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Closing the loop for patients with Parkinson disease: where are we?

Abstract

Although levodopa remains the most efficacious symptomatic therapy for Parkinson disease (PD), management of levodopa treatment during the advanced stages of the disease is extremely challenging. This difficulty is a result of levodopa’s short half-life, a progressive narrowing of the therapeutic window, and major inter-patient and intra-patient variations in the dose–response relationship. Therefore, a suitable alternative to repeated oral administration of levodopa is being sought. Recent research efforts have focused on the development of novel levodopa delivery strategies and wearable physical sensors that track symptoms and disease progression. However, the need for methods to monitor the levels of levodopa present in the body in real time has been overlooked. Advances in chemical sensor technology mean that the development of wearable and mobile biosensors for continuous or frequent levodopa measurements is now possible. Such levodopa monitoring could help to deliver personalized and timely medication dosing to alleviate treatment-related fluctuations in the symptoms of PD. Therefore, with the aim of optimizing therapeutic management of PD and improving the quality of life of patients, we share our vision of a future closed-loop autonomous wearable ‘sense-and-act’ system. This system consists of a network of physical and chemical sensors coupled with a levodopa delivery device and is guided by effective big data fusion algorithms and machine learning methods.

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Fig. 1: Oral administration of levodopa and the associated symptom fluctuations in Parkinson disease.
Fig. 2: Vision of a future closed-loop autonomous system for the management of Parkinson disease.

References

  1. Parkinson, J. An essay on the shaking palsy. J. Neuropsychiatry Clin. Neurosci. 14, 223–236 (2002).

    PubMed  Article  Google Scholar 

  2. de Lau, L. M. L. & Breteler, M. M. B. Epidemiology of Parkinson’s disease. Lancet Neurol. 5, 525–535 (2006).

    PubMed  Article  Google Scholar 

  3. Connolly, B. S. & Lang, A. E. Pharmacological treatment of Parkinson disease: a review. JAMA 311, 1670–1683 (2014).

    PubMed  Article  CAS  Google Scholar 

  4. Nord, M. Levodopa pharmacokinetics — from stomach to brain — a study on patients with Parkinson’s disease. Thesis (Linköping University, 2017).

  5. Monje, M. H. G., Foffani, G., Obeso, J. & Sánchez-Ferro, Á. New sensor and wearable technologies to aid in the diagnosis and treatment monitoring of Parkinson’s disease. Annu. Rev. Biomed. Eng. 21, 111–143 (2019).

    CAS  PubMed  Article  Google Scholar 

  6. Poewe, W. et al. Parkinson disease. Nat. Rev. Dis. Prim. 3, 17013 (2017).

    PubMed  Article  Google Scholar 

  7. Yang, W. et al. Current and projected future economic burden of Parkinson’s disease in the US. NPJ Park. Dis. 6, 15 (2020).

    Article  Google Scholar 

  8. Charvin, D., Medori, R., Hauser, R. A. & Rascol, O. Therapeutic strategies for Parkinson disease: beyond dopaminergic drugs. Nat. Rev. Drug Discov. 17, 804–822 (2018).

    CAS  PubMed  Article  Google Scholar 

  9. Birkmayer, W. & Hornykiewicz, O. Der L-Dioxyphenylalanin (=L-DOPA)-Effekt beim Parkinson-syndrom des menschen: zur pathogenese und behandlung der Parkinson-Akinese. Arch. Psychiatr. Nervenkr. 203, 560–574 (1962).

    CAS  Article  Google Scholar 

  10. Nutt, J. G. & Holford, N. H. G. The response to levodopa in Parkinson’s disease: imposing pharmacological law and order. Ann. Neurol. 39, 561–573 (1996).

    CAS  PubMed  Article  Google Scholar 

  11. Hornykiewicz, O. A brief history of levodopa. J. Neurol. 257, 249–252 (2010).

    CAS  Article  Google Scholar 

  12. Abbott, A. Levodopa: the story so far. Nature 466, S6–S7 (2010).

    CAS  PubMed  Article  Google Scholar 

  13. Obeso, J. A., Olanow, C. W. & Nutt, J. G. Levodopa motor complications in Parkinson’s disease. Trends Neurosci. 23, S2–S7 (2000).

    CAS  PubMed  Article  Google Scholar 

  14. Urso, D., Chaudhuri, K. R., Qamar, M. A. & Jenner, P. Improving the delivery of levodopa in Parkinson’s disease: a review of approved and emerging therapies. CNS Drugs 34, 1149–1163 (2020).

