Magan Dipti, Yadav Raj Kumar, Aneja Jitender, Pandey Shivam
Department of Physiology, All India Institute of Medical Sciences, Bathinda, Punjab, India.
Department of Physiology, All India Institute of Medical Sciences, New Delhi, Delhi, India.
Ann Neurosci. 2025 Jan 18:09727531241307462. doi: 10.1177/09727531241307462.
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.
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.
In the cross-sectional analytical study, a total of 137 ( = 137) participants were included. Out of the total, 107 healthy obese (BMI = 23.0-30.0 kg m; 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).
Significant ( < .05) differences were observed for neurocognitive functions for the obese group versus the control group. According to the correlation heatmaps, BMI was significantly ( < .05) negatively associated with BDNF. Multivariate linear regression analysis revealed a substantial ( < .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.
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.
研究表明,肥胖使个体易患认知功能障碍和患痴呆症的风险增加,但这种关系的本质在很大程度上仍未得到充分探索,以寻找更好的预后预测指标。
本研究是针对印度肥胖参与者的同类研究中的首例,旨在探讨在中年肥胖参与者中,随着体重指数(BMI)增加对不同神经认知指标进行量化分析的应用。此外,还使用机器学习模型来分析BMI对不同认知功能的预测性能。
在这项横断面分析研究中,共纳入了137名(n = 137)参与者。其中,从内分泌科和普通内科门诊招募了107名健康肥胖者(BMI = 23.0 - 30.0 kg/m²;年龄在36至55岁之间,男女不限),并在2023年3月至2024年2月期间招募了30名参与者作为对照组。参与者接受了神经心理学评估,包括简易精神状态检查(MMSE)、蒙特利尔认知评估(MoCA)以及脑源性神经营养因子(BDNF)的血清水平检测。
肥胖组与对照组在神经认知功能方面观察到显著(P <.05)差异。根据相关热图,BMI与BDNF显著(P <.05)负相关。多元线性回归分析显示,随着BMI的变化,BDNF显著(P <.05)下降,突出了其对认知衰老的显著影响。此外,在MoCA和MMSE中均观察到一致的下降趋势,证实了这些发现在不同分析方法中的稳健性。此外,线性回归模型和支持向量机模型为与BMI变化相关的认知衰退趋势的一致性提供了更多证据。
本研究的初步结果支持,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。