Fundamentals of Human Nutrition/Nutritional Science
1.2 Nutritional Science
1.2.1 Nutrition as a Science
Nutritional science is the study of how food affects humans and animals on a physiological level. Nutrition is a necessity in order to preserve the life of organisms and cells. It examines how food is processed, responded to, and utilized throughout the body. Nutrition is widely related to as how food follows certain biochemical sequences and transformations – metabolism and its various pathways. Through these studies it also allows researchers to understand how certain diseases and conditions are contributed to from the dietary habits of individuals. It is important to understand the science of nutrition because nutrition is at the core of all processes of life. An individual who studies nutrition is referred to as a nutritionist. This is sometimes interchangeable with a dietician, but it is to be noted that they are not the same. Dietetics is the interpretation and presentation of nutritional science to make it possible for informed rational choices pertaining to an individual’s lifestyle and food in both health and disease.
Nutritional science answers questions like:
- How can a change in diet help people with medical conditions?
- How does food contribute the body’s daily functions and mechanisms?
- What is considered a healthy diet?
- How much minerals, vitamins, and macronutrients does a person have to consume daily?
There are 3 factors that helped contribute to the understanding of essential nutrients; how diseases are associated with a person’s diet, how animal models can be used to pinpoint parts of a diet and associate it with disease symptoms, and the development of experimental diets that would investigate deficiency symptoms.
1.2.2 Nutrition Research
Epidemiology is the study of how often diseases occur in various populations and why they occur. Epidemiological studies are conducted in order to strategize how to prevent and manage these illnesses in those who have already developed it. An important factor in epidemiological studies is how it measures disease outcomes in relation to a population at risk. A population at risk is defined as a group of people, healthy or sick, who are accounted for in cases if they had the diseases being studied
Epidemiology is the study of how diseases spread and how they can be controlled throughout different populations through the use of patterns in health. Epidemiological studies are thus done on selected groups of people to allow researchers to study and learn to control the diseases. These studies are oftentimes known to generate hypotheses because while they collect data, they are not actually testing certain subjects, but observing them and cannot prove causation. The researchers observe each population with no intervention other than asking questions or carrying out simple lab tests to collect data over time (Pearce,2012). Divided into two main groups depending on whether the events are retrospective or prospective (“Epidemiology”,1993), the most common types of these studies are cohort, case-control, and cross-sectional and researchers decide on which type of study they want to use based off of their goals and constraints (such as time or costs). Cohort studies compare similar populations that have different exposures to what the researcher is interested to see whether or not the disease in question’s likelihood grows with the amount of exposure (Spears, not dated). An advantage to this type of study is that it takes place over a long period of time and ask people to keep track of their daily activities so the data can also be used to study other types of diseases. Along with that, the presence of a disease can be verified. However, a downside would be that there would need to be a large amount of participants willing to continue the study for a long period of time in order to find out if there really is a correlation between the amount of exposure and the risk of disease and the cost of this type of study would be the most expensive. The second common type of study is the case control study. In these studies, the researchers are comparing data from people who have the disease and people who do not have the disease. These studies are good when studying rare diseases because they are inexpensive and do not take long to be completed. On the other hand, the reliability of the researchers for recall is very low and it could be hard to actually compare the different data from subjects. Cross-sectional studies compare data from small groups of people and focus on observations made at only one point in time (Spears, not dated). These studies are relatively inexpensive and do not take long to complete, however they cannot reveal a sequences of events since data is only taken once. An example of a cross sectional study would be to compare the rate of a particular disease in two different places.
During the process of epidemiological studies, individuals are questioned about their current and past diet history. Epidemiological studies may also measure the levels of substances such as vitamins or minerals in blood or urine and attempt to correlate these levels with diet or health. The major strength of epidemiological studies is that they are able to look at large groups of people for a long period of time. People from more than one region or more than one country can be included. People of different ages, genders, andethnic origins can be studied. Epidemiological studies look at people, so the results of these studies are directly applicable to humans and do not have to be extrapolated from cells oranimals. On the other hand, there are limitations to epidemiological studies, particularly those that are based on people's descriptions of what they eat. There are various tools available for scientists to collect this information, but all have some limitations. They all rely on the subjects to describe accurately what they eat. Finding results from these types of studies can be difficult because it is difficult for the subject to fully describe everything they have consumed and the exact quality. When addressing epidemiological studies, there are various types that scientists can utilize.
Case-Control Studies: These studies look at the characteristics of two groups of people. The comparison is made between the experimental group, (those who already have a certain health outcome) and to the control group (a similar group of people who do not have the outcome). While case-control studies can be done quickly and relatively cheaply, they aren’t ideal for studying diet because they gather information from the past. People with illnesses often recall past behaviors differently from those without illness. This opens such studies to potential inaccuracy and bias in the information they gather.
Cohort Studies: These studies follow large groups of people over a long period of time. Researchers regularly gather information from the people in the study on a variety of dietary variables. When a specified amount of time has elapsed, the characteristics of people in the group are recorded to test the validity of the experiment’s hypothesis. Though time-consuming and expensive, cohort studies generally provide more reliable information than case-control studies because they don’t rely on information from the past. Cohort studies gather the information all along and before anyone develops the disease being studied. As a group, these types of studies have provided valuable information about the link between lifestyle factors and disease.
