Molecular Diagnostics of Ageing and Tackling Age-related Disease

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Special Issue: Precision Medicine

Review

Molecular Diagnostics of Ageing and Tackling Age-related Disease James A. Timmons1,2,* As average life expectancy increases there is a greater focus on health-span and, in particular, how to treat or prevent chronic age-associated diseases. Therapies which were able to control ‘biological age’ with the aim of postponing chronic and costly diseases of old age require an entirely new approach to drug development. Molecular technologies and machine-learning methods have already yielded diagnostics that help guide cancer treatment and cardiovascular procedures. Discovery of valid and clinically informative diagnostics of human biological age (combined with disease-specific biomarkers) has the potential to alter current drug-discovery strategies, aid clinical trial recruitment and maximize healthy ageing. I will review some basic principles that govern the development of ‘ageing’ diagnostics, how such assays could be used during the drug-discovery or development process. Important logistical and statistical considerations are illustrated by reviewing recent biomarker activity in the field of Alzheimer[27_TD$IF]'s disease, as dementia represents the most pressing of priorities for the pharmaceutical industry, as well as the chronic disease in humans most associated with age. Introduction Chronological age (CA; see Glossary) defines the time since an individual was born, but it does not accurately define the biological responses to the ‘passage of time’ and hence on its own is not useful for commenting on the health of an individual. Life expectancy as well as health-span varies between individuals due to a combination of genetic, environmental and societal factors. Medically, replacement of CA with an accurate measure of ‘biological age’ (BA) could revolutionize how we schedule medical screening and thus improve disease prevention (or ‘postponement’). For example, if by 50 years [30_TD$IF]an [31_TD$IF]individual has a very poor BA ‘score’, then more rigorous cancer[32_TD$IF] (for example) screening could be scheduled years earlier than his/her CA would otherwise indicate. Molecular diagnostics of BA could also dramatically alter the current pharmaceutical [3_TD$IF]industry [34_TD$IF]strategies for a variety of age-associated chronic [35_TD$IF]diseases. For example, rather than running individual drug discovery programs, focused on diverse ‘organ’directed therapies (e.g., pancreatic b-cell degeneration, osteopenia or prostate cancer), efforts could be directed at finding pharmacological solutions to ‘slow’ BA. Such efforts may require a precision medicine approach as it still remains controversial how to best define BA in humans and whether there may be several forms of biological ageing to target. Part of the controversy stems from a confused misuse of biomarkers of diseases that co-vary or accumulate with CA, and biological responses that relate to treatments for said diseases as markers for BA [1]. A useful assay for BA should be readily applicable to healthy middle-aged

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Trends Accurate diagnosis of biological age can be combined with disease-specific biomarkers to revolutionize drugscreening strategies for diseasepostponement. RNA is particularly promising for diagnosing biological age as RNA signatures can be readily matched to cellbased compound transcriptional signatures. A valid and useful molecular signature to guide drug-discovery ‘anti-ageing’ efforts should be based on human clinical data of diverse origin and robust design. Existing biomarkers for ageing and dementia are largely proving to be invalid, reflecting an undisciplined use of machine learning methods and naïve use of epidemiological cohorts.

1 Division of Genetics and Molecular Medicine, King's College London, London, England 2 XRGenomics Ltd, Scion House, Stirlingshire, Scotland

*Correspondence[29_TD$IF]: [email protected] (J.A. Timmons).

http://dx.doi.org/10.1016/j.tips.2016.11.005 © 2016 Elsevier Ltd. All rights reserved.

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individuals, yielding an index of the future health trajectory over the coming two decades or more. It should also be transferable to cell-based models, to facilitate drug discovery. If there is a significant developmental and epigenetic contribution to BA, then it is plausible that a biomarker for BA would demonstrate predictive capacity even when applied to young adults, opening up the possibility of long-term treatment for the prevention of some of the most devastating of agerelated diseases (e.g., dementia). However, even with an accurate assay for BA in hand, it may benefit from being combined with additional risk-factors for individual age-related chronic diseases (e.g., genetic or protein assays or quantification of environmental factors) to best direct prevention of age-related diseases that severely impact on quality of life. A robust diagnostic of BA could therefore direct the development of drugs that optimize healthspan and so ensure that people reach older age with better physical and cognitive function. The societal implications for an accurate diagnostic for BA are much broader. If we can put a number on an individual's BA, then arguably we can apply the logic articulated by Sanderson and Scherbov [2], that defining how we choose to treat and support an individual, could reflect their projected life expectancy. This would ultimately lead to personalized health surveillance plans as well as personalized pension plans and other nonmedical strategies. While this may sound daunting and highly controversial, we are in fact already making such judgements based on an individual's self-reported behaviors. In the present article, I will discuss key principles that should govern the discovery of valid ‘ageing’ diagnostics. Of central consideration, beyond technical performance, are logistical factors such as cost when being developed for population screening, or assay configuration when being used for high throughput compound screening. As age is a risk-factor for an exhaustive number of chronic human diseases, I will rely on examples of ‘biomarkers’ from dementia research to illustrate the role of molecular biomarkers in drug development for age-related disease.

