Advertisement

Quantitative Transcranial Sonography Evaluation of Substantia Nigra Hyperechogenicity Is Useful for Predicting Levodopa-Induced Dyskinesia in Parkinson Disease

  • Jia-Hui Yan
    Footnotes
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Kai Li
    Footnotes
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Yi-Lun Ge
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Wen Li
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Pu-Zhi Wang
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Hong Jin
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Jin-Ru Zhang
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Jing Chen
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Fen Wang
    Affiliations
    Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Ya-Ping Yang
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Ying-Chun Zhang
    Affiliations
    Department of Ultrasound, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
    Search for articles by this author
  • Dan Li
    Affiliations
    Department of Neurology, Suqian First People's Hospital, Suqian, Jiangsu, China
    Search for articles by this author
  • Cheng-Jie Mao
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China

    Department of Neurology, Suqian First People's Hospital, Suqian, Jiangsu, China
    Search for articles by this author
  • Chun-Feng Liu
    Correspondence
    Address correspondence to: Chun-Feng Liu, Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, 1055, Sanxiang Road, Suzhou, Jiangsu 215004, China.
    Affiliations
    Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China

    Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China

    Department of Neurology, Suqian First People's Hospital, Suqian, Jiangsu, China

    Department of Neurology, Second Affiliated Hospital of Xinjiang Medical University, Urumqi, China
    Search for articles by this author
  • Author Footnotes
    1 Jia-Hui Yan and Kai Li contributed equally to this work.
Open AccessPublished:November 28, 2022DOI:https://doi.org/10.1016/j.ultrasmedbio.2022.10.019

      Abstract

      Levodopa-induced dyskinesia (LID) is a common motor complication in Parkinson disease (PD). Abnormal substantia nigra hyperechogenicity (SN+), detected by transcranial sonography (TCS), plays an important role in the differential diagnosis of PD. The purpose of this study was to investigate the predictive performance of quantitative SN+ evaluations for LID. Five hundred sixty-two individuals were included in our analysis, and 198 individuals were followed up. These individuals were divided into two groups at baseline: the PD with LID (PD+LID) group and the PD without LID (PD-LID) group. The association between total hyperechogenic area of the SN on both sides (SNT) and LID was analyzed by binary logistic analysis. A binary logistic regression model including SNT was applied to establish a model for discriminating LID. At baseline, 105 (18.7%) individuals were diagnosed with LID. The PD+LID group had a longer disease duration, shorter education duration, higher levodopa equivalent doses, greater disease severity and larger SNT. A model combining clinical features and SNT was further established with better efficiency (area under the receiver operating characteristic curve = 0.839). One hundred ninety-eight individuals were followed up; individuals with a larger SNT and a higher predicted probability were more likely to develop LID in our follow-up. Our study determined that quantitative TCS evaluation of SN echogenicity is useful in predicting LID in PD.

      Key Words

      Abbreviations:

      PD (Parkinson’s disease), LID (levodopa-induced dyskinesia), TCS (transcranial sonography), SN (substantia nigra), SN+ (substantia nigra hyperechogenicity), SNT (total hyperechogenic area of the SN on both sides), SNL (the lager area of SN+ of two sides), UPDRS (Unified Parkinson’s Disease Rating Scale), H-Y scale (Hoehn and Yahr scale), MMSE (Mini Mental State Examination), MoCA (Montreal Cognitive Assessment), ROC (receiver-operating characteristic), AUC (area under the curves), K-M curves (Kaplan-Meier curves)

