For this purpose, simultaneous indoor (I) and outdoor (O) measurements were taken from two Polish technical universities (in Gliwice and Warsaw) located in distinct areas with respect to ambient concentrations and major sources of PM. The indoor geometric mean concentrations of Hgp were found to be 1.46 pg·m−3 and 6.38 pg·m−3 in Warsaw and Gliwice, while the corresponding outdoor concentrations were slightly lower at 1.38 pg·m−3 and 3.03 pg·m−3, respectively. A distinct pattern was found with respect to PAH concentrations with estimated I/O values of 22.2 ng·m−3/22.5 ng·m−3 in Gliwice and 10.9 ng·m−3/11.12 ng·m−3 in Warsaw. Hazard quotients (HQs) as a result of exposure to Hgp for students aged 21 ranged from 3.47 × 10−5 (Warsaw) to 1.3 × 10−4 (Gliwice) in terms of reasonable maximum exposure (RME). The non-cancer human health risk value related to Hgp exposure was thus found to be below the acceptable risk level value of 1.0 given by the US EPA. Daily exposure values for lecture hall occupants, adjusted to the benzo(a)pyrene (BaP) toxicity equivalent (BaPeq), were 2.9 and 1.02 ng·m−3 for the Gliwice and Warsaw students, respectively. The incremental lifetime cancer risk (ILCR) values with respect to exposure to PM1-bound PAHs during the students’ time of study were 5.49 × 10−8 (Warsaw) and 1.43 × 10−7 (Gliwice). Thus, students’ exposure to indoor PAHs does not lead to increased risk of lung cancer.This study assessed inhalation exposure to particulate matter (PM The European Parliament faces severe and growing challenges in its efforts to meet air quality standards with respect to PAHs and mercury pollution anthracene (BaA), chrysene (Ch), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indenopyrene (IP), dibenzo[ah]anthracene (DBA), and benzo[ghi]perylene (BghiP). In total, 60 PAH samples were determined (30 samples from the indoor and 30 samples from the outdoor environments). Information concerning PM-bound PAH extraction, their analysis, detection limits, and method validation can be found in pyrene (BaP); benzo[a]anthracene (BaA); fluoranthene (FL); chrysene (CHR); benzo[b]fluoranthene (BbF); and fluorene (Flu); while the samples taken at the Warsaw site were found to be predominantly comprised of fluoranthene (FL); benzo[a]pyrene (BaP); and chrysene (CHR). Indoor and outdoor PM1 samples showed slightly different PAH profiles. One example is the fact that concentrations of BbF were dominant in the outdoor air at the Gliwice site (1.90 ng·m−3), while the indoor concentrations were not very high, equaling 1.17 ng·m−3. More 4- and 5-ring PAHs were detected in the outdoor air as compared to the closed lecture halls, with higher contributions of 4-ring PAHs were found at the university in Gliwice.In the present study, sampling and detection of indoor and outdoor PMous work ,50. PM1 1 samples were evaluated. We found that high molecular weight (HMW) PM1-bound PAHs (4 + 5 + 6-ring PAHs) accounted for about 78–79% of total PAH concentrations in fine PM samples from lecture halls and 87% of total PAH concentrations in the ambient air (average shares in the Gliwice and Warsaw sites). Among the 16 detected PAHs included in this study, the best known, due to its mutagenic and carcinogenic effects, is benzo(a)pyrene (BaP). For many years BaP was found to be a good and sufficient indicator of human exposure to carcinogenic ambient PAHs. However, since it has been proven that other PAH conglomerates anthracene) may have higher carcinogenic potential, it has become clear that BaP is not a sufficient substitute marker for the carcinogenicity of PAHs. In this study, ∑PAHscarc (the summed up concentrations of carcinogenic PAHs) accounted for approximately 74% of the ∑PAHs indoors and 83% outdoors . BaP represented the greatest proportion of carcinogenic PAHs at the university in Gliwice, accounting for approximately 18% of ∑PAHscarc. At the university in Warsaw, fluoranthene made the greatest contribution to the total considered PAHs, on average accounting for 34% of ∑PAHscarc.The aromatic rings of PAHs influence their toxicity and carcinogenicity. The differences in the number of rings between the indoor and outdoor PMp inside the university (2.4 pg·m−3 to 27.7 pg·m−3) were much higher than outdoor concentrations (1.1 pg·m−3 to 6.1 pg·m−3); while in Warsaw the average concentrations of Hgp between indoor and outdoor environments were equal (1.4 pg·m−3) [p over the measurement period were noted in Gliwice, and can be observed by comparing the values of geometric standard deviations of the averaged 24-h Hgp concentrations from both sites.Due to the origin-related dissimilarities, it was supposed that the I/O ratios of PM-bound Hg should be quite different to those of PAHs. In Gliwice, the concentrations of Hg pg·m−3) . Greater1-bound PAHs and Hgp for students from two Polish universities, taking into account the activity patterns of students calculated on the basis of statistics from a U.S. federal survey. 1-bound compounds. The daily inhalation exposure level (IEL) values calculated as the sum of exposure to 16 priority PAHs ranged from 26 ng·day−1 (Warsaw University) to 67.94 ng·day−1 (Gliwice University). Low I/O values for benzo(a)pyrene equivalent  in Gliwice (0.85) and Warsaw (0.83) confirm the contribution of outdoor emissions from residential heating and coal plants in the case of Gliwice and the effects of adjacent road traffic in the case of atmospheric PAH levels in Warsaw [The goal of this study was to evaluate the potential cancer risk from exposure to indoor and outdoor PMn Warsaw .1-bound PAHs ranged from 5.49 × 10−8 (Warsaw) to 1.43 × 10−7 (Gliwice) . Higher ILCR values were found for the Gliwice students where the highest concentrations of ∑16PAH were obtained. The same calculations were performed for teaching staff with 40 years of work experience. In that scenario, the ILCR values were slightly increased and varied between 4.39 × 10−7 (Warsaw) and 1.16 × 10−6 (Gliwice). As in the case of the students, lecturers with prolonged (40-year) exposure are expected to have low PM-bound PAH inhalation exposure risk. 1-bound Hgp in Warsaw (3.47 × 10−5) and highest HQ was observed among the Gliwice students (1.3 × 10−4).The potential ILCR of students exposed to PMGliwice) . Our res1-bound PAHs presented in this work are much lower than the winter levels presented in numerous previous works [+ k)F and B(a)P [+ k)F at the Silesia University campus (Gliwice), located next to DTŚ highway. However, the high mean concentration of PM1-bound FL P , which mbound FL  and, as bound FL  also ref1 levels . When co0 ng·m−3 ; in the 1 ng·m−3 ; and in 0 ng·m−3 . However0 ng·m−3 ,50. This1-bound PAHs collected in the indoor environment of lecture halls in the Gliwice (a) and Warsaw (b) universities. It is well known that the distribution of LMW and HMW PAHs depends on the physical characteristics of the compounds, as well as on the physical conditions of the studied environments, such as temperature and relative humidity. The share of HMW PAHs in their total mass found in the lecture halls of Gliwice and Warsaw universities, accounting for about 78–79% of the total PAH concentrations, was, for example, much greater than the shares found in the dust from lecture theaters in Shanghai where the values were estimated to be 30–58% [1 category  were significantly correlated in Warsaw; what is more, those concentrations were also correlated with the 24-h PM1 concentrations inside the teaching room. Therefore, we suspect that in Warsaw, the entry of outdoor-generated Hgp probably influences the indoor Hgp concentrations. At the site in Gliwice, which was comprised of rather airtight rooms and used artificial ventilation, this situation was quite different. There were no significant indoor and outdoor correlations between 24-h Hgp concentrations and 24-h PM1 concentrations. High indoor concentrations of Hgp in Gliwice could be explained by the differences in the room aeration habits (rather rare in Gliwice) as compared to the Warsaw University of Life Sciences, which probably resulted in the increased accumulation of Hgp inside the Gliwice lecture hall. It was also concluded that in Gliwice, some part of Hgp migrating into the teaching room with the outdoor PM1 was probably enriched with gaseous mercury, which made the indoor Hgp levels relatively high. A full and detailed discussion concerning concentrations of PM1, PAHs, and Hgp inside and outside the lecture halls can be found in [There is no available data on PM2 pg·m−3 . When coily coal , PM-bounily coal . Therefoted data , the daifound in ,30 and, 16 and the BaP equivalent quantity (BaPeq), calculated based on the exposure to individual PAH compounds and toxic equivalent factors (TEFs) for students and lecturers via the inhalation pathway, is presented in −1 (Warsaw University) to 67.94 ng·day−1 (Gliwice University) and were much lower compared to extremely high daily PAH inhalation exposure values found among children studying in schools in Delhi, India, in close proximity to roads with heavy traffic where values reached 439.43 ng·day−1 in the winter, 232.59 ng·day−1 during the monsoon, and 171.08 ng·day−1 during the summer season [eq = 0.66 ng·m−3) and dining halls (BaPeq = 0.73 ng·m−3) in Shanghai’s universities [The mean daily intake of ∑PAHr season . The inhersities .−1 to 21.44 µg·g−1 for dust samples from lecture theatres and from 9.63 µg·g−1 to 44.13 µg·g−1 for samples from dining halls. Higher concentrations of PAHs from cooking practices, in fact, originate from coal combustion and petroleum-related sources . Since the principal source of airborne polycyclic aromatic hydrocarbons in building interiors is, by far, from the combustion of organic matter, it may be suspected that their presence in the internal environments  would be rather low. This is because the internal heat inside lecture halls in most cases is regulated by convective heaters and radiators and not by open-burning sources. For example, when comparing the concentrations of PAHs adjusted with respect to the BaP toxicity equivalent, which migrates into students’ lungs during lectures (BaPeq = 2.9 ng·m−3 in Gliwice and 1.02 in Warsaw), with the concentration of PM-bound PAHs originating from grilling food on charcoal briquettes (BaPeq = 401.6 ng·day−1) [On reviewing Equation (5), one can see that the indoor inhalation exposure values of PAHs in the lecture halls (due to longer exposure time spans) are probably 5-fold higher than the corresponding values in the outdoor environment. Similar to the observed pattern, Jyethi, et al. found that the children’s daily inhalation exposure to PAHs during the day exhibited a trend with respect to school hours (indoor activity) versus commuting to school (outdoor activities) . It is, g·day−1) , it can −6 (5.49 × 10−8 in Warsaw and 1.43 × 10−7 in Gliwice). For lecturers, the ILCR value for exposure to PAHs at the Gliwice site was higher than 10−6 but lower than the priority risk level value (10−4). On comparing those values with the values for life-long BaP inhalation lung cancer risk in the general population (women and men jointly) inhabiting the Mazowieckie