Households with children under 5 years old living in rural areas had lower food insecurity severity scores. We recommend social protection policies such as food and nutrition subsidies or conditional cash transfer to poor households with children under the age of 5.This study aimed to estimate the prevalence and profile of food insecurity in households with children under 5 years old using the Food Insecurity Experience Scale (FIES) in Thailand. We integrated FIES into the 2019 Multiple Indicator Cluster Surveys (MICS). A total of 861 households were successfully interviewed with FIES. The Rasch model was applied to examine the validity and reliability. Multiple logistic regression was used to assess the association between socio-economic status and prevalence of food insecurity, adjusting for geographical regions and characteristics of households. We found that FIES measurement is valid as Infit falls within the normal range of 0.7–1.3 and is reliable . The overall prevalence of moderate or severe food insecurity was 2.79%. The wealthiest households were less likely to suffer from food insecurity than the poorest households (adjusted OR: 0.07; 95% CI: 0.02–0.34;  One-fourth of the world’s population suffers from food insecurity. The situation is more severe in the African region, accounting for more than half of the population in the region. In 2019, it was estimated that 2 billion people worldwide (26% out of the world population of 7.7 billion) suffered from food insecurity at moderate or severe levels /[(d2/Z21−α/2 × (N − 1) + p × (1−p)]” in the OpenEpi program [For the FIES module, a subsample of MICS6 households with children under 5 was used. We calculated the sample household based on 4.8% prevalence of food insecurity in Thailand, using a formula “ program ; N = 3,5 program ; design  program , the sam program . FinallyHowever, NSO successfully interviewed 861 households using FIES. The low response rate, 57.4%, was due to the outdated household sampling frame. For example, children moved out of the households, or they were older than 5 years. Only 2% of households refused to participate in the FIES interview.The person who prepared food for family members was interviewed with FIES. Trained staffs at NSO provincial office conducted face-to-face interviews in November 2019.FIES, a standardized tool developed by FAO, was translated into Thai by International Health Policy Program (IHPP) and NSO through consultative meetings with experts, ensuring it fits with the Thai context. It was pre-tested along with MICS6 survey questions. IHPP team members attended a FAO training course (19–23 November 2018) on FIES principle, analytical technique and policy utility.This study assessed food insecurity at the household level by asking persons who prepared food for the households to respond to eight standardized questions in the FIES. Respondents were asked their experience, with reference to the last 12-month recall period: (1) Was there a time when you  were worried you would not have enough food to eat because of a lack of money or other resources? (2) Was there a time when you  were unable to eat healthy and nutritious food because of a lack of money or other resources? (3) Was there a time when you  ate only a few kinds of foods because of a lack of money or other resources? (4) Was there a time when you  had to skip a meal because there was not enough money or other resources to get food? (5) Was there a time when you  ate less than you thought you should because of a lack of money or other resources? (6) Was there a time when your household ran out of food because of a lack of money or other resources? (7) Was there a time when you  were hungry but did not eat because there was not enough money or other resources for food? (8) Was there a time when you  went without eating for a whole day because of a lack of money or other resources? The answers are Yes = 1 score, No = 0 score, and Don’t know.To construct a household asset index, we assessed the ownership of the household durables; a set of housing characteristics and household assets were asked to the head of households; see the list of household assets in the MICS6 report . RespondWe collected variables based on the context of the country and on previous studies ,12,15. TThe Rasch model was used for FIES analysis as recommended by the FAO. The model examines the validity and reliability of FIES across different country contexts . We applFirst, the infit test, with a normal range of 0.7–1.3, was used to assess the validity of the FIES. Second, the outfit test, which is more sensitive to unexpected response patterns, was used to measure the validity. These unusual patterns represented those who reported severe levels of food insecurity but did not report mild insecurity. For example, respondents who answered yes to the following question “went without eating for a whole day” but answered “no” to the following question “were you worried you would not have enough food to eat?”. The normal range of the outfit value is less than 2.Third, the residual correlation matrix was used to measure the redundancy of the questions. The normal value is −4 to 4. Lastly, the Rasch reliability test was used to measure the reliability of the FIES. The normal value is greater than 0.8. After excluding extreme values (scores of 0 and 8) as recommended by the FAO, we had 148 cases with non-extreme scores for testing the validity and reliability. With reference to FAO guidelines, respondents who reported raw scores ranging from 1 to 7 were included in the test ,24. In oAfter testing the validity and reliability of the FIES, we categorized households into two groups: food-secure households  and food-insecure households  . We combWe used the chi-square test to demonstrate the differences in the annual prevalence of food insecurity across SES and demographic profiles.We applied the two models for multiple regression. There are two main purposes for selecting the two models. First, we selected multiple logistic regression to determine the association between socio-economic status and food insecurity experience (binary outcome—did not experience/experienced). Second, we selected Tobit regression to determine the association between socio-economic status and the severity of food insecurity (raw score of food insecurity: 0–8).Multiple logistic regression analysis was employed to investigate the association between SES and the prevalence of food insecurity. We adjusted for the following covariates: source of food, regional geographical area, place of residence , household size, and whether the household received financial support from the Thai government.We also employed a Tobit regression model to investigate the association between SES and the severity of food insecurity (raw scores). The raw scores are the sum of scores given to the eight FIES questions; 0 means no food insecurity, and 8 represents the highest level of food insecurity. We used the Tobit regression model because the raw score was concentrated at 0. Therefore, we censored raw scores at 0. The model was adjusted with the same covariates as in the logistic regression model.We also attempted to investigate the association between food insecurity and malnutrition among children under 5 years old. However, the small sample size and the low prevalence of food insecurity did not allow us to perform this analysis.We found that the FIES was suited to the Thai context as the infit test was in the normal range. The outfit test resulted in two questions lying outside the normal range . HoweverOf 861 households, 24 households (2.79%) suffered from food insecurity. Of these, 22 (92%) and 2 (8%) reported moderate or severe food insecurity, respectively .The majority of the households purchased food, lived in the Northern region and rural areas, and received financial support from the Thai government. There were, on average, five members per household.p-value < 0.05). Households in the southern region had the highest prevalence of food insecurity compared to other regions   .p < 0.05) and richest tertiles  were less likely to suffer from food insecurity compared to the poorest households . Additionally, households that relied on homegrown food had higher food insecurity scores than households that relied on purchasing their food . Furthermore, we found that households in rural areas had lower food insecurity scores than households in urban areas   .The study found a low prevalence of food insecurity (2.79%) among households with children under 5 years old. We found that the FIES is a valid measure for assessing food insecurity. The study also found that the poorest household tertiles suffered higher levels of food insecurity than the richest household tertiles. Additionally, rural areas had lower food insecurity scores than households in urban areas.To the best of our knowledge, this is the first national survey conducted in Thailand to investigate the prevalence of food insecurity in households with children under 5 years old. The prevalence was lower than the 2016 FAO estimate of 4.8% for moderate or severe food insecurity in all Thai households. However, our study (households with children under 5 years old) is not comparable to the FAO survey since people of all ages were included. One of the main reasons that the result was different was because of the sampling frame and sampling methods. This research applied a NSO sampling frame while the FAO report was collected from the Gallup World Poll . When weThe study ensured the validity of the FIES questionnaires in the Thai context as the infit value laid in the normal range for all questions. As this is the first survey conducted in Thailand, the tool from this study can be applied in future studies to investigate the food insecurity situation and investigate the progress of interventions on food insecurity at national and local levels.This study is one among few conducted in the Southeast Asia Region and experienced a low prevalence of food insecurity . The finConsidering other factors, we also found that households in rural areas had lower food insecurity scores than households in urban areas. This can be explained by better access to homegrown food sources in rural households than in urban households, which rely more on purchasing food items. This result is explained by another study mentioning that in the rural areas, there is available space for gardening and access to food from neighbors .A few policies are generated by this study. First, this is the first time that the FIES has been integrated into the MICS in Thailand. This study demonstrates that FIES is a valid measure for assessing food insecurity in Thailand. It can be applied to estimate food insecurity prevalence in national representative household surveys as required by SDG 2.1.2.Second, Thailand has yet to initiate a baseline prevalence of food insecurity as called for by SDG indicator 2.1.2. This can be done by integrating the FIES module into the National Health and Welfare Survey conducted biennially, or Socio-Economic Survey conducted annually by the NSO.Third, despite a low level of food insecurity prevalence, the government should deliver its promises to SDG 2, ending hunger, achieving food security, improving nutrition, and promoting sustainable agriculture. Greater efforts should be given to end severe food insecurity (SDG indicator 2.1.2) and reduce the prevalence of under-nourishment (SDG indicator 2.1.1) in the light of increasing prevalence of stunting (SDG indicator 2.2.1), wasting, and obesity (SDG indicators 2.2.2) among children under 5 years old. All these efforts require evidence-based multi-sectoral actions for health guided by nationally representative household surveys using the FIES and MICS.Finally, we recommend the government to introduce social protection measures such as food and nutrition subsidies or conditional cash transfer to poor households with children under 5 years old. There is strong evidence that a supplemental nutrition assistance program alone or in conjunction with education is an effective means of improving food security .There are a few limitations of this study. First, the study is not a nationally representative household survey as the sample frame covered households with children aged under 5 years old. Second, a cross-sectional survey nature of this study cannot establish causality of socio-economic factors on food insecurity. Third, other socio-economic status variables such as household income were not covered by this survey. Finally, the small sample size and the low prevalence of food insecurity did not allow analysis of the association between food insecurity and malnutrition.This is the first study to determine the prevalence of food insecurity in households with children under 5 years old in Thailand. This study confirmed the validity of FIES in the Thai context. We found that the poorest households were more likely to suffer from food insecurity than the richest ones. We also found that households with children under 5 years old in rural areas suffered less severe food insecurity than households in urban areas. This suggests a need to improve social protection and provide income support to households with children under 5 years old, as food insecurity hampers child development and increases stunting and wasting. Moreover, social protection should be given to support poor households, particularly in urban areas, in ensuring food security in the light of the COVID-19 pandemic with increased unemployment and vulnerability.


