WO2016127185A1 - Methods and approach for detection and prediction of change in dementia severity or clinical diagnosis over time - Google Patents

Abstract

Certain embodiments are directed to methods or producing and assessing δ scores that are used (i) to measure dementia and track changes in dementia status over time; (ii) to equate or normalize subjects across groups, or recruited across sites or investigators, with regards to their dementia severity; or (iii) to select or classify subjects entering or applying for a study.

Description

METHODS AND APPROACH FOR DETECTION AND PREDICTION OF CHANGE IN DEMENTIA SEVERITY OR CLINICAL DIAGNOSIS OVER TIME

[0001] This Application claims priority to U.S. Provisional Patent Applications 62/112,703 and 62/113,350 filed February 6, 2015. Each of which is incorporated herein by reference in its entirety.

BACKGROUND

[0002] Despite an aggressive worldwide research effort, no new agents have been approved in the U.S. for the treatment of Alzheimer's disease (AD) and/or related dementias since 2003. An emerging consensus suggests that anti-dementia interventions may have to be applied before the clinical onset of dementia to be effective. Several recent interventions have failed to demonstrate efficacy in the general AD population, but appear to be effective in the earliest cases. Delaying the onset of dementia by only five years would greatly decrease the prevalence of this condition.

[0003] The current "State of the Art" for dementia's diagnosis is a consensus clinical diagnosis made by experienced clinicians with full access to comprehensive psychometric data, and employing standardized clinical diagnostic criteria. Such assessments are unwieldy, expensive, burdensome and necessarily limited to tertiary research centers and small sample sizes with limited generalizability. This method is unsuitable for studies in rural areas, to large samples, or in minority populations. Similarly the measurement of change in dementia severity requires either evaluation by an expert clinician, expert psychometric assessment, or both. Change in cognitive performance alone may not be clinically salient in the opinion of expert clinicians. Therefore many regulatory agencies, including the U.S. Food and Drug Administration (FDA), require a demonstration of efficacy against both cognitive and functional status measures before approval will be granted. There is a need for efficient and effective methods of defining patients, conducting clinical studies, and for assessing functionally salient clinical outcomes as they relate to anti-dementia interventions. SUMMARY

[0004] In certain embodiments δ scores are used (i) to measure dementia and track changes in dementia severity over time; (ii) to equate or normalize subjects across groups (e.g., intervention vs. placebo), or recruited across sites or investigators, with regards to their dementia severity; or (iii) to select or classify subjects entering or applying for a study. In these applications, a bifactor δ homolog targets Instrumental Activities of Daily Living (IADL) or similar functional status measure, in either cross-sectional or longitudinal data, depending on the application. In other aspects, a δ ortholog is used to predict an alternative target variable. For example, the ortholog named "dPRE" might target the direction of a patient's future change in δ scores (Δδ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in δ scores as the "Target Indicator" of a δ ortholog. dPRE scores are used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.

[0005] FIG. 10 - FIG. 12 illustrate 6's sensitivity to change over one (FIG. 10), two (FIG. 11) and three years (FIG. 12). In each figure, baseline δ score factor loadings from Visit 1 are applied consistently across waves to generate the future δ score. Each figure is marked by a line of identity. Participants mapping to that line experience no change in their dementia severity over time. Cases to the left of that line have progressed. Those to the right are recovering from a dementing process (even among the NC). A sizable fraction of cases at all diagnostic stages are becoming more demented over time. Even many non-demented controls (NC) are becoming more "demented". Regardless, other cases are improving in every diagnostic category, including many cases diagnosed with "AD" at baseline.

[0006] In certain embodiments methods described herein can be used with any psychometric assessment (e.g., both paper and pencil or electronic versions), and can be validly applied in the assessment of any recognized dementing illness [including but not limited to Alzheimer's Disease (AD), Dementia with Lewy Bodies (LBD), Vascular dementia (VaD), and /or Fronto-temporal Dementia (FTD)]. They can also be applied to the functionally salient and disabling aspects of medical conditions that are not currently recognized as "dementias", including Diabetes Mellitus (DM), Human Immune Deficiency Virus (HIV) and other infectious/viral diseases, post-menopausal cognitive decline, post- operative cognitive decline, post-chemotherapy cognitive decline (also known popularly as "Chemobrain"), traumatic brain injury (TBI), certain neuropsychiatric illnesses (e.g., major depression and schizophrenia (previously known as "dementia praecox"), and the dementia of normal aging (also referred to as "senility").

[0007] The methods can be applied to any psychometric cognitive performance measure in combination with one or more measures of Instrumental Activities of Daily Living (IADL). In this application, the item set of a single measure is treated as a cognitive battery and a δ-homolog is extracted from it by a proprietary bifactor model structure, which targets the functional status or IADL measure. Our method should improve the performance of any psychometric measure, and can be applied post hoc to existing data, or prospectively to newly acquired data. This method should be of interest to researchers, hospitals, insurance companies, pharmaceutical and other corporations, governments, the military and diagnostic test developers

[0008] Other embodiments of the invention are discussed throughout this application. Any embodiment discussed with respect to one aspect of the invention applies to other aspects of the invention as well and vice versa. Each embodiment described herein is understood to be embodiments of the invention that are applicable to all aspects of the invention. It is contemplated that any embodiment discussed herein can be implemented with respect to any method or composition of the invention, and vice versa. Furthermore, compositions and kits of the invention can be used to achieve methods of the invention.

[0009] The use of the word "a" or "an" when used in conjunction with the term "comprising" in the claims and/or the specification may mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one."

[00010] Throughout this application, the term "about" is used to indicate that a value includes the standard deviation of error for the device or method being employed to determine the value.

[00011] The use of the term "or" in the claims is used to mean "and/or" unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and "and/or." [00012] As used in this specification and claim(s), the words "comprising" (and any form of comprising, such as "comprise" and "comprises"), "having" (and any form of having, such as "have" and "has"), "including" (and any form of including, such as "includes" and "include") or "containing" (and any form of containing, such as "contains" and "contain") are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[00013] Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating specific embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

DESCRIPTION OF THE DRAWINGS

[00014] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of the specification embodiments presented herein.