      Introduction

      Parkinson disease (PD) is one of the most common neurodegenerative movement disorders, and is characterized by bradykinesia, resting tremor, rigidity and postural instability (
      • Kalia LV
      • Lang AE.
      Parkinson's disease.
      ;
      • Kulisevsky J
      • Oliveira L
      • Fox SH.
      Update in therapeutic strategies for Parkinson's disease.
      ). The pathology of PD is widely considered to be the lack of levodopa, which can be caused by the degeneration of dopaminergic neurons originating in the substantia nigra pars compacta (
      • Dauer W
      • Przedborski S.
      Parkinson's disease: Mechanisms and models.
      ;
      • Chen X
      • Wang Y
      • Wu H
      • Cheng C
      • Le W.
      Research advances on L-DOPA-induced dyskinesia: From animal models to human disease.
      ). After decades of clinical practice, levodopa has been proven to be the standard treatment for PD (
      • Radhakrishnan DM
      • Goyal V.
      Parkinson's disease: A review.
      ;
      • Armstrong MJ
      • Okun MS.
      Diagnosis and treatment of Parkinson disease: A review.
      ).
      With long-term use of levodopa, some individuals with PD may develop levodopa-induced dyskinesia (LID), which is accompanied by various abnormal movements, including chorea, akathisia and dystonia (
      • Guridi J
      • González-Redondo R
      • Obeso JA.
      Clinical features, pathophysiology, and treatment of levodopa-induced dyskinesias in Parkinson's disease.
      ). In addition to levodopa, dopamine receptor agonists (pramipexole, ropinirole, etc.) can also induce dyskinesia (
      • Tran TN
      • Vo TNN
      • Frei K
      • Truong DD.
      Levodopa-induced dyskinesia: Clinical features, incidence, and risk factors.
      ;
      • Chen X
      • Wang Y
      • Wu H
      • Cheng C
      • Le W.
      Research advances on L-DOPA-induced dyskinesia: From animal models to human disease.
      ). According to previous studies, the prevalence of LID could be as high as 10%–40% (
      • Manson A
      • Stirpe P
      • Schrag A.
      Levodopa-induced-dyskinesias clinical features, incidence, risk factors, management and impact on quality of life.
      ;
      • Tran TN
      • Vo TNN
      • Frei K
      • Truong DD.
      Levodopa-induced dyskinesia: Clinical features, incidence, and risk factors.
      ;
      • Turcano P
      • Mielke MM
      • Bower JH
      • Parisi JE
      • Cutsforth-Gregory JK
      • Ahlskog JE
      • Savica R.
      Levodopa-induced dyskinesia in Parkinson disease: A population-based cohort study.
      ;
      • Kelly MJ
      • Lawton MA
      • Baig F
      • Ruffmann C
      • Barber TR
      • Lo C
      • Klein JC
      • Ben-Shlomo Y
      • Hu MT.
      Predictors of motor complications in early Parkinson's disease: A prospective cohort study.
      ), which may greatly affect the quality of life of individuals with PD and their families. Because of this additional problem, LID is gaining increasing attention among patients and clinicians. To date, the detailed mechanism underlying LID remains unclear; however, sex, body weight, age, age at onset, duration of disease and treatment have been shown to be associated with LID (
      • Sharma JC
      • Bachmann CG
      • Linazasoro G.
      Classifying risk factors for dyskinesia in Parkinson's disease.
      ;
      • Warren Olanow C
      • Kieburtz K
      • Rascol O
      • Poewe W
      • Schapira AH
      • Emre M
      • Nissinen H
      • Leinonen M
      • Stocchi F
      Factors predictive of the development of levodopa-induced dyskinesia and wearing-off in Parkinson's disease.
      ;
      • Eusebi P
      • Romoli M
      • Paoletti FP
      • Tambasco N
      • Calabresi P
      • Parnetti L.
      Risk factors of levodopa-induced dyskinesia in Parkinson's disease: Results from the PPMI cohort.
      ;
      • Iwaki H
      • Blauwendraat C
      • Leonard HL
      • Makarious MB
      • Kim JJ
      • Liu G
      • Maple-Grødem J
      • Corvol JC
      • Pihlstrøm L
      • van Nimwegen M
      • Smolensky L
      • Amondikar N
      • Hutten SJ
      • Frasier M
      • Nguyen KH
      • Rick J
      • Eberly S
      • Faghri F
      • Auinger P
      • Scott KM
      • Wijeyekoon R
      • Van Deerlin VM
      • Hernandez DG
      • Gibbs RJ
      • Day-Williams AG
      • Brice A
      • Alves G
      • Noyce AJ
      • Tysnes OB
      • Evans JR
      • Breen DP
      • Estrada K
      • Wegel CE
      • Danjou F
      • Simon DK
      • Andreassen OA
      • Ravina B
      • Toft M
      • Heutink P
      • Bloem BR
      • Weintraub D
      • Barker RA
      • Williams-Gray CH
      • van de Warrenburg BP
      • Van Hilten JJ
      • Scherzer CR
      • Singleton AB
      • Nalls MA
      Differences in the presentation and progression of Parkinson's Disease by sex.
      ;
      • Luca A
      • Monastero R
      • Baschi R
      • Cicero CE
      • Mostile G
      • Davì M
      • Restivo V
      • Zappia M
      • Nicoletti A.
      Cognitive impairment and levodopa induced dyskinesia in Parkinson's disease: A longitudinal study from the PACOS cohort.
      ). White matter hyperintensity was reported as a risk factor for LID (
      • Chung SJ
      • Yoo HS
      • Lee YH
      • Jung JH
      • Baik K
      • Ye BS
      • Sohn YH
      • Lee PH.
      White matter hyperintensities and risk of levodopa-induced dyskinesia in Parkinson's disease.
      ). Additionally, genetic factors may also play a role in the mechanism of LID (
      • Kim HJ
      • Mason S
      • Foltynie T
      • Winder-Rhodes S
      • Barker RA
      • Williams-Gray CH.
      Motor complications in Parkinson's disease: 13-year follow-up of the CamPaIGN cohort.
      ;
      • Tirozzi A
      • Modugno N
      • Palomba NP
      • Ferese R
      • Lombardi A
      • Olivola E
      • Gialluisi A
      • Esposito T.
      Analysis of genetic and non-genetic predictors of levodopa induced dyskinesia in Parkinson's disease.
      ). And in recent studies, wearable sensors were considered useful in estimating the severity of LID (
      • Hssayeni MD
      • Jimenez-Shahed J
      • Burack MA
      • Ghoraani B.
      Dyskinesia severity estimation in patients with parkinson's disease using wearable sensors and a Deep LSTM network.
      ;
      • Knudson M
      • Thomsen TH
      • Kjaer TW.
      Comparing objective and subjective measures of Parkinson's disease using the Parkinson's KinetiGraph.
      ).
      Transcranial sonography (TCS) is a non-invasive neuroimaging technique, and is regarded as a useful method (by detecting substantia nigra [SN] echogenicity) in providing evidence for PD diagnosis (
      • Berg D
      • Godau J
      • Walter U.
      Transcranial sonography in movement disorders.
      ;
      • Prati P
      • Bignamini A
      • Coppo L
      • Naldi A
      • Comi C
      • Cantello R
      • Gusmaroli G
      • Walter U.
      The measuring of substantia nigra hyperechogenicity in an Italian cohort of Parkinson disease patients: A case/control study (NOBIS Study).
      ;
      • Zhou HY
      • Huang P
      • Sun Q
      • Du JJ
      • Cui SS
      • Hu YY
      • Zhan WW
      • Wang Y
      • Xiao Q
      • Liu J
      • Tan YY
      • Chen SD.
      The role of substantia nigra sonography in the differentiation of Parkinson's disease and multiple system atrophy.
      ) with considerable sensitivity and specificity (
      • Li DH
      • He YC
      • Liu J
      • Chen SD.
      Diagnostic accuracy of transcranial sonography of the substantia nigra in Parkinson's disease: A systematic review and meta-analysis.
      ,
      • Li K
      • Ge YL
      • Gu CC
      • Zhang JR
      • Jin H
      • Li J
      • Cheng XY
      • Yang YP
      • Wang F
      • Zhang YC
      • Chen J
      • Mao CJ
      • Liu CF
      Substantia nigra echogenicity is associated with serum ferritin, gender and iron-related genes in Parkinson's disease.
      ,
      • Li T
      • Shi J
      • Qin B
      • Fan D
      • Liu N
      • Ni J
      • Zhang T
      • Zhou H
      • Xu X
      • Wei M
      • Zhang X
      • Wang X
      • Liu J
      • Wang Y
      • Tian J.
      Increased substantia nigra echogenicity correlated with visual hallucinations in Parkinson's disease: A Chinese population-based study.
      ;
      • Toomsoo T
      • Liepelt-Scarfone I
      • Kerner R
      • Kadastik-Eerme L
      • Asser T
      • Rubanovits I
      • Berg D
      • Taba P.
      Substantia nigra hyperechogenicity: Validation of transcranial sonography for Parkinson disease diagnosis in a large Estonian cohort.
      ;
      • Xu R
      • Chen G
      • Mao Z
      • Gao H
      • Deng Y
      • Tao A.
      Diagnostic performance of transcranial sonography for evaluating substantia nigra hyper-echogenicity in patients with Parkinson's disease.
      ). In addition, abnormal SN hyperechogenicity (SN+) and a larger hyperechogenic area of the SN were indicated to be associated with the subtype of PD and disease severity (
      • Zhou HY
      • Sun Q
      • Tan YY
      • Hu YY
      • Zhan WW
      • Li DH
      • Wang Y
      • Xiao Q
      • Liu J
      • Chen SD.
      Substantia nigra echogenicity correlated with clinical features of Parkinson's disease.
      ;
      • Sheng AY
      • Zhang YC
      • Sheng YJ
      • Wang CS
      • Zhang Y
      • Hu H
      • Luo WF
      • LI CF
      Transcranial sonography image characteristics in different Parkinson's disease subtypes.
      ;
      • Yu SY
      • Cao CJ
      • Zuo LJ
      • Chen ZJ
      • Lian TH
      • Wang F
      • Hu Y
      • Piao YS
      • Li LX
      • Guo P
      • Liu L
      • Yu QJ
      • Wang RD
      • Chan P
      • Chen SD
      • Wang XM
      • Zhang W.
      Clinical features and dysfunctions of iron metabolism in Parkinson disease patients with hyper echogenicity in substantia nigra: A cross-sectional study.
      ), which revealed an important clinical application of TCS in PD. To date, studies focusing on the potential associations between SN+ and motor complications in PD are lacking. On the basis of the aforementioned evidence, we assumed that LID is associated with SN+. In this study, we attempted to determine the potential associations between SN+ and LID and to establish a prediction model for LID.

      Methods

      Individuals and study design

      We recruited 601 individuals with PD from the Department of Neurology of the Second Affiliated Hospital of Soochow University (Suzhou, China) from April 2015 to 2021. All participants fulfilled either the 2015 Movement Disorder Society clinical diagnostic criteria (
      • Postuma RB
      • Berg D
      • Stern M
      • Poewe W
      • Olanow CW
      • Oertel W
      • Obeso J
      • Marek K
      • Litvan I
      • Lang AE
      • Halliday G
      • Goetz CG
      • Gasser T
      • Dubois B
      • Chan P
      • Bloem BR
      • Adler CH
      • Deuschl G.
      MDS clinical diagnostic criteria for Parkinson's disease.
      ) or the UK Brain Bank criteria (
      • Daniel SE
      • Lees AJ.
      Parkinson's Disease Society Brain Bank, London: Overview and research.
      ) for PD. Exclusion criteria included (i) atypical and secondary parkinsonism (i.e., multiple system atrophy, progressive supranuclear palsy, brain injury, etc.); (ii) insufficient temporal bone window; and (iii) lack of full clinical assessments. We finally included 562 individuals, and 198 individuals without LID at baseline were successfully followed up every 3–6 mo (Fig. 1). The follow-up duration was determined from the baseline to the first occurrence of LID or, in the non-LID cases, from baseline to the last visit (the average follow-up duration was 37 mo). The investigation protocol was approved by the ethics committee of the Second Affiliated Hospital of Soochow University. Detailed clinical data including demographics were collected after written consent was obtained from all participants or their legally authorized representatives. All techniques were performed in accordance with the relevant guidelines and regulations.

