The Therapeutic Area Data Standards User Guide for Huntington's Disease (TAUG-HD) was developed under the CDISC Standards Development Process. It focuses on clinical trials in patients with Huntington's disease. The TAUG-HD was funded by the CHDI Foundation as part of the collaborative efforts between CHDI and Critical Path Institute (C-Path).

The development of this standard included collecting input from various stakeholders to ensure the standard is as thorough as possible. A multitude of volunteers and experts provided resources and input to support the development of this standard. The goal of this standard is to identify a core set of clinical therapeutic area concepts and endpoints for targeted therapeutic areas and translate them into CDISC standards to improve semantic understanding, support data sharing and facilitate global regulatory submission. As with all CDISC therapeutic area standards, the purpose of this standard is to describe how to use CDISC standards to represent data pertaining to a targeted therapeutic area—in this case, Huntington's disease.

It is important to note that the inclusion or exclusion of concepts in this TAUG has nothing to do with whether such concepts may, should, must, must not, should not, or need not be collected in any given Huntington's disease study. CDISC is not a regulatory, clinical, or medical expert or authority; CDISC standards are not guidelines for which data to collect, how to run clinical trials, or how to treat patients.

1. First, read the Study Data Tabulation Model (SDTM), the SDTM Implementation Guide for Human Clinical Trials (SDTMIG), the SDTMIG for Medical Devices (SDTMIG-MD), and the Pharmacogenomics/Genetics Implementation Guide (PGxIG) to gain some familiarity with the model used in this document and its general implementation in human clinical trials. These standards are the basis of this therapeutic area user guide (TAUG) and are available at http://www.cdisc.org/sdtm, https://www.cdisc.org/standards/foundational/sdtmig and https://www.cdisc.org/standards/foundational/pgx, respectively.
2. Next, read Introduction to Therapeutic Area Standards (https://wiki.cdisc.org/x/SSy8AQ) to know what to expect from this TAUG. The CDISC Training Campus (https://www.cdisc.org/education/online-courses) has a free module you can access as well.
3. Read this guide all the way through (without skipping any sections) at least once.
4. Finally, revisit any sections of particular interest as the need arises.

Some things to bear in mind while reading this document:

• This document does not replace or supersede the foundational CDISC standards or their implementation guides, and should not be used as a substitute for any other CDISC standard.
• This document does not repeat content already published in another CDISC standard.
• This document is not and does not try to be an exhaustive documentation of every possible kind of data that could be collected in relation to Huntington's disease. The team does not know the extent of every possible kind of data that could be collected in relation to Huntington's disease. They have tried to focus on those areas that CHDI subject matter experts have identified as most likely to be relevant and useful.
• The advice and examples presented in this document are influenced by ongoing internal standards development at CDISC. If a modeling approach seems inconsistent with a published standard, it may be a genuine error, but it could also be a reflection of potential or upcoming changes to the standard. Corrections for errors identified after publication can be found on the Errata page (https://wiki.cdisc.org/x/BUs8Aw) for the document.
• The examples in this document use CDISC Controlled Terminology where possible; some values that seem to be controlled terminology may still be under development at the time of publication, or even especially plausible "best-guess" placeholder values. Do not rely on any source other than the CDISC value set in the NCI Thesaurus (http://www.cancer.gov/research/resources/terminology/cdisc) for controlled terminology.
• With time, parts of this TAUG may become outdated. Please bear in mind the release date when contrasting advice and modeling in this guide against that in other CDISC standards.

Draft standards of interest to this document are listed at: Draft Standards of Interest to Huntington's Disease (https://wiki.cdisc.org/x/AKyzAg) .

All general caveats for TA standards given in the Introduction to Therapeutic Area Standards (https://wiki.cdisc.org/x/SSy8AQ) apply to this document.

This document is divided into the following sections:

• Section 1, Introduction, provides an overall introduction to the purpose and goals of the Huntington's disease project.
• Section 2, Overview of Huntington's Disease, provides an overview of what Huntington's disease is and who it affects.
• Section 3, Subject and Disease Characteristics, discusses the role of family history and genetics in Huntington's disease.
• Section 4, Disease Assessments, provides information on data used to evaluate how a subject is progressing over the course of a study.
• Appendices provide additional background material and describe other supplemental material relevant to Huntington's disease.

