High-Mortality Bridging Diseases

• High-mortality bridging diseases are conditions that connect distinct disease clusters in population-level comorbidity networks, elevating mortality risks.
• Network analyses using metrics like degree and betweenness centrality systematically identify conditions such as hypertension, IHD, and COPD as pivotal bridging nodes.
• Dynamic comorbidity models demonstrate that integrated care targeting these bridges can substantially mitigate the progression of multimorbidity across lifecycles.

High-mortality bridging diseases are defined as diseases that, due to their structural roles within population-level comorbidity or contact networks, connect otherwise disparate disease clusters or transmission pathways and are associated with elevated mortality rates. These diseases function as “bridges” linking multiple health domains—metabolic, cardiovascular, respiratory, neoplastic, or infectious—and often underlie or accelerate multimorbidity and population health deterioration, especially in aging or at-risk populations.

1. Comorbidity Networks and Identification of Bridging Diseases

High-mortality bridging diseases have been systematically identified using large-scale comorbidity network models, notably the Phenotypic Human Disease Network (PHDN) constructed via population-level claims data (1405.3801). In these networks:

• Nodes represent diseases, typically at the ICD-10 3-digit level.
• Edges represent statistically significant and clinically relevant co-occurrence (evaluated via both corrected contingency coefficients and conditional probabilities).
• The “overlap network” includes edges only when both conditional probability and statistical significance surpass empirical thresholds.

Bridging diseases correspond to nodes with high degree (number of direct connections) and/or high betweenness centrality (number of shortest paths between disease pairs passing through the node). In Austrian population data, classic high-mortality bridging diseases in the elderly include hypertension (I10), chronic ischemic heart disease (I25), and chronic obstructive pulmonary disease (J44), which structurally link multiple otherwise separated disease clusters such as metabolic, circulatory, genitourinary, and respiratory disorders.

2. Dynamics and Predictive Models in Comorbidity Networks

The structural importance of bridging diseases is elucidated via diffusion models for comorbidity progression (1405.3801). For disease $i$, the expected prevalence in the next age group is described by:

$$\hat{p}_i^{m/f}(a+1) = p_i^{m/f}(a) + (1 - p_i^{m/f}(a)) \sum_{j \neq i} \Delta P^{m/f}_{ij}(a)\, p_j^{m/f}(a)$$

where $\Delta P_{ij}$ quantifies the change in conditional risk of $i$ given $j$. This formulation demonstrates that comorbidities “diffuse” primarily via network proximity, i.e., new diseases are disproportionately acquired in the relational neighborhood of existing ones. Bridging (hub) diseases greatly increase risk for subsequent expansion of multimorbidity and mortality by connecting disease modules.

Empirical validation shows that this model explains over 85% of the variance in disease incidence in adult and elderly populations, emphasizing the predictive power of comorbidity network topology in identifying high-risk bridging diseases.

3. Lifecourse Variation in Network Structure and Bridges

Comorbidity network topology is highly dynamic across the lifespan (1405.3801, Gardinazzi et al., 27 Jun 2025):

Life Stage	Network Structure	Central Bridging Diseases
Childhood	Single, dense cluster	Acute resp. and ENT diseases
Adolescence	Cluster modularization	Emerging mental/metabolic nodes
Adulthood	Distinct modules, local hubs	Lipoprotein (E78), depression, hepatic disease
Elderly	Cluster merging, central hubs	Hypertension (I10), IHD (I25), COPD (J44), Cancers (C group), Chronic kidney (N18), Liver cirrhosis (K74), Subarachnoid hemorrhage (I60)

In late-life networks, mortality-contributing diseases (e.g., cancers, organ failures, advanced cardiovascular or renal disease) assume central, high-betweenness positions, acting as structural bottlenecks for mortality risk.

4. Gatekeeper and Divergence-Point Diseases

Longitudinal analysis of clustered patient trajectories reveals that certain conditions—particularly diabetes mellitus (E10–E14) and hypertensive diseases (I10–I15)—substantially increase the probability of transition into high-mortality multimorbidity states (Haug et al., 2019). These are termed “gatekeeper diseases,” as moving into clusters defined by them raises the probability of shifting to “sink” multimorbidity/mortality regions by 40–60%.

Multilayer comorbidity networks (Dervić et al., 2023) and temporal centrality analyses (Gardinazzi et al., 27 Jun 2025) further show that critical divergence events (“bridges”) often involve these diagnoses, marking the onset of sharply divergent, high-mortality trajectories.

5. Implications for Prevention, Risk Stratification, and Management

The network and trajectory-based identification of high-mortality bridging diseases supports both population-level and individualized intervention strategies:

• Prevention and Early Detection: Targeting gatekeeper or bridging diseases—most notably hypertension and diabetes—through screening, risk factor modification (blood pressure, lipids, glucose), and lifestyle intervention has an outsize impact on later multimorbidity and mortality (Ledebur et al., 21 Mar 2024). Modeling indicates, for example, that a 5% reduction in new hypertensive cases yields a 0.57% reduction in all-cause and cancer mortality over 15 years.
• Integrated Care: High-mortality bridges, by their structural position, link otherwise siloed disease specialties. Integrated, multidisciplinary care pathways are necessary to disrupt adverse multimorbidity cascades.
• Life-stage Adaptation: As network centrality evolves with age, the strategic focus of prevention must also adapt, with childhood interventions focusing on dense disease clusters, adult care on emergent module hubs, and late-life care on the management of central bridges.

6. Network Metrics, Validation, and Outcome Association

• Structural metrics: Disease prioritization relies on quantitative centrality (degree, betweenness), their time course (via non-negative matrix factorization), and combined Z-score product with mortality rates to highlight high-mortality network bridges (Gardinazzi et al., 27 Jun 2025).
• Robustness: Results are validated across weighting schemes (odds ratios, relative risk, phi/Jaccard), population strata (age, sex), and comorbidity definitions.
• Outcome correlation: High-mortality bridge diseases occupy the intersection of high network centrality and high in-hospital mortality, indicating both their relational and direct clinical lethality.

Disease Example	Life Stage	Centrality Role	Mortality Association
Hypertension (I10)	Elderly	Central hub/bridge	Major risk for CV death
Lipoprotein disorders (E78)	Adults	Central, high degree	Enhances CV/metabolic risk
Chronic kidney (N18)	Elderly	Late-life bridge	Organ failure mortality
Liver cirrhosis (K74)	Adult–elderly	Bridge	High all-cause mortality
Cancers (C group)	Adulthood+	Sinks/bridges	Dominant cause of death
Subarachnoid hemorrhage (I60)	Midlife	Episodic bridge	High fatality event

7. Broader Theoretical and Practical Implications

High-mortality bridging diseases emerge from and reinforce the complex, time-evolving topology of population health. Their identification via network and trajectory analysis:

• Explains why multimorbidity and the accumulation of chronic diseases intensify with age and connect to mortality surges.
• Underscores the necessity of shifting from single-disease, cross-sectional models to temporally-structured, network-aware healthcare policy.
• Provides an empirical, quantitative basis for age-specific, disease-specific, and integrated prevention and care initiatives targeting the most influential "bridges" in the disease network.

The combined evidence from comorbidity networks, trajectory models, and population-level analyses strongly supports a move toward structural (network-based) and dynamic (lifecourse) approaches to addressing high mortality and morbidity in modern healthcare systems.