Halting Old-Age Multimorbidity in Europe (HOME)

Project Proposal

Halting Old-Age Multimorbidity in Europe (HOME) by addressing old-age multimorbidity as a diagnosable and treatable medical condition

HOME project – Halting Old-age Multimorbidity in Europe – ISRLA

  1. Executive summary
  2. Rationale
  3. Methodological challenges to be solved
  4. Project aims
  5. Methodologies to be applied
  6. HOME’s 3-year project plan
  7. Team (tentative)
  8. Selected sources

Potential scientific participants and supporters are sought!

Please contact: Ilia Stambler, PhD ilia.stambler@gmail.com

 

1. Executive summary

Currently, consensus clinical criteria for the evaluation of the severity of age-related multimorbidity and for the effectiveness of interventions against it are absent. The HOME project – Halting Old-Age Multimorbidity in Europe – will tackle both the diagnostic and therapeutic parts of the problem. During the project’s diagnostic track, we will devise clinical criteria for old-age multimorbidity — utilizing diverse physiological, functional, genetic, epigenetic and other biomarkers and advanced methods of bioinformatics analysis and data-mining — that will be plausibly accepted by the European Medicines Agency (EMA). During the project’s therapeutic track, we will conduct the meta-analysis of available biomedical approaches — chiefly including diverse pharmacological and regenerative medicine approaches — for the alleviation of old-age multimorbidity and select the most promising intervention approaches for human testing. Assuming EMA approval, we will proceed toward clinical trials on the selected interventions and assessing their effectiveness using the diagnostic criteria that will be developed.

2. Rationale

Much of the Developed World, Europe in particular, is undergoing a demographic revolution, which might be called a revolution in aging. The portion of the population comprising elderly (and, increasingly, very old) people is growing rapidly, and demographers predict that the aging of our societies will continue for generations to come. This owes in part to the growing advances in biomedicine to extend human life, and to improve health and living conditions. But this revolution in aging has not been necessarily accompanied by a revolution in aging well — the quality of aging.

It is increasingly realized that population aging brings in its train a host of degenerative, malignant and other chronic diseases, such as cancer, type 2 diabetes, chronic obstructive pulmonary disease, neurodegenerative diseases, heart disease, aggravation of infectious diseases, as well as diverse other functional, physical and mental impairments. These conditions do not emerge separately from each other, but have related etiologies and mutually exacerbate each other. This multitude of morbid conditions has been often termed as “multimorbidity” or “co-morbidity” (Fried and Walston, 1999). Moreover, it has been suggested that a promising approach to address the entire host of old-age-related morbidities would be by treating their underlying determinative factors – namely fundamental degenerative processes of aging (Fontana, et al., 2014; Rae, et al., 2010; Kennedy et al., 2014; Goldman et al., 2013; Jin et al., 2015). Yet, it must be emphasized that there is currently no agreed methodology to estimate the direct effects of therapy on tackling the aging process as such (for which there is presently no agreed formal or clinical definition or criteria). Moreover, essentially, there is no agreed formal or clinical definition and criteria for multimorbidity either. Correspondingly, there are no agreed scientifically grounded criteria to select interventions against old age multimorbidity or to evaluate their effectiveness. There are clinical methodologies to diagnose individual age-related diseases and dysfunctions or assess interventions against those individual diseases and dysfunctions. Yet their integrated evaluation as a “multimorbidity” as well as the selection and evaluation of interventions against multimorbidities — remain unresolved methodological challenges (Blokh and Stambler, 2016).

Yet it is critically important to be able to develop such criteria to correctly estimate age-related multimorbidity conditions and the effectiveness of treatments against age-related multimorbidity, combining several aging-related diseases at once. This ability is critically needed for the cost-effective early detection and preventive treatment of these diseases, based on the evaluation of their underlying aging processes. The importance of quantifying the effects of “normal” or even “healthy” aging as compared to “abnormal”, “pathological”, “accelerated” or “premature” aging cannot be overestimated. It is critically important to be able to diagnose “early aging”, that is, to identify subjects in whom “biological” or “physiological age” markedly exceeds the “chronological age”. Thanks to such “early diagnosis” of aging, as a pre-clinical or concomitant condition for a variety of aging-related diseases, it may be possible to solve the problems of early diagnosis and prevention of those aging-derived diseases.