• The non-standard variable "SNR" (signal to noise ratio) is shown as a "qualifier of results" in the NSV table for the nv.xpt example in Section 4.3.2, Magnetic Resonance Spectroscopy (MRS), and is replicated in each row of the metabolite results. In fact, SNR qualifies the entire spectroscopy dataset from which these individual results were obtained. There is currently no good way to represent qualifiers of data at this level.
• NVTESTCD/NVTEST terminology shown in the example in Section 4.3.2, Magnetic Resonance Spectroscopy (MRS), overlaps with some existing controlled terminology for LB. Use of the same TESTCD and TEST terminology in multiple codelists is generally avoided, but after careful consideration and public comment the decision was made to represent these data in NV, resulting in duplication of these terms in codelists for NVTESTCD and NVTEST.

Huntington's disease (HD) is a genetic disease caused by a mutation in the huntingtin (HTT) gene. It is typically inherited in an autosomal dominant fashion from an affected parent, but a small minority of cases can be caused by a de novo mutation in an individual without a family history. The disease causes the progressive degeneration of neurons in the brain that results in cognitive impairment, psychiatric disorders, and motor dysfunction. Symptoms in affected individuals can present at any age, but onset most often occurs between the ages of 30-50. A trend in earlier onset may be present in subsequent generations in some families. There is no cure; current therapies focus on improving quality of life. Typical life expectancy from the time of onset is in the range of 15-20 years. Prevalence is similar for men and women.[1]

Subject and disease characteristics generally includes events and activities that have affected the subject prior to the study. For Huntington's disease studies, such information will include subject characteristics such as genetics and family history.

Huntington's disease (HD) is a hereditary, monogenetic autosomal-dominant neurodegenerative disorder. Monogenetic refers to the fact that a variant of a single gene—the huntingtin (HTT) gene—is sufficient to bring about the symptoms and signs of HD. In turn, the monogenetic nature of HD implies that individuals who develop symptoms and signs reminiscent of HD but do not have this gene variant (allele) by definition do not suffer from HD, despite the fact that they may look and behave like HD patients. Autosomal-dominant refers to the fact that

1. The HTT gene does not reside on 1 of the sex chromosomes (X- or Y-chromosomes), but rather an autosomal chromosome (in this case, chromosome 4). Therefore, individuals of either sex have an equal/identical chance of inheriting an HD-causing allele transmitted by an affected parent.
2. One single mutant allele is necessary and sufficient to cause disease (thus, a dominant allele).

The HTT gene codes for the huntingtin protein. The gene sequence includes a series of tandem trinucleotide repeats consisting of consecutive cytosine, adenine, and guanine (CAG) residues. In subjects who develop Huntington's disease, the CAG trinucleotide repeat has an excessive length. There is an inverse correlation between an increasing number of CAG repeats (within the HD-causing range) and age of disease onset. HD is, in most patients, of mid-life onset and of full penetrance. As a consequence of mid-life onset of symptoms and signs, many carriers of an HD-causing allele in their HTT gene have already decided to have children many years before they become aware that they inherited this allele.

To survey individuals potentially at risk for inheriting the CAG-expansion mutation, a carefully obtained family history is critical. Full penetrance of the HD-causing allele implies that each and every individual who inherits such an allele will eventually develop HD. One consequence of the full penetrance of the HD-causing allele is that most people affected with HD have a "positive family history" (i.e., a relative in the direct bloodline who was affected by HD as well). In practice, however, it is often difficult to be sure that abnormalities reported in relatives are indeed related to HD, in particular when referencing periods when confirmation by genetic testing was not available. Therefore, in obtaining a family history, raters are asked to assess HD status, designated as ranging from "unknown" to "manifest carrier"; in addition, the degree of certainty of the rater's assessment may be recorded. Family history analysis allows investigators to identify families with apparent lack of relatives affected by HD ("negative family history"). In some families, an apparently negative history may reflect censored lifespans of the older generations precluding the clinical, overt manifestations of HD. Therefore, the age at death of all family members is recorded. In others, false paternity may be the root course of a negative family history. However, in yet other families, parents may harbor a non HD-causing allele in the mutable range (i.e., 27 to 35 CAG repeats in the HTT gene), giving rise to offspring with CAG expansion sizes in the HD-causing range (≥36 CAG repeats). To identify false paternity or the presence of expandable alleles requires examination of DNA obtained from the parents. It is therefore useful to know whether DNA from relatives is available.

