Research paper Table 2 Non-linear model fits for estimated age at diagnosis associations adjusted for motor scores Variable Striatal volume Speeded finger tapping rate (mean) Consistency in self-timed finger tapping Word list learning (HVLT) Odour identification n 261 408 410 425 424 Adj R2 0.20 0.09 0.14 0.05 0.07 p Value ,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001 Non-linear df 1 3 3 1 1 Non-linear p value 0.002 ,0.01 ,0.01 0.10 ,0.01 Adj R2 = variance accounted for by estimated years to diagnosis of Huntington’s disease (HD) after accounting for covariates (see statistical methods section) and degrees of freedom used for non-linear fit. HVLT, Hopkins Verbal Learning Test; Non-linear df, degrees of freedom for non-linear fit; non-linear p value, p value for non-linear element of estimated years to HD diagnosis fit. alternative hypothesis that the mean rate of marker progression changes with estimated time to diagnosis. RESULTS A detailed example of one of our analyses is shown in fig 2 where we illustrate the relationship between estimated time to diagnosis and timing consistency measured by the self-paced tapping task. The individual data points are included to give a clear sense of the extent of model fit. In this case, approximately 20% of the subject to subject variability in performance is explained (table 1). Visually, the non-linear trend captured in the model is apparent in the raw data. Of course, it is also evident that there is additional person to person variability that our model cannot account for. The estimated relationships between years to diagnosis and the other outcomes are illustrated in fig 3. As can be seen from the figures, motor examination scores, odour recognition, striatal volumes and a wide range of cognitive performances show a consistent convergent pattern, suggesting that little impairment is detected prior to 15–20 years from estimated diagnosis. Then, after a short transition period, a fairly linear relationship is seen. Table 1 shows model results in terms of total variance explained by the expected time to diagnosis. In every instance, both the total relationship with estimated time until diagnosis and the non-linear component were highly significant. Variance explained is in the 10–20% range. It is conceivable that motor deficits may affect performance on some of the cognitive tests. To determine whether other associations were redundant with the motor association, we recomputed the corresponding models after adjusting for motor score as an additional covariate (see table 2). Although the association strengths were somewhat weakened, consistent with partial confounding, the striatal and cognitive variables still showed substantial non-linear associations. DISCUSSION Findings suggest converging evidence from varying markers for detectable abnormalities beginning between one and two decades before diagnosis of HD. The relationships between estimated years to diagnosis and motor scores, striatal volumes, odour recognition and cognitive measures were strikingly consistent. On this estimated time scale, all domains suggest a curvilinear pattern of disease commencement. This period of commencement is followed by more rapid and constant change in the last years prior to diagnosis. Our findings are consistent with previous reports suggesting an association of striatal volumes with estimated diagnosis based on CAG repeat.17 18 22 The current sample size is nearly 10 times larger than previous reports and thus serves both a confirmatory and refining role. Additionally, demonstration of similar estimated time lines for 878 cognitive, sensory and motor changes is novel and significantly extends these findings. We have illustrated a pervasive pattern of change, occurring on the same time scale, which extends from biological measures such as striatal volumes to several different clinical aspects of the HD phenotype. Our finding that associations between years to diagnosis and other variables remained, even after controlling for motor signs, strongly suggests that apparent cognitive and sensory dysfunc- tion cannot be explained solely on the basis of emerging motor signs interfering with task performance. It is noteworthy that these associations remained, regardless of whether the assess- ment was laden with motor demands (speeded finger tapping, self-timed finger tapping) or not (verbal learning, odour identification). The consistency of associations between the estimated genetic time scale and these diverse cognitive measures suggests that multiple corticostriatal circuits are simultaneously involved in early disease, although further research is needed to more explicitly track the brain behaviour associations suggested here. Striatal volumes also remained curvilinearly related to estimated years to diagnosis after controlling for motor signs. The analyses therefore indicate that neither cognitive nor striatal measurements are wholly redundant with the clinical motor examination. It follows that all of these can likely be combined and leveraged to improve the accuracy of individualised prognosis. Many of these markers may be candidate surrogate endpoints (bearing in mind that promotion from candidate to true surrogate is exceedingly difficult).32 However, markers that cannot meet criteria for this elusive role can still be useful for risk stratification—which is still quite valuable for increasing clinical trial power in a rare disease. Quantitative estimation of the impact of these markers in either role (longitudinal outcome or baseline stratification) is a substantial topic in its own right. In the near future, we will address the details of using baseline Predict-HD findings for increased statistical power and trial design efficiency in a separate manuscript. There are some important caveats to consider when inter- preting these findings. Firstly, the cross sectional associations require longitudinal validation. With planned follow-up periods of up to 7 years, the Predict-HD study will eventually yield appropriate data to attempt this validation. Furthermore, the study was designed to eventually yield a sufficient sample of individuals who become diagnosed, allowing CAG based estimates of average diagnosis age to be validated or refined. Despite excellent goodness of fit in the original model derivation,3 the time scale discussed in the current article remains provisional until longitudinal study is complete. Even if the time scale is accurate, estimated time to diagnosis is clearly an imperfect proxy for actual time to diagnosis, which is of course unknown in these subjects. Although relationships J Neurol Neurosurg Psychiatry 2008;79:874–880. doi:10.1136/jnnp.2007.128728