25  

Chapter 2

Growth characteristics, survival and brain volumetric changes in WNIN/Ob obese rats

  26  

 

2.1 Introduction

2.2 Materials and methods

2.3 Results

2.4 Discussion

2.5 Summary

  27  

 

2.1 Introduction

Lifespan is defined as the duration of time period an organism lives and

longevity as the expected average lifespan under ideal conditions. Lifespan is for an

individual and differs as per the prevailing conditions; but when we talk about

longevity the word covers mathematical terms like average lifespan of a given

population that is normally observed in absence of certain individual disorders.

Ageing is a part of the lifespan which makes the second half of the life story and is

indeed very important (Sinha and Ghosh, 2010). As discussed in chapter 1, WNIN/Ob

obese rat is the laboratory obese rat model with significantly decreased longevity and

hence could be a very useful model to study obesity associated accelerated ageing.

Although WNIN/Ob obese rats have been known to have decreased lifespan, it has

not been established nor reported yet scientifically / statistically. Therefore, in this

study an attempt has been made to scientifically establish and statistically validate the

accelerated ageing / decreased longevity of the WNIN/Ob obese rats. In order to find

out the probable mechanisms underlying the early death in these rats, it is essential to

establish them first, as models for accelerated ageing. One of the established

experiments to examine the effects of genetic manipulations and various chemical

compounds on ageing is the measurement of lifespan. This has traditionally been

accomplished by survival analysis over the lifetime during ageing (Yang et al., 2011).

One can draw interesting and useful information by appropriate statistical survival

analysis on the survival data (Valenzano et al., 2006, Harrison et al., 2009, Honjoh et

al., 2009, Yang et al., 2011).

Brain weights and volumes of different regions of brain including caudate,

hippocampus and association cortices at different ages are known to provide clues for

the progression of ageing (Ho et al., 1980, Scahill et al., 2003, Raz et al., 2005,

Driscoll et al., 2009). Therefore, the objectives of the present chapter were to do the

survival analysis in WNIN/Ob obese, WNIN/Ob lean and parental WNIN control rats

at different chronological ages, along with the estimation of their intelligence through

Cuvier’s fraction. In addition, changes if any in the volumes of important regions/

areas of brain known to be associated with/ underlie ageing were determined by

Magnetic Resonance Imaging (MRI) in the rats of the three different strains

mentioned above at comparable ages.

  28  

2.2 Materials and methods

2.2.1 Animals: Strain used and maintenance

All the experiments were carried out with the prior approval of Institutional

Animal Ethical Committee (IAEC Committee, NCLAS, National Institute of

Nutrition (ICMR), Hyderabad / Committee for the Purpose of Control and

Supervision of Experiments on Animals (CPCSEA) (Regd. No. 154/1999/CPCSEA)

and all the experiments were carried out as per the animal ethical norms. Every

careful attempt was made to minimize the number of rats used and their suffering.

The same animal feeding and maintenance protocol was used for the studies reported

in different chapters of the thesis.

Three, six, twelve, fifteen and eighteen month old (as per the requirement of

the experiment) male parental Wistar of National Institute of Nutrition (WNIN)

control, WNIN/Ob obese and WNIN/Ob lean littermates were obtained for the study

from the National Centre for Laboratory Animal Sciences (NCLAS), National

Institute of Nutrition (NIN), Hyderabad. The animals were kept in polypropylene

cages (2 animals per cage) with sterile bedding and maintained at the animal facility

of NIN at a temperature of 22 ± 2 0C with 14–16 air changes per hour and relative

humidity of 55 ± 5 % with 12h light/dark cycle.

2.2.2 Diet formulation and preparation

The animals were provided ad libitum, sterile chow (in pellet form) of

standard composition with all the recommended macro- and micronutrients (56 %

carbohydrate, 18.5 % protein, 8 % fat, 12 % fiber, and adequate amounts of minerals

and vitamins) needed for rats along with deionized water. The composition of rat

chow is as follows:

  29  

Table 2.1 Composition of the rat chow:

Ingredient %

1. Wheat Flour 22.5

2. Roasted Bengal Gram Flour 60.0

3. Skim Milk Powder 5.0

4. Casein 4.0

5. Refined Groundnut Oil 4.0

6. Salt mixture 4.0

7. Vitamin Mixture 0.5

The above ingredients were mixed thoroughly, made in to pellet form and autoclaved.