province  and the Silesia province , it was found that the additional risk resulting from the rather short 5-year exposure to PAHs in teaching rooms was virtually unnoticeable [In the present study, we estimated the potential ILCR values for students due to exposure to PAHs over 5 years and the exposure for lecturers over 40 years . Our resticeable .p were below the maximum acceptable level (HQ = 1) revealing no potential toxic effects to the student populations. Therefore, we conclude that mercury inhalation in both indoor and outdoor environments is not relevant to students. The non-carcinogenic risks associated with particulate mercury (Hgp) in the case of exposure to teaching staff were also well below 1. This demonstrates that even 40 years of exposure to Hgp is not likely to pose health risks.In all cases tested, the non-carcinogenic inhalation risks from Hgp levels. The carcinogenic risks for students in terms of individual PAHs and the combined risks associated with the sum of those substances were below the maximum acceptable level (1 × 10−6), revealing that the carcinogenic risk posed to university students via inhalation is acceptable. In addition, the non-carcinogenic risks to students aged 21 as a result of exposure to PM1-bound mercury are also far below the acceptable level of HQ = 1, indicating no toxic effects. The indoor concentrations of PAHs and Hgp varied between the considered university halls. Outdoor concentrations of ∑16PAHs were probably shaped by nearby traffic-related emission sources, while the Hgp levels  were regulated by the existence of some unidentified indoor sources enriching PM1 levels. It was found that the lecture hall in Gliwice, due to its airtightness and inappropriate air ventilation, was more detrimental to student health. The existing concentration differences between indoor and outdoor environments do not, however, allow us to state how strongly the indoor concentrations depend on the penetration of outdoor contaminated air. The effectiveness of staying indoors to reduce student exposure to outdoor-source PM compounds is, therefore, unknown. The results presented in this study indicate that there is no need to reduce the risks of adverse health effects from indoor air pollution inside the lecture halls. It must be noted, however, that providing appropriate indoor air conditions is an important task for educational institutions in order to ensure high productivity of its students. It is vitally important to keep indoor air conditions within permissible and healthy limits as established and determined by special indoor air quality standards.The main goals of this study were to determine and analyze the indoor air quality in lecture halls in terms of PM-bound PAHs and Hg


Limited research has explored clustering of lifestyle behavioral risk factors (BRFs) among university students. This study aimed to explore clustering of BRFs, composition of clusters, and the association of the clusters with self-rated health and perceived academic performance.We assessed (BRFs), namely tobacco smoking, physical inactivity, alcohol consumption, illicit drug use, unhealthy nutrition, and inadequate sleep, using a self-administered general Student Health Survey among 3,706 undergraduates at seven UK universities.A two-step cluster analysis generated: Cluster 1  with very high health awareness/consciousness, good nutrition, and physical activity (PA), and relatively low alcohol, tobacco, and other drug (ATOD) use. Cluster 2 (the abstinent) had very low ATOD use, high health awareness, good nutrition, and medium high PA. Cluster 3  included the highest regard for healthy eating, second highest fruit/vegetable consumption, and moderately high ATOD use. Cluster 4 (the risk taking) showed the highest ATOD use, were the least health conscious, least fruit consuming, and attached the least importance on eating healthy. Compared to the healthy cluster (Cluster 1), students in other clusters had lower self-rated health, and particularly, students in the risk taking cluster (Cluster 4) reported lower academic performance. These associations were stronger for men than for women. Of the four clusters, Cluster 4 had the youngest students.Our results suggested that prevention among university students should address multiple BRFs simultaneously, with particular focus on the younger students. Major modifiable detrimental behavioral risk factors (BRFs)  are known to limit peoples’ capabilities. Nevertheless, to date, these modifiable BRFs still represent significant burdens among university students –5.Studies of the health and wellbeing of university populations revealed different extents of clustering of lifestyle BRFs across students , 4, 6, 7Cluster analysis (CA) is a promising approach to assess students’ health-related lifestyle characteristics in a collective manner. CA is premised on shared characteristics to categorize a given population into mutually exclusive subgroups (clusters) for which the properties or patterns within a cluster are similar to each other than they are to properties within a different cluster , 11. IndTo bridge these knowledge gaps and to add new insights to the limited research on clustering of BRFs, and its association with health and academic achievement of students, the current study employed a large sample of students at seven universities in three countries of the United Kingdom  in order to: (1) identify and describe the clustering of five major lifestyle BRFs ; (2) characterize the student composition of each of the emerging clusters in terms of sociodemographics; and (3) examine the associations between the emerging BRFs clusters and students’ self-rated health and academic performance.n = 993, University of Gloucestershire n = 970, Bath Spa University n = 485, University of Ulster n = 475, Swansea University n = 406, Oxford Brookes University n = 208, and Plymouth University n = 169). Data quality assurance was optimized through centralized data entry using Teleform computer software.Ethical approval from the participating institutions (see below) was obtained prior to data collection. A self-administered general Student Health Survey collected health and well-being data through 2007–2008 , 13, 14 Similar to other general student health and wellbeing studies , 13, 15,Health awareness/consciousness was assessed by the question “To what extent do you keep an eye on your health?” with four response options .Consumption of fruits and vegetables was assessed with the question “How many servings of fruits and vegetables do you usually have per day ?” with four response options .Consumption of sweets was measured by the question “How often do you eat sweets ?” with five response options .The importance of healthy eating was measured with the item “How important is it for you to eat healthy?” with five response options from “Very important” to “Not at all important.”Three forms of PA  were assessed with the following questions: “On how many of the past 7 days did you: (1) participate in vigorous exercise for ≥20 min?; (2) participate in moderate exercise for ≥30 min?; (3) do exercises to strengthen or tone your muscles, such as push-ups, sit-ups, or weight lifting?” For each form of PA, students reported the number of days for which they engaged in any such activity (ranging from 0 to 7 days).Sleep/rest was assessed with the question “On how many of the past 7 days did you get enough sleep so that you felt rested when you woke up in the morning?” Students reported the number of days (ranging from 0 to 7 days).Alcohol (frequency) was assessed by the item “within the last 3 months, how often did you drink alcohol, e.g., beer?” with six response options “Several times/day,” “Everyday,” “Several times/week,” “Once a week,” “Less than once/week,” and “Never.”Alcohol (binge drinking) was measured with the question “Think back over the last 2 weeks. How many times, if any, have you had five or more alcoholic drinks at a sitting?”Alcohol problem drinking was assessed using the 4 standard items that form the CAGE screening test  for probSmoking was measured with the item “Within the last 3 months, how often did you smoke ?” with response options “Daily,” “Occasionally,” and “Never.”Illicit drug  was assessed by the question “Have you ever use/used drugs?” with response options “Yes, regularly,” “Yes but only a few times,” “Never.”Self-rated health was assessed by asking “How would you describe your general health?” with five response options “Excellent,” “Very good,” “Good,” “Fair,” and “Poor.”Self-rated academic performance was measured by the item “How do you rate your performance in comparison with your fellow students?” There were five response options: “Much better,” “Better,” “The same,” “Worse,” and “Much worse.”post hoc pairwise comparisons using the Bonferroni method  except for the comparison between Clusters 1 and 3.Cluster analysis generated 4 clusters Tables  and 3. C2 = 25.1, p = 0.003, Cramer’s Phi = 0.047). Specifically, disposable monthly income was significantly higher in Cluster 1 than Cluster 4  and in Cluster 1 than Cluster 2 .In addition, we observed significant differences by sufficiency of monthly disposable income . All differences by type of accommodation were significant  except for between Clusters 1 and 3.Further, the clusters differed significantly by type of student accommodation during the academic terms  whereby; Cluster 4 had the youngest student sample of 22.6 (SD 6.4) years, while Cluster 1 students exhibited the highest mean age .Finally, all the clusters differed significantly by students’ mean age . This cluster also had the second highest percentage of nonsmokers (78.4%) within the last 3 months, and highest proportion (77.2%) of life-time never drug users. The cluster comprised highly health conscious students who regarded healthy eating as highly important, and their fruit/vegetable consumption was third highest (though much lower than cluster 1). Sweets consumption was medium high, and cluster members had a medium high mean of 2.7 days of adequate sleep per week.This cluster comprised students with the highest regard for healthy eating, second highest fruit/vegetable consumption (but much lower than cluster 1), and their consumption of sweets was lowest among all the clusters. Their ATOD use was lower than that of Cluster 4 and Cluster 1, and on average, slept adequately for 2.9 days per week. Their PA level was not particularly high and similar to that of Clusters 2 and 4 students.This cluster included students with the highest ATOD use among the 4 clusters. Likewise, these students were the least health conscious, attached the least importance to eating healthy, had the least daily fruit intake and highest intake of sweets. Although Cluster 4 members slept adequately for the least number of days (2.1), however, their PA level was similar to that of Clusters 2 and 3.p < 0.001.Chi square and ANOVA results Table  indicate2 = 247.7, p < 0.001) between gender and the main exposure variable (cluster type). Therefore, we stratified the analysis by gender (Table p < 0.001).Students self-rated their health as good 41.8%), very