Clinical Metabolomics: The Trials of Investigating Metabolic Effects on Human Volunteers

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1 Clinical Metabolomics: The Trials of Investigating Metabolic Effects on Human Volunteers Dr. Clare A. Daykin Centre for Analytical Bioscience School of Pharmacy University of Nottingham UK 14/05/03 LC-MS User Meeting 1

2 Presentation Outline 1. Introduction - Human Diversity 2. Implicit Strategies to Deal with Diversity in Clinical Trials - Study design considerations - The problems unique to clinical trials 3. Explicit Strategies to Deal with Diversity in Clinical Trials - Options for the removal of unwanted variation in a data set 4. Example Study - Detecting SUBTLE Metabolic Effects in Human Data 2

3 Human Diversity Humans In-bred lab animals 1. Large diversity vs. less diversity 2. Many considerations in common 3. But also, many considerations unique 4. Animals: many factors can be controlled 5. Humans: little can be controlled 3

4 Strategies to Deal with Human Diversity in Clinical Trials Implicit and Explicit strategies: Implicit strategies are purely design driven. Identify factors that will be of influence and incorporate in the study design Explicit strategies will adopt a computational correction method that is predefined and uses knowledge about sample origin. Not mutually exclusive (usually used in combination) 4

5 Implicit Strategies: Study Design Considerations It is likely that EVERYTHING both genetic and environmental affects our metabolic profile. Factors which should be taken into consideration in a welldesigned study include (but are not limited to): 1) Gender 2) Age 3) BMI 4) Time of sample collection (diurnal variation) 5) Drugs (prescription and non-prescription incl. caffeine) 6) Diet (esp. vegetarian, alcohol, fish, certain vegetables) 7) Exercise 5

6 Problems with Implicit Strategies Even the Best Designed Human Trial WILL contain Flaws 6

7 Problem 1: What is Healthy? 20 PC [2] (23.1% of the total spectral variation) Volunteer Volunteer Volunteer 2-2 Volunteer 3-2 Volunteer 4-1 Volunteer 5-1 Volunteer Volunteer Volunteer 4-2 Volunteer PC [1] (51.7% of total spectral variation) SIMCA-P 10.5 Ellipse: Hotelling T2 (0.95) Volunteers may be unsuitable as study subjects because of health conditions they are unaware of. 7

8 Problem 2: Humans Lie Acesulfame K 600 MHz 1 H NMR Spectrum of Human Urine Showing the Detection of Sweetner in a Cola Light Drink Note: Withdrawing financial reward for volunteers who are proven to have not followed study instructions is allegedly unethical. 8

9 Problem 3: Imposing Dietary Restrictions on Volunteers In this case, prior to dietary intervention on day 4, the volunteers were asked to restrict their diet.* * From Daykin et al (2005), J. Food Agric. Chem., 53: Problems: 1. Large effect due to dietary restrictions 2. Dietary intervention is not studied in a normal, realistic scenario 9

10 Problem 4: Practical and Ethical Considerations Common Examples: 1. Some diseases cause incontinence How do you request a first (morning) urine sample or mid-flow urine sample? 2. Not all standard study criteria are permissible for all study groups e.g. pregnant women can not be asked to fast 10

11 Problem 5: Optimum study group size Location of optimum depends on complexity, type of analysis, S/N ratio etc Quality of Data Lower Costs High Noise High Sample Numbers High Costs Low Noise Low Sample Numbers 11

12 Problem 6: Commitment of Volunteers Reasons Why Volunteers Drop Out of Studies* 16% 20% Undeclared Health Condition 22% 29% 13% Illness During Study Voluntary Drop Out Undeclared Non Compliance Declared Non Compliance Largest reason: Voluntary Drop Out. Do not ask too much from your volunteers! *Numbers presented are based on observations over several studies in our lab 12