Table 2.2 Macromolecule content in the rat chow:

Macromolecule Grams/100g

1. Carbohydrate 58.56

2. Protein 20.94

3. Fat 6.33

4. Crude Fibre 1.06

5. Total ash 3.59

6. Moisture 9.52

7. Energy 376

  30  

Table 2.3 Composition of vitamin mixture:

Vitamin g/100kg diet

1. α-Tocopherol Acetate (E) 50% Dry powder 12.0

2. Menadione (K) 0.15

3. Thiamine (B1) 1.2

4. Riboflavin (B2) 0.5

5. Pyridoxin (B6) 0.6

6. Niacin 1.0

7. Pantothenic Acid (Calcium salt) 1.2

8. Cyanocobalamine (B12) 0.0005

9. Folic Acid 0.1

10. Paraamino Benzoic Acid (PABA) 10.0

11. Biotin 0.04

12. Inositol 10.0

13. Choline chloride 100.0

Vitamin mixture was made up to 500g with starch powder and this total 500g were

used for every 100kg diet.

Vitamin A and D (5ml of Vanitin) was mixed for every 15 kg of refined groundnut oil

to get 7000 IU of A and 400 IU of D per kg when 4 % of oil is added to the diet.

  31  

Table 2.4 Mineral (salt) mixture composition:

Salt per 100 kg diet (g)

1. DiCalcium phosphate 1250.00

2. Calcium carbonate (CaCO3) 555.00

3. Sodium chloride (NaCl) 300.00

4. Magnesium sulphate (MgSO4.7H2O) 229.20

5. Ferrous sulphate (FeSO4.7H2O) 50.00

6. Manganese sulphate (MnSO4.H2O) 16.04

7. Potassium iodide (KI) 1.00

8. Zinc sulphate 2.192

9. Copper sulphate (CuSO4.5H2O) 1.908

10. Cobalt chloride (CoCl2.6H2O) 0.012

A portion of NaCl was mixed with KI, ground well and then added to the other

salt mixtures, and finely ground with NaCl. Complete mixture was ground for the

final time and the mixture was made up to 4000 g with starch and stored in an airtight

container.

4kg of salt mixture was used for every 100kg of diet.

2.2.3 Growth characteristics and measurement of brain weights:

Rats of different strains (WNIN/Ob obese, WNIN/Ob lean littermates and

parental WNIN control rats) of 3 months age (n = 12 each) were collected from the

National Center for Laboratory Animal Sciences (NCLAS), NIN, Hyderabad and

monitored for their growth characteristics at 3 months time interval for their total

lifespan. At different age points, rats (n = 6) of all 3 strains were sacrificed and

different body parts were harvested quickly. Brain tissues were weighed and stored at

-80 0C till further experimentation.

Statistical analysis was performed by Graphpad Prism 6.0f for Mac OS X

software (Graphpad Software Inc., San Diego, CA). Significance of difference among

  32  

all groups of rats for each parameter studied was analyzed by one way analysis of

variance (ANOVA) followed by post hoc Tukey’s multiple comparison tests

appropriately. A p-value ≤ 0.05 was considered significant for the f ratio obtained by

one way ANOVA and the t values obtained in the post hoc Tukey’s multiple

comparison tests between different groups.

2.2.4 Survival analysis

Survival analyses were done to statistically measure and compare the lifespan

of the WNIN/Ob obese rats, their lean littermates and parental WNIN control rats.

Specially, the analysis of hazard function from the lifespan data is popular because the

shape of the cumulative hazard plots reflect the rate of ageing (Luder, 1993). For this

we used advanced statistical tests and canonical survival analysis by the online

application for survival analysis (OASIS) for comparing the changes in the survival

data (Yang et al., 2011).