Understanding patterns of inheritance in families may be of interest in some studies. No dataset examples of family history are provided in this TAUG. For guidance on how to represent data collected about non-study subjects associated with a subject in the study, see the SDTM Implementation Guide: Associated Persons (SDTMIG-AP; available at https://www.cdisc.org/standards/foundational/sdtmig). Further, single nucleotide polymorphisms (SNPs) are common data elements used in neurodegenerative clinical trials. No dataset examples of SNPs are provided in this TAUG, but guidance on how to represent such data is described in the SDTM Implementation Guide for Pharmacogenomics and Pharmacogenetics (SDTMIG-PGx v1.0; available at https://www.cdisc.org/standards/foundational/pgx).

Genetic testing of the HTT gene may be performed for the following reasons:

• to diagnose a subject or to confirm diagnosis of HD,
• to assess the subject's risk of developing HD at some point in life due to family history, or
• to estimate disease burden.

The genetic test focuses on the number of CAG trinucleotide repeats, and can be interpreted as shown in table below.[2] Note that any subject with an HD-causing allele is considered an HD-causing allele carrier (HDCAC) because for at least some portion of their lives they do not yet have manifest HD.

Table. HD-causing and Non HD-causing Alleles

It is important to note that sometimes 1 or more CAA trinucleotide repeats occur within the CAG repeat region of the gene, and sequencing analysis provides a more accurate and direct counting of CAG repeat numbers.

Disease assessments are typically done repeatedly over the course of a study and are used to evaluate how a subject is progressing or improving from baseline. For Huntington's disease studies these may include clinical outcomes assessments (questionnaires, ratings and scales), cerebrospinal fluid biomarkers, and imaging biomarkers.

Questionnaires, ratings, and scales (QRSs) are typically done repeatedly over the course of a study and are used to evaluate how a subject is progressing. These are often used to assess the severity of disease and improvement or worsening of specific symptoms.

QRS measures are maintained as standalone guides on the CDISC website at https://www.cdisc.org/foundational/qrs. The following table lists the assessments that have been identified as vital or useful in studies of Huntington's disease (HD). These are either being pursued as potential supplements as part of the development work for the TAUG-HD, or in some cases were already developed. Supplements may or may not be finalized at the time of publication of this TAUG, and depend on copyright approval where applicable. CDISC cannot produce supplements for copyrighted measures without the express permission of the copyright holder. The status listed was up to date as of the publication of this guide, and that status may have changed since publication.

Sponsors should refer to the QRS link above if a measure of interest is not included below, as it may have been developed for another therapeutic area, and new measures are implemented on an ongoing basis by the CDISC QRS Terminology and Standards Development sub-teams. See CDISC COP 001 (http://www.cdisc.org/bylaws-and-policies) for details on implementing or requesting development of standards for SDTM-based submissions.

Table. Measures Relevant to Huntington's Disease Research

^aSDMT is part of both the UHDRS and the HD-CAB batteries.

Sampling of cerebrospinal fluid (CSF) and blood has been identified as an important source of potential biomarkers as covariates in Huntington's disease (HD). Some of the biomarkers of interest obtained from these samples include neurofilament light chain (NFL), tau protein (total), and mutant huntingtin protein.[3]This section describes how to represent the data generated throughout the sampling process, from sample collection via lumbar puncture (spinal tap), through the aliquoting and storage of the sample and generation of the results.

