Adipose tissue, the skeleton and cardiovascular disease

Adipose tissue, the skeleton and
cardiovascular disease
Peder Wiklund
Department of Community Medicine and Rehabilitation,
Geriatric Medicine; Department of Surgical and
Perioperative Sciences, Sports Medicine; Department of
Community Medicine and Rehabilitation, Rehabilitation
Medicine
Umeå 2011
Front cover:
Original illustration by Frida Persson and Nils Persson,
based on the Vitruvian Man by Leonardo da Vinci.
Incoherent research notes supplied by Peder wiklund
Unnumbered figures:
“Obesity”, “Adipose
tissue, the skeleton…”
Original illustrations by Frida Persson
Unnumbered figure:
“Osteoporosis”
Original illustration by Frida Persson and Nils Persson
Figure 1, 7:
Original illustrations by gustav Andersson
Figure 2-4, 6, 10-12:
Original illustrations by Nils Persson
Figure 5, 8:
Pictures kindly made available by the Bone Research
Society (Picture gallery:
http://www.brsoc.org.uk/gallery/index.htm)
All previously published papers were reproduced with the kind permission of the
publishers.
Responsible publisher under Swedish law: the Dean of the Medical Faculty
This work is protected by the Swedish Copyright Legislation (Act 1960:729)
ISBN: 978-91-7459-186-6
ISSN: 0346-6612; N.S. 1417
Elektronisk version tillgänglig på http://umu.diva-portal.org/
Printed by: Arkitektkopia, Umeå
Umeå, Sweden 2011
People forget how fast you did a job - but
they remember how well you did it.
Howard Newton
1
TABLE OF CONTENTS
Table of Contents
Table of Contents
2
Abstract
4
Preface
6
Abbreviations
7
Introduction
8
Obesity
9
Epidemiology
10
Definitions of overweight and obesity
11
Pathogenesis of cardiovascular disease
12
Pathophysiology of obesity
14
Adipose tissue as an endocrine organ
15
Regulation and dysregulation of lipid metabolism
15
Obesity and CVD risk factor clustering
18
Measurement of fat mass
20
Osteoporosis
23
Epidemiology
24
Definition of osteoporosis
25
Risk factors for osteoporosis
26
Bone structure
28
Bone modelling and remodelling
29
Measurement of bone mass
Adipose tissue, the skeleton and cardiovascular disease
31
33
Fat distribution and bone mineral density
34
Bone mineral density and cardiovascular disease
34
Obesity-associated CVD risk factors and osteoporosis
35
The skeleton and energy metabolism
37
Thesis aims and hypotheses
38
Materials and methods
39
DEXA
40
Regional fat mass measurements
40
Bone mineral density measurements
41
Study populations
41
The Bone density and fat mass database
42
The MONICA database
42
The VIP database
42
Statistical methods
44
Ethics
45
Results
2
46
Study I
46
Study II
48
table of contents
Study III
50
Study IV
52
Discussion
55
Regional obesity and cardiovascular disease
56
Osteoporosis and cardiovascular disease
59
The skeleton in energy metabolism
60
Adipose tissue, the skeleton and cardiovascular disease
61
Future research
64
Summary and conclusions
65
Acknowledgements
66
References
67
3
ABSTRACT
Abstract
Cardiovascular disease (CVD) is the leading cause of death in the Western
World, although the incidence of myocardial infarction (MI) has declined
over the last decades. However, obesity, which is one of the most important
risk factors for CVD, is increasingly common. Osteoporosis is also on the rise
because of an aging population. Based on considerable overlap in the
prevalence of CVD and osteoporosis, a shared etiology has been proposed.
Furthermore, the possibility of interplay between the skeleton and adipose
tissue has received increasing attention the last few years with the discovery
that leptin can influence bone metabolism and that osteocalcin can influence
adipose tissue.
A main aim of this thesis was to investigate the effects of fat mass
distribution and bone mineral density on the risk of MI. Using dual-energy
x-ray absorptiometry (DEXA) we measured 592 men and women for
regional fat mass in study I. In study II this was expanded to include 3258
men and women. In study III 6872 men and women had their bone mineral
density measured in the total hip and femoral neck using DEXA. We found
that a fat mass distribution with a higher proportion of abdominal fat mass
was associated with both an adverse risk factor profile and an increased risk
of MI. In contrast, a higher gynoid fat mass distribution was associated with
a more favorable risk factor profile and a decreased risk of MI, highlighting
the different properties of abdominal and gynoid fat depots (study I-II). In
study III, we investigated the association of bone mineral density and risk
factors shared between CVD and osteoporosis, and risk of MI. We found that
lower bone mineral density was associated with hypertension, and also
tended to be associated to other CVD risk factors. Low bone mineral density
was associated with an increased risk of MI in both men and women,
apparently independently of the risk factors studied (study III).
In study IV, we investigated 50 healthy, young men to determine if a highimpact loading intervention in the form of a series of jumps would lead to
changes in glucose and lipid metabolism. We found that the intervention
group had significantly lowered serum glucose levels compared to the
control group. Changes in all metabolic parameters favored the intervention
group with an increase in lipolysis from baseline and a decrease in
cholesterol.
In summary, the proportion of abdominal and gynoid fat mass displayed
contrasting associations to both CVD risk factors and MI risk. Abdominal fat
mass was associated with a higher risk while a high proportion of gynoid fat
4
ABSTRACT
mass was associated with a lower risk. Bone mineral density displayed an
inverse association with MI risk, seemingly independently of CVD risk
factors, suggesting other explanations to a shared pathogenesis. Finally, high
impact loading on the skeleton in young, healthy men decreased serum
glucose levels and tended to improve other metabolic parameters, suggesting
that the skeleton can affect energy metabolism.
5
PREFACE
Preface
6
I.
Wiklund P, Toss F, Weinehall L, Hallmans G, Franks PW, Nordstrom A,
Nordstrom P (2008) Abdominal and gynoid fat mass are associated
with cardiovascular risk factors in men and women. J Clin Endocrinol
Metab 93 (11):4360-4366
II.
Wiklund P, Toss F, Jansson JH, Eliasson M, Hallmans G, Nordstrom A,
Franks PW, Nordstrom P (2010) Abdominal and gynoid adipose
distribution and incident myocardial infarction in women and men. Int
J Obes (Lond) 34 (12):1752-1758
III.
Wiklund P, Nordström A, Jansson JH, Weinehall L, Nordström P (2011)
Low bone mineral density is associated with increased risk for incident
myocardial infarction in men and women. Accepted for publication in
Osteoporosis International DOI: 10.1007/s00198-011-1631-0
IV.
Wiklund P, Nordström A, Högström M, Alfredsson H, Engström P,
Gustafsson T, Franks PW, Nordstrom P (2011) High impact loading on
the skeleton is associated with a decrease in glucose levels in young
men. Submitted for publication
ABBREVIATIONS
Abbreviations
ANOVA
BMD
BMI
CI
CT
CV
CVD
DEXA
ECG
FFA
HDL
HR
LDL
MI
MONICA
OC
pDEXA
pQCT
QCT
QUS
SAD
SAT
SD
SNS
T2DM
VAT
vBMD
VIP
VLDL
WC
WHO
WHR
Analysis of variance
Bone mineral density
Body mass index
Confidence interval
Computed tomography
Coefficient of variation
Cardiovascular disease
Dual energy x-ray absorptiometry
Electrocardiogram
Free fatty acids
High density lipoprotein
Hazard ratio
Low density lipoprotein
Myocardial infarction
Monitoring trends and determinants in cardiovascular disease
Osteocalcin
Peripheral DEXA
Peripheral quantitative computed tomography
Quantitative computed tomography
Quantitative ultrasound
Sagittal abdominal diameter
Subcutaneous adipose tissue
Standard deviation
Sympathetic nervous system
Type II diabetes
Visceral adipose tissue
volumetric BMD
Västerbotten Intervention Programme
Very low density lipoprotein
Waist circumference
World Health Organization
Waist to hip ratio
7
INTRODUCTION
Introduction
In the last century, Western countries have experienced numerous lifestyle
changes. Life expectancy has never been higher, thanks to advances in
healthcare. The abundance of food means that access to food is rarely an
issue. The downside is that an increasingly sedentary lifestyle combined with
an increased caloric intake has caused the prevalence of overweight and
obesity to skyrocket in the last decades [1]. We are also seeing a shift in
demographics; osteoporosis is increasing in prevalence as our population
grows older.
