Posttranscriptional regulation of FAN1 by miR-124-3p at rs3512 underlies onset-delaying genetic modification in Huntington's disease.

Many Mendelian disorders, such as Huntington's disease (HD) and spinocerebellar ataxias, arise from expansions of CAG trinucleotide repeats. Despite the clear genetic causes, additional genetic factors may influence the rate of those monogenic disorders. Notably, genome-wide association studies discovered somewhat expected modifiers, particularly mismatch repair genes involved in the CAG repeat instability, impacting age at onset of HD. Strikingly, FAN1, previously unrelated to repeat instability, produced the strongest HD modification signals. Diverse FAN1 haplotypes independently modify HD, with rare genetic variants diminishing DNA binding or nuclease activity of the FAN1 protein, hastening HD onset. However, the mechanism behind the frequent and the most significant onset-delaying FAN1 haplotype lacking missense variations has remained elusive. Here, we illustrated that a microRNA acting on 3'-UTR (untranslated region) SNP rs3512, rather than transcriptional regulation, is responsible for the significant FAN1 expression quantitative trait loci signal and allelic imbalance in FAN1 messenger ribonucleic acid (mRNA), accounting for the most significant and frequent onset-delaying modifier haplotype in HD. Specifically, miR-124-3p selectively targets the reference allele at rs3512, diminishing the stability of FAN1 mRNA harboring that allele and consequently reducing its levels. Subsequent validation analyses, including the use of antagomir and 3'-UTR reporter vectors with swapped alleles, confirmed the specificity of miR-124-3p at rs3512. Together, these findings indicate that the alternative allele at rs3512 renders the FAN1 mRNA less susceptible to miR-124-3p-mediated posttranscriptional regulation, resulting in increased FAN1 levels and a subsequent delay in HD onset by mitigating CAG repeat instability.

Genetic modifiers of Huntington disease differentially influence motor and cognitive domains.

Genome-wide association studies (GWASs) of Huntington disease (HD) have identified six DNA maintenance gene loci (among others) as modifiers and implicated a two step-mechanism of pathogenesis: somatic instability of the causative HTT CAG repeat with subsequent triggering of neuronal damage. The largest studies have been limited to HD individuals with a rater-estimated age at motor onset. To capitalize on the wealth of phenotypic data in several large HD natural history studies, we have performed algorithmic prediction by using common motor and cognitive measures to predict age at other disease landmarks as additional phenotypes for GWASs. Combined with imputation with the Trans-Omics for Precision Medicine reference panel, predictions using integrated measures provided objective landmark phenotypes with greater power to detect most modifier loci. Importantly, substantial differences in the relative modifier signal across loci, highlighted by comparing common modifiers at MSH3 and FAN1, revealed that individual modifier effects can act preferentially in the motor or cognitive domains. Individual components of the DNA maintenance modifier mechanisms may therefore act differentially on the neuronal circuits underlying the corresponding clinical measures. In addition, we identified additional modifier effects at the PMS1 and PMS2 loci and implicated a potential second locus on chromosome 7. These findings indicate that broadened discovery and characterization of HD genetic modifiers based on additional quantitative or qualitative phenotypes offers not only the promise of in-human validated therapeutic targets but also a route to dissecting the mechanisms and cell types involved in both the somatic instability and toxicity components of HD pathogenesis.

Genetic and Functional Analyses Point to FAN1 as the Source of Multiple Huntington Disease Modifier Effects.

