To compare samples across different individuals and settings, the conventional practice has involved the normalization of raw AFB1-lysine adduct concentrations  to the total circulating HSA concentration . It is hypothesized that this practice corrects for technical error, between-person variance in HSA synthesis or AFB1 metabolism, and other factors. However, the validity of this hypothesis has been largely unexamined by empirical analysis. The objective of this work was to test the concept that HSA normalization of AFB1-lysine adduct concentrations effectively adjusts for biological and technical variance and improves AFB1 internal dose estimates. Using data from AFB1-lysine and HSA measurements in 763 subjects, in combination with regression and Monte Carlo simulation techniques, we found that HSA accounts for essentially none of the between-person variance in HSA-normalized (R2 = 0.04) or raw AFB1-lysine measurements (R2 = 0.0001), and that HSA normalization of AFB1-lysine levels with empirical HSA values does not reduce measurement error any better than does the use of simulated data . These findings were robust across diverse populations , AFB1 exposures (105 range), HSA assays (dye-binding and immunoassay), and disease states . HSA normalization results in arithmetic transformation with the addition of technical error from the measurement of HSA. Combined with the added analysis time, cost, and sample consumption, these results suggest that it may be prudent to abandon the practice of normalizing adducts to HSA concentration when measuring any HSA adducts—not only AFB1-lys adducts—when using LCMS in serum/plasma.The assessment of aflatoxin B Conventionally, these values are transformed by dividing the measured AFB1-lys concentration by the concentration of total circulating human serum albumin (HSA), resulting in the quantitation of the AFB1-lys adduct relative to the total HSA concentration . This practice is grounded in prior analytical approaches for AFB1-lys adduct quantitation, such as immunoassays [1-lys from serum/plasma digests prior to quantification by liquid chromatography and fluorescence detection [1-lys increases assay sensitivity. However, since quantitated AFB1-lys values are no longer directly representative of the original volume of the sample from which they were derived (due to the precipitation or immunoaffinity purification), the values had to be normalized for assay input with a denominator other than the input volume. Thus, after the measurement of the total HSA concentration using one of the several available analytical approaches, AFB1-lys adduct values were normalized for assay input through their expression relative to the total HSA in the sample. These analytical methods are no longer the state-of-the-art approach for the measurement of AFB1-lys adducts, but the practice of normalization to HSA measured in a separate aliquot of serum (or plasma) has remained as a convention, including in previous reports from our group [Aflatoxin Borldwide . Hepatictabolite  results exposure . Quantitl method  results noassays  or immunetection . In bothur group ,12,13,141-lys adduct levels  to total HSA concentration  is warranted, given the added financial, labor, and sample consumption costs. Two approaches to assess this question could be: (1) to compare the internal dose values yielded by normalized vs. raw AFB1-lys adduct concentrations to measured levels of aflatoxin intake, or (2) to compare the predictive or prognostic value of normalized vs. raw AFB1-lys adduct values with disease incidence or outcomes in which aflatoxin plays an accepted etiological role. In regard to option 1, while our group has reported AFB1-lys adduct concentrations in humans with known levels of AFB1 intake [1-lys analytical methods rather than the current LCMS approach [1-lys values within associations of AFB1 and hepatocellular carcinoma incidence [1-lys adduct levels, such that the total HSA measurement was used for normalization of the assay input. Additionally, while AFB1-lys measurement was conducted in samples from individuals, the association with hepatocellular carcinoma incidence was limited to ecological analysis, limiting the precision of the results.The purpose of this study was to assess whether the conventional practice of normalizing raw AFB1 intake ,15, thesncidence ; however1-lys values determined by LCMS. To do so, we have paired a dataset of AFB1-lys adduct and HSA concentrations from nearly 800 persons with analysis by Monte Carlo simulation and regression, to interrogate whether normalization of AFB1-lys adduct levels to total HSA performs better than chance—independent of HSA assay format, study population, longitudinal changes in aflatoxin exposure, or the presence of hepatobiliary disease. Consistency in inferences between simulated and empirical data would suggest that the standardization of AFB1-lys values to HSA is not necessary and that it only reflects random error that is not associated with biological processes or technical variance.In this report, we used a simulation approach to determine the efficacy of HSA normalization of AFB1-lys (pg/mL) and total HSA (mg/mL) measurements, within normalized AFB1-lys (pg/mg HSA) values. To do so, using data from our previously published cross-sectional study of AFB1 exposure in Guatemalan adults [1-lys normalized to HSA (pg AFB1-lys/mg HSA), was regressed against the components of that calculated value, i.e., raw AFB1-lys (pg/mL) or HSA (mg/mL). In addition to AFB1-lys and total HSA measurements . However, despite the significant linear relationship and in contrast with raw AFB1-lys, total HSA was a very poor predictor of normalized AFB1-lys concentrations  than was total HSA. These results, while expected, demonstrate that HSA levels account for very little of the variance in AFB1 internal dose , bringing into question whether the HSA normalization practice removes biological or technical variance, as proposed.Initial studies compared the relative contribution of variance components derived from AFBn adults , we consurements A–C, thispulation  were inc1-lys and HSA values . As seen in 1-lys concentrations , or whether it was non-informative . To do so, we compared empirical data from the Guatemala cross-sectional study to a simulated data set with the same distribution parameters as the empirical data, but populated with “participants” assigned randomly generated AFBtrations B, and nos  to the elimination of nearly all exposure by 2012 (7%). Thus, using these data , we attempted to replicate our findings from the Guatemalan study. As seen in 1-lys . Since the HSA-normalized AFB1-lys value is, by definition, the quotient of error-prone estimates of raw AFB1-lys and HSA concentrations, this is to be expected. However, while the presence of error is to be expected, the sources and structures of the error can be informative and are worth further investigation. In this study, we sought to test the hypothesis that the normalization of AFB1-lys values are derived from the errors accompanying analytical estimates of both HSA and raw AFB1-lys adduct levels. However, in the case of the regression analysis in 1-lys value—if the dependent variable were also the raw AFB1-lys concentration instead of being the HSA-normalized AFB1-lys value, the mean squared error would be 0 and the R2 would equal 1. Thus, the regression analysis presented in 1-lys adduct estimates. Although the true error is unobservable, it is approximated by the residuals of linear regression analysis: at a given level of the independent variable, the residual is the difference between an empirical observation of the dependent variable and the best-fit regression prediction of the dependent variable. Furthermore, although the residuals shown in 1-lys adduct concentration imprecisely reflects the AFB1 internal dose or AFB1-related disease risk, and that the adjustment made by HSA normalization minimizes error and moves the estimate closer to the unknown true value. Conversely, if the HSA concentration does not adequately account for or is entirely unassociated with error contributed by the confounding variable(s), then the regression residuals document a deleterious addition of error. This would result in a diminution in statistical power, a bias towards or away from the null hypothesis, or both. Clearly, distinguishing which case is more reflective of the truth is critical to the use of the AFB1-lys adduct biomarker going forward. The regression residuals are either informative , are a result of statistical noise, or are a combination of the two. Under an assumption that HSA normalization adds information by systematically accounting for variation in hepatic function or other confounding variables, the regression residuals would reflect the gains in precision from the normalization operation. In other words, HSA normalization adds value if it is true that the raw AFB1-lys to HSA is uninformative and adds error to estimates of exposure or disease risk. On the other hand, if the observed data deviate significantly from simulated distributions, then it can be assumed that biological mechanisms, or some other systematic source of error, account for this deviation and that HSA normalization is—to some degree—informative. One way to test this question is to compare relationships observed empirically in data from human samples with those that arise from randomly simulated values obtained from distributions with parameters equivalent to those in the empirical data. If the observed data are largely identical to those derived from simulated data, it can be deduced that the normalization of AFB1-lys adduct levels, since the residuals of this regression analysis isolate the variance derived from the HSA component of the HSA-normalized AFB1-lys value. However, we must first define the null hypothesis, where there exists no biological relationship between HSA concentrations and HSA-normalization regression residuals. In fact, this null hypothesis is presented by the simulated data sets, since simulated HSA and