Body mass index, genetic susceptibility, and Alzheimer's disease: a longitudinal study based on 475,813 participants from the UK Biobank

Yuan, Shiqi; Wu, Wentao; Ma, Wen; Huang, Xiaxuan; Peng, MIn; Xu, Anding; Lyu, Jun

doi:10.1186/s12967-022-03621-2

Cited by 15 publications

(9 citation statements)

References 59 publications

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“…Nordestgaard et al 105 suggested that low BMI is not a risk factor for AD and is not associated with an increased risk of developing AD. However, contradictory findings propose that higher BMI correlates with a decreased risk of AD among individuals sharing the same genetic susceptibility 106 . It has also been postulated that both high and low BMI could augment the risk of AD development 105 .…”

Section: Discussionmentioning

confidence: 99%

Genetic causal role of body mass index in multiple neurological diseases

Wang,

Chen,

Chang

et al. 2024

Sci Rep

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Body mass index (BMI) is a crucial health indicator for obesity. With the progression of socio-economic status and alterations in lifestyle, an increasing number of global populations are at risk of obesity. Given the complexity and severity of neurological diseases, early identification of risk factors is vital for the diagnosis and prognosis of such diseases. In this study, we employed Mendelian randomization (MR) analysis utilizing the most comprehensive genome-wide association study (GWAS) data to date. We selected single nucleotide polymorphisms (SNPs) that are unaffected by confounding factors and reverse causality as instrumental variables. These variables were used to evaluate the genetic and causal relationships between Body Mass Index (BMI) and various neurological diseases, including Parkinson’s Disease (PD), Alzheimer's Disease (AD), Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Ischemic Stroke (IS), and Epilepsy (EP). The Inverse Variance Weighted (IVW) analysis indicated that there was no significant causal relationship between Body Mass Index (BMI) indicators and PD (P-value = 0.511), AD (P-value = 0.076), ALS (P-value = 0.641), EP (P-value = 0.380). However, a causal relationship was found between BMI indicators and MS (P-value = 0.035), and IS (P-value = 0.000), with the BMI index positively correlated with the risk of both diseases. The Cochran’s Q test for MR-IVW showed no heterogeneity in the MR analysis results between the BMI index and the neurological diseases (P > 0.05). The Egger intercept test for pleiotropy revealed no horizontal pleiotropy detected in any of the neurological diseases studied (P > 0.05). It was found that there was no causal relationship between BMI and PD, AD, ALS, EP, and a genetic causal association with MS, and IS. Meanwhile, the increase in BMI can lead to a higher risk of MS and IS, which reveals the critical role of obesity as a risk factor for specific neurological diseases in the pathogenesis of the diseases.

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Section: Discussionmentioning

confidence: 99%

Genetic causal role of body mass index in multiple neurological diseases

Wang,

Chen,

Chang

et al. 2024

Sci Rep

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“…There are many risk factors for the development of cognitive impairment in older adults; for example, excessive sleep duration increases the risk after adjusting for numerous relevant risk factors (Yuan et al, 2022a ), and those with a low BMI (< 23 kg/m 2 ) have a higher risk of developing dementia (Yuan et al, 2022b ). With the advent of an aging society, cognitive impairment occurrence will become more common (Afzal et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning for the prediction of cognitive impairment in older adults

Zeng

Yuan

et al. 2023

Front. Neurosci.

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ObjectiveThe purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm.MethodsThe complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application.ResultsThe study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application.ConclusionsML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

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“…Obesity in midlife, especially before the age of 50, has been linked to an increased risk of dementia, whereas in late-life, the association between high BMI and dementia often reverses. 26, 27 Prior MR studies have revealed that genetic liability to AD is linked to lower BMI, 28, 29 highlighting the role of late-life weight loss as a prodromal factor for AD. Accumulating evidence suggests that AD pathology leads to impaired functions of the hypothalamic and other brain regions crucial for metabolic regulations, which contribute to weight loss in the early stages of AD.…”

Section: Discussionmentioning

confidence: 99%

Snoring and risk of dementia: a prospective cohort and Mendelian randomization study

Gao,

Andrews,

Brenowitz

et al. 2023

Preprint

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BackgroundThe association between snoring, a very common condition that increases with age, and dementia risk is controversial. Snoring is linked to obstructive sleep apnoea and cardiometabolic conditions, both of which are associated with an increased risk of dementia. However, snoring also increases with body mass index (BMI), which in late life is linked to lower dementia risk, possibly due to metabolic changes during prodromal dementia.MethodsThe prospective cohort study used data from 450,027 UK Biobank participants with snoring measured at baseline (2006 – 2010), and followed up for dementia diagnosis (censored at 2022). Two-sample Mendelian randomization (MR) analysis used summary statistics for genome-wide association studies of Alzheimer’s disease (AD) (n = 94,437; cases = 35,274) and snoring (n = 408,317; snorers = 151,011).ResultsDuring a median follow-up of 13.5 years, 7,937 individuals developed dementia. Snoring was associated with an 8% lower risk of all-cause dementia (hazard ratio [HR] 0.92; 95% confidence interval [CI] 0.88 to 0.97) and AD (HR 0.92; 95% CI 0.86 to 0.99). The association was stronger in older individuals,APOEε4 allele carriers, and during shorter follow-up periods. MR analyses suggested no causal effect of snoring on AD, however, genetic liability to AD was associated with a lower risk of snoring. Multivariable MR indicated that the effect of AD on snoring was primarily driven by BMI.ConclusionsThe phenotypic association between snoring and lower dementia risk likely stems from reverse causation, with genetic predisposition to AD associated with reduced snoring. This may be driven by weight loss in prodromal AD.

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Body mass index, genetic susceptibility, and Alzheimer's disease: a longitudinal study based on 475,813 participants from the UK Biobank

Cited by 15 publications

References 59 publications

Genetic causal role of body mass index in multiple neurological diseases

Genetic causal role of body mass index in multiple neurological diseases

Machine learning for the prediction of cognitive impairment in older adults

Snoring and risk of dementia: a prospective cohort and Mendelian randomization study

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