Metabolism and brain health -
The Mann-Whitney U test and chi-squared χ 2 test were respectively used for continuous and categorical variables. Linear regression was used to examine associations of brain morphology with individual MetS components in the metabolic aging group. Given the moderate to high correlations observed between the five MetS components Fig.
In the next step, the residuals from the first step served as dependent variables, and the MetS component of interest as an independent variable while controlling for the demographic, socioeconomic, and lifestyle covariates listed in Table 1.
HbA1c hemoglobin A1c. The same sample size was 23, for all the statistical models. Statistical results were corrected for multiple comparisons based on random field theory RFT for cortical surface area and thickness and false discovery rate FDR for subcortical volumes at a significance level of 0.
We then examined which MetS component best explained brain morphology. We computed the Akaike information criterion AIC [ 40 ] for the above five regression models related to individual MetS components. If the deviation of the lowest AIC from others is above two, then the regression model with the lowest AIC is defined as the winning model and the corresponding MetS component is the best variable to explain brain morphology [ 41 ].
We applied this procedure at every vertex on the cortical surface and each subcortical region. Next, we employed PLS [ 42 ] to predict the brain biological age of the metabolic aging group, where the standardized cortical and subcortical morphological measures associated with any MetS component were used as features and chronological age was the predictive variable.
Ten-fold cross-validation was used to evaluate the performance of PLS. The MetS-related brain-age gap was calculated as the difference between the estimated brain age and chronological age. The chronological age was further regressed out from the brain-age gaps to adjust for age bias [ 43 ].
We employed the Kolmogorov—Smirnov KS -test to examine whether the brain-age gap distribution in participants with greater MetS severity is different from that in participants with less MetS severity. In other words, we tested whether greater MetS severity accelerates brain aging.
FDR was used to correct statistical p values at a significance level of 0. We then hypothesized that the MetS-related brain-age gap is larger in neurodegenerative diseases and stroke groups when compared to the metabolic aging group.
The brain-age prediction model trained on the metabolic aging group was directly applied to the neurodegenerative diseases and stroke groups. Moreover, we tested whether the brain-age gap estimated based on the brain morphology associated with a specific MetS component can well distinguish a specific neurodegenerative disease or stroke from the metabolic aging group.
KS-tests were used to assess the difference in the distributions of the brain-age gaps estimated by the specific-MetS-associated PLS model in the metabolic aging and specific neurodegenerative disease and stroke groups. Statistical results were corrected via FDR at a corrected p value of 0.
All analyses were carried out in MATLAB Rb The MathWorks, Inc. Table 1 lists the demographic, socioeconomic, and lifestyle characteristics in the whole imaging sample, the metabolic aging, and neurodegenerative disease groups.
The metabolic measures were acquired in the first visit to the UK Biobank study, while brain images used in this study were acquired 8. Figure 3 shows the significant associations of each MetS with cortical surface area, thickness, and subcortical volumes.
For all five MetS components, a consistent pattern emerged in the central and superior frontal gyri, whereby worse MetS status was associated with increased cortical surface area and decreased thickness.
Nevertheless, there were distinct associations of brain morphology with individual MetS components. Rows from top to bottom display the association with waist circumference a — c , triglyceride d — f , high-density lipoprotein HDL g — i , hypertension j — l , and hemoglobin A1c HbA1c m — o , respectively.
The results were adjusted for age at the MRI visit, sex, Townsend deprivation index, ethnicity, age completed full-time education, smoking status, alcohol consumption frequency, employment status, brain size, and imaging sites. L left, R right, Acc accumbens, Amyg amygdala, Caud caudate, Hipp hippocampus, Pall pallidum, Put putamen, Thal thalamus.
Higher waist circumference was additionally associated with larger cortical surface area and thinner cortical thickness in the lateral frontal lobe, supramarginal gyrus, lateral temporal lobe, posterior cingulate cortex, and insula. The thickness in the medial temporal lobe also decreased with increasing waist circumference Fig.
Moreover, greater waist circumference was associated with the larger bilateral putamen, right caudate, and thalamus, as well as right amygdala Fig.
