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. 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. 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. 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 number of studies have examined the relationship of indices of epigenetic aging (EA) to key health outcomes. Unfortunately, our understanding of the relationship of EA to mortality and substance use-related health variables is unclear. In order to clarify these interpretations, we analyzed the relationship of the Levine EA index (LEA), as well as established epigenetic indices of cigarette (cg05575921) and alcohol consumption (cg04987734), to all-cause mortality in the Framingham Heart Study Offspring Cohort (n = 2256) Cox proportional hazards regression. We found that cg05575921 and cg04987734 had an independent effect relative to LEA and vice versa, with the model including all the predictors having better performance than models with either LEA or cg05575921 and cg04987734 alone. After correction for multiple comparisons, 195 and 327, respectively, of the 513 markers in the LEA index, as well as the overall index itself, were significantly associated with cg05575921 and cg04987734 methylation status. We conclude that the epigenetic indices of substance use have an independent effect over and above LEA, and are slightly stronger predictors of mortality in head-to-head comparisons. We also conclude that the majority of the strength of association conveyed by the LEA is secondary to smoking and drinking behaviors, and that efforts to promote healthy aging should continue to focus on addressing substance use.

Smoking is the largest preventable cause of mortality and the largest environmental driver of epigenetic aging. Contingency management-based strategies can be used to treat smoking but require objective methods of verifying quitting status. Prior studies have suggested that cg05575921 methylation reverts as a function of smoking cessation, but that it can be used to verify the success of smoking cessation has not been unequivocally demonstrated. To test whether methylation can be used to verify cessation, we determined monthly cg05575921 levels in a group of 67 self-reported smokers undergoing biochemically monitored contingency management-based smoking cessation therapy, as part of a lung imaging protocol. A total of 20 subjects in this protocol completed three months of cotinine verified smoking cessation. In these 20 quitters, the reversion of cg05575921 methylation was dependent on their initial smoking intensity, with methylation levels in the heaviest smokers reverting to an average of 0.12% per day over the 3-month treatment period. In addition, we found suggestive evidence that some individuals may have embellished their smoking history to gain entry to the study. Given the prominent effect of smoking on longevity, we conclude that DNA methylation may be a useful tool for guiding and incentivizing contingency management-based approaches for smoking cessation.

BACKGROUND: The dynamics of blood clot (combination of Hb [hemoglobin], fibrin, and a higher concentration of aggregated red blood cells) formation within the hematoma of an intracerebral hemorrhage is not well understood. A quantitative neuroimaging method of localized coagulated blood volume/distribution within the hematoma might improve clinical decision-making. METHODS: The deoxyhemoglobin of aggregated red blood cells within extravasated blood exhibits a higher magnetic susceptibility due to unpaired heme iron electrons. We propose that coagulated blood, with higher aggregated red blood cell content, will exhibit (1) a higher positive susceptibility than noncoagulated blood and (2) increase in fibrin polymerization–restricted localized diffusion, which can be measured noninvasively using quantitative susceptibility mapping and diffusion tensor imaging. In this serial magnetic resonance imaging study, we enrolled 24 patients with acute intracerebral hemorrhage between October 2021 to May 2022 at a stroke center. Patients were 30 to 70 years of age and had a hematoma volume >15 cm ³ and National Institutes of Health Stroke Scale score >1. The patients underwent imaging 3×: within 12 to 24 (T1), 36 to 48 (T2), and 60 to 72 (T3) hours of last seen well on a 3T magnetic resonance imaging system. Three-dimensional anatomic, multigradient echo and 2-dimensional diffusion tensor images were obtained. Hematoma and edema volumes were calculated, and the distribution of coagulation was measured by dynamic changes in the susceptibilities and fractional anisotropy within the hematoma. RESULTS: Using a coagulated blood phantom, we demonstrated a linear relationship between the percentage coagulation and susceptibility (R ² =0.91) with a positive red blood cell stain of the clot. The quantitative susceptibility maps showed a significant increase in hematoma susceptibility (T1, 0.29±0.04 parts per millions; T2, 0.36±0.04 parts per millions; T3, 0.45±0.04 parts per millions; P <0.0001). A concomitant increase in fractional anisotropy was also observed with time (T1, 0.40±0.02; T2, 0.45±0.02; T3, 0.47±0.02; P <0.05). CONCLUSIONS: This quantitative neuroimaging study of coagulation within the hematoma has the potential to improve patient management, such as safe resumption of anticoagulants, the need for reversal agents, the administration of alteplase to resolve the clot, and the need for surgery.

Background Cognitive and motor task performance in premanifest Huntington’s disease (HD) gene-carriers is often within normal ranges prior to clinical diagnosis, despite loss of brain volume in regions involved in these tasks. This indicates ongoing compensation, with the brain maintaining function in the presence of neuronal loss. However, thus far, compensatory processes in HD have not been explicitly addressed in statistical models. Aims Using a new statistical model, which incorporates individual variability related to structural burden (i.e., loss of brain volume) and behaviour, we sought to identify functional correlates of compensation in premanifest-HD gene-carriers. Methods We investigated the modulatory effects of regional brain atrophy, indexed by structural measures of disease load, on the relationship between performance and brain activity (or connectivity) using task-based and resting-state functional MRI. Results Consistent with compensation, higher atrophy was associated with increased performance-related activity of the right parietal cortex during a working memory task. Similarly, higher functional coupling between the right dorsolateral prefrontal cortex and a left hemisphere network in the resting-state predicted better cognitive performance in individuals with higher disease burden. Such patterns were not detectable for the left hemisphere or for motor tasks. Conclusion Our findings provide evidence for active compensatory processes in premanifest-HD for cognitive demands and suggest a higher vulnerability of the left hemisphere to the effects of regional atrophy.

Huntington disease (HD) is a neurodegenerative condition with prominent motor (including oculomotor), cognitive, and psychiatric effects. While neuropsychological deficits are present in HD, motor impairments may impact performance on neuropsychological measures, especially those requiring a speeded response, as has been demonstrated in multiple sclerosis and schizophrenia. The current study is the first to explore associations between oculomotor functions and neuropsychological performance in HD. Participants with impaired oculomotor functioning performed worse than those with normal oculomotor functioning on cognitive tasks requiring oculomotor involvement, particularly on psychomotor speed tasks, controlling for covariates. Consideration of oculomotor dysfunction on neuropsychological performance is critical, particularly for populations with motor deficits.