      Transcranial sonography

      Transcranial sonography examinations were performed according to our previous studies (
      • Sheng AY
      • Zhang YC
      • Sheng YJ
      • Wang CS
      • Zhang Y
      • Hu H
      • Luo WF
      • LI CF
      Transcranial sonography image characteristics in different Parkinson's disease subtypes.
      ;
      • Li K
      • Ge YL
      • Gu CC
      • Zhang JR
      • Jin H
      • Li J
      • Cheng XY
      • Yang YP
      • Wang F
      • Zhang YC
      • Chen J
      • Mao CJ
      • Liu CF
      Substantia nigra echogenicity is associated with serum ferritin, gender and iron-related genes in Parkinson's disease.
      ) within 1 wk after the baseline clinical assessment. The brain was insonated through the right and left temporal bone windows in the axial plane by a sonographer using a 2.5-MHz sonographic device (Sequoia 512, Siemens Medical Solutions USA, Inc. 4V1C transducer) with a penetration depth of 14–16 cm and a dynamic range of 45–55 dB (
      • Berg D
      • Godau J
      • Walter U.
      Transcranial sonography in movement disorders.
      ;
      • Behnke S
      • Runkel A
      • Kassar HA
      • Ortmann M
      • Guidez D
      • Dillmann U
      • Fassbender K
      • Spiegel J.
      Long-term course of substantia nigra hyperechogenicity in Parkinson's disease.
      ). Image brightness, contrast and time-gain compensation were adapted as needed for best visualization (
      • Berg D
      • Godau J
      • Walter U.
      Transcranial sonography in movement disorders.
      ;
      • Behnke S
      • Runkel A
      • Kassar HA
      • Ortmann M
      • Guidez D
      • Dillmann U
      • Fassbender K
      • Spiegel J.
      Long-term course of substantia nigra hyperechogenicity in Parkinson's disease.
      ) (Fig. 2). The area of SN echogenicity was manually encircled with the cursor, and the planimetric area of SN and the mesencephalon was calculated automatically. TCS was performed in a darkened room by the same experienced clinician, who was blinded to the individuals’ clinical status to eliminate any bias in the examination results.
      Fig 2
      Fig. 2Transcranial sonography image of individual with Parkinson disease. The dotted area represents mesencephalon, and the white arrow, substantia nigra.
      The echogenicity of SN was divided into five grades (
      • Bartova P
      • Skoloudik D
      • Bar M
      • Ressner P
      • Hlustik P
      • Herzig R
      • Kanovsky P.
      Transcranial sonography in movement disorders.
      ), where grade I = the same as brainstem; grade II = scattered points and thin lines slightly stronger than brainstem; grade III = patches of moderate echogenicity but weaker than brain pool; grade IV = patches of hyperechogenicity similar to brain pool; grade V= patches of hyperechogenicity stronger than brain pool. Grades I and II are defined as hypo-echogenicity (SN–), and grades III and V as SN+ (
      • Berg D
      • Godau J
      • Walter U.
      Transcranial sonography in movement disorders.
      ). To quantitatively evaluate SN+, we measured the area of SN+ on the left and right sides and calculated the larger area of SN+ of two sides (SNL), the total hyperechogenic area of the SN on both sides (SNT) and the S/M ratio (defined as SNT divided by the area of mesencephalon).

      Clinical assessments

      All individuals included in our study underwent detailed clinical and neuropsychological tests at baseline and follow-up. Disease severity was evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS) (
      • Martínez-Martín P
      • Gil-Nagel A
      • Gracia LM
      • Gómez JB
      • Martínez-Sarriés J
      • Bermejo F.
      Unified Parkinson's Disease Rating Scale characteristics and structure: The Cooperative Multicentric Group.
      ) and the Hoehn and Yahr scale (H–Y scale) (
      • Goetz CG
      • Poewe W
      • Rascol O
      • Sampaio C
      • Stebbins GT
      • Counsell C
      • Giladi N
      • Holloway RG
      • Moore CG
      • Wenning GK
      • Yahr MD
      • Seidl L.
      Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: Status and recommendations.
      ) in the “off state.” PD with LID was diagnosed if the UPDRS-IV dyskinesia score (items 32–35) was >0. The participants were divided into two groups: the PD with LID (PD+LID) group and the PD without LID (PD-LID) group. Levodopa-equivalent doses (LEDs) of medications were calculated as previously described (
      • Tomlinson CL
      • Stowe R
      • Patel S
      • Rick C
      • Gray R
      • Clarke CE
      Systematic review of levodopa dose equivalency reporting in Parkinson's disease.
      ). Cognitive performance was evaluated using the Mini Mental State Examination (MMSE) (
      • Folstein MF
      • Folstein SE
      • McHugh PR.
      "Mini-mental state." A practical method for grading the cognitive state of patients for the clinician.
      ) and the Montreal Cognitive Assessment (MoCA, Beijing version) (
      • Yu J
      • Li J
      • Huang X.
      The Beijing version of the Montreal Cognitive Assessment as a brief screening tool for mild cognitive impairment: A community-based study.
      ).

      Statistical analysis

      In univariate analyses, variables were tested for normality using the Kolmogorov–Smirnov test or Shapiro–Wilk test. Differences in metric variables between the groups were evaluated with Student's t-test or the Mann–Whitney U-test. Dichotomous variables were compared between groups with the χ2-test. Binary logistic regression of LID was used to assess the associations among the demographic profiles, clinical assessments and SNT. A binary logistic regression model with backward stepwise procedure was used to build a model for discriminating LID. The Hosmer–Lemeshow test was performed to assess the goodness of fit. Receiver-operating characteristic (ROC) curves and areas under the ROC curves (AUCs) were also determined. Kaplan–Meier (K–M) curves were constructed to illustrate the effect of SNT and the predicted probability on LID. K–M curves were compared using the log-rank (Mantel–Cox) test. All statistical tests were two sided with p < 0.05 as the threshold for statistical significance. All tests were performed using SPSS Statistics, version 26.0, 64-bit (IBM Corp., Armonk, NY, USA).

