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Informative frailty indices from binarized biomarkers

Frailty indices (FIs) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FIs from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey and the Canadian Study of Health and Aging data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, and the upper limits of the FI observed in various data sets, and also highlights general difficulties in obtaining absolute scales for comparing FIs between studies.


Introduction
Poor health is often associated with aging, a decrease in functional capacity, and an increased susceptibility to illness and injury. While chronological age is a convenient proxy for aging, it cannot capture individual variability of health at a given age. The frailty index (FI) is a well-tested way of incorporating large and varied aspects of health and function that can be easily used to differentiate between individuals of the same age. Defined as the fraction of selected health attributes that are in an unhealthy state (called deficits), the FI has been shown to be a robust measure of individual health over the aging process (Mitnitski et al. 2001;Searle et al. 2008). The FI is observed to increase with age and the distribution of FIs on a population level broadens with increasing age, describing the heterogeneity of aging (Gu et al. 2009). The FI is predictive of mortality and of other adverse health outcomes (Rockwood et al. 2005;Evans et al. 2014).
The health attributes considered in the FI are typically clinically observable or self-reported, such as disabilities in activities of daily living or physical or cognitive impairments (Searle et al. 2008). Alternatively, standard laboratory measurements such as blood and urine biomarkers (Blodgett et al. 2017;Mitnitski et al. 2015;Howlett et al. 2014) as well as biomarkers of cellular senescence and oxidative stress  can be used to create a laboratory-test based FI known as FI-Lab. Cutpoints are used to binarize the quantitative biomarker measurements into deficits so that they can be naturally included in an FI. Normal reference ranges based on diagnostic or therapeutic utility (McPherson 2017) are commonly used as cutpoints.
Since the FI is an aggregate measure and is not used for the diagnosis or treatment of specific conditions, standard cutpoints are not necessarily best suited to its role of predicting risks. Furthermore, standard cutpoints are often not available for emerging biomarker measurements such as in epigenetic, proteomic, metabolomic, or other high-throughput ''omics'' approaches. Alternative ''data-based'' methods obtain cutpoints from the available data under consideration. Both normal reference ranges (Blodgett et al. 2017;Howlett et al. 2014) and data-based methods  can and have been used to create an effective FI-Lab.
One data-based method of biomarker binarization is to select cutpoints to maximize some predictive aspect of the post-binarized biomarker. For example, cutpoints can be selected to maximize the difference between survival curves of people that are on either side of the cutpoint for each biomarker . Equivalently, other predictive measures such as receiver operator characteristics (ROC) performance or mutual information (Farrell et al. 2016) could be used with respect to a particular outcome such as mortality within 5-years to generate ''optimal'' cutpoints. While attractive in principle, such individual biomarker optimization approaches run the risk of creating FIs that are overly specific to the study cohort and not generally applicable for other cohorts.
Another popular data-based method for binarizing continuous-valued data is to select cutpoints based on the quantile of the population. This approach is used in both the Fried frailty phenotype (Fried et al. 2001) (with quintiles) and in the exploration of the allostatic load theory of physiological disregulation (Seplaki et al. 2005;Juster et al. 2010) (with quartiles). Here, a risk direction is chosen for each biomarker, e.g. by how the biomarker changes with age, and the cutpoint is selected for each biomarker by the quantile of that biomarker-i.e. the fraction of the population that has values of the biomarker above the cutpoint. This approach should be less susceptible to overfitting, since the quantile is chosen globally for all biomarkers rather than individually for each biomarker. Nevertheless, it raises the question of how to choose the best quantile and of how sensitively the results depend upon the quantile chosen. Investigation of allostatic load (Seplaki et al. 2005) found that deciles and quartiles behaved similarly, implying that the quantile approach may be robust with respect to choice of quantile. Nevertheless, no systematic investigation of the quantile approach in the context of the FI has been done before.
A systematic investigation of data-based approaches for the binarization of continuous-valued biomarkers used in the evaluation of the FI can explore the questions of overfitting due to optimization raised above. At the same time, we can examine the robustness (or insensitivity) of the FI as a predictive measure of health outcomes or mortality and the robustness of the FI maximum seen in observational studies of aging (Searle et al. 2008;Mitnitski et al. 2015), with respect to the details of any binarization approach. Robust and validated data-based approaches to binarization will facilitate the future development of FIs for high-throughput 'omics data and for more model organisms of aging.
