Screening strategies and dynamic risk prediction models for Alzheimer's disease.

BACKGROUND: Characterizing the progression from Mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early AD prevention and targeted intervention. Our goal was to construct precise screening schemes for individuals with different risk of AD and to establish prognosis models for them. METHODS: We constructed a retrospective cohort by reviewing individuals with baseline diagnosis of MCI and at least one follow-up visits between November 2005 and May 2021. They were stratified into high-risk and low-risk groups with longitudinal cognitive trajectory. Then, we established a screening framework and obtained optimal screening strategies for two risk groups. Cox and random survival forest (RSF) models were developed for dynamic prognosis prediction. RESULTS: In terms of screening strategies, the combination of Clinical Dementia Rating Sum of Boxes (CDRSB) and hippocampus volume was recommended for the high-risk MCI group, while the combination of Alzheimer's Disease Assessment Scale Cognitive 13 items (ADAS13) and FAQ was recommended for low-risk MCI group. The concordance index (C-index) of the Cox model for the high-risk group was 0.844 (95% CI: 0.815-0.873) and adjustments for demographic information and APOE ε4. The RSF model incorporating longitudinal ADAS13, FAQ, and demographic information and APOE ε4 performed for the low-risk group. CONCLUSION: This precise screening scheme will optimize allocation of medical resources and reduce the economic burden on individuals with low risk of MCI. Moreover, dynamic prognosis models may be helpful for early identification of individuals at risk and clinical decisions, which will promote the secondary prevention of AD.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.

BACKGROUND: Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. METHODS: We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. RESULTS: Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset. CONCLUSIONS: The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Landmark Model-based Individual Dynamic Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Cognitive Screening.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) who are at increased risk of Alzheimer's Disease (AD) in cognitive screening is important for early diagnosis and prevention of AD. OBJECTIVE: This study aimed at proposing a screening strategy based on landmark models to provide dynamic predictive probabilities of MCI-to-AD conversion according to longitudinal neurocognitive tests. METHODS: Participants were 312 individuals who had MCI at baseline. The longitudinal neurocognitive tests were the Mini-Mental State Examination, Alzheimer Disease Assessment Scale-Cognitive 13 items, Rey Auditory Verbal Learning Test immediate, learning, and forgetting, and Functional Assessment Questionnaire. We constructed three types of landmark models and selected the optimal landmark model to dynamically predict 2-year probabilities of conversion. The dataset was randomly divided into training set and validation set at a ratio of 7:3. RESULTS: The FAQ, RAVLT-immediate, and RAVLT-forgetting were significant longitudinal neurocognitive tests for MCI-to-AD conversion in all three landmark models. We considered Model 3 as the final landmark model (C-index = 0.894, Brier score = 0.040) and selected Model 3c (FAQ and RAVLT-forgetting as neurocognitive tests) as the optimal landmark model (C-index = 0.898, Brier score = 0.027). CONCLUSION: Our study shows that the optimal landmark model with a combination FAQ and RAVLTforgetting is feasible to identify the risk of MCI-to-AD conversion, which can be implemented in cognitive screening.

Evaluating the effect of gestational diabetes mellitus on macrosomia based on the characteristics of oral glucose tolerance test.

BACKGROUND: GDM is always treated as a homogenous disease ignoring the different metabolic characteristics in oral glucose tolerance test (OGTT). We assessed the effect of GDM on macrosomia based on the different characteristics of OGTT. METHODS: We retrospectively divided 998 GDM pregnant women into 7 groups, Group A1: abnormal OGTT0h; Group A2: abnormal OGTT1h; Group A3: abnormal OGTT2h; Group B1: abnormal OGTT0h+1h; Group B2: abnormal OGTT0h+2h; Group B3: abnormal OGTT1h+2h; Group C: abnormal OGTT0h+1h+2h. RESULTS: The incidence of macrosomia in group C (21.92%) was higher than other groups. The OR of OGTT0h+1h+2h was significant (OGTT1h: OR = 1.577, 95% CI: 0.791, 3.145; OGTT2h: OR = 1.151, 95% CI: 0.572, 2.313; OGTT0h+1h: OR = 1.346, 95% CI: 0.584, 3.101; OGTT0h+2h: OR = 1.327, 95% CI: 0.517, 3.409; OGTT1h+2h: OR = 0.771, 95% CI: 0.256, 2.322; OGTT0h+1h+2h: OR = 4.164, 95% CI: 2.095, 8.278) when comparing with OGTT0h. Subgroup analysis showed abnormal OGTT0h+1h+2h might contribute more to macrosomia in pre-pregnancy BMI ≥ 24 kg/m2 than those with BMI < 24 kg/m2. CONCLUSION: The effect of abnormal OGTT0h+1h+2h on macrosomia was significantly greater than other OGTT characteristics, especially for those with pre-pregnancy BMI ≥ 24 kg/m2. Individualized management of GDM based on OGTT characteristics and pre-pregnancy BMI might be needed.