A recent genome-wide association study of Huntington disease (HD) implicated genes involved in DNA maintenance processes as modifiers of onset, including multiple genome-wide significant signals in a chr15 region containing the DNA repair gene Fanconi-Associated Nuclease 1 (FAN1). Here, we have carried out detailed genetic, molecular, and cellular investigation of the modifiers at this locus. We find that missense changes within or near the DNA-binding domain (p.Arg507His and p.Arg377Trp) reduce FAN1's DNA-binding activity and its capacity to rescue mitomycin C-induced cytotoxicity, accounting for two infrequent onset-hastening modifier signals. We also idenified a third onset-hastening modifier signal whose mechanism of action remains uncertain but does not involve an amino acid change in FAN1. We present additional evidence that a frequent onset-delaying modifier signal does not alter FAN1 coding sequence but is associated with increased FAN1 mRNA expression in the cerebral cortex. Consistent with these findings and other cellular overexpression and/or suppression studies, knockout of FAN1 increased CAG repeat expansion in HD-induced pluripotent stem cells. Together, these studies support the process of somatic CAG repeat expansion as a therapeutic target in HD, and they clearly indicate that multiple genetic variations act by different means through FAN1 to influence HD onset in a manner that is largely additive, except in the rare circumstance that two onset-hastening alleles are present. Thus, an individual's particular combination of FAN1 haplotypes may influence their suitability for HD clinical trials, particularly if the therapeutic agent aims to reduce CAG repeat instability.

Comprehensive shape analysis of the cortex in Huntington's disease.

The striatum has traditionally been the focus of Huntington's disease research due to the primary insult to this region and its central role in motor symptoms. Beyond the striatum, evidence of cortical alterations caused by Huntington's disease has surfaced. However, findings are not coherent between studies which have used cortical thickness for Huntington's disease since it is the well-established cortical metric of interest in other diseases. In this study, we propose a more comprehensive approach to cortical morphology in Huntington's disease using cortical thickness, sulcal depth, and local gyrification index. Our results show consistency with prior findings in cortical thickness, including its limitations. Our comparison between cortical thickness and local gyrification index underscores the complementary nature of these two measures-cortical thickness detects changes in the sensorimotor and posterior areas while local gyrification index identifies insular differences. Since local gyrification index and cortical thickness measures detect changes in different regions, the two used in tandem could provide a clinically relevant measure of disease progression. Our findings suggest that differences in insular regions may correspond to earlier neurodegeneration and may provide a complementary cortical measure for detection of subtle early cortical changes due to Huntington's disease.

Impairments to executive function in emerging adults with Huntington disease.

BACKGROUND AND OBJECTIVES: The clinical diagnosis of Huntington disease (HD) is typically made once motor symptoms and chorea are evident. Recent reports highlight the onset of cognitive and psychiatric symptoms before motor manifestations. These findings support further investigations of cognitive function across the lifespan of HD sufferers. METHODS: To assess cognitive symptoms in the developing brain, we administered assessments from the National Institutes of Health Toolbox Cognitive Battery, an age-appropriate cognitive assessment with population norms, to a cohort of children, adolescents and young adults with (gene-expanded; GE) and without (gene-not-expanded; GNE) the trinucleotide cytosine, adenine, guanine (CAG) expansion in the Huntingtin gene. These five assessments that focus on executive function are well validated and form a composite score, with population norms. We modelled these scores across age, and CAP score to estimate the slope of progression, comparing these results to motor symptoms. RESULTS: We find significant deficits in the composite measure of executive function in GE compared with GNE participants. GE participant performance on working memory was significantly lower compared with GNE participants. Modelling these results over age suggests that these deficits occur as early as 18 years of age, long before motor manifestations of HD. CONCLUSIONS: This work provides strong evidence that impairments in executive function occur as early as the second decade of life, well before anticipated motor onset. Future investigations should delineate whether these impairments in executive function are due to abnormalities in neurodevelopment or early sequelae of a neurodegenerative process.

Defining Clinical Meaningfulness in Huntington's Disease.

