Long‐term glucocorticoids (HairGC) measured in scalp hair have been associated with body mass index (BMI), waist circumference (WC), and waist‐hip‐ratio (WHR) in several cross‐sectional studies. We aimed to investigate the magnitude, strength, and clinical relevance of these relations across all ages.We performed a systematic review and meta‐analysis (PROSPERO registration CRD42020205187) searching for articles relating HairGC to measures of obesity. Main outcomes were bivariate correlation coefficients and unadjusted simple linear regression coefficients relating hair cortisol (HairF) and hair cortisone (HairE) to BMI, WC, and WHR.k = 146 cohorts . HairGC were positively related to all anthropometric measurements. The strongest correlation and largest effect size were seen for HairE‐WC: pooled correlation 0.18  and pooled regression coefficient 11.0 cm increase in WC per point increase in 10‐log‐transformed HairE (pg/mg) on liquid‐chromatography‐(tandem) mass spectrometry (LC–MS) . Pooled correlation for HairF‐BMI was 0.10  and pooled regression coefficient 0.049 kg/m2 per point increase in 10‐log‐transformed HairF (pg/mg) on LC–MS .We included There is a consistent positive association between HairGC and BMI, WC, and WHR, most prominently and clinically relevant for HairE‐WC. These findings overall suggest an altered setpoint of the hypothalamic–pituitary–adrenal axis with increasing central adiposity. Every centimeter of scalp hair is believed to represent the cumulative GC exposure of one month.Moreover, other anthropometric measurements than BMI are considered equally or even more relevant to cardiometabolic health, such as waist circumference (WC) and waist‐hip‐ratio (WHR), which both are markers of central adiposity.Therefore, the aim of the current systematic review and meta‐analysis was to investigate the cross‐sectional relations between HairGC levels (HairF and HairE) and anthropometric measurements  and to explore the possible influence of relevant characteristics of the population and laboratory methods.2We performed this systematic review and meta‐analysis in concordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement and Meta‐analysis of Observational Studies in Epidemiology (MOOSE) checklist.2.1A university health sciences librarian designed a comprehensive search to identify studies and conference abstracts concerning hair cortisol and/or hair cortisone and measurements of obesity. To avoid missing potentially relevant papers we designed a broad search strategy combining the elements “hair,” “cortisol/cortisone,” and “BMI/WC/WHR/anthropometrics”, including their synonyms without any restrictions other than “studies in humans”. The search was conducted in the following databases from inception up to November 16, 2020: Medline (Ovid), Embase, Cochrane, Web of Science, Scopus, Cinahl, PsycInfo, and Google Scholar. The complete search strategy is provided in the supporting information Appendix All identified studies were independently screened in two stages by two physicians  with a background in adult (EV and MM) and pediatric (OA) endocrinology. All studies that reported original HairGC data in humans were included in the title/abstract screening stage and were subsequently assessed full text. Disagreements were solved by discussion among the first authors , and the senior author (EvR) until consensus was reached. Additionally, reference lists of all included studies and relevant reviews were screened systematically for potentially relevant articles.2.2, BMI, BMI SDS, WC, and WHR. In studies presenting multiple data points of the same participants , only baseline associations were extracted. When insufficient data were reported for meta‐analysis, corresponding authors were contacted twice in a 2‐week time frame. In case of nonresponse, data were extracted from previous meta‐analyses where possible.Descriptive, methodological, and outcome data were extracted from all included studies by two researchers independently  using a predesigned standardized data extraction sheet. Discrepancies were resolved by discussion among the first authors  and the senior author (EvR). The following descriptive data were extracted: study population characteristics  and laboratory methods: liquid chromatography‐(tandem) mass spectrometry based measurements , enzyme‐linked immunosorbent assays (ELISA), or chemiluminescent immunoassays (CLIA). The reported outcomes of interest were any cross‐sectional associations between HairGC  and measurements of obesity, that is2.3Risk of bias was assessed by two researchers independently  using the Quality In Prognostic Studies (QUIPS) tool.2.4For the qualitative synthesis, we summarized all authors' conclusions regarding cross‐sectional associations between HairGC levels and obesity measurements, that is, correlation coefficients, regression coefficients, or comparison of HairGC levels and obesity measurements across categories.2.5α of 0.05.p‐values and degrees of freedom using the T‐distribution.All meta‐analyses were conducted in R version 3.6.3 with an 2.6r‐to‐z transformation was applied to transform individual correlations stratified on all combinations of HairGC (HairF and HairE) and obesity measurements . As several studies reported correlations within distinct subgroups, we calculated the pooled correlation coefficients, 95% confidence intervals (CIs) and prediction intervals (PIs) using multilevel random effects models.For all studies reporting bivariate correlations (correlation coefficients), Fisher's I2 statistic and Cochrane's Q test were used for the assessment of between‐study heterogeneity, with I2 > 25% and p‐value for Cochrane's Q test <0.05 indicating heterogeneity. For all meta‐analyses with data from at least 10 cohorts, exploratory moderator analyses were performed using mixed‐effect models for categorical parameters  and random effects models for continuous parameters . Publication bias was assessed using contour‐enhanced funnel plots.The 2.7w‐statistic described by Bini et al.For all studies reporting unstandardized simple linear regression coefficients between 10‐log transformed HairGC (HairF or HairE) in pg/mg as independent variable and untransformed obesity measurements  as dependent variable, pooled regression coefficients and 95% CIs were calculated using the statistical approach described by Bini et al. and Becker & Wu.3The literature search identified 1017 unique citation titles of which a total of 120 studies3.1n = 23,467) was 53.3 ± 18.4 years and weighted mean BMI  was 27.0 ± 5.4 kg/m2. For studies involving children, weighted mean age (n = 9904) was 7.8 ± 3.3 years and weighted mean BMI SDS (n = 4108) was 0.2 ± 1.0. Forty‐three of the 146 cohorts (29%) included children (mean age <18 years). The majority of the cohorts had a population that was predominantly female , although the proportion of females within all included subjects was 44%.The weighted mean age of cohorts involving adults (available for 2),2 or higher. Details on the mean BMI of the studies can be found in Table Of the 43 pediatric cohorts, two specifically included only children with obesity,BMI was the most commonly reported obesity measurement in 138/146 cohorts (95%), followed by WC in 30/146 cohorts (21%), WHR in 20/146 cohorts (14%), and BMI SDS in 16/43 pediatric cohorts (37%).For 145 cohorts (99%) the used laboratory method was reported, which were ELISA , LC–MS or LC–MS/MS , or CLIA . In all cohorts HairF was reported, whereas HairE was additionally reported in 19/146 cohorts (13%)..,Mean crude HairGC concentrations across the studies varied widely with reported means ranging from 1.2–592.2 pg/mg for HairF and 2.45–38.48 pg/mg for HairE. Mean HairF concentrations were higher in studies that used an ELISA (weighted mean 95.6 ± 236.4 pg/mg) compared with studies that used CLIA (24.0 ± 45.1 pg/mg) or LC–MS . The strongest pooled correlation was found for HairE versus WC .In total, 140/146 cohorts (96%) from 115 unique studies were included in the meta‐analyses of correlations, comprising data of 28,830 participants. The pooled correlation coefficients ranged from 0.10–0.18  and HairF and WHR , and studies with higher BMI SDS showed weaker correlations between HairF and BMI SDS . Visual inspection of the funnel plots showed no evidence for publication bias; that is, no systematic trends were found between standard error (as proxy for study sample size) and magnitude and direction of the reported correlation coefficients . The pooled regression coefficient for LC–MS‐based measurements was 0.049 kg/m2 . The highest pooled regression coefficient was found for HairE on dependent variable WC, where 1 point increase in 10‐log HairE was associated with 11.0 cm larger WC (95% CI 10.1–11.9 cm) on LC–MS. There was no significant between‐study heterogeneity  on LC–MS‐based assays was associated with 11 cm larger WC. For the outcome BMI, an increase of 1.15 kg/mn = 26,527 participants), we confirmed the modest positive relations in exploratory analyses of Stalder et al. and Ling et al. between HairF and BMI/BMI SDS.In the largest of our meta‐analyses, for HairF versus BMI (2 increase in BMI per 1 point increase in 10‐log transformed HairF on LC–MS), the small effect size here seems to have less clinical relevance compared with the large effect size we found for the relation HairE versus WC. We believe that the consistency of our findings across all studied outcomes is indicative of an altered setpoint of the HPA‐axis in obesity. This may induce or aggravate obesity, although causality cannot be proven by our study because of its limitation to cross‐sectional associations. Yet, the fact that HairGC apparently relate strongly to measures of abdominal obesity matches the paradigm that chronic exposure to higher levels of GCs specifically induce abdominal obesity.The current study indicates that this relation is strongest  for cortisone, the inactive form of cortisol, and WC. Although the pooled correlation coefficients and pooled regression coefficients for the most frequently studied outcome HairF versus BMI were statistically significant  as well as at tissue level, differing per tissue type.Furthermore, in contrast to Ling et al.,The percentage of males included was a significant influencer of the relation between WC and HairF, with a similar trend for WHR and HairF, but not for HairF and BMI. For both WC and WHR, cut‐off values are sex‐specific, with males generally having a larger WC and WHR than females. This might contribute to the stronger associations between HairGC and anthropometric measurements in studies that contain more males. Unfortunately, lack of raw data hampered stratification for sex.We also observed that studies that had a high percentage of participants with obesity found less strong associations between HairF and BMI. Although HairGC levels may explain less of the weight variability in cohorts with individuals with obesity compared with cohorts that include wider weight ranges, it has clearly been established that individuals with obesity in general have higher HairGC than individuals without obesity,r = 0.15 and r = 0.07, respectively, for HairF‐BMI). One possible explanation for this finding could include higher perceived weight stigma in individuals with obesity. Weight stigma is associated with adverse psychological consequences, such as anxiety, lower self‐esteem, poor quality of life, as well as with higher HairF levels.In contrast to our expectations, we found that studies using self‐reported BMI reported stronger correlations to HairGC levels than studies using objective anthropometric features . This limits the risk of publication bias, which was also confirmed by our funnel plots (supporting information Figures A limitation of our study was that we obtained data that are related to full cohorts instead of individual person‐data. This restricts our conclusions to comparisons across cohorts instead of across individuals. However, by pooling regression coefficients, we could provide an effect size that is applicable on individual level. Other limitations relate to the lack of standardization of HairGC analysis methods and the usefulness of HairGC itself, as there are still numerous issues unsolved. For example, the ubiquitously reported growth speed of scalp hair, 1 cm per month, may vary considerably by ethnicity and season.Altogether, we confirmed a consistent positive association between anthropometric measurements and hair glucocorticoids. This relation was most often studied for hair cortisol and BMI but showed the strongest correlation and largest effect size for hair cortisone and WC. These relations were not influenced by mean age, mean BMI, or mean HairGC levels nor by the used laboratory methods of the studies. However, the percentage of males, the percentage of participants with obesity, and objective measurement of weight instead of self‐reported weight represented important features to take into account when assessing hair glucocorticoids in cohorts. Although causality is not yet proven, our results suggest that higher long‐term glucocorticoid levels measured in scalp hair, especially cortisone, may contribute to or reflect the state of specifically central adiposity. Future longitudinal studies should investigate whether higher hair glucocorticoid levels can have clinical relevance in predicting the development or deterioration of obesity. Our results emphasize the importance of accounting for BMI and/or WC or WHR when interpreting hair glucocorticoid levels in individuals or on a group level.OA, BvdV, EvdA, and EvR are supported by the Elisabeth Foundation, a nonprofit organization supporting academic obesity research. EvR is supported by the Netherlands Organization of Scientific Research NWO, ZonMW Vidi Grant/Award Number: 91716453.The authors declare that there are no conflicts of interest for all authors.EvdV, OA, and MM: conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, and writing‐original draft. AA: data curation, formal analysis, investigation, visualization, and writing‐review and editing. VW, AI, EvdA, YdR, and BvdV: formal analysis, investigation, methodology, supervision, validation, and writing‐review and editing. TS: data curation, formal analysis, investigation, supervision, validation, and writing‐review and editing. SH: conceptualization, formal analysis, investigation, methodology, supervision, validation, visualization, writing‐review and editing. EvR: conceptualization, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing‐review and editing.Appendix S1. Search strategy.Table S1. Qualitative synthesis.Figure S1. Forest plot for the meta‐analysis of correlation coefficients between HairF and BMI.Figure S2. Forest plot for the meta‐analysis of correlation coefficients between HairF and BMI SDS.Figure S3. Forest plot for the meta‐analysis of correlation coefficients between HairF and WC.Figure S4. Forest plot for the meta‐analysis of correlation coefficients between HairF and WHR.Figure S5. Forest plot for the meta‐analysis of correlation coefficients between HairE and BMI.Figure S6. Forest plot for the meta‐analysis of correlation coefficients between HairE and WC.Figure S7. Bubble plot for the meta‐regression on proportion of males in the meta‐analysis of correlation coefficients between HairF and WC.Figure S8. Bubble plot for the meta‐regression on proportion of males in the meta‐analysis of correlations between HairF and WHR.Figure S9. Bubble plot for the meta‐regression on proportion of individuals with obesity in the meta‐analysis of correlations between HairF and BMI.Figure S10. Funnel plot for the meta‐analysis of correlation coefficients between HairF and BMI.Figure S11. Funnel plot for the meta‐analysis of correlation coefficients between HairF and BMI SDS.Figure S12. Funnel plot for the meta‐analysis of correlation coefficients between HairF and WC.Figure S13. Funnel plot for the meta‐analysis of correlation coefficients between HairF and WHR.Figure S14. Funnel plot for the meta‐analysis of correlation coefficients between HairE and BMI.Figure S15. Funnel plot for the meta‐analysis of correlation coefficients between HairE and WC.Click here for additional data file.


The increased elimination rate of drugs through the kidneys in patients with ARC can increase the risk of treatment failure due to the exposure to subtherapeutic serum concentrations of medications and affect the optimal management of infections, length of hospital stay, and outcomes. The main goal of this review article is to summarize the recommendations for appropriate dosing of antibiotics in patients with ARC.  Augmented renal clearance (ARC), which is commonly defined as increased renal clearance above 130 ml/min/1.73 m This article is a narrative review of the articles that evaluated different dosing regimens of antibiotics in patients with ARC. The keywords “Augmented Renal Clearance,” “Critically ill patients,” “Drug dosing,” “Serum concentration,” “Beta-lactams,” “Meropenem,” “Imipenem,” “Glycopeptide,” “Vancomycin,” “Teicoplanin,” “Linezolid,” “Colistin,” “Aminoglycosides,” “Amikacin,” “Gentamycin,” “Fluoroquinolones,” “Ciprofloxacin,” and “Levofloxacin” were searched in Scopus, Medline, PubMed, and Google Scholar databases, and pediatric, nonhuman, and non-English studies were excluded.  PK properties of antibiotics including lipophilicity or hydrophilicity, protein binding, the volume of distribution, and elimination rate that affect drug concentration should be considered along with PD parameters for drug dosing in critically ill patients with ARC.  This review recommends a dosing protocol for some antibiotics to help the appropriate dosing of antibiotics in ARC and decrease the risk of subtherapeutic exposure that may be observed while receiving conventional dosing regimens in critically ill patients with ARC. The elimination rate of drugs, especially hydrophilic antibiotics that are mainly eliminated through the kidney, increased in ARC. According to the effect of ARC on the optimal management of infections, length of hospital stay, and clinical outcomes, determination of the ARC phenomenon is necessary for adjusting the optimal treatment to reduce the risk of subtherapeutic exposure that may be observed while receiving conventional dosing regimens in critically ill patients with ARC.Augmented renal clearance (ARC) is a common phenomenon in critical care settings. The incidence of ARC was reported between 14 and 85% depending on the study population and the cutoff value of creatinine clearance (CrCl). The CrCl ≥130 ml/min/1.73 mThe higher rate of renal elimination of the medications in ARC impacts the dosing regimens of antibiotics to achieve target pharmacokinetic/pharmacodynamic (PK/PD) indices. There are some review articles aimed to recommend dosing regimens for antibiotics in patients with ARC while reviewing the related articles, but each contains only several antibiotics, particularly B-lactams and vancomycin. We tried to prepare an almost complete article that involved most antibiotics with renal excretion.2 [Infections are one of the most common problems encountered in critically ill patients and may prolong the hospital length of stay, as well as increasing patient mortality rates . The cho2 , 15, 16.2 –15. Dosi2 , 17. The2 . The majTo the best of our knowledge, there is no published guideline for drug dosing in patients with ARC. The main goal of this narrative review article is to summarize the recommendations for appropriate dosing of antibiotics in ARC to reduce the risk of subtherapeutic exposure that may be observed while receiving conventional dosing regimens in critically ill patients with ARC.This article is a narrative review of the articles that evaluated different dosing regimens of antibiotics in patients with ARC. The keywords “Augmented Renal Clearance,” “Critically ill patients,” “Drug dosing,” “Serum concentration,” “Beta-lactams,” “Meropenem,” “Imipenem,” “Glycopeptide,” “Vancomycin,” “Teicoplanin,” “Linezolid,” “Colistin,” “Aminoglycosides,” “Amikacin,” “Gentamycin,” “Fluoroquinolones,” “Ciprofloxacin,” and “Levofloxacin” were searched in Scopus, Medline, PubMed, and Google Scholar databases, and pediatric, nonhuman, and non-English studies were excluded.All studies except systematic reviews and meta-analyses and case reports were independently rated for quality by two reviewers using the National Institutes of Health (NIH) Quality Assessment Tools . The stuStudies that described the impact of ARC on the dosing regimens of antibiotics, based on achieving target pharmacokinetic/pharmacodynamic (PK/PD) indices, have increased in recent years. There are some review articles aimed to recommend dosing regimens for antibiotics in patients with ARC, but each contains only a few antibiotics, particularly B-lactams and vancomycin , 57, 58.fT > MIC) has an essential role in efficacy. In practice, depending on the pathogen and the type of B-lactam, fT > MIC for 40–70% of the dosing interval is considered an acceptable PD target [fT > 4 × MIC has been suggested to improve clinical outcomes [The pattern of B-lactams activity is time-dependent. The time that the free drug plasma concentration remains above the minimum inhibitory concentration (MIC) of the pathogen  ≥90% for optimal piperacillin regimens. The results showed the PTA was ≥90% for 50% fT > MIC in all three CI regimens and EI of 12 and 16 g piperacillin daily, whereas the PTA for 100% fT > MIC was ≥90% only in all three CI regimens [fT > MIC in 93% and 98% of patients with 130 ≤ CrCl < 200 ml/min and 80% and 90% of patients with CrCl ≥ 200 ml/min, respectively. The daily dose of 20 + 2.5 g PTZ was recommended for patients with CrCl ≥ 170 ml/min to reach the PD target with the highest probability [PK models of nine dosing regimens of PTZ , and continuous infusion (CI) of 16 g, 12 g, and 8 g daily piperacillin) have been assessed as achieving the PD targets in septic patients with different CrCls and for different MICs. The MIC of 16 mg/L has been proposed as the clinical cutoff point of piperacillin for bability . No intobability , 26. In bability .The antibacterial activity pattern of glycopeptides is both time- and concentration-dependent, and the area under the plasma concentration-time curve (AUC) for 24 hr, relative to the pathogen MIC (AUC0–24/MIC) ratio, is considered the best parameter to predict their antibacterial activity , 13 Tab. Glycope2 and higher CrCls need the larger MD to maintain the therapeutic exposure [Streptococcus intermedius stating that they did not achieve a stable vancomycin target level until they increased the dose to 1.5 g, four times a day [For vancomycin, the target of AUC0–24/MIC ≥  400 has been proposed to have optimal clinical outcomes , 35. Stues a day . Baptistes a day .Teicoplanin has approximately 90–95% affinity to binding serum protein, and hypoalbuminemia may cause a low total trough level, whereas the unbound trough concentration is in the therapeutic range (1.5–4.5 mg/L). So in settings of hypoalbuminemia, measuring total and unbound trough levels of teicoplanin is recommended , 40, 65.rd and 15th days of treatment with 400, 600, 800, and 1000 mg teicoplanin every 12 hr for four consequent doses followed by the same doses every 24 hr for 12 days was evaluated in different CrCls. The results showed the need for 800 or 1000 mg teicoplanin as the LD to provide a trough concentration ≥15 mg/L on day 3 with a PTA ≥90%. But these doses may cause trough concentrations > 60 mg/L on day 15 even in CrCls >90 mL/min/1.73 m2 [2. The clinical effectiveness rate was reported as 88% in these patients [The negative association between the trough concentrations of teicoplanin and renal function has been reported . Serum a/1.73 m2 . The admpatients . 12 mg/kpatients .Due to the limitations of clinical trials in the field of adequate dosing regimen for teicoplanin in patients with ARC, we also mentioned some studies that have suggestions for teicoplanin dosing in critically ill patients whose risk of ARC is high among them. The recommendations of these articles are summarized in No association was reported between teicoplanin-induced nephrotoxicity and its trough concentrations . TeicoplfT > MIC that are correlated with the clinical efficacy of linezolid [The antimicrobial activity of linezolid is time- and concentration-dependent. Besides AUC0–24/MIC and %inezolid , 68, itsinezolid  , the PTA was <40%. So, combination therapy with colistin in addition to high-dose regimens was recommended for these patients [Colistin is a cationic, lipoprotein, hydrophilic antibiotic . The antpatients , 52.AGs are hydrophilic antimicrobial agents with concentration-dependent killing characteristics. Although Cmax/MIC is considered the best PD index to reflect their bactericidal effect , 50, 63,Carrie et al. used the Monte Carlo simulation to report different dosing regimens of amikacin that are needed to achieve PD targets in different CrCls and for different MICs. An optimal regimen was considered the one that had fractional target attainment (FTA) >85%. They recommended 30–35 mg/kg/day amikacin in patients with ARC especially in the exposure to less-susceptible pathogens. The FTA of 30 mg/kg/day amikacin was 30–40%, 20–30%, and 29% for Cmax/MIC ≥ 8, AUC0–24/MIC ≥75, and trough concentration ≥ 2.5 mg/L, respectively, for an MIC of 8 mg/L. These rates were near 100%, 30%, and 37%, respectively, for 35 mg/kg/day amikacin . The recIn critically ill patients, 30 mg/kg/day amikacin and 7–10 mg/kg/day gentamycin and tobramycin were recommended as initial empirical doses to achieve the optimal PD targets in severe infections , 50.fT > MIC is another PD index that should be noted to decrease the risk of developing resistant pathogens against AGs. It was shown that fT > MIC for less than 60% of AG dosing intervals is associated with resistance development. Thereafter, 12.5 mg/kg amikacin every 12 hr instead of a high-dose extended-interval dosage regimen was recommended to achieve the PD target in the empiric treatment of septic patients with minimizing resistance development. The safety of the two regimens was also compared using neutrophil gelatinase-associated lipocalin (NGAL) during 7 days of treatment, and any significant difference was not detected [%detected .Although the bacterial killing property of FQs is concentration-dependent, they also show some time-dependent effects . The AUCPK properties of antibiotics including lipophilicity or hydrophilicity, protein binding, the volume of distribution, and elimination rate that affect drug concentration should be considered along with PD parameters for drug dosing in critically ill patients with ARC. This review recommends a dosing protocol for some antibiotics to help the appropriate dosing of antibiotics in ARC and decrease the risk of subtherapeutic exposure that may be observed while receiving conventional dosing regimens in critically ill patients with ARC .


The purpose of this work is to describe the association between body mass index (BMI) and (1) management option for localized prostate cancer (PCa) and (2) disease‐specific quality of life (ds‐QoL) after treatment or active surveillance.We analysed data from men with localized PCa managed with radical prostatectomy (RP), radiation therapy (RT), or active surveillance (AS) in a prospective, population‐based cohort study. We evaluated the association between BMI and management option with multivariable multinomial logistic regression analysis. The association between BMI and ds‐QoL was assessed using multivariable longitudinal linear regression. Regression models were adjusted for baseline domain scores, demographics, and clinicopathologic characteristics.2; 77% were non‐Hispanic white); 29% were obese (BMI ≥ 30). Accounting for demographic and clinicopathologic features, BMI ≥ 28 kg/m2 was inversely associated with the likelihood of receiving RP (compared with RT) and became statistically significant at BMI ≥ 33 kg/m2 . Conversely, BMI was not significantly associated with the likelihood of receiving AS compared with RT. After stratification by management option, obese men who underwent definitive treatment were not found to have clinically worse ds‐QoL. Obese men initially on AS appeared to have worse urinary incontinence than nonobese men, but this was not significant on an as‐treated sensitivity analysis.A total of 2378 men were included (medians [quartiles]: age 64 [59–69] years; BMI 27 kg/m2 were less likely to receive surgery than radiation. Obesity was not associated with ds‐QoL in men undergoing definitive treatment, nor in men who remained on AS.Among men with localized PCa, those with BMI ≥ 33 kg/m For many patients, these modalities confer similar long‐term oncologic survival,Higher body mass index (BMI) is associated with increased intra‐ and peri‐operative complications for patients undergoing RP,Assumptions about the effect of BMI on post‐treatment ds‐QoL may affect patient counselling and treatment choice. Accordingly, we evaluated the association between BMI, management option, and ds‐QoL outcomes following treatment for localized PCa using data from the prospective, population‐based Comparative Effective Analysis of Surgery and Radiations (CEASAR) study. We hypothesized that obese men are more likely to receive RT than RP and experience worse urinary and sexual function after treatment than nonobese men.22.1The CEASAR study recruited men with clinically localized PCa from five population‐based Surveillance, Epidemiology, and End Results (SEER) Program registries  and the observational Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) prostate cancer registry from 2011 to 2012 as previously described.Participants completed surveys at the time of enrolment and 6, 12, 36, and 60 months after enrolment. Trained personnel performed chart abstraction 1 year after patient enrolment to collect relevant clinical and treatment information. Men were excluded from the analytic cohort if they were missing data on BMI or if their primary treatment was not RP, RT, or AS. Institutional review board approval was obtained from each site and from Vanderbilt University Medical Center. Participants provided informed consent.2.22 vs. nonobese BMI < 30 kg/m2]) in the analysis of ds‐QoL.We utilized patient‐reported BMI at 3 years, the earliest measurement in CEASAR of this variable. It is unlikely that participants were misclassified by measuring BMI at 3 years rather than baseline because BMI is stable over this timeframe,2.3Covariates included baseline EPIC‐26 domain scores , baseline SF‐36 functional scores  and the following baseline demographic and clinical features: age at diagnosis, race/ethnicity , education , marital status , annual income , health insurance type , employment , accrual site , total illness burden index for prostate cancer comorbidity score (TIBI‐CaP) , PSA at diagnosis (continuous), clinical tumour stage , biopsy Gleason score , social support (continuous), participatory decision‐making scale (continuous), Center for Epidemiologic Studies Depression scale (continuous).2.4We considered two key outcomes. First, we examined the association between BMI and management option , determined by the combined information from 1‐year chart abstraction, patient‐reported survey, and SEER registry. Second, we examined the association between BMI and ds‐QoL, measured at baseline, 6, 12, 36, and 60 months after enrolment using the previously validated 26‐item Expanded Prostate Index Composite (EPIC‐26).2.52], overweight [BMI ≥ 25 kg/m2 and BMI < 30 kg/m2], and obese [BMI ≥ 30 kg/m2]) and compared using the Kruskal‐Wallis and chi‐squared tests for continuous and categorical variables, respectively.Clinicopathologic and sociodemographic characteristics were summarized by World Health Organization (WHO) BMI category  of receiving RP and AS compared with RT were estimated and reported with 95% confidence intervals. BMI was treated as a continuous variable in this model, and restricted cubic splines with 3 knots were used for BMI to allow nonlinear association with the outcome. This model included the covariates defined above, and an interaction term for BMI and NCCPCa risk‐category (low to favourable‐intermediate vs. unfavourable‐intermediate to high) was included in the analysis of treatment option . As the interaction was determined to not be significant, it was excluded from the final model.To estimate the association between BMI and ds‐QoL domain scores , we fit multivariable longitudinal linear regression models. In these models, BMI was dichotomized as obese vs. nonobese (BMI ≥ 30 vs. BMI < 30) according to WHO criteria to facilitate presentation of data. A sensitivity analysis for the ds‐QoL analysis was performed using WHO BMI categories of obese (BMI ≥ 30), overweight (BMI ≥ 25 and < 30), and normal or underweight (BMI < 25) and are presented in the supplement Tables  and S2. p values less than 0.05 were considered statistically significant. Analyses and figures were produced using R version 4.0.Missing covariate values were imputed using the multiple imputation by chained equations procedure.32. 582 (24%) patients were normal or underweight (BMI < 25); 1118 (47%) were overweight (BMI ≥ 25 and <30), and 678 (29%) were obese (BMI ≥ 30). Men who were younger, Black, had some‐college education or less, or had higher comorbidity scores were more likely to be obese . There were no significant differences across BMI groups in PSA at diagnosis, biopsy Gleason score, or clinical stage . Thus, 2378 study participants were included in the analytical cohort Figure ; of thes3.1p = 0.48). The logistic regression models showed that the likelihood of receiving RP compared with RT increased as BMI increased from 15 to 28 kg/m2   Table .3.2p = 0.01) and hormonal (p < 0.001) domain scores compared with overweight and normal/underweight men  when stratified by management option Table .42 were less likely to undergo RP compared with RT. We did not find an association between obesity and ds‐QoL outcomes in men treated with definitive local therapy. Conversely, among men who started on AS, obese men had worse urinary incontinence than nonobese men after controlling for covariates, which attenuated and was no longer significant in an as‐treated sensitivity analysis.In this population‐based, prospective cohort of men with localized prostate cancer, we found that obese men with BMI ≥ 33 kg/m2.To our knowledge, only one other study has assessed the association between BMI and management option in localized prostate cancer. The results of that study similarly demonstrated that increasing BMI beyond a certain threshold is associated with increased likelihood of receiving nonsurgical management (including AS) compared with RP, which was most pronounced in men with BMI ≥ 35 kg/mIn this study, we did not find clinically meaningful differences in patient‐reported functional outcomes between obese and nonobese men undergoing definitive treatment. This is consistent with several previous studies.p = 0.02) and incontinence  than nonobese men 1 year after RARP.n = 672), corroborates our results, finding no association between obesity and urinary or sexual ds‐QoL after RP.Though some studies have demonstrated worse urinary‐ and sexual‐ function in obese men who underwent RP, the larger studies were retrospective or failed to control for baseline function. The few prospective studies were small, single‐center studies.Among untreated men on AS, there were no differences in ds‐QoL scores by BMI. The finding of worse urinary incontinence in obese men on AS in our study was likely due to secondary effects of treatment. Among men initially managed with AS, obese men were more likely to undergo definitive therapy than nonobese men (46% vs. 26%) Figure .Our study's strengths include utilization of previously validated, patient‐reported ds‐QoL surveys (EPIC‐26 and SF‐36), large sample size, multivariable models controlling for baseline function, and the use of a population composed of men receiving contemporary guideline‐recommended management options, including RP (≥68% RARP), RT, and AS. There are several limitations. First, the study, like any nonrandomized observational cohort is subject to confounding by indication and unmeasured covariates. This would at least include RP factors like nerve‐sparing‐, bladder neck preservation‐ and Retzius‐space sparing‐ techniques, which are associated with functional outcomes.p = 0.32). Another study found a statistically significant difference in the BMI of men with PCa after a mean follow‐up of 2.0‐years, but it was from 26.1 to 26.3 kg/m2 . Importantly, misclassification bias from possibly undetected increase in BMI would represent crossover and increase the likelihood of a type II error.A major limitation of this study is the initial measurement of BMI in the third year. Initial surveys captured limited information to mitigate respondent burden and maximize response rates. Despite this, there exists no compelling evidence that men newly diagnosed with localized PCa experience significant changes in their BMI.52, further increases in BMI were inversely associated with the likelihood of surgical treatment compared with RT. While obesity may contribute to treatment choice, among those receiving definitive treatment, we found no evidence of worse functional outcomes in obese men compared with nonobese men undergoing definitive treatment, nor in men who remained on AS. This information may be valuable to physicians and patients considering management options for localized prostate cancer and may expand management options for obese men.In this population‐based, prospective cohort of men with localized prostate cancer, we found that, above BMI of 28 kg/mThe authors have no competing interests.Research conceptualization: all authors; Data acquisition: MC, MG, AH, MH, BO, LEP, AS, and XCW; Data curation: LCH, ZZ, TK; Data Analysis: NLS, CJW, LCH, JET, ZZ, KF, AM, TK, DAB; Funding acquisition: DFP, DAB; Methodology: NLS, CJW, LCH, JET, ZZ, SG, SK, TK, DAB; Writing—original draft: NLS, CJW, JT, TK, DAB; Writing—review and editing: all authors.Table S1 Adjusted disease‐specific functional outcomes: EPIC‐26 domain scores by WHO BMI Category, stratified by management option, adjusted for patient demographic, tumor, and baseline functional characteristics.Click here for additional data file.Table S2 Health‐Related Quality of Life Outcomes: SF‐36 domain scores by WHO BMI Category, stratified by management option, adjusted for patient demographic, tumor, and baseline functional characteristics.Click here for additional data file.Table S3 Disease‐Specific Quality‐of‐Life outcomes: EPIC‐26 domain scores by obesity status, stratified by management option and receipt of ADT, effect size adjusted for patient demographic, tumor, and baseline functional characteristics.Click here for additional data file.Table S4 General health‐related quality of life outcomes: SF‐36 domain scores by obesity status, stratified by management option, adjusted for patient demographic, tumor, and baseline functional characteristics.Click here for additional data file.Table S5 Internal cohort BMI stability from 3 years to 5 years after baselineClick here for additional data file.Table S6 Multinomial multivariable logistic regression model for the outcome of management option including all covariates.Click here for additional data file.Figure S1: Diagram of the assembly of the Comparative Effectiveness Analysis of Surgery and Radiation (CEASAR) study cohort and the final analytic cohortClick here for additional data file.Figure S2: Adjusted EPIC‐26 domain score mean‐differences between obese and non‐obese men, stratified by management option, over time in an as‐treated analysis Click here for additional data file.


