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0000-0002-8274-0357
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Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol)
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Publisher: American Association for Cancer Research (AACR)
Date: 09-2016
DOI: 10.1158/1940-6207.CAPR-15-0434
Abstract: We previously identified osteopontin (OPN) as a promising marker for the early detection of hepatocellular carcinoma (HCC). In this study, we investigated the association between prediagnostic circulating OPN levels and HCC incidence in a large population-based cohort. A nested case–control study was conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. During a mean follow-up of 4.8 years, 100 HCC cases were identified. Each case was matched to two controls and OPN levels were measured in baseline plasma s les. Viral hepatitis, liver function, and α-fetoprotein (AFP) tests were also conducted. Conditional logistic regression models were used to calculate multivariable odds ratio (OR) and 95% confidence intervals (95% CI) for OPN levels in relation to HCC. Receiver operating characteristics curves were constructed to determine the discriminatory accuracy of OPN alone or in combination with other liver biomarkers in the prediction of HCC. OPN levels were positively associated with HCC risk (per 10% increment, ORmultivariable = 1.30 95% CI, 1.14–1.48). The association was stronger among cases diagnosed within 2 years of follow-up. Adding liver function tests to OPN improved the discriminatory performance for subjects who developed HCC (AUC = 0.86). For cases diagnosed within 2 years, the combination of OPN and AFP was best able to predict HCC risk (AUC = 0.88). The best predictive model for HCC in this low-risk population is OPN in combination with liver function tests. Within 2 years of diagnosis, the combination of OPN and AFP best predicted HCC development, suggesting that measuring OPN and AFP could identify high-risk groups independently of a liver disease diagnosis. Cancer Prev Res 9(9) 758–65. ©2016 AACR.
Publisher: JMIR Publications Inc.
Date: 17-06-2020
Abstract: ARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the “prediction model risk of bias assessment” criteria, and it has not been externally validated. he aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. e followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. he internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. ur results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.
Publisher: Elsevier BV
Date: 11-2013
DOI: 10.1016/J.CGH.2013.05.029
Abstract: Few modifiable risk factors have been implicated in the etiology of pancreatic cancer. There is little evidence for the effects of caffeinated coffee, decaffeinated coffee, or tea intake on risk of pancreatic cancer. We investigated the association of total coffee, caffeinated coffee, decaffeinated coffee, and tea consumption with risk of pancreatic cancer. This study was conducted within the European Prospective Investigation into Nutrition and Cancer cohort, comprising male and female participants from 10 European countries. Between 1992 and 2000, there were 477,312 participants without cancer who completed a dietary questionnaire and were followed up to determine pancreatic cancer incidence. Coffee and tea intake was calibrated with a 24-hour dietary recall. Adjusted hazard ratios (HRs) were computed using multivariable Cox regression. During a mean follow-up period of 11.6 y, 865 first incidences of pancreatic cancers were reported. When ided into fourths, neither total intake of coffee (HR, 1.03 95% confidence interval [CI], 0.83-1.27 high vs low intake), decaffeinated coffee (HR, 1.12 95% CI, 0.76-1.63 high vs low intake), nor tea were associated with risk of pancreatic cancer (HR, 1.22, 95% CI, 0.95-1.56 high vs low intake). Moderately low intake of caffeinated coffee was associated with an increased risk of pancreatic cancer (HR, 1.33 95% CI, 1.02-1.74), compared with low intake. However, no graded dose response was observed, and the association attenuated after restriction to histologically confirmed pancreatic cancers. Based on an analysis of data from the European Prospective Investigation into Nutrition and Cancer cohort, total coffee, decaffeinated coffee, and tea consumption are not related to the risk of pancreatic cancer.
