The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors.

Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

Physical activity and fiber intake beneficial for muscle mass and strength preservation during aging: A comprehensive cross-sectional study in the UK biobank cohort.

BACKGROUND: Aging triggers intricate physiological changes, particularly in whole-body fat-free mass (FFM) and handgrip strength, affecting overall health and independence. Despite existing research, the broader significance of how muscle health is affected by the intricate interplay of lifestyle factors simultaneously during aging needs more exploration. This study aims to examine how nutrition, physical activity, and sleep impact on FFM and handgrip strength in middle-aged men and women, facilitating future personalized recommendations for preserving muscle health. METHODS: The cross-sectional analysis of the UK Biobank involved 45,984 individuals (54 % women) aged 40-70 years with a complete dataset. Multiple linear regression explored determinants of FFM and handgrip strength, considering traditional, socio-demographics, medication use and smoking as covariates, with sex and age (younger and older than 55 years) stratifications. RESULTS: In older men and women, higher physical activity beneficially affect both FFM (respectively Β = 3.36 × 10-3, p-value = 1.66 × 10-3; Β = 2.52 × 10-3, p-value = 3.57 × 10-4) and handgrip strength (Β = 6.05 × 10-3, p-value = 7.99 × 10-5, Β = 8.98 × 10-3, p-value = 2.95 × 10-15). Similar results were found in fiber intake for FFM in older men and women (respectively B = 3.00 × 10-2, p-value = 2.76 × 10-5; B = 2.68 × 10-2, p-value = 1.78 × 10-9) and handgrip strength (Β = 3.27 × 10-2, p-value = 1.40 × 10-3; Β = 3.12 × 10-2, p-value = 1.34 × 10-5). Other lifestyle factors influence FFM and handgrip strength differently. Key determinants influencing handgrip strength included higher protein intake, lower water intake, higher alcohol intake, and extended sleep duration whereas mainly higher water intake is associated with higher FFM. CONCLUSIONS: In both men and women, the main factors associated with FFM and handgrip strength are physical activity and fiber intake, which may underlie a connection between gut and muscle health. Given the observed complexity of muscle health in the age and sex strata, further longitudinal research is needed to provide personalized lifestyle recommendations.

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.

Bacterial DNAemia in Older Participants and Nonagenarian Offspring and Association With Redox Biomarkers: Results From MARK-AGE Study.

Aging and age-related diseases have been linked to microbial dysbiosis with changes in blood bacterial DNA concentration. This condition may promote chronic low-grade inflammation, which can be further aggravated by antioxidant nutrient deficiency. Low plasma carotenoids are associated with an increased risk of inflammation and cellular damage and predict mortality. However, no evidence is yet available on the relationship between antioxidants and the blood bacterial DNA (BB-DNA). Therefore, this study aimed to compare BB-DNA from (a) GO (nonagenarian offspring), (b) age-matched controls (Randomly recruited Age-Stratified Individuals from the General population [RASIG]), and (c) spouses of GO (SGO) recruited in the MARK-AGE project, as well as to investigate the association between BB-DNA, behavior habits, Charlson Comorbidity Index (CCI), leucocyte subsets, and the circulating levels of some antioxidants and oxidative stress markers. BB-DNA was higher in RASIG than GO and SGO, whereas GO and SGO participants showed similar values. BB-DNA increased in smokers and males with CCI ≥ 2 compared with those with CCI ≤ 1 within RASIG. Moreover, BB-DNA was positively associated with lymphocyte, neutrophil, and monocyte counts, but not with self-reported dietary habits. Higher quartiles of BB-DNA were associated with low lutein and zeaxanthin and elevated malondialdehyde plasma concentrations in RASIG. BB-DNA was also positively correlated with nitric oxide levels. Herein, we provide evidence of a reduced BB-DNA in individuals from long-living families and their spouses, suggesting a decreased microbial dysbiosis and bacterial systemic translocation. BB-DNA was also associated with smoking, CCI, leukocyte subsets, and some redox biomarkers in older participants.