BACKGROUND: Minimal clinically important difference (MCID) represents the smallest within-person change on an outcome measure considered meaningful to the patient. Anchor-based MCID methods evaluate the relationship between changes in an outcome measure and the patient-reported clinical importance of that change. OBJECTIVE: This study aims to estimate longitudinal MCID for clinically relevant outcome measures for individuals who have Stages 2 or 3 disease as measured by the Huntington's Disease Integrated Staging System (HD-ISS). METHODS: Data were drawn from Enroll-HD, a large global longitudinal, observational study and clinical research platform for HD family members. We analyzed HD participants (N = 11,070) by staging group using time frames ranging from 12 to 36 months. The anchor was the physical component summary score of the 12-item short-form health survey. HD-relevant motor, cognitive, and functional outcome measures were independent, external criterion outcomes. Complex analysis was conducted using multiple, independent, linear mixed effect regression models with decomposition to calculate MCID for each external criterion by group. RESULTS: MCID estimates varied by progression stage. MCID estimates increased as stage progression increased and as the time frame increased. MCID values for key HD measures are provided. For example, starting in HD-ISS stage 2, meaningful group change over 24 months equals an average increase of 3.6 or more points on the Unified Huntington's Disease Rating Scale Total Motor Score. CONCLUSIONS: This is the first study to examine MCID estimation thresholds for HD. The results can be used to improve clinical interpretation of study outcomes and enable treatment recommendations to support clinical decision-making and clinical trial methodology. © 2023 International Parkinson and Movement Disorder Society.

Genetic Modification of Huntington Disease Acts Early in the Prediagnosis Phase.

Age at onset of Huntington disease, an inherited neurodegenerative disorder, is influenced by the size of the disease-causing CAG trinucleotide repeat expansion in HTT and by genetic modifier loci on chromosomes 8 and 15. Stratifying by modifier genotype, we have examined putamen volume, total motor score (TMS), and symbol digit modalities test (SDMT) scores, both at study entry and longitudinally, in normal controls and CAG-expansion carriers who were enrolled prior to the emergence of manifest HD in the PREDICT-HD study. The modifiers, which included onset-hastening and onset-delaying alleles on chromosome 15 and an onset-hastening allele on chromosome 8, revealed no major effect in controls but distinct patterns of modification in prediagnosis HD subjects. Putamen volume at study entry showed evidence of reciprocal modification by the chromosome 15 alleles, but the rate of loss of putamen volume was modified only by the deleterious chromosome 15 allele. By contrast, both alleles modified the rate of change of the SDMT score, but neither had an effect on the TMS. The influence of the chromosome 8 modifier was evident only in the rate of TMS increase. The data indicate that (1) modification of pathogenesis can occur early in the prediagnosis phase, (2) the modifier loci act in genetic interaction with the HD mutation rather than through independent additive effects, and (3) HD subclinical phenotypes are differentially influenced by each modifier, implying distinct effects in different cells or tissues. Together, these findings indicate the potential benefit of using genetic modifier strategies for dissecting the prediagnosis pathogenic process in HD.

Phase 2 Trial of Ibudilast in Progressive Multiple Sclerosis.

BACKGROUND: There are limited treatments for progressive multiple sclerosis. Ibudilast inhibits several cyclic nucleotide phosphodiesterases, macrophage migration inhibitory factor, and toll-like receptor 4 and can cross the blood-brain barrier, with potential salutary effects in progressive multiple sclerosis. METHODS: We enrolled patients with primary or secondary progressive multiple sclerosis in a phase 2 randomized trial of oral ibudilast (≤100 mg daily) or placebo for 96 weeks. The primary efficacy end point was the rate of brain atrophy, as measured by the brain parenchymal fraction (brain size relative to the volume of the outer surface contour of the brain). Major secondary end points included the change in the pyramidal tracts on diffusion tensor imaging, the magnetization transfer ratio in normal-appearing brain tissue, the thickness of the retinal nerve-fiber layer, and cortical atrophy, all measures of tissue damage in multiple sclerosis. RESULTS: Of 255 patients who underwent randomization, 129 were assigned to ibudilast and 126 to placebo. A total of 53% of the patients in the ibudilast group and 52% of those in the placebo group had primary progressive disease; the others had secondary progressive disease. The rate of change in the brain parenchymal fraction was -0.0010 per year with ibudilast and -0.0019 per year with placebo (difference, 0.0009; 95% confidence interval, 0.00004 to 0.0017; P=0.04), which represents approximately 2.5 ml less brain-tissue loss with ibudilast over a period of 96 weeks. Adverse events with ibudilast included gastrointestinal symptoms, headache, and depression. CONCLUSIONS: In a phase 2 trial involving patients with progressive multiple sclerosis, ibudilast was associated with slower progression of brain atrophy than placebo but was associated with higher rates of gastrointestinal side effects, headache, and depression. (Funded by the National Institute of Neurological Disorders and Stroke and others; NN102/SPRINT-MS ClinicalTrials.gov number, NCT01982942 .).

