The authors show the results of an integrated model for risk management of tuberculosis in a sample of sheltered homeless in Rome. Tuberculin skin test (TST) was used for evaluating the prevalence of latent infection (LTBI). In TST positives, expectorate was collected and chest X-ray was achieved. Multiple logistic regression analysis was performed to investigate determinants of infection. Out of 288 recruited subjects, 259 returned for the TST reading; 45.56% were positive and referred to a specialized center; 70 accessed the health facility and completed the clinical pathway. The risk factors associated to LTBI were male gender (OR = 3.72), age over 60 years (OR = 3.59), immigrant status (OR = 3.73), and obesity (OR = 2.19). This approach, based on an integrated social network, guarantees high adherence to screening (89.93%), allowing patients testing positive for latent tuberculosis infection to be diagnosed and rapidly referred to a specialized center. Mycobacterium tuberculosis in overcrowded shelters and in the community .A considerable increase of people living on the road has been reported in recent years: in 1998, a figure of 5,000 homeless people was estimated in Rome, but nowadays the expected number is about 23,000 [Epidemiological studies of homeless populations have reported the prevalence rate of 1.2%–6.8% for active TB , 10. No Better understanding of the characteristics of homeless people with TB disease is important for creating strategies to reduce TB incidence in this high-risk population .Based on these assumptions, we enrolled homeless people from two shelters in Rome. A cross-sectional study was carried on with the following aims:to assess acceptance to came back for TST reading in a sample of homeless people recruited from two shelters in Rome (subjects who returned for Tuberculin Skin Test (TST) reading, after 72 hours),to measure the prevalence of skin-positive results, assumed as LTBI,to investigate the associations between social risk factors and TST positive results,to evaluate the access to a public specialized outpatient clinic for initiating an early therapeutic pathway.The protocols of this research were cleared by the local ethical committee.The eligible individuals—male and female, Italians and foreigners, adults ≥ 18 years old—were recruited from two homeless refuges in Rome (named Caritas and Acisel) in the time period November 2006-November 2007.These two shelters were selected by convenience among all the others in Rome. Individuals were recruited according to HUD (US Department of Housing and Urban Development) homeless definition, as “an individual who lacks a fixed, regular,…, and adequate night-time residence…” .The sample size was calculated using 2004 available data, the most recent ones existing when the protocol of the study was drafted, taking into account the homeless number in Rome, fixing the confidence level at 95%, estimating the LTBI prevalence as 25%, and considering a 90% study compliance. According to these parameters, 276 individuals were eligible to be recruited.Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)) and drugs use.A detailed anamnesis was collected using a form to record data about markers and risk factors: gender, age, height, and weight (for body mass index (BMI)), origin, past TB diagnosis and therapy, immigration condition , smoking habits, alcohol abuse  vaccination was investigated, with a specific question and an inspection verifying the presence of any scar on the deltoid muscle.Previous The TST (Biocine test PPD 5 UI) was performed by medical physicians in the surgery at the refuges, after informing the subjects about the study and obtaining their understanding and written consent by a translated form.TST was read after 72 hours. According to the American Thoracic Society standards, which have provided new recommendations for targeted tuberculin testing and treatment regimens for persons with latent tuberculosis infections (LTBI), a reaction with an induration diameter >10 mm and with normal chest X-ray was considered as positive and we classified this as LTBI to send the subject towards a clinical pathway, managed by a specialized hospital .Only positive subjects were invited to undertake a chest X-ray and to provide three sputum samples on alternate days. The chest X-ray was performed with an MPX+  portable equipment for radiography and read by two different radiologists. The sputum was collected with a safe disposable device .The bacterioscopic examination (Ziehl-Nielsen staining) and cultures in solid (Lowenstein-Jensen) and liquid media , were performed by the Microbiology Unit, according to internal QC procedures.M. tuberculosis .Moreover, PCR was prepared to identify and confirm M. tuberculosis isolates, a phenotypic study of drug resistance to streptomycin, isoniazid, ethambutol, and rifampicin and the genetic typing with random amplification of primers designed technique (RAPD), based on PCR technique, was prepared.In the case of The positive subjects to TST were referred to INMI  in Rome for clinical evaluation.Univariate analysis by logistic regression was performed using the chisquare test and the Mann-Whitney test for assessing differences between groups for categorical and quantitative variables, respectively.LTBI risk as the dependent variable and the following variables as independent variables: gender , marital status (single as reference group), nationality (foreigners as reference group), nutritional status (no obesity as reference group), and age category (age under 60 as reference group). The results are presented as odds ratios (OR) and 95% confidence intervals (95% CI).A multivariate analysis was performed using the dichotomous variable P  value > 0.10. The goodness of fit of the model was assessed using the Hosmer and Lemeshow test. A stepwise approach (backward elimination procedure) was followed, using the method suggested by Hosmer and Lemeshow. The variables were excluded from the model until a P ≤ 0.05.The statistical significance was set at The statistical analysis was conducted using SPSS software (release 12.0).From the two homeless shelters, 288 subjects were recruited; for each subject, a data collection form was filled in and a TST performed. In the same period, 133 expectorates were collected and 46 chest X-rays performed. Among all recruited subjects, 259 came back for the TST reading, with an acceptance rate of 89.93%. Of these, 204 subjects (78.8%) were males and 55 (21.2%) females; 31 (12%) were over 60 years old. As far as marital status, 122 subjects (47.1%) were single, 68 (26.2%) married, 51 (19.7%) divorced or separated, and 16 (6.2%) widows/widowers . Regarding the social features, 171 (66%) were immigrants and 65 (25.1%) were employed during the recruitment period. Among them, 108 (41.7%) declared to be registered within the Italian National Health Service, while 17 (2.7%) reported to be foreigners temporarily present. Regarding educational level, 13 (5%) had no education title, 124 (47.9%) had completed Primary school, 102 (39.4%) had completed Secondary school, and 12 (4.6%) were graduates.Four subjects (1.5%) reported a previous TB diagnosis. Regarding the smoking habit, 147 (56.8%) were smokers, 99 (38.2%) nonsmokers, and 13 (5%) former smokers.Twenty-two individuals (8.5%) declared to be alcohol abusers and 10 (3.9%) exalcohol abusers. Most of the participants declared not to be illicit drug users, though three subjects (1.1%) declared to be drug-users and 8 (3.1%) exdrug users.Out of the 259 compliant subjects, 141 (54.44%) were TST negative and 118 (45.56%) were positive, therefore suffering from LTBI. Among these, 18 (15.4%) were over 60 years old, 16 (13.6%) were females and 102 (86.4%) were males.No chest X-ray, sputum, and culture sample was positive, respectively, for TB and Mycobacterium tuberculosis.In All TST-positive subjects were referred to a specialized outpatient clinic and 70 of them  accessed the health facility and completed the diagnostic pathway to receive the appropriate therapy.The other 48 were lost to the study because of their homelessness condition, as all homeless were not stably residential in the shelter.One subject (3.86‰) showed clinical suspicion of active disease and was promptly referred to hospital.The results for LTBI risk, according to the multiple logistic regression analysis performed in order to discover the relationship with the risk factors, are shown in In large European metropolitan areas, for example, in London , in RottImmigration, under-nutrition , smokingIn these perspectives, the results of our study indicate a prevalence of LTBI of 45.56% among the homeless and of 3.86‰  for active TB.M. tuberculosis infection because of overcrowded environments or because they shelter in a community where the greater chance of an active TB patient transmitting the microorganism is not promptly recognized.The low rate of active TB, compared to cited experiences, could be interpreted as a sign of protection and full immune-competence of this group of subjects. It may be hypothesised that the incidence of active TB in this cohort of LTBI-positive homeless persons may be explained by the good quality of conditions available for the homeless living in Italian shelters, which warrant good nutritional status and, therefore, low risk of progression to active disease. Nevertheless, the remarkably high LTBI prevalence observed may indicate that the homeless may have a higher risk of contracting In this perspective, finally, immigrants should not be considered as spreaders of the disease but as persons at risk, and appropriate public health approaches should be oriented towards them, aimed at inclusion and at the promotion of barrier-free prevention and care measures . In Italy, health is a constitutional right for everyone, migrants included, independent of their judicial condition. Nevertheless, in practice, a high level of inequality affects access to healthcare services, as this is noted when we compare the Italian population with migrants. Available data reveal a substantial failure of health and social policies for integration .In 2008, European Centre for Disease Control (ECDC) published a framework action plan to fight TB in the European Union (EU), which recognizes the concentration of TB in “hard to find” and “hard to treat” populations as a major challenge to TB control efforts across the EU and encourages EU institutions to collaborate with partners to identify and disseminate good practice models for TB control .On these foundations, our study applies this model of TB control in advance and for the first time in Rome and produced a high compliance amongst the homeless population. In fact, a high adherence to screening was observed (89.93%) if compared with a 65% adherence to TST reading registered in a preventive therapy programe carried out in an inner city population in Atlanta in 1994–1996  and withOur data confirm that active surveillance is an excellent tool for prevention. Active research in crowded areas gave the possibility to 59.32% of the positive subjects to access health care facilities and to complete the clinical pathway.The strength of our experience was the opportunity to reach people in shelters (where people sleep and eat) and to start a clinical activity within the same place, with highly motivated staff, in order to facilitate access by the most disadvantaged persons.Nevertheless, the compliance to the TST reading decreased from 89.93% to a 59.32% clinical compliance when health and social support was missing and people were invited to go to hospital by themselves. The active role of a social network also should be strengthened with the aims of prevention, diagnosis, treatment, and followup. For example, the Dutch TB control model is based on close collaboration between hospitals and municipal public health TB .Moreover, guidelines should take into account the peculiarities of risk groups for whom health is not a priority .Firstly, concerning internal validity, a possible bias could have been introduced by a 10% of refusal to come back for the test reading.P = 0.639) and gender (P = 0.718). As such, we expect that subjects who did not return for a TST reading did not influence the statistical analysis.In any case, persons who came back for TST reading are substantially similar to the ones who did not regarding two important variables: age  because of blood sampling difficulties in the studied population, that would have been useful in the diagnosis of recent infection.Lastly, atypical infection and effects of the BCG used in the country may impact on the results of TST.


