Plasma trimethylamine N-oxide (TMAO): associations with cognition, neuroimaging, and dementia.

BACKGROUND: The gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine - have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain. METHODS: In the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function. RESULTS: Overall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses. CONCLUSIONS: Plasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.

The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data.

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.

Depressive Symptoms and Plasma Markers of Alzheimer's Disease and Neurodegeneration: A Coordinated Meta-Analysis of 8 Cohort Studies.

BACKGROUND: Depressive symptoms are associated with an increased risk of Alzheimer's disease (AD). There has been a recent emergence in plasma biomarkers for AD pathophysiology, such as amyloid-beta (Aß) and phosphorylated tau (p-tau), as well as for axonal damage (neurofilament light, NfL) and astrocytic activation (glial fibrillary acidic protein, GFAP). Hypothesizing that depressive symptoms may occur along the AD process, we investigated associations between plasma biomarkers of AD with depressive symptoms in individuals without dementia. METHODS: A two-stage meta-analysis was performed on 2 clinic-based and 6 population-based cohorts (N = 7210) as part of the Netherlands Consortium of Dementia Cohorts. Plasma markers (Aß42/40, p-tau181, NfL, and GFAP) were measured using Single Molecular Array (Simoa; Quanterix) assays. Depressive symptoms were measured with validated questionnaires. We estimated the cross-sectional association of each standardized plasma marker (determinants) with standardized depressive symptoms (outcome) using linear regressions, correcting for age, sex, education, and APOE ε4 allele presence, as well as subgrouping by sex and APOE ε4 allele. Effect estimates were entered into a random-effects meta-analysis. RESULTS: Mean age of participants was 71 years. The prevalence of clinically relevant depressive symptoms ranged from 1% to 22%. None of the plasma markers were associated with depressive symptoms in the meta-analyses. However, NfL was associated with depressive symptoms only in APOE ε4 carriers (ß 0.11; 95% CI: 0.05-0.17). CONCLUSIONS: Late-life depressive symptoms did not show an association to plasma biomarkers of AD pathology. However, in APOE ε4 allele carriers, a more profound role of neurodegeneration was suggested with depressive symptoms.

Genome-wide characterization of circulating metabolic biomarkers.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data.

BACKGROUND: There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS: We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS: The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS: Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Epigenome-wide association study of dietary fatty acid intake.

BACKGROUND: Dietary intake of n-3 polyunsaturated fatty acids (PUFA) may have a protective effect on the development of cardiovascular diseases, diabetes, depression and cancer, while a high intake of n-6 PUFA was often reported to be associated with inflammation-related traits. The effect of PUFAs on health outcomes might be mediated by DNA methylation (DNAm). The aim of our study is to identify the impact of PUFA intake on DNAm in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 cohort and the Leiden Longevity Study (LLS). RESULTS: DNA methylation levels were measured in whole blood from the population-based KORA FF4 study (N = 1354) and LLS (N = 448), using the Illumina MethylationEPIC BeadChip and Illumina HumanMethylation450 array, respectively. We assessed associations between DNAm and intake of eight and four PUFAs in KORA and LLS, respectively. Where possible, results were meta-analyzed. Below the Bonferroni correction threshold (p < 7.17 × 10-8), we identified two differentially methylated positions (DMPs) associated with PUFA intake in the KORA study. The DMP cg19937480, annotated to gene PRDX1, was positively associated with docosahexaenoic acid (DHA) in model 1 (beta: 2.00 × 10-5, 95%CI: 1.28 × 10-5-2.73 × 10-5, P value: 6.98 × 10-8), while cg05041783, annotated to gene MARK2, was positively associated with docosapentaenoic acid (DPA) in our fully adjusted model (beta: 9.80 × 10-5, 95%CI: 6.25 × 10-5-1.33 × 10-4, P value: 6.75 × 10-8). In the meta-analysis, we identified the CpG site (cg15951061), annotated to gene CDCA7L below Bonferroni correction (1.23 × 10-7) associated with eicosapentaenoic acid (EPA) intake in model 1 (beta: 2.00 × 10-5, 95% CI: 1.27 × 10-5-2.73 × 10-5, P value = 5.99 × 10-8) and we confirmed the association of cg19937480 with DHA in both models 1 and 2 (beta: 2.07 × 10-5, 95% CI: 1.31 × 10-5-2.83 × 10-5, P value = 1.00 × 10-7 and beta: 2.19 × 10-5, 95% CI: 1.41 × 10-5-2.97 × 10-5, P value = 5.91 × 10-8 respectively). CONCLUSIONS: Our study identified three CpG sites associated with PUFA intake. The mechanisms of these sites remain largely unexplored, highlighting the novelty of our findings. Further research is essential to understand the links between CpG site methylation and PUFA outcomes.