Sustainable employability (SE) has become an important factor for keeping people in the labour market and enabling the extension of working life.We developed and validated an SE index to predict assured workability in 2 years. Additionally, we developed a scoring tool to use in practice.n = 1102). The data were divided into training and validation sets. The outcome was defined as whether the employees had an assured workability after 2 years or not. Multivariable log-binomial regression was used to calculate the SE index. The area under the curve (AUC) was calculated to assess the discriminative power of the index.A questionnaire survey of postal employees aged ≥50 years was conducted in 2016 and followed up in 2018  for training was 0.79 (95% CI 0.75–0.83) and for validation data was 0.76 (95% CI 0.73–0.80). In the scoring tool, the self-rated health, workability, job satisfaction and perceived employment had the highest contribution to the index.The SE index was able to distinguish the employees based on whether they had assured workability after 2 years. The scoring method could be used to calculate the potentiality of future employability among late midlife postal employees. We developed and internally validated a sustainable employability index to predict assured workability based on nine indicators that cover the health, well-being and employability characteristics of older employees. The tool could be used by occupational healthcare, in epidemiological research, and government programmes as a screening tool for employability among older employees and could ultimately help them to conduct feasible programmes targeting employees with suboptimal employability to improve their employability. Sustainable employability has become an important factor for keeping people in the labour market and enabling the extension of working life.Several definitions of sustainable employability exist in the literature but most of them lack a clear conceptual framework.Sustainable employability is a multidimensional concept which involves the health, well-being and employability characteristics of an individual and the sustainability of those characteristics by age.An internally validated sustainable employability index to predict assured workability based on nine indicators among older postal employees.The sustainable employability index correctly predicted the assured workability of the respondents in 2 years, area under the curve for training data: 0.79  and area under the curve for validation data: 0.76 .A scoring tool to estimate assured workability in which the self-rated health, workability, job satisfaction and perceived employment had the highest contribution.The sustainable employability index can be used as a practical tool to screen the potential employability of late midlife employees.et al. [Policies for extended working lives have increased the need for sustainable employability (SE) . SE is aet al.  presenteet al.  presentePotential indicators of SE and their changes were recently studied during a 2-year follow-up among postal service workers aged 50 years and above. The indicators showed consistency with no significant change over time . In totan = 2096) replied to the survey. The follow-up survey was completed with a 76% response rate from baseline respondents (n = 1466). This study utilized data from 1102 subjects who replied to both the baseline and the follow-up surveys and had complete information on each of the measures used in the analysis.The analyses were performed using data from a prospective follow-up survey. The baseline questionnaire survey was conducted among employees of the Finnish postal services in 2016 and was followed in 2018 . A questThe study was approved by the Academic Ethics Committee of Tampere Region .The outcome variable ‘assured workability in 2 years’ was measured in the follow-up survey. Employees were asked to estimate their workability in present work from the point of view of health after 2 years on a scale of 0 (I can’t) to 10 (pretty sure). Based on the literature ,13, as wThe nine indicators of SE were measured at baseline on an ordinal scale and were used as continuous variables in the analysis.Employees were asked to rate their health from the point of view of work on a response scale of 0 (extremely bad) to 10 (extremely good) .Workability was based on the first item of the workability index (WAI), which was assessed with a single-item question ‘workability at present compared to lifetime best’ on a scale of 0–10 ,16. The The question ‘Do you have enough time and resources for your friends and hobbies?’ with the response option of 0 (hardly) to 10  was used to measure the time and resources for friends and hobbies .Employees were asked ‘how well do you feel you recover from the workload after a day/shift of work?’ with the response options 0 (very poorly) to 10 (very well) .Employees were asked ‘how satisfied are you with your current job?’ with response options forming a scale of 1–6 where 1 = very satisfied, 2 = nearly satisfied, 3 = neither satisfied nor dissatisfied, 4 = quite dissatisfied, 5 = very dissatisfied and 6 = can’t say . To makeThe employees were asked about their motivation at work on a scale of 0  to 10 (very much) .Perceived employment was measured as ‘If you now became unemployed, do you think you would get a new job which corresponds to your profession and work experience?’ with the response options 1–5, where 1 = yes, sure, 2 = probably yes, 3 = probably not, 4 = surely not and 5 = cannot say . For thiEmployees were asked if they get enough training to support their job on a scale from 0  to 10 (enough) .Employee’s experience of doing important and significant work for the company (relevance of job) was measured on a scale of 1–6, where 1 = daily, 2 = weekly, 3 = monthly, 4 = less often, 5 = never and 6 = cannot say . For thiOther baseline characteristics used in the study were: age in years, gender , work experience in years, an education level , occupational class (white-collar or blue-collar), work shifts (regular daywork or two shifts including night work) and marital status (married/living together or single/others).R2 and chi-square values for the goodness of model fit. The regression coefficients from the multivariable log-binomial regression model were then used to calculate an index as a linear combination of the estimates as follows:We first randomly divided the data into training and validation sets in a 1:1 ratio balancing by age and gender. We fitted a log-binomial regression model for the dichotomous outcome ‘assured workability in 2 years to the training data using the set of nine variables from baseline. All the variables were added to the model simultaneously. The selection of these variables was made based on previous studies ,11, whicα = constant term estimated from the regression model; β1…β9 = regression coefficient estimated from the regression model.where The regression coefficients were converted into odds ratios (ORs) and presented with their 95% confidence intervals (CIs).The SE index was then divided into quintiles. We calculated the predicted probability of having assured workability in 2 years by regressing SE index quintiles with the assured ability to work. The mean of the predicted probability of having assured workability was calculated for each quintile and the results are presented graphically.The same set of variables was used in the multivariable log-binomial model from the validation data set. The SE index was also calculated from the validation set to avoid overfitting and overestimation of the predictive ability of the index. We calculated and compared the expected and observed probabilities for both the training and validation data sets.Receiver operating characteristic was calculated for the training and validation data sets to illustrate the sensitivity and specificity of the SE index. The area under the curve (AUC) was calculated to assess the discriminative power of the index.Furthermore, we calculated the score for each of the indicators based on the coefficients from the log-binomial model calculated from the training data. The scores were calculated by dividing each estimate by the sum of the estimates of all indicators and then multiplying by 100. The scores are rounded-up.The mean age of the study population was 55.97 years (standard deviation (SD): 3.11) and 60% were male. In total, 538 subjects were selected for the training data set and 564 for the validation data . About 3The indicators show a similar type of association with the outcome in both the training and validation data sets . HoweverThe predictive ability of the SE index did not substantially differ between the training and validation data sets : AUC forThe scoring tool was calculated in the training data set based on the OR of all nine indicators of SE for its practical applicability . The scoWe developed an SE index based on nine multidimensional indicators covering individuals’ health, well-being and employability. The SE index correctly predicted the assured workability of the respondents in 2 years, with 16% in the lowest quintile having assured workability in 2 years compared to the 77% in the highest quintile of the SE index. A similar trend was found in the validation data set (10% versus 70%). This suggests that the probability of the workers having assured workability in 2 years increases with increasing levels of the SE index. The predictive ability of the SE index was high and did not differ much between the training and validation data (AUC 0.79 versus 0.76).Among individual predictors, self-rated health, workability and enough on-the-job training were significantly associated with assured workability in 2 years. Increased odds of having assured workability in 2 years were found in eight of the indicators, except for enough on-the-job training in the training data set. However, the increased odds were significant only for self-rated health and workability, whereas lower odds, but significant association was found for enough on-the-job training. Good self-rated health  and oppoThe score calculated for each indicator based on the regression coefficients was highest for self-rated health and workability, followed by job satisfaction and perceived employment. This means that self-rated health and workability were the most important indicators contributing to the SE index. The score can be used as a practical tool to screen people about their assured workability in future. A previous study shows that sustainable workability during midlife is associated with better survival compared to those with decreasing workability . TherefoWe found no previous studies that have developed an index for sustainable employability or employment index to directly compare our findings. However, a few earlier studies have presented similar sets of indicators in their definition of SE which we adapted ,4.A large and homogenous sample of older employees was used in this study which is one of its strengths. The study population includes both white- and blue-collar employees, although the majority were in blue-collar tasks. One of the limitations is the use of self-reported single-item measures as the potential indicators of SE. Earlier studies presented sustainable employability models in different populations based on self-reported but multi-item measures ,4. NeverThe duration of the follow-up was short, but we believe that a 2-year follow-up is long enough to follow employees older than 50 years, as the changes in health and co-morbidities are more prominent at older ages ,30. MoreIn conclusion, we developed and internally validated the SE index among a sample of older employees. The SE index was able to distinguish between employees with and without assured workability in 2 years. We developed a scoring method, which can be used to calculate the potentiality of future employability among late midlife employees, meaning that those with high SE scores have assured employability. Employers might screen their employees and it may ultimately help them to conduct feasible programmes targeting employees with suboptimal employability to improve employability. Following external validation, the tool may be used by occupational healthcare, epidemiological research and government programmes as a screening tool for employability among older employees.