Several large prospective studies have demonstrated that apolipoprotein B (apoB) has greater value in predicting cardiovascular risk than any other lipid measurements. Currently, however, serum apoB levels are not routinely measured, because of the additional cost. The aim of this study was to develop an equation to estimate apoB from conventional lipid measurements including total cholesterol, HDL cholesterol, and triglycerides.Data from a total of 78,127 subjects , aged 15 to 88 years (mean age 41.8 years) were reviewed to develop an apoB equation. Additional datasets from the same institution and the NHANES obtained in 2007–2008 were used for internal  and external validation , respectively.We developed an apoB equation based on a linear regression model that contains total cholesterol, triglycerides, and HDL cholesterol as terms (model 1). To more precisely estimate the serum apoB level, we adjusted mode1 1 using a cutoff serum triglyceride value of 270 mg/dl (model 2). Model 2 showed more randomly distributed residuals in patients with diabetes, atherogenic dyslipidemia, and those taking lipid-lowering agents than model 1. The residuals in the development, internal validation, and external validation datasets were also randomly distributed around 0 with no clear trends.The new equation we developed to estimate serum apoB concentrations is accurate and can be used in diverse subgroups of patients including those with diabetes, atherogenic dyslipidemia, and those taking lipid-lowering agents. Apolipoprotein B (apoB) is a major structural protein for atherogenic lipoproteins including chylomicron, VLDL, intermediate-density lipoprotein, large buoyant LDL, and small dense LDL. ApoB is required to transport lipids from the liver and gut to peripheral tissues. In general, one molecule of apoB is present on each lipoprotein particle; therefore, the total apoB level represents the total number of atherogenic particles and reflects the atherogenic potential of the whole lipoprotein fraction Several large prospective studies have demonstrated that the apoB level is a better predictor of cardiovascular risk than any other lipid measurements However, despite the clinical benefits of apoB levels over conventional lipid measurements for the assessment for cardiovascular risk, apoB levels are not routinely measured because of the additional cost. Therefore, we developed an equation to estimate the serum apoB concentration based on the conventional lipid measurements included in Friedewald's formula No specific informed consent was obtained. The requirement for written or verbal informed consent was waived by the Institutional Review Board during the planning phase of this study because researchers were only allowed to assess the database for analysis purposes, and the database did not contain any personal identifying information.Ethics approval for the study protocol and data analysis was obtained from the Institutional Review Board of Kangbuk Samsung Hospital, and the protocols used in this study complied with the Declaration of Helsinki.Study participants were recruited from subjects who visited the Health Screening Center at Kangbuk Samsung Hospital for a routine medical check-up between January 2009 and December 2009. During that period, a total of 98,248 subjects received a medical check-up. Among them, 78,127 subjects , aged 15 to 88 years (mean age 41.8 years), who had conventional lipid measurements including total cholesterol, triglycerides, HDL cholesterol, and directly measured LDL cholesterol and serum apoB levels, were enrolled in the study.In this study, diabetes mellitus was defined by the presence of one of the following: (1) fasting glucose level ≥126 mg/dl; (2) taking oral hypoglycemic agents; (3) a self-reported history of diabetes; or (4) HbA1c level ≥6.5% To validate how accurate our estimated apoB levels were based on the 2009 dataset, we used another dataset from the same institution from January 2008 and December 2008. During that period, a total of 94,027 subjects received a medical check-up and among them, 73,445 subjects , aged 18 to 87 years (mean age 41.7 years) with lipid parameters identical to patients included in the 2009 dataset were used for internal validation.We also validated how accurate our estimated apoB levels were using data from NHANES 2007–2008. During that period, a total of 3,097 subjects  with measured total cholesterol, triglyceride, HDL cholesterol, and apoB levels, were enrolled in the analysis. Data analyses were performed using special sampling weights.At baseline, a complete physical examination of each subject was performed, and a personal medical history, family history of metabolic diseases, and lifestyle factors were determined using a standardized questionnaire.All blood samples were obtained in the morning following an overnight fast of 12 to 14 hours. Serum glucose was measured by the hexokinase method using an autoanalyzer . The inter-assay coefficient of variation (CV) was 1.0%. Serum total cholesterol, triglyceride, HDL cholesterol, and LDL cholesterol levels were determined using the autoanalyzer. Serum apoB concentrations were determined by the immunoturbidometric method  with inter-assay CVs ranging from 2.9 to 4.7%. HsCRP was analyzed by particle-enhanced immunonephelometry with the BNIITM System  using a lower detection limit of 0.175 mg/l. Our clinical laboratory participates in annual inspections and surveys by the Korean Association of Quality Assurance for Clinical Laboratories, and has been accredited for quality control and performance of various measurements.Serum total cholesterol, triglyceride, and HDL cholesterol levels were determined by the enzymatic method using a Roche Modular P chemistry analyzer . Serum apoB concentrations were determined by the Dade-Behring BN ProSpec nepholometric immunoassay with a CV of 2.0–6.2%.http://www.r-project.org). All statistical tests were two-sided and P<0.05 was set as the statistical significance level.To develop an apoB equation, we examined various linear regression models that included conventional lipid measurements such as total cholesterol, triglyceride, and HDL cholesterol levels as terms. In addition, taking into account that the estimated LDL cholesterol is valid only for a limited range of triglycerides, we adjusted the regression coefficients in the final model according to a cutoff value of triglycerides. To determine the triglyceride cutoff value, we used the Bayesian information criterion (BIC). We used two additional datasets for internal and external validation of the apoB equation. For the internal dataset, errors between directly measured apoB and estimated apoB levels were plotted as residual plots. Regression coefficients estimated for the internal dataset were compared with those estimated using the apoB equation. For the external dataset, the same methodology was followed. To determine the performance of the apoB equation in various subgroups of patients, we plotted locally weighted scatter-plot smoothing (LOWESS) curves. Statistical analyses were performed with R version 2.9.2 , as shown below:IC score . We compferences . In otheferences .th percentile and the dashed lines indicate the 2.5th and 97.5th percentiles, respectively. For the internal validation dataset, the residuals were also randomly distributed around 0 and no trend was observed. For the external validation dataset, no trend was observed and the residuals were randomly distributed. Therefore, the estimated apoB predicted the directly measured apoB without bias.To illustrate the performance of the apoB equation in the development, internal validation, and external validation datasets, we constructed residual plots i.e. plots of the difference between the measured serum apoB level and the estimated apoB level versus the estimated apoB level, where the apoB level was estimated using our apoB equation . In FiguWe determined the performance of the apoB equation (model 2) in various subgroups of subjects stratified according to age, body mass index (BMI), systolic blood pressure, fasting glucose, glycated hemoglobin tertiles, or gender. Although some differences were observed according to tertiles based on the various groups and gender, the LOWESS curves were around 0 for these tertiles . The LOWNumerous studies have demonstrated an association between serum apoB levels and residual cardiovascular risk. Recently, a consensus panel convened by the American Diabetes Association and the American College of Cardiology recommended a greater focus on non-HDL cholesterol and apoB levels in patients who are likely to have small LDL particles, such as diabetic patients. The consensus panel suggested that for statin-treated patients in whom the LDL cholesterol goal is <70 mg/dl (non-HDL cholesterol <100 mg/dl), apoB should be measured and treated to <80 mg/dl. For patients on statins with an LDL cholesterol goal of <100 mg/dl (non-HDL cholesterol <130 mg/dl), apoB should be measured and treated to <90 mg/dl In the present study, we developed an equation to estimate serum apoB concentrations using a database of subjects who visited the Health Screening Center at Kangbuk Samsung Hospital for a routine medical check-up. Approximately, 100,000 subjects receive an annual medical check-up at our institution, and many of these subjects are employees who are mandated by the Industrial Safety and Health Law in Korea to participate in annual or biannual health examinations. Therefore, although our data is not derived from a nationally representative population, we are confident that our results are representative of the characteristics of the general population of South Korea.th percentile of the difference by model 1 was more than 24 mg/dl, whereas in model 2, it was about 20 mg/dl. In other words, model 2 performed better than model 1. In addition, we compared the performance of our equation with Friedewald's formula. The 75th and 95th percentiles of estimation errors in LDL cholesterol values in subjects with high triglyceride levels were more than 20 mg/dl and 40 mg/dl, respectively, when Friedewald's formula was used. In other words, application of Friedewald's formula resulted in an estimation error of more than 20 mg/dl in up to 25% of the study subjects. Moreover, the estimation error increased up to more than 40 mg/dl in 5% of the study subjects. Therefore, although some estimation errors may exist in our apoB equation, the equation effectively estimates directly-measured serum apoB concentrations in most cases, and the error was relatively small compared to that of Friedewald's formula  that are also used in Friedewald's equation to estimate LDL cholesterol  formula .2R = 0.993 and 2R = 0.994, respectively, we chose model 2 as our final model because the residuals of model 1 showed a decreasing trend. In model 2, for patients with diabetes and atherogenic dyslipidemia, apoB levels were slightly over-estimated, as the estimated apoB was more than 150 mg/dl, whereas apoB levels were under-estimated in patients taking lipid-lowering agents. It should be noted that the sample size for the subgroup taking lipid-lowering agents was only 2,650 (3.4%), indicating that it might be worthwhile to develop an apoB equation for this subgroup of patients based on a larger samples of patients taking lipid-lowering agents. However, the mean estimation error was 2.6 mg/dL , which may be acceptable in clinical practice  of triglycerides. The smaller BIC (Bayseian information criterion) is, the better the model is.(TIF)Click here for additional data file.Figure S2Performance of the apoB equation (model 2) in subgroups. The lines are LOWESS curves of errors ( = measured - estimated apoB); the closer the curve to 0, the better the fit.(TIF)Click here for additional data file.


Heme constitutes 95% of functional iron in the human body, as well as two-thirds of the average person’s iron intake in developed countries. Hence, a wide range of epidemiological studies have focused on examining the association of dietary heme intake, mainly from red meat, with the risks of common diseases. High heme intake is associated with increased risk of several cancers, including colorectal cancer, pancreatic cancer and lung cancer. Likewise, the evidence for increased risks of type-2 diabetes and coronary heart disease associated with high heme intake is compelling. Furthermore, recent comparative metabolic and molecular studies of lung cancer cells showed that cancer cells require increased intracellular heme biosynthesis and uptake to meet the increased demand for oxygen-utilizing hemoproteins. Increased levels of hemoproteins in turn lead to intensified oxygen consumption and cellular energy generation, thereby fueling cancer cell progression. Together, both epidemiological and molecular studies support the idea that heme positively impacts cancer progression. However, it is also worth noting that heme deficiency can cause serious diseases in humans, such as anemia, porphyrias, and Alzheimer’s disease. This review attempts to summarize the latest literature in understanding the role of dietary heme intake and heme function in diverse diseases. Proteins are the building blocks of life. They are also the major source of dietary nutrients. When proteins are digested, amino acids are released to the body for biosynthetic purposes or for generating cellular energy. Besides amino acids, proteins also provide other nutrients, particularly metals. Iron is the most abundant metal in the human body; one adult human body needs 3–4 g of iron. Dietary iron is found in two forms, heme and non-heme iron. Heme iron, which is present mainly in meat, poultry and fish, is well absorbed. Non-heme iron, which accounts for the majority of the iron in plants , is lessHeme is found in highest abundance in meat in the form of hemoglobin and myoglobin. Heme is released from these proteins because of the low pH in the stomach and the action of proteolytic enzymes in the stomach and small intestine ,6 Figur. Concent59Fe-hemoglobin in closed duodenal loops, it is reported that heme degradation is the rate limiting step in heme absorption, as opposed to hemoglobin degradation, heme uptake, or iron transfer to the circulation [Heme is absorbed by the mucosa as an intact metalloporphyrin [Fe(II)-protoporphyrin-IX] in the lumen by the enterocytes ,5,11,12 culation . Subsequculation ,10,11,18Research in the past decade has identified several transporters involved in maintaining heme homeostasis . Particui.e., intestinal or hepatic cells) or synthesize high levels of heme (erythroid cells). FLVCR1 exports cytoplasmic heme. Heme export by FLVCR1 is time and temperature-dependent; interference with FLVCR1 function blocks heme export and increases cellular heme content [FLVCR1, a member of the major facilitator superfamily of transporter proteins, was identified as the cell surface receptor for feline leukemia virus, subgroup C. FLVCR1 is highly expressed in tissues that either transport heme  and requires NADPH-cytochrome P-450 reductase for its catalytic turnover ,31,32,33-2 cells . HO exprBesides iron, degradation of heme by HO results in the production of carbon monoxide (CO) and biliverdin IX-α (BV) ,38. CO aDietary differences in the world likely contribute to global variations in cancer cases . Meat isA number of studies have demonstrated a positive association between high intake of red meat and colorectal cancer (CRC). However, the association between red meat intake and other cancer types such as gastrointestinal, lung cancer, pancreatic, breast and esophageal are understudied and less consistent ,45,49. TA meta-analysis performed in 2006 by Larsson and coworkers showed an elevated overall relative risk (RR) of 1.28 of colorectal cancer  = 1.15–1.42) for red meat in the highest versus lowest category of intake. They estimated the RR of 1.28 (95% CI = 1.18–1.39) for an increase of 120 g/day of red meat . Anothervs. crypt cells. These results suggest that heme inhibits the surface to crypt signaling of feedback inhibitors of proliferation and induces colonic hyperproliferation and hyperplasia, which increases the risk of colon cancer [In 2013, a meta-analysis further suggested a positive-dose response association of heme intake and the risk for colorectal cancer . In the n cancer .N-nitroso compounds (NOCs), which may increase the overall mutation rate in the DNA of colonic tissue [N-nitrosated in the presence of these nitrosating agents to produce NOCs. A majority of the NOCs investigated are shown to be carcinogens [Several potential mechanisms have been suggested to explain the association between high intake of red meat and the risk of colorectal cancer. Heme is more bioavailable and readily absorbed as compared to non-heme. However, detrimental effects are associated with heme specifically, which includes cytotoxicity and the increased formation of endogenous c tissue ,56,57. Hc tissue ,44,51,56cinogens .P for trend = 0.063) for highest versus lowest quintile of heme intake, suggesting a positive association between esophagus adenocarcinoma and heme intake [With the availability of large amounts of epidemiological data for colorectal cancer, the positive relation between colorectal cancer and high intake of red meat (high heme content) is convincing; however, limited data is available for other gastrointestinal malignancies. A 2011 study showed a positive association between red meat intake and squamous cell carcinoma of the esophagus . They obe intake . Jakszyne intake . Their se intake . Ward ane intake .Pancreatic cancer is one of the most deadly cancers. Hence, previous studies have focused on the association between risk of pancreatic cancer and diet. A meta-analysis performed in 2012 found that there is an increased risk of pancreatic cancer in men, positively associated with consumption of red meat . In that5 people a year in western countries [2 [vs. lowest quartile in their study showed a 20%–30% of higher risk associated with higher intakes of heme, with a RR of 1.24  [Endometrial cancer accounts for 10–20 per 10ountries . An assoountries  (2008) uountries . Howevertries [2 . Anothertries [2  (2012) a mg/day) . A few m mg/day) ,73,74.vs Q1) is 1.25  in men and 1.18  in women. The study showed an even higher risk of lung cancer in men with high intake of bioavailable heme and lower intakes of antioxidants [Several case control and cohort studies have been reported for an association between high meat (or heme intake) and risk for lung cancer, but these limited studies show inconsistent findings . A studyoxidants . On the oxidants . In theiTo associate a link between intake of fresh red meat and heme, Lam and coworkers performed a mechanistic study in meat-related lung carcinogenesis by using whole genome expression . They meIn Previous epidemiological studies have suggested an association between high heme iron intake and diabetes, as well as coronary heart disease. Diabetes mellitus, also referred to as diabetes, is a growing problem in the modern society characterized by impaired carbohydrate, protein and lipid metabolism caused by insulin resistance and an insufficient amount of insulin secreted. In 2000, the WHO reported that around 47 million people were suffering from diabetes. In 2004, an estimated 3.4 million people had died because of high blood sugar level, with a similar number of deaths reported in 2010. WHO projects that diabetes will be the 7th leading cause of death by 2030 with type-2 diabetes (T2D), accounting for almost 90% of the diagnosed cases .et al., authors followed 422,846 persons/year for a period of 12 years from 1986 to 1998 with 1168 reported cases of T2D, to determine possible elevated risk of T2D in men consuming red meat as source of heme intake [et al. examined the relationship between heme intake and T2D in women [et al. showed increased heme intake to be associated with gestational diabetes mellitus (GDM) in pregnant women [Several cross-sectional and cohort studies have demonstrated positive association between heme intake and T2D ,67,68,76e intake . They foe intake . In 2004in women . This stin women . The autin women , non-hemnt women . Along wnt women .The positive association found between heme and T2D is consistent with other studies evaluating the relationship between red meat (heme iron intake) and T2D. The positive association between processed red meat and T2D was confirmed by various studies, suggesting that the risk of T2D increases with higher consumption of processed red meat, regardless of gender, age, ethnicity and BMI ,76,78,79et al. reported the earliest evidence of this association in 1994 [et al. used meat intake as a measure of dietary heme and found a 60% increased risk of CHD among men who consume meat six times a week compared with men who consume meat less than once a week [et al. did not show the positive association between heme and CHD [n-3 fatty acids, which protect against CHD [It is worth noting that previous studies have also demonstrated a positive association between heme and CHD. Ascherio  in 1994 . They fo in 1994 . A study in 1994 . In anote a week . Similare a week ,83,84,85inst CHD ,87. TherAlthough the exact mechanism for association of increased dietary heme with T2D and CHD is uncertain, several mechanisms have been proposed. In western countries, heme in the diet is mainly derived from red meat. The feedback mechanism for iron absorption functions better for non-heme iron as compared to that for heme. At any serum ferritin level, the percentage of heme absorbed is higher compared to non-heme iron. As a result, heme continues to be absorbed by the body even in events of excess serum ferritin . Excess Additionally, excessive heme intake leads to deposition of iron deposition in pancreatic beta cells and other tissues, which induces insulin resistance ,92. The 2+ activated potassium channels); (3) Role in binding to different nuclear receptor proteins; (4) Role in the regulation of circadian clock (day-night cycle); (5) Role in erythrocytes [While high dietary intake of heme may have adverse effects on health, heme deficiency can also cause serious health problems. In the human body, roughly 80% of heme is present in red blood cells, 15% is synthesized and present in liver, and the rest is distributed in other tissues ,98,99. PHeme biosynthesis involves eight enzymes, and a defect in any one of them is associated with diseases. Such a defect can be caused by genetic mutations or environmental factors that suppress heme synthesis, such as the presence of lead or lack of iron. Defective heme synthesis can cause acute porphyrias, which are associated with neurological problems in peripheral nervous system  and CNS  . SiderobHeme is involved in the regulation of neuronal specific genes, particularly through the NGF signaling pathway ,102. It a1 = 7.27 × 10−6 M−1 (n1 = 1.5) and Ka2 = 2.89 × 10–6 M−1 (n2 = 1.8), forming Aβ-heme complex, which may lead to the functional heme-deficiency in the brains of AD patients [Alzheimer’s disease (AD) is an age-related neurodegenerative disorder, a common cause of age-related dementia. It is characterized by memory loss caused by a decline in synaptic function, the formation of neurofibrillary tangles, and neuronal cell death, which is followed by a further decline in cognitive and physical functions. Amyloid-β (Aβ) peptide aggregation is associated with the pathophysiology of AD and is associated with the loss of iron homeostasis and mitochondrial complex IV ,105. Altpatients . Heme bipatients .Circadian rhythm is the clock governing many important behaviors and physiological processes in humans, such as sleep/wake cycle, feeding, body temperature, hormone secretion, and metabolism . MoleculThis review offers a comprehensive analysis of the current literature on the health benefits and risks of heme as an essential nutrient. Heme constitutes 95% of the functional iron in the human body and accounts for two-thirds of iron intake for people in Western countries. Therefore, heme ought to be a crucial factor when considering human nutrition and health. This review summarizes both epidemiological and molecular studies regarding heme function in health and diseases. It can serve as a starting point for further discussion of heme function as an essential nutrient and investigation of heme function in the pathogenesis of diverse diseases.


