Background: Exposure to adverse childhood experiences (ACEs) is a risk factor for poor later life health. Here, we describe the ACE variables measured in the children of the Avon Longitudinal Study of Parents and Children (ALSPAC) study, and a method used to derive summary measures and deal with missing data in them.   Methods: The ALSPAC data catalogue (59 608 variables) was searched in September 2017 for measures on adversity exposure between birth and 18 years. 6140 adversity questions were then screened for conforming to our ACE definitions and suitability for dichotomisation. This screening identified 541 questions on ten ‘classic’ ACEs  and nine additional ACEs . These were used to derive a binary construct for exposure to each ACE. Finally, as cumulative measures of childhood adversity, different combinations of the 19 ACE constructs were summed to give total adversity scores. An appropriate strategy for multiple imputation was developed to deal with the complex patterns of missing data.Results: The ACE constructs and ACE-scores for exposure between birth and 16 years had prevalence estimates that were comparable to previous reports .Conclusions: ACE constructs, derived using a pragmatic approach to handle the high dimensional ALSPAC data, can be used in future analyses on childhood adversity in ALSPAC children. Studies show a graded relationship between ACEs and poor outcomes, with the more ACEs a person suffers the greater their risk for many health conditions  and broader experiences of household dysfunction , although it has been argued other types of adversities  should be included5 and many ACE studies incorporate additional adversities2.Exposure to Adverse Childhood Experiences (ACE) is associated with substantial health consequences2, we also derive ACE count score measures. In this Data Note, we describe the processes used to derive the ACE measures and resources available for researchers to use in their own studies, and we provide descriptive statistics of the ACE measures.Our goal was to derive measures for childhood adversity for the children of a British birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). In this cohort, a vast array of detailed adversity data has been obtained from multiple parent- and child-completed questionnaires administered throughout childhood and adolescence. Using these data presents challenges given the repeated measures, differences in measurement tools across time, and complex missing data patterns. Given the known co-occurrence of multiple forms of adversity and the potential presence of cumulative effects on healthALSPAC recruited 14,541 pregnant women resident in Avon, UK (former county covering Bristol and the surrounding areas in the South West UK) with expected dates of delivery 1st April 1991 to 31st December 1992. Each enrolled mother either returned at least one questionnaire or attended a “Children in Focus” clinic by 19/07/99. Of these initial pregnancies, there were a total of 14,676 foetuses, resulting in 14,062 live births and 13,988 children who were alive at 1 year of age.7.When the oldest children were approximately 7 years of age, an attempt was made to bolster the initial sample with eligible cases who had failed to join the study originally. As a result, when considering variables collected from the age of seven onwards there are data available for more than the 14,541 pregnancies mentioned aboveThe total sample, including later enrolment phases, is 14,775 live births and 14,701 alive at 1 year of age. Note that for reasons of confidentiality questionnaire data belonging to children from triplet or quadruplet pregnancies have been removed, resulting in 14,691 eligible participants.searchable data dictionary.The mothers, their partners and the index child have been followed-up using clinics, questionnaires and links to routine data. Please note that the study website contains details of all the data that is available through a fullyWe restricted our derivation of ACE measures to the 12087 children who answered at least 10% of the 541 questions on ACE exposure between 0–18 years.Ethical approval for the study was obtained from the ALSPAC Law and Ethics Committee and the Local Research Ethics Committees.5, previous research on childhood adversity in ALSPAC12 and discussion between LDH and LCH on ACEs to include, we searched for ALSPAC data on 20 ACEs. Ten of the twenty ACEs are frequently used in other research3: i. sexual abuse, ii. physical abuse, iii. emotional abuse, iv. emotional neglect, v. substance abuse by the parents, vi. parents have mental illness or attempted to commit suicide, vii. violence between parents, viii. parental separation, ix. bullying and x. parent convicted of an offence, whereas the other nine ACEs were either suggested more recently5, examined in ALSPAC before9 or identified as relevant by LDH and LCH when deciding which ACEs to include: xi. bond between parent and child, xii. satisfaction with neighbourhood, xiii. social support for the parent, xiv. social support for the child, xv. physical illness of a parent, xvi. physical illness of the child, xvii. financial difficulties, xviii. low social class, xix. violence between child and partner and xx. crowded housing.Based on previous literature on ACEs12. 6487 of the 12083 questions were classified into the 20 ACEs. After careful examination of the questions and response possibilities by LCH and LDH, questions were excluded if they did not conform to our ACE definitions (see section ‘ACE definitions’) or were unsuitable for dichotomisation. Crowded housing was the only ACE that was not included due to the limited number of questions on crowding and coverage of a small age range (birth- 3 years).In September 2017, text searches ;ever sexually abused, forced to perform sexual acts or touch someone in a sexual way ;adult in family was ever physically cruel towards or hurt the child ;parent was ever emotionally cruel towards the child or often said hurtful/insulting things to the child ;child always felt excluded, misunderstood or never important to family, parents never asked or never listened when child talked about their free time (5. substance use);parent was a daily cannabis or any hard drug user, or, had an alcohol problem (6. 13) ;parent was ever diagnosed with schizophrenia or hospitalised for a psychiatric problem, or, during the first 18 years of the child’s life, parent had an eating disorder (bulimia or anorexia), used medication for depression or anxiety, attempted suicide or scored above previously established cut-offs for depression  >127. parents violent towards each other);parents were ever affected by physically cruel behaviour by partner, or, ever violent towards each other, including hitting, choking, strangling, beating, shoving ;parents separated or divorced (9. bullying);child was a victim of bullying on a weekly basis (10. parent convicted);parent was convicted of a crime (11. parent-child bond)child or parent not close to each other and when growing up, child never felt loved (12. satisfaction with neighbourhood);child is not happy living in neighbourhood or would rather move, parent or child describe neighbourhood as bad ;parent never had anyone to share feelings with ;child has no friends, unhappy with number of friends or friends hardly ever support them ;parent hospitalised more than once or had cancer ;child hospitalised more than once or had a medical condition or physical disability ;very difficult to afford food or heating, or, parent was affected by becoming homeless ;highest household social class was in class V (unskilled work) or unemployed, based on mother’s and her partner’s occupations using the 1991 UK Office of Population Censuses and Surveys classification (classes I to V) (19. violence between child and partner);partner of child used physical force or violence, or, made them feel scared  and dividing the ACE-score into four categories . Similarly, an extended ACE-score  and a categorical extended ACE-score , with a similar distribution to the original scores, were derived.Similar to previous studiesR 3.3.1 will be supplied together with the data. We derived ACE measures for the time period 0–16 years, as this is a frequently used time period in previous ACE studies2, but the code can be readily adapted to different time periods between 0–18 years depending on a researcher’s needs. However, note that questions that span a larger time window (e.g. exposure 0–11 years or 0–16 years) than the period of interest (e.g. 0–8 years) would be excluded from ACE construct calculations. The questions and cut offs used to derive the dataset described below are supplied inThe R code used to derive these ACE measures in14. Therefore, for multiple imputation we recommend including two types of auxiliary variables that make the missing-at-random assumption more plausible (seeACE measures were derived for participants who responded to at least 50% of the questions (ACE-derived); these participants are more affluent than the full cohort, and including only these participants in analyses will lead to lower ACE prevalence estimates and may induce selection biasible see:1. sociodemographic indicators that are associated with both missingness and many of the ACEs   or between 18–21 years ). However, as adversity exposure may be rare we recommend only including questions with at least 50 adversity exposed participants in your own imputation model.mice package version 2.46.0 in R3.3.1 with 30 iterations per dataset15, based on the rule of thumb that the number of imputed datasets should be at least equal to the percentage of incomplete cases  and for some variables the imputation model converged after 20 iterations16.To illustrate the use of multiple imputation using these two types of auxiliary variables, we compare prevalence estimates for the imputed ACE constructs for exposure between 0–16 years to the prevalence estimates in the ACE-derived group. To preserve potential interactions between gender and adversity in relation to later outcomes, males (n=6214) and females (n=5873) were imputed separately before appending the two datasets. For both males and females, 90 imputed datasets were created using the16. For instance, our multiple imputation was carried out separately for males and females to enable examination of gender interactions in the imputed data, but to examine other interactions the imputation model would have to be adapted to reflect this.Do note that the multiple imputation process will need to be re-done for any future applications because the imputation model would have to be compatible with the analysis model(s) being used, so to avoid bias the imputation model should include the exposure, outcome and any covariates, plus any interactions and non-linearities14, sociodemographic indicators  were lower in the ACE-derived group , also our most prevalent ACE, was much higher than other ACE studies but still in line with lifetime mental health prevalence estimates in the US  and Northern Ireland . The correlation  between the individual ACEs varied from low to medium with ϕc ranging 0 to 0.32 , and were less likely to experience emotional neglect, bullying, lack of social support, violence between child and partner and physical illnesses  .Detailed, repeated measures of childhood adversity are available in the ALSPAC study. We extracted 582 variables documenting exposure to 19 different ACEs between birth and 18 years. Overall, ACE exposure prevalence did not differ by age of reporting or data source . The remainder were coded as missing and imputed using multiple imputation. This assumes that the data are missing-at-random given the variables included in the imputation model22. Also, there is more detail in the ALSPAC data that could be exploited, for instance on the subjective impact of ACE exposure. Researchers wishing to use these more detailed data could do so, but this would necessitate further data manipulation. Finally, we relied on the questionnaire and clinical childhood adversity data, but ALSPAC also has linkage data available on looked after children9 and there is future potential of linkage to criminal convictions and cautions.In our implementation of the ACE framework, we dichotomised all ACE variables based on pre-defined cut offs to capture exposure and derived an ACE count score measure that is widely used in literature as a summary variable. A limitation to our implementation is that the summing procedure implicitly assumes that each ACE has the same direction and magnitude of effect on outcomesAlthough other software packages can be used to derive the ACE measures, together with the data we will provide the R code we used to (1) dichotomise the variables, (2) derive the ACE measures for a specific time period and (3) implement multiple imputation.Overall, we describe a pragmatic method for deriving ACE constructs using a wealth of data on a UK population-based sample. Missing data was a key issue that needed to be handled, which is why, for future analyses with these ACE measures, we advise using multiple imputation by adapting the framework detailed in this Data Note.http://www.bristol.ac.uk/alspac/researchers/research-ethics/.Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. A comprehensive list of research ethics committee approval references is available to download at:ALSPAC data access is through a system of managed open access. The steps below highlight how to apply for access to ALSPAC data, including access to the data and R scripts described in this data note .ALSPAC access policy  which describes the process of accessing the data and samples in detail, and outlines the costs associated with doing so.1. Please read theresearch proposals database, which lists all research projects that have been approved since April 2011.2. You may also find it useful to browse our fully searchablesubmit your research proposal for consideration by the ALSPAC Executive Committee. You will receive a response within 10 working days to advise you whether your proposal has been approved.3. Pleasealspac-data@bristol.ac.uk.If you have any questions about accessing data, please emailThe ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access.study website.Written informed consent was obtained from the parents of participating children after receiving a full explanation of the study. Children were invited to give assent where appropriate. Study members have the right to withdraw their consent for elements of the study or from the study entirely at any time. Full details of the ALSPAC consent procedures are available of the In the last sentence of the Introduction, it would be helpful if the authors could add in additional language that helps to make clear the goals of a Data Note.  I make this comment because, having not been familiar until now with this type of paper, later statements in the paper read a bit awkwardly without this context.  I would therefore suggest a slight rephrasing to say something along the lines of:  “In this Data Note, we describe the processes used to derive the ACE measures in ALSPAC, and report the results of univariate and bivariate analyses to summarize these measures and their association with social-demographic factors.  We also provide practical guidance regarding multiple imputation strategies to employ when analyzing this ACE data as well as resources to better understand the ALSPAC data and how to access it, which we hope will be useful to both current and future users of ALSPAC data.in the cohort were approximately 7 years of age”.  In the Methods, please clarify this statement by perhaps adding “When the oldest children As information about triplets and quadruplet pregnancies is noted, it would also be helpful to report the number of twins in the sample.The “classic” ACES are defined in a way that makes clear the valence of the experience, whereas the “add-on” items are not worded as such.  I would consider possibly revising some of the labels where appropriate so that they are all worded negatively, consistent with the idea of these items tapping into adverse childhood experiences.1.The authors note: “The resulting 12083 questions were compared to previous articles using ALSPAC data to ensure that we had identified all relevant literature” – however, only 5 papers are then cited.  If the authors did search a more extensive set of literature, it might be helpful to future users to present a listing of those papers in the Supplemental Materials.  I also humbly suggest the authors consider citing one of my recent papers, which not only reported on a broad set of adversities, but also included a comprehensive strategy to address data missingness through multiple imputationThe authors use the terms “prospective” and “retrospective”, which although appear straightforward, are not so.  It would be helpful – especially for new users to this dataset – to indicate in a table footnote (such as in Table 1) how these concepts were defined.  Does prospective capture repeated events measured relatively close in time and within one year or two years of the initial event occurrence? Does retrospective capture cross-sectional assessments where participants are asked to report on the occurrence of an experience years or even a decade earlier? Though I certainly could have missed it, it does not seem that the paper includes specific time points of assessment for each of the measures.  Given all of the detailed work that was put into making this accessible, these details would help even more to assist new users in working with these data.  The data that appears in the last table of the Supplemental Materials seems to possibly do that, though it is unclear what the authors mean by start time period and end tie period ?  Although it would be tedious, it might be helpful to indicate more consistently the time frame that is captured through these measures in their description listed in Table 1.users at the time when data are requested from ALSPAC.  This code is also available, per request, for existing data users”Under “Derive ACE measures” – I suggest a small wording change: “The R code used to derive these ACE measures in R will be supplied to Is there a reason why EPDS scores are included both as socio-demographic factors and adversities, under “Multiple Imputation”?  It would be helpful if the authors could provide more detail in the supplement regarding the multiple imputation strategy.  The average reader will not be able to interpret a sentence like this: “Note the use of passive imputation for the ACE count scores and categorical ACE score variables, which are therefore constructed from the imputed ACE constructs and not used as an auxiliary variable for the imputation of the individual ACE constructs.”  If this Data Note is meant to serve as a guide for data users, it would be helpful to provide some basic introduction or literature for reference related to the type of imputation strategies employed .  Otherwise, I worry these important details will be lost in translation. Relatedly, it would also be helpful to indicate how the imputed datasets were actually combined for analysis (or how point estimates were combined/averaged).Under “Patterns of Missing Data…”, please restate to “participants with more deprivation”. Please also note the two references in this section are unnumbered.In the “Dataset Validation” section – or in the supplement, please provide one sentence to clarify “subjective impact of ACE exposure”, as this would not unclear to most users. Relatedly, the authors note that there is a subset of children who have documented cases of maltreatment, but this data is not actually readily available .  Consistent with the paper’s focus on transparency and practicality, it might be helpful to add in one additional sentence to provide a bit more context related to this issue and soften the hopefulness of that data being as rich as it now sounds.Under “Data Availability” section 3, would be helpful to add “ and what the costs are to access the data”, as data user costs vary widely across datasets. In the Supplemental…What does Criterium dichotomization mean?  How it was asked?  Or whether it is possible to dichotomize the response?I would encourage the authors to add spaces in between the variables of the “Differences” tables  or make the first column of this table wider to increase readability.  Thank you for the opportunity to review this paper, which will be a terrific addition to the literature.  As already noted by the prior reviewers, this paper is written at the perfect level to guide future researchers in using the ALSPAC data to incorporate ACES into their work.  I especially commend the authors for their thoroughness, and transparency, which is reflected in every element of this paper. Comments below are minor in nature and aimed at helping to strengthen this already strong paper.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for asking me to review this paper. It is going to be extremely useful for future researchers planning on using measures of ACEs in ALSPAC. It is pitched at the right level, putting much of the detail in supplementary tables – so as not to distract from the main purpose . I appreciate the pragmatic approach to missing data taken because of issues re: lack of complete cases and high number of variables in the dataset. I commend the authors for their careful consideration to multiple imputation and the appropriate guidance given for future researchers using this data. I do have some, mostly minor, queries that I detail below.1 and Her Majesty's Government; Department for Education. Working together to safeguard children. 20152. This information can be added as an extra column in supplementary table 2. Considering these standard definitions, I query whether “Partner/respondent was physically cruel to child” is specific enough to be considered physical abuse. I also wonder if the authors would consider whether “child never felt loved”, currently part of the parent-child bond, would be better placed as a component of neglect .I would urge the authors to consider referencing papers or government documents on the conventional definitions of some of the ACEs considered in the manuscript and how the measures used from ALSPAC compare to these definitions. See, for example Gilbert et al Lancet. 2009A footnote could be added to Table 2 explaining how mean (SE)/% is calculated for the imputed data e.g. where these calculated for each imputed dataset and then averaged across the imputed datasets?I recommend a quick re-read of the supplementary section to ensure that all the table numbers etc are accurate .How were the binary ACE measures actually created? E.g. for sexual abuse, 6 items were considered (assuming no missing data) – how were these items combined together to create the binary ‘sexual abuse’ variable.Supplementary Figure 1 is, in my view, important and a sub-section of it could be considered as a figure in the main text. Both researchers and practitioners are interested in which ACEs are correlated and which aren’t .In a few places, clarity of text can be improved e.g. “Xix: violence between child and partner”, could be re-phrased to be “violence between the child and their partner”.  I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a very clear and “researcher-friendly” paper which will be extremely useful for future users of ALSPAC data.  It describes a careful search within ALSPAC for variables that offer useful information for constructing both “classic” Adverse Childhood Experiences variables as well as several additional ones.  This has allowed construction of a “classic” ACE scale as well as an “extended” ACE scale and categorical versions of both are also offered.  Detailed information has been supplied about the way multiple imputation was used to derive more realistic ACE prevalences, since the proportion of the ALSPAC cohort with enough ACE data to construct actual ACE scores differs socio-demographically from the entire cohort.  The supplementary material is useful and, in general, the right things are kept for supplementary files so that the paper itself flows nicely. Specific points are below – all are minor and are mainly points of clarity. Page 3: Please clarify “violence between child and partner”? Reader might think “mother’s partner”.  Would be helpful to say “violence between child and child’s partner”? Becomes clearer in “definitions” but useful to mention here. Was parental death too rare to include? Might be worth mentioning. Also I notice there isn’t a “physical neglect” question as in the original Felitti questionnaire. Not necessarily a problem but worth mentioning early on.  Page 4:  When participants, in their 20s, retrospectively reported on “violent behaviour of their own partner”, do you know during what age range?  Presumably under 18 but do you know what the lowest age was?  I guess – if this occurred largely, say, between age 14 and 18, one could argue this was not really “childhood”. Page 5, second column, para 2 where you talk about categories of the extended ACE score, can you say a bit more here to justify why you picked those particular categories? This section is confusing and needs more clarification: “However, note that questions that span a larger time window (e.g. exposure 0–11 years or  0–16 years) than the period of interest (e.g. 0–8 years) would be excluded from ACE construct calculations.” The section on multiple imputation, especially the illustration on page 6 of imputation, is likely to be very helpful for future ALSPAC-users. Page 6, second column first para: might be worth expanding this paragraph a bit by commenting more on some of results in Figure 1 supplementary material.  You might want to mention both the highest correlations and also some very apparently uncorrelated variables such as emotional neglect.  I think a lot of practitioners and clinicians will want to know what is correlated with what and may be surprised at some of this. I wonder if the title of your Dataset Validation section might be expanded a bit to “Dataset validation, strengths and limitations” because you do seem to expand into general limitations here. Tiny wording points: Abstract: “the high dimensional” – should this be “the highly dimensional”? Thank you for asking me to review this very helpful paper.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Isoprenoids are amongst the most abundant and diverse biological molecules and are involved in a broad range of biological functions. Functional understanding of their biosynthesis is thus key in many fundamental and applicative fields, including systems biology, medicine and biotechnology. However, available methods do not yet allow accurate quantification and tracing of stable isotopes incorporation for all the isoprenoids precursors.Saccharomyces cerevisiae.We developed and validated a complete methodology for quantitative metabolomics and isotopologue profiling of isoprenoid precursors in the yeast This workflow covers all the experimental and computational steps from sample collection and preparation to data acquisition and processing. It also includes a novel quantification method based on liquid chromatography coupled to high-resolution mass spectrometry. Method validation followed the Metabolomics Standards Initiative guidelines.This workflow ensures accurate absolute quantification (RSD < 20%) of all mevalonate and prenyl pyrophosphates intermediates with a high sensitivity over a large linear range (from 0.1 to 50 pmol). In addition, we demonstrate that this workflow brings crucial information to design more efficient phytoene producers. Results indicate stable turnover rates of prenyl pyrophosphate intermediates in the constructed strains and provide quantitative information on the change of the biosynthetic flux of phytoene precursors.13C-metabolic flux analysis of isoprenoid biosynthesis.This methodology fills one of the last technical gaps for functional studies of isoprenoids biosynthesis and should be applicable to other eukaryotic and prokaryotic (micro)organisms after adaptation of some organism-dependent steps. This methodology also opens the way to The online version of this article (10.1007/s11306-019-1580-8) contains supplementary material, which is available to authorized users. Isoprenoids form one of the most abundant and diverse family of biological molecules on Earth , the common precursor of all isoprenoids . To test the performance of the analytical method, we constructed two strains (S023 and S037) with higher levels of isoprenoid precursors than the wild-type strain. Both had integrated one extra copy of HMG1t and ERG20 under TDH3 and PGK promoters respectively. Furthermore, in S023 and S037 the GGPP synthase from Xanthophyllomyces dendrorhous (CrtE) was expressed constitutively (TEF1p) in a centromeric plasmid. Additionally, the strain S023 also bears one integrated copy of the phytoene synthase (converts GGPP to phytoene) from Pantoea ananatis (CrtB) under the transcriptional control of the PDC1p. This strain was used to test if the GGPP pool was consumed. The genotypes of the different strains are detailed in Supporting Table S-2.All strains used in this study were derived from 4Cl (75 mM), KH2PO4 (22 mM), MgSO4 (0.4 mM) and CSM Leu−  at pH 5.0 , NH600nm of 0.007 was used to inoculate 50 mL of Verduyn medium Leu− and cells were grown at 28 °C with an agitation of 220 rpm  until harvest at mid-exponential phase. To prepare the 13C internal standard used throughout this study, strain S037 was pre-cultured in 20 mL of Verduyn medium without any amino acids and 55 mM U-13C-glucose. This preculture was used to inoculate 600 mL of the same medium at an initial OD of 0.05. Cells were harvested at mid-exponential phase.For metabolomics experiments, an initial ODg, 28 °C, 5 min), and cells were resuspended in the same medium with 55 mM of U-13C-glucose  instead of unlabeled glucose. Samples were harvested at times 0, 1, 2, 5, 10, 15, 30, 45, 60, 90 and 120 min after the switch of label input.For isotope labeling experiments (ILEs), 200 mL of the same medium was inoculated at an initial OD of 0.007. When the OD was around 3, 10 mL of culture were harvested (time zero) and centrifuged (3000×20 NH4HCO3 100 mM (50/50) mixture at 70 °C for 10 min. For absolute metabolite quantification 50 µL of 13C internal standard were added to each extract. Cellular extracts were cooled on ice and sonicated during 1 min. Cell debris was removed by centrifugation . Supernatants were evaporated overnight , resuspended in 200 μL of methanol: NH4OH 10 mM (7:3) at pH 9.5 and stored at − 80 °C until analysis.Intracellular metabolites were sampled by fast filtration .4FA, pH 9.5, in water and (B) 20 mM NH4FA, pH 9.5, in 9:1 (v/v) acetonitrile–water, with the following gradient: 0–12 min from 100% A to 100% B, 5.5 min kept with 100% B and within 0.5 min the return to the initial condition and 4 min equilibration of the column. The injection volume was 10 µL.Analysis of isoprenoids precursors was performed on a Thermo Scientific™ Hypersil C18 GOLD™, 3 µm, and 2.1 × 100 mm column. The column was kept at 25 °C and the flow rate was set to 0.3 mL/min during first 2 min and 0.4 mL/min for the rest chromatographic run. The solvent system consisted of (A) 20 mM NHMass detection was carried out in a negative electrospray ionization (ESI) mode. The settings of the mass spectrometer were as follows: spray voltage 3.2 kV, capillary and desolvation temperature were 350 and 400 °C respectively, maximum injection time 200 ms. Nitrogen was used as sheath gas (50 a.u.) and auxiliary gas (15 a.u.). The automatic gain control (AGC) was set at 106 and resolution at 70,000 from m/z 100 to 700. MS analyses were performed by targeted selected ion monitoring (tSIM) mode with 0.5 m/z isolation window for SIM and ± 10 ppm for inclusion tolerances. tSIM MS scans the targeted masses in different time segments selected based on the retention times of the analytes. Data acquisition was performed using Thermo Scientific Xcalibur software.13C-labeled extracts from yeast were used to enable isotope-dilution mass spectrometry (IDMS) . The acetone phase was transferred to a new tube and the extraction was repeated twice. Acetone extracts were pooled, centrifuged, dried under nitrogen flux and resuspended in 100 μL of acetone for HPLC analysis.Analyses were carried out on a Thermo Scientific™ Vanquish™ Focused UHPLC Plus system with DAD. 5 µL of the extraction was injected in a column YMC carotenoid (100 × 2.0 mm and 3 µm particle size) equipped with a precolumn (100 × 2.0 mm and 3 µm particle size). The mobile phases used to separate and quantify phytoene and β-apocarotenal from the ergosterol and derivatives consisted in a mixture of A methanol/water (95:5) and B dichloromethane. The flow was 0.25 mL/min with the following gradient: 0–0.1 min 5% B, 0.1–0.5 min 20% B, 0.5–2 min 60% B, 2–5 min 80% B, 5–8 min 80% B and 8–11 min 5% B. Absorbance from 210 to 600 nm was followed during all the run with a data collection rate of 2 Hz and response time of 2 s. Phytoene was quantified by its absorbance at 286 nm and β-apocarotenal at 478 nm. Reference wavelength (600 nm) was subtracted for each of the wavelengths used for metabolite quantification.13C-ILEs, isotopologue distributions were quantified from mass fractions after correction for the presence of all naturally occurring isotopes and isotopic purity of the tracer (99%) using IsoCor v2.0.4  from the isotopologue distributions using the following formula:Mi is the proportion of the isotopologue with i13C atoms for a metabolite containing n carbon atoms. IsoCor is freely available at https://github.com/MetaSys-LISBP/IsoCor and its documentation at https://isocor.readthedocs.io.MS data were processed using TraceFinder v3.2 (ThermoFisher), with a tolerance of 5 ppm to extract exact masses. In 13C-enrichments were first fitted using the following logistic model:Labeling dynamics between metabolites and strains were compared as detailed in  were analyzed by reverse phase chromatography and detected by high-resolution mass spectrometry (HRMS) operating in negative mode. The MS acquisition method was optimized to enhance sensitivity and selectivity by using the tSIM mode. Selectivity was enhanced further by setting the resolution of the Orbitrap cell to 70,000, which was sufficient to avoid mass interference with the analytes of interest, even in complex cellular extracts. Finally, the mass scan range was limited to 600 m/z (from 100 to 700 m/z) to avoid systematic under representation of heavier isotopologues  and observed a low interference (< 10%). Similarly, M5P does not interfere with measurement of MEV (< 10%).S. cerevisiae