dataesr/scientific-paragraphs-categorization huggingface dataset:
TwinsUK phenotype data The individuals included in the study attended clinical research visits during which phenotype data and biological samples were collected. During the visit height and weight were measured, and dual-energy X-ray absorptiometry whole-body scans (DXA) were carried out. The total grams of VF obtained from the trunk region in the DXA data were used to quantify VF, as previously described  [26, 27]. Briefly, DXA scans were undertaken with participants lying flat and straight, and the relevant fat mass was estimated in grams from a cross section of the whole body at L4-L5, the typical location of a CT slice. The procedure for taking these measurements, quality control and the calculation of VF has previously been described  [26, 27]. Only individuals with complete information for height, weight and VF were used in the analysis.
DXA scans were also used to estimate AGR and trunk fat mass (TFM). Fasting insulin and glucose levels were obtained from all 538 participants' whole blood samples collected during the clinical visits. Insulin resistance (IR) and type 2 diabetes (T2D) status were assessed through both questionnaire data and based on blood glucose and insulin levels. For an individual to be classified as insulin resistant, they had at least two normal fasting blood glucose readings (below 7 mmol/L) accompanied by a fasting insulin level > 50 pmol/L or over 9 μU/ml. Individuals with only one instance meeting these thresholds were removed from the controls. This categorisation resulted in 114 IR cases (HOMA-IR 3. 5 ± 1. 5) and 284 IR controls (HOMA-IR 1. 1 ± 0. 5) in the SAT dataset. An individual was categorised as a T2D case if either there were two or more indicators of T2D or T2D medication was listed in regular medications taken.
Indicators included fasting blood glucose levels greater than 7 mmol/L on any occasion and self-reporting of T2D incidence. Altogether, the SAT dataset included 15 T2D cases and 378 T2D controls. Lipid data were available for 528 twins with SAT methylation profiles. Total cholesterol, HDL, LDL and triglycerides were measured in blood serum in mmol/L. Total cholesterol, HDL and triglycerides were determined by a colorimetric enzymatic method. HDL cholesterol was determined after precipitation of larger particles (chylomicron, VLDL and LDL) by magnesium and dextran sulfate. LDL levels were estimated by using the Friedewald equation. Checks were carried out to ensure the date difference between the date of methylation measurement and lipid collection was within 5 years. Outliers were excluded based on a boxplot of lipid measurements, where data points that are located outside the whiskers of the boxplot are excluded.
Genome annotation and pathway analysis CpG annotation to genes and with respect to CpG density was based on the 450k Illumina manifest. Further genomic annotations were carried out taking into account data from the ENCODE project  [41]  and ChromHMM categorisation  [42]. This enabled an assessment of the enrichment or depletion of the differentially methylated signals relative to all tested methylation probes from the 450k array. The number of VF-DMPs mapping to each annotation category (e. g. insulators) was compared to the total number of CpGs tested mapping to that category. Fisher's exact test was used to determine whether differences were significant, with a P-value threshold of P < 0. 05. To explore functional annotations to biological processes and molecular pathways, the genes that VF-DMPs annotated to were analysed with the Ingenuity Pathway Analysis (QIAGEN Inc.
https:// www. qiage nbioi nform atics. com/ produ cts/ ingen uityp athway-analy sis) and GOmeth in missMethyl  [43]. Using IPA, we assessed evidence for enrichment of canonical pathways for the VF-DMP genes against all genes applying Fisher's exact test. We considered IPA pathway results that surpassed a P-value threshold (P < 0. 01). We also applied the GOmeth function in the missMethyl Bioconductor package in R  [43]. In GOmeth, we tested the 1181 significant CpGs against a background of all 438,594 CpGs included in the downstream analysis for enrichment in KEGG pathways. GOmeth includes allowance in the enrichment calculation for the number of probes per gene in the background set.