Studies indicate lower, comparable, and higher cardiovascular risks in women vs men in normal glucose regulation (NGR), prediabetes, and diabetes, respectively. However, this sex difference is uncertain and aging might play a part. We aimed to estimate sex differences in arterial stiffness in NGR, prediabetes, or diabetes and the potential modifications by age.We used baseline data of 9618 participants aged ≥40 years in a large community‐based cohort study in Shanghai. Glycemic status was determined by history of diabetes, fasting and 2‐h post‐load glucose levels, and hemoglobin A1c levels. Arterial stiffness was examined by brachial‐ankle pulse wave velocity (ba‐PWV). Multivariable linear regression analysis was conducted to examine the associations between sex and ba‐PWV levels in glycemic and age categories.β coefficient = 71.5; 95% confidence interval = 23.4, 119.7) and the sex difference was attenuated in the groups of prediabetes and diabetes with a borderline significant interaction between sex and glycemic status (p for interaction = .068).Before adjustment for age, women had lower, comparable, and higher ba‐PWV vs men in the NGR, prediabetes, and diabetes groups, respectively. In participants aged 40–59 years, women were associated with lower ba‐PWV levels in generally all glycemic strata after adjustment for age and other confounders. In participants aged ≥60 years, women were associated with significantly higher ba‐PWV levels (The sex difference in cardiovascular risks in adults with NGR, prediabetes, or diabetes was dependent on age. Our findings provide new evidence for prioritizing preventive treatment against atherosclerosis in men vs women with different glycemic status. HighlightsSex difference regarding arterial stiffness in different glycemic status and whether aging plays a role remains uncertain.In participants aged 40–59 years, women were associated with lower ba‐PWV levels in generally all glycemic strata after multivariable adjustment. In participants aged ≥60 years, women were associated with significantly higher ba‐PWV levels in the group of normal glycemic regulation after multivariable adjustment and this sex difference was attenuated in the groups of prediabetes and diabetes.Our findings provided additional evidence for targeted sex‐specific prevention of atherosclerosis in participants with different glycemic status. A 25% higher risk in men vs women was found by the Prospective Urban and Rural Epidemiological (PURE) study.Subclinical atherosclerosis such as arterial stiffness is an early stage of CVD before manifestation of clinical symptoms and onset of cardiovascular events. Arterial stiffness measured using the pulse wave velocity (PWV) is reported to play a critical role in the process of vascular aging, promoting the initiation and progression of CVD.22.1n = 304), and participants with missing data on ba‐PWV measurements (n = 420) or glucose measurements (n = 33). Finally, 9618 participants were included in the current analysis.Participants aged over 40 years were enrolled from an ongoing community‐based cohort with baseline examination conducted between March 2010 and August 2010 in the Jiading District of Shanghai, China. The detailed information on study design has been well illustrated elsewhere.The study protocol was approved by the Institutional Review Board of Ruijin Hospital, Shanghai Jiaotong University School of Medicine ((2011) No.14, approved on 10 March 2011). The study complied with the guidelines of the Declaration of Helsinki, and written informed consent was acquired from each participant before study.2.2A standard questionnaire was used by trained staff to collect information on demographic characteristics, socioeconomic status, lifestyle factors, disease history and medication use from each participant through a face‐to‐face interview. Education attainment was self‐reported and categorized into high school or above vs less than high school. Participants with self‐reported habits of smoking cigarettes or consuming alcohol regularly within the past 6 months were identified as current smokers or current drinkers, respectively. Physical activity was recorded using the short form of International Physical Activity Questionnaire with questions on the intensity, frequency and duration of physical activity.2). Three consecutive blood pressure measurements were conducted with 1‐min intervals between measurements after at least a 5‐min rest in the seated position using a calibrated automated electronic device  and participants were asked to avoid exercise, cigarettes, alcohol, coffee, tea, or medicine for at least half hour before measurement. We used the average of three readings in the current study. Venous blood samples were drawn from each participant after at least 10 h of overnight fasting. Participants without a history of diabetes underwent a standard oral glucose tolerance test (OGTT) and blood was drawn at 0 and 2 h during the test. Biochemical parameters including plasma glucose, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol (HDL‐c), triglycerides, serum creatinine, and serum insulin were determined by the auto‐analyzers . Hemoglobin A1c (HbA1c) was determined by high‐performance liquid chromatography . The homeostasis model assessment of insulin resistance (HOMA‐IR) was calculated as the product of fasting serum insulin (μU/ml) and fasting plasma glucose  divided by 22.5.−1.154 × [age] −0.203 × [0.742 if female] × 1.233.Anthropometric and blood pressure measurements, blood sampling and biochemical tests were performed according to standardized procedures.2.3The glycemic status including normal glycemic regulation (NGR), prediabetes, and diabetes was defined based on the American Diabetes Association criteria.2.4Ba‐PWV was measured using Colin VP‐1000  by trained staff. After resting for at least 10 min, participants were asked to keep cuffs around their left and right arms and ankles, and the pulse wave was automatically detected at the same time on each side. Ba‐PWV was calculated by dividing the distances between arm and ankle by the transit time between the start of brachial and tibial pulse waves. The larger bilateral value of ba‐PWV was used in the current analysis.2.5t tests for continuous variables and chi‐square tests for categorical variables. Restricted cubic splines were used to investigate the relationships between glucose levels, insulin levels, HOMA‐IR, and ba‐PWV levels with three knots at 5th, 50th, and 95th percentiles. Means and SDs of ba‐PWV were calculated and compared in men vs women with NGR, prediabetes, or diabetes, respectively, overall and stratified by age (40–59 vs ≥ 60 years). Smooth trajectories of ba‐PWV levels by age in men and women in strata of glycemic status were presented using generalized additive models to flexibly fit the possible trend of ba‐PWV with an increase in age. Unadjusted, age‐adjusted, and multivariable adjusted linear regression models were used to examine the association between sex and ba‐PWV levels in participants with NGR, prediabetes, or diabetes, overall and stratified by age. Covariates in the multivariable model included age, education level , current smoking (yes/no), current drinking (yes/no), physical activity , BMI, systolic blood pressure, triglycerides, LDL‐c, HDL‐c, and eGFR. Interaction terms were added to the models to examine the interaction effect of glycemic status on the association between sex and ba‐PWV, overall and stratified by age. Participants were also categorized into groups of with and without insulin resistance defined by HOMA‐IR ≥2.5.p value of less than .05 was judged as having statistical significance.Baseline characteristics of the participants are shown by glycemic status and sex. Continuous variables with normal distribution were presented as means (SDs). Skewed variables such as HbA1c and triglycerides were presented as medians (interquartile ranges) and logarithmically transformed for statistical tests. Categorical variables were presented as numbers (percentages). Comparisons were conducted between men and women in each stratum of glycemic status by Student's 3As shown in Table Associations between glucose levels, insulin levels, HOMA‐IR, and ba‐PWV are shown in Figure β coefficient = −34.4; 95% confidence interval [CI] = −55.9, −12.8), comparable ba‐PWV in the prediabetes group , and higher ba‐PWV in the diabetes group    Figure . However) Figure . Further) Figure . Similar4In this large community‐based population study, we found that the sex difference in arterial stiffness evaluated by ba‐PWV in adults with different glycemic status was highly dependent on age. In adults aged 40–59 years, the female sex was mostly “protective” regardless of the glycemic status. However, in adults aged ≥60 years, the female sex was identified as a “risk” factor of arterial stiffness in NGR, whereas this sex difference tended to diminish with the deterioration of glycemic regulation. These findings provide new evidence for prioritizing preventive treatment against adverse cardiovascular events in men and women with different glycemic status.Few studies have examined specifically the sex difference in atherosclerosis across the glucose tolerance continuum. In the general population, men were reported to have a higher burden of subclinical atherosclerosis such as the carotid or femoral atherosclerotic plaques,Aging is recognized as a key player in the progression of arterial stiffness and imposes distinctive influence on different parts of the arterial tree.p value for interaction = .760). After age 60, women were associated with significantly higher ba‐PWV values in participants with NGR. The sex difference was attenuated in participants with prediabetes and was diminished in participants with diabetes. In this age group, the p value for interaction between sex and glycemic status was marginally significant (.068), suggesting that there might be differences regarding sex disparities among participants with varied glycemic status. Previous studies have demonstrated the potential mechanisms for the augmentation of the age‐related increase in arterial stiffness in women and this could probably in part be associated with estrogen deficiency whereas at least 6 years after menopause were needed to achieve the significant risk of higher ba‐PWV.After multivariable adjustment, lower levels of ba‐PWV