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Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages

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• Open access
• Published: 29 April 2021

Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages

• Marianna Amboni, 
• Carlo Ricciardi, 
• Marina Picillo, 
• Chiara De Santis, 
• Gianluca Ricciardelli, 
• Filomena Abate, 
• Maria Francesca Tepedino, 
• Giovanni D’Addio, 
• Giuseppe Cesarelli, 
• Giampiero Volpe, 
• Maria Consiglia Calabrese, 
• Mario Cesarelli & 
• Paolo Barone 

Scientific Reports volume 11, Article number: 9297 (2021) Cite this article

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Abstract

Progressive supranuclear palsy (PSP) is a rare and rapidly progressing atypical parkinsonism. Albeit existing clinical criteria for PSP have good specificity and sensitivity, there is a need for biomarkers able to capture early objective disease-specific abnormalities. This study aimed to identify gait patterns specifically associated with early PSP. The study population comprised 104 consecutively enrolled participants (83 PD and 21 PSP patients). Gait was investigated using a gait analysis system during normal gait and a cognitive dual task. Univariate statistical analysis and binary logistic regression were used to compare all PD patients and all PSP patients, as well as newly diagnosed PD and early PSP patients. Gait pattern was poorer in PSP patients than in PD patients, even from early stages. PSP patients exhibited reduced velocity and increased measures of dynamic instability when compared to PD patients. Application of predictive models to gait data revealed that PD gait pattern was typified by increased cadence and longer cycle length, whereas a longer stance phase characterized PSP patients in both mid and early disease stages. The present study demonstrates that quantitative gait eval‎uation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. First, this might candidate gait analysis as a reliable biomarker in both clinical and research setting. Furthermore, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.

초록

진행성 핵상 마비(PSP)는 

희귀하고 빠르게 진행되는 

비정형 파킨슨병입니다. 

Progressive supranuclear palsy is a rare brain disease that affects walking, balance, eye movements and swallowing. The disease results from the damage of cells in areas of the brain that control body movement, coordination, thinking and other important functions. Progressive supranuclear palsy also is called Steele-Richardson-Olszewski syndrome. Progressive supranuclear palsy worsens over time and can lead to dangerous complications, such as pneumonia and trouble swallowing. There's no cure for progressive supranuclear palsy, so treatment focuses on managing the symptoms. 진행성 핵상 마비는 보행, 균형, 안구 운동 및 삼킴에 영향을 미치는 희귀한 뇌 질환입니다. 이 질환은 신체 움직임, 조정, 사고 및 기타 중요한 기능을 제어하는 뇌 영역의 세포가 손상되어 발생합니다. 진행성 핵상 마비는 스틸-리차드슨-올제프스키 증후군이라고도 합니다. 진행성 핵상 마비는 시간이 지남에 따라 악화되며 폐렴이나 삼킴 장애와 같은 위험한 합병증을 유발할 수 있습니다. 진행성 핵상 마비를 완치할 수 있는 치료법은 없으므로 증상을 관리하는 데 중점을 두고 치료합니다.

Symptoms

Symptoms of progressive supranuclear palsy include:

• A loss of balance while walking. A tendency to fall backward can occur very early in the disease.
• An inability to aim your eyes properly. People with progressive supranuclear palsy may not be able to look downward. Or they may experience blurring and double vision. Not being able to focus the eyes can make some people spill food. They also may appear disinterested in conversation because of lack of eye contact.

Additional symptoms of progressive supranuclear palsy vary and may mimic those of Parkinson's disease and dementia. Symptoms get worse over time and may include:

• Stiffness, especially of the neck, and awkward movements.
• Falling, especially falling backward.
• Slow or slurred speech.
• Trouble swallowing, which may cause gagging or choking.
• Being sensitive to bright light.
• Trouble with sleep.
• Loss of interest in pleasurable activities.
• Impulsive behavior, or laughing or crying for no reason.
• Trouble with reasoning, problem-solving and decision-making.
• Depression and anxiety.
• A surprised or frightened facial expression‎, resulting from rigid facial muscles.
• Dizziness.