    CAS  PubMed  Article  Google Scholar 

  15. Rovini, E., Maremmani, C. & Cavallo, F. How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front. Neurosci. 11, 555 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  16. Barbosa, W., Zhou, K., Waddell, E., Myers, T. & Dorsey, E. R. Improving access to care: telemedicine across medical domains. Annu. Rev. Public Health 42, 463–481 (2021).

    PubMed  Article  Google Scholar 

  17. van den Bergh, R., Bloem, B. R., Meinders, M. J. & Evers, L. J. W. The state of telemedicine for persons with Parkinson’s disease. Curr. Opin. Neurol. 34, 589–597 (2021).

    PubMed  PubMed Central  Google Scholar 

  18. Delrobaei, M. et al. Towards remote monitoring of Parkinson’s disease tremor using wearable motion capture systems. J. Neurol. Sci. 384, 38–45 (2018).

    PubMed  Article  Google Scholar 

  19. Evers, L. J. W., Krijthe, J. H., Meinders, M. J., Bloem, B. R. & Heskes, T. M. Measuring Parkinson’s disease over time: the real-world within-subject reliability of the MDS-UPDRS. Mov. Disord. 34, 1480–1487 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  20. AlMahadin, G. et al. Parkinson’s disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms — a patient and healthcare professional perspective. BMC Neurol. 20, 419 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  21. Teymourian, H. et al. Wearable electrochemical sensors for the monitoring and screening of drugs. ACS Sens. 5, 2679–2700 (2020).

    CAS  PubMed  Article  Google Scholar 

  22. Kim, J., Campbell, A. S., de Ávila, B. E. F. & Wang, J. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37, 389–406 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Choi, J. et al. Bio-integrated wearable systems: a comprehensive review. Chem. Rev. 119, 5461–5533 (2019).

    PubMed  Article  CAS  Google Scholar 

  24. Teymourian, H., Barfidokht, A. & Wang, J. Electrochemical glucose sensors in diabetes management: an updated review (2010–2020). Chem. Soc. Rev. 49, 7671–7709 (2020).

    CAS  PubMed  Article  Google Scholar 

  25. Gao, W. et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529, 509–514 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Goetz, C. G., Stebbins, G. T., Blasucci, L. M. & Grobman, M. S. Efficacy of a patient-training videotape on motor fluctuations for on-off diaries in Parkinson’s disease. Mov. Disord. 12, 1039–1041 (1997).

    CAS  PubMed  Article  Google Scholar 

  27. Espay, A. J. et al. Technology in Parkinson’s disease: challenges and opportunities. Mov. Disord. 31, 1272–1282 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  28. Rovini, E., Maremmani, C. & Cavallo, F. Automated systems based on wearable sensors for the management of Parkinson’s disease at home: a systematic review. Telemed. J. E. Health 25, 167–183 (2018).

    PubMed  Article  Google Scholar 

  29. Sundgren, M., Andréasson, M., Svenningsson, P., Noori, R.-M. & Johansson, A. Does information from the Parkinson KinetiGraphTM (PKG) influence the neurologist’s treatment decisions? — An observational study in routine clinical care of people with Parkinson’s disease. J. Pers. Med. 11, 519 (2021).

    PubMed  PubMed Central  Article  Google Scholar 

  30. Rob, P. et al. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson’s disease. Sci. Transl. Med. 13, eabd7865 (2021).

    Article  Google Scholar 

  31. Heldman, D. A. et al. App-based bradykinesia tasks for clinic and home assessment in Parkinson’s disease: reliability and responsiveness. J. Parkinsons Dis. 7, 741–747 (2017).

    PubMed  Article  Google Scholar 

  32. Pulliam, C. L. et al. Continuous assessment of levodopa response in Parkinson’s disease using wearable motion sensors. IEEE Trans. Biomed. Eng. 65, 159–164 (2018).

    PubMed  Article  Google Scholar 

  33. Vittal, P. et al. Does added objective tremor monitoring improve clinical outcomes in essential tremor treatment? Mov. Disord. Clin. Pract. 5, 96–98 (2018).

    PubMed  Article  Google Scholar 

  34. Nahab, F. B., Abu-Hussain, H. & Moreno, L. Evaluation of clinical utility of the personal KinetiGraph® in the management of Parkinson disease. Adv. Park. Dis. 8, 42–61 (2019).