were always found in women vs men regardless of glycemic status in participants aged 40–59 years , which is regarded as the gold standard to assess arterial stiffness. However, ba‐PWV is less operator dependent than cf‐PWV and appropriate for large‐scale epidemiological studies. Besides, ba‐PWV correlates well with cf‐PWV.In conclusion, findings from the current study unraveled that sex differences in arterial stiffness changed with aging among different glycemic status. Women might have lower risks of arterial stiffness than men regardless of glycemic status when they are with age 40–59 years. After age 60, women might have higher risks of arterial stiffness than men when glucose levels are normal but sex differences were attenuated when glucose levels are increased. The sex‐specific risk in atherosclerosis with glucose metabolism and aging warrants further investigation.Xiaoyun Zhang and Qianqian Yang made substantial contributions to the conceptualization, formal analysis, methodology, and original draft writing of the work. Ruizhi Zheng, Zhiyun Zhao, Mian Li, Tiange Wang, Min Xu, Jieli Lu, Shuangyuan Wang, and Hong Lin made substantial contributions to the data curation, investigation, review, and revision of the original work. Weiqing Wang, Guang Ning, and Yufang Bi made substantial contributions to the funding acquisition, investigation, resources, supervision, review, and revision of the original work. Yu Xu and Yuhong Chen made substantial contributions to the conceptualization, data curation, methodology, validation, review, and revision of the original work. All authors critically revised the manuscript, approved the final version of the manuscript, and agreed to be held responsible for all aspects of the work.The authors declare no conflicts of interest.


Hyperprogressive disease (HPD) is a novel pattern of the treatment course after immune checkpoint inhibitor (ICI) therapy in patients with non‐small cell lung cancer (NSCLC). This study aimed to investigate the clinical characteristics, outcomes, and associated factors of HPD using a semiautomatic volume measurement.This retrospective study enrolled patients with recurrent and/or metastatic NSCLC treated with ICIs between January 2015 and August 2019 at eight tertiary centers in Korea. HPD was defined according to the tumor growth kinetics and time to treatment failure. Tumor volume was measured using a semiautomatic software.p‐value <0.001). ICI‐OS did not significantly differ between patients with HPDv and those without HPDv . PD‐L1 expression lower than 50%, more than three metastatic sites, neutrophil‐to‐lymphocyte ratio equal to or higher than 3.3, and hemoglobin level lower than 10 were found to be associated with HPDv.A total of 219 NSCLC patients with 35 HPD by volumetric measurement (HPDv) (15.9%) were enrolled. The median duration of overall survival (OS) and OS after ICI treatment (ICI‐OS) were 34.5 and 18.4 months, respectively. HPDv patients had significantly worse progression‐free survival (PFS) than progressive disease patients without HPDv (1.16 vs. 1.82 months, There is no standardized definition of HPD. However, defining HPD in NSCLC patients treated with ICI using a semiautomatic volume measurement software is feasible. Our study evaluated hyperprogression in non‐small cell lung cancer patients treated with immune‐checkpoint inhibitor using a semiautomatic volume measurement. Patients experiencing hyperprogression by volumetric measurement had significantly worse progression‐free survival and overall survival after the initiation of immune‐checkpoint inhibitor than patients without it. Since the introduction of immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein 1 (PD‐1) and its ligand (PD‐L1) pathway, the treatment landscape for patients with lung cancer has dramatically changed. PD‐1 is an immune checkpoint receptor that is expressed in activated immune cells such as T cells, B cells, macrophages, and natural killer cells, while PD‐L1 is overexpressed in tumor cells and promotes immune escape.There has been increasing interest in this pattern of non‐small cell lung cancer (NSCLC). A study reported an incidence rate of 13.8% for HPD in patients with NSCLC receiving ICI treatment.Given the unexpected rapid progression of tumors in response to ICI treatment, HPD may relate to poor progression‐free survival (PFS) and overall survival (OS).Therefore, the present study aimed to evaluate the clinical characteristics and outcomes of HPD in patients with NSCLC treated with ICI using semiautomatic volumetric measurements.In this retrospective study, we reviewed the electronic medical records of 391 patients diagnosed with advanced‐stage NSCLC treated with humanized, high‐affinity, selective PD‐1/PD‐L1 inhibitors  at eight tertiary referral centers in South Korea between January 2015 and August 2019. Of them, 172 patients with no available CT scans before or after the treatment and had no targetable lesions according to RECIST version 1.1The following data were collected: age, sex, smoking history, Eastern Cooperative Oncology Group performance status, previous history of surgery or radiation therapy, tumor pathology, PD‐L1 expression status, sites of metastasis, previous chemotherapy, type of ICI, best overall response to ICI, NLR (by dividing neutrophil count by lymphocyte count), platelet‐to‐lymphocyte ratio , hemoglobin (Hb) level, albumin level, and lactate dehydrogenase level.This study was reviewed and approved by the Institutional Review Board of Kosin University Gospel Hospital . The study was conducted following the Declaration of Helsinki. All procedures were performed in accordance with the relevant guidelines and regulations.Three radiologists  reviewed three sequential CT images: baseline (within 1 month before ICI treatment), before baseline (within 1–2 months before baseline), and after baseline (1–2 months after ICI treatment). One target lesion was included, whereas nontarget and new metastatic lesions were excluded. The lesions were assessed on axial images using a mediastinal window . HPD was measured using two different methods: HPD by diametric measurement using the longest diameter of the tumor (HPDd) and semi‐automated volumetric assessment (HPDv).base, Dbefore, and Dafter indicate the tumor diameter at baseline, before baseline, and after ICI, respectively. The measured diameters of the target lesions were extrapolated to spherical volumes using the following formula: 4/3πr3, where r represents half the measured diameter of the tumor. This approach to measure tumor volume was used to estimate HPDd.Figure base, Vbefore, and Vafter indicate the tumor volume at baseline, before baseline, and after ICI, respectively. If the automatically drawn area included incorrect regions such as an adjacent normal organ, atelectasis, or pleural effusion, the radiologist corrected the outline.Volumetric measurements of the tumor were performed using a commercially available semiautomatic software package . Based on the longest diameter, the system automatically measured the orthogonal diameter of the tumor . Similarly, TGKpost was defined as the difference in the volume of target lesions per unit of time between after baseline and baseline: TGKpost = (Vafter−Vbase/Tafter−Tbase). The TGK ratio was defined as TGKpost/TGKpre. HPD was defined asAmong several methods for defining HPD, we adopted the TGK method introduced by Kim et al.,Complete response (CR), partial response (PR), and stable disease (SD) were defined based on RECIST version 1.1. Objective response rate (ORR) was defined as the percentage of patients who experienced CR or PR after ICI treatment, while disease control rate (DCR) was defined as the ratio of patients who experienced CR, PR, and SD after ICI treatment. PFS was used to measure the outcomes related to discontinuation of ICI treatment and defined as the period from the date of ICI initiation to the date of tumor progression or the date of death from any cause. OS was defined as the period from either the date of diagnosis or the start of cancer treatment to the date of death from any cause. OS after ICI treatment (ICI‐OS) was defined as the period from the date of ICI treatment initiation to the date of death from any cause. Patients who were still on treatment were censored at the time of data collection. In addition, TTF was introduced to define the period from the date of ICI treatment initiation to the date of study withdrawal due to patient refusal, adverse events, follow‐up loss, disease progression, or death.t‐test or Mann–Whitney U test for variables with non‐normal distribution. Before an analysis considering several covariables, we calculated the correlations of OS, ICI‐OS, and PFS with both TGK ratio by volumetric measurement (TGKv) and diametric measurement (TGKd). Survival analysis was evaluated using the Kaplan–Meier method, and the differences between the two groups were assessed using the log‐rank test. A logistic regression model was used to evaluate the odds ratio (OR) and 95% confidence interval (CI) of HPD and non‐HPD. Variables with a p‐value of <0.1 in the univariable model were included in the multivariate model. A p value of <0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 25.0 (SPSS Inc.).The patients were divided into two groups according to the status of HPD (HPDv or HPDd). The differences between the groups (HPD or non‐HPD) were analyzed using a chi‐square test or the Fisher's exact test for categorical variables and the Student's The baseline characteristics of the study participants according to the status of HPDv are shown in Table The median duration of ICI in all study participants was 3.1 months . The ORR and DCR in all patients were 21.9% and 55.7%, respectively. The best overall responses were PR in 48 patients (21.9%), SD in 74 patients (33.8%), and PD in 97 patients (44.3%). Of all study participants, 35 (15.9%) were classified as having HPDv, while 39 (17.8%) were classified as having HPDd.The correlations according to the HPD status are shown in Figure p < 0.001)   Figure . The med) Figure .p < 0.001, Figure p‐value = 0.035, Figure p = 0.105, Figure p = 0.528, Figure In terms of PFS, PD patients with HPDv were found to have poor outcomes than those without HPDv  CT images were selected, which may have led to selection bias or misclassification. The results of a multicenter hospital‐based investigation in the current study may mitigate this limitation. Second, the median follow‐up period (defined as time from ICI to death or last follow‐up) was only 6.1 months, which is relatively short for evaluating the effects of ICI treatment on survival. However, given that the current study aimed to investigate HPD that appears shortly after treatment with ICI and the RECIST working group recommended at least 4 weeks to evaluate the tumor response after treatment,In conclusion, in this study, we identified 15.1% of HPD patients with NSCLC treated with ICIs. HPD was measured using the semiautomatic volumetric method, which may be feasible in clinical practice. However, its generalizability and applicability may merit further consideration. PFS was shorter in patients with HPD than in those without HPD. Multivariable analysis showed that low PD‐L1 expression rate, high NLR, more than three metastatic sites, and low Hb levels were associated with the development of HPD. Future studies are needed to validate our results and develop practical criteria for identifying HPD after treatment with ICIs.The authors declare that they have no competing interests.Table S1 Supporting InformationClick here for additional data file.