To utilize the UK Biobank to identify genetic variants associated with the ICD10 -based diagnosis of cerebrovascular disease (CeVD).Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR  to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown.Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink’s firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD.PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10−8) and had a minor allele frequency of > 0.5%.We identified two groups of SNPs closely linked to PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies.Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between  Cerebrovascular disease (CeVD), which commonly manifests as a stroke, is one of the leading causes of serious long-term disability and the second leading cause of death worldwide . AlthougThere is considerable interest in the use of real-world data, such as EHR  to better understand disease mechanisms and to discover new treatment strategies , 5. The The current study involved de-identified data obtained from the UK Biobank Resource under Application Number 49852. It has received the proper ethical oversight, including the determination by the University of Maryland, Baltimore Institutional Review Board that the study is not human research (IRB #: HF-00088022).We carried out a GWAS using the UK Biobank to assess for statistically significant single nucleotide polymorphisms (SNPs) and the clinical manifestations of cerebrovascular disease at the population level. The UK Biobank recruited 502,682 UK participants between 2006 and 2010. The participants were between the ages of 40 and 69 at the time of recruitment. Extensive health-records were collected from participants, including clinical and genetic data with over 820,000 genotyped SNPs and up to 90 million imputed variants available for most individuals .https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22018; 1532 subjects), sex chromosome aneuploidy , mismatch between self-reported sex and genetically determined sex , recommended genomic analysis exclusions , and outliers for heterozygosity or missing rate .Using data from the UK Biobank Resource on 487,310 subjects with imputed genotypes, we performed quality control by removing those with genetic relatedness exclusions  diagnostic codes related to cerebrovascular disease as primary or secondary diagnosis at the time of this analysis (August 2021). Table 1 has a list of all ICD-10 codes included as cases in this analysis. The set of selected cases was purged of relatedness by removing one from each related pair in an iterative fashion until no subjects remained. Relatedness was defined as a kinship coefficient greater than 0.44. This treats third-degree relationships, which was defined a kinship coefficient of 0.0625, as related. Kinship coefficients for all subject pairs greater than 0.44 were provided by the UK Biobank as part of the standard set. To select the controls, all cases and related individuals were first removed from the pool. From the reduced pool, we were able to select 11 control subjects for each case, matching for age, sex and ancestry using incremental tolerances. The tolerance for age ranged from 0 (exact match) up to 7 years. Ancestry matching was performed with principal components (PCs) supplied by the UK Biobank. The mathematical distance in a graph plotting the PC1 x PC2 was used to test similarity in ancestry. The “distance” in ancestry tolerance ranged from 2 PC units to a maximum of 80 PC units with PC1 , ranging from 0 to +400 and PC2 ranging from -300 to +100 units. These tolerances were used to identify the 11 matching controls for every case.Cases were defined using the International Standard Classification of Diseases and Related Health Problems, 10The association analysis was carried out using Plink’s firth logistic regression model and adjusted for age, sex and the 5 PCs using data supplied by the UK Biobank Resource . Firth r−8). Identified variants were assessed for the presence of previously reported phenotypic associations using pheweb.org and the Cerebrovascular Disease Knowledge Portal. Pheweb.org is a GWAS dataset for electronic health record-derived disease associations from the white British participants in the UK Biobank. PheWeb utilizes a generalized mixed model association test that uses the saddle point approximation to account for case-control imbalance and imputed using the Haplotype Reference Consortium Panel [Variants were determined to be of interest if they had a minor allele frequency (MAF) of 0.5% or greater and met the standard threshold for genome-wide significance  were identified as CeVD cases at the time of this analysis (January 2021). This represents a prevalence of 22.2 cases per 1,000 people in the study population. Around 60% of the cases were male. The average age of the cases at the time of diagnosis was 63.8 years (+/- 8.3). Additionally, cases tend to have a higher BMI  and a greater waist circumference . Most cases described their ethnic background as British (89.6%) with a smaller proportion reporting their ethnic background as Irish (3.0%) and Indian (1.0%). Demographic characteristics are shown in Table 4. A full list of ICD-10 diagnoses associated with CeVD can be seen in S2 Table. Cases were more likely to be diagnosed with chronic renal failure  and acute renal failure  and had higher levels of urea  and creatinine  which is consistent with the increased rate of renal disease in the cases. Additionally, cases had higher rates of chronic ischemic heart disease , atherosclerotic heart disease , old myocardial infarction , left ventricular failure , atrial fibrillation and flutter , syncope and collapse , epilepsy  and personal use of long-term anticoagulants . Cases were also more likely to have risk factors that are typically associated with cerebrovascular disease including tobacco use , hyperlipidemia , primary hypercholesterolemia  and essential hypertension . Although the systolic blood pressure was higher in cases , there was no significant difference in the diastolic blood pressure . Finally, cases had a higher rate of type 2 diabetes mellitus  which could also be seen through cases having a higher hemoglobin A1c level  and higher serum glucose levels .Relevant ICD-10 diagnoses associated with CeVD can be seen in While cases were more likely to be diagnosed with hyperlipidemia and primary hypercholesterolemia, they had lower low-density lipoprotein (LDL)  and cholesterol levels  but higher triglycerides . Additionally, high-density lipoprotein (HDL) levels were lower in the cases . Interestingly, cases also had higher levels of C-reactive protein (CRP) .−8  were found to be associated with CeVD (S3 Table). Of this group, the intergenic SNP rs61411276  was found to be most significantly associated with the outcome, with an odds ratio (OR) of 1.11, 95% confidence interval of 1.07 to 1.15, and a p-value of 2.59 x 10−9. A second SNP (rs1922809) in close proximity to sequences encoding LRRTM4  was identified. The intronic SNP rs1922809 had an OR of 1.08, 95% confidence interval of 1.05 to 1.11, and a p-value of 4.29 x 10−8. Data from the PheWeb database was available for rs1922809 and showed that this SNP was associated with cerebrovascular disease. Data from the Cerebrovascular Disease Knowledge Portal was available for both SNPs. While the association between rs61411276 and ischemic stroke was not significant , there was a significant association between rs1922809 and ischemic stroke  and brain microbleeds .GWAS identified two group of SNPs with a MAF of ≥ 0.5% that were associated with cerebrovascular disease with a p-value of less than 5 x 10PITX2 and LRRTM4 that were significantly associated with CeVD. There has been a growing body of literature to suggest that although traditional risk factors such as smoking, diabetes and hypertension do increase the risk of CeVD, CeVD also has a genetic component that has yet to be fully understood [Through this ICD-10 based study, we identified two groups of SNPs near derstood . With thPITX2 gene encodes a protein that regulates right-left differentiation of the embryonic heart and expressed in the adult left atrium. A deficiency in PITX2 can result in electrical and structural remodeling in murine models and this may predispose patients to atrial fibrillation, a known risk factor for thromboembolic stroke [PITX2 and cardioembolic stroke [As with all GWAS studies, although we were able to demonstrate a statistically significant association between the novel loci identified in this manuscript and the development of CeVD, this does not imply causation. However, the findings of this study do have a biological plausibility that can be explained. For instance, the c stroke . Additioc stroke . In our LRRTM4 was associated with an increased risk of CeVD in our study. While the mechanism that can explain this association is not clear, this finding is consistent with the Cerebrovascular Disease Knowledge Portal. As seen in the Cerebrovascular Disease Knowledge Portal, rs1922809 was significantly associated with ischemic strokes and microbleeds in populations other than the UK Biobank [LRRTM4 is expressed in the central nervous system and the structure and expression profile of LRRTM4 mRNAs suggest that it may have a role and maintenance of the nervous system [Also, the SNP rs1922809 closely linked to  Biobank . LRRTM4 s system . Additios system , 17. We s system  we deterThere are limitations to this study that need to be highlighted. Firstly, although we were able to demonstrate a statistically significant association, this does not imply causality and the findings of the study will need to be verified in future analyses, ideally in a more diverse patient population. Due to the high prevalence of traditional risk factors associated with CeVD, it was not feasible to only include patients without traditional risk factors as this would have significantly reduced the power of this analysis. Additionally, as compared to a study that is dedicated specifically to studying CeVD, this analysis utilized data from the UK Biobank and identified cases using ICD10 codes. ICD10 based studies are limited by the fact that many diseases and medical diagnoses are often underdiagnosed. However, that does not seem to be the case here as the prevalence of cerebrovascular disease in our study group is similar to prevalences reported in recent literature . In thisPITX2 and LRRTM4) that were significantly associated with the diagnosis of CeVD in the UK Biobank (p < 5 x 10−8). The SNP (rs61411276) closely linked to PITX2 gene was associated with an increased risk of atrial fibrillation and flutter which can explain the association between this gene and the increased CeVD risk. Although the mechanism is unclear, LRRTM4 gene has been associated with cerebrovascular disease in past studies. While ICD10 based studies do have their limitations, they provide the opportunity to study a wide array of diseases and identify potential variants associated with medical conditions (both rare and common) that can then be validated and explored in further studies.In this ICD-10 based study, we have identified two groups of SNPs (S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 FigWhen selecting controls for comparison with cases, control subjects were selected from subjects within 80 units on the PC1 vs. PC2 graph. The size of 80 units is illustrated with the red boxes around subjects who are primarily European, Chinese, or African Ethnicity based on the PC1 and PC2 eigenvalues provided by the UK Biobank.(TIF)Click here for additional data file.