were collected by fast filtration to ensure rapid quenching of metabolism while reducing the concentrations of glucose, amino acids and salts that would impair both chromatographic separation and MS ionization. The extraction procedure was optimized to maximize metabolite recovery (amount recovered and reproducibility). Four different extraction procedures were tested. The first one was a cold extraction, acetonitrile/methanol/water (4:4:2) at − 20 °C during 1 h. This method has been described to be very efficient and specially for triphosphate compounds  at 70 °C during 15 min  at 70 °C during 15 min  extraction at 70 °C during 15 min  and butanol/ethanol/H2O + 100 mM NH4HCO3 (4:5:11), but the first showed a better reproducibility . We also determined the optimal incubation time. Different incubation times  were tested to maximize the intensity of the MS signals and evaluate metabolite degradation . The signals were stable for all compounds and extraction times, indicating no significant degradation occurred in our conditions. Based on these results, we selected an extraction time of 10 min.With this LC–MS method in hand, we then focused on optimizing the sampling procedure, which is of utmost importance to obtain a reliable picture of the metabolome  grown on U-13C-glucose as sole carbon source, and using the optimized sampling and extraction procedures. All intermediates showed a high 13C-incorporation (mean molecular 13C-enrichment > 98%).Finally, we implemented the isotope dilution mass spectrometry (IDMS) approach to ensure accurate absolute quantification of the different metabolites , and following the Metabolomics Standards Initiative guidelines  for all metabolites. LOD was below 0.1 pmol injected on column for most compounds, and LOQ was about 0.1–1.5 pmol over a range of concentration spanning two to three orders of magnitude. Intra- and inter-day assay reproducibility was below 20% for all compounds. Overall, the method was found to be highly sensitive and reproducible for all compounds tested.We evaluated the LC–HRMS method by preparing and analyzing in triplicate a mixture of commercial standards (at concentrations from 0.08 nM to 10 µM) prepared in a biological matrix (a uniformly S. cerevisiae. We first optimized the pool of phytoene precursors in the wild-type (WT) metabolic chassis by constructing the strain S037, which overexpresses HMG-CoA reductase (HMG1t), FPP synthase (ERG20) and GGPP synthase (CrtE). We then constructed the strain S023 by expressing a heterologous phytoene synthase (CrtB from Pantoea ananatis) which converts GGPP into phytoene. We performed metabolomics experiments to quantify the concentration of each intermediate in the three strains, and stable isotope labeling experiments which are of greatest interest to measure metabolic fluxes and infer flux control in living cells.To demonstrate the applicability of the proposed workflow, we carried out functional investigations to guide rational design of phytoene production in Metabolomics results showed very different concentration profiles in the three strains, with a good biological reproducibility for all intermediates (average RSD of 20%)   was estimated by fitting a logistic model . More surprisingly, the dynamic 13C-enrichments profiles and T50 values of all intermediates were also very similar for the three strains (p > 0.05) despite significant changes in pool sizes.Fluxes in linear pathways such as the isoprenoids biosynthetic pathway cannot be resolved using well-established stationary 13C-incorporation , which support the relevance of the proposed strategy to enhance phytoene production.Integration of metabolomics and isotopic datasets may provide quantitative information on the biosynthetic flux of isoprenoid precursors. The labeling dynamics of a given metabolite reflects its turnover rate, which is roughly equivalent to the ratio of the metabolite pool size and the flux through that metabolite pool. The strong increase of pools in S037 and S023 strains compared to the WT strain are expected to slow down the transient 13C-flux models. Still, these results already demonstrate the applicability of the proposed workflow to infer flux information on this pathway in yeast. Concentrations and isotopic labeling of the prenyl pyrophosphate intermediates could also complement other biochemical datasets (e.g. proteomics or enzyme activities) to infer quantitative information on the control exerted by each reaction step on the pathway flux, and thereby drive further strain optimization strategies.Inferring absolute flux values from these data would require more sophisticated mathematical models of isoprenoids biosynthesis, such as non-stationary 13C-incorporation provides quantitative information on the biosynthetic flux of phytoene precursors in S. cerevisiae. Overall, these results illustrate the value of the present workflow during iterative construction of a phytoene producing strain in biotechnology.We presented a workflow for functional analysis of isoprenoids biosynthesis in yeast. Procedures to quench metabolism and extract metabolic intermediates were optimized and a novel method was developed to measure concentrations and isotopic profiles of isoprenoids precursors by high pressure liquid chromatography coupled to high-resolution mass spectrometry. This workflow provides accurate quantification (RSD < 20%) over a large linear range (from 1 to 50 pmol) and showed a high sensitivity (LOD < 0.1 pmol) for all compounds tested. Integration of metabolomics data with dynamic This methodology closes one of the remaining gaps for comprehensive, quantitative understanding of isoprenoids biosynthesis. Access to absolute intracellular concentrations of all intermediates in combination with the ability to quantify their isotopologue distribution will be a valuable tool for future investigations. It may also support the development of kinetic models of isoprenoids metabolism, thus opening the way to comprehensive, mechanistic understanding of the control and regulation of their biosynthesis. This workflow was designed and validated for yeast but should be applicable to other eukaryotic and prokaryotic organisms, including bacteria, plants, and mammals, after adaptation of organism-dependent steps (in particular quenching and extraction).Supplementary material 1 (PDF 775 kb)Below is the link to the electronic supplementary material.


Understanding the potential drivers of microbial meat contamination along the entire meat supply chain is needed to identify targets for interventions to reduce the number of meatborne bacterial outbreaks. We assessed the hygienic practices in cattle slaughterhouses (28 employees) and retail shops (127 employees) through face-to-face interviews and direct personal observations. At the slaughterhouses, stunning, de-hiding and evisceration in vertical position, carcass washing and separate storage of offal were the identified good practices. Lack of hot water baths, absence of a chilling room, infrequent hand washing, insufficiently trained staff and irregular medical check-up were practices that lead to unhygienic handling of carcasses. At the retail shops, cleaning equipment using soap and hot water (81%), storing unsold meat in refrigerators (92%), concrete floors and white painted walls and ceilings were good practices. Adjacently displaying offal and meat (39%), lack of a cold chain, wrapping meat with plastic bags and newspapers, using a plastic or wooden cutting board (57%), infrequent washing of equipment and floors, and inadequately trained employees were practices that could result in unhygienic handling of beef. Our study identified unhygienic practices both at the slaughterhouses and retail shops that can predispose the public to meatborne infections, which could be improved through training and implementation of quality control systems. The global increase in human population is associated with an increased demand for foods of animal origin . ConsequMost of the meatborne bacterial outbreaks are usually attributed to contamination along the supply chain due to poor handling practices . Food-prMeat hygiene and safety is usually less controlled in many developing countries where meat for human consumption is approved based on visual inspection, if at all, without routine microbiological testing . SeveralIn Ethiopia, there are over 300 local slaughterhouses that supply meat for local consumption with different capacities and facilities, however all with low basic hygienic standards . AlthougThis study was conducted from June 2017 to May 2018 at the two local cattle slaughterhouses  found in Bishoftu, and all 127 retail shops selling beef in Bishoftu town. The town is located in East Shoa Zone of Oromia region, Ethiopia. According to the 2007 Ethiopian census report , the totn = 127) engaged in beef handling activities were included in the survey. The purpose of the study was explained to the study participants and data were collected after obtaining full written consent from the participants. At the end of each interview, completeness and accuracy of the data were checked and ensured by the principal investigator. Ethical clearance was obtained from College of Veterinary Medicine and Agriculture of Addis Ababa University, VM/ERC/06/05/09/2017), Ministry of Science and Technology of Ethiopia (Ref no.3/10/006/2018) and the University Hospital Gent, Belgium (Ref. no. 2017/0612).Data were collected through face-to-face interviews and direct personal observation using pre-tested semi-structured questionnaires and checklists to assess the beef hygienic handling practices at slaughterhouses and beef retail shops . The quep-value of less than 0.05 was set as a significance level. The hygienic handling practices at the beef retail shops were described descriptively.The collected data were entered to Microsoft Excel spread sheet  and analysed using STATA version 15.1 . Descriptive statistics such as frequency and percentage are used to summarize the data. Fisher’s exact test was used to assess the difference in the sociodemographic characteristics and hygienic handling practices of the employees between the municipal and private slaughterhouses. A n = 16) and private (n = 12) slaughterhouses. The private and the municipal slaughterhouses did not significantly differ based on the sex, age, level of education and main duty of their employees (Fisher’s exact test p > 0.05). However, there was a significant difference between the slaughterhouses with respect to years of experience of the employees (Fisher’s exact test p = 0.000). Employees at the municipal slaughterhouse had more years of work experience  than those working in the private one ). The combined mean age of the employees from the two slaughterhouses was 32.3 years (SD = 8.1) ranging from 19–50 years. Both slaughterhouses had their own veterinarian who was in charge of the supervision of slaughter process and meat inspection. Overall, the slaughter steps were similar at both slaughterhouses. The slaughtering started with the stunning of the animals by stabbing at the atlanto-occipital region using a sharp edge of knife, immediately followed by bleeding and removal of the head and the feet with the carcass in a horizontal position on the floor. The remaining slaughter steps  were performed in vertical position after manually hanging the carcass by hooks and sliding it over the rail system. Finally, the carcasses were stored and transported at room temperature.p > 0.05). The use of aprons, white coats, boots and hair covering, as well as the presence