The hands are a common predilection site of atopic dermatitis (AD). Dupilumab is licensed for the treatment of AD but not for chronic hand eczema (CHE), while CHE is challenging to treat.To evaluate the long‐term effect of dupilumab on hand eczema (HE) in patients with AD from the BioDay Registry.A prospective observational study of adult patients with HE, treated for AD with dupilumab. Patients with a HE severity of at least moderate at baseline were considered for analysis. Patients with other concomitantly systemic immunosuppressive treatments were excluded. Clinical effectiveness on HE severity, using the Hand Eczema Severity Index (HECSI) and photographic guide, and health‐related quality of life, using the Quality of Life in Hand Eczema Questionnaire (QOLHEQ), were evaluated.A total of 72 patients were included. HECSI‐75 was achieved by 54/62 patients (87.1%) and HECSI‐90 by 39/72 (62.9%) at 52 weeks. Based on the photographic guide, 56/62 patients (90.3%) achieved the endpoint of ‘clear’ or ‘almost clear’. Mean QOLHEQ reduction was −63.5% . There was no difference in response between HE subtypes.The results from this study hold promise for dupilumab to be a suitable treatment option for isolated CHE. Dupilumab is licensed for the treatment of atopic dermatitis (AD) but not for chronic hand eczema (CHE), while CHE is challenging to treat.Dupilumab gave long‐term reduction of hand eczema severity and long‐term improvement of hand eczema‐related quality of life in patients with AD and concomitant moderate to very severe hand eczema.The results from this study hold promise for dupilumab to be a suitable treatment option for isolated CHE.Dupilumab for atopic hand eczema. Systemic treatment options are scarce, because alitretinoin is the only approved treatment for all types of HE while it is primarily effective in hyperkeratotic HE.Dupilumab is a human monoclonal antibody, binding to the interleukin (IL)‐4 receptor α chain, inhibiting IL‐4 and IL‐13, both type 2 inflammatory cytokines that mediate the pathogenesis of AD. Several retrospective studies and case series have been published on the efficacy of dupilumab in patients with HE.22.1This study included patients from the Dutch BioDay Registry at the Department of Dermatology from the University Medical Center Groningen. The BioDay Registry is a prospective observational cohort study in which patients with moderate to severe AD are enrolled who receive novel systemic therapies for their AD in daily practice.2.2The study population consisted of adult patients with moderate to severe AD (≥18 years) with concomitant HE, who received treatment with dupilumab subcutaneously . Patients were included between October 2017 and June 2021. Inclusion criteria consisted of a diagnosis of AD according to the UK Working Party criteria,2.3The general course of disease severity over 52 weeks is graphically presented as the mean (percentage) change of the Hand Eczema Severity Index (HECSI). The HECSI is an instrument used to rate the severity of six efflorescences of HE  and the extent of the lesions on five distinct areas of the hand by using standard scales.Besides sociodemographic variables, the following variables were collected at baseline: smoking pack‐years, duration of HE, atopic comorbidities, Investigator Global Assessment for AD severity, occupation (including high risk of developing HE wet work),2.4t‐test were used to compare percentages and means in independent groups, respectively. Calculations were performed with IBM SPSS Statistics for Windows, version 23.0 (IBM Corp.). A P‐value of <.05 was regarded as statistically significant.All continuous outcome measures in the intention‐to‐treat population were analysed using a mixed‐effect model with repeated measures. HECSI values are presented as both the mean percentage change with 95% CIs at the various time points compared with baseline, and the percentage of patients reaching a minimum of 50%, 75%, and 90% improvement on their HECSI scores  at the various time points  compared with baseline. QOLHEQ values are presented as the mean percentage change with 95% CIs at the various time points compared with baseline for both the QOLHEQ total scores and the QOLHEQ subscale scores. Patients with missing QOLHEQ data at baseline were excluded from the analysis. Other missing QOLHEQ data, which were all missing ‘completely at random’, were imputed using multiple imputation. Fisher exact test and the independent Student 33.1In total, 72 patients were included in this study. Of these 72 patients, 48 (66.7%) were male. The mean age of the study population was 45.2 years (standard deviation [SD] 13.0). Only two clinical subtypes of HE were observed in our population: chronic fissured HE and recurrent vesicular HE. The majority (72.2%) of patients had a chronic fissured HE. Patient demographics and characteristics at baseline are presented in Table 3.29/L) was also commonly seen among the patients. At baseline . In three patients, severe conjunctivitis with limbitis was reported. Blood eosinophilia >0.40 × 1009/L was Of the 72 included patients, 62 patients completed 52 weeks of treatment with dupilumab. Among the patients who stopped treatment with dupilumab, four patients dropped out because of side effects, including severe conjunctivitis with limbitis (n = 3) and the occurrence of multiple verrucae filiformes (n = 1). Four patients dropped out because of ineffectiveness. Of the remaining two patients, one patient was lost to follow‐up and the other patient stopped treatment with dupilumab on patient's own initiative.In three out of the four patients who dropped out after 28 weeks due to ineffectiveness, improvement of HE was observed compared with baseline . Both AD and HE symptoms in the remaining patients got worse .3.3HECSI‐75 was met in 54/62 patients (87.1%) at 52 weeks Figure . FurtherP = .43). The proportion of patients reaching HECSI‐75 also did not differ between patients with or without concomitant ICD, which was 84.6% and 87.8%, respectively (P = .67).The proportion of patients achieving HECSI‐75 did not significantly differ between morphological subtypes; in patients with chronic fissured HE, 38 of the 45 (84.4%) achieved HECSI‐75 at 52 weeks, compared with 16 of the 17 patients (94.1%) with a recurrent vesicular HE subtype  achieved ‘clear’ or ‘almost clear’ at the 52 weeks assessment. Furthermore, 42/62 patients (67.7%) achieved ‘clear’ or ‘almost clear’ and at least two steps improvement on the photographic guide at 52 weeks . The QOLHEQ percentage change for all time points is presented in Figure After 52 weeks, a mean decrease of 63.5% (95% CI −71.1 to −55.9) or of 38.1 points (SD 23.3) was observed for the QOLHEQ. The mean total score of the QOLHEQ at 52 weeks was 18.2 points (SD 20.3). A total of 44/57 patients (77.2%) achieved the MIC of 22 points' reduction at 52 weeks.P < .05). Percentage change per subscale for all time points is graphically shown in Figure At 52 weeks, a mean decrease of 64.5% (95% CI −72.7 to −56.2) was observed for the domain ‘Symptoms’. For the domain ‘Emotions’ a mean decrease of –75.0% (95% CI −83.4 to −66.6) was found. For the domain ‘Functioning’ a mean decrease of –77.1% (95% CI −85.5 to −68.7) was observed. Lastly, the least mean decrease was found for the domain ‘Treatment and prevention’: −59.7% (95% CI −70.1 to −49.4%). When comparing the four subscales, the mean percentage change at 52 weeks differed significantly between each subscale . For the photographic guide, we also observed a higher proportion of patients achieving ‘clear’ or ‘almost clear’ after 52 weeks (90.3% vs 76.6% after 16 weeks) at the 52 weeks' assessment.In this study in which patients had moderate to very severe HE, a mean moderate impairment in HE‐specific HRQoL was found at baseline. This finding is in line with a recent questionnaire‐based study using the QOLHEQ to study HE‐specific HRQoL among patients with vesicular HE.IL4R was also highly upregulated in lesional HE skin compared with healthy control skin.The results of this study show that dupilumab might be an effective treatment for CHE in patients with AD. This positive effect on HE has also been shown in several case reports in isolated, nonatopic HE, including vesicular HE, hyperkeratotic HE, allergic contact dermatitis, and ICD.The main limitation in this study is the use of concomitant topical corticosteroids, up to class IV steroids including clobetasol ointment. This might have affected the observed effect of dupilumab. Furthermore, allergic factors might have influenced the severity score, because 14 of the 42 patients that performed paid work at baseline were working in a high‐risk occupation for HE. In multiple patients (n = 20), patch testing could not be performed due to the severity of their AD. It is not known whether these patients had any (relevant) contact sensitizations.In conclusion, this study showed that dupilumab might be an effective treatment for moderate to severe CHE in patients with AD, with long‐term clinical effectiveness and great improvement of HE‐specific HRQoL. The efficacy of dupilumab on isolated HE is currently being investigated in phase 2, placebo‐controlled clinical trials.Dr M.L.A.S. is an advisor, consultant, speaker, and/or investigator for AbbVie, Pfizer, LEO Pharma, Regeneron, Sanofi Genzyme, Eli Lilly, and Galderma. She has received grants from Regeneron, Sanofi Genzyme, Novartis, and Pfizer. M.S.B‐W. is a principal investigator, consultant, and advisory board member for AbbVie, Regeneron Pharmaceuticals, Inc, Sanofi Genzyme, Leo Pharma, and Pfizer; and is an advisory board member and consultant for Arena, ASLAN, Galderma, and Eli Lilly. A.N.V. and G.L.E.R. have no conflicts of interest to declare.This study did not fall under the scope of the Medical Research Involving Human Subjects Act, which was confirmed by the local Medical Ethical Review Board of the University Medical Center Groningen (METc 2018/344).Angelique N. Voorberg: Conceptualization ; data curation ; formal analysis (lead); investigation ; methodology ; project administration ; resources ; validation ; visualization ; writing – original draft (lead). Geertruida L. E. Romeijn: Data curation ; investigation ; project administration ; resources ; validation ; visualization ; writing – review and editing (supporting). Marjolein S. de Bruin‐Weller: Data curation ; funding acquisition ; investigation ; methodology ; project administration ; resources ; supervision (supporting); validation ; visualization ; writing – review and editing (supporting). Marie L. A. Schuttelaar: Conceptualization ; data curation ; funding acquisition ; investigation ; methodology ; project administration ; resources ; supervision (lead); validation ; visualization ; writing – original draft (supporting); writing – review and editing (lead).