Here, we examine the effectiveness of data-based binarization schemes for building the FI from biomarker data. We use both the NHANES and CSHA data sets to check whether cohort effects are large; we find that they are not. We examine overfitting effects with cross-validation, and find that they are present when optimal cutpoints are chosen for each biomarker-but that they are small when global cutpoints are chosen for all biomarkers. We compare the predictive performance of data-based schemes against earlier published results, and find that the data-based schemes have comparable or slightly higher predictive value than established FIs with respect to predicting mortality and clinical deficits. Overall, we find that a generic quantile data-based binarization approach performs well.
A key characteristic of the FI is the relatively insensitivity (Searle et al. 2008;Mitnitski et al. 2015) to the particular choice of deficits. We show that this also holds for choosing cutpoints for FI-Lab, and we find that a broad range of cutpoints exist where the quantile binarized FI-Lab is effective. This demonstrates both the universality of the FI and the generality of our method of choosing cutpoints. Nevertheless, we identify the best range of quantiles to use and we find that they overlap with the quintiles used in the Fried frailty phenotype (Fried et al. 2001). Furthermore, many aspects of the FI calculated at these quantiles such as maximum, minimum and overall distribution of the FI in the population overlap with results from previous FI-Lab studies.

Data, evaluation, and cross-validation
The data used in this study are from the National Health and Nutrition Examination Study (NHANES) (Centers for Disease Control and Prevention National Center for Health Statistics Updated 2014) and the Canadian Study of Health and Aging (CSHA) (Canadian Study of Health and Aging Working Group 1994). The NHANES data set consists of the 8881 individuals from the NHANES study with data for at least 11 of the 16 available biomarkers. This sample has an age range of 20 to 85. The data used from the CSHA study has 973 individuals aged 65? for whom data is available for at least 16 of the 22 biomarkers. Age distributions for these data sets are shown in Supplemental Fig. S1, which highlights the smaller and older cohort of the CSHA study.
Both of these data sets have previously been used to construct FI-Lab. Blodgett et al. (2017) considered the NHANES data set, while Howlett et al. (2014) considered the CSHA. We will compare our results with both of these in this paper. Since a much larger sample size and a much larger range of ages are available, we focus on the NHANES data set. However, major results will be also validated in the CSHA data set. Both studies' FI-Lab consist of many shared deficits and cutpoints, so the differences in the FI between the data sets are likely due to cohort effects. These two data sets have very different cohorts, so by applying our methods to both we test the generalizability of our approach.
The NHANES and CSHA cohorts differ in more than just age. In Supplemental Fig. S2 we show the distribution of FI-Lab for the CSHA cohort (white bars) together with a resampled NHANES cohort with the same (65-85 years) age distribution as the CSHA (blue bars). We see that the NHANES cohort has a significantly lower FI-Lab at the same age, i.e. it represents somewhat healthier individuals. This could be due to a large portion of the CSHA population being comprised of institutionalized individuals (Howlett et al. 2014).
The purpose of binarizing data is to construct an FI. The FI is intended to be an inclusive and general indicator of individual health; it has been shown to correlate well with mortality (Kojima et al. 2018) but also with institutionalization (Rockwood et al. 2006), postoperative complications (Velanovich et al. 2013), dementia (Song et al. 2014), recovery time in hospital (Hatheway et al. 2017), and other adverse health outcomes (Blodgett et al. 2016). Accordingly, we compare our newly constructed FI with the existing FI-Lab in their ability to predict 5 year mortality as well as by their ability to predict clinical outcomes from laboratory data. To evaluate prediction, we use the standard area under the curve (AUC) of the ROC curve. We obtain similar results using mutual information (Farrell et al. 2016), as illustrated in Supplemental Fig. S3. We also check that our new FI behave similarly to the previously published FI-Lab, with respect to the clinical FI, with respect to their distributions, and with respect the maximal observed FI in the population.
Each new FI is tested using cross-validation. Cutpoints are generated using a random half of the population, then those cutpoints are applied to the other half and the resulting FIs are evaluated. This is repeated 100 times. Cross-validation allows us to characterize any over-fitting of cutpoints.

Quantile-based cutpoints
We transform the biomarker data to a dimensionless form using quantiles. For each individual subject, each biomarker i is transformed to the proportion x i of the population that has ''less risky'' values. This is illustrated in Fig. 1 for systolic blood pressure. If an individual has a value of 140 mmHg, which places them at the upper quintile of risk for blood pressure, their systolic blood pressure score is transformed to x ¼ 0:8, corresponding to having a higher systolic blood pressure than 80% of the population.
