Beta Regression Model Research Articles

Quantifying past forest cover and biomass changes in the Ecuadorian Amazon.

Here, we developed and applied models to quantitatively reconstruct forest cover and biomass changes at three lakes in northwestern Amazonia over the past > 1500 yr. We used remotely sensed data and a modern dataset of 50 Amazonian lakes to develop generalized linear models that predict aboveground biomass, using phytolith morphotypes and forest cover as predictor variables. Also, we applied a published beta regression model to predict forest cover within 200 m of each lake, using Poaceae phytoliths. Charcoal and maize phytoliths were analysed to identify past land use. Results showed forest cover and biomass changes at our study sites ranged between 48-84% and 142-438 Mg ha-1, respectively. Human occupation was discontinuous, with major changes in forest cover and biomass coinciding with periods of land use. Forest cover and biomass decreased notably after fire (at all sites) or cultivation events (Lakes Zancudococha, Kumpaka). The timing and ecological impact of past land use were spatially and temporally variable. Our results suggest past human impact was small-scaled and heterogenous in northwestern Amazonia, with a significant impact of fire on forest cover and biomass changes.

Examining the impacts of school bus travel on students' academic performance in two major cities

AbstractSchool buses are a prevalent mode of school travel that may negatively affect students’ academic performance due to numerous factors, including longer commutes. These extended travel times can introduce or exacerbate mental and physical stressors, including air pollution exposure and bullying, while reducing opportunities for health‐promoting activities like physical activity and sleep. While a few researchers have explored the effects of school bus transportation on academic achievement, the research is limited. In this study, we used data from two major Canadian cities, Toronto and Ottawa, to investigate the relationship between the proportion of students commuting by bus to and from school and the percentage meeting standards on standardized tests. We employed beta regression models to analyze performance in reading, writing, math, and literacy across Grades 3, 6, 9, and 10 while controlling for family and income variables that may influence school travel mode and student achievement. Our findings indicate a significant inverse correlation between school bus transportation and academic achievement for Grade 10 literacy. Longer commutes appear to impair academic performance, given that Grade 10 students presumably spend more time on the bus than younger students. Notably, the negative effects on literacy outcomes are greater than on math.

Comparison between estimates of beta regression model using MSE

The multiple linear regression analysis shows the effects of explanatory variables on the dependent variable. Its function is to represent data to understand the shape and nature of the relationship between explanatory variables and the response variable. One of the main problems faced by linear regression is the nature of the data, as sometimes it does not follow a normal distribution. This has led to the emergence of different types of linear regression models such as the beta regression model, where the data of the dependent variable is less than one and confined within the period (0,1), and the data is relative and decimal. Non-traditional methods will be used to estimate beta regression parameters because some traditional methods do not provide suitable and accurate results when dealing with one of the main problems encountered in regression, which is multicollinearity. To address this issue, we used some methods include The Maximum Likelihood Estimation (MLE), ridge regression, modified Liu, and Ozkale and Kaciranlar method. Shrinking parameters are proposed for each method, and a comparison between the methods is conducted using the Mean Squared Error comparison criterion through a simulation experiment with two approaches, the simulation results showed that the ridge regression method is the best for the first and second methods of simulation. Paper type Research Paper

Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.

Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients. This study aims to determine whether experts and laypeople agree on what constitutes medical jargon. Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon. The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R2 of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms. To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

Identification of the factors influencing speeding behaviour of food delivery e-bikers in China with the naturalistic cycling data

With the rapid growth of the gig economy in China, millions of food delivery e-bikers are making their living by rushing on the street. Speeding is one of their most common risky riding behaviours, leading to severe traffic crashes. Based on 2-month naturalistic cycling data of 46 full-time food delivery e-bikers in Changsha, their speeding behaviour is deeply studied with the individual daily speeding proportion being taken as the speeding indicator. A beta regression model is built to identify the factors significantly influencing the indicator. The estimation results reveal that female riders, middle-aged riders and riders with a bachelor’s degree are less likely to engage in speeding. The same result is indicated for those working longer or experiencing more crashes. Additionally, holidays and riding distance are found to have significantly positive influences. Finally, some countermeasures are proposed to prevent speeding among food delivery e-bikers.

