WHR and cognitive performance -
For example, a 1-unit worse visual memory score was associated with a 1. Observational associations of adiposity on cognitive function are likely not causal.
In the reverse direction, our consistent findings that worse visual memory is associated with three adiposity indicators provide support for a causal link between worse visual memory and lower adiposity.
In this pseudo two-sample Mendelian randomization study using genetic instruments from large-scale genome-wide association studies and individual-level data from UK Biobank, we observed no evidence for a causal effect of adiposity on cognitive function.
Observational associations of adiposity on cognitive function are likely not to be causal. In the reverse direction, we provide support for a causal link between visual memory and adiposity. In adulthood, obesity has been consistently associated with lower cognitive function, 8 , 9 notably with poor executive function, 10 intellectual functioning, psychomotor performance and speed, and visual construction.
central vs peripheral in the adiposity-cognition relationship remains uncertain. Some studies have investigated the relationship between indicators of central adiposity [e. waist-hip ratio WHR and waist circumference WC ] and cognition, with inconsistent results.
Lower cognitive function has also been associated independently with adiposity. Mendelian randomization MR , specifically bidirectional MR, is a strategy that may help unpick the extent to which the pathways between adiposity and cognitive function represent a bidirectional causal pathway.
A limitation of their study was a lack of published genetic variants for cognitive ability at the time of publication. Therefore, single nucleotide polymorphisms SNPs for educational attainment were employed as a cognitive ability proxy. Additionally, the use of BMI as a proxy for total adiposity did not permit an investigation into whether specific adiposity indicators were differentially associated with cognitive function.
Recently, Wang and colleagues 27 performed a bidirectional MR of BMI and WHR adjusted for BMI; WHR adj BMI on cognitive performance and vice versa.
They observed conflicting findings in both directions, e. in the direction of cognition to adiposity, there was robust evidence that higher cognitive performance caused lower BMI but little evidence for an effect on WHR adj BMI.
In the reverse direction, there was no effect of BMI on cognitive performance but some evidence for a detrimental effect of higher WHR adj BMI. The study predominantly used a single indicator verbal-numerical reasoning to represent cognitive performance and thus it is not known how other indicators of cognitive performance may relate to adiposity.
Moreover, MR findings in relation to WHR adj BMI may be biased and should be avoided. We aimed to address the above identified knowledge gaps by triangulating findings using two analytical approaches.
Sample flow diagram and study design illustrating bidirectional approach. We employed a pseudo two-sample bidirectional MR design, using genetic association estimates from individual-level data of UKB participants and genome-wide association study GWAS summary statistics described below , to estimate the causal effect of five indicators of adiposity on two indicators of cognitive function and vice versa.
Adiposity measures were obtained at baseline following standardiezd protocols. BMI was positively skewed and so was transformed to the natural logarithmic scale [ln BMI ] when used as an outcome details of all parameterizations used are in Supplementary Table S1 , available as Supplementary data at IJE online.
At baseline, participants undertook cognitive assessments described elsewhere Briefly, for VM, respondents were asked to correctly identify matches from six pairs of cards after they had memorized their positions. The number of incorrect matches number of attempts made to correctly identify pairs was recorded.
A greater number VM or time RT indicates poorer cognition. Both variables were positively skewed and were transformed using natural logs when considered as outcomes Supplementary Table S1.
Potential confounders were identified from a directed acyclic graph Supplementary Figures S1 and S2 , available as Supplementary data at IJE online.
They included: the Townsend index 37 a measure of area-level deprivation , smoking status, physical activity, age years , alcohol intake, sleep duration, and comorbidities type 1 diabetes, stress, depression and chronic fatigue syndrome details in Supplementary Methods and Supplementary Table S2 , available as Supplementary data at IJE online.
For UFA and FA, the GWAS from which SNPs were obtained was performed on UKB participants. Instrument F statistics, obtained from regressions of each phenotype on the respective genetic instrument, ranged from Linkage disequilibrium clumping in PLINK1. We explored observational associations between measured adiposity and cognition and vice versa using linear regression, with and without adjustment for confounders.
To ensure comparability across observational and MR analyses, when adiposity measures were used as exposures, we rescaled them so that a 1-unit change represented a 1-standard deviation SD change.
This was not done when RT and VM were exposures of interest, as their original GWAS were performed on untransformed RT and VM Supplementary Table S1.
The following analyses were performed initially with adiposity instruments as exposures and cognitive function measures RT and VM as outcomes and then vice versa.
The inverse-variance weighted MR IVW method was our main MR model. SNP-Y associations were estimated using linear regressions, adjusted for 10 genetic principal components. SNP-X associations were extracted from the original GWAS. We report I GX 2 which quantifies the magnitude of regression dilution bias in the context of MR Egger 48 further details on MR methods are in Supplementary Methods.