AFB1-lys values within each of the n = 20,000 “participants” per data set were randomly generated and paired to produce simulated HSA-normalized AFB1-lys values. As seen in Specifically, we focused on the relationship between HSA concentrations and the residuals from the linear regression of raw vs. HSA-normalized AFB1-lys to HSA is biologically valid and effective in reducing measurement error. If low HSA concentration reflects reduced hepatic metabolic capacity or a limited availability of protein substrate for the adduction of AFB1 to HSA, one could hypothesize that in the presence of low circulating HSA levels, raw AFB1-lys adduct concentration would underestimate the AFB1 internal dose. Thus, at constant levels of AFB1 exposure, AFB1-lys adduct concentrations would decrease in some proportion with HSA and normalization could be useful to correct for the underestimation. To support this hypothesis, it would be expected that, in contrast to simulated data, the empirically observed HSA-normalization regression residuals do not deviate greatly from zero at low concentrations of HSA, as this is the range where normalization is proposed to be most effective. Instead, we consistently observed the opposite—empirical residuals at HSA concentrations lower than the normal reference range (35–55 mg/mL) exhibit a monotonic increasing trend with decreasing HSA and demonstrate no inflection point where the hypothesized normalizing effect may occur. Additionally, aside from sample size, the residuals in the empirical data and the simulated data appear nearly identical—indicating that empirically determined HSA is no better than randomly simulated values for the purposes of reducing error in AFB1-lys adduct internal dose estimates. We now compare the null hypothesis above with the alternative hypothesis, formed on the assumption that the normalization of raw AFB1 exposures, we repeated our analyses with raw data from a previously published report in Qidong, People‘s Republic of China [1-lys adduct levels progressively decreased by approximately 80%; our original publication found that this was likely due to shifts in Chinese agro-economic policy [1 exposure contributed significantly to a now 30-year trend of decreasing liver cancer mortality in Qidong [1-lys adducts in this study were measured using the same method as that used in the Guatemalan study , this Qidong ecological study utilized a different HSA assay , featured a different participant population, and exhibited a lower range of aflatoxin internal dose . Despite the differences with the Guatemalan study, we replicated our earlier findings—across four screenings spanning 14 years, HSA was consistently no better of a normalizer for raw AFB1-lys than randomly simulated values. To assure ourselves that our initial results in the Guatemalan study were not idiosyncratic to a specific population, a particular method of HSA measurement, or a particular range of AFBof China . As showc policy  and thatn Qidong . While A1 exposure may be concurrent with hepatobiliary disease, where decreases in hepatic metabolic capacity and HSA concentrations are etiologically linked. Specifically, we explored the use of HSA for normalization of raw AFB1-lys adduct levels in two case-control studies of gallbladder cancer. In contrast to the temporal stability of HSA concentrations in the longitudinal Qidong study, HSA concentrations were not equivalent between gallbladder cancer cases and controls—HSA levels in the cancer cases were significantly lower than in the controls (p < 0.0001), likely a result of hepatic injury secondary to bile duct obstruction or damage [1-lys internal dose likely also differ in hepatic function, normalization with HSA as a surrogate for liver injury may be warranted. Nonetheless, we found that regression residuals from gallbladder cancer cases and controls were indistinguishable from one another and were no different than residuals from simulated data. Thus, these results indicate that HSA normalization does not reduce the error in AFB1-lys internal dose estimates in the setting of hepatic disease secondary to gallbladder cancer, but likely adds technical variance, reducing estimate precision and potentially increasing the risk of a type 2 error in statistical inference. Finally, we wanted to test our approach in a context where AFBr damage ,19,20. I1-lys adduct levels, the various justifications for its continued use do not withstand scrutiny. Fundamentally, all rationales cited in support of HSA normalization argue that the practice corrects for between-sample variation in HSA concentration and thus the pool of HSA from which the stoichiometrically rare HSA molecules carrying an AFB1-lys adduct are sampled. This variation in true HSA concentration within a given sample originates from either random chance during sampling of a volume of serum or plasma or from biological factors , and is added to the technical variation inherent in the assay used to produce estimates of HSA concentrations (assay precision). Utility of the HSA normalization approach thus depends on both effective correction for random and/or biological variance sources and low technical variance within the assay. While the HSA normalization approach has been considered conventional practice for reporting AFB1-lys measurement, analysis is performed directly using serum/plasma samples rather than HSA precipitates or immunoaffinity purified fractions, as in prior methods [1-lys measurement error and does not necessitate correction with HSA normalization. With the current LCMS approach for AFB methods ,7. Thus,1-lys internal standard, which corrects for analyte recovery by solid-phase extraction (SPE) during processing [1-lys levels are not performed in the same aliquot of serum/plasma. Although measurement of HSA in a second aliquot clearly should produce values that are correlated with the concentration of HSA present in the aliquot used for LCMS analysis, this correlation between repeated measurements is subject to technical error within the HSA assay—error which can be substantial, depending on the approach used for HSA determination [1-lys analysis, the utility of such an approach is dependent upon the technical variance within the HSA assay being substantially smaller than the true between-person variance in HSA concentration. Furthermore, samples exhibiting significant hemolysis can lead to aberrant HSA determinations, adding additional technical error [1-lys adduct concentrations determined by LCMS in pooled serum quality control samples—measured at 3 separate levels in each of 35 separate analytical batches—was 10.8% [1-lys numerator.Additionally, the LCMS assay utilizes an isotopically labeled AFBocessing . HSA assmination . As a real error ,23. In aal error , the peras 10.8% . Thus, H1-lys adduct values: (1) AFB1-lys adduct formation likely occurs exclusively in the liver, as it is the site of both HSA synthesis and the metabolic activation of AFB1 by cytochrome P450 enzymes to its reactive carcinogenic metabolite (for which the AFB1-lys adduct is a surrogate biomarker) [Several rationales based on the following established physiological facts have been presented in support of HSA normalization of AFBomarker) . 1, HSA concentrations can be used as a surrogate biomarker of HSA synthesis and, ultimately, hepatic function. It is true that HSA synthesis and HSA levels are reduced in severe hepatic or perihepatic disease, such as cirrhosis and gallbladder cancer [First, it has been proposed that since hepatic injury or disease can broadly impair hepatic protein synthesis and metabolism, including the metabolism of AFBr cancer ,20,26,27r cancer ,29. Hower cancer ,31. Thusr cancer . In an a1-lys adducts, despite a constant level of AFB1 exposure and a constant rate of AFB1-lys adduct formation. It has been proposed that HSA concentration is similarly altered by changes in plasma volume and thus can be used to adjust for between-person variation in plasma osmolarity, comparable to how creatinine is used to normalize concentrations of urinary biomarkers [Plasma volume can be affected by hydration status , disease , and certain physiological states . A second rationale for HSA normalization suggests that alterations in plasma volume could increase or decrease the volumetric concentration of circulating AFBomarkers . Howeveromarkers , which domarkers . Thus, aomarkers ), it is omarkers ,34, enou1-lys adducts. Assuming this hypothesis is correct, given that circulating HSA concentration is generally reflective of hepatic HSA synthesis [1-lys adduct formation. However, circulating HSA is not a reliable indicator of nutritional status, except in severe cases of protein malnutrition, such as kwashiorkor [Finally, certain exogenous factors  can decrease circulating HSA concentrations. It has been proposed that decreased hepatic HSA production could reduce the availability of HSA as a substrate for the formation of AFBynthesis , the sershiorkor ,35,36. Ishiorkor . 