General Considerations for Discovering Diagnostics of Biological Age To aid both drug discovery and health surveillance, a cost-effective and accurate assay for BA would also represent a molecular assay suitable for screening compounds. Arguably the most successful period in drug-discovery history stemmed from screening of compounds in vivo, as close as possible to the physiological system central to the therapeutic indication (e.g., effectors of smooth muscle contraction for treatment of hypertension). There are numerous articles describing the core statistical considerations required to discover a valid ‘diagnostic’ (molecular signature) when using machine-learning classification [3,4]. In most cases, compromises are made reflecting the available data, and this unfortunately includes producing prototypes from a single large cohort that has been split into two groups. There is a high possibility, when built using a specific set of case and control samples from a single study, that the result reflects unknown features of that particular cohort (including laboratory factors) and thus the molecules selected will only work well for that particular cohort. Using the case[36_TD$IF]–control approach, the first data set is referred to as the ‘training dataset’ and must be used only to select the molecular markers (called ‘features’) for taking forward into independently sourced data for external validation (EV). When selecting the initial features, the modelling approach should rely on a ‘leave-one-out cross-validation’ (LOOCV) which attempts to minimise the impact of extreme samples when selecting the list of features. At this stage, the size of the shortlist of features (e.g., RNA molecules) is relatively arbitrary and partly reflective of the detection technology and the intended future use. EV relies on a second and third independent clinical dataset, where one of the new sets of samples is used with knowledge of sample identity (e.g., old or young), and the samples from the third data set classified one-byone, based on the similarity of their molecular profile to one of the two groups preidentified in the second cohort (and using only the features selected from the first cohort). When using a cohort with a continuous range for the phenotype of interest, then usually some form of regression

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Glossary Alzheimer's disease: the most common form of age-related dementia (5% prevalence in people aged over 70 years, rapidly increasing thereafter); where dementia is a class of neurological conditions reflective of the degeneration of human brain tissue starting with loss of function, for example impaired short-term memory, and eventually severe behavioral symptoms. Biological age: defines a biological programme believed to underpin health status at a given age. Biomarker: a molecular, physical, or behavioral entity that can to some extent help diagnose or predict the likelihood or presence of disease. Chronological age: defines the time since an individual was born without consideration of his/her health status. Machine-learning: a general description of the implementation of statistical methods using computers to discover discriminatory patterns of ‘markers’ between different conditions relying on the phenotypes or molecular features of the two groups. Mild cognitive impairment: represents a combination of dementia and nondementia pathologies and is not a specific disease but a clinical syndrome including impaired short-term memory. RNA: ribonucleic acid, formed during the ‘reading’ of DNA, and which controls a diverse set of cellular functions, including regulation of protein production.

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model is developed and then that formula is applied to a second or more independent datasets. The aim is to select features which relate to the primary phenotype and avoid discovering unknown and unrelated biological associations. For example, when using a cross-sectional study design and linear regression, if older subjects are drug-treated, the molecular model of ageing [37_TD$IF]will reflect the polypharmacology of age-related disease and not the biology of ageing [5]. An ideal diagnostic for BA would be one that is prognostic for health-span and is able to distinguish between asymptomatic adults who have a low or high risk for an ageing-related disease, such as dementia. The test would allow for consideration of where an individual lies on a (linear or nonlinear) trajectory in comparison to their CA (Figure 1). It is unrealistic that an assay would be sensitive and specific for BA when applied at any CA, and an assay that had optimal prognostic performance when applied to adults with a CA of 40–60 years may be ideal as this – at least in the first instance – would be the population recruited into preventative drug trials. This could reduce the cost of clinical trials because it would be expected that recruitment of asymptomatic adults with the poorest BA score would [38_TD$IF]yield clinical symptoms[39_TD$IF]/events over the duration of a trial at an enhanced rate, making the trial size smaller and/or of shorter duration. In terms of public health screening, cost effectiveness is the primary consideration[40_TD$IF], particularly when they result in expensive and less-than-conclusive follow-on imaging studies [6,7].

Biological age

One may consider that, given ageing is a universal and inevitable phenomenon, it should be easy to diagnose the ‘rate of ageing’ in middle-aged humans. Surprisingly, a valid and useful molecular test for BA has been challenging to discover. Early hopes that telomere-related assays would be informative have largely been discredited as being neither specific nor sensitive enough to be[41_TD$IF] a useful[42_TD$IF] diagnostic. More recently, biomarkers claimed to reflect human ageing have arisen from cross-sectional epidemiological cohort studies [8] [43_TD$IF]where the selected molecular markers reflect modelling of crude surrogates of physiological function or cognitive health,

Disease

Rapid ageing trajectoryy

Good health

20

S Slow ageing ttrajectory j t

40

60

80

Chronological age (years) Figure 1. The Concept of Biological versus Chronological Age. As an individual gets older, they present with agecorrelated disease, moving from the pale-blue ‘healthy’ zone into the ‘pink’ diseased zone. The chronological age at which ‘disease’ emerges depends on a complex mix of genetic, epigenetic, and lifestyle choices along with when health checks are carried out. There is no real motivation why ‘biological’ ageing would tend on average to take a linear trajectory but for simplicity the ‘mean’ trajectory is represented by the straight line (grey). Divergence below (blue [15_TD$IF]broken line) this trajectory represents either ‘slower than average’ or healthy ageing, while divergence above (purple [15_TD$IF]broken line) represents accelerated ageing. For any given chronological age, particularly [16_TD$IF]by 40 years[17_TD$IF] of CA, we can expect to observe individuals on different ageing trajectories. Diagnosis of this[1_TD$IF] trajectory, at a time where preventative strategies can [18_TD$IF]still [19_TD$IF]be effective, represents the ‘holy grail’ for promoting healthy ageing.

