Perspectives on Parkinson’s disease progression: A search for clinical, genetic, and biological determinants

Parkinson’s disease (PD) is a highly prevalent and complex neurodegenerative disorder. Considerable phenotypic heterogeneity is well recognized, with wide variability in disease progression and survival among patients. Identification of determinants of disease progression and mortality is important for prognostic characterization, disease management, and design of future clinical trials. We aimed to gain more insight in clinical, genetic, and biological determinants of mortality and disease progression in PD.

In search for clinical determinants of increased risk of mortality, we aimed to evaluate demographic factors, baseline clinical characteristics, and types of PD medications associated with early mortality in a large cohort of PD patients. This cohort consisted of 1786 early-stage PD patients, and the follow-up duration was 11 years. We found that older age at diagnosis, male gender, cognitive impairment at baseline, more severe motor symptoms (especially bradykinesia) at baseline, and higher Hoehn and Yahr (HY) stage at baseline were associated with earlier mortality. In search for genetic factors related to greater disease severity, we conducted a longitudinal study to evaluate and compare motor progression in PD patients who were carriers of the Asian LRRK2 risk variants G2385R, R1628P, and S1647T with noncarriers. Here we observed that PD patients who are LRRK2 risk variant carriers exhibited greater worsening in Unified Parkinson’s Disease Rating Scale (UPDRS) motor scores after 4 years of disease duration compared with patients who do not carry LRRK2 risk variants.

In the Progress-PD cohort, consisting of 144 Dutch PD patients, we measured blood and CSF biomarker levels and collected clinical data at baseline and at 7-10 years follow-up. We focused on biofluid biomarkers reflecting protein pathology and axonal degeneration that would discriminate between early-stage PD patients and controls. By adding CSF NfL to measurements of CSF amyloid β42 (Aβ42), phosphorylated-tau, phosphorylated-α-syn/total-α-syn (p-α-syn/t-α-syn) ratio, and oligomeric α-syn/t-α-syn (o-α-syn/t-α-syn) ratio, the best discriminating panel was formed by combining CSF NfL levels with CSF measurements of α-syn species. The discriminatory potential of this panel reached 92%. When using serum NfL instead of CSF NfL together with α-syn species, a similar discriminative potential of 90% was obtained. Our results of improved discriminatory potential using a biomarker panel confirm previous observations that the combination of CSF NfL with other CSF biomarkers more accurately differentiates PD patients from healthy controls than the single CSF biomarkers.
To find biofluid biomarkers reflecting neuropathological hallmarks that serve to predict motor progression and cognitive decline, we first performed a baseline cross-sectional study. We observed that attention and memory were the most commonly affected domains in early-stage PD. In particular CSF NfL levels were related to test scores in multiple cognitive domains: memory, attention and executive functioning. Also, CSF p-α-syn/t-α-syn ratio and CSF Aβ42 levels were related to executive and attentional functioning, respectively. Next, we performed a combined cross-sectional and longitudinal observational cohort study, in which we described the clinical outcome and measurements of blood-based biomarkers at baseline and repeated measures at 7-10 years follow-up. We found that increased blood-based levels of Aβ40 and Aβ42 were related to worse motor and global cognitive functioning. Also, higher NfL levels were associated with worse visuospatial functioning. These results provide further support for blood-based Aβ42, Aβ40 and NfL as markers for disease progression in PD.

We conclude that multiple clinical, genetic, and biological determinants all play a role in determining disease progression. To be able to provide a more reliably prognosis in individual patients and to ultimately select patients for tailored therapies, we need further studies using a big data approach that include the multiple complex and interrelated biological and genetic factors known to be involved in the disease process of PD.