good (38.5%), excellent (9%), fair (9.2%), or poor (1.4%). Ordinal regression examined the association between students’ self-rated health and cluster type. We found an interaction effect  between gender and the main exposure variable (cluster type). Therefore, we stratified the analysis by gender (Table Participants rated their academic performance compared to their peers as “the same” 65.5%), “worse” (16.9%), “much better” (1.5%), “better” (14.1%), or “much worse” (1.9%). We found an interaction effect ; and examined the associations of the resulting clusters with students’ self-rated health and academic performance. Multiple BRFs were prevalent in a significant proportion of our undergraduates, denoting the coexistence of health damaging lifestyle characteristics.Our main findings include: (a) less healthy cluster membership was associated with lower self-rated health; (b) less healthy cluster membership was associated with lower academic performance; and (c) there were subgroups of students with particularly high risk for a certain BRF and for a combination of multiple BRFs. Specifically, our findings indicated that student groups with a clustering of BRFs such as “the risk taking” or “the abstinent” and even “the moderately health conscious” exhibited lower self-rated health than “the physically active and health conscious” cluster. In addition, students in these clusters reported lower academic performance, although some effects were found only for male or for female students. This implied that certain clustering of BRFs does not only diminish the health of students, but may also affect their academic achievement negatively. Young students, financially deprived students and those living with roommates or not alone were most likely to belong to these groups with higher risk profile. Therefore, universities need to be aware that large proportions of their student population may practice harmful lifestyle behaviors. Accordingly, universities need to develop targeted interventions, e.g., programs that specifically address ATOD which tends to be high in combination with poor eating habits and poor sleep among certain groups of younger students.The current survey found distinct lifestyle behavior patterns among undergraduates. Due to different methodologies, sample selection, different BRFs examined, and the scarcity of studies that explored BRFs clustering among university students, we could compare our results only to a certain extent with other research. Generally, our findings are in line with other studies. For instance, our results are supported by research among young people (15–21 years and older) in Switzerland, where multiple risk factors were observed in a substantial proportion of young people . LikewisIn terms of university students, other research also supported our cluster findings. For instance, among young female students in the USA, where the majority of women had more than two unhealthy behaviors and a CA defined three distinct clusters ; or amonThere is a dearth of studies on the mechanisms explaining clustering of multiple risk behaviors, even though these behaviors are significant public health issues. Some authors observed “transfer” effects ; as well as “compensation” effects  . Other rThis study has some limitations. It is cross-sectional and generalizations of the findings need caution. Self-reported data could have imprecisions . Students were recruited during lessons/lectures, hence those not present in the class at the time of data collection, were not included in the survey. Some variables were measured by single items due to respondent burden, and that the study was a general student health survey undertaken within a short duration during lectures. This rendered the measurement of BRFs with more items unworkable. We did not assess serving sizes in the questions on nutrition. The extent of observed clustering of more or less favorable variables might be subject to many features  that would confound such complex and intricately associated constellations of BRFs relationships. Such confounders are usually challenging to unpack, let alone attribute to certain aspects of the university, region, country, or participating individuals . Future We conclude that the identification of subgroups of young people with a high prevalence of one or more of these risk factors allows for an optimization of the allocation of preventive measures. The identification of distinct clusters may aid in uncovering specific groups with higher risk behavior profiles. The clustering of risk factors provides support for multiple-behavior interventions. Our study calls for drug prevention campaigns and other preventive activities targeted at certain sub-groups of students who are likely to combine PA with alcohol and other drug use.This study was carried out in accordance with the recommendations of the ethics committee of the University of Gloucestershire with informed consent from all subjects. All subjects gave informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the ethics committees of the participating institutions, namely the University of Chester University of Gloucestershire, Bath Spa University, University of Ulster, Swansea University, Oxford Brookes University, and Plymouth University.WA has conceptualized the study and is responsible for the data acquisition, interpretation of results, and drafted the manuscript. DS is responsible for the data analysis and contributed to the drafting of the manuscript. CS participated in the conceptualization of the study, the interpretation of results, and drafting of the manuscript. All authors have approved the final version of 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.


Metals are key materials for modern manufacturing and infrastructures as well as transpot and energy solutions owing to their strength and formability. These properties can severely deteriorate when they contain hydrogen, leading to unpredictable failure, an effect called hydrogen embrittlement. Here we report that hydrogen in an equiatomic CoCrFeMnNi high-entropy alloy (HEA) leads not to catastrophic weakening, but instead increases both, its strength and ductility. While HEAs originally aimed at entropy-driven phase stabilization, hydrogen blending acts opposite as it reduces phase stability. This effect, quantified by the alloy’s stacking fault energy, enables nanotwinning which increases the material’s work-hardening. These results turn a bane into a boon: hydrogen does not generally act as a harmful impurity, but can be utilized for tuning beneficial hardening mechanisms. This opens new pathways for the design of strong, ductile, and hydrogen tolerant materials. All metallic alloys can suffer from it6, be it in engineering parts used in vehicles, planes or power plants or in the context of future fusion and hydrogen fuel and energy storage driven industries.Metallic materials are the manufacturing backbone of our industrialized civilization owing to their excellent load bearing capacity, ductility and damage tolerance. They occupy key roles in providing sustainable engineering and manufacturing solutions to such diverse fields as energy supply, transportation, health, infrastructure and safety. This excellent mechanical behavior can drastically deteriorate when metals are exposed to certain elements. Since 1874 it is understood that the lightest of them all, hydrogen, can be dangerous as it causes catastrophic and unpredictable failure, a phenomenon referred to as hydrogen embrittlement11, hydrogen remains not only a ubiquitous but also a threatening element in engineering metallic alloys. Once hydrogen has entered into metals it accumulates in voids and gets trapped at vacancies, dislocations and internal interfaces, i.e. at lattice defects which determine the physical, chemical and mechanical properties of metals15. Once occupying these sites, hydrogen damages the material through enhanced localized plasticity16, decohesion17, vacancy stabilization19, hydride formation21 or void coalescence22.Although these threats and opportunities have motivated nearly one and a half centuries of intense research26. This concept introduces a new path for developing advanced materials with some unique mechanical properties30. The five-component equiatomic CoCrFeMnNi alloy, also referred to as Cantor’s alloy31, is one of the most appealing HEAs due to the high thermodynamic stability of its single face-centred cubic (f.c.c.) structure and the excellent mechanical properties under various temperatures27.Although practically all metals suffer from such phenomena, the high-entropy alloy (HEA) investigated here seems to be not only less prone to hydrogen embrittlement but it even profits from its presence. HEAs are a new class of materials originally defined as solid metallic solutions composed of five or more principal elements in equimolar or near-equimolar ratios for yielding high configurational entropyOwing to these features we picked this equiatomic CoCrFeMnNi model HEA for studying its changing mechanical tensile behaviour when exposed to hydrogen. We show that this material is not only resistant to hydrogen embrittlement but we even observe its beneficial role as alloying element as it improves rather than deteriorates both, the material’s strength and ductility. The key idea behind this turnaround lies in decreasing the stability of the f.c.c. lattice structure of the matrix via hydrogen alloying to trigger more intense nanotwinning upon loading, thereby improving strain-hardening of the alloy.The equiatomic CoCrFeMnNi HEA was produced by melting and casting in a vacuum induction furnace with high-purity elemental starting materials. The cast alloy was hot-rolled and homogenized followed by water-quenching. Cold-rolling and recrystallization annealing were performed to refine the grain size. Figure in-situ hydrogen charging conditions  shows an increase in ultimate strength but a decrease in total elongation when exposed to hydrogen  shows a notable increase of both, ultimate tensile strength (571 MPa) and uniform elongation (~71%) compared to the sample tested without any preceding hydrogen alloying. Note that the diffusible hydrogen concentration in the sample under this condition (12 h pre-charged at a current density of 15 mA·cm−2) was determined to be 8.01 wt.ppm according to the hydrogen desorption rate curves  (curve #3), the ultimate tensile strength and uniform elongation are 588 MPa and ~71%, respectively, which are ~7.9% and ~4.4% larger, respectively, than those observed for the same alloy without hydrogen (curve #1). With further increasing the diffusible hydrogen concentration to 33.25 wt.ppm (240 h pre-charged at a current density of 100 mA·cm−2), the HEA (curve #4) shows a slightly reduced increment in its ultimate strength and uniform elongation compared to the alloy with a hydrogen concentration of 15.22 wt.ppm (curve #3). This observation suggests that the induced hydrogen alloying, promoting both strength and ductility, assumes certain saturation states. Recent work by Zhao et al.35 showed that a gaseous hydrogen charged equiatomic HEA with a hydrogen