In other words, it may be stated that pre-clinical diagnosis and treatment of aging-related diseases (such as Alzheimer’s disease, type 2 diabetes, cancer and heart disease) naturally belongs in the field of aging research, as aging can be seen as a pre-symptomatic, pre-clinical root determinant of a variety of aging-related diseases. Yet, to achieve this ability, it will be necessary to devise solid criteria for the “diagnosis” of premature aging and aging-related multimorbidities, for the selection of interventions against old-age multimorbidity, and at a later stage — to test the selected interventions according to the developed diagnostic criteria and to assess their actual effectiveness. Such criteria are explicitly requested by major regulatory frameworks — such as EMA, ICD, GSAP, FDA (FDA, 2012; WHO, 2015; ICD, 2015; EMA, 2016; Stambler, 2015, 2016). However the actual work of devising such criteria has yet to be done.

3. Methodological challenges to be solved

Multimorbidities are usually estimated as scores, with particular diseases and dysfunctions assigned certain scoring points (e.g. Fortin et al., 2005). Yet, such assessments are often subjective and arbitrary, and lack essential indications, such as the precise weight of each kind of morbidity within the composite “multimorbidity”, as well as their cumulative weight (which may be more or less than the sum of weights of individual morbidities) and their precise relation to each other and to their diagnostic and risk factors and their combinations.

There appears to be also no strict methodology to establish the risk factors and their combinations for several diseases and disabilities at once, comprising the “multimorbidity”. For example, even in the most authoritative studies, such as the Global Burden of Disease (GBD), the calculated risk of a disease from various risk factors, and the risk of death from various causes can exceed hundred percent (when in fact it should be no more than 100%) (Lim et al., 2012; Lozano et al., 2012). Various scoring systems are used to establish combined risk factors (e.g. Pocock, et al., 2001), which suffer from the same drawbacks as the multimorbidity scores, chiefly the inability to estimate cumulative or synergistic effects. The linear equations of the logistic regression method, that are also often used to estimate combined risk factors (e.g. Wahman et al., 2010), are usually insufficient to describe the complex non-linear relations of risk factors and morbidities.

With regard to selecting and testing of treatments against multimorbidity, currently, the effects of various treatments on human health are often examined in a disconnected manner, without knowing the precise interactions of various treatments, which can both synergistically reinforce the beneficial actions of each other, or conversely cause polypharmacy side effects that themselves become morbidity risk factors. There is an urgent need to both predictively and retroactively evaluate the effects of single treatments or combinations of various treatment factors (such as drugs, genes and lifestyle factors) on the health span and the multimorbidity status. By the precise quantitative evaluation of the influence of such factors on the health span and multimorbidity status, both synergistic positive and antagonistic adverse effects of treatment interactions must be determined.

4. Project aims

The current project aims to develop and test solid scientifically and clinically grounded criteria for old-age multimorbidities to be recognized and addressed as diagnosable and treatable conditions. There can be several classes of old-age multimorbidities that must be defined by appropriate criteria. Such criteria will not only be highly useful in the clinic, but will also assist policymakers, legislators and regulators, to stimulate relevant R&D and access to therapies for the alleviation of aging-related multimorbidities.

For old-age multimorbidities to be recognized as diagnosable and treatable medical conditions, two key advances are necessary:

  • Developing evidence-based diagnostic criteria for old-age multimorbidity. Without such science-based, clinically applicable criteria, any talk about ameliorating old-age multimorbidity will be mere slogans.
  • Establishing proof of principle that specific multimorbidity-alleviating therapies are effective. A meta-analysis of the multimorbidity-alleviating potential of candidate therapies is needed to determine which ones may be effective in humans.

The Halting Old-age Multimorbidity in Europe (HOME) project aims to tackle both sides of the problem:

  1. Our team, detailed below, of experts in mathematical modelling and analysis of biomarkers and clinical end-points in the alleviation of aging-related morbidity, will devise the initial diagnostic criteria and definitions for a clinically applicable, scientific recognition of old-age multimorbidity as a medical condition. We will then establish dialogue with regulatory agencies, primarily the EMA for their recognition of such criteria as applicable to human testing.
  2. Expert biologists and physicians in our team, specializing in experimental work, will meta-analyze the wide array of candidate multimorbidity-alleviating treatments discussed in the literature, as well as found in available databases. Based on that analysis, the most promising approaches will be selected for advanced animal and further human testing.