[00015] FIG. 1. Illustration of a structural equation model (SEM) of two latent factors: "g" and "P. Observed variables are represented by rectangles, while latent constructs are represented by circles. Arrows reflect regression weights, or factor loadings in the case of a latent variable's indicators. Bidirectional arrows represent correlations. ADL = Basic Activities of Daily Living; CDR = Clinical Dementia Rating scale sum of boxes; COWA = Controlled Oral Word Association Test; DST = Digit Span Test; IADL = Instrumental Activities of Daily Living; WMS LM II = Weschler Memory Scale: Delayed Logical Memory; WMS VR II = Weschler Memory Scale: Delayed Visual Reproduction. *A11 observed variables are adjusted for age, gender and education. Residuals and their inter- correlations not shown.

[00016] FIG. 2. Illustration of a structural equation model (SEM) of two latent factors: "g" and "P including the third latent variable "d". Observed variables are represented by rectangles, while latent constructs are represented by circles. Arrows reflect regression weights, or factor loadings in the case of a latent variable's indicators. Bidirectional arrows represent correlations. ADL = Basic Activities of Daily Living; CDR = Clinical Dementia Rating scale sum of boxes; COWA = Controlled Oral Word Association Test; DST = Digit Span Test; IADL = Instrumental Activities of Daily Living; WMS LM II = Weschler Memory Scale: Delayed Logical Memory; WMS VR II = Weschler Memory Scale: Delayed Visual Reproduction. *A11 observed variables are adjusted for age, gender and education. Residuals and their inter-correlations not shown. CDR SOB (Model 2a), MMSE (Model 2b), and GDS (Model 2c) modeled separately (Table 4), and combined in this figure.

[00017] FIG. 3. Histogram of g' scores, g' scores, a sizable fraction of the cognitive battery's total variance, are normally distributed because g', unlike d, is orthogonal to dementia status.

[00018] FIG. 4. Histogram of d scores respectively, d scores are bimodally distributed, as is the TARCC sample itself, which was composed of "dementia cases" and "controls."

[00019] FIG. 5. MCI's Boundaries on d's Spectrum.

[00020] FIG. 6. Wave 1 Histogram (TARCC).

[00021] FIG. 7. Wave 2 Histogram.

[00022] FIG. 8. Wave 3 Histogram.

[00023] FIG. 9. Wave 4 Histogram.

[00024] FIG. 10. Change in δ Over One Year.

[00025] FIG. 11. Change in δ Over Two Years.

[00026] FIG. 12. Change in δ Over Three Years.

[00027] FIG. 13. An example of a dPRE ortholog. Instead of IADL, future δ scores from a validation cohort are used as the target variable in a δ-like bifactor. dPRE's cognitive indicators are from baseline assessments. The latent variable "dPRE" is strongly associated with future (year 2) δ scores. To output dPRE as a composite variable, we would require knowledge of the future δ score. However, we have shown that a latent δ homolog can be estimated from its cognitive predictors alone, without reference to its target indicator and yet retain its diagnostic accuracy (Royall et al., in press). To estimate future δ scores, we can simply calculate such a "restricted" composite, limited to the baseline cognitive performance. This would allow a highly accurate estimation of future δ scores, and thus, conversion risk. Of course, baseline observed cognitive performance, Spearman's g, or δ scores are all associated with future δ scores. However, dPRE explains additional variance above and beyond them all.

[00028] FIG. 14. Illustrates a block diagram of a computer system configured to implement various systems and methods described herein according to some embodiments.

[00029] FIG. 15. Illustration of various modules that can be used to implement embodiments of the invention.

[00030] FIG. 16. Illustration of one embodiment of implementing aspects of the invention.

[00031] FIGs. 17A and 17B. (A) 5's incrementally better ROC /AUC relative to their indicators in two different δ-homologs. (B) dMA TARCC hispanics.

[00032] FIG. 18. dCLOX.

[00033] FIG. 19. dTEXAS.

DESCRIPTION

[00034] Neurodegenerative pathology is present in non-demented persons. This pathology represents pre-clinical stages in the development of various dementing illnesses, and non- demented persons who exhibit such lesions are at an increased near-term risk of dementia. A variety of serum, plasma, cerebrospinal fluid (CSF) and neuroimaging biomarkers have been associated with clinical dementia, but none have approached latent variable approach's (5's) ROC/AUC for the diagnosis of dementia.

[00035] A pre-clinical state labeled "mild cognitive impairment (MCI)" has been proposed and is suggested to precede the conversion to dementia by several years. The near term conversion risk of MCI cases remains controversial. Some cases convert to dementia, but those dementias are not always AD. Some MCI cases do not convert and some improve.

[00036] Clinicians are per force ill equipped to assess the "pre-clinical" stages of a disease. Because MCI cases are not yet demented, the clinical diagnosis of this condition can be challenging. MCFs case definition is therefore pegged to psychometric performance. However, this exposes it to many of the weaknesses of psychometrics. As a result, there is considerable "measurement error" in the diagnosis of MCI and this may undermine the demonstration of clinical efficacy in clinical trials directed against that condition.

[00037] There have been concerted attempts to mitigate these issues through the use of rigid consensus diagnostic rules, standardized training on clinical assessment methods, formal psychometric measures, neuroimaging, and the use of diagnostic bio-markers. However, these approaches constrain "valid" dementia case-finding to tertiary care centers, certain ethnic populations, and/or expensively acquired or burdensome assessments.