1-lys adduct formation. While HSA synthesis is indeed reduced due to liver disease or injury, even in a setting of extremely high aflatoxin exposure , HSA is present in the liver at a molar ratio of roughly 100,000:1 relative to AFB1  and  and 1-lyer (~40% ). Thus, 1-lys measurements, (2) may add more technical variance than the existing between-person biological variance that it is meant to adjust for, and (3) cannot or is not needed to control for between-person biological variance in hepatic metabolism, plasma osmolarity, or HSA synthesis. Furthermore, while AFB1-lys internal dose values can be translated to estimates of dietary intake—as we have done in a recent study of mother-child dyads in Bangladesh and Malawi [1-lys determination, there is no compelling basis for the continued use of HSA normalization in AFB1-lys adduct dosimetry when using the current state-of-the-art LCMS AFB1-lys assay. Thus, HSA normalization: (1) does not or cannot address random technical variance present in AFBd Malawi —HSA norm1-lys adduct values appears to be the result of arithmetic transformation and the addition of technical error, rather than a reduction in biological or technical variance. In future work, this conclusion could be further tested using subject-level data on hepatocellular carcinoma incidence and AFB1-lys adduct levels, assessing whether raw or HSA-normalized AFB1-lys values better recapitulate the well-established causal relationship between this cancer and AFB1 exposure, or by comparing measured AFB1 intake with raw vs. normalized AFB1-lys values. However, given our current results and combined with the added cost, analysis time, sample consumption, and heterogeneity between HSA assays, it seems prudent to abandon the practice of normalizing HSA adducts to the total circulating HSA concentration when measuring any HSA adducts—not only AFB1-lys adducts—by isotope-dilution mass spectrometry in serum or plasma. As a result, we have initiated this shift within our own laboratory, recently showing that elevated raw AFB1-lys concentrations were significantly associated with the cervical detection of oncogenic strains of HPV in Kenyan women [1-lys adducts will allow for more efficient use of precious samples and research funds, while producing superior data.Overall, the effect of HSA “normalization” in HSA-corrected AFBan women  and, sepan women . Eschewi1-lys adduct data used for this secondary analysis have been previously peer-reviewed.All HSA and AFB1-lys adduct determination by LCMS [1-lys (≥0.01 pg/μL), valid determination of HCV and HBV infection status, and self-reported weekly ethanol consumption. A total of 400 persons without any missing data were available for the present analysis. Individuals were excluded from further analysis on the basis of excessive alcohol use , positive HBV or HCV status (n = 54), excessive alcohol use and positive HBV or HCV status (n = 4), or missing HBV or HCV status (n = 4). The final analytical sample was n = 327.This study was conducted between May and October of 2016 and was approved by the institutional review boards at the Johns Hopkins Bloomberg School of Public Health and the Institute of Nutrition of Central America and Panama (INCAP). Study details have been published previously ,40. All  by LCMS , HSA by  by LCMS . Partici1-lys by LCMS [1-lys (≥0.01 pg/μL). Due to the very low rate of detectable AFB1-lys in samples from 2012 (7%), subjects from the 2012 sub-sample were excluded from further analysis in the present study. A total of 306 persons from four community screenings  had AFB1-lys and HSA data available for analysis .This study was approved by the institutional review boards at the Johns Hopkins Bloomberg School of Public Health and the Qidong Liver Cancer Institute (QDLCI). All study participants provided informed consent. Samples for this study were collected as part of screening protocols for liver cancer early detection and chemoprevention studies in Qidong, People’s Republic of China, dating to the 1980s. From our biorepository, we randomly selected age-matched subsamples of 50 males and 50 females, residing in two neighboring townships , obtained from screening protocols conducted during the years of 1995, 1999, 2003, 2009, and 2012. Each subsample consisted of a unique set of community residents; subject-level repeated sampling across survey years was not available. Serum samples from the participants were analyzed for AFB by LCMS . Partici1-lys by LCMS [1-lys (≥0.01 pg/μL); in prior publications [1-lys levels ≥0.05 pg/mg HSA were included for analysis. Data were pooled across both study sites, resulting in a final analytical sample size of n = 130 persons with GBC  and n = 85 controls . The combined data set was analyzed as one study, referred to hereafter as the “GBC case-control” study.We have previously reported positive associations between aflatoxin exposure and gallbladder cancer (GBC) in two case-control studies, conducted in the country of Chile  and in S by LCMS  and HSA  by LCMS ,12. Partications ,12, only1-lys adduct refers to serum AFB1-lys—albumin adduct concentration—in the units of pg AFB1-lys adduct/mL serum. “HSA-normalized” or “normalized” AFB1-lys adduct levels refers to the raw AFB1-lys adduct level divided by the total HSA concentration (in mg/mL), resulting in the final units of pg AFB1-lys adduct/mg HSA. Within the GBC case-control study, the distributions of HSA, raw AFB1-lys adduct levels, and HSA-normalized AFB1-lys concentrations were compared in cases and controls using the non-parametric Wilcoxon signed-rank test using the PROC NPAR1WAY procedure. All analyses were conducted using SAS 9.4 . “Raw” AFB1-lys levels, and various demographic parameters to the total observed variance in AFB1-lys internal dose in the Guatemalan dataset. Multiple models were constructed, with either raw AFB1-lys or HSA-normalized AFB1-lys as the dependent variable and the independent variables consisting of HSA, raw AFB1-lys, and various demographic factors  that we have found to be associated with aflatoxin exposure in Guatemala [2 was used to assess the proportion of explained variation (vs. coefficient of determination) of the AFB1-lys internal dose.Linear regression models were used to assess the relative contributions of HSA, raw AFBuatemala . In all 1-lys adduct concentrations, modeled on the empirical data from each study. Specifically, within each study, the empirically observed mean and standard deviation values for HSA concentration were used to construct 20 normally or lognormally distributed simulated samples of 1000 randomly generated observations each, using the RDRAND function in SAS and a seed value of 0 [1-lys adduct concentrations using the empirically observed raw AFB1-lys adduct mean and standard deviation. Next, the randomly generated HSA and AFB1-lys values in each dataset were sequentially paired, without replacement, to create a third dataset of simulated study participants, and the HSA-normalized AFB1-lys adduct values were calculated for each simulated participant. Within each study , these simulated datasets were constructed using distribution parameters drawn from the entire sample, independent of the year of sample collection (China), disease status (GBC case-control), or study site (GBC case-control). Thus, for each of the three studies, we created a unique 20,000-observation simulated dataset with distribution parameters essentially identical to the empirical data, but where intra-person relationships between raw and HSA-normalized AFB1-lys adduct levels were the result of arithmetic transformation and random variation rather than biology. A Monte Carlo simulation approach  was usedlue of 0 ; these 21-lys adduct levels and HSA-normalized adduct levels. Residuals were obtained from linear regression models in which the raw AFB1-lys adduct level was the lone independent variable and the HSA-normalized AFB1-lys concentration was the dependent variable to be predicted. Thus, the regression residuals constitute the differences between the empirically observed HSA-normalized AFB1-lys adduct level (pg/mg HSA) at a given concentration of the raw AFB1-lys adduct (pg/mL serum or plasma) vs. the predicted HSA-normalized AFB1-lys adduct level at the same concentration of the raw AFB1-lys adduct. In other words, the residuals were visualized as the ordinate (y-axis) deviation of an individual observation from the best-fit linear regression line describing the relationship between raw and HSA-normalized adduct levels. To compare residuals across five orders of magnitude of raw AFB1-lys adduct levels, Studentized regression residuals were standardized as Z-scores (Linear regression was used to assess the relationship between raw AFB


Despite the great achievements in pancreatic ductal adenocarcinoma (PDAC), identification of patients who will suffer rapid disease relapse and progression is not perfect, when definitive histology for tumor staging is not available. The Hamburg Glasgow Classification combines tumor cell dissemination in the bone marrow and systemic inflammatory response into a preoperative staging system. In this work, we assessed the Hamburg Glasgow Classification in potentially resectable PDAC as a prognostic classification for overall and progression-free survival and compared it to the UICC-TNM classification with promising results.p-value < 0.001) and progression-free survival (PFS) . These results were comparable to the UICC survival stratification for OS and PFS . Additionally, in non-metastatic PDAC, HGC III-IV was associated with shorter OS and PFS  when compared to HGC I-II. Therefore, the HGC is a promising preoperative prognostic staging classification for accurate and simple outcome stratification in patients with PDAC.This study aims to compare the Hamburg Glasgow Classification (HGC) to Union for International Cancer Control (UICC) classification in patients with pancreatic ductal adenocarcinoma (PDAC). As adequate tumor classification is only possible after tumor resection and histological evaluation, only 20% of patients with PDAC receive accurate tumor staging. Thus, an accurate preoperative staging system is still missing but urgently needed. Systemic inflammation and tumor dissemination are important factors regarding the oncological outcome. HGC integrates both into a preoperative staging system, by combining C-reactive protein (CRP), albumin, and disseminated tumor cells (DTC) in the bone marrow. In this prospective study, 109 patients underwent surgical exploration for suspected PDAC. All patients underwent a preoperative bone marrow aspiration for DTC detection. HGC showed significant preoperative risk stratification for overall survival (OS) ( Pancreatic ductal adenocarcinoma (PDAC) is the 7th leading cause of cancer death in industrialized countries . It is cAccumulating evidence indicates that early disease progression in PDAC is due to the presence of micrometastasis or the presence of disseminated tumor cells (DTC) at distant sites at the time of diagnosis. The presence of DTC in bone marrow has proven to be a prognostic indicator for progression-free and