Materiały źródłowe

• #1 Alzheimer’s Stages – Early, Middle, Late Dementia Symptoms | alz.org

  https://www.alz.org/alzheimers-dementia/stages

  Alzheimers disease typically progresses slowly in three stages: early, middle and late (sometimes referred to as mild, moderate and severe in a medical context). […] The symptoms of Alzheimer’s disease worsen over time, although the rate at which the disease progresses varies. On average, a person with Alzheimer’s lives four to eight years after diagnosis, but can live as long as 20 years, depending on other factors. […] Changes in the brain related to Alzheimer’s begin years before any signs of the disease. […] The stages are separated into three categories: mild Alzheimer’s disease, moderate Alzheimer’s disease and severe Alzheimer’s disease. […] In the middle stage, the person living with Alzheimers can still participate in daily activities with assistance. […] In the final stage of the disease, dementia symptoms are severe. Individuals lose the ability to respond to their environment, to carry on a conversation and, eventually, to control movement. […] The person living with Alzheimers may not be able to initiate engagement as much during the late stage, but he or she can still benefit from interaction in ways that are appropriate, like listening to relaxing music or receiving reassurance through gentle touch.

• #2 Prognostic and Predictive Factors in Early Alzheimer’s Disease: A Systematic Review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10894607/

  Alzheimers disease (AD) causes progressive decline of cognition and function. There is a lack of systematic literature reviews on prognostic and predictive factors in its early clinical stages (eAD), i.e., mild cognitive impairment due to AD and mild AD dementia. […] To identify prognostic factors affecting eAD progression and predictive factors for treatment efficacy and safety of approved and/or under late-stage development disease-modifying treatments. […] Analyses found data indicating that older age was and depression may be associated with progression. Greater baseline cognitive impairment was associated with progression. APOE4 may be a prognostic factor, a predictive factor for treatment efficacy and predicts an adverse response (ARIA). Elevated biomarkers (CSF/plasma p-tau, CSF t-tau, and plasma neurofilament light) were associated with disease progression.

• #3 Prognostic and Predictive Factors in Early Alzheimer’s Disease: A Systematic Review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10894607/

  Age was the strongest risk factor for progression. Biomarkers were associated with progression, supporting their use in trial selection and aiding diagnosis. Baseline cognitive impairment was a prognostic factor. APOE4 predicted ARIA, aligning with emerging evidence and relevant to treatment initiation/monitoring. […] The findings of this SLR were reviewed by content experts who provided perspectives on the data collected. […] Overall, this review identified an abundance of studies reporting prognostic factors for disease progression in eAD. Studies reporting predicting factors were more limited and the main evidence was derived from five RCTs. Older age was associated with greater risk of disease progression. Greater cognitive impairment at baseline (measured by ADAS-cog11, CDR-SB, MMSE, FAQ) was associated with increased risk of disease progression. […] Elevated biomarkers such as CSF or plasma p-tau, CSF t-tau, CSF t-tau/A ratio, CSF p-tau/A ratio, and plasma neurofilament light were all associated with increased risk of disease progression.

• #4 Predictors for survival in patients with Alzheimer’s disease: a large comprehensive meta-analysis | Translational Psychiatry

  https://www.nature.com/articles/s41398-024-02897-w

  We found that there was a poor prognosis for older patients (RR 1.05, 95% CI 1.041.07 for baseline age; RR 1.03, 95% CI 1.011.05 for age of onset), males (RR 1.58, 95% CI 1.491.68) and white patients (RR 1.36, 95% CI 1.211.53). […] In our analysis, those who had hyperlipidaemia (RR 0.69, 95% CI 0.590.80) had a lower risk of death. In contrast, we found that manifestations of movement disorders (including EPS) (RR 1.60, 95% CI 1.321.93) and cancer (RR 2.07, 95% CI 1.173.67) were more detrimental to AD patient survival. […] We found that only movement disorders (including EPS), NPS and lower MMSE scores played a meaningful role in the deterioration of progress in AD patients, which was in accordance with our primary analysis. […] Our findings indicated that hyperlipidaemia was related to longer survival of AD, and yet other VRF, including overall VRF and some separate diseases such as smoking and hypertension, did not show a similar significant association.

