Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis

Stress tests and biomarkers of resilience: Proceedings of the second state of resilience science conference

The "Stress Tests and Biomarkers of Resilience" conference, hosted by the American Geriatrics Society and the National Institute on Aging, marks the second in a series aimed at advancing the field of resilience science. Held on March 4-5, 2024, in Bethesda, Maryland, this conference built upon the foundational work from the first conference, which focused on defining resilience across various domains-physical, cognitive, and psychosocial. This year's gathering centered around three factors: the biology that underlies resilient outcomes; the social, environmental, genetic, and psychosocial factors that impact that resilience biology; and the biomarker testing and imaging that predicts resilient outcomes for older adults. The presentations and discussions around these topics were underscored by considerations around the many impacts of social determinants of health on resiliency interventions, and by advances in the modern training and research methodologies that influence data collection and experiment design.

Metrics of Physiological Network Topology Are Novel Biomarkers to Capture Functional Disability and Health

BACKGROUND

Physiological networks are highly complex, integrating connections among multiple organ systems and their dynamic changes underlying human aging. It is unknown whether individual-level network could serve as robust biomarkers for health and aging.

METHODS

We used personalized network analysis to construct a single-sample network and examine the associations between network properties and functional disability in the Rugao Longevity and Aging Study (RuLAS), the China Health and Retirement Longitudinal Study (CHARLS), the Chinese Longitudinal Healthy Longevity Survey (CLHLS), and the National Health and Nutrition Examination Survey (NHANES).

RESULTS

We observed impairments in interconnected physiological systems among long-lived adults in RuLAS. Single-sample network analysis was applied to reflect the co-occurrence of these multisystem impairments at the individual level. The activities of daily living (ADL)-disabled individuals' networks exhibited notably increased connectivity among various biomarkers. Significant associations were found between network topology and functional disability across RuLAS, CHARLS, CLHLS, and NHANES. Additionally, network topology served as a novel biomarker to capture risks of incident ADL disability in CHARLS. Furthermore, these metrics of physiological network topology predicted mortality across 4 cohorts. Sensitivity analysis demonstrated that the prediction performance of network topology remained robust, regardless of the chosen biomarkers and parameters.

CONCLUSIONS

These findings showed that metrics of network topology were sensitive and robust biomarkers to capture risks of functional disability and mortality, highlighting the role of single-sample physiological networks as novel biomarkers for health and aging.

BMI variability and cardiovascular outcomes within clinical trial and real-world environments in type 2 diabetes: an IMI2 SOPHIA study

BACKGROUND

BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability.

METHODS

We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling. BMI variability was determined using average successive variability (ASV), with first major adverse cardiovascular event of non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death (3P-MACE) as the primary outcome.

RESULTS

After adjusting for cardiovascular risk factors, a + 1 SD increase in BMI variability was associated with increased 3P-MACE risk in Harmony Outcomes (HR 1.12, 95% CI 1.08-1.17, P < 0.001). The most variable quartile of participants experienced an 87% higher risk of 3P-MACE (P < 0.001) relative to the least variable. Similar associations were found in REWIND and Tayside Bioresource. Further analyses in the EMPA-REG OUTCOME trial did not replicate this association. BMI variability's impact on 3P-MACE risk was independent of HbA1c variability.

CONCLUSIONS

In individuals with type 2 diabetes, increased BMI variability was found to be an independent risk factor for 3P-MACE across cardiovascular outcome trials and real-world datasets. Future research should attempt to establish a causal relationship between BMI variability and cardiovascular outcomes.

Tryptophan degradation as a systems phenomenon in inflammation - an analysis across 13 chronic inflammatory diseases

BACKGROUND

Chronic inflammatory diseases (CIDs) are systems disorders that affect diverse organs including the intestine, joints and skin. The essential amino acid tryptophan (Trp) can be broken down to various bioactive derivatives important for immune regulation. Increased Trp catabolism has been observed in some CIDs, so we aimed to characterise the specificity and extent of Trp degradation as a systems phenomenon across CIDs.

METHODS

We used high performance liquid chromatography and targeted mass spectrometry to assess the serum and stool levels of Trp and Trp derivatives. Our retrospective study incorporates both cross-sectional and longitudinal components, as we have included a healthy population as a reference and there are also multiple observations per patient over time.

FINDINGS

We found reduced serum Trp levels across the majority of CIDs, and a prevailing negative relationship between Trp and systemic inflammatory marker C-reactive protein (CRP). Notably, serum Trp was low in several CIDs even in the absence of measurable systemic inflammation. Increases in the kynurenine-to-Trp ratio (Kyn:Trp) suggest that these changes result from increased degradation along the kynurenine pathway.

INTERPRETATION

Increases in Kyn:Trp indicate the kynurenine pathway as a major route for CID-related Trp metabolism disruption and the specificity of the network changes indicates excessive Trp degradation relative to other proteogenic amino acids. Our results suggest that increased Trp catabolism is a common metabolic occurrence in CIDs that may directly affect systemic immunity.

FUNDING

This work was supported by the DFG Cluster of Excellence 2167 "Precision medicine in chronic inflammation" (KA, SSchr, PR, BH, SWa), the BMBF (e:Med Juniorverbund "Try-IBD" 01ZX1915A and 01ZX2215, the e:Med Network iTREAT 01ZX2202A, and GUIDE-IBD 031L0188A), EKFS (2020_EKCS.11, KA), DFG RU5042 (PR, KA), and Innovative Medicines Initiative 2 Joint Undertakings ("Taxonomy, Treatments, Targets and Remission", 831434, "ImmUniverse", 853995, "BIOMAP", 821511).

Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis

Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

Systematic analysis and optimization of early warning signals for critical transitions using distribution data

Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.

Intercorrelated variability in blood and hemodynamic biomarkers reveals physiological network in hemodialysis patients

Increased intra-individual variability of a variety of biomarkers is generally associated with poor health and reflects physiological dysregulation. Correlations among these biomarker variabilities should then represent interactions among heterogeneous biomarker regulatory systems. Herein, in an attempt to elucidate the network structure of physiological systems, we probed the inter-variability correlations of 22 biomarkers. Time series data on 19 blood-based and 3 hemodynamic biomarkers were collected over a one-year period for 334 hemodialysis patients, and their variabilities were evaluated by coefficients of variation. The network diagram exhibited six clusters in the physiological systems, corresponding to the regulatory domains for metabolism, inflammation, circulation, liver, salt, and protein. These domains were captured as latent factors in exploratory and confirmatory factor analyses (CFA). The 6-factor CFA model indicates that dysregulation in each of the domains manifests itself as increased variability in a specific set of biomarkers. Comparison of a diabetic and non-diabetic group within the cohort by multi-group CFA revealed that the diabetic cohort showed reduced capacities in the metabolism and salt domains and higher variabilities of the biomarkers belonging to these domains. The variability-based network analysis visualizes the concept of homeostasis and could be a valuable tool for exploring both healthy and pathological conditions.

A complex systems approach to aging biology

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.