The triglyceride and HDL had similar effects on brain morphology. Greater triglyceride or lower HDL was associated with increased cortical surface area and decreased cortical thickness in the bilateral central gyrus, supramarginal gyrus, and middle temporal gyrus Fig.
The putamen and pallidus showed significant associations Fig. However, only the bilateral nucleus accumbens and the right thalamus were associated with HDL but not with triglyceride. Hypertension had the least effects on brain morphology compared to the other four metabolic syndromes.
Beyond the common pattern, greater hypertension was related to reduced thickness in the parahippocampal gyrus, posterior cingulate cortex, and insula, as well as to an increased volume in the bilateral caudate and putamen Fig.
The effect of HbA1c was much stronger than that of hypertension and dyslipidemia, but weaker than that of waist circumference.
Higher HbA1c was significantly associated with larger cortical surface area and thinner cortical thickness in the lateral orbitofrontal cortex, frontal gyrus, motor region, lateral temporal lobe, and superior and inferior parietal cortex. The association between higher HbA1c and thinner cortex spread to the posterior cingulate cortex, medial temporal lobe, and precuneus Fig.
Our findings also showed HbA1c-associated thalamus, pallidus, and hippocampus volume reductions Fig. The statistical maps between the MetS severity and cortical morphology Fig.
That is, greater MetS severity was associated with increased cortical surface area and decreased thickness in the central and superior frontal gyri. Moreover, elevated MetS severity was related to cortical surface area expansion and thickness reduction in the lateral temporal lobe, posterior cingulate cortex, insula, and medial temporal lobe.
Furthermore, increased severity of MetS was associated with an increased volume in the right amygdala and caudate, and bilateral putamen, as well as with a reduced volume in the pallidus, where the strongest effects were in the bilateral putamen and pallidus Fig. Panels a — c illustrate the statistical maps for the associations between the metabolic syndrome severity MetS severity and cortical surface area, cortical thickness and subcortical volumes, respectively.
Panels d — f shows the metabolic syndrome component that most contributed to cortical surface area, cortical thickness and subcortical volumes, respectively. HDL high-density lipoprotein, HbA1c hemoglobin A1c, L left, R right.
Acc accumbens, Amyg amygdala, Caud caudate, Hipp hippocampus, Pall pallidus, Put putamen, Thal thalamus. All the above statistical analyses were run on the same sample of 23, This study employed AIC to examine which MetS component most contributed to brain morphology.
Figure 4d—f illustrates the representative MetS that most contributed to the cortical surface area, thickness, and subcortical volumes. Waist circumference best explained the considerable variance in most of the MetS-associated cortical regions, thalamus, putamen, and pallidus.
HbA1c explained the frontal and supramarginal thickness and right hippocampus volume. Dyslipidemia played a role in explaining the surface area in the frontal, parietal, and lateral medial cortex and the amygdala volume.
HDL was most associated with the temporal cortical surface area, left amygdala, and accumbens volumes. Among the five metabolic symptoms, hypertension had the least influence on brain morphology, except on the caudate volume. A root-mean-square error was 4. Panel a illustrates the scatterplot of the chronological age and the predicted brain age.
Each dot represents one participant. The color of the dots represents the number of participants in that location. Panel b shows the brain-age gap distribution in terms of the MetS severity.
The dashed line indicates the mean of each distribution. Panel c shows the cumulative distribution of the MetS-related brain-age gap at each MetS severity level.
Figure 5b, c respectively illustrate the probability and cumulative distributions of the brain-age gaps at each MetS severity level. These results suggested the acceleration of brain aging due to the elevated MetS severity.
The PLS model that was trained based on all MetS-associated brain morphology in the metabolic aging group estimated greater brain-age gaps for participants with dementia 2. These findings suggested that the MetS-related brain-age gap can be a good indicator of neurodegenerative diseases and stroke.
When the PLS regression only employed the brain morphology identified by individual MetS, the brain-age gap for dementia was much larger than that in the metabolic aging group see Fig. Similarly, waist circumference and hypertension-related PLS regressions showed the largest deviation of the brain-age gap in the stroke group from the metabolic aging group see Fig.
The PLS regressions of all MetS components showed the largest deviation of the brain-age gap in the multiple sclerosis group from the metabolic aging group see Fig. The detailed KS-test values and corresponding p values are reported in Table S2 of the Supplementary Material. The brain-age gaps were predicted using all brain morphology that were associated with any one of the five MetS components.
The dashed line represents the brain-age gaps in the aging group based on the brain morphology associated with overall MetS. Kolmogorov—Smirnov test was used to verify whether there was a significant difference in the cumulative distributions between the brain-age gap in each neurodegenerative disease group and the aging group.
Abbreviations: HDL, high-density lipoprotein; HbA1c, hemoglobin A1c. This study capitalized on the brain structural images and clinical data from the UK Biobank and explored the associations of the five metabolic components with the brain morphology, such as cortical surface area, thickness, and subcortical volumes.
Our findings suggested that widespread cortical morphology, particularly in the frontal, temporal, sensorimotor cortex, and basal ganglia were commonly associated with the five metabolic components. The elevated MetS severity accelerated brain aging. Moreover, our findings demonstrated that the MetS-related brain-age gap can well distinguish stroke and neurodegenerative diseases from aging but it does not specify the type of these diseases.
Therefore, our findings to some degree supported that the MetS-related brain morphological model can be used as a risk assessment for stroke and neurodegenerative diseases. This study conducted a comprehensive analysis of the five metabolic components and brain morphological measures based on a large sample size of the aging population without major illness.
Our findings highlighted the cortical surface area and thickness of the frontal lobe, sensorimotor region, and temporal lobe, as well as basal ganglia volumes commonly in association with all five metabolic symptoms.
These findings were largely consistent with existing findings related to the MetS dichotomous diagnosis [ 13 , 14 ]. Individuals with greater MetS severity experienced faster brain aging. Those at the greatest severity of MetS had an average 1-year larger brain-age gap than those without risk.
In particular, obesity assessed by waist circumference showed the strongest association with the widespread brain morphology among the five metabolic components even after controlling for the whole brain volume.
A recent study has shown that older brain age associated with obesity and poor metabolic components can be reversed following bariatric surgery-induced weight loss. The overall effect seemed to be driven by a global change across all brain regions and not from a specific region [ 44 ].
Hence, our findings provided further evidence that prioritizing adjusting obesity among the five metabolic components may be more helpful for improving brain health in aging populations. We discovered significant correlations between the MetS severity and the volumes of the right caudate and amygdala, in line with previous large-scale studies that showed increased volumes of these structures in association with obesity [ 28 , 45 , 46 , 47 ].
Furthermore, the distinct patterns of the brain morphological associations with the five individual MetS components mainly occur in the subcortical and cortical basal regions, particularly the basal ganglia, amygdala, and orbitofrontal cortex.
These structures have been implicated in food-related reward circuits [ 48 , 49 ]. Excessive stimulation of these circuits has been proposed to contribute to overeating and is associated with obesity and the other MetS components [ 14 , 50 ].
It is unclear whether these circuits play a crucial role in differentiating the associations of individual MetS components with brain morphology.
However, it is possible that the increase in the amygdala and caudate volumes may compensate for the cortical atrophy of these reward circuits. Our results also suggest a lateralization effect of the caudate and amygdala with the MetS severity, which persisted even after adjusting for handedness.
However, the evidence for lateralization in the food appetite network is inconclusive, with some studies indicating a left preference [ 51 , 52 ] and others pointing to a right tendency [ 53 , 54 ]. Given only two subcortical regions exhibited a significant lateralization effect related to the MetS severity in our study, we advise against drawing strong conclusions regarding the lateralization effect in the context of MetS.
Additionally, we identified two physiologically adjacent regions, the putamen and pallidum, that displayed opposite relationships with the MetS severity. A previous UK Biobank study also found contrasting associations between the putamen and pallidum volumes with obesity [ 47 ].
However, the biological significance of these opposite relationships remains unclear. Our study suggested that the MetS-associated brain morphological features can be considered as an indicator of brain aging.
That is, individuals with the most MetS severity had a greater brain-age gap than those without MetS. The PLS model can identify patients with these neurodegenerative diseases and stroke with a greater brain-age gap than that in the metabolic aging group.
These findings can also be supported by the thinning in the medial temporal lobe, including the entorhinal cortex and parahippocampal cortex, temporal pole, and posterior cingulate cortex, associated with waist circumference and the severity of MetS and the hippocampal volume reduction associated with HbA1c and the severity of MetS.
Previous histological and imaging studies have shown that the volume reduction in the entorhinal cortex, parahippocampal gyrus, and hippocampus is pathologically associated with early AD [ 55 , 56 ].
Indeed, the brain features related to waist circumference, HbA1c, and the severity of MetS can well distinguish AD from the aging group. Hence, our findings provided neural support that obesity, diabetes, and MetS were associated with an increased risk of AD [ 5 , 57 , 58 ]. Likewise, the brain morphology associated with individual five MetS components predicted greater brain-age gaps in stroke and multiple sclerosis than aging.
By comparisons, several studies have measured the brain age of patients with brain disorders and found that the brain-age gap in schizophrenia was on average 3 years larger, that in mild cognitive impairment and AD was on average 6 and 10 years larger, respectively [ 25 , 26 ]. Notably, individuals with neurodegenerative diseases or stroke also experienced a higher metabolism than healthy individuals.
Hence, the MetS-associated brain morphology is sensitive to detecting stroke and neurodegenerative diseases but not specific to any type of disease. Several limitations are worth noticing.
First, this study was cross-sectional. The longitudinal analysis would be crucial to understanding the trajectory of the MetS influence on brain morphology in aging. Second, the UK Biobank imaging sample lives in less deprived areas and is healthier than the wider UK population [ 60 , 61 , 62 ], which may limit generalizability.
Last but not least, this study limited the analysis to brain morphology. The UK Biobank study also provides other brain MRI modalities, including functional MRI and diffusion MRI [ 27 ], which need further investigation of MetS effects on brain functional and structural organization.
In conclusion, the five key metabolic syndromes significantly affected widespread brain morphology and elevated brain aging in the aging population. The MetS-related morphology well predicted elevated brain aging in stroke and neurodegenerative diseases, suggesting its role in estimating the risk of individuals.
Our study suggested that prevention and timely treatment of metabolic syndromes, especially abdominal obesity, is needed for improving brain health. All bona fide researchers can apply to access the UK Biobank research resource to conduct health-related research that is in the public interest.
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Age-related diseases including AD are directly associated with metabolic disturbances of biometallic ions e. AD may be related to changes in the distribution of iron between different cell types or between different molecular forms free iron, ferritin, transferrin [Tf], heme, etc.
Inappropriate dramatic increases in ferritin a major iron storage protein and iron deposition are strongly associated with the formation of Aβ plaques in the AD hippocampus [ ]. Since ferritin promotes the attenuation and sequestration of free iron [ ], it may cause elevated levels of labile iron, ultimately leading to increased total iron levels in the brains of AD mice.
Cellular transport of iron is regulated by iron uptake transporters transferrin receptor [TfR] and divalent metal transporter 1 and iron efflux transporters ferroportin with the assistance of the ferroxidase ceruloplasmin.
Given the association between iron accumulation in AD brains and iron shuttle dysregulation [ ], many studies have observed upregulated expression of iron storage protein, ferritin, Tf, TfR, and divalent metal transporter 1 DMT1 in neurons of AD mice, while ferroportin 1 Fpn1 and the related protein ceruloplasmin are reduced [ , , ].
A recent study confirmed that 8 weeks of chronic treadmill training reduced the levels of Tf, TfR and DMT1 and increased the levels of Fpn1 in the motor cortex of AD mice [ 20 ]. This suggests that exercise inhibits excessive iron uptake by neurons via down-regulation of iron uptake proteins, and accelerates iron release from neurons via upregulation of iron efflux and iron regulatory proteins, ultimately alleviating iron accumulation and reducing brain iron storage.
On the other hand, mitochondria play an important role in iron metabolism. Mitochondria can express DMT1 transporter, which is the major importer of iron for mitochondrial acquisition [ ].
Exercise training is known to induce an increase of mitochondrial mass in skeletal muscle [ ]. One study demonstrated that 6 months of chronic voluntary wheel running significantly increases DMT1 levels and simultaneously decreases TfR levels in the skeletal muscle of AD mice [ ].
In addition, another study found that running wheel exercise reduced iron levels in the plasma and liver, while total iron levels were elevated in tissues with high metabolic activity, such as skeletal muscle, the heart and lung [ ].
Based on the above statements, these studies suggest that regular exercise can modulate iron trafficking in AD models by reducing excess iron accumulation in the brain while inducing an increase of mitochondria in skeletal muscle increasing iron utilization by mitochondria and redistribution of iron throughout the body Fig.
Dysregulated iron metabolism and excess iron in AD contribute to amyloidogenesis. Specifically, iron can facilitate Aβ aggregation by modulating the ability of α-secretase and BACE1 to cleave APP [ ].
Furin, a ubiquitously expressed proconvertase, modulates systemic iron homeostasis through production of the soluble hemojuvelin, which strongly regulates the processing of α- and β-secretases in AD [ ]. At the cellular level in AD patients and animals, excess iron deposition mediates downregulation of furin mRNA and protein levels, impairing the α-secretase-dependent processing of APP [ ].
For this reason, enhancement of α-secretase activity by reducing iron-mediated damage could delay the harmful effects of Aβ aggregation on the brain. A study has demonstrated that chronic exercise may rectify the functional processing of APP and thus prevent Aβ formation by promoting α-secretase and inhibiting BACE-1, respectively, through low iron-induced enhancement of furin activity in AD mouse model [ 20 ], suggesting that exercise, as a means by which to prevent AD-mediated iron imbalance, may be a key modulator in reducing Aβ-induced neuronal death and restoring impaired cognitive function.
Another type of key hormone that controls iron balance and regulates iron homeostasis is iron-regulating hormones, which are responsible for negatively regulating iron uptake and efflux from cells.
Iron overload in AD patients seems to be triggered by a decrease in iron output due to an increase in hepcidin [ ]. Therefore, the reduction of hepcidin in the brain may have a beneficial effect on iron homeostasis in AD patients [ ].
The inflammatory state induced by iron load regulates the synthesis of hepcidin, of which interleukin 6 IL-6 is involved in the process of iron metabolism through hepcidin [ ]. IL-6 is increased in the AD brain as a multifunctional cytokine, and high levels of IL-6 can cause memory impairment [ ].
In contrast, regular physical exercise attenuates IL-6 expression in the brains of AD mice [ ]. Exercise-induced changes in hepcidin levels may be paramount in the regulation of cerebral iron metabolism, but the specific regulatory mechanisms need to be further explored.
Specific mechanisms by which chronic exercise improves iron metabolism. Exercise induces a synergistic improvement in the balance of iron metabolism in AD brains mainly through regulation of iron transport and related key effector molecules.
Alterations of tau protein, such as aberrant tau hyperphosphorylation, are a hallmark of AD. Increasing evidence demonstrates that tau pathology overlaps with glucose hypometabolism in the brains of AD patients [ ], along with a negative correlation between tau deposition and glucose uptake [ ] or aerobic glycolysis [ ].
Strikingly, a study has confirmed that pathological tau has a direct impact on mitochondria, inducing neuronal bioenergetic damage and leading to cognitive impairment in AD [ ].
This interesting observation has been confirmed by studies in animals expressing human tau h-tau [ ], demonstrating that aberrant tau hyperphosphorylation and aggregation are mediated by glucose hypometabolism activating the P38 MAPK pathway. Similarly, glucose hypometabolism in the AD brain may activate tau-targeting kinases and thus induce tau lesions, which is interpreted as brain bioenergetic impairment that may be up-stream of tau deposition [ ].