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combined with validated yet imprecise biochemical or physiological ‘biomarkers’ of disease [1]. For example, just because impaired glucose tolerance is more prevalent in older people does not mean it follows that it is a useful biomarker for ageing, as it is possible to demonstrate insulin resistance and impaired glucose tolerance in young adults. Thus, blood-based molecular biomarkers identified in epidemiological studies, selected using linear modelling combined with adjustment for known covariates, are difficult to interpret. The biological validity of this type of feature selection is reliant on assumptions that genes, environment, and time interact to produce a linear response, which may be very unlikely [9]. Thus, while high-throughput screening of blood samples has potential to yield diagnostics for ‘ageing’ [10–12], validation of the prototype models must address a number of common-sense criteria including the possibility of identifying biomarkers of specific diseases and not ageing per se. For example, if the assay for BA was in fact a surrogate for Type II diabetes, a prevalent disease that increases with CA, then it can yield false-positive scores in individuals that temporarily have poor glucose control, as noted previously in a personal [4_TD$IF]genome profiling study [13]. Variation in the human tissue transcriptome ([45_TD$IF]i.e., RNA) has proven a popular technology for studying the variations in physiological responses to environmental influences [14] as well as human ageing [8,11,14,15]. The abundance of RNA is influenced by genetic, epigenetic, and environmental factors and thus it is an ideal molecular entity to reflect complex physiological and pathophysiological states. Indeed, some of the most successful clinically-utilized molecular diagnostics include RNA prognostics for stratifying breast-cancer treatment [16] [46_TD$IF]and whole blood targeted RNA assays for predicting the necessity for costly cardiovascular procedures [17]. Assays for RNA are highly specific, amenable to high-throughput analysis on small roomtemperature stabilized samples [18]. In fact, RNA biomarkers rather than DNA biomarkers have much greater potential to impact across the entire drug-discovery process (Figure 2). The majority of published studies and ongoing efforts to develop RNA biomarkers for human ageing have utilized blood samples from pre-existing epidemiological cohorts, modelling RNA

Molecular drug signatures matched back to paent profiles

Molecular Diagnosis

Molecular Prognosc

Mulple Treatment Opons

Treatment Response Predictor

Discovery of New Treatments

Paent profiles used to match to or screen for new treatments

Figure 2. An Overview of the Role RNA Molecular Profiles Can Play in the Drug-discovery and Development Process. The central dogma of molecular biology, along with the fact that most pharmacological agents directly target proteins and not nucleic acids, has led to a bias that proteins (or peptides) represent the most reliable and effective molecular entity for disease biomarkers. This is probably naïve, for a number of reasons including the fact that protein abundance and activity can be entirely unrelated, protein function is highly dependent on spatial location with respect to the cell, and protein detection technologies lag far behind RNA profiling technologies in terms of cost-effectiveness, precision, through-put, and ‘biological coverage’ (unbiased detection of the products from the genome). Expression of RNA integrates influences from genetic variants, environmental disease-causing factors, and cell protein activity. Global RNA profiling represents, therefore, the ideal biomarker platform, particularly exon-level genome-wide microarrays (which provide a more streamlined and reproducible quantification of RNA from <100 nanograms) [70]. Global RNA profiling can impact on each of the steps (blue boxes) illustrated in this cartoon of the drug development process.

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expression values across individuals (related or not) with a wide CA range (e.g., 18[47_TD$IF]–100 years) with linear techniques. A recent and statistically-reproducible correlative blood RNA assay of CA was proposed to be a biomarker for BA [5]. However, the model yielded a distribution of data that had approximately equal variance across [48_TD$IF]nine decades, such that BA appeared to vary as much at 20 years as it did in people with a CA of 60 years. This somewhat illogical feature was compounded by the model's overwhelming correlation with blood pressure, as well as poor or absent correlation with cognitive health or muscle function. It is plausible that the model is reflective of vascular ageing (or disease), but there are also a number of reasons why linear modelling of epidemiological cohort data may fail to produce valid models of biological age. One is the well-known issue of population stratification [19]: that is, as 30% of all cancer-related deaths occur in people with a CA of 65 years or less,i[28_TD$IF] building a linear model using molecular data from adults aged 20[47_TD$IF]–100 years and ignoring the lack of a consistent genetic background is not valid.

Model covariate

A second major consideration is covariate adjustment. It is often claimed that confounding variables are adequately ‘adjusted’ for by removing the shared contribution to the variation between the molecular biomarkers and CA (the primary phenotype in such a study). Such an approach actually introduces influence of disease into the ‘assay’ while removing meaningful associations. For example, using blood RNA, a model for ageing was significantly correlated with impaired cognitive function and muscle strength (Table [49_TD$IF]S13 in Peters et al. [5]) but this relationship was lost after adjusting the model for various clinical variables (e.g., blood pressure, smoking) and yet [50_TD$IF]neuro-muscular [51_TD$IF]decline is the most [52_TD$IF]consistent functional [53_TD$IF]phenotype of old age. Figure 3 illustrates the potential problem with adjusting a model of ‘biological versus chronological’ age by blood pressure (a common and highly prevalent age-correlated condition). This

BP-adjusted model Untreated hypertension (160 mmHg) Treatment-controlled BP (130 mmHg) Elevated BP (130 mmHg) Healthy BP (100 mmHg)

20

40

60

80

Chronological age (years) Figure 3. When is Biological Age not Biological Age? Ideally molecular signatures of biological age would be developed from samples taken from the same person, over their entire lifespan. This is not practical or available at this time and thus most molecular descriptors of human ageing have been derived from cross-sectional cohorts. The larger the cohort, the more robust the statistical outputs can be; however, confounding variables are then typically less well considered. In this example there are four blood pressure values plotted [in millimetres mercury (mmHg)]. The black dot represents normal systolic blood pressure in a young adult, while the red dots represent subjects in a clinical cohort with hypertensive disease. The fourth person (purple dot) has elevated systolic blood pressure but is not diagnosed or treated for hypertension yet. The purple dot and the drug-treated patient with hypertension have the same blood pressure measurement. If blood pressure values are used to adjust the relationship between molecular profile (e.g., blood RNA expression values) and chronological age, in an attempt to reveal a model of ‘biological ageing’, then this integrates drug-physiology interactions and introduces bias. In short, the molecular model would not be interpretable. Instead, human ageing models should be developed using cohorts devoid of drug treatment, focused on the most practical period for implementation of an ‘age’ diagnostic (i.e., 40–50 years), and ideally include hypothesis-driven cases versus controls to evaluate links to ‘healthy ageing’ or accelerated age-related disease.