Naive CD4 and CD8 T cells are cornerstones of adaptive immunity, but the dynamics of their establishment early in life and how their kinetics change as they mature following release from the thymus are poorly understood. Further, due to the diverse signals implicated in naive T cell survival, it has been a long-held and conceptually attractive view that they are sustained by active homeostatic control as thymic activity wanes. Here we use multiple modelling and experimental approaches to identify a unified model of naive CD4 and CD8 T cell population dynamics in mice, across their lifespan. We infer that both subsets divide rarely, and progressively increase their survival capacity with cell age. Strikingly, this simple model is able to describe naive CD4 T cell dynamics throughout life. In contrast, we find that newly generated naive CD8 T cells are lost more rapidly during the first 3–4 weeks of life, likely due to increased recruitment into memory. We find no evidence for elevated division rates in neonates, or for feedback regulation of naive T cell numbers at any age. We show how confronting mathematical models with diverse datasets can reveal a quantitative and remarkably simple picture of naive T cell dynamics in mice from birth into old age. Lifelong and comprehensive adaptive immunity depends upon generating naive CD4 and CD8 T cell populations with diverse repertoires of T cell receptors (TCRs). These must be established rapidly from birth and then maintained throughout life. In mice, the number of circulating naive T cells grows from tens of thousands at birth to tens of millions in several weeks, peaking at around 2 months of age  and waniThe consensus view is that, in adult mice, naive T cells have a mean lifespan of several weeks but a mean interdivision time of several years . This diper capita rates of activation upon first exposure to environmental stimuli may increase the apparent rate of loss of naive CD4 T cells in neonatal mice. One might expect a similar process to occur with naive CD8 T cells, with substantial numbers of so-called ‘virtual’ memory CD8 T cells generated from naive T cells in the periphery soon after birth  will approach 1 within a population if it turns over completely – that is, if its donor:host composition equilibrates to that of its precursor. Saturation at To address this question, we used a well-established system that we have developed to quantify lymphocyte dynamics at steady state in healthy mice , with thlacement , which clacement , and exclacement . For thelacement , and strTo describe these data we explored variants of a structured population model in which either the rate of division or rate of loss of naive T cells varies exponentially with their post-thymic age. These models are three dimensional linear partial differential equations (PDEs) that extend those we described previously , allowin+ and Ki67- RTE with mouse age, up to a multiplicative constant which we estimated. The fitting procedure is outlined in Appendix 3, and detailed in Each model was fitted simultaneously to the measured timecourses of total naive CD4 or CD8 T cell numbers, the normalised donor fraction, and the proportions of donor and host cells expressing Ki67. To model influx from the thymus we used empirical functions fitted to the numbers and Ki67 expression levels of late stage single-positive CD4 and CD8 thymocytes (Appendix 2). Assuming that the rate of export of cells from the thymus is proportional to the number of single-positive thymocytes , we usedOur analysis confirmed support for the models of cell-age-dependent kinetics , with alAny signal of improvement in fitness with cell age, either in loss or division rates, is manifest primarily in an asymptotic value of the normalised donor fraction lower than one. For naive CD8 T cells, the normalised donor fractions at late times post-BMT exhibit considerable scatter  and MN (GFP-) cells in equal numbers, the RTE:MN ratio within both CD4 and CD8 populations decreased progressively, falling by approximately 50% at 6 weeks  CD4 and CD8 T cells to persist following co-transfer to an adult congenic recipient . This stE status . After t 6 weeks , indicatGFP reporter mice, in which information about cell age can be gleaned from GFP expression levels. In this strain, intracellular staining for Ki67 is not possible without severely compromising GFP fluorescence. Therefore, we also introduced a Ki67RFP reporter construct  modifications of the model were needed. We drew on two new data sets. One comprised the numbers and Ki67 expression of naive T cells derived from wild-type mice aged between 5 and 300 days. The other was derived from a cohort of Ragonstruct  to the sGFPKi67RFP dual reporter mice aged between 10 and 120 days, wild-type mice, and adult busulfan chimeras in which host and donor cells were pooled. The red curves show the predictions of the cell-age-dependent loss models, which were fitted to the busulfan chimera data (red points) and extrapolated back to 1 day after birth. The dual reporter mice also yielded measurements of the co-expression of GFP and Ki67. To predict the kinetics of GFP+Ki67– and GFP+Ki67+ proportions . Details of how we connected GFP measurements to the age-structured models are provided in Appendix 5, and a description of the process of predicting neonatal T cell dynamics is given in Appendix 6.portions , we need- and Ki67+ RTE as defined by GFP expression We re-analysed the data from Reynaldi et al. using a Bayesian hierarchical approach (Appendix 7) to explain the variation in the kinetics of loss of these cohorts of cells across animals and age groups. Since there was no readout of cell division in this system, we simplified the cell-age-dependent loss model by combining division and loss into a net loss rate  support . This mo support . We thenformance . See App+ Ki67– and GFP+ Ki67+ naive CD8 T cells  thymocytes , and is roughly four times shorter than the 66 day (52–90) residence time of MN CD4 T cells. Our results agree closely. We estimate that CD4 RTE (cells of age 0) have an expected residence time of 22 (18–28) days, doubling approximately every 3 months, such that in a 12-week-old mouse, the mean residence time of MN CD4 T cells aged 21 days or greater is roughly 60 days. In contrast, van Hoeven et al. concluded that naive CD8 T cells are a kinetically homogeneous population with a mean residence time of 76 (42–83) days. With our favoured age-dependent loss model, we estimate that CD8 RTE initially have an expected residence time of 40 (18–28) days, doubling every ∼5 months. However, our predicted average residence time of MN CD8 T cells (aged >21 days) in a 12 week old mouse was approximately 76 days, which agrees with their estimate. We included a similar RTE/MN model in our analysis .Another modelling study also demonstrated that CD4 RTE are lost more rapidly than MN CD4 T cells . They esanalysis  and founThe pioneering studies by Berzins et al. showed substantial and proportional increases in T cell numbers in mice transplanted with 2, 6 and 9 thymic lobes Reynaldi et al. used a novel fate-mapping system to demonstrate that the net loss rate of naive CD8 T cells  declines with their post-thymic age and is higher for CD8 RTE in neonates than in adults We do not explicitly model the mechanisms underlying adaptation in cell persistence. Modulation of sensitivity to IL-7 and signaling via Bcl-2 associated molecules has been implicated in increasing naive T cell longevity  and is cAn alternative to the adaptation model is one of selection, in which each cell’s survival capacity (loss rate) is determined during thymic development, drawn from a distribution, and subsequently fixed for its lifespan . As an iNaive T cells proliferate under severely lymphopenic conditions in mice , a phenoSJL.C57Bl/6J (CD45.1.B6) mice were treated with optimised low doses of busulfan to deplete HSC but leave peripheral T cell subsets intact. HSC were reconstituted with congenically-distinct, T cell depleted bone marrow from C57Bl/6J donors to generate stable chimeras. Details of the protocols are given in Mki67tm1.1Cle/J (Ki67-RFP) mice were generously provided by the laboratory of Prof. Hans Clevers , CD45.1 BV650, CD45.2 PE-Dazzle, TCR-+ BV711, CD44 BV785, CD25 BV650 , CD62L BUV737 (BD Biosciences), LIVE/DEAD near-IR and LIVE/DEAD blue viability dyes. For Ki67 staining, cells were fixed using the eBioscience Foxp3 /Transcription Factor Staining Buffer Set and stained with either anti-mouse Ki67 FITC or PE (both eBioscience). Cells were acquired on a BD LSR-Fortessa flow cytometer and analysed with Flowjo software (Treestar). See Single-cell suspensions were prepared from the thymus, spleen and lymph nodes of busulfan chimeric mice, wildtype control mice, or germ-free mice. Cells were stained with the following monoclonal antibodies and cell dyes: CD45.1 FITC, CD45.2 FITC, CD45.2 AlexaFluor700, TCR-+ fraction within SP thymocytes to define thymic influx (Appendix 2). Specifically, we fitted simultaneously to the time courses of total cell counts, normalised donor fraction and the fraction of cells that were Ki67+ within donor and host subsets of naive CD4 and CD8 T cells. We used a Bayesian estimation approach using R and Stan. Code and data used to perform model fitting, and details of the prior distributions for parameters, are available at this linked Github repository. Models were ranked based on information criteria estimated using the Leave-One-Out (LOO) cross validation method (+ and Ki67+ cells in RagGFP Ki67RFP reporter mice aged between 11 days and 4 months (Appendix 5), and the models fitted to data from adult mice were extrapolated back to near birth (Appendix 6). To re-analyze longitudinal data from We fitted a set of candidate mathematical models (described in Appendix 1) to the data from adult busulfan chimeric mice, using empirical descriptions of the pool sizes and Ki67n method , describ This paper challenges the widely held view that the number of naive T cells in our body is regulated through homeostatic feedback mechanisms, meaning that cells divide more frequently – or live longer – when cell numbers are low. The arguments in favor of this homeostatic regulation rely on cross-sectional data, which fail to distinguish between the effects of host age, cell age, and cell numbers, each of which separately may influence the dynamics of cells. Using a set of mathematical models and experimental data sets, this paper manages to tear these factors apart and reports that in mice there is no feedback regulation of naive T cell numbers. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tadatsugu Taniguchi as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Jose Borghans (Reviewer #1).Thank you for submitting your article "Towards a unified model of naive T cell dynamics across the lifespan" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The reviewers have noted several elements of the work that require clarification:1) Expansion of the explanation regarding several points of the model.2) Attention to revisions of the figures and figure legends and provision of additional qualifications of the models .3) Consider points raised regarding additional approaches in modelling/testing the data.Reviewer #1 (Recommendations for the authors):– Some sentences/claims in the abstract are very general; please specify that this is the situation in (clean) mice.– I found Figure S2 very helpful. I would advise incorporating it as the first main figure of the paper.– If I understood correctly, in Figure 1B, the authors have plotted the combined data of mice who underwent BMT at different ages (between 7 and 25 weeks of age). I'm wondering whether the age at BMT may have an influence on the measurements. It may be interesting to use different labels to specify the age at BMT.– Figure 2: Please plot the fits for all the models that were tested . That would help the reader judge visually how bad the fits of the alternative models were and would show in which ranges those models fit poorly. The lower relative weights of the alternative models suggest that the fits of the alternative models were really bad, but I would like to see that also visually.– In Figure 2, I noticed that in the Ki67 panels, the early data points describing the donor cells are consistently missed by the best fits. Do the authors know why this is the case? Please explain. In the last Ki67 panel (11-25 weeks), on the other hand, the donor data seem to be almost overfitted.– On page 4, the authors state that "A prediction of any progressive increase in cell division rates with age is also inconsistent with observations of the frequencies of T cell receptor excision circles (TRECs) in adult mice." The authors also report, however, that the best fit of the naive CD8 T cell data is obtained for an age-dependent division model in which the T cell division rate increases only very slightly with age. I'm not convinced that the situation would be inconsistent with the TREC data.– On the same page, it is stated "This model was very similar to a neutral, homogeneous model and predicted that the normalised donor fraction approaches 1 in aged mice. This conclusion contradicts findings from our own and others' studies that demonstrated that models assuming homogeneity in naive CD8 T cells failed to capture their dynamics in adult and aged mice (2-20 months old)." The authors explain this as follows: "The statistical support for the age-dependent division model here may derive from the relatively sparse observations in aged mice in this dataset, which define the asymptotic replacement fraction." I'm afraid I miss the point; I fail to understand this explanation.– I am struggling to understand intuitively why the age-dependent division model performs so poorly on the data of Figure 3B, while the model performs really well on the data of Figure 2. If the proliferation rate were to be fitted again on the basis of Figure 3, would the fit to the data of Figure 2 become very bad? What happens if both data sets would be fitted simultaneously?– Discussion: It would be very much appreciated if the authors could discuss their view on the translation of these findings to the human and the dirtier mouse situation.– In the Discussion it is mentioned that "Naive T cells proliferate under severely lymphopenic conditions in mice but acquire a memory-like phenotype. There is some evidence that this process occurs in healthy neonatal mice, suggesting that they are lymphopenic to some degree." I wanted to note that in Hoeven et al. (ref 22), we observed that this increased transition of RTE to the memory pool even occurs in the lympho-replete situation, suggesting that it's related to the RTE nature of the cells rather than to the cell numbers in the host. The authors may want to consider adding this to their discussion.Reviewer #2 (Recommendations for the authors):2 values) in the main body or captions, which I think would help substantiate some of the remarks.I think it would have been helpful to have seen quantitative descriptions of model fits  might be worth making to explain the quantitative discrepancy?I'd suggest that all of the figures and captions need to be re-read carefully for errors. It might be excessive, but in all captions (or recorded in SI if it is too unattractive in the main text) it would have been useful to know the number of samples/mice in each panel. These are also absent from the main text.Figure 1 is missing caption entries for panels C and D.Figure 2. Reporting goodness of fit statistics in the caption might be informative.Figure 3. Perhaps the top two panels should be A and the bottom two B, with appropriate classification in the caption? As this is an important figure, I think it would be worth expanding on the simulation, even with some brief text, as there appears to be none presently.In Figure 5B, what are the units for the y-axis?In Figure 6B, if these are paired measurements, it might be appropriate to report the Spearman correlation coefficient to quantitatively support the (obvious anyway) relationship between SP and Naive.Check that BMT, bone marrow transplant, and SP, for single positive, are defined the first time they are used.+ age-dependent division model in the first section.Did you consider fitting your models to a bootstrapped version of your data to get estimates on the influence of experimental variability? That might, e.g., have given an indication as to the strength of belief in the CD8 Essential revisions:The reviewers have noted several elements of the work that require clarification:1) Expansion of the explanation regarding several points of the model.2) Attention to revisions of the figures and figure legends and provision of additional qualifications of the models .3) Consider points raised regarding additional approaches in modelling/testing the data.We’re really grateful for the care both reviewers took in understanding and critiquing our manuscript. We feel that addressing their comments has helped us to improve it substantially.Reviewer #1 (Recommendations for the authors):– Some sentences/claims in the abstract are very general; please specify that this is the situation in (clean) mice.We now clarify in several places that we are dealing with mice.– I found Figure S2 very helpful. I would advise incorporating it as the first main figure of the paper.This is a good idea – we’ve moved the model schematics to Figure 1 – see response below, too.– If I understood correctly, in Figure 1B, the authors have plotted the combined data of mice who underwent BMT at different ages (between 7 and 25 weeks of age). I'm wondering whether the age at BMT may have an influence on the measurements. It may be interesting to use different labels to specify the age at BMT.Yes, Figure 1 didn’t present the data in the clearest way. We were indeed careful to take both mouse age and age at BMT explicitly into account in the modelling. Specifically, we used the empirical description of single-positive thymocyte numbers with mouse age as a surrogate for thymus output, and used the appropriate region of this function when modeling host and donor cell dynamics in each mouse, with its particular age at BMT.The data were all shown more clearly in Figure 2, where mouse age and age at BMT are presented more logically. So we’ve removed the raw data from Figure 1, and inserted the model sketches here instead.