Improved Liu-ridge-type estimates for the beta regression model

The main objective of this paper is to introduce a new class of estimators for the ill-conditioned beta regression model. The new class, named the Liu-ridge-type (LRT), incorporates two shrinkage parameters that allow it to include the maximum-likelihood estimator (MLE), the ridge estimator (RE), and the Liu estimator (LE) as special cases. The LRT estimator has been shown to outperform the LE, the RE, and the MLE in terms of the mean squared error (MSE) matrix under certain conditions. Simulated and real applications illustrate the potential benefits of the new LRT estimator.

Experiential Factors as a Determinant for Climate Change Mitigation Behavior: A Case of Tourist Hotel Managers in Naivasha Sub-County, Kenya

Climate change is typically abstract, large scale, slow and often unrelated to the welfare of people’s usual activities. There are however moments when the consequences of climate change are readily apparent, such as through experiencing extreme weather events. This study examined the association between personal experiences with extreme weather events together with emotions and taking actions that mitigate the consequences of climate change, a largely under researched topic. This relationship was tested among 182 randomly selected tourist hotel managers in Naivasha Sub-County in Kenya using paper and pencil survey techniques. An instrument made up of questions on the managers’ extent of their personal involvement with extreme climate risks and events was utilized. Another set of questions that rated the managers’ level of emotions towards climate change was also included. The study identified two categories of climate change mitigation behaviors (CCMB) using principle component analysis that are adopted by managers namely efficiency and curtailment practices. A beta regression model that accounts for the non-normality of the data was used to examine the relationship between experiential factors and CCMB. Findings indicate a moderate engagement in CCMB among the surveyed managers. Experiencing disasters was negatively and significantly associated with both curtailment and efficiency CCMB. Emotions were positively associated with efficiency CCMB but had a negative association with curtailment CCMB. The findings suggest that public education would benefit from strategies that focus on connecting experiencing weather related disasters and emotions with the reality of climate change.

Fungal community composition in arthropogenically-active and non-active northern Borneo caves

Fungal studies are scant within this region of Borneo, and the potential effects of anthropogenic disturbance on fungal communities in Sabahan caves have not been explored. This study involved opportunistic sampling of guano, speleothems, and cavern water from four caves in Sabah, Malaysia, categorised according to the level of anthropogenic activity. The dilution method was used for fungal isolation and abundance calculations. Morphological identification of axenic cultures was conducted, and DNA barcoding of the ITS region of cryptic species corroborated identification. A total of 170 axenic cultures were attained, composed of 69 morphological taxonomic units (MTUs), 33 genera, ten orders, and two divisions – Ascomycota and Basidiomycota. Fifty-seven of the 69 MTUs were identified to the species level. The genera Aspergillus and Penicillium were particularly prevalent, with a combined total of 86 isolations (50.6%). Notably, twelve species were documented for the first time in cave ecosystems of Sabah. The average fungal abundance across all substrates and fungal taxa occurrence was found to be higher in samples found in active caves. A significant difference in relative abundance was observed between active and non-active cave conditions. Beta regression modelling indicated that anthropogenic activity is a significant factor influencing the composition of cave fungal communities. While anthropogenic activity seems to affect cave mycobiomes, especially by selecting for ubiquitous fungi, it is likely not the sole influencing determinant of their composition.

Time toxicity among older patients with cancer treated with palliative systemic therapy.

The time toxicity of anticancer therapy, defined as days spent with healthcare contact during treatment, represents a critical but understudied outcome. This study aims to quantify time toxicity among older patients with cancer receiving palliative systemic treatment. All patients aged ≥ 65 years with metastatic cancer receiving cytotoxic chemotherapy, immunotherapy, or targeted therapy at a single center in Mexico were selected from a prospective patient navigation cohort. Patients completed a baseline assessment, including the G8 screening and quality of life measures. Physical healthcare contact days within the first 6months were extracted from medical records and divided by days alive during the same period. Beta regression models were used to identify predictors of time toxicity. We identified 158 older patients (median age 71 years); 86% received cytotoxic chemotherapy. Seventy-three percent had an impaired G8 score and were considered vulnerable/frail. Six-month overall survival was 74%. Within the first 6months, patients spent a mean of 21% (95% confidence interval (CI) 19-23%) of days with healthcare contact. Concurrent radiotherapy (odds ratio (OR) 1.55; 95%CI 1.21-1.97), cytotoxic chemotherapy versus targeted therapy (OR 1.64; 95%CI 1.13-2.37), and an impaired G8 (OR 1.27; 95%CI 1.01-1.60) were associated with increased time toxicity. Older adults with metastatic cancer spend 1 in 5days with healthcare contact during treatment, with a higher burden of time toxicity for patients receiving radiotherapy or cytotoxic chemotherapy and those with potential frailty. These findings underscore the importance of informing patients about their expected healthcare contact days within the context of a limited life expectancy.