[00038] Latent variable "measurement models" (Byrne, 2001) offer solutions to many of these problems. First, they are arguably "error free" constructs. A "latent" variable is essentially a "factor" derived from the variance shared across three or more observed variables. The individual observed measures may each have unique vulnerabilities to measurement bias, be it related to the measure's psychometric properties (skewed distributions, ceiling or floor effects), educational or cultural bias, or performance bias (hearing, visual, or motor). However, they do not all share these attributes. Thus, their shared variance is arguably measurement "error-free", and can be explicitly modeled in an SEM framework. Second, a latent variable's factor scores represent a continuously varying, and potentially normally distributed phenotype. It can be associated with bio-markers using powerful parametric statistical methods.

I. Latent Variable Methods

[00039] Cognitive impairment is widely held to be the hallmark of dementia. However, three conditions are necessary to that diagnosis (Royall et al. (2007) J Neuropsychiatry Clin Neurosci 19, 249-265): (1) there must be acquired cognitive impairment s), (2) there must be functional disability, and (3) the disability must be related to the cognitive impairment(s) that are observed. This implies that the essential feature(s) of dementing processes can be resolved to the cognitive correlates of functional status.

[00040] Psychometric and informant-based clinical measures are notoriously prone to measurement error, particularly in minority populations with limited educational attainment and culture-linguistic barriers to their assessment. Latent variable "measurement models" (Cook et al. (2001) Soc Sci Med 53(10): 1275-85) offer the potential for "error free" measures of key constructs. A latent variable model is described herein that provides both a measure of dementia severity and a continuously varying "error free" dementia-specific endophenotype. By using both cognition and functional status measures as indicators, the inventors have achieved an unprecedented ability to model dementia status from easily acquired datasets.

[00041] Target-related outcome variables can be mixed with a battery of predictors to "distill" or "refine" their shared variance into a latent variable of interest. The factor scores of the resulting latent construct can be output to create an error free continuously varying endophenotype, which can then be used as an outcome variable or predictor in its own right.

[00042] FIG. 1 presents a structural equation model (SEM) of two latent factors: "g" and "P. In SEM, observed variables are represented by rectangles, while latent constructs are represented by circles. Arrows reflect regression weights, or factor loadings in the case of a latent variable's indicators. Bidirectional arrows represent correlations. The latent variable g represents "Spearman's g", i.e., a latent variable representing the shared variance across the observed cognitive performance variables (Spearman (1904) Am J Psychol 15:201-293). In data from the Texas Alzheimer's Research and Care Consortium (TARCC), g explains 68.8% of the variance in observed psychometric performance. F represents a latent functional status factor derived from eight observed instrumental activities of daily living (IADL) items and six observed basic ADL (BADL) items. The latent variable f explains 50.67% of the variance in observed variance in care-giver rated IADL/BADL.

[00043] The observed cognitive measures all loaded significantly on g (range: r = -0.65 - -

0.79; all p <0.001). LM II loaded most strongly (r = -0.79). Digit Span loaded least strongly

(r = -0.65). The observed IADL/BADL items all loaded significantly on f (range: r = -0.37 -

-0.84; all p <0.001) (Table 1). Shopping and responsibility for medication adherence loaded most strongly (both r = -0.84). Toileting loaded least strongly (r = -0.37). [00044] In a multivariate regression (FIG. 1), g and f were each strong, significant, and independent predictors of CDR SOB. Together, g and f explained 86% of the variance in CDR scores. Nonetheless, the model did not fit adequately well. Significant inter- correlations amongst the residuals (not shown in FIG. 1) support the existence of an additional latent variable.

[00045] A third latent variable, a hybrid cognitive/functional status latent construct, is introduced "d" (FIG. 2). The latent construct d represents the variance shared between cognitive and IADL/BADL measures [i.e., any and all dementing process(es) afflicting the sample]. The creation of d attenuated the association between g and several measures of cognitive performance (range r = 0.32 - 0.48; all p < 0.001). The inventors relabeled g as "g' " to acknowledge this effect. Together, g' and d accounted for 59.6% of the variance in our cognitive battery. The latent construct d accounted for 37.2% independently of g' . The remainder was attributable to residual "measurement error".

[00046] The latent construct f was also affected by the creation of d. The latent construct f retained relatively strong associations with the BADL items (range r = 0.35 - 0.62, all p<0.001) but lost its formerly strong associations with IADL items (range r = 0.10 - 0.28), one of which (cooking) no longer loaded significantly on f (r = 0.10, p = 0.068). This shows that IADL items are more relevant to dementing illness (through d) than are BADL items.

Table 1. Selected model 1 parameters.

[00047] The latent construct d was significantly and inversely associated with each cognitive performance measure (range: r = -0.55 - -0.67; all p <0.001). It was most strongly associated with WMS VRII (r = -0.67), and least strongly associated with DST (r = -0.55). [00048] The latent construct d was also strongly and positively associated with each IADL item (range: r = 0.51 - 0.87). The latent construct d was most strongly associated with shopping (r = 0.87) and least strongly associated with laundry (r = 0.51). Each BADL item loaded significantly (and positively) on d, but the strength of these associations was relatively weak (range: r = 0.25 - 0.56). The latent construct d was most strongly associated with ADL4 (grooming) (r = 0.56) and least strongly associated with ADL 1 (toileting) (r = 0.25). Thus, in contrast to f in FIG 1, d appears to be relatively specifically related to variance in IADL and not BADL items.

[00049] As a test of d's construct validity, the inventors regressed the base model of g', d, and f onto CDR SOB (FIG. 2). Together, g', f, and d explained 90% of the variance in CDR SOB. However, this was almost entirely mediated by d (r = 0.84; p < 0.001). In contrast to FIG. 1, g's association was severely attenuated, but remained significant (r = -0.18; p = <0.001). The latent construct f s former association with dementia severity was also attenuated (partial r = 0.22; p <0.001).