The raw data of animal survival was manually recorded in tab delimited

manner. The data were then subjected to Basic Survival Analysis (BAS) in OASIS

and data outputs were calculated through Kaplan-Meier statistics, mean lifespan and

median lifespan calculations. The survival plot and log cumulative hazard plot were

made. The data were also subjected to Shapiro-Wilk Normality test, Survival Time F-

test (used to examine whether two or more normal populations have the same

variance or not) and Partial slopes rank-sum test (used for comparing the differences

in the slopes of two log cumulative hazard plots) accordingly.

2.2.5 Cuvier’s fraction (Brain-to-body weight ratio)

This is the ratio of weight of brain to that of body and is taken as a rough

estimation of the intelligence of an animal. The ratio is directly related to the animal’s

intelligence. Considering that it is very tough to establish animal intelligence, Cuvier's

fraction E/S (where E = brain weight and S = body weight) fairly gives a rough

estimate (Macphail, 1982). We calculated the Cuvier's fraction for each rat of the

three strains at different ages and assessed the significance if any of the differences in

the ratios among the three rat strains by Two way ANOVA and Tukey's multiple

comparisons test.

  33  

2.2.6 Brain volumetric analysis

MRI is based on the careful manipulation of magnetic properties of hydrogen.

Hydrogen is the most common element in the body, largely found in water (H20) and

fat (CH4 and complex C chains). Hydrogen is the simplest element with just one

proton electron pair and it has the highest sensitivity to magnetic resonance of any

element. Basically it works on the principle of nuclear magnetic resonance and uses

radiofrequency waves to probe tissue structure and function without requiring

exposure to ionizing radiation. MRI can be used for studing either live or post mortem

specimens (Oguz et al., 2013). The post mortem scans offer improved image quality

and increased signal-to-noise ratio as compared to in vivo scans due to several key

advantages (Oguz et al., 2013).

Rats were anesthetized with halothane (2-Bromo-2-Chloro-1,1,1

Trifluoroethane containing 0.01 % w/w of Thymol) (Raman and Weil Pvt. Ltd.,

Mumbai, India) and transcardially perfused with phosphate-buffered saline (PBS)

followed by 4 % paraformaldehyde in 0.1 M phosphate buffer (pH 7.4). Immediately

after perfusion, brains were carefully removed and post-fixed in 4 %

paraformaldehyde for 24 hours at 4 0C. After 24 hours, the brains were transferred to

PBS (with 0.05 % sodium azide) and stored at 4 0C until use.

Volume changes in the brain were determined in the WNIN/Ob obese and

parental WNIN control rats of comparable age using MRI. All MRI experiments were

carried out using a Bruker Biospec Tomograph (Bruker Medical, Ettlingen, Germany)

equipped with an Oxford, 33 cm bore, horizontal magnet operating at 4.7 T and a

Bruker gradient insert (maximum intensity 20 G/cm) and with T2W RARE (Rapid

Acquisition Relaxation Enhancement) imaging was utilized for determining the brain

volumes in WNIN/Ob obese and parental WNIN control rats. RARE sequences had

the following parameters: field of view (FOV) = 2.50 × 2.50 cm2; slice thickness: 1.00

mm; interslice distance: 1.00 mm; slice orient: axial; matrix size 256 × 256; echo time

(TE) = 56.0 ms; repetition time (TR) = 5000 ms; echoes = 1; averages = 4. The time

interval between two successive inversion pulses was 10 s in order to obtain complete

magnetization recovery. Starting from the olfactory bulb/anterior cortex endpoint, 25

transversal slices were acquired.

The volumes of different brain regions were determined by 3D acquisitions.

  34  

Volumes were calculated from the 3D images using the software ParaVision® 2.1.1

(Bruker, Germany) and a region growing algorithm. Unpaired t-test was applied on a

pixelwise basis to find statistically significant differences between the two groups. A

False Discovery Rate Correction (positive FDR) was applied to the obtained p values

to correct for multiple comparisons. All statistical analyses were carried out in a

custom MATLAB (The MathWorks, Inc., Massachusetts, USA) environment.

2.3 Results

2.3.1 Growth characteristics

At all the ages studied, body weight (Table 2.5) and food intake (Fig. 2.1) of

the WNIN/Ob obese rats were significantly higher (p

  35  

Figure 2.1: Average food intake at 3 months of age (in grams/ day) of the parental WNIN normal

control (WN), WNIN/Ob lean (OL) and WNIN/Ob obese (OO) rats. Values represent mean ± SEM.

