Physiology under stress in Fontan circulation: cohort, stimuli, and sampling

The Fontan circulation, a palliation for single ventricle anatomy, imposes a unique cardiopulmonary load profile that sharpens the clinical relevance of stress testing. Venous return depends on low pulmonary vascular resistance and respiratory pump mechanics, leaving patients vulnerable to limited flow augmentation during hypoxia or exercise. In this setting, inflammatory signaling has been linked to vascular tone, lymphatic function, and end-organ health, but its behavior under transient stressors is not well characterized. Serial sampling across rest, controlled hypoxic exposure, and incremental exercise can therefore illuminate dynamic set points and potential reserve in immune signaling.

Cohort characteristics and Fontan-specific physiology

Participants were individuals with established Fontan circulation, a population that typically spans adolescents to adults and includes a range of surgical pathways and conduit types. Baseline clinical descriptors such as age, sex, time since Fontan, oxygen saturation at rest, and medication exposures provide essential context for interpreting cytokine levels. Comorbidities relevant to inflammatory tone, including hepatic congestion, protein-losing enteropathy, or arrhythmia burden, can confound associations if not documented and addressed analytically. The physiologic background emphasizes how preload limitations, elevated central venous pressure, and attenuated stroke volume reserve shape responses to stress and potentially modulate inflammatory flux.

Standardized hypoxia and exercise protocols

Interventions were selected to probe oxygen delivery constraints and ventilatory mechanics alongside inflammatory signaling. A normobaric hypoxic gas mixture allows precise target saturations and predictable exposure durations, reducing variability. Incremental or ramp cardiopulmonary exercise testing complements hypoxia by adding metabolic and hemodynamic load, with breath-by-breath measurements contextualizing physiologic stress. Together, these protocols create a reproducible framework for temporal sampling windows at rest, during stress, and in early recovery, enabling paired comparisons within subjects that improve sensitivity to detect change.

Analyte panel and preanalytic control

The cytokine panel focused on canonical proinflammatory and regulatory mediators, chemokines, and growth factors measured by multiplex immunoassays or equivalent validated platforms. Preanalytic rigor is critical for reliable inference, including standardized anticoagulants, controlled time-to-processing, temperature management, and prespecified storage conditions with limited freeze-thaw cycles. Batch effects can be mitigated by randomizing sample order across plates, including internal controls, and, when possible, assaying matched timepoints for a participant on the same plate. Such controls are especially important when the expected signal under stress is subtle and when conclusions hinge on cross-condition comparisons rather than extreme contrasts.

Cytokine network analytics: from individual markers to systems view

Inflammation rarely operates as isolated markers; instead, it unfolds as interdependent modules with coordinated upregulation and damping. Accordingly, the analysis plan paired traditional per-marker contrasts with network-level assessments that capture the correlation structure among mediators across conditions. Individual differences can be quantified as within-subject changes from rest to hypoxia and exercise, while network analysis can test whether overall topology, clustering, and centrality are preserved. This systems perspective helps distinguish true biological stability from mere averaging of offsetting changes across markers.

Preprocessing, statistical contrasts, and effect size reporting

Analytic preprocessing likely included log transformation to stabilize variance, handling of values below detection limits, and outlier assessments based on prespecified rules. Paired contrasts between rest and stress conditions are best reported with effect sizes and confidence intervals, not just p values, to convey magnitude and direction. Adjustments for multiple comparisons constrain the false discovery rate when testing many analytes in parallel. Reporting patterns of change with forest plots or volcano plots further clarifies whether modest shifts cluster by cytokine class or behave idiosyncratically by individual marker.

Correlation structure and network topology

Network construction begins with correlation or partial correlation matrices that quantify co-variation among cytokines within each condition. Graph-theoretic metrics such as degree distribution, clustering coefficients, betweenness centrality, and community membership can then be compared across rest, hypoxia, and exercise. Preservation of community structure and hub identity suggests that the inflammatory system maintains its coordination architecture despite physiologic stress. A finding of stable topology, even when some individual markers nudge up or down, points to robust regulatory circuits that buffer transient perturbations.

Robustness checks, missing data, and multiplicity

Robustness depends on sensitivity analyses that test the influence of censoring, missing-at-random assumptions, and potential outliers. Excluding participants with incomplete sampling, repeating analyses with nonparametric correlation, or restricting to high-quality analytes ensures that stability is not an artifact. Multiplicity control strategies, such as false discovery rate for per-marker tests and permutation approaches for network metrics, help guard against spurious positives. Together, these checks increase confidence that a stable network reflects biology rather than analytic fragility.

