Fight Aging!

Medical research and development in the context of aging has, like all other medicine, been dominated for a long time by a model in which a single disease is identified by symptoms and then treated. In the context of infectious disease and inherited conditions, this is a good way to go about the matter of investigation, treatment, and assignment of resources. A patient typically has one disease at a time, and the symptoms are distinct and clearly caused by the disease in question. Indeed, the disease paradigm arose from the modern era's long road towards ever more effective control of infectious disease. The institutions and traditions created over that time were then turned, as-is, to the question of aging and age-related diseases.

Here, however, the disease paradigm fails miserably. Age-related diseases arise from shared causes, the underlying mechanisms of aging. Attempting to treat symptoms, the downstream consequences of accumulating cell and tissue damage, produces only modest gains rather than functional cures. Further, most aged patients exhibit multiple, interacting conditions arising from the same root causes, making it even more inefficient to try to apply the single disease model of diagnosis and treatment. Physicians and researchers specialize in only one small set of downstream consequences, and rarely interact meaningfully with other specialties.

It is far past time to move on to a better model for the research and development of therapies to treat aging and age-related disease, one focused more on underlying causes, in which there is a recognition of the mechanisms of aging as a primary target for intervention. Today's open access commentary is a discussion along these lines, but remains, I think, a little too fixated on the primacy of symptoms as a way to guide academia, industry, and clinic.

Why illness is more important than disease in old age

Currently the single disease paradigm is still dominant in medicine in general, and also plays a major role in geriatric reasoning. This paradigm (sometimes referred to as Occam's razor) aims to explain illness by looking at patients´ symptoms (subjective: e.g. pain) and clinical signs (objective: e.g. high blood pressure) in specific patient episodes and by linking these with a single disease. Thus, clinicians aim to identify the single best cause for a patient's constellation of symptoms. Diagnostic reasoning in geriatrics should however take into account the high prevalence of multimorbidity, which increases with age from around 10% at the age of 40 to 85% in those aged 75 and over. In older adults with multimorbidity, a single symptom may arise from multiple diseases (e.g. fatigue may arise from both heart failure and osteoarthritis). Parallel treatment of single diseases easily leads to a high total treatment burden, over-treatment and aggravation of disease burden due to drug-drug, drug-disease, and drug-nutrition interactions. Thus, in case of multimorbidity, the cumulative single disease approach is often inefficient and potentially harmful.

Despite the great urgency, geriatric medicine still lacks a valid and clinically applicable model for adequate diagnosis, prognosis, and treatment of multimorbidity. Commonly used epidemiological methods try to explain multimorbidity pathophysiology by using sum scores, morbidity indices, and clustering of diseases. Clinically, multimorbidity is taken into account by cumulative (sometimes weighted) comorbidity scores, if considered at all. However, these epidemiology-based methods all fail to capture the dynamics and complexity of multimorbidity and its impact on the individual patient. Moreover, these methods are quantitative, abstract figures that do not inform clinical decision-making and thus are of limited added value in clinical practice. Even the most advanced models still rely on clustering of single disease concepts, and do not explain the interactions in multiple organ systems.

Complex systems thinking implies that illness in case of multimorbidity is not caused by a simple sum of single diseases and may offer an alternative explanatory model to the biomedical model. The science serving this field is devoted to understanding the general properties of complex systems. Core hallmarks of complex systems include: [1] networks of interacting elements (e.g. interactions among aging mechanisms such as oxidative stress and amyloid aggregation in dementia or decreased mobility, depressed mood, and joint pain), [2] feedback/feedforward loops (e.g. adaptive loops such as blood pressure regulation and maladaptive loops such as higher inflammatory states in Alzheimer's disease or older COVID-19 patients), [3] a multiscale or modular hierarchical structure (e.g. accumulating cellular damage nested within organ tissue, within organisms and families), [4] non-linear dynamics (e.g. tipping points in disease trajectories that cause acute flipping from dementia to a delirious state) and [5] emergent properties: the sum of properties of system components is not equal or even similar to the whole system outcome (e.g. well-being and illness cannot be understood simply as the sum of multiple morbidities as we diagnose them when they occur individually).

We therefore propose to develop clinical dynamic symptom network (DSN) based using principles of complexity science and network analysis. First evidence for the clinical utility of this approach comes from mental health research, in which the single disease model often fails and complex psychological symptom networks have advanced understanding and treatment of mental disorders. DSNs may form the foundation of a new paradigm to understand and treat geriatric illness episodes and trajectories. These should not replace geriatric syndromes or disease thinking, but may have a strong synergistic value, as thinking in symptoms and an illness concept may be more closely related to improving well-being outcomes in older patients. In future research, DSNs may be used to: (i) understand the complex, time-varying interrelations of symptoms, signs and diseases; (ii) develop prognostic models for changes in symptoms, signs and diseases over time; and (iii) evaluate effects of therapeutic interventions on the total symptom burden.