Publisher: Cold Spring Harbor Laboratory
Date: 03-09-2020
DOI: 10.1101/2020.09.02.20185173
Abstract: COVID-19 may differentially impact people with obesity. We aimed to describe and compare the demographics, comorbidities, and outcomes of obese patients with COVID-19 to those of non-obese patients with COVID-19, or obese patients with seasonal influenza. We conducted a cohort study based on outpatient/inpatient care, and claims data from January to June 2020 from the US, Spain, and the UK. We used six databases standardized to the OMOP common data model. We defined two cohorts of patients diagnosed and/or hospitalized with COVID-19. We created corresponding cohorts for patients with influenza in 2017-2018. We followed patients from index date to 30 days or death. We report the frequency of socio-demographics, prior comorbidities, and 30-days outcomes (hospitalization, events, and death) by obesity status. We included 627 044 COVID-19 (US: 502 650, Spain: 122 058, UK: 2336) and 4 549 568 influenza (US: 4 431 801, Spain: 115 224, UK: 2543) patients. The prevalence of obesity was higher among hospitalized COVID-19 (range: 38% to 54%) than diagnosed COVID-19 (30% to 47%), or diagnosed (15% to 47%) or hospitalized (27% to 48%) influenza patients. Obese hospitalized COVID-19 patients were more often female and younger than non-obese COVID-19 patients or obese influenza patients. Obese COVID-19 patients were more likely to have prior comorbidities, present with cardiovascular and respiratory events during hospitalization, require intensive services, or die compared to non-obese COVID-19 patients. Obese COVID-19 patients were more likely to require intensive services or die compared to obese influenza patients, despite presenting with fewer comorbidities. We show that obesity is more common amongst COVID-19 than influenza patients, and that obese patients present with more severe forms of COVID-19 with higher hospitalization, intensive services, and fatality than non-obese patients. These data are instrumental for guiding preventive strategies of COVID-19 infection and complications. The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, and IQVIA. The University of Oxford received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV-016201 and INV-019257). APU has received funding from the Medical Research Council (MRC) [MR/K501256/1, MR/N013468/1] and Fundación Alfonso Martín Escudero (FAME) (APU). VINCI [VA HSR RES 13-457] (SLD, MEM, KEL). JCEL has received funding from the Medical Research Council (MR/K501256/1) and Versus Arthritis (21605). No funders had a direct role in this study. The views and opinions expressed are those of the authors and do not necessarily reflect those of the Clinician Scientist Award programme, NIHR, Department of Veterans Affairs or the United States Government, NHS, or the Department of Health, England. Previous evidence suggests that obese in iduals are a high risk population for COVID-19 infection and complications. We searched PubMed for articles published from December 2019 until June 2020, using terms referring to SARS-CoV-2 or COVID-19 combined with terms for obesity. Few studies reported obesity and most of them were limited by small s le sizes and restricted to hospitalized patients. Further, they used different definitions for obesity (i.e. some reported together overweight and obesity, others only reported obesity with BMI kg/m 2 ). To date, no study has provided detailed information on the characteristics of obese COVID-19 patients, such as the prevalence of comorbidities or COVID-19 related outcomes. In addition, despite the fact that COVID-19 has been often compared to seasonal influenza, there are no studies assessing whether obese patients with COVID-19 differ from obese patients with seasonal influenza. We report the largest cohort of obese patients with COVID-19 and provide information on more than 29 000 aggregate characteristics publicly available. Our findings were consistent across the participating databases and countries. We found that the prevalence of obesity is higher among COVID-19 compared to seasonal influenza patients. Obese patients with COVID-19 are more commonly female and have worse outcomes than non-obese patients. Further, they have worse outcomes than obese patients with influenza, despite presenting with fewer comorbidities. Our results show that in iduals with obesity present more comorbidities and worse outcomes for COVID-19 than non-obese patients. These findings may be useful in guiding clinical practice and future preventative strategies for obese in iduals, as well as provide useful data to support subsequent association studies focussed on obesity and COVID-19.
Publisher: American Association for Cancer Research (AACR)
Date: 16-07-2021
DOI: 10.1158/1055-9965.EPI-21-0266
Abstract: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.
Publisher: Cold Spring Harbor Laboratory
Date: 27-11-2020
DOI: 10.1101/2020.11.25.20229088
Abstract: To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). A network cohort study. Seven databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Hospital CDM, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19. Dialysis, tracheostomy, and ECMO. 842,928 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 77,853 from IQVIA Hospital CDM, 533,997 from IQVIA Open Claims, 36,717 from Optum EHR, 4,336 from OPTUM SES, 156,187 from Premier, and 10,951 from VA-OMOP). Across the six databases, 35,192 (4.17% [95% CI: 4.13% to 4.22%]) patients received dialysis, 6,950 (0.82% [0.81% to 0.84%]) had a tracheostomy, and 1,568 (0.19% [95% CI: 0.18% to 0.20%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was more common among patients who were younger, male, and with fewer comorbidities. Tracheostomy was broadly used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was generally used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial.
