Alzheimer’s And Parkinson’s Diseases Predict Different COVID-19 Outcomes: A UK Biobank Study Part 1

Abstract: 

In December 2019, a coronavirus, severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), began infecting humans, causing a novel disease, coronavirus disease 19 (COVID-19). This was first described in the Wuhan province of the People's Republic of China. 

Acute respiratory syndrome (SARS) is a serious respiratory disease with the main symptoms of fever, cough, dyspnea, and fatigue, which can easily lead to lung damage and death. Some studies have shown that SARS can have a long-term negative impact on patients' memory and cognitive abilities.

However, we cannot publicize the serious impact of SARS on memory because this weapon is very harmful. Many people with SARS can restore normal cognitive and memory abilities through appropriate intervention measures and rehabilitation training.

Modern medicine and rehabilitation technology have made the treatment and rehabilitation of SARS patients more targeted and personalized. The focus is on establishing a comprehensive treatment plan, including drug therapy, physical rehabilitation exercises, psychological counseling, and diet management.

In terms of drug therapy, patients can try drugs such as antiviral drugs, antibiotics, hormones, and glucocorticoids to promote the body's repair and recovery of lesions. In terms of rehabilitation exercises, physical rehabilitation exercises are very effective means. Patients can try aerobic exercise, strength training, and physical therapy.

Psychological counseling is also very important because SARS may have adverse effects on the patient's mental state. Patients can try to reduce their inner stress and anxiety through relaxation techniques, cognitive behavioral therapy, and other forms of psychotherapy.

In terms of dietary management, nutrition and a healthy diet are also critical to the patient's recovery. Patients can try to increase their intake of nutrients such as protein, vitamins, and minerals to supply the body with what it needs.

In short, although SARS may have a long-term negative impact on patients' memory and cognitive abilities, patients can achieve complete recovery through scientific treatment and rehabilitation plans. We should continue to conduct in-depth research and exploration of SARS treatment and rehabilitation techniques to facilitate the recovery of more patients in the future. This shows that we need to improve memory, and Cistanche can significantly improve memory because Cistanche is a traditional Chinese medicine with many unique effects, one of which is to improve memory. The efficacy of Cistanche comes from the various active ingredients it contains, including tannic acid, polysaccharides, flavonoid glycosides, etc., which can promote brain health in many ways.

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SARS-CoV-2 has spread throughout the world, causing a global pandemic. To date, thousands of cases of COVID-19 have been reported in the United Kingdom, and over 45,000 patients have died. 

Some progress has been achieved in managing this disease, but the biological determinants of health, in addition to age, that affect SARS-CoV-2 infectivity and mortality are under scrutiny. Recent studies show that several medical conditions, including diabetes and hypertension, increase the risk of COVID-19 and death. 

The increased vulnerability of elderly individuals and those with comorbidities, together with the prevalence of neurodegenerative diseases with advanced age, led us to investigate the links between neurodegeneration and COVID-19. We analyzed the primary health records of 13,338 UK individuals tested for COVID-19 between March and July 2020. 

We show that a pre-existing diagnosis of Alzheimer's disease predicts the highest risk of COVID-19 and mortality among elderly individuals. In contrast, Parkinson's disease patients were found to have a higher risk of SARS-CoV-2 infection but not mortality from COVID-19. We conclude that there are disease-specific differences in COVID-19 susceptibility among patients affected by neurodegenerative disorders.

Keywords: COVID-19; Parkinson's disease; Alzheimer's disease; SARS-CoV-2.

1. Introduction

The rapid emergence of coronavirus disease 19 (COVID-19) has caused over one million deaths worldwide [1]. The clinical features of patients affected by COVID-19 have been extensively explored, but the predisposing factors contributing to increased transmission and clinical severity remain unclear. 

Social and ecological health determinants such as air pollution [2–5] have been suggested to increase the risk of infection and exacerbate COVID-19-related illness. In addition, several comorbidities have been proposed to increase COVID-19 mortality rates, including cardiovascular and respiratory pathologies [6,7]. 

The predominance of these comorbidities in advanced age, combined with the increased incidence of mortality in elderly patients, suggests that age is a major risk factor for COVID-19 mortality [8]. 

Given the increased incidence of neurodegenerative diseases with aging, these observations have raised concerns about the vulnerability of patients living with chronic neurological conditions. Neurodegenerative diseases are a heterogeneous group of diseases that are characterized by the progressive loss of neurons in the central and peripheral nervous systems. 

Alzheimer's disease (AD) is the most common neurodegenerative disorder and form of dementia worldwide and is characterized by neuronal loss in the hippocampus and cortical areas leading to cognitive decline, and memory loss [9,10]. Parkinson's disease (PD) is the second most common neurodegenerative disorder, and it is characterized by neuronal loss in the substantia nigra [11]. 

In contrast to AD, only a subset of PD patients develop dementia [11]. However, in both diseases, patients experience a gradual worsening of their clinical condition as neuronal loss progresses, ultimately affecting their quality of life. Importantly, patients affected by neurodegenerative disorders often present with multiple age-related comorbidities [12]. 

These observations have led several studies to investigate the hypothesis that individuals with neurodegenerative diseases exhibit heightened susceptibility to COVID-19. 

In one of the largest cohort studies of COVID-19 in Europe, data gathered from 166 hospitals in England, Scotland, and Wales showed that dementia was among the most common comorbidities in the 20,133 patients hospitalized for COVID-19 after adjusting for age and other confounders [13]. Similarly, a single-center, retrospective, observational study of 627 patients with COVID-19 in northern Italy showed that dementia and its progressive stages were associated with increased mortality [14], consistent with previous findings [15]. 

While efforts are still ongoing to determine the exact biological basis underlying this association, a recent community-based study revealed that the APOE ε4 genotype, a genetic risk factor for both dementia and AD, is associated with an increased risk of severe COVID-19 [9,10,16]. Taken together, these findings suggest that a dementia diagnosis may represent an important risk factor for mortality in COVID-19 patients. 

However, it remains unclear whether COVID-19 susceptibility varies across distinct subtypes of dementia, namely, frontotemporal dementia, vascular dementia, and AD. In addition, reports on the association between other neurodegenerative disorders and COVID-19 are sparse, hindering both the interpretation and the implementation of related findings into clinical practice. 

For instance, it remains unclear whether individuals with Parkinsonism, and in particular PD, are at increased risk of COVID-19 death compared to the general population. Parkinsonism is an umbrella term that encompasses several different neurological disorders grouped based on several motor symptoms. The majority of patients affected by Parkinsonism have a positive diagnosis of PD [17]. 

As PD-related pathology often leads to respiratory muscle rigidity and an impairment of the cough reflex at advanced stages of the disease [18], individuals suffering from this condition may be at increased risk of COVID-19 mortality [19]. 

Although data are thus far limited, it is conceivable that PD patients may be at elevated risk of severe respiratory complications following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or even unfavorable outcomes. In addition, it has been suggested that other indirect factors of the pandemic, including increased stress, self-isolation, and anxiety, as well as prolonged immobility, may further exacerbate the outcome of PD patients affected by COVID-19 [20,21]. 

Currently, there is insufficient evidence to show that a pre-existing diagnosis of PD increases the risk of COVID-19 or mortality, and contradictory results have been reported from small samples of COVID-19 patients. A single-center, case-controlled telephone survey indicated that morbidity and mortality in patients with mild to moderate PD did not differ from those in the general population [22]. 

Conversely, a later phone survey showed that the COVID-19 prevalence among PD patients in Italy is higher than the national average [23]. These studies provide useful insights into the vulnerability of PD patients, but several methodological inconsistencies hamper their interpretation. For instance, most studies to date include data from only hospitalized or clinically suspected COVID-19 patients. 

Because a large proportion of SARS-CoV-2 infections are asymptomatic [24], these inclusion criteria likely underscore some degree of misclassification, as hospital admission rates for COVID-19 depend on the prevalence of community testing and admission criteria, which vary between countries. 

Using comprehensive clinical data from the UK Biobank, we hypothesized that a pre-existing diagnosis of dementia, AD, or PD may be associated with an increased risk of COVID-19 and related death. To assess this hypothesis, we analyzed baseline (2006–2010) demographic characteristics and pre-existing diagnoses of 13,338 COVID-19-tested volunteers in the UK Biobank. 

This dataset contains the primary health records of participants who were tested for COVID-19 since the beginning of this pandemic. We report that a preexisting diagnosis of dementia or AD predicts the largest risk of COVID-19 and mortality.

Conversely, PD diagnosis was associated with an increased risk of SARS-CoV-2 infection but not mortality from COVID-19.

2. Methods

2.1. UK Biobank Data Sources

The UK Biobank comprises health data from over 500,000 community volunteers based in England, Scotland, and Wales. Information about the geographical regions, recruitment, and other characteristics has been previously described [25]. 

Briefly, between 2006 and 2010, adults aged between 40 and 69 years within proximity to 1 of the 22 UK Biobank recruitment centers were invited to participate. Individuals had extensive demographic, lifestyle, clinical, and radiological information collected. 

Baseline assessments also included a comprehensive series of questionnaires, face-to-face interviews, physical examinations, and blood sampling, with linkages to electronic medical records. 

Clinical data on neurodegenerative disorders and other comorbidities were cross-validated by an algorithm from the UK Biobank, which took into consideration the UK Biobank baseline assessment data (verbal interview), linked hospital admissions data, and death register data [26]. 

Specifically, the diagnoses of neurodegenerative diseases and dementia relied on consensus between primary care and hospital admissions and/or mortality data [27]. 

This method has been previously validated using a subset of the UK Biobank participants and was shown to have high accuracies of detecting true positives [28]. The linkage method of COVID-19 results to UK Biobank participants has been previously published [25,29]. The full protocol is publicly available, and summary data can be viewed on the UK Biobank website: www.ukbiobank.ac.uk. UK Biobank ethical approval was granted by the North West Multi-Centre Research Ethics Committee. 

The current analysis was approved under the UK Biobank application #60124. A detailed list of the variables in the present study is presented in Supplementary Table S1. We defined hypertension using the criteria of diastolic blood pressure ≥90 mmHg or systolic blood pressure ≥140 mmHg. Individual-level data were collected from the UK Biobank on 17 August 2020.

2.2. Study Design and Exclusion Criteria

We conducted a cohort study using national primary care electronic health record data linked to in-hospital COVID-19 death data (see UK Biobank data sources). Of the 13,338 participants with available COVID-19 data, 1626 tested positive for COVID-19 between 16 March and 26 July 2020, and 11,712 were negative. 

The majority of samples tested for COVID-19 were derived from combined nose/throat swabs and analyzed by real-time polymerase chain reaction (RT-PCR). In intensive care settings, positive cases were identified by a positive test result for SARS-CoV-2 in a hospital setting (i.e., participants whose tests were taken while an inpatient or while attending an emergency department) or death with a primary or contributory cause reported as COVID-19. 

More information on the testing procedure can be found on the UK Biobank website [30]. During the same period, COVID-19 testing in England was restricted to hospitalized patients with clinical signs of the disease and healthcare workers. 

In contrast, all UK Biobank participants included in this study were subjected to COVID-19 testing since the beginning of the pandemic. For our models, we defined a positive outcome as either a positive COVID-19 diagnosis or an in-hospital death in COVID-19-positive cases. Risk factors and covariates used for the present analysis were selected based on clinical interest and prior findings. 

These risk factors and covariates are shown in Supplementary Table S1 and include dementia, AD, PD, frontotemporal, vascular dementia, cancer, diabetes, high blood pressure, blood group, age, sex, obesity, respiratory difficulties (chronic obstructive pulmonary disease (COPD) and wheezing), forced expiratory volume (FEV), grey and white matter volume, brain volume, white matter hyperintensity, and C-reactive protein (CRP) levels. 

Obesity was defined based on waist-to-hip ratio measurements. The waist-to-hip ratio is determined by dividing waist circumference by hip circumference, meaning that overweight individuals have higher ratios [31]. We grouped smoking status into current, former, and never smokers.

CRP total levels were normalized according to total protein levels and log-transformed to fit a normal distribution. Other covariates considered as potential upstream risk factors were population density, social deprivation, average household income, education level, housing type, ethnicity, environmental risk within the workplace (chemical, diesel, dusty, smoke), travel to work, and the number of people per household. 

Deprivation was measured using the Townsend Social Deprivation Index (TSDI [32], with higher values indicating higher deprivation), which was derived from the patient's postcode for a higher degree of precision. Ethnicity was grouped into white or minority ethnicities. The full list of minority groups used for our analysis can be found in Supplementary Table S2. 

For the analysis of the association between PD and COVID-19, we included both incidents (those individuals in whom the diagnosis was recorded after their UK Biobank initial assessment visit) and prevalent cases of PD (individuals diagnosed with PD before their initial assessment visit). Under all circumstances, PD diagnoses were derived from self-reports or linked Hospital Episode Statistics International Classification of Diseases (ICD) codes, as described elsewhere [33]. Our cohort included 157 participants with Parkinsonism, of whom 142 were diagnosed with PD. 

To account for potential differences between parkinsonism and PD in the context of COVID-19-related vulnerability [19], we built 2 separate models-one with individuals diagnosed with parkinsonism and one with PD patients only. Information on all covariates was obtained from primary care records provided by the UK Biobank. The geographical distribution of each subject included in the analysis is shown in Figure 1.

2.3. Statistical Analysis

For our analyses of COVID-19 and mortality, we fitted binomial regressions where the response variables were COVID-19 positivity or COVID-19-related death (Figure 1A). We defined COVID-19-related death as an individual who tested positive for COVID-19 and died. We first took an exploratory approach and included several putative comorbidities presented in the "Study section and exclusion criteria". 

More specifically, we omitted several variables, including FEV, COPD, environmental risk within the workplace, any brain-imaging-derived variable, and travel to work, due to a large number of individuals having missing data. 

We then applied an iterative variable selection procedure combining unsupervised stepwise forward and stepwise backward regression analyses to select the most suitable predictor or combination of predictors in our models based on the Akaike information criterion. We calculated the odds or risk ratios and their 95% confidence intervals to quantify the effects of the independent variables on the response variables. 

These exploratory models showed an association between pre-existing dementia diagnosis and COVID-19 and mortality. We therefore further pursued the link between neurodegenerative diseases and COVID-19 outcomes using the same analysis workflow. 

To further confirm whether diagnoses of AD or PD were associated with COVID-19 death, we created a subset of the data to include only participants with the selected neurodegenerative disease. We calculated the odds of dying from COVID-19 while accounting for the comorbidities identified in the previous model, which assessed the association between COVID-19 mortality and neurodegenerative diseases. 

All models were built using the MASS package [34] in R. The comparison tables were generated using the Stargazer package [35]. The analysis source code, detailed quality checks, and all supplementary material are available on GitHub (https://m1gus.github.io/ AD_PD_COVID19/). Statistical significance was defined as p ≤ 0.05.

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