scholarly journals Search for early pancreatic cancer blood biomarkers in five European prospective population biobanks using metabolomics

2019 ◽  
Author(s):  
Jesse Fest ◽  
Lisanne S. Vijfhuizen ◽  
Jelle J. Goeman ◽  
Olga Veth ◽  
Anni Joensuu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Logistic Regression ◽  
Predictive Value ◽  
Early Stage ◽  
Smoking Status ◽  
Large Population ◽  
Predictive Biomarkers ◽  
Increased Risk ◽  
Diabetes Mellitus Status ◽  
And Gender

AbstractBackground and aimMost patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer.MethodsThe low incidence rate complicates prospective biomarker studies. Here, we took advantage of the availability of biobanked samples from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian biobank, Rotterdam Study) and identified prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between one month and seventeen years after blood sampling, and compared these with age- and gender-matched controls from the same cohorts. We applied1H-NMR-based metabolomics on the Nightingale platform on these samples and applied logistic regression to assess the predictive value of individual metabolite concentrations, with gender, age, body mass index, smoking status, type 2 diabetes mellitus status, fasting status, and cohort as covariates.ResultsAfter quality assessment, we retained 356 cases and 887 controls. We identified two interesting hits, glutamine (p=0.011) and histidine (p=0.012), and obtained Westfall-Young family-wise error rate adjusted p-values of 0.43 for both. Stratification in quintiles showed a 1.5x elevated risk for the lowest 20% of glutamine and a 2.2x increased risk for the lowest 20% of histidine. Stratification by time to diagnosis (<2 years, 2-5 years, >5 years) suggested glutamine to be involved in an earlier process, tapering out closer to onset, and histidine in a process closer to the actual onset. Lasso-penalized logistic regression showed a slight improvement of the area under the Receiver Operator Curves when including glutamine and histidine in the model. Finally, our data did not support the earlier identified branched-chain amino acids as potential biomarkers for pancreatic cancer in several American cohorts.ConclusionWhile identifying glutamine and histidine as early biomarkers of potential biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically usefulper seas prognostic biomarkers.

scholarly journals Search for Early Pancreatic Cancer Blood Biomarkers in Five European Prospective Population Biobanks Using Metabolomics

Endocrinology ◽  
2019 ◽  
Vol 160 (7) ◽  
pp. 1731-1742 ◽  
Author(s):  
Jesse Fest ◽  
Lisanne S Vijfhuizen ◽  
Jelle J Goeman ◽  
Olga Veth ◽  
Anni Joensuu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Stage ◽  
Large Population ◽  
Predictive Biomarkers ◽  
Proton Nuclear Magnetic Resonance ◽  
First Year ◽  
Increased Risk ◽  
Young Family ◽  
Branched Chain ◽  
Potential Biomarkers

Abstract Most patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. Prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between 1 month and 17 years after sampling (N = 356) and age- and sex-matched controls (N = 887) were collected from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian Biobank, Rotterdam Study). We applied proton nuclear magnetic resonance–based metabolomics on the Nightingale platform. Logistic regression identified two interesting hits: glutamine (P = 0.011) and histidine (P = 0.012), with Westfall–Young family-wise error rate adjusted P values of 0.43 for both. Stratification in quintiles showed a 1.5-fold elevated risk for the lowest 20% of glutamine and a 2.2-fold increased risk for the lowest 20% of histidine. Stratification by time to diagnosis suggested glutamine to be involved in an earlier process (2 to 5 years before diagnosis), and histidine in a process closer to the actual onset (<2 years). Our data did not support the branched-chain amino acids identified earlier in several US cohorts as potential biomarkers for pancreatic cancer. Thus, although we identified glutamine and histidine as potential biomarkers of biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.