The main comorbidities of obesity are insulin resistance [2] and
cardiovascular disease (CVD) [3] and the main deleterious outcome of
osteoporosis is fractures [4]. Obesity and osteoporosis have understandably
been considered to be separate entities with different preventive strategies
and treatments. Interestingly, the considerable overlap in the prevalence of
CVD and osteoporosis is sufficient for a common etiology to be proposed.
Additionally, several links between the skeleton and adipose tissue have
recently been discovered expanding our knowledge of the interplay that
occurs between these tissues.
Understanding how different body compositions, both relative to regional fat
mass and bone mineral density (BMD), affects CVD risk is vital. Together
with a deeper understanding of the links between the skeleton and adipose
tissue, this could lead to improved preventive and therapeutical
interventions, reducing the mortality and morbidity associated with obesity
and osteoporosis.
8
OBESITY
Obesity
Chapters
Epidemiology
Definitions of overweight
and obesity
Pathogenesis of
cardiovascular disease
Pathophysiology of obesity
Measurement of fat mass
obesity
Obesity
Epidemiology
Obesity is a growing health concern. A combination of a sedentary lifestyle
and excessive caloric intake are causing a rapid increase in the prevalence of
overweight and obesity. This prompted the World Health Organization
(WHO) to formally recognize obesity as a global epidemic in 1997 [5]. In
Sweden about 33% of all adults are estimated to be overweight while an
additional 12% are obese [6]. Furthermore, in young men, the prevalence of
overweight and obesity have tripled and quadrupled over the last decades
[1].
Obesity is associated with several adverse outcomes. It is known to reduce
life span [7] and increase disability [8], and is associated with several serious
chronic diseases. Increasing weight corresponds to increases in the incidence
of CVD [3] and type II diabetes (T2DM) [2], as well as increased risk of
osteoarthrosis [9,10], sleep apnoea [11] and certain cancers [12].
In Sweden, CVD is the leading cause of death [13], and is strongly influenced
by obesity. However, even though the prevalence of obesity is increasing,
neither CVD nor T2DM have shown corresponding increases. In fact, CVD is
steadily declining in both men and women [14]. This discrepancy could be
because the marked increase in overweight and obesity started in the early
nineties, and the consequences have not yet surfaced. The proportions of
overweight and obese in the Swedish population in 1999 were nearly
identical to the US population in 1989, and the progression of the obesity
epidemic is similar to what was observed in the US [15]. Since year 2000, the
US has seen an increase in CVD mortality in certain age groups, after years
of decline [16]. This suggests that if the development of obesity in Sweden
continues to follow US trends, comorbidities of obesity might also increase in
prevalence.
10
Definitions of overweight and obesity
Definitions of overweight and obesity
Overweight and obesity are defined as abnormal or excess fat accumulation
that may impair health [5]. The most commonly used technique to classify
overweight and obesity is body mass index (BMI). This is defined as weight
in kilograms divided by the square of the height in meters (kg/m2) and
emphasizes total weight relative to height. BMI is the criterion for
overweight and obesity for WHO, which defines overweight as a BMI of
between 25- 29.9 and obesity defined as a BMI of 30 and above [5].
However, BMI might not be the best predictor of “dangerous” obesity.
Instead, measures of central obesity have been advocated over the use of
BMI. The central obesity criteria of waist circumference (WC) or waist to hip
ratio (WHR) vary depending on population and country. However, the WHO
criteria [17] are a WHR of >0.9 for men and >0.85 for women. The National
Institutes of Health criteria [18] are a WC of >102 cm for men and >88 cm
for women.
11
OBESITY
Pathogenesis of cardiovascular disease
Before discussing the pathophysiology of obesity, the underlying mechanism
of CVD, namely atherosclerosis, must be considered. Many of the links
between obesity and CVD that are discussed later directly concern
atherosclerosis, so a brief summary of atherosclerosis is provided.
Atherosclerosis is a slow, complex disease that starts in childhood and
progresses throughout life. It affects the walls of arteries causing a build-up
of atheromatous plaques, narrowing the inner diameter, or lumen, of the
blood vessel. The narrowing of the lumen can restrict blood flow to end
organs, causing e.g. angina pectoris or claudicatio intermittens. However,
advanced atheromatous plaques not only contribute to restriction of blood
flow, but are often unstable and prone to rupture. In the worst-case scenario
total obstruction of the vessel occurs due to thrombosis or emboli, causing
permanent damage to the heart or brain from lack of oxygen [19].
The initiating factor that is considered to be a requisite for plaque formation
is minimal injury of the endothelium, called endothelial dysfunction [20].
The first visible signs of the atherosclerotic process are small lipid deposits
in the vessel wall called fatty streaks, seen as early as a few years of age [21].
The origin of fatty streaks is low density lipoprotein (LDL) cholesterol that
becomes oxidized and entering the endothelium. The change in LDL
structure causes macrophages to phagocytose the oxidized LDL, storing the
lipids and cholesterol. Due to the lipid retention the macrophages change
appearance and become foam cells. With increasing lipid deposition and
higher LDL cholesterol concentrations in the blood overloaded foam cells die
and form extracellular pools of lipids, stimulating adjecent smooth muscle
cells to accumulate lipids [22], and turning the fatty streak into an
atheromatous plaque. Over time, and exposure to various risk factors, the
plaque builds. Extracellular pools of lipids and foam cells form the core of
the plaque, and are separated from the lumen by a fibrous cap. The cells in
the lipid core produce several pro-thrombotic factors, which can rapidly
form a thrombus when in contact with the blood, either occluding the artery
or detaching as an embolus that occludes a peripheral artery [20].
Atherosclerotic plauque progression is shown in Figure 1.
12
Pathogenesis of cardiovascular disease
Figure 1. Atherosclerotic plaque progression. The first visible sign of
atherosclerosis is a fatty streak. After additional lipid accumulation and
minor cell death, small extracellular lipid pools form, stimulating uptake into
smooth muscle cells. Growth is mainly by lipid addition. Over time, the outer
layer of the atherosclerotic plaque becomes fibrotic and prone to rupture,
creating a risk of total occlusion from thrombosis.
13
obesity
Pathophysiology of obesity
Obesity is a heterogenous disease. Although total fat mass is consistently
associated with increased CVD mortality and incident CVD, a subset of obese
individuals is metabolically healthy, while a subset of normal weight
individuals displays metabolic abnormalities [23,24].
Adipose tissue can be divided into subcutaneous adipose tissue (SAT) and
visceral adipose tissue (VAT). The SAT is located directly under the skin and
is considered as healthy adipose tissue. VAT is located inside the peritoneal
cavity, around the internal organs, and is suggested to be the driving force
for the deleterious implications of obesity (Figure 2) [25].
Figure 2. Cross-sectional image of the abdomen of a normal weight
women and an abdominally obese woman, demonstrating different
proportions of SAT and VAT.
14
Pathophysiology of obesity
The normal function of the adipose tissue is to act as an energy reservoir,
storing large amounts of triglycerides for release when the body is in need of
energy [26]. To understand why obesity is associated with chronic diseases,
it is important to consider the difference between the normal function of
subcutaneous adipocytes, and the different properties of visceral adipocytes.
Adipose tissue as an endocrine organ
Long considered inert, the finding that adipocytes can produce and secrete a
wide range of proteins and adipokines was a momentous discovery.
Adipocytes are now recognized as active participants in energy regulation
through a network of endocrine, paracrine, and autocrine signaling
pathways. Adipocytes can influence energy regulation by releasing several
adipose-tissue derived proteins, often referred to as adipokines or
adipocytokines [26].
One of the many adipokines produced by both subcutaneous and visceral
adipocytes is leptin. Leptin is secreted in direct proportion to the amount of
fat mass, and functions as a metabolic signal of energy sufficiency [27]. This
secretion is greater from subcutaneous than visceral adipocytes [28].
Adipokine secretion by visceral adipocytes is markedly different than their
subcutaneous counterparts. IL-6, an adipokine associated with an increase
in the systemic inflammation marker C-reactive protein, and TNF-α, an
adipokine associated with insulin resistance, are synthesized and secreted
more readily by VAT [29-32]. PAI-1, an adipokine involved in fibrinolysis
regulation and thrombus formation, is also increased in visceral obesity [33].