[00050] Discriminant validity is provided by multivariate regression models of Mini- Mental State Exam (MMSE) (Folstein et al. (1975) J Psychiatr Res 12, 189-198) and Geriatric Depression Scale (GDS) (Sheikh and Yesavage (1986) Clin Gerontologist 5, 165- 173) scores (FIG. 2). The MMSE is a measure of global cognition and should be more strongly associated with a dementing process than the GDS, a measure of depressed mood. As expected, d's association with these measures was weakened relative to that with CDR SOB. g's association with MMSE scores was strengthened relative to that with CDR SOB. g' and d were weakly associated with GDS scores. The latent construct f did not contribute significantly to either of those outcomes.

[00051] The latent variables g', f, and d were tested as independent predictors of TARCC consensus clinical diagnoses (i.e., "AD" vs. "control"). The latent construct d achieved the most accurate discrimination (AUC = 0.942). The latent construct g' (AUC = 0.790) was more accurate in this discrimination than was f (AUC = 0.550). When CDR scores were dichotomized about a threshold of 1.0, d again achieved the most accurate discrimination (AUC = 0.996). [00052] The latent variables d and g' can be output as case-wise factor scores, d scores uniquely can be used as a dementia endophenotype. Similarly, homologs of d created from other target indicator variables can be output as endophenotypes of their respective target conditions (e.g., age, depression, gender, schizophrenia, alcoholism, mortality, etc.).

[00053] FIGs. 3 and 4 present histograms of d and g scores respectively, d scores are bimodally distributed, as is the TARCC sample itself, which was composed of "dementia cases" and "controls". In contrast, g' scores, a sizable fraction of the cognitive battery's total variance, are normally distributed because g', unlike d, is orthogonal to dementia status.

[00054] Endophenotype Applications: Once an endophenotype has been created, it can be used as an outcome variable, a predictor, or to make categorical classifications (e.g., diagnoses), d's factor scores can be exported as a "d score". This then becomes a continuously varying dementia specific endophenotype. Thus, the interindividual variability in dementia status can be modeled, i.e., as predictors in biomarker studies.

[00055] Because d scores can be used to effectively rank order each individual in a cohort with respect to their relative position along a dementia-specific continuum, ROC analysis can be used to define optimal empirical d score boundaries for "normal cognition", "MCI" and "dementia." Thus, d scores derived from relatively simple batteries could be used to replicate the diagnoses made by experienced clinicians with full access to comprehensive psychometric data. Moreover, this can be applied to any latent d score homolog. Depression, schizophrenia, alcoholism etc. could be accurately diagnosed by the same approach.

[00056] d Model Variations: Because Spearman's g is insensitive to the measures employed in the battery, d can be derived from any desired panel of measures, i.e., measures chosen for their ease of administration, to avoid copyright controls, to reduce respondent burden, or to achieve telephone administration. Moreover, because the latent construct d is an error-free construct, it is not vulnerable to factors such as ethnicity, education, or language of administration, which potentially bias the individual measures used to create it.

[00057] Thus d, derived solely from a selection of measures that can be obtained over the telephone, is accurately predicting the blinded impressions of experienced clinicians after comprehensive in-person examinations. II. Determining a target score using a hybrid latent variable

[00058] Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in fewer unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Computationally this technique is equivalent to low rank approximation of the matrix of observed variables.

[00059] Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data. Latent variable models, including factor analysis, use regression modeling techniques to test hypotheses. The factor loadings are the correlation coefficients between the variables and factors. Analogous to Pearson's r, the squared factor loading is the percent of variance in that indicator variable explained by the factor. To get the percent of variance in all the variables accounted for by each factor, add the sum of the squared factor loadings for that factor and divide by the number of variables.

[00060] In certain embodiments methods of determining a score based on a hybrid latent variable can include one or more of the operations described below - in certain aspects these operations are executed in part by instructions provided in a tangible medium, such as a programmed computer; a network comprising one or more programmed computers; or a compact disk.

[00061] First, select a battery of behavioral measures. There must be at least three. They can be any mix of cognitive and/or behavioral measures, preferably continuously distributed, but not necessarily. The selection of behavioral indicators can be selected in order to achieve a particular application. In certain aspects, a battery of verbal measures would be selected to achieve telephonic administration. In other aspects, a battery of non-proprietary measures might be used to achieve low cost administration. In certain aspects, a battery of bedside measures might be selected to allow data collection in the field. A specific battery might be selected to allow post-hoc evaluation of an existing dataset.

[00062] Second, select a target. It can be any condition, diagnosis, mood state, behavior or biomarker related to the brain/behavior measures in the battery.

[00063] Third, select one or more measures of the target. It can be a battery of measures, or a single measure. Target measure(s) can be selected to achieve the same application(s) as the battery.

[00064] Fourth, using Structural Equation Modeling (SEM) methods, construct a latent factor indicated by the measures of the battery. In the case of cognitive measures, this will be an example of Spearman's latent intelligence factor "g".

[00065] Fifth, if the target is being defined by a battery of three or more measures, construct a latent factor indicated by the measures of the battery. In the case of functional status measures, this can be labeled "f '.

[00066] Sixth, construct a hybrid factor to be indicated by each measure in the battery and also by the measure(s) of the target measures. In the case of a cognitive performance/functional status hybrid, the resulting latent variable will represent "the cognitive correlates of functional status" and is a proxy for dementia severity (i.e., "d").

[00067] Seventh, the creation of d robs g of some of its variance, altering its factor loadings. A factor such as g should be re-labeled g' to acknowledge this change.

[00068] Eighth, d's factor loadings (or those of d's ortholog in the case of other targets) can be used to export a "d score" for each individual in the validation cohort. In the case of d, this is a continuously distributed measure of dementia severity. It can be used either as a predictor or an outcome in multivariate regression or other models (i.e., to determine d's biomarkers or to predict dementia-related clinical outcomes).

[00069] Ninth, if d scores (or those of d's ortholog in the case of other targets) are to be used to estimate clinical diagnoses, then an optimal d score threshold must be selected by Receiver Operating Curve (ROC) analysis of expert determinations of that diagnosis in the same population used to construct d.