For results from MR analyses to be valid, three key assumptions must be met: i genetic variants should be robustly associated with the exposure; ii genetic variants should be independent of confounding factors of the relationship in question; iii the association between genetic variants for the exposure and the outcome must only operate via the exposure under study.
Here we provide brief details regarding how these assumptions were assessed further details in Supplementary Methods. We explored the validity of our instruments by testing associations between SNPs and above-described potential confounders, applying a Benjamini-Hochberg false-discovery rate of 0.
Where associations were observed, MR analyses were re-run excluding potentially invalid SNPs. overestimation of causal effects in a one-sample setting , 50 using established methods. For MR analyses, we used SNP-X from sample A and SNP-Y from sample B A on B and vice versa B on A.
It was not possible to employ the split-sample strategy for analyses involving UFA and FA as either exposures or outcomes , as these phenotypes were not observable in UKB, to derive estimates of either SNP-X favourable or unfavourable or SNP-Y favourable or unfavourable betas. We used Stata16 and PLINK1.
MR analyses were performed using mrrobust in Stata. VM was 3 25th, 75th centile: 2,5. Type 1 diabetes, stress, depression and chronic fatigue syndrome see Supplementary Methods and Supplementary Table S2 , available as Supplementary data at IJE online for details.
slower, RT and with a lower number of incorrect matches, i. better VM Table 2 ; Supplementary Figures S3 and S4 , available as Supplementary data at IJE online. Higher BMI and WHR, were associated with faster RT and better VM, e. a 1-SD higher BMI was associated with 0.
Associations between measured and genetically predicted increases in one standard deviation in adiposity and percent difference in reaction time ms and visual memory number incorrect matches. Adjusted for deprivation, age at recruitment, smoking status, alcohol consumption, physical activity, sleep duration and comorbidities.
For all other adiposity-cognitive function associations, at least two of the three MR analyses agreed with adjusted observational findings, although in most situations confidence intervals were wide and included the null.
For example, a 1-SD higher BMI was associated with 0. In adjusted models, higher i. For example, a 1-ms higher RT was associated with a 0. Associations between measured and genetically predicted increases in reaction time ms and visual memory number incorrect matches on adiposity.
MR estimates of the RT-adiposity associations generally indicated that a higher i. All three MR analyses were directionally consistent with the observational analysis for the association between RT and BMI Table 3 ; Supplementary Figures S5—S7.
For example, a 1-unit worse VM score was associated with a 1. Whereas a higher worse VM score also resulted in a lower WHR in all three MR analyses, confidence intervals included the null. When removing SNPs associated with confounders from instruments, associations from adiposity to cognition in particular for VM changed direction Supplementary Table S5 , available as Supplementary data at IJE online.
In the other direction, whereas some associations from cognition to adiposity e. In addition, as per the main analyses, many of the confidence intervals were wide and included the null. Results from the split-sample strategy in which RT and VM were the exposures are presented in Supplementary Table S9 available as Supplementary data at IJE online.
The meta-analysis of estimates from MR A on B and MR B on A were smaller, but in line with those reported above. We investigated evidence for causal links between adiposity and cognitive function in UK Biobank using several complementary approaches, and found important differences in terms of the postulated direction of association.
Using a bidirectional MR design, we show the effect of adiposity on cognitive function is likely not to be causal. In the other direction, we found little evidence to support causal links between RT and adiposity; however, our findings do strengthen the evidence base for causal links between poor VM and lower adiposity.
MR estimates were imprecisely estimated and, in almost all instances, included the null. Furthermore, estimates changed direction in the main compared with the sensitivity analyses.
The lack of effect of adiposity on cognition agrees with the null MR findings between BMI and verbal-numerical reasoning observed by Hagenaars and colleagues 26 and also a recent bidirectional MR study by Wang et al.
The authors did conclude that an inverse relationship between WHR adj BMI and cognitive performance was evident, though covariable-adjusted summary associations such as WHR adj BMI should be interpreted with caution as such instruments have been found to introduce bias into MR analyses.
The consistency of findings across MR studies using different adiposity and cognitive function measures supports a likely null effect of adiposity on cognitive function. For RT, however, all confidence intervals included the null.
Our findings are in contrast with previous observational findings suggesting an association between worse cognitive function and subsequent higher BMI, 20 , 22 , 23 and this may be related to the different periods of observation across the studies e. childhood vs adulthood. Estimates from MR studies exploring the effect of cognitive ability on adiposity are conflicting.
Whereas Wang et al. That we observed some evidence suggesting that poorer visual memory i. both Wang et al.
and Davies et al. used verbal-numerical reasoning. The major strength of our study is that by using a bidirectional design, we have been able to establish the direction of causal effects between adiposity and cognitive function.