Sex and gender bias in chronic coronary syndromes research: analysis of studies used to inform the 2019 European Society of Cardiology guidelines

Sex and gender inequalities in ischemic heart diseases persist. Although ischemic heart disease is less common in women, they experience worse clinical outcomes and are less likely to receive guideline-recommended treatments. The primary scientific literature from which clinical guideline recommendations are derived may not have considered potential sex- and gender biases. This study aims to determine whether the literature cited in recent cardiovascular guidelines' clinical recommendations contain sex and gender biases. We analysed publications cited in the 2019 European Society of Cardiology (ESC) guideline recommendations on chronic coronary syndromes, using a checklist to guide data extraction and evaluate the individual studies for sex- and gender-related aspects, such as inclusion/exclusion criteria, outcome measures, and demographic data reporting. To assess representation over time, the proportion of women participants in each study was computed and analysed using a beta regression model. We also examined the associations between women's representation, journal impact factor and author gender. Among the 20 ESC recommendations on chronic coronary syndromes, four contained sex-related statements; we did not identify any gender-specific suggestions. The referenced literature upon which these recommendations were based consisted of 108 articles published between 1991 and 2019, encompassing more than 1.6 million study participants (26.8%; 432,284 women). Only three studies incorporated sex-sensitive designs; none were gender-specific. The term "gender" did not occur in 84% (n=91/108) of the publications; when used, it was exclusively to denote biological sex. The proportion of women (assumed by investigators) among study participants fluctuated over time. Having a woman as first (odds ratio (OR)=1.68, 95% CI: 1.19-2.39) or last author (OR=2.28, 95% CI: 1.31-3.97), was significantly associated with having more women participants in the study. The data underlying ESC guideline recommendations largely lack reporting of possible sex- and gender-specific aspects, and women are distinctly underrepresented. To what extent these recommendations apply to members of specific population groups who are not well-represented in the underlying evidence base remains unknown. This study is part of the Gender and Health Inequalities (GENDHI) project, ERC-2019-SyG. This project has received funding from the European Research Council (ERC).

Addition of an audience response system in a biomedical science course improves learning environment and student performance.

The effectiveness of incorporating an audience response system (ARS) in improving the learning environment and student performance was assessed in a didactic biomedical science course at a large US dental school. Instructors and students were surveyed for their experiences of using Top Hat-based ARS in a multi-disciplinary biomedical science course. Average exam scores and grade distribution for challenging sections on immunology and developmental biology were compared before and after incorporating ARS. Pearson's chi-squared test, likelihood ratio test, Student's t-test and Fisher's exact test using beta regression model were used to assess statistical significance (p < 0.05). Student survey results with 78% (82/105) response rate, indicated that incorporating ARS improved student engagement, reinforced lecture material, and prepared them better for exams (p < 0.0001). Sixty seven percent of student respondents recommended continued integration of ARS questions in lectures (p < 0.0001). Although faculty survey data (n = 5, 100% response rate) were not statistically significant, the majority of faculty agreed that real-time feedback through ARS allowed them to appropriately pace lecture delivery and restructure forthcoming material. The average exam scores and grade distribution for challenging sections showed modest improvement after incorporation of ARS (p < 0.05). There was no significant correlation between final course grades and ARS grades for participation or correctness. Incorporating the ARS in a multi-disciplinary biomedical science course is perceived by faculty and students as an effective instructional approach that improves the learning environment, teaching strategies, and student performance.