Bars sharing different superscripts are significantly different by one way ANOVA (p

  36  

Table 2.6: Mean and median lifespan measured in the parental WNIN control,

WNIN/Ob lean and WNIN/Ob obese rats (n = 12 each).

Groups

Restricted mean Age in days at % mortality

Days ± SE 95% C.I. 25% 50%

75%

90%

100%

95% Media-n C.I.

WNIN 1067 ± 12 1043 ~1090

1029

1064

1099

1120

1127

1043 ~ 1078

WNIN/Ob lean 1035 ± 11 1012 ~1057 10

10

1029

1062

1081

1107

1016 ~ 1053

WNIN/Ob obese 421 ± 7 407 ~ 435 41

3

420

427

441

483 413 ~

420

  37  

Figure 2.2: Survival plot showing the percent survival of the parental WNIN control (blue circle),

WNIN/Ob lean (red triangle) and WNIN/Ob obese rats (green square).

Figure 2.3: Log Cumulative Hazard Plot depicting the accumulation of hazard over a period of time in

the parental WNIN control (blue circle), WNIN/Ob lean (red triangle) and WNIN/Ob obese rats (green

square).

  38  

2.3.3 Measurement of brain weights and calculation of the Cuvier fraction

It is evident from the table 2.7, that there is a significant difference between

the brain-wet weights of WNIN/Ob obese rats as compared to the WNIN control rats

and WNIN/Ob lean littermates at 3 months of age. However, the brain weights were

in general significantly lower in WNIN/Ob rats than WNIN and WNIN/Ob lean

controls from 6 months of age.

Table 2.7: Average brain weight (in mg) ± S.E.M. of the parental WNIN control,

WNIN/Ob lean and WNIN/Ob obese rats at different ages.

Groups → WNIN

(WN)

WNIN/Ob lean

(OL)

WNIN/Ob obese

(OO) Age ↓

3M 1773 ± 7 1764 ± 14 1715 ± 8**

6M 1839 ± 26 1792 ± 17 1612 ± 30***

12M 1840 ± 9 1842 ± 13 1663 ± 32****

15M 1810 ± 20 1814 ± 11 1676 ± 17***

18M 1786 ± 14 1778 ± 12 1649 ± 38**

Significance of difference among all groups of rats was analyzed by one way analysis of variance

(ANOVA) followed by post hoc Tukey’s multiple comparison tests appropriately. A p-value ≤ 0.05

was considered significant for the f ratio obtained by one way ANOVA and the t values obtained in the

post hoc Tukey’s multiple comparison tests between different groups.

Cuvier fraction

Cuvier fraction was computed as brain weight/ body weight (Serendip 1996)

for all three strains of rats at all the ages mentioned earlier and the data is presented in

figure 2.4. Indeed, the Cuvier fraction was the lowest (3.12) and significant

(p

  39  

Figure 2.4: Cuvier fraction in the parental WNIN control, WNIN/Ob lean and WNIN/Ob obese rats at

3, 6, 12, 15 and 18 months of age. Significance of difference among all groups of rats was analyzed by

one way analysis of variance (ANOVA) followed by post hoc Tukey’s multiple comparison tests

appropriately. A p-value ≤ 0.05 was considered significant for the f ratio obtained by one way ANOVA

and the t values obtained in the post hoc Tukey’s multiple comparison tests between different groups.

2.3.4 Brain volumetric analysis

Figure 2.5: Volumes of the different regions of brain of parental WNIN control and WNIN/Ob obese

rats at six months of age. The bars represent mean ± SEM (n=3). Unpaired t test suggested no

significant difference between the two groups.