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.

Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population.

Biomarkers of low-grade inflammation have been associated with susceptibility to a severe infectious disease course, even when measured prior to disease onset. We investigated whether metabolic biomarkers measured by nuclear magnetic resonance (NMR) spectroscopy could be associated with susceptibility to severe pneumonia (2507 hospitalised or fatal cases) and severe COVID-19 (652 hospitalised cases) in 105,146 generally healthy individuals from UK Biobank, with blood samples collected 2007-2010. The overall signature of metabolic biomarker associations was similar for the risk of severe pneumonia and severe COVID-19. A multi-biomarker score, comprised of 25 proteins, fatty acids, amino acids, and lipids, was associated equally strongly with enhanced susceptibility to severe COVID-19 (odds ratio 2.9 [95%CI 2.1-3.8] for highest vs lowest quintile) and severe pneumonia events occurring 7-11 years after blood sampling (2.6 [1.7-3.9]). However, the risk for severe pneumonia occurring during the first 2 years after blood sampling for people with elevated levels of the multi-biomarker score was over four times higher than for long-term risk (8.0 [4.1-15.6]). If these hypothesis generating findings on increased susceptibility to severe pneumonia during the first few years after blood sampling extend to severe COVID-19, metabolic biomarker profiling could potentially complement existing tools for identifying individuals at high risk. These results provide novel molecular understanding on how metabolic biomarkers reflect the susceptibility to severe COVID-19 and other infections in the general population.

National policies for mitigating the COVID-19 pandemic include stricter measures for people considered to be at high risk of severe and potentially fatal cases of the disease. Although older age and pre-existing health conditions are strong risk factors, it is poorly understood why susceptibility varies so widely in the population. People with cardiometabolic diseases, such as diabetes and liver diseases, or chronic inflammation are at higher risk of severe COVID-19 and other infections including pneumonia. These conditions alter the molecules circulating in the blood, providing potential 'biomarkers' to determine whether a person is more likely to develop a fatal infection. Uncovering these blood biomarkers could help to identify people who are prone to life-threatening infections despite not having ever been diagnosed with a cardiometabolic disease. To find these biomarkers, Julkunen et al. studied blood samples that had been collected from 105,000 healthy individuals in the United Kingdom over ten years ago. The data showed that individuals with biomarkers linked to low-grade inflammation and cardiometabolic disease were more likely to have died or been hospitalised with pneumonia. A score based on 25 of these biomarkers provided the best predictor of severe pneumonia. This biomarker score performed up to four times better within the first few years after blood sampling compared to predicting cases of pneumonia a decade later. The same blood biomarker changes were also linked with developing severe COVID-19 over ten years after the blood samples had been collected. The predictive value of the biomarker score was similar for both severe COVID-19 and the long-term risk of severe pneumonia. Julkunen et al. propose that the metabolic biomarkers reflect inhibited immunity that impairs response to infections. The results from over 100,000 individuals suggest that these blood biomarkers may help to identify people at high risk of severe COVID-19 or other infectious diseases.

The Prognostic Value of Metabolic Profiling in Older Patients With a Proximal Femoral Fracture.

INTRODUCTION: High mortality rates of approximately 20% within 1 year after treatment are observed for patients with proximal femoral fractures. This preliminary study explores the prognostic value of a previously constructed mortality risk score based on a set of 14 metabolites for the survival and functional recovery in patients with proximal femoral fractures. MATERIALS AND METHODS: A prospective observational cohort study was conducted including patients admitted with a proximal femoral fracture. The primary outcome was patient survival, and the recovery of independence in activities of daily living was included as a secondary outcome. The mortality risk score was constructed for each patient and its prognostic value was tested for the whole population. RESULTS: Data was available form 136 patients. The mean age of all patients was 82.1 years, with a median follow-up of 6 months. Within this period, 19.0% of all patients died and 51.1% recovered to their prefracture level of independence. The mortality score was significantly associated with mortality (HR, 2.74; 95% CI, 1.61-4.66; P < 0.001), but showed only a fair prediction accuracy (AUC = 0.68) and a borderline significant comparison of the mortality score tertile groups in survival analyses (P = 0.049). No decisive associations were found in any of the analyses for the functional recovery of patients. DISCUSSION: These findings support the previously determined prognostic value of the mortality risk score. However, the independent prognostic value when adjusted for potential confounding factors is yet to be assessed. Also, a risk score constructed for this specific patient population might achieve higher accuracies for the prediction of survival and functional recovery. CONCLUSIONS: A modest prediction accuracy was observed for the mortality risk score in this population. More elaborate studies are needed to validate these findings and develop a tailored model for clinical purposes in this patient population.

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference.

BACKGROUND: DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. RESULTS: By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. CONCLUSION: We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation.

Lifestyle-Intervention-Induced Reduction of Abdominal Fat Is Reflected by a Decreased Circulating Glycerol Level and an Increased HDL Diameter.

SCOPE: Abdominal obesity is one of the main modifiable risk factors of age-related cardiometabolic disease. Cardiometabolic disease risk and its associated high abdominal fat mass, cholesterol, and glucose concentrations can be reduced by a healthier lifestyle. Hence, the aim is to understand the relation between lifestyle-induced changes in body composition, and specifically abdominal fat, and accompanying changes in circulating metabolic biomarkers. METHODS AND RESULTS: Data from the Growing Old Together (GOTO) study was used, which is a single arm lifestyle intervention in which 164 older adults (mean age 63 years, BMI 23-35 kg/m2 ) changed their lifestyle during 13 weeks by 12.5% caloric restriction plus 12.5% increase in energy expenditure. It is shown here that levels of circulating metabolic biomarkers, even after adjustment for body mass index, specifically associate with abdominal fat mass. The applied lifestyle intervention mainly reduces abdominal fat mass (-2.6%, SD = 3.0) and this reduction, when adjusted for general weight loss, is highly associated with decreased circulating glycerol concentrations and increased HDL diameter. CONCLUSION: The lifestyle-induced reduction of abdominal fat mass is particularly associated, independent of body mass index or general weight loss, with decreased circulating glycerol concentrations and increased HDL diameter.

Association of handgrip strength with patient-reported outcome measures after total hip and knee arthroplasty.

About 33% patients with osteoarthritis undergoing total hip/knee arthroplasty are not satisfied with the outcome, warranting the need to improve patient selection. Handgrip strength (HGS) has been suggested as a proxy for overall muscle strength and may be associated with post-arthroplasty function. This study aims to assess the association of pre-operative HGS with change in hip/knee function and quality of life in patients with arthroplasty. 226 hip (THA) and 246 knee (TKA) arthroplasty patients were included in this prospective cohort study. Pre-operative HGS was assessed by means of a dynamometer and the HOOS/KOOS and SF-36 questionnaires were collected before arthroplasty and 1 year thereafter. The association of HGS with score change on each sub-domain of the included questionnaires was assessed by linear regression models, adjusting for sex, body mass index and baseline score. Mean pre-operative HGS was 26 kg for patients undergoing THA and 24 kg for those undergoing TKA. HGS was positively associated with an increased improvement score on "function in sport and recreation"-domain in hip (ß = 0.68, P = 0.005) and knee (ß = 0.52, P = 0.049) and "symptoms"-domain in hip (ß = 0.56, P = 0.001). For patients with THA, HGS was associated with the "quality of life" domain (ß = 0.33, P = 0.033). In patients with TKA, HGS was associated with the physical component score (ß = 0.31, P = 0.001). All statistically significant effects were positive, indicating that with greater pre-operative HGS, an increased gain in 1-year post-surgery score was observed. HGS can be used as a tool to inform patients with OA who are future candidates for a prosthesis about the possible improvements of certain aspects of life after arthroplasty.

Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls.

BACKGROUND: Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons. METHODS: Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses. RESULTS: Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms. CONCLUSIONS: This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity.

