Does Cognitive Performance Decline as a Function of Age at Onset in Parkinson’s Disease? The Role of Age at assessment, Education, and Clinical Severity

Does Cognitive Performance Decline as a Function of Age at Onset in Parkinson’s Disease? 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The Role of Age at assessment, Education, and Clinical Severity Andrés Alberto Morales-de-Arcia, Alejandra Evelyn Ruiz-Contreras, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8905280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Parkinson’s disease (PD) may present with early-onset (EOPD) or late-onset (LOPD). Cognitive decline has been reported more frequently in LOPD, although findings remain inconsistent, partly due to insufficient control of confounding factors such as age at assessment, education, and disease severity. Aims: To evaluate the impact of age at assessment, age at onset, education, clinical severity, and emotional factors on cognitive performance in PD, and to compare cognitive outcomes between EOPD, LOPD, and healthy controls in a Mexican cohort. Methods: We analyzed 249 patients with PD and 102 healthy controls from the Mex-PD cohort. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). Multiple linear regression models were applied to identify predictors of cognition. Because of collinearity among age-related variables, moderation analyses examined interactions between age at assessment, age at onset, diagnostic group, and disease severity. Nearest-neighbor matching was used for group comparisons. Results: Age at assessment was the strongest predictor of cognitive performance, followed by education and disease severity. Age at onset was not independently associated with cognition. Cognitive differences between EOPD and LOPD disappeared after controlling for age at assessment. Disease severity significantly affected cognition from 59 years of age onward. In matched samples, both PD groups showed lower global cognition than controls, with delayed recall being the most affected domain. Conclusions: Cognitive decline in PD is mainly driven by aging and its interaction with disease severity rather than by age at onset. Education and cognitive reserve appear to exert protective effects. Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, resting tremor, and postural instability [2,21], associated with dopaminergic neuron loss in the substantia nigra [7]. In addition to motor symptoms, PD involves numerous non-motor symptoms, including cognitive impairment (CI) [15,25]. CI in PD varies in severity, ranging from subjective cognitive complaints (SCC) to mild cognitive impairment (MCI) and Parkinson’s disease dementia (PDD) [1,27]. Approximately 38.8% of patients report SCC [8], 26% develop MCI, and 13.8–27.6% progress to dementia within five years [27]. PD is often classified by age at motor onset: early-onset (EOPD, <50 years) and late-onset (LOPD, ≥50 years) [5,12,16,20]. EOPD is primarily genetic, whereas LOPD involves both genetic and environmental factors [19,23], potentially leading to distinct clinical profiles [4]. However, cognitive differences between EOPD and LOPD remain unclear. Some studies report greater decline in LOPD [5,24], while others, such as Seubert-Ravelo et al., found no association between age at onset and cognition [22]. Confounding variables—especially age at assessment—may explain these inconsistencies [11,26]. Age-related cognitive decline, involving processing speed, memory, and executive function, overlaps with PD-related mechanisms like oxidative stress and mitochondrial dysfunction [9,10]. PD may accelerate brain aging by about three years [10]. These interacting processes may intensify cognitive decline [17]. Despite progress, few studies have jointly examined the influence of age at assessment, age at onset, disease severity, education, and emotional factors on cognition in PD, particularly in Latin American populations. This study aimed to evaluate the impact of age at assessment, education, clinical severity, and emotional factors on cognition in EOPD and LOPD, and to explore group differences compared to healthy controls in a Mexican cohort. METHODS Study Participants This study is part of the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD), conducted in the Mexican cohort (MEX-PD) [13]. A total of 249 patients with Parkinson’s disease (PD) were included: 64 with early-onset PD (EOPD) and 185 with late-onset PD (LOPD). Additionally, 102 healthy controls were recruited. Inclusion criteria were: being 45 years of age or older, being born and having lived primarily in Mexico, and providing written informed consent. PD diagnosis was confirmed by neurologists using the UK Brain Bank Criteria. Healthy controls were recruited from community settings such as senior centers, cultural events, and public health campaigns, and were screened to rule out neurodegenerative conditions. Age at onset (first motor symptom) determined EOPD (<50 years) and LOPD (≥50 years) classifications [4,11,15,19]. Cognition was assessed with the MoCA (version 7.1), validated in Mexico [3]. Disease severity was measured using the Hoehn & Yahr scale. Anxiety and depression (trait/state) were assessed using IDARE and IDERE (Spanish versions of STAI and STDI). Cognitive reserve was measured using the CRIq [18], and years of education (starting from primary school) were recorded. Data collection included: (1) clinical evaluation; (2) phone administration of IDARE, IDERE, and CRIq; and (3) MoCA by video call. For group comparisons, 25 participants per group (EOPD, LOPD, and controls) were matched based on regression results. Extended Normative Control Sample The original control group from the Mex-PD cohort (n = 102) had complete clinical and cognitive data but included few participants aged 70 or older, limiting comparability with older PD patients. To improve age representation, 106 additional controls from the same cohort were added, forming an extended sample of 208. These additional participants, lacking full clinical or emotional data, were excluded from demographic and clinical comparisons and from the final matched sample. However, they were included in the moderation analysis (Figure 1B), which required only age and total MoCA score. Data Analysis All continuous variables were standardized into z-scores using the mean and standard deviation of the control group as reference. A multiple linear regression analysis was conducted to identify the most relevant predictors of the total MoCA score in patients with PD. Z-score transformed variables were included in the model, and non-significant predictors were removed iteratively. Standard errors were corrected using the Newey-West method, and multicollinearity was assessed using the variance inflation factor (VIF). Due to high collinearity between age at assessment, age at onset, and disease duration, these variables were analyzed separately. Moderation analyses were performed to examine the moderating effect of age at assessment on age at onset and total MoCA score. Further, the interaction between age at assessment and diagnostic group (EOPD vs. LOPD) was explored. Finally, a Johnson-Neyman analysis was applied to identify the specific range of age at assessment at which disease severity (Hoehn & Yahr) became significantly associated with lower cognitive function. For comparisons between groups (Controls, EOPD, and LOPD), nearest neighbor matching was used with a caliper of 0.5 and no replacement, ensuring equivalence across groups in age at assessment, sex, education, disease severity, and trait anxiety (identified as significant predictors in the multiple regression). Subsequently, global and domain-specific MoCA z-scores were compared across groups using one-way ANOVA for independent samples, followed by Tukey’s post hoc tests. All reported p-values were adjusted using the Benjamini-Hochberg method to control for type I error [14] and are referred to as adjusted p-values throughout the manuscript. All analyses were conducted using RStudio (version 4.3.1) and GraphPad Prism (version 10.2.2). RESULTS Data from 351 participants were analysed—249 with Parkinson’s disease (PD; 64 EOPD and 185 LOPD) and 102 controls ( Table 1 ). *Table 1* Descriptive data by participant group Summarizes key clinical and demographic characteristics. Variables Controls EOPD LOPD Test statistics Adjusted p-value Post hoc test Adjusted p-value Number of participants 102 64 185 NA NA Age at assessment ( years; min/max) a 56.86 ± 9.98 (43-89) 53.83 ± 7.33 (40-69) 69.07 ± 7.60 (51-86) F (2, 348) = 115.80 p=0.002** Control/LOPD: p=0.002** EOPD/LOPD: p= 0.002** EOPD/Control:p= 0.086 Sex (M/F) b 32.4%/67.6% (33/69) 62.5%/37.5% (40/24) 55.7%/44.3% (103/82) X 2 (2): 19.09 p=0.002** Control/EOPD: p= 0.002** Control/LOPD: p= 0.002** EOPD/LOPD: p= 0.44 Years of education a 14.59 ± 4.59 (0-25) 14.29 ± 4.83 (2-30) 12.72 ± 5.56 (0-28) F(2, 348) = 5.11 p=0.013* Control/LOPD: p= 0.018* Control/EOPD: p= 0.965 Age at disease onset a NA 41.1 ± 6.01 (25-49) 62.2 ± 7.93 (50-85) t(247)= -22.25 p=0.002** Significant difference Disease duration a NA 12.7 ± 7.57 (2-35) 7.07 ± 5.70 (0-38) t(247) = 5.46 p=0.002** Significant difference Disease severity (Hoehn & Yahr) a NA 2.39 ± 0.902 (0-5) 2.30 ± 0.974 (0-5) H (1) = 0.33 p=0.62 Not significant Trait anxiety a 34.02 ± 7.01 (20-60) 41.00 ± 11.0 (20-72) 37.12 ± 10.1 (20-74) F (2, 348) = 10.69 p=0.002** Control/EOPD: p= 0.002** Control/LOPD: p= 0.038* EOPD/LOPD: p= 0.026* State anxiety a 31.80 ± 6.85 (20-55) 36.61 ± 9.61 (20-65) 34.72 ± 8.92 (20-64) F (2, 348) = 6.96 p=0.002** Control/EOPD: p= 0.003** Control/LOPD: p= 0.027* EOPD/LOPD: p= 0.349 Trait depression a 31.91 ± 7.30 (17-56) 38.69 ± 9.34 (26-65) 35.89 ± 9.73 (19-69) F (2, 348) = 12.03 p=0.002** Control/EOPD: p= 0.002** Control/LOPD: p= 0.002** EOPD/LOPD: p= 0.128 State depression a 38.06 ± 5.43 (23-58) 44.09 ± 6.05 (33-64) 42.44 ± 6.55 (26-59) F (2, 348) = 23.78 p= 0.002** Control/EOPD: p= 