Minimal change disease (MCD) and primary focal segmental glomerulosclerosis (FSGS) are the main causes of primary idiopathic nephrotic syndrome in children and adults, with diagnosis being essential for the appropriate choice of therapy and requiring renal biopsy. However, the presence of only normal glomeruli on renal biopsy of FSGS patients may lead to the misclassification of these patients as having MCD. The aim of this study was to (i) compare the peptide profile of MCD and FSGS patients with that of a group of healthy subjects, (ii) generate and validate a class prediction model to classify MCD and FSGS patients and (ii) identify candidate biomarkers of these glomerular entities by analysis of the urinary peptidome.The urinary peptide profile was analyzed by magnetic bead-based technology combined with MALDI-TOF mass spectrometry in 44 patients diagnosed of MCD (n = 22) and FSGS (n = 22). The resulting spectra were compiled and analyzed using ClinProTools software.m/z 1913.60, identified as a fragment of uromodulin, and the signal with m/z 2392.54, identified as a fragment of alpha-1-antitrypsin, showed higher and lower peak areas, respectively, in FSGS patients compared with MCD patients.A class prediction model was developed to differentiate MCD and FSGS patients. The validation of this model correctly classified 81.8% (9/11) of MCD patients and 72.7% (8/11) of FSGS patients. Moreover, the signal with The simple, non-invasive technique described in the present study may be a useful tool to help clinicians by confirming diagnoses achieved by renal biopsy, thereby reducing misdiagnoses and avoiding the implementation of inappropriate therapies. Chronic kidney disease is a public health problem worldwide with an increasing incidence and prevalence, poor outcome and high associated costs Renal biopsy is needed to obtain the definitive diagnosis of glomerular diseases, to establish the prognosis, and to choose the most appropriate therapy. However, the invasiveness of this technique may result in complications and may be contraindicated in some cases Light microscopy reveals apparently normal glomeruli in MCD and segmental sclerosis in some but not all glomeruli in FSGS. Accordingly, renal biopsies of FSGS patients showing only normal glomeruli may lead to the misclassification of these patients as MCD, especially in the earlier, pre-scarring stages of the disease.Patients with MCD usually respond to corticosteroid therapy but a considerable number of patients with FSGS are dependent on or resistant to this treatment Physiological and pathological processes may be reflected by peptides and proteins present in blood, urine and other body fluids. Proteins are differentially expressed as a consequence of the development of a disease and are, thus, very valuable as potential diagnostic biomarkers. In the case of kidney diseases, the urinary proteome has been extensively investigated In the last decade, mass spectrometry (MS) has been the method of choice for the analysis of peptides and small proteins in biological fluids. To reduce the complexity of biological samples prior to MS analysis, functionalized magnetic beads have been designed, which allow the capture and purification of peptides and small proteins and also allow the removal of salts to increase the sensitivity of the analysis. The combination of magnetic beads with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS has become a promising approach in the field of biomarker discovery and proteomic pattern diagnostic since it enables the rapid study of thousands of peptides and small proteins simultaneously with only a small sample volume and with high sensitivity. Moreover, the reproducibility of this approach may be improved by automation in a liquid-handling platform. This proteomic approach has been successfully used to profile the peptidome of different biological fluids The objectives of our study were to (i) compare the peptide profile of MCD and FSGS patients with that of a group of healthy subjects, (ii) generate and validate a class prediction model able to classify MCD and FSGS patients, and (iii) identify potential biomarkers that discriminate between MCD and FSGS patients.This prospective study included Caucasian patients older than 18 years, with clinical signs of nephrotic syndrome, such as proteinuria, with stable renal function. Only patients with a clinical and histological diagnosis of MCD (n = 22) and primary FSGS  were included. Cases of clinical or pathological features indicating a secondary cause such as autoimmune diseases, infections, cancer or exposure to nephrotoxic drugs were excluded.Twenty-three of the 44 patients had also been studied in a previous report Urine and blood samples were collected the day of renal biopsy, prior to performing it.Urine samples from 16 healthy subjects  with normal renal function were collected to establish a normal urinary peptide profile.The Clinical Research Ethics Committee of Germans Trias i Pujol Hospital approved the study protocol, and all patients gave written informed consent to participate.Histological diagnosis was achieved by percutaneous renal biopsy performed before initiating corticosteroid or immunosuppressive therapy.Biopsies were carried out using a Bard Monopty Disposable Core Biopsy Instrument  and processed for light, immunofluorescence, and electron microscopy following standard procedures. Light microscopy sections were stained with hematoxylin/eosin, Schiff’s periodic acid, methenamine silver, Masson’s trichrome and Congo red. Immunofluorescence assays were performed by incubating cryostat sections with polyclonal fluorescein isothiocyanate-conjugated secondary antibodies against IgG, IgM, IgA, C3 fraction, C1q, C4, kappa and lambda chains and fibrinogen .MCD and FSGS patients were randomly subdivided into a preliminary training group for the generation of a class prediction model (11 MCD patients and 11 FSGS patients) and a validation group (11 MCD patients and 11 FSGS patients).The spectral data obtained from the whole study population was also used for the identification of peptide signals differentially expressed among MCD patients, FSGS patients and healthy subjects.Biochemical variables were determined with a routine clinical chemistry laboratory analyzer immediately after extraction. Serum levels of total cholesterol and triglycerides were determined by conventional enzymatic methods.Serum creatinine levels were determined using a modified Jaffe kinetic reaction . Twenty-four hour proteinuria was measured spectrophotometrically on a Cobas u 711 analyzer (Roche Diagnostics). The glomerular filtration rate was calculated using the Modification of Diet in Renal Disease (MDRD) formula.4HCO3, aliquoted, and immediately frozen at −80°C until processing.Urine samples were centrifuged at 2,100 g for 30 minutes at 4°C to remove cellular debris. The supernatant was recovered, adjusted to neutral pH with 1 M NHSamples were thawed and pre-fractionated using Dynabeads RPC18 . Samples were processed in duplicate following the manufacturer’s protocol but modified for optimization purposes, as described previously Automation of the complete magnetic bead pre-fractionation and AnchorChip target plate loading was performed on a liquid-handling robotic platform  to improve throughput and ensure assay reproducibility.m/z range of 1–10 kDa were recorded with the FlexControl acquisition software v3.4 .Mass spectrometry analyses were performed in an UltrafleXtreme MALDI-TOF/TOF mass spectrometer . Ionization was achieved by irradiation with a 337-nm nitrogen laser operating in linear positive ion mode geometry, with a repetition rate of 1,000 Hz. Each spectrum was acquired manually with 300 laser shots delivered randomly over the surface of the spot at a fixed laser power of 70%. Operating conditions were as follows: ion source voltages, 25 and 22.40 kV; reflector 1, 26.45 kV; reflector 2, 13.40 kV; pulsed ion extraction time, 300 ns. Spectra were externally calibrated, achieving a mass accuracy lower than 10 ppm. Peaks with a signal-to-noise ratio >3 in the m/z signals were randomly selected to calculate the coefficient of variance of their peak area.Due to the duplicates in pre-fractionation and in AnchorChip target plate loading, 4 spectra were obtained from each sample. A detailed analysis was performed with DataAnalysis software v3.4  in order to choose the best spectrum for each sample. Spectra with the highest number of peaks and the highest intensity were selected. To assess the reproducibility of the magnetic bead-based technology used in this study, we analyzed the 4 replicated spectra obtained from 5 random samples. Six ClinProTools software v2.2  was used to process MALDI-TOF MS spectra according to the following standard workflow: baseline subtraction to remove broad structures, normalization of spectra to their own total ion count, recalibration of spectra using the most prominent peaks, calculation of total average spectrum, peak area detection on the total average spectrum, and area calculation of each peak.The generation of a class prediction model able to differentiate between MCD and FSGS patients was achieved with the Support Vector Machine (SVM) algorithm. Only spectra from the training groups were used, and the number of input peaks was automatically detected based on clustering of the peak rankings as determined by the SVM. The algorithm determines optimal separation planes between the different data classes. To determine the accuracy of the class prediction model the software offers values of cross validation and recognition capability. Cross validation is a measure for the reliability of a calculated model and can be used to predict how a model will behave in the future. This method is used for evaluating the performance of a classifier for a given data set and under a given parameterization. Recognition capability describes the performance of an algorithm, i.e., the proper classification of a given data set.The model generated was further tested with spectral data from the validation groups.For statistical analyses, peak area data provided by ClinProTools software was converted to ASCII files, exported to Excel spreadsheets and analyzed with SPSS software v15.0 .Protein identification was conducted by HPLC-MS/MS as described in a previous report m/z signals were estimated using the Spearman’s correlation coefficient. Statistical analyses were performed with the SPSS software v15.0. A p-value <0.05 was considered statistically significant.Continuous variables were expressed as medians (interquartile ranges). Differences between groups were tested by the non-parametric Mann-Whitney U test. Categorical variables were analyzed with the chi-square or Fisher’s exact probability test. Associations between biochemical variables and the peak area of the Demographic and clinical characteristics of glomerular patients, randomly divided into training or validation group, are presented in Regarding age, no significant differences were found between patients in training and validation groups. There were no significant differences in patients’ age of both training groups.Differences in renal function were observed between patients of both training groups, with higher levels of serum creatinine in FSGS patients. No differences in proteinuria levels were found between these patients .m/z signals showed a mean coefficient of variance below 11%  of samples from MCD patients and 72.7% (8/11) of samples from FSGS patients .m/z 1769.38, 1898.37, 1913.60, 2713.96, 2976.97, 3004.65 and 3389.12, showed a higher peak area and 15 signals, with m/z 1945.50, 1961.71, 2305.01, 2378.07, 2392.54, 2408.26, 2491.41, 2505.64, 2521.45, 2543.26, 2642.26, 2939.95, 3161.72, 3226.48 and 4013.38, showed a lower peak area in healthy subjects.Urinary peptide profiles of glomerular patients differed significantly from those of healthy subjects . Twenty-m/z 1898.37, 1913.60, 2713.96, 2976.97, 3004.65 and 3389.12, showed a higher peak area and 14 signals, with m/z 1831.61, 1945.50, 1961.71, 2305.01, 2378.07, 2392.54, 2408.26, 2491.41, 2505.64, 2521.45, 2543.26, 2642.26, 2678.13 and 3226.48, showed a lower peak area in healthy subjects.Twenty signals showed statistically different area  on compam/z 