3M 6M 12M

15M

18M

0

2

4

6

8

10

Cuv

ier's

frac

tion

WNOLOO

****

**** ********

****

Corte

x

Hipp

ocam

pus

Caud

o Puta

men

Hypo

thalam

us

Corp

us C

allos

um

Stan

dard

PBS

0

50

100

150

200

T2 v

alue

WNOO

  40  

The volumes of different regions of the brains of WNIN control and

WNIN/Ob obese rats was determined by the MRI at six months of their age and the

results are given in figure 2.5. We observed a decrease in the regional brain volumes

of different brain parts in the WNIN/Ob obese rats as compared to the control rats but

it was not significant.

Figure 2.6: Coronal cross-sectional MRI images showing different levels of brain in parental WNIN

control and WNIN/Ob obese rats.

2.4 Discussion

Considering that in real life situations, even relatively homogenous individuals

show variation in lifespan under similar environmental conditions (Kenyon, 2010), it

is not surprising that differences in lifespan are found between populations. The obese

rodent models show reduced longevity and demonstrate improvement after dietary

restrictions (Vasselli et al., 2005, Lebel et al., 2012, Chung et al., 2013), drugs

(Pearson et al., 2008, Kaeberlein, 2010, Lebel et al., 2012) or surgical treatments

(Stylopoulos et al., 2009).

WNIN/Ob obese is a novel rat model which shows all the characteristics of

metabolic syndrome. But the peculiar characteristic that separates it from all other

obesity models is a significantly reduced lifespan of 59-60 %. To establish it as an

appropriate animal model for obesity and accelerated ageing, we need to check and

  41  

confirm the hallmarks of ageing in them. Although there are many tentative hallmarks

(Lopez-Otin et al., 2013), we evaluated only those that were feasible due to the

constraints of time, funding and availability of resources. Even before looking for

hallmarks of ageing, one must check scientifically if the animals are exhibiting

decreased lifespan or not. For this, we performed the survival analysis among the

three groups of rats, using Kaplan – Meier estimation. Survival analysis statistically

analyzes the time duration till certain event occurs. This can be anything like death in

living organisms which ceases its normal existence. In such a kind of analysis one

needs to model event data (for example, death) in comparison to time (for example,

days of life). Through this analysis one can tell the average duration of time in which

a given population shall die. Although every individual in a given population lives for

varied time period but still the analysis will tell the average lifespan after which the

probability of death would be high. In experimental facilities, animals live in very

hygienic and controlled conditions, and due to these facts individual lifespan of most

of them would be very close to one another and hence survival analyses provide very

reliable data. Survival analysis has been done in various contexts like testing lifespan

after implantation (Schultz et al., 2002, Schultz et al., 2004), after transplantation

(Chang et al., 2014), in neurodegeneration and animal models of neurological

disorders (Bowerman et al., 2012, Lee et al., 2012, Smittkamp et al., 2014) and other

studies related to longevity (Arimoto et al., 2012, van Dooremalen et al., 2012, Zarse

et al., 2012, Lionaki and Tavernarakis, 2013, Neff et al., 2013, Magana-Maldonado et

al., 2014, Zhang et al., 2014). We analyzed the lifespan of rats in all the three groups

in terms of percent survival and found significantly decreased longevity in the

WNIN/Ob obese rats compared to the other two groups. Usually cumulative hazard

plots are employed for visually examining distributional model assumptions for

reliability data and have a similar interpretation as the probability plots (Natrella,

2010). The Log Cumulative Hazard Plot shows the accumulation of hazard (here

probability of death) over time and it can be clearly seen (figure 2.3) that this occurs

quite early in the WNIN/Ob obese rats compared to WNIN parental controls and

WNIN/Ob lean littermates.

Since WNIN/Ob obese rats are genetically affected it is common to have

increased body weights in due course of time as compared to the lean littermates

perhaps due to increased food intake. But weight of brain is known to remain more or

