Dietary intake of polychlorinated dioxins and furans (PCDD/Fs) and dioxin‐like polychlorinated biphenyls (dl‐PCBs) from various foods  was investigated for various sex/age groups of the Italian population. The concentrations of PCDD/Fs and dl‐PCBs and their contribution to total TEQ values varied depending on food matrix. Fish (0.50 pg WHO‐TEQ/g wet weight) and seafood (0.16 pg WHO‐TEQ/g wet weight) showed the highest mean concentrations of PCDD/Fs plus dl‐PCBs, followed by meat (1.70 pg WHO‐TEQ/g lipid weight), meat based products (1.03 pg WHO‐TEQ/g lipid weight), milk and dairy products (0.78 pg WHO‐TEQ/g lipid weight), hen eggs (0.71 pg WHO‐TEQ/g lipid weight), fats (0.27 pg WHO‐TEQ/g lipid weight) and olive oil (0.09 pg WHO‐TEQ/g lipid weight). In all samples WHO‐TEQ PCDD/F plus dl‐PCB concentrations fulfilled the European Union food law, except in pork loin samples (1.39 pg WHO‐TEQ/g lipid weight). Differences in exposure depending on the sex/age groups (children > teenagers > adults > elders) and hypotheses considered (lower bound and upper bound) were encountered. Non‐cancer risk values showed a low exposure. Carcinogenicity risk results revealed that highly exposed individuals were distributed over all sex/age groups, even though the proportion of individuals exceeding the safe limit was higher in children. These data once again underline the importance of trying to control the levels of these contaminants in fishery products, particularly in fish, who represents one of the main exposure sources for consumers.This paper may help the consumer in making food choices to minimize the exposure risk to dioxins, furans and PCBs Anthropogenic release of PCBs has resulted in a widespread contamination of the soils and sediments. Similarly, the release of PCDD/Fs during a variety of industrial and thermal processes as well as by a number of natural processes, such as volcanic eruptions and forest fires has pervaded the environment, generating contaminated sites and hot spots. It is broadly recognized that the major route of exposure of PCBs and PCDD/Fs to humans is through dietary uptake. Due to their lipophilic character, these chemicals tend to concentrate in lipid rich foods as meat, fatty fish, milk and dairy products inducing a wide spectrum of toxic responses including infertility, reproductive system disorders, immunological toxicity, and carcinogenic effects  and polychlorinated dibenzo‐cts WHO, .The number of dietary contamination episodes occurred during the last decades has highlighted the need to strengthen the legislative measures either to reduce the presence of these pollutants in the environment or to keep their levels within safe limits in food. The European Union (EU) has, in fact, set maximum levels permissible for PCDD/Fs and dioxin‐like‐PCBs (dl‐PCBs) in foods, expressed as toxic equivalents (WHO‐TEQ), in regulation No 1259/2011  to evaluate their compliance with the maximum permissible limits (MPLs) set by European Union regulation. The dietary intake of PCDD/Fs and dl‐PCBs was subsequently estimated for age/gender subgroups of the Italian population by comparison with the new provisional tolerable weekly intake recommended by EFSA. At the end, the potential human health risks were evaluated using the hazard quotient (HQ) and lifetime cancer risk (LCR).22.1n = 30) of about 0.1−0.2 kg of muscle tissue were taken. The composite samples (only edible part) were homogenized and stored below −20°C. For each food item, two composite samples were prepared for the analytical determination.In May–July 2019, food samples were randomly acquired in supermarkets representing the five most popular retailer brands in Italy. The various supermarkets were located in 6 cities  of Southern Italy. A total of thirty‐five types of foods classified in the following groups: (1) fish  and seafood , , ; (2) meat  and meat‐based products ; (3) milk (cow whole milk) and dairy products ; (4) eggs from free foraging chickens; (5) extra virgin olive oil and fats  were acquired from five supermarkets of each city. For each food, four individual items were taken from each supermarket of the six cities and combined into a composite sample. For shellfish and some crustaceans , 10 individual units were included to prepare the composite sample. For bluefin tuna, slices  together with the seventeen 2,3,7,8‐ substituted PCDD/F congeners were determined. The analytical method has been reported in detail in previous paper : non‐2SO4 and spiked with PCB 143  and extracted with hexane. A liquid–liquid extraction with organic solvents adapted to the matrix type was applied to liquid samples , using 50 mL of a mixture of hexane/dichloromethane  for elution of the analytes. The eluate was evaporated to dryness and redissolved in 100 µl of iso‐octane.For the determination of dl‐PCBs, homogenized samples (0.5−3.0 g) were mixed with Na13‐labeled extraction standards were added to the samples in order to control the whole sample preparation process. The final obtained PCBs and PCDD/Fs extracts were injected and analyzed separately.For the determination of PCDD/Fs (US EPA method 1613), the samples extracted, as above reported, were subjected to a multistep clean up to remove the matrix and the potential interfering components. The first stage was a fat destruction step consisting of a treatment of the sample solution with sulphuric acid and base back‐extraction. The obtained extracts were then subjected to a preconditioned florisil clean‐up column, which was eluted with different solutions in order to remove interfering components. The first eluted solvent was discarded, while the second eluate containing PCDD/Fs was collected. The extracts were evaporated to dryness and redissolved in iso‐octane. Appropriate C2.3r2 > 0.999) in the linear response interval of the detector were created for the quantification. The calibration curves were prepared to result in a range of 0.5–800 ng/ml for PCB congeners and in a range of 0.025–2.00 pg/µL for PCDD/Fs.PCDD/Fs and dl‐PCBs analysis were performed on a HRGH/HRMS system consisting in a MAT 95 XL mass spectrometer, coupled with a GC Trace series 2000 . Chromatographic separation was carried out with a Trace Gold TG‐Dioxin capillary column  for PCDD/Fs and with a Trace TR‐PCB8 MS capillary column  for dl‐PCBs. Helium (99.9999% purity) at 1 ml/min was used as carrier gas and the temperatures of ion source and transfer line were set at 260 and 290°C, respectively. Injections (1 µl) were performed in split less mode on an A200S autosampler (Thermo Fisher Scientific). Electron ionization mode (E.I.), as well as voltage selected ion recording mode (VSIR) was chosen as operating method. Electron energy was 35 eV. The detector resolving power was >10.000  and the two most intense ions were monitored for the determination of the single congeners. Perfluorokerosene (PFK) was the mass reference used. The quantification was performed by isotope dilution method. Multi‐level calibration curves  was performed through the analysis of procedural blanks, quantitative control sample for each batch of samples, duplicate sample, and a standard reference material  for each set of samples. For the replicate, standard reference materials and recovery of labelled compounds , the relative standard deviations (RSD) were <10% for all the detected compounds. The recovery rates of labelled standards were between 85 and 120%. Obtained values were deviating with less than 20% from the consensus values. The limits of detection (LODs) were calculated as three times the signal‐to‐noise ratio and varied amongst analyte groups (0.0038−0.16 pg/g for PCDD/Fs and 0.04−1.40 pg/g for PCBs). The limits of quantification (LOQs) were the followings: 0.04−1.0 pg/g for PCDD/Fs and 0.12−3.80 ng/g for PCBs. Concentrations of PCBs and PCDD/Fs are expressed as pg WHO‐TEQ/g on wet or lipid weight basis in accordance with the EU legislation .The dietary intakes of PCDD/Fs plus dl‐PCBs were calculated via deterministic approach. The estimated dietary intakes were calculated by multiplying the food consumption data by mean TEQ concentrations of dioxin‐like PCBs and PCDD/Fs in each food and then dividing by the body weight. Dietary habits and biometric data of the total population and of various sex/age classes  were obtained by the Italian national food consumption survey INRAN‐SCAI  are considered unacceptable, while the US EPA  to be unacceptable.Cancer and noncancer health risks were determined on the estimated dietary intake of PCDD/Fs plus dl‐PCBs. The noncancer risk evaluation was assessed on the Hazard Quotient (HQ) and was calculated by dividing the daily intake by the reference dose (RfD). The reference dose for the sum of PCDD/Fs and dl‐PCBs is 0.7 pg TEQ/kg body weight/day US EPA, . An esti US EPA, , 1994 an US EPA,  were use US EPA,  consider33.1Concentrations of PCDD/Fs and PCDD/Fs plus dl‐PCBs expressed in picograms of WHO‐TEQ are illustrated in Tables As expected, fish (0.50 pg WHO‐TEQ/g wet weight) and seafood (0.16 pg WHO‐TEQ/g wet weight) was the food group showing the highest mean concentrations of PCDD/Fs plus dl‐PCBs. The other food categories were in the following order: meat (1.70 pg WHO‐TEQ/g lipid weight) and meat‐based products (1.03 pg WHO‐TEQ/g lipid weight), milk and dairy products (0.78 pg WHO‐TEQ/g lipid weight), eggs (0.71 pg WHO‐TEQ/g lipid weight), fats (0.27 pg WHO‐TEQ/g lipid weight), and olive oil (0.09 pg WHO‐TEQ/g lipid weight).Within seafood, excluding fish, the highest degree of contamination was in shellfish (0.32 pg WHO‐TEQ/g wet weight), followed by crustaceans (0.09 pg WHO‐TEQ/g wet weight) and cephalopods (0.03 pg WHO‐TEQ/g wet weight).For meat, a small margin between veal fillet (1.55 pg WHO‐TEQ/g lipid weight) and pork loin (1.39 pg WHO‐TEQ/g lipid weight) concentrations was observed as well as slightly higher levels were found in turkey breast (2.00 pg WHO‐TEQ/g lipid weight) compared to chicken breast (1.82 pg WHO‐TEQ/g lipid weight). Among meat‐based products, baked ham (1.13 pg WHO‐TEQ/g lipid weight) and salami (1.11 pg WHO‐TEQ/g lipid weight) appeared to be more contaminated, followed by raw ham (1.02 pg WHO‐TEQ/g lipid weight) and mortadella (0.86 pg WHO‐TEQ/g lipid weight).Concerning milk and dairy products the highest value corresponded to yoghurt (2.20 pg WHO‐TEQ/g lipid weight), followed by milk (1.37 pg WHO‐TEQ/g lipid weight) and hard cheese samples , while the remaining products exhibited a lower contamination level (0.03–0.17 pg WHO‐TEQ/g lipid weight).For fats, a large variation was observed with olive oil samples having lower levels (0.09 pg WHO‐TEQ/g lipid weight) respect to other group components (0.22–0.33 pg WHO‐TEQ/g lipid weight). The wide concentration fluctuation either in various food categories or within the same group is obviously linked to multiple factors. For instance, the structure and the dynamic of the food webs appear to be a determinant of contamination levels in marine biota. Within fish, top predator species with longer food webs tend to have higher levels than those with lower trophic levels . These findings are consistent with those found in other studies confirming that PCBs are more strongly biomagnified in food chain than PCCD/Fs  and milk (86.9%) too, the main contribution to the WHO‐TEQ came from the PCDD/Fs, whereas in eggs were more abundant dl‐PCBs (85.9%). Within meat‐based products, the contribution percentage of PCDD/Fs and dl‐PCBs to total TEQ was largely variable. Specifically, in salami (73.9%) and baked ham (64.6%) noticeable was the role of dl‐PCBs, whereas in mortadella (75.6%) and raw ham (89.2%) samples, PCDD/Fs toxicity equivalents were greater than those of dl‐PCBs.For milk and dairy products too, the contribution of PCDD/Fs or dl‐PCBs to the cumulative TEQ was different based on the food type. Both cow and goat milk cheeses and mascarpone showed a prevalence of dl‐PCBs (60.0%−82.4%) unlike milk (86.9%) and the remaining dairy products in which a predominance of dioxins was observed (53.6%−66.6%).In olive oil and fat group, the contributions of PCDD/Fs and dl‐PCBs to the total TEQ values were very similar and ranged between 55.5% and 59.3%. In general terms, these findings reflect the existence of different sources of contamination between foodstuffs of aquatic origin and land‐based products and confirm the robust dl‐PCBs contribution to the cumulative TEQ in fish , indicate that the mean concentrations of PCDD/Fs plus dl‐PCBs found here are reasonably coherent with the findings from different studies conducted in other European countries, such as Spain  on the basis of new epidemiological and experimental data on animal, has re‐assessed the human health risk related to the presence of PCDD/Fs and dl‐PCBs in food establishing a new tolerable weekly intake (TWI) value of 2 pg WHO‐TEQ/kg body weight EFSA, .As shown in Table p > 0.05), probably due to nearly similar dietary patterns and amount consumed within each age group.From these data, a decreasing trend emerged with age, with children having an intake double that of teenagers and three times that of adults and the elderly. The high value found in children should be due to the different dietary habits with the rest of the population together with their lower body weight, factors that strongly influence exposure. However teenagers were also highly exposed with a total intake of about twofold higher than in adults and elderly people. Furthermore, with regard to gender, dietary intake was higher in females teenagers and adults than males, while an opposite trend was observed in the elderly. However all intake values did not reach levels of statistical significance between genders . Particularly important in this evaluation was the observation that within the fish group, bluefin tuna was the main contributor to the intake of these pollutants for all sex/age groups. In this context, excluding tuna from the exposure estimates, there were two general considerations that capture attention and that must necessarily be discussed. The first was connected to food distribution pattern, which remained almost unchanged within the different population groups considered, with the exception of children for whom the contribution of milk to the total intake equalized that of seafood. The second aspect concerned the type of fish, the frequency of consumption and the meal size which are crucial issues to adequately balance the health benefits and risks of regular fish consumption Domingo, .This deserves even more attention in connection with the fact that potential impact of these chemicals on human changes dramatically at life stages with wider and more critical windows not only in infants and children, but also in the elderly population. In fact, the physiological changes that accompany the normal aging process result in a progressive deterioration of bodily functions and the ability to respond to environmental stress  and methods  need to be considered. In our particular case, to reduce the uncertainty associated with consumption data, more accurate information on the quantities consumed by the different sex/age groups for each item, within each food category considered, would have been helpful. In addition, the expression of the results with lower bound approach introduces a large margin of imprecision in estimated exposure levels. Furthermore, food preparation and/or cooking methods alter dioxin and PCB levels in the final product , hen eggs (14.6%), meat and meat products (12.1%), and olive oil and fats (3.7%).In children, in addition to fishery products which represented 45.0% of the total intake, a significant source of PCDD/Fs and dl‐PCBs was the milk and dairy products whose intake, two to three times higher than in other age groups, accounted 24.8% of the total. Another important route of exposure included eggs (0.93 pg WHO‐TEQ/kg body weight/week), which accounted 15.4% of the total intake, followed by meat and meat‐based products constituting 11.6%, while olive oil and other fats with a contribution equal to 3.1%, played a marginal role in total exposure. As regards free‐foraging chickens eggs it should be emphasized that their consumption can be a relevant route of exposure for human  than males (42.1%). Milk and dairy products as well as the consumption of meat and meat‐based products made an important contribution to total intake showing a higher percentage in males  respect to females , unlike eggs whose contribution to total intake was higher in females (13.9%) compared to males (12.9%), whereas olive oil and other fats gave a percentage contribution of around 3.0% in both sexes.Adult males and females received 40.7% and 44.7% respectively of their exposure from fish and other seafood, followed by milk and dairy products, whose consumption resulted of 26.0% for both genders. The category of meat and meat‐based products determined a higher percentage in males (13.8%) than females (10.9%), whereas consumption of olive oil and other fats set around at 4.0% in both sexes.Finally, in oldest age group the exposure levels from marine products were higher in males than females representing 44.6% and 39.4% of the total intake, respectively. The consumption of meat and meat‐based products , eggs  and olive oil and other fats  contributed to the total intake with percentages almost similar in both sexes, whereas the females (30.0%) were more exposed respect to males (25.1%) solely via milk and dairy product consumption.3.6−6 pg/kg body weight/day, which means that one case of cancer occurs in every 1,000,000 individuals. On this basis, it was found that intake of these chemicals on daily basis via the various foods considered by children could possibly lead to the appearance of 13 cancer cases out of 100,000 individuals. A lower, although still significant, cancer risk was also found in all other sex/age groups studied with values ranging from 4.57 × 10−5 pg/kg body weight/day to 8.01 × 10−5 pg/kg body weight/day. The risk assessment results are sensitive to the choice of dose–response functions, and the selection of a function is often an important source of uncertainty in risk assessment. Consequently, a slope factor of 1 × 10−3 pg/kg body weight/day developed on human data basis was also used as an upper bound cancer risk estimator. On this basis, LCR values ranging from 3.04 pg/kg body weight/day to 8.63 pg/kg body weight/day were above the safe limit indicating a possibility of more cancer victims (Table Health risk assessment is one of the best approaches to investigate the potential risk of exposure to toxic substances for human, offering relevant information to public health decision‐makers to protect the consumer health. The findings of analysis for both carcinogenic and noncarcinogenic risks relative for different sex/age groups are shown in Table 4PCDD/Fs and dl‐PCB levels were determined in five different food categories to estimate dietary intakes of different sex/age groups of the Italian population. Fishery products showed the highest levels, followed by milk and dairy products, meat and meat products, and to a lesser extent hen eggs and olive oil and other fats. The levels measured in the composite samples were all well below the maximum limits set by the European Commission for human consumption, with the exception of the salami samples. The estimated intake of total population was slightly above the new tolerance limit of 2 pg WHO‐TEQ/kg b.w./week. Within different age groups, children had the highest exposure followed by teenagers and by adults and elderly. Although literature data show that dietary intake of PCDD/Fs and dl‐PCBs in the population has decreased in recent decades (Gonzales et al., Grazia Barone: Data curation; methodology. Arianna Storelli: Data curation; formal analysis; reviewing and editing; Maria Maddalena Storelli: methodology; validation; writing—original draft; writing—review & editing. Antonio Busco: Software. Rosanna Mallamaci: Data curation.Authors declare to not have any conflicts of interest.


Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis.We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis.Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr . The three markers showed area under the receiver‐operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19‐9 (CA19‐9) was added to the model.The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19‐9. We identified the metabolic features of pancreatic ductal adenocarcinoma (PDAC) and developed a metabolite biomarker panel for early detection of PDAC based on tissue and serum metabolomics analysis. This panel exhibited promising performance in distinguishing PDAC from benign pancreatic neoplasms (BP) or healthy controls (Ctr), especially in combination with CA19‐9. Recent research have demonstrated that metabolic reprogramming is one of the hallmarks of cancers.Currently, most metabolic research were conducted in serum, plasma, or urine samples. However, metabolite changes in biofluids are affected by several factors, including the metabolism of the liver, kidney and muscle, dietary intake, the activity of the microbiome, and other factors. Tissue metabolomics analysis is less subject to these confounding factors and can be a valuable source of metabolic biomarkers.In the current study, based on ultrahigh performance liquid chromatography equipped with quadrupole time‐off mass spectrometry (UHPLC‐Q‐TOF/MS) platform, a combined tissue and serum nontargeted metabolomics analysis was conducted using 105 pancreatic tissue samples and 164 serum samples from patients with PDAC or benign pancreatic cystic neoplasms (BP) and healthy controls (Ctr). Additionally, AAs and medium‐ and long‐chain fatty acids (FAs)‐targeted metabolomics analyses were also conducted, using serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects, to quantify the candidate biomarkers. The primary purpose of this study is to investigate the metabolic alterations of PDAC using tissue and serum samples and to develop new potential diagnostic biomarkers.22.1Pancreatic tissues were collected at the Pancreas Biobank of the First Affiliated Hospital with Nanjing Medical University from March to July 2019. A total of 105 pancreatic tissue samples were collected, including 51 tumors and 40 noncancerous pancreatic tissues  from PDAC patients and 14 samples from BP patients. All the tissue samples were frozen in liquid nitrogen immediately after surgical resection and stored at −80°C for further analysis. All tissue samples were pathologically confirmed.g for 10 min at 4°C, and the serum samples were stored at −80°C until analysis.For the training set, 164 fasting blood samples from 80 patients with PDAC, 36 with BP, and 48 Ctr were collected at three institutions  from August 2018 to December 2019. There are 40 overlapping samples of tissue and blood from the same PDAC patients. For the validation set, 76 blood samples from an independent cohort of 22 patients with early stage PDAC, 27 with BP, and 27 with Ctr were recruited from the General Hospital of Eastern Theater Command. PDAC or BP was diagnosed pathologically after surgery, and blood samples were collected preoperatively prior to any medication. All Ctr samples were obtained from the physical examination center of Affiliated Hospital of Nanjing University of Chinese Medicine. Immediately after collection, all blood samples were centrifuged at 3000 This study was approved by the ethics committee of Affiliated Hospital of Nanjing University of Chinese Medicine (2017NL‐135‐05), and written informed consent was obtained from the participants. The results of laboratory tests, including the level of total cholesterol (TC), triglyceride (TG), and CA19‐9 were collected. The exclusion criteria were as follows: (1) a history of acute diseases in recent 3 months or other malignancies or recurrent PDAC, (2) concomitant autoimmune system disease or hematological disease, and (3) patients unable to give informed consent. Cancer stages were determined according to the AJCC TNM classification of malignant tumors, 8th edition.2.22.2.1g for 20 min). The supernatant was transferred and dried in a vacuum centrifuge and stored at −80°C. Before LC‐MS analysis, the dried supernatant was redissolved in 100 μl of acetonitrile/water , adequately vortexed, and then centrifuged at 4°C, 13,200 g for 15 min. Finally, the supernatant was collected for LC‐MS analysis.A piece of pancreatic tissue (100 mg) was mixed with 1 ml of 90% methanol and then homogenized by an MP homogenizer . After sonicating at low temperature twice (30 min/each), the mixture was centrifuged at 4°C  and vortexed for 1 min. The mixture was incubated at −20°C for 60 min and then centrifuged at 4°C . Similar to the tissue sample preparation mentioned above, the supernatant was then transferred, dried, and redissolved for LC‐MS analysis.After being thawed at 4°C, 100 μl of serum was mixed with 400 μl of cold methanol/acetonitrile , and the mixture was ultrasonicated for 30 min. The supernatant was transferred and was then added to 2 ml of 1% sulfuric acid in methanol. The mixture was incubated in an 80°C water bath for 30 min to achieve fatty acid esterification. After that, 1 ml n‐hexane and 5 ml of water were added and mixed to extract the target compounds. Finally, 500 μl of the supernatant, spiked with the internal standard , were mixed for GC‐MS analysis.After being thawed on ice, 120 μl of serum was mixed with 1 ml of chloroform–methanol  samples were prepared by mixing equal amounts of each sample in serum and tissue nontargeted and targeted metabolomics analysis, respectively.2.4t‐test or Mann–Whitney U test was further applied for group comparisons of metabolites with VIP >1.0. A p value of <0.05 was considered statistically significant. False discovery rates (FDR) were calculated using the Benjamini and Hochberg method.After log transformation and pareto scaling, multivariate analysis was performed using SIMCA‐P 14.1 (Umetrics). Unsupervised principal component analysis (PCA) was performed to assess the overall metabolic alterations among groups and evaluate the stability of the analytical system. Supervised orthogonal partial least squares discriminant analysis (OPLS‐DA) was performed to compare the metabolic profiles of PDAC versus NT, BP, or Ctr. A 200‐times permutation test was subsequently used to evaluate the robustness of the model and assess the risk of overfitting for the model. The variable importance in the projection (VIP) value of each metabolite in the OPLS‐DA model was calculated to evaluate its contribution to the classification. The Student's p < 0.05 in each comparison  were considered differential metabolites.For targeted metabolomics analysis, the Multiquant 1.2 software (AB SCIEX) was used to extract chromatographic peak area and retention time. The standard correct retention time was used to identify the metabolites. The QC samples were processed together with the serum samples. Detected metabolites in pooled samples with a coefficient of variation of <30% were considered reproducible measurements. The metabolites that Analysis of variance (ANOVA) was performed to evaluate whether the level of metabolites was affected by potential confounding factors such as “gender,” “age,” “history of type 2 diabetes,” and “history of hypertension.” Spearman correlation analysis of these metabolites and clinical parameters, including TC, TG, and CA19‐9, was also performed. Based on the potential biomarkers, unconditional binary logistic regression was conducted to build the optimal model capable of discriminating PDAC patients from non‐PDAC participants. Subsequently, we further used unconditional binary logistic regression analysis to detect whether the model can discriminate PDAC patients from Ctr independently of clinical confounding risk factors. Finally, receiver‐operating characteristic (ROC) curve analysis was carried out to evaluate the diagnostic performance of the model. All statistical analyses were performed using R software (version 3.5.1) or SPSS 24.0 (SPSS Inc.).Hierarchical clustering analysis (HCA) was performed using Cluster 3.0 and the Java Treeview software, and the heatmap was obtained. A correlation network was constructed using the Cytoscape software (version 3.7.1). Pathway analysis was conducted using MetaboAnalyst 4.0 and the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database.33.1p = 0.01).The workflow of this study is presented in Figure p < 0.05). Serum CA19‐9 level was routinely tested for PDAC and BP patients as well as controls. Among the 80 patients with PDAC in the training set, 20 (25%) were CA19‐9 negative (CA19‐9 < 37.0 IU/mL). Of the 36 BP patients, four were CA19‐9 positive. All control subjects were CA19‐9 negative. The validation serum samples were from an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr. Detailed clinical characteristics of all participants are listed in Table Serum samples of the training set were from 80 patients with PDAC, 36 with BP, and 48 with Ctr. The 80 PDAC patients included 36 early stage and 44 late‐stage tumors. The 36 patients with BP included 15 IPMN, 9 MCN, and 12 SCN cases. The age between PDAC and BP groups also showed a significant difference  of QC tissue samples Figure a,b were As shown in Figure l‐tryptophan, leucine, proline, arginine, aspartate) and dipeptides, nucleotides and nucleosides , and TCA products  were significantly downregulated in PDAC , lipids, glycolytic metabolites (lactate) were upregulated, whereas most AAs  and lipids were significantly increased in both tissue and serum samples of PDAC, while the levels of several AAs  and TCA products (succinate) were significantly decreased in PDAC.Using KEGG pathway analysis based on the differential metabolites, 11 significantly perturbed metabolic pathways in PDAC versus NT or PDAC versus Ctr were commonly found in serum and tissue samples of PDAC Table . The fou3.3l‐tryptophan, proline, creatine, arachidonic acid, and palmitic acid, remained significant (p < 0.05) and altered in the same direction as in the training set in PDAC versus Ctr. Using ANOVA tests, the five metabolites remained significantly different between the two groups after adjusting for gender, age, history of type 2 diabetes, and hypertension  (Table p < 0.001) after adjustment for these clinical characteristics  from Ctr in the training set. The panel achieved a higher AUC of 0.880 (95% CI = 0.864–0.896), and a higher sensitivity of 88.9% . Besides, for the CA19‐9‐negative patients with PDAC (n = 20) in the training set, the AUC of the panel  and TCA products (succinate) were significantly downregulated in PDAC. After systematic analyses of the data of PDAC tissue and serum metabolomics, a biomarker panel consisting of proline, creatine, and palmitic acid was identified and validated.The upregulation of FAs and lipids in our study may be attributed to the increase of various lipases and de novo FA synthesis that is observed in PDAC cells and many other cancers.n = 15),p < 0.05). Although there were some discrepancies in the concentration changes of several AAs, most AAs were downregulated in PDAC patients according to previous studies.The downregulation of AAs indicated that the anabolism of protein was vigorous, with enhanced uptake of AAs to satisfy the rapid proliferation of cancer cells. These findings are consistent with a previous study using tissues from PDAC patients  that were at risk for PDAC, but missing other populations, such as chronic pancreatitis and new‐onset diabetes. Third, further validation including other malignant tumors, such as pancreatic neuroendocrine carcinoma, cholangiocarcinoma, ampullary cancer, and gastrointestinal malignant tumor, is needed to evaluate the specificity of this panel for PDAC. Besides, the qualitative evaluation was insufficient. Of the 14 candidate metabolite biomarkers, pyroglutamic acid and 16‐hydroxypalmitic acid were not validated in this study, which need to be qualified in the future.In summary, tissue and serum metabolomics analysis of PDAC in our study showed similar metabolic changes and perturbed pathways. Based on them, a biomarker panel consisting of proline, creatine, and palmitic acid was developed and could serve as a promising biomarker panel or a complementary role to CA19‐9 for the detection of PDAC.Rui Zhao: project conception, methodology, formal analysis, and writing of the original draft. Shuai Ren: methodology and formal analysis. Changyin Li: methodology and data curation. Kai Guo: investigation and validation. Zipeng Lu, Lei Tian, and Jian He: Resources. Kai Zhang: data curation. Yingying Cao: Software. Shijia Liu: project conception. Donghui Li: Project conception, review, and revised the manuscript. Zhongqiu Wang: project conception and supervision.This work was funded by grants from the National Natural Science Foundation of China , Developing Program for High‐Level Academic Talent in Jiangsu Hospital of TCM (no. y2018rc04), and Innovative Development Foundation of Department in Jiangsu Hospital of Chinese Medicine (grant no. Y2019CX27 and Y2021CX19).The authors declare that they have no potential conflict of interest.This study was approved by the ethics committee of Affiliated Hospital of Nanjing University of Chinese Medicine (2017NL‐135‐05).Written informed consent was obtained from all the participants.Appendix S1.Click here for additional data file.