1913.60, showed a higher peak area and 4 signals, with m/z 2392.54, 2408.26, 2976.97 and 3161.72, showed a lower peak area in FSGS patients compared with MCD patients.Statistically significant differences in 5 signals were observed on comparing the peptide profile of MCD and FSGS patients . One sigm/z 1898.37 and 1913.60 have been identified in previous reports as fragments of uromodulin  m/z 1913.60 than MCD patients  m/z 2392.54 was observed in FSGS patients on comparing with MCD patients  Signal with m/z 1831.61, identified as a fragment of serum albumin protein  Signal with We analyzed correlations of signals corresponding to fragments of UMOD, A1AT, B2M and ALB with patients’ age, gender, serum creatinine, MDRD and proteinuria levels to determine whether any clinical parameter influenced these results, but no association was observed.Our results revealed differences in the urinary peptide profile on comparing glomerular patients with healthy subjects. Furthermore, a class prediction model able to classify MCD and FSGS patients was generated.During the last decade, proteomic studies based on magnetic bead technology combined with MS readout have demonstrated the utility of this approach in profiling the low-molecular-weight proteome of different biological fluids Urinary proteomics has been widely performed to identify biomarkers of clinical diseases, mainly of those affecting the kidney The model generated correctly classified 81.8% and 72.7% of MCD and FSGS patients from validation groups, respectively.In addition to creating the classification model, we considered it to be of interest to compare the urinary peptidome of patients of both glomerular entities with the aim of determining the presence of differentially expressed peptides between them. Identification of the proteins to which these peptides belong could help to gain more insight into the pathological mechanisms involved in these diseases.Interestingly, although 18 urinary peptides showed a similar peak area on comparing MCD and FSGS patients, some differences were observed in other peptides; FSGS patients showed a higher peak area in one signal corresponding to a fragment of UMOD and a lower peak area in one signal corresponding to a fragment of A1AT. Our results also showed that healthy subjects had higher areas in signals of UMOD peptides and lower areas in signals of A1AT peptides compared to patients with glomerular diseases. The area of these signals was independent of the degree of proteinuria, thus offering additional information in the diagnosis of these diseases, since proteinuria alone is not enough to differentiate between glomerular entities.m/z 1913.60, corresponding to a peptide of UMOD, showed a higher peak area in healthy subjects and also allowed differentiation between MCD and FSGS patients, with higher values in the latter; underexpression of this UMOD peptide has been previously described in patients with advanced renal disease and diabetic patients and may be due to an alteration of the apical membrane of the TAL epithelial cells UMOD, also known as the Tamm-Horsfall protein, is the most abundant urinary protein in healthy individuals; it is synthesized exclusively in the kidney, on the epithelial cells of the thick ascending limb (TAL) of Henle’s loop et al. analyzed the urinary proteome from patients with primary nephrotic syndrome by combining two-dimensional electrophoresis with MS, and found fragments of albumin and A1AT as the most abundant proteins. Although these fragments were mainly formed in plasma, a few were produced in urine, suggesting the presence in urine of specific proteases m/z 1831.61) with a higher peak area in FSGS patients compared with healthy subjects.A1AT is a major protease inhibitor in human serum that inhibits neutrophil elastase The fragmentation pattern of the proteins described above may reflect the proteolytic activity that takes place during kidney disease, and the appearance of specific peptides in the urine could consequently serve as biomarkers of the diseases studied here, MCD and FSGS.Histological diagnosis takes time and may not achieve a precise diagnosis if an adequate tissue sample is not obtained. Consequently, renal biopsy is limited, time-consuming and cannot be performed several times, thereby limiting its practice in the follow-up of the patients. The search for biomarkers in urine could replace renal biopsy as an accurate, non-invasive test and could be repeated to follow the progression of the disease and monitor the response to therapy.Although great efforts are being made in the search for biomarkers able to distinguish between MCD and FSGS, and some candidates have been proposed In conclusion, given the difficulty in differentiating between MCD and FSGS by evaluation of renal biopsies in some cases, corroboration with a simple and non-invasive technique, such as that described here, could help clinicians to confirm the diagnosis and thereby avoid unnecessary or inadequate treatments.


Interest in Internet-based epidemiologic research is growing given the logistic and cost advantages. Cohort recruitment to maximally diversify the sociodemographic profiles of participants, however, remains a contentious issue.The aim of the study was to characterize the sociodemographic profiles according to the recruitment mode of adult volunteers enrolled in a Web-based cohort.The French NutriNet-Santé Web-based cohort was launched in 2009. Recruitment is ongoing and largely relies on recurrent multimedia campaigns. One month after enrollment, participants are asked how they learned about the study  The sociodemographic profiles of participants recruited through operative communication channels  were compared with the profiles of those informed through television by using polytomous logistic regression.P<.001). Participants reporting up to secondary education were also less likely to have learned about the study through radio newscasts, newspaper articles, Internet, and advice than through television .Among the 88,238 participants enrolled through the end of 2011, 30,401 (34.45%), 16,751 (18.98%), and 14,309 (16.22%) learned about the study from television, Internet, and radio newscasts, respectively. Sociodemographic profiles were various, with 14,541 (16.5%) aged ≥60 years, 20,166 (22.9%) aged <30 years, 27,766 (32.1%) without postsecondary education, 15,397 (19.7%) with household income <€1200/month, and 8258 (10.6%) with household income €3700/month. Compared to employed individuals, unemployed and retired participants were less likely to be informed about the study through other sources than through television (adjusted ORs 0.56-0.83, Television broadcasts appear to permit the recruitment of e-cohort participants with diverse sociodemographic backgrounds, including socioeconomically disadvantaged individuals who are usually difficult to reach and retain in long-term epidemiologic studies. These findings could inform future Web-based studies regarding the development of promising targeted or general population recruitment strategies. Prospective epidemiological studies are invaluable in advancing scientific knowledge; however, they require very large samples when dealing with rare outcomes or when aiming to accurately establish small-scale associations . In addiThe inherent financial burden and the steadily declining participation rates in telephone- or mail-based data collection surveys  argue foe-epidemiology and represents the science of epidemiological assessment using Internet-enabled digital media  [In recent decades, the expansion of the Internet for personal and professional use has underscored important changes in the field of mass communication and has presented unique opportunities to enroll and follow individuals while collecting a wide range of epidemiological data . Indeed,tphones) . Also, etphones) . Over thtphones) -8, and mtphones) . In additphones)  and the tphones) . Howevertphones) -15, and Mass media campaigns, which are complementary to other outlets for the dissemination of public health messages, have shown promising results -18 and mThe NutriNet-Santé Study is a Web-based prospective cohort study launched in France in May 2009. With almost 40 million Web users older than 11 years (71% of the population), France provides an excellent context for Web-based scientific studies . Unlike recruitment in reference to the information channel by which the participants learned about the study before deciding to enroll in it.The aim of the present study was to evaluate the degree to which the sociodemographic profiles of the participants enrolled in the NutriNet-Santé Study on a voluntary basis varied across the communication source used. In particular, we expected that television, as a wide-reaching medium (99% of French households possess a television set), may aid in recruiting typically understudied population subgroups. Henceforth, we use the term The present analysis was performed on data from participants in the NutriNet-Santé Study enrolled from May 10, 2009 through December 31, 2011. The rationale, design, and implementation of the study has been reported elsewhere [In the NutriNet-Santé Study, adult participants aged 18 years and older are recruited from the general population on a voluntary basis. All data are collected through a dedicated website  via adapRecruitment is scheduled for 5 years with an additional follow-up planned at 10 years. The call for volunteers is based on a vast, biannual, multimedia campaign .At the launch of the study, the first campaign contained general information about the study and was carried out under the auspices of the Health Minister. Subsequent campaigns have been built around specific findings from the study . Before each campaign, a press conference is organized and a press release is widely disseminated within a 72-hour period. Aspects related to academic endeavor, public health interest, scientific progress, confidentiality, and convenience regarding participation  are emphasized.Additional dissemination strategies have also been used, such as short message service (SMS) text messaging for the initial mass media campaign especially , leaflets/flyers , and a nonpaid advertising display. For example, the SMS text messages were sent by a French mobile service provider to a random sample of their customers. Nonpaid advertising displays included postings in doctor’s offices, health centers, worksites, public transportation, and on billboards, as well as video clips shown in post offices.The baseline set of questionnaires includes information about date of birth, sex, area of residence, education, employment status, household composition, occupational category, and income. At 1-month follow-up, participants are asked to provide information about how they heard about the NutriNet-Santé Study and are given 18 response options including “I don’t remember” or “other.” Each participant is allowed to select only 1 response to this question. For the present analyses, these responses have been grouped into the following 9 recruitment sources: (1) general newscast or a health program on television, (2) radio newscast, (3) newspaper article, (4) Internet website or network, (5) SMS text message sent by a service provider, (6) advice from a friend/family member/health care provider, (7) leaflet/flyer, (8) nonpaid advertising display, and (9) other (including “I don’t remember”).For the present analysis, we focused on participants included in the NutriNet-Santé Study between May 10, 2009 and December 31, 2011. From the 104,020 participants meeting that criterion, we selected 88,238 individuals who provided information at 1-month follow-up on how they had learned about the study.Income per household unit was first calculated and then divided by the number of household members  . The occSociodemographic characteristics of the sample, including age , education , current employment status , occupational category , type of area of residence , and monthly household income , are presented in a frequency/percent format for the entire sample and by recruitment source.t tests, as appropriate.To better understand the selected sample, we compared the characteristics of included and excluded NutriNet-Santé Study participants using chi-square tests