title: Antemortem Prediction of Braak Stage.
abstract: We examined the extent to which tauopathy distribution, as determined by Braak staging, might be predicted by various risk factors in older individuals. The Swedish Twin Registry provided extensive information on neuropsychological function, lifestyle, and cardiovascular risk factors of 128 patients for whom autopsy data including Braak staging were available. Logistic regression was used to develop a prognostic model that targeted discrimination between Braak stages 0 to II and III to VI. The analysis showed that Braak stages III to VI were significantly predicted by having 1 or more APOE ε4 alleles, older age, high total cholesterol, absence of diabetes and cardiovascular disease, and poorer scores on the Wechsler Adult Intelligence Score Information test, verbal fluency, and recognition memory but better verbal recall. The algorithm predicted Braak stages III to VI well (receiver-operating characteristic area under curve, 0.897; 95% confidence interval, 0.842-0.951). Using a cutoff of 50% risk or more, the sensitivity was 85%, the specificity was 70%, and the negative predictive value was 69%. This study demonstrates that tauopathy distribution can be accurately predicted using a combination of antemortem patient data. These results provide further insight into tauopathy development and AD-related disease mechanisms and suggest a prognostic model that predicts the spread of neurofibrillary tangles above the transentorhinal stage.
authors.affiliation: From the Karolinska Institutet, Stockholm, Sweden (JOEC, MG, NLP, CG, IN, A-KL, LG); University of Southern California, Los Angeles, California (MG, NLP); and VU University Medical Centre, Amsterdam, The Netherlands (LG).
authors.forename: Jesper O E
authors.initials: JO
authors.lastname: Carlson
authors.forename: Margaret
authors.initials: M
authors.lastname: Gatz
authors.forename: Nancy L
authors.initials: NL
authors.lastname: Pedersen
authors.forename: Caroline
authors.initials: C
authors.lastname: Graff
authors.forename: Inger
authors.initials: I
authors.lastname: Nennesmo
authors.forename: Anna-Karin
authors.initials: AK
authors.lastname: Lindström
authors.forename: Lotte
authors.initials: L
authors.lastname: Gerritsen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D001921:Brain / Q000473:pathology*; D003933:Diagnosis; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D016874:Neurofibrillary Tangles / Q000473:pathology*; D011237:Predictive Value of Tests; D012372:ROC Curve; D012042:Registries; D012307:Risk Factors; D012720:Severity of Illness Index; D013548:Sweden; D024801:Tauopathies / Q000175:diagnosis* / Q000235:genetics