Is congenital bony stenosis of the cervical spine associated with lumbar spine stenosis? An anatomical study of 1072 human cadaveric specimens

2012 ◽  
Vol 17 (1) ◽  
pp. 24-29 ◽  
Author(s):  
Navkirat S. Bajwa ◽  
Jason O. Toy ◽  
Ernest Y. Young ◽  
Nicholas U. Ahn
Keyword(s):  
Logistic Regression ◽  
Cervical Spine ◽  
Lumbar Spine ◽  
Linear Regression Analysis ◽  
Large Population ◽  
Positive Association ◽  
Anatomical Study ◽  
Lumbar Stenosis ◽  
Increased Risk ◽  
Tandem Stenosis

Object Congenital cervical and lumbar stenosis occurs when the bony anatomy of the spinal canal is smaller than expected, predisposing an individual to symptomatic neural compression. While tandem stenosis is known to occur in 5%–25% of individuals, it is not known whether this relationship is due to an increased risk of degenerative disease in these individuals or whether this finding is due to the tandem presence of a congenitally small cervical and lumbar canal. The purpose of the present study was to determine if the presence of congenital cervical stenosis is associated with congenital lumbar stenosis. Methods One thousand seventy-two adult skeletal specimens from the Hamann-Todd Collection in the Cleveland Museum of Natural History were selected. The canal area at each level was calculated using a formula that was verified by computerized measurements. Values that were 2 standard deviations below the mean were considered to represent congenitally stenotic regions. Linear regression analysis was used to determine the association between the sum of canal areas at all levels in the cervical and lumbar spine. Logistic regression was used to calculate odds ratios for congenital stenosis in one area if congenital stenosis was present in the other. Results A positive association was found between the additive area of all cervical (that is, the sum of C3–7) and lumbar (that is, the sum of L1–5) levels (p < 0.01). A positive association was also found between the number of cervical and lumbar levels affected by congenital stenosis (p < 0.01). Logistic regression also demonstrated a significant association between congenital stenosis in the cervical and lumbar spine, with an odds ratio of 0.2 (p < 0.05). Conclusions Based on the authors' findings in a large population of adult skeletal specimens, it appears that congenital stenosis of the cervical spine is associated with congenital stenosis of the lumbar spine. Thus, the presence of tandem stenosis appears to be, at least in part, related to the tandem presence of a congenitally small cervical and lumbar canal.

scholarly journals Cadmium Is Associated with Type 2 Diabetes in a Superfund Site Lead Smelter Community in Dallas, Texas

2020 ◽  
Vol 17 (12) ◽  
pp. 4558
Author(s):  
Bert B. Little ◽  
Robert Reilly ◽  
Brad Walsh ◽  
Giang T. Vu
Keyword(s):  
Type 2 Diabetes ◽  
Logistic Regression ◽  
Fixed Effects ◽  
Smoking Status ◽  
Meta Analysis ◽  
Two Phase ◽  
Lead Smelter ◽  
Increased Risk ◽  
Cd Exposure

Objective: To test the hypothesis that cadmium (Cd) exposure is associated with type 2 diabetes mellitus (T2DM). Materials and Methods: A two-phase health screening (physical examination and laboratory tests) was conducted in a lead smelter community following a Superfund Cleanup. Participants were African Americans aged >19 years to <89 years. Multiple logistic regression was used to analyze T2DM regressed on blood Cd level and covariates: body mass index (BMI), heavy metals (Ar, Cd, Hg, Pb), duration of residence, age, smoking status, and sex. Results: Of 875 subjects environmentally exposed to Cd, 55 were occupationally exposed to by-products of lead smelting and 820 were community residents. In addition, 109 T2DM individuals lived in the community for an average of 21.0 years, and 766 non-T2DM individuals for 19.0 years. T2DM individuals (70.3%) were >50 years old. Blood Cd levels were higher among T2DM subjects (p < 0.006) compared to non-T2DM individuals. Logistic regression of T2DM status identified significant predictors: Cd level (OR = 1.85; 95% CI: 1.14–2.99, p < 0.01), age >50 years (OR = 3.10; 95% CI: 1.91–5.02, p < 0.0001), and BMI (OR = 1.07; CI: 1.04–1.09, 0.0001). In meta-analysis of 12 prior studies and this one, T2DM risk was OR = 1.09 (95% CI: 1.03–1.15, p < 0.004) fixed effects and 1.22 (95% CI: 1.04–1.44, p < 0.02) random effects. Discussion: Chronic environmental Cd exposure was associated with T2DM in a smelter community, controlling for covariates. T2DM onset <50 years was significantly associated with Cd exposure, but >50 years was not. Meta-analysis suggests that Cd exposure is associated with a small, but significant increased risk for T2DM. Available data suggest Cd exposure is associated with an increased propensity to increased insulin resistance.