and Student Crude and adjusted associations between the sociodemographic characteristics and the recruitment source were estimated using polytomous logistic regression (reference=television). Polytomous logistic regression generalizes the binary logistic regression model, allowing the dependent (outcome) variable to have more than 2 categories . The mulP<.001), more often students , and they were more likely to report a low (<€1200) monthly income .Compared to included participants, those participants excluded because of missing data from the first follow-up questionnaire were younger (39.64 years vs 43.01 years), less often retired  of the participants reported learning about it through a general newscast or a health-related broadcast on television. Meanwhile, very few participants learned about the study from a noncommercial advertising display , SMS text messaging , or leaflets .The number of participants enrolling over time is presented in P value for the income variable <.001).Crude and adjusted associations between participants’ characteristics and the principal recruitment source are presented in We examined how the sociodemographic profiles of participants in a large, Internet-based cohort differed according to the recruitment source used. The use of a number of different dissemination channels allowed the recruitment of a sizeable and diverse cohort. As expected, most participants enrolled after hearing about the study on television because this medium has the widest reach. In particular, television announcements permitted the recruitment of a larger proportion of members of population subgroups  that are not typically well represented in population-based epidemiologic research. However, the elderly were more likely to be informed about the cohort through channels other than television suggesting that television did not necessarily represent the best information medium for all population subgroups. In turn, radio, newspapers, Internet, and personal advice also proved to be substantial means of disseminating information about this epidemiologic study to encourage participation.Overall, the recruitment of participants from a wide range of sociodemographic backgrounds provides the study with a broader range of exposures and confounders than does a more homogeneous sample. This is of important concern when the overarching aim is to estimate associations between certain exposures and health/disease outcomes, as in the NutriNet-Santé Study ,13,27,28Among the initial efforts to recruit Web-based cohorts, in examining the literature we identified 1 study calling for participation in a cohort of smokers intending to quit, by using existing Internet panels intended for market research . AnotherThe main limitation of this analysis was the lack of information on participation/refusal rates because the call for participation was not delivered to a predefined and exhaustive list of randomly selected individuals. In turn, the results need to be interpreted in light of the fact that the actual effectiveness of the media channels cannot be estimated. Second, misclassification of recruitment source is also possible because the question about information source was asked 1 month after baseline. Additional misclassification about the recruitment source cannot be ruled out because participants may have been exposed to several information sources. For example, if a press release occurred during the interval between baseline and the completion of the first follow-up questionnaire, this may have led to an overreporting of the related information channels. However, the sensitivity analysis performed after removing these participants did not substantially modify the findings.Concerns have been raised regarding the selection bias potentially inherent in cohorts followed via the Internet. Web users may present particular sociodemographic profiles, especially regarding sex, age, and education ,10,29. OAs compared with national estimates -32, the As suggested in the literature , the resIt has also been recently postulated that in the context of a Web-based cohort of volunteers, the generalizability of etiologic findings depends on whether the studied associations might differ between those with and without Internet access, which seems unlikely .Along with being geographically unrestricted, Web-based prospective cohorts present with many logistic and cost advantages compared to traditional samples ,22,34,35In conclusion, the present findings fill gaps in current knowledge about cohort recruitment by providing new insights about the sociodemographic profiles of adult volunteers. Our Web-based cohort study uses recurrent mass media campaigns and numerous public outlets for encouraging participation. The various information channels allow the inclusion of individuals with diverse sociodemographic profiles. As expected, television appeared to be the most promising channel for reaching potential participants representing a wide range of sociodemographic backgrounds, including subgroups that are usually difficult to involve in long-term epidemiologic studies. Our findings could inform future Web-based studies regarding the development of operative targeted or general population recruitment strategies.


In this prospective cohort study, we estimated the risk of developing more than 1 metabolic risk factor, using different obesity indices. In addition, we investigated the relative usefulness of the obesity indices for predicting development of such risk factors and calculated optimal cutoffs for the obesity indices.The cohort comprised 10 038 representative residents of a small city and a rural county who were recruited in 2001–2002. Follow-up examinations were conducted every 2 years. Among the 3857 participants without metabolic syndrome at baseline, 1102 new cases occurred during the 6-year follow-up. Receiver operating characteristic (ROC) curves for the obesity indices were plotted to compare the usefulness of the obesity indices.2 in men and women, and the optimal WC cutoffs were 80 cm and 78 cm in men and women, respectively.The numbers of new cases of multiple metabolic risk factors among people in the highest quintiles of body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and waist-height ratio at the baseline examination were 2 to 3 times those in the lowest quintiles. The area under the ROC curve for WHR was significantly higher than that for BMI. The optimal BMI cutoff was 24 kg/mBoth overall obesity and central obesity predicted risk of developing multiple metabolic risk factors, and WHR appeared to be a better discriminator than BMI. To prevent development of metabolic diseases among Koreans, it might be useful to lower the cutoff for abdominal obesity, as defined by WC. Obesity seems to be the predominant underlying risk factor in the development of metabolic syndrome and other cardiovascular risk factors.2,3Metabolic syndrome is a cluster of interrelated risk factors of metabolic origin that is closely linked to the development of atherosclerotic cardiovascular diseases.2 or higher and a BMI of 30 kg/m2 or higher, respectively.4 Because Asians have a higher body fat percentage than whites at the same BMI level,5,6 lower cutoffs for obesity have been recommended for Asians.7 Recently, indices of abdominal obesity such as waist circumference (WC), waist-hip ratio (WHR), and waist-height ratio (WHtR) have been reported to be better discriminators of cardiovascular risk factors than an index of overall obesity such as BMI; however, variations in study design and population ethnicity have led to different conclusions regarding which indices are better discriminators of cardiovascular risk factors.8–11Body mass index (BMI) is widely used as a marker of the severity of obesity. The definitions of overweight and obesity recommended by the World Health Organization (WHO), based on data from Western populations, are a BMI of 25 kg/mMost studies of obesity indices have used a cross-sectional design, which has an inherent shortcoming: dependent variables can be affected by independent variables. For example, individuals with obesity-related diseases are likely to put more effort into losing weight, which might lead researchers to incorrect conclusions regarding the relationship between obesity and disease.12 Therefore, we considered incidence of multiple metabolic risk factors as an outcome variable. The aims of this prospective cohort study were to use different obesity indices to estimate incidence of multiple metabolic risk factors, to compare the usefulness of those obesity indices in predicting risk of developing multiple metabolic risk factors, and to identify optimal cutoffs for the obesity indices.Multiple metabolic risk factors of endogenous origin can aggregate in a single individual; indeed, this is the definition of metabolic syndrome.In 2001, the Korea Centers for Disease Control and Prevention (KCDC) established several types of cohorts as part of the Korean Genome and Epidemiology Study, which was conducted to identify environmental, genetic, and genetic–environmental interaction risk factors for developing major chronic diseases such as hypertension, type 2 diabetes, and metabolic syndrome.13,14 Briefly, the cohort is a prospective cohort of 10 038 men and women aged 40 to 69 years who were recruited from 2001 through 2002. Information on participant general characteristics, past medical history, lifestyle, physical activity, diet, reproductive factors, and psychosocial factors was obtained through structured questionnaire interviews. Anthropometric measurements such as blood pressure, height, weight, waist circumference, and body composition were also obtained. Biochemical assessments of fasting serum glucose (FSG), 75-g oral glucose tolerance test, total cholesterol, triglyceride, and high-density lipoprotein (HDL)-cholesterol, among other variables, were also conducted.For the first project, a community-based cohort was established in a rural community (Ansung) and an urban community (Ansan). Detailed information on the procedure and design of the Ansung and Ansan cohort was previously reported.n = 5503) were excluded from the analysis. Additionally, participants who did not participate in any follow-up examination (n = 591) or who had no information on metabolic risk factors (n = 87) were also excluded. Thus, the final study population was 3857. The study protocol was approved by the institutional review board of the KCDC.Among the 10 038 participants, those with existing metabolic syndrome . WC in centimeters was measured 3 times at the midpoint between the bottom of the ribs and the top of the iliac crest. The mean of the 3 readings was considered the final WC. Hip circumference in centimeters was measured 3 times at the largest posterior extension of the buttocks, and the mean of the 3 readings was considered the final hip circumference. WHR was calculated as WC divided by hip circumference, and WhtR was calculated as WC divided by height.BMI was calculated as weight in kilograms divided by the square of the height in meters , hyperglycemia (FSG ≥110 mg/dl or self-reported treatment with antihyperglycemic medication), hypertriglyceridemia (triglyceride ≥150 mg/dl), or low HDL cholesterol (<40 mg/dl for men or <50 mg/dl for women). This definition was also used in similar previous studies to estimate appropriate cutoffs of obesity indices among individuals with multiple metabolic risk factors.15–17Follow-up examinations were conducted every 2 years. The follow-up rates were 86.4%, 75.6%, and 68.8% at the first, second, and third follow-up surveys, respectively. At every follow-up examination, participants were interviewed by using a questionnaire, and blood pressure, FSG, and lipid profile were measured in the same way as at the baseline survey. A participant was considered to have incident multiple metabolic risk factors if at least 2 of the following criteria based on the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III)t test or chi-square test was used to test differences between male and female cases and non-cases in the means or proportions of baseline characteristics such as age, obesity indices, blood pressure, FSG, lipid profile, and alcohol intake.The P for trend was calculated using the