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Observational Epidemiologic Studies of Nutrition and Cancer: The Next Generation (with Better Observation) Amy F.Subar Steven Moore Yikyung Park Nancy Potischman Frances E.
Thompson Michael Leitzmann Albert Hollenbeck Kerry Grace Morrissey Victor Kipnis 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute Bethesda, MD 2 Division of Cancer Control and Population Sciences, National Cancer Institute Bethesda, MD 3 Division of Cancer Prevention, National Cancer Institute Bethesda, MD 4 AARP, Washington, DC 5 Westat, Rockville, MD Corresponding author: Arthur Schatzkin, M Jump to Buy an transportation case study original a4 (british/european) 6 hours - Best website to write a college latin american studies case study asa nbsp Many students just like you find size Best website to buy a college social sciences powerpoint presentation 35 pages / 9625 words 24 hours .Thompson Michael Leitzmann Albert Hollenbeck Kerry Grace Morrissey Victor Kipnis 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute Bethesda, MD 2 Division of Cancer Control and Population Sciences, National Cancer Institute Bethesda, MD 3 Division of Cancer Prevention, National Cancer Institute Bethesda, MD 4 AARP, Washington, DC 5 Westat, Rockville, MD Corresponding author: Arthur Schatzkin, M.
Suite 320, Rockville, MD 20886, Tele: 301-594-2931, Fax: 301-496-6829, Email: @aztahcS See other articles in PMC that cite the published article.Abstract It would be of enormous public health importance if diet and physical activity—both modifiable behavioral factors--were causally related to cancer.Nevertheless, the nutritional epidemiology of cancer remains problematic, in part because of persistent concerns that standard questionnaires measure diet and physical activity with too much error Information on other Royal Society of Chemistry activities can be found on its websites: http://www.rsc.org http://www.chemsoc.org http://www.chemsoc.org/LearnNet contains resources for teachers and students from around the world. ISBN 0–85404–388–8. British Library Cataloguing in Publication Data. A catalogue for this .
Nevertheless, the nutritional epidemiology of cancer remains problematic, in part because of persistent concerns that standard questionnaires measure diet and physical activity with too much error.
We present a new strategy for addressing this measurement error problem.
First, as background, we note that food frequency and physical activity questionnaires require respondents to report ‘typical’ diet or activity over the previous year or longer .First, as background, we note that food frequency and physical activity questionnaires require respondents to report ‘typical’ diet or activity over the previous year or longer.Multiple 24-hour recalls (24HR), based on reporting only the previous day’s behavior, offer potential cognitive advantages over the questionnaires, and biomarker evidence suggests the 24HR is more accurate than the food frequency questionnaire.The expense involved in administering multiple 24HRs in large epidemiologic studies, however, has up to now been prohibitive.In that context, we suggest that internet-based 24HRs, for both diet and physical activity, represent a practical and cost-effective approach for incorporating multiple recalls in large epidemiologic studies.We discuss 1) recent efforts to develop such internet-based instruments and their accompanying software support systems; 2) ongoing studies to evaluate the feasibility of using these new instruments in cohort studies; 3) additional investigations to gauge the accuracy of the internet-based recalls vis- -vis standard instruments and biomarkers; and 4) new statistical approaches for combining the new instruments with standard assessment tools and biomarkers The incorporation of internet-based 24HRs into large epidemiologic studies may help advance our understanding of the nutritional determinants of cancer.
Keywords: Nutrition, Diet, Physical Activity, Cancer, Measurement error There are several reasons why nutrition—conceived broadly to include diet, body size, and physical activity—should be causally linked to cancer at one or more anatomic sites.Pathophysiologic mechanisms accounting for nutrition-cancer relations are abundant and plausible (1).Animal studies have provided convincing evidence that calorie restriction and specific dietary interventions can modulate tumorigenesis (2, 3).Ecologic data—international cancer rate variation and related food consumption correlations (4), time trends(5), and migration patterns(6)—are consistent with nutritional determinants of malignant disease.Many epidemiologic studies with information on individual participants have associated various nutritional factors with cancer (1).
Especially noteworthy is the accumulating evidence that adiposity over the life span is causally related to several malignancies (7).Nevertheless, the nutrition and cancer field has been problematic, particularly with respect to the etiologic roles of dietary factors and physical activity.For a number of important foods, macronutrients, and micronutrients—total dietary fat and fat subtypes, dietary fiber and whole grains, fruits and vegetables, folate, lycopene, selenium, calcium, and vitamin D, to name just a few—the causal links to cancers at remain unclear.Consistent, though rather modest, associations have been reported between physical activity and cancers of the colorectum and, to a lesser degree, breast; but for other cancer sites the evidence is ambiguous (7).The inconsistency and uncertainty in the nutritional epidemiology of cancer can be interpreted in two ways: 1) important, public health-relevant, causal links for cancer are few, and many of the long-standing hypotheses are simply wrong; 2) many of these long-standing hypotheses are right, but methodologic difficulties have prevented us from generating the requisite evidence.
The first interpretation is really one of exclusion: as long as methodologic problems prevent us from seeing the truth we cannot rule out that truth.This paper focuses on these methodologic problems—one, in particular.Methodologic Problems in Observational Epidemiologic Studies of Nutrition and Cancer The earlier generation of case-control studies of nutrition and cancer were faced with potential recall, selection, and reverse-causation biases.These methodologic obstacles are essentially moot now that a newer generation of prospective cohort studies around the world has addressed the nutrition-cancer problem.Prospective cohort studies, however, still face two serious methodologic problems: 1) confounding by demographic, behavioral, and biologic characteristics associated with both nutritional exposures and cancer outcomes; 2) measurement error, the principal focus of this paper.
Confounding For a true causal relation between a nutritional exposure and cancer, the relative risk may well be only 1.Confounding, either by one or more unknown or inadequately measured exposures, can account for such a modest association.Confounding is a serious issue in nutritional epidemiology but is considered only briefly here.Because standard multivariate statistical techniques may not fully alleviate the problem, several investigators have proposed that sensitivity analyses be conducted to evaluate whether estimated relative risks are robust for potentially unknown confounders (8, 9).
A major alternative design strategy for dealing with confounding in nutritional epidemiology is the randomized controlled trial: the randomization process renders the treatment groups similar with respect to both known and unknown confounders.More recently, Mendelian randomization—use of a genetic variant as a proxy for a nutritional exposure--has been proposed as a strategy for addressing confounding (10).Measurement error Error in the measurement of exposures can bias epidemiologic findings.When only the main exposure is measured with error, that error generally leads to attenuation of associations between that exposure and a disease outcome and a loss of statistical power to detect exposure-disease associations (11).In spite of the loss of power, a test of the association remains statistically valid.
Things get more complicated in multivariate contexts.If, in addition to the main exposure, covariates (say, other dietary factors, including energy) are also measured with error, the association between the main exposure and disease may be attenuated or, in certain circumstances, even inflated (12).Moreover, statistical tests become invalid without a proper measurement error adjustment.When we are dealing with modest associations between nutritional exposures and malignant disease, error in the measurement of multiple nutritional factors can be a serious methodologic obstacle to observing and testing causal relations.The literature on measurement error in dietary assessment is extensive and growing (13, 14).
The instrument typically used in epidemiologic studies is the food frequency questionnaire (FFQ), which elicits information on frequency of consumption and usual portion size over the previous year, on some one hundred to 150 foods consumed.It has long been acknowledged that the FFQ assesses dietary intake with error, both systematic and random (12–16).The argument in support of use of the FFQ is that, in spite of such error, study participants are reasonably well ranked with respect to dietary consumption—especially after energy adjustment, which purportedly reduces error—such that meaningful comparisons across intake quantiles can be made.Over the last decade, investigators have used biomarkers to evaluate dietary assessment instruments.Kaaks and colleagues made an important distinction between ‘recovery’ and ‘concentration’ biomarkers (17).
Recovery biomarkers are based on precise and quantitative knowledge of the physiologic balance between intake (dietary protein, for example) and output (urinary nitrogen over 24 hours).In contrast, concentration biomarkers—including many blood nutrient levels--are based on the measurement of a specific compound’s concentration at a given point in time.One characteristic of concentration markers is that the quantitative relation between dietary intake and marker can vary substantially among individuals.This inter-individual variation results from personal lifestyle characteristics (such as smoking and other dietary constituents) as well as physiologic factors (such as absorptive and metabolic function) that influence the ‘translation’ of dietary intake into a biomarker value.Because recovery biomarkers are generally not subject to the influence of these personal characteristics—which may be correlated with true intake—they are especially useful for evaluating the measurement error structure of assessment instruments.
Studies using ‘recovery’ biomarkers such as doubly labeled water (DLW--a measure of total energy expenditure and, in the context of energy balance, total energy intake) and urinary nitrogen (UN--a measure of protein intake), however, have suggested that measurement error with the FFQ can be large.For example, the OPEN (Observing Protein Energy Nutrition) study showed that, for absolute energy and protein intakes, relative risks of 2.0 could be attenuated down to as little as 1.OPEN also showed that energy adjustment could help the situation, such that, for energy-adjusted protein (protein density) a true RR of 2.
25—an improvement, certainly, but still demonstrating substantial FFQ error and subsequent RR attenuation.This undermines the ‘ranking’ argument: with this much measurement error, even after energy adjustment, many individuals with true high intake will be ranked as low, and vice versa; moreover, there will be considerable misclassification across proximate categories (a person who truly belongs in quintile 4 is categorized in quintile 3 or 2), which further attenuates the observed relative risk.
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Recent reports from the Women’s Health Initiative (WHI), also based on use of recovery biomarkers, confirmed substantial reporting error associated with use of a FFQ (19).These studies are limited in that the number of recovery biomarkers with which to compare FFQ intake is rather small—there are no recovery biomarkers for other nutrients of interest such as dietary fat, or fiber, or fruits and vegetables—though for nutritional factors such as total dietary fat or lean meat that are strongly correlated, respectively, with energy and protein intake, the DLW and UN data may give us some indication of the extent of error produced by the FFQ.In light of these recent biomarker study findings, and with a backdrop of null findings and inconsistent results for many diet-and-cancer hypotheses, a lively debate on the ultimate utility of the FFQ—and whether it should be abandoned for newer instruments--has arisen(20–22) 30 Nov 2007 - School of Chemical Science and Engineering, Department of Chemistry,. Division of Surface AKD and ASA sizing of pilot papers – a comparative study. Surface chemical temperature of 120°C, (◇) the same sample dried at 22 °C for 24 hrs and then cured in an oven at 105 °C for 15 minutes and .In light of these recent biomarker study findings, and with a backdrop of null findings and inconsistent results for many diet-and-cancer hypotheses, a lively debate on the ultimate utility of the FFQ—and whether it should be abandoned for newer instruments--has arisen(20–22).
A heightened appreciation for the error involved in assessing physical activity has evolved in recent years.
Physical activity has generally been assessed in epidemiologic studies via frequency-type questionnaires Energy and entropy considerations are invariably important in almost all chemical studies. Chemical substances are classified in terms of their structure, phase, as well as their chemical compositions. They can be analyzed using the tools of chemical analysis, e.g. spectroscopy and chromatography. Scientists engaged in .Physical activity has generally been assessed in epidemiologic studies via frequency-type questionnaires.Several different physical activity questionnaires have been used, but in general they inquire about typical activity over time with some attempt to differentiate between different levels of activity (sedentary vs.As with the FFQ, the cognitive focus is on an extended period of time (‘typical’ activity, or activity over the previous year, etc.) and requires complex mental calculations to come up with the amount of typical activity.Investigators are now using more objective measures of physical activity, including accelerometers, heart rate monitors, or combinations.Recent reports have shown that the amount of physical activity measured by accelerometer is substantially lower than that determined by self-report (23, 24).With regard to cancer, the consistent findings of protection for physical activity vs.
colorectal cancer might in fact reflect underestimates of the true protection conferred by physical activity; whereas the inconsistent findings for other sites may, in the context of substantial physical activity assessment error, be obscuring true associations, perhaps less strong than those for colorectal cancer, but important nonetheless.New Generation of Studies, New Instruments An underlying theme emerges here: we have stronger studies—prospective cohorts set up around the world—but assessment instruments that have substantial measurement error.Moreover, we do not have accurate reference measurements (with the exception of a few recovery biomarkers like DLW and UN for diet) to adjust for measurement error in FFQs or physical activity questionnaires.In this situation, the question immediately arises: can we develop, and incorporate in prospective studies, more accurate dietary and physical activity assessment instruments.And the corollaries to that question are, what approaches, in terms of study design and statistical techniques, can we take to evaluate such new instruments, combine them with other assessment tools (if and when appropriate), and adjust for measurement error.
Diet Alternatives to the FFQ do exist, including dietary records (also called ‘diaries’) and 24-hour dietary recalls (25).Dietary records Study participants completing dietary records record, in real time, the types and amounts of foods consumed for one or more days.Typically these records have been on paper, but work is ongoing to determine whether various electronic devices, including cell phones, can be used for recording dietary data.Often some sort of tutorial, written or video, is provided to the participant to assist in filling out the records.24-hour dietary recalls 24-hour dietary recalls (24HR) are generally interview-administered, often without participants knowing in advance when they will be contacted.
The interviewer, in person or by phone, asks about all foods eaten and beverages drunk over the previous day.Department of Agriculture has adopted the Automated Multiple Pass Method (AMPM) for administering the 24HR in the U.National Health and Nutrition Survey (NHANES) (26); five distinct “passes” are used to assist the respondent in remembering what was consumed throughout the day.Given individual day-to-day variation in intake, multiple 24-hour recalls are required to estimate a study participant’s usual intake.Records and recalls, which require an individual to either record intake over several days or remember what was eaten on the previous day, have potential cognitive advantages over the FFQ, which asks respondents to provide ‘typical’ frequencies and portion sizes generally over the previous year and requires mental averaging over varying intakes and seasons.Like FFQs, however, both records and recalls also measure intake with systematic and random error.In fact, as OPEN has suggested the likely correlation of errors in FFQs and recalls (or records) means that calibration substudies using recalls (or records) as ‘reference’ instruments will underestimate the extent of error in the FFQ (18).
Recent reports, based on a comparison of FFQs to records in epidemiologic studies of dietary fat and breast cancer, are compatible with the notion that records measure diet with less error than FFQs (27, 28).For dietary records, the most common method currently used is a printed record given to the respondent to be filled in over a specified period of time.This can be done relatively inexpensively in a large cohort.Research is underway on the possibility of using cell phone digital camera technology, voice recognition or other complementary electronic devices to collect real time dietary intake data that can be completely or partially coded for analysis of daily food and nutrient intakes.These techniques are being evaluated for accuracy; further development would also be necessary to show their feasibility for large-scale research.
The coding of either paper and pencil or electronic real time diet record information, can be prohibitively expensive, $100 or more per 4-day food record per participant.Such costs can be substantially defrayed if the coding is done on a nested case-control basis: for example, records could be coded for all breast cancer cases and an appropriately selected number of controls.A more serious problem with records, however, is their propensity to influence behavior; that is, they are ‘reactive’.Because participants know in advance that they will be filling out the diary over a given series of days, even if reporting is accurate, the participant may alter dietary habits for the duration of the diary completion period.
Use of records is particularly prone to undereating and therefore an underreporting of usual diet.
In fact, diaries are often used for weight loss programs where the intent is not to assess intake but to change it.The extent of this reactivity may vary from population to population, but little is known about such variation; although there is some evidence that reactivity would be nontrivial in US populations (29), whether it is an issue in, say, the UK is not clear.Dietary recalls are among the most trusted of dietary assessment methods and used worldwide in dietary surveillance.Because it measures past diet, it is not reactive.The task of remembering the past 24 hours may be easier than forming the complex judgments needed for reporting on the past year in an FFQ.
Respondents may have difficulty remembering foods consumed the previous day and therefore may overtly omit or add foods or report inaccurate portion sizes.Nevertheless, OPEN showed considerably less measurement error in the 24HR than the FFQ (30).A major problem with the use of the 24HR in large epidemiologic studies—as the individual-level assessment tool, not as merely a reference tool on a small subset of participants--is the expense incurred in administering multiple recalls to a large cohort.Unlike the record, in which coding can be performed ad hoc in a selected manner, the costs of the 24HR are in the initial administration of the instrument.If, for example, the cost of a single interviewer-administered computer-assisted 24HR is about $100, and 6 recalls are to be administered to each of 500,000 participants in a cohort, the cost would exceed a quarter of a billion dollars just to do the dietary assessment! The administration costs may be lower in many countries relative to those in the U.
, but the costs would still be prohibitive in many cohorts around the world.Rationale for Development of Internet-Based Assessment Instruments (ASA24 and ACT24) More and more people around the world are gaining access to and using the internet.Internet usage has risen to almost 73% in the United States by June 2008; a growth of almost 130% from 200 to 2008 (31).According to Nielsen Online, over 90% of internet users have broadband connections, high-speed internet connections that allow faster data transfer needed for the new internet-based assessment instruments.
workers have broadband connections available in the workplace (32).The ‘baby boomer generation of some 81 million men and women age 43 to 63 has 60 million online consumers; more than half of persons age 64 and older in the U.are online at least monthly and 33% have broadband (up from 28% a year earlier) (33).This increasing internet use provides an attractive option for nutritional assessment on a large scale in prospective cohorts.Diet Over the past four years, the NCI has been developing a new internet-based 24-hour dietary recall, called ASA24 (‘Automated Self-Administered 24-HR’) (34).
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ASA24 is based on the USDA’s AMPM 24HR approach.The AMPM, in its series of ‘passes’, begins with a quicklist of foods recalled from the previous 24-hour period, and then successively probes the respondent for greater detail and specificity on such elements as added foods, portion size, and amount consumed.
Adaptation of the AMPM was necessary given that ASA24 takes place in a self-administered computer-based environment Best websites to write an case study chemical sciences 113 pages / 31075 words double spaced CSE Academic.Adaptation of the AMPM was necessary given that ASA24 takes place in a self-administered computer-based environment.
ASA24 uses state-of-the-art automated computer technology, including a tutorial, graphic enhancements, animated characters to guide participants, and audio language/cues.Respondents select foods consumed the previous day from USDA’s Food and Nutrient Database for Dietary Studies (FNDDS) (35) database—approximately 8000 foods are included Usual diet over the past year is queried using the National Cancer Institute's Diet History Questionnaire II.29 The second added dietary component is an Automated Self-Administered 24 hour Dietary Assessment (ASA24)30 completed online by participants, which queries intake over a 24-hour period. Dietary fibre intake will .Respondents select foods consumed the previous day from USDA’s Food and Nutrient Database for Dietary Studies (FNDDS) (35) database—approximately 8000 foods are included.Respondents find foods by browsing through food groups or by typing and searching Usual diet over the past year is queried using the National Cancer Institute's Diet History Questionnaire II.29 The second added dietary component is an Automated Self-Administered 24 hour Dietary Assessment (ASA24)30 completed online by participants, which queries intake over a 24-hour period. Dietary fibre intake will .
Respondents find foods by browsing through food groups or by typing and searching.
Information is collected about eating occasion, time of consumption, and details such as preparation methods and additions to food.To estimate amount consumed, respondents are presented with up to 8 sequentially-sized digital pictures rvijayakrishnan.com/dissertation/english-literature-dissertation-custom-writing-a4-british-european-single-spaced-plagiarism-free.To estimate amount consumed, respondents are presented with up to 8 sequentially-sized digital pictures.Pictures of beverage containers—a coffee mug or bottle of milk, for example-- include a movable ‘slider’ allowing the respondent to indicate what % of beverage in the container was actually consumed.Respondents are given multiple opportunities to modify or edit their food lists.For example, a respondent who ate Kellogg’s All Bran cereal at breakfast with skim milk can report this either by typing the food into a search box and entering or by clicking the “cereal” food group which opens subgroups in which “ready-to-eat cereals” is selected, followed by selection of the specific food term, “Kellogg’s All Bran.
” The program also contains up to 8 photographs of foods to assist in portion size designations—e., for all bran cereal, 8 photos are given, ranging from 1/4 cups to 2+ cups.Respondents have the option of reporting more than two cups by using a spin dial which increase from 2 cups up to 25 in cup increments.Modules that researchers can select choose as options for their specific research include location, with whom one ate, whether the TV was on for each meal, salt use, and dietary supplement use.
The software has the capacity to immediately compute nutrient and food group estimates for each recall day.Optional modules allow collection of data on dietary supplements, where meals are consumed, and where food is obtained.A module targeting meat preparation and consumption is also being added.After the first version is produced, NCI will proceed to translate ASA24 into Spanish.Considerable qualitative testing, including use of ‘focus groups’, has gone into the design of ASA24.
It is estimated that study participants will take about 30–40 minutes to complete ASA24 the first time.Use of the instrument appears to become easier and faster with successive completions.More information on ASA24 can be found at /tools/instruments/ .French researchers are also developing a comprehensive 24HR program, called Nutrinet Sant , which has much in common with ASA24; this program uses both successive probes and photographs but is, of course, oriented toward the French rather than U.
Investigators in other countries are also exploring development and use of automated recall; efforts are now underway, for example, to adapt ASA24 to the UK context.Physical Activity During the past 40 years, a multitude of physical activity questionnaires have been developed for use in epidemiologic studies (37).Despite these extensive development efforts, physical activity questionnaires, when compared to reference measures such as doubly labeled water, have not generally been found to measure physical activity with a high degree of accuracy (38, 39).This inaccuracy in self-reported physical activity likely reflects the cognitive challenges embedded in responding to such questionnaires.
For example, many physical activity questionnaires ask study participants to report their usual daily activity during the past year, as well as the average number of hours spent per day in sedentary, light, moderate, or vigorous activities, a task requiring complicated mental averaging across weekends, weekdays, and seasons, as well as varying activity types.In addition, few questionnaires ask for time spent according to specific activities, a practice that would likely improve accuracy of recall.Unlike the situation for diet, investigators have paid scant attention to the possibility of using the 24-hour recall approach for physical activity (40, 41).As with the dietary recall, a physical activity recall has the potential cognitive virtues of a respondent’s having to remember only the previous day’s activities, not having to average over a more extended period of time.If a sufficient number of such physical activity recalls are completed to account for variability in activity (especially on weekdays as opposed to weekends), the multiple recall approach may provide more accurate data than current questionnaires.
NCI has been developing such a physical activity recall, ACT24.This instrument asks a study participant to go through the previous 24-hour period and list all activities for the entire day.Visually, the screen is divided into two portions.The major part of the screen displays the list of common activities, such as sleeping, eating, bathing, commuting, walking, working, exercising, TV watching; participants select from this list.The minor part of the screen shows a calendar, similar in appearance to an appointment calendar, where activities appear after each entry is completed.
Participants are initially prompted to enter the activity that he/she was doing at midnight on the day of interest.For each activity, participants are asked to enter a start time (the option of ‘before midnight’ is also included) and end time, rounded to the nearest 15 minutes.After entering the activity, ACT24 probes the participants in greater detail about the activity.For example, the probes for time spent at work include queries about time spent sitting, standing, and walking at work, as well as time spent carrying weight and the amount of weight carried.When the respondent completes the entry for an activity, the corresponding time period is blocked off in the calendar portion of the screen.
This provides the participant with a way to track entries and helps set activities in the context of that particular day, potentially facilitating recall of further activities.Participants can also edit previously entered activities through the calendar directly, by dragging and dropping or by manually entering a new start or end time.After completion of an activity entry, the participant is then prompted to enter the activity for the next time slot.Participants can then enter an activity for that time or alternatively for a different time by selecting a start time of their choosing, thus giving some flexibility in the way that entries are made.Qualitative testing suggests that ACT24 will require 20–30 minutes for completion the first time.
Research Management Systems While all field studies require support systems for administration and processing of data, a study using an internet-based delivery system requires additional features.For explicitly incorporating ASA24 and ACT24 into a large cohort study such as AARP, a Website Study Management System (WSMS) has been developed.Additional internet-based instruments can be included for WSMS administration, and assignment and completion rules can be specially tailored for a given cohort.From the perspective of participant interface functions, WSMS 1) guides a participant through the consent process, captures specified demographic and contact information, and allows the participant to self-register by creating a personal user account; 2) allows a respondent to securely and seamlessly access the ASA24 and ACT24 stand-alone applications based on protocol-specified rules; 3) prompts the participant on a periodic basis, via email, to complete ASA24 or ACT24 for the previous 24-hour period; 4) links with an outbound calling system to generate reminder phone-calls and text messages to cell phone if the participants opt-in for this feature.The WSMS can, for example, prompt a participant to alternate completing ASA24 and ACT24 every month over a one-year period, resulting in 6 completions of each instrument during that year.
WSMS will mix the assignment of weekend days and weekdays in the appropriate proportions.The WSMS can be configured based on protocol rules so a participant can exit the instrument and return at a later time, with the proviso that completion take place within the calendar day following the recalled day; those going past this twenty-four hour period will be rescheduled according to protocol rules and asked to complete the recall at another time.WSMS also has specific research administration functions: it 1) receives and manages the ASA24 and ACT24 tracking data, such as completion status; and 2) provides response rates and summary statistics.Incorporating the New Assessment Tools in Epidemiologic Studies NCI investigators are initiating a study to evaluate the feasibility of using ASA24 and ACT24, with the WSMS, in the existing NIH-AARP Diet and Health Study cohort cohort of over half a million men and women and among other AARP members (42).
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The aims of this feasibility study are to 1) evaluate the response rate for the new programs; 2) determine the distribution of dietary intakes and physical activity among respondents (establishing that the ranges of dietary consumption and physical activity allow for meaningful comparisons, making sure, for example, that respondents are not all ‘vegetarian marathoners’); and 3) identify technical problems arising with ASA24 and ACT24 as well as the WSMS.
The feasibility study will also include online versions of standard questionnaires.The feasibility of using the new programs and management system in other existing cohorts and potential study populations is also being explored Help me do a college case study chemical sciences Standard US Letter Size British Bluebook.The feasibility of using the new programs and management system in other existing cohorts and potential study populations is also being explored.
Statistical Approaches for Combining New and Standard Self-Report Instruments Unlike other studies (43,44) that have used 24HRs or dietary records as ‘reference’ instruments to evaluate and adjusting for measurement error, now the former ‘reference’ instruments--the automated internet-based recalls--provide the individual-level data.If ASA24 or other new internet-based dietary recalls serve as the primary individual-level data sources in epidemiologic studies, then the information on nutrients, foods, food groups, and even dietary patterns and indexes (45) derived from the recall data can be analyzed in direct relation to cancer end points.In addition, the new digital environment will allow us to consider designs that include additional types of (web-based) dietary instruments, and explore analytic strategies that combine data from two or more instruments Aspirin | HC9H7O4 or C9H8O4 | CID 2244 - structure, chemical names, physical and chemical properties, classification, patents, literature, biological activities, safety/hazards/toxicity information, supplier lists, and more..
In addition, the new digital environment will allow us to consider designs that include additional types of (web-based) dietary instruments, and explore analytic strategies that combine data from two or more instruments.
A FFQ could give frequency information for infrequently consumed foods of importance.Our research indicates that additional information on frequency of consumption is especially helpful in allowing more precise estimates of intake of episodically consumed foods (fish, for example).One recent approach takes the FFQ data, along with other demographic and lifestyle characteristics, as covariates in calibrating a 24HR-reported food group and relating it to health end points (46).Similar arguments can be made for analysis of physical activity data in relation to cancer and other end points.First, individual-level data from ACT24 can be related in multivariable regression to the end points.
Then, given that standard physical activity questionnaires can be administered online along with ACT24, it should be possible to combine the ACT24 data with other physical activity data in ways that parallel the approaches taken for diet.Evaluation Studies We have argued that the use of the new internet based tools will give us more accurate data on diet and physical activity, and that we may get closer still to the truth with various combination approaches.We need hard data, however, to back up this assertion.In particular, we need studies that show how the new instruments compare to standard ones as well as objective ’reference’ biomarkers.
In addition, such studies may allow us to adjust for measurement error within epidemiologic studies—but again, we need to acknowledge the current limitations in the availability of dietary recovery biomarkers and objective measures of physical activity.
NCI investigators are planning a study that compares mean nutrient and food group values from ASA24 and a standardized interviewer-administered 24HR.25% will complete ASA24 twice, 50% will complete ASA24 once and AMPM once (with the order randomized), and 25% will complete the AMPM twice; this provides information on both within- and between-person comparisons.In addition, a smaller study is planned (N=60) in which NCI investigators would unobtrusively observe and record food intakes for participants who would be randomly assigned to complete one of two types of 24-hour recalls, ASA24 or the standard interviewer-administered recall.The investigators will then compare accuracy of the two methods.The feasibility study described above will permit comparisons of ASA24 to a standard FFQ (47), ACT24 to a standard physical activity questionnaire, and may also include a comparison of ACT24 with accelerometer data.
With the incorporation of the new web-based instruments into the AARP population, we hope to integrate a biomarker-based calibration study (OPEN2), using DLW, UN, possibly K+ and urinary sugars, analogous to the free-standing OPEN study (refs); physical activity monitors could also be incorporated.Such a study would have two primary objectives.First, it would allow investigators to evaluate the accuracy of the new instruments in relation to objective ‘recovery’ biomarkers: energy from ASA24 vs.DLW and UN, physical activity from ACT24 vs.Second, it would provide quantitative data on the measurement error structure of the new instruments.Such information could be used to adjust observed RRs for measurement error.This adjustment, however, could be strictly carried out only for those diet-disease associations that involve the factors reflected in the handful of available recovery markers, namely protein and energy intake, and possibly intakes of potassium and certain sugars.
It is possible, though, to carry out sensitivity analyses by assuming a similar or somewhat different measurement error structure for other nutrients or foods of interest and thereby make some estimates of cancer risk for such dietary factors.Moreover, because participants in this study will complete one or more FFQs in addition to the multiple recalls, the measurement error structure for the FFQ can also be determined in this study population.Therefore, it may be possible to evaluate the measurement error structure for the combined tools, that is, multiple recalls plus FFQ, and use these data to adjust observed RRs for measurement error derived from both instruments combined.This is an area of ongoing research, with the potential for extension to physical activity assessment as well.Conclusions—On to the Next Generation The incorporation into prospective cohort studies of new web-based assessment tools—24-hour recalls for both diet and physical activity administered multiple times over the course of, say, a year--promises a new generation of epidemiologic studies.
This new generation of studies can be diverse and international, with the appropriate adaptation of the web-based tools to various regional and ethnic cuisines and cultures.Internet access will only grow around the world and this growth will occur in most sociodemographic groups.Moreover, the digital nature of the new web-based studies allows for far greater flexibility in assessment tool modification over time—at remarkably little expense compared to that required in updating hard-copy questionnaires.We may find that relative risks for some nutrition-cancer hypotheses are indeed strengthened or even revealed for the first time (27, 28).This would clearly be of great public health importance.
For some hypotheses, however, we might find that the multiple recall-based RRs are comparable to those derived from FFQs and physical activity questionnaires (PAQs).This is likely only to strengthen the credibility of the initial FFQ- and PAQ-based findings.Either way, the new generation of studies will advance our understanding of nutrition and cancer causation.Diet, cancer, physical activity, and cancer.American Institute for Cancer Research; Washington, D.Bjorkhem-Bergman L, Torndal UB, Eken S, et al.Selenium prevents tumor development in a rat model for chemical carcinogenesis.Dietary fat in relation to tumorigenesis.Rapid increase in colorectal cancer rates in urban Shanghai, 1972–97, in relation to dietary changes.In: Schottenfeld D, Fraumeni JF, editors.New York: Oxford University Press; 2006.IARC Working Group on the Evaluation of Cancer-Preventive Strategies.
Weight control and physical activity Lyon.The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia.
A sensitivity analysis using information about measured confounders yielded improved uncertainty assessments for unmeasured confounding.
Mendelian randomization : how it can—and cannot—help confirm causal relations between nutrition and cancer.
The problem of profound mismeasurement and the power of epidemiological studies of diet and cancer.
Could exposure assessment problems give us wrong answers to nutrition and cancer questions? J Natl Cancer Inst.
Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative.
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Measurement error correction for nutritional exposures with correlated measurement error: use of the method of triads in a longitudinal setting.
New York: Oxford University Press; 1998.Is it time to abandon the food frequency questionnaire Help me write college case study chemical sciences at an affordable price Standard 14 days 43 pages / 11825 words double spaced.Is it time to abandon the food frequency questionnaire.
Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E.Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments Need to purchase an chemical sciences case study without plagiarism Turabian A4 (British/European) Proofreading College Freshman.Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments.The structure of dietary measurement error: results of the OPEN biomarker study Need to purchase an chemical sciences case study without plagiarism Turabian A4 (British/European) Proofreading College Freshman.The structure of dietary measurement error: results of the OPEN biomarker study.Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative rvijayakrishnan.com/research-paper/best-website-to-get-college-agricultural-studies-research-paper-a4-british-european-9-days-platinum-confidentially.Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative.Is it time to abandon the food frequency questionnaire.Not the time to abandon the food frequency questionnaire: point.Freedman LS, Schatzkin A, Thiebaut AC, et al.Abandon neither the food frequency questionnaire nor the dietary fat-breast cancer hypothesis.Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M.Physical activity in the United States measured by accelerometer.Phyisical activity and inactivity in an adult population assessed by accelerometry.
Nutrition in the prevention and treatment of disease.Burlington, MA: Elsevier Academic Press; 2008.Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J.An overview of USDA’s dietary intake system.Bingham SA, Luben R, Welch A, Wareham N, Khaw KT, Day N.Are imprecise methods obscuring a relation between fat and breast cancer? Lancet.Freedman LS, Potischman N, Kipnis V, et al.A comparison of two dietary instruments for evaluating the fat-breast cancer relationship.Rebro SM, Patterson RE, Kristal AR, Cheney CK.The effect of keeping food records on eating patterns.Schatzkin A, Kipnis V, Carroll RJ, et al.A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker based OPEN study.Copyright© 2008, Miniwatts Marketing Group.The state of consumers and technology: benchmark 2008.1 Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA 2 Department of Ophthalmology and Visual Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA 3 Division of Infectious Disease, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA 4 Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA 5 Department of Medical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA 6 Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA 7 Department of Bacteriology, College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA 8 William S.Middleton Veterans Affairs Medical Center, Madison, Wisconsin, USA Correspondence to Dr Nasia Safdar; ns2 at Abstract Staphylococcus aureus, vancomycin-resistant enterococci, fluoroquinolone-resistant Gram-negative bacteria and Clostridium difficile is crucial.
Evidence suggests that dietary fibre increases gut microbial diversity, which may help prevent colonisation and subsequent infection by MDROs.The aim of the Winning the War on Antibiotic Resistance (WARRIOR) project is to examine associations of dietary fibre consumption with the composition of the gut microbiota and gut colonisation by MDROs.The secondary purpose of the study is to create a biorepository of multiple body site specimens for future microbiota research.Methods and analysis The WARRIOR project collects biological specimens, including nasal, oral and skin swabs and saliva and stool samples, along with extensive data on diet and MDRO risk factors, as an ancillary study of the Survey of the Health of Wisconsin (SHOW).
The SHOW is a population-based health survey collecting data on several different health determinants and outcomes, as well as objective body measurements and biological specimens.
WARRIOR participants include 600 randomly selected Wisconsin residents age 18 and over.Specimens are screened for MDRO colonisation and DNA is extracted for 16S ribosomal RNA-based microbiota sequencing.Data will be analysed to assess the relationship between dietary fibre, the gut microbiota composition and gut MDRO colonisation.Ethics and dissemination The WARRIOR project is approved by the University of Wisconsin Institutional Review Board.The main results of this study will be published in a peer-reviewed scientific journal.
epidemiology Strengths and limitations of this study This study uses a large, non-clinical, population-based sample with a wide variety of exposures to multidrug-resistant organism risk factors.The extensive data and biological specimens collected by the Survey of the Health of Wisconsin and the Winning the War on Antibiotic Resistance project allow for future use in many more studies examining a variety of different hypotheses.The primary limitation of this study is its cross-sectional nature; however, plans for follow-up data collection are underway.1 Given their association with these varying biological mechanisms, imbalance or dysbiosis of the gut microbiota has been linked to many adverse health effects including increased risk for infection, obesity, diabetes, inflammatory bowel disease, allergic disease, frailty in ageing and mental health conditions.
1 2 There is no consensus on what microbial composition constitutes a healthy gut microbiota, although a more diverse microbiota is thought to be better, especially in the case of healthy immune response and protection against infection.3 Infection with multidrug-resistant organisms (MDROs) is increasingly common and effective treatment options are rapidly decreasing.4 Vancomycin-resistant enterococci (VRE), fluoroquinolone-resistant Gram-negative bacilli (FQRGNB), methicillin-resistant Staphylococcus aureus (MRSA) and Clostridium difficile are all MDROs with the capacity to cause seriously detrimental health effects.5 VRE often causes infections associated with hospitalisation, including urinary, bloodstream, catheter and surgical wound infections.6 FQRGNB can cause pneumonia, sepsis, meningitis and surgical site infections.
aureus is carried by approximately 30% of the US population, while MRSA is carried by about 1%.aureus carriage can be commensal but leads to increased risk for infection by MRSA.difficile causes more than 450 000 infections, leading to 15 000 mortalities annually and has exceeded MRSA as the most frequent cause of hospital-acquired infection.10 11 The lack of effective treatment options for these infections also endangers the efficacy and outcomes of other medical treatments, including surgery and those for cancer.12 MDROs are often transmitted in healthcare settings but are increasingly being acquired through community sources.13 In addition to causing clinical disease, MDROs can cause asymptomatic colonisation which is a strong predictor of future infection14 and can be a source of transmission via asymptomatic carriers of MDROs.15 Preventing colonisation by MDROs is therefore vital to preventing infection.
A balanced microbiota can prevent colonisation and infection with MDROs and other pathogens via several pathways.
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One mechanism is competitive inhibition, whereby commensal microbes compete for the same resources and mucosal binding sites as pathogenic bacteria and limit their growth.16 The makeup of the microbiota also plays a large role in the development of the immune system and continues to influence immune response and maintain homeostasis throughout our lives.17 Beneficial bacteria within the microbiota produce cytokines, short and long-chain fatty acids and other signalling molecules that increase mucus production and strengthen epithelial barriers, as well as increasing type 1 T helper cell response, all of which help to fight off pathogenic bacteria 31 Mar 2009 - Multiple 24-hour recalls (24HR), based on reporting only the previous day's behavior, offer potential cognitive advantages over the questionnaires, and biomarker The earlier generation of case-control studies of nutrition and cancer were faced with potential recall, selection, and reverse-causation biases..17 Beneficial bacteria within the microbiota produce cytokines, short and long-chain fatty acids and other signalling molecules that increase mucus production and strengthen epithelial barriers, as well as increasing type 1 T helper cell response, all of which help to fight off pathogenic bacteria.
18 Many factors are known to influence the composition of the human gut microbiota, including age, sex and genetics, as well as modifiable factors including birth mode, diet, exercise, environment, smoking, cohabitation, animal contact and use of antibiotics, probiotics and prebiotics.
19–23 Recent literature suggests dietary factors can alter the gut microbiota and may play a role in the risk of infection by gut pathogens .19–23 Recent literature suggests dietary factors can alter the gut microbiota and may play a role in the risk of infection by gut pathogens.24 Dietary fibre appears promising in promoting a diverse, healthy gut microbiota by selecting for fibre-degrading microbes that produce immune-enhancing compounds like butyrate.25 Butyrate and other short-chain fatty acids are end-products of microbial fermentation that can enter systemic circulation and inhibit the expression of specific proinflammatory cytokines.26 Moreover, disease-causing disturbances to the gut microbiota may be due to Western diets abundant in fats and simple carbohydrates but lacking in fibre.The purpose of the Winning the War on Antibiotic Resistance (WARRIOR) study is to examine the relationships between dietary fibre, the gut microbiota and colonisation by MDROs in a statewide, non-clinical, population-based sample of adults and to further create a microbiome sample repository for future research.We aim to determine the association between diets either high or low in fibre and gut microbial diversity to examine the different effects of specific types of dietary fibre on the gut microbiota and MDRO colonisation.The primary hypothesis is that higher dietary fibre consumption will be associated with increased gut microbial diversity and lower prevalence of MDRO colonisation.Methods/design Overview The WARRIOR project aims to collect data and biological samples from 600 Wisconsin residents age 18 and over.WARRIOR is an ancillary study of the ongoing Survey of the Health of Wisconsin (SHOW), for which methods have been previously published.
28 A description of the WARRIOR project and the full SHOW protocol are available on the SHOW website ( ).The SHOW is an annual cross-sectional, statewide, population-based health survey, modelled after the National Health and Nutrition Examination Survey, which collects a wide range of health, behaviour and environment data as well as objective body measurements and biological specimens.The SHOW was initiated in 2008 and the WARRIOR project is a 2-year ancillary study that began at the start of the 2016 survey year.Survey components that were added to the SHOW by the WARRIOR project include additional dietary assessments, questions about MDRO risk factors and additional specimen collection including swabs of oral, skin and nasal tissues, as well as saliva and stool samples.A study schematic outlines the various study components in figure 1.
Figure 2 A conceptual model illustrating the pathways between dietary fibre consumption and MDRO colonisation, including mediators and confounding factors.Biological sampling In addition to the blood and urine specimens collected by the SHOW, the WARRIOR project collects saliva and stool samples and separate swabs of the nose, mouth and skin (combined axilla/groin).Participants self-collect a stool sample at home using a collection kit provided by the SHOW interviewer that includes a stool collection hat, a sterile 60 mL specimen cup, a sterile wood tongue depressor, gloves, a specimen label, a biohazard bag, a brown paper bag and an instruction sheet.
Participants collect the stool sample within the 24 hours prior to their SHOW clinic visit and refrigerate the sample until submitting it at their appointment.
At the clinic appointment approximately 1–2 mL of saliva is collected using a sterile collection aid and a sterile tube, and swabs of the axilla/groin, nares and buccal mucosa and tonsils are taken using a dual head BBL CultureSwab with liquid stuart transport medium (Becton, Dickinson and Company, Franklin Lakes, New Jersey, USA).All WARRIOR samples are then shipped and received at the Infectious Disease Research Laboratory at the University of Wisconsin—Madison within 24 hours, where they are immediately processed for MDRO colonisation testing and then frozen at −80°C for later use in microbiome analysis.While stool collection and shipment proved to be easier for participants than anticipated, saliva collection was more inconsistent than expected, as ease and rate of saliva production can vary greatly among individuals.Microbiological analysis In 2016, swabs, saliva and stool were screened for the presence MRSA, VRE and FQRGNB; in 2017 screening for C.Specimens are processed immediately on receipt by the laboratory.Swabs are vortexed in 1 mL of tryptic soy broth (TSB) (Remel, Lenexa, Kansas, USA) while 100L of saliva and 0.1 g of stool are used to inoculate the TSB, resulting in a total of five assays per subject that completes all biological components of the WARRIOR project.Broths are incubated overnight aerobically at 36°C.Aliquots of each broth are plated to mannitol salt agar (Remel, Lenexa, Kansas, USA) supplemented with 4 mg/L of cefoxitin (Sigma-Aldrich, St Louis, Missouri) for MRSA detection,31 enterococcosel agar (BD/Difco, Sparks, Maryland) supplemented with 6 mg/L of vancomycin (Sigma-Aldrich, St Louis, Missouri) for VRE detection and MacConkey’s agar (BD/Difco, Sparks, Maryland, USA) supplemented with 4 mg/L of ciprofloxacin (Sigma-Aldrich, St Louis, Missouri) for detection of FQRGNB.
Colonies matching suspected morphology on selective agar are subcultured on blood agar plates (BAP) (BD, Sparks, Maryland) for identification.Identification of isolates is performed using conventional biochemical methods and identification is confirmed via sequencing of the 16S ribosomal RNA (rRNA) gene.Resistance to cefoxitin and ciprofloxacin are determined using Kirby-Bauer disc diffusion susceptibility testing methods and breakpoints published in the Clinical Laboratory Standards Institute documents M07-A10 and M100-S26.32 33 The E-test (Bio-Merieux, Marcy l’Etoile, France) is used to determine the minimum inhibitory concentration (MIC) of vancomycin.1 g of stool is inoculated into 1 mL of prereduced C.difficile Brucella Broth and incubated anaerobically at 36°C overnight.difficile Brucella agar plate and incubated for 48 hours at 36°C.
Colonies matching suspected colony morphology are subcultured to a prereduced BAP and subsequently identified using Gram staining and catalase testing.Presence of toxin genes is assessed using an in-house PCR assay and bacterial identification is confirmed via sequencing of the 16S rRNA gene.35 All positive antibiotic resistant isolates are stocked and stored at −80°C for future unspecified research.Microbiota analysis is performed using DNA extracted and purified from stool samples to address the aims of the WARRIOR project, and DNA extracted from other sample matrices will be used for future unspecified research.The purified DNA is then normalised to a concentration of 5 ng/ L and amplified using PCR with barcoded primers to the 16S V4 region and sequenced on an Illumina Miseq (2×250 bp reads).
36 Stored DNA samples are available as a resource for additional metagenomic research and additional analyses as new technologies are developed.Stool genomic DNA extraction Approximately 180–220 mg of each faecal sample is added to a 2 mL bead-beating tube containing 500L 2× sodium chloride-tris-EDTA (STE) buffer, 300 mg of 1.0 mm diameter zirconia/silica beads and vortexed to homogenise the stool.The sample is then centrifuged for 15 min at 4°C at 500 g.A total of 800L of 2× STE buffer is added to the supernatant and up to 1000L is transferred to a new bead-beat tube containing 0.
1 mm diameter zirconia/silica beads and one 4 mm stainless steel bead.For chemical lysis, 115L of an enzymatic cocktail containing 50L lysozyme (10 mg/mL), 10L mutanolysin (1 mg/mL), 5L lysostaphin (5 mg/mL) and 50L 20% sodium dodecyl sulfate is added to each tube.Additionally, 700L phenol:chloroform:isoamyl alcohol is added to the sample.Bead-beat tubes are then vortexed and incubated at 56°C for 30 min.For mechanical lysis, bead-beat tubes are vortexed and then placed in a Mini-BeadBeater-24 (Cat 112011, Biospec Products, Bartlesville, Oklahoma, USA) and beat for 3 min.
Tubes are centrifuged at 16 000x g for 10 min at 4°C.The top aqueous layer is transferred to a clean 2 mL tube and washed with an additional 500L phenol:chloroform:isoamyl alcohol and vortexed.The sample is then centrifuged at 16 000× g for 10 min at 4°C.The phenol:chloroform:isoamyl alcohol wash is repeated between 2 and 10 times to remove impurities from the sample until the aqueous layer is clean.
