The Impact of Nonmotor Symptoms on Health-Related Quality of Life in Parkinson’s Disease: A Network Analysis Approach

Impact of depressive symptoms on medication adherence in older adults with chronic neurological diseases

BACKGROUND

Nonadherence to medication contributes substantially to worse health outcomes. Especially among older adults with chronic illness, multimorbidity leads to complex medication regimes and high nonadherence rates. In previous research, depressive symptomology has been identified as a major contributor to nonadherence, and some authors hypothesize a link via motivational deficits and low self-efficacy. However, the exact mechanisms linking depressive symptomology and nonadherence are not yet understood. This is in part because the often-employed sum scores cannot do justice to the complexity of depressive symptomology; instead, it is recommended to assess the influence of individual symptoms.

METHODS

Following this symptom-based approach, we performed correlation, network and regression analysis using depressive symptoms as depicted by the items of the revised Beck Depression Inventory II (BDI) to assess their influence with nonadherence in N = 731 older adults with chronic neurological diseases. Nonadherence was measured with the self-report Stendal Adherence to Medication Score (SAMS).

RESULTS

Even when controlling for sociodemographic and health-related covariates, the BDI remained the most influential contributor to nonadherence. Across different methods, Loss of Interest and Difficulty with Concentration were identified as particularly influential for nonadherence, linking nonadherence with other affective or somatic BDI items, respectively. Additionally, Fatigue, Problems with Decision Making, Suicidal Thoughts, and Worthlessness contribute to nonadherence.

CONCLUSION

Using a symptom-driven approach, we aimed to understand which depressive symptoms contribute to higher levels of nonadherence. Our results refine previous hypotheses about motivation and control beliefs by suggesting that it is not merely a lack of beliefs in the efficacy of medication that connects depressive symptoms and nonadherence, but rather an overall lack of interest in improving one's health due to feelings of worthlessness and suicidal tendencies. This lack of interest is further substantiated by already sparse resources caused by changes in concentration and fatigue. In order to improve health outcomes and reduce nonadherence, these associations between depressive symptoms must be further understood and targeted in tailored interventions.

Urinary extracellular vesicle dynamics in Parkinson's disease patients with urinary dysfunction

Parkinson's disease (PD) presents with severe motor manifestations and a plethora of non-motor symptoms. Urinary dysfunctions are one of the most common non-motor symptoms of PD patients responsible for reduced quality of life. Urinary extracellular vesicles (EVs) are mostly considered to originate from the cells in the urogenital tract. In this study, we have performed urinary EV analysis in 29 PD cases with varied severity of urinary dysfunction and correlated it with the EV dynamics in 29 age-matched controls. In the studied cases, apart from urinary dysfunction, symptoms of depression, anxiety, cognitive dysfunction, sleep, and wakefulness were observed in >75% of the cases. No significant difference in urinary EV size, concentration and urinary EV protein concentration was observed between PD cases with urinary dysfunction and controls. However, a significant positive association was observed between urinary EV concentration and motor scores (p = 0.042), while no association was observed between urinary EV concentration and urinary dysfunction scores. Chronic stress induced by motor symptoms could be one of the reasons for excessive EV production in PD patients with urinary dysfunctions. Large-scale studies on the association of urinary EV dynamics with motor and non-motor symptoms may provide additional information on urinary dysfunction in PD.

Increased Serum S100β Concentration is Associated with Depression in Parkinson's Disease

Purpose

To explore the relationship between the serum level of S100 calcium-binding protein, beta chain (S100β) and Parkinson's disease (PD) with depression.

Patients and Methods

A total of 145 patients with PD and 60 healthy controls matched for sex, age, and years of education in our hospital were selected. Fluorescence quantitative immunochromatography was used to quantify the level of S100β in serum. Clinical manifestations were assessed by Unified Parkinson's Disease Rating Scale part-III (UPDRS-III), Hoehn & Yahr (H-Y) stage and 17-item Hamilton Rating Scale for Depression (HAMD-17). According to the results of HAMD-17, PD patients were divided into PD with depression group and PD without depression group. The relationship between serum S100β and HAMD-17 scores in PD patients with depression was investigated through correlation analysis and multivariate regression analysis, and receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of serum S100β.

Results

The level of serum S100β in PD with depression group was significantly higher than that in PD without depression group and control group. In PD patients with depression, serum S100β level was positively correlated with UPDRS-III score, H-Y Scale and HAMD-17 score. The HAMD-17 score was positively correlated with the UPDRS-III and H-Y scales, and the increase in the HAMD-17 score was associated with women. Elevated serum S100β level and UPDRS-III score are independent risk factors for PD with depression. Analysis of receiver operating characteristic (ROC) curves showed that the serum S100β level with a cutoff of 0.28 ng/mL distinguished patients with PD with or without depression with an area under the ROC curve (AUC) of 0.742, sensitivity of 0.696, and specificity of 0.779.

Conclusion

The serum S100β level could be a biomarker of PD with depression.

Depressive Symptoms and Their Impact on Quality of Life in Parkinson's Disease: An Exploratory Network Analysis Approach

The clinical presentation of Parkinson's disease (PD) is often dominated by depressive symptoms, which can significantly impact the patients' quality of life (QoL). However, it is not clear how these depressive symptoms are interconnected, or if some symptoms are more influential in affecting QoL. In the Cohort of Patients with Parkinson's Disease in Spain (COPPADIS) study, 686 patients with PD were analyzed using network analyses. The patients completed the Beck Depression Inventory II (BDI-II) and provided their overall QoL (EUROHIS-QOL) at the beginning of the study. The study used centrality measures such as Expected Influence and Bridge Expected Influence to identify depressive symptoms that had the greatest impact on overall QoL. The results of exploratory network analyses indicate that the BDI-II items related to loss of energy, past failure, and tiredness or fatigue have the greatest impact on overall QoL as measured by the EUROHIS-QOL 8-item index. The loss of energy and tiredness or fatigue BDI-II items are also strongly associated with a number of different EUROHIS-QOL items, according to Bridge Expected Influences. For individuals suffering from PD, network analysis can aid in identifying significant non-motor symptoms that impact their QoL, thus paving the way for potential improvements.