Assessment of Baseline Ultrawidefield Fluorescein Angiographic Quantitative Leakage Parameters with Ultrawidefield Fundus Features and Clinical Parameters in Diabetic Retinopathy in Protocol AA

PurposeEvaluate quantitative leakage parameters on ultra-widefield fluorescein angiography (UWF-FA) images and explore their association with Diabetic Retinopathy Severity Scale (DRSS), predominantly peripheral lesions (PPLs), visual acuity, and clinical characteristics. DesignA post-hoc analysis of baseline UWF-FA images in the DRCR Retina Network observational study Protocol AA. ParticipantsN = 575 eyes from 384 adults across 38 sites in the US and Canada with gradable UWF-FA. MethodsA machine learning-enhanced feature extraction platform provided initial leakage segmentation of UWF-FA images sequentially reviewed and corrected by two certified readers for segmentation accuracy. UWF-FA leakage was measured in five retinal zones: panretinal (whole retina), central macular (3-disc diameter fovea-centered circle), posterior pole (6-disc diameter fovea-centered circle), peripheral (outside 6-disc diameter circle) and widefield far peripheral (outside 9-disc diameter circle); associations with clinical factors were evaluated with marginal beta regression models. Main Outcome MeasuresUWF-FA leakage index, calculated as the area with leakage divided by the analyzable retinal area. ResultsThe mean quantitative leakage index was 3.5% for panretinal, 6.6% for macular, 4.8% for posterior pole, 3.3% for peripheral and 2.8% for wide-field far peripheral retinal zones. Panretinal leakage was associated with DRSS (mean 2.2% for no to mild NPDR, 3.4% for moderate NPDR, 4.2% for moderately severe NPDR, 4.8% for severe NPDR and 5.1% for PDR; P<.001), HbA1c (3.2% for HbA1c <8% vs. 3.8% for HbA1c ≥8%; P=.01 for continuous HbA1c), visual acuity (3.3% for 20/25 or better vs. 4.7% for 20/32 or worse; continuous P<.001), and UWF-FA-PPL types of IRMA (4.3% vs. 3.3%; P=.005) or NVE (5.7% vs. 3.4%; P=.003). DRSS was also statistically significant for leakage within all retinal zones (P<.001); eyes with non-central DME versus no DME had higher mean leakage in the central macular (11.2% vs. 5.9%; P=0.005) and posterior pole regions (9.2% vs. 4.2%; P=.002). ConclusionQuantitative UWF-FA leakage analysis identified associations between leakage and DRSS, visual acuity, and presence of DME. In the future, quantitative UWF-FA leakage parameters may be explored as potential biomarkers for disease progression risk.

Identifying Key Determinants of Civil Society Robustness in Southeast Asia

The research examines the key determinants of civil society robustness in Southeast Asia, addressing a significant gap in quantitative research within the region. Utilizing the beta regression model on data spanning from 1986 to 2023, the researcher explores the impact of participatory democracy, civil liberty, political stability, the rule of law, and neopatrimonialism on civil society’s robustness. Moderating effects of GDP per capita, control of corruption, freedom of expression, and digital freedom accessibility are also considered. Findings reveal that participatory democracy and civil liberties significantly enhance civil society’s robustness, whereas political instability tends to undermine it. Surprisingly, neopatrimonialism shows a positive influence, suggesting complex adaptive strategies by civil organizations. Along with the insignificant result of the rule of law, neopatrimonialism challenges conventional views, standing out among the variables analyzed. The research contributes to understanding the multifaceted dynamics of civil society in Southeast Asia, offering insights for policymakers and Civil Society Organizations (CSOs) to foster democratic governance and social justice. It underscores the necessity for region-specific strategies acknowledging the unique political, economic, and cultural landscapes of Southeast Asian countries.

Mapping the Lequesne Functional Index Into the EQ-5D-5L Utility Index in Patients With Knee Osteoarthritis

ObjectiveThis study aimed to map the Lequesne index onto the EuroQol 5 Dimension (EQ-5D-5L) utility index for patients with knee osteoarthritis. MethodsBaseline data from a previous randomized controlled trial were used; 461 patients were involved in the mapping development, and 230 in the validation phase. Various modeling techniques, including generalized linear models, tobit, and beta regression, were used. Factors such as age, sex, and body mass index were considered as covariates. Model selection was based on criteria such as Akaike and Bayesian information criteria, adjusted R2, mean absolute error, and root mean squared error. Validation involved assessing the preselected models using mean absolute error, root mean squared error, and intraclass correlation coefficient. This study follows the Mapping Onto Preference-Based Measures Reporting Standards statement. ResultsFive models were developed, with 2 incorporating age, sex, with or without body mass index along with the Lequesne index showing the best fit across regressions. Validation results were similar for the 3 regressions, with beta regression models exhibiting wider ranges closer to the validation data set. Intraclass correlation coefficient values were better for beta regression models. Both models tended to overpredict for lower EQ-5D-5L values and underpredict for better health status. ConclusionThese mapping functions, the first of their kind, effectively translate the Lequesne index to EQ-5D-5L values in patients with knee osteoarthritis. They demonstrate satisfactory fit and precision, providing valuable tools for clinicians and researchers, particularly in situations where generic preference-based health-related quality of life instruments are inaccessible for utility derivation in cost-effectiveness studies.

