Biology of Nutrition
A.Y. 2020/2021
Learning objectives
Aims of the course are: (i) the understanding of how a correct nutritional state is important in order to assure an optimal health state and to compensate the energy expenditure related physical activity; (ii) the knowledge of principles at the basis of an optimal and healthy diet according to scientific national (LARN and National guidelines) and international guidelines (EFSA) and to deal with physical and sport activity; (iii) the knowledge of the features and properties of the various nutrients required in a balanced diet; (iv) the knowledge and understanding of the concepts and procedures of statistics applied to biomedical sciences, including hypothesis testing in the analysis of continuous and categorical variables, correlation and regression.
Expected learning outcomes
At the end of the course, students should know principles for defining a dietary scheme and to critically analyze some of the most common dietary regimes. Through specific practical exercises, students should have become able to know and select most appropriate sources of energetic nutrients and their best assortment to compose diets suited for specific type of physical activities and sports. Students should have become able to know pros and cons of dietary supplements. Moreover, students are expected to become able to know the usefulness of linear and non-linear regression and how to apply it to generate predictive models. By means of examples of application of statistics to nutritional topics, students should have become able to interpret the results of statistical analyses published in the biomedical literature, and should have acquired the ability to select the best statistical approach to analyze different datasets.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
During the emergency teaching phase, the educational program is maintained with the changes shown below. The lectures for both modules will be mainly supplied through the Microsoft Teams platform and they may be attended either live (synchronous mode) based on the schedules for the first trimester, or pre-recorded (asynchronous mode, by means of Power point with audio) as in the case of the Biology of nutrition exercises. The lectures will be recorded and made available to the students using the same platform. Attendance to the lessons in synchronous mode is strongly recommended. Program and reference material are unchanged. The verification method for the Nutrition Biology module remains unchanged (open questions) but is carried out in Teams, according to the indications provided by the University. For the Data analysis and predictive modeling module is a written test, consisting of a series of problems covering the topics explained during the course. The exam will take place using the Microsoft Teams platform and the website exam.net, in agreement with university policies (cfr: https://www.unimi.it/it/studiare/frequentare-un-corso-di-laurea/seguire-il-percorso-di-studi/esami/esami-distanza-faq-gli-studenti/esami-scritti-su-piattaforma-online). The expected duration of the test is about one hour.
Prerequisites for admission
For Biology of nutrition module knowledge of biochemistry, biochemistry of nutrition and physiology of nutrition are required.
For Data analysis and predictive modeling module knowledge of basic maths principles is recommended
For Data analysis and predictive modeling module knowledge of basic maths principles is recommended
Assessment methods and Criteria
For Biology of nutrition module the verification of students' preparation is in form of an oral test. During the oral examination, open questions about the basic nutrition and the nutrition topics for physical activity and sport will be done. Exercises on diet planning may also be required. The expected duration of the test is 30 minutes. The evaluation expressed in thirtieths is the result of both the knowledge of the topics covered and the student's critical analysis ability.
For the Data analysis and predictive modeling module is a written test, consisting of a series of problems covering the topics explained during the course. The expected duration of the test is three hours
The final score is the weighted average of the individual marks relating to the credits of the two modules
For the Data analysis and predictive modeling module is a written test, consisting of a series of problems covering the topics explained during the course. The expected duration of the test is three hours
The final score is the weighted average of the individual marks relating to the credits of the two modules
Modulo: Biologia della nutrizione
Course syllabus
Nutritional requirements according to the LARN, EFSA documents, the Guidelines for the Italian population, the documents of the Italian Society of Human Nutrition, SINU
Nutrients: characteristics, properties and food sources
Antioxidants and bioactive compounds
Hydration, nervous and alcoholic drinks
The methodology to estimate the basal expenditure and the energy requirements
The use of food databases
Setting balanced eating patterns for healthy adults
