Aims
To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.
Materials and ...methods
Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.
Results
In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized −0.20, 95% confidence interval CI −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).
Conclusions
Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Observational and Mendelian randomization (MR) studies link obesity and cancer, but it remains unclear whether these depend upon related metabolic abnormalities.
We used information from 321,472 ...participants in the UK biobank, including 30,561 cases of obesity-related cancer. We constructed three genetic instruments reflecting higher adiposity together with either "unfavourable" (82 SNPs), "favourable" (24 SNPs) or "neutral" metabolic profile (25 SNPs). We looked at associations with 14 types of cancer, previously suggested to be associated with obesity.
All genetic instruments had a strong association with BMI (p < 1 × 10
for all). The instrument reflecting unfavourable adiposity was also associated with higher CRP, HbA1c and adverse lipid profile, while instrument reflecting metabolically favourable adiposity was associated with lower HbA1c and a favourable lipid profile. In MR-inverse-variance weighted analysis unfavourable adiposity was associated with an increased risk of non-hormonal cancers (OR = 1.22, 95% confidence interval CI:1.08, 1.38), but a lower risk of hormonal cancers (OR = 0.80, 95%CI: 0.72, 0.89). From individual cancers, MR analyses suggested causal increases in the risk of multiple myeloma (OR = 1.36, 95%CI: 1.09, 1.70) and endometrial cancer (OR = 1.77, 95%CI: 1.16, 2.68) by greater genetically instrumented unfavourable adiposity but lower risks of breast and prostate cancer (OR = 0.72, 95%CI: 0.61, 0.83 and OR = 0.81, 95%CI: 0.68, 0.97, respectively). Favourable or neutral adiposity were not associated with the odds of any individual cancer.
Higher adiposity associated with a higher risk of non-hormonal cancer but a lower risk of some hormone related cancers. Presence of metabolic abnormalities might aggravate the adverse effects of higher adiposity on cancer. Further studies are warranted to investigate whether interventions on adverse metabolic health may help to alleviate obesity-related cancer risk.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Insulin resistance (IR) is predictive for type 2 diabetes and associated with various metabolic abnormalities in fasting conditions. However, limited data are available on how IR affects metabolic ...responses in a non-fasting setting, yet this is the state people are mostly exposed to during waking hours in the modern society. Here, we aim to comprehensively characterise the metabolic changes in response to an oral glucose test (OGTT) and assess the associations of these changes with IR.
Blood samples were obtained at 0 (fasting baseline, right before glucose ingestion), 30, 60, and 120 min during the OGTT. Seventy-eight metabolic measures were analysed at each time point for a discovery cohort of 4745 middle-aged Finnish individuals and a replication cohort of 595 senior Finnish participants. We assessed the metabolic changes in response to glucose ingestion (percentage change in relative to fasting baseline) across the four time points and further compared the response profile between five groups with different levels of IR and glucose intolerance. Further, the differences were tested for covariate adjustment, including gender, body mass index, systolic blood pressure, fasting, and 2-h glucose levels. The groups were defined as insulin sensitive with normal glucose (IS-NGT), insulin resistant with normal glucose (IR-NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and new diabetes (NDM). IS-NGT and IR-NGT were defined as the first and fourth quartile of fasting insulin in NGT individuals.
Glucose ingestion induced multiple metabolic responses, including increased glycolysis intermediates and decreased branched-chain amino acids, ketone bodies, glycerol, and triglycerides. The IR-NGT subgroup showed smaller responses for these measures (mean + 23%, interquartile 9-34% at 120 min) compared to IS-NGT (34%, 23-44%, P < 0.0006 for difference, corrected for multiple testing). Notably, the three groups with glucose abnormality (IFG, IGT, and NDM) showed similar metabolic dysregulations as those of IR-NGT. The difference between the IS-NGT and the other subgroups was largely explained by fasting insulin, but not fasting or 2 h glucose. The findings were consistent after covariate adjustment and between the discovery and replication cohort.
Insulin-resistant non-diabetic individuals are exposed to a similar adverse postprandial metabolic milieu, and analogous cardiometabolic risk, as those with type 2 diabetes. The wide range of metabolic abnormalities associated with IR highlights the necessity of diabetes diagnostics and clinical care beyond glucose management.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal ...associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs.
Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex.
Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men.
We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women.
Experimental evidence connects autophagy and lysosomal dysfunction with neuro-degeneration, but the relevance to human dementia remains uncertain. Gao et al. report that late-onset Alzheimer's and ...Parkinson's diseases are associated with autophagic and endolysosomal genes. This population-based evidence supports the endolysosomal system as a potential therapeutic target in dementia.