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illustration shows that two of the four individuals have the same elevated blood-pressure value record in a cohort, but one individual is relying on antihypertensive drugs to achieve that bloodpressure reading, while the other one is not. In this case both blood pressure measures cannot simply be treated as equal, and used as covariates in a linear model of ageing, as many have done [5]. From the perspective of understanding the underlying biology of ageing, the presence of the antihypertensive drug treatment will impact on the data modelling and also directly on gene expression. Further, the relationship between many physiological parameters and disease will not be linear, reflecting the well-known principles of autoregulation, compensatory responses, and the somewhat arbitrary nature of clinical thresholds. Other examples provide illustrations of the potential limitations of using linear modelling to discover assays for BA, including DNA methylation [20]. A DNA methylation model of age was built using a vast array of biobank data, with little or no information on clinical phenotype or physiology. Spanning the majority of human CA, analysis of DNA methylation patterns using a moderate throughput microarray technology, yielded a quasilinear model that distinctly fitted samples under 20 years of age, [54_TD$IF]from those from 20 to 60 years [43_TD$IF]while [5_TD$IF]demonstrating a poor relationship with CA in samples aged 80 years or more [21]. While developmental and growth influences probably explain the divergence of the model in samples aged 20 years or younger, the divergence of the model from CA at 80 years or more could be seen as a positive feature. A useful assay that quantified ageing would be expected to distinguish itself from CA, otherwise it would not replace CA. Nevertheless, for use in drug screening or health surveillance in asymptomatic middle-aged adults, a DNA methylation assay [56_TD$IF]has [57_TD$IF]several [58_TD$IF]failings. The DNA methylation model [20] was highly statistically significant in numerous independent cohorts (although the true-positive rate is unknown as it is unclear to how many cohorts the assay has been applied) but it appears to have next to no clinical utility so far because it fails to explain a meaningful amount of variance in long-term health [10]. As emphasised above, the aim is to produce a practical assay that informs about BA more so than CA, and in my opinion it is unlikely that an assay would be sensitive and specific when applied to the key target audience, middle-aged adults. Indeed, it has been found that the majority of DNA methylation differences between two individuals for the ‘epigenetic clock’ score were established prior to the age of 20 years (possibly explaining the original quasilinear model) while only modest changes occur longitudinally in an individual [22]. Whether the poor reproducibility of DNA methylation assays versus RNA profiling or genotyping can be overcome is another concern. Clearly the ideal clinical materials for building valid assays of BA would be longitudinal in nature and included detailed physiological, molecular, and behavioural records. Unfortunately, there are few suitable longitudinal datasets that facilitate valid model building, and for this approach to be more robust, it is likely that longitudinal trials specifically designed for studying ageing will be required to discover or validate emerging biomarkers of ageing, rather than ad hoc epidemiological cohorts. This will take decades to complete. In the absence of time-travel, alternative strategies are required and some modelling strategies that can rely on epidemiological cohorts are proving to be more promising. Levine and Crimmins took a hypothesis-driven case[36_TD$IF]–control approach whereby they used genotyping to discover biomarkers for surviving a behavior that substantially reduces both life-span and health-span, namely smoking [23]. They found 215 single nucleotide polymorphisms (SNPs) enriched in the group of long-lived older smokers and demonstrated that a risk-score DNA biomarker was related to cancer risk in independent cohorts. The RNA expression levels for the 215 genes nearest the genomic markers identified by Levine and Crimmins can be shown to be a robust classifier of young (25 years) versus older (65 years) tissue CA in our [59_TD$IF]skeletal muscle tissue biobanks as well as[60_TD$IF] being a[61_TD$IF] potential biomarker for having coronary artery disease [11]. [62_TD$IF]The [63_TD$IF]Levine and Crimmins study illustrates the principle of using a hypothesis-driven approach to