      Results

      Demographics and clinical assessments at baseline

      This study recruited 601 individuals with PD; 39 individuals were then excluded (Fig. 1). Therefore, 562 individuals were included in the analysis. At baseline, 105 individuals (18.7%) had experienced LID. Table 1 outlines the demographic variables and clinical assessments in the PD-LID and PD+LID groups. Sex composition and age did not significantly differ between the two groups (p = 0.266 and 0.244, respectively). Compared with the PD-LID group, the PD+LID group had an earlier age at onset (p = 0.012), longer disease duration (p < 0.001), shorter education duration (p < 0.001) and higher LEDs (p < 0.001). With respect to disease severity, the PD+LID group had a more advanced H–Y scale (p < 0.001) and higher UPDRS scores (p = 0.004 for UPDRS-I, p < 0.001 for UPDRS-II and p < 0.001 for UPDRS-III, respectively). However, there were no significant differences in cognitive performance between the two groups (p = 0.078 for the MMSE and p = 0.082 for the MoCA, respectively) (Table 1).
      Table 1Demographic variables and clinical assessments in the PD-LID and PD+LID groups at baseline
      Total (n = 562)PD-LID (n = 457)PD+LID (n = 105)p Value
      Sex (male)370 (65.8%)296 (64.8%)74 (70.5%)0.266
      p Value estimated using the χ2-test.
      Age (y)61.4 ± 9.861.2 ± 9.962.4 ± 9.60.244
      p Values estimated using the t-test.
      Age at onset (y)57.5 ± 9.858.0 ± 9.855.3 ± 9.90.012
      p Values estimated using the t-test.
      Disease duration (mo)48.2 ± 44.238.8 ± 34.989.0 ± 56.0<0.001
      p Values estimated using the Mann–Whitney U-test.
      Education duration (y)7.4 ± 4.67.8 ± 4.65.7±4.3<0.001
      p Values estimated using the Mann–Whitney U-test.
      LEDs (mg)422.0 ± 241.6376.7 ± 203.9619.5 ± 290.0<0.001
      p Values estimated using the Mann–Whitney U-test.
      Hoehn and Yahr scale (“off” state)2.1 ± 0.82.0 ± 0.72.5 ± 0.9<0.001
      p Values estimated using the Mann–Whitney U-test.
      UPDRS-I2.7 ± 2.02.6 ± 2.03.2 ± 2.10.004
      p Values estimated using the Mann–Whitney U-test.
      UPDRS-II10.2 ± 6.39.3 ± 5.414.1 ± 8.1<0.001
      p Values estimated using the Mann–Whitney U-test.
      UPDRS-III (“off” state)23.1 ± 12.821.5 ± 11.430.1 ± 15.8<0.001
      p Values estimated using the Mann–Whitney U-test.
      MMSE25.1 ± 4.425.2 ± 4.324.6 ± 4.50.078
      p Values estimated using the Mann–Whitney U-test.
      MoCA21.0 ± 5.121.2 ± 5.020.2 ± 5.20.082
      p Values estimated using the Mann–Whitney U-test.
      SNT (cm2)0.39 ± 0.360.35 ± 0.340.55 ± 0.37<0.001
      p Values estimated using the Mann–Whitney U-test.
      LID = levodopa-induced dyskinesia; LEDs = levodopa-equivalent doses; PD = Parkinson disease; PD+LID = PD with LID; PD-LID = PID without LID; MoCA = Montreal Cognitive Assessment; MMSE = Mini Mental State Examination; SNT = total hyperechogenic area of the substantia nigra on both sides; UPDRS = Unified Parkinson's Disease Rating Scale.
      Values are expressed as the number (%) or mean ± standard deviation.
      low asterisk p Value estimated using the χ2-test.
      p Values estimated using the Mann–Whitney U-test.
      p Values estimated using the t-test.

      Quantitative SN+ evaluations between the PD-LID and PD+LID groups

      We detected 406 (72.2%) individuals with SN+, 316 in the PD-LID group and 90 in the PD+LID group. Compared with the PD-LID group, the D+LID group had a higher proportion of SN+ (p = 0.001), larger SNL, larger SNT and higher S/M ratio (p < 0.001, respectively) (Table 2).
      Table 2SN+ evaluations in the PD-LID and PD+LID groups
      Total (n = 562)PD-LID (n = 457)PD+LID (n = 105)p Value
      SN+406 (72.2%)316 (69.1%)90 (85.7%)0.001
      p Value estimated using the χ2-test.
      SNL (cm2)0.28 ± 0.230.25 ± 0.220.37 ± 0.22<0.001
      p Values estimated using the Mann–Whitney U-test.
      SNT (cm2)0.39 ± 0.360.35 ± 0.340.55 ± 0.37<0.001
      p Values estimated using the Mann–Whitney U-test.
      S/M ratio9.4% ± 22.7%7.6% ± 7.9%17.3% ± 61.7%<0.001
      p Values estimated using the Mann–Whitney U-test.
      PD+LID = PD with LID; PD-LID = PID without LID; SN+ = substantia nigra hyperechogenicity; SNL = the larger area of SN+ on both sides; SNT = total hyperechogenic area of the substantia nigra on both sides; S/M ratio = SNT divided by the area of midbrain.
      Values are expressed as the number (%) or mean ± standard deviation.
      low asterisk p Value estimated using the χ2-test.
      p Values estimated using the Mann–Whitney U-test.
      In Figure 3 are the ROC curves of the SN+ evaluations (SNT, SNL and S/M ratio) for discriminating LID in individuals with PD. The ideal diagnostic threshold should yield the highest sum of sensitivity and specificity, and the point situated at the top left corner of the curve would be the best diagnostic cutoff value. Among three ROC curves, the curve of SNT reached the highest AUC (0.657), and we marked the point in this curve for the cutoff value of 0.315 cm2. At this point, the sensitivity was 71.4% and the specificity was 53.8%. Thus, we included SNT in our further analysis.
      Fig 3
      Fig. 3(A) ROC curve of SNT for discriminating LID. Asterisk marks the point in the SNT curve for the cutoff of 0.315 cm2. At this point, the sensitivity value was 71.4%, and the specificity value was 53.8%. The AUC was 0.657 (95% CI: 0.599–0.715, p < 0.001). (B) ROC curve of SNL for discriminating LID. The asterisk marks the point in the SNL curve for the cutoff of 0.205 cm2. At this point, the sensitivity value was 83.8%, and the specificity value was 41.6%. The AUC was 0.650 (95% CI: 0.592–0.707, p < 0.001). (C) ROC curve of S/M ratio for discriminating LID. The asterisk marks the point in the SNT curve for the cutoff of 7.97%. At this point, the sensitivity value was 66.7%, and the specificity value was 58.9%. The AUC was 0.654 (95% CI: 0.596–0.712, p < 0.001). AUC = area under the ROC curve; CI = confidence interval; LID = levodopa-induced dyskinesia; ROC = receiver operating characteristic; S/M = SNT divided by the area of mesencephalon; SN = substantia nigra; SNL = larger area of SN of two sides; SNT = total hyperechogenic area of the SN on both sides.