overall survival in PDAC. This suggests that the bone marrow might be a reservoir for disseminating PDAC cells ,5.However, patients’ outcomes are not solely based on tumor-related factors such as tumor size, grade, lymph node involvement, and systemic tumor dissemination ,5,6 but Over the last few decades, the American Joint Committee on Cancer (AJCC) created the TNM staging system, which is the most commonly used staging system to differentiate between groups with different survival outcomes . HoweverThe Glasgow Prognostic Score (GPS) is based on two acute phase proteins, albumin, and C-reactive protein (CRP). This represents an indicator of SIR, which has been proven to be a useful tool for risk stratification in patients with colorectal, esophageal, and lung cancer ,12.Merging the DTC status with the GPS combines SIR and the disseminated tumor load forming a preoperative staging classification, namely the Hamburg-Glasgow Classification (HGC). This can stratify patients into different risk groups before oncologic resection. Previously, we assessed HGC in non-metastatic esophageal cancer which resulted to be a strong, significant, and independent predictor of overall survival and disease-free survival .This study aims to assess the Hamburg-Glasgow Classification in patients with PDAC as a prognostic classification for overall and progression-free survival.All patients enrolled in this study underwent surgical exploration for suspected PDAC at the University Hospital Hamburg-Eppendorf. The indication for treatment was according to the German S3 guidelines . The stuPreoperative routine workup included patient’s history, physical examination, routine blood tests, thoracic and abdominal CT-scans, in selected cases endoscopy with fine-needle aspiration. In the case of non-resectable PDAC, the cTNM stage was used to classify patients in their respective UICC stages. The database included 194 patients who had bone marrow aspiration at the time of surgery between July 2004 and March 2010. We retrospectively included only patients with histological evidence of pancreatic ductal adenocarcinoma (PDAC). Therefore, 16 patients with distal cholangiocarcinoma and 22 patients with benign pancreatic lesions were excluded. After excluding 47 for missing preoperative Albumin or CRP, 109 patients were enrolled in this study. Post-operative follow-up was conducted according to the S3 German guidelines  at 3-monIn this study, all patients who underwent surgical exploration for PDAC received a preoperative bone marrow aspiration. This was done under aseptic measures. 10 mL of bone marrow was taken from the upper right iliac crest. Mononuclear cells were enriched using the Ficoll gradient. Disseminated tumor cells were detected using an anti-cytokeratin antibody A45-B/B3). This immunocytochemical assay for DTC detection in bone marrow has a false-positive rate of a maximum of 1% in control cases . All sta-B/B3. Th−1), and (iii) hypoalbuminemia (<35 g L−1)  .2 test. Survival curves for progression-free and overall survivals of the patients were plotted using the Kaplan–Meier method and analyzed using the log-rank test. Results are presented as median survival in months with a 95% confidence interval (CI) and the number of patients at risk.For statistical analysis, SPSS 26 for Windows  was used. Descriptive statistics were used to describe patient baseline characteristics. The association between the HGC and clinicopathological parameters was evaluated using the XThe overall survival was computed as the period from the date of surgery to either the date of death or last follow-up, whichever occurred first. The progression-free survival was defined from the date of surgery to the date of evidence of recurrence, last follow-up, or date of death, whichever occurred first.p < 0.05.Multivariate analysis using Cox regression was used to assess the independent prognostic influence of HGC and other parameters on progression-free survival and overall survival. Results are presented as hazard ratio (HR) with 95% CI. Significant statements refer top-values of two-tailed tests that were 6 mononuclear cells. We assessed the association of HGC with sex, age, and the following histopathological parameters: tumor grade, tumor size, lymph node stage, metastatic status, resection margin, and post-operative Union for International Cancer Control (UICC) TNM classification. There was no association between the above-mentioned parameters and the HGC  were male. The median age was 65 (range 42–84 years). This cohort included 3 patients with UICC stage I, 49 patients with UICC stage II, 14 patients with UICC stage III, and 43 patients with UICC stage IV. DTCs were detected in 20 patients preoperatively. The overall frequency of DTC was 1–3 per 2 × 10 the HGC . Furtherp-value < 0.001, respectively). These results were comparable to the survival stratification of the UICC TNM classification for OS  and PFS   compared to HGC I–II  and PFS  compared to HGC I–II  . In patiHGC I–II . In metaHGC I–II .p-value < 0.001) and shorter PFS   and shorter PFS  to R2 resections but significantly shorter OS  and PFS  compared to R0 and R1 resections, respectively.  compared to patients with no detectable DTCs  and OS  compared to patients with HGC I–II.The presence of distant metastases at the time of exploration was associated with shorter OS (= 0.002) . Patientctively. . Moreoveble DTCs . When tap = 0.001; HR 2.49, 95% CI, 1.47–4.22) and progression-free survival . . .p-value = 0.002) and UICC  were independent prognostic factors for overall survival. Regarding progression-free survival, only HGC reached significance . A detailed analysis is available in the Supporting data  is the most used classification for PDAC. It is a well-defined cancer staging system that depends on the anatomic extension of the tumor ,10. HoweFurthermore, TNM classification does not take into consideration other factors like tumor biology, or patient-related factors, which could explain why patients assigned the same stage may vastly vary in terms of tumor behavior and associated outcomes. This is supported by our findings, in that HGC stages are not associated with the degree of nodal involvement or with distant metastasis, but with progression-free survival and overall survival, which emphasizes the hypothesis that not only anatomic extension but also other biological factors can affect survival outcomes or that systemic micrometastasis is present early in the course of PDAC .It is well known that even after R0 resection of PDAC and aggressive adjunct multimodal therapy, metastasis and tumor recurrence still occur . A recenThe GPS is a marker for the patient’s systemic immune response and is based on two acute phase reactant proteins, C-reactive protein (CRP) and Albumin. An elevated GPS reflects a compromised cell-mediated immunity . This inHypoalbuminemia is also a well-known negative prognostic factor in cancer patients undergoing surgery. Albumin is a nutritional marker that identifies poor nutritional status in cancer patients and is a negative acute-phase reactant that decreases in inflammatory states indicating a compromised immune system ,24. ConsIn 2017, the definition of borderline resectable pancreatic ductal adenocarcinoma (BR-PDAC) was expanded from solely depending on anatomic criteria to include biological factors like questionable metastatic disease and conditional host factors such as suboptimal performance status, or severe medical comorbidities . For thiIn this study, we showed that the HGC is a strong predictor of oncological outcome in PDAC and is associated with shorter OS and PFS in patients with PDAC. Hamburg-Glasgow classification seems to be an objective, easily available, and significant prognosticator for the survival of patients with PDAC which should be used in addition to the TNM classification. Even in non-metastatic PDAC, the HGC showed significant survival stratification between the groups HGC I/II and III/IV, so that patients with resectable PDAC and HGC III/IV should be considered as borderline resectable PDAC, who could benefit from a neoadjuvant approach. Patients with HGC III/IV had a mean overall survival of 9.3 and 4.8 months, respectively, regardless of their TNM stage.In this cohort, we included patients with different tumor stages, with localized and metastatic disease as well as patients in resectable and non-resectable settings, which simulates real-life situations that regularly encounter surgeons treating PDAC, where a preoperative prognostic classification is lacking and the decision for surgical exploration mostly depends on imaging studies. We were able to show that the HGC is independent of the TNM system. The implementation of the HGC into standard clinical preoperative staging might add significant information on tumor biology to the TNM classification, which might improve treatment in patients with PDAC. However, larger prospective validation studies are required before the implementation of HGC in TNM classification. Furthermore, our study could serve as a ground platform for the integration of new forms of liquid biopsies, like CTC, cfDNA, microRNA, and ctDNA ,30 into The HGC is a promising staging classification system for PDAC. However, we recognize that there are limiting factors to our study: (i) single-institution study (ii) this study is from an era in which modern chemotherapy (such as gem nab-paclitaxel and FOLFIRINOX) for either adjuvant or systemic disease was not in use (iii) the lack of comparative data, for example, ASA or ECOG scores. However, we report on a homogenous and large study population in different stages of PDAC who had no preoperative systemic therapy which might affect the DTC and SIR status.The HGC is a promising preoperative staging classification that shows significant risk stratification for overall survival and progression-free survival in patients with PDAC. Our results suggest that HGC can enable accurate preoperative staging in addition to the TNM classification which might improve the treatment of patients with PDAC.