Clinical interpretation, limitations, and future directions

What does a stable cytokine network mean in the Fontan context? First, it implies that short-lived reductions in oxygen content or increased metabolic demand do not substantially rewire inflammatory coordination, at least within the timeframe and intensity of the applied stressors. Second, it suggests that between-person differences in inflammatory tone may dominate over within-person transient shifts during controlled stress, which has implications for prognostication. Finally, it highlights a potential homeostatic resilience, in which inflammatory modules remain tethered by feedback loops even when physiologic reserve is taxed.

What network stability suggests for risk stratification

For clinicians seeking risk markers, a stable network under hypoxia and exercise indicates that resting measurements could approximate stressed-state ranking for many cytokines and modules. If topology is preserved, network-derived indices such as module eigenvalues or hub centrality might be consistent across conditions and usable as baseline risk indicators. This aligns with the observation that chronic drivers in Fontan physiology, including venous congestion and limited preload reserve, are persistent rather than episodic. It also means that dynamic testing may add limited discriminatory value for inflammatory profiling compared with its strong value for exercise capacity and ventilatory efficiency.

Implications for biomarker development and trials

Translating these findings to development pathways, the apparent stability favors parsimonious panels that target modules or hub markers rather than exhaustive profiling across conditions. This could streamline assay burden in longitudinal registries and interventional trials, using resting draws to anchor inflammatory trajectories. From a validation standpoint, module preservation across stress states bolsters construct validity for composite indices and supports their use as pharmacodynamic or enrichment biomarkers. The results also underscore the need to align inflammatory endpoints with mechanisms of candidate therapies, especially those targeting vascular tone, lymphatic function, or endothelial dysfunction.

Caveats, confounders, and generalizability

Several caveats merit emphasis. Sample sizes in specialized cohorts are often modest, which can limit power to detect small but meaningful changes and to resolve condition-specific reweighting of edges in the cytokine network. Heterogeneity in age, time since Fontan, conduit types, and medication exposures can blur subtle effects without stratification or interaction modeling. Circadian timing, nutritional state, and recent infections influence systemic inflammation, making protocol standardization vital and residual confounding a persistent concern.

Furthermore, while hypoxic exposure and exercise are relevant real-world stressors, they are relatively brief and controlled, and may not capture inflammatory responses to sustained hemodynamic decompensation, arrhythmic events, or intercurrent illness. The stability observed here does not preclude time-lagged responses that emerge hours after stress, nor does it address end-organ-specific inflammation that may not be mirrored in plasma. External validity beyond the specific testing environment, equipment, and analyte platform will require replication. Finally, it remains to be shown whether network stability holds in subgroups with advanced liver disease, significant collaterals, or concomitant pulmonary hypertension.

Mechanistic context and comparator physiology

The lack of large network shifts under stress can be interpreted in light of compensatory mechanisms evident in Fontan physiology. Respiratory-driven augmentation of venous return, neurohumoral activation, and peripheral extraction adjustments may stabilize the internal milieu across short perturbations. Inflammatory signaling may be dominated by chronic stimuli from hepatic congestion and tissue hypoperfusion rather than acute shifts in oxygen content. Comparing these dynamics with biventricular controls or patients with acquired heart disease could help clarify whether the observed stability is unique to Fontan or shared across limited-reserve states.

Design considerations for future studies

Future protocols could expand sampling windows to include delayed timepoints, enabling detection of slower inflammatory cascades. Incorporating endothelial biomarkers, lymphatic markers, and metabolomic readouts would test cross-domain coherence and triangulate mechanisms. Stratified analyses by training status, hepatic stiffness, or conduit type might expose condition-specific vulnerabilities masked in aggregate. Preplanned external replication across centers and assay platforms would strengthen generalizability and inform platform harmonization in multicenter trials.

Clinical utility beyond inflammation

Even if inflammatory networks prove stable under short stressors, exercise testing remains indispensable for functional classification, rehabilitation planning, and evaluating interventions that target the respiratory pump or peripheral conditioning. Integrating inflammatory modules with cardiorespiratory indices could still enhance composite risk scores, particularly if certain cytokines correlate with ventilatory inefficiency or chronotropic incompetence. Systems-level modeling that couples hemodynamics and immune signaling may reveal thresholds where compensation gives way to decompensation. Such multiscale tools will be crucial to personalizing care in this complex physiology.

Overall, the reported stability of cytokine networks during hypoxia and exercise in Fontan physiology focuses attention on chronic drivers of inflammation rather than acute stress responses. For biomarker development, resting measurements may suffice to capture individual inflammatory set points and module architecture, reducing sampling burden in trials and registries. For mechanistic science, the findings motivate exploration of upstream determinants like endothelial shear, lymphatic load, and hepatic congestion, and how these shape immune tone over longer timescales. As a next step, linking network features to clinical outcomes will determine whether stable topology still carries prognostic weight and whether targeting specific modules can modify trajectories in this vulnerable population. For access to the abstracted details and broader context, see the PubMed record at https://pubmed.ncbi.nlm.nih.gov/40921273/.