Publisher: Cold Spring Harbor Laboratory
Date: 30-10-2020
DOI: 10.1101/2020.10.29.20222083
Abstract: To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children/adolescents diagnosed or hospitalized with COVID-19. Secondly, to describe health outcomes amongst children/adolescents diagnosed with previous seasonal influenza. International network cohort. Real-world data from European primary care records (France/Germany/Spain), South Korean claims and US claims and hospital databases. Diagnosed and/or hospitalized children/adolescents with COVID-19 at age between January and June 2020 diagnosed with influenza in 2017-2018. Baseline demographics and comorbidities, symptoms, 30-day in-hospital treatments and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome (ARDS), multi-system inflammatory syndrome (MIS-C), and death. A total of 55,270 children/adolescents diagnosed and 3,693 hospitalized with COVID-19 and 1,952,693 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were all more common among those hospitalized vs diagnosed with COVID-19. The most common COVID-19 symptom was fever. Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital treatments for COVID-19 included repurposed medications ( %), and adjunctive therapies: systemic corticosteroids (6.8% to 37.6%), famotidine (9.0% to 28.1%), and antithrombotics such as aspirin (2.0% to 21.4%), heparin (2.2% to 18.1%), and enoxaparin (2.8% to 14.8%). Hospitalization was observed in 0.3% to 1.3% of the COVID-19 diagnosed cohort, with undetectable (N per database) 30-day fatality. Thirty-day outcomes including pneumonia, ARDS, and MIS-C were more frequent in COVID-19 than influenza. Despite negligible fatality, complications including pneumonia, ARDS and MIS-C were more frequent in children/adolescents with COVID-19 than with influenza. Dyspnea, anosmia and gastrointestinal symptoms could help differential diagnosis. A wide range of medications were used for the inpatient management of pediatric COVID-19. Most of the early COVID-19 studies were targeted at adult patients, and data concerning children and adolescents are limited. Clinical manifestations of COVID-19 are generally milder in the pediatric population compared with adults. Hospitalization for COVID-19 affects mostly infants, toddlers, and children with pre-existing comorbidities. ⍰ This study comprehensively characterizes a large international cohort of pediatric COVID-19 patients, and almost 2 million with previous seasonal influenza across 5 countries. ⍰ Although uncommon, pneumonia, acute respiratory distress syndrome (ARDS) and multi-system inflammatory syndrome (MIS-C) were more frequent in children and adolescents diagnosed with COVID-19 than in those with seasonal influenza. ⍰ Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more frequent in COVID-19, and could help to differentiate pediatric COVID-19 from influenza. ⍰ A plethora of medications were used during the management of COVID-19 in children and adolescents, with great heterogeneity in the use of antiviral therapies as well as of adjunctive therapies.
Publisher: Elsevier BV
Date: 08-2016
Publisher: American Physiological Society
Date: 12-2014
DOI: 10.1152/PHYSIOLGENOMICS.00061.2014
Abstract: Epigenetic regulation of imprinted genes is regarded as a highly plausible explanation for linking dietary exposures in early life with the onset of diseases during childhood and adulthood. We sought to test whether prenatal dietary supplementation with docosahexaenoic acid (DHA) during pregnancy may modulate epigenetic states at birth. This study was based on a randomized intervention trial conducted in Mexican pregnant women supplemented daily with 400 mg of DHA or a placebo from gestation week 18–22 to parturition. We applied quantitative profiling of DNA methylation states at IGF2 promoter 3 ( IGF2 P3), IGF2 differentially methylated region (DMR), and H19 DMR in cord blood mononuclear cells of the DHA-supplemented group ( n = 131) and the control group ( n = 130). In stratified analyses, DNA methylation levels in IGF2 P3 were significantly higher in the DHA group than the control group in preterm infants ( P = 0.04). We also observed a positive association between DNA methylation levels and maternal body mass index IGF2 DMR methylation was higher in the DHA group than the control group in infants of overweight mothers ( P = 0.03). In addition, at H19 DMR, methylation levels were significantly lower in the DHA group than the control group in infants of normal weight mothers ( P = 0.01). Finally, methylation levels at IGF2/H19 imprinted regions were associated with maternal BMI. These findings suggest that epigenetic mechanisms may be modulated by DHA, with potential impacts on child growth and development.
