To determine when a bone scintigraphy investigation is appropriate in patients with newly diagnosed prostate cancer (PCa).We retrospectively reviewed 703 newly diagnosed PCa patients who were referred for bone scintigraphy. The association between age, prostate specific antigen (PSA), Gleason score (GS) and bone scintigraphy result were investigated by series of crude or stratified analysis.Overall, 15.08% (106/703) patients had bone metastases. PSA and GS between positive bone scan group and negative bone scan group were significantly different, while age was not. The incidence of bone metastasis in patient with PSA < 20 ng/ml or GS < 8 was less than 10%, but increased dramatically with rising PSA and upgrading GS. In multivariate analysis, PSA ≥ 20 ng/ml ) and GS ≥ 8 ) were independently predictive of positive bone scan.Patients with PSA ≥ 20 ng/ml or GS ≥ 8 were in higher risk of bone metastasis, bone scintigraphy was recommended. But a bone scintigraphy is of limited value in PCa patients with PSA ≤ 20 ng/ml and GS ≤ 7. Prostate cancer (PCa) is one of the leading malignant diseases in male in Western countries but it is relatively uncommon in China. However, recent widespread use of serum prostate-specific antigen (PSA) made PCa a growing health problem in China, with a bulky increase in incidence . Early dHowever, routine use of bone scintigraphy staging is controversial. Subgroups of PCa patients with minimal risk for bone metastases can be safely excluded from bone scintigraphy. Numerous studies have demonstrated serum PSA, Gleason score (GS) and clinical tumor stage can be successfully used as indicators to predict which patients required bone scintigraphy for bone metastasis. According to a novel risk stratification tool proposed by Briganti et al., bone scThe current study was approved by ethics committee at our hospital. It included newly diagnosed PCa patients from January 2011 to December 2014 in the Department of Urology at the First Affiliated Hospital of Zhejiang University in China. The consecutive 703 records of the patients were reviewed retrospectively for bone scan images, PSA levels and GS in biopsy, after excluding those with prior 5-alpha reductase inhibitors medication or surgical treatment for benign prostatic hyperplasia. All the bone scans were carried out as a routine clinical evaluation to further stage the prostate cancer prior to prostatectomy, radiotherapy or androgen deprivation therapy. The PSA tests were within 30 days of bone scan, and the most recent bone scan results after prostate biopsy were included in the study to avoid bias caused by delayed diagnosis of bone metastases during tumor progession.The primary outcome measured was the presence of bone metastasis on bone scintigraphy. Whole body bone scintigraphy was performed using Tc-99-methylenediphosphonate (Tc 99 m MDP) and reviewed by 2 certified nuclear medicine physicians with extensive experience. The skeletal metastasis on bone scintigraphy was defined as either solitary or multiple asymmetric areas of increased tracer uptake presence, excluding tracer accumulations related to previous trauma and degenerative bone disease . Patientp value <0.05.The association between age, PSA, GS and bone scintigraphy result were investigated by series of crude or stratified analysis. Chi-square test was used for the comparison of proportions. Multivariate logistic regression analysis was used to compare the association between the independent variables (PSA and GS) and bone scan findings. To counter the skewness of the PSA values, a natural logarithm of PSA, lnPSA was used to generate a receiver operating characteristic (ROC) curve. All statistical analyses were performed using STATA, version 11.0  and the outcome was considered significant only when the p = 0.13; for stratified subgroups, p = 0.96). However, there was a significant difference in PSA and GS between two groups (p <0.05).We identified 703 men who had been diagnosed with PCa and fulfilled our inclusion criteria. The mean age of included 703 patients was 69.9 years. The biopsy GS ranged from 6 to 10 with a mean value of 7.4, and the median PSA value was 22.1 ng/ml . The proportion of patients with highly suspicious findings of bone metastases based on bone scan in our cohort was 15.08% (106/703). The comparative baseline characteristics of the two groups with different bone metastasis status were listed in Table p = 0.21). The sensitivity and specificity of PSA at a cut-off (88 ng/ml) with maximal Youden’s index was 67.9% and 89.1% respectively, while GS at a cut-off (≥8) was a little bit more sensitive (69.81%) and less specific (68.01%) for diagnosing bone metastasis.Moreover, we evulated the predictivity of PSA and GS on bone metastasis. Logistic regression with bony metastasis as the dependent variable and lnPSA and GS as independent variables was carried out to determine whether PSA and Gleason scores were additive. Both ln PSA and GS were statistically significant predictors of bone metastasis. However, receiver operating characteristic (ROC) curve for accuracy of ln PSA on bone metastasis has an area under curve (AUC) 0.829, which was superior over GS (AUC = 0.698) Fig. . The mulWe further stratified the PSA into 3 groups: ≤10 ng/ml, 10-20 ng/ml, and ≥20 ng/ml. The prevalence of bone metastases increased progressively with PSA level. Bone metastasis was detected in 6  patients with PSA below 10 ng/ml, and 9  with PSA between 10 and 20 ng/ml. For the patients with PSA above 20 ng/ml, there was a dramatic increase in bone metastasis proportion . Correspondingly, multivariate analysis revealed higher risk ) of bone metastasis in patient with PSA > 20 ng/ml , 8.17% (25/306) and 27.92% (74/265) patients were found with bone metastasis in GS < 7, =7 and >7 respectively (Table 32, 8.17%Numerous studies have been conducted to find out proper criteria for ruling out bone metastases in patients with low risk of bone metastasis. PSA and biopsy GS were the most common parameters for assessing the bone metastasis risk. Oesterling et al.  were theIn China, the incidence of PCa is rapidly increasing, ranking eighth among the most common cancers in the male population in 2008 . AlthougSeveral studies have been conducted to search for the optimal bone scan indications for Chinese PCa patients, but with heterogeneous results , 10, 18.Meanwhile, several other issues still should be addressed when interpreting our results. Firstly, the follow-up time is short, which would prevent delayed diagnosis of bone metastatases. Then, metastatic lesions detected by bone scan were not histologically confirmed, thus the inconsistence between bone scan and histology would biased our results.Based on our findings, patients with PSA ≥ 20 ng/ml or GS > 7 were in higher risk of bone metastasis, and bone scintigraphy was strongly recommended for these patients. For the patients with PSA < 20 ng/ml or GS < 8, bone scintigraphy is of limited value unless a curative treatment is contemplated. On the other hand, considering the presence of metastatic disease in these low risk patients, a more precise stratification tool should be established to safely eliminate bone scintigraphy.


High protein diets shift the faecal microbiota into a more unfavourable composition in obese humans. In lean dogs, higher protein consumption is accompanied with increased production of putrefactive fermentation products, whereas obese dogs have a different gut microbiota compared to lean dogs. Still, the impact of high dietary protein on gut microbiota in obese dogs remains unclear. The aim of this study was to investigate faecal microbial changes in lean and obese dogs in response to two different levels of dietary protein.Six healthy lean and six obese Beagles were fed a high protein diet (HP) and a low protein diet (LP) for 28 days each in a crossover design. Denaturing gradient gel electrophoresis and quantitative PCR were performed on faecal samples for microbial profiling. Plasma acylcarnitine and fermentation metabolites were measured.Lactobacillus and clostridial cluster I. The similarity coefficient of faecal microbiota between the two diets was smaller in obese dogs than in lean dogs.Dogs fed HP had higher concentrations of protein fermentation metabolites including faecal ammonia, isovalerate, isobutyrate, phenol, indole, serum indoxyl sulphate and plasma 3-OH isovalerylcarnitine compared to dogs fed LP, whereas no changes in faecal concentrations of acetate and butyrate were observed. The abundances of clostridial clusters IV and XIVa, covering the majority of butyrate-producing bacteria, and of the butyrate kinase gene, one of the terminal genes of the butyrate synthesis pathway were higher in dogs on HP compared to LP. Significant interactions between diet and body condition were found for the abundance of Firmicutes, High protein diet increased the abundance and activity of butyrate-producing bacteria in Beagles independent of the body condition. In addition, increasing dietary protein content had a greater overall impact on faecal microbiota in obese compared to lean dogs.The online version of this article (10.1186/s12917-017-1276-0) contains supplementary material, which is available to authorized users. Obesity is defined as an accumulation of excessive amounts of adipose tissue in the body, and it is the most common nutritional disorder in companion animals . The preClostridium perfringens and to reduce the abundance of clostridial cluster XIVa in dogs [Gut microbiota are mainly influenced by undigested dietary carbohydrates and protein. In dogs, there is increasing interest in feeding high protein diets, e.g. raw meat-based diets and weight management diets , 10. In  in dogs . HoweverThe interactions between gut microbiota and host metabolism are of great importance thus receiving increased attention more recently . Short-cButyrate-producing bacteria supply energy to the gut epithelium, regulate host cell responses, and therefore, are considered to exert health-promoting effects on the colon . The redThe aim of the present study was (1) to investigate the impact of dietary protein on faecal microbial profile and functionality especially focusing on butyrate-producing microbiota and the concomitant fermentation and host metabolic profile in dogs; and (2) to compare the response of lean and obese dogs to two different levels of dietary protein. We hypothesize that high protein diet alters the profile and functionality of faecal microbiota and thus induces associated changes to host metabolism. The diversity and composition of faecal microbiota were measured through DGGE and quantitative PCR. The functionality of faecal microbiota was assessed by quantifying faecal fermentation metabolites and terminal genes expression for butyrate synthesis. The effect on the host metabolism was evaluated through the concentrations of plasma acylcarnitines and serum indoxyl sulphate.This study was approved by the Ethical Committee of the Faculty of Veterinary Medicine, Ghent University, Belgium (EC 2011/056).Twelve healthy Beagles  with a mean age of 6.0 years (range 3.8–8.3 year) were included in this study. Six Beagles  were lean with a body condition score (BCS) of 4–5/9 and six Beagles  were obese with a BCS of 8–9/9 . ObesityTwo isocaloric experimental diets Table , a high The study was designed as a crossover with two 4-weeks periods. The first 3 weeks were an adaptation period and samples were taken in the fourth week. In the first period, three lean and three obese dogs were randomly selected and assigned to LP first and the other three lean and obese dogs first received HP. In the second period, diets were switched. Each dog was therefore assigned to one of four groups .g for 15 min at 4 °C and stored at −20 °C until assayed. On day 27 of each period, fresh faecal samples (± 10 g) were collected within 10 min after spontaneous