    Google Scholar 

  35. Pahwa, R. et al. Role of the Personal KinetiGraph in the routine clinical assessment of Parkinson’s disease: recommendations from an expert panel. Expert Rev. Neurother. 18, 669–680 (2018).

    CAS  PubMed  Article  Google Scholar 

  36. Fokkenrood, H. J. P. et al. Physical activity monitoring in patients with peripheral arterial disease: validation of an activity monitor. Eur. J. Vasc. Endovasc. Surg. 48, 194–200 (2014).

    CAS  PubMed  Article  Google Scholar 

  37. Taylor, L. M. et al. Validation of a body-worn accelerometer to measure activity patterns in octogenarians. Arch. Phys. Med. Rehabil. 95, 930–934 (2014).

    PubMed  Article  Google Scholar 

  38. Yeung, J. et al. Evaluating the sensoria smart socks gait monitoring system for rehabilitation outcomes. PMR 11, 512–521 (2019).

    Article  Google Scholar 

  39. Universitat Politècnica De Catalunya BarcelonaTech. STAT-ON, a new device that helps monitor the symptoms of patients with Parkinson’s. Universitat Politècnica De Catalunya BarcelonaTech https://www.upc.edu/en/press-room/news/stat-on-a-new-device-that-helps-monitor-the-symptoms-of-patients-with-parkinson2019s (2019).

  40. Pérez-López, C. et al. Assessing motor fluctuations in Parkinson’s disease patients based on a single inertial sensor. Sensors 16, 2132 (2016).

    PubMed Central  Article  Google Scholar 

  41. Samà, A. et al. Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor. Comput. Biol. Med. 84, 114–123 (2017).

    PubMed  Article  Google Scholar 

  42. Piromalis, D. D. et al. Commercially available sensor-based monitoring and support systems in Parkinson’s disease: an overview. 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) 430–438 (IEEE, 2021).

  43. Washabaugh, E. P., Kalyanaraman, T., Adamczyk, P. G., Claflin, E. S. & Krishnan, C. Validity and repeatability of inertial measurement units for measuring gait parameters. Gait Posture 55, 87–93 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  44. Morris, R. et al. Validity of mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson’s disease. Physiol. Meas. 40, 95003 (2019).

    Article  Google Scholar 

  45. Zampieri, C. et al. The instrumented timed up and go test: Potential outcome measure for disease modifying therapies in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 81, 171–176 (2010).

    PubMed  Article  Google Scholar 

  46. Mancini, M. & Horak, F. B. Potential of APDM mobility lab for the monitoring of the progression of Parkinson’s disease. Expert Rev. Med. Devices 13, 455–462 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Weiss, D. et al. Long-term outcome of deep brain stimulation in fragile X-associated tremor/ataxia syndrome. Park. Relat. Disord. 21, 310–313 (2015).

    Article  Google Scholar 

  48. Lyons, K. E. & Pahwa, R. The impact and management of nonmotor symptoms of Parkinson’s disease. Am. J. Manag. Care 17, S308–S314 (2011).

    PubMed  Google Scholar 

  49. Armstrong, M. J. & Okun, M. S. Diagnosis and treatment of parkinson disease: a review. JAMA 323, 548–560 (2020).

    PubMed  Article  Google Scholar 

  50. Schapira, A. H. V., Chaudhuri, K. R. & Jenner, P. Non-motor features of Parkinson disease. Nat. Rev. Neurosci. 18, 435–450 (2017).

    CAS  PubMed  Article  Google Scholar 

  51. Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601 (2015).

    PubMed  Article  Google Scholar 

  52. Velseboer, D. C., de Haan, R. J., Wieling, W., Goldstein, D. S. & de Bie, R. M. A. Prevalence of orthostatic hypotension in Parkinson’s disease: a systematic review and meta-analysis. Park. Relat. Disord. 17, 724–729 (2011).

    Article  Google Scholar 

  53. Shahdost-Fard, F., Bigdeli, A. & Hormozi-Nezhad, M. R. A smartphone-based fluorescent electronic tongue for tracing dopaminergic agents in human urine. ACS Chem. Neurosci. 12, 3157–3166 (2021).

    CAS  PubMed  Article  Google Scholar 

  54. Chen, C. et al. Blue and green emission-transformed fluorescent copolymer: specific detection of levodopa of anti-Parkinson drug in human serum. Talanta 214, 120817 (2020).