0.002** Control/LOPD: p= 0.002** EOPD/LOPD: p= 0.207 Cognitive Reserve Index a 110.31 ± 20.5 (66.7-176) 89.62 ± 8.63 (66-114) 90.56 ± 12.1 (52-128) F (2, 348)= 67.97 p=0.002** Control/EOPD: p= 0.002** Control/LOPD: p= 0.002** EOPD/LOPD: p= 0.946 a Mean ± Standard Deviation b Percentage, with absolute values in parentheses NA = Not applicable The LOPD group was significantly older at assessment than the EOPD and control groups (adjusted p = 0.002**). Educational attainment was lowest in the LOPD group, while both EOPD and LOPD had lower cognitive reserve index scores than controls (adjusted p = 0.002**). Among PD patients, EOPD cases had an earlier onset and longer disease duration than LOPD, as expected. Compared to controls, both EOPD and LOPD groups showed lower cognitive reserve (CRIq) scores, while EOPD participants also had higher levels of trait and state anxiety and depression. Full clinical and demographic comparisons are presented in Table 1. A multiple linear regression model was conducted to identify the most relevant predictors of the total MoCA score in PD patients. The model identified age at assessment as the strongest predictor (ß = -0.445, adjusted p = 0.002**), followed by years of education (ß = 0.222, adjusted p = 0.002**), trait anxiety (ß = -0.183, adjusted p = 0.002**), disease severity (ß = -0.168, adjusted p = 0.004**), , and cognitive reserve (ß = 0.138, adjusted p = 0.004**). These variables explained 41.4% of the total variance (adjusted R² = 0.4136, adjusted p = 0.002**). Since age at assessment is the sum of age at onset and disease duration, multicollinearity between these three variables was evaluated using the Variance Inflation Factor (VIF). High collinearity was identified between age at assessment (VIF = 21.590), age at onset (VIF = 28.291), and disease duration (VIF = 8.285). Therefore, it was decided to exclude age at onset and disease duration from the main model and analyze them separately. The disease duration was included in a multiple regression model with the significant variables from the first model, excluding only age at onset. The results showed that disease duration was not a significant predictor (ß = 0.030, adjusted p = 0.620) and even slightly reduced the variance (adjusted R² = 0.4119), compared to the base model without disease duration (adjusted R² = 0.4136). This lack of significance and the reduced explanatory contribution led to its exclusion in subsequent analyses. In a second analysis, age at onset was included in the original multiple regression model, excluding only disease duration. Although collinearity with age at assessment was moderate (VIF = 4.169) for age at assessment and 4.054 for age at onset, the results indicated that age at onset was not a significant predictor when analyzed together with age at assessment (ß = -0.039, adjusted p = 0.699). Furthermore, when evaluating its effect in isolation, without including age at assessment, it barely improved the explained variance (adjusted R² = 0.4154), compared to the base model. To further explore this relationship, a moderation analysis was conducted, which revealed a significant interaction between age at onset and age at assessment (ß = -0.180, adjusted p = 0.002**), because age at onset, alone, was not significant (ß = 0.089, adjusted p = 0.421). Age at assessment remained the strongest predictor of MoCA score (ß = -0.605, adjusted p = 0.002**), and the model explained 31% of the variance (adjusted R² = 0.311). To visualize this effect, the sample was stratified by standard deviations of age at assessment: when age at assessment was -1SD (55.1 years), age at onset significantly predicted MoCA scores (adjusted p = 0.022*), but this effect disappeared at average (65.15 years) and high (75.21 years) age at assessment (adjusted p = 0.395 and adjusted p = 0.534, respectively ( Fig. 1 ). A second moderation analysis was conducted to evaluate whether age at assessment and cognitive performance vary by diagnostic group (Controls, EOPD, LOPD). This model revealed that, when controlling for age at assessment, neither the EOPD group (ß = -0.2198, adjusted p = 0.254) nor the LOPD group (ß = -0.0681, adjusted p = 0.590) showed significant differences in cognitive performance compared to controls. However, the interaction between age at assessment and diagnostic group was significant only for the LOPD group (ß = -0.5597, adjusted p = 0.002**), but not for the EOPD group (ß = 0.001, adjusted p = 1.000). When comparing the slopes, LOPD showed a cognitive decline more than three times greater than that observed in EOPD (ß = -0.811, adjusted p = 0.020** vs ß = -0.250, adjusted p = 0.020**), with the latter being very similar to that observed in controls (ß = -0.251, adjusted p = 0.995). Despite this, when controlling for age at assessment, the LOPD group alone did not significantly predict cognitive function, suggesting that the interaction between age and disease—and not the PD group per se —that explains cognitive decline (see Fig. 2 ). Given this pattern, further exploration was conducted to determine whether other clinical factors may amplify the effect of age on cognition. In particular, the interaction between age at assessment and disease severity was analyzed. The results showed that both disease severity (ß = -0.418, adjusted p = 0.002**) and age at assessment (ß = -0.234, adjusted p = 0.002**) negatively contributed to cognitive performance, and their interaction was also significant (ß = -0.145, adjusted p = 0.038*). This model explained 32% of the variance in total MoCA (R² = 0.3195). The Johnson-Neyman analysis identified the point at which disease severity begins to impact cognition, with a significance threshold at 59.22 years. Above this point, its effect progressively became more relevant (see Fig. 3 ). Finally, to reduce potential biases introduced by differences in age, sex, education, disease severity, and trait anxiety (all identified as significant or relevant in the multiple regression analyses), nearest neighbor matching was applied using these five variables. This allowed for the creation of three comparable groups of 25 participants each (Controls, EOPD, and LOPD) to evaluate the MoCA score. The groups had similar average ages (Control: 62.0 ± 5.9; EOPD: 61.2 ± 5.6; LOPD: 62.8 ± 6.1), a balanced sex ratio, comparable education (Control: 14.4 ± 3.6; EOPD: 13.8 ± 3.9; LOPD: 13.6 ± 3.4), similar motor severity levels (Hoehn & Yahr: EOPD: 2.2 ± 0.7; LOPD: 2.3 ± 0.6), and similar trait anxiety scores (Controls: 34.92 ± 6.10; EOPD: 40.08 ± 11.4; LOPD: 35.68 ± 10.4; F(2,72) = 2.11, adjusted p = 0.180). The cognitive reserve index (CRIq) was not included as a matching criterion, significant differences were found between the groups: controls had a higher average score (115.9 ± 22.8) compared to EOPD (89.1 ± 9.4) and LOPD (91.9 ± 10.3) (Control vs EOPD: adjusted p = 0.002** and Control vs LOPD: adjusted p = 0.002**) (see Discussion). In this matched sample, controls had a higher average MoCA score (0.00 ± 1.00 z, 27.32 ± 2.25 points) compared to EOPD (-1.44 ± 1.90 z, 24.08 ± 4.27 points) and LOPD (-1.21 ± 1.86 z, 24.60 ± 4.17 points). Significant differences were found between controls and both PD groups (Control vs EOPD: adjusted p = 0.013*; Control vs LOPD: adjusted p = 0.046*), but not between EOPD and LOPD. The effect size was moderate (η² = 0.134), and the statistical power was 0.857 ( Table 2 ). MoCA domain analysis revealed that delayed recall was the only domain with significant differences (F(2,72) = 5.96, adjusted p = 0.002**), where controls had higher scores than patients with EOPD (adjusted p = 0.046*) and LOPD (adjusted p = 0.010**), with no differences between the latter. In the z-score analysis, controls had an average score of 0.00 ± 1.00 (4.00 ± 0.91 points, maximum value); EOPD -1.10 ± 1.76 (3.00 ± 1.61 points), and LOPD -1.36 ± 1.56 (2.76 ± 1.42 points). The effect size was moderate (η² = 0.142 for delayed recall), and statistical power was 0.90 ( Table 2 ). No statistically significant differences were found between the groups in the remaining MoCA domains. Although controls tended to score higher than PD patients, comparisons did not reach significance for visuospatial (F(2,72) = 2.03, adjusted p = 0.180), naming (F(2,72) = 0.85, adjusted p = 0.493), attention (F(2,72) = 1.99, adjusted p = 0.282), abstraction (F(2,72) = 1.28, adjusted p = 0.414), orientation (F(2,72) = 1.03, adjusted p = 0.430), or language (F(2,72) = 3.23, adjusted p = 0.067). The average z-scores ranged from -0.18 to -0.88 in the EOPD and LOPD groups, without exceeding the significance threshold ( Table 2 ). *Table 2* Comparison of MoCA Total and Domain Scores Between Matched Groups MoCA Domain Controls (z) (real) EOPD (z) (real) LOPD (z) (real) F (2,72) Adjusted p-value Post Hoc Test Adjusted p-value η² β Total MoCA 0.00 ± 1.00 (27.32 ±2.25) -1.44 ±1.90 (24.08 ±4.27) -1.21 ± 1.86 (24.60 ± 4.17) 5.58 0.011* Control/EOPD: p=0.013* Control/LOPD: p=0.046* 0.134 0.857 Visuospatial 0.00 ± 1.00 (3.91 ± 1.12) -0.88 ± 1.84 (3.48 ± 1.52) -0.83 ± 1.65 (3.51 ± 1.39) 2.03 0.180 NA NA NA Naming 0.00 ± 1.00 (2.95 ± 0.28) -0.18 ± 0.94 (2.91 ± 0.26) -0.21 ± 0.88 (2.90 ± 0.25) 0.85 0.493 NA NA NA Attention 0.00 ± 1.00 (5.52 ± 0.68) -0.49 ± 1.51 (5.27 ± 0.83) -0.53 ± 1.30 (5.24 ± 0.71) 1.99 0.282 NA NA NA Language 0.00 ± 1.00 (5.16 ± 0.84) -0.36 ± 1.49 (4.88 ± 1.25) -0.62 ± 1.33 (4.72 ± 1.12) 3.23 0.067 NA NA NA Abstraction 0.00 ± 1.00 (1.98 ± 0.76) -0.34 ± 1.30 (1.82 ± 0.99) -0.28 ± 1.11 (1.84 ± 0.87) 1.28 0.414 NA NA NA Delayed Recall 0.00 ± 1.00 (4.00 ± 0.91) -1.10 ± 1.76 (3.00 ± 1.61) -1.36 ± 1.56 (2.76 ± 1.42) 5.96 0.002** Control/EOPD: p=0.046* Control/LOPD: p=0.010** 0.142 > 0.85 Orientation 0.00 ± 1.00 (5.91 ± 0.29) -0.53 ± 1.43 (5.72 ± 0.58) -0.66 ± 1.64 (5.66 ± 0.67) 1.03 0.430 NA NA NA Values are presented as z-scores with real scores shown in parentheses. Post hoc comparisons were conducted using Tukey’s test. Effect size is reported