voiding. An aliquot of ±2 g was put into a sterile plastic tube, frozen immediately on dry ice and stored at −80 °C for microbial analyses, and the rest was stored at −20 °C for chemical analyses.Leftover food was collected and weighed after each feeding. Body weight and BCS were measured weekly. After overnight fasting, blood samples were drawn from the jugular vein on day 27 of each period. Heparinized plasma and serum were obtained by centrifugation at 1620 Dry matter of diets and faecal samples was analyzed by drying to a constant weight at 103 °C , and crude ash was determined by combustion at 550 °C . Dietary crude protein was calculated from Kjeldahl nitrogen , ether extract was analyzed by the Soxhlet method  and crude fibre was determined by acid-alkali digestion . Nitrogen-free extract was calculated by subtracting crude ash, crude protein, crude fat, and crude fibre of the DM content. Total dietary fibre (TDF) and insoluble dietary fibre was measured with a Total Dietary Fibre Assay Kit (Sigma–Aldrich Co.), using procedures based on a combination of enzymatic and gravimetric methods . SolubleFaecal pH was measured with a portable pH meter (Hanna Instruments). Faecal ammonia was analysed by steam distillation and titration . Faecal i System (Abbott Diagnostics). Plasma acylcarnitine profile was determined according to Zytkovicz et al. [Serum cobalamin and folate concentrations were measured using commercially available ARCHITECT B12 and ARCHITECT Folate assays, respectively, on ARCHITECT z et al. . Serum iz et al. .Total bacterial DNA extractions from 500 mg faeces were performed according to Boon et al. . IsolateThe quantification of DNA by qPCR was performed with a C1000 Thermal Cycler (Bio-Rad). The amplification and detection were carried out in 96-well plates using SensiMixTM SYBR No-ROX Kit (Bioline Reagents Ltd). Each reaction was done in triplicate in 12 μL total reaction mixture using 2 μL of 50 ng of the DNA sample except for BK where 2 μL of undiluted DNA was used. All qPCR results were expressed as gene copies per g of fresh faeces. The primer sets used in this study are listed in Table Statistical analyses were performed with RStudio  using the gamm4 package for R (version 0.2–2).To test the effect of both dietary protein levels on composition and functionality of microbiota in lean and obese dogs and their effect on host metabolism, a general additive mixed model was used: Y = μ + dog + diet + BC + group + D × BC + ε, where μ is the overall mean, Dog a random effect; diet refers to HP or LP; BC is the body condition of the dogs (lean vs. obese), group is the order by which dogs received the diets (groups 1–4) and refers to the carry-over effect, D × BC is the interaction between diet and body condition and refers to the direction or size of the effect of dietary protein on lean and obese animals; and ε is the error term.Faecal concentration of valerate and p-cresol were only detected in three and one samples, respectively, and hence these parameters were not taken into consideration when performing the statistical analysis.For the D × BC, the nparcomp package of R was used (version 2.0) for post-hoc test by creating dummy variables and an own contrast matrix.P < 0.05.Statistical significance was accepted at P < 0.001).Two obese dogs (one from group 3 and one from group 4) were excluded from the study due to injuries not associated with the present study. Significant D × BC interactions were observed on BW and BCS, and post hoc analysis could only detect the differences between lean and obese dogs (Table P < 0.001), isovalerate (P < 0.001), isobutyrate (P < 0.001), phenol (P = 0.004) and indole (P = 0.002) were higher in dogs on HP compared to LP (Table P = 0.010) and isobutyrate (P = 0.009) concentrations were higher in obese than lean dogs. A significant D × BC interaction was observed on faecal propionate (P = 0.017), however, a carry-over effect was observed for faecal propionate . No differences in faecal concentrations of acetate, butyrate, and total SCFA were observed between diets or BC  (Table P = 0.012) for dogs fed HP compared to LP (Table Serum indoxyl sulphate concentration was higher in dogs fed HP than LP (7) Table . Within LP Table .Table 4BP = 0.050)  Fig. .Fig. 1DiP = 0.025), XIVa (P = 0.001) and of the BK gene (P = 0.019) were observed when dogs were fed HP compared to LP (Table P = 0.007), Lactobacillus (P = 0.017) and clostridial cluster I (P = 0.022). Compared to LP, HP increased the abundance of Firmicutes in lean dogs (P < 0.05), while no changes were observed in obese dogs. Clostridial cluster I was more abundant in lean dogs when fed HP than LP (P < 0.05), whereas post-hoc test did not reveal any differences between the four groups for Lactobacillus. No effects of diet or BC were found for faecal concentrations of total bacteria, Bacteroidetes, Enterobacteriaceae and gene express of BCoAT.Higher faecal abundances of clostridial cluster IV P = 0.02, XIVa  can switch from butyrate to propionate production [Understanding how dietary components alter the composition and activity of gut microbiota direct nutritional interventions and disease prevention strategies. The present study has demonstrated an impact of dietary protein content on both composition and activity of faecal microbiota in dogs. In particular, the major butyrate producing bacterial groups,  harvest , 6; and  harvest . In addi harvest . Therefooduction , 39. Wheoduction . PossiblThe accumulation of butyrate-producing bacteria is often associated with intake of fibre. In this study, TDF in HP was two-fold higher compared to LP. Generally, TDF is comprised of plant fibre which in this study was provided by brewer’s rice, rice meal, and beet pulp Table , with TDAlthough TDF was higher, soluble fibre was much lower in HP compared to LP. In contrast to insoluble fiber which cannot be fermented, soluble fibre is readily fermentable . Thus, iC. perfringens dominated in many Carnivores  and non-carnivorous Carnivora (e.g. red panda and giant panda) [C. perfringens was not measured, but it is the major component of clostridial cluster I. The clostridial cluster I was increased when dogs were fed HP, however, it still needs to be confirmed if increased clostridial cluster I abundance is due to increases in C. perfringens numbers. Nevertheless, other canine studies have also shown that high protein diets promoted the growth of C. perfringens [As dogs may use a different pathway for butyrate production, it raises the question whether dogs possess the same predominant butyrate producers as humans . The butyrate kinase gene pathway is linked to t panda) . In thisfringens . Thus, cIn contrast to our results, another study reported high dietary protein decreased the abundance of clostridial cluster XIVa in dogs . This coLactobacillus and clostridial cluster I in lean dogs whereas no changes were observed obese dogs  and branched-chain fatty acids  compared to dogs consuming LP. These results are in agreement with previous studies in dogs fed high protein diets , 51. In ein diet . Therefoein diet .Lactobacillus and clostridial cluster I) that are body condition dependent. Further, faecal microbiota in obese dogs seemed to be less stable compared to that of lean dogs. Together, the results of the present study suggest that diet composition per se had an important effect on the faecal microbiota, however, body condition also affected microbiota composition in dogs. This should be taken into consideration in future nutritional interventions and disease prevention.In our study, high protein diet promoted the growth of butyrate-producing bacteria which includes clostridial clusters I, IV and XIVa in dogs independent of body condition, and importantly this butyrate synthesis is suggested to relate to the BK pathway as compared to BCoAT pathway in humans. Thus, the different metabolic pathway used for butyrate production provides valuable information for modulation of gut microbiota and their fermentation metabolites in dogs. In addition, HP induced several bacterial changes (including Firmicutes,


Iron deficiency is the most common micronutrient deficiency worldwide and children are at an increased risk due to the rapid growth occurring during early life. The developing brain is highly dynamic, requires iron for proper function, and is thus vulnerable to inadequate iron supplies. Iron deficiency early in life results in altered myelination, neurotransmitter synthesis, neuron morphology, and later-life cognitive function. However, it remains unclear if dietary iron repletion after a period of iron deficiency can recover structural deficits in the brain.n = 14; 23.5 mg Fe/L milk replacer) or an iron-deficient diet  for phase 1 of the study, from postnatal day (PND) 2 until 32. Twenty pigs (n = 10/diet from phase 1) were used in phase 2 of the study from PND 33 to 61, all pigs were provided a common iron sufficient diet, regardless of their early-life dietary iron status. All pigs remaining in the study were subjected to magnetic resonance imaging (MRI) at PND 32 and again at PND 61 using structural imaging sequences and diffusion tensor imaging (DTI) to assess volumetric and microstructural brain development, respectively. Data were analyzed using a two-way ANOVA to assess the main and interactive effects of early-life iron status and time.Twenty-eight male pigs were provided either a control diet  in the cerebellum, olfactory bulb, and putamen-globus pallidus. Main effects of early-life iron status, regardless of imaging time point, were noted for decreased relative volumes of the left hippocampus, right hippocampus, thalamus, and increased relative white matter volume in ID pigs compared with CONT pigs. DTI indicated interactive effects for fractional anisotropy (FA) in the whole brain, left cortex, and right cortex. Main effects of early-life iron status, regardless of imaging time point, were observed for decreased FA values in the caudate, cerebellum, and internal capsule in ID pigs compared with CONT pigs. All comparisons described above were significant at Results from this study indicate that dietary iron repletion is able to compensate for reduced absolute brain volumes early in life; however, microstructural changes and altered relative brain volumes persist despite iron repletion. Iron is an essential micronutrient that is important for many physiological processes. Despite its importance in the body, iron deficiency remains the most pervasive micronutrient deficiency worldwide , 2. MoreIron is known to be important for neuronal growth and development. Research in 4-week-old pigs suggests that postnatal iron deficiency results in altered hippocampal gene expression for axon growth and guidance, as well as genes responsible for blood brain barrier integrity, angiogenesis, and hypoxia . MoreoveThe presence of iron in the brain is also required for proper myelination . OligodeTo date, most studies focus on the influence of iron deficiency on hippocampal development; however, high concentrations of iron are found throughout the brain. Therefore, our study sought to characterize the global influence of iron deficiency on brain development and to assess if particular regions are more vulnerable at specific time points. Additionally, this study aimed to non-invasively characterize changes in brain development that occurred as a result of dietary iron deficiency up to 32 days of age, and if these changes could be corrected by 61 days of age, after a period of dietary iron repletion. In doing so, we used the pig because it is a superior model for assessing the influence of nutrition on neurodevelopment  and is aAll animal and experimental procedures were in accordance with the National Research Council Guide for the Care and Use of Laboratory Animals and approved by the University of Illinois at Urbana-Champaign Institutional Animal Care and Use