Tissue specificity The SAT samples included in the study consisted of subcutaneous fat. Previously published datasets  [44, 45]  were used to explore differences between subcutaneous fat methylation and visceral fat methylation levels at the VF-DMPs. Our first assessment  [44]  estimated the number of VF-DMPs that fell in genomic regions that showed over 10% methylation differences between the SAT and VAT  [44]. Our second assessment explored how many VF-DMPs were also previously identified by Macartney-Coxon et al. [45]  to be differentially methylated after multiple testing adjustment between SAT and VAT. To assess the level of tissue specificity across SAT and blood samples, we analysed the VF-methylation associations at the 1181 VF-DMPs in the 901 whole blood twin samples, with and without taking into account BMI.
Gene expression profiles and analyses RNA-seq data  [48]  were available for 720 individuals with available genotype information (median age 60, age range 38-64, median BMI 25, BMI range , and these included the 538 twins in the current study. RNA-seq generation and pre-processing in the SAT samples have been previously described  [24]. In summary, STAR software v2. 4. 0. 1  [49]  was used to align reads to hg19. Samples were excluded if they had less than 10 million reads sequenced to known genes. Samples were also excluded if reads were not properly paired. Gene counts were transformed into trimmed mean of M-values (TMM)-adjusted counts per million (CPMs) and inverse-normalised prior to all downstream analyses. The expression dataset was filtered to a minimum of 5 gene counts in 25% of the subjects.
Genetic data, heritability and QTL analyses A twin-based heritability analysis was carried out to estimate heritability of the 1181 VF-DMPs using ACE modelling  [50] , which assumes that phenotypic variance is the sum of additive genetic effects (A), common environmental effects (C) and unique environmental effects (E). Genotypes were available for all 538 individuals in the sample and were used for the identification of methylation Quantitative Trait Loci (meQTLs) and expression Quantitative Trait Loci (eQTLs). Genotyping of the full TwinsUK genetic dataset has been described previously  [51]. Briefly HumanHap300, HumanHap610Q, Human-Hap1M Duo and HumanHap1. 2M Duo 1M arrays were used to genotype the sample. Following pre-phasing using IMPUTE2 without a reference panel, the resulting haplotypes were used to perform fast imputation from 1000 Genome phase1 dataset.
Following imputation, quality control measures included the exclusion of SNPs which failed Hardy Weinberg equilibrium (P < 1e-6), had a MAF < 0. 01, had missingness of more than 5% or had an info score < 0. 8. Individuals with discordant sex were removed. Outliers were also removed using PLINK 2. 0 (unrelated participants) and GENESIS (related participants) where a deviation of more than 7 SD from the mean was considered an outlier. The data was further pruned for relatedness with participants with IBS > 0. 125 (calculated using PLINK 2. 0) removed. A meQTL analysis was performed to test for the association between genetic variation and DNA methylation levels at the 1181 VF-DMPs in SAT. SNPs that were meQTLs were determined by fitting a linear model in MatrixEQTL R package  [52] , where the corrected methylation beta-values were the response variables, and dosage of minor allele was the predictor.
Genetic effects at SNPs for each gene in a cis-window (a 1-MB region around the transcription start site (TSS) of each gene) were analysed. SNPs with a MAF <5% were excluded. As described in Glastonbury et al. [24] , the analysis was performed on the inverse-rank-normalised gene expression residuals corrected for family and zygosity and RNA extraction batch. The QTLtools package in R  [55]  was used for the analysis, adjusting for BMI, genotyping chip and 40 PEER factors. For consistency with meQTL results, cis eQTL significant threshold matched that applied for cis meQTLs (P-value = 1 × 10 -5 ). To assess co-localisation of meQTLs and eQTLs, a simplified approach was taken, where a direct comparison was made between meQTL SNPs and eQTL SNPs.
A consistent linear model was used to assess the methylation-diet associations with covariates including age, BMI, smoking status, cell composition, batch effects as fixed effects, family and zygosity as random effects. To assess evidence for significant VF-DMP associations with dietary variables, we considered results that surpassed Bonferroni multiple testing correction (P = 4. 2 × 10 -5 ). To assess the mediation effect of methylation on the impact of diet on VF, we carried out a mediation analysis using the mediation package in R  [65]. Methylation at CpGs identified to harbour differential methylation effects for both VF and diet (VF/diet-DMPs) was used as a mediator for the causal effect of the relevant diet variable on VF. Before undertaking a mediation analysis, the association between VF and the dietary variables where overlapping methylation associations were found (fibre, magnesium, DASH score, Nordic score, DQI-I score) was determined.
A consistent linear mixed effect model (lme4) was used with VF as response variable, the dietary variable as predictor, and fixed effect covariates including age, smoking, BMI, age, cell composition and batch, and random effects covariates zygosity and family. All of the dietary variables were significantly associated with VF at P <0. 05 (Additional file 2: Table  S5 ). The R mediation package uses a single mediator and fibre, magnesium and DASH diet score all had more than one associated VF/diet-DMP. For each of these dietary variables, the CpG site with the most significant association with the diet variable was used as the mediator. Outcome models were constructed using mixed effects models (lme4) with the diet variable as the predictor and fixed effect covariates smoking, age, BMI, cell composition, batch and random effect covariates family and zygosity.
Insulin resistance classifier We tested whether insulin resistance status could be predicted using DNA methylation levels at the most significantly differentially methylated site associated with IR, namely in FASN (cg11950105 Altogether, 20 random samples were selected creating 20 random training and test set combinations. For each of the training datasets, a generalised linear model was fitted with IR as the outcome and predictors including unadjusted DNA methylation levels and covariates (age, sex, BMI and smoker status) using the R glm function. The sensitivity and specificity were assessed using receiver operative curve (ROC), implemented using the pROC package in R  [66]. Given the strong relationship between IR and BMI, we tested the performance of the methylation classifier compared to the performance of BMI alone.
Second, we pursued replication of selected phenotype associations observed with the 19 VF-DMPs in 9 genes, which in our data showed significant associations with a wide range of metabolically relevant phenotypes, including insulin resistance, triglycerides, HDL and amino acid metabolites. We sought replication in two further cohort samples, a T2D case-control study from Nilsson et al. [22]  and a set of unrelated obese individuals from Allum et al. [20]. The Nilsson et al. [22]  Scandinavian case-control study dataset included 28 T2D cases (BMI 27. 4±3. 6, mean age 74. 5±4. 2, 46% female) and 28 controls (BMI 27. 0±3. 6; mean age 74. 3±4. 3; 46% female) from the Danish Twin Registry, described previously  [22]. DNA extraction, methylation profiling and data quality control has been described previously  [22].
Differential methylome signature associated with VF The association between VF, assessed using dual-energy X-ray absorptiometry (DXA) whole-body scans, and DNA methylation was explored in the primary dataset of 538 SAT samples from TwinsUK (Fig. 1 , Table  1 ), using linear regression models correcting a number of covariates (see 'Methods'). An epigenome-wide association analysis identified 1181 CpG sites (VF-DMPs) annotated to 788 genes that were statistically differentially methylated with VF, controlling for BMI, cell composition and further biological and technical covariates (see 'Methods'), at a Bonferroni multiple testing threshold (P = 1. 14 × 10 -7 ; Additional file 2: Table  S1 ; Fig. 2a ). Of the 1181 VF-DMPs, 660 (56%) were hypermethylated with increasing VF, where VF-DMP effect sizes were estimated by repeating the association analysis without methylation normalisation.
2 × 10 -5 ) with the same direction of effect as that observed for VF. A subset of 1172 VF-DMPs was then explored in the independent dataset of 104 younger male and female participants of mixed ethnicity from the LEAP cohort within the New England Family Study (mean BMI 30. 9 ± 7. 03, mean age 47 ± 1. 7, 48% male), described in detail elsewhere  [8]. We observed that 92% (1073 of 1172) of tested VF-DMPs showed a consistent direction of methylation association with AGR in the LEAP cohort participants (Fig. 2c ), with 21% (220 of 1172) displaying nominally significant effects, with 9 sites significant after multiple testing correction (P = 4. 2 × 10 -5 ). Two follow-up analyses explored the VF associated methylation signature, first, over larger genomic regions, and second, by minimising effects of genetic variation.
VF-DMP tissue specificity This study was undertaken in SAT and identification of VF-DMPs took into account SAT cell composition variation. We sought to assess whether the 1181 VF-DMPs showed methylation differences between the SAT and the visceral adipose tissue (VAT) methylome in independent published datasets. In our first assessment, only 127 out of 1181 VF-DMPs (10. 8%) fell in genomic regions that showed over 10% methylation differences between the SAT and VAT methylomes in an independent dataset of three SAT and VAT samples  [44]. Our second comparison focused on SAT and VAT methylomes of 15 obese individuals  [45] , and only 5 of the 1181 VF-DMPs showed significant differences between tissue types. The results suggest that the majority of VF-DMPs in our SAT sample have similar DNA methylation profiles across VAT and SAT.
In line with these methylation results, gene expression profiles from the Genotype-Tissue Expression (GTEx) resource were also highly consistent between SAT and VAT at VF-DMP genes, but the majority showed differences across SAT and whole blood samples (Fig. 2e , Additional file 1: Fig. S5 , Additional file 2: Table  S10 ). These findings provide a strong argument that the SAT-DMPs identified are reflective of VAT. Tissue specificity of VF-DMPs was also explored between SAT and whole blood, in both the secondary TwinsUK dataset of 901 twins with blood methylation profiles and in a subset of 222 twins with both SAT and blood methylation profiles. Using the subset of 222 twins, we assessed the correlation between SAT and blood DNA methylation at the 1181 VF-DMPs. The majority of VF-DMPs (888, 75%) show very low correlation between blood and SAT methylation levels (-0.
Subsets of VF-DMPs exhibit corresponding gene expression changes with VF To characterize the functional signature of the SAT VF methylome signals, we explored SAT gene expression profiles at the 788 genes annotated to VF-DMP. We compared SAT DNA methylation and gene expression levels in the primary dataset of 538 twins, seeking to identify expression quantitative trait methylation signals (eQTMs). DNA methylation and expression levels were regressed at 848 CpG-gene pairs in cis, comprising 807 CpGs annotated to 717 genes after expression quality control assessments. Significant associations were observed at 109 CpG sites (13%) with 72 genes (10%) after multiple testing correction (Additional file 2: Table  S3 ). The most significant correlation was obtained between SAT DNA methylation at cg04029738 (in FASN) and FASN expression levels (P = 2.
5 × 10 -23 ) where an increase in methylation leads to a decrease in expression. Methylation at a further 12 CpGs annotated to FASN also significantly associated with its expression levels (Fig. 3 ). Further significant methylation-expression associations were observed at multiple CpG sites, including in the PC (6 CpGs) and SREBF1 (5 CpGs) genes. We next investigated the association between SAT gene expression and VF at the 717 genes in the gene expression dataset of 720 TwinsUK participants. Pairwise gene expression and VF associations identified 227 genes (32%) to be significantly differentially expressed with VF after Bonferroni correction, with 415 genes (58%) at nominal significance. Next, conditional analyses explored whether methylation or expression was the likely driver of the association with VF.
Integrative genetic analyses highlight FASN Obesity and metabolic health traits exhibit a clear genetic component, and multiple studies have now shown that a proportion of the human methylome is under strong influence of genetic variation. We assessed if the differential methylation signature of VF may be influenced by genetic variants, or DNA methylation quantitative trait loci (meQTLs). Due to the study participants being twins, we first applied twin-based heritability to explore evidence for genetic effects underlying DNA methylation variation in the primary dataset of 538 TwinsUK participants. Heritability results showed that 33% of the SAT VF-DMPs had evidence for substantial influence of additive genetic effects (A > 0. 4). We then explored the association between genetic variation in cis and DNA methylation levels at the 1181 VF-DMPs in SAT, identifying 203 VF-DMP CpG sites (17%) with at least one genome-wide significant cis meQTL SNP (P = 1 × 10 -5 ; Additional file 2: Table  S4 ) in the primary dataset of 538 TwinsUK participants.