[00070] Tenth, once the optimal threshold has been selected and its accuracy established, the threshold is applied dichotomously to the d score obtained in any individual unknown case.

[00071] Eleventh, to obtain the d score in the unknown case, they are first administered the same set of measures used to construct d in the validation cohort. The scores are entered into a computer program that encodes d's factor loadings. The program is executed on a suitable platform (phone, tablet, computer, or internet-based server). The unknown case is assigned a d score. The d score is compared to the validated reference threshold.

[00072] Certain embodiments include the analysis of various cognitive assessment tests and functional assessment tests. The following provide examples of some of the tests that may be provided in isolation or included in a cognitive testing battery. One skilled in such assessments will recognize that other known and novel tests may be applied or used with the methods described herein. Additionally, the tests may be grouped into specific classifications and groups. The collection and arrangement of tests in a battery may be in accordance with a particular cognitive limitation or other criterion. One of skill in such assessments will recognize that the specific tests may be altered and substituted without affecting the novelty of the methods described herein, as may the groupings and ordering of the tests within a test battery.

III. Clinical Application of Latent Variable Method

[00073] In certain embodiments δ scores are used (i) to measure dementia and track changes in dementia status over time; (ii) to equate or normalize subjects across groups (e.g., intervention vs. placebo), or recruited across sites or investigators, with regards to their dementia severity; or (iii) to select or classify subjects entering or applying for a study. In other aspects δ ortholog, "dPRE", is used to predict the direction of a patient's future change in δ scores (Δδ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in δ scores as the "Target Indicator" of a δ ortholog. dPRE scores are used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.

[00074] Certain embodiments are directed to methods using δ scores (1) as an outcome measure in dementia clinical trials, (2) to equate subjects across groups (i.e., intervention vs. placebo) or recruited across sites or investigators, with regards to their dementia severity, (3) to use a δ ortholog, "dPRE", to predict the direction of a patient's future change in δ scores (Δδ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in δ scores as the "Target Indicator" of a δ ortholog. dPRE scores then could be used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process. FIG. 13 presents an example of a dPRE ortholog. Instead of IADL, future δ scores from a validation cohort are used as the target variable in a δ-like bifactor. dPRE's cognitive indicators are from baseline assessments. The latent variable "dPRE" is strongly associated with future (year 2) δ scores. To output dPRE as a composite variable, we would require knowledge of the future δ score. However, we have shown that a latent δ homolog can be estimated from its cognitive predictors alone, without reference to its target indicator and yet retain its diagnostic accuracy (Royall et al., in press). To estimate future δ scores, we can simply calculate such a "restricted" composite, limited to the baseline cognitive performance. This would allow a highly accurate estimation of future δ scores, and thus, conversion risk. Of course, baseline observed cognitive performance, Spearman's g, or δ scores are all associated with future δ scores. However, dPRE explains additional variance above and beyond them all.

[00075] 5's factor scores can be exported as a composite variable, i.e., as a "δ Score". This then becomes a continuously varying dementia specific phenotype. It can be used to effectively rank order each individual in a cohort with respect to their relative position along a "dementia"-specific continuum.

[00076] FIG. 3 and FIG. 4 present histograms of d's and g' (respectively) distributions in TARCC data. MCI cases have been excluded. The g' distribution is approximately normal. However, this is a convenience sample composed of AD cases and controls. 5's bimodal distribution better reflects this fact. The latent variable g' normal distribution betrays its irrelevance to the dementia/control discrimination, as is also evidenced by its low AUC (= 0.59 in this cohort).

[00077] MCI is likely then to represent the middle ground of 5's distribution. Its boundaries can be estimated by ROC analysis. Optimal empirical boundaries have been calculated for "normal cognition", "MCI" and "dementia" using another δ homolog in a Japanese sample ("dJ"). dJ is indicated by CLOX: An Executive Clock-Drawing Task (CLOX) (Royall et al. Journal of Neurology, Neurosurgery and Psychiatry (1988) 64:588- 94), the Executive Interview (EXIT25) (Royall et al. Journal of the American Geriatrics Society (1992) 40, 1221-26), the Frontal Assessment Battery (FAB) (Dubois, et al., Neurology (2000) 55: 1621-26), and IADL scores, dJ is constructed from a 30 minute bedside battery that does not overlap with the 80 minute psychometric battery used to construct d in TARCC.

[00078] A dJ score of 0.85 best discriminates between AD v. MCI, with 78% sensitivity and 83% specificity. A dJ score of 1.12 best discriminates between NC v. MCI, with 73% sensitivity and 77% specificity. FIG. 5 presents these thresholds for the discriminations between AD v. MCI (a) and NC v. MCI (b). dJ's AUCs for these discriminations are in Table 2.

Table 2. Diagnostic Performance of δ Homologs.

[00079] δ scores (d scores) predict longitudinal cognitive decline: δ scores predict longitudinal cognitive decline among initially non-demented persons (Royall and Palmer Journal of Neuropsychiatry and Clinical Neurosciences (2012) 24:37-46). A latent growth curve of change in δ scores as a predictor of prospective Year 4 dementia status was constructed. The DST, Controlled Oral Word Association (COW A), Boston, and IADL showed significant declines over time while LMII and Visual Recall (VRII) demonstrated significant increases [χ2 = 1152 (df =229); CFI = 0.968; RMSEA = 0.043].

[00080] All indicator loadings were significant for the four latent variables: g', Ag δ and Δδ, yielding four distinct factors. This model demonstrated good fit to the data χ2 = 543 (df =245); CFI = 0.991; RMSEA = 0.023]. After adjustment for demographic covariates and baseline CDR-SB scores, δ and Δδ were significantly independently associated with CDR- SB4, explaining 25% and 49% of its variance, respectively. The latent variable g' significantly explained 3% of CDR-SB4 variance independently of δ and Δδ. Ag' was not significantly associated with CDR-SB4. Baseline CDR-SB explained 16% of CDR-SB 4 variance, independently of δ, Δδ, and g' . These findings have recently been confirmed.