Committee. Twenty-eight, naturally farrowed, intact male pigs were obtained from Carthage Veterinary Services and transferred to the University of Illinois Piglet Nutrition and Cognition Laboratory (PNCL) at PND 2. Per standard agricultural protocol, pigs were provided an intramuscular injection of a prophylactic antibiotic  within 24 h of birth. Contrary to typical agricultural procedures, pigs on this study were not provided an iron dextran shot, as iron is the nutrient of interest in the experimental diet. Recent pig studies observed hippocampal transcriptome changes  after irL × W × H), which were composed of three acrylic walls, one stainless steel wall, and vinyl-coated metal flooring. Each caging unit was designed for pigs to see, hear, and smell, but not touch neighboring pigs. Pigs were allowed to physically interact with one another for approximately 15 min each day, and each pig was provided a toy for enrichment in their home-cage. Pig rearing environment was maintained on a 12 h light and dark cycle from 0800 to 2000 hours, with ambient temperature set at 26.6°C for the first 21 days of the study and gradually lowered to 22°C during the last 7 days of phase 1.For phase 1 of this study, 28 pigs were housed individually in custom pig rearing units  immediately following magnetic resonance imaging (MRI) at PND 32 or 33 and housed there until the end of the study. At VMRF, pigs were housed in floor pens (1.5 sq meters) and the rearing environment was maintained on a 12 h light and dark cycle from 0800 to 2000 hours, with ambient temperature set at approximately 22°C.N = 28, n = 14/phase 1 diet) were provided one of the two dietary treatments with varying iron contents. The control diet (CONT) was formulated to meet all of the nutrient requirements of the growing pig and was formulated to contain 117.5 mg Fe/kg milk replacer powder. The ID diet was based off of the CONT diet; however, iron was only formulated to be supplemented at 7.8 mg Fe/kg milk replacer powder. Additionally, both diets were formulated to contain arachidonic acid (ARA) (2.08 g ARA/kg milk replacer powder) and docosahexaenoic acid (DHA) (1.04 g DHA/kg milk replacer powder). Milk replacer was reconstituted fresh daily with 200 g of milk replacer powder per 800 g water. Thus, formulated iron concentrations in reconstituted pig milk replacers were CONT (23.5 mg Fe/L milk replacer) and ID (1.56 mg Fe/L milk replacer). All pigs were provided ad libitum access to liquid diets from PND 2 until PND 32 or 33.For phase 1 of this study, pigs  were weaned onto the same series of iron-adequate diets, regardless of their phase 1 dietary iron status. Pigs were provided ad libitum access to standard complex diets  and standard agricultural feeding practices were followed by sequentially switching from stage 1 diets, to stage 2 diets, to stage 3 diets, on PND 32, 41, and 50, respectively. During this phase of the study, all diets were formulated to meet all nutrient requirements of growing pigs  were subjected to neuroimaging procedures and for phase 2, 20 pigs (n = 10 per phase 1 diet) were subjected to neuroimaging procedures. Imaging procedures were performed using a Siemens MAGNETOM Trio 3 T MRI, with a custom pig-specific 8-channel head coil at PND 32 and a human 8-channel head coil at PND 61. The pig neuroimaging protocol included three magnetization prepared rapid gradient-echo (MPRAGE) sequences and diffusion tensor imaging (DTI) to assess brain macrostructure and microstructure. Upon arrival to the imaging facility, anesthesia was induced using an intramuscular injection of telazol  administered at 0.07 mL/kg BW, and maintained with inhalation of isoflurane . Pigs were immobilized during all MRI procedures. Visual observation of each pig’s well-being, as well as observations of heart rate, PO2 and percent of isoflurane were recorded every 5 min during the procedure. Total scan time for each pig was approximately 60 min. Upon completion of the scan, pig respiration and heart rate were monitored every 15 min until complete recovery from anesthesia. Imaging techniques are briefly described below.All pigs remaining in each phase underwent MRI procedures on PND 32 or 33 for phase 1 and again at PND 61 or 62 for phase 2, at the Beckman Institute Biomedical Imaging Center. For phase 1, 28 pigs (1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence was used to obtain anatomic images of the pig brain, with a 0.7 mm isotropic voxel size. The following sequence-specific parameters were used to acquire T1-weighted MPRAGE data: repetition time (TR) = 1,900 ms; echo time (TE) = 2.49 ms; inversion time (TI) = 900 ms; 224 slices; field of view (FOV) = 180 mm; flip angle = 9°. Pig brains were manually extracted as previously described  images were assessed in FSL for fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) using the previously described methods .All researchers involved in this study  remained blinded to dietary treatment identity until final data analyses had been completed. Data were analyzed by using the MIXED procedure of SAS 9.4 . All longitudinal measures reported herein were obtained from pigs on both PND 32 and 61, thus all data were analyzed using a two-way repeated measures ANOVA . Interactive effects were defined as an interaction between diet (CONT vs ID) and MRI day (PND 32 vs 61). Number of animals per treatment group was based on a power analysis using variability estimates from previous studies to detect differences with sufficient power of 80% at a significance of 0.05. Data were analyzed for outliers  and outliers were removed prior to statistical analysis. Significance was accepted at Pig health measures were assessed at the end of each phase, and all outcomes are presented as means ± SD. At the end of phase 1, body weights of ID and CONT pigs were 6.24 ± 1.26 and 10.78 ± 1.45 kg, respectively, and at the end of phase 2 body weights of ID and CONT pigs were 17.05 ± 2.62 and 24.95 ± 2.40 kg, respectively. Average daily liquid milk intake for ID and CONT pigs during phase 1 was 0.93 ± 0.18 and 1.74 ± 0.24 L, respectively, and for phase 2, the average daily feed intake (as-is basis) for ID and CONT pigs was 0.73 ± 0.10 and 0.92 ± 0.13 kg, respectively. Hematocrit concentrations for ID and CONT pigs at the end of phase 1 were 14 ± 3 and 32 ± 3% packed cell volume (PCV), respectively, while at the end of phase 2, hematocrit concentrations in ID and CONT pigs were 35 ± 3 and 36 ± 2%, respectively. Hemoglobin concentrations for ID and CONT pigs at the end of phase 1 were 4.8 ± 0.9 and 10.86 ± 1.1 g/dL, respectively, while at the end of phase 2, hemoglobin concentrations for ID and CONT pigs were 11.85 ± 1.15 and 12.13 g/dL, respectively.P = 0.02), Figure P < 0.01) brain volumes compared with CONT pigs, and by PND 61 brain volumes were larger (P < 0.001) than volumes observed at PND 32, but were not different (P = 0.12) between ID and CONT pigs. Interactive effects were also observed for absolute volumes of the caudate (P = 0.01), gray matter (P < 0.01), hypothalamus (P = 0.01), internal capsule (P = 0.03), lateral ventricle (P = 0.05), olfactory bulb (P = 0.01), putamen-globus pallidus (P < 0.01), and right cortex (P = 0.02), Table S1 in Supplementary Material. Notably, these interactions are likely confounded by the difference in pig whole brain volumes, thus relative brain volumes are analyzed below. Also of note, a main effect of MRI day (P < 0.001) was observed for absolute volume of every brain region, indicating that all brain regions increased in size from PND 32 to 61, regardless of dietary treatment.An interactive effect of diet and MRI day was observed for whole brain volumes (P < 0.001), olfactory bulb (P = 0.03), and putamen-globus pallidus (P = 0.01), Figure P < 0.01) in size from PND 32 to 61 in the CONT pigs and a decrease (P = 0.01) in relative size from PND 32 to 61 in the ID pigs. Analysis of the olfactory bulb indicated no change (P = 0.28) in relative size from PND 32 to 61 in the CONT pigs, whereas ID pigs exhibited an increase (P < 0.001) in relative size of the olfactory bulb from PND 32 to 61. The putamen-globus pallidus relative size decreased (P < 0.01) from PND 32 to 61 in the CONT pigs, but did not change (P = 0.60) during the same timeframe in ID pigs. Main effects of diet were also observed for relative volumes in the left hippocampus (P < 0.01), right hippocampus (P < 0.01), thalamus (P = 0.04), and white matter (P = 0.03), Figure P = 0.05), gray matter (P < 0.001), olfactory bulb (P < 0.001), and right cortex (P = 0.01), Table S2 in Supplementary Material.Due to observed differences in absolute whole brain volumes, all regions of interest were assessed relative to whole brain volume. Relative brain volume measures were generated by dividing the absolute volume for an individual region by the whole brain volume within subject and multiplying by 100, resulting in a percent of total brain volume measure. Interactive effects of diet and MRI day were observed for relative brain volumes in the cerebellum , right cortex (P = 0.05), and DTI-generated whole brain (P < 0.001), Figure P = 0.03) FA values compared with CONT pigs at PND 32. By PND 61, left cortex FA values in both dietary treatments were increased (P < 0.001) compared with FA values at PND 32, but FA values for ID pigs remained lower (P < 0.001) than CONT pigs at PND 61. FA values in the right cortex and whole brain indicated no difference (P = 0.36) between ID and CONT pigs at PND 32, but by PND 61 ID pigs exhibited decreased (P < 0.01) FA values compared with CONT pigs. Main effects of diet were observed for FA values in the caudate (P = 0.04), cerebellum (P < 0.01), and internal capsule (P = 0.04), in all instances ID pigs exhibited decreased FA values compared with CONT pigs, Figure P < 0.001), corpus callosum (P < 0.001), and internal capsule (P < 0.001), Table P < 0.001), Figure S1 in Supplementary Material.Diffusion measures of FA indicated interactive effects of diet and MRI day in the left cortex , Table S3 in Supplementary Material, right cortex (P < 0.001), and whole brain (P < 0.01), Figure P < 0.05), except for the left hippocampus (P = 0.23), Table S3 in Supplementary Material. No interactive effects of diet and MRI day were observed for measures of MD. Main effects of diet were observed for MD in the right cortex (P < 0.01) and whole brain (P < 0.01), Figure P < 0.05), except for the cerebellum (P = 0.11) and left hippocampus (P = 0.11), Table S4 in Supplementary Material. No interactive effects of diet and MRI day were observed for measures of AD. Main effects of diet were observed for AD in the right cortex (P = 0.01) and whole brain (P < 0.01), Figure P < 0.05), except for the cerebellum (P = 0.66), corpus callosum (P = 0.25), and left hippocampus (P = 0.05), Table S5 in Supplementary Material.No interactive effects of diet and MRI day were observed for measures of RD. Main effects of diet were observed for RD in the left cortex (Iron deficiency influences structural brain development, which results in lasting functional deficits in animals and humans alike , 28, 29.Analysis of whole brain volumes revealed smaller brains in dietary ID pigs at PND 32 when compared with CONT pigs. Interestingly, after a 30-day period of dietary iron repletion, ID and CONT pig brain volumes were not different, thus suggesting compensatory brain volume growth in the ID pig brain. It has previously been reported that iron deficiency influences expression of genes related to axon guidance and expansion in the 4-week-old pig hippocampus . Thus, iDue to the differences in absolute brain volumes between dietary treatment groups, individual brain regions were assessed as a percent of total brain volume. Our analyses indicated volumes of the cerebellum, olfactory bulb, and putamen-globus pallidus were influenced by dietary iron status over time. Interestingly, cerebellar