Model 2 was based on model 1, with additional adjusting for sociodemographic variables age, sex, nation, education, marital status and family economics. Model 3 was based on model 2, with additional adjustments of lifestyles smoking, drinking and physical exercise and health variables hypertension, stroke and depression.

All statistical analyses were performed by SAS V. No patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for design or implementation of the study.

No patients were asked to advise on interpretation or writing up of results. There are no plans to disseminate the results of the research to study participants or the relevant patient community. Of the subjects, The mean age of all subjects was More than a half Among the subjects with cognitive impairment, the mean MMSE score was The mean values of BMI and WHR were Differences of BMI and WHR between the two groups were both statistically significant.

The subjects with cognitive impairment tended to be older, female, minority ethnic group, without physical exercise, with hypertension, with stroke, with depression. Also, cognitive impairment was associated with education, marital status, family economics, smoking and drinking.

More details are shown in table 1. The mean MMSE scores were calculated by quartiles of BMI. Subjects in the highest BMI quartile category had the highest mean MMSE score Compared with the second quartile of BMI, the OR of the lowest quartile was 1.

In model 3, the OR of Q1 BMI was close to being statistically significant, and these results were essentially unchanged after adjustment for more covariates table 2.

Further, the association between WHR and cognitive impairment was assessed under each BMI group. Under the lowest BMI group, the association between WHR and cognitive impairment was not statistically significant. Similar results were found in the second and third quartiles of BMI. In the highest BMI group, each 0.

The OR value remained significant after adjusting for more covariates in model 2 and model 3 which were 1. Similarly, we assessed the associations between waist circumference and cognitive impairment within various BMI levels.

When BMI, age, sex, nation, education, marital status, family economics, smoking, drinking, physical exercise, hypertension, stroke and depression were controlled, each 1-unit higher waist circumference corresponded to a 1.

In this cross-sectional study of Chinese elderly aged 60 years and older, we investigated the associations between BMI, WHR and cognitive impairment risk. We found that each 0. In previous studies, some reported that high BMI tended to be a risk factor for cognitive decline, 9—11 while others observed a negative association between high BMI and cognitive function.

Zhou et al 21 suggested that subjects who were both with obesity and dementia had a high mortality rate which might very likely remove those with high BMI and dementia, and leave moderate or severe dementia subjects with low BMI, thus enforce the association between BMI and dementia.

Assuming the survivor bias existed, the observed association between high BMI and cognition impairment would be biased towards the null, and such bias would be even more serious in cross-sectional study if it exists.

Nevertheless, the hypothesis is not enough to explain the relationship between low BMI and cognitive impairment. Furthermore, several cohort studies reported that both persons with low BMI and persons with high BMI had lower cognitive functions in later life.

Among the participants of this study, the mean value of WHR tended to increase within the higher BMI group. The association remained after adjusting for covariates. Similar results were observed when evaluating the association between waist circumference and cognitive impairment.

These findings led us to speculate that body fat and muscle had a reverse effect on cognition. Adipokines might be a link between body fat and dementia. Adipokines include hundreds of polypeptides secreted by the cells of white adipose tissue. The action of adipokines could be altered during neurodegenerative events and might feedback to contribute to neurodegeneration.

The association between muscle and cognition could mainly be derived from muscle strength. It is noteworthy to mention that previous studies have reported high-fat diet exacerbates cognitive decline. Some limitations of the present study should be noted. One limitation is that high-fat diet, which is an important influence factor for cognitive function as mentioned above, was not included in this study.

It is probable that high-fat diet leads to central obesity with high BMI and WHR among Chinese elderly. Further studies are needed to explore the relationship within diet, WHR and cognitive impairment.

Besides, caution would be needed when generalising the present findings, as our results were based on a cross-sectional study. The results of this study suggest that it is of benefit to the elderly with high BMI to control WHR.

Table 1 XLS File Sociodemographics and health characteristics of participants by cognitive status. Table 2 XLS File Association between body mass index and cognitive impairment. Table 3 XLS File Association of waist-to-hip ratio per 0. Table 4 XLS File Association of waist circumference with cognitive impairment under different body mass index groups.

Contributors: JL, RY, TZ, QC, XY, YZ, FL, XW, FH and CY participated in the design of the study, collection of data, data cleaning. TZ, RY, YZ, FL, XW and CY conducted the statistical analyses.

TZ wrote the manuscript. RY, QC, XY and JL contributed to the interpretation of the results and revised the manuscript critically. All authors approved the final version of the manuscript. Funding: This work was supported by Zhejiang Provincial Medical and Health Science and Technology Project KYB, KY, RC , Zhejiang Provincial Natural Science Foundation of China Q19H , and Science and Technology Bureau of Yuhuan Ethics approval: Ethics Committee of Zhejiang Provincial Center for Disease Control and Prevention.

Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: Data are not publicly available due to local ethical restrictions. We acknowledge the invaluable contributions made by all the interviewers of the Zhejiang Ageing and Health Cohort Study.

Source Information January , Volume 8 Issue 10 - BMJ Open. Authors Tao Zhang Rui Yan Qifeng Chen Xuhua Ying Yujia Zhai Fudong Li Xinyi Wang Fan He Chiyu Ye Junfen Lin. Table of Contents Introduction Materials and methods Study population Cognitive function Body mass index Waist-to-hip ratio Covariates Statistical analysis Patient and public involvement Results Sociodemographics and health characteristics Association between BMI and cognitive impairment Association between WHR and cognitive impairment Association between waist circumference and cognitive impairment Discussion Conclusions.

Study population The present study used data collected from the baseline survey of a community-based cohort study focusing on ageing and health problems among the elderly in Zhejiang province, China, since Sociodemographics and health characteristics Of the subjects, Tables Table 1 XLS File.

Sociodemographics and health characteristics of participants by cognitive status. Table 2 XLS File. Table 3 XLS File. Association of waist-to-hip ratio per 0. Table 4 XLS File. Association of waist circumference with cognitive impairment under different body mass index groups.

Footnotes Number Reference 1. Competing interests: None declared. Patient consent: Obtained. Reviewer comments Download.

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Second, previous studies found sex differences in the effects of BMI and FM on cognitive impairment [ 11 , 17 ], but our sample size is not large enough to observe such differences.

Third, all our study subjects are community-dwelling men and women aged 65 or older from Taichung City, which can reduce the generalizability of our results. Last, the data were collected at baseline, and one-year and year follow-up.

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Our findings suggest that FM and AF trajectories with consistent high levels and WHR trajectory with high level with U-shaped group are associated with low risks of incident cognitive impairment in older adults. Similarly, WHR trajectory with low but slowly increasing trend is associated with a decreased risk of cognitive decline.

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Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan. Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan.

School of Medicine, College of Medicine, China Medical University, No. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan.

You can also search for this author in PubMed Google Scholar. TCL and CCL contributed equally to the design of the study and the direction of its implementation, including supervision of the field activities, quality assurance and control.

CIL, CSL, and CHL supervised the field activities. TCL and CCL helped conduct the literature review and prepare the Methods and the Discussion sections of the text.

All authors read and approved the final manuscript. Correspondence to Cheng-Chieh Lin. Written informed consent was obtained from all the study participants.

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Reprints and permissions. Li, TC. et al. Obesity marker trajectories and cognitive impairment in older adults: a year follow-up in Taichung community health study for elders. BMC Psychiatry 22 , Download citation. Received : 03 August Accepted : 24 November Published : 30 November Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background Obesity and cognitive impairment prevalence increases as age increases.

Methods A total of older adults aged 65 and older were involved in the study, with at least two repeated measurements at baseline, one-year or year follow-up. Results After a year follow-up, older adults with incident cognitive impairment and with rapid cognitive decline were defined as the top 25th percentile of cognitive decline.

Conclusion FM and AF trajectories with consistent high levels and WHR trajectory with high level with U-shaped group are associated with low risks of incident cognitive impairment in older adults. Background Approximately 9.

Method Study design and subjects A community-based prospective cohort study, namely, TCHS-E, was conducted in residents aged 65 and over in the North District of Taichung City, Taiwan in Measurements Sociodemographic factors, lifestyle behaviors, and disease histories The standardized questionnaire comprises sociodemographic characteristics, educational attainment levels, marital statuses, income levels, smoking habits, habitual alcohol intake levels, leisure-time physical activities, and personal histories of diagnosed hypertension; diabetes mellitus; heart disease; hyperlipidemia; stroke; and cancer, including current anti-diabetes, hypertension, heart, and hyperlipidemia medications.

Anthropometric measurements The anthropometric measurements include body height, weight, BMI, WC, hip circumference HC , and WHR. Body composition DXA Lunar DPX, General Electric, Madison, WI, USA was used to determine the whole body and regional distributions of fat and lean mass.

Frailty status Frailty is defined on the basis of well-established, standardized, and widely accepted phenotype described by Fried et al. Cognitive function assessment MMSE scale is widely used to assess cognitive function in older adults.

Results Of the older adults who participated in the study, had incident cognitive impairment and had cognitive decline higher than the 75th percentile during the year follow-up period. Table 1 Comparisons of baseline socio-demographic factors, lifestyle behaviors, disease history, and frailty status according to cognitive impairment and cognitive decline Full size table.

Full size image. Table 2 Comparisons of pattern of body mass index, fat mass, waist, WHR and abdominal fat trajectories according to cognitive impairment and cognitive decline Full size table. Discussion Our study is the first to examine the effects of BMI, FM, WC, WHR, and AF trajectories on cognitive impairment and cognitive decline in older adults.