증상

진행성 핵상 마비의 증상은 다음과 같습니다:

• 보행 중 균형 감각 상실. 뒤로 넘어지는 경향이 질병 초기에 발생할 수 있습니다.
• 눈을 제대로 조준하지 못함. 진행성 핵상 마비 환자는 아래를 보지 못할 수 있습니다. 또는 시야가 흐려지고 복시가 발생할 수 있습니다. 눈의 초점을 맞추지 못하면 음식을 흘리기도 합니다. 또한 눈을 맞추지 못해 대화에 무관심한 것처럼 보일 수도 있습니다.

진행성 핵상 마비의 추가 증상은 다양하며 파킨슨병과 치매의 증상과 유사할 수 있습니다. 증상은 시간이 지남에 따라 악화되며 다음과 같은 증상이 나타날 수 있습니다:

• 특히 목이 뻣뻣해지고 움직임이 어색해집니다.
• 넘어짐, 특히 뒤로 넘어짐.
• 말이 느리거나 어눌해짐.
• 연하 곤란으로 인해 토하거나 질식할 수 있음.
• 밝은 빛에 민감해짐.
• 수면 장애.
• 즐거운 활동에 대한 흥미 상실.
• 충동적인 행동을 하거나 이유 없이 웃거나 우는 경우.
• 추론, 문제 해결 및 의사 결정에 문제가 있습니다.
• 우울증과 불안.
• 얼굴 근육 경직으로 인한 놀람 또는 겁에 질린 표정.
• 현기증.

PSP에 대한 기존의 임상 기준은 

특이성과 민감도가 우수하지만, 

조기에 객관적인 질환별 이상을 포착할 수 있는 

바이오마커가 필요합니다.

 이 연구는 

초기 PSP와 

특별히 연관된 보행 패턴을 식별하는 것을 

목표로 했습니다. 

연구 대상은 연속적으로 등록된 104명의 참가자(PD 환자 83명, PSP 환자 21명)로 구성되었습니다. 보행은 보행 분석 시스템을 사용하여 정상 보행과 인지적 이중 과제를 수행하는 동안 조사했습니다. 단변량 통계 분석과 이원 로지스틱 회귀를 사용하여 모든 PD 환자와 모든 PSP 환자, 새로 진단된 PD 환자와 초기 PSP 환자를 비교했습니다. 

PSP 환자는 

초기 단계부터 PD 환자보다 보행 패턴이 더 나빴습니다. 

PSP 환자는 

PD 환자에 비해 보행 속도가 감소하고 

동적 불안정성 측정치가 증가했습니다. 

보행 데이터에 예측 모델을 적용한 결과, PD 보행 패턴은 케이던스 증가와 더 긴 사이클 길이로 대표되는 반면, PSP 환자는 질병 중기와 초기 단계 모두에서 더 긴 자세 단계가 특징적인 것으로 나타났습니다. 본 연구는 정량적 보행 평가가 질병의 초기 단계부터 PSP 환자와 PD 환자를 명확하게 구분할 수 있음을 보여줍니다. 

첫째, 임상 및 연구 환경에서 신뢰할 수 있는 바이오마커로 보행 분석을 후보로 삼을 수 있습니다. 또한, 연구 결과는 초기 질환별 재활 전략을 구상하기 위한 추측적 단서를 제공할 수 있습니다.

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Introduction

Progressive supranuclear palsy (PSP) is a rare and rapidly progressing neurodegenerative disease classified among atypical Parkinsonisms, with a preval‎ence of 5–6 cases per 100,0001. PSP Richardson’s syndrome, the most frequent form of the disease, is characterized by vertical supranuclear gaze palsy and postural instability with early falls2. Extant evidence suggests that the clinical spectrum of PSP is larger than originally described. In particular, the second most common form of disease, accounting for a third of cases, is characterized by a parkinsonian syndrome resembling Parkinson’s disease (PD) especially in the earliest stages3.

Recently, several PSP variants were detailed in the International Parkinson and Movement Disorder Society criteria for diagnosis of PSP (MDS-PSP)4 and subsequently characterized in real-life clinical settings5,6.