An exposure to sweetened and fatty foods early in life may be involved in high liking later in life. The objective is to investigate the association between dietary exposure to carbohydrate, sugars and fat in infancy, with liking for sweetness, fattiness and fattiness-and-sweetness sensations at 8-to-12-year-old. Analyses were conducted on 759 French children from the EDEN mother-child cohort. Carbohydrate, sugar or fat intake, being a consumer of added sugars or added fats were assessed at 8 and 12 months using 3-day food records. The liking score (0–10) for the different sensations was assessed through an online child-completed questionnaire. Associations were tested by linear regressions adjusted for main confounders and the interaction with sex was tested. None of the early dietary exposure variables was related to fattiness liking. Carbohydrate intake at 8 months was positively but weakly associated with liking for sweetness-and-fattiness. In girls only, carbohydrate intake at 12 months was positively associated with liking for sweetness. Globally, no marked associations were observed between infant dietary exposure to sweet and fat and liking for sweetness and fattiness in young children. The positive link in girls between early carbohydrate exposure and later liking for sweetness needs to be confirmed in further studies. According to the “developmental origin of health and disease” hypothesis, an unbalanced diet in early life may contribute to adverse cardiometabolic outcomes later in life . A body Taste preference is usually referring to the comparison of individual reaction to different concentrations of tastants  or to a tastant relative to water ,17,18,19In contrast to liking for sweetness which is innate , liking Sex-related differences in dietary behavior have already been noted in children, regarding both food intake ,40 and fThe present study aimed to investigate the associations between infant dietary exposure to carbohydrate, sugars and fat and liking for sweetness, fattiness and fattiness-and-sweetness sensations in 8-to-12-year-old children, accounting for a potential moderating effect of sex on the associations. We hypothesized that a high intake of carbohydrate and fat may lead to a high liking for sweetness, fattiness and fattiness-and-sweetness sensations and that these associations may differ by sex.The present study was based on the EDEN  mother-child cohort study. A detailed study protocol was published previously . BrieflyFor the present study, data about consumption in infancy (when children were aged 8 or 12 months) were used. Moreover, families accepting to pursue participation beyond the initially planned follow-up were contacted to let their children complete an online questionnaire on food liking. Between May and September 2015 (when the children were aged 8 to 12 years), parents were invited to participate either by electronic mail (78%) or by paper mail (22%), when no valid electronic mail address was available.Infant food intake was reported on a daily basis (according to the instructions) by mothers using a 3-day food record including two weekdays and one weekend day when their child was aged 8 and 12 months. The food record included information about the provided and the uneaten amount of food, the food brand name or the recipe for home-made foods. Nutrient intake was calculated based on food composition databases from the French baby foods industry group (SFAE 2005) and from the French Observatory of Food Nutritional Quality (CIQUAL 2020). Carbohydrate, sugar, fat and energy intake were calculated only among non-breastfed infants, as breast milk intake could not be measured.No information about free sugars was available in the food composition databases. However, consumption of natural sweeteners , sweetened beverages  were collected from food records. Sweetened beverages and natural sweeteners were grouped into added sugars group. The consumption of added fats  was also collected. Infants were categorized as consumers or non-consumers of added sugars or added fats.Children’s liking for sweetness and fattiness sensations was assessed when they were aged between 8 and 12 years by a specific and validated questionnaire. The liking questionnaire, completed by children, was initially developed for adults  but adapLiking for sweetness and fattiness was built on 54 items divided into 3 sections. Liking for sweetness, fattiness and fattiness-and-sweetness, had a singular factor structure composed of multiple latent constructs (factors), themselves composed of several items. Liking for sweetness (16 items) was composed of 3 latent constructs: liking for sweet foods, liking for added sugars and liking for added jam. Liking for fattiness was composed of liking for fattiness-and-saltiness sensation and by liking for fattiness-and-sweetness sensation. Liking for fattiness-and-saltiness sensation was built on 3 latent constructs: liking for added fats and salt (9 items), liking for cheesy foods (3 items) and liking for fatty-salty foods (10 items). Liking for fattiness-and-sweetness sensation was based on 3 latent constructs: liking for added whipped cream (5 items), liking for fatty-sweet foods (9 items) and liking for added chocolate spread (2 items). Using the previously published factor structure , goodnesMaternal characteristics  were collected during a face-to-face interview between 24 and 28 weeks of amenorrhea. Information about the children sex, birth weight, and gestational age was collected from the obstetrical and pediatric records. Children weight and height information were reported at the 8–12-year follow-up as a sub-part of the liking questionnaire. Child’s overweight (including obesity) was defined according to the IOTF cut-offs .Maternal diet during the last trimester of pregnancy was assessed using a validated food frequency questionnaire (FFQ), completed at birth . MaternaAny breastfeeding duration and age at complementary food introduction were calculated from the data collected at ages 4, 8 and 12 months . Breastfn = 60 and n = 113, respectively) and children no more breastfed ) were kept in the main analysis . Complete-case analysis at 8 and 12 months was run on 555 and 543 children respectively, excluding children with missing data for covariates and confounders at 8 and 12 months (n = 61 and n = 67).The different steps of the sample selection process are described in t-test and Chi-squared test were used to compare the characteristics of included and non-included participants; and the dietary intakes and the liking scores of boys and girls. Paired Student’s t-test was used to compare the liking scores between sweetness, fatty and fattiness-and-sweetness sensations.Student’s Unadjusted and adjusted associations between dietary exposure variables in infancy and liking outcome variables in young children were tested either using linear or logistic regressions. Each dietary intake variable  was tested individually, i.e., not concomitantly with another dietary variable within the same model. Carbohydrate intake, sugar intake and consumption of added sugars were consecutively analyzed in association with liking for sweetness sensation. Fat intake and consumption of added fats were successively investigated in relation with liking for fattiness sensation. Finally, carbohydrate, sugar or fat intake, being a consumer of added sugars or added fats were consecutively studied in association with liking for fattiness-and-sweetness sensation. For all the studied associations, potential confounding factors were identified in the literature and selected using the directed acyclic graph (DAG) method . Hence, Missing values for confounders, which were assumed to be missing at random, were handled using multiple imputations. Five independent datasets were generated using the Markov chain Monte Carlo method (PROC MI with FCS LOGISTIC and FCS REGPMM statements) and pooled effect estimates were calculated (PROC MIANALYZE). The multiple imputation method is detailed in the p < 0.05 except for interaction tests (p < 0.10).All analyses were performed with SAS software . Significance was set at n = 1297), compared with included children (n = 610), were more likely to be born to a mother who is younger , with higher rate of obesity , an educational level lower than a high school diploma , a household income lower than €1 500/month . Excluded children were also more likely to be never breastfed  and introduced to complementary foods before 4 months .Sample characteristics are presented in p < 0.0001) and the liking for fattiness-and-sweetness sensation score (p < 0.0001). Compared with girls, boys had higher carbohydrate intake at age 8 and 12 months and higher liking for sweetness in young children. The percentage of added sugar consumers at age 8 and 12 months did not differ between sex even though boys had higher sugar intake at 12 months (but not at 8 months) than girls. Boys also had higher fat intake at age 12 months (but not at 8 months) and higher liking for fattiness and fattiness-and-sweetness sensations at 8 months. No difference in the percentage of added fats consumer was observed between sexes.The liking for sweetness, fattiness and fattiness-and-sweetness sensation scores were 4.6 ± 1.3, 5.7 ± 1.2 and 5.8 ± 1.7, respectively . The likpinteraction = 0.14 and 0.33, respectively) . The association between carbohydrate or sugar intake at 12 months and the liking for sweetness score differed by sex (pinteraction = 0.003 for carbohydrate intake and pinteraction = 0.07 for sugar intake). A significant, positive but weak association was found between carbohydrate intake and liking for sweetness in 8-to-12-year-old girls; and a negative trend was observed in boys. For sugar intake, only a positive trend was observed in girls. Directions of the associations remained unchanged in the complete-case analysis.Few associations were highlighted between dietary intake in infancy and liking for sweetness sensation in young children. Being a consumer of added sugars at ages 8 and 12 months was not related to the liking for sweetness score and no interaction with sex was observed at 8 months and 12 months (ctively) . Regardipinteraction > 0.50). Similar findings were found in the complete-case analysis.Neither being a consumer of added fats nor fat intake at ages 8 or 12 months were associated with the liking for fattiness sensation score in young children . No intepinteraction = 0.26 and all other pinteraction > 0.60). The complete-case analysis showed similar trends.Overall, the dietary intake variables at ages 8 and 12 months were not related to the liking for fattiness-and-sweetness sensation score in young children, except for a positive link between carbohydrate intake at age 8 months and the liking for fattiness-and-sweetness sensation score . No inteIn the present study, no marked associations between infant dietary exposures to sugars and fat and liking for sweetness, sweetness-and-fattiness or fattiness sensations in 8-to-12-year-old children were observed. Positive links were observed, between carbohydrate intake at age 12 months and later liking for sweetness in girls only; and between carbohydrate intake at age 8 months and later liking for fattiness-and-sweetness sensation.Previous studies found inconsistent associations between exposure to sweetness and liking for sweetness sensation. In fact, American children frequently exposed to sweetened water during infancy were more likely to prefer solutions with higher concentrations of sucrose at ages 6–10 years compared with children rarely exposed to sweetened water in infancy . In an 8Sex differences in children’s diet, food preferences and feeding practices were previously observed ,44,45,56Regarding liking for fattiness sensation or fattiness-and-sweetness sensation, we did not find any association with fat intake during infancy. Consistently, in a cross-sectional study, a frequent consumption of fatty foods was not related to the preference for foods with higher fat in children aged 6 to 9 years . In contIn the present study, infant dietary intake was assessed by 3-day food records which were checked a posteriori and validated by a dietician. As we were not able to directly isolate free sugar intake from total simple sugar intake, we identified consumers of added sugars, but only one third of the sample consumed added sugars. Later dietary exposure to sweetened foods, added sugars and/or sugars in childhood could have a more impactful contribution to children liking for sweetness sensation than exposure during the first year. Regarding children’s liking, it was assessed by children themselves, which limits reporting bias compared with an indirect measurement through parental report. This measurement is not comparable with an actual tasting test since it is based on their representation of the food items and on their eating habits . NeverthAs in most cohort studies, mothers from our study sample were relatively more frequently highly educated and had higher household incomes than the national average. This could explain the low level and the lack of variability in the infant dietary exposure to sugar and fat as such feeding practices vary according to a socioeconomic gradient ,45. ConsTo conclude, this study did not show marked associations between dietary exposure to fat and sugar in infancy and liking for sweetness and fattiness sensations in 8-to-12-year-old children. In girls, the exposure to carbohydrate at age 12 months was positively related to the liking for sweetness; and the exposure to carbohydrate at age 8 months was positively related to the liking for fattiness-and-sweetness. More studies, ideally conducted in populations with a wider variability and a more specific characterization of the exposure to sugar and fat in infancy are needed to further explore these associations.