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The top aqueous layer is then transferred to a clean 2 mL microcentrifuge tube containing 70L of 3M sodium acetate and 700L isopropanol.
The samples are inverted several times and subsequently incubated at −20°C for 30 min to 1 hour.Each sample is centrifuged at 16 000x g (4°C) for 20 min to collect the DNA pellet, which is then washed with 500L cold 70% ethanol Butthe duties to society asa whole merit onlyone of the12 topical sections,“The Researcherin Society” (see Box 2);the first page of this short section describesthe duties ofthe researcherto the public, thesecond presents ahistorical case (Committee on Science, Engineering, andPublic Policy 2009, pp. 29–43, 48–49)..Each sample is centrifuged at 16 000x g (4°C) for 20 min to collect the DNA pellet, which is then washed with 500L cold 70% ethanol.
The ethanol wash is repeated, and sample DNA pellets are dried for 5 min using a Savant SpeedVac (DNA120-230, Thermo Scientific, Waltham, Massachusetts, USA).Finally, dried DNA pellets are resuspended in 100L TE buffer and stored overnight at 4°C or at 37°C for 1 hour to dissolve the DNA pellet.Samples are then purified using NucleoSpin Gel and PCR clean-up kit according to manufacturer’s directions (Macherey-Nagel, Germany) and eluted in 40L TE buffer.
DNA is quantified using PicoGreen in a microplate reader (BioTek Instruments) and stored long term at −80°C rvijayakrishnan.com/thesis/how-to-buy-an-criminal-law-thesis-standard-bluebook-us-letter-size-100-plagiarism-free.DNA is quantified using PicoGreen in a microplate reader (BioTek Instruments) and stored long term at −80°C.Swab and saliva genomic DNA extraction The swab head is placed into a 2 mL bead-beating tube containing 750L 1× PBS and 500 mg of 0.For chemical lysis, 25L of an enzymatic cocktail containing 5L lysozyme (10 mg/mL), 15L mutanolysin (1 mg/mL) and 5L lysostaphin (5 mg/mL) is added to each bead-beat tube and vortexed.
The bead-beat tubes are then incubated at 37°C for 30 min before 60L of a second enzymatic cocktail containing 10L proteinase K (20 mg/mL) and 50L 10% sodium dodecyl sulfate is added to each tube.
Bead-beat tubes are then vortexed and incubated at 55°C for 45 min.For mechanical lysis, bead-beat tubes are vortexed and then placed in a Mini-BeadBeater-24 (Cat 112011, Biospec Products, Bartlesville, Oklahoma, USA) and beat for 3 min.Tubes are centrifuged at 16 000 g for 3 min at 4°C.The top aqueous layer is transferred to a clean 2 mL microcentrifuge tube containing 70L of 3M sodium acetate and 700L isopropanol.The samples are inverted several times and subsequently incubated at −20°C for 30 min to 1 hour.
The following ethanol wash, pellet drying and resuspension, column purification, DNA quantification and storage steps are identical to those used in the stool genomic DNA extraction method above.Statistical considerations The proposed sample size of 600 subjects will provide 80% power to detect a partial correlation (after adjustment for covariates) of 0.125 between dietary fibre intake and the primary outcome, a diversity index using a two-sided 2.Raw sequencing data will be processed using mothur.
36 Contigs (overlapping sequences) will be compiled, and low-quality reads will be removed.Sequences of short length and chimaeras will be detected and removed using UCHIME.37 Sequences will be assigned to operational taxonomic units (OTUs) at the species level (97% similarity) using the GreenGenes database.38 OTU counts and the diversity (Shannon and Simpson) and richness (ACE and Chao) indices will be calculated.39–41 Several different regression methods will be used to assess the association of the usual intake of total dietary fibre and fibre from specific sources to gut microbial diversity, as well as the relationship between fibre consumption and MDRO colonisation.
Usual grams of daily dietary fibre intake will be assessed by quantiles of consumption as fits the distribution of the data.Control variables will be added sequentially in groups; initial models will adjust only for demographic factors, subsequent models will add medications, and final models will add comorbidity and other risk factor data.Each variable in the model building process will be assessed individually and variables that are not significant at the ≤0.2 level will not be included in the final model.
Discussion Emergence of antibiotic resistance and MDROs are a global public health crisis.These infections are often very serious, leading to increased medical care usage and death.Gaining a better understanding of how the gut microbiota influence colonisation of MDROs will help in developing new therapeutic and prevention strategies.This is the first statewide microbiota study assessing the relationship of MDROs and diet in a random, non-clinical, general population sample.
Studies of community acquired MDROs are becoming more common; however, many of these sample from community-living facilities, daycares or within livestock workers.42–44 This study is innovative in that it samples by household within census block groups, and participants have a wider variety of exposure levels to different community acquired MDRO risk factors.Other than low rates of ASA24 completion, participation in the added WARRIOR project components exceeded expectations.We anticipated approximately 50% of the SHOW participants would be willing to enrol in the WARRIOR project.In the first year of recruitment however, participation rates were much higher.
Most people were willing to participate by submitting one or more biological samples.Having a large part of the compensation structured around the WARRIOR project components also helped with recruitment.Incorporating the MDRO risk factor questions within the usual SHOW survey likely also helped bolster completion rates.While this study will help us better understand the relationship of dietary fibre, the gut microbiome and MDRO colonisation and serves as a biorepository for future analysis using the other biological samples collected, there are some limitations.Dietary intake data and many confounding variables to be considered are collected by self-report, although there are important exceptions (eg, physical activity and sleep are assessed by multiday accelerometry).
The current WARRIOR project protocols are cross-sectional; however, the recently funded Population-based Microbiome Research Core (PMRC)45 will conduct longitudinal follow-up of the WARRIOR sample.PMRC will collect an additional stool sample, environmental samples and reassess MDRO risk factor exposures, including questions about infection history after the WARRIOR project.This data will be useful for many future studies, including analysis assessing infection risk in addition to MDRO colonisation analysed by the WARRIOR project.The data collected for the WARRIOR project, in addition to the extensive SHOW data, creates a rich resource that can be used for many future studies.Future directions include investigating other components of the diet, and other exposures that may be associated with the gut microbiota and MDRO colonisation.
Given the many varied biological samples taken, a variety of relationships with the oral, skin and nasal microbiota could also be examined.Further assessment of the stool samples, including metagenomics and strain typing, is also a likely future direction.The established study infrastructure provided by the SHOW also allows for the possibility of collecting additional specimens in the future, for example, environmental samples such as water and dust or additional analysis of individual-level data generated from the SHOW biorepository.The ongoing infrastructure also supports additional data collection and longitudinal follow-up using these same protocols.The WARRIOR project serves as a model for population-based microbiome research and findings will provide important insights into human variability and the role of the microbiome in protection or exacerbation of the global MDRO crisis.