It is important to note that several factors of the sample collection methods may affect the results, and therefore should be tightly controlled and recorded. For example, it is possible for tau protein in a biofluid sample to react with the material of the sample collection tube, producing differing results in the downstream processing of the same sample if the sample were to be split into 2 tubes of differing composition. It is not within the purview of this TAUG to dictate to investigators which protocol should be followed in the acquisition of these biomakers. Rather, the sample processing examples included in this guide address this issue from the perspective of showing how to represent the details of these processes as data in SDTM-based datasets. Having these data available as shown below provides analysts with the necessary information to interpret the results in the context of the parameters under which they were acquired. Though the examples focus on CSF sample processing, they are applicable to blood samples as well.

The following concept map shows the provenance of a biomarker value (represented in the Laboratory Test Results [LB] domain) obtained from CSF, starting with the generation of the CSF sample via lumbar puncture and following through the various events the sample may undergo (centrifugation, aliquoting, freezing, thawing) in the process of generating actual endpoints. Each step of the process shows at a high level the different types of data generated that should be represented.

Dashed lines point to the corresponding SDTM domains (or groups of domains, such as those for devices) in which the data generated are represented. Refer to the individual domains referenced in the concept map for a detailed view of how the datasets should be populated using more detailed, realistic example values as endpoints. The red lines indicate steps in the process that may occur in differing order; once collected, samples may be processed immediately, or frozen for processing later.

CSF sampling begins with a lumbar puncture, represented in the Procedures (PR) domain. The origin of the CSF sample in the Biospecimen Events (BE) domain is linked to the lumbar puncture via the REFID variable: PRREFID=BEREFID when BETERM=COLLECTING.

From this point, the CSF sample is usually aliquoted. Each aliquot is assigned a PARENT value (in the RELSPEC dataset; Concept Map. Biofluid RELSPEC-RELREC) that corresponds to the REFID value of the collected sample, thus identifying the aliquot as a "child" of that sample. In addition, each aliquot would be assigned a unique REFID value that may be based on the REFID of the parent. For example, if the PRREFID/BEREFID for the sample collection is 100, then BEREFID for the first aliquoting event could be 100.1, and so on. From this point on, all other events and findings using the sample aliquot will be assigned a REFID value in those subsequent domains corresponding to the unique REFID for the aliquot used. In this way, the REFID values can be viewed as a unique sample ID across all observations recorded on that sample in subsequent domains.

The SPDEVID variable is used to identify devices associated with events or findings in other domains. In the PR domain record for Lumbar Puncture, it identifies the spinal needle used to draw the sample. In the biospecimen domains (BE/BS), it identifies the tube lots used to collect/store the sample and aliquots; it may also be used to represent a separate device identifier for a freezer used for storage when freezing is represented in BE. All SPDEVID values also match their respective device identifiers and properties represented in the device domains.

In this section, we show examples of how to represent the various types of data discussed above with the SDTM, and how to relate them to each other and to the biomarker results represented in the LB domain. The examples that follow are intended to illustrate the data modeling of these various properties and findings and are not meant to serve as strict requirements nor as a comprehensive list of the properties and findings which should be provided in a regulatory submission. Sponsors should refer to their regulatory agencies and review divisions for guidance on which data should be included in their submission(s).

The following table lists other examples of biomarkers that may be used in HD clinical trials, for which CDISC Controlled Terminology is either published or proposed. Regardless of the analyte (biomarker) and specimen type, the data would be handled the same as shown in the preceding examples. Users should always refer to the most recently published controlled terminology before implementing the terms with a status of "requested" shown below.

Biomarkers for Use in Huntington's Disease Clinical Trials

¹Refers to the values in the LBTEST and LBTESTCD columns. Values in the LBSPEC column already exist as controlled terminology.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) or positron emission tomography/computerized tomography (PET/CT) are valuable tools for assessing imaging biomarkers in Huntington's disease (HD). In more recent years, magnetic resonance spectroscopy (MRS) has emerged as a potentially useful research tool in HD as well. The following subsections describe these procedures at a high level, with an emphasis on the data that are generated and how they should be represented in SDTM-based datasets.

In Huntington's disease (HD), magnetic resonance imaging (MRI) is primarily used for obtaining volumetric brain measurements, as evidence suggests these biomarkers correlate with disease progression.[4] HD is characterized pathologically by selective neurodegeneration of specific brain regions. Neuroimaging measures associated with HD signs and symptoms are tightly associated with clinical outcomes and can precede onset of motor symptoms. Selective neurodegeneration of medium spiny neurons (MSNs) results in measurable atrophy in the striatum (composed of the caudate and putamen). Putamen volume is related to the number of MSNs and correlates with motor impairment in HD. Neuroanatomic regions of interest include:

• Basal ganglia: Collective term referring to clusters of neurons (caudate nucleus, putamen, and globus pallidus) that are responsible for involuntary movements such as tremors, and chorea.
• Striatum: Term that describes a collection of nuclei that mediate the refinement and control of motor movement; regions of the brain that comprise the striatum include caudate, putamen, and nucleus accumbens.

In a brain MRI procedure, the subject lies down and is entered into the MRI scanner. Once the subject is in the scanner, the procedure begins by collecting a series of image slices across the region of interest (in this case, the head). Multiple sets of image slices—commonly referred to as scans—are typically collected across the entire volume of the head, with each new set (scan) potentially using different properties than the scan before. This is important to note: The changing properties between each scan may affect how that image series can be used or interpreted, and is usually controlled by protocol and/or recorded for later reference by analysts. To reiterate: A single MRI procedure (which would be represented in the Procedures [PR] domain) results in multiple scans, each differentiated by the set of properties that were in place for that scan. These changeable settings that may vary from one scan to the next are represented in the Device In-Use (DU) domain. Examples include, but are not limited to, properties such as image weighting, matrix size, slice thickness, repetition time (TR), echo time (TE), inversion time (TI), and anatomical plane of image acquisition. These parameters are usually represented in standardized reports such as the DICOM header.

In addition, MRI scanners have inherent onboard properties which generally would not change in the course of a study. These unchanging properties are represented in the Device Properties (DO) domain. Finally, information such as make, model, and serial number would be represented in the Device Identifiers (DI) domain.

This section provides examples of how to represent these types of data in SDTM-based datasets and how to relate them to each other and to the volumetric findings represented in the Nervous System Findings (NV) domain. The examples are only intended to illustrate the data modeling of these various properties and findings, and are not meant to serve as strict requirements nor as a comprehensive list of the properties and findings which should be provided in a regulatory submission. Sponsors should seek guidance from their regulatory agencies and review divisions regarding which data should be recorded and submitted as appropriate for their individual studies.

The following concept map shows the provenance of a brain morphological measurement starting with the generation of the images via MRI and following through to the derivation of the endpoint. Each step of the process shows at a high level the different types of data that are generated and how they should be represented.

For ease of reading, only the salient concepts are shown. Refer to the individual domain examples referenced in the concept map below for a detailed view of how the datasets should be populated.

The MRI imaging procedure is represented in the PR domain. During this procedure, multiple scans may be conducted. Each scan is identified by a unique set of properties that apply to the scan, and those properties are grouped in the Device In-Use (DU) domain by DUREFID. The link between the findings and the individual scan from which they are derived is maintained by having PRREFID=NVREFID=DUREFID. This allows for linking the findings to a unique scan and its associated parameters within the procedure and the results. When multiple scans are conducted for a single imaging procedure each scan will have a unique PRREFID, but each overall procedure will have a unique PRLINKID. In all cases below, SPDEVID refers to the unique identifier for the MRI scanner.

Note that if a contrast agent was used, it would be placed in the Procedure Agents (AG) domain according to the PET imaging example provided in Section 4.3.3, Positron Emission Tomography (PET).

In Huntington's disease (HD), magnetic resonance spectroscopy (MRS) procedures use a magnetic resonance imaging (MRI) scanner to obtain spectroscopy data for the purposes of tracking changes in concentrations of metabolites present in brain tissue over time. Some of these changes correlate with disease progression. MRS scans do not produce anatomical (image) data; rather, they produce spectra. Investigators look for peaks whose position and total area on the graph correspond to the presence of specific molecules and their concentrations, respectively. Obtaining meaningful spectroscopy data does, however, require image data in order to localize the source of these spectra to specific regions of the brain that can only be chosen from anatomical images. Therefore, an anatomical scan is first conducted on the subject. If the resulting 3-D images are of sufficient quality, the investigator will manually select a voxel in a region of interest and immediately run a subsequent MRS scan to obtain spectroscopy data from that voxel while the subject is still in position. Both scans—the anatomical image acquisition and the subsequent spectroscopy data acquisition—use the same MR scanning device running a different set of acquisition parameters relevant to the different types of data they are intended to acquire.