Conclusion This study demonstrates that FM, WHR, and AF trajectories are associated with incident cognitive impairment, and WHR trajectory is a key predictor for the cognitive decline in older adults. Abbreviations MCI: Mild cognitive impairment BMI: Body mass index FM: Fat mass WC: Waist circumference WHR: Waist-to-hip ratio AF: Abdominal fat DXA: Dual-energy X-ray absorptiometry BIA: Bioimpedance analysis MMSE: Mini-Mental State Examination TCHS-E: Taichung Community Health Study for Elders HC: Hip circumference CT: Computerized tomography MRI: Magnetic resonance imaging ORs: Odds ratios CIs: Confidence intervals.

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Acta Physiol Oxf. Engler-Chiurazzi EB, Brown CM, Povroznik JM, Simpkins JW. WC showed stronger associations with cognition and cognitive decline compared to BMI, although most associations did not remain significant after controlling for age since individuals with obesity tended to be older in their study population Deckers et al.

Other obesity associated measures show inverse associations with cognition as well; hypertension and systolic blood pressure were for example associated with a decline in executive function Debette et al. More evidence about the link between cognition and obesity measures derives from weight loss studies, as previous studies have observed cognitive improvement after weight loss that followed bariatric surgery Gunstad et al.

Overall, there is a consistent link found in literature between obesity indices and cognitive function, however, the exact mechanisms are still unsolved. One of the proposed mechanisms involved between obesity and cognitive function concerns the gut microbiome. As dietary patterns largely influence the gut microbiome, it may not be surprising that a high fat diet HFD in animals affects gut health and its microbiome.

These changes in abundance were closely associated with increased body weight in mice Lam et al. In contrast, mice that showed resistance to diet-induced obesity DIO through HFD had a shift toward a lean microbiota composition phenotype compared to DIO mice.

Interestingly, these mice also showed lower levels of inflammation and no impairment in memory, whereas the DIO mice showed impaired spatial recognition and discrimination Zhang et al. The gut microbiota is involved in energy metabolism, partly through fermenting complex dietary fibers into short-chain fatty acids SCFAs Baothman et al.

The main SCFAs present in the gut are acetate, propionate, and butyrate, of which acetate and propionate are mainly produced by Bacteroidetes , and butyrate by Firmicutes see Figure 1 ; Baothman et al.

SCFAs are highly important for gut health by maintaining the mucosal epithelium and are used as energy source for gut epithelial cells Davis, ; Xu et al.

Some studies showed that SCFAs increase tight junction proteins in the gut therewith increasing gut barrier integrity Stilling et al. A transgenic mouse model for obesity showed impaired gut permeability in the colon Schroeder et al.

Moreover, HFD induced obesity in mice decreased tight junction protein zonulin-1 ZO-1 causing increased gut permeability Lam et al. Gut permeability is also affected by the microbiome Leigh and Morris, , as for example an increased amount of Oscillibacter is associated with decreased expression of ZO-1 Lam et al.

Oscillibacter is a Gram-negative species, which contains lipopolysaccharide LPS. LPS may change the structure of tight junction proteins, thereby increasing gut permeability Muscogiuri et al. Thus, many animal studies show higher levels of LPS and increased permeability in the gut in obesity, which may contribute to increased systemic inflammation.

Figure 1. A schematic overview of the effects of a high fat diet on the gut microbiome. High fat diet is associated with increased Firmicutes phyla and decreased Bacteroidetes phyla abundance. This leads to a change in the produced metabolites, as there is an increase in butyrate and a decrease in propionate levels.

High fat diets are also associated with increased Gram-negative bacteria, which activate the immune system via LPS. This LPS can enter the bloodstream through decreased gut permeability, as seen in decreased levels of tight junction proteins zonulin-1 and occludin.

ATP, adenosine triphosphate; LPS, lipopolysaccharide; SCFA, short chain fatty acids; ZO-1, zonulin As mentioned above, some studies showed that SCFAs increase the amount of tight junction proteins in the gut thereby increasing gut barrier function Stilling et al.

A high fiber diet has also been shown to increase SCFA production in the gut and was shown to attenuate inflammatory cell infiltration in mice Matt et al. Furthermore, butyrate, when administered with HFD in mice, is found to be protective against DIO Lin et al.

However, ex vivo studies in human biopsies of the colon showed that butyrate did not increase tight junction proteins, such as occludin Tabat et al. Therefore, these discrepancies between animal and human studies ex-vivo need further research.

Interestingly, these obesity-associated changes in gut microbiota composition have been shown to be reversable. This was associated with increased SCFA levels while gut permeability and body weight continued to be higher in obese mice compared to the lean mice Battson et al.

Vice versa, transplanting microbiota from obese mice to lean mice induced increased inflammation and gut permeability Bruce-Keller et al. Administering Akkermansia muciniphila to HFD-induced obese mice reduced body weight and fat mass Everard et al.

These results show both the effect of diet on microbiota, as well as the impact of microbiota on body weight and inflammation.

Gut microbiota and gut permeability are likewise associated with peripheral and central inflammation. In mice, HFD-induced obesity increased macrophage infiltration and expression of pro-inflammatory cytokines such as tumor necrosis factor α TNF-α and interleukin 6 IL-6 in mesenteric WAT, and increased TNF-α expression in the gut Lam et al.

Another study showed that HFD-induced obesity in mice increased the proinflammatory M1 macrophage phenotype levels in the colon Zhang et al. One mechanism involved in the association between gut microbiota and inflammation may be the increase in Gram negative bacteria in obesity such as Bacteroidetes and Oscillibacter , which contain LPS de La Serre et al.

As mentioned earlier, LPS may change the structure of tight junction proteins, through which it may increase gut permeability Muscogiuri et al. Thereby LPS can cross the gut barrier into the circulation, bind to Toll-like receptor 4 TLR4 and lead to inflammation by stimulating pro-inflammatory cytokine production and activating the innate immune system Saad et al.

However, the causality of these associations is still unknown. The distinct gut microbial composition in adults with obesity was associated with inflammation, as reflected in increased high-sensitivity C-reactive protein CRP plasma levels Verdam et al. Another study demonstrated that a healthy eating pattern in humans which included more fruit, yogurt, and less sugar compared to a less healthy diet showed improvements in both microbiota diversity and inflammation, in the blood as well as in WAT Kong et al.

In WAT, lower levels of circulating pro-inflammatory monocyte chemoattractant protein-1 MCP1 and a shift toward anti-inflammatory M2 macrophages were seen Kong et al. Supplementary Table 1 gives an overview of the studies discussed and Figure 1 provides an illustrative summary focusing on the effects of a HFD on the gut microbiome.

Moreover, the gut microbiome is associated with changes in cognition and brain structure. For example, germ-free mice exhibit memory impairment Gareau et al.

Hence, gut microbiota may affect cognition in mice. Interestingly, HFD-induced obese mice show a reduced occludin and ZO-1 expression in the gut, as well as increased gut inflammation, which was accompanied by impaired spatial and object recognition memory.

Object recognition memory was furthermore positively associated with Bacteroidetes abundance. DIO resistant mice showed no increased gut permeability, gut inflammation, or memory impairment Zhang et al. Interestingly, when mice on a standard chow diet received microbiota from HFD-induced obese mice, they showed increased inflammation, gut permeability, blood-brain barrier BBB permeability, anxiety and decreased memory performance Bruce-Keller et al.

Furthermore, mice that received microbiota from humans experiencing obesity, showed decreased inhibition, just like the human donors, compared to lean human donors and mice who received their microbiota Arnoriaga-Rodriguez et al.

Altogether, this indicates the importance of a healthy gut microbiota for optimal cognitive function in animals. In humans, studies have also shown associations between gut microbiota and cognition. Firmicutes bacteria are positively associated with memory performance, whereas Bacteroidetes and Proteobacteria are inversely associated with memory Arnoriaga-Rodriguez et al.

Adults with obesity showed lower scores in a Stroop test, which was positively associated with Eubacterium and Firmicutes bacterium abundance, and inversely associated with Bacteroidetes abundance Arnoriaga-Rodriguez et al.

One way the gut microbiota may influence cognition is through inflammation: Kreutzer et al. Proteobacteria and Marinilabiliaceae Bacteroidetes. Furthermore, when mice received fecal transplants from these humans experiencing obesity, they showed decreased memory function, as well as increased inflammatory gene expression in the prefrontal cortex Arnoriaga-Rodriguez et al.

Thus, research indicates that gut microbiota is one of the factors which may affect cognition through inflammation see Figure 2. Figure 2. Overview of mechanisms underlying cognitive impairment in obesity. Both excess WAT and altered gut microbiota have a direct and indirect effect on brain functioning.

In dysbalanced WAT, adipocytes secrete pro-inflammatory adipokines in the circulation, leading to a more pro-inflammatory state. Adipokines like leptin, PAI-1 and SAA thereby, affect vascular health via promoting atherosclerosis, hypertension and thrombosis.

Gut microbiota in obesity is linked to higher levels of LPS and increased gut permeability, which may contribute to increased systemic inflammation.

Both excess WAT and altered gut microbiota add to blood brain barrier BBB dysfunction, which leads to increased neuroinflammation amongst other. The hypothalamus, amygdala and hippocampus seem to be the most vulnerable regions for obesity related changes and are all three highly important in cognitive functioning.