      Independent risk factors associated with LID

      Table 3 outlines the results of binary logistic regression analysis of the risk factors and SNT in the PD-LID and PD+LID groups. Following binary logistic regression analysis, we found that disease duration (odds ratio [OR] = 1.015, 95% confidence interval [CI]: 1.003–1.026, p = 0.014), education duration (OR = 0.923, 95% CI: 0.865–0.984, p = 0.015), LEDs (OR = 1.003, 95% CI: 1.002–1.004, p < 0.001) and UPDRS-III score (OR = 1.026, 95% CI: 1.003–1.049, p = 0.026) were independently associated with LID. After adjustments for sex, age, age at onset, disease duration, education duration, treatment, disease severity and cognitive performance, the SNT was found to be significantly larger in the PD+LID group (OR = 2.601, 95% CI: 1.267–5.337, p = 0.009) (Table 3).
      Table 3Binary logistic regression of risk factors and SNT in the PD-LID and PD+LID groups
      PD-LID (n = 457)PD+LID (n = 105)Odds ratio (95% CI)p Value
      p Values were estimated from binary logistic regression models adjusted for sex, age, age at onset, disease duration, education duration, LEDs, UPDRS-III and MoCA.
      Sex (male)296 (64.8%)74 (70.5%)0.863 (0.474–1.571)0.630
      Age (y)61.2 ± 9.962.4 ± 9.61.003 (0.884–1.138)0.962
      Age at onset (y)58.0 ± 9.855.3 ± 9.90.975 (0.856–1.111)0.706
      Disease duration (mo)38.8 ± 34.989.0 ± 56.01.015 (1.003–1.026)0.014
      Education duration (y)7.8 ± 4.65.7 ± 4.30.923 (0.865–0.984)0.015
      LEDs (mg)376.7 ± 203.9619.5 ± 290.01.003 (1.002–1.004)<0.001
      UPDRS-III (“off” state)21.5 ± 11.430.1 ± 15.81.026 (1.003–1.049)0.026
      MoCA21.2 ± 5.020.2 ± 5.20.985 (0.930–1.043)0.599
      SNT (cm2)0.35 ± 0.340.55 ± 0.372.601 (1.267–5.337)0.009
      CI = confidence interval; LID = levodopa-induced dyskinesia; LEDs = levodopa-equivalent doses; MoCA = Montreal Cognitive Assessment; PD = Parkinson disease; PD+LID = PD with LID; PD-LID = PID without LID; SNT = total hyperechogenic area of the substantia nigra on both sides; UPDRS = Unified Parkinson's Disease Rating Scale.
      The UPDRS score and Hoehn and Yahr scale were synergistic, and the MoCA and Mini Mental State Examination were synergistic, so only the UPDRS-III score and MoCA were included for analysis.
      Values are expressed as the number (%) or mean ± standard deviation unless otherwise noted.
      low asterisk p Values were estimated from binary logistic regression models adjusted for sex, age, age at onset, disease duration, education duration, LEDs, UPDRS-III and MoCA.

      Development of model for discriminating LID

      The above-mentioned associated risk factors, including disease duration, education duration, LEDs, UPDRS-III score and SNT, were included in the binary logistic regression analysis with the backward stepwise procedure. Figure 4 illustrates the ROC curve of the probability for discriminating LID, which indicates that the AUC was 0.839 (95% CI: 0.795–0.883, p < 0.001) with an acceptable goodness of fit (Hosmer–Lemeshow, p > 0.05). We also marked the point in the curve for the cutoff value of 0.2025. At this point, the sensitivity was 71.4%, and the specificity was 82.3% (Fig. 4).
      Fig 4
      Fig. 4Receiver operating characteristic curve of predicted probability for discriminating levodopa-induced dyskinesia in individuals with Parkinson disease. The predicted probability was used to plot the receiver operating characteristic curve. The predicted probability was calculated with the prediction model, established by binary logistic regression analysis with the backward stepwise procedure using disease duration, education duration, levodopa-equivalent doses, Unified Parkinson's Disease Rating Scale-III score and SNT. The asterisk marks the point in the model curve for the cutoff of 0.2025. At this point, the sensitivity value was 71.4%, and the specificity value was 82.3%. The area under the receiver operating characteristic curve was 0.839 (95% confidence interval: 0.795–0.883, p < 0.001). SNT = total hyperechogenic area of the SN on both sides.

      Confirmation of SNT and the prediction model with follow-up

      We successfully followed up 198 individuals from the PD-LID group at baseline; the average follow-up duration was 37 mo, and 29 individuals (14.6%) developed LID during the follow-up. We first divided 198 patients into two groups, according to the SNT cutoff value (0.315 cm2). Follow-up duration, change in LEDs and UPDRS-III score between follow-up and baseline did not significantly differ between the two groups (Table 4). From the K–M curves, individuals with a lager SNT were more likely to develop LID as compared with individuals with a smaller SNT (Fig. 5).
      Table 4Clinical assessments at follow-up (grouped by SNT)
      Total (n = 198)SNT <0.315 (n = 118)SNT ≥0.315 (n = 80)p Value
      Follow-up duration (mo)37.1 ± 18.738.9 ± 19.234.4 ± 17.80.055
      p Values estimated between two groups using the Mann–Whitney U-test.
      Change in LEDs (mg)68.7 ± 179.762.8 ± 184.877.3 ± 172.70.896
      p Values estimated between two groups using the Mann–Whitney U-test.
      Change in UPDRS-III2.4 ± 11.43.2 ± 12.61.3 ± 9.30.189
      p Values estimated between two groups using the Mann–Whitney U-test.
      LID during follow-up29 (14.6%)11 (9.3%)18 (22.5%)0.010
      p Value estimated between two groups using the χ2-test.
      LEDs = levodopa-equivalent doses; LID = levodopa-induced dyskinesia; SNT = total hyperechogenic area of the substantia nigra on both sides; UPDRS = Unified Parkinson's Disease Rating Scale.
      Values are expressed as the number (%) or mean ± standard deviation.
      low asterisk p Values estimated between two groups using the Mann–Whitney U-test.
      p Value estimated between two groups using the χ2-test.
      Fig 5
      Fig. 5Kaplan–Meier curves of developing levodopa-induced dyskinesia during follow-up (grouped by SNT). p Values were estimated using the log-rank test: p1 = comparison between the SNT <0.315 and SNT ≥0.315 groups; p2 = comparison between the SNT ≥0.315 group and all individuals followed up. SNT = total hyperechogenic area of the SN on both sides.
      We then divided 198 patients according to the baseline-predicted probability cutoff value (0.2025). Follow-up duration, change in LEDs and UPDRS-III score between the follow-up and baseline groups did not significantly differ between the two groups (Table 5). From the K–M curves, individuals with a higher predicted probability were more likely to develop LID as compared with individuals with a lower predicted probability (Fig. 6).
      Table 5Clinical assessments at follow-up (grouped by predicted probability)
      Total (n = 198)Predicted probability <0.2025 (n = 167)Predicted probability ≥0.2025 (n = 31)p Value
      Follow-up duration (mo)37.1 ± 18.737.6 ± 19.034.4 ± 17.40.407
      p Values estimated between two groups using the Mann–Whitney U-test.
      Change in LEDs (mg)68.7 ± 179.776.0 ± 184.329.1 ± 190.50.335
      p Values estimated between two groups using the Mann–Whitney U-test.
      Change in UPDRS-III2.4 ± 11.42.7 ± 11.00.6 ± 13.40.110
      p Values estimated between two groups using the Mann–Whitney U-test.
      LID during follow-up29 (14.6%)19 (11.4%)10 (32.3%)0.003
      p Value estimated between two groups using the χ2-test.
      LEDs= levodopa-equivalent doses; LID = levodopa-induced dyskinesia; UPDRS = Unified Parkinson's Disease Rating Scale.
      low asterisk p Values estimated between two groups using the Mann–Whitney U-test.
      p Value estimated between two groups using the χ2-test.
      Fig 6
      Fig. 6Kaplan–Meier curves of developing levodopa-induced dyskinesia during follow-up (grouped by predicted probability). p Values were estimated using the log-rank test: p1 = comparison between the predicted probability <0.2025 group and the predicted probability ≥0.2025 group; p2 = comparison between the predicted probability ≥0.2025 group and all individuals followed up.