Moreover, the synthesis of adiponectin, an adipokine suggested to be antiinflammatory that is able to increase insulin sensitivity [34], is decreased in
obesity [28].
Regulation and dysregulation of lipid metabolism
The main function of adipose tissue is the storage and release of
triglycerides. Triglycerides contain three fatty acids, esterified to one glycerol
molecule, with the fatty acids ultimately used as cellular energy. The main
regulators of this process are insulin, which increases storage and inhibits
lipolysis in periods of energy surplus, and cathecholamines, which increase
lipolysis and the release of free fatty acids (FFA) into the bloodstream in
periods of energy shortage.
One difference between SAT and VAT is the anatomical location of the fat
depots. Venous blood from SAT is returned to the heart via the systemic
15
obesity
veins, while venous blood from VAT is instead returned via the portal vein,
creating a direct link between VAT and the liver [35].
Subcutaneous adipocytes are highly insulin sensitive. A high avidity for FFA
and triglyceride storage allows the SAT to act as a metabolic sink, controlling
the amount of FFA in the circulation [36]. VAT is less insulin sensitive than
SAT [37]. Visceral adipocytes are, on the other hand, susceptible to
cathecholamine-induced lipolysis. This altered inhibition/stimulation of
lipolysis leads to increased FFA concentrations in the blood and a
consequent increase of FFA influx to the liver. This causes an increase in FFA
oxidation and increased very low density lipoprotein (VLDL) cholesterol
secretion [38]. Additionally, glyconeogenesis is upregulated, which leads to
hyperglycemia in addition to endogenous hyperlipidemia [39]. This
dysregulation of lipid metabolism can cause ectopic fat accumulation in the
liver, skeletal muscles and heart, inducing peripheral insulin resistance and
aggravating the metabolic disturbances associated with visceral obesity [40].
In summary, because VAT drains via the portal vein and has a unique
adipokine profile, it is thought to induce a prothrombotic and
proinflammatory state that results in endothelial dysfunction [41]. This
endothelial dysfunction precedes atherosclerotic plaques [42], and is now
considered a pivotal component in the atherosclerotic process [20]. SAT,
however, might confer protection against the metabolic deterioration
associated with VAT. According to the post-prandial model of insulin
resistance [43] chylomicrons derived from a meal are hydrolyzed by VAT.
Because VAT is unable to effectively store the released FFA, increased flux to
the liver ensues, ultimately causing ectopic fat accumulation. SAT and VAT
compete to hydrolyze chylomicrons, and a higher amount of SAT might
ameliorate the increased postprandial FFA flux attributable to VAT (Figure
3).
16
Pathophysiology of obesity
Figure 3. Adverse properties of excess visceral fat accumulation. VAT has
an altered adipokine profile and dysregulates lipid metabolism. This leads to
adverse lipid levels, an increase in insulin resistance, and accumulation of
ectopic fat, aggravating the atherosclerotic process. Subcutaneous fat
accumulation works as a metabolic sink when in positive energy balance,
providing protection from the metabolic disturbances of VAT accumulation.
17
obesity
Obesity and CVD risk factor clustering
The role of obesity in CVD, and in MI in particular, is part of a web of
causation. The etiology of MI is multi factorial and influenced by several risk
factors in addition to obesity. Hypertension, physical inactivity, smoking,
age, heredity, sex, T2DM, and dyslipidemia are some of the strongest risk
factors [44], and obesity interacts with several of these.
Obesity and hypertension
Hypertension and obesity are strongly associated, with a linear relationship
between increasing BMI and systolic and diastolic blood pressure [45].
Obesity is suggested to influence blood pressure through several
mechanisms, including alterations in the renin-angiontensin system [46],
insulin resistance, increased sympathetic activity [47], and hyperlipidemia
[48].
Essential hypertension is one of the most important risk factors for CVD.
CVD risk increases linearly with increasing systolic and diastolic blood
pressures [49]. The pathophysiology of hypertension in the development of
CVD lies in both changes in hemodynamics and vascular physiology. With
increasing blood pressure, end-organ damage occurs in the heart [50] and
kidneys [51], among others. Hypertension is also associated with endothelial
dysfunction [52], leading to an aggravated atherosclerotic process.
Obesity and smoking
The relationship between obesity and smoking is complex. Nicotine can
increase energy expenditure [53] and lower appetite, thereby promoting
weight loss, explaining the lower body weight found in smokers in some
studies [54]. On the other hand, heavy smoking can increase the risk of
obesity compared to light smoking, perhaps partly through the clustering of
risk factors, such as physical inactivity, unhealthy diet, and high alcohol
intake [55]. Smoking is also associated in a dose-dependent fashion with
higher WHR, which is indicative of increased accumulation of VAT [56,57].
The risk for CVD among smokers is dose-dependent, with a doubled-totripled risk for CVD when smoking as few as 1-4 cigarettes a day [58]. After
smoking cessation CVD risk quickly reverts to the level of non-smokers [59],
suggesting that smoking-related changes are at least partly reversible.
Smoking can affect CVD risk through several mechanisms. It can directly
accelerate atherosclerosis by inducing a prothrombotic state and endothelial
dysfunction [60]; it can also interact with other CVD risk factors. Smoking
18
Pathophysiology of obesity
has been shown to be unfavorably associated with lipid profile [61], and can
contribute to insulin resistence [62] and abdominal obesity [56,57], factors
that in turn can accelerate atherosclerosis.
Obesity and type II diabetes
The prevalence of obesity and T2DM are largely overlapping, enough that
obesity is recognized as the most prominent cause of T2DM [63]. The risk of
T2DM increases linearly with increasing body weight. Weight gain is
associated with an increased risk, and even moderate weight loss can
decrease risk [2,64]. Obesity may induce T2DM by several mechanisms. It
can cause insulin resistance through the release of FFA into the liver, and by
secreting pro-inflammatory cytokines from the VAT, and also by increasing
cellular stress signaling [65,66]. T2DM is one of the main comorbidities of
obesity as T2DM is associated with increased mortality and morbidity,
especially in CVD [67].
Diabetes is associated with a doubled-to-tripled risk of MI [68]. Diabetes can
affect CVD risk through different mechanisms. Hyperglycemia can cause
increased glycosylation of almost any protein. Glycosylated proteins form
advanced glycosylation end products that in turn can induce endothelial
dysfunction, and accumulation and oxidation of LDL particles [69]. Aside
from the effects of hyperglycemia, diabetes is also closely related to other
CVD risk factors. Insulin resistance, together with a combination of central
obesity, hypertension, and dyslipidemia, is called the metabolic syndrome
[70], highlighting the strong relationships between obesity, T2DM, and other
CVD risk factors.
Obesity and dyslipidemia
Obesity, and particularly abdominal obesity, is strongly associated with the
presence of several lipid metabolism abnormalities. Increased triglycerides,
LDL and VLDL cholesterol; and reduced high density lipoprotein (HDL)
cholesterol are all associated with abdominal obesity [71,72]. Abdominal
obesity can cause hypertriglyceridemia through the abnormal release of
FFAs to the liver, inducing increased endogenous secretions of triglycerides
in the form of VLDL [73]. Increased triglycerides are inversely associated
with HDL cholesterol levels, and also inversely associated with LDL particle
size and density, leading to a more atherogenic LDL particle [74,75]. The
associations between triglyceride levels and nontraditional risk factors
(increased insulin, apolipoprotein B levels and small, dense LDL particles)
suggest that hypertriglyceridemia could be used with central obesity as a
screening tool to identify persons at high risk of CVD [76].
19
obesity
Measurement of fat mass
Several methods for measuring fat mass have been developed. The most
commonly used are BMI, WC and WHR. For measuring VAT computed
tomography (CT) is the gold standard [77], although the high cost and low
availability makes this method less commonly used. Alternatives for
measuring fat mass include skinfold measurements, bioelectrical impedance,
quantitative ultrasound (QUS), and dual energy x-ray absorptiometry
(DEXA) [78,79].
Anthropometry
Body composition, especially fat mass, can be measured in many different
ways. All have advantages and disadvantages. The most common methods
are anthropometric and thus easy, quick and readily available. BMI is
calculated as weight divided by the squared height and is a measure of
general obesity. Although widely used in the clinical setting this method has
several drawbacks [80]. Although predictive of the risk of MI, T2DM, and
other adverse conditions, BMI gives only a crude measure of general obesity.