• #5 Predictors of long-term cognitive outcome in Alzheimer’s disease | Alzheimer’s Research & Therapy | Full Text

  https://alzres.biomedcentral.com/articles/10.1186/alzrt85

  The MMSE mean difference from the baseline score was -0.6 after one year of ChEI treatment, -2.3 after two years, and -3.2 after three years. The ADAS-cog mean difference from the baseline score was -1.8, -4.8, and -7.3, at one, two, and three years after the start of treatment, respectively. […] Using mixed models, we found that a higher mean dose of ChEI, male gender, older age, NSAID/acetylsalicylic acid therapy, and absence of the APOE 4 allele were predictors of a better short-term ChEI-treatment response and long-term outcome. […] In conclusion, this study showed that male gender, older age, absence of the APOE 4 allele, and NSAID/acetylsalicylic acid treatment or a higher mean dose of ChEI were predictors of better response to ChEI therapy and of a more favorable longitudinal outcome. Lower cognitive ability at baseline was a predictor of improved response to ChEI treatment.

• #6 Predictors for survival in patients with Alzheimer’s disease: a large comprehensive meta-analysis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11006915/

  The prevalence of Alzheimers disease (AD) is increasing as the population ages, and patients with AD have a poor prognosis. However, knowledge on factors for predicting the survival of AD remains sparse. […] We observed that AD patients who had hyperlipidaemia (RR: 0.69) were at a lower risk of death. In contrast, male sex (RR: 1.53), movement disorders (including extrapyramidal signs) (RR: 1.60) and cancer (RR: 2.07) were detrimental to AD patient survival. […] Our study comprehensively summarized risk factors affecting survival in patients with AD, provided a better understanding on the role of different factors in the survival of AD from four dimensions, and paved the way for further research. […] In total, 26 probable prognostic factors were finally explored for AD survival, and 17 factors were identified as possibly related to the survival of AD. Among them, hyperlipidaemia and higher MMSE scores were predictors of longer survival. However, males, features of movement disorders (including EPS) and cancer showed a worse prognosis.

• #7 Prediction of clinical diagnosis of Alzheimer’s disease, vascular, mixed, and all-cause dementia by a polygenic risk score and APOE status in a community-based cohort prospectively followed over 17 years | Molecular Psychiatry

  https://www.nature.com/articles/s41380-020-0764-y

  The strongest genetic risk factor for Alzheimers disease (AD) is the 4 allele of Apolipoprotein E (APOE) and recent genome-wide association meta-analyses have confirmed additional associated genetic loci with smaller effects. […] A PRS encompassing genetic variants reaching genome-wide significant associations to AD (excluding APOE) from the most recent genome-wide association meta-analysis data was calculated and APOE status was determined in 5203 participants. […] The PRS per standard deviation increase in score and APOE4 positivity (1 4 allele) were significantly associated with greater odds of AD (OR, 95% CI: PRS: 1.70, 1.451.99; APOE4: 3.34, 2.244.99) and AD prediction accuracy was significantly improved when adding the PRS to a base model of age, sex, and education (ASE) (c-statistics: ASE, 0.772; ASE+PRS, 0.810).

• #8 Predictors of long-term cognitive outcome in Alzheimer’s disease | Alzheimer’s Research & Therapy | Full Text

  https://alzres.biomedcentral.com/articles/10.1186/alzrt85

  The longitudinal course of AD is complex and several sociodemographic and clinical factors, such as younger age or higher education, being a carrier of the apolipoprotein E (APOE) 4 allele, or moderate-to-severe level of dementia have been suggested to increase the rate of cognitive decline in untreated patients. […] In this study, we used mixed-effects models (linear and nonlinear) to achieve a higher resolution in the analysis of the long-term association between potential predictive characteristics, including a comparison of the three ChEI agents, on the cognitive outcome of AD patients in a routine clinical setting. […] The aims of this study were: 1) to identify the sociodemographic and clinical factors that influence the longitudinal cognitive outcome and response to ChEI treatment, and 2) to study the impact of different ChEI agents and dosages.

• #9 Predictors for survival in patients with Alzheimer’s disease: a large comprehensive meta-analysis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11006915/

  Our findings indicated that hyperlipidaemia was related to longer survival of AD, and yet other VRF, including overall VRF and some separate diseases such as smoking and hypertension, did not show a similar significant association. […] In our analysis, no difference was observed for APOE 4 carriers and different levels of CSF biomarkers in disease progression, similar to previous studies.

