scholarly journals Are Drugs that Cause Dysbiosis Longitudinally Associated with Cognitive Scores, Cognitive Impairment, & Dementia?

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 645-645
Author(s):  
Nicholas Resciniti ◽  
Alexander McLain ◽  
Anwar Merchant ◽  
Daniela Friedman ◽  
Matthew Lohman
Keyword(s):  
Cognitive Impairment ◽  
Dose Response ◽  
Mixed Models ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Linear Mixed Models ◽  
Cognitive Outcomes ◽  
Binary Outcomes ◽  
Cognitive Changes ◽  
Drug Study ◽  
Over Time

Abstract Recent research has examined how the microbiome may influence cognitive outcomes; however, there is a paucity of research understanding how medication associated with dysbiosis may be associated with cognitive changes. This study used data from the Health and Retirement Study and the Prescription Drug Study subset for adults 51 and older (n=3,898). Continuous (0-27) and categorical (cognitively normal=12-27; cognitive impairment=7-11; and dementia=0-6) cognitive outcomes were used. Prescriptions utilized were proton pump inhibitors, antibiotics, selective serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotics, antihistamines, and a summed dose-response measure. Linear mixed models (LMM) and generalized linear mixed models (GLMM) were used for continuous and binary outcomes. For the LMM model, the main effect for those taking one medication was insignificant; however, the interaction with time showed a significant decrease over time (β: -0.07; 95% confidence interval (CI): -0.14, 0.01). The mean cognitive score was lower for those taking two or more medications (β: -1.48; 95% CI: -2.70, -0.25), although the interaction with time was insignificant. GLMM results showed those taking two or medications had odds that are 612% larger (odds ratio (OR): 7.12; 95% CI: 3.03, 16.71) of going from cognitively healthy to dementia but the interaction with time showed decreased odds over time (OR: 0.92; 95% CI 0.86, 0.97). For cognitive impairment, those who took two or more medications had odds that were 45% larger (OR: 1.45; 95% CI: 1.05, 2.00) of going from cognitively healthy to cognitively impaired. This study indicated a dose-response aspect to taking medications on cognitive outcomes.

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales
Keyword(s):  
Parameter Estimation ◽  
Medical Treatment ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Obesity Prevalence ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Public Health Reporting ◽  
Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

scholarly journals Cross-National Outcomes of a Digital Weight Loss Intervention in the United States, Canada, United Kingdom and Ireland, and Australia and New Zealand: A Retrospective Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiuchen Yang ◽  
Ellen Siobhan Mitchell ◽  
Annabell S. Ho ◽  
Laura DeLuca ◽  
Heather Behr ◽  
...  
Keyword(s):  
United States ◽  
Weight Loss ◽  
United Kingdom ◽  
New Zealand ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
The United States ◽  
Weight Loss Intervention ◽  
The Us ◽  
Over Time

Mobile health (mHealth) interventions are ubiquitous and effective treatment options for obesity. There is a widespread assumption that the mHealth interventions will be equally effective in other locations. In an initial test of this assumption, this retrospective study assesses weight loss and engagement with an mHealth behavior change weight loss intervention developed in the United States (US) in four English-speaking regions: the US, Australia and New Zealand (AU/NZ), Canada (CA), and the United Kingdom and Ireland (UK/IE). Data for 18,459 participants were extracted from the database of Noom's Healthy Weight Program. Self-reported weight was collected every week until program end (week 16). Engagement was measured using user-logged and automatically recorded actions. Linear mixed models were used to evaluate change in weight over time, and ANOVAs evaluated differences in engagement. In all regions, 27.2–33.2% of participants achieved at least 5% weight loss by week 16, with an average of 3–3.7% weight loss. Linear mixed models revealed similar weight outcomes in each region compared to the US, with a few differences. Engagement, however, significantly differed across regions (P < 0.001 on 5 of 6 factors). Depending on the level of engagement, the rate of weight loss over time differed for AU/NZ and UK/IE compared to the US. Our findings have important implications for the use and understanding of digital weight loss interventions worldwide. Future research should investigate the determinants of cross-country engagement differences and their long-term effects on intervention outcomes.