By performing both observational and MR analyses and via the use of multiple indicators of adiposity and cognitive function, we have also been able to triangulate findings to more comprehensively explore the adiposity-cognitive function relationship.
Within our bidirectional MR framework, we used three different methods which have distinct strengths and assumptions.
We acknowledge some limitations. Our observational analysis was cross-sectional; direction of causality cannot be inferred from such study designs. We investigated the extent of the bias resulting from sample overlap 51 and found it to be small. Furthermore, when we investigated the extent of this bias for the RT and VM instruments by employing a split-sample strategy, we observed estimates which were directionally consistent with those from the full sample.
This resulted in a smaller number of SNPs in our instruments than otherwise would have been possible, which likely reduced the power of our MR analyses and may have contributed to some weak instrument bias. The VM assessment performed less well in terms of reliability, compared with the other cognitive function assessments in UKB.
Our results have important public health implications. We demonstrate that the effect of adiposity on cognitive function is likely not to be causal. Findings should be interpreted in the context of the limitations of the study and should be triangulated using other cognitive outcomes and complementary methods to determine their robustness.
Participants provided informed consent; ethical approval was given by the North-West Multicentre Research Ethics Committee. Supplementary data are available at IJE online.
and S. initiated the idea and design of the study. performed all statistical analyses and wrote the first draft of the manuscript.
All authors contributed to the interpretation of the results, provided important intellectual input and approved the manuscript. guarantees the work carried out, had access to all of the data and takes responsibility for the integrity of the data and the accuracy of the data analysis.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study has been conducted using the UK Biobank Resource Application Number We express our gratitude to the participants and researchers involved in UK Biobank.
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Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan.
Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan. You can also search for this author in PubMed Google Scholar. Wan-Yu Lin conceived the study design, applied for the TWB data, developed the analysis tool, performed the analyses, interpreted the results, and wrote the manuscript.
Correspondence to Wan-Yu Lin. Written informed consent was obtained from each participant and the study was carried out in accordance with institutional requirements and the principles of the Declaration of Helsinki.
This study further received approval from the Research Ethics Committee of National Taiwan University Hospital NTUH-REC no.
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Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Research Open access Published: 07 November Associations of five obesity indicators with cognitive performance in 30, Taiwan Biobank participants Wan-Yu Lin ORCID: orcid.
Abstract Background Obesity adversely influences the central nervous system and cognitive functions. Methods The Taiwan Biobank TWB administered the Chinese version of the Mini-Mental State Examination MMSE to 30, participants 12, males and 18, females aged 60 to 70 years. Results Abdominal obesity defined by WHR was significantly associated with poor cognitive performance.
Conclusion The results consistently agreed that preventing abdominal obesity is associated with better cognitive performance in both males and females. Background The pace of population aging is increasing.
Methods Taiwan Biobank Since October , the TWB has recruited over , community-based volunteers in Taiwan to build a biobank. Mini-mental state examination The MMSE evaluates the performance of orientation, attention, calculation, memory, language, and visual-spatial skills [ 6 ].
Covariates adjusted in all models Ten covariates adjusted in all models included age, smoking status, drinking status, regular exercise, chronic disease status, depression status, blood pressure level, total cholesterol, fasting glucose, and educational attainment.
Definition of obesity This cross-sectional study assesses the associations of five obesity indicators with cognitive performance.
Statistical analysis Males and females are different in body composition and risks of cognitive impairment [ 35 ]. Table 1 Basic characteristics of the 30, TWB participants Full size table. Results Basic characteristics Table 1 summarizes the characteristics of 12, males Discussion This study showed that WHR was most relevant to cognitive performance among five commonly used obesity indicators.
Abbreviations BFP: body fat percentage. BMI: body mass index. FDR: false discovery rate. MMSE: Mini-Mental State Examination. HC: hip circumference.
TWB: Taiwan Biobank. WC: waist circumference.
Holistic mental wellness is known to have WHR and cognitive performance negative adn on cardiovascular and metabolic health, but Holistic mental wellness also xognitive brain structure WR function; it is associated with both gray and white matter integrity loss, adn well as performancf cognitive function, including the domains of executive function, WHR and cognitive performance, Mindful eating for appetite regulation, and language. Especially midlife obesity perdormance associated with both cognitive impairment and an increased risk of developing dementia at later age. However, underlying mechanisms are not yet fully revealed. Here, we review recent literature published between and March and discuss the effects of obesity on brain structure and cognition, with a main focus on the contributions of the gut microbiome, white adipose tissue WATinflammation, and cerebrovascular function. Obesity-associated changes in gut microbiota composition may cause increased gut permeability and inflammation, therewith affecting cognitive function. Moreover, excess of WAT in obesity produces pro-inflammatory adipokines, leading to a low grade systemic peripheral inflammation, which is associated with decreased cognition.
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