Since Dr. Sugimura’s discovery of heterocyclic aromatic amines (HAA) in broiled fish, many epidemiological studies have been conducted to investigate their role in human cancers, often focusing on colorectal cancer. The difficulty in measuring HAA exposure from meat and fish intake in these studies has resulted in inconsistent findings. Because studying individuals who may be particularly susceptible to the carcinogenic effects of HAA might facilitate the demonstration of a link with cancer, multiple studies have focused on individuals with the high activity phenotype for CYP1A2 and/or NAT2, the two main metabolic enzymes involved in the bioactivation of HAA. These investigations have also yielded inconsistent results. Two recent large pooled analyses of colorectal cancer studies have helped clarify the overall evidence. One was conducted in whites and reported no interaction of red meat intake and NAT2 genotype on risk in Whites. The other was conducted in Japanese and African Americans, two populations with high rates of the disease and a prevalence of the at-risk rapid NAT2 phenotype 10- and 2-fold greater than in whites, respectively. In those groups, a significant interaction was found, with the association of red meat with colorectal cancer being strongest among individuals with the rapid NAT2 phenotype, intermediate among those with the intermediate phenotype and not significant among those with the slow NAT2 phenotype. Recent research on biomarkers has focused on PhIP hair content, as a marker of exposure to HAA, and on DNA adducts using new sensitive quantitative methods, as markers of early biological effects. These advances, when brought to bear, may contribute greatly to the further elucidation of the carcinogenicity of HAA in humans. Salmonella typhimurium TA98, launching a field of study that spanned over four decades [N-acetyl-transferase (NAT), which is mainly expressed in the liver and intestinal epithelium [Making an analogy between cigarette smoke and the enticing smell emanating from his wife’s kitchen, Prof. Takashi Sugimura astutely wondered one day whether smoke from broiled fish also contained mutagens . Confirm decades . Several decades . The ani decades . To act ithelium . I revieMany epidemiological studies have investigated the association reported for meat consumption and risk of several cancers of the colorectum, breast and prostate . A 2015 Given the difficulties in assessing dietary exposure to HAAs through dietary questionnaires, it was proposed that focusing on individuals who can be a priori inferred to have a heightened susceptibility to the carcinogenic effect of HAAs might facilitate the demonstration of a link with human cancer. As mentioned above, CYP1A2 and NAT2 has been shown to play an important role in the bioactivation of HAAs –12. In oN-acetylation phenotype [A larger number of studies have focused on NAT2, red meat intake and CRC. The slow activity phenotype for this enzyme varies widely across populations, from ~ 5% in Eskimos to 10% in Japanese, 50% in Europeans and 90% in North Africans  Interesthenotype .NAT2 gene. Several combinations of SNPs have been proposed to classify NAT2 genotypes and best infer the phenotype. A panel of seven SNPs for NAT2 has been shown to be optimal [More than 25 single-nucleotide polymorphisms that modify NAT2’s catalytic activity for HAAs have been identified in the  optimal , 20, alt optimal . CRC ris optimal –24.Additional studies have reported an interaction between intake of red meat or well-done meat, or HAA, and NAT2 acetylator status on the risk of colorectal neoplasia –30. HoweNAT2, as similar associations with CRC were found with red meat for individuals with the combined rapid/intermediate NAT2 phenotype as for those with the slow NAT2 phenotype (Table interaction = 0.99). No result was given separately for the rapid phenotype.The first study used the data from the Colon Cancer Family Registry and the Genetics and Epidemiology of Colorectal Cancer Consortium with a total of 8290 CRC cases and 9115 controls from 11 individual studies . NAT2 php = 0.001), whereas the genetically-inferred NAT2 phenotype was not. A statistically significant interaction was observed between red meat intake and NAT2 activity in both populations combined (pinteraction = 0.03), with the association of red meat with CRC being strongest among individuals with the rapid NAT2 phenotype, intermediate among those with the intermediate phenotype and non-significant among those with slow NAT2 phenotype  in hair, adjusted for melanin hair content, seems to be the most promising for use in molecular epidemiologic studies. PhIP measured in the hair represents an integrated exposure over a time period of weeks to months and has been shown to be relatively constant over time. Feeding studies with well-done red meat have shown a very good correlation between ingested dose and PhIP hair level [With regard to biomarkers of exposure, the measurement of the HAA 2-amino-1-methyl-6-phenylimidazo [4,5-ir level , 38. Howir level . The devir level  and a rair level  which alMuch research have been conducted on the role of HAA in human cancer since Dr. Sugimura’s discovery of HAA in his wife’s broiled fish. Colorectal cancer has been the most studied by epidemiologists. The difficulty in assessing HAA exposure from diet has resulted in inconsistent findings. Focusing on genetically susceptible individuals was favored to demonstrate a link with cancer. Two recent large pooled analyses of colorectal cancer studies, one of European-descent individuals, the other of Japanese and African Americans, have suggested that the modifying effect of NAT2 on the association between red meat intake and CRC may be limited to populations with a high prevalence of the rapid acetylation phenotype . In those groups, the association of red meat with colorectal cancer was found to be strongest among individuals with the rapid NAT2 phenotype, intermediate among those with the intermediate phenotype and non-significant among those with slow NAT2 phenotype. Recent research on biomarkers have focused on PhIP hair content to assess usual exposure to HAA and on DNA adducts using new sensitive quantitative methods to demonstrate early biological effects. These studies, when matured, have the potential to contribute greatly to the further elucidation of the carcinogenicity of HAA in humans.