This section provides examples of how to represent types of data generated through MRS procedures (more specifically, single-voxel selection MRS) in SDTM datasets, and how to relate them to each other and to the metabolite concentration findings. No details about the anatomical MRI scan are provided here; for examples, see Section 4.3.1, Magnetic Resonance Imaging (MRI).

The examples provided here are only intended to illustrate the data modeling of these various properties and findings and are not meant to serve as strict requirements for data submissions nor as a comprehensive list of the properties and findings which should be provided in a regulatory submission. Study sponsors should seek guidance from their regulatory agencies and review divisions, as applicable, regarding which of these data should be recorded and submitted as appropriate for their individual studies.

The concept map below provides an overview of MRS, from data acquisition to the metabolite results. Each step of the process shows at a high level the different types of data that are generated and where they are represented in SDTM. Note that although both the scans use the same device, the parameters used for each scan differ (e.g., water suppression methods apply to MRS scans, not imaging scans).

For ease of reading, only a few salient concepts are shown. Refer to the individual domain examples referenced in the concept map for more details on the types of data that would be represented in those domains.

Unlike volumetric magnetic resonance imaging (MRI) imaging, positron emission tomography (PET) imaging is used to characterize molecular or functional processes in the brain. PET imaging requires the intravenous administration of a radiopharmaceutical (or radiolabeled) tracer to the subject in order to target the region or molecule of interest for imaging. Tracers are specific to the processes they are designed to characterize. These tracers are molecules with radioactive elements designed to bind to specific molecules or be taken up by specific regions of the body. When the tracer accumulates in the region it is designed to bind, the radioactive signal emitted from this target rises relative to the signal emitted from regions it is not designed to bind. PET scanners collect the radioactivity emitted and localize this signal to the region where it has accumulated. These tracers require a specific uptake time—the time from when they were administered to the subject to when the imaging procedure can begin—in order to ensure time is given for them to accumulate at their target location in the brain. Consistent uptake times are required to ensure comparability of results across subjects and visits within a given protocol. These tracers are represented in the Procedure Agents (AG) domain. It is important to record the start time of tracer administration, as well as the start time of the imaging procedure (represented, as in the MRI examples, in the PR domain). This ensures that anyone analyzing the data can determine if uptake time was comparable across observations.

Once the specified uptake time has passed, the PET imaging procedure begins. As in MRI, a single PET procedure may result in multiple scans. As in the MRI examples, the set of changeable properties that define a scan are grouped in the Device In-Use (DU) domain, and linked to the findings in the Nervous System Findings (NV) domain, by using the same --REFID value. PET scanners also have unchanging properties and identifiers analogous to MRI scanners, which are represented in the Device Properties (DO) and Device Identifiers (DI) domains, respectively, as described in Section 4.3.1, Magnetic Resonance Imaging (MRI).

The ultimate endpoint generated by a PET procedure comes in the form of a Standard Uptake Value (SUV) or Standard Uptake Value Ratio (SUVR). SUV is the ratio of radioactivity signal coming from the brain region of interest (which the tracer is designed to target) and the signal coming from the whole body concentration of injected radioactivity. SUVR is a ratio of ratios; it compares the SUV of the region of interest to the SUV of some reference region in the brain. Reference regions are chosen based on the likelihood that the tracer will not be taken up significantly there, and thus serve as a background signal for normalization. In any case, SUV and SUVR are derived from the image as opposed to being direct measurements of radioactivity.

The anatomical data provided by PET scanners are often not sufficient to localize the radioactivity they are designed to image to the level of specificity desired by investigators. For this reason, PET scans are often conducted concurrently with computerized tomography (CT) or with MRI scans, which allow co-registration of more detailed anatomical data with the SUVR data obtained from the PET portion of the image. This is performed concurrently in a single procedure using a hybrid PET/CT or PET/MRI scanner.

