MEANS AND METHODS FOR ASSESSING HUNTINGTON'S DISEASE OF THE PRE-MANIFEST STAGE

Abstract

The present invention relates to the field of diagnostics. Specifically, it relates to a method for assessing Huntington's disease of the pre-manifest stage in a subject comprising the steps of determining at least one performance parameter from a dataset of fine motoric measurements from said subject, comparing the determined at least one performance parameter to a reference, and assessing Huntington's disease of the pre-manifest stage in the subject based on said comparison. Yet, the invention contemplates a device and a system for carrying out the aforementioned methods and the use of such device or system for assessing Huntington's disease of the pre-manifest stage in the subject.

Claims

1. A method for assessing Huntington's disease of the pre-manifest stage in a subject comprising the steps of: a) determining at least one performance parameter from a dataset of fine motoric measurements from said subject; b) comparing the determined at least one performance parameter to a reference; and c) assessing Huntington's disease of the pre-manifest stage in the subject based on said comparison.

2. The method of claim 1, wherein the said of fine motoric measurements comprise measurements of at least one of the following: finger movement accuracy, dexterity, and/or movement speed.

3. The method of claim 2, wherein said measurements of at least one of the following: finger movement accuracy, dexterity movement speed are carried out during tapping movements and/or drawing movements of fingers.

4. The method of claim 1, wherein said measurements are carried out using a mobile device.

5. The method of claim 4, wherein said mobile device is comprised in a smartphone, smartwatch, wearable sensor, portable multimedia device or tablet computer.

6. The method of claim 1, wherein said method is computer-implemented.

7. The method of claim 1, wherein the reference is at least one performance parameter from a dataset of fine motoric measurements from said subject, wherein said dataset has been obtained prior to the dataset of step a).

8. The method of claim 1, wherein the reference is at least one performance parameter from a dataset of fine motoric measurements from at least one subject known to suffer from Huntington's disease of the pre-manifest stage.

9. The method of claim 8, wherein at least one performance parameter being identical to the reference is indicative for a subject suffering from Huntington's disease of the pre-manifest stage.

10. The method of claim 1, wherein the reference is at least one performance parameter from a dataset of fine motoric measurements from at least one subject known not to suffer from Huntington's disease of the pre-manifest stage.

11. The method of claim 10, wherein at least one performance parameter which differs from the reference is indicative for a subject suffering from Huntington's disease of the pre-manifest stage.

12. The method of claim 1, wherein said assessing Huntington's disease of the pre-manifest stage comprises diagnosing predicting Huntington's disease of the pre-manifest stage.

13. A mobile device comprising a processor, at least one sensor and a database as well as software which is tangibly embedded to said device and, when running on said device, carries out the method of claim 1.

14. A system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database as well as software which is tangibly embedded to said device and, when running on said device, carries out the method of claim 1, wherein said mobile device and said remote device are operatively linked to each other.

15. Use of the mobile device according to claim 13 for assessing Huntington's disease of the pre-manifest stage in a subject using at least one performance parameter from a dataset of measurements of fine motoric measurements from said subject.

16. The method of claim 12, wherein said assessing Huntington's disease of the pre-manifest stage further comprises recommending therapies against Huntington's disease of the pre-manifest stage.

17. Use of the mobile device according to or the system of claim 14 for assessing Huntington's disease of the pre-manifest stage in a subject using at least one performance parameter from a dataset of measurements of fine motoric measurements from said subject.

Description

FIGURES

[0161] FIG. 1: Boxplots comparing the pre-defined features of each mobile device implemented test between the cohorts. Legend: *: p<0.05; **: p<0.01; ***: p<0.001. (A) Cognitive tests SDMT and SWR, (B) Stability and Gait Tests Balance, 2-minute walking, U-turn (C) Upper-Body Motion Tests Draw a Shape, Chorea and speeded tapping. “Dominant” refers to the dominantly used had, “non-dominant” refers to the non-dominantly used hand.

[0162] FIG. 2: Exploratory analysis of features discriminating the pre-manifest and healthy control cohorts. Legend: *: p<0.05; **: p<0.01; ***: p<0.001. Shown are features from handed tests which had a p<0.01 for at least one hand and p<0.05 for both hands. The up-time standard deviation feature from the Speeded Tapping test is defined as the standard deviation of the time where the finger was lifted off the screen between consecutive taps; p=0.002 (dominant hand); p<0.001 (non-dominant hand).