– Figure 2: Please plot the fits for all the models that were tested . That would help the reader judge visually how bad the fits of the alternative models were and would show in which ranges those models fit poorly. The lower relative weights of the alternative models suggest that the fits of the alternative models were really bad, but I would like to see that also visually.Good idea – we’ve now added these to the SI. None of the alternative models look atrocious, even when viewed under the transformations they were fitted on . When fitting to so many observations and different timecourses simultaneously, we find visual inspection doesn’t often square neatly with likelihood measures.This problem  highlights one reservation we have about relying on information criteria alone. The validity of any likelihood-based measure depends on the assumptions underlying it – in particular, the error distributions . While we do our best to transform data to satisfy these error models, departures will occur. For this reason we used these model weights as a guide only, and focused instead on out-of-sample prediction.– In Figure 2, I noticed that in the Ki67 panels, the early data points describing the donor cells are consistently missed by the best fits. Do the authors know why this is the case? Please explain. In the last Ki67 panel (11-25 weeks), on the other hand, the donor data seem to be almost overfitted.Thank you for picking this up! This was actually just an error in plotting the Ki67 predictions. They involve a subtraction step (age of mouse minus age at BMT) and this was done correctly for the fits to cell numbers and donor chimerism, but was incorrect for the Ki67 panels – so the fitted curves were slightly offset. We’ve corrected this.– On page 4, the authors state that "A prediction of any progressive increase in cell division rates with age is also inconsistent with observations of the frequencies of T cell receptor excision circles (TRECs) in adult mice." The authors also report, however, that the best fit of the naive CD8 T cell data is obtained for an age-dependent division model in which the T cell division rate increases only very slightly with age. I'm not convinced that the situation would be inconsistent with the TREC data.Thanks, our logic was incorrect here – we’ve removed this argument.– On the same page, it is stated "This model was very similar to a neutral, homogeneous model and predicted that the normalised donor fraction approaches 1 in aged mice. This conclusion contradicts findings from our own and others' studies that demonstrated that models assuming homogeneity in naive CD8 T cells failed to capture their dynamics in adult and aged mice (2-20 months old)." The authors explain this as follows: "The statistical support for the age-dependent division model here may derive from the relatively sparse observations in aged mice in this dataset, which define the asymptotic replacement fraction." I'm afraid I miss the point; I fail to understand this explanation.We’ve tried to clarify. Essentially it was about information. The cell-age-dependent kinetics models are constrained most tightly by the donor replacement curve, whose asymptote is rather noisy for CD8s. With these data, therefore, our intuition is that we are not able to reliably discriminate between age-dependent loss and division for CD8s based on information criteria alone.– I am struggling to understand intuitively why the age-dependent division model performs so poorly on the data of Figure 3B, while the model performs really well on the data of Figure 2. If the proliferation rate were to be fitted again on the basis of Figure 3, would the fit to the data of Figure 2 become very bad? What happens if both data sets would be fitted simultaneously?This comment was helpful – we now explain that Figure 3 just serves as confirmation of strong cell-age effects for both CD4 and CD8 T cells; therefore, it excludes homogeneous models and lends weight to our decision to reject the fitted age-dependent division model for CD8s.The age-dependent division models do poorly in Figure 3 because, for both CD4 and CD8 T cells, the predicted magnitudes of the division rate and its rate of increase with cell age are both very small when they are fitted to the data in Figure 2. This makes both the CD4 and CD8 age-dependent division models similar to neutral models with constant rates of division and loss, with no difference in the loss rates of RTE vs mature naive T cells.Direct fitting of the age-dependent division model to the data in Figure 3 is difficult because the age of the mice in that experiment were not specified precisely, the MN cell donors were a mix of WT and thymectomised mice, and there is also ambiguity in the mapping of GFP-positivity to cell age, needed to define an RTE. We have extended our discussion of this point.We went back-and-forth on the issue of fitting the models to everything at once vs fitting on data from one set of experiments and using out-of-sample prediction to challenge the models. Batch effects can be quite significant — for example, when comparing the dynamics of endogenous cells in Figure 2 to those of adoptively transferred cells in Figure 3; different housing facilities, reagents, and so on. So the correct way to fit simultaneously to observations from different labs and experimental systems would be to use hierarchical models. That would leave only information criteria with which to weigh support for different models, which as we describe above we find a little unsatisfactory. So in this study we tried to use out-of-sample prediction — even if just describing trends in new datasets — as a means of evaluating the models.– Discussion: It would be very much appreciated if the authors could discuss their view on the translation of these findings to the human and the dirtier mouse situation.Yes, this is important. We have added a section to the Discussion regarding self-renewal clonality, and added comments on T cell behaviour in humans and ‘dirty’ mice at other points in the text.– In the Discussion it is mentioned that "Naive T cells proliferate under severely lymphopenic conditions in mice but acquire a memory-like phenotype. There is some evidence that this process occurs in healthy neonatal mice, suggesting that they are lymphopenic to some degree." I wanted to note that in Hoeven et al. (ref 22), we observed that this increased transition of RTE to the memory pool even occurs in the lympho-replete situation, suggesting that it's related to the RTE nature of the cells rather than to the cell numbers in the host. The authors may want to consider adding this to their discussion.Thank you – we have added this point to our discussion and reworded that argument. . Your study also reminded us to discuss the CD4 co-transfer experiment in Dong et al.. We also have difficulty reconciling their results with other studies, but we speculate that it may derive from the step of labelling one of the transferred populations  with CFSE, but not the other.Reviewer #2 (Recommendations for the authors):2 values) in the main body or captions, which I think would help substantiate some of the remarks.I think it would have been helpful to have seen quantitative descriptions of model fits .2 is just a measure of closeness of fit and tends to 1 for overfitted data – so we don’t feel it is so helpful here where our goal to trade off goodness of fit vs. model complexity.RFor the qualitative fit in Figure 3, perhaps some speculative statement (if you have one) might be worth making to explain the quantitative discrepancy?We did speculate in the discussion, but have expanded on it. We believe the disparity stems from a combination of the unknown ages of the mice used in their experiments ; potential effects of cell manipulation (adoptive transfer vs dynamics of endogenous cells); and uncertainty in the age cut-off defining RTE.I'd suggest that all of the figures and captions need to be re-read carefully for errors. It might be excessive, but in all captions (or recorded in SI if it is too unattractive in the main text) it would have been useful to know the number of samples/mice in each panel. These are also absent from the main text.Figure 1 is missing caption entries for panels C and D.Thanks – we’ve gone through all of the captions, made corrections and added sample sizes.Figure 2. Reporting goodness of fit statistics in the caption might be informative.See above for our comment re: goodness of fit.Figure 3. Perhaps the top two panels should be A and the bottom two B, with appropriate classification in the caption? As this is an important figure, I think it would be worth expanding on the simulation, even with some brief text, as there appears to be none presently.We reframed the discussion of Figure 3 and have actually compressed the figure a little — we feel it’s clearer now. We have also added a new section in the SI that details how the simulations were performed.In Figure 5B, what are the units for the y-axis?Net loss rate (loss rate – division rate) – this is the inverse of the expected clonal lifespan. We’ve added this. Also for panel 5A.In Figure 6B, if these are paired measurements, it might be appropriate to report the Spearman correlation coefficient to quantitatively support the (obvious anyway) relationship between SP and Naive.This is a good idea – we now quote the Spearman rank correlation coefficients.Check that BMT, bone marrow transplant, and SP, for single positive, are defined the first time they are used.Thank you for picking these up.+ age-dependent division model in the first section.Did you consider fitting your models to a bootstrapped version of your data to get estimates on the influence of experimental variability? That might, e.g., have given an indication as to the strength of belief in the CD8We are using a Bayesian fitting approach – the envelopes we show on the model predictions are essentially an analog of the envelopes you would get by bootstrapping the data and refitting .