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform.

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Mapping of the gene network that regulates glycan clock of ageing.

Glycans are an essential structural component of immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation), a trait which was shown to be associated with ageing. We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in the in vitro IgG expression system. Upregulation of three genes, EEF1A1, MANBA and TNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in this in vitro expression system.

Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome.

BACKGROUND: B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. RESULTS: Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals. CONCLUSION: Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.

Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification.

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

The association of blood biomarkers with treatment response and adverse health outcomes in older patients with solid tumors: A systematic review.

INTRODUCTION: Blood biomarkers are potentially useful prognostic markers and may support treatment decisions, but it is unknown if and which biomarkers are most useful in older patients with solid tumors. The aim of this systematic review was to evaluate the evidence on the association of blood biomarkers with treatment response and adverse health outcomes in older patients with solid tumors. MATERIALS AND METHODS: A literature search was conducted in five databases in December 2022 to identify studies on blood biomarkers measured before treatment initiation, not tumor specific, and outcomes in patients with solid tumors aged ≥60 years. Studies on any type or line of oncologic treatment could be included. Titles and abstracts were screened by three authors. Data extraction and quality assessment, using the Quality in Prognosis Studies (QUIPS) checklist, were performed by two authors. RESULTS: Sixty-three studies were included, with a median sample size of 138 patients (Interquartile range [IQR] 99-244) aged 76 years (IQR 72-78). Most studies were retrospective cohort studies (63%). The risk of bias was moderate in 52% and high in 43%. Less than one-third reported geriatric parameters. Eighty-six percent examined mortality outcomes, 37% therapeutic response, and 37% adverse events. In total, 77 unique markers were studied in patients with a large variety of tumor types and treatment modalities. Neutrophil-to-lymphocyte ratio (20 studies), albumin (19), C-reactive protein (16), hemoglobin (14) and (modified) Glasgow Prognostic Score ((m)GPS) (12) were studied most often. The vast majority showed no significant association of these biomarkers with outcomes, except for associations between low albumin and adverse events and high (m)GPS with mortality. DISCUSSION: Most studies did not find a significant association between blood biomarkers and clinical outcomes. The interpretation of current evidence on prognostic blood biomarkers is hampered by small sample sizes and inconsistent results across heterogeneous studies. The choice for blood biomarkers in the majority of included studies seemed driven by availability in clinical practice in retrospective cohort studies. Ageing biomarkers are rarely studied in older patients with solid tumors. Further research is needed in larger and more homogenous cohorts that combine clinical parameters and biomarkers before these can be used in clinical practice.

Increasing number of long-lived ancestors marks a decade of healthspan extension and healthier metabolomics profiles.

Globally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by the increasing lifespan of study participants with each additional long-lived family member. Here we investigate whether the healthspan in such families follows a similar quantitative pattern using three-generational data from two databases, LLS (Netherlands), and SEDD (Sweden). We study healthspan in 2143 families containing index persons with 26 follow-up years and two ancestral generations, comprising 17,539 persons. Our results provide strong evidence that an increasing number of long-lived ancestors associates with up to a decade of healthspan extension. Further evidence indicates that members of long-lived families have a delayed onset of medication use, multimorbidity and, in mid-life, healthier metabolomic profiles than their partners. We conclude that both lifespan and healthspan are quantitatively linked to ancestral longevity, making family data invaluable to identify protective mechanisms of multimorbidity.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk.