Quantiles are implemented on a population scale by performing a rank normalization of the data, where each biomarker is sorted in ascending risk, then the ranks (position in the sorted list) are divided by the number of individuals. The rank normalized values x i are given by and so x i 2 ½0; 1. Implementing binarization is straightforward using these quantiles. We apply a global cutpoint (GCP) as a threshold value of the rank normalized values, X GCP , applied identically across all biomarkers. We build the resulting FI as the average over these binarized deficits, where N is the number of measured biomarkers. For each biomarker, a deficit d i ¼ 1 is assigned when x i is above the threshold in the direction of risk.

Direction of risk
We determine a direction of risk for each biomarker, before applying quantile-based cutpoints. We then binarize with respect to the at-risk direction, as discussed above. We do not assert that biomarkers only have one direction of risk, but we do find that most biomarkers have one direction that is most often explored by the population, and so we assume this is the dominant direction of risk during aging. This is illustrated in the Supplemental Fig. S4. We prefer a mortality-free approach to determining direction of risk to reduce potential over-fitting. We simply use the aging trends of the biomarkers to determine the risk direction. The relation between age and each biomarker is determined by the sign of the Spearman's rank correlation. A positive value indicates the risk direction is towards large values of the biomarker, a negative value indicates risk towards small values. This method is effective at determining risk directions if the population has a reasonably large distribution of ages. Aging trends effectively classify risk direction in both the CSHA (ages 65-104 years) and NHANES (ages 20-85) data sets. However, we restrict the age range for calculating risk directions to ages 35? to calculate relations based on normal aging behaviour.
Another method of determining risk direction is to use mortality data, or some other adverse health  [45,65], and oldest [65, 85] age groups respectively (in years). The trend during aging is an upward shift of blood pressure. b The rank normalized score x vs the corresponding systolic blood pressures. The median corresponds to x ¼ 0:5. For this and other measures, the nonlinear mapping between x and corresponding value is always monotonic-but is either increasing or decreasing depending on the direction of risk outcome. For each biomarker ROC curves can be generated with respect to the binary outcome (e.g. 5 year mortality) and an AUC can be calculated. An AUC above 0.5 indicates the primary risk direction is towards high values, an AUC below 0.5 indicates risk towards the low end. Equivalently, one could do a logistic regression of the biomarker against an adverse outcome and use the sign of the beta value (positive beta would indicate risk towards high values). This type of approach ensures that the risk directions generate the best FI for predicting that outcome, but they are potentially over-fit to that outcome. We find that risk directions from mortality data are predominantly the same as the aging trend directions. The AUC of the resulting FI with respect to 5 year mortality is also essentially the same as with aging trends, as shown in Supplemental Fig. S5.
We have also considered a simple approach for two-sided cutpoints. For simplicity, we consider symmetric cutpoints with both x i [ X GCP and x i \1 À X GCP assigned as deficits with d i ¼ 1. The predictive AUC of the resulting FI is significantly worse than the one-sided approach, as indicated by the Supplemental Fig. S6. Accordingly, we focus on one-sided cutpoints in this paper.

Optimally predictive binarization
In addition to quantile binarization, we also compare with two different FIs created with cutpoints selected for optimal prediction with respect to mortality. For both methods we treat the population uniformly; we do not stratify or control for possible cohort effects such as age or sex. Additional details are provided in the Supplemental Information.
The first, FI logrank , based on the separation of survival curves, has been used to create an FI-Lab . For each biomarker, the cutpoint is found that maximizes the significance of separation between survival curves of individuals with and without the deficit by minimizing the p-value from a logrank test (Mantel 1966).
The second method for generating optimal cutpoints is based on information theory. FI info uses cutpoints selected for the highest possible mutual information with respect to mortality at 5 years. In a manner similar to FI logrank every possible cutpoint is tested for every biomarker and the cutpoints which maximize the mutual information with respect to mortality are selected.

Results
To evaluate the various data-based approaches to binarization, we have calculated the AUC with respect to 5-year mortality for both the NHANES and CSHA cohorts. The results are shown in Fig. 2. The performance of all measures was qualitatively similar for both the NHANES and CSHA data sets, though due to the smaller cohort the CSHA data showed greater variability in cross validation.
