Dataset Information

Plasma Transthyretin as a Predictor of Amnestic Mild Cognitive Impairment Conversion to Dementia.

ABSTRACT: Amnestic mild cognitive impairment (MCI) is a prodromal stage of dementia, with a higher incidence of these patients progressing to Alzheimer's disease (AD) than normal aging people. A biomarker for the early detection and prediction for this progression is important. We recruited MCI subjects in three teaching hospitals and conducted longitudinal follow-up for 5 years at one-year intervals. Cognitively healthy controls were recruited for comparisom at baseline. Plasma transthyretin (TTR) levels were measured by ELISA. Survival analysis with time to AD conversion as an outcome variable was calculated with the multivariable Cox proportional hazards models using TTR as a continuous variable with adjustment for other covariates and bootstrapping resampling analysis. In total, 184 MCI subjects and 40 sex- and age-matched controls were recruited at baseline. At baseline, MCI patients had higher TTR levels compared with the control group. During the longitudinal follow-ups, 135 MCI patients (73.4%) completed follow-up at least once. The TTR level was an independent predictor for MCI conversion to AD when using TTR as a continuous variable (p?=?0.023, 95% CI 1.001-1.007). In addition, in MCI converters, the TTR level at the point when they converted to AD was significantly lower than that at baseline (328.6?±?66.5 vs. 381.9?±?77.6?ug/ml, p?

SUBMITTER: Tien YT

PROVIDER: S-EPMC6904474 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Plasma Transthyretin as a Predictor of Amnestic Mild Cognitive Impairment Conversion to Dementia.

Tien Yi-Ting YT Lee Wei-Ju WJ Liao Yi-Chu YC Wang Wen-Fu WF Jhang Kai-Ming KM Wang Shuu-Jiun SJ Fuh Jong-Ling JL

Scientific reports 20191210 1

Amnestic mild cognitive impairment (MCI) is a prodromal stage of dementia, with a higher incidence of these patients progressing to Alzheimer's disease (AD) than normal aging people. A biomarker for the early detection and prediction for this progression is important. We recruited MCI subjects in three teaching hospitals and conducted longitudinal follow-up for 5 years at one-year intervals. Cognitively healthy controls were recruited for comparisom at baseline. Plasma transthyretin (TTR) levels ...[more]

PMID: 31822765

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Project description:PurposeAmnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer's disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients' conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient.MethodsWe prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients.ResultsAmong the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient.ConclusionWe were able to propose a predictive algorithm for each aMCI individual's conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.

Project description:BackgroundMild cognitive impairment (MCI) of an amnestic type is a common condition in older people and highly predictive of Alzheimer's disease (AD). To date, there is no clear consensus regarding the best antecedent biomarker to predict early conversion to AD.ObjectiveThe aim of the study is to demonstrate that (1)H magnetic resonance spectroscopy (MRS) of the brain in MCI patients may predict early conversion to dementia within the 2-year period after baseline assessment.MethodsA cohort of patients fulfilling the criteria of amnestic MCI were enrolled consecutively. At baseline the patients underwent neuropsychological examination, standard blood tests and APOE genotype. (1)H-MRS (1.5 T) of the brain was carried out by exploring two areas: the posteromedial bilateral parietal lobe and left medial occipital lobe. The patients were followed up to detect conversion to probable AD according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association group criteria.ResultsAfter a 2-year follow-up, 27 (38%) patients converted to AD. The mean N-acetyl-aspartate/creatine (NAA/Cr) ratio in the posteromedial bilateral parietal cortex was 1.38 in converters versus 1.49 in non-converters (p<0.0001). An NAA/Cr ratio equal to or lower than 1.43 in this area predicted conversion to probable AD at 74.1% sensitivity and 83.7% specificity (area under the curve: 0.84; 95% CI 0.73 to 0.92). The cross-validated accuracy of classification was 82%, which reaches 85% when the APOE4 genotype and memory test are included in the analysis. In the left medial occipital lobe, the predictive value was somewhat lower with 85.2% sensitivity and 61.4% specificity (area under the curve: 0.8; 95% CI 0.69 to 0.89). Neither the APOE4 genotype nor leuco-araiosis was predictive of conversion to dementia.ConclusionMRS is a valuable biomarker to predict early conversion to dementia in patients with amnestic MCI.

Project description:BackgroundAbout 40-50% of patients with amnestic mild cognitive impairment (MCI) are found to have no significant Alzheimer's pathology based on amyloid PET positivity. Notably, conversion to dementia in this population is known to occur much less often than in amyloid-positive MCI. However, the relationship between MCI and brain amyloid deposition remains largely unknown. Therefore, we investigated the influence of subthreshold levels of amyloid deposition on conversion to dementia in amnestic MCI patients with negative amyloid PET scans.MethodsThis study was a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain magnetic resonance imaging, and [18F]-florbetaben (FBB) positron emission tomography scan (PET). Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Regional cortical amyloid levels were calculated, and a receiver operating characteristic (ROC) curve for conversion to dementia was obtained. To increase the reliability of the results of the study, we analyzed the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset together.ResultsDuring the follow-up period, 36% (39/107) of patients converted to dementia from amnestic MCI. The dementia converter group displayed increased standardized uptake value ratio (SUVR) values of FBB on PET in the bilateral temporal, parietal, posterior cingulate, occipital, and left precuneus cortices as well as increased global SUVR. Among volume of interests, the left parietal SUVR predicted conversion to dementia with the highest accuracy in the ROC analysis (area under the curve [AUC] = 0.762, P < 0.001). The combination of precuneus, parietal cortex, and FBB composite SUVRs also showed a higher accuracy in predicting conversion to dementia than other models (AUC = 0.763). Of the results of ADNI data, the SUVR of the left precuneus SUVR showed the highest AUC (AUC = 0.596, P = 0.006).ConclusionOur findings suggest that subthreshold amyloid levels may contribute to conversion to dementia in patients with amyloid-negative amnestic MCI.

Dataset Information

Plasma Transthyretin as a Predictor of Amnestic Mild Cognitive Impairment Conversion to Dementia.

Publications

Plasma Transthyretin as a Predictor of Amnestic Mild Cognitive Impairment Conversion to Dementia.

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