Abstract
Late-onset Alzheimer's disease is the most common dementia type, yet no treatment exists to stop the neurodegeneration. Evidence from monogenic lysosomal diseases, neuronal pathology and experimental models suggest that autophagic and endolysosomal dysfunction may contribute to neurodegeneration by disrupting the degradation of potentially neurotoxic molecules such as amyloid-β and tau. However, it is uncertain how well the evidence from rare disorders and experimental models capture causal processes in common forms of dementia, including late-onset Alzheimer's disease. For this reason, we set out to investigate if autophagic and endolysosomal genes were enriched for genetic variants that convey increased risk of Alzheimer's disease; such a finding would provide population-based support for the endolysosomal hypothesis of neurodegeneration. We quantified the collective genetic associations between the endolysosomal system and Alzheimer's disease in three genome-wide associations studies (combined n = 62 415). We used the Mergeomics pathway enrichment algorithm that incorporates permutations of the full hierarchical cascade of SNP-gene-pathway to estimate enrichment. We used a previously published collection of 891 autophagic and endolysosomal genes (denoted as AphagEndoLyso, and derived from the Lysoplex sequencing platform) as a proxy for cellular processes related to autophagy, endocytosis and lysosomal function. We also investigated a subset of 142 genes of the 891 that have been implicated in Mendelian diseases (MenDisLyso). We found that both gene sets were enriched for genetic Alzheimer's associations: an enrichment score 3.67 standard deviations from the null model (P = 0.00012) was detected for AphagEndoLyso, and a score 3.36 standard deviations from the null model (P = 0.00039) was detected for MenDisLyso. The high enrichment score was specific to the AphagEndoLyso gene set (stronger than 99.7% of other tested pathways) and to Alzheimer's disease (stronger than all other tested diseases). The APOE locus explained most of the MenDisLyso signal (1.16 standard deviations after APOE removal, P = 0.12), but the AphagEndoLyso signal was less affected (3.35 standard deviations after APOE removal, P = 0.00040). Additional sensitivity analyses further indicated that the AphagEndoLyso Gene Set contained an aggregate genetic association that comprised a combination of subtle genetic signals in multiple genes. We also observed an enrichment of Parkinson's disease signals for MenDisLyso (3.25 standard deviations) and for AphagEndoLyso (3.95 standard deviations from the null model), and a brain-specific pattern of gene expression for AphagEndoLyso in the Gene Tissue Expression Project dataset. These results provide evidence that a diffuse aggregation of genetic perturbations to the autophagy and endolysosomal system may mediate late-onset Alzheimer's risk in human populations.
We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable ...metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Metabolite associations with insulin resistance were studied in 7,098 young Finns (age 31 ± 3 years; 52% women) to elucidate underlying metabolic pathways. Insulin resistance was assessed by the ...homeostasis model (HOMA-IR) and circulating metabolites quantified by high-throughput nuclear magnetic resonance spectroscopy in two population-based cohorts. Associations were analyzed using regression models adjusted for age, waist, and standard lipids. Branched-chain and aromatic amino acids, gluconeogenesis intermediates, ketone bodies, and fatty acid composition and saturation were associated with HOMA-IR (P < 0.0005 for 20 metabolite measures). Leu, Ile, Val, and Tyr displayed sex- and obesity-dependent interactions, with associations being significant for women only if they were abdominally obese. Origins of fasting metabolite levels were studied with dietary and physical activity data. Here, protein energy intake was associated with Val, Phe, Tyr, and Gln but not insulin resistance index. We further tested if 12 genetic variants regulating the metabolites also contributed to insulin resistance. The genetic determinants of metabolite levels were not associated with HOMA-IR, with the exception of a variant in GCKR associated with 12 metabolites, including amino acids (P < 0.0005). Nonetheless, metabolic signatures extending beyond obesity and lipid abnormalities reflected the degree of insulin resistance evidenced in young, normoglycemic adults with sex-specific fingerprints.
Introduction
Meta-analysis is the cornerstone of robust biomedical evidence.
Objectives
We investigated whether statistical reporting practices facilitate metabolomics meta-analyses.
Methods
A ...literature review of 44 studies that used a comparable platform.
Results
Non-numeric formats were used in 31 studies. In half of the studies, less than a third of all measures were reported. Unadjusted P-values were missing from 12 studies and exact P-values from 9 studies.
Conclusion
Reporting practices can be improved. We recommend (i) publishing all results as numbers, (ii) reporting effect sizes of all measured metabolites and (iii) always reporting unadjusted exact P-values.
Abstract
Large-scale epidemiological and population data provide opportunities to identify subgroups of people who are at risk of disease or exposed to adverse environments. Clustering algorithms are ...popular data-driven tools to identify these subgroups; however, relying exclusively on algorithms may not produce the best results if the dataset does not have a clustered structure. For this reason, we propose a framework (the R-library Numero) that combines the self-organizing map algorithm, permutation analysis for statistical evidence and a final expert-driven subgrouping step. We used Numero to define subgroups in two examples without an obvious clustering structure: a biomedical dataset of kidney disease and another dataset of community-level socioeconomic indicators. We benchmarked the Numero subgroupings against popular clustering algorithms (principal components, K-means and hierarchical clustering). The Numero subgroupings were more intuitive and easier to interpret without losing mathematical quality. Therefore, we expect Numero to be useful for exploratory analyses of population-based epidemiological datasets.
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