    CAS  PubMed  Article  Google Scholar 

  55. Wang, L., Su, D., Berry, S. N., Lee, J. & Chang, Y.-T. A new approach for turn-on fluorescence sensing of l-DOPA. Chem. Commun. 53, 12465–12468 (2017).

    CAS  Article  Google Scholar 

  56. Park, S. W., Kim, T. E. & Jung, Y. K. Glutathione-decorated fluorescent carbon quantum dots for sensitive and selective detection of levodopa. Anal. Chim. Acta 1165, 338513 (2021).

    CAS  PubMed  Article  Google Scholar 

  57. Brunetti, B., Valdés-Ramírez, G., Litvan, I. & Wang, J. A disposable electrochemical biosensor for l-DOPA determination in undiluted human serum. Electrochem. Commun. 48, 28–31 (2014).

    CAS  Article  Google Scholar 

  58. Goud, K. Y. et al. Wearable electrochemical microneedle sensor for continuous monitoring of levodopa: toward Parkinson management. ACS Sens. 4, 2196–2204 (2019).

    CAS  PubMed  Article  Google Scholar 

  59. Tai, L.-C. et al. Wearable sweat band for noninvasive levodopa monitoring. Nano Lett. 19, 6346–6351 (2019).

    CAS  PubMed  Article  Google Scholar 

  60. Moon, J.-M. et al. Non-invasive sweat-based tracking of L-dopa pharmacokinetic profiles following an oral tablet administration. Angew. Chem. Int. Ed. 60, 19074–19078 (2021).

    CAS  Article  Google Scholar 

  61. Yang, Y. & Gao, W. Wearable and flexible electronics for continuous molecular monitoring. Chem. Soc. Rev. 18, 1465–1491 (2019).

    Article  Google Scholar 

  62. Bandodkar, A. J. & Wang, J. Non-invasive wearable electrochemical sensors: a review. Trends Biotechnol. 32, 363–371 (2014).

    CAS  PubMed  Article  Google Scholar 

  63. Tang, W. et al. Touch-based stressless cortisol sensing. Adv. Mater. 33, 2008465 (2021).

    CAS  Article  Google Scholar 

  64. Lin, S. et al. Natural perspiration sampling and in situ electrochemical analysis with hydrogel micropatches for user-identifiable and wireless chemo/biosensing. ACS Sens. 5, 93–102 (2020).

    CAS  PubMed  Article  Google Scholar 

  65. Teymourian, H., Tehrani, F., Mahato, K. & Wang, J. Lab under the skin: microneedle based wearable devices. Adv. Healthc. Mater. 10, 2002255 (2021).

    CAS  Article  Google Scholar 

  66. Samant, P. P. et al. Sampling interstitial fluid from human skin using a microneedle patch. Sci. Transl. Med. 12, eaaw0285 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Tran, B. Q. et al. Proteomic characterization of dermal interstitial fluid extracted using a novel microneedle-assisted technique. J. Proteome Res. 17, 479–485 (2018).

    CAS  PubMed  Article  Google Scholar 

  68. Nilsson, A. K., Sjöbom, U., Christenson, K. & Hellström, A. Lipid profiling of suction blister fluid: comparison of lipids in interstitial fluid and plasma. Lipids Health Dis. 18, 164 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  69. Niedzwiecki, M. M. et al. Human suction blister fluid composition determined using high-resolution metabolomics. Anal. Chem. 90, 3786–3792 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Kolluru, C., Williams, M., Chae, J. & Prausnitz, M. R. Recruitment and collection of dermal interstitial fluid using a microneedle patch. Adv. Healthc. Mater. 8, 1801262 (2019).

    CAS  Article  Google Scholar 

  71. Rawson, T. M. et al. Microneedle biosensors for real-time, minimally invasive drug monitoring of phenoxymethylpenicillin: a first-in-human evaluation in healthy volunteers. Lancet Digit. Health 1, e335–e343 (2019).

    PubMed  Article  Google Scholar 

  72. Tehrani, F. et al. An integrated wearable microneedle array for the continuous monitoring of multiple biomarkers in interstitial fluid. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00887-1 (2022).

    Article  PubMed  Google Scholar 

  73. Senek, M. & Nyholm, D. Continuous drug delivery in Parkinson’s disease. CNS Drugs 28, 19–27 (2014).

    CAS  PubMed  Article  Google Scholar 

  74. Dhall, R. & Kreitzman, D. L. Advances in levodopa therapy for Parkinson disease. Neurology 86, S13–S24 (2016).

    CAS  PubMed  Article  Google Scholar 

  75. Tsunemi, T. et al. Intrajejunal Infusion of levodopa/carbidopa for advanced Parkinson’s disease: a systematic review. Mov. Disord. 36, 1759–1771 (2021).

