 11 differentially expressed genes (DEG). Out of these 11 genes, the median Gene Expression Profile value decreased from TP1 to TP2 in 10 genes. The only gene that did not follow this pattern was CCDC181. By performing 1-way ANOVA, we identified 8/11 genes in each of the two groups (HV versus HF) while 5 of the genes (TTC34, TMEM119, C19orf33, ID1, TKTL2) overlapped between the two groups. We found 265 genes which are differentially expressed between those who survived and those who died. Conclusions: From our proof-of-concept heart failure study, we conclude that gene expression correlates with VO2 peak in both healthy individuals and HF patients, potentially by regulating various physiological processes involved in oxygen uptake and utilization during exercise. Multi-omics profiling may help identify novel biomarkers for assessing exercise capacity and prognosis in HF patients, as well as potential targets for therapeutic intervention to improve VO2 peak and quality of life. We anticipate that our results will provide a novel metric for classifying immune health.

Keywords: heart failure; immunological fitness; peripheral blood mononuclear cell transcriptome.

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Conflict of interest statement

The authors G.B. and M.C.D. are co-founding equity holders of LeukoLifeDx, Inc., the developer of MyLeukoMAPTM biomarker test. T.-M.B. was an employee of LeukoLifeDx. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Figures

Figure 1
Figure 1
Study Design.
Figure 2
Figure 2
Average expression of 11 genes across all three time points and 20 subjects. Color-coding identifies upper quartile (blue) and lower quartile (red).
Figure 3
Figure 3
Gene Expression of 11 genes at Time Points 1, 2, and 3. The left columns represent the normalized signal values for Time Point 1, the middle columns for Time Point 2, and the right columns for Time Point 3.
Figure 4
Figure 4
The Venn diagram depicts the 11 genes described in Figure 3 and Table S1. While 5 of the 11 genes overlap between the group HV and HF, 6 of the 11 genes are specific to either HV (n = 4) or HF (n = 16).
Figure 5
Figure 5
Cluster entities diagram for all 20 subjects and TP1, TP2 and TP3 with FC cut-off 2.0 and 11 genes. Top to bottom: TTC34, DPYD-AS1, CCDC181, TKTL2, THSD7A, TMEML19, TNRFSF12A, CD300LD, MGAT5B, C19orf33, ID1.
Figure 6
Figure 6
Covariate regression analysis shows an expression of the 11 genes across all three TPs for HV and HF groups in correlation to VO2max and % predicted oxygen uptake. The color coding depicts a positive (red) and a negative (blue) correlation. The darker the color the stronger the respective correlation. Top to bottom: TTC34, DPYD-AS1, CCDC181, TKTL2, THSD7A, TMEM119, TNFRSF12A, CD300LD, MGAT5B, C19orf33, ID1.
Figure 7
Figure 7
HF group (n-9), analyzed by time-point (TP1, TP2, TP3) and as a shared gene set. Comparison of Z test results for death-associated heart failure group and three time points separately.

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