In turn, pathological tau impairs mitochondrial function, exacerbating the lack of energy production and its own phosphorylation state. Correspondingly, these results also suggest that the reduced bioenergy in AD may be a trigger for the development of tau lesions.
Experimental studies have extensively reported that exercise inhibits the abnormal tau hyperphosphorylation state in the AD brain and exerts neuroprotective effects [ , ]. Notably, dysregulated glucose metabolism in the AD brain mediates abnormal levels of tau O-GlcNAcylation and consequently its hyperphosphorylation [ ].
On the other hand, decreased expressions of GLUT1 and GLUT3 in the brains of AD patients trigger low levels of tau O-GlcNAcylation, resulting in abnormal tau hyperphosphorylation and exacerbating the course of AD[ ], whereas four weeks of regular swimming training suppress the decreases of GLUT1 and GLUT3 levels in the brains of AD mice and also downregulate the expressions of Aβ and phosphorylated tau proteins, restoring learning and memory capacity [ 41 ].
This points to a strong pathological link between energy production caused by glucose metabolism and changes in tau phosphorylation status in the AD brain.
Excessive oxidative stress [ ], metabolic disturbances [ ] and neuroinflammation [ ] in the brain mediate the accumulation of abnormal proteins in the context of AD. Long-term regular endurance exercise acts as an effective physiological regulator to alleviate the pathological state of AD, with multiple neuroprotective effects, and is also essential for maintaining metabolic health.
Physical exercise plays a key regulatory role in the enhancement of neuronal activity and neuroprotection by activating signaling molecules including BDNF and elevating the levels of CLUTs in neurons to maintain energy metabolism in the AD brain.
On the other hand, the mechanisms associated with the ability of exercise to delay AD pathology Aβ and tau involve the improvement of glucose metabolism.
For instance, IDE not only degrades Aβ in the brain during exercise interventions [ 76 ], but also regulates the impaired insulin resistance [ ].
In addition to its key role in the translocation of cleared Aβ, LRP1 interacts with insulin receptor β in the brain and regulates insulin signalling and glucose uptake [ ]. These diverse, interrelated and interacting molecular mechanisms work together to regulate glucose metabolism in the more complex setting of AD, and also highlight that physical exercise has integrated multi-targeting effects.
Impaired cholesterol homeostasis can cause neurodegenerative diseases. Throughout the clinical phase of AD, high cholesterol levels in the cell membrane lead to high activities of β- and γ-secretases and high production of toxic Aβ peptides [ , ].
Also, studies have confirmed that the change in cholesterol distribution in the plasma membrane is related to Aβ production [ ]. In addition, changes in cholesterol levels can also mediate changes in tau phosphorylation status [ ], but the exact molecular mechanisms are unknown and further studies are needed to explain the pathological relationship between cholesterol, Aβ and tau.
Based on the current evidence, it is hypothesized that exercise reduces the formation of Aβ peptides and AD pathology by lowering intracellular cholesterol or altering cholesterol distribution.
The triggering receptor expressed on myeloid cells 2 TREM2 is a lipid and lipoprotein receptor on microglia, and loss-of-function variants of TREM2 lead to impaired cholesterol metabolism and increased incidence of AD [ 99 , ].
A study found that 3 months of voluntary running inhibited TREM2 shedding, maintained TREM2 protein levels, promoted microglial glucose metabolism in the hippocampus of AD mice, and delayed the disease process [ 42 ].
This study, however, did not further explore the changes in lipid levels. Therefore, future studies are needed to determine if exercise improves AD lipid metabolism by affecting TREM2 levels.
Steady-state Aβ levels are the result of the balance between its production and clearance. Based on the above studies, the mechanism by which exercise clears Aβ from the AD brain is more complex and may involve many proteins operating in parallel.
In general, this is reflected in the fact that exercise decreases BACE1 and increases α-secretase secretion to reduce toxic Aβ production, upregulates NEP or IDE expression to accelerate Aβ proteolytic degradation, as well as elevates LRP1 and downregulates RAGE levels to facilitate Aβ efflux across the BBB through relevant signaling pathways.