Although semi-quantitative rating scales such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)7 and PSP Rating Scale (PSP-RS)8 provide a clinician-based quantification of disease burden, these scales do not provide objective quantitative measures. Indeed, there is a need to employ quantitative tools for eval‎uating motor function in parkinsonism9; among those, gait analysis is one of the main instruments used to assess locomotion. Gait analysis is a non-invasive, 3-dimensional computerized examination of gait, commonly used in the literature to investigate and distinguish various diseases10.

Gait analysis has been employed for different objectives in PD patients, including the investigation of pathophysiological mechanisms underpinning the disease, eval‎uation of treatment outcomes, automatic recognition of PD symptoms, and implementation of algorithms for PD diagnosis and staging11,12,13,14. In addition, gait analysis has been used to explore the association between specific gait patterns and specific symptoms of PD, such as mild cognitive impairment15,16 and freezing of gait17.

Quantitative tools for assessing locomotion have also been applied in PSP patients. Amano et al. 18 examined the biomechanical features of dynamic postural control during gait initiation and ambulation in PSP patients using a gait analysis system. Hatanaka et al. compared the gait features of PSP, PD patients, and controls using a portable triaxial accelerometer rhythmogram19. Other studies have employed sensor-based approaches. In particular, Raccagni et al. investigated the ability of a gait assessment system to detect differences in gait parameters in atypical parkinsonian disorders20, whereas Gaβner et al. assessed whether sensor-based gait parameters could serve as a complementary tool to clinical scores for distinguishing atypical parkinsonism from PD21. More recently, machine-learning approaches have been introduced to assess the gait patterns of PSP patients, although the rarity of this disease poses challenges to obtaining large datasets for analysis. In our previous work, we distinguished de novo PD, stable PD, and PSP patients using machine-learning techniques applied to gait parameters after artificial data augmentation and achieved promising results22. Subsequently, De Vos et al. performed a similar study using wearable technology23.

Albeit the MDS-PSP criteria4 have recently proven to have good specificity and sensitivity24, there is a need for biomarkers able to capture early objective disease-specific abnormalities and to monitor disease progression over time. The main aim of the present study was to identify gait patterns specifically associated with PSP with two-fold impact: (1) providing a proof of concept that quantitative gait eval‎uation may represent a reliable biomarker since the earliest stages of disease; (2) recognizing early disease-specific gait patterns useful to design tailored rehabilitation programs.

소개

진행성 핵상 마비(PSP)는

비정형 파킨슨병으로 분류되는

희귀하고 빠르게 진행되는 신경 퇴행성 질환으로,

유병률은 100,000명당 5~6건입니다1.

가장 흔한 형태의 질환인

PSP 리차드슨 증후군은

수직 핵상 시선 마비와

조기 낙상을 동반한 자세 불안정성이 특징입니다2.

현존하는 증거에 따르면 PSP의 임상 스펙트럼은 원래 설명된 것보다 더 넓은 것으로 나타났습니다. 특히, 3분의 1을 차지하는 두 번째로 흔한 형태의 질환은 특히 초기 단계에서 파킨슨병(PD)과 유사한 파킨슨 증후군이 특징입니다3.

최근 국제 파킨슨병 및 운동 장애 학회의 PSP 진단 기준(MDS-PSP)4에 여러 가지 PSP 변종이 자세히 설명되어 있으며, 이후 실제 임상 환경에서 특징화되었습니다5,6.

운동 장애 학회 통합 파킨슨병 평가 척도(MDS-UPDRS)7 및 PSP 평가 척도(PSP-RS)8와 같은 반정량적 평가 척도가 임상의사 기반의 질병 부담 정량화를 제공하지만, 이러한 척도는 객관적인 정량적 측정치를 제공하지 못합니다. 실제로 파킨슨병의 운동 기능을 평가하기 위한 정량적 도구가 필요하며9, 그 중 보행 분석은 운동 기능을 평가하는 데 사용되는 주요 도구 중 하나입니다. 보행 분석은 보행에 대한 비침습적 3차원 컴퓨터 검사로, 문헌에서 다양한 질환을 조사하고 구별하는 데 일반적으로 사용됩니다10.