FI GCP , assembled from quantile based global cutpoints, performed well. For all tested values of X GCP the cross-validated and full dataset results agree, indicating minimal overfitting. For the extreme values of X GCP equal to 0 (where all biomarkers are at risk) or 1 (where none are), there is no predictive value of FI GCP and the AUC is equal to 0.5-as expected. Between these extremes, we see a broad maximum of the AUC. Indeed, for global cutpoints between 0.5 and 0.9 FI GCP slightly outperforms the published FI-Lab for both the NHANES and CSHA datasets.
The binarization approaches to maximize the mortality prediction for the full datasets gave comparable AUC values, as indicated by the columns to the right in Fig. 2. However, cross-validation of FI info and FI logrank showed significantly lower AUC when compared to the full dataset calculation. The decreased performance in cross validation indicates that these cutpoints have poor out of sample performance and do not represent a generalizable risk threshold. While the cross-validated FI info , using maximum information cutpoints, appears to perform as well as FI GCP -the cross-validated FI logrank does not. Since using individual cutpoints optimized for each biomarker to predict mortality leads to an FI that is prone to overfitting, we believe our quantile cutpoints method will apply more generally to more datasets (especially those with smaller cohorts), and so we focus on quantile cutpoints for the remainder of this paper.
We were surprised that the quantile-based cutpoints performed similarly well for both the NHANES and CSHA datasets, despite their significantly different age, health, and cohort sizes. Since quantile-based cutpoints are extracted from the cohorts being characterized, we wanted to investigate cohort effects more directly. Since the NHANES dataset has a large population and a large range of ages, we obtained quantile-based cutpoints from sub-cohorts of NHANES for young (25-45), middle (45-65), or old (65-85) age groups. Remarkably, the cutpoints obtained from any one sub-cohort worked reasonably well applied to any other cohort. However, the range of X GCP for best prediction decreased and shifted closer to 1 as shown in Supplemental Fig. S7. This supports our observation that cohort effects are not large with quantile-based cutpoints.
A crucial test of FI-Lab behavior is how well it corresponds to an established FI-Clin. The coloured lines in Fig. 3 shows average FI GCP values binned by their corresponding FI-Clin values. For intermediate values of X GCP , we see that FI GCP is monotonically increasing with FI-Clin. Indeed, the published FI-Lab appears to correspond to X GCP values between 0.8 and 0.9-where the FI GCP also performs well with respect to both AUC and cohort effects. Conversely, for much larger or smaller values of X GCP , where the AUC is significantly worse than for the published FI-Lab, we see that the FI GCP is not strongly dependent on FI-Clin or even becomes non-monotonic.
We can test the versatility of the FI by its ability to predict outcomes other than mortality. In Fig. 4 we evaluate the prediction of four binary clinical deficits, where the blue lines indicate the AUC for FI GCP vs the global cutpoint X GCP . The corresponding AUC of the (a) (b) Fig. 2 Cross-validated AUC of different data-based FIs with respect to 5 year mortality for the a NHANES and b CSHA cohorts. The dotted line indicates the AUC of the quantile-based FI GCP vs the global cutpoint X GCP . Box and whisker plots display the data from cross-validation: the boxes represent the upper and lower quartiles, the whiskers go to the 99th and 1st percentiles, and the circles are remaining outliers. The short dashed line within each box is the median, the solid line the mean, and the star is the AUC for the full data set without cross validation. The horizontal grey dashed line shows the AUC of the published FI (Blodgett et al. 2017;Howlett et al. 2014). The rightmost two columns, as indicated, show the AUC for FI info constructed from maximum information cutpoints and FI logrank constructed from logrank minimum p-value cutpoints published FI-Lab (Blodgett et al. 2017) is indicated by the horizontal orange lines. We see that FI GCP is as good as FI-Lab for a range of cutpoints-approximately where mortality prediction also performs best.
(All clinical deficits are tested in Supplemental Fig. S8 for NHANES and Fig. S9 for CSHA.).
