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Advanced Neurology Alpha-synuclein, depression and neurodegeneration

prevalence of specific disorders. In the current study, it is Repeated measures analysis of variance was used to
possible that depression, PD, and AlzD share common risk examine the influence of SNCA polymorphisms and the
factors and depression may predict the subsequent risk baseline levels of depression on changes in the prevalence
of developing PD or AlzD. To test these hypotheses and of PD and AlzD across countries.
minimize the risk of false-positive findings, the analysis of All analyses were carried out using Statistical Package
the aforementioned data was carried out according to the for the Social Sciences, version 26.0 (SPSS 26.0). All tests
following steps: were two-tailed, and a significance level of P < 0.05 was

(i) Direct bivariate correlations were computed between used for all analyses in this study.
the prevalence of depression, PD, and AlzD at both
time points (1990 and 2019); 3. Results
(ii) A cross-lagged regression analysis was carried out Epidemiological and genetic data analyses were carried
between the two time points (1990 and 2019); in out for 204 countries and 32 countries, respectively,
this model, a significantly greater “cross-correlation” while environmental data analyses in relevance to PM2.5
between depression in 1990 and PD/AlzD in 2019 and pesticide were carried out for 193 countries and
than that between PD/AlzD in 1990 and depression 155 countries, respectively. The estimated prevalence
in 2019 would strengthen the possibility of a causal of depression, PD, and AlzD in 1990 was 3.46%, 0.07%,
relationship ; and 0.40%, respectively, while that in 2019 was 3.95%,
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(iii) Bivariate correlations were computed to examine the 0.11%, and 0.69%, respectively. The mean increases in
association between the prevalence of depression in the prevalence of each disorder over this 30-year period
1990 and the percentage of change in the prevalence were 15.95% for depression, 11.30% for PD, and 66.67%
of PD and AlzD between 1990 and 2019; a positive for AlzD. All these changes were statistically significant
correlation between these variables would also suggest (depression: t = 14.93, P < 0.001; PD: t = 14.06, P < 0.001;
a causal relationship; AlzD: t = 12.13, P < 0.001), indicating substantial increases
(iv) For population genetic data, estimated allele in the prevalence of all three disorders over this period.
frequency distributions were correlated with both the 3.1. Bivariate correlations between the prevalence
prevalence of each disorder at each time point and of depression and both PD and AlzD
the percentage of change in the prevalence of each
disorder over time; The results of direct correlations between the estimated
(v) A general linear model was used to examine the prevalence of depression and both PD and AlzD in the
influence of SNCA gene polymorphisms and baseline years 1990 and 2019 are presented in Table 2. From these
prevalence of depression on changes in the prevalence results, it can be seen that the prevalence of all forms of
of PD and AlzD over the study period (1990–2019); depression (major depression, dysthymia, and depressive
(vi) All the aforementioned analyses were adjusted for
PM2.5 levels and pesticide exposure. Table 2. Correlations between the prevalence of depression
and both Parkinson’s disease and Alzheimer’s disease in 1990
2.3. Data analysis and 2019
All study variables were tested for normality prior to Year Variable Parkinson’s disease Alzheimer’s disease
further analysis using the Shapiro-Wilk test. Paired- a a
samples t-test was performed to determine if the changes 1990 Depression 0.49 (<0.001) a 0.53 (<0.001) a
0.43 (<0.001)
0.38 (<0.001)
MDD
in the prevalence of depression, PD, and AlzD over the Dysthymia 0.62 (<0.001) a 0.61 (<0.001) a
study period (1990 – 2019) were statistically significant. Depression* 0.45 (<0.001) a 0.51 (<0.001) a
MDD* 0.35 (<0.001) a 0.41 (<0.001) a
Bivariate correlations were computed using Pearson’s Dysthymia* 0.59 (<0.001) a 0.56 (<0.001) a
coefficient (r) and Pearson’s partial coefficient (partial r). 2019 Depression 0.31 (<0.001) a 0.34 (<0.001) a
Analyses of epidemiological data were corrected for MDD 0.19 (0.004) b 0.25 (<0.001) a
multiple comparisons using Bonferroni’s method. Analyses Dysthymia 0.47 (<0.001) a 0.35 (<0.001) a
of genetic data were not subjected to this correction due Depression** 0.24 (0.004) b 0.22 (0.006) b b
0.16 (0.054)
to the small number of cases available for study. Adopting MDD** 0.34 (<0.001) a 0.19 (0.022) b
0.17 (0.037)
Dysthymia**
such a method would raise the possibility of a false-
a
negative finding. The strength of bivariate correlations was MDD: Major depressive disorder. Significant at P < 0.05 after applying
Bonferroni’s correction. Significant at P < 0.05, uncorrected. *Adjusted
b
quantified using standard guideline values for biomedical for life expectancy. **Adjusted for life expectancy, particulate matter
research . (PM2.5) pollution, and pesticide exposure
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Volume 2 Issue 1 (2023) 4 https://doi.org/10.36922/an.326