[00081] δ scores are sensitive to change across 6's entire range. FIG. 6 - FIG. 9 show the distribution of δ scores in TARCC over four waves of longitudinal follow-up. Over time, a dementia group emerges in 5's lower (demented) range.

[00082] FIG. 10 - FIG. 12 show the correlations between baseline δ scores and those obtained after 1, 2, and 3 years of follow-up (respectively). Baseline δ score factor loadings from Visit 1 are applied consistently across waves to generate the future δ score. Each figure is marked by a line of identity - participants mapping to that line experience no change in their dementia severity over time. Cases to the left of that line have progressed. Those to the right are recovering from a dementing process (even among the NC). It is easily determined that a sizable fraction of cases at all diagnostic stages are becoming more demented over time. Even many non-demented controls (NC) are becoming more "demented". Regardless, other cases are improving in every diagnostic category, including many cases diagnosed with "AD" at baseline.

[00083] These trends are not-likely to reflect psychometric measurement error, (1) latent variables are inherently resistant to non-systematic measurement error of any kind, and (2) the δ score's AUC for AD v. control does not attenuate over time Table 3, even though cases are rated in the future with δ score factor weights derived from baseline data. Table 3. AUC of baseline d score factor weighs as predictors of dementia status (AD vs. control)at baseline and in subsequent years (TARCC data).

[00084] FIG. 9 - FIG. 11 also code clinical conversions from "NC" to MCI or AD (open circle), and from MCI to AD (closed circle). These conversions are concentrated in an intermediate range of d Scores and among cases with worsening d Scores (to the left of the line of identity). Furthermore, MCI conversions to AD occur at lower (more "demented") baseline d Scores than do conversions from NC status.

[00085] This analysis confirms that (1) δ scores are sensitive to change at all stages of dementia (NC, MCI and AD), (2) δ scores can detect clinically salient change among MCI and NC, and (3) δ scores can detect improvement or progression of dementia status at intervals as short as one year and (4) δ scores can predict conversion to MCI or AD from non-demented baselines over up to four years of follow-up.

[00086] These findings also illustrate the δ score's potential to serve as a sensitive measure of functionally (IADL) salient (dementing) cognitive change in demented and pre-demented subjects, and at intervals as short as 1 year. Because δ scores are continuously distributed, they obviate the need for difficult to make categorical clinical distinctions (such as "MCI"). Any desired δ score can be used to select cases for treatment and to ensure the recruitment of comparably demented subjects across sites, investigators (even across linguistic or cultural barriers (Royall et al., Journal of Alzheimer 's Disease 2016;49:561-570). In certain aspects subjects can be selected at some confidence level that a selected subject's untreated dementia status will worsen over time.

[00087] The results above also reveal that neither MCI nor AD cases inevitably progress. A sizable fraction of both groups improve their dementia status over time. We are currently pursuing the biomarkers of those outcomes. However, clinical improvement in a study's placebo group has been observed in several failed AD clinical trials and can undermine study power and performance (Schneider & Sano, 2009). [00088] In other embodiments the structure of our proprietary bifactor model ensures that all δ-homologs are necessarily more accurate assessments of dementia than will be any of their indicator variables. This is because any observed cognitive performance measure will be additionally burdened by variance that is empirically UN-RELATED to dementia (i.e., measure specific measurement error and g', which is 5's residual in Spearman's general intelligence factor "g". FIGs 17A and 17B illustrate 5's incrementally better ROC /AUC relative to their indicators in two different δ-homologs.

[00089] Since δ is necessarily "greater than the sum of its parts" (Royall & Palmer in press) our method will improve upon the scoring of any single psychometric measure when its items or a subset of its items, are used as a cognitive "battery" for the purposes of constructing an item level δ-homolog. For example dTEXAS and dCLOX treat items from the Executive Interview (EXIT25) (Royall, Mahurin & Grey, 1992) and CLOX: An Executive Clock-drawing Task (Royall, Cordes & Polk, 1998) as such a "battery". The resulting latent δ-homolog (dTEXAS and dCLOX, respectively) has a higher AUC/ROC than the traditionally sum scored measures from which their indicators were taken. dTEXAS 's AUC = 0.92 for the diagnosis of dementia vs, 0.89 for the EXIT25 (Matsuoka et al., 2014). dCLOX's AUC = 0.92, vs. 0.78 for the CLOX (Matsuoka et al., 2014). dCLOX correlates r = 0.92 with CDR Sum of Boxes. dTEXAS correleates r = 0.78. A dTEXAS score of -0.58 best discriminated between dementia v. all others with 90.1% sensitivity and 80.0% specificity. In each case, only a subset of the measure's items were required to construct their item level homologs. Thus, we have also shortened the time required for this assessment and the administrative burden to the patient. In the case dTEXAS, the EXIT25 items chosen as its indicators can also be given over the phone. Thus, not only is dTEXAS briefer and more accurate than the EXIT25, it can potentially be applied via telephone, whereas the full EXIT25 cannot.

[00090] These observations have profound implications for dementia's assessment: 1. The majority of the information obtained by ANY psychometric battery will not relate to IADL, and therefore to dementia. The latent variable δ will necessarily improve upon any observed measure or battery's diagnostic performance because it is free of that measurement error. 2. The latent variable δ can be constructed from any cognitive battery. This is because our proprietary bifactor model's structure extract's δ from Spearman's general intelligence factor g. It is widely accepted that g is "indifferent to its indicators" meaning that it contributes variance to every cognitive performance measure, regardless of its face validity as a measure of any individual cognitive domain (e.g., memory, attention, executive function, etc). 3. δ can be constructed from item-level data, without loss of its diagnostic performance. Therefore, the diagnostic performance of ANY cognitive performance measure might be improved by rescoring it as a δ homolog composite.