However, calculating r provides information about the relationship between two parameters, but does not provide any statement about the agreement or concordance of two testing procedures. Hence, to assess replaceability, the concordance correlation coefficient (ρc) and the Bland-Altman analysis including the mean absolute error (MAE) and the mean absolute percentage error (MAPE) seem to be more appropriate. Therefore, an exemplary model based on r = 0.55 showed ρc = 0.53, A MAE of 413.58 N and a MAPE = 23.6% with a range of −1,000–800 N within 95% Confidence interval (95%CI), while r = 0.7 and 0.92 showed ρc = 0.68 with a MAE = 304.51N/MAPE = 17.4% with a range of −750 N–600 N within a 95% CI and ρc = 0.9 with a MAE = 139.99/MAPE = 7.1% with a range of −200–450 N within a 95% CI, respectively. This model illustrates the limited validity of correlation coefficients to evaluate the replaceability of two testing procedures. Interpretation and classification of ρc, MAE and MAPE seem to depend on expected changes of the measured parameter. A MAPE of about 17% between two testing procedures can be assumed to be intolerably high.Measuring maximal strength (MSt) is a very common performance diagnoses, especially in elite and competitive sports. The most popular procedure in test batteries is to test the one repetition maximum (1RM). Since testing maximum dynamic strength is very time consuming, it often suggested to use isometric testing conditions instead. This suggestion is based on the assumption that the high Pearson correlation coefficients of  Producing high maximal strength is of crucial importance to reach high level performance in many sports such as American football and rugby –6, baskeEspecially in elite sports and competitive sports, the effectiveness of training routines is evaluated with performance testing  to monitAdvocates of isometric testing highlight the supposed advantages regarding the quantification of various force-time characteristics , 38–40, Additionally, measuring force-time characteristics like rate of force development (RFD) or isometric impulse is considered to provide information on various dynamic strength qualities , 43. HowAccordingly, a potentially higher transfer of dynamic testing measures towards speed strength performances like sprinting, jumping, rapidly performed directional changes  seems rational , 64, 68.Still, based on the supposed advantages of isometric testing conditions and the additional information on force-time characteristics testing has led multiple authors to suggest substituting 1RM testing with isometric testing to monitor athletes' training progress  McGuiganr = 0.2–0.5 small, r > 0.5–0.7 moderate, r > 0.7 high correlation) (Investigating the concordance between two measurement devices is a well-known problem in medicine –88. In telation) , it is selation) , 13.3% welation)  or 12 ± elation) , the poselation)  describeelation) .r = 0.9 and r = 0.7. Data were compiled and added from previous investigations. The MAE was determined using, n = number of participants, ix = the isometric strength value, and iy = dynamic strength valueConsequently, assuming high correlation coefficient, the calculation of the concordance correlation coefficient and Bland-Altman analysis including MAPE, and MAE were carried out assuming r ≥ 0.7), which are partially higher than the stated correlations in literature with r = 0.52–0.97 , classification of effect sizes should be considered in the light of content. Accordingly, stated substantial higher borders made by Cataldi et al.  error between two testing methods, it cannot be assumed that both testing method measure the same parameter and a replacement of one of both testing methods should be avoided. Therefore, the objective of an investigation and the chosen procedure to evaluate strength capacity should be selected carefully, as it can be assumed that 1RM and isometric testing conditions will not measure the same parameter. There are some hypotheses explaining differences in strength values dependent on measurement procedure.From a physical and mechanical point of view, force is defined as F = m * a, so force capability can be described as the ability of the body to accelerate a mass. Maximal strength in a dynamic strength measurement is only assumed to be maximal if the gravitational force acting on the resistance and the force output exerted on the resistance by the individual are equal, so no movement or acceleration of the mass (resistance) would be present. With isometric force measurements, Newton's third principle [for every action (force) in nature there is an equal and opposite reaction] is used to measure the opposing force to an insurmountable resistance. Since performing a one repetition maximum involves moving a surmountable resistance through a range of motion, it is not the same as assessing maximal strength/MVC, leading to the assumption that the 1RM performance would be lower than MVIC. Furthermore, when performing a 1RM, once the initial sticking point is surpassed, the force to move the weight decreases due to inertia . In contIn science and diagnostics, there are high demands on standardization to ensure equal testing conditions and exclude external factors influencing the results. Angle specificity of maximal isometric strength produces different maximal strength values when performed at different joint angles , 112. AnThe 1RM bench press, back squat and clean are the most common methods of assessing maximum strength in athletes –120. Usir = 0.79–0.86) in maximal isometric strength with 1RM in the squat indicating that “these findings demonstrate a degree of joint angle specificity to dynamic tasks for rapid and peak isometric force production”   , 80 can 35–0.99)  might in35–0.99) –20, bask35–0.99)  and weig35–0.99) –134. TheSeveral factors influencing the estimation of maximal strength can lead to significant errors dependent on testing conditions in cross sectional study designs. Since high specificity in training regimes can be assumed  a questiConsequently, if Yahata and colleagues  would teThe use of correlation coefficients to justify the replaceability of 1RM testing with isometric testing seems invalid, since the MAPE and MAE between both measurement procedures is intolerably high, even when high correlation coefficients with high sample sizes were used. Investigating the agreement between two measurement conditions requires further analytic approaches, such as concordance- and Bland-Altman analyses with classification of MAPE and MAE values. Investigations considering adequate analyses are very rare in exercise science. Results showing that both 1RM and MVIC present a different estimation of the maximal strength capacity of the participant. Therefore, assuming there are equivalent measures between dynamic performance and isometric testing conditions , 84 shouUsing maximal strength tests in practice—performance diagnostics in sports or pre-post-test designs in scientific studies—authors should consider limitations which should be minimized. Since a higher transfer of 1RM to sport specific movements can be assumed and most athletes using dynamic movements in their daily training routines, a higher application of dynamic testing protocols can be hypothesized in field tests , 109 “Fr


P < 0.05). Multivariate logistic regression analysis showed that higher D‐dimer value, higher incidence of lower limb edema, and TB were risk factors for VTE; OR (95%CI) = 8.840 (2.383–32.794); OR (95%CI) = 4.957 (1.219–20.161); OR (95%CI) = 16.216 (4.779–55.025). However, the use of Rifamycin was found to be a protective factor against VTE [OR (95%CI) = 0.170(0.073–0.395)]. Receiver operating characteristic curve (ROC) curve of D‐dimer  and the cut‐off value of 1855 μg/L was obtained according to the Youden index, with a sensitivity and a specificity rate of 82.2% and 74.3%, respectively. The risks of VTE seem higher in TB patients with fever, dyspnea, lower limb edema, and D‐dimer levels of more than 1855 μg/L; therefore, it should be actively screened, and prophylactic anticoagulation given if necessary. Effective directly observed treatment plus short‐course chemotherapy (DOTS) protocol anti‐TB therapy helps reduce the probability of VTE in TB patients.Tuberculosis (TB) patients who develop venous thromboembolism (VTE) have poor clinical outcomes. This study aimed to explore the risk factors and the prognosis of TB patients with VTE. A total of 11 267 with TB patients from the Zhejiang University‐affiliated Chest Hospital, China, were enrolled between January 2016 and January 2020. A total of 107 TB patients with VTE were selected as the VTE group. Patients in the control group were randomly screened in a 1:1 ratio between the VTE and control groups. Univariate and multivariate logistic regression analysis was used to evaluate the factors associated with VTE in TB patients. Of the 214 patients, 145 (60.17%) were male, 69 (32.2%) were female, with an average age of 62.21 ± 19.35. The incidence of VTE in TB patients was 0.95%. Using a univariate analysis, it was found that age, fever, dyspnea, lower limb edema, respiratory failure, malignant tumor, prothrombin time, activated partial thromboplastin time, D—dimer, and hemoglobin levels were different between the two groups ( Venous thromboembolism (VTE) risks seem higher in tuberculosis (TB) patients, and therefore, there is a need for active prevention and treatment. Patients with fever, dyspnea, lower limb edema, and D‐dimer levels of more than 1855 μg/L should be actively screened for VTE and prophylactic anticoagulation given if necessary. Effective directly observed treatment plus short‐course chemotherapy (DOTS) protocol anti‐TB therapy helps reduce the probability of VTE in TB patients. We retrospectively analyzed all inpatient cases diagnosed with a clinically confirmed TBA total of 107 TB patients with VTE were identified.2.2Mycobacterium tuberculosis complex was identified by Biochip assays using sputum specimens with acid‐fast bacilli that were smear‐positive or culture‐positive. (2) Extrapulmonary TB (EPTB): patients with at least one specimen extracted from infected lesions other than the lung that was a confirmed M. tuberculosis infection using biochip assays. Rifamycin therapy was defined as the inpatient use of at least 3 days before or after hospitalization.Active TB was defined as follows: (1) pulmonary TB: 2.3−X ± s. Continuous variables were analyzed with the independent samples t‐test. Categorical variables were compared with Pearson's Chi‐square test. We included variables with a p‐value of <0.05 into a multivariate logistic regression model with stepwise forward selection to identify independent risk factors. Then, we used the receiver operating characteristic curve (ROC) to describe the continuous variables with statistical significance and derive the cut‐off value per the Youden index. The criterion for statistical significance was p < 0.05.The data were entered into SPSS for Windows, version 24. Continuous data were described as 2.4This study was approved by the Hangzhou Red Cross Hospital Ethics Committee (2020) Quick review no. 187).3p = 0.007) as compared with those without VTE (median age 58.64 ± 21.19).A total of 11 267 patients were included in the study, with 7713 males (68.5%) and 3554 females (31.5%). Of this, 107 patients had confirmed concurrent TB with VTE. The incidence of VTE in TB patients was 0.95%. The median age of all patients included in the study was 62.21 ± 19.35 years; however, those with VTE were older (median age 65.78 ± 16.66；p = 0.001). Those with dyspnea were 67 (31.31), of which 41 (61.19%) were confirmed with VTE. Similarly, the analysis showed that patients with dyspnea were more likely to have VTE (p = 0.027). A total of 27 (12.62%) patients