scholarly journals Evaluating the Serum Carbon Isotope Ratio as a Biomarker for Animal Protein Ratio in a Controlled Feeding Study of US Adults

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 1068-1068
Author(s):  
Diane O'Brien ◽  
Natasha Tasevska ◽  
Virag Sagi-Kiss ◽  
Susana A Palma-Duran ◽  
Brian Barrett ◽  
...  
Keyword(s):  
Protein Intake ◽  
Isotope Ratio ◽  
Cross Validation ◽  
Disease Risk ◽  
Carbon Isotope Ratio ◽  
Animal Protein ◽  
Calibration Equation ◽  
Chronic Disease Risk ◽  
Feeding Study ◽  
Fold Cross Validation

Abstract Objectives Recent studies have identified the serum natural abundance carbon isotope ratio (CIR) as a candidate biomarker of animal protein intake in postmenopausal women. Such a biomarker would help clarify the contribution of dietary protein source (animal vs. vegetable) to chronic disease risk. Here we evaluate biomarker performance and develop a biomarker calibration equation in a mixed-age and – gender cohort. Methods We conducted a 15-d feeding study of 100 adults (age 18–70, 55% women) in Phoenix, AZ. Participants were provided individualized diets that approximated habitual food intakes. Total CIR and nitrogen isotope ratio (NIR) were measured in sera collected at the end of the feeding period. We expressed animal protein as a ratio of total protein intake (APratio). We evaluated a model of serum CIR based on APratio, the serum NIR, gender, age and body weight, and the resulting regression equation was inverted to develop an equation for the APratio that we call the calibrated biomarker. We evaluated the association of the calibrated biomarker with actual APratio using Pearson correlation and 5-fold cross validation. Results Animal protein intake in this study was 73 ± 30 g/d (mean ± SD) and the APratio was 0.63 ± 0.13. Our model explained a large proportion of the variation in serum CIR (R2 = 0.77) and APratio was the only significant model effect (coefficient = 6.22, SE = 0.44, P < 0.0001). Inverting that model generated the following biomarker calibration equation: APratio = (CIR – 26.35 – 0.06 (gender) + 0.068 * In age – 0.215 * In body weight – 0.204 * serum NIR)/6.22, where gender = 1,0 (male, female). There was a strong correlation between model-predicted and actual APratio (rP = 0.85, P < 0.0001), with the mean model-predicted APratio differing from mean actual APratio by 0.0015 (SE = 0.0077). The standard deviation of the prediction error was 0.076. The 5-fold cross validation procedure produced very similar model R2, effects, and prediction errors. Conclusions These data suggest that the serum CIR has potential as a predictive biomarker of APratio, providing a useful tool for objectively assessing dietary protein intake patterns. Such a tool could help resolve the contribution of dietary patterns favoring animal protein intake to chronic disease risk. Funding Sources This work was funded by NIH U01 CA197902.

scholarly journals The Carbon Isotope Ratio of Breath Is Elevated by Short and Long-Term Added Sugar and Animal Protein Intake in a Controlled Feeding Study

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 1069-1069
Author(s):  
Diane O'Brien ◽  
Natasha Tasevska ◽  
Virag Sagi-Kiss ◽  
Susana A Palma-Duran ◽  
Brian Barrett ◽  
...  
Keyword(s):  
Carbon Isotope ◽  
Isotope Ratio ◽  
Carbon Isotope Ratio ◽  
Animal Protein ◽  
Sample Collection ◽  
Short Term ◽  
Added Sugar ◽  
Feeding Study ◽  
Short And Long Term