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discovering biomarkers for ‘successful ageing’ or in this case ‘survival’ in the face of life- and health-span-reducing behavior (smoking), and such a strategy may be more useful than applying linear models to[64_TD$IF] ad hoc epidemiological cohorts. We have also relied on a hypothesis-driven approach to produce biomarkers of human ageing [11]. In this case, muscle tissue RNA from younger adults (25 years) and older adults (65 years) were contrasted, that is, a CA range that should avoid the complications of growth/maturation in the young group and the impact of population stratification in the older group. The hypothesis was that by studying ‘ordinary’ older subjects that had reached 65 years without being diagnosed with cardiovascular or metabolic disease and with good fitness despite leading a normal sedentary lifestyle we could discover RNA markers which reliably reflected this ‘good fortune’. The younger subjects were also sedentary but otherwise healthy. In this case 150 RNA expression values were sufficient to correctly distinguish the CA of thousands of human muscle, brain and skin tissue samples. This[65_TD$IF] new assay for tissue age, which Sood et al. went on to demonstrate had some key characteristics of a useful assay for BA [11], was not correlated with lifestyle-related diseases. The RNA signature [6_TD$IF]can be used to configure a drug-screening assay in human cells [24] and was shown to respond to rapamycin, a drug shown to extend life-span in animal models. Both of these[67_TD$IF] examples of hypothesis-driven ‘ageing’ assays [11,23] could be utilized for establishing either drug-screens or clinical screening tools. To be useful as clinical tools they should be offered to individuals at a fixed ‘middle-aged’ CA timepoint (e.g., 50 years) as this would avoid assumptions around creating a model with linear adjustments for CA. Each healthy middle-aged person could be ranked for their ‘ageing’ score, and along with other informative lifestyle variables, a healthy-ageing plan offered. Follow-on support could involve specific assays for age-correlated diseases (biochemical, clinical, and potentially genetic). The aim from both a public health (preventative) perspective and improving the efficacy of clinical trials is to estimate the likely risk that individual may have of developing age-associated disease earlier in older age. How soon we will be able to successfully validate an appropriate set of biomarkers is partly dependent on how readily the field accepts a definition of biological ageing [5,8,25,26]. A second essential change in mindset will be for the pharmaceutical industry to move away from drug development activities focused on individual chronic age-related disease, towards efforts to target ageing, with the hope that this will reduce the prevalence or severity of a variety of diseases that increase with age [1]. In summary, it is possible to produce potentially useful molecular diagnostics that will contribute to diagnosing how successfully a person is ageing or predicted to cope with the ageing process [11,23], using hypothesis-driven case[36_TD$IF]–control models followed by extensive independent validation. Alzheimer[27_TD$IF]'s disease (AD) is an example of a human clinical condition where the prevalence increases dramatically with each decade of CA, beyond 70 years. I will now discuss the various attempts to identify molecular diagnostics for AD and how these, as well as robust diagnostics for human ageing, could be directed to accelerate potential drug treatments for AD.

Bioassays for Diagnosing AD Dementia UK estimate that £26 billion is spent on health and social care activities for the 850 000 dementia patients in the UK alone and 250 000 new cases are expected each year (Dementia UK 2014 report). The shift in population demographics in the coming decades will mean that >1.2 billion people will be aged over 65 years worldwide [27]. Approximately 7% of [68_TD$IF]the over-70 population has dementia, with at least 60% of these having AD. AD is thought to be the single largest healthcare cost [28] and there are currently no drug treatments for AD that cure the disease [29] and consensus is that only the earliest possible intervention is likely to significantly prevent neurodegeneration. While CA is the single most significant ‘predictor’ for AD in adults [30], it is clear that the majority of 65-year-old individuals do not have an AD diagnosis, and thus CA cannot be a ‘diagnostic’ for AD. It has been argued that prescreening with an effective

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community-administered blood-based biomarker with a false positive rate at double the current prevalence of AD would more than halve the cost of imaging-based screening overall [31]. This has led to widespread consideration of simple and cheap genetic assays (e.g., APOE genotyping) and single-study validated biomarkers for stratifying and recruitment into clinical trials. The APOE genotyping assay is useless as an AD diagnostic[69_TD$IF] in asymptomatic people and not even able to quantify relative risk very well. The Alzheimer[27_TD$IF]'s disease/cognitive health field has[70_TD$IF] however been collaborative and forwardthinking – engaging worldwide public–private partnership to accelerate progress in the dementia field [32]. This has included the pharmaceutical industry through the innovative medicines initiative (IMI) and through the European Medical Information Framework (EMIF). Both are focused on delivering a large-scale electronic and biological Alzheimer[27_TD$IF]'s disease resource to European researchers. This mirrors the US NIH-funded initiative – Alzheimer[27_TD$IF]'s Disease Neuroimaging Initiative (ADNI). ADNI focuses on detailed confirmative technologies for AD, yet only cheap high-throughput biomarkers fit with population demographics, prevalence, and economic considerations. A useful diagnostic for AD needs to be scalable and cost-effective for mass population screening [33]. Criteria for tracking AD when a diagnosis has already been established will have a distinct set of criteria and is considered in greater detail elsewhere [34]. Advances have been made in medical imaging and biochemical bioassays to confirm evidence of (already extensive) neurodegeneration, for example, cerebral atrophy on magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF) levels of b-amyloid species. These assays are expensive, invasive, and restricted to specialist centers, which are too few in number. For example, in the USA there are 11 000 MRI scanners, which could screen 10 patients each per day if used exclusively for dementia (which is not plausible) [31]. An imaging-based screening program would not be affordable – to screen 40 million USA citizens aged 65 years or older would cost $40 billion [33,35]. The capacity to screen using newer positron emission tomography (PET) ligands for amyloid and tau imaging is several orders of magnitude more limited than MRI. Further, imaging technologies are far from proven to be specific diagnostics for AD [36], and elevated levels of amyloid occur in older people without clear evidence for cognitive impairment or AD [37]. More often than not, terminology related to ‘risk’ and ‘diagnosis’ are confused. A biomarker may be associated with an increased risk above a population average but this does not make it necessarily a good diagnostic. As the clinical diagnosis of AD is imperfect, this has implications for interpretation of the real-world performance claimed for any new assay. It is[71_TD$IF] also important to appreciate that the ‘less severe’ stage of cognitive impairment – mild cognitive impairment (MCI) – represents a combination of pathologies and not a specific disease [38,39] with 60% presenting with AD in the medium term. Thus it does not make sense to present a diagnostic area-under-curve (AUC) analysis for MCI [11]. There have been several studies claiming that blood-based proteins and metabolites ([72_TD$IF]combined with or without clinical data) are useful for diagnosing AD [40–46]. The develop of a reliable multiplex protein detection assay is challenging and when a centralised laboratory is required to ensure technical performance, sample collection and transport costs are high (Table 1)[73_TD$IF]. The ‘ideal’ deal assay would use a room temperature stable biological sample and be carried out at the point of care. There is also the issue of ‘added value’ [74_TD$IF], for example, using a global unbiased assay (such as a transcriptome-wide gene chip). Not only is it possible to extract, mathematically, a BA or AD biomarker, but data is available from[75_TD$IF] the[76_TD$IF] remaining transcriptome to evolve the assay (to include more false-positive ‘checks’) and provide additional assays, [7_TD$IF]including [78_TD$IF]calculation of drugresponse predictors (Figure 2). Targeted assays do not deliver this ‘added value’[79_TD$IF]. Nevertheless, most neuroscience laboratories and drug-companies have focused on discrete protein assays, partly due to the belief that a good AD biomarker should reflect molecular