Trends in plant cover derived from vegetation plot data using ordinal zero‐augmented beta regression

AbstractQuestionsPlant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non‐equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.LocationThe Netherlands, Western Europe.MethodsWe used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero‐augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four‐year period. Thereafter we assessed trends in cover and in occurrence across all periods.ResultsWe found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.ConclusionsOur model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.

Performance evaluation of different regression models: application in a breast cancer patient data

This paper provides a comprehensive analysis of linear regression models, focusing on addressing multicollinearity challenges in breast cancer patient data. Linear regression methodologies, including GAM, Beta, GAM Beta, Ridge, and Beta Ridge, are compared using two statistical criteria. The study, conducted with R software, showcases the Beta regression model’s exceptional performance, achieving a BIC of − 5520.416. Furthermore, the Ridge regression model demonstrates remarkable results with the best AIC at − 8002.647. The findings underscore the practical application of these models in real-world scenarios and emphasize the Beta regression model’s superior ability to handle multicollinearity challenges. The preference for AIC over BIC in Generalized Additive Models (GAMs) is rooted in the AIC’s calculation framework, highlighting its effectiveness in capturing the complexity and flexibility inherent in GAMs.

Thinking Inside the Bounds: Improved Error Distributions for Indifference Point Data Analysis and Simulation Via Beta Regression using Common Discounting Functions

Standard nonlinear regression is commonly used when modeling indifference points due to its ability to closely follow observed data, resulting in a good model fit. However, standard nonlinear regression currently lacks a reasonable distribution-based framework for indifference points, which limits its ability to adequately describe the inherent variability in the data. Software commonly assumes data follow a normal distribution with constant variance. However, typical indifference points do not follow a normal distribution or exhibit constant variance. To address these limitations, this paper introduces a class of nonlinear beta regression models that offers excellent fit to discounting data and enhances simulation-based approaches. This beta regression model can accommodate popular discounting functions. This work proposes three specific advances. First, our model automatically captures non-constant variance as a function of delay. Second, our model improves simulation-based approaches since it obeys the natural boundaries of observable data, unlike the ordinary assumption of normal residuals and constant variance. Finally, we introduce a scale-location-truncation trick that allows beta regression to accommodate observed values of 0 and 1. A comparison between beta regression and standard nonlinear regression reveals close agreement in the estimated discounting rate k obtained from both methods.

Tilted beta regression and beta-binomial regression models: Mean and variance modeling

Tilted beta regression and beta-binomial regression models: Mean and variance modeling

Modulation of the Association Between Corticospinal Tract Damage and Outcome After Stroke by White Matter Hyperintensities.

Motor outcomes after stroke relate to corticospinal tract (CST) damage. The brain leverages surviving neural pathways to compensate for CST damage and mediate motor recovery. Thus, concurrent age-related damage from white matter hyperintensities (WMHs) might affect neurologic capacity for recovery after CST injury. The role of WMHs in post-stroke motor outcomes is unclear. In this study, we evaluated whether WMHs modulate the relationship between CST damage and post-stroke motor outcomes. We used data from the multisite ENIGMA Stroke Recovery Working Group with T1 and T2/fluid-attenuated inversion recovery imaging. CST damage was indexed with weighted CST lesion load (CST-LL). WMH volumes were extracted with Freesurfer's SAMSEG. Mixed-effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment, controlling for age, days after stroke, and stroke volume. A total of 223 individuals were included. WMH volume related to motor impairment above and beyond CST-LL (β = 0.178, 95% CI 0.025-0.331, p = 0.022). Relationships varied by WMH severity (mild vs moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (β = 0.888, 95% CI 0.604-1.172, p < 0.001) with a CST-LL × WMH interaction (β = -0.211, 95% CI -0.340 to -0.026, p = 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (β = 0.299, 95% CI 0.008-0.590, p = 0.044), but did not significantly relate to CST-LL or a CST-LL × WMH interaction. WMHs relate to motor outcomes after stroke and modify relationships between motor impairment and CST damage. WMH-related damage may be under-recognized in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury.

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines.

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.