Abstract Objectives Objective biomarkers would help to clarify relationships between added sugar (AS) intake and chronic disease. A recent study identified the breath carbon isotope ratio (CIR) as a potential short-term AS biomarker. To further evaluate the biomarker potential of the breath CIR, we evaluate the effects of both short and longer-term intakes of AS in the context of normal dietary intake patterns, and also evaluate animal protein (AP), another dietary factor known to influence CIR. Methods We conducted a 15-d controlled feeding study of 100 adults (age 18–70, 55% women) in Phoenix, AZ. Participants were provided individualized diets that approximated habitual food intakes and recorded the time that all foods were consumed throughout each day. Three breath samples were collected on each of 3 nonconsecutive, randomly selected study days: one fasting sample, one “morning” sample (collected 10:00–14:00) and one “evening” sample (collected 14:00–20:00). We used a linear mixed model to evaluate the effects of AS and AP intake in each of 8 hours preceding collection of the breath sample (t1 = 0–1 hour prior, t2 = 1–2 hours prior, etc.). Besides daily intake, models also included 15-d mean AS and AP intake, as well as sex, age and BMI. Coefficients are presented as (β (SE), P). Results Mean (±SD) intakes of AS and AP in our study were 67 ± 34 and 73 ± 30 g/d, respectively. The breath CIR was increased by AS consumed 1–4 hours prior to sample collection (βt2 = 0.014 (0.005), P = 0.0025; βt3 = 0.0094 (0.004), P = 0.02; βt4 = 0.012 (0.005), P = 0.02) and AP consumed 3–6 hours prior to sample collection (βt4 = 0.012 (0.005), P = 0.03; βt5 = 0.0092 (0.004), P = 0.03; βt6 = 0.010 (0.006), P = 0.09). In addition, the breath CIR increased with higher 15-d intakes of both AS and AP (βAS = 0.012 (0.003), P < 0.0001 and βAP = 0.014 (0.004), P = 0.0003, respectively). Conclusions Both short-term and longer-term intakes of AS and AP increased the breath CIR. Short-term AS intake had a more rapid effect on the breath CIR than short-term AP intake, although effects were of similar size. Furthermore, the size of short-term effects were similar to the size of long-term effects. Thus, breath CIR is influenced by both short and long-term intakes of AS and AP and could have potential for evaluating dietary patterns. Funding Sources This work was funded by NIH U01 CA197902.

scholarly journals The Relationship between Protein Intake and Source on Factors Associated with Glycemic Control in Individuals with Prediabetes and Type 2 Diabetes

Nutrients ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2031
Author(s):  
Neda S. Akhavan ◽  
Shirin Pourafshar ◽  
Sarah A. Johnson ◽  
Elizabeth M. Foley ◽  
Kelli S. George ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Body Mass ◽  
Protein Intake ◽  
Cell Function ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Animal Protein ◽  
Protein Consumption

Type 2 diabetes (T2D) is a major contributor to morbidity and mortality largely due to increased cardiovascular disease risk. This study examined the relationships among protein consumption and sources on glycemic control and cardiovascular health in individuals with prediabetes and T2D. Sixty-two overweight or obese participants with prediabetes or T2D, aged 45–75 years were stratified into the following three groups based on protein intake: <0.8 g (gram)/kg (kilogram) body weight (bw), ≥0.8 but <1.0 g/kg bw, and ≥1.0 g/kg bw as below, meeting, and above the recommended levels of protein intake, respectively. Body mass, body mass index (BMI), hip circumference (HC), waist circumference (WC), lean mass, and fat mass (FM) were significantly higher in participants who consumed below the recommended level of protein intake as compared with other groups. Higher animal protein intake was associated with greater insulin secretion and lower triglycerides (TG). Total, low-density, and high-density cholesterol were significantly higher in participants who met the recommended protein intake as compared with the other groups. These data suggest that high protein consumption is associated with lower BMI, HC, WC, and FM, and can improve insulin resistance without affecting lipid profiles in this population. Furthermore, higher intake of animal protein can improve β-cell function and lower plasma TG.