To study the potential of quantitative MRI (qMRI) fat fraction (FF) as a biomarker in nonambulant patients with Duchenne muscular dystrophy (DMD), we assessed the additive predictive value of elbow flexor FF to age at loss of hand-to-mouth movement.Nonambulant patients with DMD (age ≥8 years) were included. Four-point Dixon MRI scans of the right upper arm were performed at baseline and at the 12-, 18-, or 24-month follow-up. Elbow flexor FFs were determined from 5 central slices. Loss of hand-to-mouth movement was determined at study visits and by phone calls every 4 months. FFs were fitted to a sigmoidal curve by use of a mixed model with random slope to predict individual trajectories. The added predictive value of elbow flexor FF to age at loss of hand-to-mouth movement was calculated from a Cox model with the predicted FF as a time-varying covariate, yielding a hazard ratio.p = 0.002). This corresponded to a 3.13-fold increase in the instantaneous risk of loss of hand-to-mouth movement in patients with a 10–percent points higher elbow flexor FF at any age.Forty-eight MRIs of 20 patients with DMD were included. The hazard ratio of a percent-point increase in elbow flexor FF for the time to loss of hand-to-mouth movement was 1.12 (95% confidence interval 1.04–1.21; In this prospective study, elbow flexor FF predicted loss of hand-to-mouth movement independently of age. qMRI-measured elbow flexor FF can be used as a surrogate endpoint or stratification tool for clinical trials in nonambulant patients with DMD.This study provides Class II evidence that qMRI FF of elbow flexor muscles in patients with DMD predicts loss of hand-to-mouth movement independently of age. Quantitative MRI (qMRI) fat fraction (FF) of the vastus lateralis muscle has been shown to predict loss of ambulation in DMD.5 This predictive value must be additive to age because any parameter that consistently changes over time will correlate to a decline in function in a progressive disease. Upper arm qMRI FF increases over time and correlates with function cross-sectionally.7 We studied the additive predictive value of elbow flexor FF (FFEF) for LoHM to age in a prospective study in nonambulant patients with DMD.Duchenne muscular dystrophy (DMD) is characterized by muscle weakness in a proximal-to-distal gradient. Independent ambulation is generally lost in the early teens and occurs years before loss of hand-to-mouth movement (LoHM).8 and through Dutch outpatient clinics and patient organizations. Exclusion criteria were MRI contraindications , exposure to an investigational drug ≤6 months before participation, and recent (≤6 months) upper extremity surgery or trauma. One hundred twenty-two eligible patients were approached for participation; details on this recruitment have been reported previously.9 Patients visited the Leiden University Medical Center (LUMC) for a half-day of assessments at baseline and 12 and 18 months. Due to the coronavirus disease 2019 (COVID-19) pandemic, some follow-up visits were postponed from 12 to 18 months and from 18 to 24 months or missed. Telephone calls every 4 months were used to evaluate LoHM.We included male nonambulant patients with genetically confirmed DMD who were ≥8 years of age between March 2018 and July 2019. Patients were recruited from the Dutch Dystrophinopathy DatabaseThe medical ethics committee of the LUMC approved the study, and we obtained written informed consent from patients and legal representatives. The study was registered on ToetsingOnline (NL63133.058.17).EF was calculated as a weighted mean value by averaging elbow flexor voxels of all eroded ROIs from the reconstructed fat and water images and correcting for partial saturation due to T1 effects:Four-point Dixon scans were acquired of the right upper arm on a 3T MRI scanner  with 2 circular 15-cm coils. Participants were positioned on the right side with the right shoulder and elbow in 90° flexion because pilot experiments suggested this to be the most comfortable position and this position placed the upper arm muscles more toward the center of the MRI scanner. If this was uncomfortable, a supine position was chosen. Dixon scans were acquired with 33 slices and a voxel size of 1 × 1 × 10 mm  and aligned perpendicular to the humerus bone. Dixon water and fat images were generated with in-house developed software  with a 6-peak lipid spectrum; B0 maps were calculated from the phase data of the first and last echoes. Regions of interest (ROIs) were drawn on the muscle border of the elbow flexors  by 1 reviewer (K.J.N.) on 5 contiguous slices around a central slice  using thPerformance of the upper limb (PUL) 2.0 was assessed for the right arm at all visits. LoHM was defined as the inability to move a filled glass independently to the mouth with the right hand and allowing support of the elbow on a table, similar to the PUL hand-to-mouth item in which a 200-g weight is used. Age at LoHM was prospectively established to a month's precision. If LoHM had occurred before baseline, month and year were established retrospectively through a detailed interview and clinical documentation.EF between baseline and the 12-month follow-up was assessed with the Wilcoxon signed-rank test. The additive predictive value of FFEF to age at LoHM was calculated with a Cox proportional hazards model as described previously.4 For this, we applied a logit transformation to FFEF to allow use of standard statistical methods that rely on a gaussian distribution. A linear (mixed) model was fitted to the transformed data with age as the only covariate and a random slope per individual. The fitted lines were transformed back to the original scale with a logistic transformation, after which individual FFEF trajectories were predicted at any time. The Cox proportional hazards model was fitted with the predicted FFEF as a time-varying covariate, yielding a hazard ratio .The difference in FFEF have additive predictive value to age at LoHM in nonambulant patients with DMD? The level of evidence was assigned as Class II during the review process.The primary research question of this study was the following: does FFAnonymized data and analysis software can be made available to qualified investigators.EF results and PUL scores at different time points are presented in the FE of 5.9 ± 5.4% (p < 0.01).Twenty-two patients with DMD participated, but 2 patients refused the MRI. One patient switched to a medication trial after baseline; 1 patient discontinued after 12 months of follow-up because of traveling distance; and 8 visits were canceled due to COVID-19 restrictions. One 12-month follow-up scan was excluded due to insufficient signal. Forty-eight MRIs of 20 patients with DMD were included: 12 patients had 3 MRIs; 4 patients had 2; and 4 patients had 1. All patients used glucocorticoids, but 1 patient had temporarily ceased treatment 6 weeks before baseline due to weight gain. Patients' characteristics at baseline and FFEF data and predicted FFEF at age at LoHM are shown in EF for the time to LoHM was 1.12 . This hazard ratio corresponds to a 3.13-fold increase in the instantaneous risk of LoHM in patients with a 10–percent points higher FFEF at any age. An FFEF growth chart (EF curves and LoHM trajectories.Acquired and predicted FFth chart  and survth chart  illustraEF predicts LoHM in nonambulant patients with DMD on top of age. This added predictive value is essential because parameters that consistently change over time will always correlate with functional tests in a progressive disease.In this prospective study, we show that FF11 However, any outcome measure that consistently changes over time will correlate to declining measures of function in a progressive disease. Two previous studies demonstrated the added predictive value of vastus lateralis FF on top of age on the clinical outcome loss of ambulation and thus showed for the first time that muscle FF adds to the assessment of disease severity.5Previous studies demonstrated that qMRI muscle FF increases over time and correlates with function cross-sectionally