as η² and statistical power as β. NA = Not Applicable. DISCUSSION The most consistent finding of this study was that age at assessment, rather than age at onset, is the primary predictor of cognitive decline in Parkinson’s disease (PD). In the full patient sample, we observed that age at assessment, clinical severity, and trait anxiety were negatively associated with cognitive performance, while education and cognitive reserve had a protective effect. The moderation analysis confirmed that the effect of age at onset on cognition is only significant in younger patients, and becomes statistically non-significant at mid or later ages. This suggests that the impact of age at onset is mediated by age at assessment, aligning with the meta-analysis by Guo et al. [11], which identified age at assessment as a more robust predictor of cognitive decline (RR = 1.07–1.11), in contrast to the risk attributed to late-onset age (RR = 4.43), which may have been overestimated in studies that did not adequately control for chronological age. Additionally, it was observed that the effect of age at assessment on cognition varies by diagnostic subtype. While patients with EOPD showed a decline similar to that of controls, LOPD patients exhibited a three times steeper slope (ß = -0.8113). However, after controlling for age, neither subgroup differed significantly from controls, reinforcing that cognitive decline depends not solely on the onset subtype of PD, but on the interaction between aging and disease. This pattern was accentuated when analyzing the interaction between age at assessment and disease severity (Hoehn & Yahr), which was significant (ß = -0.145, p = 0.007). The Johnson-Neyman analysis revealed that at 59.22 years, disease severity began to significantly affect cognition. This finding is consistent with the results of the PRECODE-1 study [25], which reported an increased risk of cognitive decline at 36 months in patients with onset age ≥60 years, high motor severity, and low baseline performance in verbal memory and semantic fluency. However, that study used age at onset as the main criterion, which may have underestimated the influence of age at assessment, which, as shown in the present study, represents a more direct and robust predictor of cognitive decline. This work provides a more nuanced view of the role of age in PD cognition. Unlike previous studies [5,24] that did not adjust for age at assessment, we demonstrate that cognitive differences between EOPD and LOPD disappear when rigorously controlling for age, severity, education, and trait anxiety. This highlights the need to incorporate these factors in future clinical and experimental designs. Moreover, this study corroborates the proposal by Eickhoff et al. [10], who suggested that PD accelerates brain aging by approximately three years, a phenomenon linked to longer disease duration, motor severity, and cognitive decline. In this framework, age at assessment not only reflects the passage of time, but also the accumulation of neurodegenerative damage and its interaction with other vulnerability factors. When comparing EOPD and LOPD in matched groups by age at assessment, education, sex, trait anxiety, and severity, no differences were found in the total MoCA score (EOPD: 24.08 ± 4.27; LOPD: 24.60 ± 4.17), which contrasts with studies by Bovenzi et al. [5] and Tang et al. [24], who reported greater decline in LOPD without adjusting for age at assessment. In contrast, the results are consistent with those of Seubert-Ravelo et al. [22], who also found no association between age at onset and cognitive decline, although their sample had low variability in age at assessment, which limits direct comparisons. Regarding cognitive domains, the only one with significant differences was delayed recall (η² = 0.142; β > 0.85), with lower scores in EOPD and LOPD compared to controls, reinforcing the vulnerability of episodic memory in PD, as also noted by Jellinger. No significant differences were found in attention, visuospatial functions, or language, possibly due to the strict matching of sociodemographic variables. Regarding matching, the cognitive reserve index (CRIq) was not included as a criterion for methodological reasons. Although it is valuable as a predictor, its explanatory capacity was less than that of variables such as age, education, or severity. Additionally, including it would have reduced the sample size. This led to significant differences in CRIq between groups, with lower scores in PD patients. While these differences may contribute to lower MoCA performance, they should not be interpreted as exclusively reflecting a lower prior reserve, since in PD, a decrease in the work and recreational dimensions of CRIq is expected due to functional limitations imposed by the disease. Limitations: The cognitive evaluation was based solely on the MoCA, without applying a broader neuropsychological battery. Genetic variables and non-motor symptoms such as fatigue, apathy, or psychotic symptoms were also not considered, which could enrich predictive models. Furthermore, although the effect of education was controlled in both matching and statistical analyses, the cognitive reserve index (CRIq) was not used as a matching criterion. This methodological decision was made because its inclusion would have considerably reduced the size of the matched sample by making it difficult to form equivalent groups across multiple key variables. Conclusion: The results of this study support the notion that age at assessment is the primary factor of cognitive decline in PD, beyond age at onset. Cognitive differences seem to depend more on the interaction between age and clinical severity than on the diagnostic subtype itself, although there are motor differences between EOPD and LOPD. From the age of 59, this interaction becomes critical, highlighting the importance of more intensive monitoring in older patients. These findings have important clinical implications. On one hand, they help identify patients at higher risk and prioritize interventions. On the other hand, they reinforce the protective role of education and cognitive reserve and support the use of MoCA as a useful tool to detect early vulnerable domains such as episodic memory. Thus, more than a homogeneous condition, cognition in PD reflects a delicate balance between neurodegeneration, aging, reserve, and adaptation; understanding this is the first step toward truly personalized care. Declarations Funding Sources and Conflict of Interest This project is part of the Mexican Parkinson’s Disease Research Network (MEX-PD), supported by the American Parkinson’s Disease Association through a Parkinson’s Disease Diversity Research Grant (APDA/D07) and by CONAHCYT-FORDECYT-PRONACES grant no. [11311, 6390]. Andrés Alberto Morales de Arcia is a student of the Programa de Maestría en Ciencias (Neurobiología), Universidad Nacional Autónoma de México, and received a scholarship (1314724) from Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI). Alejandra Lázaro Figueroa is a doctoral student of the Programa de Doctorado en Psicología, Universidad Nacional Autónoma de México, and received a grant (1222481) from SECIHTI for her PhD studies. The authors declare that there are no conflicts of interest relevant to this work. Financial Disclosures for the Previous 12 Months The authors declare that there are no additional disclosures to report. AI Use Statement During the preparation of this manuscript, generative artificial intelligence tools (ChatGPT by OpenAI) were used solely to improve the language writing. The content was reviewed and edited by the authors, who assume full responsibility for its accuracy and integrity. Acknowledgments We are deeply grateful to the following neurologists for their valuable support in data acquisition during the development of this study: Dr. Anke Paula Ingrid Kleinert Altamirano, Dr. Sara Isaís Millán, Dr. Diana Carolyn Deras Gaucin, Dr. Omar Cárdenas Sáenz, Dr. Ildefonso Rodríguez Leyva, Dr. Lucero de María Ugalde Mejía, Dr. Maritza Jacqueline Valadez, Dr. Moisés Rubio, Dr. Damaris Daniela Vázquez Guevara, Dr. Carlos Martínez, and Dr. Teresa Pérez. We are also grateful to Luis Aguilar, Alejandro León, and Jair García of the Laboratorio Nacional de Visualización Científica Avanzada for their important support. We thank Carina Uribe Díaz, Alejandra Castillo Carbajal, and Christian Molina for their technical support, and to Mario Nandayapa Quartet, Yoloxochitl Sánchez-Guevara, Instituto de Biotecnología UNAM, and Centro Cultural Manuel Gómez Morin, who facilitated the recruitment of participants for this research. Finally, we acknowledge the Programa de Maestría en Ciencias (Neurobiología) UNAM for its academic and institutional support. Ethical Compliance Statement The study was approved by the Ethics Committee of the Instituto de Neurobiología of the Universidad Nacional Autónoma de México (UNAM). Written informed consent was obtained from all participants. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. 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Int J Neurosci. 2016;126(3):227–34. https://doi.org/10.3109/00207454.2015.1010646 Tolosa E, Garrido A, Scholz SW, Poewe W. Challenges in the diagnosis of Parkinson’s disease. Lancet Neurol. 2021;20(5):385–97. https://doi.org/10.1016/S1474-4422(21)00030-2 Wilson H, Pagano G, Yousaf T, Chandra A, Niccolini F, Politis M. Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1). J Neural Transm. 2020;127(1):51–59. https://doi.org/10.1007/s00702-019-02125-6 Xiao Y, Ou R, Yang T, Liu K, Wei Q, Hou Y, et al. Different associated factors of subjective cognitive complaints in patients with early- and late-onset Parkinson’s disease. Front Neurol. 2021;12:749471. https://doi.org/10.3389/fneur.2021.749471 Additional Declarations No competing interests reported. Supplementary Files STROBEChecklistMoralesdeArciaetal.