Ambient temperature observations from single monitoring stations  are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error.We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution.Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center.Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.The online version contains supplementary material available at 10.1186/s12940-021-00735-w. Studies of the relationship between ambient temperature and adverse health outcomes routinely utilize observations at single airport weather stations to derive exposure estimates. While airport stations have long time series and high-quality data, their measurements may not accurately reflect average exposure experienced by the surrounding study population. Specifically, airport monitors cannot capture intra-urban spatial variability driven by features of the built and natural environment , 2, espeExposure measurement error that arises from insufficient characterization of exposure spatial variability may lead to attenuated health effect estimates , 6. PrevOur study has two objectives. The first is to evaluate the potential benefits of using fine-scale meteorology data products when estimating acute health effects of high temperature, as opposed to a single monitoring station. We are particularly interested in comparing health effect estimates obtained from exposure assessment methods that account for spatial variability and those that assume spatially homogenous exposures. The second objective of this paper is to fill an important knowledge gap on the association between high temperature and morbidity as measured by emergency department (ED) visits. Previous work has predominantly focused on mortality –21, and To achieve these objectives, we conducted time-series analyses of warm-season daily temperature (maximum and minimum) and all-age ED visits in five US cities from different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. To estimate exposures, we used Daymet , a publiMulti-year, patient-level ED records were obtained from individual hospitals in the Atlanta metropolitan area (1993–2004) and the Georgia Hospital Association (2005–2012), the Arizona Department of Health Services, Bureau of Public Health Statistics (2008–2016), the California Health and Human Services Agency, Office of Statewide Planning and Development; 2005–2016), and the Utah Department of Health, Office of Health Care Statistics (2005–2016). Records included admission date and International Classification of Diseases (ICD) diagnosis codes. ICD 9th revision (ICD-9) codes were used for ED visits prior to October 1, 2015, followed by the use of ICD 10th revision (ICD-10) codes. Our definition of an ED visit included patients seen in the ED as outpatients and discharged, as well as patients admitted as inpatients from the ED. The analysis was restricted to the warmest 6 months of the year, May to October.We used both the primary and secondary diagnosis codes to identify ED visits for specific health outcomes that have been found to be associated with high temperature in previous studies . The hea2) for Atlanta, 2 counties  for Los Angeles, 2 counties  for Phoenix, 3 counties  for Salt Lake City, and 5 counties (6136 km2) for San Francisco. Based on the Köppen-Geiger climate classification [Analytic datasets of daily ED visit counts for each city were obtained by aggregating ED visits by day based on patient residential location in each corresponding MSA. Specifically, an ED visit was included in the study if the patient’s residential ZIP code corresponded to a ZIP code tabulation area (ZCTA) that overlapped with the MSA definition for a city, defined by the U.S. Office of Management and Budget based on contiguous counties. The five MSAs varied in the number of counties (and area size): 20 counties  gridded surfaces and identified all 1 km × 1 km grid cells that are within the MSA boundary. The Daymet temperature product ingests all surface observations from the Global Historical Climatology Network. The algorithm also performs spatial-temporal interpolation using surrounding observations, as well as information on other weather variables, elevation, and land/water masks. A Daymet grid cell is linked to an MSA if the centroid of the 1 km grid cell falls within any of the MSA’s county boundary. Daymet temperature data were used to develop three different daily exposure temperature metrics: a daily simple spatial average of all 1 km grid cells over each MSA, a daily weighted average based on county population, and a daily weighted average based on ZCTA population. For time-series analysis of aggregated health outcomes, the optimal exposure metric should correspond to the average of exposures of all at-risk individuals. Data on population size were obtained from the US Census at the county-level  and at the ZCTA-level (2000 and 2010). Annual populations were based on linear interpolation of decadal Census data. Daily minimum and maximum temperature from the different exposure assessment methods were also averaged to obtain daily mean temperature.Daily maximum temperature, minimum temperature, and average dewpoint temperature observations were obtained from monitors at the major international airport serving each MSA. We also obtained Daymet  temperature and ED visit outcome counts, modeled using natural cubic splines with 4 degrees of freedom (3 equidistant interior knots). While mortality studies have usually utilized longer lags , previous analyses have found that acute effects of heat on ED visits operate on shorter lags , 31. We All time-series models included the following variables for confounder adjustments. We controlled for daily mean dew-point temperature using natural cubic splines with 4 degrees of freedom. Long-term and seasonal temporal trends were modeled as a smooth function of day-of-year with 6 degrees of freedom. We added interaction terms between time spline coefficients and indicators for year, resulting in year-specific seasonal trends. For heat-related illnesses, we only included interactions between linear day-of-year and indicators of year due to the smaller number of events. We also included indicators for day-of-the-week, federal holidays, and hospital-specific indicators to account for hospitals’ contributions to the total ED visits in the city.To better compare the strength of non-linear associations between ED counts and temperature across different exposure assessment methods, we report relative risk based on two different contrasts along the exposure range: 75th percentile versus 25th percentile, and 95th percentile versus 50th percentile. The percentile values were specific to each city and exposure metric. Some studies have defined a common reference temperature by identifying the temperature value that corresponds to the minimum risk on the non-linear function . We opteIt is difficult to statistically assess differences in relative risks estimated from to the use of different exposure assessment methods because these models were fit to the same health outcome and were non-nested. In addition to qualitatively examining the magnitude of associations, we considered two model comparison tools: (1) the Akaike information criterion (AIC) assuming the outcome is Poisson and (2) the estimated overdispersion parameter. Lower values of AIC and overdispersion are preferred, and may indicate better model fit and out-of-sample prediction performance. We note that these are general model comparison tools that do not necessarily reflect the accuracy and precision in inference for the relative risk parameters of interest. We conducted three sensitivity analyses to examine the robustness of the estimated associations and comparison across exposure assessment methods. First, we varied the natural cubic spline’s degrees of freedom for the exposure of interest to 3, 5, or 6. Second, we evaluated an additional exposure assessment method by considering only the Daymet grid cell linked to the airport monitor. Third, we modified the temperature exposure metric to a 3-day moving average .Descriptive statistics (mean and standard deviation) of the four temperature metrics are given in Table Pairwise correlations between the four temperature metrics are given in Supplementary Table Outcome-specific total and mean daily ED visits are shown in Table To better visualize differences in the entire exposure-response function due to exposure assessment methods, Fig. In sensitivity analyses, when considering using Daymet values at the airport monitors, we found that estimated RRs were either similar to those using airport observations  or similar to those using population-weighted Daymet data , which comes from several factors. First, Daymet uses stations compiled within the Global Historical Climatology Network-Daily (GHCN-D) dataset, from the US National Centers for Environmental Information. For the US, the GHCN-D dataset compiles US automated surface observing stations (ASOS) which include airports, cooperative network stations , and some regional mesonets , providing roughly 10–15,000 temperature observations a day across the contiguous US through the study period , which mPossible extensions to alleviate these concerns include use of supplemental station networks, use of other geophysical attributes as predictors in statistical models, or incorporation of urban models within the general interpolation framework to explicitly represent UHIs. For example, there are many thousands more stations available in the past 10–15 years through local mesonets that may improve representation of UHIs. There are also high-resolution land use/land cover maps that allow for inclusion of other geophysical predictors in a statistical model, or use of physically based urban models , that exOur study also has several limitations common to those that utilize administrative health databases. First, our exposure estimates do not account for individual activity patterns and indoor temperature. However, by using only temporal contrast in exposure, long-term trends that impact personal exposures should not confound estimated relative risks. Moreover, quantifying risks associated with ambient temperature is more relevant for designing local heat warning systems. Second, we did not control for ambient air pollution, such as ozone and fine particulate matter, which have been associated with various cardiorespiratory outcomes. Higher temperature often increases pollutant concentrations due to increased emission  and favorable conditions for pollutant formation and transport. Hence, controlling for ambient pollution may reduce the total association of temperature on ED visits . HoweverIn summary, this study found positive health associations between high temperature and emergency department visits in five US cities located in different climate regions. The comparison of different exposure metrics suggests that epidemiological studies based on single monitoring stations may underestimate the effect of temperature on morbidity when monitoring station is less representative of the exposure of the at-risk population. Our results further demonstrate the potential advantages of using spatially-resolved, population-weighted exposure estimates in estimating health effects of short-term heat exposure, and can benefit from future studies that examine additional locations, health outcomes, and exposure lag structures.Additional file 1: Table S1. Descriptive statistics of exposures by city, temperature metric and exposure assessment methods. Table S2. Quantile values of exposures by city and exposure metrics. Table S3. Difference in AIC and ratio of overdispersion by city, outcome and temperature exposure. Figure S1. Maps of average May-October daily Daymet 1km temperature estimates by city. Figure S2. Associations with minimum temperature between the 75th and 25th percentile of exposures. Figure S3. Associations with maximum temperature between the 75th and 25th percentile of exposures. Figure S4. Associations with average temperature between the 95th and 50h percentile of exposures. Figure S5. Associations with average temperature between the 75th and 25h percentile of exposures. Figure S6. Associations with minimum temperature with Daymet grid cell link to the monitor. Figure S7. Associations with maximum temperature with Daymet grid cell link to the monitor. Figure S8. Sensitivity analyses for different natural cubic spline degrees of freedom for minimum temperature. Figure S9. Sensitivity analyses for different natural cubic spline degrees of freedom for maximum temperature. Figure S10. Associations for 3-day moving average of minimum temperature between the 95th and 50th percentile of exposures. Figure S11. Associations for 3-day moving average of maximum temperature between the 95th and 50th percentile of exposures. Primary.csv Model results for primary analysis for same-day exposure . Primary.csv Model results for primary analysis for same-day exposure . Primary_Ref25.csv Model results for primary analysis for same-day exposure . PrimaryMV.csv Model results for primary analysis for 3-day moving average exposure .