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Epigenome-Wide Association Study Reveals CpG Sites Associated with Thyroid Function and Regulatory Effects on KLF9.

Background: Thyroid hormones play a key role in differentiation and metabolism and are known regulators of gene expression through both genomic and epigenetic processes including DNA methylation. The aim of this study was to examine associations between thyroid hormones and DNA methylation. Methods: We carried out a fixed-effect meta-analysis of epigenome-wide association study (EWAS) of blood DNA methylation sites from 8 cohorts from the ThyroidOmics Consortium, incorporating up to 7073 participants of both European and African ancestry, implementing a discovery and replication stage. Statistical analyses were conducted using normalized beta CpG values as dependent and log-transformed thyrotropin (TSH), free thyroxine, and free triiodothyronine levels, respectively, as independent variable in a linear model. The replicated findings were correlated with gene expression levels in whole blood and tested for causal influence of TSH and free thyroxine by two-sample Mendelian randomization (MR). Results: Epigenome-wide significant associations (p-value <1.1E-7) of three CpGs for free thyroxine, five for free triiodothyronine, and two for TSH concentrations were discovered and replicated (combined p-values = 1.5E-9 to 4.3E-28). The associations included CpG sites annotated to KLF9 (cg00049440) and DOT1L (cg04173586) that overlap with all three traits, consistent with hypothalamic-pituitary-thyroid axis physiology. Significant associations were also found for CpGs in FKBP5 for free thyroxine, and at CSNK1D/LINCO1970 and LRRC8D for free triiodothyronine. MR analyses supported a causal effect of thyroid status on DNA methylation of KLF9. DNA methylation of cg00049440 in KLF9 was inversely correlated with KLF9 gene expression in blood. The CpG at CSNK1D/LINC01970 overlapped with thyroid hormone receptor alpha binding peaks in liver cells. The total additive heritability of the methylation levels of the six significant CpG sites was between 25% and 57%. Significant methylation QTLs were identified for CpGs at KLF9, FKBP5, LRRC8D, and CSNK1D/LINC01970. Conclusions: We report novel associations between TSH, thyroid hormones, and blood-based DNA methylation. This study advances our understanding of thyroid hormone action particularly related to KLF9 and serves as a proof-of-concept that integrations of EWAS with other -omics data can provide a valuable tool for unraveling thyroid hormone signaling in humans by complementing and feeding classical in vitro and animal studies.

Pooled analysis of epigenome-wide association studies of food consumption in KORA, TwinsUK and LLS.

PURPOSE: Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation. METHODS: Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group. RESULTS: We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others. CONCLUSION: This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content.

Gut microbiome changes due to sleep disruption in older and younger individuals: a case for sarcopenia?

Major hallmarks of functional loss, loss of metabolic and musculoskeletal health and (multi)morbidity with aging are associated with sleep disturbances. With poor sleep shifts in gut microbial composition commonly manifest, which could mediate the pro-inflammatory state between sleep disturbances and sarcopenia. This systematic review presents the recent evidence on how sleep disturbances throughout the lifespan associate with and contribute to gut microbial composition changes, proposing a mechanism to understand the etiology of sarcopenia through sleep disturbances. The relationship between disturbed sleep and clinically relevant gut microbiota composition on health aspects of aging is discussed. A search was performed in PubMed, Cochrane Library, Scopus, Web of Science using keywords including (microbio* OR microflora) AND (sleep OR sleep disorder). Six cross-sectional population-based studies and five experimental clinical trials investigating healthy individuals with ages ranging from 4 to 71 were included. The cross-sectional studies reported similarities in associations with sleep disturbance and gut microbial diversity. In older adults, shorter sleep duration is associated with an increase in pro-inflammatory bacteria whereas increasing sleep quality is positively associated with an increase of beneficial Verrucomicrobia and Lentisphaerae phyla. In young adults, the effect of sleep disruption on gut microbiome composition, specifically the ratio of beneficial Firmicutes over Bacteroidetes phyla, remains contradictory and unclear. The findings of this review warrant further research in the modulation of the gut microbiome linking poor sleep with muscle-catabolic consequences throughout the lifespan.