Estimating Bidirectional Transitions and Identifying Predictors of Mild Cognitive Impairment.

BACKGROUND AND OBJECTIVES: Various resources exist for treating mild cognitive impairment (MCI) or dementia separately as terminal events or for focusing solely on a 1-way path from MCI to dementia without taking into account heterogeneous transitions. Little is known about the trajectory of reversion from MCI to normal cognition (NC) or near-NC and patterns of postreversion, which refers to cognitive trajectories of patients who have reversed from MCI to NC. Our objectives were to (1) quantitatively predict bidirectional transitions of MCI (reversion and progression), (2) explore patterns of future cognitive trajectories for postreversion, and (3) estimate the effects of demographic characteristics, APOE, cognition, daily activity ability, depression, and neuropsychiatric symptoms on transition probabilities. METHODS: We constructed a retrospective cohort by reviewing patients with an MCI diagnosis at study entry and at least 2 follow-up visits between June 2005 and February 2021. Defining NC or near-NC and MCI as transient states and dementia as an absorbing state, we used continuous-time multistate Markov models to estimate instantaneous transition intensity between states, transition probabilities from one state to another at any given time during follow-up, and hazard ratios of reversion-related variables. RESULTS: Among 24,220 observations from 6,651 participants, there were 2,729 transitions to dementia and 1,785 reversions. As for postreversion, there were 630 and 73 transitions of progression to MCI and dementia, respectively. The transition intensity of progression to MCI for postreversion was 0.317 (2.48-fold greater than that for MCI progression or reversion). For postreversion participants, the probability of progressing to dementia increased by 2% yearly. Participants who progressed to MCI were likely to reverse again (probability of 40% over 15 years). Age, independence level, APOE, cognition, daily activity ability, depression, and neuropsychiatric symptoms were significant predictors of bidirectional transitions. DISCUSSION: The nature of bidirectional transitions cannot be ignored in multidimensional MCI research. We found that postreversion participants remained at an increased risk of progression to MCI or dementia over the longer term and experienced recurrent reversions. Our findings may serve as a valuable reference for future research and enable health care professionals to better develop proactive management plans and targeted interventions.

Long-term exposure to low concentrations of polycyclic aromatic hydrocarbons and alterations in platelet indices: A longitudinal study in China.