    CAS  PubMed  Article  Google Scholar 

  76. Olanow, C. W. et al. Continuous subcutaneous levodopa delivery for Parkinson’s disease: a randomized study. J. Park. Dis. 11, 177–186 (2021).

    CAS  Google Scholar 

  77. Ray Chaudhuri, K. et al. Non-oral dopaminergic therapies for Parkinson’s disease: current treatments and the future. NPJ Park. Dis. 2, 16023 (2016).

    CAS  Article  Google Scholar 

  78. Bogachkov, Y. Y. JUBIwatch device targets medication adherence. Parkinson’s News Today https://parkinsonsnewstoday.com/2021/11/02/jubiwatch-medication-management-device-parkinsons-disease/ (2021).

  79. Frankel, J. P., Lees, A. J., Kempster, P. A. & Stern, G. M. Subcutaneous apomorphine in the treatment of Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 53, 96–101 (1990).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. Trenkwalder, C. et al. Expert Consensus Group report on the use of apomorphine in the treatment of Parkinson’s disease–clinical practice recommendations. Park. Relat. Disord. 21, 1023–1030 (2015).

    Article  Google Scholar 

  81. LeWitt, P. A. Subcutaneously administered apomorphine. Neurology 62, S8–S11 (2004).

    CAS  PubMed  Article  Google Scholar 

  82. Pahwa, R. et al. Early morning akinesia in Parkinson’s disease: effect of standard carbidopa/levodopa and sustained-release carbidopa/levodopa. Neurology 46, 1059–1062 (1996).

    CAS  PubMed  Article  Google Scholar 

  83. Kempster, P. A. et al. Levodopa peripheral pharmacokinetics and duration of motor response in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 52, 718–723 (1989).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Antonini, A. & Tolosa, E. Apomorphine and levodopa infusion therapies for advanced Parkinson’s disease: selection criteria and patient management. Expert Rev. Neurother. 9, 859–867 (2009).

    CAS  PubMed  Article  Google Scholar 

  85. Olanow, C. W., Poewe, W., Rascol, O. & Stocchi, F. On-demand therapy for OFF episodes in Parkinson’s disease. Mov. Disord. 36, 2244–2253 (2021).

    PubMed  Article  Google Scholar 

  86. Hauser, R. A., LeWitt, P. A. & Comella, C. L. On demand therapy for Parkinson’s disease patients: opportunities and choices. Postgrad. Med. 133, 721–727 (2021).

    PubMed  Article  Google Scholar 

  87. Amjad, F. et al. Current practices for outpatient initiation of levodopa-carbidopa intestinal gel for management of advanced Parkinson’s disease in the United States. Adv. Ther. 36, 2233–2246 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  88. Müller, T. An evaluation of subcutaneous apomorphine for the treatment of Parkinson’s disease. Expert Opin. Pharmacother. 21, 1659–1665 (2020).

    PubMed  Article  Google Scholar 

  89. Nutt, J. G. & Woodward, W. R. Levodopa pharmacokinetics and pharmacodynamics in fluctuating parkinsonian patients. Neurology 36, 739 (1986).

    CAS  PubMed  Article  Google Scholar 

  90. Nutt, J. G., Carter, J. H., Lea, E. S. & Woodward, W. R. Motor fluctuations during continuous levodopa infusions in patients with Parkinson’s disease. Mov. Disord. 12, 285–292 (1997).

    CAS  PubMed  Article  Google Scholar 

  91. Gancher, S. T., Nutt, J. G. & Woodward, W. R. Time course of tolerance to apomorphine in parkinsonism. Clin. Pharmacol. Ther. 52, 504–510 (1992).

    CAS  PubMed  Article  Google Scholar 

  92. Nutt, J. G. Pharmacokinetics and pharmacodynamics of levodopa. Mov. Disord. 23, S580–S584 (2008).

    PubMed  Article  Google Scholar 

  93. Scholten, K. & Meng, E. A review of implantable biosensors for closed-loop glucose control and other drug delivery applications. Int. J. Pharm. 544, 319–334 (2018).