concentration of ~76.5 wt.ppm exhibitts a slight decrease in elongation, although it showed resistance to gaseous hydrogen embrittlement that is superior to that of 304 and 316 L austenitic stainless steels. This observation further indicates that hydrogen induced enhancement of strength and ductility are strongly dependent on the hydrogen concentration in the material. This is related to the competition between the beneficial and the unfavorable mechanisms during the deformation of the hydrogen charged samples as discussed below.The CoCrFeMnNi HEA studied here shows exactly this opposite trend when exposed to hydrogen as demonstrated in Fig. −2) (curve #3) has the highest strain-hardening rate, which is consistent with the strength and elongation results shown in Fig. We also find that the hydrogen alloying did not exert any significant effects on the yield strength of the equiatomic CoCrFeMnNi HEA Fig. . This mein-situ tensile testing. Figure To understand the fracture behavior of the hydrogen alloyed CoCrFeMnNi HEA, we studied the morphology of the fracture surfaces of all samples after in-situ hydrogen charging conditions, a gradient in hydrogen concentration from the edge to the inner regions of the samples could have been induced according to previous studies on hydrogen embrittlement of other materials4. For this reason associated with the charging process, the intergranular fracture feature observed in the edges is attributed to the high hydrogen concentration near the edges of the samples during the tensile testing in alkaline solution. The fracture mode of microvoid coalescence in the inner regions suggests lower hydrogen concentration in these zones. We also observe that the very fine (<1 μm) ductile dimples in the inner regions of the fracture surface in the hydrogen alloyed samples  shows a very high density of nanotwins compared to the HEA without hydrogen. We reveal the nanotwinning here by ECCI analysis37 using bulk samples to avoid any thin slice size and related effects which alter the stress and microstructure state of the material.To reveal the micro-mechanisms behind the hydrogen induced joint increase in both, strength and ductility, we probed the deformation microstructures in the cross-sections of the CoCrFeMnNi HEA samples tensile tested under various hydrogen charging conditions. Figure loy Fig. . For ins2)38, which explains the occurrence of mechanical twinning at room temperature without hydrogen charging. The strong increase in twinning density in the hydrogen alloyed HEA samples reveals that hydrogen reduces the stacking fault energy of the equiatomic CoCrFeMnNi HEA. This is consistent with the previous studies on the effect of hydrogen on the stacking fault energy in other f.c.c. structured alloys40. Moreover, no other phase formed during the in-situ tensile testing with hydrogen charging according to the XRD results shown in Supplementary Fig. 41, thereby increasing strength and ductility simultaneously.The equiatomic CoCrFeMnNi HEA has a relatively low intrinsic stacking fault energy . All the pure metals were cleaned properly and the carbon content was then controlled to be as low as possible to avoid the influence of interstitial carbon on the mechanical properties45. Samples with dimensions of 10 × 25 × 60 mm3 machined from the original cast were subsequently hot-rolled at 900 °C to a thickness reduction of 50% (thickness changed from 10 to 5 mm). Homogenization was conducted at 1200 °C for 2 h in Ar atmosphere followed by water-quenching. To refine the grain size, samples were further cold-rolled to a thickness reduction of 60%, and subsequently annealed at a furnace temperature of 900 °C for 3 min in Ar atmosphere followed by water-quenching. Note that the true temperature that the samples actually reached during annealing might be lower than the furnace temperature (900 °C) due to the short annealing time.The equiatomic CoCrFeMnNi HEA ingot with dimensions of 25 × 60 × 65 mmThe microstructure of the recrystallized alloy (grain-refined) was analyzed using various methods. X-ray diffraction (XRD) measurements were performed using an X-Ray equipment ISO-DEBYEFLEX 3003 equipped with Co Kα (λ = 1.788965 Å) radiation operated at 40 kV and 30 mA. Electron backscatter diffraction (EBSD) measurements were carried out by a Zeiss-Crossbeam XB 1540 FIB scanning electron microscope (SEM) with a Hikari camera and the TSL OIM data collection software. Electron channeling contrast imaging (ECCI) analyses of the deformation microstructures were performed on a Zeiss-Merlin instrument. The elemental distributions in the recrystallized alloy were investigated using energy-dispersive X-ray spectroscopy (EDS). The fracture morphology was observed by a Zeiss-Merlin instrument.−1 CH4N2S at pH = 13. A platinum wire was used as the counter electrode. The samples were pre-charged for 12 h at 15 mA·cm−2, 72 h at 25 mA·cm−2, and 240 h at 100 mA·cm−2, respectively. Then the samples were continuously in-situ charged during the entire tensile testing to avoid the release of hydrogen. The tensile tests were conducted in an Instron tensile machine at the tensile rate of 1 × 10−4 S−1. Uniaxial tensile tests were conducted using specimens with thickness of 1.5 mm and gauge length of 10 mm. Three samples for each condition were tested to confirm reproducibility. The hydrogen desorption rates were measured by using a custom-designed UHV-based Thermal Desorption Analysis instrument in conjunction with a Mass Spectrometer detector set up (TDA-MS) from 25 °C to 800 °C, and the corresponding heating rate was 26 °C min−1. The diffusible hydrogen concentration was determined by measuring cumulative desorbed hydrogen from 25 °C to 600 °C.Hydrogen was introduced into the specimens by electrochemical charging with various current densities at ambient temperature (25 °C) in 0.1 M NaOH solution plus 0.05 g·LSupplementary information


Combining the pore density and intermediate Pacific δ13C records shows an elevated nitrate inventory of >10% during the Last Glacial Maximum relative to the Holocene, consistent with a δ13C-based and δ15N-based 3D ocean biogeochemical model and previous box modeling studies.Anthropogenic impacts are perturbing the global nitrogen cycle via warming effects and pollutant sources such as chemical fertilizers and burning of fossil fuels. Understanding controls on past nitrogen inventories might improve predictions for future global biogeochemical cycling. Here we show the quantitative reconstruction of deglacial bottom water nitrate concentrations from intermediate depths of the Peruvian upwelling region, using foraminiferal pore density. Deglacial nitrate concentrations correlate strongly with downcore δ 13C, the authors present a quantitative record of deglacial nitrate from the intermediate Pacific and infer close coupling between carbon and nitrogen cycles.Understanding controls on past nitrogen budgets can improve predictions for future global biogeochemical cycling. Here, using foraminiferal pore density and δ The increasing use of chemical fertilizers to provide food for a growing global population and the burning of fossil fuels lead to a severe rise of fixed nitrogen in the biosphere2. Nitrate (NO3−) is one of the main limiting nutrients in the modern ocean3 and nitrate fertilization is considered to contribute to the ongoing ocean deoxygenation5. A strong climate sensitivity has been predicted for the global NO3− inventory and feedbacks on climate by the coupling of the biogeochemical carbon (C) and nitrogen (N) cycles through the biological pump1. A quantitative reconstruction of past reactive N inventories and feedbacks on other biogeochemical cycles throughout time might help us to predict scenarios for the future. Nevertheless, despite different estimates from numerical models, quantitative paleo-records for past reactive N budgets in the oceans are not yet available.Nitrogen (N) is a fundamental component of amino acids and thus, essential for all living organisms2-fixation, performed by cyanobacteria7, while the main loss of mineralized N results from denitrification and anaerobic ammonium oxidation (Anammox) in oxygen deficient zones (ODZs) in the sediments as well as in the water column9. Estimates of past inventories of reactive N species are mainly based on geological records of the fractionation between the N isotopes 15N and 14N (given as δ15N in ‰) in bulk sedimentary organic matter (δ15Nbulk). The complexity of various Ncycle processes influencing δ15N  complicates a quantitative reconstruction of the N budget based on δ15N alone.The main source for bioavailable N in the modern ocean is N15Nbulk records for estimating changes in the past N budget. Box modeling studies11 agree that the inventory of reactive N was likely elevated during cold phases mainly due to a reduction of denitrification in the water column and seafloor sediments, related to enhanced O2 solubility in colder seawater and decreased area of shelf sediments from lower sea level, respectively. Additionally, enhanced N2 fixation from atmospheric iron deposition has been proposed12. Estimated changes from box models based on δ15Nbulk range from 5 to 100%11 increase of reactive N during glacials as compared to interglacials. Another box model study, which is not based on δ15Nbulk13, predicts changes in the global oceanic nutrient budgets due to changes in sea-level, dust deposition, and ocean circulation. This study estimates an increase in dissolved N (DN) of ~16% during the late Holocene compared to the Last Glacial Maximum. This is generally consistent with a study representing glacial nitrogen cycling constrained by isotopes in a 3D global ocean biogeochemical model considering LGM boundary conditions that predicts a glacial Nbio increase between 6.5 and 22%7.Models have used global δ3− concentrations ([NO3−]) using the pore density of benthic foraminifera. Foraminifera are one of the rare examples of eukaryotes which are able to use NO3− as an electron acceptor when oxygen is depleted within their habitats and play an important role in the oceanic benthic nitrogen cycle16. The pore density in the shells of Bolivina spissa is significantly correlated to the [NO3−] in their habitats because the pores facilitate the uptake of electron acceptors for respiration17. A comprehensive review about the functionality of pores in benthic foraminifera can be found in ref. 18. The functionality of pores in Foraminifera ranges from gas exchange for the uptake of electron acceptors and the release of metabolic waste products like CO219 to the uptake of dissolved organic material20. Foraminifera from oxygen depleted environments typically show an increased porosity21 and often a clustering of mitochondria under the pores22. Several recent studies describe the influence of oxygen availability on foraminiferal pore characteristics25. While some species adapt their porosity by changing the size of their pores25, other species are adapting the numbers of pores (pore density) in their tests24.A main focus of our study is the reconstruction of past NO3− as electron acceptor15. At least one species, B. spissa, from the Peruvian ODZ, adapts its pore density to the availability of NO3− in its habitat17. A comparison of 232 measurements of the pore density in B. spissa to the bottom water nitrate concentrations  at the Peruvian continental margin over the last deglaciation. The foraminiferal pore density is providing a tool to reconstruct past [NO3−] in a high lateral and temporal resolution allowing to test model predictions. A comparison of the reconstructed [NO3−] to the stable carbon isotope ratio (δ13C) in our sedimentary record shows the same correlation as in intermediate depths of the modern Pacific, enabling us to reconstruct regional differences in deglacial [NO3−]. A first analysis of deglacial δ13C records reveals the same trend in deglacial [NO3−] change as reconstructed by the pore density and predicted by the different model studies.We determined the pore density of the benthic foraminiferal species 3− concentrations ([NO3−]BW) using sediment core M77/2 52-2  from the Peruvian continental margin over the last deglaciation. Past [NO3−]BW was reconstructed using the pore density of the benthic foraminiferal species B. spissa  occurred during the LGM. This difference is highly significant compared to all other individual time intervals . The highest pore densities, and thus lowest [NO3−]BW, have been found for the MLH. This difference is also highly significant compared to all other time intervals .We reconstructed bottom water NOion Fig. . We dist13 provided evidence that the NO3− inventory at this location is driven by fluctuations of the global reactive N-inventory. A plot which compares relative changes in the global reactive N budget over the last deglaciation from the different modeling approaches with our quantitative [NO3−]BW record is shown in Fig. 3− inventory during the LGM was elevated compared to the Holocene which corroborates estimations from previous biogeochemical model studies13 although it also reveals fluctuations in [NO3−]BW in a much higher temporal resolution. A 50-100% higher reactive N inventory is suggested for the LGM by another box model study by Eugster et al.11 and thus probably overestimates this change by one order of magnitude according to our reconstruction.A comparison with continuous transient global box model simulations covering the last deglaciation15Nbulk record of our sediment core  in seawater is controlled by the balance between terrestrial and marine carbon sources and sinks32, while the spatial distribution of δ13CDIC is mainly controlled by photosynthesis, respiration, and the ventilation and mixing between different water masses33. Autotrophic organisms preferably take up the lighter isotope 12C during photosynthesis. Thus, surface water masses have more positive δ13CDIC and are depleted in DIC, since 12C-carbon is preferably exported as organic matter. In intermediate to deep water masses organic matter is readily remineralized by respiration, which leads to an increase in DIC and a decrease of δ13CDIC within these water masses. An increase in photosynthesis leads to a higher export productivity and thus a stronger gradient in δ13CDIC between surface and deep water masses established through the biological carbon pump.A comparison between the reconstructed [NO13CDIC and [NO3−] and [PO43−] in the modern ocean, with a stronger correlation to [NO3−] than [PO43−]33. The distributions of [NO3−] and δ13CDIC in water masses of the modern Pacific, taken from the GLODAPv2 database34, are shown in Fig. 13C also affect the NO3− distribution. Surface water masses show high δ13CDIC and low [NO3−] through primary productivity, while the intermediate to deep water masses show low δ13CDIC and higher [NO3−] through remineralization of exported organic matter. All these processes define the endmembers of δ13CDIC and [NO3−] in different water masses and thus the mixing processes between different water masses follow the same trend.Since photosynthesis and respiration both influence the distribution of major nutrients in the ocean, there is an inverse relationship between δ3−]BW and δ13CFORAM, and the correlation of dissolved NO3− and δ13CDIC in intermediate water depths (700–2000 m) of the recent Pacific34, is shown in Fig. P < 0.001) and neither slopes nor intercepts significantly differed from each other . Thus, [NO3−]BW and δ13C in our downcore record showed basically the same correlation over the last 22 kyrs as [NO3−] and δ13C of DIC in intermediate water depths of the modern Pacific. We propose that the linear regression between [NO3−] and δ13CDIC , while the δ13CFORAM record consists only of a single measurement on a bulk sample of a few specimens (N ~ 5). Another reason might be an influence of microhabitat preferences of U. peregrina on δ13CFORAM  to the late Holocene (0–6 kyrs BP), we calculated the average relative change of [NO3−] between both time intervals. Once again, this result indicates that [NO3−] was 3.0  µmol/kg higher during the LGM , which supply the tropical regions with preformed nutrients, affecting NO3− limitation at lower latitudes7.We found a distinct offset of BW was depleted for ~4 kyrs  depleted center of the ODZ near the thermocline. Deglacial water column denitrification mainly occurred in ODZs, and was probably stimulated by an enhanced supply of preformed nutrients that led to an increase in export production. As such, more organic N was transferred to intermediate water depths by the biological pump where it was decomposed to NO3− and increased ambient [NO3−]BW despite the N loss at shallower water depths.A comparison between the reconstructed [NOlk. Fig.  in our s13CFORAM to the reconstructed [NO3−]BW using the pore density proxy show how closely the oceanic carbon and nitrogen cycle were coupled over the last glacial/interglacial cycle in the Pacific. δ13CFORAM has extensively been used as a proxy of paleoproductivity before but also as proxy for ventilation and oxygenation43. Nevertheless, our study shows that the ratio between [NO3−]BW and δ13CFORAM over the last 22 kyrs at our sampling location remained unchanged and implies the possibility that δ13CFORAM might also be used as a quantitative NO3− proxy at intermediate water depths. Several locations of the modern Pacific show relatively low δ13CDIC values  do not individually influence δ13CDIC in the same direction. It is possible that the main background driver controlling both processes is the deglacial change in sea level. The decreased area of continental shelves during the LGM in comparison to interglacial conditions led to a lower benthic denitrification and thus higher [NO3−], and a lower burial rate of organic carbon and thus a lower mean oceanic δ13C. Strong nitrogen cycle feedbacks are required to realistically model deglacial δ15N10. In this case, the main factor controlling the oceanic NO3− budget would indeed be the change in benthic denitrification due to the extension of shelf seafloor. The mean oceanic δ15N is controlled by the ratio of pelagic to benthic denitrification28 and the balance from N2 fixation. The decrease of δ15Nbulk in the Eastern Tropical North and South Pacific starting ~12 kyr BP can indeed be modeled by increasing the ratio of benthic to pelagic denitrification28 since benthic denitrification fractionates δ15N much less than pelagic denitrification.The fact that the correlation between δ13CDIC and [NO3−] in different water masses is the ventilation and thus their reservoir age. Consistent evidence from different studies indicate a poorly ventilated deep Pacific during the LGM47. Data from the Eastern Equatorial Pacific (EEP) is very scarce, though, and shows some strong contrasts between different sampling locations and approaches49. Nevertheless, it is likely that deep water masses at the EEP were also poorly ventilated during the LGM. This older water mass would increase [NO3−] and reduce δ13CDIC by remineralization, as well as reduce oxygen concentration ([O2]) at these depths.Another factor, controlling both δ2] during the LGM at depths similar to our sampling location50. This is consistent with other redox proxy records from shallower depths in the Peruvian upwelling region, which indicated a less pronounced ODZ and lower primary productivity during the LGM51. Indeed, the accumulation rates of organic carbon (Acc. Rate. Corg) at our sampling site were lower during the LGM52 which also indicates a lower primary productivity above this sampling site , but also is affected by iron fertilization at high latitudes. Iron fertizliation decreases preformed nutrients in SAMW and shallow Antarctic Intermediate Water (AAIW), where our core location exists, of the Pacific due to the transfer of more remineralized nutrients to the deep Pacific. This process is observationally constrained in the 3D model by direct comparison to δ15Nbulk across the Southern Ocean  measurements of the cores were done with three to six individuals of benthic foraminiferal species Uvigerina peregrina. The tests of single species were crushed. Isotopic measurements were done with a Thermo Scientific MAT253 mass spectrometer equipped with an automated CARBO Kiel IV carbonate preparation device at GEOMAR, Kiel. Isotope values were reported in per mil (‰) relative to the VPDB (Vienna Pee Dee Belemnite) scale and calibrated vs. NBS 19  as well as to an in-house standard (Solnhofen limestone). Long-term analytical accuracy (1-sigma) for δ18OFORAM and δ13CFORAM was better than 0.06‰ and 0.03‰ on the VPDB scale. The data for δ18O and δ13C is listed in the Supplementary Table Core M77/2-52-2 was recovered during R/V 15N) measurements, 10–15 mg of freeze dried bulk sediments were analyzed using a Thermo Scientific Flash 2000 Elemental Analyzer coupled to a Thermo Scientific Delta V Advantage isotope ratio mass spectrometer (IRMS) at NIOZ, Texel. Results were expressed in standard δ-notation relative to atmospheric N2 and the precision as determined using laboratory standards calibrated to certified international reference standards were <0.3‰. The data for δ18O, δ13C, and δ15N are listed in the Supplementary Table For the stable nitrogen isotope δ5N measur14C AMS dating measurements performed at Beta Analytic, Inc., Florida, USA, on the planktonic foraminifera species Neogloboquadrina dutertrei57. Conventional radiocarbon datings were calibrated applying the marine calibration set Marine 1358 and using the software Calib 7.059. Reservoir age of 102 yrs was taken into account according to the marine database . Ages are expressed in thousands of years (kyr) before 1950 AD . The radiocarbon based chronology of the core was supplemented and tuned using Analyseries software with δ18O record from a nearby core M77/2-059-160 and the Antarctic EPICA δ18O reference stack61.The already published age model is based on five Bolivina spissa specimens were picked of the >63 µm fraction from each sample of sediment core M77/2-52-2. All of the 819 specimens were mounted on aluminum stubs by the use of adhesive carbon pads. They were not sputter coated to preserve them for future geochemical analyses. Scanning electron micrographs were produced for every single individual using a Hitachi table top scanning electron microscope (TM3000 accelerating voltage of 5–15 kV and using back-scattered electrons (BSE) detector. Following the method published in ref. 17 to minimize ontogenetic effects we determined the pore density of the first (youngest) ten chambers only, relating to an area of about 50,000–60,000 µm2. For the calculation of [NO3−]BW we used the data shown in Fig. 7d of ref. 17. We plotted the data inversely to achieve a function in the form of Eq. 3−]BW is the reconstructed bottom water nitrate concentration and PD is the pore density of B. spissa. The corresponding linear fit is shown in the Supplementary Figure 3−]BW.Depending on the availability 7–22 3−]BW a complete error propagation has been done including both the uncertainty of the mean PD within the samples and the uncertainties of the calibration function. The error propagation has been applied to Eq. 3−]BW, a, b and PD). Considering Eq. For the calculation of the errors for the reconstructed NO−BW a comn is the number of specimens analyzed in each sample. The results for each sample are summarized in Supplementary Table The standard error of the mean (SEM) for one sample was then calculated according to Eq. mentclass2pt{minim13CDIC and [NO3−] are taken from the GLODAPv2 database34. The Ocean Data View (ODV) software has been used to compile the plots for Fig. 2a, b62. The dataset, which is shown in Fig. 13C on DIC and [NO3−]. Due to graphical reasons, all δ13C below −1‰ have been cut Fig. 13C mainly follow the distribution of anthropogenic CO2 in the Pacific ; 5°31’S; 85°50’W; 1278 m; [NO3−] = 41.1 µmol/l). This concentration was also used to calculate the Δ[NO3−] for the downcore data from the pore density shown in Fig. All data for recent δ7, which is based on the UVic Earth System Climate Model63 with a version of Kiel biogeochemistry64. The physical ocean-atmosphere-sea ice model includes a three-dimensional  general circulation model of the ocean (Modular Ocean Model 2) with parameterizations such as diffusive mixing along and across isopycnals, eddy-induced tracer advection65, computation of tidally-induced diapycnal mixing over rough topography including sub-grid scale66, as well as anisotropic viscosity67 and enhanced zonal isopycnal mixing schemes in the tropics to mimic the effect of zonal equatorial undercurrents68. A two-dimensional, single level energy-moisture balance atmosphere and a dynamic-thermodynamic sea ice model are used, forced with prescribed monthly climatological winds69 and ice sheets70.We use an improved model version of Somes et al.71 protocols as closely as possible with our model setup. This includes lower atmospheric concentrations of the greenhouse gases carbon dioxide, nitrous oxide, and methane, changes of Earth’s orbit, and the increased area and height of ice sheets70. The ocean grid bathymetry and total ocean volume remains unchanged relative to the pre-industrial simulation. However, effects of reduced sea level on sedimentary N loss are accounted for by calculating a new sub-grid scale bathymetry scheme assuming a constant 120 meters sea level reduction. An improved atmospheric Fe mask based on Somes et al., 20177 was applied by optimizing the model with δ15N observations  and organic (DON and DOP) phases, 2 phytoplankton (ordinary and N2-fixing diazotrophs), zooplankton, sinking detritus, as well as dissolved O2, dissolved inorganic carbon, alkalinity, and Δ14C64. Iron limitation is calculated using monthly surface dissolved iron fields prescribed from the BLING model72.The marine ecosystem-biogeochemical model coupled within the ocean circulation includes 2 nutrients in the inorganic  and internal-cycling  processes in the model73. N2 fixation introduces isotopically light atmospheric nitrogen into the ocean (δ15NNfix = −1 ‰), whereas N loss fractionates strongly in the water column (ɛWCNl = 20‰) and lightly in the sediments (ɛSedNl = 3.75‰). NO3− uptake by phytoplankton fractionates NO3− at 6‰ in the model. Zooplankton excretion fractionates at 4‰ enriching its biomass in 15N relative to phytoplankton. DON remineralizes with a fractionation factor of 1.5‰ to reproduce upper ocean δ15N-DON observations mainly occurring within the range of 3–6‰74. For a detailed discussion about offsets between measurements and modeling of the modern δ13CDIC-[NO3−]-coupling see Supplementary Note 4 and Supplementary Figure Nitrogen isotopes are fractionated by inventory-altering  and Late Holocene (0–6 kyrs BP). Four downcore datasets from two additional references76 were added which were not included in ref. 37. The results are compiled in Supplementary Table 3− inventories which is shown in Fig. Cibicidoides spp. were used. These stations are clearly marked in Supplementary Table 3−] was 3.0  µmol/kg higher during the LGM. The mean offset between LGM and Holocene is slightly lower (2.3 ± 0.5 µmol/kg (1 SEM); N = 23) if also the sampling locations outside the Pacific are included. For a regional comparison of deglacial NO3− changes, including all stations see Supplementary Figure 3− inventory during the LGM. Only individual stations in the Sea of Japan, the East China Sea and on the Southern Australian Continental Margin indicate no changes between LGM and Late Holocene. One station at the Southern Australian Continental Margin even indicated a depletion of NO3− during the LGM compared to the late Holocene. The highest deglacial [NO3−] changes are located a station south of New Zealand and in the Sea of Okhotsk, which is probably related to the high latitudes of these sediment cores. The Sea of Okhotsk was still covered by ice during the LGM.A compilation of deglacial δ3−]BW, δ18O, δ13C and δ15N for sediment record M77/2-52-2 is available in Supplementary Table 13CFORAM and the reconstructed deglacial [NO3−] offsets for all records from the intermediate Pacific is available in Supplementary Table 13CDIC-[NO3−] coupling are available on the GEOMAR Thredds Server (https://thredds.geomar.de).All data which support the findings of this study are either available online or within the Supplementary material. The foraminiferal pore density, reconstructed NO−BW, δ18OSupplementary Information(PDF 2844 kb)Peer Review File(PDF 1999 kb)


Neisseria meningitidis is a Gram-negative bacterial pathogen that causes life threatening meningitis and septicemia. Neisseria Heparin Binding Antigen (NHBA) is an outer membrane protein that binds heparin and heparan sulfate and DNA. This protein is one of the four antigens in the meningococcal serogroup B vaccine Bexsero. In the current study, we sought to define the full glycan-binding repertoire of NHBA to better understand its role in meningococcal pathogenesis and vaccine efficacy. Glycan array analysis revealed binding to 28 structures by recombinant NHBA. Surface plasmon resonance was used to confirm the binding phenotype and to determine the affinity of the interactions. These studies revealed that the highest affinity binding of NHBA was with chondroitin sulfate (KD = 5.2 nM). This affinity is 10-fold higher than observed for heparin. Analysis of binding with well-defined disaccharides of the different chondroitin sulfate types demonstrated that the most preferred ligand has a sulfate at the 2 position of the GlcA/IdoA and 6 position of the GalNAc, which is an equivalent structure to chondroitin sulfate D. Chondroitin sulfate is widely expressed in human tissues, while chondroitin sulfate D is predominantly expressed in the brain and may constitute a new receptor structure for meningococci. Neisseria meningitidis is a Gram-negative bacterial pathogen that causes life threatening meningitis and septicemia1. The organism is susceptible to antibiotics; however, it is difficult to diagnose at early stages and can rapidly progress to a life-threatening disease. The combination of difficult diagnosis and rapid progression indicates that vaccination is the most effective and appropriate public health response to this organism. There are 13 serogroups of N. meningitidis based on the expression of different polysaccharide capsule structures. Serogroup A is the dominant serogroup in sub-saharan Africa, where the highest burden of disease exists2. Serogroups B and C predominate in the developed world3, although serogroups X and W-135 are also emerging as a major cause of disease in several regions worldwide3. The polysaccharide capsules that are the basis of these serogroups have been used as antigens in highly successful conjugate vaccines, including monovalent serogroup A and C vaccines, and four-valent vaccines comprising A, C, W-135 and Y capsule polysaccharides4. The same capsule-based vaccine approach cannot be used for serogroup B strains, as the alpha-2-8 polysialic acid expressed by these strains is a structural mimic of human neural cell adhesion molecule (NCAM)5 and may induce immunopathology if used as a vaccine antigen. Therefore, serogroup B vaccine development had to move in a different direction, utilizing surface proteins of this highly variable pathogen.7. These outer membrane proteins provide targets for complement-mediated serum bactericidal activity6. They also provide an opportunity for functional blocking as each is proposed to play a key role in N. meningitidis pathogenesis, and understanding the functional roles of these surface antigens enables a better understanding of how the vaccine is functioning to protect against infection. NadA is an adhesin8 and fHBP binds to human factor H in serum reducing the efficiency of complement mediated killing10. NHBA binds heparin11 and heparan sulfate proteoglycans12 via an arginine (Arg)-rich region. Two proteases, the phase variable meningococcal NalP and human lactoferrin have been shown to cleave NHBA upstream and downstream of the central Arg-rich region, respectively11. The functional significance of these cleavage events is yet to be elucidated. NHBA binding to heparin mediates increased serum resistance11, potentially via interactions between heparin and factor H or C4b binding protein13. NHBA is also involved in meningococcal adherence to epithelial cells, via binding to heparan sulfate proteoglycans12. NHBA also binds to DNA, and although this interaction has not been fully characterized, NHBA-DNA binding promotes biofilm formation14. The nhbA gene is found in all meningococcal strains tested15.In 2013, a serogroup B vaccine  was licensed for use. This vaccine contains four components; outer membrane vesicles from a serogroup B strain, formulated with three recombinant proteins NadA, fHBP and NHBAThe lectin (carbohydrate binding) activity of NHBA for heparin and heparin sulfate has been well characterized and is believed to be important for meningococcal pathogenesis. In the current study, we sought to investigate the full range of lectin activity of NHBA using glycan array analysis.nhbA mutant strain (Ȼ3ΔnhbA). Fluorescently labelled bacteria were incubated on the Institute for Glycomics’ v3.0 glycan array16. The results shown in Fig. nhbA strain does not bind. Direct binding to glycans by NHBA was also investigated using purified recombinant NHBA, which revealed binding to 28 glycan structures on the array. Twenty-two of these structures were in common with those bound by the NHBA expressing strain. Seven structures bound both the recombinant NHBA and the wild type strain (but not the Ȼ3ΔnhbA strain) indicating NHBA-specific and -dependent binding 18 or pili19 that can negatively affect interactions mediated by other structures. The observed NHBA-dependent lectin activity includes the glycosaminoglycans (GAGs) heparin, heparan sulfate, heparinase III digests, hyaluronan, chondroitin sulfate, as well as glucose and the ganglioside GT3  in comparison with an isogenic GT3 Fig. .Figure 1nhba mutant strain) and that either also bound recombinant NHBA (Table D) of 5.2 nM. NHBA was named based on its interaction with heparin11, and binding to heparan sulfate has subsequently been described12. The affinity of NHBA with heparin (13J) was KD = 52 nM, and its affinity with heparan sulfate (14J) was KD = 1.4 µM. Heparin is more sulfated than heparan sulfate20, and these data suggest that the sulfation of heparin may be required for high affinity binding by NHBA. We conducted studies with disaccharides representing all possible sulfations of heparin. However, all of these disaccharides showed low affinity binding to NHBA, indicating that NHBA requires a larger binding epitope for the higher affinity interaction observed with the heparin polymer structure . In these studies, recombinant NHBA was bound covalently to a biosensor chip and various glycans were analyzed in flow for binding to NHBA , which is equivalent structure to chondroitin sulfate D. Reduced affinity was observed with a disaccharide that has sulfation at the 2 position of the GlcA/IdoA and 4 position of the GalNAc (KD = 500 nM). No binding was seen if the disaccharide was sulfated at all three positions  or with disaccharides sulfated at a single position  is flown across the bound protein while the first analyte (A) is present. Analysis of the resultant sensorgrams showed that the binding of chondroitin disaccharide D to NHBA was not able to inhibit the subsequent binding of heparin or heparan sulfate polymers to the NHBA protein, as an additive effect was observed when the disaccharide was added Fig. . When he14 so this was further investigated using SPR and ITC. SPR analysis demonstrated interaction between DNA and NHBA with a KD of 0.071 µM (±0.017), but saturation was not reached indicating an interaction that did not have 1:1 stoichiometry 18, capsule18 and pili19; which have been shown to negatively affect interactions mediated by other structures.The initial screen using glycan array analysis and the N. meningitidis Ȼ3 using whole fluorescently labelled bacteria. These data indicate that NHBA has a much broader lectin activity than binding to heparin; the ligand binding activity after which it was named. The majority of the newly identified glycans bound by NHBA are sialylated and/or sulfated, which is consistent with the previously described negatively charged ligands of NHBA, which include heparin11, heparan sulfate12 and DNA14. Furthermore, the majority of the glycans to which NHBA bound were GAGs and included chondroitin sulfate polymers and digests as well as hyaluronan.NHBA lectin activity was confirmed using recombinant NHBA protein. The recombinant NHBA protein bound to 28 structures on the glycan array, and binding to 22 of these structures was also observed by the wild type 26. Heparan sulfate proteoglycans are typically found on epithelial cells, endothelial cells and fibroblasts27, while chondroitin sulfate proteoglycans are typically found on lymphocytes27, within the central nervous system28, and are highly expressed on endothelial cells29. It is important to note that the expression of different chondroitin sulfate types is tissue and host specific and is affected by factors such as age and disease26. Chondroitin sulfate D is found in the adult human brain, while chondroitin sulfate A and C are typically found in cartilage and other tissues26.Heparan sulfate and chondroitin sulfate are found on a range of host cell surfaces and in the extracellular matrixD = 5.2 nM). The chondroitin sulfate used is a natural product that contains a mixture of sulfation patterns . Therefore, alternative sources of chondroitin sulfate polymers were also used to address the question of whether the alternate sulfation patterns played a role in NHBA affinity. Results indicate that NHBA prefers chondroitin sulfate D , as there was a 10,000-fold decrease in affinity to chondroitin sulfate C and no binding to chondroitin sulfate A or B. A source of pure chondroitin sulfate D was not available, however studies using the repeating subunit disaccharide structures that make up the chondroitin sulfate polymer confirmed that the chondroitin sulfate type D disaccharide was the preferred substrate with a KD of 9.5 nM. This is a remarkably high affinity considering that it is a small disaccharide subunit that does not have the polyvalency of high molecular weight chondroitin sulfate polymer.In this study, for the first time, we present a quantitative measure of the binding affinity to the known ligands of NHBA, heparin and heparan sulfate, and also to the newly discovered glycan structures. The highest affinity interaction was between NHBA and chondroitin sulfate (KD = 52 nM), and 200-fold higher than for heparan sulfate (KD = 1.362 µM). It is interesting to consider that the binding to heparin with an affinity of 52 nM can only be achieved with the polymer. This is unlike chondroitin sulfate, which has nanomolar affinity for both polymer and disaccharide. Heparin disaccharides all had affinities approximately 500-fold weaker (KD > 2.5 µM) than we observed for the equivalent chondroitin sulfate disaccharide. Unlike chondroitin sulfate, this indicates the high affinity interaction observed in SPR with heparin requires polyvalent binding. Previous studies have indicated that NHBA-mediated adherence to epithelial cells  is heparan sulfate-dependent and chondroitin sulfate-independent12. This observation is not consistent with the high affinity NHBA-chondroitin sulfate interaction that we observed herein. However, the expression of different chondroitin sulfate types is variable between different tissues26 and all cancer cell lines that have been investigated do not express the preferred NHBA ligand, chondroitin sulfate D31. As a result, one would not expect to see NHBA binding to the atypical chondroitin sulfate types expressed by transformed cell lines typically used in cell association studies.The affinity of NHBA for chondroitin sulfate is 10-fold higher than for the previously described ligand heparin  that is transiently expressed in the brain during embryonic development33 and is found on adult oligodendrocyte progenitor cells in the human adult brain34. GT3 consists of three negatively charged side branching sialic acid residues that may mimic the presentation of the sulfated GAGs and explain the binding of NHBA to this otherwise unrelated structure.The only other high affinity NHBA interaction in the nanomolar range that was observed was with a non-GAG structure, the ganglioside GT3  and the isogenic Ȼ3∆nhbA mutant (generated as described previously11) were grown on BHI supplemented with 10% Levinthal’s base and incubated overnight at 37 °C at 5% CO2. Levinthal’s base is prepared by adding defibrinated horse blood to three volumes of BHI broth, heating at 95 °C for 40 mins, followed by centrifugation  to remove to remove insoluble material from the lysed blood.nhbA gene was amplified from N. meningitidis MC58  and cloned into pET19b (Invitrogen). Protein expression and purification was performed as described previously35.The et al. using array v3.0 from the Institute for Glycomics and a ProScan Array Microarray scanner (Perkin Elmer) 555ex/568em19. Briefly, 125 μL of Bodipy558-succinimidyl ester labelled bacteria (OD 600 = 0.1) were added to the array slide and hybridized for 30 min. Analysis of recombinant NHBA was performed as per Day et al.36, using 1–2 μg of His-tagged protein. Binding was defined as positive if the average fluorescence intensity of the glycan spots was greater than one-fold above the adjusted background (average of the slide background plus three standard deviations) in three independent replicates (Student’s t-test p < 0.001).Glycan array analysis was performed as per Mubaiwa D) of each interaction, with analytes run in a 1:5 dilution series at concentrations ranging between 1 nM to 100 µM. Glycans or DNA were injected at a flow rate of 10 μl/min, or 30 μl/min for polymers larger than 10 kDa, with a contact time of 60 sec and a dissociation time of 600 sec. Duplicate datasets was analyzed using the BIAcore T200 evaluation software 2.0.2; sensorgrams were double reference subtracted.SPR was performed using a BIAcore T200 instrument and Series S CM5 sensor chips . Recombinant NHBA (100 µg/ml) was immobilized by amine coupling (10 µL/min flow rate onto flow cells 2–4), resulting in ~8000 response units (RU) of immobilized protein. The reference surface (flow cell 1) underwent the same treatment, without protein injection. Single cycle kinetics was used to generate the affinity (KD) and PBS controls, with 120 second injections of analyte A to ensure saturation or near-saturation was reached prior to competition with analyte B. The results were analyzed using BIAcore S200 evaluation software.SPR competition assays were performed by using a BIAcore S200 instrument and the A-B-A inject function. Recombinant NHBA was immobilized as above. The A-B-A was used with combinations of each of the glycan substrates (at concentration 10 × KN. meningitidis genomic DNA (using primers 5′-GGTCGTGTTCCAAAAGACGGG-3′ and 5′-GAAGCCTGAATGCCTTTCCAGC-3′), cleaned up using a QIAGEN PCR purification kit and quantified using a nanodrop. ITC was performed using a nano-ITC (TA instruments) with 10 µM NHBA titrated into 1 µM of DNA . PBS-NHBA interactions were run as a negative control. This was also background subtracted. Affinity data was determined for a minimum of two repeats and the average KD values and stoichiometry (n) are reported.A 503 bp DNA fragment was amplified from Supplementary information