Nutritional investigation methods
The regulation CE 1169/11 for nutritional labeling
The energy needs and fuel requirements to support training program
Quantity, quality of nutrients and their correct distribution in the daily diet during training, competition and the recovery phase
The timing of the introduction of nutrients in the various phases of physical activity
Integration and supplements
Hydration for physical activity and sports
Nutrients: characteristics, properties and food sources
Antioxidants and bioactive compounds
Hydration, nervous and alcoholic drinks
The methodology to estimate the basal expenditure and the energy requirements
The use of food databases
Setting balanced eating patterns for healthy adults
Nutritional investigation methods
The regulation CE 1169/11 for nutritional labeling
The energy needs and fuel requirements to support training program
Quantity, quality of nutrients and their correct distribution in the daily diet during training, competition and the recovery phase
The timing of the introduction of nutrients in the various phases of physical activity
Integration and supplements
Hydration for physical activity and sports
Teaching methods
Lectures supported by projected material and exercises; collective critical analysis of the scientific literature
Class attendance is strongly recommended
Class attendance is strongly recommended
Teaching Resources
A pdf copy of the power point presentations shown in class will be available on ARIEL
Costantini, Cannella, Tomassi, "Alimentazione e nutrizione umana" IL PENSIERO SCIENTIFICO EDITORE
LARN 2014, Italian guide lines 2018
Riccardi, Pacioni, Rivellese,"Manuale di nutrizione applicata" ILDESON GNOCCHI
Mc Ardle, Katch, Katch "Alimnetazione nello sport" Casa Editrice Ambrosiana
LARN 2014
Costantini, Cannella, Tomassi, "Alimentazione e nutrizione umana" IL PENSIERO SCIENTIFICO EDITORE
LARN 2014, Italian guide lines 2018
Riccardi, Pacioni, Rivellese,"Manuale di nutrizione applicata" ILDESON GNOCCHI
Mc Ardle, Katch, Katch "Alimnetazione nello sport" Casa Editrice Ambrosiana
LARN 2014
Modulo: Analisi e modellistica predittiva dei dati
Course syllabus
The principal topics of the course are illustrated in the followimgs:
Structure of a scientific paper and role of data analysis.
Descriptive statistics: tables, graphical plots, summary measures of central tendency and variability (mean, variance, standard deviation, median, percentiles, quartiles).
The Gaussian (a.k.a, Normal) distribution.
Basic concepts of inferential statistics
Hypothesis testing and statistical significance. Statistical errors of the first and second type.
How to compare a mean against a reference value: one-sample z test and t test
How to compare two means:
How to compare three or more means: the F distribution and ANOVA.
How to manage situations in which the data distribution is not normal: introduction to nonparametric statistics.
How to analyze the relationship between two variables by means of correlation analysis.
Predictive modelling: linear and nonlinear regression.
Categorical data analysis: the chi squared test.
The software R: fundamentals of programming and statistical analysis
Structure of a scientific paper and role of data analysis.
Descriptive statistics: tables, graphical plots, summary measures of central tendency and variability (mean, variance, standard deviation, median, percentiles, quartiles).
The Gaussian (a.k.a, Normal) distribution.
Basic concepts of inferential statistics
Hypothesis testing and statistical significance. Statistical errors of the first and second type.
How to compare a mean against a reference value: one-sample z test and t test
How to compare two means:
How to compare three or more means: the F distribution and ANOVA.
How to manage situations in which the data distribution is not normal: introduction to nonparametric statistics.
How to analyze the relationship between two variables by means of correlation analysis.
Predictive modelling: linear and nonlinear regression.
Categorical data analysis: the chi squared test.
The software R: fundamentals of programming and statistical analysis
Teaching methods
The module is divided in 12 lessons, two hours each. Lessons are divided in didactics, for learning the fundamental principles included in the objectives of the course, and exercises, for strengthening the comprehension and providing critical reasoning skills through the application of the acquired concepts.
Attendance to the lessons is strongly recommended.
Attendance to the lessons is strongly recommended.
Teaching Resources
Course lecture notes, available on Ariel
Pagano, Gauvreau; Principles of Biostatistics, 2003 (optional)
Pagano, Gauvreau; Principles of Biostatistics, 2003 (optional)
Modulo: Analisi e modellistica predittiva dei dati
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
MAT/06 - PROBABILITY AND STATISTICS
MAT/06 - PROBABILITY AND STATISTICS
Lessons: 24 hours
Professor:
Marano Giuseppe
Modulo: Biologia della nutrizione
MED/49 - FOOD AND DIETETIC SCIENCES - University credits: 6
Practicals: 8 hours
Lessons: 44 hours
Lessons: 44 hours
Professor:
Ferraretto Anita
Professor(s)