volumes were not different at PND 32, but by PND 61 dietary ID pigs exhibited smaller relative cerebellar volumes. Iron deficiency has been shown to delay cerebellar myelination in rodents , which mInterestingly, relative volumes of the olfactory bulb were increased, relative to CONT, in ID pigs at PND 61, corroborating findings from a recent pig study, which reported increased gray matter in the olfactory bulb of ID pigs at 4 weeks of age . It remaRegardless of age, pigs on the ID diet exhibited decreased relative volumes of the left and right hippocampi, as well as the thalamus. The observed effects in the hippocampi support previous work, which indicated prenatal, postnatal, and perinatal iron deficiency in rodents resulted in reduced hippocampal subfield volumes . In a seResults from our study also indicate that relative volumes of white matter were increased in dietary ID pigs. At first pass, this finding might seem opposite of what has been reported, as iron deficiency is known to reduce myelination, which certainly contributes to white matter volume and maturation. However, when focusing on the absolute volumes of white matter in the CONT and ID pigs, we observed no differences between the groups at PND 32 or 61, yet whole brain volumes changed over time. Moreover, the absolute volume of gray matter increased to a larger extent in the ID pigs from PND 32 to 61 than it did in CONT pigs, suggesting that the observed whole brain compensatory growth is likely due to disproportionate growth in gray matter-rich regions. Thus, the larger relative white matter volumes in ID pigs may be driven by smaller absolute brain volumes at PND 32. Our results are in contrast to a previous pig study, which indicated region-specific decreases in white matter concentrations and FA measures of ID pigs . HoweverDiffusion tensor measures provide non-invasive measures of microstructural water movement and are related to axonal growth and myelinating events . FA is aDietary iron deficiency also appeared to decrease FA measures in the cerebellum, caudate, and internal capsule, regardless of imaging time point. Indications of delayed myelination in the cerebellum of our pigs support observations in rodents, which suggest decreased myelination due to dietary iron deficiency . BecauseAnalysis of whole brain and right cortex MD, AD, and RD measures indicated increased rates of diffusion in the ID pigs compared with the CONT pigs, regardless of imaging time point. Leyshon and colleagues previously reported increased MD, AD, and RD in 4-week-old pigs; however, their observations were located in the hippocampi and thalamus , rather A notable strength of this study is the implementation of a longitudinal dietary intervention in which brain development was measured at two time points. To our knowledge, no other neuroimaging study has comprehensively assessed changes in volumetric and diffusion measures over time in the pig. In doing so, this study provides insight into the growth and development of specific brain regions, and can therefore be used as normative data to which other studies can be compared when using the pig as a biomedical model. Analysis of absolute brain volumes and regions within the brain indicated all brain regions increased in size from PND 32 to 61. When assessing regional brain growth relative to whole brain volumes, the proportion of gray matter in the brain decreased and the olfactory bulb exhibited increases as a proportion of total brain volume. Assessment of diffusion tensor measures indicated region-specific FA values increased from PND 32 to 61, which supports observations in human neurodevelopment , 36. FutUsing multiple imaging techniques, we were able to non-invasively characterize changes in brain development related to early-life dietary iron deficiency followed by dietary iron repletion. Measures of total brain volume at PND 61 suggest dietary iron repletion is able to compensate for delayed whole brain growth at PND 32; however, diffusion FA measures suggest dietary iron repletion was not able to correct myelination and tissue organization in specific brain regions by PND 61. Many previous studies have shown structural changes in brain development after a period of iron deficiency, and our study expands upon this work by identifying aspects of brain development, which do not appear to benefit from iron repletion later in life. Thus, these data highlight the importance of the critical window during which adequate dietary iron is necessary to ensure proper brain growth trajectories are established. Moreover, this research suggests possible heightened sensitivity of myelination to dietary iron status, and future work should seek to expand upon the mechanisms through which these changes develop and persist despite iron repletion.All animal and experimental procedures were in accordance with the National Research Council Guide for the Care and Use of Laboratory Animals and approved by the University of Illinois at Urbana-Champaign Institutional Animal Care and Use Committee.AM, LK, and RD were involved in study design and implementation. All authors were involved in data acquisition, analysis, and interpretation. All authors read and approved the final version of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MI) requires clarity. We hypothesized that among patients referred for diagnostic coronary angiography without acute MI, hsTnI concentrations would correlate with prevalent coronary artery disease (CAD) and predict incident cardiovascular events and mortality.The meaning of high‐sensitivity troponin I (hsTnI) concentrations in patients without acute myocardial infarction  and incident MI , cardiovascular death , and all‐cause death . In those with >70% coronary stenosis, hsTnI ≥99th percentile independently predicted incident MI , cardiovascular mortality , and the composite end point of MI and all‐cause death . In participants with coronary stenosis <70%, hsTnI ≥99th percentile even more strongly predicted incident MI , cardiovascular mortality , and the composite end point of MI and all‐cause death .We measured hsTnI using a single‐molecule counting assay  in samples from 991 patients obtained at the time of angiography. Concentrations of hsTnI were assessed relative to the severity of MI and undergoing diagnostic coronary angiography, hsTnI concentrations were associated with higher prevalence of CAD and predicted incident MI, cardiovascular death, and all‐cause death.In a large prospective cohort of patients who were free of prevalent URL: http://www.clinicaltrials.gov. Unique identifier: NCT00842868. In patients undergoing diagnostic coronary angiography, high‐sensitivity troponin I concentrations were associated with higher prevalence of coronary artery disease and independently predicted cardiovascular events in patients with obstructive and nonobstructive coronary artery disease.These data highlight the diagnostic and prognostic utility of high‐sensitivity troponin I in patients with coronary artery disease for identifying patients who may benefit from more aggressive medical therapy to reduce atherothrombotic risk.Cardiovascular disease is a major modern health concern; it is projected that by 2030, >23.3 million people globally will die annually from acute myocardial infarction (MI), stroke, and other cardiovascular diseases.In recent years, biomarkers have been identified as important tools for prognostication in patients with CAD beyond that with conventional risk factors, and most data focused on the value of biomarkers for those with acute syndromes such as MI.http://Clinical Trials.Gov identifier NCT00842868).Given these open questions, we examined the diagnostic and prognostic utility of hsTnI in 991 patients without acute MI referred for diagnostic coronary angiography for various indications who were enrolled in the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) study  were noted at the time of the procedure. As previously reported, patients were mainly referred for evaluation of CAD Table .13Immediately before angiography angiographic procedures, 15 mL of blood was obtained through a centrally placed vascular access sheath. All samples were promptly centrifuged for 10 minutes. Samples were aliquoted on ice and then immediately placed in a −80°C freezer for permanent storage.Using aliquots that had undergone their first thaw, concentrations of hsTnI were measured using a single‐molecule counting assay  performed on an Erenna platform. This very highly sensitive assay has a limit of detection of 0.5 ng/L and a 99th percentile reference limit of 6 ng/L in apparently healthy individuals.Incident MI, heart failure (HF), cardiovascular death, and all‐cause mortality were recorded over a median follow‐up period of 1486 days . The mean follow‐up period was 1338 days (SD: 367 days). A detailed definition of each end point for CASABLANCA has been described previously.For the purposes of the present analysis, obstructive CAD was defined as ≥70% stenosis in at least 1 vessel, and nonobstructive CAD was defined as stenosis >0% but <70%.2 or Fisher exact test for dichotomous variables, the Cochran–Mantel–Haenszel test for categorical variables, and the t test or Kruskal–Wallis test for continuous variables. The following covariates were included in all cross‐sectional and prognostic analyses in addition to hsTnl: age, sex, heart rate, systolic blood pressure, diastolic blood pressure, atrial fibrillation or flutter, hypertension, CAD, chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, medications used , sodium, glomerular filtration rate, glucose, hemoglobin, myeloperoxidase, NT‐proBNP , and cystatin C.Baseline characteristics between those with hsTnl >99th versus <99th percentile were compared using the χLogistic regression was first used to assess the cross‐sectional relationship between obstructive CAD and the above set of individual predictors. Covariates significant at 0.10 level were then included into a stepwise logistic regression, with hsTnl, age, and sex forced into the model.Cox proportional hazards models were used to predict the following outcomes in all patients, in the subset of patients with obstructive CAD, and in the subset of patients with nonobstructive CAD, using the set of covariates listed above: incident MI, incident HF, cardiovascular death, all‐cause death, and the combined outcomes of incident MI, HF, and all‐cause death; incident MI and cardiovascular death; and incident MI and all‐cause death. Again, covariates significant at 0.10 level were then included in a stepwise Cox proportional hazards model, with hsTnl, age, and sex forced in. The analyses that included MI as an outcome were additionally adjusted for previous MI; all analyses that included HF as an outcome were additionally adjusted for previous HF.P value of <0.05 was considered statistically significant. All analyses were performed using the SAS version 9.4 (SAS Institute).Kaplan–Meier curves were compared using the log‐rank test. In all statistical analyses, a 2‐tailed The median hsTnI concentration in the study sample was 4.19 ng/L Table . ElevatiBaseline characteristics of study participants, dichotomized as a function of elevated hsTnI ≥99th percentile, are detailed in Table P=0.006).Of the 991 patients included in this study, 619 patients were found to have obstructive CAD, 226 patients had nonobstructive CAD, and the remaining 146 patients either had no evidence of coronary disease or were missing information. Patients with an hsTnI ≥99th percentile had a higher degree and number of stenotic coronary lesions , yielding a C statistic of 0.82 .In multivariate analysis, dichotomized hsTnI (≥99th percentile) independently predicted obstructive CAD . Similar patterns were seen relative to cardiovascular death , incident HF , cardiovascular death , and all‐cause death . In addition, an elevated hsTnI concentration independently predicted composite end points of incident MI, HF, and all‐cause mortality ; incident MI and cardiovascular death , and incident MI and all‐cause death  compared with men  and incident HF in men  compared with women . Similarly, hsTn ≥99th percentile predicted incident MI  and cardiovascular death , and the composite end point of MI and all‐cause death . In patients with nonobstructive CAD (n=226), hsTnI ≥99th percentile even more strongly predicted incident MI , and the composite end point of MI and all‐cause death  and cardiovascular mortality . This is a cohort that is often overlooked in clinical practice when discovered to have nonobstructive disease; however, over a 1486‐day follow‐up period, our study found that the cumulative incidence of MI was actually higher in patients with nonobstructive disease and elevated hsTnI ≥99th percentile (23%) compared with patients with obstructive CAD and an hsTnI concentration <99th percentile (12%); the incidence of incident MI in patients with obstructive CAD and hsTnI ≥99th percentile was 30%. Further studies should evaluate the utility of hsTnI to trigger application of more aggressive medical therapies to reduce atherothrombotic risk  in this cohort.Notably, we found hsTn concentrations ≥99th percentile to be a robust independent predictor of all‐cause mortality, cardiovascular death, incident MI, and incident HF. Interestingly, in patients with nonobstructive CAD, we found that an elevated hsTnI concentration was a powerful predictor of incident MI ; however, it is possible that some patients would have been recognized as having MI if adjudicated with the hsTnI under study in the present analysis. This issue of equipoise is an inevitable consequence of higher sensitivity, and it is fair to expect such limitations to affect any study examining hsTn methods in patients with ischemic heart disease. Concentrations of hsTnI were measured at a single point in time at the time of angiography and may not reflect levels at future time periods. It is necessary to note the hsTnI assay used in this analysis is time‐consuming compared with processing of conventional troponin assays; more efficient versions of such highly sensitive methods would be needed to translate these results for efficient clinical use. Our results need further validation and should not be extrapolated to the general population without suspected CAD because these patients were not included in our study. Whether aggressive treatment strategies in patients with an elevated hsTn concentration would modify their risk of future cardiovascular events remains unknown and requires further investigation.myocardial injury that lack therapeutic imperatives. Our results suggest even slight elevation in hsTnI concentrations predicted the presence of obstructive CAD and independently predicted major cardiovascular events. Our results add novel and substantial clinical context to hsTnI concentrations in a commonly encountered patient population (those sent for angiography) while setting the stage for analyses of how hsTn may be leveraged in clinical trials of therapeutic intervention for such patients.With assays of increasing sensitivity coming into more widespread use, clinicians face detection of abnormal values in patients not thought to have acute MI. This has led to increased use of nonspecific terms such as HsTnI is a powerful predictor of the presence of CAD in patients sent for angiography not thought to have acute MI. In addition, hsTnI predicts incident cardiovascular events in patients with either obstructive or nonobstructive CAD. Our results will help to clarify the meaning of elevated hsTn in patients without MI. Further studies should evaluate the utility of hsTnI to trigger application of more aggressive medical therapies to reduce atherothrombotic risk  in this cohort.Dr Ibrahim is supported by the Dennis and Marilyn Barry Fellowship in cardiology research. Dr Januzzi is supported in part by the Hutter Family Professorship in Cardiology. Dr Gaggin is supported in part by the Ruth and James Clark Fund for Cardiac Research Innovation. This work was supported by a grant from Singulex.Dr Januzzi has received grant support from Roche Diagnostics, Siemens, Cleveland Heart Labs and Prevencio, consulting income from Roche Diagnostics, Critical Diagnostics, Philips, and Novartis, and participates in clinical end point committees/data safety monitoring boards for Abbvie, Bayer, Pfizer, Novartis, Amgen, Janssen, and Boehringer Ingelheim. Dr Gaggin has received grant support from Roche and Portola; consulting income from Roche Diagnostics, American Regent, Amgen, Boston Heart Diagnostics and Critical Diagnostics; research payments for clinical end point committees for EchoSense. The remaining authors have nothing to disclose. All authors have approved the final article.Table S1. Reason for Referral for Angiography for the 991 Patients Included in the StudyTable S2. Baseline High‐Sensitivity Troponin I Concentrations Among All Patients and Subgroups Stratified by Coronary Artery Disease StatusTable S3. Multivariate Logistic Regression on Obstructive Coronary Artery Disease (≥70% Stenosis) With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S4. Multivariate Logistic Regression on Obstructive Coronary Artery Disease (≥70% Stenosis) With Age, Sex, Continuous High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S5. Multivariate Logistic Regression on Obstructive Coronary Artery Disease (≥70% stenosis), With Age, Sex and Natural‐Log Transformed Continuous Singulex High‐Sensitivity Troponin and Baseline Biomarkers* and Other Covariates** Table S6. Multivariate Logistic Regression on Obstructive Coronary Artery Disease (≥70% Stenosis) With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers and Other Baseline Covariates Table S7. Negative Predictive Value for Obstructive Coronary Artery Disease by Concentration of High‐Sensitivity Troponin ITable S8. Multivariate Cox Regression on Incident Myocardial Infarction With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S9. Multivariate Cox Regression on Incident Myocardial Infarction With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S10. Multivariate Cox Regression on Incident Heart Failure With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S11. Multivariate Cox Regression on Incident Heart Failure With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S12. Multivariate Cox Regression on Cardiovascular Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers and Other Baseline Covariates Table S13. Multivariate Cox Regression on Cardiovascular Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S14. Multivariate Cox Regression on All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S15. Multivariate Cox Regression on All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S16. Multivariate Cox Regression on Composite End Point of Myocardial Infarction, Heart Failure, and All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers and Other Baseline Covariates Table S17. Multivariate Cox Regression on Composite End Point of Myocardial Infarction, Heart Failure, and All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S18. Multivariate Cox Regression on Composite End Point of Myocardial Infarction and Cardiovascular Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S19. Multivariate Cox Regression on Composite End Point of Myocardial Infarction and Cardiovascular Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S20. Multivariate Cox Regression on Composite End Point of Myocardial Infarction and All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S21. Multivariate Cox Regression on Composite End Point of Myocardial Infarction and All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Stepwise Selection on Baseline Biomarkers* and Other Baseline Covariates** Table S22. Multivariate Cox Proportional Hazards Regression on Incident Myocardial Infarction With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S23. Multivariate Cox Proportional Hazards Regression on Incident Myocardial Infarction With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S24. Multivariate Cox Proportional Hazards Regression on Incident Congestive Heart Failure With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Forced Into the Model Table S25. Multivariate Cox Proportional Hazards Regression on Incident Congestive Heart Failure With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S26. Multivariate Cox Proportional Hazards Regression on Cardiovascular Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S27. Multivariate Cox Proportional Hazards Regression on Cardiovascular Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S28. Multivariate Cox Proportional Hazards Regression on All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S29. Multivariate Cox Proportional Hazards Regression on All‐Cause Death With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S30. Multivariate Cox Proportional Hazards Regression on Incident Myocardial Infarction With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S31. Multivariate Cox Proportional Hazards Regression on Cardiovascular Mortality With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S32. Multivariate Cox Proportional Hazards Regression on Incident Congestive Heart Failure With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S33. Multivariate Cox Proportional Hazards Regression on All‐Cause Mortality With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S34. Multivariate Cox Proportional Hazards Regression on Incident Heart Failure, Myocardial Infarction, and All‐Cause Death, With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S35. Multivariate Cox Proportional Hazards Regression on Composite Outcome of Myocardial Infarction and All‐Cause Death, With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S36. Multivariate Cox Proportional Hazards Regression on Composite Outcome of Myocardial Infarction and Cardiovascular Death, With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S37. Multivariate Cox Proportional Hazards Regression on Incident Myocardial Infarction With Age, Sex, Dichotomized Singulex hsTnl and Other Covariates* Table S38. Cox Proportional Hazards Regression on Incident Congestive Heart Failure With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S39. Multivariate Cox Proportional Hazards Regression on Cardiovascular Mortality With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S40. Multivariate Cox Proportional Hazards Regression on All‐Cause Mortality With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S41. Multivariate Cox Proportional Hazards Regression on Incident Heart Failure, Myocardial Infarction, and Death, With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S42. Multivariate Cox Proportional Hazards Regression on Composite Outcome of Myocardial Infarction and All‐Cause Death, With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates* Table S43. Multivariate Cox Proportional Hazards Regression on Composite Outcome of Myocardial Infarction and Cardiovascular Death, With Age, Sex, Dichotomized Singulex High‐Sensitivity Troponin I, and Other Covariates Table S44. Proportionality Test Result for Myocardial Infarction (MI); Heart Failure (HF); Cardiovascular Death; MI and Cardiovascular Death; MI and All‐Cause Death; and HF, MI, and All‐Cause Death Figure S1. Kaplan–Meier curve for cumulative incidence of myocardial infarction with obstructive coronary artery disease (≥70% stenosis).Figure S2. Kaplan–Meier curve for cumulative incidence of myocardial infarction with obstructive (≥70% stenosis) vs nonobstructive coronary artery disease (<70% but >0% stenosis).Figure S3. Kaplan–Meier curve for cumulative incidence of myocardial infarction with nonobstructive coronary artery disease (<70% but >0% stenosis).Click here for additional data file.


Testosterone represents an interesting hormonal marker, because sex differences in IUD have been observed, e.g., males show higher tendencies toward Internet gaming disorder (IGD) or females toward overusage of online social networks (both compared to the contrary sex). In N = 217 participants associations between the 2D:4D marker of the hand and both unspecified IUD and specific forms of IUD were investigated. It appeared that more female hands  were associated with lower IGD . This effect was driven by the facet of loss of control of Internet Gaming in the whole sample  and the female subsample  = 137). Aside from this, a negative association appeared between the facet of loss of control of generalized IUD and the right digit ratio in males underlining earlier work. In sum, the present work demonstrates that the 2D:4D marker is an interesting marker for Internet addiction and can be easily included as a biomarker to understand the biological underpinnings of Internet (over-)usage.Internet use disorder (IUD) presents a growing problem worldwide. Among others, it manifests in loss of control over Internet usage and social problems due to problematic Internet use. Although IUD currently is not an official diagnosis in DSM-5 or ICD-10, mounting evidence suggests that IUD indeed could be categorized as a behavioral addiction. On a systemic neuroscientific level, IUD is well characterized and dysfunctions in the fronto-striatal-limbic loop have been observed in persons being afflicted with IUD. On a molecular level underlying these neural dysfunctions less is known. Therefore, the present research investigates the influence of prenatal testosterone as measured  Currently, about 3.75 billion users of the world population are online.compulsive Internet use, problematic Internet use, Internet addiction, and due to recent advances in DSM-5 also Internet use disorder (IUD). IUD was coined in line with the inclusion of the term Internet gaming disorder (IGD) in DSM-5 in its appendix . As examples, lower 2D:4D ratios have been associated with autism , 37, andRecently, the 2D:4D ratio of the hand was also investigated in the context of IUD. Lower 2D:4D values of the right hand  in males were associated with unspecified IUD , an effeAs these first works did not address potential associations between 2D:4D and the manifold specific forms of IUD, we aimed with the present research to answer several questions. First of all, we aimed at the replication of the finding that lower digit ratios would predict higher tendencies toward IGD, in particular in males . Finally, with this study, it is the first time that data on possible associations between other specific forms of IUD and the digit ratio are presented. For IUD in the realm of online activities being more visible in females , we expected more female hands (hence higher digit ratios), for the remaining online channels , we expected higher tendencies toward IUD being more associated with more male hands (lower 2D:4D ratio). As Montag et al.  demonstrN = 217 participants from the Ulm Gene Brain Behavior Project (UGBBP). The mean age of our sample was M = 23.41 years (SD = 7.77) and consisted of 77 men and 140 women. Most of them (83.9%) were students (182). A total of 204 participants had German (94.0%) as a mother tongue including bilingually grown up participants; however, the remainder of the participants could understand and use the German language. Overall, 76.5% (166) of the participants were qualified for university (German “Abitur”), seven had a baccalaureate (“Fachabitur”), 15 participants finished the 10th grade on a German secondary school (“mittlere Reife”) and one finished the German “Hauptschule,” i.e., completed high school. A 1st university degree including one polytechnic degree was held by 27 participants and one holds an advanced technical college certificate. Regarding handedness, 91.7% were right-, 6.9% left-handed and 1.4% stated to use both hands equally. All participants filled in the questionnaires described in the next section and provided a scan of the left and right hand. The study was approved by the local ethic committee of Ulm University, Germany.For this study, we used data collected from November 2016 till May 2017 of LoCTM = 0.79, CSP: αCSP = 0.74).We collected data on unspecified IA tendencies with the German s-IAT based on the original test by Young . The s-IA1 = 0.87, αA2 = 0.64, αA3 = 0.77, αA4 = 0.83, αA5 = 0.79.The s-IAT represents five specific problematic Internet use categories—online computer gaming (A1), online gambling (A2), compulsive online buying (A3), online pornography (A4), online social networking (A5). Each category consists out of four items whereas two items each are used to collect data for the factors LoCTM and CSP, respectively. Items to gather information on LoCTM where phrased as follows: “How often do you find that you spend more time with  than you intended?” and “How often do you neglect household chores to spend more time with ?” The following items were used to retrieve information on CSP: “How often do you feel preoccupied with the  when offline, or fantasize about ?” and “How often do you choose to spend more time with  over going out with others?” A 5-point-likert scale (same as in the s-IAT) was used. Cronbach’s alpha for these five scales each consisiting of four anchor items showed the following reliabilities: αICC(left) = 0.97 and ICC(right) = 0.96. Correlations between the 2D:4D values of the two raters were r(left) = 0.93 and r(right) = 0.93 (both at p < 0.01).To determine 2D:4D ratios, we used scans of both hands. The length of the fingers was measured in pixels (scan resolution 300–400 dpi) starting at the middle of the closest crease to the palm to the fingertip. The measurement was conducted with the graphical software GIMP version 2.8.14.t-test was used. We conducted a Mann–Whitney U test to check for differences in Internet related variables depending on gender. Spearman correlations were applied to analyze associations between age, Internet variables, and finger ratios.Statistical analyses were executed with the SPSS version 24.0.0.1 for MAC. Non-parametric testing was applied due to the not normally distributed variables age, s-IAT, and the s-IAT scales. The data of both left and right 2D:4D hand ratios were normally distributed. To identify potential differences between these groups, an independent sample Since a broken or injured finger can lead to length variations with tremendous influences on the 2D:4D ratio, the following participants were excluded from the sample.NL = 208 left 2D:4D ratios and NR = 212 right 2D:4D ratios could be used for the analyses. Additionally, one participant did not give information on the s-IAT scale online gaming, which leads to a sample size reduced by 1 for analyses including the specific IA factor online gaming. Moreover, another participant gave no information for the 3rd item of online shopping that is why N is reduced by 1 in some tables.Six participants reported a broken index finger  and seven a broken ring finger . Furthermore, a total of 24 participants reported no information concerning broken fingers that is why we inspected the scans of their hands visually. Since no abnormalities were found, these participants remained in the sample. Moreover, the left index finger of one participants was extremely short . Since we did not have any further information rendering a reasonable explanation for this fact, we decided to exclude this participant (only the left hand) from the sample. Consequently, data of t(212) = −2.34, p = 0.02]. Gender also influenced several IUD scores , online gambling addiction  and in online pornography addiction . For online communication addiction, females showed higher scores than males . No significant differences were found for s-IAT scores and online shopping addiction.The means (M) and SD for all questionnaire measures and 2D:4D ratios are presented in Table t22 = −2.34Our results show that age is partially correlated with both right hand ratios and online gaming behavior in females, but not in males. Noteworthy, evidence from literature shows, that 2D:4D markers are stable over life (please see introduction). Nevertheless, we assume that age could be a nuisance variable that might affect some of the following analysis .rho = 0.40; p < 0.01). The second highest associations could be observed between both Internet Pornography Disorder   and IGD (A1) correlated with unspecified IUD . Going beyond these findings, it is noticeable that general s-IAT values correlated highest with IGD (A1) in males  and with ICD (A5) in females . Therefore, we consider gender as an important variable to get more detailed information about these associations. Please note, that the trend-results remained unchanged when using partialized correlations controlling for age. Only in males, Internet Shopping Disorder   correlated significantly and higher than in usual bivariate correlations with general s-IAT .A correlation matrix of all IUD scales shows several significant correlations . Of note and in line with an earlier study by Montag et al. , the higrhoR = −0.24, p = 0.04, NR,m = 74) and a negative correlation independent of gender between the 2D:4D ratio of the right hand and IGD (A1) . The effect is driven by the facet of loss of control of Internet Gaming in the whole sample  and the sub-facet LoCTM in the female sample . For online gambling, we discovered a negative association  driven by the sub-facet CSP in the female sample .We found an inverse association between the finger ratios in right male hands and the sub-facet LoCTM of the s-IAT  triggered by both sub-facets (rhoR(LoCTM) = −0.15, p = 0.03, NR,m = 212, rhoR(CSR) = −0.15, p = 0.03, NR,m = 212) but could not be found in male or female subsamples. No significant associations for the left hand could be observed  = −0.20, p = 0.02, N = 137]. LoCTM was not associated with age on the