[00091] Traditional cognitive assessment is inefficient and burdensome to patients. Cognitive performance is significantly, but only weakly related to functional outcomes (Royall et al., 2007). The U.S. Preventive Services Task Force (USPSTF) has even opined against widespread screening for cognitive impairment among older persons (Moyer et al., in press). However, latent variable "measurement models" (Byrne, 2001) offer solutions to many of these problems. First, they are arguably "error free" constructs. A "latent" variable is essentially a "factor" derived from the variance shared across three or more observed variables. The individual observed measures may each have unique vulnerabilities to measurement bias, be it related to the measure's psychometric properties (skewed distributions, ceiling or floor effects), educational or cultural bias, or performance bias (hearing, visual, or motor). However, they do not all share these attributes. Thus, their shared variance is arguably measurement "error-free", and can be explicitly modeled in an SEM framework. Second, a latent variable's factor scores represent a continuously varying, and potentially normally distributed phenotype. It can be associated with bio-markers using powerful parametric statistical methods.

[00092] Certain aspects of the methods described herein improve the diagnostic performance of psychometric measures by using their itemsets to generate δ scores. This should both improve diagnostic accuracy while potentially also reducing administration burden, and /or improving the ease of administration.

[00093] 5's factor scores can be exported as a composite variable, i.e., as a "δ Score". This then becomes a continuously varying dementia specific phenotype. It can be used to effectively rank order each individual in a cohort with respect to their relative position along a "dementia"-specific continuum. The itemset of any cognitive performance measure is amenable to this approach because all measures are affected by Spearman's general intelligence factor g, from which δ can be derived. It is further unprecedented to apply this approach to item level data. That opens the possibility of rescoring and cognitive performance measures as a δ homolog, thereby improving the original measure's sensitivity to functionally salient cognitive changes and dementia.

IV. Biomarkers Related to Dementia Status

[00094] Biomarkers can be used to both define a disease state as well as to provide a means to predict physiological and clinical manifestations of a disease. Three commonly discussed ways in which biomarkers could be used clinically are: (1) to characterize a disease state, i.e. establish a diagnosis, (2) to demonstrate the progression of a disease, and (3) to predict the progression of a disease, i.e. establish a prognosis. Establishing putative biomarkers for such uses typically requires a statistical analysis of relative changes in biomarker expression either cross-sectionally and/or over time (longitudinally). For example, in a state or diagnostic biomarker analysis, levels of one or more biomarkers are measured cross-sectionally, e.g. in patients with disease and in normal control subjects, at one point in time and then related to the clinical status of the groups. Statistically significant differences in biomarker expression can be linked to presence or absence of disease, and would indicate that the biomarkers could subsequently be used to diagnose patients as either having disease or not having disease. In a progression analysis, levels of one or more biomarkers and clinical status are both measured longitudinally. Statistically significant changes over time in both biomarker expression and clinical status would indicate that the biomarkers under study could be used to monitor the progression of the disease. In a prognostic analysis, levels of one or more biomarkers are measured at one point in time and related to the change in clinical status from that point in time to another subsequent point in time. A statistical relationship between biomarker expression and subsequent change in clinical status would indicate that the biomarkers under study could be used to predict disease progression.

[00095] Results from prognostic analyses can also be used for disease staging and for monitoring the effects of drugs. The prediction of variable rates of decline for various groups of patients allows them to be identified as subgroups that are differentiated according to disease severity (i.e., less versus more) or stage (i.e., early versus late). Also, patients treated with a putative disease-modifying therapy may demonstrate an observed rate of cognitive decline that does not match the rate of decline predicted by the prognostic analysis. This could be considered evidence of drug or treatment efficacy.

[00096] Various multi-analyte type analyses have been described, for example, WO 2004/104597, "Method for Prediction, Diagnosis, and Differential Diagnosis of AD" describes methods of predicting disease status via an x/y ratio of Αβ peptides; WO 2005/047484, "Biomarkers for Alzheimer's Disease" describes a series of markers that can be used for the assessment of disease state; WO 2005/052592, "Methods and Compositions for Diagnosis, Stratification, and Monitoring of Alzheimer's Disease and Other Neurological Disorders in Body Fluids" teaches methods and markers gleaned from plasma for the monitoring of Alzheimer's disease; and WO 2006/009887, "Evaluation of a Treatment to Decrease the Risk of a Progressive Brain Disorder or to Slow Brain Aging" teaches methods and ways to use brain imaging to measure brain activity and/or structural changes to determine efficacy of putative treatments for brain-related disorders. Embodiments of the current invention can be used to improve and identify novel biomarkers and methods for the treatment and assessment of a variety of disease states that result in cognitive impairments, alterations, and/or deficiencies. For example, the protein thrombopoeitin, measured in serum, has been shown to predict future d-scores, but only via the contemporaneously measured intercept of the longitudinally measured growth process of d-score change, not the growth process' slope (Royall & Palmer, in press). Thus, our method can be used to ascertain the time point in a dementing illness when a specific biomarker is involved (Royall and Palmer, Alzheimer 's & Dementia: Diagnosis, Assessment & Disease Monitoring, in press.).

[00097] In order to develop or improve diagnosis, prognosis, and/or treatment of such disease states clinical trials and other studies must use cognitive testing to assess progression of the disease in order to determine whether the therapy under study has a positive effect on disease progression. However, the variability in patient response associated with cognitive testing, due to the progressive and variable course of the disease, is large enough to inhibit the ability of these tests to detect alteration in the status of an individual. The current methods can be used to detect and evaluate such alterations in the status of an individual. Each quintile in the d-score of MCI cases increases their five year risk of dementia conversion almost threefold (Royall and Palmer, Journal of Prevention of Alzheimer 's Disease. 2015 2:337-38.). [00098] δ scores can be used (1) as an outcome measure in dementia clinical trials, and (2) to equate subjects across groups (i.e., intervention vs. placebo) or recruited across sites or investigators, with regards to their dementia severity. A δ ortholog, "dPRE", can also be used to predict the direction of a patient's future change in δ scores (Δδ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in δ scores as the "Target Indicator" of a δ ortholog. dPRE scores then could be used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.

