Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition

Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study

It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021. Predicted metabolomic age was trained and tested based on 168 metabolomics. Metabolomic age was linked to the risk of 50 diseases in the validation dataset. The median follow-up duration for individual diseases ranged from 11.2 years to 11.9 years. After controlling for false discovery rate, chronological age-adjusted age gap (CAAG) was significantly associated with the incidence of 25 out of 50 chronic diseases. After adjustment for full covariates, associations with 15 chronic diseases remained significant. Greater CAAG was associated with increased risk of eight cardiometabolic disorders (including cardiovascular diseases and diabetes), some cancers, alcohol use disorder, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease and age-related macular degeneration. The association between CAAG and risk of peripheral vascular disease, other cardiac diseases, fracture, cataract and thyroid disorder was stronger among individuals with unhealthy diet than in those with healthy diet. The association between CAAG and risk of some conditions was stronger in younger individuals, those with metabolic disorders or low education. Metabolomic age plays an important role in the development of multiple chronic diseases. Healthy diet and high education may mitigate the risk for some chronic diseases due to metabolomic age acceleration.

Trace Elements in Alzheimer's Disease and Dementia: The Current State of Knowledge

Changes in trace element concentrations are being wildly considered when it comes to neurodegenerative disorders, such as Alzheimer's disease and Parkinson's disease. This study aims to present the role that trace elements play in the central nervous system. Moreover, we reviewed the mechanisms involved in their neurotoxicity. Low zinc concentrations, as well as high levels of copper, manganese, and iron, activate the signalling pathways of the inflammatory, oxidative and nitrosative stress response. Neurodegeneration occurs due to the association between metals and proteins, which is then followed by aggregate formation, mitochondrial disorder, and, ultimately, cell death. In Alzheimer's disease, low Zn levels suppress the neurotoxicity induced by β-amyloid through the selective precipitation of aggregation intermediates. High concentrations of copper, iron and manganese cause the aggregation of intracellular α-synuclein, which results in synaptic dysfunction and axonal transport disruption. Parkinson's disease is caused by the accumulation of Fe in the midbrain dopaminergic nucleus, and the pathogenesis of multiple sclerosis derives from Zn deficiency, leading to an imbalance between T cell functions. Aluminium disturbs the homeostasis of other metals through a rise in the production of oxygen reactive forms, which then leads to cellular death. Selenium, in association with iron, plays a distinct role in the process of ferroptosis. Outlining the influence that metals have on oxidoreduction processes is crucial to recognising the pathophysiology of neurodegenerative diseases and may provide possible new methods for both their avoidance and therapy.

Higher baseline resting metabolic rate is associated with 1-year frailty decline among older adults residing in an urban area

BACKGROUND

Dysregulated energy metabolism is one hypothesized mechanism underlying frailty. Resting energy expenditure, as reflected by resting metabolic rate (RMR), makes up the largest component of total energy expenditure. Prior work relating RMR to frailty has largely been done in cross section with mixed results. We investigated whether and how RMR related to 1-year frailty change while adjusting for body composition.

METHODS

N = 116 urban, predominantly African-American older adults were recruited between 2011 and 2019. One-year frailty phenotype (0-5) was regressed on baseline RMR, frailty phenotype, demographics and body composition (DEXA) in an ordinal logistic regression model. Multimorbidity (Charlson comorbidity scale, polypharmacy) and cognitive function (Montreal Cognitive Assessment) were separately added to the model to assess for change to the RMR-frailty relationship. The model was then stratified by baseline frailty status (non-frail, pre-frail) to explore differential RMR effects across frailty.

RESULTS

Higher baseline RMR was associated with worse 1-year frailty (odds ratio = 1.006 for each kcal/day, p = 0.001) independent of baseline frailty, demographics, and body composition. Lower fat-free mass (odds ratio = 0.88 per kg mass, p = 0.008) was independently associated with worse 1-year frailty scores. Neither multimorbidity nor cognitive function altered these relationships. The associations between worse 1-year frailty and higher baseline RMR (odds ratio = 1.009, p < 0.001) and lower baseline fat-free mass (odds ratio = 0.81, p = 0.006) were strongest among those who were pre-frail at baseline.

DISCUSSION

We are among the first to relate RMR to 1-year change in frailty scores. Those with higher baseline RMR and lower fat-free mass had worse 1-year frailty scores, but these relationships were strongest among adults who were pre-frail at baseline. These relationships were not explained by chronic disease or impaired cognition. These results provide new evidence suggesting higher resting energy expenditure is associated with accelerate frailty decline.