Illustrated in Figure 4 are two individuals with the same weight and height,
one of them lean and muscular and the other abdominally obese. They have
the same BMI but different risks for CVD.
Figure 4. Comparison of two individuals with
widely different body composition, but the same
BMI.
20
Measurement of fat mass
Other easily obtained anthropometric measures are WC (measured as the
horizontal circumference approximately at the umbilicus), WHR (ratio
between WC and hip circumference, measured at the trochanters) and
sagittal abdominal diameter (SAD, the distance from the back to the upper
abdomen, measured in the supine position). Instead of general obesity, these
methods all in some way assess the amount of VAT. As VAT is a stronger
predictor of the adverse outcomes of obesity than total fat mass these
measures are more predictive of CVD risk than BMI [81]. WC emphasizes the
absolute amount of abdominal fat mass, similar to the SAD, while the WHR
instead emphasizes the proportion of abdominal fat relative to hip size.
Whether any of these measures are superior to the others is inconclusive.
When assessing CVD risk factors such as triglycerides, HDL cholesterol,
insulin, and glucose levels, most studies find WC to be as good or better than
WHR [82-84]. On the other hand, several large studies evaluating the risk of
incident CVD found that WHR was more strongly associated [85], or tended
to be more strongly associated [86] with the risk of incident CVD. In any
case, the advantage of WHR, WC and SAD are attributed to the ability to
estimate abdominal fat mass. However, these measures are still only
anthropometric. A direct measure of fat mass requires other methods.
Computed tomography
CT is the gold standard for quantifying abdominal fat mass and VAT. VAT is
usually measured by CT by taking a cross-sectional scan at the umbilicus.
The contours of the SAT and the parietal peritoneum are manually traced
and the intraperitoneal (visceral) fat volume calculated by subtracting the
subcutaneous fat volume from the total fat volume of the scan. This has been
shown to be highly correlated to total VAT in cadaver studies, and CT is thus
used as the reference when investigating how well other methods can assess
VAT [77].
Dual energy x-ray absorptiometry
DEXA is a low-dose radiation method that is gaining in popularity. It is
based on a model that separates the body into three different compartments:
bone mineral, lean mass and fat mass. It is primarily used to measure BMD,
but also gives an accurate estimate of regional fat mass and lean mass. The
advantage of DEXA is that since it is x-ray-based, it can accurately
differentiate fat mass from other tissue, thereby circumventing many of the
inherent drawbacks of anthropometry [87]. It can also accurately measure
total body fat mass, abdominal fat mass, and any other regional fat depot
using only a single measurement. The drawbacks of DEXA are that, while
21
obesity
strongly correlated to CT-measured total abdominal fat mass, it cannot
differentiate between SAT and VAT [88]. Also while a DEXA-measurement
involves less radiation and is more readily available than CT, it is still an
operator-dependent method of measuring fat mass.
More information on the accuracy and precision of DEXA is in Materials
and methods.
22
osteoporosis
Osteoporosis
Chapters
Epidemiology
Definition of osteoporosis
Risk factors for
osteoporosis
Bone structure
Measurement of bone
mass
23
osteoporosis
Osteoporosis
Epidemiology
Osteoporosis is generally a silent disease, characterized by reduced bone
mass and deterioration of the bone tissue microstructure, leading to bone
fragility and an increased risk of fractures (Figure 5). The development of
osteoporosis often spans over several decades, gradually weakening the
bone. Since the bone loss is asymptomatic, a low-energy fracture is often the
first presenting sign [4].
Figure 5. Comparison of a bone with normal bone microstructure with
an osteoporotic bone with decreased BMD and microstructural
deterioration.
Osteopenia and osteoporosis currently are common, with an exponential
increase in prevalence after 50 years of age [89]. Additionally, the risk of
sustaining an osteoporotic fracture is 50% in women and 25% in men [90].
In Sweden, hip fractures alone occurs in 18,000 individuals each year [91],
greatly affecting quality of life. Hip fractures are associated with a high
morbidity and a considerable increase in mortality [92]. Hip fractures
worldwide are predicted to increase fourfold by 2050 because of an
increasingly elderly population [93].
24
Definition of osteoporosis
Definition of osteoporosis
BMD is a continuous variable, with an inverse linear relationship to the risk
of a fracture. The diagnosis of osteoporosis is therefore an arbitrary cut-off
that represents a greatly increased risk of fracture compared to a normal
bone [94].
The diagnosis of osteoporosis is based on measurements of BMD as specified
by the WHO in 1994 [94], and later updated by the International
Osteoporosis Foundation [89]. The preferred method for BMD
measurements is DEXA. A BMD ≤2.5 standard deviations (SDs) below that
of young adult females from the same ethnic group is considered
osteoporosis, while ≤-1 to >-2.5 SD is considered osteopenia. These reference
values were defined in women, but the same values are used in men [89].
Figure 6. Schematic illustration of the accumulation and loss of bone mass
during a lifetime for men and women. Peak bone mass occurs between the
third and fourth decade of life, after which men have a continuous decline in
bone mass. In women however, a faster decline in bone mass occurs the first
years after menopause, after which the decline slows to be comparable to
men.
25
osteoporosis
Risk factors for osteoporosis
The risk of sustaining a fracture is inversely related to bone strength,
estimated by BMD, and is directly related to the severity of trauma [95]. This
thesis focuses on the association between BMD and CVD, and will be limited
to risk factors for reduced BMD. Risk factors associated with obesity and
osteoporosis, including smoking, hypertension, diabetes and hyperlipidemia
are discussed in further detail in the chapter Adipose tissue, the
skeleton and cardiovascular disease.
Risk factors can be divided into non-modifiable and modifiable risk factors
(Table 1). Non-modifiable risk factors include genetic factors that are
estimated to determine 50-80% of the variance in bone mass [96,97]. These
are thus the strongest determinant of bone mass. Age is also an important
risk factor, as with advancing age, the risk for osteoporosis increases linearly.
Both men and women reach peak bone mass somewhere between the end of
longitudinal growth and third or fourth decade, depending on bone site [98100]. After peak bone mass is reached, a decrease in bone mass occurs
throughout life. However, men and women do not have similar patterns of
decrease. Men display a slow and continuous decrease while women have an
accelerated bone loss in the years immediately after menopause, because of
menopause-associated estrogen deficiency (Figure 6) [101-103].
Modifiable risk factors include physical inactivity, body weight, smoking,
alcohol, malnutrition and several diseases and their treatments [104-107].
Physical inactivity is one of the most important risk factors. Physical activity
is associated with increased BMD in children and adults [108,109] and also
to amelioration of bone loss associated with aging [110]. Lifestyle factors
such as smoking [111] and excess alcohol consumption [112] can also
negatively influence BMD.
26
Risk factors for osteoporosis
Table 1.
Risk factors for osteoporosis
Non-modifiable risk factors
Modifiable risk factors
Age
Physical inactivity
Sex
Low body weight
Heredity
Smoking
Premature menopause
Excess alcohol consumption
Malabsorption
Hypogonadism
Hyperparathyroidism
Prolonged glucocorticoid use
Vitamin D or calcium deficiency
27
osteoporosis
Bone structure
The skeleton has several important functions in the body. It regulates
calcium homeostasis in the blood, and is responsible for our ability to move,
serving as levers for the skeletal muscles and protecting vital structures from
trauma. To fullfill these roles, the skeleton must be able to withstand
considerable force [113].
Figure 7. Bone anatomy of the proximal femur.
All bones consist of two types of bone tissue, namely cortical and trabecular
bone (Figure 7). Cortical bone comprises the outer surface of bone and is
much denser than the trabecular bone, and provides most of the structural
integrity of bone, contributing about 80% of the skeletal weight. Cortical
bone consists of a system of cylindershaped units called osteons, which are
running parallel to the length of the bone. In the center of each osteon is a
28
Bone structure
canal, the Haversian canal, housing blood vessels and nerves. The osteons
are in contact with the marrow cavity, the periosteum and other osteons
through oblique and transverse canals. The structure of the cortical bone is,
because of the dense organization of the osteons, very resilient to mechanical
loading force [113].