We also carried out expression quantitative trait locus (eQTL) analysis in cis at the 717 genes in the larger dataset of 720 twins with SAT expression profiles. Cis eQTLs were identified at 125 genes (17%) with at least one genome-wide significant cis eQTL SNP (P = 1 × 10 -5 ). Altogether, meQTLs were also eQTLs at a little more than a quarter (54, 27%) of the 203 VF-DMPs under genetic influence (Additional file 2: Table  S4 ). For example, FASN had 211 eQTL SNPs that were also meQTL SNPs for FASN VF-DMP cg06906087. We then assessed whether meQTL SNPs for VF-DMPs may also influence VF, integrating eQTL and eQTM findings. Our SAT meQTLs did not overlap with previously identified GWAS signals for VF or waist hip ratio adjusted for BMI  [53] ; therefore, we assessed the association between the 203 most associated meQTL SNPs and VF in the 538 individuals in this study.
DNA methylation classifier of insulin resistance Lastly, we investigated the extent to which DNA methylation levels could be used to classify insulin resistance. There was insufficient data to carry out the same analysis for T2D and no other clinical traits were considered in this study. Given the strong relationship between DNA methylation in FASN and IR, we focussed on this gene only. We assessed the performance of DNA methylation levels at FASN as a classifier of IR and therefore risk of metabolic disease in the primary dataset of TwinsUK participants. Given the strong relationship between IR and BMI, we assessed the performance of a classifier based on DNA methylation levels and BMI together, compared to the predictive power of BMI alone. Methylation at cg11950105 in FASN combined with BMI was a strong predictor of insulin resistance with an average AUC of 0.
The VF-DMP signals we obtained showed overall concordant DNA methylation levels across SAT and VAT samples, with consistent gene expression changes. Although our discovery sample is among the largest adipose tissue methylation datasets profiled so far  [21] , a sample of 538 is nevertheless relatively modest for epigenetic analyses aiming to identify moderate to small effects. The Mendelian randomisation analysis included in the paper was limited by the sample size and more robust conclusions would be drawn from larger sample sizes. Our sample consisted of female-only White British twins, and therefore, results may not translate to males and to other ethnicities, although a proportion of our findings were replicated in males and in mixed sex cohort samples. The SAT samples were also predominantly from individuals who were not affected by major disease  [21] , which may miss epigenetic changes relating to obesity-related disease.
Utilising a Bonferroni adjustment for multiple testing is a conservative approach as it assumes that DNA methylation levels at CpG sites are independent of each other, while multiple studies have reported evidence for co-methylation  [94]. Furthermore, although the key clinical phenotypes were obtained at time of adipose tissue biopsy in the discovery dataset, the metabolomic profiles were obtained within up to 5 years of time of biopsy sampling, where the time differences may limit the interpretation of these results. A natural extension to this work would be to carry out a longitudinal follow-up and to explore how baseline methylation levels and their longitudinal trajectory relate to metabolic disease incidence, to better disentangle cause from effect at the identified changes.
). The analyses were carried out in the IR data set of 397 people (114 cases, age 58. 3±8. 5, BMI 30. 9±5. 3; 284 controls, age 59. 2±10. 2, BMI 24. 8±3. 6). Training datasets were created by selecting 60% of the full dataset at random. Test datasets were created with the remaining 40% of the full dataset.
Scenario Formulation A literature search was performed with a selection of papers including randomized trials, systematic literature reviews, meta-analyses, and guidelines on peri-operative prophylaxis for the prevention of SSIs during cardiac surgery and thoracic surgery. The literature search was performed on the PubMed database, with a selection of Englishlanguage articles published from 2000 to 2020. In specific cases where recent literature was lacking, articles published since 1995 were also considered. Key search terms such as: "an-timicrobial prophylaxis" OR "antibiotic prophylaxis" AND "thoracic surgery" OR "cardiac surgery" OR "heart pediatric surgery" OR "catheterization" OR "pacemaker implant" OR "sternotomy" OR "ECMO" OR "extracorporeal circulation" OR "congenital cardiopathy" OR "cardiac prosthesis" were used.
Two-Round Consensus Process Based on the scenarios, the questionnaire was submitted to experts on the online platform REDCap. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing: (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for data integration and interoperability with external sources. Each question included the clinical scenario, and possible answers were whether or not SAP was recommended for the scenario and, in the case of its recommendation, a list with all the antibiotics available on the European market so that the expert could select the antibiotics that were considered by that individual as first choice.