PLIS: A metabolomic response monitor to a lifestyle intervention study in older adults.

The response to lifestyle intervention studies is often heterogeneous, especially in older adults. Subtle responses that may represent a health gain for individuals are not always detected by classical health variables, stressing the need for novel biomarkers that detect intermediate changes in metabolic, inflammatory, and immunity-related health. Here, our aim was to develop and validate a molecular multivariate biomarker maximally sensitive to the individual effect of a lifestyle intervention; the Personalized Lifestyle Intervention Status (PLIS). We used 1 H-NMR fasting blood metabolite measurements from before and after the 13-week combined physical and nutritional Growing Old TOgether (GOTO) lifestyle intervention study in combination with a fivefold cross-validation and a bootstrapping method to train a separate PLIS score for men and women. The PLIS scores consisted of 14 and four metabolites for females and males, respectively. Performance of the PLIS score in tracking health gain was illustrated by association of the sex-specific PLIS scores with several classical metabolic health markers, such as BMI, trunk fat%, fasting HDL cholesterol, and fasting insulin, the primary outcome of the GOTO study. We also showed that the baseline PLIS score indicated which participants respond positively to the intervention. Finally, we explored PLIS in an independent physical activity lifestyle intervention study, showing similar, albeit remarkably weaker, associations of PLIS with classical metabolic health markers. To conclude, we found that the sex-specific PLIS score was able to track the individual short-term metabolic health gain of the GOTO lifestyle intervention study. The methodology used to train the PLIS score potentially provides a useful instrument to track personal responses and predict the participant's health benefit in lifestyle interventions similar to the GOTO study.

Fat metabolism is associated with telomere length in six population-based studies.

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold Pmeta = 6.5 × 10-4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-CE %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-CE) became significantly associated with LTL (P = 3.6 × 10-4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (P = 1.9 × 10-4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.

Higher thyrotropin leads to unfavorable lipid profile and somewhat higher cardiovascular disease risk: evidence from multi-cohort Mendelian randomization and metabolomic profiling.

BACKGROUND: Observational studies suggest interconnections between thyroid status, metabolism, and risk of coronary artery disease (CAD), but causality remains to be proven. The present study aimed to investigate the potential causal relationship between thyroid status and cardiovascular disease and to characterize the metabolomic profile associated with thyroid status. METHODS: Multi-cohort two-sample Mendelian randomization (MR) was performed utilizing genome-wide significant variants as instruments for standardized thyrotropin (TSH) and free thyroxine (fT4) within the reference range. Associations between TSH and fT4 and metabolic profile were investigated in a two-stage manner: associations between TSH and fT4 and the full panel of 161 metabolomic markers were first assessed hypothesis-free, then directional consistency was assessed through Mendelian randomization, another metabolic profile platform, and in individuals with biochemically defined thyroid dysfunction. RESULTS: Circulating TSH was associated with 52/161 metabolomic markers, and fT4 levels were associated with 21/161 metabolomic markers among 9432 euthyroid individuals (median age varied from 23.0 to 75.4 years, 54.5% women). Positive associations between circulating TSH levels and concentrations of very low-density lipoprotein subclasses and components, triglycerides, and triglyceride content of lipoproteins were directionally consistent across the multivariable regression, MR, metabolomic platforms, and for individuals with hypo- and hyperthyroidism. Associations with fT4 levels inversely reflected those observed with TSH. Among 91,810 CAD cases and 656,091 controls of European ancestry, per 1-SD increase of genetically determined TSH concentration risk of CAD increased slightly, but not significantly, with an OR of 1.03 (95% CI 0.99-1.07; p value 0.16), whereas higher genetically determined fT4 levels were not associated with CAD risk (OR 1.00 per SD increase of fT4; 95% CI 0.96-1.04; p value 0.59). CONCLUSIONS: Lower thyroid status leads to an unfavorable lipid profile and a somewhat increased cardiovascular disease risk.