of sinks for hand washing were good practices observed at both slaughterhouses. However, none of the employees wore hand gloves during operations. We also observed lack of hot water for hand washing and dipping of knives.Both slaughterhouses reported the use of water from the municipal city supply. Hand washing was not a frequent practice during slaughter operations according to 53.6% of the respondents . There wn = 127) were males with a mean age of 25.3 years (SD = 5.9) ranging from 18 to 56 years. Most (70.1%) respondents at retail shops attended only up to primary school and 85.8% of them did not receive training on the best practices of handling meat.The sociodemographic characteristics of the study participants from the retail shops are indicated in According to the respondents, carcasses are transported from the slaughterhouses to the retail shops using closed vehicles without a cooling facility. The municipal water supply was the source of water for all retail shops. Of the retail shops, 39.4% displayed offal  and meat next to each other on the same display cabinet, 4.7% used the same knife for cutting offal and meat. Among the respondents, 85.0% of them used the same coat for the entire day; 9.0% did not wash hands before touching meat; 11.8% did not use soap for hand washing, and 2.4% collected money while handling meat. Ninety-two percent had a refrigerator for storage of leftover meat . A variable frequency of washing equipment, display cabinet, and floor was reported. In most of the retail shops (>70%) equipment, floors and the display cabinet were cleaned once per day. The majority (81.1%) of retail shops reported cleaning their equipment with soap and hot water . All respondents wore a white coat, but none of them put on gloves. In all retail shops, there were light bulbs, either concrete or tile floors and white painted walls and ceilings. However, in all shops meat was displayed at room temperature, with no covering, being exposed to dust particles and domestic flies. All shops used either plastic bags or newspapers for wrapping the meat . Among tProper meat handling practices play a significant role in ensuring meat quality and safety . KnowledIn the present study, lack of hot water baths for hand washing and dipping of knives, infrequent hand washing, insufficiently trained operational employees, lack of regular medical check-up and lack of cooling facilities were bad practices identified both at the slaughterhouses and retail shops. Hot water, which is essential for hand and knife washing to remove potential surface contaminants and to prevent further cross contamination of meat, was lacking at washing basins of both at slaughterhouses and retail shops . Even thAccording to 53.6% of the respondents at slaughterhouses hand washing was not a frequent practice during slaughter operations, and few (9.4%) employees at retail shops did not wash their hands before touching meat. This practice is not consistent with the requirements of the CAC which recommends that food handlers should wash their hands at every stage of food production to safeguard the consumer from foodborne diseases .About 40% of slaughterhouses and 85.8% of retails shops employees did not receive training on hygienic handlings of meat. Previous studies also reported that a considerable proportion of meat processing employees ,40,41 anAll employees at the slaughterhouses and 98% of the respondents at retail shops confirmed having had a medical check-up. However, when asked about the frequency of the check-up, answers were variable and not in line with the actual requirement by the Ethiopian regulatory body. Having a periodic medical check-up would partly limit the transmission of pathogens from sick or potentially carrier employees . In addiCarcasses were stored at room temperature at the slaughterhouses and transported to beef retail shops using vehicles without cooling facilities. At all retail shops, meat was displayed openly with no cooling and no cover, being exposed to dust particles and domestic flies. The meat could remain as such for hours until sold. The mean annual temperature of the study area is estimated at 20.2 °C (range: 10.9–29.5 °C) , which iNone of the employees in slaughterhouses and retail shops wore hand gloves during handling of meat. The use of gloves may protect the meat against contamination . In counAt the slaughterhouses, the use of aprons, white coats, boots and hair covering, as well as the presence of sinks for hand washing were good practices observed at both slaughterhouses. These practices are important to protect both the personnel and the meat from exposure to pathogens . Stunning of the animals, the hanging of carcasses over the rail system for dehiding and eviscerations, and carcass washing after eviscerations were good practices identified at the slaughterhouses. These practices are essential to ensure production of quality and safe meat and needs to be maintained at all times ,34,35,36E. coli O157 and Salmonella in cattle feces and on hides and the possibility of their transfer to carcass during slaughter operations [According to the respondent’s perception, feces during evisceration, hides, handler’s hands and knifes were the potential sources of carcass contamination at the slaughterhouses whereby feces as well hides were identified as the major sources by 36% of them. This was consistent with previous reports ,51. Preverations ,54,55,56At retail shops, the use of soap and water for hand and equipment washing, storing leftover meat in refrigerators, concrete/tile made floors, and white painted walls and ceilings were the identified good practices. These were in line with the basic requirements of Ethiopian proclamations and can contribute to hygienic handling of meat ,36. HoweThe use of plastic bags or newspapers were contrary to the requirements of the Ethiopian Food, Medicine and Healthcare Administration and Control Authority Proclamation (No. 661/2009) that require packaging material to be made out of substances, which are safe and suitable for their intended use, and the product to be packed in container which will safeguard its hygienic, safety, quality and food grade. Furthermore, the proclamation states that “no packaging material shall be put into use unless it complies with the international and national safety and quality standards”, which was lacking in the beef retails shops in Bishoftu town .In most of the retail shops (>70%) equipment, floors and the display cabinet were cleaned once per day. Unclean retail shops ceilings and white walls with observable dirty spots were noticed in 79% of the shops. Frequent and scheduled cleaning of equipment and working environments at food establishments are the basic essential requirements to ensure the continuing effective control of food hazards likely to contaminate food .In general, the observed unhygienic practices at the slaughterhouses and retail shops can be linked with lack or inadequate knowledge of basic hygienic practices ,58,59,60The study has some limitations. The study used questionnaires as a data collection tool, which relies totally on the answers of the respondents that might not necessarily correspond to the actual situation. For example, 91% of the employees at the retail shops and 46% of employees at slaughterhouses responded that they washed their hands before touching the meat and between activities during work, which was contrary to our observations. All the respondents confirmed having had a medical check-up. However, when asked about the frequency of the check-up, answers were variable and not in line with the actual requirement by the regulatory body. Combining questionnaires with personal observations reduced the study limitations in part, while of course, the presence of the study team might have induced practice changes.The study showed a combination of good and unhygienic meat handling practices in slaughterhouses and retail shops. The unhygienic handling practices potentially lead to a higher possibility for contamination and cross-contamination of the meat and may have serious public health implications. The unhygienic handling practices coupled with consumption of raw or under cooked meat which is a common habit in Ethiopia ,63 could


Brain-specific SIRT6-KO mice present increased DNA damage, learning impairments, and neurodegenerative phenotypes, placing SIRT6 as a key protein in preventing neurodegeneration. In the aging brain, SIRT6 levels/activity decline, which is accentuated in Alzheimer’s patients. To understand SIRT6 roles in transcript pattern changes, we analyzed transcriptomes of young WT, old WT and young SIRT6-KO mice brains, and found changes in gene expression related to healthy and pathological aging. In addition, we traced these differences in human and mouse samples of Alzheimer’s and Parkinson’s diseases, healthy aging and calorie restriction (CR). Our results define four gene expression categories that change with age in a pathological or non-pathological manner, which are either reversed or not by CR. We found that each of these gene expression categories is associated with specific transcription factors, thus serving as potential candidates for their category-specific regulation. One of these candidates is YY1, which we found to act together with SIRT6 regulating specific processes. We thus argue that SIRT6 has a pivotal role in preventing age-related transcriptional changes in brains. Therefore, reduced SIRT6 activity may drive pathological age-related gene expression signatures in the brain. During aging, there is an increase in the incidence of several age-related diseases including neurodegeneration, in which aging itself is the main risk factor . TechnolTo understand the molecular mechanism of aging, several genes have been studied – specifically, the Sirtuin deacylase and ADP-ribosylase gene family, which are highly conserved proteins with important roles in preventing age-related diseases –9. For eSince one of the driving forces of aging is the gradual accumulation of DNA damage, we speculated that brain-specific SIRT6-deficient mice could be used as a model for sporadic neurodegeneration. Indeed, this animal model presents accelerated DNA damage accumulation, learning and memory impairments, increased neuronal cell death, and the appearance of hyper-phosphorylated Tau and hyper-acetylated Tau , 21. SimTherefore, we used SIRT6 as a focusing lens to understand the changes in gene expression that could lead to pathological brain aging. Overall, we found that changes due to the lack of SIRT6 are commonly seen in aging and age-related neurodegenerative diseases. We classified the results into four main gene expression categories that change with age, either in a pathological or a non-pathological age-related manner, and groups of genes whose age-related expression changes are either or not reversed by CR. Our results underline a specific pathological signature that could be further used to predict the risk for developing a neurodegenerative disease.To characterize the aging signature of the brain, we measured gene expression in three young WT mice (21 days), three SIRT6-KO mice of the same age, and three old WT mice (22-26 month)  gene categories that are significantly changed in SIRT6KO in relation to both WT and aged mice groups. Both these comparisons only show the downregulation of significant categories, which we decided to further inspect , 1B. In NFKB were in the top 50 categories, we clustered them together as immune response   / [number of occurrences in Aging and Neurodegeneration