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Rentería","email":"","orcid":"","institution":"School of Biomedical Sciences, Faculty of Health, Medicine, and Behavioural Sciences, The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"E.","lastName":"Rentería","suffix":""}],"badges":[],"createdAt":"2026-02-18 03:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8905280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8905280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104994390,"identity":"48518127-0a4b-4c2c-9882-e4e5d0298747","added_by":"auto","created_at":"2026-03-19 16:00:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271201,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of age at onset on total MoCA score with moderation by age at assessment (z-scores). Lines represent standard deviations (–1 SD, 0 SD, +1 SD) of the moderating variable (age at assessment in z-scores). F(3,245)=32.99; adjusted R²=0.311; adjusted p=0.002**. Symbols: grey = Controls; blue = EOPD; red = LOPD.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8905280/v1/ec1840d1da16198da5527842.png"},{"id":104994389,"identity":"336f9080-248a-423f-95e6-2b2db67d4acf","added_by":"auto","created_at":"2026-03-19 16:00:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":305411,"visible":true,"origin":"","legend":"\u003cp\u003eModeration analysis of diagnostic group (Controls, EOPD, LOPD) × age at assessment predicting MoCA score (z-scores). F(5,451)=37.35; R²=0.2928; adjusted R²=0.285; adjusted p=0.002**. Lines = regression slopes for Controls (grey), EOPD (blue), and LOPD (red).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8905280/v1/3047eaab8be481d218f5f765.png"},{"id":105035368,"identity":"84448f49-7d02-4608-92d3-b867981baa1b","added_by":"auto","created_at":"2026-03-20 07:25:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116126,"visible":true,"origin":"","legend":"\u003cp\u003eJohnson–Neyman plot of the age threshold at which Hoehn \u0026amp; Yahr severity begins to significantly affect MoCA performance. The analysis identifies 59.22 years as the point beyond which disease severity (Hoehn \u0026amp; Yahr) is significantly associated with lower MoCA scores (p \u0026lt; 0.05). Shaded areas indicate non-significant (pink) versus significant (blue) regions; real age values are shown on the secondary x-axis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8905280/v1/4940d290996eb617b86cba31.png"},{"id":105036697,"identity":"dd920c45-8e74-44b0-aa9f-2b6e67aa1d37","added_by":"auto","created_at":"2026-03-20 07:35:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1648336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8905280/v1/4a4fa190-dbfa-4870-9e90-5aa41dc5a451.pdf"},{"id":104994392,"identity":"e1f25ea4-392b-4617-9c5b-7ee7051b5834","added_by":"auto","created_at":"2026-03-19 16:00:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3173,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEChecklistMoralesdeArciaetal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8905280/v1/ad9ff03aa7c8557c38ae9d11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does Cognitive Performance Decline as a Function of Age at Onset in Parkinson’s Disease? The Role of Age at assessment, Education, and Clinical Severity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, resting tremor, and postural instability [2,21], associated with dopaminergic neuron loss in the substantia nigra [7]. In addition to motor symptoms, PD involves numerous non-motor symptoms, including cognitive impairment (CI) [15,25].\u003c/p\u003e\n\u003cp\u003eCI in PD varies in severity, ranging from subjective cognitive complaints (SCC) to mild cognitive impairment (MCI) and Parkinson\u0026rsquo;s disease dementia (PDD) [1,27]. Approximately 38.8% of patients report SCC [8], 26% develop MCI, and 13.8\u0026ndash;27.6% progress to dementia within five years [27].\u003c/p\u003e\n\u003cp\u003ePD is often classified by age at motor onset: early-onset (EOPD, \u0026lt;50 years) and late-onset (LOPD, \u0026ge;50 years) [5,12,16,20]. EOPD is primarily genetic, whereas LOPD involves both genetic and environmental factors [19,23], potentially leading to distinct clinical profiles [4].\u003c/p\u003e\n\u003cp\u003eHowever, cognitive differences between EOPD and LOPD remain unclear. Some studies report greater decline in LOPD [5,24], while others, such as Seubert-Ravelo et al., found no association between age at onset and cognition [22]. Confounding variables\u0026mdash;especially age at assessment\u0026mdash;may explain these inconsistencies [11,26].\u003c/p\u003e\n\u003cp\u003eAge-related cognitive decline, involving processing speed, memory, and executive function, overlaps with PD-related mechanisms like oxidative stress and mitochondrial dysfunction [9,10]. PD may accelerate brain aging by about three years [10]. These interacting processes may intensify cognitive decline [17].\u003c/p\u003e\n\u003cp\u003eDespite progress, few studies have jointly examined the influence of age at assessment, age at onset, disease severity, education, and emotional factors on cognition in PD, particularly in Latin American populations.\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the impact of age at assessment, education, clinical severity, and emotional factors on cognition in EOPD and LOPD, and to explore group differences compared to healthy controls in a Mexican cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is part of the Latin American Research Consortium on the Genetics of Parkinson\u0026rsquo;s Disease (LARGE-PD), conducted in the Mexican cohort (MEX-PD) [13]. A total of 249 patients with Parkinson\u0026rsquo;s disease (PD) were included: 64 with early-onset PD (EOPD) and 185 with late-onset PD (LOPD). Additionally, 102 healthy controls were recruited.\u003c/p\u003e\n\u003cp\u003eInclusion criteria were: being 45 years of age or older, being born and having lived primarily in Mexico, and providing written informed consent. PD diagnosis was confirmed by neurologists using the UK Brain Bank Criteria. Healthy controls were recruited from community settings such as senior centers, cultural events, and public health campaigns, and were screened to rule out neurodegenerative conditions.\u003c/p\u003e\n\u003cp\u003eAge at onset (first motor symptom) determined EOPD (\u0026lt;50 years) and LOPD (\u0026ge;50 years) classifications [4,11,15,19]. Cognition was assessed with the MoCA (version 7.1), validated in Mexico [3].\u003c/p\u003e\n\u003cp\u003eDisease severity was measured using the Hoehn \u0026amp; Yahr scale. Anxiety and depression (trait/state) were assessed using IDARE and IDERE (Spanish versions of STAI and STDI). Cognitive reserve was measured using the CRIq [18], and years of education (starting from primary school) were recorded.\u003c/p\u003e\n\u003cp\u003eData collection included: (1) clinical evaluation; (2) phone administration of IDARE, IDERE, and CRIq; and (3) MoCA by video call. For group comparisons, 25 participants per group (EOPD, LOPD, and controls) were matched based on regression results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Normative Control Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original control group from the Mex-PD cohort (n = 102) had complete clinical and cognitive data but included few participants aged 70 or older, limiting comparability with older PD patients. To improve age representation, 106 additional controls from the same cohort were added, forming an extended sample of 208. These additional participants, lacking full clinical or emotional data, were excluded from demographic and clinical comparisons and from the final matched sample. However, they were included in the moderation analysis (Figure 1B), which required only age and total MoCA score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll continuous variables were standardized into z-scores using the mean and standard deviation of the control group as reference.\u003c/p\u003e\n\u003cp\u003eA multiple linear regression analysis was conducted to identify the most relevant predictors of the total MoCA score in patients with PD. Z-score transformed variables were included in the model, and non-significant predictors were removed iteratively. Standard errors were corrected using the Newey-West method, and multicollinearity was assessed using the variance inflation factor (VIF).\u003c/p\u003e\n\u003cp\u003eDue to high collinearity between age at assessment, age at onset, and disease duration, these variables were analyzed separately. Moderation analyses were performed to examine the moderating effect of age at assessment on age at onset and total MoCA score. Further, the interaction between age at assessment and diagnostic group (EOPD vs. LOPD) was explored. Finally, a Johnson-Neyman analysis was applied to identify the specific range of age at assessment at which disease severity (Hoehn \u0026amp; Yahr) became significantly associated with lower cognitive function.\u003c/p\u003e\n\u003cp\u003eFor comparisons between groups (Controls, EOPD, and LOPD), nearest neighbor matching was used with a caliper of 0.5 and no replacement, ensuring equivalence across groups in age at assessment, sex, education, disease severity, and trait anxiety (identified as significant predictors in the multiple regression). Subsequently, global and domain-specific MoCA z-scores were compared across groups using one-way ANOVA for independent samples, followed by Tukey\u0026rsquo;s post hoc tests.\u003c/p\u003e\n\u003cp\u003eAll reported p-values were adjusted using the Benjamini-Hochberg method to control for type I error [14] and are referred to as adjusted p-values throughout the manuscript.\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using RStudio (version 4.3.1) and GraphPad Prism (version 10.2.2).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eData from 351 participants were analysed\u0026mdash;249 with Parkinson\u0026rsquo;s disease (PD; 64 EOPD and 185 LOPD) and 102 controls (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Table 1* Descriptive data by participant group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummarizes key clinical and demographic characteristics.