    CAS  PubMed  Article  Google Scholar 

  94. Khodaei, M. J., Candelino, N., Mehrvarz, A. & Jalili, N. Physiological closed-loop control (PCLC) systems: review of a modern frontier in automation. IEEE Access. 8, 23965–24005 (2020).

    Article  Google Scholar 

  95. Schmitzer, J., Strobel, C., Blechschmidt, R., Tappe, A. & Peuscher, H. Efficient closed loop simulation of do-it-yourself artificial pancreas systems. J. Diabetes Sci. Technol. 16, 61–69 (2021).

    PubMed  PubMed Central  Article  Google Scholar 

  96. Wolkowicz, K. L. et al. A review of biomarkers in the context of type 1 diabetes: biological sensing for enhanced glucose control. Bioeng. Transl. Med. 6, e10201 (2021).

    CAS  PubMed  Article  Google Scholar 

  97. Espay, A. J., Brundin, P. & Lang, A. E. Precision medicine for disease modification in Parkinson disease. Nat. Rev. Neurol. 13, 119–126 (2017).

    PubMed  Article  Google Scholar 

  98. Lang, A. E. & Espay, A. J. Disease modification in Parkinson’s disease: current approaches, challenges, and future considerations. Mov. Disord. 33, 660–677 (2018).

    PubMed  Article  Google Scholar 

  99. Luis-Martínez, R., Monje, M. H. G., Antonini, A., Sánchez-Ferro, Á. & Mestre, T. A. Technology-enabled care: integrating multidisciplinary care in Parkinson’s disease through digital technology. Front. Neurol. 11, 575975 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  100. Wang, C. et al. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nat. Biomed. Eng. 2, 687–695 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  101. Goud, K. Y. et al. Wearable electrochemical microneedle sensing platform for real-time continuous interstitial fluid monitoring of apomorphine: toward Parkinson management. Sens. Actuators B Chem. 354, 131234 (2022).

    CAS  Article  Google Scholar 

  102. Ji, D. et al. Smartphone-based square wave voltammetry system with screen-printed graphene electrodes for norepinephrine detection. Smart Mater. Med. 1, 1–9 (2020).

    Article  Google Scholar 

  103. Imani, S. et al. A wearable chemical–electrophysiological hybrid biosensing system for real-time health and fitness monitoring. Nat. Commun. 7, 11650 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. Sempionatto, J. R. et al. An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic biomarkers. Nat. Biomed. Eng. 5, 737–748 (2021).

    CAS  PubMed  Article  Google Scholar 

  105. Belić, M. et al. Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease — a review. Clin. Neurol. Neurosurg. 184, 105442 (2019).

    PubMed  Article  Google Scholar 

  106. Patel, S. et al. Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 13, 864–873 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  107. Nancy Jane, Y., Khanna Nehemiah, H. & Arputharaj, K. A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson’s disease. J. Biomed. Inform. 60, 169–176 (2016).

    CAS  PubMed  Article  Google Scholar 

  108. Lawton, M. et al. Parkinson’s disease subtypes in the Oxford Parkinson Disease Centre (OPDC) discovery cohort. J. Park. Dis. 5, 269–279 (2015).

    Google Scholar 

  109. Brichta, L., Greengard, P. & Flajolet, M. Advances in the pharmacological treatment of Parkinson’s disease: targeting neurotransmitter systems. Trends Neurosci. 36, 543–554 (2013).

    CAS  PubMed  Article  Google Scholar 

  110. LeWitt, P. A. Norepinephrine: the next therapeutics frontier for Parkinson’s disease. Transl. Neurodegener. 1, 4 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. Devos, D., Defebvre, L. & Bordet, R. Dopaminergic and non-dopaminergic pharmacological hypotheses for gait disorders in Parkinson’s disease. Fundam. Clin. Pharmacol. 24, 407–421 (2010).

    CAS  PubMed  Article  Google Scholar 

  112. Stocchi, F. Prevention and treatment of motor fluctuations. Park. Relat. Disord. 9, 73–81 (2003).

    Article  Google Scholar 

  113. Dijk, J. M., Espay, A. J., Katzenschlager, R. & de Bie, R. M. A. The choice between advanced therapies for Parkinson’s disease patients: why, what, and when? J. Park. Dis. 10, S65–S73 (2020).

    Google Scholar 

  114. Larson, D. & Simuni, T. New dopaminergic therapies for PD motor complications. Neuropharmacology 204, 108869 (2022).

