S100B levels are affected by older age but not by alcohol intoxication following mild traumatic brain injury

Introduction: Biomarkers of brain damage and head injury are potentially useful tools in the management of afflicted patients. Particularly S100B has received much attention and has been adapted into clinical guidelines. Alcohol intoxication and higher age (65 years and over) have been used as risk factors for serious complications following head injury. The effect of these factors on S100B levels has not been fully established in a relevant patient cohort. Methods: We prospectively included 621 adult patients with mild traumatic brain injury (TBI) and S100B sampling. Mild TBI was defined as Glasgow Come Scale 14-15 with loss of consciousness and/or amnesia, but without high-risk factors for intracranial complications. These patients would normally require CT scanning according to local and most international guidelines. S100B was sampled within 3 hours following trauma. Results: 280 patients (45%) were intoxicated by alcohol. Alcohol intoxication had no effect on S100B levels (p = 0.65) and the performance of S100B remained unchanged in these patients. 115 patients (22%) were 65 years or older with elevated S100B levels being more common in this group compared to patients under 65 (p = 0.029). Although the sensitivity of S100B was unchanged in older patients, the specificity was poorer. Conclusion: S100B can be used reliably in mild TBI patients with alcohol intoxication. The clinically utility of S100B in older patients may be limited by very poor specificity leading to only a small decrease in CT scanning

Geo-mapping of time trends in childhood caries risk - a method for assessment of preventive care

Background: Dental caries is unevenly distributed within populations with a higher burden in low socio-economy groups. Several attempts have been made to allocate resources to those that need them the most; there is a need for convenient approaches to population-based monitoring of caries risk over time. The aim of this study was to develop the geo-map concept, addressing time trends in caries risk, and demonstrate the novel approach by analyzing epidemiological data from preschool residents in the region of Halland, Sweden. Methods: The study population consisted of 9,973 (2006) and 10,927 (2010) children between 3 to 6 years of age (similar to 77% of the eligible population) from whom caries data were obtained. Reported dmfs >0 for a child was considered as the primary caries outcome. Each study individual was geo-coded with respect to his/her residence parish (66 parishes in the region). Smoothed caries risk geo-maps, along with corresponding statistical certainty geo-maps, were produced by using the free software Rapid Inquiry Facility and the ESRI (R) ArcGIS system. Parish-level socioeconomic data were available. Results: The overall proportion of caries-free (dmfs = 0) children improved from 84.0% in 2006 to 88.6% in 2010. The ratio of maximum and minimum (parish-level) smoothed relative risks (SmRRs) increased from 1.76/0.44 = 4.0 in 2006 to 2.37/0.33 = 7.2 in 2010, which indicated an increased geographical polarization of early childhood caries in the population. Eight parishes showed evidential, positional changes in caries risk between 2006 and 2010; their corresponding SmRRs and statistical certainty ranks changed markedly. No considerable parallel changes in parish-level socioeconomic characteristics were seen during the same time period. Conclusion: Geo-maps based on caries risk can be used to monitor changes in caries risk over time. Thus, geo-mapping offers a convenient tool for evaluating the effectiveness of tailored health promotion and preventive care in child populations

Influence of temperature during pyrolysis of Fe-alginate: Unraveling the pathway towards highly active Fe/C catalysts

Transition metals supported on carbons play an important role in catalysis and energy storage. By pyrolysis of metal alginate, highly active catalysts for the Fischer-Tropsch synthesis (FTS) can be produced. However, the evolution of the carbon (alginate) and transition metal (Fe3+) during pyrolysis remains largely unknown and was herein corroborated with several advanced in situ techniques. Initially, Fe3+ was reduced to Fe2+, while bound to alginate. FeO nucleated above 300 °C, destabilizing the alginate functional groups. Increasing temperatures improved carbonization of the carbon support, which facilitated reduction of FeO to α-Fe at 630 °C. Catalysts were produced by pyrolysis between 400 and 700 °C, where the highest FTS activity (612 µmolCO gFe−1 s−1) was achieved for the sample pyrolyzed at low temperature. Lower metal loading, due to less decomposition of alginate, moderated sintering and yielded larger catalytic surface areas. The results provide valuable knowledge for rational design of metal-alginate-based materials.publishedVersio

Evaluation of four novel genetic variants affecting hemoglobin A1c levels in a population-based type 2 diabetes cohort (the HUNT2 study)

<p>Abstract</p> <p>Background</p> <p>Chronic hyperglycemia confers increased risk for long-term diabetes-associated complications and repeated hemoglobin A1c (HbA1c) measures are a widely used marker for glycemic control in diabetes treatment and follow-up. A recent genome-wide association study revealed four genetic loci, which were associated with HbA1c levels in adults with type 1 diabetes. We aimed to evaluate the effect of these loci on glycemic control in type 2 diabetes.</p> <p>Methods</p> <p>We genotyped 1,486 subjects with type 2 diabetes from a Norwegian population-based cohort (HUNT2) for single-nucleotide polymorphisms (SNPs) located near the <it>BNC2</it>, <it>SORCS1</it>, <it>GSC </it>and <it>WDR72 </it>loci. Through regression models, we examined their effects on HbA1c and non-fasting glucose levels individually and in a combined genetic score model.</p> <p>Results</p> <p>No significant associations with HbA1c or glucose levels were found for the <it>SORCS1</it>, <it>BNC2</it>, <it>GSC </it>or <it>WDR72 </it>variants (all <it>P</it>-values > 0.05). Although the observed effects were non-significant and of much smaller magnitude than previously reported in type 1 diabetes, the <it>SORCS1 </it>risk variant showed a direction consistent with increased HbA1c and glucose levels, with an observed effect of 0.11% (<it>P </it>= 0.13) and 0.13 mmol/l (<it>P </it>= 0.43) increase per risk allele for HbA1c and glucose, respectively. In contrast, the <it>WDR72 </it>risk variant showed a borderline association with reduced HbA1c levels (<it>β </it>= -0.21, <it>P </it>= 0.06), and direction consistent with decreased glucose levels (<it>β </it>= -0.29, <it>P </it>= 0.29). The allele count model gave no evidence for a relationship between increasing number of risk alleles and increasing HbA1c levels (<it>β </it>= 0.04, <it>P </it>= 0.38).</p> <p>Conclusions</p> <p>The four recently reported SNPs affecting glycemic control in type 1 diabetes had no apparent effect on HbA1c in type 2 diabetes individually or by using a combined genetic score model. However, for the <it>SORCS1 </it>SNP, our findings do not rule out a possible relationship with HbA1c levels. Hence, further studies in other populations are needed to elucidate whether these novel sequence variants, especially rs1358030 near the <it>SORCS1 </it>locus, affect glycemic control in type 2 diabetes.</p

Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine’s polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100–12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of ‘pleiotropic’ variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

To dissect the genetic architecture of blood pressure and assess effects on target-organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure loci, of which 17 were novel and 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target-organ damage in multiple tissues, with minor effects in the kidney. Our findings expand current knowledge of blood pressure pathways and highlight tissues beyond the classic renal system in blood pressure regulation

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.Peer reviewe