IL-1β, interleukin 1β; IL-6, interleukin 6; PAI-1, plasminogen activator inhibitor 1; TNF-α, tumor necrosis factor α; SAA, serum amyloid; ZO-1, zonulin-1; LPS, lipopolysaccharide. Supplementary Table 2 gives an overview of the studies discussed based on the influence of gut microbiota on cognition.

Obesity has been associated with increased peripheral inflammation as shown in multiple human studies. This inflammation is described to start in excess WAT, where adipocytes increase in size as they store more free fatty acids FFA. In the context of obesity, the adipocytes may also produce more FFA, attracting pro-inflammatory cells, such as macrophages and mast cells to the adipose tissue.

Macrophages subsequently shift to a proinflammatory M1 phenotype, and the adipocytes secrete pro-inflammatory adipokines such as leptin, TNF-α, IL-1β, and IL-6 Illan-Gomez et al. Adipokines have several functions among others involvement in thrombosis, hypertension, metabolism and inflammation.

For example, leptin regulates metabolism and contributes to vascular disease via atherosclerosis and thrombosis Wang et al. Another inflammatory adipokine which shows increased levels in obesity is serum amyloid A SAA. SAA is considered a biomarker for inflammation as well as for cardiovascular disease, indicating the strong inverse correlation between inflammation and vascular health Zhao et al.

Altogether, excess WAT in obesity is associated with decreased fibrinolysis, which increases the risk of thrombosis and atherosclerosis, which in the end affects vascular health as well Figure 2 ; Kiliaan et al.

Moreover, by releasing cytokines into the circulation, a low but chronic systemic inflammation is induced Illan-Gomez et al. For example, bariatric surgery patients with higher levels of circulating LPS show increased inflammation in VAT and subcutaneous adipose tissue SCAT , including infiltration of macrophages and increased levels of IL-6 and MCP1 Clemente-Postigo et al.

However, inflammatory markers have been shown to differ between SCAT and VAT with a more proinflammatory profile in VAT McLaughlin et al.

The reviewed studies focusing on WAT inflammation, adipokines in obesity are summarized in Supplementary Table 3. Obesity-associated inflammation is not limited to the peripheral parts of the body, but it is observed in the brain as well. Neuroinflammation is observed in neurodegenerative diseases and is seen as a mediator of cognitive impairment Kumar, Many animal studies have shown the neuroinflammatory effects of HFD mainly on the amygdala, hippocampus, and hypothalamus, structures involved in regulating emotional behavior, learning and memory, and homeostasis, respectively Guillemot-Legris and Muccioli, Both HFD-induced obesity and transgenic mice models of obesity show inflammation in the hypothalamus Buckman et al.

DIO in mice showed increased expression of TNF-α and IL-6 in the hypothalamus, and increased TNF-α only in male mice Lainez et al. Furthermore, it has been shown that in HFD-induced obese rodents cellular immune responses are activated as seen as increased microglia and astrocyte activation in the hypothalamus Thaler et al.

Interestingly, this increased inflammation was associated with alterations in cognitive function and could be transferred to wild type mice by WAT transplantation or attenuated by exercise and IL-1β receptor blocker via IL-1 receptor antagonist infusion in the hippocampus Erion et al.

This thereby highlights both the role of VAT as a source of inflammation affecting other organs in the body, as well as the role of IL-1β and its receptors in neuroinflammation.

In mice, HFD intake for approximately 4 months exactly 16 and 18 weeks is associated with increased IL-1β and TNF-α expression in the amygdala and hippocampus, which correlated with cognitive impairment and specifically decreased spatial learning and memory performance Almeida-Suhett et al.

Furthermore, HFD induced obese rats showed increased oxidative stress and activated microglia in the hippocampus as well as decreased dendritic spine density Saiyasit et al. While HFD for 3 months additionally showed increased synaptic internalization by microglia in the hippocampus, as well as impaired memory, which could be reversed by switching the mice to a low-fat diet for 2 months Hao et al.

Furthermore, peripheral inflammation markers are inversely associated with cognition. Higher lean muscle mass and lower non-VAT and VAT mass were associated with better fluid intelligence in older adults, and was mediated by circulating leukocytes Klinedinst et al.

Serum inflammation markers are also associated with structural changes in the brain. For example, metabolic risk factors, including the presence of diabetes, hypertension and obesity, were associated with a thinner cortical thickness of the inferior frontal gyrus, and was mediated by higher serum pro-inflammatory interleukin 2 levels Kaur et al.

A recent method used to indirectly measure neuroinflammation is imaging water content in the brain. Here, a higher free water content is hypothesized to indicate increased neuroinflammation. Indeed, results mirror those found in animal models as BMI was associated with higher free water content, mainly in the cerebellum, subcortical areas, and the WM tracts between these areas Kullmann et al.