    CAS  PubMed  Article  Google Scholar 

  115. Athauda, D. & Foltynie, T. The ongoing pursuit of neuroprotective therapies in Parkinson disease. Nat. Rev. Neurol. 11, 25–40 (2015).

    CAS  PubMed  Article  Google Scholar 

  116. Kim, K. S. Toward neuroprotective treatments of Parkinson’s disease. Proc. Natl Acad. Sci. USA 114, 3795–3797 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. Salamon, A., Zádori, D., Szpisjak, L., Klivényi, P. & Vécsei, L. Neuroprotection in Parkinson’s disease: facts and hopes. J. Neural Transm. 127, 821–829 (2020).

    PubMed  Article  Google Scholar 

  118. Savitt, D. & Jankovic, J. Targeting α-synuclein in Parkinson’s disease: progress towards the development of disease-modifying therapeutics. Drugs 79, 797–810 (2019).

    CAS  PubMed  Article  Google Scholar 

  119. Genius, J. et al. Results from a phase 1b study of UCB0599, an orally available, brain-penetrant inhibitor of alphasynuclein (ASYN) misfolding in people living with Parkinson’s disease (PD) (2025). Neurology 96, 2025 (2021).

    Google Scholar 

  120. Sardi, S. P. & Simuni, T. New era in disease modification in Parkinson’s disease: review of genetically targeted therapeutics. Park. Relat. Disord. 59, 32–38 (2019).

    Article  Google Scholar 

  121. Nutt, J. G. et al. Randomized, double-blind trial of glial cell line-derived neurotrophic factor (GDNF) in PD. Neurology 60, 69–73 (2003).

    CAS  PubMed  Article  Google Scholar 

  122. Lang, A. E. et al. Randomized controlled trial of intraputamenal glial cell line–derived neurotrophic factor infusion in Parkinson disease. Ann. Neurol. 59, 459–466 (2006).

    CAS  PubMed  Article  Google Scholar 

  123. Axelsen, T. M. & Woldbye, D. P. D. Gene therapy for Parkinson’s disease, an update. J. Parkinsons Dis. 8, 195–215 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  124. Chen, W., Huang, Q., Ma, S. & Li, M. Progress in dopaminergic cell replacement and regenerative strategies for Parkinson’s disease. ACS Chem. Neurosci. 10, 839–851 (2019).

    CAS  PubMed  Article  Google Scholar 

  125. Kim, T. W., Koo, S. Y. & Studer, L. Pluripotent stem cell therapies for Parkinson disease: present challenges and future opportunities. Front. Cell Dev. Biol. 8, 729 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  126. Hung, A. Y. & Schwarzschild, M. A. Approaches to disease modification for Parkinson’s disease: clinical trials and lessons learned. Neurotherapeutics 17, 1393–1405 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. Kalia, L. V., Kalia, S. K. & Lang, A. E. Disease-modifying strategies for Parkinson’s disease. Mov. Disord. 30, 1442–1450 (2015).

    CAS  PubMed  Article  Google Scholar 

  128. Silva, R. C., Domingues, H. S., Salgado, A. J. & Teixeira, F. G. From regenerative strategies to pharmacological approaches: can we fine-tune treatment for Parkinson’s disease? Neural Regen. Res. 17, 933–936 (2022).

    PubMed  Article  Google Scholar 

  129. Del Din, S., Kirk, C., Yarnall, A. J., Rochester, L. & Hausdorff, J. M. Body-worn sensors for remote monitoring of Parkinson’s disease motor symptoms: vision, state of the art, and challenges ahead. J. Park. Dis. 11, S35–S47 (2021).

    Google Scholar 

  130. Dorsey, E. R., Bloem, B. R. & Okun, M. S. A new day: the role of telemedicine in reshaping care for persons with movement disorders. Mov. Disord. 35, 1897–1902 (2020).

    PubMed  Article  Google Scholar 

  131. Evans, L., Mohamed, B. & Thomas, E. C. Using telemedicine and wearable technology to establish a virtual clinic for people with Parkinson’s disease. BMJ Open Qual. 9, e001000 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  132. Barker, R. A. et al. GDNF and Parkinson’s disease: where next? A summary from a recent workshop. J. Park. Dis. 10, 875–891 (2020).

    CAS  Google Scholar 

  133. Jankovic, J. Motor fluctuations and dyskinesias in Parkinson’s disease: clinical manifestations. Mov. Disord. 20, S11–S16 (2005).