Trabecular bone is much more porous than cortical bone, and houses blood
vessels and hematopoeic stem cells. The osteons in the trabeculae are called
packets, with the strongest and thickest trabeculae arranged in the direction
of the greatest loading force on the bone. This allows for mechanical strength
without unnecessary weight. Bone turnover is also higher in trabecular bone
than cortical bone, signifying its important role in mineral metabolism [113].
Despite these differences, both bone types contain the same materials:
organic bone matrix, bone mineral, and several types of bone cells. The
organic bone matrix is the framework for bone mineral deposition. Adult
bone is mainly composed of type I collagen, produced by osteoblasts
(described below). The inorganic component of bone, the bone mineral,
consists primarily of calcium and phosphorus, organized into hydroxyapatite
crystals. The collagen gives the bone its resistance to torsional forces, while
the bone mineral makes the bone stiff and provides resistance to
compressional forces [113].
Bone modelling and remodelling
The skeleton is in a constant state of regeneration with two processes
occurring simultaneously. Bone resorption is the process that breaks down
bone. Bone formation is responsible for building up bone. Bone modelling is
when bones change their overall shape in response to mechanical loading
and other factors, while remodelling is the process by which bone is renewed
to maintain bone strength [113]. Several factors influence bone turnover,
most notably hormonal stimulation by testosterone and estrogen [114,115].
The cells responsible for constantly renewing bone tissue are osteoblasts,
osteoclasts and osteocytes. Osteoblasts are bone-forming cells that
synthesize and secrete type I collagen and enzymes that facilitate the
mineralization process. Osteoclasts promote bone resorption by secreting
acids and lysosomal enzymes onto the mineralized bone surface (Figure 8).
The third type of cells is the osteocyte. Osteocytes are terminally
differentiated, inactive osteoblasts. When osteoblasts secrete unmineralized
bone matrix, some osteoblasts become trapped by the secreted collagen, and
become incorporated into the matrix. These osteocytes develop long
cytoplasmic processes, which connect them to other osteocytes via tiny
29
osteoporosis
canals called canaliculi, forming a network of osteocytes [113]. The
osteocytes sense changes in mechanical loading, and can influence
osteoblastic and osteoclastic activity. This leads to altered bone turnover, in
order to adapt to the current state of physical activity [116].
The renewal of bone tissue is continuous and allows the bone structure to
adapt to the current loading state. Osteoclasts and osteoblasts move together
as a single unit, called a basic multicellular unit, strongly coupling bone
resorption and bone formation under most conditions. The resorptional
phase of remodelling takes about three weeks, while bone formation takes up
to three months [117].
Figure 8. Osteoclast resorbing bone.
30
Measurement of bone mass
Measurement of bone mass
Several different techniques are used to measure bone mass. DEXA is the
most common, but peripheral DEXA (pDEXA), QUS and peripherial
quantitative computed tomography (pQCT) are also used. Other less
commonly used techiques are quantitative computed tomography (QCT) and
magnetic resonance imaging to investigate the microstructure of the skeleton
[118].
Dual energy x-ray absorptiometry
DEXA is currently the most common bone densitometry technique, and the
WHO criteria for osteoporosis are based on DEXA BMD measurements [89].
DEXA is based on a model that separates the body into three different
compartments: bone mineral, lean mass and fat mass. It can measure total
body BMD as well as BMD at specific skeletal sites, e.g. the spine, femoral
neck or radius. The advantages of DEXA are high accuracy [119], short scan
time, and a relatively low radiation dose. A limitation of DEXA is that all
DEXA scans are two-dimensional so BMD measurements can be affected by
bone size. When two different bones with the same volumetric BMD (vBMD,
g/cm3) but different width and height are measured using DEXA, the larger
bone will appear to have a higher BMD [118,120].
More information on the accuracy and precision of DEXA in Materials and
methods.
Peripheral dual energy x-ray absorptiometry
pDEXA uses the same technique as DEXA. Because the device is smaller, the
most notable difference is that pDEXA can measure BMD only at
perhipheral sites, such as the radius or calcaneus. The main advantage of
pDEXA is that the equipment is portable, cheaper, and easier to use than a
regular DEXA scanner [118]. Since hip fractures are the most serious
complication of osteoporosis, BMD at the hip is of great interest. How well
BMD measured peripherally correlates with BMD at the hip is unclear. Even
though a low BMD at peripheral sites has been shown to increase the risk of
future fractures, BMD of the hip is a better predictor of fracture risk [118].
31
osteoporosis
Quantitative ultrasound
QUS uses sound waves instead of x-rays to assess bone strength. With sound,
the properties measured are different than DEXA. QUS measures how fast
sound travels through the bone and how the sound waves are absorbed. This
is combined into a stiffness index that correlates to DEXA-derived BMD
values [121]. Advantages of QUS include its low cost, the lack of ionizing
radiation and the portability of the equipment [122]. The limitations are that
different QUS devices can vary significantly when measuring the same
parameter, as no universal standard exists for QUS [123]. Also, the WHO
osteoporosis criteria are not applicable for QUS, and correlation coefficients
between QUS measurements and DEXA measurements are in the range of
0.4-0.7 [124,125].
Computed tomography and peripheral computed tomography
QCT and pQCT are the most precise methods for measuring true vBMD.
While the other methods rely on two-dimensional imaging, QCT scans
several cross-sectional scans, measuring with high accuracy both the bone
area and bone mineral content. It can also differentiate between the
trabecular and cortical bone compartments [126]. The advantages of QCT lie
in the high accuracy and ability to see not only BMD but also bone structure.
The limitations are that it is expensive and results in higher radiation
exposure than DEXA. Additionally, the WHO osteoporosis criteria are not
applicable for QCT measurements [126,127].
32
adipose tissue, the skeleton and cardiovascular disease
Adipose tissue, the skeleton
and cardiovascular disease
Chapters
Fat distribution and bone
mineral density
Bone mineral density and
cardiovascular disease
The skeleton and energy
metabolism
adipose tissue, the skeleton and cardiovascular disease
Adipose tissue, the skeleton and
cardiovascular disease
While traditionally regarded as unrelated diseases, new evidence suggests
that the roles and functions of the skeleton and adipose tissue are more
highly intertwined than previously thought. Leptin, a peptide hormone
primarily manufactured by adipocytes, increases and decreases bone
formation through actions in the hypothalamus, in addition to its appetiteregulating properties [128]. Additionally, osteocalcin (OC), a protein
secreted solely by osteoblasts, has been shown to regulate glucose
homeostasis through actions on the pancreas, as well as on adipose tissue, in
animal models [129].
Fat distribution and bone mineral density
Low body weight is an important risk factor for hip fracture, since BMD is
often correspondingly low. Obesity, on the other hand, is often associated
with higher BMD. This has been attributed to the differences in load on the
skeleton for different body weights, which results in differences in
mechanical stimuli [130]. Several studies have found a correlation between
total fat mass and BMD [131-133], and while the explanation of increased
mechanical load on the skeleton is straightforward and logical, other
explanations have been proposed. Adipocytes are an important source of
estrogen production in post-menopausal women, and estrogen inhibits bone
resorption [134]. Also, adipocytes secrete several adipokines, some of which
may influence BMD [135]. But since neither adipose tissue nor the skeleton
are inert organs, the ramifications of increasing body weight on bone are
complicated. For example, an increased risk of fractures has been observed
in obese children [136], and aging and menopause are both associated with
increased fat mass, but a decline in BMD [101-103,137].
While total body weight might positively correlate to BMD, different
distributions of body fat mass seem to differently influence BMD. Earlier
studies investigating fat distribution and BMD found negative correlations
between central obesity and BMD [138,139], suggesting that VAT might be a
factor that cause lower BMD.
Bone mineral density and cardiovascular disease
BMD is usually described in the context of fracture risk. However,
osteoporosis and CVD are often seen in the same individuals, suggesting that
these two diseases might be linked. Prevalent CVD is related to low bone
34
Bone mineral density and cardiovascular disease
mass [140,141], and low bone mass has been shown to be associated with
increased CVD mortality [142,143]. This relationship is partly regarded as a
result of the normal aging process, as osteoporosis and CVD are very
common diseases, but as noted above, a shared etiology has been proposed,
independent of age. Suggested ethiologies include hypertension [144],
smoking [111], hyperlipidemia [145,146], and atherosclerosis [147,148].
Interestingly, these risk factors are, as discussed previously, also involved in
a complex interplay with VAT (Figure 9).