Further, in individuals with the highest inflammation values, associations were found between higher neuroinflammation and higher BMI, fat mass, CRP, and worse overall cognitive performance Puig et al. The reviewed studies focusing on neuroinflammation are summarized in Supplementary Table 4.

Obesity and especially increased VAT is highly associated with hypertension Hall et al. Adipokines can directly regulate this link between obesity and vascular function via their influence on endothelial cells, arterial smooth muscle cells and macrophages in the vessel wall Ntaios et al.

Nowadays, vascular health is thought to be an important mediator in the link between obesity and cognitive function. Moreover, multiple studies showed associations between co-morbidities of obesity such as hypertension and atherosclerosis and cognitive decline. Underlying mechanisms involved in development of cognitive dysfunction and linked to hypertension may include, among others, cerebral vessel remodeling, endothelial dysfunction and oxidative stress Mansukhani et al.

Furthermore, atherosclerosis is characterized by elevated low-density lipoproteins, that become oxidized which subsequently attracts macrophages, in the end leading to a chronic state of inflammation Cunningham and Hennessy, As hypertension and atherosclerosis often co-occur in individuals with obesity, the link between obesity and cognitive function is often mediated by these obesity-related comorbidities Dye et al.

Cognitive impairment in obesity is hypothesized to be associated with impaired cerebrovascular function. Mild obese mice showed less glucose transporter 1 GLUT-1 in the endothelium of blood vessels in the hippocampus and thalamus compared to control mice Arnoldussen et al.

GLUT-1 is important for glucose uptake from the blood into in the brain tissue. Less GLUT-1 in these areas, however, did not show differences in behavioral tests measuring cognitive function Arnoldussen et al. Another study using a high fat, high sugar diet HFHS to induce obesity in rats also showed reduced GLUT-1 expression in the hippocampus, as well as learning impairment compared to rats on a control diet Hargrave et al.

HFD-induced obesity in mice is furthermore associated with increased vasodilation in cerebral vessels, as well as a higher vascular density in the brain Cao et al.

In the brain of obese Zucker rats, vasodilation, a decreased inner diameter of the middle cerebral artery and decreased nitric oxide bioavailability was observed Katakam et al. In Wistar rats, 8 weeks of HFD caused cerebrovascular dysfunction and reduced CBF Li et al.

Overall, impaired neurovascular function, especially during midlife, is associated with impaired cognition and increased risk of dementia Iadecola and Gottesman, In humans, adults with obesity showed significantly reduced cerebrovascular reactivity CVR compared to lean adults.

Weight, BMI, and WC were furthermore inversely associated with CVR in a simple linear model in the complete study population adults with and without obesity Rodriguez-Flores et al.

However, in another study when the entire study group was considered lean controls and adults with overweight or obesity , this inverse association between BMI and CVR disappeared after controlling for insulin resistance Frosch et al.

Compared to lean adults, adults experiencing obesity showed decreased overall CBF in the brain when performing a response inhibition and attention test Willeumier et al.

This increased CBF was positively associated with body fat percentage assessed by bio-electrical impedance analysis Silvah et al. Furthermore, decreased regional CBF in the left frontal superior orbital and right frontal cortex, cerebellum, right precentral and right postcentral cortex was correlated with increasing BMI Willeumier et al.

Tarantini et al. However, they did not find changes in synaptic function of neurons in the hippocampus, and underlying mechanisms are therefore not yet clear Tarantini et al.

Obesity-associated changes in inflammation and vascular function might lead to increased BBB permeability. It has been shown using radioactively labeled triolein that triglycerides can cross the BBB and affect leptin sensitivity in the brain, specifically in the striatum, hypothalamus, occipital cortex, cerebellum and the midbrain Banks et al.

BBB leakiness in mice was associated with hippocampal inflammation as demonstrated with increased cytokine production, macrophage infiltration, and cognitive impairment Stranahan et al. Furthermore, both long term exposure of HFD and HFHS were associated with increased BBB permeability Cao et al.

Interestingly, some studies have shown that SCFAs modulate tight junction protein expression not just in the gut, but also in the BBB, and may therewith be associated with BBB integrity. In germ free mice, fecal transfer from pathogen-free control mice was shown to upregulate the tight junction protein occludin expression in the frontal cortex and striatum, and claudin-5 and ZO-1 expression in the hippocampus and striatum, thereby decreasing BBB permeability Braniste et al.

Moreover, in germ free mice whose intestines were mono-colonized with a single bacteria strain producing either butyrate or acetate and propionate, a normalized BBB permeability was shown, and germ-free mice who received sodium butyrate showed increased occludin expression in the frontal cortex and hippocampus Braniste et al.