  42  

less same across the species. According to the calculations of George Sacher using

least squares regression of log lifespan (Sacher, 2008), there is relationship between

brain weight and lifespan. He also concluded that brain weight by itself is a better

predictor of lifespan than is body weight. As said earlier in this chapter, normally

there is a positive correlation between brain size and body size (i.e. large animals

commonly have larger brains than smaller animals). Nevertheless this relationship is

not linear. In such a case the brain weight to body weight ratio cannot be taken as a

direct indicator of intelligence as such albeit a rough estimate can be made. It is

baffling to establish intelligence in an animal, but Cuvier's fraction gives a rough

estimate. Although the brain-to-body mass ratio seems to be simpler in calculation but

is a useful tool for comparing encephalization within species or between fairly closely

related species (Serendip 1996). Brain-to-body mass ratio has been considered a

rough estimate of the intelligence of an animal (Serendip 1996). Because there have

been studies showing a direct correlation between the ratio and cognitive function of

animals, as a part of studying the ageing changes in the WNIN/Ob obese rats, Cuvier's

fraction was estimated in these rats as compared to their lean littermates and parental

WNIN control rats. Cuvier's fraction was computed using the respective average

weights of brain and body in different groups of rats. At the age of 3 months this

fraction was found to be very low (3.12±0.031) in WNIN/Ob obese rats as compared

to 7.68±0.167 and 7.22±0.131 in parental WNIN control rats and lean littermates

respectively. The brain weight was found to be significantly low and hence even if we

consider the body weight to be normal like WNIN or WNIN/Ob lean, the Cuvier’s

fraction remains low at around 4.27 as compared to around 5.36 in normal controls at

the age of 12 months. Therefore, as a rough estimation it can be considered that

WNIN/Ob obese rats have lower intelligence than other groups of rats. To confirm

this, future studies need to be planned to test learning and memory, anxiety and social

behavior in the WNIN/Ob obese rats.

Cortical thickness has been associated with better preservation of cognitive

functions (Burzynska et al., 2012) whereas the possibility of structural brain

abnormalities and cognitive dysfunction has been proposed in the obese as compared

to the lean individuals (Stanek et al., 2013). Studies have shown a reduction in the

prefrontal and cingulated cortices’ volume (Shamy et al., 2011), thinning of prefrontal

and superior temporal cortices (Alexander et al., 2008), reduction in gray and white

  43  

matter volume (Wisco et al., 2008), thinning of somatosensory and motor cortices and

thickening of superior temporal and cingulate cortices (Koo et al., 2012) with

advancing age (Raz et al., 2005). With increasing age, grey matter volume decreases

globally and there is a bilateral accelerated loss in the insula, superior parietal gyri,

central sulci, and cingulate sulci (Good et al., 2001). But areas like amygdala,

hippocampi, and entorhinal cortex were seen to exhibit meager or no changes as a

function of age along with no global changes in the white matter (Good et al., 2001).

To delineate the neuroanatomical correlates of decline in the volume of various brain

regions, we measured the volumes of different regions of the brain using the MRI. To

our surprise, although we observed a decreasing trend but did not find any significant

changes in the volume of the different brain regions of the WNIN/Ob obese rats as

compared to parental WNIN control rats. This is an interesting finding as normally

with significant decrease in total brain weight it is common to surmise shrinkage of

brain tissue or at least some change in regional brain volumes (Witelson et al., 2006).

The possible reason behind this remains obscure and needs to be examined in future.

2.5 Summary:

• Survival analysis showed around 60% decrease in the longevity of WNIN/Ob

obese rats as compared to the lean littermates and parental WNIN control rats.

Cumulative hazard plots obtained from lifespan analysis data, reflect that the

rate of ageing in WNIN/Ob obese rats is significantly faster; this is evident

from the results that accumulation of hazards appear as early as 400 days in

WNIN/Ob obese rats as compared to 1000 days or more in lean littermates and

parental WNIN control rats.

• A significant drop in brain weight (hallmark of brain ageing) was observed at

early age of 3 months in the WNIN/Ob obese rats. Cuvier’s fraction that is a

rough estimate of intelligence was found to be very low in the WNIN/Ob

obese rats.

• Volumetric analysis through Magnetic Resonance Imaging (MRI) showed a

decrease, though not significant, in the total brain volume as well as in the

brain regions like cerebral cortex, hippocampus, caudate putamen,

hypothalamus and corpus callosum of WNIN/Ob obese rats.

  44  

• Even in the absence of any significant volumetric change in the brain, there

remains a possibility of compromised microenvironment causing the

molecular pathways to go awry. Therefore, we conducted cellular studies

comprising glial and neuronal evaluations in the next chapter.