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest statistics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost hoc test\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of participants\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at assessment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(\u003cem\u003eyears; min/max)\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e56.86 \u0026plusmn; 9.98 (43-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53.83 \u0026plusmn; 7.33\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(40-69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e69.07 \u0026plusmn; 7.60\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(51-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF (2, 348) = 115.80\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/LOPD: p=0.002**\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eEOPD/Control:p= 0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003cem\u003e(M/F)\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e32.4%/67.6% (33/69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e62.5%/37.5% (40/24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e55.7%/44.3%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(103/82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(2): 19.09\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/EOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of education\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14.59 \u0026plusmn; 4.59\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e14.29 \u0026plusmn; 4.83\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e12.72 \u0026plusmn; 5.56\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF(2, 348) = 5.11\u003c/p\u003e\n \u003cp\u003ep=0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/LOPD: p= 0.018*\u003c/p\u003e\n \u003cp\u003eControl/EOPD: p= 0.965\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at disease onset\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e41.1 \u0026plusmn; 6.01\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(25-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e62.2 \u0026plusmn; 7.93\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(50-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003et(247)= -22.25\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eSignificant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease duration\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e12.7 \u0026plusmn; 7.57\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.07 \u0026plusmn; 5.70\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0-38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003et(247) = 5.46\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eSignificant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease severity (Hoehn \u0026amp; Yahr)\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.39 \u0026plusmn; 0.902\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.30 \u0026plusmn; 0.974\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eH (1) = 0.33\u003c/p\u003e\n \u003cp\u003ep=0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait anxiety\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e34.02 \u0026plusmn; 7.01\u003c/p\u003e\n \u003cp\u003e(20-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e41.00 \u0026plusmn; 11.0\u003c/p\u003e\n \u003cp\u003e(20-72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e37.12 \u0026plusmn; 10.1\u003c/p\u003e\n \u003cp\u003e(20-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF (2, 348) = 10.69\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/EOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eControl/LOPD: \u0026nbsp;p= 0.038*\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.026*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState anxiety\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31.80 \u0026plusmn; 6.85\u003c/p\u003e\n \u003cp\u003e(20-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e36.61 \u0026plusmn; 9.61\u003c/p\u003e\n \u003cp\u003e(20-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e34.72 \u0026plusmn; 8.92\u003c/p\u003e\n \u003cp\u003e(20-64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF (2, 348) = 6.96\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/EOPD: \u0026nbsp;p= 0.003**\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p= 0.027*\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait depression\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31.91 \u0026plusmn; 7.30\u003c/p\u003e\n \u003cp\u003e(17-56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e38.69 \u0026plusmn; 9.34\u003c/p\u003e\n \u003cp\u003e(26-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e35.89 \u0026plusmn; 9.73\u003c/p\u003e\n \u003cp\u003e(19-69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF (2, 348) = 12.03\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/EOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState depression\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38.06 \u0026plusmn; 5.43\u003c/p\u003e\n \u003cp\u003e(23-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e44.09 \u0026plusmn; 6.05\u003c/p\u003e\n \u003cp\u003e(33-64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e42.44 \u0026plusmn; 6.55\u003c/p\u003e\n \u003cp\u003e(26-59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF (2, 348) = 23.78\u003c/p\u003e\n \u003cp\u003ep= 0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/EOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive Reserve Index\u003c/strong\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e110.31 \u0026plusmn; 20.5\u003c/p\u003e\n \u003cp\u003e(66.7-176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e89.62 \u0026plusmn; 8.63\u003c/p\u003e\n \u003cp\u003e(66-114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e90.56 \u0026plusmn; 12.1\u003c/p\u003e\n \u003cp\u003e(52-128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eF (2, 348)= 67.97\u003c/p\u003e\n \u003cp\u003ep=0.002**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eControl/EOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p= 0.002**\u003c/p\u003e\n \u003cp\u003eEOPD/LOPD: p= 0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eMean \u0026plusmn; Standard Deviation\u003cbr\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003ePercentage, with absolute values in parentheses\u003cbr\u003e\u0026nbsp;NA = Not applicable\u003c/p\u003e\n\u003cp\u003eThe LOPD group was significantly older at assessment than the EOPD and control groups (adjusted p = 0.002**). Educational attainment was lowest in the LOPD group, while both EOPD and LOPD had lower cognitive reserve index scores than controls (adjusted p = 0.002**). Among PD patients, EOPD cases had an earlier onset and longer disease duration than LOPD, as expected. Compared to controls, both EOPD and LOPD groups showed lower cognitive reserve (CRIq) scores, while EOPD participants also had higher levels of trait and state anxiety and depression. Full clinical and demographic comparisons are presented in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multiple linear regression model was conducted to identify the most relevant predictors of the total MoCA score in PD patients. The model identified age at assessment as the strongest predictor (\u0026szlig; = -0.445, adjusted p = 0.002**), followed by years of education (\u0026szlig; = 0.222, adjusted p = 0.002**), trait anxiety (\u0026szlig; = -0.183, adjusted p = 0.002**), disease severity (\u0026szlig; = -0.168, adjusted p = 0.004**), , and cognitive reserve (\u0026szlig; = 0.138, adjusted p = 0.004**). These variables explained 41.4% of the total variance (adjusted R\u0026sup2; = 0.4136, adjusted p = 0.002**).\u003c/p\u003e\n\u003cp\u003eSince age at assessment is the sum of age at onset and disease duration, multicollinearity between these three variables was evaluated using the Variance Inflation Factor (VIF). High collinearity was identified between age at assessment (VIF = 21.590), age at onset (VIF = 28.291), and disease duration (VIF = 8.285). Therefore, it was decided to exclude age at onset and disease duration from the main model and analyze them separately.\u003c/p\u003e\n\u003cp\u003eThe disease duration was included in a multiple regression model with the significant variables from the first model, excluding only age at onset. The results showed that disease duration was not a significant predictor (\u0026szlig; = 0.030, adjusted p = 0.620) and even slightly reduced the variance (adjusted R\u0026sup2; = 0.4119), compared to the base model without disease duration (adjusted R\u0026sup2; = 0.4136). This lack of significance and the reduced explanatory contribution led to its exclusion in subsequent analyses.\u003c/p\u003e\n\u003cp\u003eIn a second analysis, age at onset was included in the original multiple regression model, excluding only disease duration. Although collinearity with age at assessment was moderate (VIF = 4.169) for age at assessment and 4.054 for age at onset, the results indicated that age at onset was not a significant predictor when analyzed together with age at assessment (\u0026szlig; = -0.039, adjusted p = 0.699). Furthermore, when evaluating its effect in isolation, without including age at assessment, it barely improved the explained variance (adjusted R\u0026sup2; = 0.4154), compared to the base model.