Association Between BMI and Neurocognitive Functions Among Middle-aged Obese Adults: Preliminary Findings Using Machine-learning (ML)-based Approach.

IF 1.8 Q4 NEUROSCIENCES Annals of Neurosciences Pub Date : 2025-01-18 DOI:10.1177/09727531241307462
Dipti Magan, Raj Kumar Yadav, Jitender Aneja, Shivam Pandey
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Abstract

Background: Studies suggest that obesity predisposes individuals to developing cognitive dysfunction and an increased risk of dementia, but the nature of the relationship remains largely unexplored for better prognostic predictors.

Purpose: This study, the first of its kind in Indian participants with obesity, was intended to explore the use of quantification of different neurocognitive indices with increasing body mass index (BMI) among middle-aged participants with obesity. Additionally, machine-learning models were used to analyse the predictive performance of BMI for different cognitive functions.

Methods: In the cross-sectional analytical study, a total of 137 (n = 137) participants were included. Out of the total, 107 healthy obese (BMI = 23.0-30.0 kg m-2; age between 36 and 55 years of both genders) were recruited from the out-patient department of the Department of Endocrinology and General Medicine, and 30 participants were recruited as the control group, between March 2023 to February 2024. The participants underwent neuropsychological assessments, including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA) and serum levels of brain-derived neurotrophic factor (BDNF).

Results: Significant (p < .05) differences were observed for neurocognitive functions for the obese group versus the control group. According to the correlation heatmaps, BMI was significantly (p < .05) negatively associated with BDNF. Multivariate linear regression analysis revealed a substantial (p < .05) decline in BDNF with a change in BMI, accenting its significant impact on cognitive ageing. Additionally, consistent decreasing trends were observed across the MoCA and MMSE, confirming the robustness of the findings across diverse analytical methodologies. Furthermore, the linear regression model and super vector machine model contributed additional evidence to the consistency of the trends in cognitive decline linked to BMI variations.

Conclusion: The preliminary results of the present study support that increased BMI is an important physiological indicator that influences neurocognition and neuroplasticity in individuals with obesity.

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中年肥胖者BMI与神经认知功能之间的关系:基于机器学习(ML)方法的初步发现
背景:研究表明,肥胖使个体易患认知功能障碍和痴呆风险增加,但这种关系的性质在很大程度上仍未得到更好的预后预测因子的探索。目的:本研究首次在印度肥胖参与者中进行此类研究,旨在探讨中年肥胖参与者随着体重指数(BMI)的增加,不同神经认知指数的量化使用。此外,机器学习模型用于分析BMI对不同认知功能的预测性能。方法:采用横断面分析研究,共纳入137名(n = 137)受试者。其中,健康肥胖(BMI = 23.0-30.0 kg -2) 107例;于2023年3月至2024年2月期间,从内分泌与普通内科门诊部招募年龄在36 - 55岁之间的男性和女性,并招募30名参与者作为对照组。参与者接受神经心理学评估,包括迷你精神状态检查(MMSE)、蒙特利尔认知评估(MoCA)和血清脑源性神经营养因子(BDNF)水平。结果:肥胖组与对照组在神经认知功能方面有显著差异(p < 0.05)。根据相关热图,BMI与BDNF呈显著负相关(p < 0.05)。多元线性回归分析显示,BDNF随BMI变化而显著下降(p < 0.05),强调其对认知衰老的显著影响。此外,在MoCA和MMSE中观察到一致的下降趋势,证实了不同分析方法中发现的稳健性。此外,线性回归模型和超级向量机模型为与BMI变化相关的认知衰退趋势的一致性提供了额外的证据。结论:本研究初步结果支持BMI升高是影响肥胖个体神经认知和神经可塑性的重要生理指标。
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来源期刊
Annals of Neurosciences
Annals of Neurosciences NEUROSCIENCES-
CiteScore
2.40
自引率
0.00%
发文量
39
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