Cognitive function and its relationship with brain structure in myotonic dystrophy type 1.

Studies have shown relationships between white matter abnormalities and cognitive dysfunction in myotonic dystrophy type 1 (DM1), but comprehensive analysis of potential structure-function relationships are lacking. Fifty adult-onset DM1 individuals (33 female) and 68 unaffected adults (45 female) completed the Wechsler Adult Intelligence Scale-IV (WAIS-IV) to determine the levels and patterns of intellectual functioning. Neuroimages were acquired with a 3T scanner and were processed with BrainsTools. Regional brain volumes (regions of interest, ROIs) were adjusted for inter-scanner variation and intracranial volume. Linear regression models were conducted to assess if group by ROI interaction terms significantly predicted WAIS-IV composite scores. Models were adjusted for age and sex. The DM1 group had lower Perceptual Reasoning Index (PRI), Working Memory Index (WMI), and Processing Speed Index (PSI) scores than the unaffected group (PRI t(113)  = -3.28, p = 0.0014; WMI t(114)  = -3.49, p = 0.0007; PSI t(114)  = -2.98, p = 0.0035). The group by hippocampus interaction term was significant for both PRI and PSI (PRI (t(111)  = -2.82, p = 0.0057; PSI (t(112)  = -2.87, p = 0.0049)). There was an inverse association between hippocampal volume and both PRI and PSI in the DM1 group (the higher the volume, the lower the intelligence quotient scores), but no such association was observed in the unaffected group. Enlarged hippocampal volume may underlie some aspects of cognitive dysfunction in adult-onset DM1, suggesting that increased volume of the hippocampus may be pathological.

Tracking Huntington's Disease Progression Using Motor, Functional, Cognitive, and Imaging Markers.

BACKGROUND: Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants. OBJECTIVE: The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups. METHODS: Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups. RESULTS: Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression. CONCLUSIONS: Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.

AHRR methylation predicts smoking status and smoking intensity in both saliva and blood DNA.

Many existing DNA repositories do not have robust characterizations of smoking, while for many currently ongoing studies, the advent of vaping has rendered traditional cotinine-based methods of determining smoking status unreliable. Previously, we have shown that methylation status at cg05575921 in whole blood DNA can reliably predict cigarette consumption. However, whether methylation status in saliva can be used similarly has yet to be established. Herein, we use DNA from 418 biochemically confirmed smokers or nonsmokers to compare and contrast the utility of cg05575921 in classifying and quantifying cigarette smoking. Using whole blood DNA, a model incorporating age, gender, and methylation status had a receiver operating characteristic (ROC) area under the curve (AUC) for predicting smoking status of 0.995 with a nonlinear demethylation response to smoking. Using saliva DNA, the ROC AUC for predicting smoking was 0.971 with the plot of the relationship of DNA methylation to daily cigarette consumption being very similar to that seen for whole blood DNA. The addition of information from another methylation marker designed to correct for cellular heterogeneity improved the AUC for saliva DNA to 0.981. Finally, in 31 subjects who reported quitting smoking 10 or more years previously, cg05575921 methylation was nonsignificantly different from controls. We conclude that DNA methylation status at cg05575921 in DNA from whole blood or saliva predicts smoking status and daily cigarette consumption. We suggest these epigenetic assessments for objectively ascertaining smoking status will find utility in research, clinical, and civil applications.

Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures.

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

Testing a longitudinal compensation model in premanifest Huntington's disease.