\u003c/p\u003e\n\u003cp\u003eTo further explore this relationship, a moderation analysis was conducted, which revealed a significant interaction between age at onset and age at assessment (\u0026szlig; = -0.180, adjusted p = 0.002**), because age at onset, alone, was not significant (\u0026szlig; = 0.089, adjusted p = 0.421). Age at assessment remained the strongest predictor of MoCA score (\u0026szlig; = -0.605, adjusted p = 0.002**), and the model explained 31% of the variance (adjusted R\u0026sup2; = 0.311). To visualize this effect, the sample was stratified by standard deviations of age at assessment: when age at assessment was -1SD (55.1 years), age at onset significantly predicted MoCA scores (adjusted p = 0.022*), but this effect disappeared at average (65.15 years) and high (75.21 years) age at assessment (adjusted p = 0.395 and adjusted p = 0.534, respectively (\u003cstrong\u003eFig. 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eA second moderation analysis was conducted to evaluate whether age at assessment and cognitive performance vary by diagnostic group (Controls, EOPD, LOPD). This model revealed that, when controlling for age at assessment, neither the EOPD group (\u0026szlig; = -0.2198, adjusted p = 0.254) nor the LOPD group (\u0026szlig; = -0.0681, adjusted p = 0.590) showed significant differences in cognitive performance compared to controls. However, the interaction between age at assessment and diagnostic group was significant only for the LOPD group (\u0026szlig; = -0.5597, adjusted p = 0.002**), but not for the EOPD group (\u0026szlig; = 0.001, adjusted p = 1.000). When comparing the slopes, LOPD showed a cognitive decline more than three times greater than that observed in EOPD (\u0026szlig; = -0.811, adjusted p = 0.020** vs \u0026szlig; = -0.250, adjusted p = 0.020**), with the latter being very similar to that observed in controls (\u0026szlig; = -0.251, adjusted p = 0.995). Despite this, when controlling for age at assessment, the LOPD group alone did not significantly predict cognitive function, suggesting that the interaction between age and disease\u0026mdash;and not the PD group \u003cem\u003eper se\u003c/em\u003e\u0026mdash;that explains cognitive decline (see \u003cstrong\u003eFig. 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGiven this pattern, further exploration was conducted to determine whether other clinical factors may amplify the effect of age on cognition. In particular, the interaction between age at assessment and disease severity was analyzed. The results showed that both disease severity (\u0026szlig; = -0.418, adjusted p = 0.002**) and age at assessment (\u0026szlig; = -0.234, adjusted p = 0.002**) negatively contributed to cognitive performance, and their interaction was also significant (\u0026szlig; = -0.145, adjusted p = 0.038*). This model explained 32% of the variance in total MoCA (R\u0026sup2; = 0.3195). The Johnson-Neyman analysis identified the point at which disease severity begins to impact cognition, with a significance threshold at 59.22 years. Above this point, its effect progressively became more relevant (see \u003cstrong\u003eFig. 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFinally, to reduce potential biases introduced by differences in age, sex, education, disease severity, and trait anxiety (all identified as significant or relevant in the multiple regression analyses), nearest neighbor matching was applied using these five variables. This allowed for the creation of three comparable groups of 25 participants each (Controls, EOPD, and LOPD) to evaluate the MoCA score.\u003c/p\u003e\n\u003cp\u003eThe groups had similar average ages (Control: 62.0 \u0026plusmn; 5.9; EOPD: 61.2 \u0026plusmn; 5.6; LOPD: 62.8 \u0026plusmn; 6.1), a balanced sex ratio, comparable education (Control: 14.4 \u0026plusmn; 3.6; EOPD: 13.8 \u0026plusmn; 3.9; LOPD: 13.6 \u0026plusmn; 3.4), similar motor severity levels (Hoehn \u0026amp; Yahr: EOPD: 2.2 \u0026plusmn; 0.7; LOPD: 2.3 \u0026plusmn; 0.6), and similar trait anxiety scores (Controls: 34.92 \u0026plusmn; 6.10; EOPD: 40.08 \u0026plusmn; 11.4; LOPD: 35.68 \u0026plusmn; 10.4; F(2,72) = 2.11, adjusted p = 0.180). The cognitive reserve index (CRIq) was not included as a matching criterion, significant differences were found between the groups: controls had a higher average score (115.9 \u0026plusmn; 22.8) compared to EOPD (89.1 \u0026plusmn; 9.4) and LOPD (91.9 \u0026plusmn; 10.3) (Control vs EOPD: adjusted p = 0.002** and Control vs LOPD: adjusted p = 0.002**) (see Discussion).\u003c/p\u003e\n\u003cp\u003eIn this matched sample, controls had a higher average MoCA score (0.00 \u0026plusmn; 1.00 z, 27.32 \u0026plusmn; 2.25 points) compared to EOPD (-1.44 \u0026plusmn; 1.90 z, 24.08 \u0026plusmn; 4.27 points) and LOPD (-1.21 \u0026plusmn; 1.86 z, 24.60 \u0026plusmn; 4.17 points). Significant differences were found between controls and both PD groups (Control vs EOPD: adjusted p = 0.013*; Control vs LOPD: adjusted p = 0.046*), but not between EOPD and LOPD. The effect size was moderate (\u0026eta;\u0026sup2; = 0.134), and the statistical power was 0.857 (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMoCA domain analysis revealed that delayed recall was the only domain with significant differences (F(2,72) = 5.96, adjusted p = 0.002**), where controls had higher scores than patients with EOPD (adjusted p = 0.046*) and LOPD (adjusted p = 0.010**), with no differences between the latter. In the z-score analysis, controls had an average score of 0.00 \u0026plusmn; 1.00 (4.00 \u0026plusmn; 0.91 points, maximum value); EOPD -1.10 \u0026plusmn; 1.76 (3.00 \u0026plusmn; 1.61 points), and LOPD -1.36 \u0026plusmn; 1.56 (2.76 \u0026plusmn; 1.42 points). The effect size was moderate (\u0026eta;\u0026sup2; = 0.142 for delayed recall), and statistical power was 0.90 (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eNo statistically significant differences were found between the groups in the remaining MoCA domains. Although controls tended to score higher than PD patients, comparisons did not reach significance for visuospatial (F(2,72) = 2.03, adjusted p = 0.180), naming (F(2,72) = 0.85, adjusted p = 0.493), attention (F(2,72) = 1.99, adjusted p = 0.282), abstraction (F(2,72) = 1.28, adjusted p = 0.414), orientation (F(2,72) = 1.03, adjusted p = 0.430), or language (F(2,72) = 3.23, adjusted p = 0.067). The average z-scores ranged from -0.18 to -0.88 in the EOPD and LOPD groups, without exceeding the significance threshold (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Table 2*\u0026nbsp;\u003c/strong\u003eComparison of MoCA Total and Domain Scores Between Matched Groups\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (z) (real)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEOPD (z) (real)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOPD (z) (real)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF (2,72)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost Hoc Test\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal MoCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (27.32 \u0026plusmn;2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.44 \u0026plusmn;1.90\u003c/p\u003e\n \u003cp\u003e(24.08 \u0026plusmn;4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.21 \u0026plusmn; 1.86 (24.60 \u0026plusmn; 4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eControl/EOPD: p=0.013*\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p=0.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisuospatial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (3.91 \u0026plusmn; 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.88 \u0026plusmn; 1.84\u003c/p\u003e\n \u003cp\u003e(3.48 \u0026plusmn; 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.83 \u0026plusmn; 1.65 (3.51 \u0026plusmn; 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNaming\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (2.95 \u0026plusmn; 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.18 \u0026plusmn; 0.94\u003c/p\u003e\n \u003cp\u003e(2.91 \u0026plusmn; 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.21 \u0026plusmn; 0.88 (2.90 \u0026plusmn; 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (5.52 \u0026plusmn; 0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.49 \u0026plusmn; 1.51\u003c/p\u003e\n \u003cp\u003e(5.27 \u0026plusmn; 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.53 \u0026plusmn; 1.30 (5.24 \u0026plusmn; 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (5.16 \u0026plusmn; 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.36 \u0026plusmn; 1.49\u003c/p\u003e\n \u003cp\u003e(4.88 \u0026plusmn; 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.62 \u0026plusmn; 1.33 (4.72 \u0026plusmn; 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbstraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (1.98 \u0026plusmn; 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.34 \u0026plusmn; 1.30\u003c/p\u003e\n \u003cp\u003e(1.82 \u0026plusmn; 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.28 \u0026plusmn; 1.11 (1.84 \u0026plusmn; 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed Recall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (4.00 \u0026plusmn; 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.10 \u0026plusmn; 1.76 (3.00 \u0026plusmn; 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.36 \u0026plusmn; 1.56 (2.76 \u0026plusmn; 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eControl/EOPD: p=0.046*\u003c/p\u003e\n \u003cp\u003eControl/LOPD: p=0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026gt; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.00 (5.91 \u0026plusmn; 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.53 \u0026plusmn; 1.43\u003c/p\u003e\n \u003cp\u003e(5.72 \u0026plusmn; 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.66 \u0026plusmn; 1.64 (5.66 \u0026plusmn; 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eValues are presented as z-scores with real scores shown in parentheses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePost hoc comparisons were conducted using Tukey\u0026rsquo;s test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEffect size is reported as \u0026eta;\u0026sup2; and statistical power as \u0026beta;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNA = Not Applicable.