It is important to realize that SUVR is based on quantifying regions of signal in a series of images. The only factors that differentiate one SUVR from the next are the region of interest and the tracer used. It is therefore essential to record the region of interest and represent this in the NVLOC variable. Associated qualifiers of NVLOC (NVLAT, NVDIR) may be used where appropriate. It is also essential to maintain the link between the SUVR value (in the NV domain) and the tracer that produced it (in the AG domain).

Uptake of specific tracers in the striatum is an indicator of the functional integrity of the basal ganglia in the brain. Recent development of selective tracers that bind the molecule phosphodiesterase 10A has provided a useful tool for in vivo assessment of striatal neuronal injury and loss. PDE10A is selectively expressed in medium spiny neurons (MSNs). PDE10 levels—as assessed by PET neuroimaging—are reduced early in HD and decline in uptake as HD disease progresses. This process can be detected by the use of PDE10-targeted tracers in longitudinal PET imaging studies.

Hypometabolism is another feature of HD and other neurodegenerative disorders (such as Alzheimer's) that can be assessed via PET. ¹⁸F-fluorodeoxyglucose (FDG) is a radiolabeled glucose analog used as a tracer in PET imaging to assess in vivo glucose metabolic activity in the brain. In Alzheimer's disease FDG-PET imaging shows hypometabolism in frontal, temporal, and parietal cortices and can help in differentiating Alzheimer's from other causes of dementia. In HD, decreased cortical metabolism in the early stage of HD is considered indicative of rapid progression. Cortical metabolism in the frontotemporal and parietal cortices has been reported to be significantly lower in patients with early HD with faster progression of the disease as measured using the Unified Huntington's Disease Rating Scale (UHDRS) for clinical outcomes.

The examples in this section illustrate how to represent these various types of data in SDTM-based datasets, and how to maintain and represent the relationship between the tracer administration, the PET procedure, the individual scan properties, and the SUVR findings represented in the NV domain. These examples are only intended to illustrate the data modeling of these various properties and findings, and are not meant to serve as strict requirements or a comprehensive list of the properties and findings which should be provided in a regulatory submission. Sponsors should seek guidance from their regulatory agencies and review divisions regarding which data should be recorded and submitted as appropriate to their individual protocol(s).

The concept map below shows the provenance of a brain physiological or functional measurement (measured as SUVR, represented in the NV domain) of various PET tracers. The process starts with the administration of the tracer, followed by the acquisition of images, and, ultimately, the derivation of the endpoint. Each step of the process shows in high-level detail the different types of data generated that should be represented. Dashed lines lead to the corresponding SDTM domains in which the data generated are represented.

For ease of reading, only the salient concepts are shown. Refer to the individual domain examples referenced in the concept map for detailed views of how the datasets should be populated.

Similar to MRI imaging, PET imaging begins with the imaging procedure, represented in the PR domain. The structure and relationships of the data generated are handled exactly as in the MRI example, except that PET imaging involves the administration of a tracer, which is represented in the AG domain (Concept Map. Relating Records in PET Imaging). In these cases, the LNKID variable serves as a 3-way link between the procedure, the tracer administration, and the results. It is particularly important to maintain the link between AGLNKID and NVLNKID as the tracer information in the AG domain describes the target biomarker being imaged, and on which the SUVR in the NV domain is based. Note that this approach would also work for MRI use cases that involve an exogenous contrast-agent administration. In the PET and PET/CT examples, SPDEVID refers to the PET or PET/CT scanner used.