scholarly journals Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

2011 ◽  
Vol 11 ◽  
pp. 42-76 ◽  
Author(s):  
Daniel T. L. Shek ◽  
Cecilia M. S. Ma
Keyword(s):  
Longitudinal Data ◽  
Generalized Linear Models ◽  
Mixed Models ◽  
Linear Models ◽  
Linear Mixed Models ◽  
Hierarchical Linear Models ◽  
Data Analyses ◽  
Basic Concepts ◽  
User Friendly ◽  
Over Time

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

Subjective cognition in chimeric antigen receptor T-cell therapy recipients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 7020-7020
Author(s):  
Anna Barata ◽  
Aasha Hoogland ◽  
Kelly Hyland ◽  
Anuhya Kommalapati ◽  
Reena Jayani ◽  
...  
Keyword(s):  
T Cell ◽  
Chimeric Antigen Receptor ◽  
Divided Attention ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Antigen Receptor ◽  
Allogeneic Hct ◽  
Visuospatial Abilities ◽  
Car T ◽  
Over Time

7020 Background: Chimeric antigen receptor T-cell (CAR-T) therapy can lead to durable responses in chemorefractory patients with hematologic malignancies. CAR-T, however, can be associated with neurotoxicity. There is a significant body of literature describing patient-reported concerns with cognition in hematopoietic cell transplant (HCT) recipients, a similar treatment group. However, little is known about subjective cognition in CAR-T patients. This study examined changes in subjective cognition over time in CAR-T recipients and compared their outcomes with allogeneic HCT recipients. Methods: At baseline and 90 days after infusion, participants completed the Everyday Cognition Questionnaire (ECog). The ECog provides scores for total cognition, memory, language, visuospatial abilities, planning, organization, divided attention, and satisfaction with cognition. Comparison data from allogeneic HCT recipients came from a previous observational study. Linear mixed models compared changes in subjective cognition between recipients of CAR-T and allogeneic HCT over time. Models were adjusted by age, marital status, education, and Karnofsky performance status. Results: Participants were 111 CAR-T recipients (mean age 60 years, 37% female) and 190 allogeneic HCT recipients (mean age 53, 42% female). Linear mixed models indicated CAR-T recipients’ subjective cognition didn’t change within the 90 days after infusion (p’s > .05). At baseline, there were no group differences between CAR-T and allogeneic HCT recipients in subjective cognition (p’s > 0.05). Over time, however, subjective cognition between groups differed. Specifically, CAR-T recipients reported stable subjective cognition whereas allogeneic HCT recipients reported worsening total subjective cognition (p = 0.04), memory (p = 0.02), visuospatial abilities (p = 0.01), planning (p = 0.01), and divided attention (p = 0.01). At follow-up, CAR-T recipients reported better total subjective cognition (p < 0.01), memory (p < 0.01), language (p = 0.01), visuospatial abilities (p < 0.01), planning (p < 0.01), and divided attention (p < 0.01) than allogeneic HCT recipients. Conclusions: Despite the neurotoxicity associated with CAR-T, patients can expect to perceive similar subjective cognition at day 90 compared to baseline. Future studies should also evaluate objective cognition in CAR-T recipients.

scholarly journals An introduction to joint models—applications in nephrology

2020 ◽  
Vol 13 (2) ◽  
pp. 143-149
Author(s):  
Nicholas C Chesnaye ◽  
Giovanni Tripepi ◽  
Friedo W Dekker ◽  
Carmine Zoccali ◽  
Aeilko H Zwinderman ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Joint Model ◽  
Joint Models ◽  
Introductory Article ◽  
Clinical Interest ◽  
Main Components ◽  
Over Time

Abstract In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques.

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