right-hand side. For online gambling, the sub-facet CSP is also associated with 2D:4D . Due to the significant association between age and craving for online gambling, we controlled for age , these results would not hold for multiple testing. Nevertheless, we present them for future research endeavors.Before coming to the first hypothesis, we revisit the question if in particular female hands show higher digit ratios than male ones. We could find such a significant difference in the right hand of our participants, which is in line with what has been observed most robustly in the literature . To furtN = 27 online gaming addicts with N = 27 healthy controls. They used the CSAS II measure for assessing and classifying video gaming addiction. The authors report that “CSAS II is based on the Internet Addiction Scale ISS-20, which has been extended and adapted to assess video game addiction” (p. 2). In Canan et al.  and streaming videos (n = 206) and others (n = 74). The resulting statistic in Canan et al.  we assessed IUD in five different specific domains  as well as unspecified IUD. Furthermore, and (b) the present work did not use a cut-off to search for subgroups of addicted or non-addicted persons. Instead we searched for continual associations between healthy and pathological usage of the Internet (and specific online channels) in the complete investigated sample of r et al.  and Canar et al. , namely p < 0.001)”. In light of this, we believe that our samples do not strongly differ in these areas. Asides, we assessed addictive tendencies in the “Big Five of IUD” as mentioned above, whereas the Canan et al.  males and naturally their sample is hard to compare with the participants from the present work. Canan et al.  n et al.  study onn et al.  to some n = 77 males vs. n = 140 females), because we recruited the present sample mainly in psychological classes. Therefore, our statistical power to detect the same effects for males as for females is smaller. In contrast to the power hypothesis, there is a low observed correlation between right hand digit ratio and IGD in the male group of participants in Table rho = −0.04, p = 0.72, N = 74. In sum, the digit ratio of the right hand might not be exclusively linked to IGD in males . This underlines that prenatal testosterone might be exclusively linked to IGD/unspecified IUD but not to any other areas when assessing IUD in different contexts. Nevertheless, this statement might be revised, when other researchers also observe our associations between 2D:4D and gambling/pornography usage. It needs to be remembered that the present study investigated a largely healthy student sample population (and the scores on the IUD questionnaires where highly skewed toward the lower end of the distribution). In particular with the sex-dimorphisms observed in many areas of online usage, clinical samples might reveal different outcomes.r = 0.40**, see Table S5 in Supplementary Material). Splitting these results into a male and female subsample reveals that this association is driven by the female subsample (r = 0.50**), whereas in males it is weaker (r = 0.29**). The second and third strongest association between specific forms and unspecified IUD was shopping (r = 0.34**) and pornography (r = 0.28**) in females. In males, significant associations occurred most prominently with gaming (r = 0.57**), pornography (r = 0.53**) and shopping (r = 0.39**). Perhaps except for the pornography-unspecified IUD link, the associations are in line with the gender differences observed in the literature.The last aim of our study was to revisit findings from an earlier study by Montag et al.  observinrho = 0.68, p < 0.01; males: rho = 0.67, p < 0.01; females: rho = 0.70, p < 0.01, . In males, a negative association appeared between the digit ratio of the right hand and generalized IUD (facet loss of control) supporting the findings of Canan et al.  are associated with higher tendencies toward IGD. Noteworthy, our findings could only be observed in females and not males, something, which could only be partly explained . All statistical analyses were checked by BL. MM and JM carried out the measurement of the hands. CM wrote the first draft of the introduction. CM and MM wrote the first draft of the discussion. MB, RS, and BL critically revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Escherichia coli periplasm by the General Secretory (Sec) pathway, but Sec‐based production chassis cannot handle many potential target proteins. The Tat pathway offers a promising alternative because it transports fully folded proteins; however, yields have been too low for commercial use. To facilitate Tat export, we have engineered the TatExpress series of super‐secreting strains by introducing the strong inducible bacterial promoter, ptac, upstream of the chromosomal tatABCD operon, to drive its expression in E. coli strains commonly used by industry . This modification significantly improves the Tat‐dependent secretion of human growth hormone (hGH) into the bacterial periplasm, to the extent that secreted hGH is the dominant periplasmic protein after only 1 hr induction. TatExpress strains accumulate in excess of 30 mg L−1 periplasmic recombinant hGH, even in shake flask cultures. A second target protein, an scFv, is also shown to be exported at much higher rates in TatExpress strains.Numerous high‐value proteins are secreted into the  This minimizes downstream processing (DSP) costs because the target protein can be purified from the relatively simple periplasmic contents, and this strategy avoids debris and DNA contamination, which are serious DSP problems  is only capable of transporting proteins in an unfolded state .The bacterial strains and plasmids used in this work are listed in Table 2.2ptac is inserted upstream of the tatA promoter, gene doctoring methodology was used  and tatBRev(check), and the purified PCR products were sequenced to confirm strain construction. P1 transduction was used to transfer the TatExpress cassette, still carrying its kanamycin resistance gene marker, from E. coli K‐12 TatExpress Km1 to E. coli BL21 , after which protein overexpression was induced by adding either 10 or 100 µm IPTG . Samples were taken for fractionation prior to induction, and at 1, 2, 3, 4, and/or 5 hr post induction. Periplasmic (P) fractions were collected using an EDTA/lysozyme/cold osmotic shock method previously described , and detection of C‐terminal‐His6 tagged proteins using anti‐His6  raised in mouse  and an anti‐mouse‐HRP secondary antibody (Promega). Blots were developed using Clarity Western ECL substrate  and images collected using a Bio‐Rad ChemiDoc XRS+ molecular imager. All gels and blots shown are representative. Quantitation was carried out using Bio‐Rad Image Lab software and calculated using an average of two gels or blots (within linear range) of biological triplicates against a standard curve of purified hGH‐His6. Periplasmic hGH was assayed using a hGH ELISA kit as per the manufacturer's protocol . Standardized OD10 periplasmic fractions were diluted 1:500,000 using PBS and absorbance was read using a BMG Labtech Spectrostar microplate reader at 405 nm, with a reference wavelength at 490 nm. Concentrations were calculated from two independent experiments and all samples measured in duplicate, and used to calculate average periplasmic yield (in mg/L) by factoring in culture OD readings at 600 nm.The preparation of normalized protein samples were carried out as detailed in our previous work , which is regulated by the LacI repressor and IPTG , upstream of the chromosomal tatABCD operon in E. coli strains W3110 and BL21, using gene doctoring methodology  , and the strains’ export capacities were tested using a construct in which mature hGH was expressed with an N‐terminal TorA signal peptide and a C‐terminal hexa‐histidine tag (His6) from a pEXT22 based plasmid.Growth of both TatExpress derivatives was indistinguishable from their respective WT strains in shake‐flask cultures using standard LB medium Figure . Similar3.2E. coli W3110 cells and the TatExpress W3110 strain; expression was induced using 10 µm IPTG and cells were fractionated immediately prior to induction, and at 1, 2, 3, and 4 hr post induction, into cytoplasm, membrane/insoluble, and periplasm samples . Fractionated samples were analyzed by Coomassie stained reducing SDS–PAGE  gave a figure of 22 mg L−1 hGH.Similar tests using TatExpress BL21 cells are shown in Figure Similar tests were carried out using a second target molecule: a single chain antibody fragment (scFv) that has been previously shown to be efficiently exported by Tat if a TorA signal peptide is attached  generating strains that stably overexpress the Tat system from the chromosome and (ii) matching target protein expression levels to the Tat secretion capacity of the cell. Our results clearly show that combined high‐level expression of both the Tat system and recombinant hGH protein leads to the formation of inclusion bodies and/or degradation of the precursor protein. Lower level expression from a low copy number plasmid, together with induction using an optimal concentration of IPTG, leads to much more efficient secretion rates. The net result is a platform that exports hGH by the Tat pathway at extremely high rates, to the extent that the target protein is the most abundant periplasmic protein within a few hours of induction. The system is also capable of exporting high levels of other proteins, for example a previously tested single chain antibody fragment  that are sufficient for many purposes.C.R., D.F.B., and S.J.W.B. conceived and designed the research program. K.L.R., A.P., J.R., and D.F.B. performed the experiments. C.R., K.LR., and D.F.B. wrote the manuscript with input from all authors.Additional Supporting Information may be found online in the supporting information tab for this article.Supporting Data S1.Click here for additional data file.