datasets]).Confidence Scores for Pathological genes =  * [ND Score].For each of the 4 categories, the genes and their Confidence Scores can be found in For each category defined in the Results section, the list of genes was tested for enrichment of transcription factor binding sites and binding site combinations in mice and human via oPOSSUM . We ran i + ABS) to allow a simple ranking of all TFs. We then calculated the TF Prominence Score = Hit scorei * Zi, and multiplied the Prominence Scores of Human with Mouse to get the Combined Prominence Score. The TFs and their related Scores can be found in We then calculated a Normalized Z-Score for gene i = ZTFs were then ranked by their Combined Prominence Scores, and TFs from the top of the lists were manually chosen for each category to plot https://human.brain-map.org/) RNA Microarray data [SIRT6 and YY1 RNA expression data were obtained from Allen Brain Atlas (ray data . A diffehttps://human.brain-map.org/) human brain microarray data [The top 2000 probes co-expressed with either SIRT6 or YY1 were downloaded using Allen Brain Atlas . For enrichGO, all 743 genes served as input. For enrichKEGG, only 629 out of 743 were recognized.Next, the GO analysis was simplified using ‘simplify’ function with the following parameters: cutoff = 0.5, by = "p.adjust", select_fun = min. The top unique categories appear in the main Published SIRT6 and YY1 ChIP-seq data on H1 and K562 cell lines from the ENCODE database were obtained  –70. BED-Peaks were then annotated to the closest gene using ChIPseeker R package. Significance of overlapping peaks among chosen ChIP-seq data sets was found using enrichPeakOverlap function from the ChIPseeker R package . Genes that overlap among all ChIP-seq datasets were further analyzed for common pathways using ClusterProfiler R package, as previously mentioned (function: enrichKEGG). SIRT6 and YY1 ChIP-seq genes overlapping Venn diagram was generated using ggVennDiagram R package.YY1 ChIP-seq data was viewed in UCSC Genome Browser , using 'All cells were grown in DMEM , supplemented with 1% L-glutamine , 1% Penicillin/Streptomycin antibiotics mix  and 10% FBS . Incubation was in 37° C, 5% CO2.In Vitro DNA Transfection Reagent , according to manufacturer’s protocol.Transfections were conducted using PolyJet™ 70% confluent SH-SY5Y cultures were transfected with pCMV-Sirt6-Flag and pCMV-YY1-Flag plasmids, mock cells were used as negative controls. 24 hours after transfection, cells were collected and re-suspended in 150mM lysis buffer . Cell lysates were cleared by centrifugation at maximum speed, and the supernatant was incubated with Anti-Flag Affinity gel , during 2 hours at 4° C under rotatory agitation. After Anti-Flag Affinity gel incubation, the samples were washed 3 times with 150mM lysis buffer, and two times with 300mM Lysis buffer (same as 150mM buffer but with 300mM KCl). Flag-purified proteins were eluted with an excess of flag peptide. Same volumes of eluted samples and input samples were analyzed by immunoblotting.Cells were plated and transfected 24 hours afterward with null-T7-RenillaLuc, hSIRT6p-FireflyLuc and either an empty or pCMV-YY1-Flag plasmids. Lysis, luciferase substrates and luminescence readings were conducted using Dual- Luciferase® Reporter Assay System , according to manufacturer’s protocol.Cells were plated in 8-microchmaber slides (10k cells/chamber) and transfected 24 hours afterwards with hSIRT6p-GFP-CMV-mCherry plasmid, together with either an empty or pCMV-YY1-Flag plasmids. One chamber served as a fluorescence control (containing only the fluorescent reporter plasmid) and another chamber served as a background and autofluorescence control (non-transfected cells). They were then fixated in 2% paraformaldehyde 72h post transfections (when fluorescence in both channels was clearly visible in the control chamber). The slide was then visualized and photos were captured using Zeiss fluorescent system . Analysis was conducted using a manually adjusted automated pipeline in CellProfiler program [Cells were plated in 6-multiwell plate (200k cells/well) and transfected 24 hours afterwards with either an empty or pCMV-YY1-Flag plasmids. RNA was purified using EZ-RNA II Kit , according to manufacturer’s protocol.Then, cDNA was reverse-transcribed using qScript® cDNA Synthesis Kit .cDNA levels were measured using LightCycler® 480 Probes Master . All probes in use were manufactured by IDT : human ACTB (Hs.PT.39a.22214847); human SIRT6 .anti-SIRT6 and anti-YY1 were diluted 1:1000 in 5% BSA in TTBS, with 0.02% sodium azide. Antibodies that were in use: anti-SIRT6 , anti-YY1 , anti-H3 .Supplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7


Extra virgin olive oil (EVOO) is suggested to be cardioprotective, partly due to its high phenolic content. We investigated the effect of extra virgin high polyphenol olive oil (HPOO) versus low polyphenol olive oil (LPOO) on blood pressure (BP) and arterial stiffness in healthy Australian adults. In a double-blind, randomized, controlled cross-over trial, 50 participants  were randomized to consume 60 mL/day of either HPOO (360 mg/kg polyphenols) or LPOO (86 mg/kg polyphenols) for three weeks. Following a two-week washout period, participants crossed over to consume the alternate oil. Anthropometric data, peripheral BP, central BP and arterial stiffness were measured at baseline and follow up. No significant differences were observed in the changes from baseline to follow up between the two treatments. However, a significant decrease in peripheral and central systolic BP (SBP) by 2.5 mmHg (95% CI: −4.7 to −0.3) and 2.7 mmHg (95% CI: −4.7 to −0.6), respectively, was observed after HPOO consumption. Neither olive oil changed diastolic BP (DBP) or measures of arterial stiffness. The reductions in SBP after HPOO consumption provide evidence for a potentially widely accessible dietary intervention to prevent cardiovascular disease in a multiethnic population. Longer intervention studies and/or higher doses of EVOO polyphenols are warranted to elucidate the potential effect on DBP and arterial stiffness. Cardiovascular disease (CVD) is the leading cause of mortality and morbidity worldwide. Established risk factors such as hypertension, dyslipidaemia, diabetes and obesity contribute to 9.7 million annual deaths related to CVD globally . The mosExtensive evidence indicates that certain dietary patterns are cardioprotective . The traThe reported cardioprotective benefits of OO have been mostly attributed to the presence of variable concentrations of bioactive compounds, including polyphenols , mainly known for their anti-inflammatory and antioxidant properties ,19,20. OOur research team recently conducted a meta-analysis of the published literature to determine the effects of HPOO consumption, compared with LPOO, on cardiovascular markers . This meThe OLIVAUS study  was cond2. Exclusion criteria included non-English-speaking individuals, pregnant or lactating women, smokers, individuals on a special type of diet for medical reasons  and/or with a high habitual OO intake (>1 tablespoon/day). Exclusion also applied if individuals were taking vitamins or antioxidant supplements as part of a regular regime and were unable to discontinue their use for the duration of the trial . Finally, study subjects taking prescribed medication  and those with diagnosed chronic diseases , gut-related diseases or any other condition that could impair adherence were also excluded.All participants were recruited in Melbourne, Australia, via social media and La Trobe University email database advertising, word of mouth and posters on campus. A standardized screening procedure was followed in order to identify eligible participants, who were required to be within the age range of 18–75 years and a body mass index (BMI) of 18.5–40 kg/mThe OLIVAUS study was a double-blind, cross-over, randomized controlled trial (RCT) aiming to evaluate the effect of extra virgin HPOO consumption on CVD risk markers in comparison with a commercially available OO which was low in polyphenols (LPOO). Prior to the main study, a pilot study was conducted with five study participants in order to test the feasibility of the study protocol and the data collection tools . EnrolleStudy participants were requested to consume a daily dose of 60 mL of either type of raw OO over 2 intervention periods of 3 weeks each, in conjunction with their habitual diet. The two types of OO varied only in their phenolic content  but did not differ with respect to the rest of their nutrient composition, including their fatty acid profile. Two washout periods, of 2 weeks each, during which study participants were instructed to avoid olives and OO consumption, preceded the first and the second intervention periods of OO administration. The intervention in the present study was designed with a daily dose of 60 mL OO, which reflects the habitual intake in populations where the cardioprotective benefits of virgin OO have been previously reported ,25,26.Participants were provided with OO bottles at the beginning of each intervention period. The OOs were supplied in dark coloured glass containers to minimise phenolic content loss due to sunlight. To ensure blinding of the researchers to the OO type, each bottle was assigned a different code number that was concealed from study participants and research team members. This was disclosed only after the completion of the statistical analyses. To assess the level of adherence to the intervention, participants were instructed to return the containers at the end of each intervention period so that the daily amount of unconsumed OO could be measured and recorded. Study participants were also instructed to keep a written record of daily OO consumed during each intervention period using a checklist provided to them. This information was recorded by research team members after the end of each intervention period. Full details of the study protocol, including a comparison of the concentrations of total polyphenols and polyphenol subclasses in each of the two types of OOs, are provided elsewhere .Socio-demographic data were collected from eligible participants during a scheduled interview at our trial clinic room located at La Trobe University. Trained researchers conducted all interviews using a standardized questionnaire. Specifically, the socio-demographic data collected during this interview included age, gender, language(s) spoken at home, level of education, ethnicity and parental country of birth. Any medications and dietary supplements taken by the study participants were also recorded.