보행 분석은 파킨슨병 환자의 병리 생리학적 메커니즘 조사, 치료 결과 평가, 파킨슨병 증상 자동 인식, 파킨슨병 진단 및 병기 설정 알고리즘 구현 등 다양한 목적으로 사용되어 왔습니다11,12,13,14. 또한 보행 분석은 특정 보행 패턴과 경도 인지 장애15,16 및 보행 동결17과 같은 PD의 특정 증상 간의 연관성을 탐색하는 데 사용되었습니다.

보행 평가를 위한 정량적 도구도

PSP 환자에게 적용되었습니다.

아마노 등18은 보행 분석 시스템을 사용하여 PSP 환자의 보행 시작 및 보행 중 동적 자세 제어의 생체역학적 특징을 조사했습니다. 하타나카 등은 휴대용 3축 가속도계 리듬그램을 사용하여 PSP, PD 환자 및 대조군의 보행 특징을 비교했습니다19. 다른 연구에서도 센서 기반 접근법을 사용했습니다. 특히 Raccagni 등은 보행 평가 시스템이 비정형 파킨슨 장애에서 보행 매개변수의 차이를 감지하는 능력을 조사했으며20, Gaβner 등은 센서 기반 보행 매개변수가 비정형 파킨슨과 PD를 구별하기 위한 임상 점수를 보완하는 도구로 사용될 수 있는지 평가했습니다21. 최근에는 PSP 환자의 보행 패턴을 평가하기 위해 머신러닝 접근법이 도입되었지만, 이 질환의 희귀성으로 인해 분석을 위한 대규모 데이터 세트를 확보하는 데 어려움이 있습니다. 이전 연구에서는 인공 데이터 증강 후 걸음걸이 매개변수에 머신러닝 기법을 적용하여 신병성 PD, 안정형 PD, PSP 환자를 구분하여 유망한 결과를 얻었습니다22. 이후 De Vos 등은 웨어러블 기술을 사용하여 유사한 연구를 수행했습니다23.

최근 MDS-PSP 기준4이 우수한 특이도와 민감도를 가지고 있음이 입증되었지만24, 조기에 객관적인 질병별 이상을 포착하고 시간이 지남에 따라 질병 진행을 모니터링할 수 있는 바이오마커가 필요합니다.

본 연구의 주요 목표는

(1) 정량적 보행 평가가 질병의 초기 단계부터 신뢰할 수 있는 바이오마커가 될 수 있다는 개념 증명 제공,

(2) 맞춤형 재활 프로그램 설계에 유용한 초기 질병별 보행 패턴 인식이라는

두 가지 효과를 가진 PSP와 특별히 관련된 보행 패턴을 식별하는 것이었습니다.

Methods

Study design and population

The study population consisted of 104 participants (83 PD and 21 PSP patients) consecutively enrolled between February 2018 and July 2020. Participants were selected from patients referred to the Movement Disorders Unit of the Institute for Diagnosis and Care Hermitage-Capodimonte of Naples and Center for Neurodegenerative Diseases of the University of Salerno. All PD patients fulfilled the Movement Disorder Society (MDS) clinical diagnostic criteria for PD25. Newly diagnosed PD patients presented with symptom onset within 1 year from enrolment and were included after undergoing a [123I]FP-CIT SPECT examination for dopamine transporter assessment which indicated nigro-striatal degeneration. All PSP patients met the clinical diagnostic criteria proposed by MDS4,26 and qualified for diagnosis of probability. Of patients, 11 (52.3%) presented with Richardson’s syndrome. The remaining patients presented with other variant syndromes of PSP (five patients exhibited PSP with predominant parkinsonism and four patients exhibited PSP with predominant gait freezing). Of the PD patients, 56 were stable; i.e., they received stable treatment during the 4 weeks preceding enrolmentt, and 27 were newly diagnosed PD patients who had never received treatment. Of the PSP patients, 12 were early PSP patients (disease duration less than 2 years). The exclusion criteria for all patients were as follows: gait requiring assistance; dementia according to the DSM-V criteria; clinically significant comorbidities, including other neurologic disorders, orthopedic diseases, or cardiovascular/respiratory diseases; anticholinergic or neuroleptic treatment; and/or brain surgery. All participants were eval‎uated using an assessment including demographic, clinical, and anthropometric data. All participants were eval‎uated in the self-defined best “on-state” while receiving their typical dopaminergic drugs.