Senescent human melanocytes drive skin ageing via paracrine telomere dysfunction.

Cellular senescence has been shown to contribute to skin ageing. However, the role of melanocytes in the process is understudied. Our data show that melanocytes are the only epidermal cell type to express the senescence marker p16INK4A during human skin ageing. Aged melanocytes also display additional markers of senescence such as reduced HMGB1 and dysfunctional telomeres, without detectable telomere shortening. Additionally, senescent melanocyte SASP induces telomere dysfunction in paracrine manner and limits proliferation of surrounding cells via activation of CXCR3-dependent mitochondrial ROS. Finally, senescent melanocytes impair basal keratinocyte proliferation and contribute to epidermal atrophy in vitro using 3D human epidermal equivalents. Crucially, clearance of senescent melanocytes using the senolytic drug ABT737 or treatment with mitochondria-targeted antioxidant MitoQ suppressed this effect. In conclusion, our study provides proof-of-concept evidence that senescent melanocytes affect keratinocyte function and act as drivers of human skin ageing.

Repeat variations in polyglutamine disease-associated genes and cognitive function in old age.

Although the heritability of cognitive function in old age is substantial, genome-wide association studies have had limited success in elucidating its genetic basis, leaving a considerable amount of "missing heritability." Aside from single nucleotide polymorphisms, genome-wide association studies are unable to assess other large sources of genetic variation, such as tandem repeat polymorphisms. Therefore, here, we studied the association of cytosine-adenine-guanine (CAG) repeat variations in polyglutamine disease-associated genes (PDAGs) with cognitive function in older adults. In a large cohort consisting of 5786 participants, we found that the CAG repeat number in 3 PDAGs (TBP, HTT, and AR) were significantly associated with the decline in cognitive function, which together accounted for 0.49% of the variation. Furthermore, in an magnetic resonance imaging substudy, we found that CAG repeat polymorphisms in 4 PDAGs (ATXN2, CACNA1A, ATXN7, and AR) were associated with different imaging characteristics, including brain stem, putamen, globus pallidus, thalamus, and amygdala volumes. Our findings indicate that tandem repeat polymorphisms are associated with cognitive function in older adults and highlight the importance of PDAGs in elucidating its missing heritability.

Validated inference of smoking habits from blood with a finite DNA methylation marker set.

Inferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUCcrossvalidation 0.925 ± 0.021, AUCexternalvalidation0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications.

Frailty Questionnaire Is Not a Strong Prognostic Factor for Functional Outcomes in Hip or Knee Arthroplasty Patients.

INTRODUCTION: Up to 33% and 25% of patients with end-stage hip and knee osteoarthritis (OA) are considered frail by the Groningen Frailty Indicator (GFI). This study aims to assess whether frail patients have lower functional gains after arthroplasty and to assess GFI as a tool to discriminate between good and adverse change score. MATERIALS AND METHODS: Patients with end-stage hip/knee OA scheduled for arthroplasty were recruited from the Longitudinal Leiden Orthopaedics Outcomes of Osteo-Arthritis Study. Functional outcome was measured as change score on the Hip Osteoarthritis Outcome Score/Knee Osteoarthritis Outcome Score (HOOS/KOOS), by subtracting preoperative score from 1-year postsurgery score and then dichotomized based on a cutoff of 20 points. For each HOOS/KOOS subscale, 3 models were estimated: GFI univariate (model 1), GFI and baseline score (model 2), and baseline score univariate (model 3). A receiver operating characteristic analysis was performed to assess the discriminative ability of each model. RESULTS: Eight hundred five patients with end-stage hip OA (31.4% frail) and 640 patients with end-stage knee OA (25.4% frail) were included. Frail patients were older, had a higher body mass index, had more comorbidities, and lived more often alone. Persons considered frail by GFI had significant lower baseline score; however, except for "function in sports and recreation" and "quality of life," change scores were similar in frail and nonfrail persons. The discriminatory value of GFI was negligible for all HOOS/KOOS subscales. Baseline score, however, was adequate to discriminate between total knee arthroplasty patients with more or less than twice the minimally clinically important difference on KOOS symptoms subscale (area under the curve = 0.802). DISCUSSION/CONCLUSION: Although frail patients with OA have lower functioning scores at baseline, the change scores on HOOS/KOOS subscales are similar for both frail and nonfrail patients. Exploring other heath assessements may improve patient-specific outcome prediction.