      Discussion

      Levodopa-induced dyskinesia is a common motor complication of PD that is often caused by levodopa treatment (
      • Chen X
      • Wang Y
      • Wu H
      • Cheng C
      • Le W.
      Research advances on L-DOPA-induced dyskinesia: From animal models to human disease.
      ). The prevalence of LID varies from 10% to 40% in different studies depending on the progression of the disease (
      • Manson A
      • Stirpe P
      • Schrag A.
      Levodopa-induced-dyskinesias clinical features, incidence, risk factors, management and impact on quality of life.
      ;
      • Tran TN
      • Vo TNN
      • Frei K
      • Truong DD.
      Levodopa-induced dyskinesia: Clinical features, incidence, and risk factors.
      ;
      • Turcano P
      • Mielke MM
      • Bower JH
      • Parisi JE
      • Cutsforth-Gregory JK
      • Ahlskog JE
      • Savica R.
      Levodopa-induced dyskinesia in Parkinson disease: A population-based cohort study.
      ;
      • Kelly MJ
      • Lawton MA
      • Baig F
      • Ruffmann C
      • Barber TR
      • Lo C
      • Klein JC
      • Ben-Shlomo Y
      • Hu MT.
      Predictors of motor complications in early Parkinson's disease: A prospective cohort study.
      ). In the present study, the incidence was 18.7%, which is consistent with previous studies (
      • Manson A
      • Stirpe P
      • Schrag A.
      Levodopa-induced-dyskinesias clinical features, incidence, risk factors, management and impact on quality of life.
      ;
      • Tran TN
      • Vo TNN
      • Frei K
      • Truong DD.
      Levodopa-induced dyskinesia: Clinical features, incidence, and risk factors.
      ;
      • Turcano P
      • Mielke MM
      • Bower JH
      • Parisi JE
      • Cutsforth-Gregory JK
      • Ahlskog JE
      • Savica R.
      Levodopa-induced dyskinesia in Parkinson disease: A population-based cohort study.
      ;
      • Kelly MJ
      • Lawton MA
      • Baig F
      • Ruffmann C
      • Barber TR
      • Lo C
      • Klein JC
      • Ben-Shlomo Y
      • Hu MT.
      Predictors of motor complications in early Parkinson's disease: A prospective cohort study.
      ). LID may greatly affect the quality of life of individuals with PD and their families; therefore, discriminating individuals at higher risk of LID may be of importance. In this study, using binary logistic regression analysis, we found that disease duration, education duration, LEDs and disease severity were independent risk factors for LID, which is consistent with previous studies (
      • Sharma JC
      • Bachmann CG
      • Linazasoro G.
      Classifying risk factors for dyskinesia in Parkinson's disease.
      ;
      • Walker RW
      • Howells AR
      • Gray WK.
      The effect of levodopa dose and body weight on dyskinesia in a prevalent population of people with Parkinson's disease.
      ;
      • Manson A
      • Stirpe P
      • Schrag A.
      Levodopa-induced-dyskinesias clinical features, incidence, risk factors, management and impact on quality of life.
      ;
      • Warren Olanow C
      • Kieburtz K
      • Rascol O
      • Poewe W
      • Schapira AH
      • Emre M
      • Nissinen H
      • Leinonen M
      • Stocchi F
      Factors predictive of the development of levodopa-induced dyskinesia and wearing-off in Parkinson's disease.
      ;
      • Tran TN
      • Vo TNN
      • Frei K
      • Truong DD.
      Levodopa-induced dyskinesia: Clinical features, incidence, and risk factors.
      ;
      • Luca A
      • Monastero R
      • Baschi R
      • Cicero CE
      • Mostile G
      • Davì M
      • Restivo V
      • Zappia M
      • Nicoletti A.
      Cognitive impairment and levodopa induced dyskinesia in Parkinson's disease: A longitudinal study from the PACOS cohort.
      ).
      In our study, all 562 individuals underwent TCS. In total, we found 406 (72.2%) individuals with SN+, which is consistent with our previous study (
      • Li K
      • Ge YL
      • Gu CC
      • Zhang JR
      • Jin H
      • Li J
      • Cheng XY
      • Yang YP
      • Wang F
      • Zhang YC
      • Chen J
      • Mao CJ
      • Liu CF
      Substantia nigra echogenicity is associated with serum ferritin, gender and iron-related genes in Parkinson's disease.
      ). The proportions of SN+ reported in other studies ranged from 60% to 80% (
      • Yu SY
      • Cao CJ
      • Zuo LJ
      • Chen ZJ
      • Lian TH
      • Wang F
      • Hu Y
      • Piao YS
      • Li LX
      • Guo P
      • Liu L
      • Yu QJ
      • Wang RD
      • Chan P
      • Chen SD
      • Wang XM
      • Zhang W.
      Clinical features and dysfunctions of iron metabolism in Parkinson disease patients with hyper echogenicity in substantia nigra: A cross-sectional study.
      ;
      • Zhou HY
      • Huang P
      • Sun Q
      • Du JJ
      • Cui SS
      • Hu YY
      • Zhan WW
      • Wang Y
      • Xiao Q
      • Liu J
      • Tan YY
      • Chen SD.
      The role of substantia nigra sonography in the differentiation of Parkinson's disease and multiple system atrophy.
      ); this difference may be due to different components, diagnostic criteria and the operator. Therefore, because of its convenience and high diagnostic value, TCS currently has a wide range of applications in various movement disorders, especially in individuals without obvious motor symptoms.
      We quantitatively analyzed the association between SN+ (detected by TCS) and LID in individuals with PD. Sex, age, age at onset, disease duration, education duration, treatment, disease severity and cognitive performance were included as covariates. We found that individuals with PD+LID had significantly larger SNT compared with individuals with PD-LID. We then constructed an ROC curve of SNT and detected moderate sensitivity (71.4%) and specificity (53.8%); the AUC was 0.657. We further established a model including four independent risk factors (disease duration, education duration, LEDs and UPDRS-III score) and SNT. The sensitivity and specificity of our model were 71.4% and 82.3%, and the AUC reached 0.839, which indicated the credibility and validity of our established model. In addition, our findings were also validated by follow-up observations, where individuals with a lager SNT or a higher predicted probability were more likely to develop LID during follow-up. These findings indicated that the quantitative characteristics of SN+ had good values in discriminating LID to a certain extent, which had not been reported before and may also increase the scope of the application of TCS in PD.
      To date, the detailed mechanism causing SN+ and the causal relationship between SN+ and LID remain unknown. However, abnormal iron deposition has been reported to be associated with SN+ (
      • Berg D
      • Siefker C
      • Ruprecht-Dörfler P
      • Becker G.
      Relationship of substantia nigra echogenicity and motor function in elderly subjects.
      ,
      • Berg D
      • Godau J
      • Walter U.
      Transcranial sonography in movement disorders.
      ), which may provide some clues to understanding the detailed mechanism underlying SN+. Interestingly, excessive iron deposition may lead to abnormal oxidative stress in the brain, which could result in dopaminergic neuron degeneration and death (
      • Gaasch JA
      • Lockman PR
      • Geldenhuys WJ
      • Allen DD
      • Van der Schyf CJ.
      Brain iron toxicity: Differential responses of astrocytes, neurons, and endothelial cells.
      ;
      • Berg D
      • Godau J
      • Riederer P
      • Gerlach M
      • Arzberger T.
      Microglia activation is related to substantia nigra echogenicity.
      ;
      • Carta AR
      • Mulas G
      • Bortolanza M
      • Duarte T
      • Pillai E
      • Fisone G
      • Vozari RR
      • Del-Bel E.
      L-DOPA-induced dyskinesia and neuroinflammation: Do microglia and astrocytes play a role?.
      ;
      • Zhang S
      • Tao K
      • Wang J
      • Duan Y
      • Wang B
      • Liu X.
      Substantia nigra hyperechogenicity reflects the progression of dopaminergic neurodegeneration in 6-OHDA rat model of Parkinson's disease.
      ). Nigrostriatal pathology may then further increase the risk of LID. Functional analyses are warranted in the future for further clarification.
      To our knowledge, this is the first study to investigate the association between the hyperechogenic area of the SN and LID in individuals with PD. In addition, we provided an effective tool that is helpful in discriminating LID. However, our study has some limitations. First, our sample size was relatively small. Second, our patients were from one center. Further studies with a larger sample size and in different populations are warranted.