The 2020 National Comprehensive Cancer Network guidelines recommend neoadjuvant FOLFIRINOX or neoadjuvant gemcitabine plus nab‐paclitaxel (G‐nP) for borderline resectable/locally advanced pancreatic ductal adenocarcinoma (BR/LA PDAC).The purpose of our study was to compare treatment outcomes, toxicity profiles, costs, and quality‐of‐life measures between these two treatments to further inform clinical decision‐making.We developed a decision‐analytic mathematical model to compare the total cost and health outcomes of neoadjuvant FOLFIRINOX against G‐nP over 12 years. The model inputs were estimated using clinical trial data and published literature. The primary endpoint was incremental cost‐effectiveness ratios (ICERs) with a willingness‐to‐pay threshold of $100 000 per quality‐adjusted‐life‐year (QALY). Secondary endpoints included overall (OS) and progression‐free survival (PFS), total cost of care, QALYs, PDAC resection rate, and monthly treatment‐related adverse events (TRAE) costs (USD). FOLFIRINOX was the cost‐effective strategy, with an ICER of $60856.47 per QALY when compared to G‐nP. G‐nP had an ICER of $44639.71 per QALY when compared to natural history. For clinical outcomes, more patients underwent an “R0” resection with FOLFIRINOX compared to G‐nP (84.9 vs. 81.0%), but FOLFIRINOX had higher TRAE costs than G‐nP ($10905.19 vs. $4894.11). A one‐way sensitivity analysis found that the ICER of FOLFIRINOX exceeded the threshold when TRAE costs were higher or PDAC recurrence rates were lower.Our modeling analysis suggests that FOLFIRNOX is the cost‐effective treatment compared to G‐nP for BR/LA PDAC despite having a higher cost of total care due to TRAE costs. Trial data with sufficient follow‐up are needed to confirm our findings. Several clinical trials have evaluated the effectiveness of therapies such as FOLFIRINOX and gemcitabine plus nab‐paclitaxel (G‐nP) for PDAC patients.In a previous study, we compared the effectiveness and cost‐effectiveness of neoadjuvant versus adjuvant therapies for BR/LA PDAC.To date, the efficacy and cost‐effectiveness of neoadjuvant FOLFIRINOX and G‐nP in BR/LA PDAC have not been compared in a randomized, prospective clinical trial. Because of this lack of clinical trial data, we developed a decision‐analytic model incorporating the best available published data to simulate a hypothetical clinical trial between FOLFIRNOX and G‐nP. The aim of this study is to compare these two neoadjuvant strategies in terms of cost‐effectiveness for BR/LA PDAC treatment.22.1We developed a Markov model, using Python 3.7, to follow and track hypothetical cohorts of BR/LA PDAC patients undergoing neoadjuvant therapy prior to resection through a simulated trial Figure . The modFurthermore, we included a hypothetical scenario analyses to our model. This scenario explored the base analysis in a center of excellence setting. For this scenario, the dropout rates, PDAC recurrence rates, and R0 rates were adjusted to align with published literature from centers of excellence.2.2We assessed the incremental cost‐effectiveness of G‐nP versus FOLFIRINOX from the perspective of the U.S. healthcare system. The primary endpoint was the optimal neoadjuvant treatment, defined as the highest QALYs with an incremental cost‐effectiveness ratio (ICER) below a willingness‐to‐pay threshold (WTP) of $100 000 (2021 USD). Secondary endpoints included overall survival (OS), progression‐free survival (PFS), total cost of care, R0 status, PDAC resection rate, and monthly treatment‐related adverse events (TRAE) costs. Costs and QALYs were discounted at 3% per year, and a half‐cycle correction was applied to QALYs and unadjusted life‐years.2.3We estimated the transition probabilities of our Markov model from published literature and clinical trial data Tables  and 2. W2.4,Costs were calculated from a payer perspective and no indirect costs were included in the analysis. The costs of chemotherapy drugs were based on Centers for Medicare and Medicaid (CMS) 2020 average sale price. Capecitabine and radiation therapy costs for the neoadjuvant arms were estimated from published literature.2.5We performed one‐way deterministic sensitivity analyses, which involved changing each parameter individually across a plausible range of values, to determine the robustness of our base‐case results Tables  and 2. P33.1Neoadjuvant FOLFIRINOX treatment was the optimal strategy in the base‐case model with a total cost of $240 877 and 2.99 QALYs. Furthermore, patients in this arm had a resection rate of 67.32%, with 84.90% of those undergoing surgery achieving an R0 resection. Patients in the FOLFIRINOX arm had TRAE costs of $10 905 per month. FOLFIRINOX yielded an ICER of $80 862 compared to neoadjuvant G‐nP treatment, which was below the $100 000 WTP threshold. G‐nP had a total cost of $205 161, 2.54 QALYs, and an ICER of $49 196 compared to natural history Figure . The G‐n3.2In the center‐of‐excellence scenario, FOLFIRINOX remained the optimal strategy. In this scenario, FOLFIRINOX had an ICER of $73 124, total cost of $251 718, and 3.42 QALYs. The G‐nP strategy resulted in an ICER of $45 795, total cost of $217 753, 2.96 QALYs. Both FOLFIRINOX and G‐nP had higher median, 5‐year, and 10‐year OS and PFS in this scenario compared to the base case, but a smaller percentage of the cohort underwent resection compared to the base case scenario , and that overall survival was longer in this scenario compared to the FOLFIRINOX arm in the base‐case analysis. This result may be explained by a sicker patient population at the center‐of‐excellence, but improved surgical outcomes compared to patients treated in centers without such a designation.To the best of our knowledge, this is the first study to directly compare the cost‐effectiveness of neoadjuvant FOLFIRINOX and neoadjuvant G‐nP for BR/LA PDAC patients. Our previous study compared the cost‐effectiveness of neoadjuvant FOLFIRINOX to adjuvant therapies, but did not include a comparator neoadjuvant treatment arm. Additionally, this study is the first to analyze the cost‐effectiveness of neoadjuvant BR/LA PDAC treatment strategies in a center‐of‐excellence setting, albeit through a “thought experiment.” Simulation modeling analysis enables comparisons that would be difficult or impossible to evaluate in a clinical trial. These outcomes include 10‐year overall survival and progression‐free survival, QALYs, and costs. In the future, additional clinical trial data will be incorporated into the model to update the analyses to the latest PDAC findings.Our results must be interpreted with study limitations in mind. Due to the scarcity of prospective, randomized clinical trial data for patients with BR/LA PDAC treated with neoadjuvant chemotherapies, model assumptions and inputs relied on preliminary clinical trial data and retrospective published data. Using these data sources may have incorporated bias in our analysis, including referral bias, and/or selection bias of healthier patients compared to the average BR/LA PDAC patient. In instances where BR/LA data was limited, we consistently used the more conservative estimates from metastatic patient cohorts, biasing our results against neoadjuvant FOLFIRINOX or the null. Additionally, FOLFIRINOX trials have a selection bias, often enrolling healthier patients compared to G‐nP trials. We attempted to reconcile this selection bias by calibrating to a more conservative estimate of the overall survival and progression‐free survival confidence interval in FOLFIRINOX trials so that G‐nP and FOLFIRINOX could be compared with minimal bias. When there was limited cost and utility data for the neoadjuvant setting, we used published data from metastatic PDAC cohorts to derive conservative estimations. Lastly, we also assumed uniform comorbidity status across patients assigned to all treatment arms in the model, which is likely an oversimplification of real‐life clinical practice. Given high rates of treatment related toxicity, comorbidities and patient performance status play an important role in individualizing a neoadjuvant treatment plan for patients with BR/LA PDAC. Because of these data limitations, we assuaged concerns of uncertainty about our model by verifying our model outputs through rigorous sensitivity analyses and aligning our outcomes with published clinical data.5In summary, our analysis found that FOLFIRNOX is the optimal neoadjuvant chemotherapy regimen compared to gemcitabine plus nab‐paclitaxel for BR/LA PDAC, despite having a higher cost of total care due to TRAE costs. This comparison of neoadjuvant PDAC chemotherapy strategies gives new insight into the selection of treatment regimens for BR/LA patients. Data from prospective randomized clinical trials with sufficient follow‐up are needed to confirm our findings.The authors declare no conflict of interest.Study concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, statistical analysis, technical support, M.A.I.; Study concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, statistical analysis, technical support, B.L.; Acquisition of data, analysis and interpretation of data, drafting of the manuscript, Y.P.; Acquisition of data, analysis and interpretation of data, critical revision of manuscript for important intellectual content, J.P.; Acquisition of data, analysis and interpretation of data, critical revision of manuscript for important intellectual content, F.L.; Analysis and interpretation of data, critical revision of the manuscript for important intellectual content, technical support, S.B.; Analysis and interpretation of data, revision of the manuscript for important intellectual content, F.K.; Critical revision of the manuscript for important intellectual content, analysis and interpretation of data, G.A.M.; Critical revision of the manuscript for important intellectual content, analysis and interpretation of data, C.Y.K.; Study concept and design, critical revision of the manuscript for important intellectual content, obtained funding, technical and material support, study supervision, C.H.This study was approved by the Institutional Review Board at Columbia University Medical Center, New York.Appendix S1: Supporting InformationClick here for additional data file.