in DMD.EF increased according to a sigmoidal curve, similar to the vastus lateralis FF in ambulant patients.5 The hazard ratio of 1.12 was comparable to that of the vastus lateralis FF for the time to loss of ambulation.4 These data thus support the use of qMRI FF as an objective biomarker in different stages of the disease. Predicted FF curves can be used for stratification in clinical trials or as a surrogate endpoint, limiting sample size and duration.FF10The rate of change in FF, for instance, 1-year change, could be used as a biomarker in trials in which the therapeutic effect over that period of time can be compared to placebo or another therapy, and the power calculation could be based on the more or less “linear” middle part of the FF curve because that is where the fastest change is expected to happen. This will require stratification of the cohort with respect to baseline FF, as is now commonly done for functional tests.13We assessed the timing of reaching the clinical endpoint via regular phone calls between clinical assessments. In our experience, patients and caregivers are able to define such important endpoints within a month's precision. This increases the power of our survival analyses compared to standard natural history studies in which clinical assessments are performed at 6- or 12-month intervals only. It reduced the burden for participants and allowed continuation of the protocol despite COVID-19–related restrictions. The importance of hand-to-mouth movement is stressed by its incorporation into the widely used Brooke upper extremity rating scale, the PUL, and the DMD Upper Limb Patient Reported Outcome Measure, in which patients and families confirmed its clinical relevance.EF curves. Restrictions due to the COVID-19 pandemic also led to some missing data. It is important to replicate results in other cohorts with different steroid regimens.Limitations of this study include the small sample size, which did not allow modeling of the intercept of the FFEF predicted LoHM independently of age in nonambulant patients with DMD. This establishes qMRI FF as a biomarker in DMD and potentially facilitates the design of clinical trials, through either stratification or use as a surrogate endpoint.FF


Clinical pathways are changing to incorporate support and appropriate follow-up for people to achieve remission of type 2 diabetes, but there is limited understanding of the prevalence of remission in current practice or patient characteristics associated with remission.2. A total of 7,710 people  were in remission of type 2 diabetes. Factors associated with remission were older age (odds ratio [OR] 1.48 [95% CI 1.34 to 1.62] P < 0.001) for people aged ≥75 years compared to 45 to 54 year group), HbA1c <48 mmol/mol at diagnosis (OR 1.31 [95% CI 1.24 to 1.39] P < 0.001) compared to 48 to 52 mmol/mol), no previous history of GLT (OR 14.6 [95% CI 13.7 to 15.5] P < 0.001), weight loss from diagnosis to 2019 (OR 4.45 [95% CI 3.89 to 5.10] P < 0.001) for ≥15 kg of weight loss compared to 0 to 4.9 kg weight gain), and previous bariatric surgery (OR 11.9 [95% CI 9.41 to 15.1] P < 0.001). Limitations of the study include the use of a limited subset of possible definitions of remission of type 2 diabetes, missing data, and inability to identify self-funded bariatric surgery.We carried out a cross-sectional study estimating the prevalence of remission of type 2 diabetes in all adults in Scotland aged ≥30 years diagnosed with type 2 diabetes and alive on December 31, 2019. Remission of type 2 diabetes was assessed between January 1, 2019 and December 31, 2019. We defined remission as all HbA1c values <48 mmol/mol in the absence of glucose-lowering therapy (GLT) for a continuous duration of ≥365 days before the date of the last recorded HbA1c in 2019. Multivariable logistic regression in complete and multiply imputed datasets was used to examine characteristics associated with remission. Our cohort consisted of 162,316 individuals, all of whom had at least 1 HbA1c ≥48 mmol/mol (6.5%) at or after diagnosis of diabetes and at least 1 HbA1c recorded in 2019 (78.5% of the eligible population). Over half (56%) of our cohort was aged 65 years or over in 2019, and 64% had had type 2 diabetes for at least 6 years. Our cohort was predominantly of white ethnicity (74%), and ethnicity data were missing for 19% of the cohort. Median body mass index (BMI) at diagnosis was 32.3 kg/mIn this study, we found that 4.8% of people with type 2 diabetes who had at least 1 HbA1c ≥48 mmol/mol (6.5%) after diagnosis of diabetes and had at least 1 HbA1c recorded in 2019 had evidence of type 2 diabetes remission. Guidelines are required for management and follow-up of this group and may differ depending on whether weight loss and remission of diabetes were intentional or unintentional. Our findings can be used to evaluate the impact of future initiatives on the prevalence of type 2 diabetes remission. Mireille Captieux and co-workers report on population-level evidence for remission of type 2 diabetes in Scotland. Feasibility of diabetes remission has been demonstrated in research settings and after bariatric surgery, but we do not know how many people in the general population achieve remission of type 2 diabetes.Informed decisions need to be made about which people are most likely to achieve and maintain remission; to do this, we need to better understand the characteristics of people who are currently in remission.Estimating the prevalence of remission of type 2 diabetes in Scotland in 2019 creates a baseline to evaluate the impact of future initiatives to support remission and for future studies of duration of remission and effect on risk of complications of diabetes.We calculated how many people were in remission of type 2 diabetes in 2019 in Scotland from a national type 2 diabetes register. This register contains 99% of people with diabetes in Scotland.We described the characteristics of people who were in remission of type 2 diabetes compared to people who were not in remission and created a mathematical model that shows the probability of achieving remission in 2019 based on these characteristics.We found that about 1 in 20 of people with type 2 diabetes in the study population were in remission of type 2 diabetes.Compared to people who did not achieve remission, people in remission of type 2 diabetes tended to be older; have a lower HbA1c at diagnosis; have never taken any glucose-lowering medication; have lost weight since the diagnosis of diabetes; and have had bariatric surgery.There is a sizeable proportion of people who achieve remission of type 2 diabetes outside research trials and without bariatric surgery. These people should be recognised and coded appropriately so they can be supported by their clinicians. The clinical progress of these people can now be followed by researchers.People who have not yet been prescribed drugs to treat diabetes may be the most appropriate group for clinicians to initiate discussions around remission and weight management options.Guidelines for supporting people who achieve remission of diabetes must recognise differences between people that lose weight intentionally and those that lose weight because of severe illness. Clinicians also need greater clarity on how to manage older or frailer people who achieve remission criteria. There were an estimated 463 million people with diabetes in the world in 2019, of whom 90% to 95% have type 2 diabetes   registry, which is a population-level diabetes registry derived from NHS primary and secondary care data. SCI-Diabetes contains demographic and clinical data for over 99.5% of people with a diagnosis of diabetes in Scotland . Type ofWe extracted data for people with a diagnosis of type 2 diabetes who were registered as alive and resident in any Scottish health board on December 31, 2019. From this population, we selected people who were aged ≥30 years who were diagnosed with type 2 diabetes between January 1, 2004 (when registration to SCI-Diabetes was near complete across Scotland) and December 31, 