Short-term changes in added sugar consumption by adolescents reflected in the carbon isotope ratio of fingerstick blood

2018 ◽  
Vol 24 (4) ◽  
pp. 251-259 ◽  
Author(s):  
Sarah V Liu ◽  
Lori B Moore ◽  
Tanya M Halliday ◽  
A Hope Jahren ◽  
Jyoti Savla ◽  
...  
Keyword(s):  
Carbon Isotope ◽  
Isotope Ratio ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Dietary Assessment ◽  
Carbon Isotope Ratio ◽  
Population Level ◽  
Added Sugars ◽  
Sweetened Beverages ◽  
Dietary Assessment Methods

Background: Consumption of added sugars (AS) and sugar-sweetened beverages (SSB) may adversely affect adolescents’ weight and cardiovascular disease risk. Reliance on self-reported dietary assessment methods is a common research limitation, which could be overcome by dietary intake biomarkers. Aim: The investigation was a proof-of-concept study to evaluate the proposed carbon isotope ratio (δ13C) biomarker of AS intake in adolescents, using a controlled feeding design. Methods: Participants ( n = 33, age 15.3 years, 53% female) underwent two seven-day controlled feeding periods in a randomly assigned order. Diets were matched in composition except for AS content (5% or 25% of total energy). Fasting fingerstick blood samples were collected daily during each diet period. Results: Fingerstick δ13C values changed from day 1 to 8 by –0.05 ± 0.071‰ on 5% AS, and +0.03 ± 0.083‰ on 25% AS ( p ≤ 0.001). Reliability was demonstrated between day 7 and 8 δ13C values on the 5% (ICC = 0.996 , p ≤ 0.001) and 25% (ICC = 0.997, p ≤ 0.001) AS diets. Conclusions: Larger scale investigations are warranted to determine if this technique could be applied to population-level research in order to help assess the effectiveness of interventions aimed at reducing the consumption of AS or SSB intake.

Lands DSS

2021 ◽  
Vol 13 (1) ◽  
pp. 21-33
Author(s):  
Gianni Fenu ◽  
Francesca Maridina Malloci
Keyword(s):  
Cross Validation ◽  
Environmental Changes ◽  
Disease Risk ◽  
Risk Index ◽  
Human Action ◽  
Weather Data ◽  
Feed Forward Neural Network ◽  
Crop Disease ◽  
Fold Cross Validation

Decision support systems (DSSs) are used in precision farming to address climate and environmental changes due to human action. However, increments in the amount of data produced continuously by the latest sensor and satellite technologies have recently incentivized the integration of artificial intelligence (AI). A review of research dedicated to the application of DSSs and AI in forecasting crop disease is proposed. In this paper, the authors describe the DSS LANDS developed for monitoring the main crop productions in Sardinia and the case study conducted to forecast potato late blight. A feed-forward neural network was implemented to investigate if weather data provided by regional stations could be used to predict a disease risk index using an AI technique. The test performed by stratified k-fold cross validation achieved an accuracy of 96%.

scholarly journals Serum Nitrogen and Carbon Stable Isotope Ratios Meet Biomarker Criteria for Fish and Animal Protein Intake in a Controlled Feeding Study of a Women's Health Initiative Cohort

2018 ◽  
Vol 148 (12) ◽  
pp. 1931-1937 ◽  
Author(s):  
Hee Young Yun ◽  
Johanna W Lampe ◽  
Lesley F Tinker ◽  
Marian L Neuhouser ◽  
Shirley A A Beresford ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Dietary Intake ◽  
Postmenopausal Women ◽  
Protein Intake ◽  
Isotope Ratios ◽  
Animal Protein ◽  
Stable Isotope Ratios ◽  
Feeding Study ◽  
Participant Characteristics ◽  
Biomarker Evaluation

