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Objective: To investigate the association between long-term weight change and blood metabolites., Methods: Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3-79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow-up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study (n = 1,760; age range: 25-70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study (n = 471; age range: 55-74, baseline BMI: 27.24 ± 5.37)., Results: Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta-analysis β [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10 -19 ), gamma-glutamyl valine (β [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10 -20 ), butyrylcarnitine (β [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10 -12 ), and 3-phenylpropionate (β [95% CI] = -0.03 [-0.041 to -0.019]; P = 9.8 × 10 -8 ), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO., Conclusions: Metabolites linked to higher oxidative stress are associated with increased long-term weight gain., (© 2017 The Authors. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS).)

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 × 10(-)(7)) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 × 10(-10)) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average., (Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Objectives: Obesity as measured by body mass index (BMI) is one of the major risk factors for osteoarthritis. In addition, genetic overlap has been reported between osteoarthritis and normal adult height variation. We investigated whether this relationship is due to a shared genetic aetiology on a genome-wide scale., Methods: We compared genetic association summary statistics (effect size, p value) for BMI and height from the GIANT consortium genome-wide association study (GWAS) with genetic association summary statistics from the arcOGEN consortium osteoarthritis GWAS. Significance was evaluated by permutation. Replication of osteoarthritis association of the highlighted signals was investigated in an independent dataset. Phenotypic information of height and BMI was accounted for in a separate analysis using osteoarthritis-free controls., Results: We found significant overlap between osteoarthritis and height (p=3.3×10(-5) for signals with p≤0.05) when the GIANT and arcOGEN GWAS were compared. For signals with p≤0.001 we found 17 shared signals between osteoarthritis and height and four between osteoarthritis and BMI. However, only one of the height or BMI signals that had shown evidence of association with osteoarthritis in the arcOGEN GWAS was also associated with osteoarthritis in the independent dataset: rs12149832, within the FTO gene (combined p=2.3×10(-5)). As expected, this signal was attenuated when we adjusted for BMI., Conclusions: We found a significant excess of shared signals between both osteoarthritis and height and osteoarthritis and BMI, suggestive of a common genetic aetiology. However, only one signal showed association with osteoarthritis when followed up in a new dataset.

Background: Snacking is a common diet behaviour which accounts for a large proportion of daily energy intake, making it a key determinant of diet quality. However, the relationship between snacking frequency, quality and timing with cardiometabolic health remains unclear. Design: Demography, diet, health (fasting and postprandial cardiometabolic blood and anthropometrics markers) and stool metagenomics data were assessed in the UK PREDICT 1 cohort (N = 1002) (NCT03479866). Snacks (foods or drinks consumed between main meals) were self-reported (weighed records) across 2–4 days. Average snacking frequency and quality [snack diet index (SDI)] were determined (N = 854 after exclusions). Associations between snacking frequency, quality and timing with cardiometabolic blood and anthropometric markers were assessed using regression models (adjusted for age, sex, BMI, education, physical activity level and main meal quality). Results: Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/m2 and were predominantly female (73%). 95% of participants were snackers (≥ 1 snack/day; n = 813); mean daily snack intake was 2.28 snacks/day (24 ± 16% of daily calories; 203 ± 170 kcal); and 44% of participants were discordant for meal and snack quality. In snackers, overall snacking frequency and quantity of snack energy were not associated with cardiometabolic risk markers. However, lower snack quality (SDI range 1–11) was associated with higher blood markers, including elevated fasting triglycerides (TG (mmol/L) β; – 0.02, P = 0.02), postprandial TGs (6hiAUC (mmol/L.s); β; – 400, P = 0.01), fasting insulin (mIU/L) (β; – 0.15, P = 0.04), insulin resistance (HOMA-IR; β; – 0.04, P = 0.04) and hunger (scale 0–100) (β; – 0.52, P = 0.02) (P values non-significant after multiple testing adjustments). Late-evening snacking (≥ 9 pm; 31%) was associated with lower blood markers (HbA1c; 5.54 ± 0.42% vs 5.46 ± 0.28%, glucose 2hiAUC; 8212 ± 5559 vs 7321 ± 4928 mmol/L.s, P = 0.01 and TG 6hiAUC; 11,638 ± 8166 vs 9781 ± 6997 mmol/L.s, P = 0.01) compared to all other snacking times (HbA1c remained significant after multiple testing). Conclusion: Snack quality and timing of consumption are simple diet features which may be targeted to improve diet quality, with potential health benefits. Clinical trial registry number and website: NCT03479866, https://clinicaltrials.gov/ct2/show/NCT03479866?term=NCT03479866&draw=2&rank=1 [ABSTRACT FROM AUTHOR]