• #10 Prediction of clinical diagnosis of Alzheimer’s disease, vascular, mixed, and all-cause dementia by a polygenic risk score and APOE status in a community-based cohort prospectively followed over 17 years | Molecular Psychiatry

  https://www.nature.com/articles/s41380-020-0764-y

  The addition of the PRS to the base model of age sex and education (ASE) significantly improved AD prediction (Fig. 2, c-statistic: ASE, 0.772; ASE+PRS, 0.810, p0.01). […] Participants that were both PRS+ APOE4+ had a 4.6-fold increased risk in developing AD compared with PRS APOE4 participants (OR, 95% CI: 4.59, 2.967.11). […] In a prospective community-based cohort independent of the IGAP consortia, PRS positivity expressed significant predictive ability of AD diagnosis beyond APOE status, with stronger associations to AD than VD, MD, or all-cause dementia. […] The PRS expressed a greater ability to predict AD than VD, MD, or all-cause dementia.

• #11 Hypertension a Predictive Risk Factor on Progression to Alzheimer’s Disease Using APOEε4 as a Benchmark

  https://www.mdpi.com/2076-3425/15/5/434

  Hypertension is the most significant risk factor comparable to the genetic risk factor APOEε4 allele. […] MCI patients who subsequently develop hypertension within 18 months after their first diagnosis of MCI have a significantly higher risk of AD onset (HR = 2.77, 95%CI (1.66, 4.65), p value < 0.0001), compared to MCI patients with no hypertension or a late occurrence of hypertension after 18 months. [...] Early hypertension has a significantly higher risk and HR of AD onset at follow-up using unadjusted and adjusted Cox models: HR = 2.56, 95% CI (1.55, 4.22), p = 0.0002, and HR = 2.77, 95% CI (1.66, 4.65), p < 0.0001, respectively. [...] The HR was higher than for those carrying APOEε4 allele (HR = 2.30, 95% CI (1.75, 3.02, p < 0.0001)). [...] Early hypertension was consistently ranked as the most significant risk factor among non-genetic factors, similar to the genetic risk factor of the APOEε4 allele.

• #12 Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study | BMJ Open

  https://bmjopen.bmj.com/content/15/2/e092293

  Alzheimers disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. […] This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques. […] The proposed classifier can help guide preclinical screening of AD in the elderly population. […] The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. […] The combination of LASSO and SVM-RFE techniques has demonstrated promising diagnostic performance in AD. […] Using this hybrid feature selection method, we extracted a feature subset consisting of 11 variables. The XGBoost model built on this subset achieved the best classification performance (accuracy: 0.843, precision: 0.919, specificity: 0.929, recall: 0.762, F1 score: 0.833, AUC: 0.915). […] Our study underscores the robust performance of the XGBoost model in predicting AD risk, leveraging straightforward, relevant and easily identifiable risk variables.

• #13 Late-Life Alzheimer’s Disease (AD) Detection Using Pruned Decision Trees

  https://www.clinmedjournals.org/articles/ijbdt/international-journal-of-brain-disorders-and-treatment-ijbdt-6-033.php?jid=ijbdt

  Machine learning (ML) is a category of an algorithm that allows software applications to become more accurate in outcome prediction without being explicitly programmed. […] Present concentrate on late-life AD detection with the help of MRI demographic data and AD prediction were evaluated with feature characteristics. […] Model performance was evaluated by accuracy, precision, and area under receiver operating characteristic curve (AU-ROC). […] In AD diagnosis of most MCI studies, MRI demographic information along with other features highly important in AD forecasting. […] The outcome tree was generated with different sub-branches and left a decision at the end, considered as a leaf of the corresponding branch. In the end, outcomes suggesting that pruned decision tree models are one of the best approaches with an accuracy of 88.7%.

• #14 Late-Life Alzheimer’s Disease (AD) Detection Using Pruned Decision Trees

  https://www.clinmedjournals.org/articles/ijbdt/international-journal-of-brain-disorders-and-treatment-ijbdt-6-033.php?jid=ijbdt

  ROC curve value was evaluated as fundamental analysis in medical diagnosis, and it’s a plot of true positive rate on y-axis and false positive rate on x-axis. According to, in diagnosis classification an excellent model possess ROC near to one that means it has effective measure of separability. If it near to zero said to have worst measure of separability. In this experiment, we got ROC of AD classification is 0.962, which means that comprehensive classification of AD patients was done. […] Therefore, there is more chance to save AD patients in future before they turn into helpless situations.