Standard protocol approvals, registrations, and patient consent

This study was performed in accordance with the 1964 Declaration of Helsinki and was approved by Campania Sud, the reference ethics committee of the Center for Neurodegenerative Diseases of the University of Salerno. Written informed consent was obtained from all participants.

Gait analysis

Gait analysis was performed in all subjects using a BTS Bioengineering system. The SMART DX is an optical system equipped with six infrared cameras, two video cameras, two force plates, a set of passive markers, and an elaborator. The Davis protocol was used for all subjects27, comprising the following phases. Anthropometric measurements of the patients (height, weight, leg length, etc.) were obtained. In total, 22 reflective markers were positioned on specific points of the body. The standing phase consisted of assessments of the patient while standing up on a force plate. This was followed by the walking phase on a 10-m path. All patients were eval‎uated on the straight pathway during two different tasks: (1) GAIT: normal gait, namely the single task; (2) COG: walking while serially subtracting 7 s starting from 100, namely the dual task; each task was performed four times. Prior to commencing the trials, all participants were trained to walk at a normal pace at their usual speed, without any instructions to prioritize walking or calculating. This procedure generated a report from which spatial and temporal parameters were extracted.

보행 분석
모든 피험자의 보행 분석은 BTS 바이오엔지니어링 시스템을 사용하여 수행되었습니다. SMART DX는 6개의 적외선 카메라, 2개의 비디오 카메라, 2개의 힘판, 패시브 마커 세트, 정교기가 장착된 광학 시스템입니다. 모든 피험자에게 데이비스 프로토콜27이 사용되었으며, 다음 단계로 구성되었습니다. 환자의 인체 측정치(키, 몸무게, 다리 길이 등)를 얻었습니다. 총 22개의 반사 마커를 신체의 특정 지점에 배치했습니다. 기립 단계는 환자가 힘판 위에 서 있는 동안 평가하는 것으로 구성되었습니다. 그 다음에는 10m 경로에서 걷기 단계가 이어졌습니다.

모든 환자는 직선 경로에서

(1) 정상 보행, 즉 단일 과제,

(2) 100에서 7초를 연속적으로 빼면서 걷기,

즉 이중 과제, 즉 두 가지 과제를 수행하는 동안 평가받았습니다.

각 과제는 4회 수행되었습니다.

실험을 시작하기 전에 모든 참가자는 걷기나 계산의 우선순위에 대한 지시 없이 평소 속도로 정상 속도로 걷도록 훈련받았습니다. 이 절차를 통해 공간적 및 시간적 매개변수가 추출된 보고서가 생성되었습니다.

Statistical analysis

IBM SPSS v.25 was used to perform all the statistical analyses. For univariate statistical analysis, the Shapiro Wilk and Kolmogorov Smirnov tests were used to assess normality according to the sample size (the former for n < 50, and the latter for n > 50). For normally distributed data, the Levene test was used to assess the homoscedasticity of the variances between the compared groups. A t-test for independent samples was employed when both of the previous assumptions were verified; a Mann Whitney test was otherwise employed. Univariate statistical analysis was performed to quantify the effects of the COG task on the two groups. Binary logistic regression28 was computed to produce models capable of classifying patients into a diagnostic group (PD or PSP) starting from spatial and temporal parameters of gait. The presence of multicollinearity among variables and outliers was verified. The former was assessed by computing the coefficients of correlation, and all variables with a correlation greater than 0.80 were removed; the latter was verified by computing the a-dimensional Cook’s distance and Center Leverage Value. The odds ratios with a confidence interval of 95% and relative p-values were provided for each variable included in the models. The Hosmer Lemeshow goodness-of-fit test was computed to eval‎uate whether the observed event rates matched expected event rates in subgroups of the model population. Finally, the overall accuracy of the models and capacity to detect each group were determined. Alpha significance level was set to p < 0.05 for all statistical analyses.

Results

Univariate statistical analysis and binary logistic regression were performed twice: first, all PD patients (N = 83) were compared with all PSP patients (N = 21). Subsequently, the analysis was restricted to newly diagnosed PD patients (N = 27) and early PSP patients (N = 12).