2018 ) were in remission in 2019. Three sensitivity analyses for estimates of the prevalence of remission were carried out. First, assuming that none of the 35,321 patients without an HbA1c reading in 2019 was in remission and adding them to the denominator resulted in an estimated prevalence in the whole cohort of 206,856 of 3.7% (95% CI 3.6 to 3.8). Second, inclusion of the 9,219 patients who had never had an HbA1c ≥48 mmol/mol in both numerator and denominators meant that estimated prevalence of diabetes remission in the 171,535 patients with at least 1 HbA1c in 2019 was 9.9% (95% CI 9.7 to 10.0). Third, changing the remission duration cut point from 12 to 10 months to also include people whose last HbA1c was between 10 and 12 months changed the prevalence estimate to 4.9% (95% CI 4.8 to 5.0).Key differences in characteristics of people in remission in 2019 compared to people who were not in remission in 2019 were older age ; greater weight loss between diagnosis of diabetes and 2019; lower proportions with previous prescriptions of GLT (25% of people in remission had been prescribed GLT compared to 84% of people not in remission); lower mean HbA1c at diagnosis ; and higher prevalence of previous history of bariatric surgery  see . After sP < 0.001)  . Bariatr< 0.001) . Multiva< 0.001)  Figs. Wh< 0.001)  Figs.2 or <27 kg/m2 (4.9% of our population), previous insulin use (7.9% of our population), and HbA1c ≥108 mmol/mol at diagnosis (1.5% of our population). The proportion of people in remission of type 2 diabetes in the subset of our population who were eligible for participation in the DiRECT trial was 4.7% (95% CI 4.4 to 5.0).People aged ≥75 years showed the most weight loss . Only 12We found that the prevalence of type 2 diabetes remission in the study population was 4.8% in our primary analysis. Prevalence of remission was estimated to be 3.7% if none of the 17% of people with missing HbA1c data for 2019 was in remission. Conversely, including people without any record of HbA1c ≥48 mmol/mol (6.5%) in our cohort would increase the estimate of remission prevalence to 9.9%. The latter group includes people whose diagnosis of type 2 diabetes was based on glucose criteria alone, people who had recently moved to Scotland with a previous diabetes code, or who have been misclassified as having diabetes.Prevalence of remission in the study cohort increased with age and was highest in women aged over 75 years 8.2%). Median weight decreased between diagnosis of type 2 diabetes and 2019 across the whole population, with people who were in remission in 2019 losing a median 6.5 kg of weight compared to 3.3 kg among people not in remission. We found that remission prevalence was lower in more socioeconomically deprived groups in univariable analyses. In multivariable models, factors associated with remission were age ≥65 years, HbA1c <48 mmol/mol (6.5%) at diagnosis, no history of GLT prescription, any weight loss from diagnosis to 2019, and a previous history of bariatric surgery. It is unsurprising that people who are closer to meeting the criteria for remission at diagnosis (HbA1c <48 mmol/mol and/or no prior GLT) have higher odds of remission. Older people develop diabetes at lower BMI and lower HbA1c than their younger counterparts . It is p%. MedianThe study has a number of limitations. As for all studies using routine data, missing data were fairly common, although the proportions of missing data in all covariates were low enough to justify the use of multiple imputation and findings from both complete and imputed datasets were similar. We estimated prevalence under 3 sets of assumptions finding a range of 3.7% to 9.9%, although we believe the most appropriate estimate is of 4.8% from our primary analysis. The prevalence estimates are influenced by our definition of type 2 diabetes remission. In our data, remission definition had to be based on HbA1c alone, because FPG is rarely measured after diagnosis of diabetes in the UK. Our remission estimate therefore does not account for people whose diabetes was diagnosed on the basis of fasting glucose values or 2-hr PG alone and who never had a record of HbA1c ≥48 mmol/mol 6.5%) after diagnosis of diabetes. A widely cited 2009 report by a multidisciplinary expert group was ambiguous as to whether both HbA1c and FPG measures are required to define remission .5% after. Other pKarter and colleagues also used a diabetes registry to describe the epidemiology of remission in a Northern California population with health insurance, using a more conservative definition of remission (HbA1c <39 mmol/mol) based on the ADA diagnostic prediabetes criteria. They also excluded people who had bariatric surgery, and end of follow-up was December 31, 2011, prior to wider interest in the use of very low calorie diets to achieve remission of diabetes. They found a cumulative incidence of achieving remission over 7 years (1.60% [95% CI 1.53 to 1.68]) . In our Our prevalence estimates suggest that a reasonably large proportion of people achieve remission of type 2 diabetes in routine clinical care outside trial or bariatric surgery settings. The immediate implications for practice are that these people should be recognised and coded appropriately so they can be given adequate support and followed up to ensure continued care consistent with diabetes management guidelines. It is important to recognise that remission of diabetes may not be permanent. The Look Action for Health in Diabetes (AHEAD) trial among overweight and obese United States adults with type 2 diabetes found that, of the participants who attained remission of type 2 diabetes, a third of the group that received an intensive lifestyle intervention, and 40% to 50% of the control group returned to type 2 diabetes status each year . FurtherOur findings highlight that there is no differentiation between intentional and nonintentional weight loss in current definitions of remission, and people who attain remission by unintentional weight loss caused by severe illness could still be defined as being in remission of type 2 diabetes ,8. ThereOur results may also be relevant in supporting people to attain remission in the future; recognising characteristics that make type 2 diabetes remission more likely could facilitate discussions about remission, weight loss, or bariatric surgery. Further cohort studies could build on our results to develop and validate risk scores for remission of diabetes. Our results suggest that supporting clinicians to discuss remission and to refer to weight management services with early or diet-controlled diabetes is a rational approach. However, more data are needed to establish whether a very low calorie diet approach will be effective or appropriate in people that have been excluded from trials. Our estimates for prevalence of type 2 diabetes remission reflect the period prior to the widespread introduction of the use of very low calorie diets in routine clinical care and the COVID-19 pandemic so further estimates of remission prevalence estimates will be required in the future.In conclusion, we have shown that approximately 5% of people with type 2 diabetes in Scotland in 2019 appear to be in remission and that history of never having GLT, weight loss since diagnosis, HbA1c <48 mmol/mol (6.0%) at diagnosis, age ≥65 years, and previous bariatric surgery are associated with remission. Our findings provide a useful basis for the evaluation of the multifactorial approaches to both remission and prevention of diabetes that are currently being introduced and also highlight the need for guidelines to support definition of remission, management, and follow-up of people that achieve remission.S1 TableSTROBE, STROBE, Strengthening The Reporting of OBservational Studies in Epidemiology.(DOCX)Click here for additional data file.S2 TableNumber of procedures for people with an obesity code are given against each OPCS code for the present analysis. ICD-10, International Classification of Diseases-10th revision; OPCS-4, Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4.