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe most consistent finding of this study was that age at assessment, rather than age at onset, is the primary predictor of cognitive decline in Parkinson\u0026rsquo;s disease (PD). In the full patient sample, we observed that age at assessment, clinical severity, and trait anxiety were negatively associated with cognitive performance, while education and cognitive reserve had a protective effect.\u003c/p\u003e\n\u003cp\u003eThe moderation analysis confirmed that the effect of age at onset on cognition is only significant in younger patients, and becomes statistically non-significant at mid or later ages. This suggests that the impact of age at onset is mediated by age at assessment, aligning with the meta-analysis by Guo et al. [11], which identified age at assessment as a more robust predictor of cognitive decline (RR = 1.07\u0026ndash;1.11), in contrast to the risk attributed to late-onset age (RR = 4.43), which may have been overestimated in studies that did not adequately control for chronological age.\u003c/p\u003e\n\u003cp\u003eAdditionally, it was observed that the effect of age at assessment on cognition varies by diagnostic subtype. While patients with EOPD showed a decline similar to that of controls, LOPD patients exhibited a three times steeper slope (\u0026szlig; = -0.8113). However, after controlling for age, neither subgroup differed significantly from controls, reinforcing that cognitive decline depends not solely on the onset subtype of PD, but on the interaction between aging and disease. This pattern was accentuated when analyzing the interaction between age at assessment and disease severity (Hoehn \u0026amp; Yahr), which was significant (\u0026szlig; = -0.145, p = 0.007). The Johnson-Neyman analysis revealed that at 59.22 years, disease severity began to significantly affect cognition. This finding is consistent with the results of the PRECODE-1 study [25], which reported an increased risk of cognitive decline at 36 months in patients with onset age \u0026ge;60 years, high motor severity, and low baseline performance in verbal memory and semantic fluency. However, that study used age at onset as the main criterion, which may have underestimated the influence of age at assessment, which, as shown in the present study, represents a more direct and robust predictor of cognitive decline.\u003c/p\u003e\n\u003cp\u003eThis work provides a more nuanced view of the role of age in PD cognition. Unlike previous studies [5,24] that did not adjust for age at assessment, we demonstrate that cognitive differences between EOPD and LOPD disappear when rigorously controlling for age, severity, education, and trait anxiety. This highlights the need to incorporate these factors in future clinical and experimental designs.\u003c/p\u003e\n\u003cp\u003eMoreover, this study corroborates the proposal by Eickhoff et al. [10], who suggested that PD accelerates brain aging by approximately three years, a phenomenon linked to longer disease duration, motor severity, and cognitive decline. In this framework, age at assessment not only reflects the passage of time, but also the accumulation of neurodegenerative damage and its interaction with other vulnerability factors.\u003c/p\u003e\n\u003cp\u003eWhen comparing EOPD and LOPD in matched groups by age at assessment, education, sex, trait anxiety, and severity, no differences were found in the total MoCA score (EOPD: 24.08 \u0026plusmn; 4.27; LOPD: 24.60 \u0026plusmn; 4.17), which contrasts with studies by Bovenzi et al. [5] and Tang et al. [24], who reported greater decline in LOPD without adjusting for age at assessment. In contrast, the results are consistent with those of Seubert-Ravelo et al. [22], who also found no association between age at onset and cognitive decline, although their sample had low variability in age at assessment, which limits direct comparisons.\u003c/p\u003e\n\u003cp\u003eRegarding cognitive domains, the only one with significant differences was delayed recall (\u0026eta;\u0026sup2; = 0.142; \u0026beta; \u0026gt; 0.85), with lower scores in EOPD and LOPD compared to controls, reinforcing the vulnerability of episodic memory in PD, as also noted by Jellinger.\u003c/p\u003e\n\u003cp\u003eNo significant differences were found in attention, visuospatial functions, or language, possibly due to the strict matching of sociodemographic variables.\u003c/p\u003e\n\u003cp\u003eRegarding matching, the cognitive reserve index (CRIq) was not included as a criterion for methodological reasons. Although it is valuable as a predictor, its explanatory capacity was less than that of variables such as age, education, or severity. Additionally, including it would have reduced the sample size. This led to significant differences in CRIq between groups, with lower scores in PD patients. While these differences may contribute to lower MoCA performance, they should not be interpreted as exclusively reflecting a lower prior reserve, since in PD, a decrease in the work and recreational dimensions of CRIq is expected due to functional limitations imposed by the disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u0026nbsp;\u003c/strong\u003eThe cognitive evaluation was based solely on the MoCA, without applying a broader neuropsychological battery. Genetic variables and non-motor symptoms such as fatigue, apathy, or psychotic symptoms were also not considered, which could enrich predictive models. Furthermore, although the effect of education was controlled in both matching and statistical analyses, the cognitive reserve index (CRIq) was not used as a matching criterion. This methodological decision was made because its inclusion would have considerably reduced the size of the matched sample by making it difficult to form equivalent groups across multiple key variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u0026nbsp;\u003c/strong\u003eThe results of this study support the notion that age at assessment is the primary factor of cognitive decline in PD, beyond age at onset. Cognitive differences seem to depend more on the interaction between age and clinical severity than on the diagnostic subtype itself, although there are motor differences between EOPD and LOPD. From the age of 59, this interaction becomes critical, highlighting the importance of more intensive monitoring in older patients.\u003c/p\u003e\n\u003cp\u003eThese findings have important clinical implications. On one hand, they help identify patients at higher risk and prioritize interventions. On the other hand, they reinforce the protective role of education and cognitive reserve and support the use of MoCA as a useful tool to detect early vulnerable domains such as episodic memory.\u003c/p\u003e\n\u003cp\u003eThus, more than a homogeneous condition, cognition in PD reflects a delicate balance between neurodegeneration, aging, reserve, and adaptation; understanding this is the first step toward truly personalized care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Sources and Conflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is part of the Mexican Parkinson\u0026rsquo;s Disease Research Network (MEX-PD), supported by the American Parkinson\u0026rsquo;s Disease Association through a Parkinson\u0026rsquo;s Disease Diversity Research Grant (APDA/D07) and by CONAHCYT-FORDECYT-PRONACES grant no. [11311, 6390].\u003c/p\u003e\n\u003cp\u003eAndr\u0026eacute;s Alberto Morales de Arcia is a student of the Programa de Maestr\u0026iacute;a en Ciencias (Neurobiolog\u0026iacute;a), Universidad Nacional Aut\u0026oacute;noma de M\u0026eacute;xico, and received a scholarship (1314724) from Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SECIHTI).\u003c/p\u003e\n\u003cp\u003eAlejandra L\u0026aacute;zaro Figueroa is a doctoral student of the Programa de Doctorado en Psicolog\u0026iacute;a, Universidad Nacional Aut\u0026oacute;noma de M\u0026eacute;xico, and received a grant (1222481) from SECIHTI for her PhD studies.\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest relevant to this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosures for the Previous 12 Months\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no additional disclosures to report.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Use Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, generative artificial intelligence tools (ChatGPT by OpenAI) were used solely to improve the language writing. The content was reviewed and edited by the authors, who assume full responsibility for its accuracy and integrity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to the following neurologists for their valuable support in data acquisition during the development of this study: Dr. Anke Paula Ingrid Kleinert Altamirano, Dr. Sara Isa\u0026iacute;s Mill\u0026aacute;n, Dr. Diana Carolyn Deras Gaucin, Dr. Omar C\u0026aacute;rdenas S\u0026aacute;enz, Dr. Ildefonso Rodr\u0026iacute;guez Leyva, Dr. Lucero de Mar\u0026iacute;a Ugalde Mej\u0026iacute;a, Dr. Maritza Jacqueline Valadez, Dr. Mois\u0026eacute;s Rubio, Dr. Damaris Daniela V\u0026aacute;zquez Guevara, Dr. Carlos Mart\u0026iacute;nez, and Dr. Teresa P\u0026eacute;rez. We are also grateful to Luis Aguilar, Alejandro Le\u0026oacute;n, and Jair Garc\u0026iacute;a of the Laboratorio Nacional de Visualizaci\u0026oacute;n Cient\u0026iacute;fica Avanzada for their important support. We thank Carina Uribe D\u0026iacute;az, Alejandra Castillo Carbajal, and Christian Molina for their technical support, and to Mario Nandayapa Quartet, Yoloxochitl S\u0026aacute;nchez-Guevara, Instituto de Biotecnolog\u0026iacute;a UNAM, and Centro Cultural Manuel G\u0026oacute;mez Morin, who facilitated the recruitment of participants for this research. Finally, we acknowledge the Programa de Maestr\u0026iacute;a en Ciencias (Neurobiolog\u0026iacute;a) UNAM for its academic and institutional support.