The 24-h rest and activity behaviors  are fundamental human behaviors essential to health and well-being. Functional principal component analysis (fPCA) is a flexible approach for characterizing rest-activity rhythms and does not rely on a priori assumptions about the activity shape. The objective of our study is to apply fPCA to a nationally representative sample of American adults to characterize variations in the 24-h rest-activity pattern, determine how the pattern differs according to demographic, socioeconomic and work characteristics, and examine its associations with general health status.The current analysis used data from adults 25 or older in the National Health and Nutrition Examination Survey . Using 7-day 24-h actigraphy recordings, we applied fPCA to derive profiles for overall, weekday and weekend rest-activity patterns. We examined the association between each rest-activity profile in relation to age, gender, race/ethnicity, education, income and working status using multiple linear regression. We also used multiple logistic regression to determine the relationship between each rest-activity profile and the likelihood of reporting poor or fair health.We identified four distinct profiles  that together accounted for 86.8% of total variation in the study sample. We identified numerous associations between each rest-activity profile and multiple sociodemographic characteristics. We also found evidence suggesting the associations differed between weekdays and weekends. Finally, we reported that the rest-activity profiles were associated with self-rated health.Our study provided evidence suggesting that rest-activity patterns in human populations are shaped by multiple demographic, socioeconomic and work factors, and are correlated with health status.The online version contains supplementary material available at 10.1186/s12966-022-01274-4. Physical activity, sedentary behaviors and sleep are fundamental human movement behaviors organized in a 24-h rhythmic cycle. These behaviors are orchestrated by the internal circadian timing system, and influenced by common environmental exposures  . The conMulti-day accelerometry collects rich and complex data for investigating movement behaviors, and its increasing popularity in population studies has presented as both a challenge and opportunity for characterizing 24-h rest-activity rhythms. Using accelerometry data, previous studies have derived both parametric   and nonpCharacterizing rest-activity patterns in the general population and by demographic, socioeconomic and work characteristics provides key information to identify subgroups with sub-optimal rest-activity patterns, and shed light on individual and environmental factors shaping the rest-activity behaviors. To date, few studies examined demographic and lifestyle correlates with rest-activity patterns –17, and NHANES is a biennial cross-sectional survey designed to assess the health and nutritional status of non-institutionalized population in the United States and conducted by the National Center for Health Statistics at the Center for Disease Control and Prevention . The NHADetails of the physical activity monitor protocol have been published online . BrieflyN = 288/day) across all available days and fitted a curve with Fourier bases to create a single “smoothed” activity pattern for the 24-h period. We then performed standard PCA on this activity pattern, and the eigenvectors from the fPCA were extracted for the top components, each representing a distinct feature, or profile, of the rest-activity cycle. For each PCA component, the associated eigenvalue quantifies to what degree the participent’s 24-h rest-activity pattern reflects the specific rest-activity profile characterized by this PCA component, and was used in subsequent association studies. The number of principle components was determined by a scree plot. Our code for the fPCA and a sample dataset are available at https://github.com/cicibauer/fpca_NHANES.We used R packages fda to perform the fPCA  to derivTrained interviewers gathered sociodemographic information from the participant family and sample person, and we focused on the following: 1) age ; 2) gender ; 3) race/ethnicity ; 4) education ; 5) household income ; 6) work status in the past week . Study participants also reported self-rated health status as excellent, very good, good, fair or poor, from which we created a dichotomous variable indicating whether a participant reported poor or fair health. Distribution of sociodemographic variables and self-rated health are presented in Supplementary Table N = 10,275) because socioeconomic indicators assessed among younger adults in the early 20 s may not accurately reflect their lifelong SES. We excluded those missing education (N = 11), income (N = 894) and work status (N = 6) . We further excluded those with no actigraphy data  or missing one or more 5-min epochs across all seven days (N = 329). The overall analysis included 7,657 participants. The stratified analysis investigating the weekday-weekend differences had 7,041 participants with both weekday and weekend rest-activity profiles (detailed below). The analysis on self-rated health excluded participants with missing self-reported health status (N = 528).A total of 19,931 participants were in NHANES 2011–2014. We focused on adults 25 or older  because it was presumed to be the healthiest. For gender and race/ethnicity, we chose men and NH White because they had the larger/largest sample size. For all SES and work variables, we chose the group with presumably the highest SES . To determine the association between rest-activity profiles and self-rated health, we first divided the eigenvalue for each component, or rest-activity profile, into quintiles and then used multiple logistic regression to calculate the odds ratio (OR) and 95% confidence intervals (CI) for reporting fair or poor health comparing Q1-Q4 to Q5 (reference group), adjusting for age, gender, race/ethnicity, education, income and work status. We performed analyses for overall activity and for weekday- and weekend-specific patterns separately. All analyses accounted for the complex sample design by including NHANES full sample weights . We performed fPCA using R package fda (version 5.1.9) and the rest of the analyses were performed in SAS .Figure Each rest-activity profile was associated with multiple participant characteristics Fig. . A higheTable Q1 vs. Q5 (95% CI), 2.61 , p-trend < 0.0001), and the results were similar for both weekday and weekend profiles. The early rise profile showed a suggestive inverse U-shaped association, with Q2 and Q3 each exhibiting a 28% increase in the OR for poor and fair health. Moreover, the results appeared more pronounced for the weekend early rise profile, showing a 43–44% increase. Participants in the lowest quintile of the eigenvalue for the prolonged activity window profile  were 38% less likely to report poor or fair health, when compared to those in the highest quintile ). However, the association was weaker in the analysis focusing on weekday and weekend profiles specifically. Finally, participants in the lower quintiles (Q1-Q4) of the eigenvalues for the biphasic activity profile exhibited a 47%-62% increase in the odds of reporting poor or fair health when compared to those in the Q5, and the results were fairly consistent between weekday and weekend profiles.Table In a nationally representative sample of US adults, we identified four distinct profiles for the 24-h rest-activity cycle. We found considerable variation in these profiles across different subgroups by age, gender, race/ethnicity, SES and work status. We also observed associations between rest-activity profiles and self-rated health status.The High amplitude profile (PCA1) appeared to be primarily driven by a daytime physical activity levels. Previous studies using fPCA also found the high amplitude profile as the primary component substantially explaining the population variance –14, 16, higheractivity levels in men [The high amplitude profile was significantly associated with almost every participant characteristic in our study, although some findings were consistent with previous literature while others not. For example, lower amplitude was positively associated with age, which was consistent with the well-established evidence suggesting a decline in physical activity levels in older adults , 25. In s in men . The diss in men .High amplitude profile was positively associated with income and the association was more pronounced on weekends than on weekdays. A previous study in NAHNES 2003–2006 also reported that higher income was associated with higher activity levels, especially less frequent but higher intensity physical activity on weekends , suggestThe early rise profile (PCA2) captures the timing aspect of the rest-activity cycle. All previous fPCA studies identified at least one component related to early vs. late activity window, explaining 11.4%-23.0% of total activity variation –14, 16. The strong positive relationship between age and the early rise profile observed in our study has been consistently reported previously , 35. ThePrevious studies on the relationship between SES  and timing of rest-activity patterns were inconclusive: Mitchell et al. reported an association between earlier acrophase and lower education, but no difference across income groups,  while DiThe prolonged activity window profile is characterized by an early rise time but also a later time in the declining phase of the rest-activity pattern, resulting in a longer day activity window, and a similar profile was reported in previous studies in the MrOS  and a saWe found Black participants were more prone to exhibit the prolonged activity window profile, consistent with the current literature invariantly reported that Black Americans had shorter sleep duration than their White counterparts . We alsoThe biphasic profile was reported by previous fPCA studies –14, 16, The biphasic profile was more pronounced among participants with higher education and income, and who reported working 40 h or more. Moreover, the biphasic pattern was only present on weekdays, suggesting a role of occupational factors. Therefore, the morning and evening peaks of activity in this profile may correspond to work-related commute and social and family activities before and after work hours , 47, whiA major strength of our study is its large, diverse and nationally representative sample, which allowed us to evaluate rest-activity patterns in the general US population and make comparisons across subgroups by demographic, socioeconomic and work characteristics. Moreover, we took a flexible, data-driven approach without a priori assumptions to characterize rest-activity patterns, and derived multi-dimensional features of the 24-h movement behaviors. Finally, we performed stratified analysis by weekdays/weekends, allowing for the assessment of potential impact of workday schedule on the rest-activity cycles.Our study also has several limitations. First, although fPCA is an innovative method with many advantages, it also has some limitations. Most notably, the derived profiles are sample-specific and may not be applicable to other populations. It is thus