PD versus PSPUnivariate statistical analysis

Univariate statistical analysis comparing demographic and clinical features, and spatial and temporal gait parameters for both GAIT and COG tasks between PD and PSP patients are presented in Tables 1 and 2, respectively.

Table 1 Comparison of demographic and clinical features between PD and PSP patients.

Full size table

Table 2 Univariate statistical analysis comparing all gait parameters (mean ± SD) between PD and PSP patients.

Full size table

In the GAIT task, PSP patients exhibited poorer gait patterns when compared to PD patients. Namely, relative to PD patients, PSP patients exhibited reduced velocity and cadence, shortened step and cycle lengths, increased cycle duration mainly due to longer double support stance phase duration, and increased swing duration variability (Table 2).

In the COG task, PSP patients exhibited the same gait features as those displayed during the single task, with the exception of two gait variables, namely swing duration and step length variability (Table 2). For both GAIT and COG tasks, the difference in step width between the two groups was not statistically significant. When comparing the effect of the dual task on gait measures in PD and PSP patients, the simultaneous performance of the secondary task significantly worsened most gait measures in both groups with the exception of the step width that was significantly increased in PSP patients but not in PD patients (Table S1).

Binary logistic regression

Binary logistic regression was performed to distinguish PD and PSP patients. The results for GAIT and COG tasks are presented in Table 3.

보행 과제에서

PSP 환자는

PD 환자에 비해 보행 패턴이 더 나빴습니다.

즉,

PD 환자에 비해 PSP 환자는

속도와 케이던스 감소,

스텝 및 사이클 길이 단축,

주로 이중 지지 자세 단계 지속 시간 증가로 인한 사이클 지속 시간 증가,

스윙 지속 시간 변동성 증가를 나타냈습니다(표 2).

COG 과제에서 PSP 환자는 스윙 지속 시간과 스텝 길이 변동성이라는 두 가지 보행 변수를 제외하고는 단일 과제에서 나타난 것과 동일한 보행 특징을 나타냈습니다(표 2). GAIT와 COG 과제 모두에서 두 그룹 간의 보폭 차이는 통계적으로 유의미하지 않았습니다. 이중 과제가 PD 및 PSP 환자의 보행 측정에 미치는 영향을 비교했을 때, 보조 과제의 동시 수행은 PSP 환자에서는 유의하게 증가했지만 PD 환자에서는 증가하지 않은 보폭을 제외하고 두 그룹 모두에서 대부분의 보행 측정치를 유의하게 악화시켰습니다(표 S1).

이원 로지스틱 회귀 분석
이원 로지스틱 회귀를 수행하여 PD와 PSP 환자를 구분했습니다. GAIT 및 COG 작업에 대한 결과는 표 3에 나와 있습니다.

Table 3 Multinomial logistic regression results for GAIT and COG tasks to distinguish PD and PSP.

Full size table

Two outliers were removed from the GAIT model, and five outliers were removed from the COG model. The graphs depicting Cook’s distance versus center leverage values are presented in the supplemental data (Figs. S1 and S2). The overall accuracies for the GAIT and COG models were 92.3% and 95%, respectively. The capacities to detect PD patients were 96.5% and 96.4%, and the capacities to identify PSP patients were 73.7% and 88.2% for the GAIT and COG models, respectively. The positive predictive value and the negative predictive value were 82.3% and 94.2% for the GAIT task, 83.3% and 97.6% for the COG task. Table 4 shows the confusion matrix of each model.

Table 4 Confusion matrix of binary logistic regression (PD Vs PSP) for GAIT and COG tasks.

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The odds ratios of the variables included in the GAIT model revealed that longer swing duration and increased cadence and cycle length were associated with a higher probability of being in the PD group. Conversely, longer stance phase was associated with a higher probability of being in the PSP group. The COG model confirmed these results, with the exception of swing duration, which was not entered in this model. The Hosmer Lemeshow goodness-of-fit test demonstrated good overall quality of the models, with p-values of 0.976 and 1.000 for the GAIT model and COG model, respectively.

Newly diagnosed PD versus early PSPUnivariate statistical analysis

Univariate statistical analysis was performed to compare spatial and temporal parameters of gait between newly diagnosed PD and early PSP patients. The results for GAIT and COG tasks are presented in Table 5.