Randomized Trials: These studies are similar to cohort studies in the case that they follow a group of people over time. However, with randomized trials, the researchers actually intervene to see how a specific behavior change or treatment, for example, affects a health outcome. They are called “randomized trials” because people in the study are randomly assigned either to receive or not receive the intervention. This randomization helps researchers hone in on the true effect the intervention has on the health outcome. However, randomized trials also have drawbacks, especially when it comes to diet. While they are good at looking at topics like vitamin supplements and cancer, when the change in diet is more involved than say taking a vitamin pill, participants begin to have trouble keeping to their prescribed diets. Such involved interventions can also become very expensive
Cells are the fundamental units of life and are the smallest components considered inside the human body.These body cells are constantly communicating with each other, responding to the environment and to the signals they receive from a person's five senses. If the cells cannot operate efficiently, the functioning of the tissues and organs the cells make up, will become compromised. As a result, a person can experience a diminishment of physical functioning and the onset of a host of health conditions and diseases. By keeping a person's cells well nourished, the individual will also be well nourished.
Cells are analyzed in laboratories to look for the presence of diseases and to understand how it actively effects cells. Cells can be collected from urine, blood, and through the process of scraping the surface of organs. Cell studies are conducted to see what types of cells there are based on their appearance and characteristics. The study of cells, including their origin, function, and structure is known as cytology. The analysis of cells is referred to as cytopathology.
Animal studies have highly contributed to our current knowledge of metabolism and nutrition through the experimentation of other species besides humans; these experiments predict what might happen in the physiology of humans. Animal studies have been utilized to solve nutrition x nutrient interactions, bioavailability of nutrients and its precursors, and nutrient tolerance and toxicity levels
Before animal experiments, people were already beginning to understand how certain foods and their elements affected health. For example, scurvy had developed amongst sailors in the 1800’s, and it was discovered that scurvy developed due to a lack of food that contained citrus, like limes (Baker 2008). Nutritional scientist took it further by using animal studies to understand how minerals, vitamins and macronutrients can develop into diseases, deficiencies, and toxicities. David H. Baker’s journal explains and gives examples of how nutritional scientist use animals to understand the elements of nutrition.
The types of animals used in these studies are generally rats, pigs, dogs, cats, farm animals, rabbits, and primates. Animal testing is viewed as a way for medical progress without harming people. Dogs were used in animal models that helped understand the role of the pancreas and insulin, and how pellagra develops from a deficiency of specific foods. However these experiments can give faulty data, due to the effects of the experiment giving different results for specific species (Baker 2008). In one experiment, rats, chicks, dogs, cats, and pigs were used to see how amino acids could have a negative affect on one another (Baker, 2008). In the experiment lysine affected arginine in chicks and dogs, but not for pigs and cats, which leads to the question, will this also be the same for humans (Baker 2008)?
Animal Welfare Act Animal studies are often a controversial topic; there is always a fear for the animal’s well being. In the U.S., one can rest assured that the government has taken this into consideration; there are laws and regulations on behalf of the animal’s welfare. The Animal Welfare Act is a law that protects animals from maltreatment and sets minimum requirements for housing and care (Animal Welfare Act, 2015). Animals are not used strictly for human benefit, but also to better their future as well. In animal studies, scientists learn more about the species, which leads to advances in the animal’s care.
Benefits and Advantages There are a plethora of benefits from animal studies, some of which include the eradication of nutrient deficiency diseases in most countries. With the advances being made in technology along with the usefulness of animal studies, this will lead to the understanding of the affects of diets on diseases, reproduction, brain function, and even longevity. There are also benefits in animal studies in a general sense: more economical for larger group studies, easy to control and monitor dietary consumption and living conditions, short life spans improve the practicality and efficiency of developmental, longevity, or multiple generation studies, and controlled breeding reduces biological variability. Of course, the most obvious advantage is that this safer for humans, but thanks to the Animal Welfare Act, this demands that an ethical approach be taken towards the animals.
Procedures Scientists do not just use any animal for their research. Specifically for nutritional studies, a great deal of research is put into strategically choosing an animal model (Baker, 2003). Scientists need their animal model’s diet, nutrient metabolism, and requirements to be similar to humans in order for the studies to be applicable to human nutrition. For example, humans have the uncommon requirement for vitamin C. For studies on vitamin C, scientists often look to use guinea pigs that also have this uncommon requirement. More often than not, rats are the go-to animal models for human nutrition. Rats share human’s omnivorous eating pattern, which makes them better candidates for nutrition studies (Macrae, 1993).
Application Most vitamins were actually discovered through animal studies, simply by changing the animal’s diet. Animal studies in nutrition have prevented diseases due to deficiencies and even the eradication of some food preparation methods. For example, nitrogen trichloride bleaching used to bleach flour was discontinued for human consumption after the incidental discovery that it causes hysteria in dogs, cats, ferrets, and rabbits. Animal models have led to the discovery of gastric juice, essential amino acids, and matched deficiency diseases and symptoms with the lacking vitamin (Macrae, 1993).