Obesity-associated CVD risk factors and osteoporosis
Hypertension
Hypertension has, in several studies, been associated with lower BMD
[149,144]. It has been postulated to decrease BMD by mobilizing calcium
from the bone, leading to hypercalciuria and causing increased calcium
losses [150]. In contrast, treatment with thiazide diuretics seems to improve
BMD by reducing urinary calcium excretion [151,152].
Hyperlipidemia
Hyperlipidemia has been shown to be associated with BMD in several
studies [153,154]. LDL cholesterol has been shown to reduce calcium uptake
in bone and to promote vascular calcification [145], and is also associated
with increased osteoclastic differentiation [146]. Furthermore, statins, which
are widely used to treat hyperlipidemia, have been associated with increased
BMD in some studies [155,156].
Physical inactivity
Physical activity has been shown in all ages to be beneficial for BMD, for
both reaching a higher peak bone mass [157-159] and reducing postmenopausal bone loss [160,161]. Sedentary men and women have
significantly lower BMD compared to active controls [162,163]. Additionally,
performing light physical activity regularly has been shown to be associated
with a 70% decreased risk of ischemic heart disease [164]. The level of
physical activity throughout life is thus an important modifiable risk factor.
Smoking
Smoking is, in addition to CVD, also associated with lower BMD and greater
rates of bone loss [165]. One factor is the lower body weight found in
smokers [54], but nicotine might also have a direct effect on bone formation
35
adipose tissue, the skeleton and cardiovascular disease
[166]. Smoking may influence hormone levels [167,168] and increase
atherosclerotic burden [60].
Diabetes
The association between diabetes and BMD is not completely understood. In
cases of diabetes type 1, BMD is decreased [169], while studies on individuals
with T2DM have shown conflicting associations with BMD. Even though the
fracture risk can be markedly increased in persons with T2DM [170], often
average or even higher BMD is found in T2DM compared to healthy controls
[171]. While T2DM is seemingly not associated with decreased BMD, other
aspects of bone strength are influenced by this condition. Bone area is
smaller, which can partially explain the increased BMD seen in some studies,
as a specific amount of bone mineral within a smaller bone area would be
interpreted as a higher BMD [172]. Nonetheless, diabetes is associated with
changes in BMD and an increased risk of fractures, and is also associated
with increased risk of CVD.
Figure 9. Schematic model of the association between BMD and CVD. CVD
risk factors are associated with both obesity and BMD, and may contribute to
the association between lower BMD and increased risk of CVD.
36
The skeleton and energy metabolism
The skeleton and energy metabolism
Obesity-associated CVD risk factors appear to negatively influence BMD, and
adipocyte-derived leptin can both up-regulate and down-regulate bone
formation [128]. Adipose tissue might be able to influence skeletal
metabolism in several ways, and the skeleton may be able to influence
adipose tissue [129]. Only a few molecules produced in the skeleton are
released into the systemic circulation, one of them OC.
In animals
OC, traditionally used as a bone formation marker [173], is able to influence
glucose homeostasis by influencing adipose tissue in mice. OC knockout
mice (OC-/-) have increased VAT, are insulin resistant, and have altered
pancreatic β-cell function. Supplementing OC-/- mice with recombinant OC
increases insulin sensitivity and lower fasting plasma glucose. The metabolic
effects of OC are partly mediated through an increased expression of
adiponectin in adipocytes [129].
In humans
The endocrine properties of the skeleton have also been investigated in
humans. In mice, OC seems to mainly influence glucose metabolism, and
whether this is similar in humans is of great interest. OC has in crosssectional and longitudinal studies been shown to be associated with glucose
metabolism. OC is a negative predictor of glucose levels [174,175], and
persons with diabetes have lower OC levels than healthy controls [174,176].
When improvement occurs in individuals with poor glycemic control, an
increase in OC is seen [177]. Studies investigating OC and lipid metabolism
are inconclusive. While Fernández-Real et al. [178] showed that OC is
inversely associated with triglycerides Zhou et al. [176] found a direct
association between triglycerides and OC, and an inverse association
between HDL cholesterol and OC. In addition to glucose and lipid
metabolism, OC has also been shown to be associated with body
composition. Inverse associations have been found for OC and BMI
[174,179], total fat mass [174], and VAT [180], suggesting that the skeleton
could be involved in fat accumulation.
The precise role of the skeleton, and particularly OC, in energy metabolism is
not known. OC might as noted above be a marker of a beneficial metabolic
phenotype, as it is associated with glycemic control, lower glucose levels, and
body fat distribution. OC, however, is also associated with decreased femoral
neck BMD [181], increased risk of MI [182] and increased risk of death [183].
37
thesis aims and hypotheses
Thesis aims and hypotheses
The overall aims of this thesis were to investigate the associations of body
composition to CVD risk factors and incident MI, and to investigate whether
the proposed interplay between the skeleton and energy metabolism might
be influenced by high impact loading on the skeleton.
Study I-II. Regional fat mass is associated to CVD risk factors and incident
CVD, but shows differential associations that depend on the location of the
regional fat mass. By measuring regional fat mass with DEXA, the aims were
to investigate how different regional fat depots are associated to CVD risk
factors and the risk of MI.
Study III. BMD is suggested to be inversely associated with CVD risk. Several
CVD risk factors also can negatively influence BMD, so a common etiology
has been proposed between osteoporosis and CVD.
An aim was to investigate if BMD of the femoral neck and hip is associated
with the risk of MI, and whether the association could be explained by
associations between BMD and traditional CVD risk factors.
Study IV. The skeleton seems to be involved in various aspects of energy
metabolism, with OC as the proposed link. We hypothesized that bone
formation and skeletal metabolic signalling are coupled. The aim was to
determine if a high impact exercise intervention would lead to changes in
glucose and lipid metabolism in young men, and whether these changes
could be explained by changes in OC levels.
38
materials and methods
Materials and methods, Results
Chapters
DEXA
Study populations
The Bone mineral and fat
mass database
The MONICA database
The VIP database
Statistical methods
Ethics
Results
Study I-IV
39
materials and methods
Materials and methods
DEXA
In Study I-IV, a table-top DEXA (GE Lunar, Madison, WI, USA) was used for
all bone mineral and regional fat mass investigations (Figure 10). Until
1998, a Lunar DPX-L was used. From 1998 a Lunar IQ was used.
Figure 10. Table-top DEXA machine.
Regional fat mass measurements
DEXA accurately measures total fat mass (r2=0.85-0.90 compared to
hydrostatic weighing) [184], abdominal fat mass (r2=0.76-0.92 compared to
CT) [88,185] and VAT (r2=0.44-0.62 compared to CT) [88,186].
The coefficient of variation (CV, [mean/SD] x 100) is a measure of
dispersion, with a low CV indicating a lower dispersion in repeated
measurements. CVs were calculated from repeated scans and were 2% for
total fat mass and 2% for abdominal fat mass.
40
Study populations
Bone mineral density measurements
DEXA is the gold standard for measuring BMD, with the WHO basing the
definitions of osteopenia and osteoporosis on DEXA BMD measurements
[94]. DEXA accurately measures BMD in both total body and specific
subregions, such as the spine and hip. Both femoral neck and total hip had a
CV for BMD measurements of 1%.
Study populations
For Study I-III, the original cohort was gathered from the Bone density and
fat mass database (described below). Study I, which investigated body
composition and the association to CVD risk factors, included 592 men and
women who had their total fat mass measured using DEXA. Data on blood
pressure, lipids, and glucose were collected through the Västerbotten
Intervention Programme (VIP, described below), and were available for all
participants.
Study II investigated body composition and the risk of MI, and included
2336 women and 922 men who had their total fat mass measured. A subset
of 1523 women and men also participated in the VIP. This subset was used to
investigate the association of blood pressure, lipids, and glucose to regional
fat mass and to investigate whether these factors would affect the association
between regional fat mass and the risk of MI.
Study III investigated BMD and the risk of MI in 5490 women and 1382 men
who had their BMD measured. A subset of 2730 women and 574 men had
also participated in the VIP and thus had blood pressure, lipids, and glucose
measured. This subset was used to investigate the association between the
above background variables and BMD, and whether these variables affect the
association between BMD and MI.