®9 software . A 3-day food diary was used to collect information on the dietary intake of study participants during two weekdays and one weekend day (preferably non-consecutive) at baseline and follow up of each intervention period. Specifically, study participants were instructed to record details on their intake of food and beverages, including information on the quantity, type/brand and cooking methods of the consumed items. The level of detail required to be recorded in the diary as well as additional strategies on how to incorporate raw, uncooked OO in their habitual diet were provided to study participants at a pre-baseline meeting by a trained nutritionist. The completed food diaries were returned and checked by the research team members for potential wrong or missing entries during the scheduled interviews with the study participants. All dietary intake data were analyzed for energy, macro- and micronutrient content using FoodWorksPhysical activity (PA) was assessed using the Active Australia Survey (AAS) questionnaire , a tool 2). Using World Health Organization (WHO) cut-off points for BMI, study participants were classified as underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2) or obese (BMI ≥ 30 kg/m2) [Anthropometric measurements were conducted four times during the study, i.e., at baseline and follow up of each intervention period. Body weight and standing height were measured with study participants in light clothing and barefoot, using a digital scale , to the closest 0.1 kg and a wall-mounted stadiometer  to the nearest 0.1 cm, respectively. Waist circumference (WC) was measured to the nearest 0.1 cm, using a flexible steel tape calibrated in cm with mm graduations  directly over the skin at the umbilicus level. Body mass index (BMI) was calculated using Quetelet’s equation (weight (kg)/height (m)0 kg/m2) . FurtherBMI 25.0–.9 kg/m2 Peripheral  and central (aortic) blood pressure (BP) were measured using applanation tonometry with a SphygmoCor XCEL device , at baseline and follow-up examinations at each intervention period. Following a minimum of 5 min rest in the supine position, peripheral brachial systolic BP (SBP) and diastolic BP (DBP) was measured using a blood pressure cuff affixed to the upper left arm. Three consecutive BP recordings were made and the average of the last two recordings was used for data analysis. In addition, central SBP and DBP, as well as PP measures were automatically derived via the brachial BP cuff. The BP categories recommended by the American College of Cardiology (ACC)/American Heart Association (AHA) were used to classify study participants into those with Normal BP (SBP/DBP < 120/80 mmHg), Elevated BP (SBP 120–129 mmHg and DBP < 80 mmHg), Hypertension Stage I (SBP 130–139 mmHg or DBP 80–89 mmHg) and Hypertension Stage II (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg) [AIx = AP/PP × 100). PWV was measured using a tonometer to capture the carotid waveform, while a femoral cuff was placed high on the left thigh in order to capture the femoral waveform. The PWV was then calculated by dividing the distance between the carotid and femoral measurement sites by the transit time. This method is considered the gold standard technique for assessing central arterial stiffness. Measures of peripheral and central arterial stiffness, using pulse wave analysis (PWA) and pulse wave velocity (PWV), were obtained non-invasively with the SphygmoCor XCEL device . This was carried out using the standard procedure as outlined in our previous paper . PWA is Power calculations showed that a sample size of 40 was adequate to provide sufficient statistical power to detect a statistically significant between-group difference of 5% and a standard deviation (SD) of 11 in HDL-C efflux levels , with 80% power and 5% level of significance . The totn) and percentage (%) for categorical ones. All reported p values are two tailed, and the level of statistical significance is set at p < 0.05.All statistical analyses were conducted using the SPSS statistical software for Windows . For all continuous variables, the Kolmogorov–Smirnov test was performed to examine the normality of their distribution. A general linear model, i.e., repeated-measures ANOVA , was used to examine the between-group differences  of mean values at each time point of measurement, the within-group changes (time effect) from baseline to follow up in each intervention arm, and the differences in the changes from baseline to follow up between the two intervention arms (treatment × time interaction effect). Both per protocol (PP) and intention-to-treat (ITT) analyses were performed. The PP analyses were conducted in study participants who had full data from baseline to follow up in the first or the second intervention period. For the ITT analyses, multiple imputations were conducted in order to compensate for all missing values. Five imputed models derived from this process. Considering that the PP and the ITT analyses provided similar results , the results coming from the latter are presented in this article. In all statistical analyses, adjustments were made for gender and age. Data are presented either as the mean ± SD, as estimated marginal means and standard errors (SE) or as the mean change and 95% confidence interval of change (CI) for continuous variables and as frequency , from 105 interested individuals who agreed to be screened, were eligible and enrolled in the study from July 2018 through to October 2019. Seven participants discontinued the intervention, due to inability to comply (n = 4) and for personal reasons, (n = 3) and therefore 43 participants completed the study. Fifty volunteers (n = 50) and by gender. Study participants had a mean age of 38.5 ± 13.9 years (the age range was between 20 and 70 years) and their mean years of education was 17.3 ± 3.5. In addition, the majority of study participants were females (66%), had a tertiary education (86%) and were born in Australia (70%). No significant gender differences were observed in any of these socio-demographic characteristics. The mean BMI and WC was 24.7 ± 3.5 kg/m2 and 86.9 ± 11.2 cm, respectively, with no significant differences between genders. In addition, 44% of study participants were overweight and 4% were obese. Based on their WC measurements, 16% had a high cardiometabolic risk and 24% had very high risk. Although there were no significant differences between genders observed in BMI and WC, compared to females, male study participants were taller  and had a higher body weight . At baseline, the mean peripheral SBP and DBP for the cohort was 120.0 ± 13.4 and 69.9 ± 8.4 mmHg, respectively, while 18% of study participants were categorised as having elevated BP, 20% had Stage 1 Hypertension and 8% had Stage 2 Hypertension. Mean central SBP and DBP was 106.8 ± 13.3 and 70.6 ± 8.7 mmHg, respectively, mean heart rate was 61.5 ± 10.2 bpm and PWV was 9.5 ± 1.4 m/s. There were no significant differences between genders in any of these hemodynamic indices. The changes observed in dietary energy, macro- and micronutrient intake from baseline to follow up, as well as the differences between treatment arms are summarized in The effect of the two intervention OOs on peripheral and central BP are illustrated in Compliance to treatment was high, as reflected in the OO volume returned by participants after each intervention period. Based on the measured actual remaining OO, compliance was found to be 92% for both the LPOO and HPOO group after the first intervention period, while 92% for the LPOO group and 90% for the HPOO group after the second intervention period. Nevertheless, compliance was not found to differ significantly between the two groups .The present double-blind, cross-over, randomized controlled trial investigated the effect of daily consumption of 60 mL raw extra virgin HPOO in comparison with LPOO, each for 3 weeks, on BP and arterial stiffness in Australian adults. The key finding was that peripheral and central SBP decreased significantly by 2.5 and 2.7 mmHg, respectively, after extra virgin HPOO (phenolic content 360 mg/kg) consumption. However, no significant differences were observed between the two interventions with regards to the changes in peripheral and central SBP. No significant within-group changes or between-group differences were either observed on diastolic BP, and measures of arterial stiffness.The significant decrease reported from our study in peripheral SBP is consistent with the limited number of RCTs that have examined the effect of HPOO consumption on peripheral BP, but after providing different doses of OO, different phenolic content of the administered oil, and varying intervention duration. In this regard, Moreno-Luna et al.  describeIt is noteworthy, that other clinical trials also examining the effect of HPOO on peripheral SBP and DBP reported either significant results only for peripheral DBP or no significant findings on BP. In this regard, the EUROLIVE study demonstrated that 3 weeks of daily consumption of 25 mL EVOO, containing 366 mg/kg of polyphenols, significantly reduced peripheral DBP, but had no effect on SBP in healthy men , while aTo the best of our knowledge, this is the first study reporting a significant reduction in central SBP after consumption of HPOO. This is of importance, considering that raised central BP has been positively associated with cardiovascular risk and mortality ,35. The Dietary intake and body weight changes observed in our study deserve comment in the context of the favourable effect of HPOO on peripheral and central SBP. In this regard, the addition of 60 mL of OO in participants’ habitual diet resulted in significant increases in caloric intake leading to weight gain in both intervention groups. Previous cross-sectional and prospective studies have reported that body weight gain is directly associated with increases in arterial BP in normotensive subjects , with a Further to dietary energy intake, our study also recorded the intake of macro-  and micronutrients  that have established effects on BP levels ,40,41,42To the best of our knowledge, the OLIVAUS study is the first human clinical trial to investigate the effect of OO polyphenols on measures of arterial stiffness through applanation tonometry. Stiffening of the arterial wall in the larger central arterial system represents an important CVD risk marker  and the The findings reported in our study should be interpreted in light of its strengths and limitations. The main strength of the present study is its randomized, double-blind, cross-over design that reduces interindividual variability and increases the external validity of the study findings. The use of applanation tonometry represents another strength, since it is a state-of-the-art, non-invasive method to measure BP and arterial stiffness. In addition, in a multiethnic population that is not accustomed to a high consumption of OO, our participants’ compliance was overall high throughout both intervention periods. On the other hand, one of the limitations of the present study is that the sample size was calculated on the basis of the expected differences only in its primary outcome . Another limitation could be the potential effect of seasonality on the examined outcomes, due to the fact that the participants were enrolled in the study gradually . Lastly, despite the inclusion of a washout period before the initiation of the intervention and between the intervention periods, there is no guarantee that any potential carry-over effect on the examined hemodynamic markers was completely avoided. However, pairwise comparisons that examined potential carry-over effects were insignificant for all hemodynamic markers.To our knowledge, the OLIVAUS study is the first to examine the effect of OO polyphenols on peripheral and central SBP and DBP as well as on measures of arterial stiffness in Australian adults. Although there were no significant differences between OO treatments in any of the examined outcomes, there was a significant reduction in peripheral and central SBP after daily consumption of extra virgin HPOO for 3 weeks. This provides evidence for a potentially widely accessible dietary intervention that can reduce CVD risk in a multicultural context, such as in Australia. However, additional clinical trials of longer duration and use of EVOO with different phenolic content and profile are required to shed more light on the potential effect of OO polyphenols on other CVD risk markers, including DBP and arterial stiffness.