Extent of nodal dissection and patient age impact recurrence-free survival (RFS) after surgery for non-small cell lung cancer (NSCLC)

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 7613-7613
Author(s):  
G. K. Dy ◽  
P. K. Dy ◽  
P. C. Chua ◽  
G. D. Nelson ◽  
N. R. Foster ◽  
...  
Keyword(s):  
Local Recurrence ◽  
Surgical Resection ◽  
Early Stage ◽  
Smoking Status ◽  
Cox Proportional Hazards Model ◽  
Stage Number ◽  
T Stage ◽  
Early Stage Nsclc ◽  
Post Surgery ◽  
Increased Risk

7613 Background: Factors influencing the pattern of disease relapse after curative surgical resection in early stage NSCLC is unknown. We sought to evaluate the effect of tumor- and patient-related characteristics on the pattern of disease recurrence (local relapse and/or metastatic disease) and survival in relation to surgical resection for NSCLC. Methods: A consecutive retrospective series of 488 patients seen at Mayo Clinic Rochester who had complete resection of NSCLC between 1997 and 1998 was utilized. Cox proportional hazards model was used to evaluate the effect of age, gender, smoking status, TNM stage, number of lymph node (LN) involved, number of LN resected, and histopathologic diagnosis on RFS. Logistic regression was used to evaluate the effect on recurrence or death within the first 2 years after surgical resection. Results: Data on 342 patients with a median follow-up of 85 months (range: 0.1 to 162) are reported. 60% were male, and 81% had N0 stage at diagnosis. There were 26 (7.6%) never smokers. Median number of LN resected was 20 (interquartile range [IQR]: 14–29). Median age at surgery was 71 years (IQR: 64 to 76). A bi-modal pattern of local recurrence (N=62) after surgery for early stage NSCLC was observed, with 57% of patients having a local recurrence within 2 years and 21% of patients having a local recurrence from 5–7 years post-surgery. The median duration from surgery to first documented recurrence was 16 months (IQR: 8 to 39). While age, N stage, number of LN involved, and T stage were significant predictors of RFS univariately, only lesser LN resected (p=0.008), older age (p<0.0001) and higher T-stage (p=0.003) were significant adverse predictors of RFS in the multivariate analysis when adjusted for all factors. Higher T-Stage was associated with a significantly increased risk of recurrence or death within the first 2 years after surgical resection (p =0.004). Updated results using data from all 488 patients will be presented in the meeting. Conclusions: A trend towards a bimodal distribution of local recurrences after surgical resection of early stage NSCLC was observed. Increased number of LN resected, younger age and lower T stage were associated with better RFS. No significant financial relationships to disclose.

scholarly journals Revisiting the Association Between Cardiovascular Risk Factors and Diabetes

2009 ◽  
Vol 35 (5) ◽  
pp. 770-777 ◽  
Author(s):  
Kyeongra Yang ◽  
Eileen R. Chasens ◽  
Susan M. Sereika ◽  
Lora E. Burke
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Smoking Status ◽  
Strong Predictor ◽  
Large Population ◽  
The United States ◽  
Behavioral Risk ◽  
Increased Risk

Purpose The purpose of this study was to examine the association between cardiovascular risk factors and the presence of diabetes in a large population-level dataset. Methods A secondary analysis was conducted using data from the 2007 Behavioral Risk Factor Surveillance System, a population-based survey (n = 403,137) conducted in the United States. Results The majority of the respondents were middle-aged and overweight. Approximately half of the sample reported little or no physical activity. Estimates from a logistic regression model for a weighted sample of white, black, and Hispanic adults revealed that having hypertension or elevated cholesterol was a strong predictor of diabetes even when controlling for age, gender, race, education, income, body mass index, smoking status, and physical activity. Conclusions The results confirmed the importance of diabetes educators counseling patients with hypertension or hypercholesterolemia about their increased risk for developing diabetes.

scholarly journals Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

2020 ◽  
Author(s):  
Kai-Yang Lin ◽  
Han-Chuan Chen ◽  
Hui Jiang ◽  
Sun-Ying Wang ◽  
Hong-mei Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Serum Creatinine ◽  
Logistic Regression Analysis ◽  
Predictive Value ◽  
Independent Predictor ◽  
Multivariate Logistic Regression Analysis ◽  
Increased Risk ◽  
Long Term Mortality

Abstract Background DD was found to be associated with acute myocardial infarction (AMI) and renal insufficiency. However, it is uncertain whether DD is an independent risk factor of CI-AKI in patients undergoing pPCI. Methods We prospectively enrolled 550 consecutive patients with STEMI undergoing pPCI between January 2012 and December 2016. The predictive value of admission DD for CI-AKI was assessed by receiver operating characteristic(ROC) and multivariable logistic regression analysis. CI-AKI was defined as an absolute serum creatinine increase ≥0.3 mg/dl or a relative increase in serum creatinine ≥50% within 48 h of contrast medium exposure. Results Overall, the incidence of CI-AKI was 13.1%. The ROC analysis showed that the cutoff point of DD was 0.69 ug/ml for predicting CI-AKI with a sensitivity of 77.8% and a specificity of 57.3%. The predictive value of DD was similar to the Mehran score for CI-AKI (AUC DD =0.729 vs AUC Mehran =0.722; p =0.8298). Multivariate logistic regression analysis indicated that DD >0.69 ug/ml was an independent predictor of CI-AKI (odds ratio[OR]=3.37,95%CI:1.80-6.33, p <0.0001). Furthermore, DD >0.69 ug/ml was associated with an increased risk of long-term mortality during during a mean follow-up period of 16 months(hazard ratio=3.41, 95%CI:1.4-8.03, p =0.005). Conclusion admission DD >0.69 ug/ml is a significant and independent predictor of CI-AKI and long-term mortality in patients undergoing pPCI.

scholarly journals Association of smoking and cancer with the risk of venous thromboembolism: the Scandinavian Thrombosis and Cancer cohort

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Benedikte Paulsen ◽  
Olga V. Gran ◽  
Marianne T. Severinsen ◽  
Jens Hammerstrøm ◽  
Søren R. Kristensen ◽  
...  
Keyword(s):  
Risk Factor ◽  
Venous Thromboembolism ◽  
Cancer Patients ◽  
Smoking Status ◽  
Large Population ◽  
Population Based ◽  
Cancer Site ◽  
Established Risk Factor ◽  
Increased Risk ◽  
Active Cancer

AbstractSmoking is a well-established risk factor for cancer, and cancer patients have a high risk of venous thromboembolism (VTE). Conflicting results have been reported on the association between smoking and risk of VTE, and the effect of smoking on VTE-risk in subjects with cancer is scarcely studied. We aimed to investigate the association between smoking and VTE in subjects with and without cancer in a large population-based cohort. The Scandinavian Thrombosis and Cancer (STAC) cohort included 144,952 participants followed from 1993–1997 to 2008–2012. Information on smoking habits was derived from self-administered questionnaires. Active cancer was defined as the first two years following the date of cancer diagnosis. Former smokers (n = 35,890) and those with missing information on smoking status (n = 3680) at baseline were excluded. During a mean follow up of 11 years, 10,181 participants were diagnosed with cancer, and 1611 developed incident VTE, of which 214 were cancer-related. Smoking was associated with a 50% increased risk of VTE (HR 1.49, 95% CI 1.12–1.98) in cancer patients, whereas no association was found in cancer-free subjects (HR 1.07, 95% CI 0.96–1.20). In cancer patients, the risk of VTE among smokers remained unchanged after adjustment for cancer site and metastasis. Stratified analyses showed that smoking was a risk factor for VTE among those with smoking-related and advanced cancers. In conclusion, smoking was associated with increased VTE risk in subjects with active cancer, but not in those without cancer. Our findings imply a biological interaction between cancer and smoking on the risk of VTE.

scholarly journals Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

2020 ◽  
Author(s):  
Kai-Yang Lin ◽  
Han-Chuan Chen ◽  
Hui Jiang ◽  
Sun-Ying Wang ◽  
Hong-mei Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Serum Creatinine ◽  
Logistic Regression Analysis ◽  
Predictive Value ◽  
Independent Predictor ◽  
Multivariate Logistic Regression Analysis ◽  
Increased Risk ◽  
Long Term Mortality

Abstract Background DD was found to be associated with acute myocardial infarction (AMI) and renal insufficiency. However, it is uncertain whether DD is an independent risk factor of CI-AKI in patients undergoing pPCI. Methods We prospectively enrolled 550 consecutive patients with STEMI undergoing pPCI between January 2012 and December 2016. The predictive value of admission DD for CI-AKI was assessed by receiver operating characteristic(ROC) and multivariable logistic regression analysis. CI-AKI was defined as an absolute serum creatinine increase ≥0.3 mg/dl or a relative increase in serum creatinine ≥50% within 48 h of contrast medium exposure. Results Overall, the incidence of CI-AKI was 13.1%. The ROC analysis showed that the cutoff point of DD was 0.69 ug/ml for predicting CI-AKI with a sensitivity of 77.8% and a specificity of 57.3%. The predictive value of DD was similar to the Mehran score for CI-AKI (AUC DD =0.729 vs AUC Mehran =0.722; p =0.8298). Multivariate logistic regression analysis indicated that DD >0.69 ug/ml was an independent predictor of CI-AKI (odds ratio[OR]=3.37,95%CI:1.80-6.33, p <0.0001). Furthermore, DD >0.69 ug/ml was associated with an increased risk of long-term mortality during during a mean follow-up period of 16 months(hazard ratio=3.41, 95%CI:1.4-8.03, p =0.005). Conclusion admission DD >0.69 ug/ml is a significant and independent predictor of CI-AKI and long-term mortality in patients undergoing pPCI.

Predicting hospital readmissions in the oncology population.

2016 ◽  
Vol 34 (26_suppl) ◽  
pp. 177-177
Author(s):  
Sarah Thirlwell ◽  
Kristine A. Donovan ◽  
Mary Turney ◽  
C. Edward Emnett ◽  
Amber Lamoreaux ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Predictive Value ◽  
Emergency Department Visits ◽  
Cancer Center ◽  
Medical Setting ◽  
Cutoff Score ◽  
Operating Characteristics ◽  
Important Indicator ◽  
Increased Risk ◽  
Receiver Operating

177 Background: The 30-day readmission rate is established as an important indicator of quality of care. The LACE index is commonly used in the general medical setting to predict readmission but its ability to predict readmission with sensitivity and specificity in the oncology population has not yet been examined. At our cancer center, palliative care (PC) consultation is associated with an increased risk for readmission but it is not an element in the LACE index. Methods: We sought to characterize the operating characteristics of the LACE Index using receiver operating characteristics analyses to predict unplanned readmissions to our cancer center over a 6-week period beginning March 2016. Data was gathered from chart review to calculate a total LACE score for each unplanned admission. Logistic regression was used to examine the individual components of the LACE index and whether a PC consult improved the performance of the index. Results: A total of 329 patients with unplanned admissions were included. Fifty-nine (17.9%) were readmitted within 30 days of discharge. There was no difference between the median LACE scores of those readmitted compared to those who were not (Md = 10.0; p = .93). Receiver operating characteristic (ROC) curve analyses of LACE scores yielded an area under the curve estimate relative to 30-day readmissions of .45 indicative of poor overall accuracy. ROC analyses also showed that the previously established LACE cutoff score of 10 had sensitivity of .54 and specificity of .57 relative to readmissions. The positive predictive value was .81 and the negative predictive value was .18. In logistic regression analysis, only direct referral center/emergency department visits were an independent predictor of readmission, with a c-statistics of .64 for readmission. The inclusion of a PC consult did not improve the performance of the index. Conclusions: The LACE Index performed poorly in predicting 30-day readmission in the oncology setting; the inclusion of whether a PC consult took place did not improve the index’s utility. Further research is required to create a new tool or enhance existing indices to predict readmission in the oncology population.

An Efficient Artificial Neural Network for Coronary Heart Disease Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1474-1483
Author(s):  
Priyam Vinay Sheta
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Heart Disease ◽  
Early Stage ◽  
Large Population ◽  
Input Feature ◽  
The Neural Network ◽  
Artificial Neural

Abstract: Coronary heart disease is rapidly increasing over these days also with a significant number of deaths. A large population around the world is suffering from the disease. When surveys were carried out of the death rate and the number of people suffering from the coronary heart disease, it was understood that how important is the diagnosis of this disease at an early stage. The old way for detecting the disease was not found effective. This paper suggests a different method and technology to detect the disease and the proposed method is more effective than the old traditional methods. In this paper, an artificial neural network that predicts the coronary heart disease is used with 14 features as the input. Feature selection, data preprocessing, and removing irrelevant data was done before training the neural network. The backpropagation algorithm was used for making the neural network learn the features. The output of data was basically binary but the neural network was trained to give the output as a probability between 0 and 1. Two algorithms were proposed for this prediction named Logistic Regression and Artificial Neural Network but the later was selected because of the accuracy of 94%. The accuracy of Logistic Regression was 87%.

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