Ferroptosis is a unique type of non-apoptotic regulated cell death triggered by acute or chronic cellular stress under aberrant metabolic and biochemical processes, ending in overwhelming iron-dependent lipid peroxidation and cellular rupture [ ].
In addition, age-related defects in brain glucose metabolism appear to be associated with the progression of tau protein pathology and cognitive impairment in AD [ ], that is, mitochondrial dysfunction in the AD state causes bioenergetic impairment that exacerbates abnormal tau phosphorylation and aggregation into NFTs.
More longitudinal studies are needed to clarify the specific molecular mechanisms by which physical exercise and energy metabolism alter tau protein pathology and to assess the impact of both on the extent of tau O-GlcNAcylation.
Chronic treadmill running for 6 months improves cognitive and executive function and provides many benefits for AD patients by increasing brain glucose disposal [ ], and there is growing evidence to support their protective effect against AD [ 33 , 72 ].
Many of the results on the effects of exercise on AD metabolism as discussed in this paper are mainly based on studies obtained in animal models.
However, the study duration and the sample size, which are less limiting in animal studies than in human trials, often lead to discrepancies in results; therefore, further large-scale clinical trials in AD patients are still urgently needed.
Exercise helps maintain a healthy cardiovascular system, increases blood flow to the brain and promotes efflux of Aβ, which in turn is directly degraded and cleared by the liver and kidneys, thereby reducing the risk of cognitive decline. Future research is needed to investigate and elucidate the role of peripheral organs in exercise interventions of AD metabolism.
To conclude, exercise is a non-invasive way to affect multiple metabolic mechanisms to alter AD pathology. The neuroprotective effects of physical exercise against AD may be due to the synergistic improvement in overall brain metabolism via multiple metabolic targets, ultimately mitigating pathophysiological features and improving cognition Fig.
New insights into the underlying mechanisms linking how exercise biologically affects the metabolic profile of AD and different brain cells can facilitate identification of new and effective targets for AD screening, diagnosis and treatment, as well as the development of promising and tailored combined intervention strategies, effective drug candidates, functional foods and exercise mimetics.
AD pathophysiology is multifaceted and involves a combination of genomic, metabolomic, interactomic and environmental factors. Although preclinical studies have proposed potential mechanisms by which exercise can benefit abnormal AD metabolism, there is still a lack of data from human trials to support this.
More human studies should be performed in future to unveil the exact biological underpinnings supporting exercise benefits, and to pave the way for personalized physical exercise interventions. Biometabolic pathways modified by acute or chronic exercise that reduce the risk of AD. Exercise can affect glucose metabolism, Aβ metabolism, lipid metabolism, iron metabolism and tau health, and directly influence AD pathology.
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If you go to the gym Ketosis Weight Loss, Immune system support might have heard about creatine. Taking hsalth as a supplement is Metabolism and brain health bain among athletes and bodybuilders. It Immune system support their muscles Metabolksm energy during Metablism workouts and therefore improves exercise performance. The magic behind this practice is that the high-energy phosphate moiety of adenosine triphosphate ATP can be rapidly transferred to creatine to generate phosphocreatine PCr in a reversible reaction catalyzed by the enzyme creatine kinase CKand the PCr is used to regenerate ATP when the demand for energy is high. Thus, PCr acts as an energy reservoir and maintains relative stable ATP levels during exercises. Metabooism you for visiting nature. You anf using Sports nutrition and mental health browser version with limited support for CSS. To braun the best experience, Immune system support recommend you use a more up to date healrh or turn off compatibility Thermogenic foods list in Metabolism and brain health Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Metabolic syndrome MetS is characterized by a constellation of metabolic risk factors, including obesity, hypertriglyceridemia, low high-density lipoprotein HDL levels, hypertension, and hyperglycemia, and is associated with stroke and neurodegenerative diseases. This study capitalized on brain structural images and clinical data from the UK Biobank and explored the associations of brain morphology with MetS and brain aging due to MetS. Cortical surface area, thickness, and subcortical volumes were assessed using FreeSurfer.
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