The principals of assessing energy
balance and metabolic rate in mice
Energy balance- the game has changed.
Tschop et al Nature methods Dec 2012
What is energy balance? And what is metabolic rate? How do they differ?
1. ENERGY BALANCE
Energy balance describes whether an organism will lose or gain weight. The size
Of the organism is actually irrelevent for considering energy balance
30g
31g
30g of energy out
31g of food in
1 tonne and 1g
Of food in
1 tonne
+ 1g
1 tonne of energy out
1. ENERGY BALANCE and rate of weight gain
31g of food in
1 tonne and 1g
Of food in
30g of energy out
1 tonne of energy out
2 Metabolic rate
With metabolic rate then the size of organism is very important, as we care about
The rate of energy expenditure per g of the organism
30g
5g of energy out
5g of food in
30g
7g of food in
7g of energy out
How do we measure food intake, energy balance and
metabolic rate?
To assess metabolic rate and energy balance we need
3 pieces of information
1. The body weight of the animal
2. The amount of food it has consumed*
3. How much energy it has expended
*This correctly should be the amount it has assimilated. See point at the end.
How to measure food intake and body weight
How to measure energy expenditure
Direct Methods:
Calorimetry
Indirect methods:
Indirect calorimetry using gas exchange
Doubly labelled water
Direct Calorimetry
Advantage – Most accurate method – does exactly what it says on the tin.
Disadvantage – Sealed unit so (very) limited time scale for experiments.
Indirect calorimetry 1 - Doubly labelled water
Indirect calorimetry 2 – Gas exchange indirect calorimetry.
What is an indirect calorimeter?
Why does the volume of the chamber matter matter?
1.2
1
0.8
6.2l chamber
2.7l chamber
10l chamber
0.6
0.4
0.2
0
0
20
40
60
80
100
70
60
50
40
uncorrected for lag
corrected for lag
30
20
10
0
0
10
20
30
40
50
60
What is Energy expenditure and how does it relate to VO2 and VCO2?
EEJ = 15.818xV02 + 5.176*VCO2
Indirect calorimetery
Oxygen consumption gives an indication of how
much energy an animal is using as an animal will produce 4.8 KJ
of energy per litre of oxygen with an error of around 6%.
Carbon dioxide production is not a 1 to 1 ratio with oxygen
consumption and the amount of CO2 produced varies dependent
on the source of energy (Carbohydrate, Fat or Protein)
Combining the amount of O2 consumed and the amount of CO2
produced gives us a value called the RQ which provides some
information about fuel usage.
RQ varies from 0.7-1 dependent on metabolic status of the animal
What is Energy expenditure and how does it relate to VO2 and VCO2?
EEJ = 15.818xV02 + 5.176*VCO2
Varies in ratio to oxygen by about 30%
Therefore about 75% of the EE equation is driven by VO2
25% is driven by VCO2, which varies from equalling V02 to -30%.
Therefore 25% of 30 ~6% - the error based on using 02 alone
How about RQ
A bit more on RQ
RQ is VC02/VO2
Burning carbohydrate has an RQ of 1 because….
H
O
Therefore
C
O
1 molecule of C02
C
O
Requires 1 molecule
H H
O
of O2
H
n
Burning fat has an RQ of 0.7 because….
H
O
C
O
C
H n
H H
O
Therefore
1 molecule of C02
Requires 1.5 molecules
of O2 (1/1.5 =0.7)
What does RQ mean?
-Gives information about substrate utilisation
-Mice fed a constant diet, so in mice probably gives more information about
Energy balance
-Can exceed 1 during periods of lipogenesis as lipogenesis has an RQ of about 5
Important points about body weight and experimental design
Important point 1
EE J/min/mouse
A larger mouse WILL expend more energy than a smaller mouse.
50
45
40
35
30
25
20
15
10
5
0
y = 0.8886x + 9.4426
2
R = 0.3757
P=0.0004
0
10
20
Bodyweight (g)
30
40
Important point 2
Food intake in (kj/mouse/8 days)
A larger mouse WILL eat more energy than a smaller mouse.
700
600
500
400
y = 22.081x - 143.72
R2 = 0.8604
300
200
100
0
0
10
20
bodyweight (g)
30
40
Important point 3
Bodyweight
When you study your mouse is very important.
Time
If a larger animal expends more energy, and my genotype of interest is obese and
getting more obese relative to a control when I measure it, will it have higher EE?
Probably yes
Therefore what I need to know is if my animal has a DISPROPORTIONATELY
Low EE for its body weight.
How to normalise for body weight
Traditionally people have used different comparators to
straight body weight.
A particularly common comparator is BW0.75
However this, along with BW0.72 and BW0.66 were developed for comparing across
species, ie elephants and mice, rather than within species.
Evidence suggests that these are not valid comparators for comparing
within species, as we do.
So then describing oxygen consumption as
Ml/min/kg0.75 is not a good idea
Is there a better method?
ANCOVA
How ANCOVA works
Oxygen consumption
A group with increased oxygen consumption per gram of animal
Body weight
Oxygen consumption
A group with the same oxygen consumption per gram of animal
Body weight
When regression lines are not equal
Oxygen consumption
Idealised regression
Body weight
How about some real data?
WT
WT
WT
WT
WT
WT
WT
KO
KO
KO
KO
KO
KO
KO
KO
O2 ml/min/mouse
2.1425
2.3217
2.108415148
2.3652
2.240591387
2.2635
2.40299321
1.5767
2.0381
1.9817
2.110144446
2.031
1.979966916
2.318167748
2.220327835
Body weight g
34
32
31.1
29
34.3
35
37
29
33
29
33.3
33
37.7
37.6
35.4
Using traditional ml/min/kg0.75
NS
Now to do some ANCOVAring
When regression lines are not equal and cross in the area of interest
Oxygen consumption
Idealised regression
Body weight
So the model is valid!
Oxygen consumption rates from 7 month old male mice fed a
standard laboratory chow diet. Data collected from free living
mice with ad libitum access to food over a period of 48 hours.
N=8 per group. Chow diet. Oxygen consumption expressed as
adjusted means based on a normalised mouse weight of
33.36g determined using ANCOVA. P<0.05
Other types of data from metabolic caging systems
Other types of data
Delta Body weight: weight out - weight in
Important for understanding RQ and energy balance. Mice in negative energy
balance will have lower RQ values (they are oxidising fat) and potentially
disproportionately low EE (as they try to maintain fat reserves)
Activity
A very poorly understood variable and would take about another hour to discuss
fully. I would recommend entirely ignoring this for the time being.
Water intake
May give very useful information regarding diabetic mice/kidney function. In general
not a major variable in energy balance studies, however a dramatic loss in weight
may be attributable to a failure to drink. Important to check this.
Gut assimilation efficiency
Not all food that is consumed will be absorbed into the body. Mice general operate in
The mid-80% range for assimilation efficiency. Assimilation efficiency can be
assessed using a bomb calorimeter to measure fecal energy content.
Conclusions
Energy balance describes the processes by which animals get fat.
Metabolic rate describes the unitary mass energy expenditure.
Assessing these variables accurately is essential for determining why your mouse
is obese, lean or has altered metabolic function.
Practical session
1. Testing that your data is valid
2. How to analyse your data.
Testing that your data is valid
Basic checks
An important point about how a calorimeter measures CO2 and O2
CO2 = Air out – air in to give VCO2
O2 = Air in – Air out to give VO2
This is not a trivial difference!!!
Lets imagine that we have a lot of water in our cage…
The water dilutes the gases in the cage, reducing the CO2 and O2 measurements
Coming out of the chamber
02in = 100% of normal
CO2in = 100% of normal
02out = 90% of normal
CO2out = 90% of normal
These errors do not cancel because:
CO2 = Air out – air in to give VCO2
O2 = Air in – Air out to give VO2
CO2 = 90 -100% of correct values so error is negative
O2 = 100 – 90% of correct values so error is positive
Therefore excessive moisture will drive down RQ as RQ = VCO2/VO2
Unusually low RQ values are indicative of an issue with moisture or
drying…
If all cages drift down, as well as the room air value, it may suggest the
Air drying on your system is not working.
If one animal has a drift in RER it may be a problem with a specific cage
Exercise 1 – checking the data
On your computer you will find the EXCEL sheet ‘training analysis’.
Please open this file and proceed to tab ‘training data 1’
On this data set please graph (scatter plot) the O2% for room air
Also place on a separate graph the RQ values for each mouse.
You can compare this to training data 2, which does not have any problems
Can we see any problems?
Exercise 2
Look at training data 1
Please graph the EE column for each mouse
Can we see a problem? What might have caused it?
Summary of checks:
Make sure zero gas = 0
Make sure span gas gives same reading as the bottle
Make sure room air gives stable readings
Make sure RQ average is sensible (average of 0.9 for high carb diet,
nearer to 0.78 for HFD)
Make sure there are no freak out lying values (may happen occasionally for CO2).