[0163] FIG. 3: Exploratory analysis of features discriminating the pre-manifest and healthy control cohorts. Legend: *: p<0.05; **: p<0.01; ***: p<0.001. Shown are features from handed tests which had a p<0.01 for at least one hand and p<0.05 for both hands. The Draw a Shape test feature was the same as the coefficient of variance for the spiral drawing speed; p=0.04 (dominant hand); p=0.007 (non-dominant hand).

EXAMPLES

[0164] The following Examples merely illustrate the invention. Whatsoever, they shall not be construed in a way as to limit the scope of the invention.

Example 1

Identification of Novel Features that Discriminate Between Healthy Controls and Pre-Manifest Subjects

[0165] Study

[0166] A smartphone application comprising seven active tests (Symbol Digit Modalities Test [SDMT], Stroop Word Reading test [SWRT], Speeded tapping, Chorea, Balance, U-turn, 2-minute walk) and continuous passive monitoring was deployed in an observational study (Digital-HD) of participants with pre-manifest Huntington's disease (HD), manifest HD and healthy controls (HC).

[0167] Presented here are results from 79 participants enrolled at time of data cut-off. Predefined active test measurements were aggregated over Weeks 5 and 6 post-screening to minimize practice effects.

[0168] This 1-year study aimed to enrol 80 participants (manifest HD: 40, pre-manifest HD: 20, HC: 20). Data were captured from continuous passive monitoring and daily active tests. Passive monitoring involves wearing a smartwatch and carrying a GPS-enabled smartphone, both containing tri-axial accelerometers and gyroscopes. Active tests measure motor and non-motor manifestations of HD, and include questions about motor tasks, cognitive tests, QoL and mood.

[0169] Demographic details of the patients are summarized in Table 1, below.

TABLE-US-00001 TABLE 1 Pre-manifest Manifest HC HD HD Total N 20 20 39 79 Age, mean (standard 47.8 44.4 56.2 51.1 deviation [SD]), (14.2) (10.0) (11.2) (12.7) years Female, % 35.0 50.0 46.2 44.3 Right-hand 85.0 100.0 74.4 83.5 dominance, % Total Functional 13 12.9 10.6 11.8 Capacity, mean (SD) (0.0) (0.3) (2.2) (2.0) Total Motor Score, 1.4 4.9 32.9 17.8 mean (SD) (2.5) (3.9) (16.8) (19.2)

[0170] Participants were trained on active tests during an on-site equipment issue visit (EIV) and performed these tests at home upon prompting.

[0171] During the EIV, participants underwent clinical assessments using the Unified HD Rating Scale motor, cognitive and functional subscales, as well as the Timed-up-and-go test and selected items from the Berg Balance Scale alongside a Kinect sensor.

[0172] Encrypted phone data were securely transferred via the internet and analysed to extract clinically meaningful measurements for group discrimination and correlation with clinical parameters.

[0173] Feature differences between the groups were calculated using the Mann-Whitney test. Correlations with in-clinic counterparts were calculated using Spearman's correlation coefficient.

[0174] Results

[0175] The SDMT and SWR test showed excellent correlation with their in-clinic counterparts . The Chorea test show good correlation with the UHDRS Maximal Chorea Upper Limb item. All 8 pre-defined features had significant differences between the Manifest and Healthy Control Cohorts with all p<0.001 apart from the gait tests (U-Turn: p=0.009; 2-minute walking: p=0.004) (see FIG. 1). Results are also shown in Table 2, below.