ST2 mRNA, disrupted a putative binding site for miR202‐3p. Functional analyses suggested that miR‐202‐3p significantly decreased soluble ST2‐G mRNA stability and inhibited its endogenous expression. Furthermore, we found increased plasma‐soluble ST2 (sST2) level was highly associated with CC genotype of rs3821204 in vivo. Taken together, our findings provide the first evidence that genetic variants in ST2 gene are associated with EH risk and variant rs3821204 may influence the development of EH by controlling sST2 expression.Recent studies have suggested that interleukin 1 receptor‐like 1 (ST2) plays a critical role in pathogenesis of several cardiovascular disease conditions. In this study, we examined association of 13 single nucleotide polymorphisms (SNPs) of ST2 gene with essential hypertension (EH) risk in 1151 patients with EH and 1135 controls. Our study showed that variants rs11685424, rs12999364 and rs3821204 are highly associated with an increase in risk of EH, while rs6543116 is associated with a decrease risk of EH. Notably, in silico analyses suggested the G>C change of rs3821204, which located within the 3′UTR of soluble  Hypertension is a significant public health problem worldwide and affects more than 30% of the population ST2 gene is located on chromosome 2q12 and contains 11 exons, a proximal promoter and a distal promoter. This gene encodes an IL‐33 receptor which consists of two main isoforms, a transmembrane ligand (ST2L) and a soluble component  It is well known that the interleukin 33 (IL‐33)/interleukin 1 receptor‐like 1 (ST2) pathway plays an important role in pathogenesis of cardiovascular disease such as atherosclerosis, heart failure and hypertension ST2 polymorphisms and EH has not been systematically investigated. Accordingly, we suggested that genetic variations of the ST2 gene influence EH development by interfering the function or expression of ST2. To investigate this, we performed a genetic association analysis of ST2 and EH risk in a case–control study of the Han Chinese population. Further, we performed functional analyses to investigate the underlying functional role of ST2 variants in EH development.Although higher sST2 concentration has been reported in association with hypertension, association between The sample population included 1151 EH patients and 1135 controls. Patients were unrelated to each other and recruited from the Han Chinese population. EH patients were recruited from three hospitals  in Wuhan City, Hubei province, China http://www.hapmap.org; with CHB  and JPT  as the reference set]. According to the criteria of r2 ≥ 0.8 and minor allele frequency (MAF) ≥0.05, to cover the distal promoter region of ST2, we extended 30 kb into the 5′ and 2000 bp into the 3′ end of ST2, and selected six tagSNPs to be analysed. Another seven SNPs were selected from previous reports of coronary heart disease (CHD) (‐27307T/A and ‐27614C/A), asthma and atopic dermatitis TagSNPs were selected based on the HapMap phase I & II database [http://snpinfo.niehs.nih.gov/snpinfo/snp func.htm and http://epicenter.ie-freiburg.mpg.de/services/microsniper/.Appropriate bioinformatics websites were used to predict the putative binding site for rs3821204: Fasting venous blood was collected from the peripheral vein. DNA was extracted using a Puregene kit . Twelve SNPs were genotyped using the Sequenom MassArray system . The primers and probes used are listed in Table S1. The remaining SNP, rs3755278, was genotyped by TaqMan SNP allelic discrimination . The catalogue number for genotyping rs3755278 was C_27477453_10. TaqMan data collection and analysis were performed with SDS 2.2.1. Overall, all 13 SNPs were successfully genotyped with a call rate >96%. About 10% of the samples were randomly selected for repeat genotyping of the three SNPs for quality control, and the results were 100% concordant.The human umbilical vein cell line, EA.hy926, human embryonic kidney cell line, HEK293, and T‐acute lymphoblastic leukaemia cell line, KOPTK1, were obtained from American Type Culture Collection (ATCC) and cultured according to the manufacturer's protocols.Genomic DNA was isolated from cells using the Guide Cell and Tissue Genomic DNA Extraction Kit . The genotypes of cell lines were determined by Taqman genotyping assay.Total cholesterol (TC), triglyceride (TG) and fasting blood glucose (FBG) levels were measured by standard laboratory procedures at the Department of Clinical Laboratory, Union Hospital.® Frist‐Strand Synthesis System (Life Technologies). Gene expression was monitored by PCR assay. The primer sequences were as follows: sST2 forward, 5′‐ GGCACACCGTAAGACTAAGTAG‐3′ and sST2 reverse, 5′‐CAATTTAAGCAGCAGAGAAGCTCC‐3′; β‐actin forward, 5′‐CCCAGCCATGTACGTTGCTAT‐3′ and β‐actin reverse, 5′‐TCACCGGAGTCCATCACGAT ‐3′. Levels of miR‐202‐3p and U6 were monitored by quantitative RT‐PCR using the Bulge‐Loop™ miRNA RT‐PCR primer set .Total RNA was extracted using Trizol . For reverse transcription PCR (RT‐PCR), cDNA was obtained using the SuperScriptIIIST2 was amplified with primers: forward, 5′‐GTCTCTAGAATCCCCCACTCCCTCC‐3′ and reverse, 5′‐CAGTCTAGAATCTGTGTTCCTGCCC‐3′. A point mutation (G>C) was introduced by recombinant PCR using the primers: forward, 5′‐GTTTTTCTGGTCATAATGAAC‐3′ and reverse, 5′‐GTGTTCATTATGACCAGAAAAACGTATAGAACGG‐3′. Fragments were digested with XbaI and inserted into the luciferase reporter vector, pGL3‐control .The 3′‐untranslated region (UTR) fragment of Renilla luciferase readings were calculated.HEK293 cells were seeded into 24‐well plates at 20,000 cells/well, and incubated at 37°C for 24 hrs. Luciferase constructs (with ~1‐kb sST2 3′‐UTR mRNA) and micro RNA (miRNA) mimics were cotransfected using lipofectamine 2000 (Life Technologies), following the manufacturer's instructions. At 48 hrs after transfection, cells were lysed and luciferase assays performed with the Dual‐Luciferase Reporter Assay System (Promega) on a single automatic injection Mithras luminometer , following the manufacturer's instructions. Ratios of firefly luciferase to Fifty participants were selected from the control group. Plasma sST2 concentrations were measured using a highly sensitive sandwich immunoassay: Pressage ST2 . Test procedures were performed according to the manufacturer's instructions. The lower limit of sST2 detection was 3.1 ng/ml and the upper limit 200 ng/ml.t‐test and those with unequal variance or without a normal distribution were analysed by Mann–Whitney rank‐sum tests. Chi‐square tests were used to compare categorical variables and Hardy–Weinberg equilibrium (HWE) of SNPs. Unconditional logistic regression analysis was used to determine association between SNPs and hypertension risk, after adjustment for age, sex, smoking, drinking, body mass index (BMI), TG, FBG and family history of hypertension by odds ratios (ORs) and 95% confidence intervals (CIs). Power calculations were performed with the QUANTO software program (Version 1.2.3) . One‐way analysis of variance (anova) was used to examine differences in sST2 levels in patients with different rs3821204 genotypes. All other statistical analyses were performed with the statistical analysis software package SPSS 12.0 . Differences or associations with two‐sided P‐values <0.05 were considered statistically significant.Continuous variables were expressed as mean ± standard deviation (S.D.). Distribution of numerical data was determined by the Kolmogorov–Smirnov normality test. Continuous data with a normal distribution were compared by Student's General participant characteristics are presented in Table P > 0.05), except for rs1041973 and rs10515922, which significantly deviated from HWE in EH patients and controls (P < 0.05) (see Table S2). MAFs for ‐27307T/A and ‐27614C/A were 0, and according to the HapMap database, these alleles have not previously been identified in the Chinese Han population. Consequently, we examined association between the remaining nine SNPs and EH risk. To determine association between these SNPs and EH risk, we analysed genotypes using additive and recessive models. Four of these SNPs  were significantly associated with EH after adjusting for conventional EH risk factors such as age, sex, smoking, drinking, BMI, TG, FBG and family history of hypertension  and recessive  models. In contrast, the rs6543116 A allele was associated with decreased EH risk in both additive  and recessive  models. Additionally, there was a dose–response effect for increasing number of rs6543116 A alleles and decreased EH risk. Both rs12999364 and rs3821204 were also associated with EH risk in both additive  and recessive  models. Overall, our findings suggest increased EH risk in individuals carrying these homozygous ST2 variants. Except for rs11685424 and rs6543116 in the recessive model, P‐values for these SNPs reached significance after Bonferroni correction . There were no significant associations between the remaining five SNPs and EH risk in our population (P > 0.05). Given the frequency of CHD within this case–control sample population, we analysed subgroups without CHD patients to determine whether the effect was influenced by CHD risk. Exclusion of individuals with CHD did not change the results (see Table S3).Specifically, we found that the A allele of rs11685424 was associated with increased EH risk in both additive . Together, this suggests these variants may influence EH susceptibility by controlling their own expression. Previous studies have confirmed this, and shown that rs11685424 and rs6543116 are associated with elevated EH risk and increased sST2 expression As miRNAs often function as negative regulators of gene expression, we performed luciferase assays to determine the effect of rs3821204 on sST2 expression. We found that miR‐202‐3p significantly decreased RLA of ST2‐G but not ST2‐C, while miR‐548 m did not alter RLA of ST2‐G or ST2‐C  . Plasma sST2 concentrations were significantly lower in patients with GG genotypes compared to those with CC genotypes  by modulating immune and inflammatory responses Previous epidemiological studies have shown that elevated plasma sST2 concentration is associated with risk and outcome of hypertension In our present study, we had >90% power to detect association between the SNPs and EH risk, at a 0.05 significance level under both additive and recessive models, except for rs11685424, which had 84% power to detect association under the recessive model. Nevertheless, our study has several limitations. First, the study sample was not sufficiently large, especially when CHD patients were excluded. Further studies are needed to replicate our results in a larger sample size population. Second, our sample population was not well matched, but we addressed this using unconditional logistic regression analysis with adjustment for age, sex, smoking, drinking, BMI, TG, FBG and family history of hypertension. Third, all the volunteers recruited in our study were ethnic Han Chinese, and association between these SNPs and EH risk in other ethnic groups needs further validation in larger prospective studies.ST2 polymorphism and risk of EH. Additionally, our findings further implicate rs3821204 as a functional polymorphism that contributes to increase EH risk and enhanced sST2 expression by disruption of a miR‐202‐3p binding site. Thus, our findings imply that ST2 may strongly influence development of EH, further supporting the important role of ST2 in EH, and highlighting ST2 as a valuable target for prevention and treatment of EH.In conclusion, we have found significant association between a The authors confirm that there are no conflict of interests.Table S1. Primer sequences for genotyping 12 SNPs from the ST2 gene using the Sequenom platform.Click here for additional data file.Table S2. Frequencies of 13 ST2 SNPs in the study population.Click here for additional data file.Table S3. Secondary analyses of four SNPs significantly associated with EH risk in the subgroup without CHD subjects.Click here for additional data file.Table S4. Functional prediction of ST2 variants.Click here for additional data file.