Users and non-users of web-based health advice service among Finnish university students – chronic conditions and self-reported health status (a cross-sectional study)

<p>Abstract</p> <p>Background</p> <p>The Internet is increasingly used by citizens as source of health information. Young, highly educated adults use the Internet frequently to search for health-related information. Our study explores whether reported chronic conditions or self-reported health status differed among Finnish university students using the Finnish Student Health Services web-based health advice service compared with those not using the service.</p> <p>Methods</p> <p>Cross-sectional study performed by a national postal survey in 2004. Material: A random sample (n = 5 030) of a population of 101 805 undergraduate Finnish university students aged 19–35. The response rate: 63% (n = 3 153). Main outcome measures: Proportion of university students reporting use a of web-based health advice service, diagnosed chronic conditions, and self-reported health status of users and non-users of a web-based health advice service. Statistical methods: Data were presented with frequency distributions and cross-tabulations and the χ²test was used.</p> <p>Results</p> <p>12% (n = 370) of Finnish undergraduate students had used the web-based health advice service and were identified as 'users'. The proportion of male students reporting allergic rhinitis or conjunctivitis was greater among users than non-users (24%, n = 22 vs. 15%, n = 154, χ², P = .03). The proportion of female students reporting chronic mental health problems was greater among users than non-users (12%, n = 34 vs. 8%, n = 140, χ², P = .03). There was no statistical significance between the group differences of male or female users and non-users in self-reported health status (good or fairly good, average, rather poor or poor).</p> <p>Conclusion</p> <p>Among young, highly educated adults the use of a web-based health advice service is not associated with self-reported health status. However, a web-based health advice service could offer support for managing several specific chronic conditions. More research data is needed to evaluate the role of web-based health advice services that supplement traditional forms of health services.</p

Novel AlkB Dioxygenases—Alternative Models for In Silico and In Vivo Studies

Background: ALKBH proteins, the homologs of Escherichia coli AlkB dioxygenase, constitute a direct, single-protein repair system, protecting cellular DNA and RNA against the cytotoxic and mutagenic activity of alkylating agents, chemicals significantly contributing to tumor formation and used in cancer therapy. In silico analysis and in vivo studies have shown the existence of AlkB homologs in almost all organisms. Nine AlkB homologs (ALKBH1–8 and FTO) have been identified in humans. High ALKBH levels have been found to encourage tumor development, questioning the use of alkylating agents in chemotherapy. The aim of this work was to assign biological significance to multiple AlkB homologs by characterizing their activity in the repair of nucleic acids in prokaryotes and their subcellular localization in eukaryotes. Methodology and Findings: Bioinformatic analysis of protein sequence databases identified 1943 AlkB sequences with eight new AlkB subfamilies. Since Cyanobacteria and Arabidopsis thaliana contain multiple AlkB homologs, they were selected as model organisms for in vivo research. Using E. coli alkB2 mutant and plasmids expressing cyanobacterial AlkBs, we studied the repair of methyl methanesulfonate (MMS) and chloroacetaldehyde (CAA) induced lesions in ssDNA, ssRNA, and genomic DNA. On the basis of GFP fusions, we investigated the subcellular localization of ALKBHs in A. thaliana and established its mostly nucleo-cytoplasmic distribution. Some of the ALKBH proteins were found to change their localization upon MMS treatment. Conclusions: Our in vivo studies showed highly specific activity of cyanobacterial AlkB proteins towards lesions and nucleic acid type. Subcellular localization and translocation of ALKBHs in A. thaliana indicates a possible role for these proteins in the repair of alkyl lesions. We hypothesize that the multiplicity of ALKBHs is due to their involvement in the metabolism of nucleo-protein complexes; we find their repair by ALKBH proteins to be economical and effective alternative to degradation and de novo synthesis

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (P$_v$), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (P$_m$). Correlations between P$_v$ and P$_m$ were stronger for SNPs with established marginal effects (Spearman's ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When P$_v$ and P$_m$ were compared for all pruned SNPs, only BMI was statistically significant (Spearman's ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the P$_v$ distribution (P$_{binomial}$ <0.05). SNPs from the top 1% of the P$_m$ distribution for BMI had more significant Pv values (P$_{Mann-Whitney}$ = 1.46×10$^{-5}$), and the odds ratio of SNPs with nominally significant (<0.05) P$_m$ and P$_v$ was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (P$_{int}$<0.05) were enriched with nominally significant P$_v$ values (P$_{binomial}$ = 8.63×10$^{-9}$ and 8.52×10$^{-7}$ for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them.This research was undertaken as part of a research program supported by the European Commission (CoG-2015_681742_NASCENT), Swedish Research Council (Distinguished Young Researchers Award in Medicine), Swedish HeartLung Foundation, and the Novo Nordisk Foundation, all grants to PWF. DS is supported by the Swedish Research Council International Postdoc Fellowship (4.1-2016-00416). TVV is supported by the Novo Nordisk Foundation Postdoctoral Fellowship within Endocrinology/ Metabolism at International Elite Research Environments via NNF16OC0020698. TWW was supported by the grants "Bundesministerium fur Bildung und Forschung": BMBF-01ER1206, BMBF- 01ER1507. APM is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant WT098017). LAC acknowledges funding for the Framingham Heart Study: This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (Contract No. N01-HC-25195 and Contract No. HHSN268201500001I) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This research was partially supported by grant R01-DK089256 from the National Institute of Diabetes and Digestive and Kidney Diseases (MPIs: I.B. Borecki, LAC, K. North). TOK was supported by the Danish Council for Independent Research (DFF—1333-00124) and Sapere Aude program grant (DFF—1331-00730B). RM would like to acknowledge the High Performance Computing Center of University of Tartu. EGCUT was supported by EU H2020 grants 692145, 676550, 654248, 692065, Estonian Research Council Grant IUT20-60, and PerMed I NIASC, EIT—Health and European Union through the European Regional Development Fund (Project No, 2014-2020.4.01.15-0012 GENTRANSMED)

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe

Associations between a polymorphism in the pleiotropic GCKR and Age-related phenotypes: the HALCyon programme.

Background: The glucokinase regulatory protein encoded by GCKR plays an important role in glucose metabolism and a single nucleotide polymorphism (SNP) rs1260326 (P446L) in the gene has been associated with several age-related biomarkers, including triglycerides, glucose, insulin and apolipoproteins. However, associations between SNPs in the gene and other ageing phenotypes such as cognitive and physical capability have not been reported. Methods: As part of the Healthy Ageing across the Life Course (HALCyon) collaborative research programme, men and women from five UK cohorts aged between 44 and 90+ years were genotyped for rs1260326. Meta-analysis was used to pool within-study genotypic associations between the SNP and several age-related phenotypes, including body mass index (BMI), blood lipid levels, lung function, and cognitive and physical capability. Results: We confirm the associations between the minor allele of the SNP and higher triglycerides and lower glucose levels. We also observed a triglyceride-independent association between the minor allele and lower BMI (pooled beta on zscore = 20.04, p-value = 0.0001, n = 16,251). Furthermore, there was some evidence for gene-environment interactions, including physical activity attenuating the effects on triglycerides. However, no associations were observed with measures of cognitive and physical capability. Conclusion: Findings from middle-aged to older adults confirm associations between rs1260326 GCKR and triglycerides and glucose, suggest possible gene-environment interactions, but do not provide evidence that its relevance extends to cognitive and physical capability

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe