The largest database of trusted experimental protocols
> Objects > Food > Red Meat

Red Meat

Red meat refers to the flesh of mammals, such as beef, pork, lamb, and goat.
It is a rich source of protein, iron, zinc, and other essential nutrients.
However, overconsumption of red meat has been linked to an increased risk of certain health conditions, including heart disease and cancer.
Researchers studying the effects of red meat consumption on human health can optimize their research protocols using tools like PubCompare.ai to ensure reproducibility and accuracy.
This platform allows researchers to easily locate protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the best protocols and products.
By improving the quality and rigor of their red meat research, scientists can contribute to a better understanding of the potential benefits and risks associated with red meat consumption.

Most cited protocols related to «Red Meat»

We created an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). The procedure we used to create these indices is similar to the one used by Martínez-González et al. [13 (link)]; their “provegetarian food pattern” is similar in composition to our PDI. Frequencies of consumption of each food were converted into servings consumed per day. Then the number of servings of foods that belonged to each of 18 food groups were added up. The 18 food groups were created on the basis of nutrient and culinary similarities, within larger categories of animal foods and healthy and less healthy plant foods. We distinguished between healthy and less healthy plant foods using existing knowledge of associations of the foods with T2D, other outcomes (CVD, certain cancers), and intermediate conditions (obesity, hypertension, lipids, inflammation). Plant foods not clearly associated in one direction with several health outcomes, specifically alcoholic beverages, were not included in the indices. We also excluded margarine from the indices, as its fatty acid composition has changed over time from high trans fat to high unsaturated fat. We controlled for alcoholic beverages and margarine consumption in the analysis.
Healthy plant food groups included whole grains, fruits, vegetables, nuts, legumes, vegetable oils, and tea/coffee, whereas less healthy plant food groups included fruit juices, sugar-sweetened beverages, refined grains, potatoes, and sweets/desserts. Animal food groups included animal fats, dairy, eggs, fish/seafood, meat (poultry and red meat), and miscellaneous animal-based foods.
S1 Table details examples of foods constituting the food groups. The 18 food groups were divided into quintiles of consumption, and each quintile was assigned a score between 1 and 5. For PDI, participants received a score of 5 for each plant food group for which they were above the highest quintile of consumption, a score of 4 for each plant food group for which they were above the second highest quintile but below the highest quintile, and so on, with a score of 1 for consumption below the lowest quintile (positive scores). On the other hand, participants received a score of 1 for each animal food group for which they were above the highest quintile of consumption, a score of 2 for each animal food group for which they were between the highest and second highest quintiles, and so on, with a score of 5 for consumption below the lowest quintile (reverse scores). For hPDI, positive scores were given to healthy plant food groups, and reverse scores to less healthy plant food groups and animal food groups. Finally, for uPDI, positive scores were given to less healthy plant food groups, and reverse scores to healthy plant food groups and animal food groups. The 18 food group scores for an individual were summed to obtain the indices, with a theoretical range of 18 (lowest possible score) to 90 (highest possible score). The observed ranges at baseline were 24–85 (PDI), 28–86 (hPDI), and 27–90 (uPDI) across the cohorts. The indices were analyzed as deciles, with energy intake adjusted at the analysis stage.
Publication 2016
Alcoholic Beverages Animals Cereals Coffee Diet Eggs Fabaceae Fats Fats, Unsaturated Fatty Acids Feeds, Animal Fishes Food Fowls, Domestic Fruit Fruit Juices High Blood Pressures Inflammation Lipids Malignant Neoplasms Margarine Meat Nutrients Nuts Obesity Plants Plants, Edible Red Meat Seafood Solanum tuberosum Sugar-Sweetened Beverages Vegetable Oils Vegetables Whole Grains
Self-reported FFQs were designed to assess average food intake over the preceding year. A standard portion size and nine possible frequency of consumption responses, ranging from “never, or less than once per month” to “six or more times per day” were given for each food. Total energy and nutrient intake was calculated by summing up energy or nutrients from all foods. Previous validation studies in this cohort revealed good correlations between nutrients assessed by the FFQ and multiple weeks of food records completed over the preceding year10 . For example, correlation coefficients between 1986 FFQ and 4 weeks of diet records obtained in 1986 were 0.68 for saturated fat and 0.78 for crude fiber. The mean correlation coefficient between frequencies of intake of 55 foods assessed by two FFQ 12 months apart was 0.5710 , 11 (link).
The aMed score was adapted from the Mediterranean diet scale by Trichopoulou et al8 (link). Our components include vegetables (excluding potatoes), fruits, nuts, whole grains, legumes, fish, monounsaturated-to-saturated fat ratio, red and processed meats, and alcohol. Participants with intake above the median intake received 1 point for these categories; otherwise they received 0 points. Red and processed meat consumption below the median received 1 point. We assigned 1 point for alcohol intake between 5-15 g/d. This represents approximately one 12-oz can of regular beer, 5 oz of wine, or 1.5 oz of liquor. The possible score range for aMed was 0–9, with a higher score representing closer resemblance to the Mediterranean diet. Table 1 shows the intake of aMed components during the follow-up periods. Consumption of each food group was stable across time except for a trend toward a decrease in alcohol and red/processed meat intake.
Publication 2009
Amniotic Fluid Beer Diet, Mediterranean Eating Ethanol Fabaceae Fibrosis Fishes Food Fruit Meat Nutrient Intake Nutrients Nuts Potato Red Meat Saturated Fatty Acid Vegetables Whole Grains Wine
Lifestyle habits of interest were physical activity, television watching, alcohol use, sleep duration, and diet, and cigarette smoking was a potential confounding factor (Table 1, and Tables 1 and 2 in the Supplementary Appendix, available with the full text of this article at NEJM.org). On the basis of their plausible biologic effects, the dietary factors we assessed included fruits, vegetables, whole grains, refined grains, potatoes (including boiled or mashed potatoes and french fries), potato chips, whole-fat dairy products, low-fat dairy products, sugar-sweetened beverages, sweets and desserts, processed meats, unprocessed red meats, fried foods, and trans fat (see Table 1 in the Supplementary Appendix). We also evaluated nuts, 100%-fruit juices, diet sodas, and subtypes of dairy products and potatoes. Different types of alcohol drinks were also evaluated. To assess aggregate dietary effects, changes in each dietary factor independently associated with weight gain were categorized in quintiles and assigned ascending values (1 to 5) or descending values (5 to 1) for habits inversely or positively associated with weight gain, respectively; these ordinal values were summed to generate an overall score for dietary change.
Publication 2011
Biopharmaceuticals Cereals Dairy Products Diet Dietary Modification DNA Chips Fat-Restricted Diet Food Fruit Fruit Juices Meat Nuts Potato Red Meat Sugar-Sweetened Beverages Therapy, Diet Vegetables Whole Grains

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2015
Berries Brain Bread Butter Cheese Diet Diet, Mediterranean Dietary Approaches To Stop Hypertension Disorders, Cognitive Fast Foods Fishes Food Fowls, Domestic Margarine Nutrients Nuts Oil, Olive Plant Leaves Red Meat Saturated Fatty Acid Sodium Vaginal Diaphragm Vegetables Whole Grains Wine

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2015
Berries Brain Butter Cheese Diet Dietary Approaches To Stop Hypertension Disorders, Cognitive Fast Foods Food Fowls, Domestic Margarine Nutrients Nuts Oil, Olive Plant Leaves Presenile Dementia Red Meat Seafood Vegetables Whole Grains Wine

Most recents protocols related to «Red Meat»

A semi-quantitative FFQ to estimate dietary Zn intake in the Indonesian population was developed, focusing particularly on pregnancy and the period of infancy. In an initial phase, participants filled out an online questionnaire to report their recollection of all foods consumed in the previous 24 h (Q-24 h) to gather information on foods commonly consumed in Indonesia. The food items gathered from the Q-24 h were used in the development of a FFQ to be used to estimate habitual Zn intake over a longer period (LFFQ). As this study focused on Zn intake, food items not captured through the Q-24 h but known to be good sources of Zn were added. The LFFQ comprised 82 food items.
A series of food photographs were produced to enable participants to estimate their usual food portion and were based on the recommendations of a previous study (22 (link)). Each food was presented as four portion sizes comprising 25%, 50%, 100% and 125% of a portion commonly consumed or portion on the package label of commercial products. Portions were measured out using an electrical scale (TANITA digital food scale). The amount of Zn in each food was obtained from USDA Nutrient Database for Standard Reference (United States Department of Agriculture 2013; https://data.nal.usda.gov/dataset/usda-national-nutrient-database-standard-reference-legacy-release), the Indonesian food database Nutrisurvey 2007 (http://www.nutrisurvey.de), or from previous studies (23 (link), 24 (link)). A plate or bowl containing the food was arranged together with a spoon and fork on each side. Food was photographed on a white background using a digital camera with a macro lens (Nikon 3100D) and photographs were printed at a size of 4 cm × 8 cm. In parallel, a shorter version of the FFQ (S-FFQ), which comprised fewer food items (28 items), was developed with the aim of reducing the required time for completion and thus pressure on the interviewer and participant during the clinic visit. To develop the S-FFQ, the number of food items was reduced by focusing on Zn-rich foods, such as red meat, offal, avocado, broccoli, spinach, grouping vegetables with lower Zn content, such as cabbage, carrot and lettuce, into a category of “other vegetables” and excluding items that were found to be rarely or never consumed by this population, such as brown rice, veal and pork. The L-FFQ and S-FFQ were compared with one and other and with a 3-day food record (Q3-d).
Both the L-FFQ and the S-FFQ consisted of five sections, which were: (1) personal information about child and parents, which included name, date of birth, birth weight, parents' educational background and occupation; (2) prenatal and birth history; (3) post-natal history, including feeding in the first six months, weaning age and foods, and consumption of food supplements; (4) retrospective record of foods consumed during pregnancy; (5) retrospective record of foods consumed by the child during infancy (from weaning up to age one year old); and (6) record of foods consumed by the child at the point of sampling. The S-FFQ is included as supplementary information.
Publication 2023
Birth Weight Broccoli Cabbage Carrots Child Childbirth Clinic Visits Dietary Supplements Electricity Fingers Food Interviewers Lactuca sativa Lens, Crystalline Nutrients Oryza sativa Parent Persea americana Pork Pregnancy Pressure Red Meat Spinach Veal Vegetables
A paper-based questionnaire was designed to obtain information on the demographics, diagnostic investigations, gastrointestinal cancers, year of diagnosis and treatment(s) received by the study participants. Additional information on the smoking status, alcohol consumption, sources of dietary proteins, daily intake of water, and frequency of fruit intake were obtained from the participants using the questionnaire. The information on nutrition obtained from the study participants relied on their ability to vividly recollect the constituent composition of their dietary foods. All data were handled anonymously and confidentially. Anonymity was ensured by the use of codes generated from the respondents’ initials. Data were stored and analyzed electronically using Microsoft Office (Excel) version 2016. The occurrence of all categorical data obtained in this study were expressed as percentages or cumulative frequencies.
A 2×2 contingency table was generated and used to calculate the odds ratio at 95% confidence interval for each category of GI cancers and a specified lifestyle.17 For each contingency table, patients diagnosed with a category of GI cancers under consideration was considered as bad outcome and all other patients were classified as a control group for that category of GI cancers. P-values were calculated in Microsoft Office (Excel) version 2016 using the upper and lower limits of the 95% confidence interval and z-statistic from each odds ratio calculation. P-value<.05 was considered statistically significant. The lifestyle of the study participants that were investigated for their association with gastrointestinal cancers were history of smoking, alcohol consumption, red meat consumption, seafood consumption, poultry and products consumption, and consumption of dietary plant proteins. Infrequent intake of fruits and insufficient daily water intake were also investigated for their association with gastrointestinal cancers.
The reporting of this study conforms to STROBE guidelines.18 (link)
Publication 2023
Diet Dietary Proteins Food Fowls, Domestic Fruit Gastrointestinal Cancer Mental Recall Patients Plant Proteins, Dietary Red Meat Seafood Water Consumption
The prevalent levels of saturated fat in meat, especially red meat, give it a bad reputation regarding people’s health. Therefore, it is recommended to limit one’s consumption of meat, particularly red meat, in order to reduce an individual’s chance of developing certain disorders and diseases including cardiovascular (CV) diseases (Giromini & Givens, 2022 (link)). However, this perspective takes into account the fact that meat is a rich source of several micronutrients, including vitamins and trace minerals that are either absent from meals derived from plants or have a low bioavailability in those foods (Biesalski, 2005 (link)). In addition, because it is high in protein and low in carbohydrates, meat has a lesser glycemic index (GI), which is thought to be advantageous in preventing obesity, diabetes, and cancer. Meat is a product that is rich in protein and low in carbohydrates (Biesalski, 2005 (link)).
Publication 2023
Carbohydrates Cardiovascular Diseases Diabetes Mellitus Food Malignant Neoplasms Meat Micronutrients Obesity Plants Proteins Red Meat Saturated Fatty Acid Trace Minerals Vitamins
Body mass index (BMI) was determined through physical measurement, and data on sex, age, ethnicity, education level, employment, socioeconomic status, drinking, food, and physical activity were collected through touchscreen questionnaires and interviews. Ethnicity was categorized as White, Black, Asian and other. Education level was classified as either college/university or other (vocational, lower secondary, or upper secondary). Employment status was categorized as employed versus unemployed. Townsend deprivation index (TDI) was used to define socioeconomic level [21 (link)], and classified as low, middle, or high according to tertiles [22 (link)]. Non-smokers and smokers (former or current smokers) were the two categories for smoking status. The threshold for excessive alcohol consumption was set at > 14 g per day for female and > 28 g per day for male [23 ]. Less than 10 MET hours per week of physical activity was classified as low, 10 to 49.9 MET hours per week as moderate, and more than 50 MET hours per week as high [24 (link)]. At least three of these five regularly consumed food groups must be consumed in sufficient amounts to constitute a healthy diet (Vegetables ≥3 servings/day; Fruits ≥3 servings/day; Unprocessed red meats ≤1.5 servings/week; Processed meats ≤1 serving/week; Fish ≥2 servings/week) [25 (link)]. BMI was grouped as < 25, 25 to 30, and ≥ 30 kg/m2. Heart disease and stroke was diagnosed according to self-reported and medical records. Depression symptoms were assessed using the Patient Health Queationanaie-4 (PHQ-4), self-reported, or medical records. Diabetes was ascertained based on self-reported, medical records, and using anti-diabetic agents.
Publication 2023
Antidiabetics Asian Americans Cerebrovascular Accident Depressive Symptoms Diabetes Mellitus Ethnicity Fishes Food Fruit Heart Diseases Index, Body Mass Males Meat Non-Smokers Patients Physical Examination Red Meat Vegetables Woman
Covariates included age, sex (male, female), ethnicity (white, mixed, south Asian, Black), height (cm), family history of cancer (yes, no), smoking status (never smoked, ex-smoker, current smoker), physical activity level (low, moderate, high), average household income (<£18,000, £18,000-£30,999, £31,000-£51,999, >£52,000), highest educational attainment (university degree, A levels or equivalent, O levels or equivalent, vocational qualification, none of the above), Index of Multiple Deprivation (IMD) quintile, geographical region, alcohol intake (g/day), body mass index (BMI) categorised as underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), or obese (≥30 kg/m2), total energy intake (kcal/day), and female-specific characteristics including menopausal status (pre-menopausal, post-menopausal, unsure because of hysterectomy, unsure because of other reason, unknown), use of oral contraceptives (never, ever, unknown), use of hormone-replacement therapy (never, ever, unknown), and parity (0, 1–2, ≥3, unknown). IMD is a composite measure of deprivation for each small area of the UK based on participants' postcode, and we derived IMD quintiles based on deprivation scores.16 Additional covariates considered in sensitivity analysis included intake of sodium, total fat, carbohydrate, red meat, processed meat, fibre, and calcium; and presence of diabetes, cardiovascular disease (angina, myocardial infarction, and stroke), depression, and hypertension at baseline. Missing data were under 3% except for physical activity (15.1% missing) and average household income (9.4% missing). We used multiple imputation by chained equation with 10 imputed datasets to estimate missing covariate data under assumption of missing at random and the analytical results were combined using Rubin's rule.
Publication 2023
acid-fuchsin Angina Pectoris Calcium, Dietary Carbohydrates Cardiovascular Diseases Cerebrovascular Accident Contraceptives, Oral Diabetes Mellitus Ethnicity Ex-Smokers Females Fibrosis High Blood Pressures Households Hypersensitivity Hysterectomy Index, Body Mass Males Malignant Neoplasms Meat Menopause Myocardial Infarction Obesity Postmenopause Premenopause Red Meat Sodium South Asian People Therapy, Hormone Replacement

Top products related to «Red Meat»

Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
Sourced in United States, Japan, Austria, Germany, United Kingdom, France, Cameroon, Denmark, Israel, Sweden, Belgium, Italy, China, New Zealand, India, Brazil, Canada
SAS software is a comprehensive analytical platform designed for data management, statistical analysis, and business intelligence. It provides a suite of tools and applications for collecting, processing, analyzing, and visualizing data from various sources. SAS software is widely used across industries for its robust data handling capabilities, advanced statistical modeling, and reporting functionalities.
Sourced in United States, Japan, United Kingdom, Austria, Germany, Czechia, Belgium, Denmark, Canada
SPSS version 22.0 is a statistical software package developed by IBM. It is designed to analyze and manipulate data for research and business purposes. The software provides a range of statistical analysis tools and techniques, including regression analysis, hypothesis testing, and data visualization.
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
Sourced in United States, Japan, Germany, United Kingdom, Belgium, Spain, Poland
SPSS Statistics 26 is a comprehensive software package for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of SPSS Statistics 26 is to enable users to perform a wide range of statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing.
Sourced in Germany
The Seca medical Body Composition Analyser is a device that measures an individual's body composition, including body weight, body fat percentage, and other related metrics. It utilizes bioelectrical impedance analysis technology to provide these measurements.
Sourced in United States, United Kingdom, Austria, Denmark
Stata 15 is a comprehensive, integrated statistical software package that provides a wide range of tools for data analysis, management, and visualization. It is designed to facilitate efficient and effective statistical analysis, catering to the needs of researchers, analysts, and professionals across various fields.
Sourced in United States
SPSS is a computer software package used for statistical analysis. Version 16.0 provides a range of data manipulation, analysis, and visualization tools. The core function of SPSS 16.0 is to enable users to analyze data and generate reports.

More about "Red Meat"

Red meat, a rich source of protein, iron, zinc, and other essential nutrients, refers to the flesh of mammals such as beef, pork, lamb, and goat.
While red meat can be a valuable part of a balanced diet, overconsumption has been linked to an increased risk of certain health conditions, including heart disease and cancer.
Researchers studying the effects of red meat consumption on human health can utilize powerful tools like PubCompare.ai to optimize their research protocols for reproducibility and accuracy.
This platform allows researchers to easily locate protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the best protocols and products.
By improving the quality and rigor of their red meat research, scientists can contribute to a better understanding of the potential benefits and risks associated with red meat consumption.
When conducting red meat research, researchers may also benefit from using statistical software like SAS 9.4, SPSS version 22.0, or Stata 15 to analyze their data.
These tools can help researchers uncover insights and patterns in their red meat consumption data, leading to more robust and reliable findings.
Additionally, the use of medical measurement devices like the Seca Body Composition Analyser can provide valuable data on body composition, which may be relevant to understanding the impact of red meat consumption on health.
By leveraging the latest tools and technologies, researchers can ensure that their red meat research is of the highest quality and contributes to the growing body of knowledge on this important topic.
Whether you're studying the nutritional benefits of red meat or exploring its potential health risks, the insights and resources available can help you optimize your research protocols and drive meaningful discoveries.