The initial stages of neurodegeneration are commonly marked by normal levels of cognitive and motor performance despite the presence of structural brain pathology. Compensation is widely assumed to account for this preserved behaviour, but despite the apparent simplicity of such a concept, it has proven incredibly difficult to demonstrate such a phenomenon and distinguish it from disease-related pathology. Recently, we developed a model of compensation whereby brain activation, behaviour and pathology, components key to understanding compensation, have specific longitudinal trajectories over three phases of progression. Here, we empirically validate our explicit mathematical model by testing for the presence of compensation over time in neurodegeneration. Huntington's disease is an ideal model for examining longitudinal compensation in neurodegeneration as it is both monogenic and fully penetrant, so disease progression and potential compensation can be monitored many years prior to diagnosis. We defined our conditions for compensation as non-linear longitudinal trajectories of brain activity and performance in the presence of linear neuronal degeneration and applied our model of compensation to a large longitudinal cohort of premanifest and early-stage Huntington's disease patients from the multisite Track-On HD study. Focusing on cognitive and motor networks, we integrated progressive volume loss, task and resting state functional MRI and cognitive and motor behaviour across three sequential phases of neurodegenerative disease progression, adjusted for genetic disease load. Multivariate linear mixed models were fitted and trajectories for each variable tested. Our conceptualization of compensation was partially realized across certain motor and cognitive networks at differing levels. We found several significant network trends that were more complex than that hypothesized in our model. These trends suggest changes to our theoretical model where the network effects are delayed relative to performance effects. There was evidence of compensation primarily in the prefrontal component of the cognitive network, with increased effective connectivity between the left and right dorsolateral prefrontal cortex. Having developed an operational model for the explicit testing of longitudinal compensation in neurodegeneration, it appears that general patterns of our framework are consistent with the empirical data. With the proposed modifications, our operational model of compensation can be used to test for both cross-sectional and longitudinal compensation in neurodegenerative disease with similar patterns to Huntington's disease.

AHRR Methylation is a Significant Predictor of Mortality Risk in Framingham Heart Study.

Background.-The ability to predict mortality is useful to clinicians, policy makers and insurers. At the current time, prediction of future mortality is still an inexact process with some proposing that epigenetic assessments could play a role in improving prognostics. In past work, we and others have shown that DNA methylation status at cg05575921, a well-studied measure of smoking intensity, is also a predictor of mortality. However, the exact extent of that predictive capacity and its independence of other commonly measured mortality risk factors are unknown. Objective.-To determine the capacity of methylation to predict mortality. Method.-We analyzed the relationship of methylation at cg05575921 and cg04987734, a recently described quantitative marker of heavy alcohol consumption, to mortality in the Offspring Cohort of the Framingham Heart Study using proportional hazards survival analysis. Results.-In this group of participants (n = 2278) whose average age was 66 ± 9 years, we found that the inclusion of both cg05575921 and cg04987734 methylation to a base model consisting of age and sex only, or to a model containing 11 commonly used mortality risk factors, improved risk prediction. What is more, prediction accuracy for the base model plus methylation data was increased compared to the base model plus known predictors of mortality (CHD, COPD, or stroke). Conclusion.-Cg05575921, and to a smaller extent cg04987734, are strong predictors of mortality risk in older Americans and that incorporation of DNA methylation assessments to these and other loci may be useful to population scientists, actuaries and policymakers to better understand the relationship of environmental risk factors, such as smoking and drinking, to mortality.

A Four Marker Digital PCR Toolkit for Detecting Heavy Alcohol Consumption and the Effectiveness of Its Treatment.