Cardiovascular diseases continue to be the most imminent health care problems in the western world, accounting for numerous deaths per year. Heart failure (HF), namely the reduction of left ventricular function, is one of the major cardiovascular disease entities. It is chronically progressing with relapsing acute decompensations and an overall grave prognosis that is little different if not worse than most malignant diseases. Interestingly acute metabolically and/or immunologically challenging events like infections or major surgical procedures will cause relapses in the course of preexisting chronic heart failure, decrease the patients wellbeing and worsen myocardial function. HF itself and or its progression has been demonstrated to be driven at least in part by inflammatory pathways that are similarly turned on by infectious or non-infectious stress responses. These thus add to HF progression or relapse. TNF-α plasma levels are associated with disease severity and progression in HF. In addition, several cytokines  are involved in deteriorating left ventricular function. Those observations are based on clinical studies using inhibitors of cytokines or their receptors or they stem from animal studies examining the effect of cytokine mediated inflammation on myocardial remodeling in models of heart failure. This short review summarizes the known underlying immunological processes that are shared by and drive all: chronic heart failure, select infectious diseases, and inflammatory stress responses. In conclusion the text provides a brief summary of the current development in immunomodulatory therapies for HF and their overlap with treatments of other disease entities. Chronic heart failure (HF) is in part driven by inflammatory processes. Ventricular function in chronic HF patients worsens if the patient experiences acute illnesses or stresses. Some of these share inflammatory motifs with those active in progression of HF. We will focus on the overlap of key pathways in chronic inflammation in HF, sepsis, viral infection  and non-infectious stressors to shed light on their potentially synergistic adverse effects in HF progression and deterioration .HF can present as a chronic disease or in an acute setting with rapidly decreasing myocardial function either as the primary presentation or as acute decompensation of preexisting disease. Acute events (like operations or infections) can trigger a reduction of myocardial function that frequently will not return to prior levels .HF has a prevalence of 11.8 % in people aged 60 or older . An obseThe mainstay of HF is the loss of cardiomyocyte  mass due to noxious stimuli ranging from ischemia via pressure overload-induced remodeling processes to viral infection and genetic causes see . CardiomRecently, the recognition of the role of the immune system in chronic HF is evolving . The mosAfter an ischemic insult macrophages infiltrate the site of injury and alter their polarization around 1 week after the acute injury from classical proinflammatory to alternative anti-inflammatory phenotypic characteristics, the latter expressing IL-10. Inflammatory macrophages express tumor necrosis factor a (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β)  see Fig. The immIn vivo animal experiments have shown that overexpression of TNF-α leads to chronic inflammation in the heart, to dilatative cardiomyopathy and in the end to HF  , mostly There are parallels between the cytokine profile in HF patients and patients suffering from bacterial sepsis. TNF-α is elevated in septic patients . SurvivoThe increased susceptibility of HF patients to infection is conveyed through several, partly independent, partly overlapping mechanisms in addition to circulating cytokines. Infections increase the metabolic demand, leading to a mismatch of oxygen demand to supply. In HF the metabolic alterations are increased oxidative stress , energy One of the key therapeutic measures for patients in septic shock is however maintenance of intravascular volume status by infusion of fluids. Patients with preexisting HF are at high risk of acute decompensation with development of pulmonary edema due to fluid substitution. HF patients in septic shock have been shown to receive less fluids than non-HF patients. Among the HF patients those who received less fluids have a higher mortality . On the Taken together these data suggest that it is important for HF patients to prevent infection altogether. Said that, all measures to avoid infections are effective. A very effective way to do so is vaccination. Vaccination against influenza reduces all-cause mortality and rate of hospitalization due to cardiovascular events as well as to all-causes –68 whichSince 2020 the world has been confronted with the SARS-CoV2 pandemic. Preexisting HF increases mortality of COVID-19 . ExacerbLike in HF and sepsis macrophage-derived cytokines are increased in COVID-19 , 78. AnaThere is contradicting data about the levels of IL-1β in COVID-19 patients. A small study showed an increase in IL-1β in COVID-19 patients correlating with the severity of the disease  but a laIL-6 is significantly elevated in severe COVID-19 according to a meta-analysis and levels of IL-6 like in sepsis correlated with adverse outcomes . EspeciaPost-acute sequelae of COVID-19, also called Long-COVID, is as HF potentially caused by incomplete resolution of inflammation and characterized by a long lasting elevation of IL-1β, IL-6 and TNF-α .Cardiomyocytes can be directly infected with SARS-CoV2 via the angiotensin-converting enzyme type 2 (ACE2) which is then cleaved by transmembrane protease serine 2 (TMPRSS2) . Due to ACE2 counterbalances the activated RAAS in HF by transforming Angiotensin II to angiotensin 1-7. Increased ACE2 levels thus even prevent or improve preexisting HF . The effAutopsies have shown that both the innate and the adaptive immune systems are activated in the heart of patients with SARS-CoV2-Infection . An autoOne of the cytokines indicted in inflammation in HF as well as in sepsis is TNF-α. Contrary to the promising animal experiments the use of infliximab, a TNF-α antibody resulted in a reduced cardiac output in patients with rheumatoid arthritis without known HF . The ATTNumerous studies have examined the effect of monoclonal and polyclonal antibodies against TNF-α in septic patients. Both reduce over-all mortality in patients with severe sepsis. Monoclonal anti-TNF-α-antibodies improve survival of patients in septic shock . In COVIThese results suggest that even though TNF-α is upregulated in HF as well as in other infections, blocking it by using antibodies has negative effects on HF despite positive effects on some select infectious diseases.In the CANTOS-trial canakinumab, an IL-1β antibody, in patients with myocardial infarction and active inflammation resulted in less all-cause mortality and a lower rate for HF hospitalization , 99. AlsA positive effect of the anti-IL-6 antibody tocilizumab has been seen for the cardiac function of patients without HF , 29. Up In a broader attempt to address the immunological aspects in HF the ACCLAIM study showed better over-all survival in the mildly impaired (NYHA II) group after using a non-specific immunomodulatory therapy (using the Celacade system™) . SurprisCytoResc, a recent, not yet published, trial uses an extracorporeal polystyrene-based hemoadsorber in COVID-19 patients . Prior tThe use of corticosteroids is a treatment option for patients in septic shock if treatment with vasopressors and fluids is not successful in improving the hemodynamic situation . For a lThe aim of this short review was to delineate parallels in the immunological processes in HF and inflammatory responses to infections or non-infectious stresses, which can also be viewed as an opportunity for using immune based strategies to fight deteriorating HF in cases with such comorbidities.Influencing the release and circulation of cytokines could be an interesting starting point for HF therapy but is not yet ready to be administered in a broad setting. Inhibition of IL-6-signaling seems to be one of the more promising strategies for treatment of HF as well as infections but it has to be considered, that there is a lot of contradictory data so that a clear recommendation for immunomodulatory therapy cannot be given yet.TLR4 is involved in the development of various diseases connected to inflammation is. To date TLR4 signaling cannot be therapeutically targeted, while this may be a promising avenue to improve cardiac function in HF patients as well as to reduce myocardial dysfunction in infectious diseases .One thing one should keep in mind while discussing immunomodulatory therapy is that these medications have a risk of increasing susceptibility for new infections necessitating a strict indication and close monitoring during and after application.The manuscript was written by LH and GT and revised by JW and AE. All authors contributed to the article and approved the submitted version.Funding for LH was provided through the University of Oldenburg (Forschungspool-Projekt 2018-030).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


E coli or Klebsiella spp.The aim of this study was to emulate the MERINO trial of piperacillin-tazobactam vs meropenem for the definitive treatment of bloodstream infection (BSI) caused by ceftriaxone-nonsusceptible Data from an observational study of BSI and a randomised controlled trial of a rapid diagnostic in BSI were used to emulate the MERINO trial. The primary outcome of the emulated trial was 28-day mortality after blood culture. Outcomes were compared using logistic regression adjusted for propensity score for emulated intervention.with E. coli or Klebsiella spp. of which 121 met the eligibility criteria. In the emulated trial, a total of 14/82 patients (17.1%) allocated to piperacillin-tazobactam met the primary outcome compared with 6/39 (15.4%) in the meropenem group (unadjusted odds ratio 1.13 (95% CI 0.40 to 3.21)). After adjustment for propensity score, the odds ratio increased to 1.31 (95% CI 0.40 to 4.26). This difference is in the same direction but of a smaller magnitudethan observed in the MERINO trial, where 30-day mortality was met by 23/187 patients (12.3%) in the piperacillin-tazobactam and 7/191 (3.7%) in the meropenem group (unadjusted odds ratio of 3.69 (95% CI 1.48 to 10.41)).Of the 6,371 observational study and RCT participants, 1,968 had a bloodstream infection The mortality rate in an emulated trial population was more than double the mortality rate in the MERINO trial. The methodology used attempts to address the concern that previous results could be explained by biases such as selection bias and uncontrolled confounding and provides information on how a trial such as the MERINO trial may have performed in the NHS. E.coli or K.pneumoniae [Extended-spectrum β-lactamase (ESBL) producing bacteria are a frequent cause of bloodstream infection (BSI). Carbapenems have been regarded as the antibiotic of choice for treatment of infections caused by ESBL producers . Howevereumoniae . A noninAn RCT is considered the gold standard design in clinical research, however, they are not always financially or ethically feasible to conduct. Therefore, an observational approach is often used but these studies are subject to bias and confounding. Emulating a target trial is an approach designed to “mimic” trial practice using observational data and if successful, should yield similar results .The aim of this study was to use data from an observational study and a trial of a rapid diagnostic to emulate the MERINO trial to explore whether an emulated trial would yield consistent results using data from the UK.Patient-level data from the two studies performed as part of the BSI-FOO Programme , 15 wereThe research programme on which this work is based was approved by Southwest Research Ethics Committee (10/HO102/51). The National Information Governance Board approved the use of routinely-collected patient data without specific consent