      Conclusions

      This study found that quantitative TCS evaluations of SN+ are associated with LID and are useful in predicting LID in individuals with PD.
      Acknowledgments—We express our gratitude to all the individuals who participated in the study. This work was supported by the National Key R&D Program of China (2017YFC 0909100), Jiangsu Provincial Key R&D Program (BE2018658), Jiangsu Provincial Medical Key Discipline Project (ZDXKB2016022), Discipline Construction Program of the Second Affiliated Hospital Soochow University (XKTJ-XK202001), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), National Natural Science Foundation of China (81801120), Pre-research Project for Doctors of the Second Affiliated Hospital of Soochow University (SDFEYBS1702) and Pre-Research Project for Doctors of the Second Affiliated Hospital of Soochow University (SDFEYBS2014).
      Conflict of interest disclosure—The authors declare no competing interests.
      Data availability statement—The data that support the findings of this study are available from the corresponding author on reasonable request.

      References

        • Armstrong MJ
        • Okun MS.
        Diagnosis and treatment of Parkinson disease: A review.
        JAMA. 2020; 323: 548-560
        • Bartova P
        • Skoloudik D
        • Bar M
        • Ressner P
        • Hlustik P
        • Herzig R
        • Kanovsky P.
        Transcranial sonography in movement disorders.
        Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2008; 152: 251-258
        • Behnke S
        • Runkel A
        • Kassar HA
        • Ortmann M
        • Guidez D
        • Dillmann U
        • Fassbender K
        • Spiegel J.
        Long-term course of substantia nigra hyperechogenicity in Parkinson's disease.
        Mov Disord. 2013; 28: 455-459
        • Berg D
        • Siefker C
        • Ruprecht-Dörfler P
        • Becker G.
        Relationship of substantia nigra echogenicity and motor function in elderly subjects.
        Neurology. 2001; 56: 13-17
        • Berg D
        • Godau J
        • Walter U.
        Transcranial sonography in movement disorders.
        Lancet Neurol. 2008; 7: 1044-1055
        • Berg D
        • Godau J
        • Riederer P
        • Gerlach M
        • Arzberger T.
        Microglia activation is related to substantia nigra echogenicity.
        J Neural Transm (Vienna). 2010; 117: 1287-1292
        • Carta AR
        • Mulas G
        • Bortolanza M
        • Duarte T
        • Pillai E
        • Fisone G
        • Vozari RR
        • Del-Bel E.
        L-DOPA-induced dyskinesia and neuroinflammation: Do microglia and astrocytes play a role?.
        Eur J Neurosci. 2017; 45: 73-91
        • Chen X
        • Wang Y
        • Wu H
        • Cheng C
        • Le W.
        Research advances on L-DOPA-induced dyskinesia: From animal models to human disease.
        Neurol Sci. 2020; 41: 2055-2065
        • Chung SJ
        • Yoo HS
        • Lee YH
        • Jung JH
        • Baik K
        • Ye BS
        • Sohn YH
        • Lee PH.
        White matter hyperintensities and risk of levodopa-induced dyskinesia in Parkinson's disease.
        Ann Clin Transl Neurol. 2020; 7: 229-238
        • Daniel SE
        • Lees AJ.
        Parkinson's Disease Society Brain Bank, London: Overview and research.
        J Neural Transm Suppl. 1993; 39: 165-172
        • Dauer W
        • Przedborski S.
        Parkinson's disease: Mechanisms and models.
        Neuron. 2003; 39: 889-909
        • Eusebi P
        • Romoli M
        • Paoletti FP
        • Tambasco N
        • Calabresi P
        • Parnetti L.
        Risk factors of levodopa-induced dyskinesia in Parkinson's disease: Results from the PPMI cohort.
        NPJ Parkinsons Dis. 2018; 4: 33
        • Folstein MF
        • Folstein SE
        • McHugh PR.
        "Mini-mental state." A practical method for grading the cognitive state of patients for the clinician.
        J Psychiatr Res. 1975; 12: 189-198
        • Gaasch JA
        • Lockman PR
        • Geldenhuys WJ
        • Allen DD
        • Van der Schyf CJ.
        Brain iron toxicity: Differential responses of astrocytes, neurons, and endothelial cells.
        Neurochem Res. 2007; 32: 1196-1208
        • Goetz CG
        • Poewe W
        • Rascol O
        • Sampaio C
        • Stebbins GT
        • Counsell C
        • Giladi N
        • Holloway RG
        • Moore CG
        • Wenning GK
        • Yahr MD
        • Seidl L.
        Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: Status and recommendations.
        Mov Disord. 2004; 19: 1020-1028
        • Guridi J
        • González-Redondo R
        • Obeso JA.
        Clinical features, pathophysiology, and treatment of levodopa-induced dyskinesias in Parkinson's disease.
        Parkinsons Dis. 2012; 2012943159
        • Hssayeni MD
        • Jimenez-Shahed J
        • Burack MA
        • Ghoraani B.
        Dyskinesia severity estimation in patients with parkinson's disease using wearable sensors and a Deep LSTM network.
        Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 6001-6004
        • Iwaki H
        • Blauwendraat C
        • Leonard HL
        • Makarious MB
        • Kim JJ
        • Liu G
        • Maple-Grødem J
        • Corvol JC
        • Pihlstrøm L
        • van Nimwegen M
        • Smolensky L
        • Amondikar N
        • Hutten SJ
        • Frasier M
        • Nguyen KH
        • Rick J
        • Eberly S
        • Faghri F
        • Auinger P
        • Scott KM
        • Wijeyekoon R
        • Van Deerlin VM
        • Hernandez DG
        • Gibbs RJ
        • Day-Williams AG
        • Brice A
        • Alves G
        • Noyce AJ
        • Tysnes OB
        • Evans JR
        • Breen DP
        • Estrada K
        • Wegel CE
        • Danjou F
        • Simon DK
        • Andreassen OA
        • Ravina B
        • Toft M
        • Heutink P
        • Bloem BR
        • Weintraub D
        • Barker RA
        • Williams-Gray CH
        • van de Warrenburg BP
        • Van Hilten JJ
        • Scherzer CR
        • Singleton AB
        • Nalls MA
        Differences in the presentation and progression of Parkinson's Disease by sex.
        Mov Disord. 2021; 36: 106-117
        • Kalia LV
        • Lang AE.
        Parkinson's disease.
        Lancet. 2015; 386: 896-912
        • Kelly MJ
        • Lawton MA
        • Baig F
        • Ruffmann C
        • Barber TR
        • Lo C
        • Klein JC
        • Ben-Shlomo Y
        • Hu MT.
        Predictors of motor complications in early Parkinson's disease: A prospective cohort study.
        Mov Disord. 2019; 34: 1174-1183
        • Kim HJ
        • Mason S
        • Foltynie T
        • Winder-Rhodes S
        • Barker RA
        • Williams-Gray CH.
        Motor complications in Parkinson's disease: 13-year follow-up of the CamPaIGN cohort.
        Mov Disord. 2020; 35: 185-190
        • Knudson M
        • Thomsen TH
        • Kjaer TW.
        Comparing objective and subjective measures of Parkinson's disease using the Parkinson's KinetiGraph.
        Front Neurol. 2020; 11570833
        • Kulisevsky J
        • Oliveira L
        • Fox SH.
        Update in therapeutic strategies for Parkinson's disease.