In Study IV, 50 healthy men aged 20-32 were enrolled. Participants were
notified of the study through posters at sporting facilities and at Umeå
University campus, Umeå, Sweden.
41
materials and methods
The Bone density and fat mass database
DEXA has been used since 1991 at the Sports Medicine Unit, Umeå
University, Sweden, to measure BMD and total and regional fat mass. All
individuals who have been measured by DEXA at the unit have been
included in the Bone density and fat mass database. In early 2007, 12302
individuals had had BMD measured at some site. A subset of 4957
individuals had measured both total body BMD and regional fat mass.
Reasons for BMD measurements were either a referral, most often from a
general practitioner, or participation in a research project conducted at the
Sports Medicine Unit. In 2005 and 2006, all individuals measured were
cathegorized by the reason for DEXA scan. The most common causes were
previous fracture (28.6%), fear of osteoporosis (23.1%), taking oral
corticosteroid therapy (20.1%) or involvement in research projects (15.1%).
The MONICA database
The WHO MONICA project (MONitoring trends and determinants In
CArdiovascular disease) was started by the WHO in the early 1980s with
participating centres from all over the world. Its purpose was to gather
information about the trends in MI incidence [187]. The project was
discontinued in 1995, but continues as a regional project in Northern
Sweden [188]. The original project included only patients between 25-65
years; however, since 2000 also patients aged 65-75 years have been
included in the Northern Sweden MONICA project. To ensure high validity
of MI diagnosis, strict critera are used to validate MI. Originally, a MI
diagnosis required either clear electrocardiogram (ECG) changes typical of
MI according to the Minnesota code or cardiac enzyme elevation together
with either typical chest pain or “probable” ECG changes and lesser
symptoms. If the event was fatal it was considered to be a definite MI if they
had findings visible to the naked eye of a fresh MI or coronary thrombosis
[188]. In the late 1990s, troponins were introduced as biomarkers, and as of
2000 are used by all hospitals in northern Sweden. From 2000, all MI
diagnoses are based on the presence of typical chest pain and troponin. If
only one marker indicate a MI an ECG is analyzed before a diagnosis is made
[189].
The VIP database
The VIP is an ongoing community intervention programme that annually
invites all residents of Västerbotten county aged 40, 50 and 60 to a
systematic risk factor screening and individual health counselling [190]. The
VIP started in 1985 as the Norsjö Project. At that time, Västerbotten had one
42
The VIP database
of the highest incidences of CVD in Sweden and the project was initiated to
reduce morbidity and mortality from CVD and diabetes. Residents 30 years
of age were initially invited but participation after 1995 is restricted to those
40, 50 and 60 years of age. At screening, height and weight are measured in
light clothing. Blood pressure is measured in the supine position using a
mercury sphygmomanometer after 5 minutes of rest. An oral glucose
tolerance test is performed with a 75 g oral glucose load according to WHO
standards. Total cholesterol and triglycerides are analysed using a Reflotron
bench-top analyser (Roche Diagnostics). Until 2004, this was also used to
analyze plasma glucose. Since 2004, a Hemocue bench-top analyser (Quest
Diagnostics) has been used. In addition to anthropometry and blood tests, all
VIP participants are also invited to fill in a comphrehensive questionnaire
detailing factors such as social support, working environment, self-percieved
health, previous illness, family history of disease, physical activity, tobacco,
alcohol, and eating habits. In addition, the screening concludes with
individual counselling in which the participants discuss the results of the
screening. The results are stored in a database that is frequently used for
research. All applications to extract data from the database are evaluated by
a scientific board connected to the programme [190].
43
materials and methods
Statistical methods
Study I. Bivariate correlations between the different regional fat mass
measures and continuous CVD risk factors were tested using Pearson’s
coefficient of correlation. Differences between three or more groups were
analysed using analysis of variance (ANOVA) and Bonferroni’s post hoc test.
The relationships between different regional fat mass measures and
categorical CVD risk factors were determined using logistic regression.
Study II. Differences between two groups were tested using Students t-test
for independent samples. The relationships between different regional fat
mass measures and MI events were determined using Cox proportional
hazards models. The area under the reciever operator characteristic curve
was compared for different regional fat mass measures in relation to the risk
of MI. Logistic regression was used to investigate the relationships between
quartiles of the different regional fat mass measures and categorical CVD
risk factors.
Study III. Differences between two groups were estimated using the
Student’s t-test for independent samples and the Mann-Whitney U test for
the non-normally distributed variables. Correlations between BMD and risk
factors were evaluated using Pearson’s correlation coefficient. Variables nonnormally distributed were transformed using the natural logarithm. The
relationships between BMD and vBMD, and MI risk were determined using
Cox proportional hazards models. Logistic regression was used to investigate
the relationship between different estimates of bone mass and different risk
factors for MI, such as hypertension, diabetes, hypertriglyceridemia, and
smoking.
Study IV. Differences between two groups were tested using the Student’s ttest for independent samples and the Mann-Whitney U-test for variables
non-normally distributed. Variables were then transformed to normality
using the square root of the variable in multivariate analyses. Changes in
variables between baseline and study endpoint were tested using a paired
samples t-test for the intervention and control group separately. To test for
between-group differences between baseline and study endpoint, linear
regression was used. Between-group differences in changes in metabolic
variables were tested by several models: an unadjusted model, a model
adjusted for baseline measurements of the dependent variable, a model
adjusted for age, weight, and baseline measurements, and a model adjusted
for baseline measurements and changes in accelerometer-measured physical
activity.
44
Ethics
A p-value of less than 0.05 was considered to be significant in all studies.
SPSS for the PC (version 15 and 17, SPSS Inc., Chicago, IL, USA) was used
for statistical analyses.
Ethics
All studies (Study I-IV) were approved by the Regional Ethical Review Board
in Umeå, Sweden.
45
results
Results
Study I
Abdominal and gynoid fat mass are associated with cardiovascular risk
factors in men and women.
Aim
To explore the association between CVD risk factors and regional fat mass,
with a focus on the contributions of abdominal fat mass and gynoid fat mass.
Methods
The study cohort consisted of 175 men and 417 women with a mean age of
45.3 years and 46.8 years, respectively. Information regarding CVD risk
factors was collected through the VIP database (described above) and was
available for all participants.
Results
In bivariate analyses, abdominal fat mass generally had the strongest
correlation to CVD risk factors in men. In women the correlations of
abdominal fat mass and the abdominal to gynoid fat mass ratio to CVD risk
factors were similar. Gynoid fat mass per se and the gynoid to total fat mass
ratio showed contrasting correlations with CVD risk factors. While gynoid fat
mass showed a positive correlation, the gynoid to total fat mass ratio was
negatively correlated to CVD risk factors.
To further investigate the associations of regional fat mass to CVD risk
factors, a logistic regression was performed with the presence of impaired
glucose tolerance (IGT), hypercholesterolemia, triglyceridemia or
hypertension as dependent variables. After adjusting for age, follow-up time,
physical activity, and smoking, the amount of abdominal fat mass was the
strongest predictor for most CVD risk factors in men. In women, similar
associations were found for abdominal fat mass and the abdominal to gynoid
fat mass ratio for the CVD risk factors.
The gynoid to total fat mass ratio displayed significant negative associations
for the risk of hypertriglyceridemia and hypertension for both men and
women.
46
Study I
Conclusions
In both men and women, abdominal fat mass was strongly associated with
the measured CVD risk factors. While gynoid fat mass per se was positively
associated with CVD risk factors, the gynoid to total fat mass ratio was
negatively associated with CVD risk factors.
These results suggest that abdominal fat mass is the strongest predictor of
CVD risk factors, and that gynoid fat mass could have independent beneficial
properties for the CVD risk factor profile.
47
results
Study II
Abdominal and gynoid adipose distribution and incident myocardial
infarction in women and men
Aim
To explore the associations between the risk of MI and regional fat mass,
with a focus on the contributions of abdominal fat mass and gynoid fat mass.
A secondary objective was to determine if any association could be explained
by the presence of CVD risk factors.
Methods
Total and regional fat mass was measured using DEXA in 2336 women and
922 men with a mean age of 56.2 years and 51.9 years, respectively.
Information on MI incidences was from the MONICA database (described
above). Altogether 104 first ever MIs were identified in the study population.
Information regarding CVD risk factors was collected through the VIP
database (described above) and the MONICA database (described above)
and was available for 1523 subjects.