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Table 1. Technical Considerations for Various Laboratory Biomarkersa[20_TD$IF] Blood-[21_TD$IF]based [2_TD$IF]detection [23_TD$IF]molecule

Targeted [24_TD$IF]proteins

Targeted [25_TD$IF]metabolites

Targeted RNA

Global RNA

DNA

Physiological

Sample collection & transport costs

High

High

Low

Low

Low

High

Sample processing costs

Moderate

Moderate

Low

Low

Low

Moderate

Added value

No

No

No

Yes

Limited

Unclear

Potential throughput

Moderately high

Moderate

High

High

High

Moderate

[26_TD$IF]Table 1 addresses various logistical considerations for the development and use of a blood-based bioassay for Alzheimer[27_TD$IF]'s disease that would be used in primary care setting. It would ideally identify those most at risk, for referral to specialist centers and provision of earlier advice to the patient (for planning and lifestyle changes). In most cases room temperature stable assays can be used for whole blood RNA (collected in a stabilizing reagent) and DNA while protein and metabolomics assays can require more sophisticated cold-storage and centralized analysis. a

'leakage' from the CNS. In each case, between four and 20 proteins were discovered to classify samples[80_TD$IF], in the training phase as controls or patients (MCI or AD)[81_TD$IF] , using either Myriad[82_TD$IF] Inc (USA) multiplex immunoassay platform, SomaLogic[83_TD$IF] Inc aptamer platform, or one LCSM study in the training phase, or a Luminex-based antibody platform in the discovery phase (screening up to 200 analytes). Samples originated from USA community cohorts, AddNeuroMed, DCR, Texas ADRC, AIBL, and ADNI research consortium samples.[84_TD$IF] Claimed levels of performance[85_TD$IF] for these protein assays ranged from 76 to 95% with sensitivity 75–90%. However, none of these studies used independent validation [86_TD$IF]procedures, instead relying only on splitting a cohort for test and validation. This is much used, but not reliable as it retains systematic technical variance/bias common to both halves of the dataset. Most assays included the addition of APOE genotyping and clinical chemistry data to achieve the reported performance, and perhaps most critically, despite measuring similar panels of proteins, there is no clear overlap in the predictive protein list in these seven studies. The language used to report[87_TD$IF] alleged replication is often unhelpful. For example the Lovestone laboratory claimed to replicate diagnostic performance of a small number of plasma proteins selected from 160 literature observations [47], but the replication was for single-variant correlation, at a frequency equivalent to chance [48] and not[8_TD$IF] as a combined diagnostic[89_TD$IF] assay. In fact, these protein measures contributed, after overfitting, an additional 6% above using age and gender alone. A related study of 691 subjects also by Lovestone et al. went back to a wider protein screen (1001 proteins) but again overfitted a new set of plasma proteins, identifying [90_TD$IF]a distinct set of 13 proteins that yielded an assay performance of AUC = 0.7 (in the other half of the same cohort). In short, the claimed performances of blood protein AD ‘diagnostics’ are usually very misleading. Several other logistical considerations also need to be taken into account when considering the potential of protein-based assays for mass screening (Table 1). As mentioned above, detection of RNA can be highly specific and high-throughput, making it suitable as a clinical screen and a response signature for pharmacological activity [24,49]. Measures of microRNA levels, short 20 nucleotide molecules, with or without combined clinical and genetic data, have been used to distinguish AD from controls [50–53]. MicroRNAs are interesting as they may be rather stable RNA molecules in biological samples. Two USA-based companies (DiaMir and Eisai) utilised multiplex arrays or RNA sequencing to discover candidate miRNAs and then implemented a targeted approach using real-time PCR (TaqMan or Qiagen assays). The resultant assays have relied on between two and 17 miRNA assays to[91_TD$IF] (unfortunately) reassess the same controls and patient samples. The samples originated either from the same USA commercial supplier (PrecisionMed CA), Melbourne case[36_TD$IF]–control samples or the RosKamp memory clinic. While the reported specificity and sensitivity of these assays was remarkably high (95%), neither Leidinger [51] (who used the[92_TD$IF] exact same control samples for test and validation!), nor Kumar [50] (who presented ‘performance’ values from a single dataset) used acceptable validation processes. Interestingly the ‘12-Mir model’ used in each case did not