Human studies are conducted to test the safety and efficiency of specific treatments. They are designed to improve health and quality of life for patients, however, there is a risk that patients will be given treatments that do not work or may even be detrimental to health. Clinical trials are carried out in many stages or phases. Phase 1 is when a new treatment is developed and tested within a few individuals to estimate how safe it is. The treatment is usually given to all those who take part in it, rather than being compared against another form of treatment. Phase 2 treatment focuses on a larger population of people to effectively measure out safety, possible side-effects, or positive effects patients may experience. Phase 2 can involve comparison with another treatment, but does not have to. Phase 3 trials are tested within a large population as well and compare the effects of newer drugs or treatments with standard treatments. Phase 3 trials are typically randomized clinical trials. In these trials one group of people, the experimental group, is given the new treatment, while the other, the control group, is given the standard treatment or a placebo; some phase 3 trials may compare more than two groups
1.2.3 Analyzing Research Data
Analyzing research data allows researchers to answer questions about specific conditions or diseases – such as its distribution, possible causes and correlations, and related issues. This makes it possible to create and monitor specific procedures that may improve the situation
Motivation for Data Analysis
Data analysis by use of statistical methods developed for the human sciences is an essential step in drawing conclusions about human nutritional trials and other nutritional research activities. A properly conducted statistical analysis summarizes collected data in a meaningful, unbiased manner, so long as the experiment and data collection were conducted in the same vein.
In the nutritional sciences, the final product of data analysis is used to gain a quantitative understanding of nutrients and their interactions with humans. This information is used to inform public health policy on important nutritional guidelines, recommendations, and safe levels of consumption. In releasing their 2015 Dietary Guidelines for Americans, the Department of Health and Human Services (HHS) and U.S. Department of Agriculture (USDA) received recommendations from the Dietary Guidelines Advisory Committee, which compiled many scientific journals using statistical analysis (USDOH, 2015).
Basic Categories of Data Analysis
Data analysis in any discipline (economics, epidemiology, sociology, etc.) falls into two basic categories: descriptive and inferential statistics.
Descriptive statistics aims to summarize collected data in a meaningful way. A descriptive analysis reveals certain properties and characteristics about the collected data, but does not extrapolate or make predictions about trends if the experiment were conducted in different environments with different conditions. Properties such as central tendencies (mean and median), data patterns (mode), measures of dispersion (variance, standard deviation), distributional qualities (normal, uniform, gamma) give researchers an initial insight into important qualities about the data. This also gives researchers an opportunity to find errors in the data collection process or biases in the research method used to collect the data.
Also known as predictive statistics, inferential statistics uses collected data from a sample (a small group of people) to make predictions or extrapolations about the entire relevant population. Inferential statistics is also used to find whether an event occurred purely by chance, or was possibly caused (correlated) by a preceding event.
Hypothesis testing, parameter estimation, and confidence interval estimation are all examples of methods in inferential statistics. In a hypothesis test, nutritional researchers might use a statistical test to determine whether a treatment given to a Group A (called the treatment group) is significantly different from a placebo given to another (control) Group B. Significantly different, or statistically significant, means the hypothesis test has rejected the notion that the effect of the treatment on Group A is the same as a placebo on Group B. In laymen’s terms, the treatment is statistically effective. It is important to understand that a statistically significant result does not translate to an actual difference in the treatment’s effect on any population. See Disadvantages of Data Analysis.
Disadvantages and Challenges of Data Analysis
Experiments and epidemiological analyses are never perfect. In the realm of nutritional science, there are many inherent ethical, legal, and logistical restrictions placed on research and collection of data. Other problems arise when statistics is used to prove questionable or unethical reasons, such as proving a drug or remedy is effective when in fact it is not. Still other issues arise when the wrong statistical tools are used, or these tools are used by those inexperienced with statistical analysis. There are many other issues and challenges that arise, and so this list is not all inclusive.
Strict and correct use of statistical tools are futile if the sample used to draw descriptions and inferences is not representative of the relevant population. This bias can occur intentionally or unintentionally. Sometimes legal or ethical considerations prevent researchers from correctly composing the sample.
Incorrect Use of Statistical Tools, or Using Incorrect Tools
There are many tools in statistics that have a more effective counterpart based on the situation. There are tools used to correct the problem of drawing inferences on smaller than usual sample sizes, tools for making inferences on data that don’t follow a normal distribution, among many others. Inexperience or lack of prudence usually results in incorrect and often misleading predictions.
Missing Data and Data Replacement
In both epidemiological and experimental research, there is near certainty that some data will not lend itself to collection or measurement. Sometimes, proxy variables will be used as a surrogate to missing data. Test scores, for example, may be used in place of IQ scores. However, if researchers are not prudent in their proxy selection, they may skew the data (Hall, 2004).
Dietary Guidelines. (2015). Retrieved December 2, 2015, from http://health.gov/dietaryguidelines/
Hall, B. (2004, May 11). A Note on Measurement Error and Proxy Variables. Retrieved December 2, 2015, from https://eml.berkeley.edu/~bhhall/papers/BHH04_measerr.pdf