likelihood ratio test.BMI, WC, WHR, and WHtR were divided into quintiles. To determine whether these obesity indices were associated with development of multiple metabolic risk factors, the Cox proportional hazards model was used and modeled for men and women after adjustment for age, residential area, education level, history of cigarette smoking, history of alcohol drinking, and number of metabolic risk factors at baseline. n = 3153). False discovery rates (FDR) were calculated to adjust for the increase in α error due to multiple comparisons.To identify the obesity index that best predicted development of multiple metabolic risk factors, receiver operating characteristic (ROC) curves were plotted for each obesity index, and the areas under the ROC curve (AUC) were compared among participants who completed the 6-year follow-up  was calculated, and the corresponding value for the maximum of the Youden index was considered the optimal cutoff point. All statistical analyses were conducted using SAS 9.1 .Table Table To identify the obesity index that best predicted development of multiple metabolic risk factors, the ROC curves and the AUC of the obesity indices in relation to multiple metabolic risk factors were plotted and calculated . In pred2  and the optimal WC cutoff was 80 cm . Among women, the optimal BMI cutoff was 24 kg/m2  and the optimal WC cutoff was 78 cm . Among the measured obesity indices, the Youden index for WHR was highest in men and women.Table 2 in both men and women. The optimal WC cutoffs were 80 cm in men and 78 cm in women, which are lower than those specified by current obesity criteria in Korea.1In a community-based cohort of middle-aged Koreans, we found that the presence of overall obesity and central obesity increased the risk of developing multiple metabolic risk factors and that WHR appeared to be a better discriminator than BMI in predicting short-term incidence of multiple metabolic risk factors. The optimal BMI cutoff was 24 kg/m5,6,18 Therefore, at a given level of BMI, WC, and WHR, the absolute risk of developing metabolic risk factors appears to be higher among Asians than among whites.19 Although the WHO has recommended that overweight and obesity in Asian populations be defined as a BMI of 23 kg/m2 or higher and a BMI of 25 kg/m2 or higher, respectively,7 a BMI less than 25 kg/m2 was associated with development of multiple metabolic risk factors in our study, especially among men. The International Diabetes Federation (IDF) and the National Cholesterol Education Program (NCEP) suggested that the optimal WC cutoff is 90 cm for men and 80 cm for women in South Asian, Chinese, and Japanese populations.1,12 However, our results showed that a WC between 75 cm and 80 cm increased the risk of developing multiple metabolic risk factors, even though this range is within the defined normal range for WC. When the IDF and NCEP criteria for WC were applied to the present male participants, the sensitivity and specificity in predicting development of multiple metabolic risk factors were 16.7% and 91.8%, respectively. These discrepancies indicate that the WHO, IDF, and NCEP criteria for obesity may not be appropriate for predicting short-term risk of developing metabolic risk factors in middle-aged or elderly Koreans.Recent studies have shown that, as compared with Western populations, Asians have less lean muscle mass and more visceral fat mass at a lower BMI and WC.2 for BMI and 85 to 90 cm for WC for men and 23 to 24.7 kg/m2 and 78 to 84 cm, respectively, for women.15,17,20 A Chinese cross-sectional study of obesity index cutoffs for predicting prevalence of multiple metabolic risk factors suggested cutoffs of 24.0 to 24.2 kg/m2 for BMI and 80 to 90 cm for WC for men and 24 to 24.7 kg/m2 and 80 to 85 cm, respectively, for women.21,22 Previous Korean studies suggested that the WC cutoff for predicting prevalence of multiple metabolic risk factors was 80 to 86 cm in men and 76 to 80 cm in women.11,23,24 Our results are consistent with those from a previous Korean cross-sectional study based on Korean National Health and Nutrition Examination data in which the study participants were a representative sample population. However, our WC cutoff is somewhat lower than those reported in Japanese and Chinese studies, although the BMI cutoff was similar.Japanese cross-sectional studies of obesity index cutoffs for predicting incidence of multiple metabolic risk factors suggested cutoffs of 24.1 to 24.2 kg/m25; however, except for 1 prospective study, all the evaluated studies were cross-sectional. Huxley et al19 systematically reviewed ethnic differences in the cross-sectional relationship among obesity and diabetes and hypertension and found that measures of central obesity were better discriminators of prevalent diabetes and hypertension in Asians and whites.Although many studies have attempted to identify the obesity index that best predicts metabolic risk factors, most such studies were cross-sectional. One meta-analysis suggested that measures of abdominal obesity, and in particular WHtR, are better predictors than BMI of cardiovascular disease risk factors such as hypertension, diabetes, and dyslipidemia9 In Jamaica, WC and BMI were similarly accurate predictors of incident diabetes.26 The San Antonio Heart Study suggested that BMI and WC had similar power in predicting development of metabolic syndrome in non-Hispanic whites and Mexican Americans.27 In Iranian men and women, WHtR appeared to be better than BMI in predicting the risk of developing type 2 diabetes.28,29 These ethnic differences in results suggest that the predictive power of each obesity index varies by ethnic group.30,31 In our study, waist-related indices were better predictors of metabolic syndrome.Although cross-sectional studies of representative populations may have sufficient external validity for the results to be applicable to the general population, the findings of such studies might be biased due to uncertain causal relationships. Previous prospective cohort studies reported somewhat inconsistent results across ethnic groups, and few studies have examined the risk of developing multiple metabolic risk factors. The Health Professionals Follow-Up Study found that WC and BMI were better than WHR in predicting the risk of developing type 2 diabetes in white men.32 In our study population, loss to follow-up was positively associated with high obesity index (among men but not women), low education level, and smoking. Unfortunately, we cannot identify whether loss to follow-up was associated with incidence of multiple metabolic risk factors. If it was, relative risk would be affected in women. Nevertheless, our follow-up rate was relatively high; therefore, the effect of selective follow-up is probably limited.Our study has several limitations. First, our study population might not be a representative sample of the Korean general population, which could restrict the applicability of cutoffs from our study. Second, although obesity indices might change due to lifestyle modification during the follow-up period, only baseline measurements were used in this analysis, and chronological changes in individual obesity were not considered. However, in men, the correlations of BMI at baseline with BMI at 2, 4, and 6 years were 0.94, 0.92, and 0.91, respectively, and the correlations of WC at baseline with WC at each follow-up survey were 0.85, 0.82, and 0.77, respectively (data not shown). In women, the correlations between obesity indices at baseline and obesity indices at each follow-up survey were also high: 0.94, 0.91, and 0.89, respectively, for BMI and 0.81, 0.76, and 0.71, respectively, for WC. Therefore, it is likely that misclassification bias due to change in obesity indices during follow-up had little effect on our study results. Third, although loss to follow-up is inevitable in most cohort studies, it can lead to selection bias, which occurs when loss to follow-up is nonrandom and is related to both the exposure and the outcome.In conclusion, overall obesity and central obesity predicted the risk of developing multiple metabolic risk factors among Koreans, and WHR appeared to be a better discriminator than BMI for predicting the incidence of risk factors. To identify middle-aged and elderly Koreans at high risk of developing multiple metabolic factors, we should consider lowering the current WC cutoffs, especially for men.


Igeo), calculated to estimate the enrichment of metal concentrations in soil, showed that the level of metal pollution in most surveyed areas is significant, especially for Pb and Cd. It is thus important to keep systematic and continuous monitoring of heavy metals and their derivatives to manage and suppress such pollution.There is a growing public concern over the potential accumulation of heavy metals in soil, owing to rapid industrial development. In an effort to describe the status of the pollutions of soil by industrial activities, relevant data sets reported by many studies were surveyed and reviewed. The results of our analysis indicate that soils were polluted most significantly by metals such as lead, zinc, copper, and cadmium. If the dominant species are evaluated by the highest mean concentration observed for different industry types, the results were grouped into Pb, Zn, Ni, Cu, Fe, and As in smelting and metal production industries, Mn and Cd in the textile industry, and Cr in the leather industry. In most cases, metal levels in the studied areas were found to exceed the common regulation guideline levels enforced by many countries. The geoaccumulation index ( Industrial pollution has been and continues to be a major cause of environmental degradation. Numerous studies have already demonstrated that areas in close proximity to industrial activities are marked by noticeable contamination of air, soil, and water –3. HenceAmong various toxic substances released by industrial activities, heavy metals have been seen as a key marker because they may be analysed effectively and consistently in most environmental matrices. Unlike organic pollutants which may degrade to less harmful components as a result of biological or chemical processes, metals are not degradable by natural processes especially when elemental metallic content is considered . The effIn most parts of the world, large quantities of trace metals are directly discharged to nearby land and into surface waters. This activity adversely affects the quality of air, soil, and ground water, such that it becomes a subject of serious concern worldwide –8. In ren = 11), four case studies were mainly conducted near chemical and petrochemical (CE + PE) types, while all others concerned various industrial complexes (n = 7). Metal pollution in the water phase was investigated mainly in the brewery (BE = 4), tannery (LE = 2), and textile industries (TE = 2 cases). During the past few decades, industrial activities have increased greatly around the world with rapid economic growth. This has been accompanied by severe environmental pollution. Many studies have been carried out to evaluate the status of industrial pollution and its environmental impacts in a broad and aggregative manner. There are, however, very few studies that describe the impact of such pollution with respect to the spread of key pollutants across various environmental media. In order to build a database to assess the basic features of heavy metal pollution due to industrial activities, we conducted a literature survey of major articles dealing with this topic that have been published since 1996 . If these metal pollution data are sorted by matrix type, 42 of the articles dealt with the soil phase. The remaining ones dealt with the air (11) and water (8) phases. As the classification of the data sets surveyed is important, we used criteria provided by the International Standard Industrial Classification (ISIC) division of the United Nations  10]. Th. Th10]. In this study, considering the availability of data, our analysis concentrated on the soil matrix. In this respect, we analyzed the basic methodological approaches employed for data acquisition in all the selected references. Based on this analysis, we evaluated the fundamental features of metal pollution in soil layers