Publisher: Elsevier BV
Date: 02-2016
DOI: 10.1016/J.CANEP.2016.01.002
Abstract: Serum liver biomarkers (gamma-glutamyl transferase, GGT alanine aminotransferase, ALT aspartate aminotransferase, AST alkaline phosphatase, ALP total bilirubin) are used as indicators of liver disease, but there is currently little data on their prospective association with risk of hepatobiliary cancers. A nested-case control study was conducted within the prospective EPIC cohort (>520,000 participants, 10 European countries). After a mean 7.5 mean years of follow-up, 121 hepatocellular carcinoma (HCC), 34 intrahepatic bile duct (IHBC) and 131 gallbladder and biliary tract (GBTC) cases were identified and matched to 2 controls each. Circulating biomarkers were measured in serum taken at recruitment into the cohort, prior to cancer diagnosis. Multivariable adjusted conditional logistic regression was used to calculate odds ratios and 95% confidence intervals (OR 95%CI). In multivariable models, 1SD increase of each log-transformed biomarker was positively associated with HCC risk (OR(GGT)=4.23, 95%CI:2.72-6.59 OR(ALP)=3.43, 95%CI:2.31-5.10 OR(AST)=3.00, 95%CI:2.04-4.42 OR(ALT)=2.69, 95%CI:1.89-3.84 OR(Bilirubin)=2.25, 95%CI:1.58-3.20). Each liver enzyme (OR(GGT)=4.98 95%CI:1.75-14.17 OR(AST)=3.10, 95%CI:1.04-9.30 OR(ALT)=2.86, 95%CI:1.26-6.48, OR(ALP)=2.31, 95%CI:1.10-4.86) but not bilirubin (OR(Bilirubin)=1.46,95%CI:0.85-2.51) showed a significant association with IHBC. Only ALP was significantly associated with GBTC risk (OR(ALP)=1.59, 95%CI:1.20-2.09). This study shows positive associations between circulating liver biomarkers in sera collected prior to cancer diagnoses and the risks of developing HCC or IHBC, but not GBTC.
Publisher: Wiley
Date: 06-07-2015
DOI: 10.1002/IJC.29643
Abstract: The role of amount and type of dietary fat consumption in the etiology of hepatocellular carcinoma (HCC) is poorly understood, despite suggestive biological plausibility. The associations of total fat, fat subtypes and fat sources with HCC incidence were investigated in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, which includes 191 incident HCC cases diagnosed between 1992 and 2010. Diet was assessed by country-specific, validated dietary questionnaires. A single 24-hr diet recall from a cohort subs le was used for measurement error calibration. Hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated from Cox proportional hazard models. Hepatitis B and C viruses (HBV/HCV) status and biomarkers of liver function were assessed separately in a nested case-control subset with available blood s les (HCC = 122). In multivariable calibrated models, there was a statistically significant inverse association between total fat intake and risk of HCC (per 10 g/day, HR = 0.80, 95% CI: 0.65-0.99), which was mainly driven by monounsaturated fats (per 5 g/day, HR = 0.71, 95% CI: 0.55-0.92) rather than polyunsaturated fats (per 5 g/day, HR = 0.92, 95% CI: 0.68-1.25). There was no association between saturated fats (HR = 1.08, 95% CI: 0.88-1.34) and HCC risk. The ratio of polyunsaturated/monounsaturated fats to saturated fats was not significantly associated with HCC risk (per 0.2 point, HR = 0.86, 95% CI: 0.73-1.01). Restriction of analyses to HBV/HCV free participants or adjustment for liver function did not substantially alter the findings. In this large prospective European cohort, higher consumption of monounsaturated fats is associated with lower HCC risk.
Publisher: Wiley
Date: 24-09-2014
DOI: 10.1002/IJC.29214
Publisher: Springer Science and Business Media LLC
Date: 21-12-2014
Publisher: Elsevier BV
Date: 08-2013
Publisher: JMIR Publications Inc.
Date: 05-04-2021
DOI: 10.2196/21547
Abstract: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the “prediction model risk of bias assessment” criteria, and it has not been externally validated. The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.
Location: Spain
Location: Spain
Location: Spain
No related grants have been discovered for Talita Duarte-Salles.