    PubMed  Article  Google Scholar 

  134. Vijiaratnam, N. & Foltynie, T. Therapeutic strategies to treat or prevent off episodes in adults with Parkinson’s disease. Drugs 80, 775–796 (2020).

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

The authors’ work is supported by the NIH National Institute of Neurological Disorders and Stroke (grant number R21 NS114764-01A1).

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Contributions

H.T., F.T. and K.L. along with J.W. and I.L. all contributed to researching data for the article, discussing the content and writing the article, as well as editing before submission. K.M. contributed to researching data for the article, discussing the content and writing the article. T.P., J.M., Y.G.K. and J.S. contributed to researching data for the article and discussing the content.

Corresponding authors

Correspondence to Irene Litvan or Joseph Wang.

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Competing interests

J.W.’s research is supported by the National Institutes of Health (NIH) grant 1R21NS114764-01A1. I.L.’s research is supported by the NIH grants 2R01AG038791-06A, U01NS100610, U01NS80818, R25NS098999, U19 AG063911-1 and 1R21NS114764-01A1, the Michael J Fox Foundation, Parkinson Foundation, Lewy Body Association, CurePSP, Abbvie, Biogen, Biohaven Pharmaceuticals, Brain Neurotherapy Bio, Centogene, EIP Pharma, Novartis, Roche, United Biopharma and UCB. She is a Scientific adviser for Amydis and The Rossy Center for Progressive Supranuclear Palsy at the University of Toronto. K.L.’s research is partially supported by NIH grant 1R21NS114764-01A1. The other authors declare no competing interests.

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Related links

HeartGuide: https://omronhealthcare.com/products/heartguide-wearable-blood-pressure-monitor-bp8000m/

Kinesia 360: http://glneurotech.com/kinesia/products/kinesia-360/

MoveMonitor: https://www.mcroberts.nl/products/movemonitor/

Personal Kinetigraph: https://pkgcare.com/

SleepImage: https://sleepimage.com/

Glossary

Accelerometers

Electronic tools that measure linear acceleration along one or several axes.

Amperometric transduction

The electrochemical recording of current signals; a constant potential is maintained between the electrodes, with the resulting current related to the concentration of a target biomarker.

Bradykinesia

Slowness in the execution of movement; also implies hypokinesia (low amplitude) of the movement.

Chemical sensors

Devices capable of converting a chemical quantity into a measurable signal.

Diffusion mechanism

The underlying mechanism governing the diffusion of biomolecules from blood capillaries to other bodily fluids such as interstitial fluid or sweat.

Disposable electrode strip

An electrode pattern, usually screen-printed on semi-rigid plastic material, for one-time measurements of a chemical quantity.

Dyskinesias

Involuntary, erratic, rocking, twisting or writhing movements reflecting high levodopa levels.

Electrochemical devices

Devices that quantify the concentration of target analytes (for example, drugs, biomarkers or metabolites) by converting electrode–analyte interactions into measurable electrical signals (that is, current or voltage).

Freezing of gait

Brief episodes during which the patient is unable to generate active stepping movements.

Gyroscopes

Devices that measure angular rotational velocity, also known as angular velocity sensors.

Holter device

A monitoring device consisting of signal recording hardware and data analysis software; this technology is most commonly used for monitoring heart rhythm and rate.

Internal consistency testing

Assessing the correlation between multiple items in a single test to provide a measure of the reliability of the test overall.

Magnetometers

Devices that measure orientation by sensing the direction of the earth’s magnetic field; ideal for measuring falls and changes in position (seated versus standing).

Microneedle sensor patches

An epidermal device with micro-dimensional needle-shaped projections that attach painlessly to the skin for minimally invasive detection of biomarkers.

‘Off’ states

When medication is no longer working well and parkinsonian symptoms re-emerge.

‘On’ states

When a patient experiences a good response to medication and parkinsonian symptoms (for example, tremor, stiffness, slowness) are well controlled.

Test–retest reliability

A measure of reliability, where the given test is applied twice over a period of time to the same group of individuals.

The Internet of things

A network of physical objects that can connect and exchange data over the internet through their embedded sensors, software and other technologies.

Voltammetric

The electrochemical recording of current signals by varying the applied potential between the electrodes.

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Teymourian, H., Tehrani, F., Longardner, K. et al. Closing the loop for patients with Parkinson disease: where are we?. Nat Rev Neurol (2022). https://doi.org/10.1038/s41582-022-00674-1

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