resulting from industrial activities. The basic information concerning the methodologies for sampling, sample treatment, and analysis for metal content is summarized in The metal data in soils derived from random and grid sampling are often used to represent an entire area. In this respect, grid sampling can be particularly useful when prior knowledge of the likely spatial variability is limited. This technique also avoids any sampling bias that could result from the collection of an unrepresentative average sample as a result high portion of subsamples from the same region. Two subtypes of grid sampling such as gridcell and gridpoint have commonly been employed .3 within Teflon bombs in a microwave digester [3, and H2O2  may cause instrumental drift during analysis [Soil samples were commonly dried at room temperature and sorted via sieving . They were then mineralized in a single acid like HNOdigester . In manynd H2O2  HCl, and HF , (3) HClnd HClO4 , (4) HClHClO4 [4 , and (5)HClO4 [4 ). ReagenHClO4 [4 . The diganalysis .Flame/atomic absorption spectrometer (FAAS/AAS) is the dominant technique employed for metal analysis in soil , 27, 32.AAS is also used in combination with a graphite furnace (GF) mode—known as GF-AAS , 25. It Many authors have also used inductively coupled plasma with mass spectrometry (ICP-MS) or atomic emission spectrometry (ICP-AES) for the simultaneous analysis of multiple metals , 20, 33.ICP-MS is a multielement technique that also uses an ICP plasma source to dissociate the sample into its constituent atoms or ions . HoweverNondestructive methods for analysis such as X-ray fluorescence (XRF) and instrumental neutron activation analysis (INAA) have also been used commonly in many recent studies , 24, 28.QA procedures are made to ensure that the approach is properly validated, under control at all stages, and employed appropriately. Validation of analytical methodologies via the measurement of matrix-certified reference materials is good examples of a necessary QA activity. Another important performance criteria in QA/QC is the method detection limit (MDL). The MDL is usually defined as the lowest quantity or concentration of a component that can be reliably detected for a given analytical method .Standard reference materials  or certified reference materials ) are materials used to check the quality and traceability of results and can be divided into two categories: calibrants and matrix RMs (as mentioned above). The former is mono- or multielemental standard solutions used for calibrating instruments and ensuring traceability in measurement results, while the latter is a material of a similar matrix to the sample being analyzed which has been certified for homogeneity and its content of relevant analytes . Matrix RMs are used for method validation, rather than for calibration.Most of the reference materials used in soils have been formulated by several NMIs like National Institute of Standards and Technology (NIST), USA, Institute for Reference Materials and Measurements , and so forth. In the industrial soils of Baoji city, China, soil standards NIST-2709, 2710, and 2711 were used along with Canadian certified reference materials such as SO-1, 2, 3, and 4 . In the As one of the major indices of environmental pollution, trace element concentration data in the soil phase affected by industrial activities complied in this study is listed in Trace metals occur naturally in soils as a result of diverse geological processes such as chemical reaction and erosion of underground geological materials . Beside Severe metal pollution has been reported from areas surrounding mines and smelters in many countries , 59–63. −1, resp.) were found near metal industries in Thessaloniki [−1) of Mn (652), Pb (85), Zn (92), and Cu (47) were also found in soils surrounding the mining and smelting areas in Tharsis, Spain [−1) of metals in soil dry matter (DM): Cd (14), Cr , Cu , Ni , Pb (172), and Zn . If one refers to the report of Kabata-Pendias and Pendias [Fairly high concentrations of Mn, Zn, and Cr  of Mn 2, Pb (85 Pendias  appear t−1) for Pb, Zn, and Cu of 294, 196, and 37.7, respectively. Borgna et al. [Borgna et al.  measureda et al.  also nota et al. . The anaa et al. , 67, 68.a et al.  found tha et al.  was alsoa et al. , and in a et al. .Considering horizontal distributions, metal concentrations in soil usually decrease with increasing distance from the mining or smelter site, generally following an exponential or negative-power decay function. In most cases, metal concentrations in topsoil layers significantly exceeded those of background levels (background levels being considered as those many kilometers from the smelter facilities). For instance, contamination was found to be detectable up to 33 km for a copper smelter or up to 217 km for a zinc smelter in Canada . It was −1) of Pb, Cr, As, and Cd resulting from this industry type were 25.2, 16.6, 5.97, and 0.51, respectively.Chemical and petrochemical industries have been identified as large emitters of not only metals but also a wide variety of pollutants , polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), etc.) . As thes−1) of Zn (126), Mn (277), Ni (88.2) and Cu (63.5) were detected in soil samples [2 emissions from the acid-producing industries [In a study conducted near a sulphuric acid plant in Bangladesh, very high concentrations  of 191 (Pb), 668 (Mn), 109 (Cu), 586 (Cr), 380 (Fe), and 83.6 (Cd) [−1, respectively [Textile industries can act as one of the major sources of metal pollution in the environment . There iectively . All met3.6 (Cd) . In addiectively . Althoug−1) of trace metals were determined to be Cr (744), Zn (0.97), Cu (0.04), Fe (37.7), and As (0.04) in soil samples in the vicinity of leather industries in India [−1, resp.) in soils affected by tannery effluent in Mexico [−1 in tannery waste contaminated soil in the Vellore district of Tamil Nadu, India [Solid and liquid wastes emanating from the tanning industry are known to contain various toxic trace metals . In mostin India . On the n Mexico . Cr was u, India .−1, resp.) were recorded in the soil samples close to the cement plant [−1and 290 mg kg−1, respectively, in the soil samples of ceramic industry sites in Bangladesh. High levels (mg kg−1) of Pb (268) and Zn (169) were also found near battery manufacturing facilities, which are suspected to pollute the soil in the industrial area of Baoji city, China [Nonmetallic mineral industries such as cement, ceramic, and battery manufacturing facilities can act as a major source of trace metal pollution in soil. In a study made in Jordan, it was suggested that cement factory emissions might represent the most important pollution source in the area investigated . High Pbnt plant . This mant plant . Kashem nt plant  found Niy, China . It was y, China .To learn more about the relative dominance of a given metal between different industry types, we evaluated our soil metal data by various classification criteria . Conside−1 in −1, respectively (−1 in −1) close to a number of industries such as mining and metal, chemical, textile, ceramic, and industrial complexes. In the case of Cu, concentrations above the regulatory value  were mainly seen from textile and metal industries. In textile processing, a number of heavy metals  are used in dying and printing processes [−1, resp.). The contents of Cr and Cd in topsoil were reported to increase due to the release of various industrial wastes such as tannery wastes, electroplating sludges, leather manufacturing wastes, and so forth [−1) was found only in the case of mining and metal industries.The rapid growth in industrial activities has increased the pressure on environmental sustainability. For a healthy and balanced environment, several regulations have been set out by different governments recognizing the need for improved environmental management. In case of soil, the regulations are set on total metals in the soil, and as such it is important to consider bioavailability of the metals in relation to soil physiochemical properties . The regulations for the maximum allowable soil metal levels established in different countries are presented in ectively . Hence, ly −1 in . The mearocesses . As suchso forth . In contIgeo) [Igeo ≤ 0: practically uncontaminated, (2) 0 < Igeo < 1: uncontaminated to moderately contaminated, (3) 1 < Igeo < 2: moderately contaminated, (4) 2 < Igeo < 3: moderately to heavily contaminated (5) 3 < Igeo < 4: heavily contaminated (6) 4 < Igeo < 5: heavily to extremely contaminated, and (7) 5 ≤ Igeo: extremely contaminated [Cn is the measured concentration of the trace element in the soil samples and Bn is the geochemical background value in the earth's crust. The constant 1.5 allows us to account for the natural fluctuations in the content of a given substance in the environment and the possible influence of nonlocalized anthropogenic sources. Results for calculation of the geo-accumulation index using our survey soil data are summarized in Igeo values of the trace metals in our soil database, they can be arranged in ascending order: Zn (0.81), Ni (1.26), Cu (1.28), As (1.49), Cr (1.60), Pb (2.06), and Cd (3.60). According to this geo-accumulation index calculation, it can be seen that the extent of soil pollution is the least significant for such metals as Mn and Fe, despite the influence of industrial activities. This indicates that these two metals are commonly derived from natural (geogenic) processes. It is also found that Zn, Ni, and Cu remain below class 3 level, while the highest Igeo were recorded for Cr, As, Pb, and Cd such as 3.57, 3.84, 5.02, and 9.33, respectively , 8.38 (Pb), 6.64 (Zn), 4.77 (As), 3.63 (Cu), 3.52 (Mn), and 3.49 (Cr) [Igeo of Ca, Cu, Mn were found 1.21, 1.36, and 2.96, respectively [In order to learn more about the level of metal pollution in soil and around industrialized areas, we employed a common approach to estimate the enrichment of metal concentrations in soil relative to background concentration, by computing the geoaccumulation index (Igeo) , 97. Thiaminated . This inectively . Among t.49 (Cr) . Around ectively .Igeo values, was classified as moderately-to-extremely contaminated. Most of the samples exhibited the strongest contamination in Pb and Cd. However, the samples were not greatly polluted with respect to Mn and Fe. Considering that there are no regulatory guidelines regarding soil pollution in many developing countries, more efforts should be made to characterize the soil pollution in relation to various industrial activities. This may also help us set proper soil regulation guidelines for sustaining a healthy and balanced environment and protecting human health.To learn more about the effect of industrial activities on environmental pollution, we performed a comprehensive survey of metal pollution in different environmental media. Although database which our survey drew upon is limited in terms of number of studies and the range of industry types, it is representative of the diversity and type of studies in the literature and hence the results of our analysis still provide valuable insights into metal pollution in the soil environment. The data obtained in this survey demonstrate that the metal concentrations in soil generally reflect the influence of various local industrial activities which include metal and mining, chemical and petrochemical, textile, leather, cement, and ceramic industries. Observations of generally enhanced metal levels in soils around various industrial facilities are explainable by unregulated, untreated solid and fluid wastes released by the industries to the nearby land. Among the 9 reference trace metals examined, it is seen that Cu, Cd, Pb, and Zn were monitored the most frequently. Evaluation of soil metal data indicates that their maximum values occur in relation to particular industry types, that is, Pb, Zn, Ni, Cu, Fe, and As in smelter and metal industries, Mn and Cd in the textile industry, and Cr in leather industry studies. The observed metal levels in many of the mining and metal industry samples frequently exceeded the guidance levels set by the environmental legislation in the relevant country. The status of soil pollution in this study, if assessed according to