P < 3.39 × 10− 7) was tested using generalized estimating equation models. The longitudinal EWASs revealed novel relations of five SNVs to renal function-related traits. Cross-sectional data for renal function-related traits in 7699 Japanese subjects were examined in a replication study. Among the five SNVs, rs55975541 in CDC42BPG was significantly (P < 4.90 × 10− 4) related to the serum concentration of uric acid in the replication cohort. We also examined the SNVs detected in our longitudinal EWASs with the information on P values in GKDGEN meta-analysis data. Four SNVs in SLC15A2 were significantly associated with the estimated glomerular filtration rate in European ancestry populations, although these SNVs were related to the serum concentration of uric acid with borderline significance in our longitudinal EWASs. Our findings indicate that CDC42BPG may be a novel susceptibility locus for hyperuricemia.Chronic kidney disease and hyperuricemia are serious global health problems. Recent genome-wide association studies have identified various genetic variants related to these disorders. However, most studies have been conducted in a cross-sectional manner. To identify novel susceptibility loci for chronic kidney disease or hyperuricemia, we performed longitudinal exome-wide association studies (EWASs), using ~ 244,000 genetic variants and clinical data of Japanese individuals who had undergone annual health checkups for several years. After establishing quality controls, the association of renal function-related traits in 5648 subjects  with 24,579 single nucleotide variants (SNVs) for three genetic models (The online version of this article (10.1007/s00438-017-1394-1) contains supplementary material, which is available to authorized users. UMOD gene as a CKD susceptibility locus  with rs12917707 was not strong (r2 = 0.02), although rs12917707 was excluded from the GWAS meta-analysis because of low minor allele frequency (MAF < 0.01)  and hyperuricemia are caused by renal function abnormalities and are multifactorial disorders resulting from interactions between genetic background and environmental factors. The goal of the present study was to identify novel susceptibility loci for CKD or hyperuricemia in Japanese individuals. CKD is a global health problem and an independent risk factor for cardiovascular diseases   with the use of exome-array-based genotyping methods to explore novel susceptibility loci for CKD or hyperuricemia in the Japanese cohort.A total of 5648 community-dwelling individuals were recruited from those who visited the Health Care Center of Inabe General Hospital  for an annual health check-up from April 2003 to March 2014. All participants had each undergone 1–11 medical examinations, and the average follow-up period was 5 years. We refer to this cohort as the “discovery cohort.” Methods for the collection and storage of medical examination data and genomic DNA samples have been described previously  = 194 × [age (years)]−0.287 × [serum creatinine (mg/dL)]−1.094 × [0.739 if female]. The National Kidney Foundation-Kidney Disease Outcomes Quality Initiative guidelines recommend a diagnosis of CKD if eGFR is < 60 mL min− 1 1.73 m− 2  for the diagnosis of CKD. The control individuals for the EWAS of CKD had an eGFR of ≥ 90 mL min− 1 1.73 m− 2  and did not appear to have functional or structural abnormalities of the kidneys or a history of renal disease. Subjects with hemodialysis or peritoneal dialysis were excluded from the study.Glomerular filtration rate was estimated with the use of the simplified prediction equation derived from the modified version of that described in the Modification of Diet in Renal Disease (MDRD) Study, as proposed by the Japanese Society of Nephrology , as proposed by the Japanese Society of Gout and Nucleic Acid Metabolism  in controls, and (4) SNVs located on mitochondrial DNA or sex chromosomes. Among Inabe subjects, four were identified as population outliers by principal component analysis (PCA) of SNVs, using the EIGENSTRAT method  were used for genotyping subjects in the discovery cohort. These arrays include putative functional exonic variants selected from > 12,000 individual exome and whole-genome sequences across diverse ethnic populations  of P values ranged from 1.05 to 1.21.Next, we converted the genotyping data of the subjects into numeric data with dominant, additive, and recessive models, resulting in 24,579 SNVs. The dominant and recessive models were defined as “0, AA; 1, AB + BB” and “0, AA + AB; 1, BB” , respectively, whereas the additive model was defined as “0, AA; 1, AB; 2, BB”. Quantile–quantile plots for the P < 3.39 × 10− 7  for each genetic model, after Bonferroni’s correction to compensate for multiple comparisons of genotypes with the clinical parameters. Sitlani et al. , we used the generalized estimating equation (GEE) model .The association of candidate SNVs identified from our longitudinal EWASs was tested in the replication cohort using the Chi-square test  or linear regression analysis (for quantitative data). In addition, we also examined relations of the candidate SNVs identified, using information on https://analysistools.nci.nih.gov/LDlink/,  of rs11543349 in OGFR to serum creatinine concentration and eGFR  associated with the prevalence of hyperuricemia or serum concentration of uric acid in the three genetic models, and their approxdf values were > 30 (Online Resource 4). Of these SNVs, nine are located at seven gene loci  that might be novel susceptibility loci for hyperuricemia. However, five candidate SNVs at 12q24.1 exhibited moderate or strong LD (0.43 ≤ r2 ≤ 0.99) with rs671 of ALDH2 that has been reported to be associated with renal function-related traits   were identified as candidates for novel susceptibility loci that might be related to the serum concentration of uric acid. Of these candidates, the association of rs55975541 in CDC42BPG showed the significance level with P = 3.7 × 10− 12 in the dominant model (Online Resource 4).The GEE model with adjustments for age and gender showed that 16 SNVs were significantly (CDC42BPG was significantly [P < 4.90 × 10− 4 (0.05/17 SNVs × 6)] related to the serum concentration of uric acid. We also assessed relations of the candidate SNVs detected in the discovery cohort with CKD or hyperuricemia, using GKDGEN meta-analysis data on European or African ancestry populations (Online Resource 7). The in silico replication study did not show significant association with the five candidate SNVs that were newly identified in the discovery cohort. However, four SNVs  in SLC15A2 were significantly (P < 4.90 × 10− 4) associated with eGFR in European ancestry populations, although these SNVs were related to the serum concentration of uric acid with borderline significance (P = 5.50 × 10− 7 to 6.40 × 10− 7) in our longitudinal EWASs.We examined the relation of 17 candidate SNVs identified from our longitudinal EWASs to CKD or hyperuricemia using the Chi-square test or linear regression analysis, using cross-sectional data for CKD and hyperuricemia-related traits in 7699 Japanese subjects of the replication cohort (Online Resource 6). In this replication study, rs55975541 in CDC42BPG was affected by other previously reported SNVs that were not included in the exome arrays used and which exhibited strong LD. LDproxy, an LDlink application, indicated rs55975541 in CDC42BPG was in LD  with rs80094634 only in JPT  from the 1000 Genomes Project database [http://www.internationalgenome.org, . According to the Ensembl human database (http://www.ensembl.org/Homo_sapiens/), it is possible that the nonsynonymous substitution has an effect on the protein function because the amino acid substitution was predicted to be “probably damaging” with scores of zero by SIFT prediction and 0.998 by PolyPhen analysis. Therefore, rs55975541 is more likely to be related to hyperuricemia than, the non-genotyped SNV, rs80094634. The LDproxy analysis also showed that rs55975541 in CDC42BPG was not in LD with any genetic variants in the established SLC22A11, SLC22A12, and NRXN2 loci (Online Resource 8).We further investigated whether the allele frequency of the replicated target SNV in ABCG2, GCKR, and UMOD, were significantly (P < 3.39 × 10− 7) associated with renal function-related traits in any of the three genetic models. In the discovery cohort, MAFs of some previously reported SNVs were less than 5%. For instance, the SNV rs121907892 of SLC22A12 was significantly associated with the prevalence of hyperuricemia and serum concentration of uric acid in all the genetic models (P < 2.0 × 10− 16). However, rs121907892 was excluded from our longitudinal EWAS because of low minor allele frequency (MAF < 0.03). The differences in significance levels between the present and previous studies might be due to sample sizes, statistical methods, or differences in genetic background among the populations examined.We comprehensively assessed significance levels of 291 SNVs at 55 previously known CKD- or hyperuricemia-associated loci (Online Resource 9). Of the SNVs examined, 103 at 37 loci, including CDC42BPG. In the longitudinal EWAS for the discovery cohort, the SNV in CDC42BPG was significantly (P = 3.7 × 10− 12) associated with the serum concentration of uric acid. The association of rs55975541 was determined using cross-sectional data for the serum concentration of uric acid of 7699 Japanese subjects in the replication cohort. The LDproxy analysis suggests that this SNV independently affects the incidence of hyperuricemia.In the present study, 17 SNVs showed significant relations with CKD- or hyperuricemia-related traits. Five of these SNVs have not been reported in previous studies. However, the relation of these SNVs to CKD or hyperuricemia could not be found in the replication study, except for rs55975541 in CDC42BPG is ubiquitously expressed in a variety of tissues and organs including the kidney, according to The Human Protein Atlas database (http://www.proteinatlas.org/). This protein may act as a downstream effector of CDC42 in the regulation of cytoskeletal reorganization . The amino acid change may have an effect on the protein function, as predicted by SIFT and PolyPhen. Although CDC42BPG may be a susceptibility locus for renal function, the functional relevance of the candidate SNV to the serum concentration of uric acid remains unclear.MRCKγ encoded by SLC genes and hyperuricemia-related traits in diverse ethnic groups  in SLC15A2 were significantly associated with eGFR in European ancestry populations from GKDGEN meta-analysis data, whereas these SNVs were related to the serum concentration of uric acid with a borderline significance in our longitudinal EWASs.Despite many studies of associations between several There were some limitations to the present study. First, the longitudinal EWAS was conducted in a local Japanese population only. Although we examined relations of candidate SNVs to CKD- or hyperuricemia-related traits in the replication studies, these studies were conducted in a cross-sectional manner. Therefore, replication of longitudinal EWASs in other Japanese populations or other ethnic groups is required to verify the relations of the identified SNVs to the relevant diseases. Second, the functional relevance of the candidate SNV to the pathogenesis of the diseases examined remains unclear. Therefore, further functional analysis is required to clarify the results of this study.CDC42BPG was significantly associated with the serum concentration of uric acid in our EWASs. Genotyping for the SNV detected in our longitudinal EWAS may be informative for assessment of genetic risk for hyperuricemia.In conclusion, our findings indicated that rs55975541 in Below is the link to the electronic supplementary material.Supplementary material 1 (PDF 222 KB)Supplementary material 2 (PDF 228 KB)Supplementary material 3 (PDF 222 KB)Supplementary material 4 (PDF 206 KB)Supplementary material 5 (PDF 208 KB)Supplementary material 6 (PDF 242 KB)Supplementary material 7 (XLSX 17 KB)Supplementary material 8 (PDF 158 KB)Supplementary material 9 (XLSX 93 KB)