        Curr Opin Neurol. 2018; 31: 439-447
        • Li DH
        • He YC
        • Liu J
        • Chen SD.
        Diagnostic accuracy of transcranial sonography of the substantia nigra in Parkinson's disease: A systematic review and meta-analysis.
        Sci Rep. 2016; 6: 20863
        • Li K
        • Ge YL
        • Gu CC
        • Zhang JR
        • Jin H
        • Li J
        • Cheng XY
        • Yang YP
        • Wang F
        • Zhang YC
        • Chen J
        • Mao CJ
        • Liu CF
        Substantia nigra echogenicity is associated with serum ferritin, gender and iron-related genes in Parkinson's disease.
        Sci Rep. 2020; 10: 8660
        • Li T
        • Shi J
        • Qin B
        • Fan D
        • Liu N
        • Ni J
        • Zhang T
        • Zhou H
        • Xu X
        • Wei M
        • Zhang X
        • Wang X
        • Liu J
        • Wang Y
        • Tian J.
        Increased substantia nigra echogenicity correlated with visual hallucinations in Parkinson's disease: A Chinese population-based study.
        Neurol Sci. 2020; 41: 661-667
        • Luca A
        • Monastero R
        • Baschi R
        • Cicero CE
        • Mostile G
        • Davì M
        • Restivo V
        • Zappia M
        • Nicoletti A.
        Cognitive impairment and levodopa induced dyskinesia in Parkinson's disease: A longitudinal study from the PACOS cohort.
        Sci Rep. 2021; 11: 867
        • Manson A
        • Stirpe P
        • Schrag A.
        Levodopa-induced-dyskinesias clinical features, incidence, risk factors, management and impact on quality of life.
        J Parkinsons Dis. 2012; 2: 189-198
        • Martínez-Martín P
        • Gil-Nagel A
        • Gracia LM
        • Gómez JB
        • Martínez-Sarriés J
        • Bermejo F.
        Unified Parkinson's Disease Rating Scale characteristics and structure: The Cooperative Multicentric Group.
        Mov Disord. 1994; 9: 76-83
        • Postuma RB
        • Berg D
        • Stern M
        • Poewe W
        • Olanow CW
        • Oertel W
        • Obeso J
        • Marek K
        • Litvan I
        • Lang AE
        • Halliday G
        • Goetz CG
        • Gasser T
        • Dubois B
        • Chan P
        • Bloem BR
        • Adler CH
        • Deuschl G.
        MDS clinical diagnostic criteria for Parkinson's disease.
        Mov Disord. 2015; 30: 1591-1601
        • Prati P
        • Bignamini A
        • Coppo L
        • Naldi A
        • Comi C
        • Cantello R
        • Gusmaroli G
        • Walter U.
        The measuring of substantia nigra hyperechogenicity in an Italian cohort of Parkinson disease patients: A case/control study (NOBIS Study).
        J Neural Transm (Vienna). 2017; 124: 869-879
        • Radhakrishnan DM
        • Goyal V.
        Parkinson's disease: A review.
        Neurol India. 2018; 66: S26-S35
        • Sharma JC
        • Bachmann CG
        • Linazasoro G.
        Classifying risk factors for dyskinesia in Parkinson's disease.
        Parkinsonism Relat Disord. 2010; 16: 490-497
        • Sheng AY
        • Zhang YC
        • Sheng YJ
        • Wang CS
        • Zhang Y
        • Hu H
        • Luo WF
        • LI CF
        Transcranial sonography image characteristics in different Parkinson's disease subtypes.
        Neurol Sci. 2017; 38: 1805-1810
        • Tirozzi A
        • Modugno N
        • Palomba NP
        • Ferese R
        • Lombardi A
        • Olivola E
        • Gialluisi A
        • Esposito T.
        Analysis of genetic and non-genetic predictors of levodopa induced dyskinesia in Parkinson's disease.
        Front Pharmacol. 2021; 12640603
        • Tomlinson CL
        • Stowe R
        • Patel S
        • Rick C
        • Gray R
        • Clarke CE
        Systematic review of levodopa dose equivalency reporting in Parkinson's disease.
        Mov Disord. 2010; 25: 2649-2653
        • Toomsoo T
        • Liepelt-Scarfone I
        • Kerner R
        • Kadastik-Eerme L
        • Asser T
        • Rubanovits I
        • Berg D
        • Taba P.
        Substantia nigra hyperechogenicity: Validation of transcranial sonography for Parkinson disease diagnosis in a large Estonian cohort.
        J Ultrasound Med. 2016; 35: 17-23
        • Tran TN
        • Vo TNN
        • Frei K
        • Truong DD.
        Levodopa-induced dyskinesia: Clinical features, incidence, and risk factors.
        J Neural Transm (Vienna). 2018; 125: 1109-1117
        • Turcano P
        • Mielke MM
        • Bower JH
        • Parisi JE
        • Cutsforth-Gregory JK
        • Ahlskog JE
        • Savica R.
        Levodopa-induced dyskinesia in Parkinson disease: A population-based cohort study.
        Neurology. 2018; 91: e2238-e2243
        • Walker RW
        • Howells AR
        • Gray WK.
        The effect of levodopa dose and body weight on dyskinesia in a prevalent population of people with Parkinson's disease.
        Parkinsonism Relat Disord. 2011; 17: 27-29
        • Warren Olanow C
        • Kieburtz K
        • Rascol O
        • Poewe W
        • Schapira AH
        • Emre M
        • Nissinen H
        • Leinonen M
        • Stocchi F
        Factors predictive of the development of levodopa-induced dyskinesia and wearing-off in Parkinson's disease.
        Mov Disord. 2013; 28: 1064-1071
        • Xu R
        • Chen G
        • Mao Z
        • Gao H
        • Deng Y
        • Tao A.
        Diagnostic performance of transcranial sonography for evaluating substantia nigra hyper-echogenicity in patients with Parkinson's disease.
        Ultrasound Med Biol. 2020; 46: 1208-1215
        • Yu J
        • Li J
        • Huang X.
        The Beijing version of the Montreal Cognitive Assessment as a brief screening tool for mild cognitive impairment: A community-based study.
        BMC Psychiatry. 2012; 12: 156
        • Yu SY
        • Cao CJ
        • Zuo LJ
        • Chen ZJ
        • Lian TH
        • Wang F
        • Hu Y
        • Piao YS
        • Li LX
        • Guo P
        • Liu L
        • Yu QJ
        • Wang RD
        • Chan P
        • Chen SD
        • Wang XM
        • Zhang W.
        Clinical features and dysfunctions of iron metabolism in Parkinson disease patients with hyper echogenicity in substantia nigra: A cross-sectional study.
        BMC Neurol. 2018; 18: 9
        • Zhang S
        • Tao K
        • Wang J
        • Duan Y
        • Wang B
        • Liu X.
        Substantia nigra hyperechogenicity reflects the progression of dopaminergic neurodegeneration in 6-OHDA rat model of Parkinson's disease.
        Front Cell Neurosci. 2020; 14: 216
        • Zhou HY
        • Sun Q
        • Tan YY
        • Hu YY
        • Zhan WW
        • Li DH
        • Wang Y
        • Xiao Q
        • Liu J
        • Chen SD.
        Substantia nigra echogenicity correlated with clinical features of Parkinson's disease.
        Parkinsonism Relat Disord. 2016; 24: 28-33
        • Zhou HY
        • Huang P
        • Sun Q
        • Du JJ
        • Cui SS
        • Hu YY
        • Zhan WW
        • Wang Y
        • Xiao Q
        • Liu J
        • Tan YY
        • Chen SD.
        The role of substantia nigra sonography in the differentiation of Parkinson's disease and multiple system atrophy.
        Transl Neurodegener. 2018; 7: 15