We illustrate the distribution of FI GCP in Supplemental Fig. S10 for X GCP ¼ 0:85 and for X GCP ¼ 0:4. Both perform as well as FI-Lab in terms of predicting mortality (see Fig. 2). However they have very different distributions when using the same NHANES population. While using X GCP ¼ 0:85 leads to a similar distribution as the published FI-Lab, X GCP ¼ 0:4 leads to significantly higher FI values. While this is not unexpected, since the extreme value of X GCP ¼ 0:0 would lead to all FIs being equal to 1, it does lead us to systematically examine the upper and lower limits of the FI. In Fig. 5 we show the upper (light blue) and lower (dark blue) 1% of the FI GCP distributions in the NHANES dataset vs X GCP . We see that as X GCP increases both the maximum and the minimum FI GCP decrease. For X GCP J0:7 the minimum is zero. For X GCP ¼ 0:85 the range of maximal FI approximately corresponds to the range observed for the published FI-Lab (indicated in red, and labeled ' 'Blodgett''). We also show that the 1st and 99th percentiles of FI GCP in the CSHA dataset (black dashed lines) are similar to those of the NHANES dataset, despite the large differences in, e.g., the age distribution between these cohorts.

Discussion
For a large range of global cutpoints we have shown FI GCP to predict mortality and adverse clinical outcomes as well or better than FI-Lab created using established clinical risk thresholds. This result was replicated in the NHANES and CSHA data sets. Furthermore, FI GCP was as informative in crossvalidation, where cutpoints were calculated in one cohort and tested on another. Indeed, even applying cutpoints calculated in one age group to a cohort 20 to 40 years older remained effective. These results show FI GCP is an effective method for generating an FI from biomarkers without prior knowledge of cutpoints, at least for cohorts of thousands of individuals or more.
The FI created using optimal cutpoints for each biomarker, FI logrank and FI info , although highly informative, did not fare as well in cross-validation. Using these methods in one cohort did not yield an FI which was equivalently predictive in another cohort. Both the logrank and maximum-information based cutpoints strongly depend on the mortality of the particular cohort used and, as a result, do not represent general risk thresholds. We suggest that cross-validation of cutpoints should always be done to ensure general applicability. Specifically, we caution against determining optimal cutpoints with respect to the outcome that the resulting FI will be tested on without extensive out of sample validation.
Notwithstanding this, there is room for improvement in the optimal cutpoint approaches. Considering the whole population uniformly ignores cohort differences which can be crucial when relating measurements of health to adverse outcomes. Accounting for these differences when calculating optimal cutpoints could increase the out of sample performance of these methods.
An important question to address when implementing FI GCP is which global cutpoint is appropriate. We suggest that the cutpoint be selected such that the FI has good predictive value with respect to both health outcomes and mortality. However, in both the NHANES and CSHA data-sets there is a large range of cutpoints which are similarly predictive across many of these measures. Close study of Fig. 2 indicates that X GCP of 0.6 or 0.7 would build FI GCP that best predicts mortality, though this range of optimal X GCP may depend on the cohort. Indeed, when we consider which X GCP best predicts clinical deficits, the ranges of optimal cutpoints vary significantly (see Supplemental Figs. S8 and S9, particularly). It appears that there is no one ''best'' global cutpoint for general prediction of health outcomes, or that applies equally well across cohorts.
Another criterion for picking the global cutpoint is the interpretability of the FI within and across studies. Within the range of cutpoints which are highly predictive there are large differences in the distributions of FI GCP . Changing how the FI is constructed changes how individual values of the FI are assessed. For example, an FI of 0.2 has very different meaning depending on how biomarkers are binarized (see Supplemental Fig. S10).
In the context of current FI studies, an appropriate global cutpoint appears to be X GCP ¼ 0:85. The resulting FI 0:85 is highly predictive of both mortality and many of the clinical outcomes. Furthermore, the maximum, minimum, and mean of FI 0:85 are similar to the previously published medical threshold FI-Lab. As a result, individual values of FI 0:85 can be more easily interpreted between studies.
FI GCP also provides a framework for investigating many aspects of the FI. Indeed, we find that some common characteristics of the FI are not generally applicable. One of the results of changing X GCP is the systematic change of the extremely high (or low) FIs observed in a population, as shown in Fig. 5. Variations of the maximum FI has been observed in FI-Clin (Searle et al. 2008;Gu et al. 2009;Bennett et al. 2013;Hubbard et al. 2015;Armstrong et al. 2015), FI-Lab (Blodgett et al. 2017;Howlett et al. 2014), between SHARE and SAGE multi-nation studies (Harttgen et al. 2013), in an FI assembled from electronic health records (Clegg et al. 2016) or primary care data (Drubbel et al. 2013), and were found to be necessary in network models of the FI (Farrell et al. 2016). We have shown that any explicit choice of binarization changes the observed FI GCP limits. Indeed, any evaluation of binarized deficits-whether biomarker or clinical-should have similar effects. Because of the broad AUC maximum with respect to X GCP we have shown that such variations of the FI-max do not imply that the quality of predictions of mortality or adverse health should be adversely affected. While cohort effects contribute to observed differences of FImax between studies, we suggest that binarization effects may dominate. In Fig. 5, the difference between the upper and lower 1% of FI GCP between the NHANES and CSHA cohorts is less than when X GCP is changed by only 0.1.