Repeat length variations in polyglutamine disease-associated genes affect body mass index.

BACKGROUND: The worldwide prevalence of obesity, a major risk factor for numerous debilitating chronic disorders, is increasing rapidly. Although a substantial amount of the variation in body mass index (BMI) is estimated to be heritable, the largest meta-analysis of genome-wide association studies (GWAS) to date explained only ~2.7% of the variation. To tackle this 'missing heritability' problem of obesity, here we focused on the contribution of DNA repeat length polymorphisms which are not detectable by GWAS. SUBJECTS AND METHODS: We determined the cytosine-adenine-guanine (CAG) repeat length in the nine known polyglutamine disease-associated genes (ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, TBP, HTT, ATN1 and AR) in two large cohorts consisting of 12,457 individuals and analyzed their association with BMI, using generalized linear mixed-effect models. RESULTS: We found a significant association between BMI and the length of CAG repeats in seven polyglutamine disease-associated genes (including ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, TBP and AR). Importantly, these repeat variations could account for 0.75% of the total BMI variation. CONCLUSIONS: Our findings incriminate repeat polymorphisms as an important novel class of genetic risk factors of obesity and highlight the role of the brain in its pathophysiology.

Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults.

BACKGROUND: DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health. METHODS: We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), diastolic, and systolic blood pressure (BP). FINDINGS: Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, lower DNA methylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 × 10-7 < P < 0.01). Similarly, lower DNA methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 × 10-8 < P < 0.001). Lower DNA methylation at all the CpGs was consistently associated with higher TG levels. INTERPRETATION: Epigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. FUND: European Union's Horizon 2020 research and innovation programme under grant agreement no. 633595 DynaHEALTH.

Genome-wide identification of directed gene networks using large-scale population genomics data.

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10-10), among which transcription factors were overrepresented (Fisher's P = 3.3 × 10-7). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.

Genome-Wide Association Study on Immunoglobulin G Glycosylation Patterns.

Immunoglobulin G (IgG), a glycoprotein secreted by plasma B-cells, plays a major role in the human adaptive immune response and are associated with a wide range of diseases. Glycosylation of the Fc binding region of IgGs, responsible for the antibody's effector function, is essential for prompting a proper immune response. This study focuses on the general genetic impact on IgG glycosylation as well as corresponding subclass specificities. To identify genetic loci involved in IgG glycosylation, we performed a genome-wide association study (GWAS) on liquid chromatography electrospray mass spectrometry (LC-ESI-MS)-measured IgG glycopeptides of 1,823 individuals in the Cooperative Health Research in the Augsburg Region (KORA F4) study cohort. In addition, we performed GWAS on subclass-specific ratios of IgG glycans to gain power in identifying genetic factors underlying single enzymatic steps in the glycosylation pathways. We replicated our findings in 1,836 individuals from the Leiden Longevity Study (LLS). We were able to show subclass-specific genetic influences on single IgG glycan structures. The replicated results indicate that, in addition to genes encoding for glycosyltransferases (i.e., ST6GAL1, B4GALT1, FUT8, and MGAT3), other genetic loci have strong influences on the IgG glycosylation patterns. A novel locus on chromosome 1, harboring RUNX3, which encodes for a transcription factor of the runt domain-containing family, is associated with decreased galactosylation. Interestingly, members of the RUNX family are cross-regulated, and RUNX3 is involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis. Besides the involvement of glycosyltransferases in IgG glycosylation, we suggest that, due to the impact of variants within RUNX3, potentially mechanisms involved in B-cell activation and T-cell differentiation during the immune response as well as cell migration and invasion involve IgG glycosylation.