Our project is inspired by the encouraging precedent of the FDA-approved ‘Targeting Aging with Metformin’ (TAME) study in the US (Hall, 2015; Healthspan Campaign, 2015). In November, 2015, the American Food and Drug Administration (FDA) approved the human testing of Metformin — a well-established anti-diabetic — as the first drug candidate for delaying old-age multimorbidity. It may do so by intervening in the core aging process (mainly glycation), rather than treating disparate diseases. We propose to transpose this approach to Europe, with more elaborate criteria for the definition of multi-morbidity.

Moreover, instead of focusing on Metformin we propose to first conduct the meta-analysis of the available approaches and potential therapies, and, next, apply to the European Medical Agency (EMA) for human testing to target old-age multimorbidity using the most promising approaches as determined by our meta-analysis. For assessing the effectiveness of the interventions, we will use evidential criteria, biomarkers and clinical end-points that will be established in the diagnostic track of this project and that would be acceptable to the EMA.

5. Methodologies to be applied

In order to develop theoretically grounded and adequate methodologies to measure multimorbidities, as well as their combined risk factors and interventions, we will strongly rely on methods of information-theoretical analysis, utilizing such measures as normalized mutual information and entropy (Blokh and Stambler, 2016; Blokh and Stambler, 2015a,b; Blokh and Stambler, 2014).

For example, the use of normalized mutual information as a measure of correlation between the morbidities and risk factors/interventions, will allow to establish the cumulative and non-linear influences and to compare those influences. In other words, out of several individual risk factor/intervention variables, a single multiple risk factors’/interventions’ variable can be created combining the individual risk factors/interventions. Similarly, out of several individual disease variables, a single “multimorbidity” variable can be established composed of several diseases (diabetes andheart disease and dementia, etc.). And these two composite variables can be correlated by normalized mutual information. In addition, the estimation of influence of individual risk factors and interventions on the multimorbidity, as well as of multiple risk factors and interventions on an individual disease considered as a weighted part of the multimorbidity, will also be possible. One of the advantages for the evaluation of the risk factors and interventions is that the value of correlation will never exceed unity or 100% due to the property of normalized mutual information.

Such a systemic approach may help to quantitatively and formally investigate the process of aging, as an underlying and common factor of multiple age-related diseases. In this way, the process of aging may be explored as a complex phenomenon depending on multiple factors of different etiology. That is to say, rather than attempting to infer from the poorly defined concept of biological aging toward its derivative conditions (diseases and morbidities), it may be possible to attempt to formally or operatively define pathological or “early aging” from the diagnosable morbidities, seeking common age-related denominators between them. It may also be possible to designate various types of “multimorbidity” – for example “degenerative multimorbidity” (combining various degenerative diseases) or “proliferative multimorbidity” (combining various forms of cancer and other proliferative diseases), which may exhibit discrepant patterns that could be compared (Foster et al., 2012; Musicco et al., 2013). Other information-theoretical measures, such as entropy in such different multimorbidity systems, may also vary.

A major methodological advantage of the proposed project will be its integrative approach, aiming to bridge several gaps in the current variety of approaches to old-age health and morbidity evaluation. Rather than restricting the study to particular “domains”, we will endeavour to utilize the widest available selection of diagnostic parameters, including genetic and epigenetic loci, combined with physiological and functional parameters, as well as environmental and behavioural factors, using up-scaled bioinformatic analysis. This will provide major advantages in terms of combining research and technological capabilities and shortening the pathways between different areas of research and from research to application. One of the crucial disparities in the current variety of approaches to “healthy aging” or conversely to aging-related “multimorbidity” and “frailty” is the perceived opposition between “external” or “environmental” factors for healthy aging, and “internal” or “genetically determined” factors. On the one hand, it is often assumed that environmental, behavioural and lifestyle factors alone are sufficient to promote healthy aging, disregarding genetic composition, the inner structure and function of the body. On the other hand, there is a “genetic” or “biological deterministic” approach that assumes the strict genetic and other biological determinants of health in old age that virtually cannot be influenced by environmental factors. The proposed project aims to bridge this gap through the study of physiological, in particular metabolic, neuro-hormonal and epigenetic influences on healthy aging, which recognizes the crucial regulatory role of the environment on gene expression and internal physiological function.

The initial development of criteria for the diagnosis of old-age multimorbidity and for the selection of prospective interventions against it will be mainly based on data from available clinical databases, combining diverse diagnostic and therapeutic parameters. We will seek to utilize data from databases such as the Gani_med and SHIP (Greifswald, Germany), Finrisk, the Estonian Biobank, “Digital Health” of the Israeli Ministry of Health, and many others. We will also use emerging databases compiling biological data on aging at various levels, and for potential interventions (Moskalev et al, 2015; Craig et al., 2015).