GALT sequencing was performed if CG could not be excluded following review of medical records. 328 infants with TGAL 1.0–1.49 mmol/L on NBS were identified, of whom 35 had ICD‐10 codes relevant to CG including vomiting, poor feeding, weight loss, failure to thrive, jaundice, hepatitis, Escherichia coli urinary tract infection, sepsis, intracranial hypertension and death. CG could be excluded in 34/35, due to documentation of clinical improvement with continued dietary galactose intake, or a clear alternative aetiology. GALT sequencing in the remaining individual confirmed Duarte‐variant galactosaemia (DG). In conclusion, undiagnosed CG appears to be rare in those with TGAL 1.0–1.49 mmol/L on NBS; however, our recent experience with missed cases is nevertheless concerning. Further work is required to establish the optimum screening strategy, to maximize the early detection of CG without excess false‐positives.Newborn screening (NBS) for classical galactosaemia (CG) facilitates early diagnosis and treatment to prevent life‐threatening complications, but remains controversial, and screening protocols vary widely between programmes. False‐negatives associated with first‐tier screening of total galactose metabolites (TGAL) are infrequently reported; however, newborns with TGAL below the screening threshold have not been systematically studied. Following the diagnosis of CG in two siblings missed by NBS, a retrospective cohort study of infants with TGAL just below the cut‐off (1.5 mmol/L blood) was conducted. Children born in New Zealand (NZ) from 2011 to 2019, with TGAL 1.0–1.49 mmol/L on NBS were identified from the national metabolic screening programme (NMSP) database, and clinical coding data and medical records were reviewed.  False‐negative newborn screening results for classical galactosaemia are reported in association with primary measurement of total galactose metabolites; however, such patients are likely to present clinically.1GALT variants (OMIM#230400), at an incidence of ~1/50 000 live‐births in Caucasians.Classical galactosaemia (CG) caused by galactose‐1‐phosphate uridyltransferase (GALT) deficiency occurs secondary to bi‐allelic Newborn screening approaches for CG vary widely between screening programmes.The NMSP detected 10 patients with CG from 2000 to 2008, and all had significantly elevated TGAL (range 2.24–16.6 mmol/L). The TGAL cut‐off during this period was 0.8 mmol/L, which was associated with a positive predictive value (PPV) well below 10%. Therefore, the cut‐off was increased to 1.5 mmol/L, but remained 0.8 mmol/L for infants in neonatal intensive care, due to potentially inadequate milk intake in this population. In 2019, two siblings with TGAL <1.5 mmol/L were missed by NBS then subsequently diagnosed with CG after presenting clinically with liver failure, raising concern that other CG patients had been missed by the NMSP. A retrospective cohort study of infants with TGAL 1.0–1.49 mmol/L was conducted, to estimate the incidence of false‐negatives associated with a cut‐off of 1.5 mmol/L. It was hypothesized that missed cases would become progressively unwell unless CG was diagnosed clinically, liver‐transplantation was performed, or low‐galactose feeds were started as treatment for an alternative disorder mimicking CG.2In NZ all infants are assigned a unique national health identifier (NHI) at birth, which is recorded on the NBS sample and all subsequent healthcare episodes. All NBS results are recorded in the NMSP database. Participation in NBS in NZ is voluntary, with high uptake (>99.5% births). The vast majority of secondary and tertiary healthcare episodes for children in NZ occur at public hospitals, and each healthcare episode is coded according to the International Classification of Disease‐10 (ICD‐10). Therefore significant childhood illnesses requiring hospital care are reliably captured, and ICD‐10 coding data can be readily matched to NBS results as previously described.Escherichia Coli infection, cataract, renal tubulopathy, developmental delay, intracranial hypertension, encephalopathy, seizures, coma and death. If relevant coding criteria were present, review of clinical records was conducted to determine if the clinical course was potentially consistent with CG. Documented resolution of symptoms or normal growth and development, in the presence of ongoing dietary galactose intake, was considered to be inconsistent with CG.TGAL was measured in whole‐blood using a fluorescent galactose oxidase method. NHI numbers were obtained for all NBS samples from July 2011 to December 2019 with TGAL 1.0–1.49 mmol/L. ICD‐10 codes linked to these NHI were obtained from the Ministry of Health (MOH). Coding criteria considered consistent with a missed diagnosis of CG included: galactosaemia, poor feeding, poor growth, weight loss, vomiting, lactose intolerance, neonatal jaundice, liver disease/failure, hepatomegaly, cholestasis, hepatitis, coagulopathy, liver transplantation, sepsis, GALT sequencing was performed on DNA extracted from the DBS sample. The coding regions of the GALT gene, flanking ±20 base‐pairs (encompassing the splice sites) and the 3′ end of the promoter region were sequenced by Sanger‐based sequencing. Analysis of sequence data was performed using Variant Reporter Software v2.If clinical records were potentially consistent with CG, GALT sequencing results for known CG patients missed by NBS in the study period are also described.Data were also collected regarding total NBS samples during the study period, and those with TGAL >1.5 mmol/L including true‐positives and false‐positives. A positive screen was defined as an NBS result triggering additional actions including request for repeat DBS samples or clinical referral. The clinical presentation, NBS data and 3E. Coli urinary tract infection (2), coma (2), seizures (1), developmental delay (1), hepatitis (1), intracranial hypertension (1) and death (1). One individual developed intracranial hypertension, and died at 3 years of age, and was found to have a large posterior fossa tumour at post‐mortem. Of the other cases, all but one were noted to have clinical improvement or normal growth and development, with evidence of ongoing regular dietary lactose intake.503 938 infants underwent NBS in the study period. Of these, 27 positive screens for CG were detected. CG was confirmed in only 2/27 (PPV 7.4%). No patients were diagnosed with galactokinase, epimerase or mutarotase deficiency. 328 infants were identified with TGAL 1.0–1.49 mmol/L  variant and the Duarte (D2) variant c.‐119_‐116delGTCA, consistent with DG. This result was not considered relevant to the clinical presentation.One individual was identified for whom CG could not be excluded following review of clinical records. This individual presented to hospital at 6 days of age with 15% weight loss since birth, jaundice, and apnoea. Formula feeds were given by nasogastric tube during this admission. However note was made of a change of formula prior to discharge; unfortunately the new formula preparation was not documented. Subsequent records at 3 years of age documented hearing loss, hypotonia, global developmental delay and normal liver enzymes, but dietary history was not recorded. During the study period, two individuals presented clinically with CG, and were missed by NBS. These siblings were born at term, and were breastfed prior to NBS at 2 days of age Table . TGAL waPatient A was fed with standard infant formula containing lactose prior to presentation with liver failure at 4 weeks of age. TGAL was 1.28 mmol/L at 35 days of age, with galactose 0.9 mmol/L, galactose‐1‐phosphate 0.62 mmol/L and GALT activity 43% of the mean normal value. He had received intravenous fluids and red cell transfusion in the preceding 4 days, thus explaining the normal GALT activity. Extensively hydrolysed formula feeds were started for management of cholestasis, due to their high medium‐chain triglyceride (MCT) content; however, this preparation was also low in galactose. He made a full clinical recovery with no evidence of liver disease or cataract at 14 months of age.GALT variant c.563A > G, p.(Gln188Arg), which was then detected in Patient A by Sanger sequencing.Patient B was born 13 months after Patient A, and presented with liver failure at 10 days of age. TGAL was 9.2 mmol/L at 17 days with GALT activity <10% of the mean normal value. Whole exome sequencing in Patient B identified the homozygous 4GALT alleles, or when galactose intake prior to sampling is significantly limited.Population NBS for CG has been feasible since 1964,In this cohort of 328 newborns with TGAL in the range of 1.0–1.49 mmol/L, no missed cases of CG were identified, and therefore the risk of undiagnosed CG in this group appears to be low. It is unlikely but plausible that further individuals with CG in this cohort escaped detection by this review; for example, if low‐galactose feeds were initiated in primary care for another indication, in a newborn with very mild symptoms not requiring hospital care.GALT sequencing was only performed in one individual, who was found to have DG. It is likely that many others also had DG, or were heterozygous CG carriers. However neither DG or CG‐heterozygosity is associated with short‐ or long‐term medical complications.GALK1 or GALM may also have been informative; however, detection of galactokinase or mutarotase deficiency was not the purpose of this study.The primary limitation of this study is that Newborns with TGAL just below the screening threshold of 1.5 mmol/L were selected for inclusion, due to the presumed higher risk of false‐negatives in this group. However it is noteworthy that the two index cases missed on NBS had TGAL well below the cut‐off and were not captured by the study criteria. It is also noteworthy that while both siblings were homozygous for the common pathogenic c.563A > G, p.(Gln188Arg) variant associated with severe disease,The prevalence of CG diagnosed on NBS in the study period was unexpectedly low. 19 individuals were diagnosed with CG in NZ over 20 years (incidence 1/63 000 live‐births), consistent with the expected population incidence.Beyond Australasia, the GALT assay is more commonly used as the primary screening test, and false‐negatives using this approach appear to be rare.Finally, the authors acknowledge that large‐scale prospective studies confirming the benefit of NBS for CG are lacking.5Clinically presenting patients with CG, with TGAL well below cut‐off, have been missed by the NMSP and other screening programmes that utilize a primary TGAL method. In this retrospective cohort study of newborns with TGAL 1.0–1.49 mmol/L on NBS, no additional missed cases of CG were identified. This suggests that false‐negatives associated with TGAL in this range are not common, and such patients are likely to present clinically. The occurrence of clinically presenting false‐negative cases with TGAL between 0.5–1.0 mmol/L is nevertheless concerning, and warrants consideration of a significantly lower threshold or alternative screening strategy. NBS for CG may significantly reduce morbidity and mortality in affected infants; however, the optimum screening strategy and thresholds remain to be clarified.Isaac Bernhardt declares no conflict of interest. Emma Glamuzina declares no conflict of interest. Bryony Ryder declares no conflict of interest. Detlef Knoll declares no conflict of interest. Natasha Heather declares no conflict of interest. Mark De Hora declares no conflict of interest. Dianne Webster declares no conflict of interest. Callum Wilson declares no conflict of interest.Participation for Newborn Screening (NBS) in New Zealand is voluntary. Since July 2011, routine informed consent for NBS has included consent for additional uses of the dried blood‐spot sample, for activities including quality control and audit by the National Metabolic Screening Programme, as well as for research approved by an ethics committee and the Ministry of Health. Therefore, only samples obtained subsequent to the implementation of this policy were included. This study was approved by the Health and Disability Ethics Committee. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation  and with the Helsinki Declaration of 1975, as revised in 2000 (5). Written consent for publication of de‐identified information has been obtained from the parent/guardian of Case A and Case B.