How might we compare FIs that use different binarization approaches within the same cohort? Perhaps we shouldn't: since the ability of the FI to predict various clinical outcomes sometimes improves and sometimes degrades as X GCP is changed, we can't expect one FI to behave exactly like another. However, qualitative comparisons may be possible with reference to extremal values of the FI such as shown in Fig. 5. For quantile cutpoints, we also have a formal relationship between the global cutpoint and the population average of the FI that should facilitate such qualitative comparisons: This follows since it is precisely the fraction 1 À X GCP of the biomarkers which are labelled at risk, across all biomarkers. This relationship is shown as a dotted black line in Fig. 5 and appears to hold approximately for the NHANES dataset. This remains to be better explored in future work. Cohort effects become evident when the same cutpoint approaches are used between studies. While FI GCP behaved qualitatively similarly in the NHANES and CSHA cohorts, it exhibits quantitative differences (see e.g. Fig. 2) that indicate cohort effects. FI GCP is convenient for exploring cohort effects since it allows a complete separation of the cohorts at the level of biomarker binarization. For example, in previous work on FI-Lab in the CSHA and NHANES studies (Blodgett et al. 2017;Mitnitski et al. 2015) some cutpoints were sex specific (blood pressure, creatinine, blood urea, and hemoglobin) and some were not. Using FI GCP we could treat all biomarkers in a generic sex specific manner by first separating the population by sex then calculating the rank normalized scores. This approach does not require previous knowledge of the cohort dependence of the biomarkers, and should be useful in future studies of general cohort dependence of the FI-including sex differences. Brief analysis using sex-specific cutpoints show only marginal improvements in predictive value of FI GCP over the favourable range of X GCP (Supplemental Fig. S11). However, qualitative differences in the sex specific FI GCP were also observed which suggests that it could provide a new tool for analyzing sex differences in FIs. Detailed analyses of cohort effects are beyond the scope of this paper but FI GCP is well suited for these questions.
More generally, we have shown that FIs created using population-based approaches can effectively treat biomarkers without prior medical knowledge. The same data-based approaches could also be useful in approaching FIs for metabolomics, proteomics, and other omics-style applications. There is no Henry's clinician's handbook (McPherson 2017) to select omics cutpoints from, and the large number of measurements in an omics dataset necessitates an automated method for treating potential deficits. An FI based on omics data (FI-omics) would provide insight into how frailty manifests itself on the most fundamental levels, and a quantile approach should facilitate FI-omics.
Similarly, a general method of creating an FI from biomarker measurements opens the door to many more animal model applications. Previous work has been done to create an FI-Lab in laboratory mice (Kane et al. 2019). Since there is no clinical guide for treating mice, cutpoints were selected in reference to measurements in young mice. This requirement of having a healthy cohort to use as a benchmark is incompatible with studies where there is no clearly defined healthy group available. A generic approach which can be applied to any set of biomarker measurements allows the FI to be used more generally, and should then facilitate comparisons of health and aging between organismal models and human studies.
In this study we have created and explored an effective quantile-based method of creating FIs from biomarker data. We demonstrated that our methods performs as well as or better than established methods which use diagnostic thresholds. Furthermore, we show that they are more robust to cohort effects than methods based on optimal prediction. These methods are applicable to any set of continuous valued data where information on the age or mortality of the population is available, and they do not require previous knowledge of how each measurement relates to health. We believe that our global cutpoint approach will be a powerful tool for examining cohort differences since cutpoints can be calculated for that cohort without prior knowledge of the biomarkers. We found that the main limitations in our approach are based on choosing an appropriate global cutpoint for a given study. Accordingly, we have raised the question of how to compare individuals across studies which have used different approaches for creating an FI. Nevertheless, we show that there is overlap between FIs created using global cutpoints around X GCP ¼ 0:85 and other methods for creating FI-Lab.