(Parenthesis indicates CDISC/NCI codelist)

Works Cited

1. Ross CA, Aylward EH, Wild EJ, et al. Huntington disease: natural history, biomarkers and prospects for therapeutics. Nature Rev Neurol, 2014;10(4):204-216. doi:10.1038/nrneurol.2014.24
2. Warby SC, Graham RK, Hayden MR. Huntington disease. In: Adam MP, Ardinger HH, Pagon RA, et al., eds., GeneReviews. Seattle, WA: University of Washington. https://www.ncbi.nlm.nih.gov/books/NBK1305/. Published October 23, 1998. Updated July 5, 2018. Accessed September 5, 2018.
3. Byrne LM, Wild EJ. Cerebrospinal fluid biomarkers for Huntington's disease. J Huntingtons Dis, 2016;5(1):1-13. doi:10.3233/JHD-160196
4. Georgiou-Karistianis N, Scahill R, Tabrizi SJ, Squitieri F, Aylward E. Structural MRI in Huntington's disease and recommendations for its potential use in clinical trials. Neurosci Biobehav Rev, 2013;37(3):480-490. doi:10.1016/j.neubiorev.2013.01.022
5. Russell DS, Jennings DL, Barret O, et al. Change in PDE10 across early Huntington disease assessed by [18F]MNI-659 and PET imaging. Neurology, 2016;86(8):748-754. doi:10.1212/WNL.0000000000002391

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"CDISC grants open public use of this User Guide (or Final Standards) under CDISC's copyright."

Each Participant in the development of this standard shall be deemed to represent, warrant, and covenant, at the time of a Contribution by such Participant (or by its Representative), that to the best of its knowledge and ability: (a) it holds or has the right to grant all relevant licenses to any of its Contributions in all jurisdictions or territories in which it holds relevant intellectual property rights; (b) there are no limits to the Participant's ability to make the grants, acknowledgments, and agreements herein; and (c) the Contribution does not subject any Contribution, Draft Standard, Final Standard, or implementations thereof, in whole or in part, to licensing obligations with additional restrictions or requirements inconsistent with those set forth in this Policy, or that would require any such Contribution, Final Standard, or implementation, in whole or in part, to be either: (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works (other than as set forth in Section 4.2 of the CDISC Intellectual Property Policy ("the Policy")); or (iii) distributed at no charge, except as set forth in Sections 3, 5.1, and 4.2 of the Policy. If a Participant has knowledge that a Contribution made by any Participant or any other party may subject any Contribution, Draft Standard, Final Standard, or implementation, in whole or in part, to one or more of the licensing obligations listed in Section 9.3, such Participant shall give prompt notice of the same to the CDISC President who shall promptly notify all Participants.

No Other Warranties/Disclaimers. ALL PARTICIPANTS ACKNOWLEDGE THAT, EXCEPT AS PROVIDED UNDER SECTION 9.3 OF THE CDISC INTELLECTUAL PROPERTY POLICY, ALL DRAFT STANDARDS AND FINAL STANDARDS, AND ALL CONTRIBUTIONS TO FINAL STANDARDS AND DRAFT STANDARDS, ARE PROVIDED "AS IS" WITH NO WARRANTIES WHATSOEVER, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE, AND THE PARTICIPANTS, REPRESENTATIVES, THE CDISC PRESIDENT, THE CDISC BOARD OF DIRECTORS, AND CDISC EXPRESSLY DISCLAIM ANY WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR OR INTENDED PURPOSE, OR ANY OTHER WARRANTY OTHERWISE ARISING OUT OF ANY PROPOSAL, FINAL STANDARDS OR DRAFT STANDARDS, OR CONTRIBUTION.

Limitation of Liability

IN NO EVENT WILL CDISC OR ANY OF ITS CONSTITUENT PARTS (INCLUDING, BUT NOT LIMITED TO, THE CDISC BOARD OF DIRECTORS, THE CDISC PRESIDENT, CDISC STAFF, AND CDISC MEMBERS) BE LIABLE TO ANY OTHER PERSON OR ENTITY FOR ANY LOSS OF PROFITS, LOSS OF USE, DIRECT, INDIRECT, INCIDENTAL, CONSEQUENTIAL, OR SPECIAL DAMAGES, WHETHER UNDER CONTRACT, TORT, WARRANTY, OR OTHERWISE, ARISING IN ANY WAY OUT OF THIS POLICY OR ANY RELATED AGREEMENT, WHETHER OR NOT SUCH PARTY HAD ADVANCE NOTICE OF THE POSSIBILITY OF SUCH DAMAGES.

Note: The CDISC Intellectual Property Policy can be found at: cdisc_policy_003_intellectual_property_v201408.pdf.