had lower limb edema, and 22 (81.48%) confirmed with VTE. Likewise, patients with lower limb edema were more likely to be diagnosed with VTE (p = 0.000). Lastly, respiratory failure (p = 0.007) and malignant tumor (p = 0.017) had a statistically significant association with VTE occurrence in TB patients  patients had a fever, of which 45 (66.18%) were confirmed with VTE. Analysis showed that patients with VTE were more likely to have fever , APTT (p = 0.000), and D‐dimer (p = 0.000) were all significantly higher in the VTE group than in the control group. However, the hemoglobin level  was significantly lower in the VTE group than in the control group. White blood cells and platelet count were not associated with the occurrence of VTE , while the use of Rifamycin reduced the risks of VTE occurrence (p = 0.000). The anatomical sites of TB were not associated with VTE risks (Table Active TB as a disease was associated with the occurrence of VTE (ks Table .p = 0.001), lower limb edema , and active TB  all contributed to occurrence of VTE in TB patients . The cut‐off value was 1855 μg/L, as calculated by the Youden index, with a sensitivity of 82.2% and a specificity of 74.3%.D‐dimer was analyzed using the ROC Figure . The res4Epidemiological studies estimate the incidence of VTE to be around 0.1% to 0.2% in the general population and 0.7% to 3.9% in patients with active pulmonary TB.In our analysis, patients in the VTE group were older than those in the control group, which indicates that older age could be at a higher risk of developing VTE. However, older patients generally have other comorbidities, and sometimes their symptoms could overlap with VTE. Other studies also show that the clinical manifestations of VTE are usually diverse and sometimes nonspecific; hence, misdiagnosis and treatment delay are common.p < 0.05), with an AUC value of 0.78–0.89 and a cut‐off value of 1855 μg/L. Therefore, clinicians should pay more attention to patients with a high D‐dimer level, especially if higher than 1855 μg/L.Hypercoagulability is one component of the Virchow triad. There is evidence that TB patients are in a hypercoagulable state with a higher D‐dimer level than the general population.Currently, the mechanisms underlying the increased risk of VTE in TB patients remain unknown. Some researchesWhite et al.Moreover, the data by White and colleagues are from the period between 1978 and 1986, an era in which short‐course therapy with isoniazid and rifampicin was the standard of care, as compared with modern‐day directly observed treatment plus short‐course chemotherapy (DOTS); therefore, absolute causation of Rifamycin with VTE cannot be concluded. Similarly, our study is a retrospective study with a small sample size, and therefore, larger studies are needed to confirm the effect of Rifamycin in TB patients with VTE.5VTE risks seem higher in TB patients, and therefore, there is a need for active prevention and treatment. Patients with fever, dyspnea, lower limb edema, and D‐dimer levels of more than 1855 μg/L should be actively screened for VTE and prophylactic anticoagulation given if necessary. Effective DOTS protocol anti‐TB therapy helps reduce the probability of VTE in TB patients.The authors declare no conflict of interest. The manuscript has been read and approved by all the authors and that each author believes that the manuscript represents honest work.The permission was obtained for the study from the Local Ethics Committee of Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine. This study was exempted from signing patient consent statement, because this study only uses patient data with identifiable information and the research project does not involve personal privacy and commercial interests.Guo Yi Nan collected patients' data and carried out statistical analysis and article writing. He Fei wrote the original manuscript; Wang Zhen offered guidelines for the research design; Duan Tian Yun: research design idea, proofreading, and offered guidance on statistical analysis.Data S1. Supporting InformationClick here for additional data file.


Synovial sarcoma (SS) accounts for 8%–10% of all soft‐tissue sarcomas. Clinical presentation and outcomes vary, yet discrete risk groups based on validated prognostic indices are not defined for the full spectrum of patients with SS.n = 1063).We performed a retrospective cohort study using data from the SEER  database of SS patients who were <70 years of age at diagnosis. We constructed a recursive partitioning model of overall survival using a training cohort of 1063 patients with variables: Age at diagnosis, sex, race, ethnicity, primary site, tumor size, tumor grade, and stage. Based on this model, we grouped patients into three risk groups and estimated 5‐year overall survival for each group. We then applied these groups to a test cohort (p < 0.0001).Our model identified three prognostic groups with significantly different overall survival: low risk , intermediate‐risk  and high risk . Prognostic groups were applied to the test cohort, showing significantly different survival between groups (Our analysis yields an intuitive risk‐classification tree with discrete groups, which may provide useful information for researchers, patients, and clinicians. Prospective validation of this model may inform efforts at risk‐stratifying treatment. We used a multivariable recursive partitioning model to define prognostic groups for children and adults with synovial sarcoma. These risk groups provide clinicians and researchers with an intuitive framework for estimating individual patient risk. The annual incidence is 2–3 cases per 100,000 people and typically presents between the ages of 15 and 30 years. The disease is genetically characterized by a A prior analysis of SEER data from 1983 to 2005 for patients with SS demonstrated that children (<18 years) had superior outcomes to adults, and that the variables of female sex, extremity tumors, localized tumors, and tumors <5 cm were all associated with more favorable outcomes.Discrete risk groups have not been defined for the full spectrum of patients presenting with SS. Identification of discrete risk‐groups, especially to identify patients at higher risk of poor outcomes, is essential for the deployment of risk‐adapted therapy, which has led to improved outcomes for many cancer histologies. With multiple new therapeutic strategies on the horizon, identifying those patients who are unlikely to be cured with standard therapy is critical.To address these gaps in SS risk‐stratification, we have analyzed data from the SEER database and utilized recursive partitioning to identify discrete prognostic groups. The database was parsed into training and test cohorts to build a classification and regression tree, define prognostic groups, and independently validate the designated groups.22.1We performed a retrospective cohort study of patients with SS  70 years of age or younger with complete staging information. SEER diagnoses, whenever possible are based on pathologic diagnosis, as mentioned in the SEER Program Coding and Staging Manual 2018. Age 70 was used as a cut‐off given that treatment strategies and goals of care may differ significantly in much older patients with greater number of co‐morbidities. Life expectancy in the United States was between 70 and 80 years for the study period and in this dataset, death from non‐oncologic causes becomes a significant competing risk factor for patients above age 70. We utilized data from the SEER18 database for patients diagnosed during the years of 2000 to 2017 to define prognostic groups . Data from the SEER18 system were derived from 34.6% of the US population and provided data on cancer incidence, age at diagnosis, primary tumor site, tumor histology, grade/stage at diagnosis, and survival.2.2Clinical variables from the SEER database were abstracted in the following manner: age at diagnosis, sex , race , primary site , tumor size (cm), tumor histopathologic grade  and stage  Table .6 Prim2.3The primary clinical endpoint used in this study was overall survival (OS), which was defined as the time from diagnosis to the time of death due to any cause, with surviving patients censored at the time of last follow‐up.2.4Patient characteristics were summarized using descriptive statistics. Categorical variables were summarized using frequencies and proportions. Continuous variables were summarized using means, medians, and ranges.p‐value) was selected for branching and split using a goodness of split measure that optimizes between node separation using log‐rank statistics.Optimal cut‐point analysis for the continuous variables age and tumor size were performed using the R package “partykit” version 1.2.7.p‐value <0.05. When there was no longer a statistically significant (p < 0.05) split, or a resultant node had fewer than 20 patients, the tree was stopped. For branch points of variables with missing data, cases without that data available were dropped from further analysis within that branch. Proportional hazards were checked for each model.Once each variable was dichotomized, the classification tree was built using manual Cox proportion‐hazards regression models.After defining terminal nodes, the 5‐year OS and 95% confidence interval for each derived node was estimated using Kaplan–Meier methods. The relative hazard ratio for death was calculated for each node in reference to the lowest risk group (Node 1).Following creation of the survival tree using the training data, each node was assigned to a pre‐defined, clinically relevant, low‐risk (5‐year OS ≥80%), intermediate‐risk (5‐year OS 35%–79%), or high‐risk (5‐year OS <35%) group based on the 5‐year OS of the node. The exact cutoffs for these prognostic groups were assigned using clinical judgment, and closely resembled OS survival rates of assigned groups in similar types of analyses.We then applied these prognostic groups to the test cohort and used Kaplan–Meier methods to estimate 5‐year overall survival for each group. An overall log‐rank test was performed to determine whether the prognostic groups retained statistical significance in the test cohort.p‐values <0.05 were considered statistically significant. Analyses were performed in R/RStudio version 4.0.0 and Stata 14.2.The SEER database was accessed using SEER Stat version 7.0.5. Two‐sided 33.1n = 1063) and a validation test cohort  and did not differ significantly between the training and test groups  compared to 3.55  for 7.5 cm. These variables were dichotomized for the entire cohort based on optimized cut‐points.For the purpose of utilizing clinically meaningful variables at the outset, we used regression analysis to identify the optimal cut‐points for age and tumor size in the training cohort. The cut‐point for age was determined to be 21 years, and the cut‐point for tumor size was 7.5 cm Figure . For com3.2Using the defined predictor variables, a classification tree with terminal nodes was generated using all patients in the training cohort Figure . The strp < 0.0001).We then grouped each terminal node into the pre‐defined low‐risk, intermediate‐risk, and high‐risk groups. The 5‐year overall survival estimates and characteristics for each prognostic group in the training cohort are shown in Table 3.3p < 0.0001). We found that within the test cohort, patients in the low‐risk group had a 5‐year OS of 84% (95% CI 79–88%), whereas patients in the intermediate‐risk group had a 5‐year OS of 66% (95% CI 60–71%) and those in the high‐risk group had a 5‐year OS of 19%  compared to 27% (95% CI 9–49%) for patients <21 years of age at diagnosis with metastatic disease.Young patients (≤ 18 years) with synovial sarcoma have been shown previously to demonstrate superior outcomes relative to patients who are older.Our study has several limitations. The SEER database provides an exceptional resource for the study of rare tumors such as SS, yet data were not complete for some variables utilized in our cohort . Furthermore, tumor grade as recorded in SEER does not follow the FNCLCC tumor grading system which has been reported to be prognostic in other cohorts.In summary, our analysis provides a classification tree applicable to children, adolescents, and adults with SS. We identify a particularly high‐risk group of adult patients with large localized, non‐appendicular tumors, who may in part have poor outcomes given that local control can be more challenging in non‐appendicular sites than in appendicular sites. Conversely, we validate prior reports demonstrating more favorable outcomes for young patients, regardless of other clinical variables. While these findings provide an intuitive risk‐classification tree, this study supports the need for potentially more informative prognostic molecular markers in SS. This is particularly true given the recent major advances in the molecular mechanisms of oncogenesis in this fusion‐driven sarcoma with altered chromatin remodeling complexes.Conceptualization: David S. Shulman. Study design and methodology: David S. Shulman, Dylan V. Neeland Clement Ma. Statistical analysis: David S. Shulmanand Dylan V. Neel. Writing‐original draft: David S. Shulmanand Dylan V. Neel. Writing‐review & editing – Clement Ma, George D. Demetri, Jason L. Hornick, Natalie B. Collins.This work was supported by Alex's Lemonade Stand Foundation (DSS) and the Harvard Catalyst KL2/CMeRIT program 5KL2TR002542 (DSS). The project described was also supported by award number T32GM007753 from the National Institute of General Medical Sciences (DVN). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.GDD: Scientific consultant with sponsored research to Dana‐Farber: Bayer, Pfizer, Novartis, Epizyme, Roche/Genentech, Epizyme, LOXO Oncology, AbbVie, GlaxoSmithKline, Janssen, PharmaMar, Daiichi‐Sankyo, AdaptImmune. Scientific consultant: GlaxoSmithKline, EMD‐Serono, Sanofi, ICON plc, MEDSCAPE, Mirati, Synlogic, WCG/Arsenal Capital, MJ Hennessey/OncLive, C4 Therapeutics, McCann Health, Rain Therapeutics. Consultant/SAB member with minor equity holding: G1 Therapeutics, Caris Life Sciences, Erasca Pharmaceuticals, RELAY Therapeutics, Bessor Pharmaceuticals, Champions Biotechnology, CellCarta, ikena Oncology, Kojin Therapeutics. Board of Directors member and Scientific Advisory Board Consultant with minor equity holding: Blueprint Medicines. Patents/Royalties: Novartis royalty to Dana‐Farber for “use patent” of imatinib in GIST. Non‐Financial Interests: AACR Science Policy and Government Affairs Committee Chair, Alexandria Real Estate Equities.Ethical approval and informed consent for human subjects were not necessary for this study, as it was conducted on publicly available de‐identified data (SEER).Table S1Figure S1Figure S2Click here for additional data file.


Cerebral small vessel disease (SVD) is an important cause of dementia that lacks effective treatment. We evaluated the efficacy and safety of cilostazol, an antiplatelet agent with potential neurovascular protective effects, in slowing the progression of white matter hyperintensities (WMHs) in stroke‐ and dementia‐free subjects harboring confluent WMH on magnetic resonance imaging (MRI).In this single‐center, randomized, double‐blind, placebo‐controlled study, we randomized stroke‐ and dementia‐free subjects with confluent WMHs to receive cilostazol or placebo for 2 years in a 1:1 ratio. The primary outcome was change in WMH volume over 2 years. Secondary outcomes were changes in brain volumes, lacunes, cerebral microbleeds, perivascular space, and alterations in white matter microstructural integrity, cognition, motor function, and mood.p = 0.167). Secondary outcomes, bleeding and vascular events, were also not statistically different between the two groups.We recruited 120 subjects from October 27, 2014, to January 21, 2019. A total of 55 subjects in the cilostazol group and 54 subjects in the control group were included for intention‐to‐treat analysis. At 2‐year follow‐up, the changes in WMH volume were not statistically different between cilostazol treatment and placebo (0.3±1.0 mL vs −0.1±0.8 mL, In this trial with stroke‐ and dementia‐free subjects with confluent WMHs, cilostazol did not impact WMH progression but demonstrated an acceptable safety profile. Future studies should address the treatment effects of cilostazol on subjects at different clinical stages of SVD. We assigned stroke‐ and dementia‐free subjects with moderate‐to‐severe confluent WMH in a 1:1 ratio to either cilostazol (at a dosage of 100 mg twice daily per os) or placebo for 104 weeks using computer‐generated randomization codes. Randomization was concealed until the participants were assigned to cilostazol or placebo.2.2We recruited participants from the prospective Chinese University of Hong Kong RISK Index for Screening Subclinical Brain Lesions in Hong Kong (CU‐RISK) cohort that screened community‐dwelling individuals with subclinical WMHs.2.3Age, sex, education level, smoking and drinking status, medications, and co‐morbidities  were recorded at baseline. In addition, we assessed blood pressure, body mass index, complete blood count, renal and liver function, fasting plasma glucose, glycosylated hemoglobin, and lipid profile  at 52‐week intervals throughout the 104‐week study period.Systematic review: Some clinical studies suggested that cilostazol may improve cognitive outcomes in patients with cerebral small vessel disease (SVD) who had history of lacunar strokes or intracerebral hemorrhage. However, treatment effect of cilostazol in stroke‐ and dementia‐free patients harboring moderate‐to‐severe SVD is unknown.Interpretation: Cilostazol treatment did not impact the progression of white matter hyperintensity volume. Our study findings did not support the routine use of cilostazol in patients with SVD without overt clinical symptoms.Future directions: Future cilostazol trials should address treatment effects on subjects at different clinical stages of SVD.The efficacy and safety of cilostazol in stroke‐ and dementia‐free subjects with cerebral small vessel disease (SVD) are unclear.In this single‐center, randomized, double‐blind, placebo‐controlled study, cilostazol did not impact white matter hyperintensity progression on brain magnetic resonance imaging over a period of 2 years compared to placebo.Changes in cognition, bleeding, and vascular events were not statistically different between cilostazol and placebo treatment.Further studies should address treatment effects of cilostazol on subjects at different clinical stages of cerebral SVD.2.43); T2‐weighted ; 3D fluid‐attenuated inversion recovery ; diffusion‐weighted  and venous BOLD . WMHs, lacune, cerebral microbleed (CMB), and perivascular space (PVS) were defined and visually rated according to the standards for reporting vascular changes on neuroimaging (STRIVE) criteria.Brain MRI was performed at baseline and 2 years. MRI was performed using the Philips Achieva 3.0 T Tx series . MRI sequences were acquired as follow: three‐dimensional (3D) T1‐weighted .For diffusion‐weighted imaging, the pre‐processing steps included susceptibility‐induced distortion, eddy‐current, and head motion corrections. Because the diffusion‐weighted data were acquired without reversed phase‐encoding directions, we first employed Synb0‐DISCO v2.0, a tool that synthesizes an “undistorted” b0 image through a deep learning approach , based on the geometry of the given structural T1‐weighted scans.2.6For quantification of longitudinal changes in WMH, the automatic lesion segmentation tool (LPA) from LST, an open‐source toolbox for SPM was applied. A joint lesion map was rendered from lesions maps of both time points to identify lesion (of any time point) and non‐lesion voxels. The distribution of FLAIR intensity differences was estimated within the non‐lesion voxels to enable statistical quantification of intensity changes within the joint lesion map. Significant changes were labeled as increase or decrease in lesion volume, whereas non‐significant changes but different cross‐sectional lesion segmentation results were counted as lesions at both time points.http://www.fil.ion.ucl.ac.uk/spm/software/spm12).All images were visually checked and de‐identified prior to any analyses. The imaging analyses were performed mainly on the Functional MRI of the Brain Software Library (FSL) v6.0 tools2.7Assessments on cognition, mood, and motor functions were performed at baseline, 1 year, and 2 years. We assessed cognition using the Chinese Montreal Cognitive Assessment (MoCA) and the 30‐min battery of the National Institute of Neurological Diseases and Stroke – Canadian Stroke Network Vascular Cognitive Impairment (NINDS‐CSN VCI) Neuropsychology Protocol, which consists of 1‐min animal fluency for executive function, symbol digit modalities test (SDMT) for processing speed, and Hong Kong list learning test (HKLLT) for memory and verbal learning.2.8The primary outcome was the absolute change in WMH volume on MRI over 2 years. Secondary imaging outcomes included changes in diffusion measures, lacunes, CMB, PVS, total brain parenchymal volume, and hippocampal volume. Secondary clinical outcomes were changes in MoCA, NINDS‐CSN VCI Neuropsychology Protocol 30‐min battery, GDS, 8‐m walk time, and single leg stance test over 2 years. Safety outcomes were intra‐ or extra‐cranial bleeding, vascular events, and death during the study period. No interim analyses were performed.2.9A detailed study protocol is provided in the supplementary materials. The protocol was approved by the local institutional review board (Joint CUHK‐NTEC CREC Reference No. 2013.680‐T). Written informed consent was obtained from all participants. The trial was registered with the Center for Clinical Research and Biostatistics, the Chinese University of Hong Kong (CUHK‐CCT00430).2.103 in the placebo group and 1.7 (0–13.6) cm3 in the treatment group. Assuming the effect of cilostazol upon WMH progression in subjects with confluent WMH over a 2‐year period was comparable to that of B vitamins, to detect a 1.66 cm3 difference in median WMH volume change with 80% power, the sample size required for each of the 2 groups was 53. Based on our experience from VITATOPS MRI sub‐study and previous trials using cilostazol among stroke subjects, we estimated that about 20% of subjects with confluent WMH may drop out over a study period of 2 years. Hence, this study required 70 subjects per group, that is, a total of 140 subjects. Recruitment of this study was prematurely stopped in 2019 due to the social unrest in 2019.Sample size estimation was based on the data from the VITATOPS MRI substudy.2.11t‐test or Mann‐Whitney U test for continuous variables after normality examination in Kolmogorov‐Smirnov test. Analysis of covariance (ANCOVA) for continuous variables and logistic regression for categorical variables were used to compare changes in outcome measures between cilostazol and placebo groups. Because lacune, CMB, and PVS score were not normally distributed, a cutoff of >0 was applied on the presence lacunes and microbleeds, whereas a cutoff of >1 was applied on PVS rating (ranging 0–4) to dichotomize the variables in order to perform logistic regression. Years of education, background hypertension, baseline hippocampal ratio, and CMB were included as covariates for all analyses. Baseline clinical or imaging measures were included as covariates in relevant longitudinal analyses on change in clinical or imaging measurements. Inter‐rater agreement was gauged by the κ statistics. Primary analyses were intention‐to‐treat, defined as all randomized subjects who had taken at least one dose of the study medication. Per‐protocol analyses were performed for subjects who had completed the trial regimen covering the entire study period. We performed efficacy analysis of change in all the outcome measures at the first and second year follow‐up. A two‐sided p < 0.05 was considered statistically significant. All statistical analyses were performed using Python 3.7.15.Continuous variables were expressed as mean ± SD, whereas categorical variables were expressed in number (percentage). We compared baseline characteristics between the two treatment groups by chi‐square or Fisher exact test for categorical variables and independent 2.12n = 28).We performed a secondary analysis using a propensity score‐matching model in view of the unbalanced baseline characteristics  between the two groups despite randomization. In a logistic regression model, we estimated the propensity score of each patient using age, years of education, hypertension, baseline hippocampal ratio, and CMB as covariates subjects with extreme propensity scores were removed (33.1n = 60 per group). Seven subjects in the treatment group and six subjects in the placebo group withdrew before the first dose of cilostazol or placebo after randomization. Therefore, the intention‐to‐treat sample consisted of 53 subjects in the cilostazol group and 54 subjects in the placebo group. A total of 37 subjects in the cilostazol group and 44 subjects in the placebo group completed the entire trial regimen for per‐protocol analyses  and had a higher proportion of hypertensive subjects . Other baseline demographics, medical co‐morbidities, cognitive, gait, and balance performance were similar between the two groups (Table p = 0.012). There were fewer subjects with CMB in the cilostazol group   of the subjects were female, 71 (66.4%) had hypertension, 20 (18.7%) had diabetes mellitus, 29 (27.1%) had hyperlipidemia. The placebo group received a shorter duration of education . Changes in the normalized brain volume, hippocampal ratio, number of CMBs, lacunes, PVS, and diffusion metrics were also not statistically different between the two groups. Other secondary imaging outcomes are illustrated in Table From the intention‐to‐treat analysis, the difference in WMH volume changes between cilostazol and placebo were not statistically significant . Per‐protocol analysis of clinical outcomes was consistent with the intention‐to‐treat analysis , mood, gait, and stance were not statistically different between the two groups Table . One patis Table .3.4Within‐group comparisons of clinical and radiological outcomes are shown in Table 3.5p = 0.013). Palpitation and headache were more common with cilostazol treatment but did not reach statistical significance. Cilostazol use was not associated with increased bleeding, vascular events, or death compared to placebo  Ltd. This funding source had no role in the design, execution, analyses, interpretation of the data, or decision to submit results of this study. Bonaventure Y.M. Ip, Bonnie Y.K. Lam, Vincent M.H. Hui, Lisa W.C. Au, Mandy W.T. Liu, Vivian W.Y. Lee, Winnie C.W. Chu, Thomas W. Leung, Ho Ko, and Vincent C.T. Mok declare that they have no potential conflicts of interest that might be relevant to the contents of the manuscript. L. Shi is the director of BrainNow Medical Technology Limited, which assisted with the quantification of hippocampal ratio. Author disclosures are available in the SUPPORTING INFORMATIONClick here for additional data file.SUPPORTING INFORMATIONClick here for additional data file.SUPPORTING INFORMATIONClick here for additional data file.


Cushing’s syndrome (CS) is a rare condition of chronically elevated cortisol levels resulting in diverse comorbidities, many of which endure beyond successful treatment affecting the quality of life. Few data are available concerning patients’ experiences of diagnosis, care and persistent comorbidities.To assess CS patients’ perspectives on the diagnostic and care journey to identify unmet therapeutic needs.A 12-item questionnaire was circulated in 2019 by the World Association for Pituitary Organisations. A parallel, 13-item questionnaire assessing physician perceptions on CS patient experiences was performed.P = 0.008) and decreased libido (P = 0.03). Forty physicians completed the parallel questionnaire which showed that generally, physicians poorly estimated the prevalence of comorbidities, particularly initial and persistent cognitive impairment. Only a minority of persistent comorbidities  were managed by specialists other than endocrinologists. 63% of patients were satisfied with treatment.Three hundred twenty CS patients from 30 countries completed the questionnaire; 54% were aged 35–54 and 88% were female; 41% were in disease remission. The most burdensome symptom was obesity/weight gain (75%). For 49% of patients, time to diagnosis was over 2 years. Following treatment, 88.4% of patients reported ongoing symptoms including, fatigue (66.3%), muscle weakness (48.8%) and obesity/weight gain (41.9%). Comparisons with delay in diagnosis were significant for weight gain (This study confirms the delay in diagnosing CS. The high prevalence of persistent comorbidities following remission and differences in perceptions of health between patients and physicians highlight a probable deficiency in effective multidisciplinary management for CS comorbidities. Cushing’s syndrome (CS) is a morbid endocrine condition due to prolonged exposure to high circulating cortisol levels , 2, 3. HGiven the burden of the disease, ideal CS treatment would include early diagnosis, curative surgery and multidisciplinary care of comorbidities both pre- and post-cure of CS, including the psychological dimension of the patient’s disease experience . Few datA 12-item patient questionnaire was developed based on the generally understood clinical characteristics and symptomology of CS, aiming to assess patients’ experiences of symptoms, diagnosis, care and treatment satisfaction  the time between first symptoms and diagnosis and the persistence of symptoms and (ii) persistence of symptoms, with the specialities of the physicians currently treating the respondents. Frequency distribution of a particular variable was displayed and compared with the frequency distribution of the comparator variable. A significance level of 0.05 was applied.n  = 87) coming from the United Kingdom and 14% (n  = 44) from the United States of America. More than half  of the patients were aged between 35 and 54 years, and 88.4% (n  = 283) were female. The majority of patients  had undergraduate or postgraduate qualifications  (n  = 156) and over 3 years in 30.3% (n  = 97).The time to diagnosis from first reporting of CS symptoms was declared to be within 2 years for 48.4% (  = 155)  and was n = 240) as being burdensome. Fatigue, feelings of depression or mood problems, sleep disturbances, muscle weakness and hirsutism were also very commonly (>40%) mentioned as being burdensome. Burdensome symptoms classified as ‘other’ were rare (<1%) and included issues such as hormonal problems and dental problems.A broad range of signs and symptoms were initially noticed by patients, with weight gain, hirsutism or acne, fatigue, sleep disturbances, depressive symptoms, muscle weakness, anxiety and hypertension all being reported in over 50% of patients . Obesityn  = 172) of cases, an endocrinologist made the initial diagnosis of CS or prescribed the first screening tests, n  = 58), in the remaining cases a diversity of other physicians directly or indirectly contributed to make the diagnosis, as indicated in n = 18) of patients self-diagnosed and then convinced their physician to order the diagnostic tests.In 53.8% (n  = 178) of patients were not in remission. 40.8% of patients (n  = 130) were in true biochemical remission (n = 52), ‘Received an operation to remove adrenal glands’  and ‘Treated with hydrocortisone’ . Thirteen percent of the patients (n  = 41) were on cortisol-lowering treatment and 6.6% of the patients (n  = 21) had not had or were awaiting surgery. Following treatment for CS, 11.6% of the patients (n  = 37) reported having no further symptoms related to the condition, with 88.4% (n  = 283) still symptomatic. Of the total population (n  = 320), the most bothersome symptoms were fatigue , muscle weakness  and obesity/weight gain   .Figure n  = 283) with those who did not (n  = 37) in terms of time to diagnosis. Patients with a longer time to diagnosis reported significantly more frequent weight gain (P = 0.008), and more frequent reduced libido (P = 0.03) after treatment. Although not statistically significant, there was a strong trend towards patients reporting a longer time to diagnosis and a greater frequency of persistent perceived bone issues after treatment (P = 0.053), as well anxiety (P = 0.07) and depression/mood concerns (P = 0.08).To compare the time to diagnosis and the persistence of symptoms following treatment, an analysis of a number of variables was performed comparing the group with persistent symptoms after treatment (n = 313) were managed by an endocrinologist, followed by a GP/family doctor . A psychiatrist/psychologist was involved in 23.4% (n  = 75), followed by a physiotherapist , rheumatologist , gynecologist , cardiologist , dermatologist  and a dietician   .n  = 212), whereas only 15.9% (n  = 51) were receiving ongoing care for fatigue and persistent muscle weakness was reported in 48.8% (n  = 156) with 17.2% (n  = 55) of patients being treated for this  were seeing a psychiatrist/psychologist . Endurinn = 240). Social life , family life , interpersonal relationships , and sexual life  had also been significantly affected by their illness. Thirty-seven percent of the patients (n  = 118) reported that their economic situation had been negatively affected. ‘Other’ responses for this question included reductions in self-esteem, self-image and self-confidence. Sixty-three percent of patients (193/305) were satisfied with their treatment and 36.7% (n  = 112) were not.Three-quarters of patients reported that their work life had been affected , unlike the patient questionnaire where most respondents were from the United Kingdom, the United States of America, the Netherlands and France, most of the physicians surveyed were from Western Europe, although there were representatives from other parts of the world. In the physician questionnaire, 83% (n  = 33) were endocrinologists, 13% (n  = 5) internal medicine specialists and 5% (n  = 2) other disciplines. Sixty percent (n  = 24) had over 10 years clinical experience, and 93% (n  = 37) were experienced in the treatment of CS, seeing an average of 10 patients per year. Of the specialities involved in the care of CS, 96% of physicians (n  = 38) considered endocrinologists to be involved, 48% (n  = 19) included family doctors/GPs, 20% (n  = 8) cardiologists, 28% (n  = 11) psychiatrists/psychologists and 28% (n  = 11) included dieticians. These results are consistent with the patients’ perceptions, with the exception of dieticians, who only 10% of patients reported seeing  in patients with hypercortisolism and has received consulting fees from Corcept Therapeutics and HRA Pharma Rare Diseases. R F has received research grants from Strongbridge and Recordati Rare Diseases and honoraria for talks at symposia and for participating in advisory boards from HRA Pharma Rare Diseases, Corcept, Ipsen, Novartis and Recordati Rare Diseases. M A H and S I are employees of HRA Pharma Rare Diseases. R A F is a member of the editorial board of Endocrine Connections. He was not involved in the editorial or review process of this paper, on which he is listed as an authors.This work did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.