It is known that the BBB endothelium also expresses monocarboxylate SCFA receptors Vijay and Morris, Furthermore, studies in rodents showed that supplementation with sodium butyrate or monobutyrin increased the expression of occludin and ZO-1 in the brain, in contexts of HFD Nguyen et al.

This indicates that the BBB may be vulnerable to changes in the gut microbiota Kelly et al. On a structural level, the brains of people experiencing obesity show thinner cortices and lower brain volumes, particularly in the hippocampus and hypothalamus, as well as decreased WM integrity compared to lean adults Puig et al.

On a cognitive level, this is reflected in lower memory, verbal fluency, and executive functions Kesse-Guyot et al. Recent evidence of both preclinical and clinical studies has shown multiple mechanisms underlying cognitive impairment in obesity.

DIO in rodents shifted the microbiome composition, particularly increasing Firmicutes abundance and decreasing Bacteroidetes abundance Lam et al.

This neuroinflammation is primarily found in the amygdala, hippocampus and hypothalamus, being areas of emotion regulation, learning and memory, and energy metabolism, respectively Duparc et al. Moreover, obesity is associated with impaired cerebrovascular and BBB function, particularly once more in the hippocampus and hypothalamus Hargrave et al.

All together this indicates the vulnerability of brain regions such as the hippocampus and hypothalamus in obesity.

The multiple underlying processes as described above do not act alone on cognition but are closely interconnected. Both excess WAT and altered gut microbiota in obesity add to systemic inflammation Illan-Gomez et al.

Interesting points for future research may include differences between race and ethnicity and sex in the role of obesity on cognitive function Lainez et al.

However, many rodent studies use only male animals as a homogenic group, whereas many clinical samples include more women than men. One of the well-known sex differences in obesity include differences in fat distribution, as men typically store more fat in the abdomen and women more in gluteofemoral WAT White and Tchoukalova, Especially VAT is associated with dysregulated adipokine production and consequently inflammation and vascular disease, while gluteofemoral obesity is often associated with lower risk of metabolic disorders, however, the literature is inconsistent on this point Kiliaan et al.

These sex differences in fat distribution and therefore inflammatory state, might contribute to the sex differences seen in dementia risk factors Azad et al. Furthermore, there is an interaction between sex and vascular risk factors in association with cognitive outcomes Gannon et al. For example, in postmenopausal women hypertension and diabetes are more likely to increase to the risk of developing cognitive impairment compared to men and pre-menopausal women Gannon et al.

More research is needed to investigate the interaction between sex, differences in risk factors and cognitive outcomes.

This will help to unravel underlying mechanisms and ultimately the development of personalized tailor-made treatments and preventatives such as a healthy diet and exercise. The obesity indices used have received more attention over the last years, as it has been found that WC and WHR are more sensitive obesity indices for associations with cognitive outcomes compared to BMI.

As mentioned before, VAT and SCAT were found to contain different levels of inflammation markers McLaughlin et al.

For future research it might be interesting to discriminate between VAT and SCAT via for example the use of MRI to quantify the different WAT compartments in humans. Furthermore, including various obesity indices, as well as multiple metabolic and inflammation measures are recommended to find the underlying mechanisms causing these different outcomes in cognition.

Nowadays, novel imaging techniques can measure neuroinflammation and BBB function in humans providing more information about the underlying link between obesity and cognition Albrecht et al. Furthermore, much evidence about the link between obesity and cognitive function in humans is based on observational cross-sectional studies which excludes any information about causality.

More research is needed to study cognitive changes in obesity over time, including more information on the role of gut microbiota, inflammation and cerebrovascular function. Additionally, more research on obesity treatment is needed to investigate whether weight loss or treatment of comorbidities leads to improvement of cognitive function.

Overall, obesity is associated with lower cognitive performance in the following domains: executive function, memory, inhibition, and language Kesse-Guyot et al. Underlying mechanisms may include changes in gut microbiome composition which are associated with increased gut permeability and inflammation.

Moreover, excess WAT and especially VAT produces pro-inflammatory adipokines, leading to low chronic systemic inflammation and reduced cerebral vascular function leading to increased BBB permeability and neuroinflammation, which may lead to neurological damage and impaired cognition Arnoldussen et al.

Future research is needed to investigate these pathways longitudinally, including various obesity indices and an equal gender distribution to study for example deviations in the associations between obesity measures and cognition.