Background.-Heavy alcohol consumption (HAC) is a shared concern of the forensic, medical and insurance underwriting communities. Unfortunately, there is a relative lack of clinically employable tools for detecting HAC and monitoring treatment response. Building on the results of 3 genome wide methylation studies, we have previously shown in a small group of samples that methylation sensitive digital PCR assays (MSdPCR) have the potential to accurately classify individuals with respect to HAC in a small set of individuals. Objective.-We now expand on those earlier findings using data and biomaterials from 143 participants with current HAC and 200 abstinent controls. Results.-We show that a set of 4 digital PCR assays that have a receiver operating characteristic (ROC) area under the curve (AUC) of 0.96 for detecting those with HAC. After a mean of 21 days of inpatient enforced abstinence, methylation status at one of these markers, cg04987734, began to revert to baseline values. Re-examination of methylation data from our smaller 2014 study with respect to this locus demonstrated a similarly significant reversion pattern at cg04987734 in association with treatment enforced abstinence. Conclusions.-We conclude that clinically implementable dPCR tools can sensitively detect the presence of HAC and that they show promise for monitoring alcohol treatment results. These dPCR tools could be useful to clinicians and researchers in monitoring those enrolled in substance use disorder treatment, employee wellness programs and insurance underwriting.

Identification of symbol digit modality test score extremes in Huntington's disease.

Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers.

Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.

BACKGROUND: Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington's disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis. METHODS: Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status. RESULTS: The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC = .83 (range .77-.90), and the 10-year mean AUC = .86 (range .82-.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington's disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes. CONCLUSIONS: Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.

A Droplet Digital PCR Assay for Smoking Predicts All-Cause Mortality.

OBJECTIVES: -Determine whether an epigenetic assay for smoking predicts all-cause mortality in adults participating in a longitudinal study of Iowa adoptees. BACKGROUND: -Improved biomarkers for smoking are needed given its large public health impact and significant limitations of both self-report and current biomarkers, such as cotinine in detecting smoking. In the past 5 years, multiple epigenome-wide association studies of smoking have identified loci suitable for translation as epigenetic biomarkers for smoking, in particular the CpG cg05575921. Digital polymerase chain reaction methods hold promise for the development of this and other epigenetic biomarkers. METHODS: -Participants in the Iowa Adoption Studies were interviewed regarding their smoking habits. DNA was prepared from whole blood and bisulfite-converted for methylation analysis and digital droplet polymerase chain reaction assay of methylation at cg05575921 was performed. National Death Index records were requested for 584 study participants, resulting in 24 complete matches, 210 partial matches and 350 non-matching records. Complete matches were coded as deceased while the remainder were coded as alive (ie, censored). In total, methylation data and vital status information were available for a total of N = 193 subjects, including 15 deceased and 178 non-deceased. Cox regression was used to examine the ability of cg05575921 methylation as a continuous value to predict the timing of mortality with and without the inclusion of age, sex, race, BMI, marital status, educational status, socioeconomic status, cardiovascular risk factors, and a history of cancer as covariates. RESULTS: -Methylation at cg05575921 predicted the hazard of mortality as the sole predictor and after accounting for major demographic and clinical risk factors. The fitted model showed the hazard ratio increased by 3.5% for every 1% decrease in methylation. CONCLUSIONS: -Decreased methylation at cg05575921, an emerging epigenetic biomarker for smoking, was associated with early mortality in a longitudinal study of adults after accounting for the impact of major demographic and clinical risk factors for all-cause mortality. This approach may be useful in clinical research or actuarial assessments.

Measuring compensation in neurodegeneration using MRI.

PURPOSE OF REVIEW: Despite signs of cortical and subcortical loss, patients with prodromal and early-stage neurodegenerative disease are able to perform at a level comparable to the normal population. It is presumed that the onset of compensatory processes, that is changes in brain activation within a function-specific network or in the recruitment of a region outside of the task-network, underlies this maintenance of normal performance. However, in most studies to date, increased brain activity is not correlated with indices of both disease and performance and what appears to be compensation could simply be a symptom of neurodegeneration. RECENT FINDINGS: MRI studies have explored compensation in neurodegenerative disease, claiming that compensation is evident across a number of disorders, including Alzheimer's and Parkinson's disease, but generally always in early stages; after this point, compensation is generally no longer able to operate under the severe burden of disease. However, none of these studies explicitly adopted a particular model of compensation. Thus, we also discuss our recent attempts to operationalize compensation for empirical testing. SUMMARY: There is clear evidence of compensatory processes in the early stages of neurodegenerative disease. However, for a more complete understanding, this requires more explicit empirical modelling.