Make sure data shows expected circadian rhythms (EE and RQ high at night, low in
the day)
Check all cages have roughly similar SD for RQ and EE.
The minimum EE would normally be expected to be the first value
(due to time constant of cage).
How to analyse the data
At this stage we will just take the average RER and average EE for the data set
We have
I have generated this data for you in the sheet called training analysis 1
Please calculate the average EE, body weight and perform a ttest between them
Formula:
=TTEST(G3:G9,G10:G16,2,2)
So the animals have a lower average energy expenditure, while they do not have
A significant difference in body weight there is a tendency for it to be lower.
We now want to know if they also have a lower metabolic rate (EE per KG)
To do so we need to normalise for body weight using ANCOVA.
First we can get a feel for this by plotting BW on the x axis and EE on the y axis of
A scatter plot. If the data fall on the same line, then the reduced EE is just caused by
Reduced body weight.
Try and make a plot with two data series placing BW on the x axis and EE on the y axis
This is what you should get. As we can see the lines are parralel. We now need to see
If they are statistically different…
EE vs Average BW
y = 0.5948x + 20.747
R2 = 0.6232
44
y = 0.4974x + 22.004
R2 = 0.5976
EE J/min/mouse
42
40
Series1
38
Series2
36
Linear (Series2)
Linear (Series1)
34
32
30
25
27
29
31
Weight (g)
ANCOVA.
33
35
37
How to actually RUN the ANCOVA
SPSS data input screen – copy data into here
Select variable tab to show this screen – type in names and change measure type.
Select variable tab to show this screen – type in names and change measure type.
Select ‘data’ menu and select define variable properties
Select ‘genotype’ and press continue
Type ‘WT’ and KO into the label section. Press ‘ok’
Select analysis menu > general linear model > univariate
Make EE the dependent variable, genotype the fixed factor and body weight the
covariate
Select options and then ‘display means for:’ genotype. Tick compare main
Effects box. Hit ok! The analysis will run.
Test of between subject effects should be signficant for BW and genotype.
Estimated marginal means show mean and standard error based on a 32.56g mouse
Testing model validity…
Go back to: analysis menu > general linear model > univariate
Select model button.
Select ‘custom’ model. Move genotype and BW indidually, then select both and
Move them across to test for an interaction. Press continue and run the model.
Ignore every thing but the genotype* BW value – this should be NON-significant
If your model is valid…
As you can see the lines do not cross in the region of interest…
EE vs Average BW
y = 0.5948x + 20.747
R2 = 0.6232
44
y = 0.4974x + 22.004
R2 = 0.5976
EE J/min/mouse
42
40
Series1
38
Series2
36
Linear (Series2)
Linear (Series1)
34
32
30
25
27
29
31
Weight (g)
33
35
37
Exercise 3
Open the ‘formatted for SPSS tab’
Copy the data into SPSS and run the analysis.
Refer to the hand out for instructions on how to do this…
Compare the results of the ANCOVA analysis to the EE/BW and EE/BW^0.75
Data in the excel sheet ‘training analysis 1’ tab. What do you find?
Bonus exercises
If you have not already done so try plotting delta body weight against RER
For the data on ‘training analysis 1’.
What do you find and why?
Secondly look at bonus training analysis.
Check the average and the significance (t-test) of all the different values.
Are any significant? If so what do you think this may tell us biologically?
So overall…
1. Analysis of energy expenditure for Energy balance should not be normalised
to body weight
2. Normalisation of energy expenditure or food intake should ideally be conducted
Using ANCOVA. If this is not possible, then stick with per mouse.
3. Activity data can simply be expressed as counts per mouse.
4. Metabolic chambers will give you VAST quantities of data but much of it is
meaningless unless you consider how you are analysing it carefully.
Answer to bonus question 1
RER vs delta body weight
4
3.5
y = -39.852x + 38.606
R2 = 0.7325
3
weight loss
2.5
2
Series1
1.5
Linear (Series1)
1
0.5
0
0.88
-0.5
0.9
0.92
0.94
-1
RER
0.96
0.98
Answer to bonus question 2
Lipolysis at E max is impaired in basal and B3-adrenergic receptor stimulated
states
KO
WT
Answer to bonus question 2
Lipid Clearance TG
12
10
TG
8
WT
6
PPARy2
4
2
0
0
2
4
6
Time (Hours)
8
10
And in human obesity this is what we see…