Moreover, early preventive measures against obesity, such as lifestyle interventions targeting healthy diet and physical activity are highly recommended to reduce the detrimental effects of obesity on brain function and structure. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. BBB, blood-brain barrier; BMI, body mass index; BRID, brain resource international database; CBF, cerebral blood flow; CRP, C-reactive protein; CVR, cerebrovascular reactivity; DIO, diet-induced obesity; FA, fractional anisotropy; FFA, free fatty acids; GLUT-1, glucose transporter 1; GM, gray matter; HFD, high fat diet; HFHS, high fat: high sugar; IL-1β, interleukin 1β; IL-6, interleukin 6; LABS, longitudinal assessment of bariatric surgery; LPS, lipopolysaccharide; MCP-1, monocyte chemoattractant protein-1; PAI-1, plasminogen activator inhibitor-1; SAA, serum amyloid A; SCAT, subcutaneous adipose tissue; SCFAs, short-chain fatty acids; TLR4, Toll-like receptor 4; TNF-α, tumor necrosis factor α; VAT, visceral adipose tissue; WAT, white adipose tissue; WC, waist circumference; WHR, waist-to-hip ratio; WM, white matter; WMH, white matter hyperintensities; ZO-1, zonulin Albrecht, D.

In vivo imaging of human neuroinflammation. ACS Chem. doi: PubMed Abstract CrossRef Full Text Google Scholar. Alexopoulos, N. Visceral adipose tissue as a source of inflammation and promoter of atherosclerosis. Atherosclerosis , — Almeida-Suhett, C.

Behavioral changes in male mice fed a high-fat diet are associated with IL-1beta expression in specific brain regions.

Alosco, M. Cognitive function after bariatric surgery: evidence for improvement 3 years after surgery. Ambikairajah, A. Longitudinal changes in fat mass and the hippocampus. Obesity Silver Spring 28, — Arnoldussen, I. Adiposity is related to cerebrovascular and brain volumetry outcomes in the RUN DMC study.

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Early intake of long-chain polyunsaturated fatty acids preserves brain structure and function in diet-induced obesity.

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Gut 70, — Asghar, A. Role of immune cells in obesity induced low grade inflammation and insulin resistance. Cell Immunol. Ashrafian, F. Average age of the sample was Cognitive function was measured at baseline and four years later using the Minimental Cabán MMC , a previously validated Spanish-language instrument.

We examined bivariate correlations between these measures at baseline as well as covariate-adjusted regression models with cognitive decline as an outcome.

Our findings suggest that higher WHR, but not BMI, may be associated with adverse cognitive outcomes in older Puerto Ricans. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

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Volume 2. Article Contents Abstract. Journal Article. WAIST-HIP RATIO, BODY MASS INDEX, AND COGNITIVE DECLINE IN OLDER PUERTO RICANS.

M Crowe , M Crowe. Oxford Academic. Google Scholar. A Dávila-Roman. School of Public Health, University of Puerto Rico, San Juan. C Barba. B Downer. R Andel. University of South Florida, School of Aging Studies.

Cognitive decline can start from age 45 [ 43 ]. Although these 30, individuals aged 60 to 70 years were relatively young considering cognitive impairment, identifying the obesity indicator most associated with cognitive performance at age 60—70 can help prevent cognitive impairment later.

Results consistently agreed that, among the five obesity indicators, WHR was most relevant to cognitive performance. WC was generally the second indicator that was associated with cognitive performance. These results highlighted the importance of abdominal obesity to the risk of cognitive impairment.

Both the results from males and females showed the tendency of increased risk of poor cognitive performance given elevated BMI levels, but these associations were not statistically significant Table 2. Due to this nonsignificant result, the associations of obesity with cognitive function may be overlooked.

This study illustrates that abdominal obesity, rather than general obesity, is associated with cognitive performance. A large WHR is a threat to cognitive health. This result was in line with the results shown here. However, the current work has three strengths over the previous study [ 8 ].

First, this work was based on a much larger sample size 30, than that of the previous research 1, [ 8 ]. Second, more obesity indicators were assessed five than in the previous research only BMI and WHR [ 8 ]. Finally, I performed an analysis within each sex stratum. A recent study has shown that aging is associated with different obesity indicators in males and females [ 44 ].

I performed a sex-specific analysis to clarify whether this is also the case in cognitive performance. Abdominal obesity indicates excess truncal particularly visceral fat [ 45 ], which specifically increases the risk of developing insulin resistance [ 46 ].

Insulin resistance further drives metabolic syndromes and cognitive declines [ 47 ]. By analyzing the MMSE results of more than 30, TWB individuals, this study confirmed the link between abdominal obesity and poor cognitive performance.

Finally, the main limitation of this work is that it is a cross-sectional study, and the associations observed here cannot be explained as causality.

Residual confounding and reverse causation are possible. Furthermore, the lack of association of BMI or BFP with poor cognitive performance could be because of insufficient power.

Medication information was not collected by the TWB, and therefore it was not adjusted in the analyses. An even more extensive study will be needed to replicate these results. TWB approved my application to access the data on February 18, application number: TWBR; principal investigator: Wan-Yu Lin.

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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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Abstract Similar to many areas in the mainland U.