Reacción a "Reactions: movement data can predict Parkinson's risk years before diagnosis"

A team of UK researchers at Cardiff University have analysed accelerometry patterns collected from handheld devices and selected from a subgroup of over 100,000 patients randomly selected from the more than half a million patients registered in the UK biobank (UKBB). The main objective is to assess whether such accelerometry data can be considered as a relevant biomarker for the early diagnosis of Parkinson's disease in the general population. These researchers have compared their results with those obtained with other predictive models using other types of biomarkers such as genetic, blood, biochemical, lifestyle and prodromal symptoms. Accelerometry data were collected every hour of the day for seven consecutive days.The results of the analysis show that years before the diagnosis of Parkinson's disease, accelerometric patterns are already altered and, moreover, that no other neurological pathology shows a similar reduction. Another noteworthy observation is the finding that sleep is more impaired in Parkinson's patients, who get out of bed more frequently during the night than the general population. Finally, and due to its importance at a clinical level, it is very relevant to have proven that accelerometry data with portable devices can even predict the conversion time until the diagnosis of Parkinson's disease, that is, how long it will take for a person with altered accelerometric patterns to suffer from this neurodegenerative process.Parkinson's disease is characterised as a slow neurodegenerative process, with an initial diagnosis at around 65 years of age on average, although the neurodegenerative process is known to have started much earlier (even up to 20 years earlier), although the high plasticity of the dopaminergic system can compensate for initial losses of dopamine in the brain, so that typical symptoms do not appear until much later and, like any other disease, it cannot be diagnosed in the absence of symptomatology. There is enormous scientific and clinical interest in identifying patients in prodromal stages (before initial diagnosis), for which aspects such as the presence of REM sleep disorders (patients with such disorders have a high rate of conversion to Parkinson's disease), constipation, alterations in smell (hyposmia), excessive daily sleepiness, orthostatic hypotension and urinary incontinence, among others, have usually been assessed. However, the predictive power of these symptoms has so far been low.The main value of this study is that it has demonstrated that accelerometry measurements obtained using wearable devices (such as a smartwatch or other similar devices) are more useful than the assessment of any other potentially prodromal symptom in identifying which people in the normal population are at increased risk of developing Parkinson's disease in the future, as well as being able to estimate how many years it will take to start suffering from this neurodegenerative process. In these diseases, early diagnosis is to some extent questionable, as early diagnosis is of little use if neuroprotective treatment is not available. However, it is of great importance for use in clinical trials aimed at evaluating the efficacy of new potentially neuroprotective treatments whose main objective is to slow down - and ideally even halt - the clinical progression that typically characterises Parkinson's disease.In today's digital and permanently connected world, the use of portable devices of this type to telemetrically measure different parameters in neurological pathologies is rapidly consolidating. In fact, in June 2022, the Food and Drug Administration (FDA; the regulatory body for the pharmaceutical market in the USA), approved the use of a software called StrivePD Ecosystem as an application for the Apple Watch to monitor different parameters in Parkinsonian patients, such as motor and non-motor aspects, episodes of rigidity and resting tremor, as well as the efficacy of the response to antiparkinsonian medication. Similarly, another company with an interest in digital telemedicine, NeuroRPM (Washington DC, USA), has obtained FDA approval for an Apple Watch app that monitors common symptoms of Parkinson's disease such as bradykinesia (slowness of movement), tremor and dyskinesia (abnormal movements induced by medication). Another Apple Watch app recently approved by the FDA for similar indications is "Parky", developed by the Ankara-based Turkish company H2o Therapeutics. Within the promising field of wearable devices in the context of Parkinson's disease, a somewhat different approach has been taken by Cala Health (California, USA), who have designed a wristwatch capable of sending electrical stimuli to the patient at the onset of tremors so that they can be corrected.