Purpose: In this study, we explore the relationship between social jetlag (SJL), a parameter of circadian misalignment, and gut microbial composition, diet and cardiometabolic health in the ZOE PREDICT 1 cohort (NCT03479866). Methods: We assessed demographic, diet, cardiometabolic, stool metagenomics and postprandial metabolic measures (n = 1002). We used self-reported habitual sleep (n = 934) to calculate SJL (difference in mid-sleep time point of ≥ 1.5 h on week versus weekend days). We tested group differences (SJL vs no-SJL) in cardiometabolic markers and diet (ANCOVA) adjusting for sex, age, BMI, ethnicity, and socio-economic status. We performed comparisons of gut microbial composition using machine learning and association analyses on the species level genome bins present in at least 20% of the samples. Results: The SJL group (16%, n = 145) had a greater proportion of males (39% vs 25%), shorter sleepers (average sleep < 7 h; 5% vs 3%), and were younger (38.4 ± 11.3y vs 46.8 ± 11.7y) compared to the no-SJL group. SJL was associated with a higher relative abundance of 9 gut bacteria and lower abundance of 8 gut bacteria (q < 0.2 and absolute Cohen's effect size > 0.2), in part mediated by diet. SJL was associated with unfavourable diet quality (less healthful Plant-based Diet Index), higher intakes of potatoes and sugar-sweetened beverages, and lower intakes of fruits, and nuts, and slightly higher markers of inflammation (GlycA and IL-6) compared with no-SJL (P < 0.05 adjusted for covariates); rendered non-significant after multiple testing adjustments. Conclusions: Novel associations between SJL and a more disadvantageous gut microbiome in a cohort of predominantly adequate sleepers highlight the potential implications of SJL for health. [ABSTRACT FROM AUTHOR]

Background: The Dietary Approaches to Stop Hypertension (DASH) diet is beneficial in reducing blood pressure; however, this may be a consequence of concurrent weight reduction. In the present study, we investigated whether body mass index (BMI) mediates the association between the DASH diet and hypertension and investigate common metabolic pathways. Methods: We included 2424 females from the cross‐sectional TwinsUK cohort, with blood pressure, BMI and dietary intake measured within 1.01 (SD = 0.68) years and serum metabolomics profiling (591 metabolites). We constructed a mediation model to test the mediation effects of BMI on the total effect of the DASH diet on hypertension. To identify a metabolite panel associated with the DASH diet and BMI, we built random forest models for each trait, and selected the common metabolic contributors using five‐fold cross‐validation error. Results: We found that BMI fully mediates the association between the DASH diet and hypertension, explaining 39.1% of the variance in hypertension. We then identified a panel of six common metabolites predicting both the DASH diet and BMI with opposing effects. Interestingly, at the univariate level, the metabolites were also associated with hypertension in the same direction as BMI. The strongest feature, 1‐nonadecanoyl‐GPC (19:0), was positively associated with the DASH diet (β [SE] = 0.65 [0.12]) and negatively with BMI (β [SE] = −1.34 [0.12]) and hypertension (odds ratio = 0.71, 95% confidence interval = 0.6–0.84). Conclusions: We highlight the role of BMI in the mechanisms by which the DASH diet influences hypertension and also highlight common metabolic pathways. Further studies should investigate the underlying molecular mechanisms to increase our understanding of the beneficial ways of treating hypertension. Key points: Adherence to the DASH diet can reduce blood pressure; however, this may be a consequence of concurrent weight reduction.In a large sample of 2424 females from TwinsUK, we report that BMI fully mediates the association between the DASH diet and hypertension, explaining 39.1% of the variance in hypertension.We also identify a panel of six common metabolites predicting both the DASH diet and BMI with opposing effects, highlighting common metabolic pathways.Further studies should investigate the underlying molecular mechanisms to increase our understanding of the beneficial ways of treating hypertension. [ABSTRACT FROM AUTHOR]