(DOCX)Click here for additional data file.S3 TableICD-9, International Classification of Diseases-Ninth revision; ICD-10, International Classification of Diseases-10th revision.(DOCX)Click here for additional data file.S4 TableOdds of missing data in people with type 2 diabetes in Scotland diagnosed ≥30 years of age who had at least 1 HbA1c ≥48 mmol/mol (6.5%) after diagnosis of diabetes and who were alive and had at least 1 HbA1c recorded in 2019.(DOCX)Click here for additional data file.S5 TableAdjusted for all variables in the model (listed in first column of the table). CCA, Complete case analysis.(DOCX)Click here for additional data file.S6 Table1 Adjusted for all variables in the model (listed in first column of the table). N = 152 934 (multiple imputation) and N = 110,814 (CCA). CCA, complete case analysis.Unadjusted and adjusted odds for remission of type 2 diabetes from logistic regression model using complete case and multiple imputation datasets. (DOCX)Click here for additional data file.S1 FigStratified by sex and age in 2019.(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 FigN = 117,048. CI, confidence interval; OR, odds ratio.(DOCX)Click here for additional data file.S5 FigN = 110,814. CI, confidence interval; OR, odds ratio.People with previous history of dementia, end-stage renal disease, liver cirrhosis, cancer, or metastases in the last 5 years removed. (DOCX)Click here for additional data file. 9 Jun 2021Dear Dr Captieux, Thank you for submitting your manuscript entitled "Epidemiology of type 2 diabetes remission in Scotland in 2019: a cross-sectional population-based study" for consideration by PLOS Medicine.Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external assessment.However, before we can send your manuscript for assessment, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. 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Throughout the text, please remove the spaces from the square brackets containing reference call-outs.Please include a completed checklist for the most appropriate reporting guideline, e.g., STROBE or RECORD, as an attachment with your revision, labelled "S1_RECORD_Checklist" or similar and referred to as such in your Methods section. In the checklist, please refer to individual items by section  and paragraph number rather than by line or page numbers, as the latter generally change upon publication. Comments from Academic editor:I read this manuscript with interest. As pointed out by the authors and reviewers, diabetes remission is an area of much interest and public health significance. Hence this study, one of the very few that has examined prevalence of diabetes remission using population-based data, is of great interest. Although the findings are not too surprising, the results, highlighting the potential possibility of diabetes remission outside of intensive interventions in trial settings using VLC diets, is still of much public health interest.I do, however, have some concerns about the analysis. In addition to points raised by the reviewers, I agree with one of the reviewers that on close inspection, many of the procedures included in Supplementary table 1 are not procedure codes appropriate for bariatric surgery, but include many procedural codes associated with major GI surgery (e.g. gastrectomy for stomach cancer). This raises the issue of the marked weight loss and DM remission being related to co-existing malignancy and the associated weight loss due to the underlying disease or GI surgery.The authors would need to provide a detailed explanation to address this concern, which would very much impact on the validity of the analysis. Another major concern, which has not been discussed or addressed, is the potential remission of diabetes that occurs when patients develop significant chronic kidney disease or end stage renal disease, whereby medications are no longer necessary, and A1c may have become normal (or low), due to both diabetes remission as well as co-existing anaemia of chronic disease. A sensitivity analysis to ensure that patients with concomitant malignancy or chronic kidney disease have been excluded would be necessary, in my view.Comments from the reviewers:*** Reviewer #1: This study utilises Scottish population data to explore the prevalence of remission of type 2 diabetes. The authors are able to assess a number of key measures including age, diabetes duration, BMI and HbA1c at diagnosis, weight loss, diabetes treatment and presence of other diagnoses to identify the measures associated with remission (defined as >1 year with an HbA1c <48 mmol/mol). This is an important study as it tries to quantify the prevalence of remission in the general diabetes population. Thus, moving the conversation forward as to how we address remission in both epidemiology and clinical practice.I have some issues with the presentation of the results and areas which I think need to be more clearly described.1. I found table S3 hard to follow and would appreciate greater explanations as to how to interpret the displayed data. 2. Figure S2 shows the prevalence of remission by age and HbA1c group at diagnosis but the figure header says the results are by duration of type 2 diabetes and age in 2019. I can not see how duration is included in this figure.3. Greater weight loss is associated with remission - did you consider looking at weight change as a percentage of baseline weight? Similarly did change in BMI show any association?4. The cut-point of over a year of HbA1c values <48 mmol/mol is valid due to the annual nature of HbA1c measures among people with type 2 diabetes. Did you look at people who failed to meet the criteria because their last HbA1c was just under a year (ie 11 months rather than 12 months)? Would a cut point of at least 10 months change your results?5. Any data on complications - specifically retinopathy around the time of diagnosis which indicates potentially delayed diagnosis?6. You do not find that diabetes duration is associated with risk of remission after adjustment for other covariates. I think this is worthy of some discussion as it seems intuitive that remission is easier in people with new onset diabetes but that is not the case here.7. The association with use of GLT is interesting. I would appreciate some discussion of your interpretation of this - do you think this association is because GLT correlates with sustained hyperglycaemia and symptoms or could it also suggest that treatment with hypoglycaemic agents is deleterious to chances of remission? I appreciate your desire not to overly interpret your findings but I think some discussion is warranted.8. Can you include some comments on the ethnic make up of your study? It being Scotland I presume it is primarily Caucasian but do you have any estimate on the prevalence of Asian and Black participants? 9. Have you any data for the length of remission observed in the population. How often do you see a period of an HbA1c <48mmol/mol for at least a year followed by HbA1c values >48mmol/mol. Is the remission you observe likely to be prolonged or more akin to the honeymoon period sometimes observed in early diabetes?*** Reviewer #2 : This cross-sectional study aims to estimate the prevalence of remission of type 2 diabetes in all adults in Scotland aged >30 years diagnosed with type 2 diabetes and alive on 31/12/2019.Comments:"Factors associated with remission were: older age (OR 1.48 (95% CI 1.34-1.62) for people aged >75 years compared to 45-54 year group), HbA1c <48mmol/mol at diagnosis (OR 1.31 (95%CI 1.24-1.39) compared to 48-52mmol/mol), no previous history of glucose lowering therapy , weight loss from diagnosis to 2019 (OR 4.45 (95%CI 3.89-5.10) for >15kg of weight loss compared to 0-4.9kg weight gain) and previous bariatric surgery ."Did the authors consider investigating interactions between factors?"For the purposes of this analysis we defined remission on the basis of all HbA1c values being <48mmol/mol in the absence of glucose lowering treatment (GLT) for a duration of >365 days before the date of the last recorded HbA1c in 2019, that is on the basis of at least two HbA1c values <48mmol/mol at an interval of at least 365 days. "Did the authors consider undertaking any sensitivity analyses on different definitions of remission?"Demographic variables recorded at date of diagnosis of type 2 diabetes were: sex and age ."Can the authors please confirm if age was categorised at the point of data collection, or during data processing? If the latter, did the authors consider including age as a continuous variable in their analyses?"People with no record of an HbA1c >48mmol/mol (6.5%) after diagnosis of diabetes or no HbA1c recorded during 2019 were excluded initially but included for sensitivity analyses for estimates of prevalence of remission."The authors have conducted a valuable sensitivity analysis which helps to demonstrate the robustness in their study findings.The authors have applied comprehensive and rigorous statistical modelling methods, which they describe clearly and concisely. They have appropriately handled missing data by applying MICE, and have suitably conducted a complete case sensitivity analysis. The main study limitations have been thoroughly explored in the discussion section.*** Reviewer #3: This paper has addressed the rates of remission of T2D in the Scottish adult population. Their main findings were that age ≥65, no previous glucose lowering medication, weight loss since the diagnosis of T2D and previous bariatric surgery were associated with a higher chance of being one of the 5% of patients with T2D who achieved remission. There are some issues that need clarification. 1. The finding that previous bariatric surgery was associated with remission of T2D is somewhat confusing. If I understand the methods correctly these patients underwent bariatric surgery before the diagnosis of T2D. What was the time interval between the surgery and diagnosis of T2D? It would seem likely that it was short as it has been shown that new cases of T2D after bariatric surgery is unusual (particularly with modern procedures). Thus, the remission of T2D was not associated with previous bariatric surgery but the effect of the surgery, which is consistent with the effect of bariatric surgery on T2D. Why was not bariatric surgery after the diagnosis of T2D studied?2. In addition, the surgical codes presented in Table S1 are confusing. I am not familiar with the OPCS codes but bases on the written description many of the codes used do not represent bariatric surgery, but other procedures performed on the upper gut. Many are procedures done for cancer surgery. Some will have a similar impact on changing the anatomy of the foregut as bariatric surgery with similar impact on gut physiology as bariatric surgery. Yet, they are not bariatric surgery and surgery for cancer comes with other confounding factors. 3. The authors emphasize that weight loss (achieved without surgery) is an important factor. However, it needs to be pointed out that persistent weight loss without bariatric surgery is difficult to achieve. The findings are also necessary to put in the perspective of impact of remission on hard endpoints. Lessons learned from the look AHEAD study.*** Reviewer #4: Epidemiology of type 2 diabetes remission in Scotland in 2019: a cross-sectional population-based studySummary/ Overall CommentsThank you for the opportunity to review this submission. The authors conducted a very well-designed study describing the prevalence of T2DM remission in a large cohort of patients and uses sound statistical methods to describe the predictors of remission. The authors capitalize on a very extensive national database with great details and follow-up data on patients with diabetes in Scotland. This study reflects a very effective use of this valuable database to answer important clinical questions to guide patient care and future research. The manuscript is well-written and the rationale, methods, and findings are easy to follow throughout the manuscript. I commend the authors on this excellent work. I only have a few questions and minor comments regarding the study.Abstract- Well-written and clearly summarizes the study.Introduction- Well-written Introduction section, provides the right amount of background and literature review to help readers understand the purpose of the study.- The authors describe the study aims on Lines 105-107. I would suggest that one of the aims of the study was to identify predictors of T2DM remission in their multivariable regression analysis, in addition to their stated aim of comparing the characteristics of patients with vs. without remission.Methods- The overall study methods, eligibility criteria, definition of variables, and statistical methods are well-described.- Thank you for including a section describing patient/ public involvement. Although patients and the public were not involved in the design of the study, inclusion of this section and details is of utmost importance and unfortunately, often neglected in manuscripts.- Approach to missingness was described very well.- Please describe how the decision regarding which covariates to include was derived? If based on existing literature on the predictors of T2DM remission, please cite all relevant papers. Were the covariates selected based on a series of studies that found an association with each of the selected covariates separately or studies that evaluated all the covariates? Or were these covariates decided solely based on clinical rationales? Please describe.- Many of the covariates selected are part of the DiaREM score which consists of similar, albeit fewer variables, that have been developed and validated to predict T2DM remission following RYGB surgery . Although this predictive score is limited to remission post bariatric surgery, perhaps the authors can cite this score and/ or any other scores in a more general setting unrelated to bariatric surgery, that consist of a similar set of predictors.- I'm curious as to why the authors chose to treat age as a categorical variable in its inclusion as a covariate, rather than continuous despite this being available to the authors. Would it not be more optimal to maintain continuous variables as continuous whenever possible?- Why was weight (mentioned in Line 157) mentioned as a covariate? Would BMI, already a covariate, be a more accurate reflection in this case as it accounts for height?- Did the database contain information about the type of bariatric surgery that patients underwent? Were there any gastric restrictive surgeries  or were they all malabsorptive ? If any details on these are available in the database or perhaps based on local surgical practices during the timeframe of the study (perhaps only certain types of bariatric procedures are offered in Scotland?), it would be great to describe these.Results- The Results section is well-structured, beginning with a detailed description of the sample and flows nicely through description of the various findings.- Great use of figures to summarize a large number of findings.- Figure 1 - This figure should be mentioned in the first part of the Results section when the included population is first described, rather than in the Methods section.- Figure 3 - Very helpful forest plot. I would suggest adding labels to the x-axis for easier interpretation for readers.- Was bariatric surgery associated with higher SES? Perhaps this association is strong enough to account for the fact that SES was not a predictor in the multivariable analysis (once SES was factored in)?- Please briefly describe the DiRECT trial that is discussed in the final paragraph of the Results section.- It seems that bariatric surgery was a predictor of T2DM remission independent of weight loss since both were statistically significant predictors in the multivariable model. If that's the case, this is an interesting finding that may be worth explicitly mentioning in the Results & discussing further in the Discussion section.Discussion- This section provides adequate details to help contextualize the study findings yet is succinct and clear to follow.- Minor comment - Lines 310-311 - This statement "... inversely associated with socioeconomic deprivation..." suggests that remission was associated with higher SES but in its current form describing the inverse relationship with socioeconomic deprivation, the statement is somewhat difficult to understand and could be simplified.***Any attachments provided with reviews can be seen via the following link:[LINK] 5 Sep 2021AttachmentResponse to reviewers.pdfSubmitted filename: Click here for additional data file. 28 Sep 2021Dear Dr. Captieux,Thank you very much for re-submitting your manuscript "Epidemiology of type 2 diabetes remission in Scotland in 2019: a cross-sectional population-based study" (PMEDICINE-D-21-02522R2) for consideration at PLOS Medicine.I have discussed the paper with editorial colleagues and our academic editor, and it was also seen again by four reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:[LINK]***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. 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Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.plosmedicine@plos.org.Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at Please let me know if you have any questions, and we look forward to receiving the revised manuscript.   Sincerely,Richard Turner, PhDSenior Editor, PLOS Medicinerturner@plos.org------------------------------------------------------------Requests from Editors:Please finalize the arrangements for data deposition, and add the details to the data statement (submission form). We suggest removing "who wish" at line 32 (abstract). Please convert the three subtitles into boldface text; and add bullets to the individual points in the Author Summary.Please remove the point at line 70 ("Remission of type 2 diabetes ...").Please trim the point at line 101 .Throughout the text, please incorporate single spaces before the reference call-outs .In the abstract and at any other instances, please use square brackets within parentheses where needed, e.g., "".Please remove reference 23 . Noting reference 25, can a URL or publication details be added?Please break the STROBE checklist out into a separate attached file, labelled "S1_STROBE_Checklist" or similar and referred to as such in the text. Comments from Reviewers:*** Reviewer #1: I am happy that the authors have addressed my prior comments.*** Reviewer #2: The authors have satisfactorily considered and responded to each comment in turn.*** Reviewer #3: I am happy with the changes made*** Reviewer #4: I thank the authors for considering my comments on this manuscript. They have addressed my comments and I have no further suggestions for the manuscript. Thank you for the opportunity to review this interesting study.***Any attachments provided with reviews can be seen via the following link:[LINK] 30 Sep 2021Dear Dr Captieux, On behalf of my colleagues and the Academic Editor, Dr Ma, I am pleased to inform you that we have agreed to publish your manuscript "Epidemiology of type 2 diabetes remission in Scotland in 2019: a cross-sectional population-based study" (PMEDICINE-D-21-02522R3) in PLOS Medicine.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. 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PLEKHS1) and the Insulin-like Growth Factor (IGF) axis. Seven literature searches were carried out to investigate the backgrounds of these potential links. PLEKHS1 is a candidate biomarker in BCa, with mutations that are easily detectable in urine and increased expression seemingly associated with worse disease states. PLEKHS1 has also been implicated as a potential mediator for the onset of T2DM in people with obesity. The substantial evidence of the involvement of IGF in BCa, the role of the IGF axis in obesity and T2DM, and the global prevalence of T2DM and obesity suggest there is scope for investigating the links between these components. Preliminary findings on the relationship between PLEKHS1 and the IGF axis signal possible associations with BCa progression. This indicates that PLEKHS1 plays a role in the pathogenesis of BCa that may be mediated by members of the IGF axis. Further detailed research is needed to establish the relationship between PLEKHS1 and the IGF axis in BCa and determine how these phenomena overlap with T2DM and obesity.There is considerable evidence of a positive association between the incidence of type 2 diabetes mellitus (T2DM) and obesity with bladder cancer (BCa), with the link between T2DM and obesity having already been established. There also appear to be potential associations between Pleckstrin homology domain containing S1 ( Bladder cancer (BCa) is the twelfth most common cancer worldwide, responsible for 3% of annual cancer cases and 2.1% of cancer-related deaths . All cases of CIS were concomitant to visible tumours   suggest ours see . Paired PLEKHS1 mutations compared to the wildtype . By contrast, PLEKHS1 expression was significantly increased in the presence of CIS (p = 0.02). The presence of CIS influenced multiple genes in the IGF axis, with significantly reduced expression of PLEKHS1 expression was significantly increased in G3T1 compared to G1Ta tumours (p = <0.01), whilst IGFBP2, IGFBP4, and IGF1R were significantly reduced in the more advanced tumour group , which were similar results to those observed in CIS. This also confirms the results of Tang et al., showing that IGFBP-2 expression is significantly reduced as BCa progresses  plays a role in such putative mechanisms and is likely one of several influencing factors. Furthermore, the studies described above highlight the potentially dynamic, plastic, and complex nature of IGF signalling within different BCa disease states. The weak, albeit recurrent, associations between T2DM and obesity and the risk of disease and adverse outcomes in BCa suggest that the biology of these phenomena is worthy of further detailed investigation. Although unlikely to be fundamental initiators or drivers of disease for most current BCa cases, the global prevalence of T2DM and obesity means that such investigations may be important in the future. The evidence that we have presented here indicates that perturbations in the IGF axis are the likely mediators of these associations, and we speculate that alteration in PLEKHS1 expression is significantly associated with high-risk NMIBC (in both G3T1 tumours and CIS) and significant reductions in the expression of IGFBP-2, IGFBP-4, and IGF-1R. Given that, in independent analyses, IGFBP-2 expression is significantly reduced as BCa progresses [IGFBP-2 may be of importance in this pathway. Regardless, despite the frequency of non-coding PLEKHS1 mutations in BCa [PLEKHS1 expression between the mutant and the wild type, which is consistent with the results of Pignot et al. Our ‘in-house’ RNAseq data demonstrate that increased ogresses , IGFBP-2s in BCa , our datn = 85); nevertheless, clear associations have been observed between PLEKHS1 and components of the IGF axis. Therefore, in future, we plan to analyse larger, publicly available BCa datasets. One of the limitations of the ‘in-house’ data generated is that the sample size is relatively small (PLEKHS1 and the IGF axis, although it remains to be elucidated if these also translate to associations between the proteins. Further investigations are needed to determine the phenotypic links between PLEKHS1 and the IGF axis components and to examine how these may be altered when cells are exposed to an altered metabolic milieu, such as high-glucose and inflammatory cytokines. These studies may lead to the identification of biomarkers of progression, in addition to preventative and therapeutic strategies for those at increased risk of bladder cancer due to lifestyle factors.Our hypothesis is novel and speculative, yet our preliminary data support potential associations between PLEKHS1 plays a role in the pathogenesis of BCa and that this may be mediated via members of the IGF pathway. Further research is warranted to establish the relationship between PLEKHS1 and the IGF axis in BCa, as well as how such putative relationships overlap with T2DM and obesity and the pathogenesis and progression of BCa. This could lead to the identification of novel biomarkers or potential drug targets that could result in improvements in the diagnosis and treatment of patients. We suggest that


The immune system matures throughout childhood; therefore, evidence about the safety and efficacy of vaccines for the prevention of COVID-19 in the paediatric population is important. Efficacy and safety have not been established for COVID-19 vaccines in a large part of the paediatric population at the time of the initial approval for use in adults. This study aims to provide an overview of planned and ongoing paediatric clinical trials investigating the safety and/or efficacy of COVID-19.We identified all paediatric clinical trials investigating the safety and/or efficacy of COVID-19 vaccines in clinicaltrials.gov and clinicaltrialregister.eu, as well as all clinical trials planned in agreed PIPs  as of 11 June 2021. Information about the study design, the paediatric age groups that they included, and the primary and secondary safety and efficacy outcomes were collected, together with expected timelines for the studies.21 clinical trials were identified through the clinical trial registries and 19 clinical trials were specified in 6 agreed PIPs, 5 of these trials were also in the trial registers. All PIPs stipulated development of the COVID-19 vaccines for the full paediatric population, with a deferral. The earliest expected completion date of a PIPs is March 2024. The majority (14/21) of registered trials are randomised double-blinded studies. All investigated safety, 20 have a surrogate efficacy outcome (immunogenicity), of these 7 also measure clinical efficacy (COVID-19 infections). 18 studies were initiated, of these, all but one is still ongoing and one in adolescents has been finalised.Even though several trials have been planned in agreed PIPs, the registered paediatric clinical trials identified are most often not part of a PIP.The online version contains supplementary material available at 10.1007/s43441-021-00356-y. The rapid development and implementation of vaccines against COVID-19 are society’s best weapon against the social, economic and human devastation brought about by the COVID-19 pandemic. Along with the approval of COVID-19 vaccines for the adult population, many have highlighted the urgency of an ethical and effective strategy to implement the COVID-19 vaccines for the paediatric population as rapidly and safely as possible . The paeIn the EU (European Union), four COVID-19 vaccines have currently been granted a CMA  –8. They Since the EPR  came intThis study aims to provide insight into the timely drug development of COVID-19 vaccines for the paediatric population by investigating registered clinical trials on the safety and the efficacy of COVID-19 vaccines along with the planned clinical trials agreed in a PIP and the expected timelines for completion.All paediatric clinical trials investigating a COVID-19 vaccine registered at clinicaltrials.gov or clinicaltrialregister.eu were identified as of 11 June 2021. A paediatric trial was defined as any clinical trial including subjects from any of the paediatric subgroups  as defined by the International Conference on Harmonization (ICH) Topic E 11, 2001 . ClinicaThen, we identified all agreed PIPs with an active substance investigated for the prevention of COVID-19 in the EU as of 17 June 2021, through the website of the European Medicines Agency’s (EMA) . The PaeFor clinical trials registered in the clinicaltrialregister.eu, we recorded if the sponsor reported the trial to be part of a PIP (section A.7. in the EudraCT form). For clinical trials registered in the Clinicaltrial.gov, we evaluated if a current trial was likely to be part of a PIP by matching the trial IDs provided, trial design, outcome measures and the age groups involved.A total of 21 clinical trials testing the safety and efficacy of a COVID-19 vaccine in the paediatric population were identified in clinicaltrial.gov and/or clinicaltrialregister.eu . However, most often the registered trials included adolescents and children. A discrepancy was observed between planned clinical trials in agreed PIPs and registered clinical trials for infants and toddlers, and term newborn. Only one study was registered for term newborn even though thirteen were planned in the agreed PIPs for COVID-19 vaccines. Similarly, there was a discrepancy observed between planned clinical trials with immunosuppressed paediatric patients in agreed PIPs and registered paediatric studies in this population. This may indicate that such trials although agreed with the authorities have not yet been fully planned and initiated.In general, only a few of the trials registered were conducted as part of an agreed PIP, which indicated that the development is not driven by the EPR. However, if an agreed PIP contains requirements that has already been studied, this should not lead to duplication of research. The initial agreed PIPs have shown to be dynamic with many modifications introduced based on feasibility and changed in the development . Also, eAlso, the paediatric COVID-19 vaccine development has progressed very fast with only five months delay from approval in individuals above 16 years of age to the extension of the indication to cover individuals 12 years of age and older as well . For innChronological age will in many cases be an inappropriate proxy for physiological development and disease presentation relevant to medicines development in the paediatric population , 21. TheFour vaccines are currently  under rolling review by EMA  . Only onAs mentioned, the Pfizer-BioNTech Covid-19 vaccine was approved for adolescents 12 to 17 years of age on 28 May 2021. The study providing the basis for the paediatric indication was carried out in accordance with the PIP for the Pfizer-BioNTech COVID-19 vaccine and enrolled 2260 adolescents aged 12–15 .We found that for both registered studies and planned studies agreed as part of the PIP, efficacy is mostly measured as immunogenicity. Only a few measured incidences of COVID-19 infection after vaccination. A similar pattern was observed by Kesselheim, et.al. 2020 showing that for vaccines approved by the FDA between 2006 and 2020, 65% of the pivotal studies supporting the approval have immunogenicity as primary end point. In the paediatric population, this was even higher (80%) . Immunog. found that pivotal studies supporting the approval of a vaccine by the FDA between 2006 and 2020, had a median follow-up of 18 months, and for the studies in the paediatric population, the duration was at least 12 months [All registered clinical trials had a primary or secondary safety outcome, most often in a randomised control trial, with a median safety follow-up of 12 months after last dose. Kesselheim et al2 months . The vac2 months –7. The r2 months . The CMA2 months .For the paediatric population, COVID-19 does not seem to be a seriously debilitating or life-threatening disease. Studies have suggested that paediatric individuals are at a lower risk of developing COVID-19 and that they have the disease more mildly than adults do –34. The The overview of clinical trials for vaccines for the prevention of COVID-19 in the paediatric population provided here only represents a snapshot in time. The development is moving at unprecedented speed, as is obvious from the recent expansion in the approved indication of the Pfizer-BioNTech vaccine to adolescents 12–15 years of age in May 2021 , 37, 38,This study shows that clinical trials to investigate the safety and efficacy of all currently approved COVID-19 vaccines had been registered in one of the clinical trials databases or planned in an agreed PIP for most of the paediatric population (neonates to adolescents); however, this development does not seem to be driven by the agreed PIPs.Supplementary file1 (DOCX 20 kb)Below is the link to the electronic supplementary material.