Long-term exposure to low polycyclic aromatic hydrocarbon (PAH) concentration may ave detrimental effects, including changing platelet indices. Effects of chronic exposure to low PAH concentrations have been evaluated in cross-sectional, but not in longitudinal studies, to date. We aimed to assess the effects of long-term exposure to the low-concentration PAHs on alterations in platelet indices in the Chinese population. During 2014-2017, we enrolled 222 participants who had lived in a village in northern China, 1-2 km downwind from a coal plant, for more than 25 years, but who were not employed by the plant or related businesses. During three follow-ups, annually in June, demographic information and urine and blood samples were collected. Eight PAHs were tested: namely 2-hydroxynaphthalene, 1-hydroxynaphthalene, 2-hydroxyfluorene, 9-hydroxyfluorene (9-OHFlu), 2-hydroxyphenanthrene (2-OHPh), 1-hydroxyphenanthrene (1-OHPh), 1-hydroxypyrene (1-OHP), and 3-hydroxybenzo [a] pyrene. Five platelet indices were measured: platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), platelet crit, and the platelet-large cell ratio. Generalized mixed and generalized linear mixed models were used to estimate correlations between eight urinary PAH metabolites and platelet indices. Model 1 assessed whether these correlations varied over time. Models 2 and 3 adjusted for additional personal information and personal habits. We found the following significant correlations: 2-OHPh (Model1 ß1 = 18.06, Model2 ß2 = 18.54, Model ß3 = 18.54), 1-OHPh (ß1 = 16.43, ß2 = 17.42, ß3 = 17.42), 1-OHP(ß1 = 13.93, ß2 = 14.03, ß3 = 14.03) with PLT, as well as 9-OHFlu with PDW and MPV (odds ratio or Model3 ORPDW[95%CI] = 1.64[1.3-2.06], ORMPV[95%CI] = 1.33[1.19-1.48]). Long-term exposure to low concentrations of PAHs, indicated by2-OHPh, 1-OHPh, 1-OHP, and 9-OHFlu, as urinary biomarkers, affects PLT, PDW, and MPV. 9-OHFlu increased both PDW and MPV after elimination of the effects of other PAH exposure modes.

A Stacking Framework for Multi-Classification of Alzheimer's Disease Using Neuroimaging and Clinical Features.

BACKGROUND: Alzheimer's disease (AD) is a severe health problem. Challenges still remain in early diagnosis. OBJECTIVE: The objective of this study was to build a Stacking framework for multi-classification of AD by a combination of neuroimaging and clinical features to improve the performance. METHODS: The data we used were from the Alzheimer's Disease Neuroimaging Initiative database with a total of 493 subjects, including 125 normal control (NC), 121 early mild cognitive impairment, 109 late mild cognitive impairment (LMCI), and 138 AD. We selected structural magnetic resonance imaging (sMRI) feature by voting strategy. The imaging feature, demographic information, Mini-Mental State Examination, and Alzheimer's Disease Assessment Scale-Cognitive Subscale were combined together as classification features. We proposed a two-layer Stacking ensemble framework to classify four types of people. The first layer represented support vector machine, random forest, adaptive boosting, and gradient boosting decision tree; the second layer was a logistic regression classifier. Additionally, we analyzed performance of only sMRI feature and combined features and compared the proposed model with four base classifiers. RESULTS: The Stacking model combined with sMRI and non-imaging features outshined four base classifiers with an average accuracy of 86.96%. Compared with using sMRI data alone, sMRI combined with non-imaging features significantly improved diagnostic accuracy, especially in NC versus LMCI. CONCLUSION: The Stacking framework we used can improve performance in diagnosis of AD using combined features.

The path linking excessive daytime sleepiness and activity of daily living in Parkinson's disease: the longitudinal mediation effect of autonomic dysfunction.

BACKGROUND: Excessive daytime sleepiness (EDS) and autonomic dysfunction have been verified to impair activity of daily living (ADL) in patients with Parkinson's disease (PD). Whether EDS can affect ADL in PD patients through autonomic dysfunction is still unclear. The purpose of this study is to explore the longitudinal mediation effect of autonomic dysfunction between EDS and ADL. METHODS: Data used in this study were from six-follow-up visits of 413 patients with newly diagnosed PD from the Parkinson's Progression Markers Initiative (PPMI). We used latent growth mediation modeling (LGMM) to explore whether the autonomic dysfunction is a longitudinal mediator between EDS and ADL. RESULTS: The results showed that as the disease progresses, EDS (P < 0.001) and autonomic dysfunction (P < 0.001) gradually worsened and ADL (P < 0.001) gradually decreased in PD patients. In addition, the more severe the patients' EDS symptom, the more worsened the symptoms of autonomic dysfunction, which result in a decrease in ADL. Both the intercept (95% CI: 0.142, 0.308) and the slope (95% CI: 0.083, 0.331) of autonomic dysfunction showed a partial mediating effect, and a longitudinal mediation effect was presented. CONCLUSION: Longitudinal changes in EDS affect the ADL of PD patients directly or indirectly by affecting the symptoms of autonomic dysfunction. Controlling the symptoms of autonomic dysfunction may improve the ADL of PD patients with EDS.