Background: COVID‐19 has a broad clinical spectrum. We investigated the role of serum markers measured on admission on severity as assessed at discharge and investigated those which relate to the effect of BMI on severity. Methods: Clinical and laboratory data from 610 COVID‐19 cases hospitalized in the province of Zheijang, China were investigated as risk factors for severe COVID‐19 (assessed by respiratory distress) compared to mild or common forms using logistic regression methods. Biochemical markers were correlated with severity using spearman correlations, and a ROC analysis was used to determine the individual contribution of each of the biochemical markers on severity. We carried out formal mediation analyses to investigate the extent of the effect of body mass index (BMI) on COVID‐19 severity mediated by hypertension, glycemia, Lactose Dehydrogenase (LDH) at the time of hospitalization and C‐Reactive Protein levels (CRP), in units of standard deviations. Results: The individual markers measured on admission contributing most strongly to prediction of COVID‐19 severity as assessed at discharge were LDH, CRP and glucose. The proportion of the effect of BMI on severity of COVID‐19 mediated by CRP, glycemia or hypertension, we find that glucose mediated 79% (p <.0001), LDH mediated 78% (p <.0001), hypertension mediated 66% (p <.0001); however, only 44% (p <.005) was mediated by systemic inflammation (CRP). Conclusion: Our data indicate that a larger proportion of the effect of BMI on severity of COVID‐19 is mediated by glycemia and LDH levels whereas less than half of it is mediated by systemic inflammation. [ABSTRACT FROM AUTHOR]

Objectives: To determine the proportion of people with gout who self-report triggers of acute attacks; identify the commonly reported triggers, and examine the disease and demographic features associated with self-reporting any trigger(s) of acute attacks of gout. Methods: Individuals with gout were asked to fill a questionnaire enquiring about triggers that precipitated their acute gout attacks. Binary logistic regression was used to compute odds ratio (OR) and 95% confidence intervals (CI) to examine the association between having ≥1 self-reported trigger of acute gout and disease and demographic risk factors and to adjust for covariates. All statistical analyses were performed using STATA. Results: 550 participants returned completed questionnaires. 206 (37.5%) reported at least one trigger of acute attacks, and less than 5% reported >2 triggers. Only 28.73% participants reported that their most recent gout attack was triggered by dietary or lifestyle risk factors. The most frequently self-reported triggers were alcohol intake (14.18%), red-meat or sea-food consumption (6%), dehydration (4.91%), injury or excess activity (4.91%), and excessively warm or cold weather (4.36% and 5.45%). Patients who had onset of gout before the age of 50 years were significantly more likely to identify a trigger for precipitating their acute gout attacks (aOR (95%CI) 1.73 (1.12–2.68) after adjusting for covariates. Conclusion: Most people with gout do not identify any triggers for acute attacks, and identifiable triggers are more common in those with young onset gout. Less than 20% people self-reported acute gout attacks from conventionally accepted triggers of gout e.g. alcohol, red-meat intake, while c.5% reported novel triggers such as dehydration, injury or physical activity, and weather extremes. [ABSTRACT FROM AUTHOR]

Background Osteoarthritis (OA) and cardiovascular disease (CVD) are the two most prevalent conditions in the population aged over 70 in developed countries. Both conditions share common risk factors, in particular age and body mass index. However, the very high level of co-occurrence of both diseases cannot be accounted by common risk factors alone. Materials and Methods We reviewed the recent literature published in English in PubMed for articles relating to osteoarthritis and cardiovascular disease. Results On the one hand, the disability caused by OA increases the risk of CVD and in particular of ischemic events and mortality beyond what can be explained by known common risk factors, such as ageing and obesity. Moreover, the presence of OA has a synergistic effect on CVD symptoms considerably worsening them. On the other hand, at least in women, there appears to be a common pathogenic mechanism underlying atherosclerosis (but not hypertension) and actual joint damage. Conclusion There are some possible molecular mechanisms underlying both diseases, in particular relating to low grade inflammation and female hormones. However, the data available to date also indicate that OA may be considered as an indirect cause of CVD by increasing walking disability and the use of analgesic medication such as NSAIDs. We discuss future directions that need to be taken to address these highly prevalent, costly and disabling morbidities. [ABSTRACT FROM AUTHOR]

