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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Impact of Tooth Loss and Other Risk Factors on Cognitive Impairment in Saudi Female Population

Atheer Abdulhade Ganem¹, N C Sandeepa², Afnan Hassan Alkhayri³

¹Department of Orthodontics, College of Dentistry, King Khalid University, Abha, Saudi Arabia.

Neuroscience Journal

|August 21, 2019

Abstract

Introduction:

It is known that cognitive impairment is linked to aging and neurobiological, psychological, and social factors. Recently, however, mastication and the number of teeth has also attracted attention, with a previous case control study reporting a correlation between the loss of teeth and Alzheimer's disease.

Objective:

To investigate possible relationships between cognitive function and various demographic variables, stress, medical history, and number of natural teeth in a specified female population.

Materials & Methods:

A sample of the Saudi female population, 40-65 years of age, who visited the King Khalid University College of Dentistry (Abha, Saudi Arabia), was studied. Education, occupation, perceived stress, and medical history, along with the number of remaining teeth, were assessed. The Mini-Mental State Examination (MMSE) was used to assess cognitive performance and the results were statistically analyzed.

Results:

Subjects were divided into those with mild, moderate, and severe impairment based on MMSE score; the association between age, education, occupation, medical history, and cognitive function demonstrated statistically significant results. Fifty percent of subjects with 0-16 teeth exhibited severe cognitive impairment. Of the cognitive abilities, attention, recall, and language skills were linked to the number of remaining teeth. When subjects were categorized into only high and low cognitive impairment based on MMSE score, regression analysis did not reveal a significant correlation between any of the studied variables and cognitive impairment.

Conclusion:

Results of the present study add to the recent data and head towards the theory of likely connection between the number of teeth and hippocampus-dependent cognitive functioning. Results of regression analysis revealed an absence of conclusive relation in the latter part of study. Longitudinal analyses including comprehensive clinical dental data with brain-imaging will shed further light on probable causal relationship(s).

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