At a later stage, if granted the approval of the EMA for the developed diagnostic and therapeutic criteria, we will proceed toward testing the selected interventions in human subjects using these criteria. For that stage, subject recruitment and testing will be conducted by the clinical facilities performing the study to ensure full control over the quality and uniformity of testing conditions.

The prospective test subject sample for this later stage should be at least 2000, to be examined during at least 2 years, from several considerations:

  1. At least 200-250 subjects are needed to create a single component category for a particular age-related morbidity within the general multicomponent multimorbidity (with at least 8 components: e.g. cancer, dementia, type 2 diabetes, metabolic syndrome; COPD, heart disease, osteoporosis, frailty).
  2. In the analysis of tables of conjunction, we assume that for almost all the cells, the expected number of elements should be no less than 5 in each cell (Kullback, 1958; Pollard, 1977). We consider discrete parameters, assuming 3 values (i.e. below, equal or above some normative of delimiting value). As a rule, to fulfil this sufficiency criteria, 200-250 subjects are needed to create a correlation between a single risk factor and a single disease category. 500-700 subjects are sufficient to create a correlation between a combined risk factor, composed of 2 single risk factors, with a single disease. To establish a combined risk factor composed of 3 individual risk factors, with a disease, the sample size requirement needs to increase to at least 2000 subjects.
  3. Moreover, in order to test the most promising approaches (at least 2 or more), it is necessary to divide the number of subjects by 2 (gender) and by n+2 (n approaches + 1 combined treatment + 1 control). Thus for testing 2 approaches, 250×8=2000 subjects appears to be the minimal requirement.
  4. 2000 subjects is also the typical number involved in FDA phase 3 clinical trials (FDA, 2016).

The subjects should be from different age groups, ideally 50 to 100+. Special attention should be paid to distinctions between subjects characterized by “healthy aging” (exhibiting greater resilience or reduced mortality as shown by longitudinal observations), as compared to subjects characterized by “accelerated aging” (high mortality from aging-related diseases as determined during the same observation period), with and without the treatments.

Yet, it should also be noted, that the choice of the subject subsets is not restrictive.  The proposed model will be an open one, capable to accommodate any additional number of parameters and any additional number of subjects from different age groups — improving the model’s diagnostic ability, depending on the availability of data.

6. HOME’s 3-year project plan

Year 1 — During the diagnostic track, we will devise clinical criteria for old-age multimorbidity that will plausibly be accepted by the EMA (see the Methodology section above). For the therapeutic track, we will conduct the meta-analysis of available approaches and select the most promising for human testing. Total cost for Year 1, mainly comprising salaries for the analysts, information collection and analysis costs: ‎200,000 euros.

Year 2 and 3 — Assuming EMA approval, we will proceed toward clinical trials on the selected interventions and assessing their effectiveness using the diagnostic criteria that will be developed. The number of the subjects recruited can be scalable, but should preferably be at least 2000 subjects, mainly from the older age categories. The annual costs, comprising salaries and costs of analysis, as well as costs of clinical testing of the therapeutic and diagnostic approaches: from 2 million euros annually and upward, depending on the number of subjects recruited and analysed (this estimate is at least for 2,000 people).

Even though limited in scope, the pioneering HOME study will blaze the trail for large-scale additional R&D and clinical applications, thanks to the recognition of old-age multimorbidity as a diagnosable and treatable medical condition in the EU.

7. Team (Tentative. The project can be scaled up. Additional scientific participants and supporters are sought)

Prof. Andreas Simm. Clinician. Director of Interdisciplinary Centre on Ageing Halle (IZAH), Martin Luther University Halle-Wittenberg, Germany. andreas.simm@uk-halle.de

Prof. Dr. Georg Fuellen. Analyst. Director of the Institute for Biostatistics and Informatics in Medicine and Ageing Research, IBIMA, Medical Faculty, Rostock, Germany. fuellen@uni-rostock.de

David Blokh. PhD. Technology Officer. Algorithm developer. C.D. Technologies Inc. Beer Sheba. Israel. david_blokh@012.net.il

Ilia Stambler. PhD. Executive officer (Please contact regarding project-related queries) Analyst. Department of Science, Technology and Society, Bar Ilan University, Israel. Chair Israeli Longevity Alliance.  ilia.stambler@gmail.com

 

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