ABSTRACT Background Natural abundance stable isotope ratios are candidate biomarkers of dietary intake that have not been evaluated in a controlled feeding study in a US population. Objectives Our goals were to evaluate dietary associations with serum carbon (CIR), nitrogen (NIR), and sulfur (SIR) isotope ratios in postmenopausal women, and to evaluate whether statistical models of dietary intake that include multiple isotopes and participant characteristics meet criteria for biomarker evaluation. Methods Postmenopausal women from the Women's Health Initiative (n = 153) were provided a 2-wk controlled diet that approximated each individual's habitual food intake. Dietary intakes of animal protein, fish/seafood, red meat, poultry, egg, dairy, total sugars, added sugars, sugar-sweetened beverages (SSBs), and corn products were characterized during the feeding period with the use of the Nutrition Data System for Research (NDS-R). The CIR, NIR, and SIR were measured in sera collected from fasting women at the beginning and the end of the feeding period. Linear models based on stable isotope ratios and participant characteristics predicted dietary intake. The criterion used for biomarker evaluation was R2 ≥ 0.36, based on the study's power to detect true associations with R2 ≥ 0.50. Results The NIR was associated with fish/seafood intake and met the criterion for biomarker evaluation (R2 = 0.40). The CIR was moderately associated with intakes of red meat and eggs, but not to the criterion for biomarker evaluation, and was not associated with intake of sugars (total, added, or SSB). A model of animal protein intake based on the NIR, CIR, and participant characteristics met the criterion for biomarker evaluation (R2 = 0.40). Otherwise, multiple isotopes did not improve models of intake, and improvements from including participant characteristics were modest. Conclusion Serum stable isotope ratios can, with participant characteristics, meet biomarker criteria as measures of fish/seafood and animal protein intake in a sample of postmenopausal women. This trial was registered at clinicaltrials.gov as NCT00000611.

Physician Advice to Reduce Chronic Disease Risk Factors

1994 ◽  
Vol 10 (6) ◽  
pp. 367-371 ◽  
Author(s):  
Carol Friedman ◽  
Ross C. Brownson ◽  
Dan E. Peterson ◽  
Joan C. Wilkerson
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Disease Risk ◽  
Chronic Disease Risk ◽  
Physician Advice

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

Klasifikasi Berita Kriminal Menggunakan Naïve Bayes Classifier (NBC) dengan Pengujian K-Fold Cross Validation

2019 ◽  
Vol 5 (2) ◽  
pp. 108-117
Author(s):  
Herfia Rhomadhona ◽  
Jaka Permadi
Keyword(s):  
Cross Validation ◽  
Online Media ◽  
Bayes Classifier ◽  
Ve Bayes ◽  
Fold Cross Validation

Berita kriminalitas merupakan berita yang selalu menjadi trending topik di setiap media massa, khususnya media massa online. Media massa online terlah menyediakan beberapa fasilitas untuk mempermudah masyarakan dalam mencari sebuah berita berdasarkan topik. Media massa online melabeli suatu berita berdasarkan kategorinya. Namun, media massa online tidak memberikan sub kategori pada berita tersebut. Sebagai contoh jika seorang pengguna membuka kategori kriminal, maka yang ditampilkan adalah semua jenis berita kriminal tanpa memberikan informasi yang spesifik dari jenis kriminalitasnya. Permasalahan tersebut dapat diatasi dengan mengklasifikasikan berita kriminalitas berdasarkan subkategori. Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC)  untuk mengklasifikasi berita berdasarkan sub kategorinya. Adapun subkategori terbagi kedalam 5 kategori yaitu korupsi, narkoba, pencurian, pemerkosaan dan pembunuhan. Penelitian ini bertujuan untuk mengetahui kemampuan NBC dalam mengklasifikasi berita dengan melakukan pengujian menggunakan teknik K-Fold Cross Validation dengan nilai K dari 3 sampai 10. Hasil pengujian menyatakan bahwa NBC memiliki kemampuan dalam klasifikasi berita kriminal dengan nilai precision sebesar 98,53 %, nilai recall sebesar 98,44 % dan nilai accuracy sebesar 99,38 %.

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