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include any miRNAs in common. There are notable differences in conservation of miRNAs across species, potentially limiting the utility of any clinical biomarker to human-only assays. Cheng et al. used a complex laboratory process to isolate serum exosomes and took a splitcohort partial validation approach. This assay will therefore not be terribly practical for the purposes of a cost-effective screen for use in general practice (Table 1). Their best model (16miRs) was 87% sensitive and 77% specific when combined with APOE genotyping[5_TD$IF]. Two results published by DiaMir claimed 95% specificity and sensitivity for MCI, while their published model did not work in AD. DiaMir achieved replication of two from 13 miRs in univariate analysis, but importantly their model [53] using TaqMan and plasma samples did not replicate. This is a very important [93_TD$IF]and common failing. Replication of the diagnostic assay ‘model’ is a critical validation step, replication of univariate components of the assay is not. None of the DiaMir microRNA markers appear in the other published models and little is known about the patient samples DiaMir used. [94_TD$IF]Indeed, most published models were built using patient materials (typical train/test split) and thus the models will also incorporate unknown bias based on single cohorts or sample handling procedures. So far, despite some attractive properties, there are no validated biomarkers for AD using miRNA assays, nor any that are able to inform about biological age. There has been more [95_TD$IF]robust work with measuring RNA levels to produce biomarkers of AD [54–58]. Numerous laboratories have worked with whole-blood RNA and produced assays incorporating 20–225 RNA molecules to accurately classify samples during the training phase. Three reports are from a Norwegian company (DiaGenic) relying on a large number of TaqMan assays following an initial microarray screen. A fourth report originates from a French company (ExonHit, now called Diaxonhit), which relied on an Affymetrix EXON gene chip to select a biomarker model. Clinical samples originated from a Norway/Sweden community cohort, a French multicenter study (EHTAD-002), a USA-commercial source (Precision Medicine, again), and the EU AddNeuroMed and DCR cohorts. Some of the claimed specificity and sensitivity reported are plausible (sensitivity 80%, specificity 70–90%) but only Rye et al.’s [54] assay has been evaluated in two or more independent cohorts. Rye et al. (DiaGenic) reported an ROC AUC performance of 0.74 using TaqMan assay plates. [96_TD$IF]In a separate study, the Lovestone lab used a train/test split model to validate their model yielding specificity of 69% and sensitivity of 80%. This gene-list did not reach statistical significance when applied[97_TD$IF], on its own, to independent samples [11] and thus is not valid. The French company ExonHit have not yielded any independent validation of their AD test (AclarusDxTM), and given the original performance was achieved by removing patients on the boundary of the predictions, it is unlikely to be a valid model. To date, the relationship between any of these molecular biomarkers and drug activity[98_TD$IF] or disease pathways has not been reported in any detail.

Biological [9_TD$IF]Age and AD

Thus prototype blood diagnostics have been found to be 75–85% specific at distinguishing AD patients from control samples. However most have not been validated using independently processed samples and have failed to replicate in independent studies [31,59][10_TD$IF] and may only reflect later stages of the disease. [10_TD$IF]2Furthermore, blood-based protein signatures[103_TD$IF] which can diagnose mild cognitive impairment from controls in single studies [41–43,45,46], [104_TD$IF]have not identified a common set of proteins[6_TD$IF] across multiple studies. Further, the candidate [105_TD$IF]cognitive disease marker proteins included cytokines or other markers of metabolic–cardiovascular disease [60] and thus these simply will not be specific for AD, when applied to older populations [61]. Neuroscientists and drug companies working on dementia [106_TD$IF]often [107_TD$IF]prioritise [108_TD$IF]consideration [109_TD$IF]for whether a new biomarker is specific for AD over other neurological conditions of modest prevalence, rather than a range of very prevalent ‘age-correlated’ disease such as macrovascular disease.

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If one [10_TD$IF]considers Bayes Theorem, and calculates the probabilities associated with assay performances described above, given the prevalence[1_TD$IF] of AD at various CA's and the certainty of clinical diagnosis, it would actually seem impossible to develop what would be considered a useful diagnostic for early AD applied to people aged 50 to 70 years of age (chronological) using the current clinical resources[12_TD$IF] and an prototype assay with <98% specificity. This is partly because an entirely definitive diagnosis is challenging in living subjects, and histopathology data is available only post-mortem. Still, even this may not reveal subtitles in dementia diagnosis and more than one type of dementia can coexist. Thus, unlike a nosebleed, the accuracy of a clinical diagnosis of AD [13_TD$IF]remains <100%. Current clinical cohorts being used for biomarker development rely largely on symptomatic diagnosis[14_TD$IF] of AD, which is perhaps 90% accurate. If, for example, a new AD diagnostic assay was 85% specific, this would translate to a positive predictive value of less than 50%, when applied to people aged 65 years (when prevalence is <10%). Thus even though a blood based bioassay with a[8_TD$IF] reported accuracy [15_TD$IF]of [16_TD$IF]90% [17_TD$IF]would [18_TD$IF]be [19_TD$IF]considered ‘exceptional’, it would still yield far too many false-positives in a population aged 65 years, given the prevalence of AD [48], [120_TD$IF]and so would not be a useful ‘diagnostic’ of AD. [12_TD$IF]By [12_TD$IF]contrast, if the aim was to substantially enrich the ‘event rate’ at which diagnosis of AD appear in a group of older asymptomatic people recruited for a clinical trial (e.g., 60 years CA), then this [123_TD$IF]is far more plausible. In reality,[124_TD$IF] as a molecular biomarker assay that correctly diagnoses AD in people aged <80 years is probably implausible, [125_TD$IF]making the current trend for claiming such [126_TD$IF]possibilities naïve [46,62,63]. To refocus the biomarker efforts towards providing tools for enrichment of clinical trials we need to rethink priorities within the large well-funded AD consortium initiatives, including more open access to public biobanks, and employing far greater rigor to the process of biomarker discovery and validation. One alternative strategy for attempting to locate disease biomarkers for AD would be to produce molecular diagnostics of future risk. If we reflect on the fact that ‘age’ is the biggest risk factor for AD starting with a prevalence of 7% at 70 years and rising rapidly thereafter, then an accurate diagnosis of BA could be informative about an individual's future risk of AD when measured earlier in adulthood. Further, combining a valid assay for BA, with other risk factors for AD (e.g., early-adulthood cognitive status, gender, etc.), could allow for the recruitment of ‘higher risk’ asymptomatic (in cognitive or imaging tests) individuals into AD drug trials that aim to prevent or delay the onset of AD. We have produced one example of such an assay, a 150-RNA signature for BA, which was developed as a tissue age diagnostic, particularly human muscle tissue. We noticed during the validation process that this muscle-derived 150-gene signature was highly regulated in older ‘disease-free’ hippocampus but not in the ‘age-stable’ cerebellum (Figure 4 from reference [11]) and also able to ‘diagnose’ human brain tissue age. Given the role of ageing and the hippocampus in AD [64], this 150-gene assay for BA has some biological credibility to be linked to neurodegeneration as well as ageing, and thus illustrates the potential of an assay for ‘ageing’ to be informative about age-related disease. The RNA test for BA was also able to distinguish AD samples from control samples (age- and gender-matched) with the same performance as the other AD diagnostic assays (i.e., modest) and thus not a stand-alone AD diagnostic assay[127_TD$IF] (not the aim as illustrated above). However, this assay of BA has greater independent validity than any other AD assay discussed above, as the model was built using thousands of tissue samples and around the hypothesis of healthy tissue ageing and not clinical AD samples.[128_TD$IF] This assay for healthy neuro-muscular ageing was composed of genes that demonstrated no bias for any particular canonical [129_TD$IF]pathway. Recent evidence suggests a role for mTOR signalling in AD [65] and we have[1_TD$IF] demonstrated that the neuromuscular BA assay is responsive to mTOR inhibition in a variety of human cell types. This example illustrates the potential for robust biomarker models to aid drug-discovery efforts and is consistent with[130_TD$IF] successful efforts to link in vitro and clinical drug efficacy data in oncology. The development and refinement of additional models of biological age, along with further validation of the