Background: Despite evidence of antioxidant effects of vitamin E in vitro and in animal studies, large, randomized clinical trials have not substantiated a benefit of vitamin E in reducing inflammation in humans. An individual's genetic background may affect the response to α-tocopherol supplementation, but this has rarely been investigated. Objective: The aim of this study was to explore the role of genetic polymorphisms on changes in LPS-stimulated inflammatory cytokine production from peripheral blood mononuclear cells (PBMCs) after α-tocopherol supplementation. Design: A total of 160 healthy, middle-aged male volunteers (mean age: 52.7 y) were given dietary supplements of either 75 IU (low dose; n = 57) or 600 IU (high dose; n = 103) α-tocopherol/d for 6 wk. The production of TNF-α and IL-lβ, -6, and -10 by PBMCs after LPS stimulation was measured at baseline and after 6 wk. Polymorphisms in 15 genes involved in inflammation or responses to oxidative stress were characterized in the subjects. Results: The ability of a-tocopherol to affect TNF-α production by LPS-stimulated PBMCs was influenced by the TNFA -238 polymorphism (P = 0.016). The ability of α-tocopherol to affect IL-6 production was influenced by the GSTP1 313 polymorphism (P = 0.019). The ability of α-tocopherol to affect IL-lβ production was influenced by the IL10 -592 and -1082 polymorphisms (P = 0.025 and P = 0.016, respectively). Conclusions: In healthy control subjects, the effect of α-tocopherol supplementation on the production of inflammatory cytokines appears to be dependent on an individual's genotype. These genotype-specific differences may help explain some of the discordant results in studies that used vitamin E. [ABSTRACT FROM AUTHOR]

The article examines the differences in risk factors influencing total hip replacement (THR) and total knee replacement (TKR) according to the presence and absence of multiple interphalangeal nodes in patients with large joint osteoarthritis (OA). The results revealed that age, sex and body mass index (BMI) are highly associated with total joint replacement in nodal OA cases than non-nodal OA cases. It was also found out that GDF5 polymorphism was associated with THR in nodal OA cases and increased height was a risk factor for THR in non-nodal OA cases.

Objectives: To validate the gout activity score (GAS) against the gout impact scale in a primary care based gout cohort.Methods: This was a single-centre cross-sectional study. People with gout who participated in previous research at academic rheumatology, University of Nottingham, UK, and consented for participation in future studies were mailed a questionnaire in September 2015. Those returning completed questionnaires were invited to attend for a study visit at which blood was collected and musculoskeletal examination was performed. The Gout Assessment Questionnaire, which contains the gout impact scale (GIS), and short form (SF) 36v2 questionnaires were completed. The GAS3-step-c score was calculated. Spearman's correlation coefficient was calculated to examine correlation between GAS and SF-36 v2, and GIS. Statistical analyses were performed using PASW v22.Results: One hundred and two (93% men) of the 150 participants who were mailed a questionnaire attended the study visit. Their mean (SD) age, body mass index, serum uric acid and GAS were 67.94 (9.93) years, 29.96 (4.57) kg/m2, 5.25 (1.75) mg/dl, and 2.99 (0.74) respectively. There was moderate correlation between GAS and gout concern overall, unmet gout treatment need, and gout concern during an attack components of GIS (r=0.306 to 0.453), but no to poor correlation between GAS and summary scores and scales of SF-36 v2 (r=-0.090 to -0.251).Conclusion: This first study to validate GAS against the GIS found moderate correlation. However, this study did not examine the predictive validity of GAS, and prospective studies are needed before GAS can be used widely. [ABSTRACT FROM AUTHOR]