important to include a large, diverse and representative sample such as the NHANES to improve the generalizability of study findings. Moreover, the method can be subject to over-fitting, and profiles obtained can be difficult to interpret. However, we did identify distinct and interpretable profiles in our sample. Second, we only examined six participant characteristics, while many other factors such as lifestyle, health conditions, and social interactions may play a role in shaping rest-activity behaviors. Third, the work variable in our study was crude and only collected information on the work status in the previous week. Given our finding of an important role of work status, future studies should include more detailed information and long-term assessment to better examine employment and occupational factors in relation to rest-activity patterns. Fourth, despite the representativeness of our study sample, the profiles and associations reported by our study may not be generalizable to other samples, especially populations with highly distinct rest-activity patterns such as night-shift workers and severely ill patients. Fifth, we focused on self-rated health as a general measure of health, whereas rest-activity profiles may have different associations with different disease outcomes. Finally, NHANES is a cross-sectional study, and therefore we could not determine the direction of certain associations, particularly those related to SES, work status, and self-rated health.Our study identified distinct rest-activity profiles among the US population and provided supporting evidence that human rest-activity patterns are shaped by demographic, socioeconomic and work factors. Some of the results, especially those for the high amplitude component, were consistent with previous studies focusing on individual behavior patterns such as overall physical activity. However, we also identified and examined profiles that captured additional aspects of the behavior  of the diurnal rest-activity patterns, for which there has been limited study on their sociodemographic, occupational and health correlates. Currently, behavioral interventions largely focus on altering individual behaviors, such as increasing physical activity levels, reducing prolonged sitting, and improving sleep quality. More recently, researchers in the field of circadian physiology have also developed strategies that may help improving the rhythmicity of diurnal behaviors, such as timed melatonin treatment, light exposure, and exercise regimes , 49. We Overall, the relationships between environmental exposures and behavior patterns are complicated, and may also intertwine with the internal circadian clock. We encourage future studies to focus on identifying additional individual and environmental factors that explain rest-activity patterns, and.Additional file 1: Supplementary Figure 1. Weekday rest-activity profiles of 24-hour actigraphy data from adults in the National Health and Nutrition Examination Survey (2011-2014). Each panel depicts the mean 24-hour activity patterns for participants with high (solid line) and low (dotted) eigenvalues of the first four components derived from the functional principal component analysis (PCA): A) The first component (48.5% variance), with higher eigenvalues representing a higher amplitude; B) the second component (20.6% variance), with higher eigenvalues representing earlier rise time; C) the third component (10.1% variance), with higher eigenvalues representing a more prolonged daytime activity window; D) the forth component (6.2% variance), with higher eigenvalues representing a more pronounce biphasic pattern characterized by a mid-day dip in activity. Supplementary Figure 2. Weekend rest-activity profiles of 24-hour actigraphy data from adults in the National Health and Nutrition Examination Survey (2011-2014). Each panel depicts the mean 24-hour activity patterns for participants with high (solid line) and low (dotted) eigenvalues of the first four components derived from the functional principal component analysis (PCA): A) The first component (42.4% variance), with higher eigenvalues representing a higher amplitude; B) the second component (21.8% variance), with higher eigenvalues representing earlier rise time; C) the third component (10.1% variance), with higher eigenvalues representing a more prolonged daytime activity window; D) the forth component (6.7% variance), with higher eigenvalues representing a later activity peak. Supplementary Table 1. Distribution of study characteristics among 7,657 participants aged 25 or older in the National Health and Nutrition Examination Survey (2011-2014). Supplementary Table 2. Pearson correlation coefficients between overall, weekday and weekend rest-activity profiles among adults in the National Health and Nutrition Examination Survey (2011-2014).


Ile-de-France region (Paris Area).During the SARS-CoV-2 pandemic, the French Government imposed various containment strategies, such as severe lockdown (SL) or moderate lockdown (ML). The aim of this study was to evaluate the effect of both strategies on severe trauma admissions and ICU capacity in stJanuary 2017 to 31th December 2020, including all consecutive trauma patients admitted to the trauma centers of Ile-de-France region participating in the national registry (Traumabase®). Two periods were defined, the “non-pandemic period” (NPP) from 2017 to 2019, and the “pandemic period” (PP) concerning those admitted in 2020. The number of ICU beds released during 2020 pandemic period  was estimated by multiplying difference in trauma admissions by the median length of stay during the same week of pandemic period (ICU day-beds in 2020).We conducted a multicenter cohort-based observational study from 1Ile-de-France.A 15% yearly reduction of trauma patients was observed during the PP, associated with the release of 6422 ICU day-beds in 2020. During SL and ML, the observed decrease in trauma admission was respectively 49 and 39% compared with similar dates of the NPP. The number of beds released was 1531 days-beds in SL and 679 day-beds in ML. Those reductions respectively accounted for 4.5 and 6.0% of the overall ICU admission for COVID-19 in The lockdown strategies during pandemic resulted in a reduction of severe trauma admissions. In addition to the social distancing effect, lockdown strategies freed up an important number of ICU beds in trauma centers, available for severe COVID-19 patients. As ats (ICU) , indicatstJanuary 2017 to 31th December 2020. During each of these 4 years, all consecutive trauma admitted to the trauma centers of Ile-de-France region (12.2 million of inhabitants) participating in the national registry (Traumabase®) were included . Patients were categorized in two groups according to their year of admission, allocating those admitted within years 2017, 2018 and 2019 to the “non-pandemic period” group and those admitted in 2020 to the “pandemic period” group. Each variable result over the non-pandemic period were summarized using its median (and interquartile) or number (and relative percentage).We conducted a multicenter cohort-based observational study from 1Ile de France area:in 2020: leaving home was only allowed for essential purchases, medical care, exercise (1-h session per day), and work journeys if work from home was impossible.Severe lockdown (SL) from week 12 to week 19 in 2020: leaving home was only allowed for essential purchases, medical care, exercise (1-h session per day), and work journeys . Kindergarten, schools and high schools were opened.Moderate lockdown (ML) from week 45 to week 51 During the pandemic period, two types of lockdown strategies were applied in The number of ICU beds released during 2020 pandemic period  was estimated by multiplying difference in trauma admissions (difference between median number of trauma admissions each week during the pandemic and the non-pandemic period) by the median length of stay during the same week of pandemic period (ICU day-beds in 2020).Ile-de-France region are available on open access data [Data for SARS-CoV-2 patients admitted in ICU in ess data .Ile-de-France.Over the 4 year-period, 11,230 trauma patients were admitted in the participating trauma centers, 8749 during the non-pandemic period (average 2916 per year) and 2481 during the pandemic period, representing a 15% yearly reduction during the pandemic period (Table p = 0.68). Mechanisms of injury differed markedly between both period: road traffic accidents decreased during both lockdown whereas proportion of penetrating trauma and fall from height significantly increased (but not the absolute numbers of event) (Table The characteristics of the population during pandemic and non-pandemic period are described Table t) Table .These results highlight how trauma epidemiology in Paris Area has been impacted during the pandemic period and especially during the SL and the ML. The national lockdowns imposed by authorities to control the pandemic reduced severe trauma incidence. This study suggests that lockdown benefice on SARS-CoV-2 pandemic management is not only related to the prospect of controlling virus circulation. The reduction of trauma center workflow during lockdown freed up an important number of ICU beds for critical care patients and this effect seems to be more pronounced with a severe lockdown rather than a moderate one. This “collateral effect” of lockdown partly protected our health care system when facing the overwhelming and sudden surge in the number of patients in need of urgent treatment, it allowed to reallocate these free ICU beds to SARS-CoV-2 patients and probably contributed to maintain a part of scheduled major surgical activity such as cancer, cardio-vascular surgery or transplantation. This important observation might help public health stakeholders when preparing for potential future pandemic waves.Furthermore, during this period, trauma centers did not restrict their admission policy, as indicated by the similar median ISS across the study periods. The proportion of patients operated within the first 24 h remained identical suggesting timely access to complex trauma surgery was not impeded. The trauma system analyzed in this study provides adequate and appropriate care equivalent to non-pandemic period, while participating significantly to the 2020 COVID-19 surge.Study limitations first include a lack of specific data on SARS-CoV-2 admission in Trauma centers. However, the official occupation rate of ICU in Paris area during pandemic waves was over 100% during SL and ML compared with ICU standard capacity. Secondly, this study only represents urban territory and should not be transposed in rural territories. Thirdly, data for non-trauma centers are not available; then the effect of lockdown on ICU beds release in non-trauma center could not be analyzed.The lockdown strategies during pandemic resulted in a reduction of severe trauma admissions. This reduction did not appear to be driven by modification of severe trauma admission policy or quality of care, although surge capacity may have impacted both. In addition to the social distancing effect, lockdown strategies freed up an important number of ICU beds in trauma centers, available for severe COVID-19 patients.