请用中文对下列段落的主要内容进行翻译：Multivariate prediction performance of all four neuropathology scales is shown in Fig. 5. The prediction performance of each variable pool and variables selected are summarized in Table 3. The highest predictive performance among all neuropathology scales was seen with ADNC prediction. The best prediction variable pool for elevated ADNC was the inclusion of plasma biomarkers plus demographic variables plus cognitive scores with a CV AUC [CI] of 0.798 [0.753, 0.843]. Final variables selected in this model included all plasma variables except Aβ42/40 ratio, APOE ε4 carrier status, and Mayo-PACC. Without the Mayo-PACC scores in the variable pool, CV AUC was lowered to 0.777 [0.714, 0.841] with age at visit replacing cognitive scores and Aβ42/40 ratio entering the model. Plasma biomarkers alone had a performance of 0.734 [0.623, 0.846] whereas a variable pool that included all demographics and cognitive scores, but no plasma biomarkers achieved the worst performance with CV AUC of 0.681 [0.565, 0.797] using all variables. Groupwise comparisons in predictive performance between variable pools showed no statistical difference despite a trend towards better performance in the largest variable pool. Braak stage was best predicted with the plasma biomarkers plus demographics plus cognitive scores and the inclusion of GFAP, p-tau181, and Mayo-PACC scores, as predictors achieved a CV AUC [CI] of 0.774 [0.680, 0.868]. With plasma biomarkers alone, GFAP, p-tau181, and NfL were selected from the plasma biomarker pool with CV AUC [CI] of 0.699 [0.632, 0.767]. The plasma biomarker plus demographics model achieved slightly better performance in which NfL was removed and APOE ε4 carrier status was included to achieve a CV AUC [CI] of 0.713 [0.650, 0.776]. A variable pool without plasma biomarkers, i.e., demographics and cognitive scores performed similarly with CV AUC of 0.696 [0.594, 0.797] using all variables available. Groupwise comparisons between variable pools showed that prediction with the full variable pool was statistically higher than demographics plus Mayo-PACC (p = 0.02) and plasma plus demographics (p = 0.08) but not plasma alone (p = 0.055).

Neuritic plaque scores were best predicted with the plasma biomarker pool, achieving a CV AUC [CI] of 0.770 [0.717, 0.822] with all four plasma biomarkers being selected. The pool of plasma biomarkers and demographics achieved similar performance with CV AUC [CI] of 0.761 [0.702, 0.822] in which all plasma biomarkers were selected with the additional inclusion of APOE ε4 carrier status. The complete variable pool consisting of plasma biomarkers plus demographics plus cognitive scores achieved slightly lower performance with CV AUC of 0.742 [0.677, 0.808] where all plasma biomarkers were selected and Mayo-PACC replaced APOE ε4 carrier status. Again, the lowest overall predictive performance was observed in the variable pool that excluded plasma biomarkers with CV AUC of 0.647 [0.600, 0.694] using all demographics and cognitive scores available. Groupwise comparisons in predictive performance between variable pools showed demographics plus Mayo-PACC performance was statistically lower against all other groups with plasma biomarkers including plasma alone (p = 0.008), plasma plus demographics (p = 0.009) and plasma plus demographics plus Mayo-PACC (p = 0.011). There was no significant difference between two pools where both included plasma biomarkers.

A high Thal amyloid phase was best predicted using the complete variable pool of plasma biomarkers, demographics, and cognitive scores with a CV AUC of 0.754 [0.674, 0.833]. Variables selected included GFAP, NfL, p-tau181, APOE ε4 carrier status, and Mayo-PACC cognitive scores. The variable pool consisting of only plasma biomarkers achieved a CV AUC of 0.731 [0.687, 0.775] with selection of all plasma biomarkers as predictors. Inclusion of demographics showed a slightly better performance with CV AUC of 0.740 [0.711, 0.769] replacing Aβ42/40 ratio with APOE ε4 carrier status. The variable pool that included only demographics and cognitive scores had the lowest performance in the prediction of elevated Thal phase with a CV AUC of 0.704 [0.624, 0.784] using APOE ε4 carrier status and Mayo-PACC scores. Groupwise comparisons between variable pools showed a statistical increase in predictive performance between the complete variable pool and the pool with demographics plus Mayo-PACC (p = 0.023).

Regression coefficients and odds ratio of the ordinal logistic regression are shown in the Supplemental Table 2 (online resource). Overall, GFAP, NfL, p-tau181, Mayo-PACC score, and APOE ε4 carrier status were all significant predictors of increasing degree of neuropathology across all four metrics (p < 0.05). Aβ42/40 ratio was a significant predictor of ADNC (p < 0.01), neuritic plaque score (p < 0.01), and Thal phase (p < 0.01) but not Braak stage (p = 0.13). Age at time of visit was a significant predictor of ADNC (p < 0.01), Braak stage (p = 0.036), and Thal phase (p = 0.026) but not neuritic plaque score (p = 0.070). Female sex was not a significant predictor of increasing burden across any of the neuropathology metrics.