Football in the community schemes: Exploring the effectiveness of an intervention in promoting healthful behaviour change

This study aims to examine the effectiveness of a Premier League football club’s Football in the Community (FitC) schemes intervention in promoting positive healthful behaviour change in children. Specifically, exploring the effectiveness of this intervention from the perspectives of the participants involved (i.e. the researcher, teachers, children and coaches). A range of data collection techniques were utilized including the principles of ethnography (i.e. immersion, engagement and observations), alongside conducting focus groups with the children. The results allude to the intervention merely ‘keeping active children active’ via (mostly) fun, football sessions. Results highlight the important contribution the ‘coach’ plays in the effectiveness of the intervention. Results relating to working practice (i.e. coaching practice and coach recruitment) are discussed and highlighted as areas to be addressed. FitC schemes appear to require a process of positive organizational change to increase their effectiveness in strategically attending to the health agenda

Genetic variants and blood pressure in a population-based cohort: the cardiovascular risk in young Finns study

Clinical relevance of a genetic predisposition to elevated blood pressure was quantified during the transition from childhood to adulthood in a population-based Finnish cohort (N=2357). Blood pressure was measured at baseline in 1980 (age 3–18 years) and in follow-ups in 1983, 1986, 2001, and 2007. Thirteen single nucleotide polymorphisms associated with blood pressure were genotyped, and 3 genetic risk scores associated with systolic and diastolic blood pressures and their combination were derived for all of the participants. Effects of the genetic risk score were 0.47 mm Hg for systolic and 0.53 mm Hg for diastolic blood pressures (both P&lt;0.01). The combination genetic risk score was associated with diastolic blood pressure from age 9 years onward (β=0.68 mm Hg; P=0.015). Replications in 1194 participants of the Bogalusa Heart Study showed essentially similar results. The participants in the highest quintile of the combination genetic risk score had a 1.82-fold risk of hypertension in adulthood (P&lt;0.0001) compared with the lowest quintile, independent of a family history of premature hypertension. These findings show that genetic variants are associated with preclinical blood pressure traits in childhood; individuals with several susceptibility alleles have, on average, a 0.5-mm Hg higher blood pressure, and this trajectory continues from childhood to adulthood.<br/

A genome-wide association meta-analysis on lipoprotein (a) concentrations adjusted for apolipoprotein (a) isoforms.

High lipoprotein (a) [Lp(a)] concentrations are an independent risk factor for cardiovascular outcomes. Concentrations are strongly influenced by apo(a) kringle IV repeat isoforms. We aimed to identify genetic loci associated with Lp(a) concentrations using data from five genome-wide association studies (n = 13,781). We identified 48 independent SNPs in the &lt;i&gt;LPA&lt;/i&gt; and 1 SNP in the &lt;i&gt;APOE&lt;/i&gt; gene region to be significantly associated with Lp(a) concentrations. We also adjusted for apo(a) isoforms to identify loci affecting Lp(a) levels independently from them, which resulted in 31 SNPs (30 in the &lt;i&gt;LPA&lt;/i&gt; , 1 in the &lt;i&gt;APOE&lt;/i&gt; gene region). Seven SNPs showed a genome-wide significant association with coronary artery disease (CAD) risk. A rare SNP (rs186696265; MAF ∼1%) showed the highest effect on Lp(a) and was also associated with increased risk of CAD (odds ratio = 1.73, &lt;i&gt;P&lt;/i&gt; = 3.35 × 10 &lt;sup&gt;-30&lt;/sup&gt; ). Median Lp(a) values increased from 2.1 to 91.1 mg/dl with increasing number of Lp(a)-increasing alleles. We found the &lt;i&gt;APOE2&lt;/i&gt; -determining allele of rs7412 to be significantly associated with Lp(a) concentrations ( &lt;i&gt;P&lt;/i&gt; = 3.47 × 10 &lt;sup&gt;-10&lt;/sup&gt; ). Each &lt;i&gt;APOE2&lt;/i&gt; allele decreased Lp(a) by 3.34 mg/dl corresponding to ∼15% of the population's mean values. Performing a gene-based test of association, including suspected Lp(a) receptors and regulators, resulted in one significant association of the &lt;i&gt;TLR2&lt;/i&gt; gene with Lp(a) ( &lt;i&gt;P&lt;/i&gt; = 3.4 × 10 &lt;sup&gt;-4&lt;/sup&gt; ). In summary, we identified a large number of independent SNPs in the &lt;i&gt;LPA&lt;/i&gt; gene region, as well as the &lt;i&gt;APOE2&lt;/i&gt; allele, to be significantly associated with Lp(a) concentrations

Genetic variants and blood pressure in a population-based cohort: the cardiovascular risk in young Finns study

Clinical relevance of a genetic predisposition to elevated blood pressure was quantified during the transition from childhood to adulthood in a population-based Finnish cohort (N=2,357). Blood pressure was measured at baseline in 1980 (age 3–18 years) and in follow-ups in 1983, 1986, 2001 and 2007. Thirteen single nucleotide polymorphisms associated with blood pressure were genotyped and three genetic risk scores associated with systolic and diastolic blood pressure and their combination were derived for all participants. Effects of the genetic risk score were 0.47 mmHg for systolic and 0.53 mmHg for diastolic blood pressure (both p<0.01). The combination genetic risk score was associated with diastolic blood pressure from age 9 onwards (β=0.68 mmHg, p=0.015). Replications in 1194 participants of the Bogalusa Heart Study showed essentially similar results. The participants in the highest quintile of the combination genetic risk score had a 1.82-fold risk of hypertension in adulthood (p<0.0001) compared with the lowest quintile, independent of a family history of premature hypertension. These findings show that genetic variants are associated with preclinical blood pressure traits in childhood, individuals with several susceptibility alleles have on average a 0.5 mmHg higher blood pressure and this trajectory continues from childhood to adulthood

A genome-wide association meta-analysis on apolipoprotein A-IV concentrations.

Apolipoprotein A-IV (apoA-IV) is a major component of HDL and chylomicron particles and is involved in reverse cholesterol transport. It is an early marker of impaired renal function. We aimed to identify genetic loci associated with apoA-IV concentrations and to investigate relationships with known susceptibility loci for kidney function and lipids. A genome-wide association meta-analysis on apoA-IV concentrations was conducted in five population-based cohorts (n = 13,813) followed by two additional replication studies (n = 2,267) including approximately 10 M SNPs. Three independent SNPs from two genomic regions were significantly associated with apoA-IV concentrations: rs1729407 near APOA4 (P = 6.77 × 10 (-)  (44)), rs5104 in APOA4 (P = 1.79 × 10(-)(24)) and rs4241819 in KLKB1 (P = 5.6 × 10(-)(14)). Additionally, a look-up of the replicated SNPs in downloadable GWAS meta-analysis results was performed on kidney function (defined by eGFR), HDL-cholesterol and triglycerides. From these three SNPs mentioned above, only rs1729407 showed an association with HDL-cholesterol (P = 7.1 × 10 (-)  (07)). Moreover, weighted SNP-scores were built involving known susceptibility loci for the aforementioned traits (53, 70 and 38 SNPs, respectively) and were associated with apoA-IV concentrations. This analysis revealed a significant and an inverse association for kidney function with apoA-IV concentrations (P = 5.5 × 10(-)(05)). Furthermore, an increase of triglyceride-increasing alleles was found to decrease apoA-IV concentrations (P = 0.0078). In summary, we identified two independent SNPs located in or next the APOA4 gene and one SNP in KLKB1 The association of KLKB1 with apoA-IV suggests an involvement of apoA-IV in renal metabolism and/or an interaction within HDL particles. Analyses of SNP-scores indicate potential causal effects of kidney function and by lesser extent triglycerides on apoA-IV concentrations

Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets

In recent years, there has been a notably increased interest in the study of multivariate interactions and emergent higher-order dependencies. This is particularly evident in the context of identifying synergistic sets, which are defined as combinations of elements whose joint interactions result in the emergence of information that is not present in any individual subset of those elements. The scalability of frameworks such as partial information decomposition (PID) and those based on multivariate extensions of mutual information, such as O-information, is limited by combinational explosion in the number of sets that must be assessed. In order to address these challenges, we propose a novel approach that utilises stochastic search strategies in order to identify synergistic triplets within datasets. Furthermore, the methodology is extensible to larger sets and various synergy measures. By employing stochastic search, our approach circumvents the constraints of exhaustive enumeration, offering a scalable and efficient means to uncover intricate dependencies. The flexibility of our method is illustrated through its application to two epidemiological datasets: The Young Finns Study and the UK Biobank Nuclear Magnetic Resonance (NMR) data. Additionally, we present a heuristic for reducing the number of synergistic sets to analyse in large datasets by excluding sets with overlapping information. We also illustrate the risks of performing a feature selection before assessing synergistic information in the system.</p

Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data

Aims  Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests. Methods and results  The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to improve the performance of the artificial intelligence models. An extension study was carried out using data from the Young Finns Study (YFS) to assess the results’ generalizability. Upon applying virtual augmented data, accuracy increased by approximately 5%, from 0.75 to –0.79 for random forests (RFs), and from 0.76 to –0.80 for Gradient Boosting (GB). Sensitivity showed a significant boost for RFs, rising by about 9.4% (0.81–0.89), while GB exhibited a 4.8% increase (0.83–0.87). Specificity showed a significant boost for RFs, rising by ∼24% (from 0.55 to 0.70), while GB exhibited a 37% increase (from 0.51 to 0.74). The extension analysis aligned with the initial study.Conclusion  Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease

Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency &ge;0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated

The impact of low-frequency and rare variants on lipid levels

Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

HapMap imputed genome-wide association studies (GWAS) have revealed &gt;50 loci at which common variants with minor allele frequency &gt;5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value &lt; 5 × 10(-8) previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR &lt; 0.05) genes and 127 significantly (FDR &lt; 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples