Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles.Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms.In the cross-sectional KarMeN (Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy.The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies. The human metabolome is influenced by a number of endogenous factors, such as age, sex, and body mass index (BMI) –7, as weMost of the early studies were conducted with a rather small number of participants n = 60–150) that were not very well characterized in terms of diet or health status. Further, biological samples were often analyzed using nuclear magnetic resonance spectroscopy (NMR) as the only analytical method, data analysis was mainly based on principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), and thus results have been rather descriptive 0 that we, 18, 22.1H-NMR. With this multi-platform metabolomics analysis—combining targeted and non-targeted methods—we were able to cover more than 400 analytes in plasma and over 500 analytes in urine from a wide range of chemical classes. The aim of this study was to identify metabolite patterns, i.e. a set of metabolites that in combination and their relation to one another are predictive for sex or age in plasma and 24 h urine of healthy men and women. For this purpose, we performed predictive modelling using three different established machine learning algorithms on the combined data from the different analytical platforms.We performed a cross-sectional study  to invesKarlsruhe Metabolomics and Nutrition (KarMeN) study is a cross-sectional study that was performed at the Max Rubner-Institut in Karlsruhe, Germany, between 2011 and 2013, and was described in detail in Bub et al. [The b et al. .2, who gave their written informed consent were recruited. Participants were included, if they were free from prevalent diseases, had no history of a chronic disease, were non-smokers, did not take any medication, hormones, or supplements, and were willing and able to perform all the examinations.Briefly, 301 healthy adults, 172 men, 129 women, aged 18–80 y, BMI 17.8–31.4 kg/mParticipants were subjected to a standardized examination schedule  and visiIn addition, the menopausal status in women was determined by anamnestic interview and follicle stimulating hormone (FSH) measurements. For premenopausal women the study days were scheduled such that sample collection on day 2 fell into the luteal phase of their menstrual cycle, since this is the phase of the cycle with the least hormonal variation.The study was approved by the ethics committee of the State Medical Chamber of Baden-Württemberg, Stuttgart, Germany (F-2011-051) and was in accordance with the 1964 Helsinki declaration and its later amendments. The study was registered at the German Clinical Study Register (DRKS00004890). WHO universal trial number: U1111-1141-7051. Written informed consent was obtained from all individual participants included in the study.On the morning of study day 2, fasting plasma samples were collected by drawing blood into 9 mL ethylenediaminetetraacetic acid (EDTA) plasma tubes  from an antecubital vein. Blood was centrifuged at 1850 x g at 4°C and aliquoted into small portions. In addition, serum samples  were collected for standard clinical biochemistry analyses.Subjects collected 24 h urine, starting the morning prior to study day 2 up to the morning of study day 2. Collection bottles were kept in cool bags with cooling units throughout. Upon delivery of the 24 h urine samples to the study center, the volume was recorded, 2 x 14 mL were centrifuged at 1850 x g at 20°C and aliquoted into small portions.All samples were initially frozen at -20°C for one day and then cryopreserved at -196°C until analysis as previously found to be an acceptable procedure .Quality control (QC) samples were prepared by pooling fasting plasma samples and 24 h urine samples, respectively, from KarMeN participants. These QC samples were used for all analytical methods applied.In order to obtain a preferably broad coverage of the metabolome of human biofluids, a number of different targeted and non-targeted analytical methods were applied. This section provides a short overview on the different analytical methods used. A detailed description of the analytical procedures can be found in the All 24 h urine and fasting plasma samples were analyzed by untargeted GC×GC-MS using a Shimadzu GCMS QP2010 Ultra instrument equipped with a ZOEX ZX2 modulator. The method was originally developed and described for the analysis of urine samples , but herAs some isomeric sugar species cannot be sufficiently resolved with the untargeted GC×GC-MS approach  but may The chromatographic separation of plasma fatty acids, especially the cis/trans isomers usually requires the application of specialized polar columns and can thus not be done adequately using a standard apolar × medium-polar GC×GC column setup. For this reason, we used the method described by Ecker et al.  to deterAcylcarnitines, amino acids, biogenic amines, phosphatidylcholines and sphingomyelins were determined by LC-MS in fasting plasma samples using the Absolute IDQ™ p180 kit developed by Biocrates AG . The 96 well plate kit includes a detailed description for the extraction procedure, instrument settings and quantification software (MetIDQ version 4.5.2). A description of the preparation and quantification process can be found in Romisch-Margl et al. . For chrFor details regarding reliability of results refer to section E of the l-carnitine, choline, and trimethylamine-N-oxide (TMAO), was established [A targeted quantification UPLC-MS/MS method for seven amino compounds in plasma, including ablished  using anAnalyses of 14 bile acids were done from fasting plasma using a 1200 series HPLC system  coupled with a Q-Trap 3200 mass spectrometer . Samples were prepared by SPE procedure prior to LC-MS/MS analyses using negative ESI and MRM mode as described in detail in Frommherz et al. [1H-NMR spectroscopy. Plasma samples were measured at 310 K on an AVANCE II 600 MHz NMR spectrometer equipped with a 1H-BBI probehead and a BACS sample changer . Urine samples were analyzed at 300 K on a Bruker 600 MHz spectrometer ) equipped with either SampleXpress or BACS sample changer, respectively, as described in Rist et al. [All plasma and urine samples were analyzed by 1D-t et al. . TypicalCalcium, chloride, potassium, sodium, and phosphate concentrations were determined in a 24 h urine specimen. Calcium, chloride, potassium, sodium, phosphate, and also iron concentrations, as well as creatinine, bilirubin, LDL-, HDL-, and total cholesterol, triglycerides, glucose, uric acid, urea, free T3 and free T4 thyroid hormone concentrations were determined in blood serum. Analyses were carried out by the medical laboratory MVZ Labor PD Dr. Volkmann und Kollegen GbR , which is an accredited lab according to DIN EN ISO 15189:2001, using standard analytical procedures. Creatinine was quantified in-house in 24 h urine specimens using a photometric assay based on the Jaffé reaction . Total urinary nitrogen was quantified by the Kjeldahl method. FSH was quantified in blood serum by an enzyme-linked immunosorbent assay (ELISA) . Urine osmolality was directly determined by freezing-point depression, using a micro-osmometer .GC×GC-MS raw data files were processed by untargeted alignment by in-house developed R-modules, as described recently . Signal To analyze the samples of the entire study, five Absolute IDQ™ well plates were used. To account for possible batch effects between the plates, data normalization as described by the manufacturer’s user manual was applied based on the pooled QC samples which were extracted and measured ten times on each well plate in between the study samples. The efficacy of the procedure was verified by comparing PCA plots and coefficients of variation before and after plate correction.All spectra were automatically phased with the Bruker AU program apk0.noe. Using the program AMIX 3.9.14  plasma spectra were then referenced to the ethylenediaminetetraacetic acid (EDTA) signal at 2.5809 ppm and bucketed graphically, such that buckets wherever possible contained only one signal or group of signals and no peaks were split between buckets. Urine spectra were resampled to bring them to a uniform frequency axis. Then, spectra were aligned by “correlation optimized warping”  and buckData of the different analytical platforms were integrated into a combined data matrix, consisting of 301 samples and > 1000 analytes (including knowns and unknowns). Analytes with a detected frequency lower than 75% in the study samples were eliminated from the data matrix prior to statistical analysis. Non-detected values were replaced by values corresponding to 1/10 x limit of quantitation (LOQ) in targeted methods, where no limit of detection (LOD) was determined; 1/2 x LOD in methods, where LOD was determined/available; or 1/2 x minimal intensity for non-targeted MS-based methods.2 were calculated to estimate performance of the predictions. Details on nested cross-validation can be found in section I of the The analytes were arranged in columns of this common data matrix, which were mean centered and scaled by standard deviation prior to analysis. This resulting matrix was used as input for three different prediction models: support vector machine (SVM) with linear kernel, generalized linear model net (glmnet), and PLS. Three algorithms were used in order to support the interpretation of the results, assuming that metabolites appearing as important in different algorithms are biologically relevant. The prediction performance of these models is dependent of model specific hyperparameters which have to be optimized. For example, SVM uses a cost parameter C that controls the trade-off between complexity of the decision function and training error. The parameters α and λ are tuned in glmnet, and the number of components (ncomp) in PLS. In order to find the optimal value for the hyperparameter, a grid search in conjunction with a nested 5x10-fold cross validation scheme  was applFor the prediction of sex, due to the relatively low number of young females compared to young men, only participants in the age range of 36–80 were included. In this age range the number of males and females is comparable .Each prediction model yields a metabolite pattern, i.e. a combination of analytes that is important for the correct prediction. In case of linear SVM and glmnet negative and positive weights occur, e.g. favoring the male or female class, respectively. In case of PLS positive and negative weight are distributed over multiple components and can only be summarized into a single value by summing up the squared weights from the different components. For the presentation of results, analytes were assigned a rank for each algorithm according to their weight, the ranks of the three algorithms were averaged, and analytes sorted according to mean rank.Identification of unknown substances from non-targeted analyses that are important for the prediction of sex or age was performed by comparison with databases, as described in Weinert et al. and Egert et al. , 33 for Table 1.Basic characteristics of the KarMeN study participants as well as selected anthropometric, physiological and functional parameters assessed are listed in All plasma samples were analyzed by GC×GC-MS, GC-MS, LC-MS/MS, NMR, and classical clinical biochemistry. After quality checks and data filtering, 442 analytes were included in the metabolomics data analyses. Of these, 174 were derived from targeted analyses and thus known a priori. Of the detected analytes from non-targeted analyses, approximately 40% could be identified or putatively annotated by comparison with databases. For 24 h urine samples, in addition to classical clinical biochemistry analyses, metabolomics analyses were performed using GC×GC-MS, GC-MS, and NMR, yielding 531 analytes after data filtering. Targeted analyses contribute 57 a priori known metabolites, whereas from the non-targeted analyses approximately 28% of analytes could be identified or putatively annotated. The complete dataset is shown in Table 2). The combination of plasma and urine profiles did not improve the accuracy of prediction.In order to determine if the metabolite profiles of the KarMeN study participants are associated with sex, predictive modelling was performed on the combined data matrix including analytes form all analytical methods used. Prediction of sex was possible from plasma metabolite profiles with generally more than 95% accuracy, whereas accuracy of prediction of sex from urine metabolite profiles was possible with about 90%, with PLS yielding the highest accuracy for both sample matrices .For men, all algorithms used showed clear associations of metabolite profiles with age , with PLl-methionine, which show negative weights, i.e. generally higher concentrations in younger men. In contrast, myo-inositol, arabitol, isocitric acid, glucuronic acid, l-ornithine, TMAO, SM C16:1, l-tyrosine, hippuric acid, choline, pseudouridine, and potassium show positive weights and therefore tend to have a higher concentration in older men . The most important mean rank metabolites for the correct prediction of age are shown in For women, the associations of metabolite profiles with age were less strong than for men  with PLSd-glucuronic acid, glucose, and phosphatidylcholine (PC).aa.C32:2, which tend to be higher in older women, whereas PC.ae.C44:5, isoleucine, aspartic acid, malic acid, and tryptophan tend to be higher in younger women C14:1, SM(OH)C16:1, cholesterol, er women . Also, mer women .Several metabolites were found to be associated with age in both sexes and could therefore be described as sex-independent markers of age. They include ornithine, hippuric acid, choline, pseudouridine, glucuronic acid, phenylalanine, potassium in plasma and glutaric acid, creatinine, N-acetylaspartic acid, 2,5-furandicarboxylic acid, sedoheptulose, and several unknown metabolites in urine .Prediction of age in women was less accurate than prediction of age in men, and concentrations of many metabolites showed a discontinuous trajectory with age, with a sudden increase around the age of 50 . Thus, tBased on plasma metabolome data, prediction of menopausal status was possible with a high accuracy of about 88%, whereas prediction based on urine metabolome data was possible with up to 85% accuracy, with PLS being the algorithm producing the best results in both cases . Combinimyo-inositol, carnitine, and others  of one metabolite depends on all others, a single importance value cannot be interpreted outside this context, and even the signs of the importance values do not necessarily show the correct concentration trend for all metabolites. For the discussion, however, metabolites that were important in all three algorithms were considered biologically relevant for the respective questions and are discussed here further see also .Using different machine learning algorithms, it was possible to correctly predict the sex of the study participants with > 95% accuracy from plasma and > 90% accuracy from urine metabolite profiles. Metabolites that are important for correct prediction from plasma include creatinine detected by different analytical methods, lipids (mainly phospholipids), and amino acids . EspeciaUnfortunately, many of the metabolites that are important for the prediction of sex from urine metabolite profiles could not yet be identified. Some of the ones that could be annotated or were derived from targeted analyses have been described before to differ between men and women, such as creatinine , 20, 52,l-Threonine [The metabolite with the highest importance value for the prediction of sex from urine metabolite profiles, 4-deoxythreonic acid, has not been mentioned in literature in the context of sex differences before. It is known to be present in urine , 58, andhreonine . Its funAlthough we cannot explain the functional role of the described metabolites with this study, it is easy to imagine that the hormonal differences between men and women, leading among other effects to different body composition, and behavioral or life-style differences are the underlying cause for many of the observed metabolite differences.One limitation of our study is that we were not able to recruit an adequate number of young females due to exclusion of hormonal contraceptive use. Therefore, prediction models for sex are based on participants ranging from 36 to 80 years of age. Also, due to the fact that we did not further explore analytes that occurred in less than 75% of all study samples, we may have missed metabolites that are specific to one sex.Based on our results and observations in other studies, we argue that sex has to be taken into account as a potentially confounding factor for all metabolomics analyses that are based on study populations including men and women.Since sex has a large influence on the metabolome, associations of metabolite profiles with age were investigated separately for men and women. Employing different machine learning algorithms, it was possible to predict the chronological age of the study participants in a continuous model based on metabolite profiles from plasma or urine for men and women. However, associations with urine were weaker than with plasma, and for women they were weaker than for men. The latter could be caused by the lower number of women than men or due to menopausal effects.It has been reported before , 19 and Generally, as reported by others , 50, 60,Most metabolites in urine that were found to be important for the prediction of age in women have not been reported in this context before with a few exceptions. N-acetyl-aspartic acid was found by Thévenot et al. to be associated with age, but also with sex and BMI . The sugSedoheptulose was also important for the correct prediction of age in men, but with a lower rank than in women . FurtherThe urinary metabolite with the highest importance value for the prediction of age in men was 4-hydroxymandelic acid with higher concentrations in younger men. Unfortunately, not much has been reported about this molecule. It has been found as a metabolite in human urine after consumption of a polyphenol mix from red wine and red grape juice extracts , suggestmyo-inositol, arabitol, isocitric acid, TMAO, and others (higher in older men).When looking at plasma metabolites contained in the metabolite pattern for age of men, analogous to the pattern in women, mainly lipids, amino acids, and organic acids are important . SeveralOne has to keep in mind that our continuous models were based on chronological age but not on biological age as suggested by Hertel et al. , whereasIn general, we listed and discussed metabolites only if they had high ranks in all three algorithms. This way we may have missed some metabolites that are important for the prediction of sex or age in only one or two algorithms, but we concluded that metabolites with high ranks in all three algorithms may have a high biological relevance and are not method-specific.As elaborated above, many of the metabolites found in this study to be important for the prediction of sex or age have been reported multiple times in different studies. Therefore, these metabolites seem to be reliable and robust markers and thus likely also biologically relevant, since study participants, analytical methods, and statistical methods vary considerably between these studies. While most other studies looked at significant differences in metabolite concentrations between sexes or age groups, our predictive modelling approach identified metabolite patterns that are important for the correct prediction of sex or age, i.e. a set of metabolites that in combination and in their relation to one another are associated with sex or age. If metabolites are found to be associated with sex or age in both approaches, this may indicate their fundamental functions in the metabolism with respect to sex and age.In addition, due to the wide analytical coverage based on different analytical techniques and the predictive modelling approach we found several metabolites that have not been reported before to be associated with sex or age. Nevertheless, these may also be relevant in other study populations.Based on our results and observations in other studies –7, we arThe clear associations of metabolite patterns of the participants of the KarMeN study with sex and age demonstrate that the human urine and plasma metabolome varies with age and differs between sexes. Using different machine learning algorithms, it was possible to predict sex and age from metabolite profiles of plasma and urine. Due to the fact that we employed a wide spectrum of analytical methods, that the participants of this study were very well characterized, and sampling and examinations were performed in very standardized conditions, several metabolites were found to be important for the correct prediction of sex or age that have not been described before. Other metabolites identified as being important for the prediction of sex or age, have been reported previously. This suggests that these are robust markers for sex or age in healthy humans. Several metabolites were found to be associated with age in men and women. Thus, they could potentially be considered general markers of age. Due to the associations of sex and age with the metabolome and the different age-trends in men and women, we recommend to include not only sex and age but also sex-age-interactions in statistical data analyses of metabolomics studies.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Introduction: General practitioners (GPs) are key participants in osteoporosis (OP) management. The aim was to evaluate their adherence to lege artis management of the disease, potential barriers, and to discuss differences observed in comparison with the baseline survey carried out in 2007; the focus was on secondary prevention.Methods: On behalf of two professional associations, 2-round postal survey among randomly selected GPs  was performed in 2014. The questionnaire covered areas concerning GP's role in the fight against OP, knowledge about OP, management of OP-related fractures, barriers to the management of OP, system- and patient-related in particular, and availability and use of information sources.Results: The overall questionnaire return rate was 37% (551 respondents); mean age of the respondents was 53 year (37% men). The GP's role in the treatment of OP was rated as essential in 28 and 37% of men and women, respectively (P = 0.012). The guideline for diagnosis and treatment of OP for GPs was considered accessible by 92% of respondents. As much as 60% of the respondents were adherent to the guideline, i.e., used it repeatedly. The knowledge of several risk factors was very good, however, recommended daily intake of calcium was stated correctly by only 41% of respondents, and daily intake of vitamin D by only 40%. Three quarters reported active steps after a fracture: referral to a specialist, life-style recommendations, prescription of calcium/vitamin D supplements. Half of the respondents focus on fall prevention. System-related barriers, such as lack of possibility to prescribe selected drugs (61%) and financial limits set by health insurance company (44%) were most frequently reported. Patient-related barriers were also common, patient's non-adherence (reported by 29%) and patient's reluctance to go to a specialist (18%).Conclusion: GPs adhered to OP management more than in 2007. Knowledge of risk factors and involvement in post-fracture care was relatively high. Compared to baseline survey, patient-related barriers, patient non-adherence in particular, were more common. Prescribing conditions are still an important issue. Among GPs, education should be focused on calcium and vitamin D intake, doses, sources, and supplements. Osteoporosis (OP) is a systemic disease characterized by low bone mass and deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture  should be based on evaluation of fracture risk as a whole and skeletal health . Antiresorptive drugs may be prescribed by physicians specialized in rheumatology, endocrinology, orthopedics, clinical osteology, internal medicine, and gynecology for patients with OP (T-score below −2.5 SD) or for patients who experienced low-trauma fracture. Prescribing conditions of most antiresorptives have been liberated to allow for prescribing by general practitioners (GPs), i.e., after initial check-up by a specialist, prescription can be delegated to GP.Our survey carried out in 2007 (baseline survey) showed insufficient knowledge and engagement in care for patients at risk of fracture with most commonly reported barriers to be financial limits set by health insurance companies and lack of authorization to prescribe antiresorptive drugs  were addressed again (second round). All questionnaires received within 2 months from the start of the second round were included in analyses. The flow of the responses through the study is shown in Figure Questionnaire form consisted of nine multiple choice questions and was sent together with stamped reply envelope. A cover letter signed by heads of the above mentioned professional associations was included. Besides demographic data, i.e., age, gender, length of professional experience, and community size, the questionnaire covered areas concerning  and recommended calcium and vitamin D intake (2 items with open responses). One point was assigned to each correct, minus one point to each incorrect, and 0 to unmarked response. Knowledge score was calculated as a sum of all values assigned to each response. Maximum of the knowledge score was therefore 15 points.Regarding the management of fractures, GPs were asked about steps which are taken when facing a patient with a fracture as a consequence of OP (multiple choice).Simple chi-square test for two dichotomous variables,Kendall correlations for two continuous  variables,Mann-Whitney test for dichotomous and continuous  variables.Since most variables did not follow a normal distribution, non-parametric statistics were used. Associations between variables were studied as follows:Statistical analyses were calculated using PASW software (version 18.0). P < 0.05 was considered statistically significant.The overall questionnaire return rate was 37% (551 respondents); 33% in men and 39% in women, respectively. Characteristics of respondents are summarized in Table P = 0.012). The GP's role was perceived as more important by respondents from larger communities (P = 0.039) and was not associated with either age or length of professional experience.The GP's role in the fight against OP is rated as essential by 28 and 37% of men and women, respectively  and length of professional experience (P = 0.011): mean 8.7 points (58%) in the youngest decile vs. 7.0 points (47%) in the oldest decile. The knowledge score was associated with the higher perceived significance of the GP's role in fight against OP (P < 0.001) and was not associated with community size. Knowledge of the risk factors, such as anorexia nervosa, smoking, and low body mass index (p < 0.01 for each) as well as knowledge score itself (P < 0.027) was better in women than in men.The mean knowledge score was 7.7 ± 2.9 (51% of possible points) out of 15 points. The knowledge score correlated negatively with age (The steps taken by respondents when facing a patient with fracture as a consequence of OP are summarized in Table p = 0.010). On the contrary, other health problems perceptive as more serious (p = 0.011) and lack of time (p = 0.040) were more frequently marked by younger respondents.Perceived barriers to OP management are listed in Table This survey among GPs is a simplified follow-up to our previous baseline survey with similar goals and organization , was relatively high, compared to recent surveys among GPs in which the rates were up to one third of addressees. In Belgium, the response rate of 28% in the survey on perception and knowledge of the FRAX tool . It is based on individual risk factors and country-specific fracture epidemiology. Although mentioned in the guideline for GPs, Czech reimbursement rules and prescribing conditions do not take into account FRAX-based evaluations, and its use is rare in the Czech Republic even in comparison with Poland, Hungary or Slovakia  to find respective information at the time of the survey and mark correct answers due to social desirability. Gender-specific perception of OP-related topics has been published: e.g., greater concern about vitamin D intake than about skin cancer was expressed by women in the above mentioned survey  in performing all key steps, such as giving instructions on fall prevention, prescribing calcium/vitamin D and recommendation of lifestyle changes Table . SlightlAlmost three quarters of respondents stated they prescribe calcium/vitamin D supplements to their patients after a fracture. In the survey of Buckinx, more than half of GPs systematically prescribe vitamin D to their patients in nursing homes  was addressed on behalf of the two most relevant professional associations, which was probably the reason of relatively high response rate. The rate, however, does not ensure a representative sample of Czech GPs. Factors which should be considered when extrapolating to all GPs are, similarly to other surveys, probable bias of volunteering (less knowledgeable GPs did not participate in the survey) and social desirability bias (tendency to provide answers which are expected by the investigator). However, potential overestimation of the knowledge and activities is probably very similar to the baseline survey, since the same method was used and similar response rate was achieved. The results provoke hypotheses possibly calling for further investigation, such as tracking real-world evolution of prescribing rate of antiresorptive drugs by GPs, which would help to uncover the changes in the barrier of prescribing. Such data analyses were not in scope of the present survey therefore may be a topic for future research.GPs adhered to the selected areas of OP management more than in 2007 (baseline survey). Knowledge of most risk factors for OP was relatively high and much better than in 2007, probably due to educational activities targeted at GPs and the updated guideline. Relatively high self-estimated adherence to secondary prevention was observed. GPs reported to be more involved in post-fracture care, i.e., recommendation of lifestyle changes, prevention of falls, referral to a specialist, and prescribing calcium/vitamin D supplements. However, awareness regarding intake of calcium and vitamin D, and several risk factors (family history in particular) was still insufficient. Systemic regulations, such as liberation of prescribing conditions, less strict annual prescription budget limits, and influx of generics of bisphosphonates probably helped much to eliminate major obstacles in the management of OP perceived by GPs. However, impact of delegated prescription and prescribing of antiresorptives by GPs are unknown. Prescribing conditions therefore remain an important issue. Compared to the baseline survey, patient-related barriers to the management, patient non-adherence in particular, were more common, which seems striking. Education focused on calcium and vitamin D intake , and support of the FRAX instrument use would be appropriate. It seems important to increase awareness about OP and its consequences among general public, elderly in particular, and to study patient-related barriers in detail.MV: Conceptualized and organized the study. Carried out literature review, handled data, performed statistics, interpreted the results, drafted and revised the manuscript. TT: Handled the patient data and prepared the data for analysis. Carried out analyses and statistics. Prepared the figures. Drafted and revised the manuscript. LF: Conceptualized the study. Interpreted the results, revised the manuscript. RK: Organized the study. Handled the patient data and prepared the data for analysis. Carried out analyses and statistics. Prepared the figures and revised the manuscript. VP: Conceptualized the study. Contributed to interpretation of the results and revised the draft. SB: Conceptualized the study. Contributed to interpretation of the results and revised the draft. JS: Conceptualized the study. Contributed to interpretation of the results and revised the draft.The study was supported by Charles University in Prague, the projects SVV 260 295, SVV 260 417, by MH CZ–DRO  and by the programme PRVOUK P37/11.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Borrelia burgdorferi spirochetes, has been found, thus raising the possibility of a link. We aimed to determine if infection with Borrelia contributes to an increased risk of celiac disease.Environmental factors, including infectious agents, are speculated to play a role in the rising prevalence and the geographic distribution of celiac disease, an autoimmune disorder. In the USA and Sweden where the regional variation in the frequency of celiac disease has been studied, a similarity with the geographic distribution of Lyme disease, an emerging multisystemic infection caused by Borrelia infection and celiac disease, we also examined the risk of subsequent Lyme disease in patients with a diagnosis of celiac disease.Biopsy reports from all of Sweden’s pathology departments were used to identify 15,769 individuals with celiac disease. Through linkage to the nationwide Patient Register, we compared the rate of earlier occurrence of Lyme disease in the patients with celiac disease to that in 78,331 matched controls. To further assess the temporal relationship between Twenty-five individuals (0.16%) with celiac disease had a prior diagnosis of Lyme disease, whereas 79 (0.5%) had a subsequent diagnosis of Lyme disease. A modest association between Lyme disease and celiac disease was seen both before , 1.06–2.47) and after the diagnosis of celiac disease , with the risk of disease being highest in the first year of follow-up.Borrelia infection is not a substantive risk factor in the development of celiac disease.Only a minor fraction of the celiac disease patient population had a prior diagnosis of Lyme disease. The similar association between Lyme disease and celiac disease both before and after the diagnosis of celiac disease is strongly suggestive of surveillance bias as a likely contributor. Taken together, the data indicate that  Celiac disease is an autoimmune enteropathy with genetic, environmental, and immunologic components . It is cBorrelia burgdorferi sensu lato spirochetal bacteria. B. burgdorferi sensu stricto is the primary cause of Lyme borreliosis in the USA, whereas B. afzelii, B. garinii, and B. burgdorferi are the causative agents in Europe [Borrelia infection-induced autoimmunity and is associated with autoantibodies [Lyme disease, also known as Lyme borreliosis, is caused by the n Europe . Lyme din Europe , 20. Altn Europe . The anttibodies , 23, an tibodies , and thetibodies . The contibodies .Borrelia to a later onset of celiac disease or gluten sensitivity has recently garnered attention and is discussed in various Internet forums [B. burgdorferi infection also occur [Borrelia increases the risk of celiac disease.The potential for the contribution of infection with t forums –29. In at forums , where tso occur . In Swedso occur , where Lso occur . DespiteFor this study, celiac disease was defined as a patient’s having villous atrophy (Marsh 3 histopathology grade) . EarlierBorrelia infection was defined as having the relevant International Classification of Diseases (ICD) code for Lyme disease in the Swedish Patient Register (ICD-10: A69.2) [Exposure to : A69.2) . The regWe matched each individual with celiac disease with up to five controls for sex, age, county, and calendar year. Controls were selected by the Statistics Sweden government agency, using the Swedish Total Population Register .Since information regarding diagnosis of Lyme disease was available starting on January 1, 1997, we only included study participants on or after January 1, 1998 . This resulted in 15,769 individuals with celiac disease and 78,331 matched controls.We used conditional logistic regression to calculate odds ratios (ORs) for later celiac disease among individuals with Lyme disease. The conditional approach means that each individual with celiac disease was only compared with his/her matched control (within a stratum). In this way, age, sex, county, and calendar year do not affect risk estimates. Furthermore, we examined the risk of celiac disease according to the time since the diagnosis of Lyme disease . We also adjusted for country of birth (Nordic vs. not Nordic) and educational level using four a priori-defined categories . Four peTo assess the temporal relationship between celiac disease and Lyme disease, we also examined the risk of later Lyme disease in patients with an initial diagnosis of celiac disease. This analysis was restricted to 15,742 individuals without a prior Lyme disease diagnosis at first biopsy with celiac disease and 78,230 matched controls. Cox regression was used to estimate the hazard ratio (HR). The follow-up period continued for each celiac disease patient and control until the first Lyme disease diagnosis, death, emigration, or December 31, 2009.We used SPSS 22  for all analyses. The OR and HR findings with 95% confidence intervals that did not overlap with 1.0 were regarded as statistically significant.This study was approved by the Ethics Review Board of Stockholm, Sweden. According to the board’s decision, no study participant was contacted, and the study was strictly register-based .The demographic characteristics of the study participants are shown in Table P for interaction, 0.962).Twenty-five 0.16%) individuals with celiac disease and 73 (0.09%) controls had a record of earlier Lyme disease  . The relative risk in the prospective analysis was not significantly different from that in the main analysis.In the prospective analysis, we found that 79 individuals with celiac disease (0.50%) and 215 controls (0.27%) had a record of later Lyme disease   reported a rate of about 4% [The strengths of this study are its nationwide population-based design and high statistical power. The prevalence of celiac disease in Sweden is among the highest in the world , making about 4% .Borrelia infection for markers of celiac disease (and vice versa). Given that there is some overlap between the systemic symptoms of Lyme disease and celiac disease, it is reasonable to assume that such surveillance bias would have increased the probability of celiac disease diagnosis in this study.Infectious agents have been linked to the pathogenesis of a number of autoimmune diseases, with one or more mechanisms, such as molecular mimicry, epitope spreading, bystander activation, and suppression of regulatory T cell function, being implicated , 44. In We acknowledge that patients with celiac disease may be different from controls in a number of ways that have not been measured. Though our cases were matched to controls by age, sex, county, and calendar year, there could be differences between cases and controls in relationship to both celiac disease and Lyme borreliosis which may contribute to the observed results. Therefore, while strongly suggestive of surveillance bias as the likely source for the association, other components, such as shared genetic, environmental, or other factors between celiac disease and Lyme disease might be potential additional contributors. For example, a variable previously found to be associated with Lyme borreliosis and celiac disease is income , 46. FurBorrelia infection with celiac disease.In summary, we found that, despite the high rates of occurrence of both celiac disease and Lyme disease in the population for this study, only a minor fraction of the celiac disease patients had an earlier record of Lyme disease. Furthermore, the modest relationship between Lyme disease and celiac disease both before and after the diagnosis of celiac disease points to surveillance bias or shared risk factors as the likely contributors. Taken together, our data argue against a causal association of


Few studies have shown nomograms that may predict disease-specific survival (DSS) probability after curative D2 gastrectomy for advanced gastric cancer (AGC), particularly among Chinese patients. This study sought to develop an elaborative nomogram that predicts long-term DSS for AGC in Chinese patients.A retrospective study was conducted on 6753 AGC patients undergoing D2 gastrectomy between January 1, 2000 and December 31, 2012 from three large medical hospitals in China. We assigned patients from Sun Yat-sen University Cancer Center to the training set, and patients from the First Affiliated Hospital of China Medical University and Tianjin Medical University Cancer Hospital to two separate external validation sets. A multivariate survival analysis was performed using Cox proportional hazards regression model in a training set, and a nomogram was constructed. Harrell’s C-index was used to evaluate discrimination and calibration plots were used to validate similarities between survival probabilities predicted by the nomogram model and actual survival rates in two validation sets.P < 0.001). In two validation sets, the nomogram’s discrimination power was also excellent relative to TNM classification . After calibration, the nomogram produced survival predictions that corresponded closely with actual survival rate.The multivariate Cox regression model identified age, tumor size, location, Lauren classification, lymphatic/venous invasion, depth of invasion, and metastatic lymph node ratio as covariates associated with survival. In the training set, the nomogram exhibited superior discrimination power compared with the 8th American Joint Committee on Cancer TNM classification (Harrell’s C-index, 0.82 vs. 0.74; The established nomogram was able to predict 3-, 5-, and 10-year DSS probabilities for AGC patients. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacity, suggesting its clinical utility. Despite a downward trend in incidence worldwide, gastric cancer remains both the second most prevalent cancer and the second most frequent cause of cancer-related death in China . AnnuallThe only proven curative treatment of gastric cancer without distant metastasis is radical resection combined with regional lymphadenectomy (mostly with D2 resection in Asian countries) . AccordiOur previous study demonstrated that age, sex, tumor size, tumor location, and lymphatic/venous invasion (LVI) could also be considered for predicting survival probabilities . TherefoAmong the available decision aids, nomograms currently represent the most accurate and discriminatory tools for predicting outcomes in patients with cancer . AccordiIn the past few years, nomograms have been successfully established to quantify risk by combining prognostic factors in certain malignancies –14. HoweTo date, no large cohort study has been conducted to establish the nomogram for the Chinese population with gastric cancer. Additionally, the demographic features, staging distributions, and treatment strategies in Chinese population differ from those in Western and Korean/Japanese populations.In the present study, we aimed to develop and externally validate a more elaborative nomogram that predicts 3-, 5-, and 10-year disease-specific survival (DSS) probabilities in Chinese patients who have undergone curative resection for AGC. This study included a large cohort of patients from three high-capacity hospitals from southern to northern China obtained over a relatively short period of time (2000–2012). This study represented the current treatment protocol in China.Between January 1, 2000 and December 31, 2012, patients who underwent gastric cancer resection at the Department of Gastric and Pancreatic Surgery at Sun Yat-sen University Cancer Center , the Department of Surgical Oncology at the First Affiliated Hospital of China Medical University , and the Department of Gastric Cancer Surgery at Tianjin Medical University Cancer Hospital  were included in the study. Data from patients who met the following criteria were collected: the pathologic diagnosis of primary gastric adenocarcinoma; absence of residual gastric cancer after surgery; no other synchronous malignancy; no preoperative chemotherapy; no distant metastasis; postoperative advanced disease (tumor invasion beyond the submucosa); D2 lymphadenectomy  ; R0 reseThe DSS was defined as interval between the date of diagnosis and the date of gastric cancer-related death. Patients with no documented evidence of death were censored at the date of last follow-up. To determine prognostic values related to DSS, we collected the following variables for survival analysis and the further establishment of the nomogram: sex, age, tumor size (the longest diameter of tumor), primary site , Lauren classification , lymphatic/venous invasion (present or absent), depth of invasion , number of metastatic lymph nodes resected, and number of total lymph nodes resected. As part of our surgical procedure, accurate count of all resected lymph nodes, which were carefully retrieved by the surgeon, was recorded. The exact number of retrieved lymph nodes and detailed information about the tumors were recorded in the pathologic results.A strict disease monitoring program was conducted every 3 months for the first 2 years after surgery, every 6 months from the third year through the fifth year, and annually after 5 years. The monitoring program included abdominal ultrasonography (or computed tomography [CT] if necessary), chest radiographs, gastroduodenal endoscopy examinations, and blood examinations for carcinoembryonic antigens CA199 and CA724. Regular follow-up results were obtained from outpatient records, telephone interviews, and brief messages. This study was approved by the institutional review boards of SYSUCC, CMU, and TJMU.The present study was approved by the ethics committees of the three hospitals and complied with the seventh version of Declaration of Helsinki in 2013.For nomogram construction and external validation, we assigned patients from SYSUCC to the training set and patients from CMU to TJMU to two separate external validation sets. A multivariate survival analysis was conducted using a Cox proportional hazards regression model without violation of the proportional hazards assumption.The clinicopathologic characteristics of the training and validation sets were evaluated. The proportional hazards (PH) assumption and linearity assumption for continuous variables  were examined using restricted cubic splines. To maximize predictive ability, continuous variables were transformed to adequately fit the PH and linearity assumptions, if possible . Using P < 0.05 in a two-tailed test. All statistical analyses were performed using SPSS version 20  and R software version 2.13.2 (http://www.r-project.org) with the design and survival packages.The performance of the nomogram consisted of discrimination and calibration using the external validation set. Discrimination was evaluated using a concordance index (C-index), which quantifies the probability that given two random patients, and the patient who relapses first had a higher probability of the event of interest. Harrell’s C-index, which is appropriate for censored data, was used for evaluating discrimination . CalibraFinally, 6753 patients were enrolled in the present study, of which 2169 were from SYSUCC, 2353 were from CMU, to 2231 were from TJMU .In the Cox PH regression model, age, tumor location, Lauren classification, LVI, T category, and metastatic lymph node ratio were found to be independently associated with prognosis. Table P < 0.001).Figure P < 0.001 for both comparisons).In the CMU and TJMU validation sets, discrimination was also better for the nomogram than for AJCC TNM staging , and the nomogram had a higher precision. Furthermore, our nomogram considered the unique characteristics of the patient population, thus addressing discrepancies in previous models regarding demographic features, stage distribution, and treatment strategies of US, Korean, or Japanese gastric cancer patients.In the present study, we established and externally validated a nomogram model for predicting DSS after D2 gastrectomy for AGC based on a large Chinese cohort. This nomogram was more significantly predictive than the eighth edition AJCC staging, with a C-index of 0.82 and good calibration. Compared with previous studies , 15, 16,P = 0.360). We believe that this finding represents one of the most important differences in demographic features and that it may be due, in part, to disadvantages in terms of socioeconomic status of the female population in China.In contrast to the previous nomogram based on US , Korean In our nomogram, Lauren classification was included because of its independent prognostic effect, which corresponds with the US  databaseOne important improvement of our nomogram is that we chose to include metastatic lymph node ratio, which refers to the number of metastatic lymph nodes resected divided by the number of total lymph nodes resected. In our previous studies, we successfully verified that the metastatic lymph node ratio had theoretical and practical advantages for decreasing the staging migration phenomenon and improving prognostic prediction –24. HoweThe accuracy of a predictive model refers to the ability of the model to discriminate between patients with or without the outcome of interest, which is demonstrated by the C-index for censored data. In our nomogram model, the C-index values were high: 0.82 in the training set and 0.83 and 0.81 in validation sets. Furthermore, a model’s accuracy indicates not only the overall ability to predict the outcome of interest but also the ability to predict the outcome of interest in specific patient groups or according to risk level. For this purpose, calibration plots should be obtained for both internal and external data. Unlike previous studies, our study investigated all three independent datasets.We hypothesize that the inclusion of complete and precise data , the conversion of continuous data, and the stage distribution contributed to that our study has a higher C-index value compared with previous studies , 15, 16.This study has some limitations. The main limitation of the present study was that adjuvant therapy, such as chemotherapy or chemoradiotherapy, was not included as a variable in the predictive model. In the multivariate analysis using the Cox regression model, adjuvant therapy did not appear to be important for prognosis. In clinical practice in China, patients with stage II or III disease usually receive adjuvant chemotherapy with a relatively uniform protocol including 5-fluorouracil- and/or platinum-based therapeutics. In some patients with high-risk recurrent diseases, adjuvant chemoradiotherapy is also recommended. Therefore, adjuvant therapy was not included as a variable in the present study. Another limitation was that our external validation set was based on the Chinese population. This is a large and multicenter database on Chinese patients has been used to establish such a model. In addition, the baseline parameters at the three hospitals were different because the patients from each hospital come from specific regions of China and thus have comparatively different demographics. However, despite these differences, the nomogram demonstrated great accuracy as shown by the C-indexes . Whether this predictive model is suitable for Western, US, or other Asian countries is unknown. Therefore, in future studies, it will be necessary to validate this model using data from other countries.In conclusion, in the present study, we established a nomogram for precisely and individually predicting DSS for Chinese patients with AGC who underwent radical resection with D2 lymphadenectomy based on databases from multiple centers. Its superiority to the traditional TNM staging system may enhance its clinical use in the near future.


The prevalence of gestational diabetes mellitus is increasing in parallel with the rising prevalence of type 2 diabetes and obesity around the world. Current evidence strongly suggests that women who have had gestational diabetes mellitus are at greater risk of cardiovascular disease later in life. Given the growing prevalence of gestational diabetes mellitus, it is important to identify appropriate reliable markers of cardiovascular disease and specific treatment strategies capable of containing obesity, diabetes, and metabolic syndrome in order to reduce the burden of cardiovascular disease in the women affected. It affects from 5-6% to 15–20% of pregnancies worldwide, depending on population demographics, screening methods, diagnostic criteria in use, and maternal lifestyle , 4. PregWomen with GDM are at greater risk of metabolic syndrome  and type 2 diabetes years after their pregnancy , 7. GDM Women who develop GDM are also at higher risk of overt cardiovascular disease (CVD) later in life . While aBe that as it may, the effect of GDM on the risk of CVD remains to be fully elucidated: it is still not clear whether the association existing between GDM and CVD is independent of the increased risk of CVD associated with type 2 diabetes.In this paper, we review the relationship between common CVD risk factors and a history of GDM and take a look at potential new markers of CVD in such women .A review of the international literature was conducted as regards the cardiovascular risk for women with GDM or a history of GDM. The keywords used were as follows: gestational diabetes mellitus, cardiovascular disease, cardiovascular risk, vascular disease, pregnancy, and pregnancy complication. Only data deriving from human studies and produced from 2005 onwards were considered in order to ensure that the evidence was topical. Literature dating from before 2005 was only included if it was particularly relevant. Data regarding patients with prior diabetes (type 1 or type 2) were not considered.p < 0.001) and that any hypertension would be diagnosed at an earlier age in the former than in the latter . Kaul et al. [In the literature, there is plenty of evidence of a greater risk of hypertension in women with a history of GDM. Carr et al.  demonstrl et al.  studied l et al.  recentlyp < 0.05), to take medication for dyslipidemia , and to be diagnosed with dyslipidemia at a younger age  than women with no history of GDM [p = 0.001), LDL-cholesterol , and triglyceride levels  and lower HDL-cholesterol levels . In a similar study population, Retnakaran et al. found GDM to be an independent predictor of total cholesterol, LDL-cholesterol, and triglyceride levels measured 3 months after delivery. These authors also demonstrated a stronger correlation between the area under the curve on the antepartum oral glucose tolerance test and postpartum levels of LDL-cholesterol and triglycerides, total cholesterol to HDL ratio, apoB, and apoB to apoA1 ratio  and an inverse relationship with HDL-cholesterol , after adjusting for age, ethnicity, and family history of diabetes [Numerous published reports demonstrate that women with a history of GDM are more dyslipidemic. Like the situation seen for hypertension, Carr et al. showed that women who developed GDM were more likely to report a history of acquired dyslipidemia , when NCEP, ATP III criteria were applied [p < 0.001) [Women who have had GDM are at high risk of developing metabolic syndrome. In a cohort of Caucasian women, for instance, the prevalence of metabolic syndrome 16 months after delivery was 9% among the women with a history of GDM and only 1% among controls ( applied . This pr< 0.001) . Other s< 0.001) –12. A re< 0.001) . Noctor < 0.001)  recently< 0.001) . Their sEven women with a history of GDM who have no common CV risk factors are at greater risk of CVD than those with normal glucose tolerance in pregnancy. GDM seems to have a significant impact on endothelial function and structure, triggering the first step towards the development of atherosclerosis.p < 0.01). It is important to mention, however, that these groups also differed in terms of the principal components of metabolic syndrome , which were all significantly higher in the GDM women than in the controls. In a population-based, multicenter, longitudinal, and observational study conducted by Gunderson et al. [Carotid artery intima-media thickness (cIMT) is a subclinical measure of early atherosclerosis that strongly predicts heart disease and stroke, particularly in women . In recen et al. , 898 womp = 0.003). They also reported finding no correlation between FMD and inflammatory parameters, lipid profile, or insulin resistance indices; they did not consider glucose AUC.Another proposed surrogate marker for the early detection of atherosclerosis is the flow-mediated dilation (FMD) of the brachial artery , which ip = 0.61) [p < 0.0001) in the third trimester of pregnancy [p < 0.0001). In another cross-sectional study on pregnant women with GDM (n. 19) or preeclampsia (n. 42) and controls with normal glucose tolerance and blood pressure (n. 19), Guimarães et al. also demonstrated a significantly reduced FMD in the women with GDM or preeclampsia by comparison with the controls, and they suggested the possibility of endothelial injury in such patients [Hannemann et al., on the other hand, found no differences in FMD between women who had experienced GDM five years earlier and control women matched for age, BMI, and smoking habits . Brewste = 0.61) . There iregnancy . They fopatients .p < 0.001) and also that insulin resistance, hyperglycemia, and oxidative stress markers were negatively associated with CVFR. On multivariate analysis, the authors also found an independent association between CFVR and GDM (p = 0.02) [In this setting, Caliskan et al. recently studied the coronary flow velocity reserve (CFVR), which reflects coronary microvascular function, in women with a history of GDM 6 months after delivery. They found this parameter to be significantly reduced in the GDM women by comparison with controls whose glucose tolerance remained normal in pregnancy  .Endothelial dysfunction is believed to be an important initiating factor in the development of atherosclerosis . Like ciIn a recent study, 141 women with a history of GDM and 49 age- and BMI-matched healthy control women were tested for circulating apelin, IL-6, and plasminogen activator inhibitor levels and IMT and took an oral glucose tolerance challenge. The results showed that plasma apelin levels were lower in women with a history of GDM and, in multiple regression analysis, they were negatively associated with fasting and postload glucose, IL-6, and carotid IMT. Suppressed apelin levels are therefore associated with a higher cardiovascular risk in women with a history of GDM .Subclinical inflammation is another major risk factor for future CVD in the general population, and the higher risk of CVD later in life for women with a history of GDM is potentially at least partly due to inflammatory mechanisms . AlthougOsteoprotegerin (OPG) is a soluble member of the tumor necrosis factor (TNF) receptor superfamily that inhibits osteoclast maturation and protects bone from normal osteoclast remodeling . OPG hasPentraxin 3 (PTX3) is an essential component of innate immunity induced by various inflammatory stimuli. It is produced by endothelial cell macrophages and granulocytes at sites of inflammation  and may Lekva et al. considered oral glucose tolerance test findings, lipid profiles, PTX3 levels, and arterial stiffness in 300 women during pregnancy and 5 years afterwards. Early in pregnancy and 5 years later, PTX3 levels were lower in the women who developed GDM, and they were associated with BMI. Low PTX3 levels in early pregnancy were inversely correlated with metabolic risk factors for CVD  5 years after delivery. Low plasma concentrations of PTX3 in early pregnancy are therefore associated with the subsequent onset of GDM and a higher risk of CVD later on .In conclusion, numerous studies have demonstrated an increased risk of type 2 diabetes, metabolic syndrome, and CVD after pregnancy in women who develop GDM, but the mechanisms contributing to the vascular dysfunction seen in GDM women remain uncertain. For the time being, no validated markers of this vascular risk are identifiable before the onset of diabetes, metabolic syndrome, or cardiovascular morbidity. Novel potential early markers have recently been proposed, but further investigations on larger samples and longitudinal studies are needed to confirm their value. Given the rising prevalence of GDM, future studies should aim to identify strong early markers of CVD in women who develop this condition, and specific strategies are warranted to prevent or reduce obesity, diabetes, metabolic syndrome, and consequent CVD, in this particular population.


This observational epidemiological study investigates sex/gender and racial differences in prevalence of COPD among never-smokers. Data were derived from the 2012 Center for Disease Control's Behavioral Risk Factor Surveillance System. The sample consisted of 129,535 non-Hispanic whites and blacks 50 years of age and older who had never smoked. Descriptive and multivariable analyses were conducted, with the latter using a series of logistic regression models predicting COPD status by sex/gender and race, adjusting for age, height, socioeconomic position (SEP), number of household members, marital status, and health insurance coverage. Black women have the highest prevalence of COPD (7.0%), followed by white women (5.2%), white men (2.9%), and black men (2.4%). Women have significantly higher odds of COPD than men. When adjusting for SEP, black and white women have comparably higher odds of COPD than white men , while black men have significantly lower odds . This research provides evidence that racial inequalities in COPD (or lack thereof) may be related to SEP. Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in the US  and a prCOPD prevalence has been higher among women than men in most age groups for more than two decades –8. As woAnother group experiencing detrimental trends surrounding COPD is black Americans, whose mortality from the disease has been increasing faster than other racial groups . Higher The purpose of the current study is to investigate sex/gender and racial inequalities in COPD among never-smokers, in a large, representative sample of black and white Americans over the age of 50. We hypothesize that women and black Americans will have higher odds of COPD and that height (a proxy for lung size) and socioeconomic position (SEP) will help explain the association. We also expect a significant interaction between sex and race, such that black never-smoking women will have the highest odds of COPD compared to other groups.n = 129,535). Our final sample contained 8,674 black women, 2,708 black men, 80,317 white women, and 37,836 white men.Data for the current study are drawn from the 2012 Behavioural Risk Surveillance System. The BRFSS collects ongoing data assessing state-specific behavioral risk factors in the adult population . On a moSelf-report of a medical diagnosis of COPD was the outcome measure. It was ascertained by positive response to the question of whether a “doctor, nurse or another health professional” ever told the respondent that they had “Chronic Obstructive Pulmonary Disease or COPD, emphysema or chronic bronchitis.” There were very little missing data for this question (<0.5% of full sample).While gender was not measured per se, sex was assessed by self-report using two categories of male and female. Race was also determined by self-report. We included in the analysis those who are identified as non-Hispanic and either white or black/African American . There were no missing data on sex and 1.4% missing on race.Based on a review of the literature, we included height and SEP in the modeling as theoretically important explanatory factors. Height was self-reported (in inches) and entered into the models as a continuous variable (missing = 1.2%). SEP was measured by highest level of education (missing = 0.4%) and net annual household income (missing = 14%). Both were included in the modeling as categorical variables. To prevent losing a large amount of data on household income, a missing category was included.We controlled for a number of variables in the modeling, including age  and health care coverage . Also, as an attempt to account for whether the respondent was living or married to a smoker, we adjusted for the number of people in the household  and marital status (missing = 0.5%). A missing category is included for household size as it was only asked among those sampled over a landline.t-test for continuous variables. To test our primary hypothesis, we undertook a series of logistic regression models. First, we tested for an interaction between sex/gender and race for predicting COPD status using a likelihood ratio test that compared a model with the main effects and age only with one that also included the interaction terms. Consistent with our initial hypothesis, a significant interaction was found . To increase its interpretability, we created a four-level variable representing the combination of sex/gender and race as follows: black women, white women, black men, and white men (the reference group). In the first and each subsequent model, we predicted COPD status using this 4-level variable, while controlling for age. In the second model, we also included height. The third model included SEP. The final model included all variables simultaneously . As group comparisons in this model are all relative to white men, we also conducted contrasts that compare black and white women. To account for the multiple comparisons required to test our hypothesis, we used an adjusted p value of <0.01 to establish statistical significance. All data were weighted to account for selection and nonresponse bias.All analyses were undertaken using IBM SPSS version 23. The prevalence of COPD was estimated and differences in COPD status across study variables were tested using the chi-square test for ordinal/categorical variables and p < 0.001) and with decreasing education and income levels (both p < 0.001). Those with COPD are significantly shorter than those without this condition . A higher prevalence of COPD is found among those who live alone (p < 0.001) and who have a health insurance plan (p = 0.001).As shown in p < 0.001), there is no difference between black and white men. When height is added to model 2, the relationships are attenuated, but the difference between black and white women remains . When SEP is included in model 3, black and white women continue to have significantly higher odds of COPD than white men; however, two important changes happen. First, the odds of COPD for black women are attenuated much more than they are for white women , which results in the elimination of difference in the odds of COPD between black and white women using the adjusted p value . Second, the odds of COPD among black men become significantly lower than those among white men . Finally, in the fully adjusted model, both black and white women have significantly higher odds of COPD compared to white men and there is no difference between black and white women using the adjusted p value . Also, consistent with the model including SEP, black men have significantly lower odds of COPD than white men .The objective of this study was to investigate sex/gender and racial inequalities in COPD among never-smokers in a population-based sample of Americans. Consistent with our hypotheses, we find that sex/gender and race are associated with COPD prevalence but that the relationships are uniquely influenced by SEP. Specifically, we show that women (both black and white) have higher odds of COPD than men. This finding is consistent with prior research  and the Importantly, the current study also demonstrates that, among never-smokers, black women have higher odds of COPD than white women but that SEP appears to explain the difference. This finding contradicts an earlier study using the NHANES that found no age-adjusted black/white differences in the odds of self-report of physician-diagnosed COPD among never-smoking women . The lat lower odds of COPD than white men. This finding is consistent with prior research on never-smokers [Another important finding of the current study is that, among never-smoking men, controlling for SEP allows for a racial effect to emerge, such that black men are found to have-smokers , 27. Whi-smokers  and/or p-smokers ), it is -smokers . HoweverThis study's findings must be considered in light of several limitations. One is that COPD was based on self-report of a medical diagnosis. Some smaller studies have shown self-report of a diagnosis of COPD to be quite accurate, when compared to medical charts  and, thThe frequent focus of health research on proximal risk factors obscures the role that more distal causes, such as structural conditions, play in making some groups more vulnerable to illness than others. While biological factors, such as sex, may be critical for understanding COPD prevalence among nonsmokers, our research suggests that structural factors—gender, race, and SEP—may also be important to consider, not only in research but also in policy development.


Iliodromiti et al., on page 1514.This editorial refers to ‘The impact of confounding on the associations of different adiposity measures with the incidence of cardiovascular disease: a cohort study of 296 535 adults of white European descent’,There has been much discussion in the epidemiological literature about the so-called ‘obesity paradox’, where being overweight or obese appears to be protective for mortality in studies including individuals with prior cardiovascular disease (CVD).2. Interestingly, the associations between the other adiposity measures considered  and incidence of CVD did not appear to be influenced by confounding or reverse causality. The associations were unchanged by the exclusion of smokers and individuals with prior co-morbidities, as well as by omitting the first 2 years of follow-up. This could be a result of BMI being a poor discriminator of body fat and lean mass. Chronic illness can lead to a reduction in lean mass and subsequent weight loss,In this issue of the journal, Iliodromiti and colleagues2), WC, and waist-hip-ratio and incidence of CVD reported by Iliodromiti and colleagues2 higher BMI is associated with a 23% increased risk of CVD, defined as a composite of coronary heart disease and ischaemic stroke . They also estimate that 12.6 cm higher WC and 0.083 higher waist-hip-ratio are associated with increases in CVD risk of 27% and 25%, respectively . There are several possible explanations for these discrepancies, including: (i) differences in covariate adjustment; (ii) lack of specificity in CVD outcome definition; and (iii) measurement error.The associations between BMI . This is often referred to as ‘regression dilution bias’ and can be corrected by multiplying the regression coefficient from analyses using baseline levels of the risk factor by 1/R, where R is the regression dilution ratio (which can be estimated by the correlation between pairs of measurements some time apart).,P-values associated with the HRs would remain unchanged by this correction.Iliodromiti and colleaguesUsing data from the 20 000 UK Biobank participants who attended the first repeat assessment visit, regression dilution ratios for BMI, WC, and waist-hip-ratio are estimated to be 0.94, 0.83, and 0.64, respectively, for men and 0.94, 0.86, and 0.64, respectively, for women. Applying these to the HRs that have not been adjusted for the intermediate risk factors of diabetes and blood pressure , the HRs for BMI remain roughly the same . The strengths of the associations for WC have increased slightly , but the associations for waist-hip-ratio have nearly doubled . However, the corrected associations for all three measures are still weaker than those observed in the ERFC.r = 0.95).The waist to height ratio was not reported in the ERFC meta-analysis as it is highly correlated with WC  and hence are susceptible to regression dilution bias, and so their associations with CVD may be stronger than suggested by Iliodromiti and colleagues.Conflict of interest: the CTSU has a staff policy of not accepting honoraria or other fees from the pharmaceutical industry, expect for the reimbursement of costs to participate in scientific meetings (www.ctsu.ox.ac.uk). N.S. reports grants from Boehringer Ingelheim, outside the submitted work.


Our purpose is to examine the relationship of Health related quality of life measured by EORTC QLQc30, QLQ-LC13; FACT-L, LCSS, Eq5D) with survival in advanced lung cancer patients. A total of 299 Lung Cancer (LC) patients were, included in this national multicenter Project entitled of “the LC Quality of Life Project (AKAYAK). Baseline scores were analyzed by using Cox’s proportional hazard regression to identify factors that influenced survival. Univariate and multivariate models were run for each of the scales included in the study.Mean and median survival were 12.5 and 8.0 months respectively. Clinical stage (as TNM), comorbidity; symptom scales of fatigue, insomnia, appetit loss and constipation were associated with survival after adjustment for age and sex. Global, physical and role functioning scales of QLQc30; physical and functional scales of LCS and TOI of the FACT-L was also associated with survival. Mobility and Usual activities dimensions of the Eq5D; Physical functioning and the constipation symptom scale of the QLQ-c30; and LCS and TOI scores of the FACT-L remained statistically significant after adjustment. LC13 and LCSS scales were not predictors of survival.HRQOL serves as an additional predictive factor for survival that supplements traditional clinical factors. Besides the strong predictive ability of ECOG on survival, FACT-L and the Eq5D are the most promising HRQOL instruments for this purpose. Health-related quality of life (HRQOL)  is one of the common cancers in men worldwide, and survival, treatment modalities, and HRQOL of LC patients are issues that need to be addressed in a clinical context. It is both an important and difficult task for clinicians to predict prognosis in cancer patients and especially for patients having advanced lung cancer. Until recent decades, health professionals used objective performance indicators in a dominant role to predict the prognosis of lung cancer. Several studies have shown the ability of HRQOL instruments to predict survival based on different cancer sites . We contacted 299 LC patients undergoing active chemotherapy, surgery, or post-therapy follow-up from April 2010 to February 2012 in an inpatient setting or at the outpatient clinics of five comprehensive cancer centres in western TurkeyHRQOL and performance scales were completed at baseline for all patients regardless of the treatment type .During these visits, HRQOL instruments were applied to the patients via interviewer assistance and the Karnofsky performance status (KPS) and Eastern Cooperative Oncology Group (ECOG) performance status  in this trial were as follows:Patients who were diagnosed with Primary LC as stage IIIB or IV .Age between 18 and 76 years.Previously untreated and planning to undergo chemotherapy, radiotherapy, or chemo-radiotherapy.Ability to read and complete questionnaires,Agreed to participate in the study and volunteer to attend the control visits.Written informed consent form was provided.The inclusion criteria for the patients  consists of 13 questions assessing lung cancer-associated symptoms , treatment-related side effects , and pain medication” .Tumour characteristics, such as histology, tumour type, stage, duration of illness, type of treatment, and patient characteristics, including age, gender, education, and co-morbidities at the time of diagnosis, were obtained. All histological types were included in our study. The 7th edition of lung cancer TNM classification and staging system was used in this study .Survival curves were estimated using the Kaplan–Meier method. The log-rank test was used to determine the statistical significance of the differences between curves. The level of significance was set at 0.05. Statistical analyses were performed using the SPSS software (version 15.0 for PC).All patients (n = 299) had TNM system stage III (38.5 %) or IV 61.5 %) disease and an ECOG performance status of 0 or 1 (70.9 %) at baseline , as several studies have demonstrated in advanced LC patients, although some results have been contradictory. In the study by Herndon et al., for example, global QOL was not found prognostic for survival. As Qi et al.  noted, tWe found the fatigue, appetite loss, insomnia, and constipation symptom scales of the QLQ-C30 to be significant predictors of survival after adjustment in our study. When the generic scales of the QLQ-C30 were included in the models, the constipation scale remained as a unique independent significant predictor of OS. Our findings are consistent with previous reports for appetite loss , and comorbidities. Eaton et al.  evaluateAs previously confirmed in many studies , the pain and self-care dimensions were excluded from the final reduced model, leaving mobility and usual activities dimensions as strong independent predictors of survival. These two dimensions remained in the model because both refer to “physical independence,” as already confirmed by the findings of QLQ-C30 and FACT-L in this study. In fact, Jang et al.  demonstrIn this study, we included a battery of HRQOL instruments to evaluate predictors of survival. Among them, none of the LC13 scales or LCSS index or symptom scores were found to be predictive of survival. These results are consistent with findings in previous studies (LC13: Montazeri et al. This study has some limitations. One is that this study merged all types of lung cancer, and the findings could not be expressed purely for any specific type of lung cancer. Second, we could not use the “duration of cancer” variable in this study, because most of the patients were newly diagnosed, but this may be strength of the study too. A third limitation was the lack of FLIC scale in the questionnaire battery, due to the unavailability of a validated Turkish version (Ganz et al. HRQOL serves as an additional predictive factor for survival that supplements ‘traditional’ clinical factors, such as age, gender, stage, and comorbidity in LC patients. Besides the strong predictive ability of the ECOG performance status on survival, FACT-L and the Eq5D were the most promising QOL instruments for the purpose in this study.


There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD.We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3–5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD): the Kidney Failure Risk Equation  and the Diabetic Nephropathy Score ). They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI) of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported  and was not significantly improved by adding the D-score . Only continuous NRI was positive after adding the D-score to the KFRE .We found that the predict values of the KFRE and the D-score were not as good as reported, and combining the D-score with the KFRE did not significantly improve prediction of the risk of ESRD in advanced diabetic nephropathy. To improve prediction of renal prognosis for advanced diabetic nephropathy may require different approaches with combining clinical and pathological parameters that were not measured in the KFRE and the RPS DN Classification. Despite advances over the past 20 years in delaying the progression of diabetic nephropathy, it is still a leading cause of end-stage renal disease (ESRD) worldwide and imposes a heavy burden not only on individual patients but also on society . Therefoet al. developed the kidney failure risk equation (KFRE) for patients with stage 3 to 5 CKD to identify those at high risk of developing ESRD based on demographic, clinical, and laboratory variables [P<0.001) and has been validated in more than 30 countries with very similar c-statistics [Tangri ariables . Withoutet al., on the other hand, developed a pathological score for diabetic nephropathy (D-score), based on a pathological classification of diabetic nephropathy by the Renal Pathology Society (RPS DN Classification) [Hoshino ication) , to predication) . Its preIt has been unclear whether the KFRE can identify ESRD in patients with diabetic nephropathy who have a much higher risk of end-stage disease than other CKD patients, and whether there is any value in combining pathological information on diabetic nephropathy with the KFRE. Therefore, we performed the present study to assess the performance of the KFRE for predicting ESRD in patients with biopsy proven diabetic nephropathy, to investigate the incremental value of combining the D-score with the KFRE, and to validate a new combined model (KFRE + D-score) for predicting ESRD in these patients.To identify patients with type 2 diabetes, we retrospectively reviewed all renal biopsies performed from 1985 to 2013 at the following four nephrology centers: Toranomon Hospital , Toranomon Hospital Kajigaya , Kanazawa University Hospital , and Kanazawa Medical Center . The indications for biopsy were (i) renal impairment, (ii) urinary abnormalities such as albuminuria, proteinuria, hematuria, or casts, and (iii) suspected concomitant renal disease with diabetic nephropathy. Patients were excluded if they had protocol renal transplant biopsy, confirmed concomitant renal disease (except for nephrosclerosis), or inadequate tissue for diagnosis. Among 521 biopsies, 493 were performed in patients with diabetic nephropathy. Among them, 296 patients with stage 3 to 5 CKD at the time of biopsy were followed up for at least three months, and these patients were enrolled.This study was approved by the institutional review boards of Toranomon Hospital, Toranomon Hospital Kajigaya, Kanazawa University Hospital, and Kanazawa Medical Center. The study design, clinical setting, eligibility criteria, variables investigated, and statistical analysis were in conformity with the STROBE Statement . This woClinical characteristics at the time of biopsy, such as the age, gender, duration of diabetes, BMI, hypertension, and dyslipidemia, were ascertained from the medical records.Laboratory data at the time of biopsy were also obtained from the medical records, including hemoglobin A1c, serum creatinine, eGFR , and ACRenal biopsy specimens were processed for light microscopy, immunofluorescence, and electron microscopy. All biopsies were evaluated with three pathologists according to the RPS DN Classification . If all The primary endpoint of this study was ESRD, which was defined as initiation of hemodialysis or peritoneal dialysis, or renal transplantation, or death from uremia. Death unrelated to ESRD was also extracted and loss to follow-up was considered a censoring event.ACR) = 1.32 × ln(PCR) − 2.64 [t-test or the Wilcoxon test for continuous variables, while the chi-square test or Fisher’s exact test was used for categorical variables.There were no missing data for age, gender, eGFR, and the D-score. However, 207 of 493 patients had missing ACR data but had PCR data. In these patients, we converted PCR to ACR using a converting formula: ln − 2.64 . Differeet al [The 296 patients were randomized 2:1 to the development cohort of 198 and validation cohort of 98 patients. In the development cohort, the KFRE index, the D-score, and the incremental value of the D-score were assessed with using Cox proportional hazard models to test whether predictors were associated with the primary outcome. Model performance was also assessed with calculating the global chi-square, Akaike information criterion, and Harrell’s c-statistics. Furthermore, the area under the receiver operating characteristic curve, net reclassification improvement, integrated discrimination improvement, integrated sensitivity, and integrated specificity for the 3-year risk of ESRD were investigated in the development cohort. To assess incremental values, we followed the guidelines of Kerr Results are expressed as the mean with standard deviation or the median with interquartile range for continuous data and as percentages for categorical data.p<0.05 (two-sided). All statistical analyses were conducted using Stata version 14.1 .Statistical tests were considered significant at 2) who all had biopsy proven diabetic nephropathy without other renal diseases. A total of 296 patients followed for at least three months were randomized at a 2:1 ratio to two groups, which were a development cohort (n = 198) and a validation cohort (n = 98). The median follow-up period (25th-75th percentiles) was similar in both cohorts, being 1.8 (1.0–5.0) years in the development cohort and 2.0 (1.0–3.8) years in the validation cohort (p = 0.89). The study group comprised four independent cohorts of patients  with type 2 diabetes and advanced CKD , and logarithmic urine ACR (mg/g). In this study, we used the KFRE index reported by Lennartz et al [In the original paper by Tangri et al. , the KFRtz et al ., with het al. [Based on the Pathological Classification of Diabetic Nephropathy by Tervaert et al. , a pathoet al. ) was cal2:The risk of ESRD was calculated according to the KFRE as reported in the original articleIn addition, the predicted risk of ESRD according to the KFRE+D-score was calculated as follows:nonevents showed no significant change, there was a significant change of NRIevents that resulted in a positive overall NRI . The change of integrated discrimination improvement was similar to that of NRI, but was also not significant . Integrated sensitivity was improved in the KFRE+D-score model, whereas integrated specificity was not improved in the KFRE+D-score model. The actual differences from the KFRE model were small.We assessed the incremental value of the D-score for predicting ESRD at three years in the development cohort from the two-category threshold at a prevalence of 46.5% based on the percentage of patients with events. As shown in This study demonstrated that the KFRE and the D-score were independent predictors of ESRD in patients with advanced diabetic nephropathy. However, its predict values were not as good as reported, and combining the KFRE with the D-score did not significantly improve prediction of the risk of ESRD.et al. developed the KFRE to identify patients with stage 3 to 5 CKD at high risk of progressing to ESRD based on demographic, clinical, and laboratory variables [et al. . The following points can be given as this reason. First, the KFRE may not be able to capture a phenotype of diabetic nephropathy—nonproteinurics. The simple version of KFRE consists of only four variables—age, sex, estimated GFR, and ACR—and from their hazard ratios we see ACR is the strongest predictor of ESRD among them. However, in our cohort population, we see an increasing frequency of impaired renal function with normoalbuminuria or with microalbuminuria—ACR<300 mg/g (113/296 = 38%)—of our study group. We think this population is a different phenotype of diabetic nephropathy and they are not well captured by KFRE. Second, diabetic nephropathy has a lot of different risk factors of ESRD compared to other CKDs. Other than four variables of KFRE, we have other known risk factors of developing diabetic nephropathy—poor glycemic control, duration of diabetes, hypertension, dislypidemia—and unknown risk factors of developing diabetic nephropathy—race, genetics, and novel biomarkers. Combining these factors may improve prediction of ESRD in patients with diabetic nephropathy. Unfortunately, adding known risk factors did not improve prediction of ESRD with KFRE in our cohort.A number of authors have proposed prognostic scores for ESRD in patients with advanced CKD, but none of these methods have been widely accepted –16. For ariables . Withoutet al. developed a consensus classification of diabetic nephropathy on behalf of the Renal Pathology Society [For many years, diabetic nephropathy was clinically diagnosed from the presence of macroalbuminuria and renal impairment in patients with diabetes and so there have been a limited number of biopsy-based studies on diabetic nephropathy. Even after Tarvaert  Society , only a  Society –19 and nDespite this result, the main clinical importance of renal biopsy in patients with diabetes is probably related to differentiating diabetic nephropathy from other renal diseases or categorizing concomitant renal pathology. Indeed, several studies have indicated that renal biopsy is useful for differentiating pure diabetic nephropathy from pure non-diabetic nephropathy or combined states since the renal prognosis is different –23. We aOur study had a number of strengths. It was based on a large multicenter cohort of patients with biopsy proven diabetic nephropathy recruited from across Japan. In addition, follow-up was adequate to assess renal outcomes, enabling robust survival analysis of the incremental value combining of renal biopsy data with clinical variables.et al. [However, a number of limitations of this study should be considered. First, there may be a possibility of confounding by indication—the study population was potentially biased by nephrologists who were interested in diabetic nephropathy, since usually it was diagnosed clinically from overt albuminuria and renal impairment in patients with diabetes. Second, the D-score was based on the Pathologic Classification of Diabetic Nephropathy, in which certain pathological changes such as exudative lesions or mesangiolysis, which Furuichi et al.  reportedet al. . HoweverIn conclusion, the kidney failure risk equation was a good instrument for identifying a high risk of progression to ESRD among patients with diabetic nephropathy and advanced CKD. However, its predictive value was weaker than in the miscellaneous CKD cohort originally reported by Tangri et al and adding pathological information based on the Diabetic Nephropathy Classification by the Renal Pathology Society to the KFRE did not significantly improve prediction of ESRD. Accordingly, to improve prediction of renal prognosis for advanced diabetic nephropathy may require different approaches with combining unmeasured clinical or pathological features.