13 Explicit Strategies: Removing Interperson Variation Computationally 1. In most cases, application of standard tools such as discriminant analysis will filter out unwanted variation 2. Where metabolic effects under study are much smaller than inter-individual effects (e.g. nutritional studies), additional tools are required 3. It is advisable to keep methods as simple as possible. Pre-processing is part of the model: it adds to model complexity and has to be included in all validation steps 13

14 Explicit Strategies: Artificial (Illustration) Dataset 3 Metabolites, 2 volunteers, 4 treatments A B C Data is Mean Centred Samples/Scores Across Plot the of Whole Dataset Dataset (Overall) Person-Treatment Scores on PC 2 (22.19%) PCA Scores on PC 1 (74.20%)

15 Explicit Strategies: Correction per person before PCA 10 8 Data is Samples/Scores Mean Centred Plot per of Dataset Person 2-4 Scores on PC 2 (15.33%) Scores on PC 1 (84.51%) /Std (Person) -Avg (Person) Scores on PC 2 (18.10%) Data Samples/Scores is Autoscaled Plot per of Dataset Person Scores on PC 1 (81.63%) 15

16 Explicit Strategies: Summarised The most appropriate method depends on research question: Do you want everybody to be the same? Use Mean Centring to remove a personal base level Use Auto Scaling to also remove personal sensitivities to treatments Express data as difference to or fraction of t n measurement to enhance dynamic effects Normalise to a certain metabolite or external measurement (food intake, total body fluid volume produced, physiological data ) 16

17 Example Study The Effect of Potential Functional Food Ingredients (Anti-Oxidant Vitamins) on Recovery After Exercise S. Coolen 1, J. van Duynhoven 1, F. Wulfert 2, C.A. Daykin 3 1 Unilever Research and Development, Vlaardingen, The Netherlands 2 Division of Food Sciences, School of Biosciences, University of Nottingham, UK 3 Centre for Analytical Biosciences, School of Pharmacy, University of Nottingham, UK Publication Submitted to Journal of Vascular Research 17

18 Example Study: Study Design 4 weeks vitamin treatment (vitamins C and E) 8 weeks wash-out period 18 volunteers A B C A B C T1 Exercise T2 1 min after T3 5 mins after T4 60 mins after T5 120 mins after 18

19 Example Study: Major Variation in Data due to Inter-Individual Variation Correction for Effect Required Scores on PC 2 (16.65%) Scores on PC 1 (28.49%) 19

Effect After 4 Weeks Vitamin C and E Treatment (Appointment B)

20 Example Study: The Exercise Metabolome at Appointments A and B Exercise Effect At Start of Study (Appointment A) Exercise Effect After 4 Weeks Vitamin C and E Treatment (Appointment B) Appointment C profile similar to A except that recovery complete by T4 4 20

21 Example Study: The Anti-Oxidant (and Cheating!) Metabolome at Time-Point T3 Appointment C After 8 week wash out period Appointment A Start of Study Appointment B Mid study after 4 weeks vitamin treatment 21

22 Conclusions 1. Design of human trials is fraught with problems 2. Minimising effects by good study design helps but some rogue samples will always slip through the net 3. Data can be further noise-reduced with simple data preprocessing strategies 4. After implementing these steps, metabolomics can even be used to distinguish SUBTLE metabolic effects 5. This is essential in a changing health care market, where interests increasingly focus on prevention rather than cure of disease 22

23 Acknowledgements University of Nottingham, all members of the Centre for Analytical Biosciences and in particular: Dr. Dave Barrett, Dr. Catharine Ortori, Ali Awwad, Xinru Liu University of Nottingham (Division of Food Sciences) Dr. Florian Wulfert Unilever Research and Development, Vlaardingen, The Netherlands Dr. John van Duynhoven, Dr. Stefan Coolen 23

Urinary metabolic profiling in inflammatory bowel disease. Dr Horace Williams Clinical Research Fellow Imperial College London

Urinary metabolic profiling in inflammatory bowel disease Dr Horace Williams Clinical Research Fellow Imperial College London Background: Metabolic profiling Metabolic profiling or metabonomics describes