• #15 A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset

  https://www.mdpi.com/2227-7390/12/14/2204

  Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. […] The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. […] A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net.

• #16 A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset

  https://www.mdpi.com/2227-7390/12/14/2204

  This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field. […] The proposed transfer learning approach model with neural network approach algorithms with modalities of VGG16, VGG19, and AlexNet are used for building the model by choosing inputs and evaluating them on healthy datasets from the Behavioral Risk Surveillance Systems to classify and predict Alzheimer’s disease and enhance diagnostic accuracy by providing a more comprehensive view of an individual’s health. […] The proposed study has a beneficial outcome, and continued research efforts are required to identify and assess their impact on onset using various approaches. Efforts to address Alzheimer’s disease, which is a growing concern, have significance in research and development to understand the cause, its treatment, and finding its potential cure.

• #17 It is possible to predict cognitive decline in Alzheimer’s | Amsterdam UMC

  https://www.amsterdamumc.org/en/spotlight/it-is-possible-to-predict-cognitive-decline-in-alzheimers.htm

  Alzheimer Centre has developed a prediction model that can predict cognitive decline in patients with mild cognitive impairment or mild dementia due to Alzheimer’s disease. […] Although the predictions do not provide absolute certainty, the model does give an indication of the course of the disease over a period of 5 years. […] The prediction model is based on data from nearly 1000 patients with Alzheimer’s disease. […] „As a result, it gives a prediction that is really tailored to each individual person,” says Van der Veere. […] „Previous research shows that people still want information about their prognosis, even if this information is uncertain. An app with our prediction model can therefore meet an important need.” […] „In the future, this will become even more important if we can treat Alzheimer’s disease,” says Wiesje van der Flier, Research Director at Alzheimer Centre Amsterdam. „Doctors can use the prediction model to explain what the possible effect of a treatment can be.”

• #18 Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer’s disease | Alzheimer’s Research & Therapy | Full Text

  https://alzres.biomedcentral.com/articles/10.1186/s13195-022-01053-0

  Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. […] We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. […] The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD. […] Discriminative performance was higher in SCD/MCI patients than in patients with AD dementia for both institutionalization and mortality. […] Models based on amyloid-positive patients only had similar discrimination. […] We developed validated models to predict institutionalization and mortality along the AD continuum. […] The models can be used to provide patients in both pre-dementia and dementia stages and their caregivers prognostic information on the time to institutionalization and mortality.

• #19 Alzheimer’s: Will doctors soon be able to predict cognitive decline?

  https://www.medicalnewstoday.com/articles/researchers-figure-out-how-they-could-predict-individual-stages-of-alzheimers

  This is encouraging research as it suggests that researchers can produce a model which is able to predict how the symptoms experienced by people with mild cognitive impairment or early Alzheimers disease might change. […] The predictions can also be used by a clinician to discuss with a patient the potential effect of a treatment. […] This research lays the foundation for the types of prognostic tools that can not only give us a sense of what might be expected along the road ahead, but also, how we might alter that course by addressing a variety of modifiable risk factors to change the disease trajectory and improve our chances of maintaining higher levels of cognitive health and function over a longer horizon.

• #20

  https://link.springer.com/article/10.1007/s10916-023-01906-7

  Alzheimers disease (AD) is thought to develop when abnormal amounts of amyloid beta (Aβ) build up in the brain, either extracellularly as amyloid plaques, tau proteins or intracellularly as neurofibrillary tangles, affecting neuronal function, connectivity and leading to progressive brain function loss. […] The amyloid beta (Aβ) hypothesis and the cholinergic hypothesis are two competing theories presented to explain the underlying cause of AD. […] Dementia is ranked on the seventh place in the leading causes of deaths in the world. […] The number of dementia patients is rapidly increasing worldwide, and statistical projections suggest that 135 million people might be affected by dementia by 2050. […] While age is the most prominent risk factor for dementia; figures suggest that a person at the age of 65 years old has 12% risk of developing dementia disease. By the age of 85 years old, this risk can reach to 30%.

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