TABLE-US-00002 TABLE 2 test feature description SPEEDED- uptime_std standard deviation of the time the 0.001059054 0.000288768 0.000553011 TAPPING finger is lifted between to two taps SPEEDED- longgapcntmad 0.002600484 0.000451095 0.001083082 TAPPING SPEEDED- uptime_max maximum time the finger is lifted 0.003149447 0.000403984 0.001127975 TAPPING between to two taps SPEEDED- uptime_cv coefficient of variance of the time the 0.001059054 0.00159178 0.001298376 TAPPING finger is lifted between to two taps SPEEDED- down_dt_std 0.002362109 0.000859704 0.001425032 TAPPING SPEEDED- down_dt_max 0.002863746 0.001439337 0.002030245 TAPPING SPEEDED- down_dt_cv 0.003149447 0.001942154 0.002473199 TAPPING DRAW-A- SPIRAL_sp_cov SPIRAL drawing speed coefficient 0.016064013 0.002448697 0.006271834 SHAPE of variance SPEEDED- nexttapdist_cv coefficient of variance of the distance 0.003800152 0.014058605 0.00730923 TAPPING between two taps DRAW-A- SQUARE_overShoot_ mean 0.026547517 0.002238179 0.007708314 SHAPE SPEEDED- downtime_std standard devation of the time the finger 0.005480406 0.014058605 0.008777635 TAPPING touches the screen during a tap DRAW-A- CIRCLE_sp_cov CIRCLE drawing speed coefficient 0.026547517 0.00292502 0.008812038 SHAPE of variance SPEEDED- downtime_cv coefficient of variance of the time the 0.00926984 0.010090761 0.009671594 TAPPING finger touches the screen during a tap SPEEDED- downtime_max maximum time the finger touches the 0.012955226 0.026291395 0.018455649 TAPPING screen during a tap DRAW-A- CIRCLE_acc_celerity 0.05829058 0.006830256 0.019953435 SHAPE DRAW-A- CIRCLE_celerity 0.05829058 0.006830256 0.019953435 SHAPE SPEEDED- tapmovecnt_std 0.020911079 0.019344679 0.020112636 TAPPING SPEEDED- tapmovecnt_max 0.014048096 0.03401407 0.021859389 TAPPING SPEEDED- tapmovecnt_cv 0.032828498 0.016516759 0.023285626 TAPPING DRAW-A- OVERALL_mean_celerity 0.08835202 0.006830256 0.024565563 SHAPE DRAW-A- SPIRAL_mag_error_time 0.05146206 0.011881032 0.02472696 SHAPE DRAW-A- SPIRAL_sp_mean SPIRAL mean drawing speed 0.048307143 0.012824421 0.024889981 SHAPE SPEEDED- tapposchange_max 0.026291395 0.026291395 0.026291395 TAPPING SPEEDED- downtime_min 0.011919855 0.059324888 0.02659218 TAPPING DRAW-A- SPIRAL_error_time 0.045316297 0.016064013 0.026980763 SHAPE DRAW-A- SPIRAL_MerrorValue 0.051462076 0.016064013 0.028752173 SHAPE SPEEDED- tapposchange_std 0.032828498 0.026291395 0.029378683 TAPPING DRAW-A- SQUARE_celerity 0.065855484 0.01491205 0.031337521 SHAPE DRAW-A- SQUARE_acc_celerity 0.065855484 0.01491205 0.031337521 SHAPE SPEEDED- down_dt_p95 0.079410626 0.012955226 0.032074642 TAPPING DRAW-A- LINE_TOP_TO_BOTTOM_err_sd 0.019857262 0.052533903 0.032298289 SHAPE DRAW-A- SPIRAL_hausD_t 0.048307143 0.026547517 0.035811097 SHAPE DRAW-A- SPIRAL_celerity 0.034866372 0.039801146 0.037252135 SHAPE DRAW-A- SPIRAL_acc_celerity 0.034866372 0.039801146 0.037252135 SHAPE SPEEDED- touch_x_std_h1 0.037921338 0.037921338 0.037921338 TAPPING DRAW-A- CIRCLE_mag_error_time 0.074211716 0.02475803 0.042864157 SHAPE SPEEDED- tapposchange_mean 0.046811892 0.040710278 0.043654612 TAPPING DRAW-A- SPIRAL_err_sum 0.074211716 0.030463803 0.047547567 SHAPE SPEEDED- nexttapdist_std 0.061337166 0.037921338 0.048228491 TAPPING DRAW-A- SPIRAL_aDS_apen 0.045316297 0.054787666 0.049827444 SHAPE SPEEDED- nexttapdist_max 0.040710278 0.061337166 0.049970522 TAPPING SPEEDED- tapposchange_median 0.079410626 0.040710278 0.056857969 TAPPING DRAW-A- SQUARE_mag_error_time 0.08835202 0.037264273 0.057379211 SHAPE DRAW-A- FIGURE_8_mag_error_time 0.078703997 0.04248308 0.057823768 SHAPE DRAW-A- CIRCLE_aDS_apen 0.08835202 0.04248308 0.061265536 SHAPE DRAW-A- FIGURE_8_aDS_apen 0.098931599 0.054787666 0.073622221 SHAPE SPEEDED- nexttapdist_mean 0.095631733 0.074529135 0.084423636 TAPPING CHOREA DIST 0.083415083 0.098931599 0.090842653 CHOREA AREA 0.083415083 0.098931599 0.090842653

[0176] 2 candidate features (see FIGS. 2 and 3) where identified that showed promise in discriminating the pre-manifest and healthy control cohorts using speeded tapping and Draw a Shape tests.

CITED REFERENCES

[0177] The Huntington Group, 1996, Movement Disorders, 11(2): 136

[0178] Rao 2009, Gait Posture. 29 (3): 433-6

[0179] WO 2019/081640