Reduced Rank Regression –
This presentation is the property of its rightful owner.
Sponsored Links
1 / 18

Reduced Rank Regression – a powerful statistical method for identifying empirical dietary patterns PowerPoint PPT Presentation


  • 101 Views
  • Uploaded on
  • Presentation posted in: General

Reduced Rank Regression – a powerful statistical method for identifying empirical dietary patterns Gina Ambrosini PhD Senior Research Scientist MRC Human Nutrition Research, Cambridge EUCCONET International Workshop, Bristol October 2011. Why dietary patterns ?.

Download Presentation

Reduced Rank Regression – a powerful statistical method for identifying empirical dietary patterns

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Reduced rank regression a powerful statistical method for identifying empirical dietary patterns

Reduced Rank Regression –a powerful statistical method for identifying empirical dietary patterns

Gina Ambrosini PhD

Senior Research Scientist

MRC Human Nutrition Research, Cambridge

EUCCONET International Workshop, Bristol October 2011


Why dietary patterns

Why dietary patterns ?

The human diet is complex – we do not eat nutrients or foods in isolation

  • Single food/nutrient studies are frequently null e.g. fat intake and obesity; these do not consider total dietary intake

  • Strong co-linearity between dietary variables; ; difficult to separate effects, may be too small to detect

  • Numerous dietary variables (foods & nutrients) lead to too many statistical tests

    Studies of dietary patterns i.e. combinations of total food intake can overcome many of these problems


What nutrition epidemiologists want to know

What nutrition epidemiologists want to know …

Reduced Rank Regression

?

?

Disease

or

Health Outcome

Dietary

Pattern

PCA or Factor Analysis

Cluster Analysis

Dietary Indices

Eg. Healthy Eating Index


Empirical dietary patterns

Empirical Dietary Patterns

E.g. Principal Components Analysis (PCA), Factor Analysis and Cluster Analysis

  • Data reduction techniques; identify latent constructs in data = patterns

  • Take advantage of co-linearity

  • Consider total diet; ‘real-life’ consumption and synergism

  • Produce uncorrelated dietary patterns (or clusters) suitable for multivariate models

  • Exploratory, data-driven, study specific: reproducibility unknown in different populations

  • Explain variation in food intakes but not necessarily nutrients – the end product of diet

  • Not disease-specific or hypothesis-based

Food Intakes

Dietary Patterns


Reduced rank regression a novel empirical approach

Reduced Rank Regression – a novel empirical approach


Reduced rank regression rrr

Reduced Rank Regression (RRR)

  • A hypothesis-based empirical method for identifying dietary patterns

  • Similar to PCA and factor analysis but requires a 2nd set of data = response variables

  • Response variables should be on the pathway between food intake and outcome of interest

    RRR dietary patterns are linear combinations of food intake

    that explain the maximum variation in a set of response variables

Dietary Pattern

Disease

or

Outcome

of Interest

Food

Intake

Nutrients

Or

Biomarkers

Predictors

Responses


Example alspac

Example - ALSPAC

  • Measured dietary intake using a 3d food diary at 7, 10 and 13 years of age

  • We hypothesised that:a dietary pattern that could explain the variation in dietary energy density, % energy from fat, and fibre at 7, 10 and 13 ywould be prospectively assoc with body fatness measured at 9, 11, 13, 15 y


Example rrr alspac

Example RRR - ALSPAC

1st Dietary Pattern:

Energy-dense,

high in fat,

low in fibre

Responses

Nutrient Intakes

Predictors

Food Group Intakes

Dietary

Pattern 1

Fat

Fruit

Veg

Dietary

Pattern 2

OBESITY(fat mass)

F3

F4

3-day food

diary

Fibre

F6

F5

Energy

Density

Dietary

Pattern 3

F7

F8…

Each dietary pattern is a linear combination of weighted food intakes

that explains the max variation in ALL response variables -1st pattern often explains the most

Such that for each dietary pattern a z-score is calculated as

= W1(Food1 Intake) + W2(Food2 Intake) + W3(Food3 Intake) + …


Alspac energy dense high fat low fibre dietary pattern

ALSPAC energy-dense, high fat, low fibre dietary pattern


Reduced rank regression a powerful statistical method for identifying empirical dietary patterns

ALSPAC – change in Fat Mass Index (z-score) with a SD increase in energy-dense, high fat, low fibre dietary patternz-score


Cross cohort comparisons alspac v raine study

Cross-cohort comparisons: ALSPAC v Raine Study

PhD project – Geeta AppannahUniversity of Cambridge and MRC Human Nutrition Research:

  • An almost identical energy-dense, high fat, low fibre dietary pattern seen at 14 and 17 y in The Western Australian Pregnancy Cohort (Raine) Study, a contemporaneous birth cohort.

  • Similar factor loadings for an energy-dense, high fat, low fibre dietary pattern in a FFQ and a food diary at 14 y of age in the Raine Study

Geeta Appannah, MRC Human Nutrition Research


Comparisons of rrr and pca patterns

Comparisons of RRR and PCA patterns

  • Although the PCA and RRR patterns in these studies had similar nutrient profiles; these studies reported stronger associations between RRR-based dietary patterns and outcomes

  • RRR patterns explain more variation in the response variables

Gina Ambrosini


Caution using biomarkers as response variables

Caution - using biomarkers as response variables

Biomarkers as response variables should be chosen carefully:

  • So they are true intermediates and not a proxy for the outcome of interest

  • Should be on pathway;

  • Therefore must be susceptible to dietary intake – relevant to more novel biomarkers

Dietary Pattern

Diabetes

Blood Glucose

Insulin Resist.

Food

Intake

Responses

Predictors

Gina Ambrosini


Generalisability of rrr patterns

Generalisability of RRR patterns

  • Imamura et al (2010) applied RRR dietary patterns that were associated with type 2 diabetes in three different cohorts to the Framingham Offspring Study

  • All patterns were characterised by high intakes of meat products, refined grains and soft drinks

Imamura F et al. Generalizability of dietary patterns associated with type 2 diabetes mellitus. AJCN 2010; 90(4):1075-83

Gina Ambrosini


Limitations

Limitations

RRR appears to be a robust and powerful method, however:

  • Reproducibility, generalisability of patterns – only 1 published study

  • RRR depends on existing knowledge in order to choose response variables

  • Response variables must be chosen very carefully to avoid circular analysis

  • Biomarkers as response variables: must be an intermediate and not a proxy for the outcome/disease

Gina Ambrosini


Acknowledgements

Acknowledgements

Dr Pauline Emmett, Dr Kate Northstone, & the ALSPAC Study Team

Ms Geeta Appannah, PhD scholar, MRC Human Nutrition Research

Mr David Johns, PhD scholar, MRC Human Nutrition Research

Dr Anna Karin Lindroos, Swedish Food Authority, Uppsala (prev. HNR)

Funding from:


Reduced rank regression a powerful statistical method for identifying empirical dietary patterns

[email protected]

MRC Human Nutrition Research

Cambridge, UK


Reported associations with other rrr dietary patterns

Reported Associations with Other RRR Dietary Patterns

Gina Ambrosini


  • Login