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Compliance Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Instituto de Neurobiolog\u0026iacute;a of the Universidad Nacional Aut\u0026oacute;noma de M\u0026eacute;xico (UNAM).\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eWe confirm that we have read the Journal\u0026rsquo;s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAarsland D, Batzu L, Halliday GM, Geurtsen GJ, Ballard C, Ray Chaudhuri K, Weintraub D. Parkinson disease-associated cognitive impairment. 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J Parkinsons Dis. 2019;9(3):501\u0026ndash;15. https://doi.org/10.3233/JPD-191683\u003c/li\u003e\n\u003cli\u003eChua CY, Koh MRE, Chia NSY, Ng SYE, Saffari SE, Wen MC, et al. Subjective cognitive complaints in early Parkinson\u0026rsquo;s disease patients with normal cognition are associated with affective symptoms. Parkinsonism Relat Disord. 2021;82:24\u0026ndash;8. https://doi.org/10.1016/j.parkreldis.2020.11.013\u003c/li\u003e\n\u003cli\u003eCollier TJ, Kanaan NM, Kordower JH. Aging and Parkinson\u0026rsquo;s disease: Different sides of the same coin? Mov Disord. 2017;32(7):983\u0026ndash;90. https://doi.org/10.1002/mds.27037\u003c/li\u003e\n\u003cli\u003eEickhoff CR, Hoffstaedter F, Caspers J, Reetz K, Mathys C, Dogan I, et al. Advanced brain ageing in Parkinson\u0026rsquo;s disease is related to disease duration and individual impairment. Brain Commun. 2021;3(3):fcab191. https://doi.org/10.1093/braincomms/fcab191\u003c/li\u003e\n\u003cli\u003eGuo Y, Liu FT, Hou XH, Li JQ, Cao XP, Tan L, et al. Predictors of cognitive impairment in Parkinson\u0026rsquo;s disease: A systematic review and meta-analysis of prospective cohort studies. J Neurol. 2021;268(8):2713\u0026ndash;22. https://doi.org/10.1007/s00415-020-09757-9\u003c/li\u003e\n\u003cli\u003eKukkle PL, Goyal V, Geetha TS, Mridula KR, Kumar H, Borgohain R, et al. Clinical study of 668 Indian subjects with juvenile, young, and early onset Parkinson\u0026rsquo;s disease. Can J Neurol Sci. 2022;49(1):93\u0026ndash;101. https://doi.org/10.1017/cjn.2021.40\u003c/li\u003e\n\u003cli\u003eL\u0026aacute;zaro-Figueroa A, Reyes-P\u0026eacute;rez P, Morelos-Figaredo E, Guerra-Galicia CM, Estrada-Bellmann I, Salinas-Barboza K, et al. MEX-PD: A National Network for the Epidemiological \u0026amp; Genetic Research of Parkinson\u0026rsquo;s Disease. medRxiv. 2023. https://doi.org/10.1101/2023.08.28.23294700\u003c/li\u003e\n\u003cli\u003eLi G, Zhang X. E-values, multiple testing and beyond. Biometrika. 2023;110(4):867\u0026ndash;883. https://doi.org/10.1093/biomet/asad045\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Fern\u0026aacute;ndez R, Gasca-Salas C, S\u0026aacute;nchez-Ferro \u0026Aacute;, Obeso JA. Actualizaci\u0026oacute;n en la enfermedad de Parkinson. Rev Med Clin Condes. 2016;27(3):363\u0026ndash;79. https://doi.org/10.1016/j.rmclc.2016.06.010\u003c/li\u003e\n\u003cli\u003eMehanna R, Smilowska K, Fleisher J, Post B, Hatano T, Pimentel Piemonte ME, et al. Age cutoff for early-onset Parkinson\u0026rsquo;s disease: Recommendations from the International Parkinson and Movement Disorder Society Task Force on Early Onset Parkinson\u0026rsquo;s Disease. Mov Disord Clin Pract. 2022;9(7):869\u0026ndash;78. https://doi.org/10.1002/mdc3.13523\u003c/li\u003e\n\u003cli\u003eMurman DL. The impact of age on cognition. Semin Hear. 2015;36(3):111-121. https://doi.org/10.1055/s-0035-1555115. PMID: 27516712; PMCID: PMC4906299\u003c/li\u003e\n\u003cli\u003eNucci M, Mapelli D, Mondini S. Cognitive Reserve Index questionnaire (CRIq): a new instrument for measuring cognitive reserve. Aging Clin Exp Res. 2012;24(3):218-26. https://doi.org/10.3275/7800\u003c/li\u003e\n\u003cli\u003ePang SYY, Ho PWL, Liu HF, Leung CT, Li L, Chang EES, et al. The interplay of aging, genetics and environmental factors in the pathogenesis of Parkinson\u0026rsquo;s disease. Transl Neurodegener. 2019;8(1):23. https://doi.org/10.1186/s40035-019-0165-9\u003c/li\u003e\n\u003cli\u003ePost B, van den Heuvel L, van Prooije T, van Ruissen X, van de Warrenburg B, Nonnekes J. Young onset Parkinson\u0026rsquo;s disease: A modern and tailored approach. J Parkinsons Dis. 2020;10(s1):S29\u0026ndash;36. https://doi.org/10.3233/JPD-202135\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Jord\u0026aacute;n A, Medina-Rioja R, Boll-Woehrlen MC. Diagn\u0026oacute;stico cl\u0026iacute;nico de los parkinsonismos at\u0026iacute;picos. Rev Mex Neurociencia [Internet]. 2017 [cited 2025 Jul 4];18(2):88\u0026ndash;99. Available from: https://previous.revmexneurociencia.com/wp-content/uploads/2017/03/RevMexNeu-2017-182-88-99-R.pdf\u003c/li\u003e\n\u003cli\u003eSeubert-Ravelo AN, Y\u0026aacute;\u0026ntilde;ez-T\u0026eacute;llez MG, Salgado-Ceballos H, Escart\u0026iacute;n-P\u0026eacute;rez RE, Neri-Nani GA, Vel\u0026aacute;zquez-Osuna S. Mild cognitive impairment in patients with early-onset Parkinson\u0026rsquo;s disease. Dement Geriatr Cogn Disord. 2016;42(1\u0026ndash;2):17\u0026ndash;30. https://doi.org/10.1159/000447533\u003c/li\u003e\n\u003cli\u003eSimon DK, Tanner CM, Brundin P. Parkinson disease epidemiology, pathology, genetics, and pathophysiology. Clin Geriatr Med. 2020;36(1):1\u0026ndash;12. https://doi.org/10.1016/j.cger.2019.08.002\u003c/li\u003e\n\u003cli\u003eTang H, Huang J, Nie K, Gan R, Wang L, Zhao J, et al. Cognitive profile of Parkinson\u0026rsquo;s disease patients: A comparative study between early-onset and late-onset Parkinson\u0026rsquo;s disease. Int J Neurosci. 2016;126(3):227\u0026ndash;34. https://doi.org/10.3109/00207454.2015.1010646\u003c/li\u003e\n\u003cli\u003eTolosa E, Garrido A, Scholz SW, Poewe W. Challenges in the diagnosis of Parkinson\u0026rsquo;s disease. Lancet Neurol. 2021;20(5):385\u0026ndash;97. https://doi.org/10.1016/S1474-4422(21)00030-2\u003c/li\u003e\n\u003cli\u003eWilson H, Pagano G, Yousaf T, Chandra A, Niccolini F, Politis M. Predict cognitive decline with clinical markers in Parkinson\u0026rsquo;s disease (PRECODE-1). J Neural Transm. 2020;127(1):51\u0026ndash;59. https://doi.org/10.1007/s00702-019-02125-6\u003c/li\u003e\n\u003cli\u003eXiao Y, Ou R, Yang T, Liu K, Wei Q, Hou Y, et al. Different associated factors of subjective cognitive complaints in patients with early- and late-onset Parkinson\u0026rsquo;s disease. Front Neurol. 2021;12:749471. https://doi.org/10.3389/fneur.2021.749471\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8905280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8905280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eParkinson\u0026rsquo;s disease (PD) may present with early-onset (EOPD) or late-onset (LOPD). Cognitive decline has been reported more frequently in LOPD, although findings remain inconsistent, partly due to insufficient control of confounding factors such as age at assessment, education, and disease severity.\u003c/p\u003e\u003ch2\u003eAims:\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of age at assessment, age at onset, education, clinical severity, and emotional factors on cognitive performance in PD, and to compare cognitive outcomes between EOPD, LOPD, and healthy controls in a Mexican cohort.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe analyzed 249 patients with PD and 102 healthy controls from the Mex-PD cohort. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). Multiple linear regression models were applied to identify predictors of cognition. Because of collinearity among age-related variables, moderation analyses examined interactions between age at assessment, age at onset, diagnostic group, and disease severity. Nearest-neighbor matching was used for group comparisons.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAge at assessment was the strongest predictor of cognitive performance, followed by education and disease severity. Age at onset was not independently associated with cognition. Cognitive differences between EOPD and LOPD disappeared after controlling for age at assessment. Disease severity significantly affected cognition from 59 years of age onward. In matched samples, both PD groups showed lower global cognition than controls, with delayed recall being the most affected domain.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eCognitive decline in PD is mainly driven by aging and its interaction with disease severity rather than by age at onset. Education and cognitive reserve appear to exert protective effects.\u003c/p\u003e","manuscriptTitle":"Does Cognitive Performance Decline as a Function of Age at Onset in Parkinson’s Disease? The Role of Age at assessment, Education, and Clinical Severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:00:06","doi":"10.21203/rs.3.rs-8905280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-05T10:51:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T16:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130152833849891911678609252550390571940","date":"2026-04-07T09:17:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T07:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313791908014140329120624170418698556077","date":"2026-04-04T17:21:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117916036720290299491699236405589072695","date":"2026-03-18T15:15:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T09:21:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T07:50:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T09:49:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2026-02-18T02:51:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"48280841-b921-423f-94a8-8e141955bba7","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-05T10:51:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T21:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:00:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8905280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8905280","identity":"rs-8905280","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}