Background: The treatment of depression is a main strategy for suicide prevention in older adults. We aimed to calculate suicide rates by antidepressant prescription patterns in persons aged ≥ 75 years. A further aim was to estimate the contribution of antidepressants to the change in suicide rates over time.Methods: Swedish residents aged ≥ 75 years  were followed between 2007 and 2014 in a national register-based retrospective cohort study. Biannual suicide rates were calculated for those with selective serotonin reuptake inhibitor (SSRI) single use, mirtazapine single use, single use of other antidepressants and use of ≥ 2 antidepressants. The contribution of antidepressants to the change in biannual suicide rates was analyzed by decomposition analysis.Results: There were 1,277 suicides. About one third of these were on an antidepressant during their last 3 months of life. In the total cohort, the average biannual suicide rate in non-users of antidepressants was 13 per 100,000 person-years. The corresponding figure in users of antidepressants was 34 per 100,000 person-years. These rates were 25, 42 and 65 per 100,000 person-years in users of SSRI, mirtazapine and ≥ 2 antidepressants, respectively. In the total cohort, antidepressant users contributed by 26% to the estimated increase of 7 per 100,000 in biannual suicide rates. In men, biannual suicide rates increased by 11 suicides per 100,000 over the study period; antidepressant users contributed by 25% of the change. In women, those on antidepressant therapy accounted for 29% of the estimated increase of 4.4 per 100,000.Conclusion: Only one third of the oldest Swedish population who died by suicide filled an antidepressant prescription in their last 3 months of life. Higher suicide rates were observed in mirtazapine users compared to those on SSRIs. Users of antidepressants accounted for only one quarter of the increase in the suicide rate. The identification and treatment of suicidal older adults remains an area for prevention efforts. The oldest segment of the population is accountable for the highest suicide rates in many countries . DepressCurrently, over one fifth of the Swedish population aged 75 and above is prescribed antidepressants (ADs), mainly SSRIs and mirtazapine . AntidepSome previous research suggested an association between the increase in AD use and a decrease in suicide rates , 15, 16.The use of nationwide, register-based data can accrue the large sample sizes needed to examine suicide deaths with sufficient power and minimize the risk of selection bias. We aimed to investigate suicide rates in adults aged 75 years and above for the most common antidepressant prescription patterns. A further aim was to estimate the contribution of antidepressants to the change in suicide rates over time.We conducted a retrospective cohort register-based study of all Swedish residents aged 75 years and older between January 1, 2007 and December 31, 2013. Individuals had to be recorded as living in Sweden during the year prior to study entry in order to be included and were followed until December 31, 2014 or censored in case of death due to causes other than suicide.Data from various registers were linked using the personal identity number. All Swedish residents aged 75 years and older during the study period were identified from the Total Population Register at Statistics Sweden. The Swedish Prescribed Drug Register was employed to capture AD use. The register has full coverage of dispensed prescriptions in outpatient care and in residential care . It incln = 46,244), in order to reduce confounding by indication. The rationale for this was that these medications are no longer recommended for the treatment of depression in older adults in Sweden and are instead used for treatment of chronic pain  during the year that preceded the suicide and recorded the date of last refill and the specific AD prescribed . We exclnic pain .In order to ensure sufficient power, we considered categories of the prescription patterns most commonly seen in older adults in Sweden: (a) single use of SSRI, (b) single use of mirtazapine, (c) single use of other AD, and (d) use of ≥ 2 different ADs.The use of AD was considered as a time-varying exposure. In Sweden, patients may purchase up to 3 months' supply when redeeming a prescription. Alternatively, they may use the multidose system, which provides an automatic refill every 2 weeks. A person was considered as being on continuous exposure to AD treatment if she/he had ≥ 3 refills of AD – or ≥ 20 automatic refills for those using multidose – over a 12-month period. This approach ensured that AD medication covered 75% of days as a minimum. For individuals who died during the observation period, we calculated the proportion of days covered by AD during their last year of life to determine whether they should be considered to be on AD therapy.We collected data as recorded during the year participants entered the cohort. Characteristics included: sex, age group , previous episode of self-harm, residence in institution, use of other psychoactive medications  and use of cardiovascular medications (beta-blockers and statins). The selected characteristics were previously found to be associated with both antidepressant use and suicide. The use of specialized care for depression was considered as a proxy for serious depression. The relationship of the selected variables with suicide in users and non-users of antidepressant older than 75 years was previously investigated in our published study .Suicides were identified from the Cause of Death Register based on ICD-10 codes: Intentional self-harm (X60-X84), harm of undetermined intent (Y10-Y34), and sequelae of intentional self-harm and of events of undetermined intent (Y87.0 and Y87.2).t-test for continuous variables and Pearson's chi square test for categorical variables. The latter test was also employed to test for differences in proportions of individuals using other psychoactive medications among AD user groups compared with those using solely SSRI.First, we compared the baseline characteristics of users and non-users of AD using In order to distinguish random fluctuations due to small numbers from true changes, biannual suicide rates were calculated by AD use group and period for all AD users and non-users as well as for the most commonly prescription patterns.A decomposition technique was used to calculate the contribution of users of antidepressants to the change in the overall suicide rate. The decomposition technique is a method derived from demographic studies, widely used in health and behavior studies when rates of a phenomenon within the same population are compared at two different time points. This method takes into account the difference in the population composition between the time points in order to adjust for the confounding effect of population composition on the measured rates and accounts for the impact of compositional factors on the observed rates . In our As the analyses were based on existing population register data, informed consent from subjects was not required. Approval was granted by register holders. Statistics Sweden replaced the Personal identity number by a study number prior to data delivery and data were analyzed anonymously. The study was approved by the Regional Ethical Review Board in Gothenburg .A total of 1,401,349 individuals  were included in the cohort and followed for up to 8 years. This corresponds to 7,191,803 person-years . Key baseline characteristics are presented by AD use in In the total group, there were 1,277 suicides during the study period, and 70% of these occurred among men. Overall, 404 (31.6%) filled at least one AD during their final 3 months of life (28.6% of men and 38.6% of women) .In the total cohort, the average biannual suicide rates in those who were not on AD was 13 per 100,000 person-years . The corDuring the study period, in the total cohort, the percentage of person-years spent in AD treatment increased from 8.1% in 2007–2008 to 11.7% in 2013–2014 and the overall suicide rate increased from 12 to 19 suicides per 100,000 during the same period . In men,As seen in This nationwide cohort study covering Swedish residents aged 75 and above found that about one third of those who died by suicide filled an AD prescription during their final 3 months of life. Individuals who were on single AD treatment with mirtazapine, as well as those using more than one AD had higher suicide rates than those on a single SSRI. Older individuals not on AD therapy were accountable for the major share of the increase in the suicide rate during the study period while those on AD therapy made a small contribution to the overall change.Only about one third of those who died by suicide in our national cohort of individuals aged 75 and above were on AD. Considering that two-thirds of persons over 75 who die by suicide may be depressed , there iSeemingly, no previous study has calculated suicide rates by type of AD in this age group. A Canadian population-based study conducted in a general adult population found suicide rates in the range of those in our population . In our Our decomposition analysis suggests that non-users of AD contributed the most to the change increase in the suicide rate, which may in part be explained by the role of other factors affecting suicide such as social factors and other comorbidities , 34. TheThis is to our knowledge the first study to calculate nationwide suicide rates for individuals aged 75 and above treated with AD. Complete and systematically assembled register data with no loss to follow-up are some of the strengths of this study. This large national cohort provided enough power to assess rates of a rare event like suicide, allowing us to decompose the contribution of ADs to the change in the suicide rate in the Swedish population aged 75 and above. A main limitation is the lack of diagnosis-specific data from primary care as this information is not available in the National Patient Register. We could therefore not adjust for confounding by indication. This is problematic as antidepressants may have been prescribed for conditions other than depression , also associated with suicidality in older adults even in the absence of depression , 31. MorWe considered AD use as a time-varying exposure which allowed us to take into account changes in prescribing patterns that may have occurred during the 8-year study period. Our strict definition of continuous use of AD increased the likelihood that subjects had enough exposure to AD to derive therapeutic benefit. While we grouped biannual suicide rates to increase study power, the number of suicides for each specific substance (with the exception of mirtazapine) was small and did not allow for reasonable and stable estimation of suicide rates. The biannual rates in men and women should also be interpreted with caution due to small numbers. Comparison of our suicide rates with other countries should be interpreted with caution due to differences in population composition. Further, prescribing patterns may vary; some countries may still prescribe TCA for late-life depression. The results of this research may not be generalized to other countries or settings as availability of healthcare and reporting of suicide varies widely in a global perspective.Only one third of those aged 75 and above who died by suicide in this Swedish national cohort were on an antidepressant (non-tricyclic) during their last 3 months of life. Users of antidepressants accounted for only one quarter of the overall increase in the suicide rate in this age group between 2007 and 2014. Our findings, taken together with previous results demonstrating high rates of depression among older adults who die by suicide, highlight the need for intervention research to prevent suicide in this growing age group.https://scb.se/vara-tjanster/bestalla-mikrodata/, and https://www.socialstyrelsen.se/statistik-och-data/bestalla-data-och-statistik/.The data analyzed in this study is subject to the following licenses/restrictions: The datasets generated and/or analyzed during the current study are not publicly available due confidentiality but aggregated data are available from Statistics Sweden or the National Board of Health and Welfare upon request. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved by Regional Ethical Review Board in Gothenburg . Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.KH, MW, and JF designed the study. KH was the guarantor, coordinated the statistical analysis, and drafted the manuscript. KH, MW, JF, and AE interpreted the data and participated to the critical revision of the manuscript. All authors had full access to all of the data  in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis.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.