Frailty as a Predictor of Poor Rehabilitation Outcomes among Older Patients Attending a Geriatric Day Hospital Program: An Observational Study.

BACKGROUND The Geriatric Day Hospital (GDH) is an important outpatient geriatric service, but there are few data on the role of frailty as a potential predictor of poor outcomes in this setting. METHODS Data were analyzed from 499 patients aged ≥ 60 years attending a 12-week GDH program between 2018 and 2021. Frailty status was defined as non-frail (68, 13.6%), mild/moderate frailty (351, 70.3%), and severe frailty (80, 16.0%) based on the Clinical Frailty Scale (CFS). Outcomes were defined as (1) poor outcome (hospital readmission, death, or medical deterioration) during the program and (2) admission to permanent nursing home care upon completion of the program. Multivariate logistic models were used for predictive analyses. RESULTS The mean age was 80.3 (standard deviation 7.0); 58.3% were women. Overall, 77 patients (15.4%) had a poor outcome, and 48 (9.6%) were admitted to permanent nursing home care. Poor outcome was experienced by none of the non-frail patients (0%), by 49 (14.0%) patients with mild/moderate frailty, and 22 (27.5%) patients with severe frailty (adjusted OR, 2.0; 95% CI 1.3, 3.2; p < 0.01). Admission to a permanent nursing home care was experienced by none of the non-frail patients (0%), 20 (5.7%) of those with mild/moderate frailty, and 28 (35.0%) with severe frailty (adjusted OR, 2.9; 95% CI 1.3, 6.3; p < 0.01). CONCLUSIONS The CFS is a promising risk predictor of poor outcome and admission to permanent nursing home discharge among older patients attending a GDH program

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-omic signature of body weight change: results from a population-based cohort study

BACKGROUND: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study. METHODS: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits. RESULTS: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10−4 to 1.2 × 10−24). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules. CONCLUSIONS: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function

Body Fat Free Mass Is Associated with the Serum Metabolite Profile in a Population-Based Study

To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study. Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs). We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism. A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network

Associations between thyroid hormones and serum metabolite profiles in an euthyroid population

The aim was to characterise associations between circulating thyroid hormones—free thyroxine (FT4) and thyrotropin (TSH)—and the metabolite profiles in serum samples from participants of the German population-based KORA F4 study. Analyses were based on the metabolite profile of 1463 euthyroid subjects. In serum samples, obtained after overnight fasting (≥8), 151 different metabolites were quantified in a targeted approach including amino acids, acylcarnitines (ACs), and phosphatidylcholines (PCs). Associations between metabolites and thyroid hormone concentrations were analysed using adjusted linear regression models. To draw conclusions on thyroid hormone related pathways, intra-class metabolite ratios were additionally explored. We discovered 154 significant associations (Bonferroni p < 1.75 × 10(−04)) between FT4 and various metabolites and metabolite ratios belonging to AC and PC groups. Significant associations with TSH were lacking. High FT4 levels were associated with increased concentrations of many ACs and various sums of ACs of different chain length, and the ratio of C2 by C0. The inverse associations observed between FT4 and many serum PCs reflected the general decrease in PC concentrations. Similar results were found in subgroup analyses, e.g., in weight-stable subjects or in obese subjects. Further, results were independent of different parameters for liver or kidney function, or inflammation, which supports the notion of an independent FT4 effect. In fasting euthyroid adults, higher serum FT4 levels are associated with increased serum AC concentrations and an increased ratio of C2 by C0 which is indicative of an overall enhanced fatty acyl mitochondrial transport and β-oxidation of fatty acids. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-013-0563-4) contains supplementary material, which is available to authorized users

Ldlr and ApoE mice better mimic the human metabolite signature of increased carotid intima media thickness compared to other animal models of cardiovascular disease

International audienceBackground and aims: Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD. Methods: A targeted mass spectrometry assay was used to quantify and compare a series of blood me-tabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE À/À , Ldlr À/À , and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates. Results: In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcar-nitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr À/À and ApoE À/À mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr À/À mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE À/À mice shared 10. Conclusions: The human cIMT signature was partially mimicked by Ldlr À/À and ApoE À/À mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD

Gaussian graphical model of serum amino acids and acylcarnitine metabolite concentrations in KORA S4.

<p>Each node represents a metabolite, whereas edges represent significant partial correlations. Nodes were coloured according to the β-estimate and the p-value from the linear models (red  =  positive association with FFMI; blue  =  negative association with FFMI; white  =  not significant association with FFMI).</p

Selected metabolic traits significantly associated with FFMI^a in a linear regression model adjusted for age, and sex for the non-obese and obese participants in the KORA S4 sample.

a<p>Fat Free Mass Index;</p>b<p>for multiple testing adjusted p-value;</p>c<p>adjusted R² of the linear model; AAs, amino acids; Σ aromatic amino acids, sum of tyrosine, phenylalanine, and tryptophan; Σ BCAAs, sum of valine, isoleucine, and leucine; Σ glucogenic amino acids, sum of alanine, glycine, and serine.</p

Characteristics (mean ± SD; % or n absolute) of male and female subjects in the analysed samples of KORA S4 and F4.

a<p>Parameters derived from the bioelectrical impedance analysis measurements which were only conducted in KORA S4;</p>b<p>Fat Free Mass Index;</p>c<p>Body Fat Mass Index;</p>d<p>Sports (active: >1 h leisure time of sports per week on a regular basis; inactive: less than 1 h sports per week).</p

Boxplot of serum BCAA concentrations (µmol/l), by quintiles of the FFMI, for the KORA S4 population.

<p>Boxplot of serum BCAA concentrations (µmol/l), by quintiles of the FFMI, for the KORA S4 population.</p