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aforementioned assays, can create an alternative way to approach the pharmaceutical development of novel treatments for age-related chronic diseases.

Concluding Remarks An emerging strategy to treat chronic diseases of old age is to pharmacologically target the underlying ageing process. The hope is that by altering ‘biological’ age[13_TD$IF] (Figure 1) we can simultaneously reduce the prevalence of a number of age-correlated pathologies, including bcell degeneration, osteopenia, neurodegeneration, and certain cancers. To be successful in such an endeavor, it is critical that robust molecular ‘signatures’ are produced from [132_TD$IF]human studies[13_TD$IF] rather than disease models, signatures that can be used as reliable readouts in compound screening programs [24,49,66]. Not only is it a benefit to direct a drug-discovery program using human clinical data [67], but this strategy also speaks to recent ideas around the promise or logic of polypharmacological drug development [68]. In terms of human ageing, our current view is that it is possible to identify a robust OMIC signature for neuromuscular ageing while vascular ageing may represent distinct biological phenomena. To generate greater opportunity for academic and biotechnology laboratories to contribute to this new drug-development strategy, significant funding will be needed to create large open-access ‘compound’ OMIC signature databases, using a variety of human primary cell types, screened with multiple doses and durations, and accompanied by the detailed physico–chemical characteristics of the compound [69] to better aid interpretation of in vitro activities (see Outstanding Questions). There is also a pressing need to educate biomedical and clinical researchers to better appreciate that given a typical desktop computer and a set of OMIC data, it can be relatively trivial for a bioinformatician to develop a ‘highly statistically significant’ (but subsequently irreproducible and flawed) biomarker signature. In the absence of a more disciplined approach we may well undermine efforts to better link drug discovery programs to human clinical ‘biomarker’ data. Drug discovery effort would[12_TD$IF] fail because the OMIC signatures used are irreproducible, as in the case of many of the emerging biomarkers for AD [46,58,62]. So in addition to producing openaccess availability to much more substantial drug–OMIC signature databases [49], research leaders need to better integrate best practice from the biological, clinical, mathematical, and computer sciences to help avoid basic research errors. Greater use of genuinely independent ‘external’ validation prior to publication [11] would help address such problems, although it will substantially reduce the frequency of positive findings and high-profile publication. Nevertheless this should be welcomed, as the ultimate aim is to facilitate genuine progress towards tackling some of the most devastating of human diseases.

[134_TD$IF]Disclaimer [135_TD$IF]Statement JAT holds Medical Research Council and European Union funding and has consulted for PepsiCo (Purchase, NY, USA). He is also a Director and shareholder at XRGenomics LTD (Scion House, Stirling). Resources i

www.cdph.ca.gov

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Outstanding Questions While the potential for in vitro generated drug/compound RNA signatures to identify interesting compounds or match drug efficacy with individual patient responses is proven, how will a universal and open-access database of compound signatures from diverse chemical space and cell types be generated? Since the accessibility of machinelearning methods has made it trivial to produce superficially impressive but unreliable biomarker models of disease, how do we [136_TD$IF]improve [137_TD$IF]peer-review [138_TD$IF]and improve transparency to better manage finite resources and[139_TD$IF] manage expectations? Given Bayes’ Theorem, the prevalence of Alzheimer[27_TD$IF]'s disease in the population under 80 years of age, and the real-life precision of clinical diagnosis, can molecular biomarkers ever yield clinically acceptable diagnostics or should the focus shift to using molecular profiling for[140_TD$IF] predicting BA, pharmacodynamics and pharmacological screening? As multidisciplinary skills are required to integrate biological, clinical, mathematical, and computer sciences for successful biomarker development, how do we support the production of principal investigators equally skilled in all domains?

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