The current study aims to investigate the combined association of walking pace and grip strength with incident type 2 diabetes (T2D). A total of 205 738 participants  without diagnosed or unknown diabetes at baseline from the UK Biobank study were included in this prospective study. Walking pace was self‐reported as slow, average, or brisk. Grip strength was measured using a dynamometer and classified as weak, average, and strong. The combined association of walking pace and grip strength with incident T2D was investigated using Cox‐proportional hazards models with a 2‐year landmark analysis. The additive interaction was conducted by estimating relative excess risk due to interaction (RERI). After the median follow‐up period of 5.4 years (interquartile range: 4.8–6.5), 5082 (2.5%) participants were diagnosed with T2D. Compared to brisk‐strong individuals (reference group), people who were slow‐weak had a higher risk of T2D  after adjusting for all covariates. There were dose–response gradients across both walking pace and grip strength variables. There was a modest amount of negative additive interaction (RERI; −0.06 . To conclude, slower pace and weaker grip strength were associated with a higher risk of developing T2D, independent of sociodemographics, lifestyle, and adiposity. Combining walking pace and grip strength might be a practical approach to screening people who are at increased risk of developing T2D. For example, walking pace and grip strength have both been associated with all‐cause mortality, cardiovascular disease (CVD), and cancer incidence and mortality.From a physiological point of view, walking pace and grip strength reflect different underlying physiological processes, grip strength is a simple contraction measuring strength whereas walking pace integrates strength with other processes such as balance and coordination, and so it is possible that the combination of both will result in a stronger association with health outcomes than individually. Indeed, previously we have demonstrated the additive effects of walking pace and grip strength for CVD risk prediction22.1n = 273 991), participants who had diabetes (n = 12 967) or unknown diabetes (n = 1589) at baseline, and missing exposure or covariates (n = 8173).The UK Biobank study recruited approximately 502 000 participants between 2006 and 2010  from the general population.2.2http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/primary_care_data.pdf. We defined incident T2D as primary care diagnosed with ICD‐10  code E11. READ codes used in the primary care data were converted into ICD‐10 codes using UK Biobank's look‐up table.Incident T2D was derived from linkage to primary care data in the UK Biobank. Records were extracted for 45% of the UK Biobank cohort (228 467 participants). The end of coverage (extract date) was May 2017 for Scotland, September 2017 for Wales, and August 2017 for England. Detailed linkage procedures are available at 2.3Participants self‐reported their usual walking pace on a touch‐screen questionnaire at the baseline assessment visit. The question asked was “How would you describe your usual walking pace?” and they could select one of the three following options: brisk (>4 miles/h), average (3–4 miles/h), and slow walking pace (<3 miles/h), as described elsewhere.2.4The covariates included age, fruit and vegetable intake, red meat intake, processed meat intake, body mass index (BMI), as well as categorical variables sex, ethnicity, deprivation index, smoking status, alcohol intake, total sedentary time, sleep duration, and multimorbidity.2), normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥30 kg/m2). Multimorbidity was derived from participants who self‐reported chronic illnesses at baseline that included 43 medically diagnosed diseases. This covariate was categorized as a binary variable that as having no, or have 1, or more chronic illnesses. Additional details about these measurements can be found in the UK Biobank online protocol.Age was calculated from birth and baseline assessment dates; sex was self‐reported; ethnicity was self‐reported and categorized as white, South Asian, Black, Chinese, and mixed. The deprivation index, an area‐based measure of socioeconomic status, was derived from the postal code of residence using the Townsend deprivation score.2.5Continuous variables are expressed as mean with SD and categorical variables are expressed as frequency and percentage. Cox‐proportional hazard models were used to investigate the associations of walking pace, grip strength, and the combination of walking pace and grip strength with incident T2D with follow‐up as the timeline variable. The results were reported as hazard ratios (HR) together with 95% confidence intervals (CI). The analyses were conducted within a 2‐year landmark period, which excludes any incident T2D occurring in the first 2 years of the follow‐up period and excluded all participants with prevalent diabetes (type 1 or type 2) or undiagnosed diabetes (HbA1c ≥ 48 mmol/mol), as well as those with missing data on walking pace, grip strength, and covariates.The associations between walking pace, grip strength, and the combination of walking pace and grip strength and T2D incidence were adjusted for confounders with three models that included an increasing number of covariates. Model 1  was adjusted for age, sex, deprivation index, and ethnicity. Model 2 (lifestyle model) was adjusted for all variables in model 1 plus, fruit and vegetable intake, red meat intake, processed meat intake, smoking status, alcohol intake, total sedentary time, sleep time, and multimorbidity. Model 3 (adiposity model) was adjusted for all variables in model 2 plus BMI.The additive interaction was conducted to test the interaction between walking pace and grip strength with incident T2D. The analyses were adjusted for confounders with three models as mentioned above. Relative excess risk due to interaction (RERI) was estimated to measure the additivity of the associations.p‐values <0.05 were regarded as statistically significant.The proportional hazard assumption was tested by Schoenfeld residuals. Statistical analyses were performed using the statistical software STATA 17 (StataCorp LP) and R v4.0.2. 2.6http://www.ukbiobank.ac.uk/). This research has been conducted using the UK Biobank resource under application number 7155.The UK Biobank study was approved by the North West Multi‐Centre Research Ethics Committee  and all participants provided written informed consent to participate in the UK Biobank study. The study protocol is available online .Of the 502 458 participants, 205 738 participants who had full data available for incident T2D, walking pace, grip strength, and covariates were included in this study Figure . The medTable Table The combined associations between walking pace and grip strength and T2D incidence are shown in Table 4The main finding of this study is that the combination of slow walking pace and weak grip strength were associated with a higher risk of T2D, independently of sociodemographics, lifestyles, and adiposity, than either walking pace or grip strength alone. Additive interaction analysis showed only a slight overlap for the association between grip strength and walking pace, reassuring the importance of considering both markers. These findings have important public health relevance as both walking pace and grip strength are easy to measure and are inexpensive,Our findings agree with and extend the findings of previous studies on this topic. In the latest prospective cohort study investigating walking pace and T2D risk, conducted in Japan with a 3‐year follow‐up, data indicated that a fast walking pace was associated with a lower risk of T2D after adjusting for sociodemographic and lifestyle factors including BMI and systolic blood pressure.A prospective cohort study, with 1085 older Japanese aged 65–89 years who were followed up for 10 years, investigated the combination of walking pace, grip strength, and standing balance to predict CVD, cancer, and all‐cause mortality. They indicated that adding grip strength to walking pace increased the ability to predict all‐cause mortality.To the best of our knowledge, our study was the first study that provided the additive interaction of the joint effect of two exposures between walking pace and grip strength on incident T2D. The results showed that the joint association was slightly lower (−6%) than the sum of their independent associations.The strengths of the present study are that a large number of participants provided a sufficient sample size to undertake the analysis. We were also able to show that the associations were independent of various lifestyle factors and adiposity. Walking pace and grip strength are low cost, easy to administer, and, therefore, relatively simple to implement into clinical practice. Even though our study derived 45% of incident T2D linked to primary care data in the UK Biobank, these data have external validity for studying the prevalence and incidence of T2D.To conclude, this is the first study to report the combined association of walking pace and grip strength and T2D incidence. The findings provide evidence that the combination of walking pace and grip strength are associated with T2D incidence, more than either alone, and this might be a practical approach to screening people who are at high risk of developing diabetes.5Physical activity is a crucial component of care in people with T2D,J.B., F.K.H., C.C‐M., and S.R.G. contributed to the study conception and design, advised on all statistical aspects, and interpreted the data. J.B., F.K.H., C.C‐M., and S.R.G. performed the statistical analyses. J.B., F.K.H., C.C‐M., and S.R.G. drafted the manuscript. J.B., S.P‐S., F.P‐R., F.K.H., C.C‐M., and S.R.G. reviewed the manuscript and approved the final version to be published. J.B., F.K.H., C.C‐M., and S.R.G. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.The authors report no conflict of interest.Appendix S1Click here for additional data file.