Table 5 Univariate statistical analysis comparing all gait parameters (mean ± SD) between newly diagnosed PD and early PSP patients.

Full size table

For the single task (GAIT), early phase PSP patients exhibited poorer walking parameters when compared with PD patients. In particular, compared to newly diagnosed PD patients, early PSP patients exhibited reduced velocity and cadence, shortened step and cycle length, and increased cycle duration; these patients tended to rely on a longer double support stance phase (Table 5).

In the dual task (COG), compared to newly diagnosed PD patients, early PSP patients exhibited a gait pattern similar to that during the single task with the exception of two gait variables, namely, swing duration and swing duration variability (Table 5). For both GAIT and COG tasks, the differences in step length variability and step width between the two groups were not statistically significant. When comparing the effects of the dual task on gait measures in newly diagnosed PD patients versus early PSP patients, most gait parameters were similarly altered in the dual task condition in both groups, with the exception of step length variability and step width, which were influenced by the dual task in PSP patients but not in PD patients (Table S2).

Binary logistic regression

The results of the binary logistic regression conducted to differentiate newly diagnosed PD patients and early PSP patients are presented in Table 6 for both GAIT and COG tasks.

Table 6 Multinomial logistic regression results for GAIT and COG tasks to distinguish newly diagnosed PD and early PSP patients.

Full size table

Two outliers were removed from each of the two models. The graphs depicting Cook’s distance versus center leverage values are presented in the supplemental data (Figs. S3 and S4). For the GAIT and COG models. The overall accuracies were 89.2% and 91.9% for the GAIT and COG models, respectively. The capacities to detect newly diagnosed PD patients were 92.3% and 96.3%, and the capacities to identify early PSP patients were 81.8% and 80.0% for the GAIT and COG models, respectively. The positive predictive value and the negative predictive value were 81.8% and 92.3% for the GAIT task, 88.9% and 96.2% for the COG task. Table 7 shows the confusion matrix of each model.

Table 7 Confusion matrix of binary logistic regression (newly diagnosed PD vs early PSP) for GAIT and COG tasks.

Full size table

The odds ratios of the variables included in the GAIT model revealed that longer cycle length was associated with a higher probability of being in the newly diagnosed PD group. In contrast, longer stance phase was associated with a higher probability of being in the early PSP group. The COG model confirmed the same predictor, i.e. longer stance phase, for PSP diagnosis, whereas it disclosed that increased cadence raises the probability of PD diagnosis. The Hosmer Lemeshow goodness-of-fit test demonstrated good overall quality of the models, with p-values of 0.846 and 0.195 for the GAIT model and COG model, respectively.

Discussion

Here, we demonstrated that PSP patients exhibited

disease-specific gait pattern

when compared to PD patients,

even during the earliest stages of disease.

In particular,

PSP patients exhibited reduced velocity and

increased measures of dynamic instability

when compared to PD subjects.

Furthermore,

application of predictive models to gait data revealed that increased cadence and longer cycle length characterized PD gait pattern, whereas longer stance phase distinguished PSP subjects in both mid and early stages.

토론

여기서 

우리는 PSP 환자가 PD 환자와 비교했을 때 

질병의 초기 단계에서도 

질병 특유의 보행 패턴을 보인다는 것을 보여주었습니다. 

특히

PSP 환자는 

PD 환자에 비해 보행 속도가 감소하고 

동적 불안정성 측정값이 증가했습니다. 

또한 

보행 데이터에 예측 모델을 적용한 결과, 

케이던스 증가와 사이클 길이가 길수록 

PD 보행 패턴의 특징인 반면, 

자세 단계가 길수록 중기와 초기 단계 모두에서 

PSP 피험자를 구별할 수 있는 것으로 나타났습니다.