[00099] 6's factor scores can be exported as a composite variable, i.e., as a "δ Score". This then becomes a continuously varying dementia specific phenotype. It can be used to effectively rank order each individual in a cohort with respect to their relative position along a "dementia"-specific continuum.

[000100] δ ortholog, "dPRE", can be used to predict the direction of a patient's future change in δ scores (Δδ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in δ scores as the "Target Indicator" of a δ ortholog. dPRE scores then could be used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.

V. Computer Implementation

[000101] Embodiments of hybrid latent variable system may be implemented or executed by one or more computer systems. One such computer system is illustrated in FIG. 14. In various embodiments, computer system may be a server, a mainframe computer system, a workstation, a network computer, a desktop computer, a laptop, or the like. For example, in some cases, the system shown in FIG. 14, FIG. 15, FIG. 16 or the like may be implemented as computer system. Moreover, one or more of servers or devices may include one or more computers or computing devices generally in the form of a computer system. In different embodiments these various computer systems may be configured to communicate with each other in any suitable way, such as, for example, via a network.

[000102] As illustrated, the computer system includes one or more processors 510 coupled to a system memory 520 via an input/output (I/O) interface 530. Computer system 500 further includes a network interface 540 coupled to I/O interface 530, and one or more input/output devices 550, such as cursor control device 560, keyboard 570, and display(s) 580. In some embodiments, a given entity (e.g., hybrid latent variable system) may be implemented using a single instance of computer system 500, while in other embodiments multiple such systems, or multiple nodes making up computer system 500, may be configured to host different portions or instances of embodiments. For example, in an embodiment some elements may be implemented via one or more nodes of computer system 500 that are distinct from those nodes implementing other elements (e.g., a first computer system may implement an assessment of a hybrid latent variable assessment or system while another computer system may implement data gathering, scaling, classification etc.).

[000103] In various embodiments, computer system 500 may be a single-processor system including one processor 510, or a multi-processor system including two or more processors 510 (e.g., two, four, eight, or another suitable number). Processors 510 may be any processor capable of executing program instructions. For example, in various embodiments, processors 510 may be general -purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, POWERPC®, ARM®, SPARC®, or MIPS® ISAs, or any other suitable ISA. In multi-processor systems, each of processors 510 may commonly, but not necessarily, implement the same ISA. Also, in some embodiments, at least one processor 510 may be a graphics-processing unit (GPU) or other dedicated graphics-rendering device.

[000104] System memory 520 may be configured to store program instructions and/or data accessible by processor 510. In various embodiments, system memory 520 may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. As illustrated, program instructions and data implementing certain operations, such as, for example, those described herein, may be stored within system memory 520 as program instructions 525 and data storage 535, respectively. In other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory 520 or computer system 500. Generally speaking, a computer-accessible medium may include any tangible storage media or memory media such as magnetic or optical media— e.g., disk or CD/DVD-ROM coupled to computer system 500 via I/O interface 530. Program instructions and data stored on a tangible computer-accessible medium in non-transitory form may further be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 540.

[000105] In an embodiment, I/O interface 530 may be configured to coordinate I/O traffic between processor 510, system memory 520, and any peripheral devices in the device, including network interface 540 or other peripheral interfaces, such as input/output devices 550. In some embodiments, I/O interface 530 may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 520) into a format suitable for use by another component (e.g., processor 510). In some embodiments, I/O interface 530 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface 530 may be split into two or more separate components, such as a north bridge and a south bridge, for example. In addition, in some embodiments some or all of the functionality of I/O interface 530, such as an interface to system memory 520, may be incorporated directly into processor 510.

[000106] Network interface 540 may be configured to allow data to be exchanged between computer system 500 and other devices attached to a network, such as other computer systems, or between nodes of computer system 500. In various embodiments, network interface 540 may support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fiber Channel SANs, or via any other suitable type of network and/or protocol.

[000107] Input/output devices 550 may, in some embodiments, include one or more display terminals, keyboards, keypads, touch screens, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system 500. Multiple input/output devices 550 may be present in computer system 500 or may be distributed on various nodes of computer system 500. In some embodiments, similar input/output devices may be separate from computer system 500 and may interact with one or more nodes of computer system 500 through a wired or wireless connection, such as over network interface 540.

[000108] As shown in FIG. 14, memory 520 may include program instructions 525, configured to implement certain embodiments described herein, and data storage 535, comprising various data accessible by program instructions 525. In an embodiment, program instructions 525 may include software elements of embodiments described in the claims or illustrated in FIG. 14, FIG. 15, or the like. For example, program instructions 525 may be implemented in various embodiments using any desired programming language, scripting language, or combination of programming languages and/or scripting languages (e.g., C, C++, C#, JAVA®, JAVASCRIPT®, PERL®, etc). Data storage 535 may include data that may be used in these embodiments. In other embodiments, other or different software elements and data may be included.

[000109] A person of ordinary skill in the art will appreciate that computer system 500 is merely illustrative and is not intended to limit the scope of the disclosure described herein. In particular, the computer system and devices may include any combination of hardware or software that can perform the indicated operations. In addition, the operations performed by the illustrated components may, in some embodiments, be performed by fewer components or distributed across additional components. Similarly, in other embodiments, the operations of some of the illustrated components may not be performed and/or other additional operations may be available. Accordingly, systems and methods described herein may be implemented or executed with other computer system configurations.

Claims

1. A method of evaluating dementia status over time comprising:

(a) conducting an evaluation of a subject and determining a δ score at an initial time point;

(b) conducting a second evaluation and determining a δ score at a second time point; and

(c) assessing the change in the δ score to determine the likelihood of dementia progression in the subject.

2. The method of claim 1, wherein the subjects are classifies as non-progressors or progressors.

3. The method of claim 1, wherein the subjects are classified as non-converters or converters.