for the BSI-FOO observational study and collection of full data for patients who died before being approached for consent in RAPIDO. North Bristol NHS Trust acted as Sponsor.E. coli or Klebsiella spp. that was resistant to Ceftriaxone and/or Cefotaxime and susceptible to both meropenem and piperacillin-tazobactam and treatment was started within 72 hours of blood culture (date and time 0 for BSI-FOO and RAPIDO). Patients that would have otherwise been eligible but did not start any of the study drugs (meropenem or piperacillin-tazobactam) within the 72 hours window were considered ineligible and excluded from the emulated trial population. All participants were aged 18 years and over. Informed consent was not required for the BSI-FOO observational study and RAPIDO patients who died before being approached for consent, so it was not possible to replicate consent for this population, but all surviving RAPIDO participants provided written informed consent to join the trial. Exclusion criteria are given in The MERINO trial inclusion/exclusion criteria were applied to BSI-FOO and RAPIDO participants . PatientThe trial interventions in the MERINO trial were treatment with piperacillin-tazobactam or meropenem. These were to be administered for a minimum of 4 days and maximum of 14 days, with duration determined by the treating clinician. In the emulated trial population patients were assigned to a “emulated intervention” based on their treatment timeline and allocated to the first study drug received. The start of follow-up was defined as the date in which the patient started their first dose of their assigned intervention.The primary outcome of the MERINO trial was all-cause mortality at 30 days after randomisation. It was not possible to analyse 30-day mortality for the emulated trial as follow-up in BSI-FOO and RAPIDO was limited to 28 days, where start of follow-up was defined as the date the blood sample was taken. All patients started their emulated intervention within 3 days of blood sample; therefore, 25-day mortality was analysed to ensure full follow-up was available for all patients.The MERINO trial analysis population was defined as any randomised participant receiving at least 1 dose of the allocated drug. This was supported by an analysis of the per-protocol population. By definition, in the emulated trial population, all participants received at least one dose of allocated drug and were therefore included in the primary analysis population. We did not emulate the per-protocol analysis as few participants received allocated treatment for the required four days.Continuous data were summarised using mean and standard deviation (or median and interquartile range (IQR) if distributions were skewed) and categorical data as numbers and percentages. Demographics, comorbidities and medical history were summarised by emulated intervention. Standardised mean differences were calculated to quantify imbalances in baseline characteristics by the treatment group . Mortalia priori. Factors included in the propensity score model were: centre, age, sex, temperature at time 0, neutrophil count on day 0 or closest, systolic blood pressure on day 0 or closest, on IV fluids at day, on ventilation at day 0, cerebrovascular disease, Charlson score and source of infection. The number of participants and deaths in each emulated intervention group was examined within strata defined by propensity score quantiles. Participants in strata for which there were no participants or deaths in either group were excluded to ensure the analyses were restricted to participants eligible to receive either treatment strategy, ensuring the assumption of positivity was met.To emulate the trial analyses, absolute risk differences were calculated using generalised linear models. As the emulated trial was not randomised, potential confounding factors needed to be accounted for in the analysis. Propensity score models were developed using a logistic regression model with emulated intervention as the outcome. Variables included in the propensity score model were age and sex and any potential confounders based on clinician expertise. These were specified th, 50th and 90th percentiles to capture potential non-linear associations with the outcome. Missing values were imputed with age- and sex-adjusted averages.Convergence was not achieved when fitting an adjusted generalised linear model and therefore outcomes were compared using logistic regression adjusted for the propensity score. Unadjusted odds ratios were calculated for the MERINO trial to provide a comparison. Propensity scores were modelled using restricted cubic splines with 3 knots at 10th, 50th and 75th percentiles to assess the robustness of the results to the location of knots; (c) excluding participants that switch to the other intervention during follow-up.Three sensitivity analyses were carried out: (a) imputing missing categorical values with worst case values i.e. disease present; (b) propensity score model using restricted cubic splines at 25Model fit was assessed using standard methods. Meropenem was the reference group in all analyses. Results are reported as effect estimates with 95% confidence intervals (CI). In all tables missing data are described in footnotes.All analyses were performed in Stata version 16.0 .Klebsiella spp. of which 163 had proven non-susceptibility to cephalosporins and proven susceptibility to meropenem and piperacillin-tazobactam. Of these, 34 were not in receipt of meropenem or piperacillin-tazobactam within 72 hours of blood culture. Of the remaining 129 participants, 4 repeat episodes and 4 polymicrobial infections were excluded, thus 121 met the eligibility criteria ) compared to the piperacillin-tazobactam group ) and duration of allocated treatment was also longer in the meropenem group  vs. 3 days ). Of those allocated to meropenem, 31/39 (79.5%) were in receipt of their study drug for four days  and of those allocated to piperacillin-tazobactam, 38/82 (46.3%) were in receipt of their study drug for four days. After the first dose of emulated intervention, 1/39 (2.6%) participants allocated to meropenem switched to piperacillin-tazobactam, conversely, 39/82 (47.6%) switched from piperacillin-tazobactam to meropenem  compared to 30/378 (7.9%) 30-day mortality in the MERINO trial population [The overall mortality rate in the emulated trial was more than double the mortality rate in the MERINO trial but similar to mortality rates reported in other observational studies , 6. AlthThere are several differences in the study design and population characteristics that could explain the lower mortality rates observed in the MERINO trial compared to the emulated trial. Firstly, there are differences in the demographics, comorbidities and severity of illness between the emulated trial population and the MERINO trial population. Secondly, the process of obtaining patient consent in MERINO could result in the sickest patients not being captured. Consent was not required for the BSI-FOO observational study and therefore all eligible patients were included. In addition, the exclusion of individuals who were deemed unlikely to survive beyond 96 hours in MERINO may have also resulted in the sicker patients who would otherwise be eligible for the trial being excluded which could lead to an underestimation of the true mortality rate. This was acknowledged by the authors as a limitation of the study. We did not impose the 96-hour restriction in the trial emulation as this could have introduced survival bias. Finally, over half of the infections in the MERINO trial were urinary tract which are known to be more responsive to treatment. The treatment effect may differ across different levels of infection severity/presence of comorbidities e.g. piperacillin-tazobactam may be inferior to carbapenems in patients with severe infections but non-inferior in less severe infections such as urinary tract infections , 11, howOne of the strengths of this study is the trial emulation approach used in the design and analysis. Trial emulation using observational data ensures that eligibility criteria and assumptions are explicit before analysis and minimises common biases that arise in observational data analyses. In addition, a number of the published observational studies exclude patients who are not in receipt of either intervention for >48hours –9, meaniThere are several limitations to this study. First, it was not possible to emulate all elements of the MERINO trial. The MERINO trial had 30-day follow-up, we did not have 30-day follow-up for all BSI-FOO and RAPIDO participants and therefore we analysed 25-day mortality. However, this is unlikely to have a significant impact as later deaths are less unlikely to be a result of treatment. Upon examination of the Kaplan-Meier curves, there is a larger difference in mortality rate up to 14 days. We did not formally compare mortality rates at 14 days due to the small number of events and lack of statistical power, however we feel that 14-day mortality may be a more clinically meaningful outcome for studies investigating mortality in BSI as it is the time period most reflective of death attributable to suboptimal therapy. In addition, we did not explore other outcomes other than mortality as this was beyond the scope of the trial emulation, however other outcomes such as composite outcome of treatment failure and mortality may merit analysis in future research.Secondly, it was not possible to emulate the per-protocol analysis fully as few participants received allocated treatment for four days, and restricting analyses to those who are in receipt for four or more days would introduce immortal time bias . Our approach made it hard to attribute differences in the intention-to-treat analysis because some participants received both treatments with many of the participants assigned to piperacillin-tazobactam swapping to meropenem leading to contamination of drug exposure. This compromises our ability to draw any firm conclusions from this study. We excluded participants who switched to the other intervention during follow-up in a sensitivity analysis and this gave similar results, although it is worth noting that this sensitivity analysis had a smaller sample size, and this analysis is likely to be subject to selection bias as switching is usually related to prognosis. In addition, participants’ empirical treatment and treatment pathways other than the “allocated” intervention were not controlled for so any observed differences could be attributed to the effects of empirical therapy or treatments received after classification of “trial drug”. There were also differences in the time to receipt of “allocated” treatment between the two groups.Finally, it was not possible to emulate blinding, so the validity of our estimates depends on the assumption that all confounding factors were correctly adjusted for. We allowed for differences in baseline characteristics by adjusting for propensity score. However, due to the retrospective nature of the study we were only able to control for variables that had been collected and there is the risk that unmeasured confounders may impact the results.In summary, the mortality rate in an emulated trial population was more than double the mortality rate in the MERINO trial and the difference between mortality rates in piperacillin-tazobactam and meropenem was weaker but the direction of effect was consistent with the MERINO trial. Our findings suggest that the discrepancies between the MERINO trial and observational studies estimates could be partly explained by differences in the populations and also due to the bias that arises from observational studies e.g. survival bias and bias from unmeasured or uncontrolled confounding. This methodology attempts to address the concern that previous results could be explained by such biases and provides information on how a trial such as the MERINO trial may have performed in the NHS.S1 File(PDF)Click here for additional data file.