Posters

Presenting Author

Stephen Michael Garcia

Presenting Author Academic/Professional Position

Medical Student

Academic Level (Author 1)

Medical Student

Academic Level (Author 2)

Medical Student

Academic Level (Author 3)

Medical Student

Academic Level (Author 4)

Faculty

Discipline/Specialty (Author 4)

Population Health and Biostatistics

Presentation Type

Poster

Discipline Track

Community/Public Health

Abstract Type

Research/Clinical

Abstract

Background: Alzheimer’s disease (AD) is a growing problem among the elderly and has several risk factors. The Rio Grande Valley has specific demographic and socioeconomic factors that may influence the likelihood of developing this neurodegenerative disease. Elevated cholesterol is a risk factor for the development of Alzheimer’s; however, this correlation has not been fully explored in the population within the Rio Grande Valley. This area of study is important due to the rapidly aging population within the RGV, as well as the high prevalence of comorbidities such as elevated lipid profiles.

Methods: This study analyzed a case series of 6 people with Alzheimer's Disease and an extensive group of controls attending the same clinic at RGV. Based on the distribution of 6 people, we used their rank in age (minimum minus five years and maximum plus five years) and BMI (ranges identical to age) to select the case sample.

The dataset was declared a panel using longitudinal modeling and visualization of repeated measures per subject. Individual lipid trajectories for subjects with Alzheimer’s disease were visualized and compared with those of control subjects.

A mixed-effects model with a random intercept and random slope model was fit. This model estimated the fixed effects by group and visit time, allowing individual-specific random slopes and intercepts to account for within-subject correlation over time.

Results: BMI, height, and LDL-c levels showed non-significant trends toward lower values in the AD group. Weight was significantly lower in the AD group (median 63.9 kg vs. 80.2 kg in controls; p = 0.044). Smoking and vaping history exhibited notable differences, although small cell sizes limited formal testing. Notably, 50% of AD participants reported former vape use compared to < 1% in controls (p < 0.001), though this finding should be interpreted with caution.

Lipids related to cholesterol (HDL-c, total cholesterol, and LDL-c values) did not significantly differ. However, triglycerides were unexpectedly higher in the AD group despite lower weight and BMI (p < 0.001).

Conclusion: This study presents a case series of individuals with Alzheimer’s disease (AD) compared to a large matched control group to explore potential differences in lipid metabolism. Within similar age and BMI ranges, AD participants exhibited lower body weight and a trend toward lower LDL-c levels. Notably, triglyceride levels were higher in the AD group despite their lower BMI — a paradoxical finding suggesting potential metabolic dysregulation in this population. While the small number of AD cases limits the generalizability and statistical power of the study, the use of longitudinal modeling and repeated measures strengthens the internal validity of observed trends. These preliminary findings offer valuable insights and generate hypotheses that merit further investigation into larger, more definitive cohort studies.

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Lipid Profiles in Alzheimer’s Disease: A Case Series with Longitudinal Comparison to Controls

Background: Alzheimer’s disease (AD) is a growing problem among the elderly and has several risk factors. The Rio Grande Valley has specific demographic and socioeconomic factors that may influence the likelihood of developing this neurodegenerative disease. Elevated cholesterol is a risk factor for the development of Alzheimer’s; however, this correlation has not been fully explored in the population within the Rio Grande Valley. This area of study is important due to the rapidly aging population within the RGV, as well as the high prevalence of comorbidities such as elevated lipid profiles.

Methods: This study analyzed a case series of 6 people with Alzheimer's Disease and an extensive group of controls attending the same clinic at RGV. Based on the distribution of 6 people, we used their rank in age (minimum minus five years and maximum plus five years) and BMI (ranges identical to age) to select the case sample.

The dataset was declared a panel using longitudinal modeling and visualization of repeated measures per subject. Individual lipid trajectories for subjects with Alzheimer’s disease were visualized and compared with those of control subjects.

A mixed-effects model with a random intercept and random slope model was fit. This model estimated the fixed effects by group and visit time, allowing individual-specific random slopes and intercepts to account for within-subject correlation over time.

Results: BMI, height, and LDL-c levels showed non-significant trends toward lower values in the AD group. Weight was significantly lower in the AD group (median 63.9 kg vs. 80.2 kg in controls; p = 0.044). Smoking and vaping history exhibited notable differences, although small cell sizes limited formal testing. Notably, 50% of AD participants reported former vape use compared to < 1% in controls (p < 0.001), though this finding should be interpreted with caution.

Lipids related to cholesterol (HDL-c, total cholesterol, and LDL-c values) did not significantly differ. However, triglycerides were unexpectedly higher in the AD group despite lower weight and BMI (p < 0.001).

Conclusion: This study presents a case series of individuals with Alzheimer’s disease (AD) compared to a large matched control group to explore potential differences in lipid metabolism. Within similar age and BMI ranges, AD participants exhibited lower body weight and a trend toward lower LDL-c levels. Notably, triglyceride levels were higher in the AD group despite their lower BMI — a paradoxical finding suggesting potential metabolic dysregulation in this population. While the small number of AD cases limits the generalizability and statistical power of the study, the use of longitudinal modeling and repeated measures strengthens the internal validity of observed trends. These preliminary findings offer valuable insights and generate hypotheses that merit further investigation into larger, more definitive cohort studies.

 

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