BACKGROUND: Defining reliable markers of conversion to dementia could be the first step in order to identify appropriate treatment strategies for mild cognitive impairment (MCI) patients. OBJECTIVE: To develop a tool able to predict the risk of progression from MCI to Alzheimer's disease (AD). METHODS: 406 MCI patients were included and followed for a one-year period. Demographic characteristics, vascular risk factors, extent of cerebrovascular lesions, markers of carotid atherosclerosis investigated with an ultrasonographic assessment (plaque index and intima-media thickness) and cerebrovascular reactivity to apnea (breath-holding index) were considered as potential predictors of conversion. RESULTS: 106 (26%) MCI patients showed a conversion to AD. Plaque index, intima-media thickness, and breath-holding index were relevant predictors of conversion (p = 0.042; p = 0.003; p < 0.001, multivariate logistic regression analysis). A simplified scoring system was devised based on the magnitude of the estimated multinomial logistic regression β coefficient results. A total score was calculated as the sum of each predictive factor which resulted in a 0-5 range. The optimal cut-off score was ≥3 (sensitivity, 23.6%, 95% CI 15.9%-32.8%; specificity, 97.7%, 95% CI 95.3%-99.1%; positive likelihood ratio, 10.1, 95% CI 4.5%-22.7%; negative likelihood ratio, 0.78, 95% CI 0.70%-0.87%). The AUC was 0.71 (95% CI, 0.65-0.77). CONCLUSIONS: Our findings show the possibility to obtain a predictive indicator of the risk of conversion from MCI to dementia by considering the presence of both atherosclerotic changes in the carotid district and impairment of cerebral hemodynamics. Such an approach may allow us to formulate a correct prognosis in more than 70% of patients with amnesic MCI.
BACKGROUND: Defining reliable markers of conversion to dementia could be the first step in order to identify appropriate treatment strategies for mild cognitive impairment (MCI) patients. OBJECTIVE: To develop a tool able to predict the risk of progression from MCI to Alzheimer's disease (AD). METHODS: 406 MCI patients were included and followed for a one-year period. Demographic characteristics, vascular risk factors, extent of cerebrovascular lesions, markers of carotid atherosclerosis investigated with an ultrasonographic assessment (plaque index and intima-media thickness) and cerebrovascular reactivity to apnea (breath-holding index) were considered as potential predictors of conversion. RESULTS: 106 (26%) MCI patients showed a conversion to AD. Plaque index, intima-media thickness, and breath-holding index were relevant predictors of conversion (p = 0.042; p = 0.003; p < 0.001, multivariate logistic regression analysis). A simplified scoring system was devised based on the magnitude of the estimated multinomial logistic regression β coefficient results. A total score was calculated as the sum of each predictive factor which resulted in a 0-5 range. The optimal cut-off score was ≥3 (sensitivity, 23.6%, 95% CI 15.9%-32.8%; specificity, 97.7%, 95% CI 95.3%-99.1%; positive likelihood ratio, 10.1, 95% CI 4.5%-22.7%; negative likelihood ratio, 0.78, 95% CI 0.70%-0.87%). The AUC was 0.71 (95% CI, 0.65-0.77). CONCLUSIONS: Our findings show the possibility to obtain a predictive indicator of the risk of conversion from MCI to dementia by considering the presence of both atherosclerotic changes in the carotid district and impairment of cerebral hemodynamics. Such an approach may allow us to formulate a correct prognosis in more than 70% of patients with amnesic MCI.
Authors: Thomas Polak; Martin J Herrmann; Laura D Müller; Julia B M Zeller; Andrea Katzorke; Matthias Fischer; Fabian Spielmann; Erik Weinmann; Leif Hommers; Martin Lauer; Andreas J Fallgatter; Jürgen Deckert Journal: J Neural Transm (Vienna) Date: 2017-09-01 Impact factor: 3.575
Authors: Binu P Thomas; Min Sheng; Benjamin Y Tseng; Takashi Tarumi; Kristen Martin-Cook; Kyle B Womack; Munro C Cullum; Benjamin D Levine; Rong Zhang; Hanzhang Lu Journal: J Cereb Blood Flow Metab Date: 2016-01-01 Impact factor: 6.200
Authors: A Trovato; R Siracusa; R Di Paola; M Scuto; M L Ontario; Ornella Bua; Paola Di Mauro; M A Toscano; C C T Petralia; L Maiolino; A Serra; S Cuzzocrea; Vittorio Calabrese Journal: Immun Ageing Date: 2016-07-09 Impact factor: 6.400
Authors: Sara Mazzucco; Linxin Li; Maria A Tuna; Sarah T Pendlebury; Rose Wharton; Peter M Rothwell Journal: Int J Stroke Date: 2016-07-26 Impact factor: 5.266
Authors: Hannah Gardener; Michelle R Caunca; Chuanhui Dong; Ying Kuen Cheung; Mitchell S V Elkind; Ralph L Sacco; Tatjana Rundek; Clinton B Wright Journal: Stroke Date: 2017-06-19 Impact factor: 7.914