Comparison of gait patterns in PD and PSP patients

Itn the single task, PSP subjects exhibited reduced velocity and cadence, shortened step and cycle length, increased cycle duration (mainly due to longer double support stance phase), and increased swing time variability when compared to PD patients. These data indicating that PSP gait pattern is characterized by dynamic instability are consistent with previous findings in smaller samples19,21,29. These results suggest that complex dysfunction of internal motor programming is more prominent in PSP patients than in PD patients, with the former group exhibiting dysfunction in both spatial and temporal domains, and the latter group exhibiting predominantly spatial dysfunction30,31. Notably, because dysfunction of spatial gait variables are generally responsive to dopaminergic treatment in PD patients32, the different gait patterns of the two patient groups could at least partly depend on the effectiveness of medication that is notoriously poor in PSP patients4. Gait patterns of the two groups were similar in both the single and dual tasks, with non-specific detrimental effects of the secondary task on both PSP and PD patients. Importantly, the major effect of the dual task in PSP patients was an increase in step width that could represent an attempt to counteract the lateral instability commonly observed as balance and walking deficits in PSP patients19.

Application of predictive models on the single task data revealed that longer swing duration and increased cadence and cycle length were characteristic gait features of PD patients. In contrast, longer stance phase was a gait feature that typified the gait pattern of PSP patients. Data from the dual task condition confirmed the single task data with the exception of swing duration. These results indicate that relative to PSP patients, PD patients display more stable locomotion, even under the dual task condition, and exhibit superior performance in temporal gait parameters that thus represent the variables better discriminating between PD and PSP30,31.

Comparison of gait patterns in newly diagnosed PD versus early PSP patients

Whole-group differences were recapitulated in the comparison of gait features between newly diagnosed PD and early PSP patients. From early stages, patients with PSP exhibited reduced velocity and cadence, shortened step and cycle length, and increased cycle duration which was predominantly underpinned by a longer double support stance phase. These findings were independent of dopaminergic effects, since newly diagnosed PD patients were drug-naïve. The dual task condition significantly modified most gait measures in both patients groups. Notably, step length variability and step width were influenced by the dual task in PSP patients but not in PD patients. These data highlight two important points. First, early stage PD and PSP patients exhibit distinct gait patterns, which likely reflect different underlying pathophysiological mechanisms. Second, in the early stage, the dual task condition may be more detrimental in PSP patients than in PD patients. Our findings are consistent with the recent observation that gait impairments in PSP are associated with an imbalance in the control of indirect and direct locomotor pathways. In particular, PSP patients display specific dysfunction of the indirect prefrontal–subthalamic–pedunculopontine loop of locomotor control, which drives modulated gait, and increased activity in the direct loop, which regulates stereotyped gait33. Given that the dual task condition mainly involves cognitive-mediated gait34 that is underpinned by the indirect pathway, it is unsurprising that PSP patients exhibit greater effects in the dual task condition, at least in the early stage. With disease progression, this effect tends to be masked by the general deterioration of all gait features, hence the loss of disease specificity of the dual task effect in more advanced patients, as reported above.

Application of predictive models on single task data revealed that increased cycle length was a gait variable characteristic of newly diagnosed PD patients, whereas longer stance phase was a gait feature that typified early PSP gait pattern. In the dual task condition, longer stance phase was confirmed as the best predictor for PSP diagnosis, whereas increased cadence was the strongest predictor for PD diagnosis. These findings indicate that from an early stage, PSP patients exhibit more alterations in temporal gait features when compared to PD patients. Further, they suggest that the dual task condition exerts a major effect on step length35 in PD patients from a very early stage, as reflected by increased cadence.

The present study has some limitations. First, the sample size of PSP patients was relatively small, mainly due to the low preval‎ence of the disease and the early loss of independent walking. Second, when comparing gait patterns in newly diagnosed PD and early PSP patients, we indeed confronted patients with (PSP) and without (PD) dopaminergic treatment. Nevertheless, given the poor response to dopaminergic treatment in PSP patients4 and the general confirmation of the different gait patterns between the two patients groups in both early and mid-stages of disease, we hypothesize that medication status had a minimal impact on our findings. Finally, we admit that the PSP group included different phenotypes and the “stable” PD group consisted of patients in slightly different Hoehn and Yahr stages; nevertheless, predictive models were able to capture disease-specific gait patterns.

In conclusion, the present study demonstrates that quantitative gait eval‎uation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. Our findings indicate that gait analysis could be candidate as a reliable biomarker in both clinical and research setting. In addition, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.

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