Results
In women, after adjusting for age and smoking, the abdominal to gynoid fat
mass ratio was the strongest predictor of MI risk (hazard ratio [HR]= 2.44,
95% confidence interval [CI]: 1.79-3.32). In contrast, the gynoid to total fat
mass ratio was negatively associated with MI risk (HR=0.57, 95% CI: 0.430.77). In men, no estimate of abdominal fat mass was associated with MI
risk, but gynoid fat mass was negatively associated with MI risk (HR=0.69
95% CI: 0.48-0.98).
In women, after adjusting for the presence of confounding variables such as
smoking, hypertension, hypertriglyceridemia or diabetes, the abdominal to
gynoid fat mass ratio was still associated with an increased MI risk (p<0.05).
In men, after adjustment for smoking, hypertension, hypertriglyceridemia,
and diabetes, total fat mass and gynoid fat mass were both associated with a
decreased MI risk (p<0.05).
Conclusion
In women the proportions of abdominal fat mass and gynoid fat mass
displayed contrasting associations with MI risk. In men no association was
48
Study II
found for the different measures of abdominal fat mass and MI risk.
However, gynoid fat mass was negatively associated with MI risk, seemingly
independently of smoking, hypertension, hypertriglyceridemia or diabetes.
49
results
Study III
Low bone mineral density is associated with increased risk for myocardial
infarction in men and women
Aim
To explore the association between BMD and MI risk. A secondary objective
was to investigate whether shared risk factors for osteoporosis and CVD
could explain any association.
Methods
Femoral neck and total hip BMD were measured using DEXA in 5490
women and 1382 men with a mean age of 53.1 and 58.0 years, respectively.
Information on MI incidence was collected from the MONICA database.
Altogether, 196 first-ever MIs were identified in the study population.
Information on CVD risk factors was collected through the VIP database and
the MONICA database, and was available for 2730 women and 574 men.
Results
After adjustment for age and BMI, lower BMD of the femoral neck and total
hip was associated with increased MI risk for both women (HR= 1.33, 95%
CI: 1.08-1.66 per SD decrease in femoral neck BMD) and men (HR= 1.74,
95% CI: 1.34-2.28 per SD decrease in total hip BMD).
In both women and men, lower BMD of the femoral neck was associated
with hypertension. In women, BMD of the femoral neck was associated with
diabetes (p<0.05), BMD of the total hip was associated with
hypertriglyceridemia and BMD of the femoral neck and total hip was
associated with smoking.
Adjusting for smoking, hypertension, diabetes, and hypertriglyceridemia in
survival analysis did not materially change the results. After adjustment, the
associations between BMD and MI risk were slightly attenuated in men (HR:
1.42-1.88 in the age and BMI-adjusted model versus 1.33-1.77 in the fully
adjusted model) and similar attenuations were seen in women (HR: 1.061.25 versus 1.05-1.22).
50
Study III
Conclusion
Lower BMD was associated with an increase in MI risk for both men and
women. Women had consistently lower HRs than men in all models.
Adjusting for smoking, hypertension, diabetes, and hypertriglyceridemia
only slightly attenuated these associations.
51
results
Study IV
High impact loading of the skeleton is associated with decreased fasting
glucose in young men
Aim
To explore whether an intervention of high impact loading on the skeleton
without cardiorespiratory demand would lead to changes in glucose and lipid
metabolism. A secondary objective was to determine if changes in OC could
explain any associations.
Methods
We investigated 50 men allocated to a control group or an intervention
group. The intervention was an exercise protocol of six different jumps in
sets of five, that subjected the lower extremities to high impact loading. In
week 1 three training days were to be completed. For week 2 and 3 four
training days were to be completed. For each subsequent week, an increase
in the number of training days was made until every day was a training day.
The baseline characteristics of the control group were a mean age of 26.5
years and a BMI of 23.7. The intervention group had a mean age of 24.8
years and a BMI of 24.5. Blood samples were taken for OC, adrenalin,
noradrenalin, glucose, HbA1c, insulin, and cholesterol. Microdialysis
sampling was used to measure glycerol in the abdominal SAT as a marker of
fasting lipolysis. Total physical activity was measured using an
accelerometer.
Results
OC correlated to fat distribution by bivariate analysis, with a borderline
significant association with abdominal fat mass (p=0.05) and significant
correlation to the abdominal/gynoid fat mass ratio (p=0.03). Since OC also
displayed a strong association with age (r=-0.59, p<0.01) a partial
correlation was tested adjusting for age. In this model, OC showed no
significant correlation with any of the anthropometric variables and was not
correlated with any of the investigated metabolic parameters (glucose,
HbA1c, insulin or cholesterol). OC in unadjusted models tended to correlate
to high impact physical activity (p=0.05), but when adjusting for age, this
correlation disappeared (p=0.20).
52
Study IV
In the intervention group, total cholesterol decreased (p=0.03), glycerol
increased two-fold (p<0.01) while glucose and HbA1c tended to decrease
(p=0.07).
Between-group differences were tested thoroughly using several linear
regression analysis models using the delta value as the dependent variable.
In unadjusted analysis, the intervention group had significantly decreased
glucose compared to the control group (p=0.02), with a tendency toward a
difference for HbA1c (p=0.08) and noradrenalin (p=0.05). OC significantly
increased in the control group (p=0.005) compared to the intervention
group. When baseline values of the dependent variable were included in the
model, the intervention showed borderline significant changes in glucose
(p=0.07), and significant changes in adrenalin and noradrenalin (p=0.003
and 0.02, respectively). Additionally adjusting for age and weight did not
change these associations. Adjusting for baseline values of the dependent
variable and changes in accelerometer-measured physical activity, showed
an association between the intervention and a decrease in glucose (p=0.046)
and adrenalin (p=0.03), and tended to be associated with a decrease in
noradrenalin (p=0.06). Changes in OC were significantly different between
the intervention and control group in all models. Using the follow-up trait
instead of the delta value as the dependent variable did not change the
results.
Conclusion
The results suggested that high impact loading on the skeleton influences
glucose metabolism, perhaps involving mechanisms other than only OC.
53
results
54
Discussion
Discussion
Chapters
Regional obesity and
cardiovascular disease
Osteoporosis and
cardiovascular disease
The skeleton in energy
metabolism
Adipose tissue, the
skeleton and
cardiovascular disease
Future research
55
discussion
Discussion
Regional obesity and cardiovascular disease
Obesity is rapidly increasing in prevalence. Today more than half of all men
and a third of all women in Sweden are considered overweight or obese [6].
Central obesity is recognized as the driving force behind the deleterious
effects of obesity, as illustrated by definitions of metabolic syndrome. Central
obesity is strongly associated with risk factor clustering, and is in the
different criteria either a requisite for metabolic syndrome diagnosis, or a
factor strongly indicative of metabolic syndrome [17,70,191]. The most
common methods for measuring central fat distribution are anthropometric,
such as WC or WHR. WHR not only measures abdominal obesity, but
provides the context of hip circumference. An increased hip circumference is
hypothesized to be protective against CVD [192,193]. However, since hip
circumference is also affected by skeletal structure and lean mass, the effects
of fat mass alone are difficult to discern.
In study I and II, we investigated the contributions of regional fat mass to
traditional CVD risk factors, and to the risk of MI. We measured regional fat
mass using DEXA. In study I, we showed that abdominal fat mass had the
strongest association to CVD risk factors in men, and while in women the
associations of the abdominal to gynoid fat mass ratio and abdominal fat
mass per se was similar. Gynoid fat mass was positively associated with CVD
risk factors. This was not surprising since gynoid fat mass was strongly
correlated to total fat mass. When gynoid fat mass was expressed as the ratio
between gynoid fat mass to total fat mass, associations to risk factors were
inverted, with a high proportion of gynoid fat mass associated with lower
risk.
In study II we investigated the association of the same fat mass depots to MI
risk. The results showed that, in women, the proportion of abdominal fat
mass expressed as the ratio of abdominal to gynoid fat mass was the
strongest predictor of MI. The ratio of gynoid to total fat mass, on the other
hand, was associated with a decreased MI risk. Surprisingly, in men
abdominal fat mass was not significantly associated to the MI risk. Instead,
gynoid fat mass per se was associated with a decreased MI risk. These
observations
remained
after
adjusting
for
hypertension,
hypertriglyceridemia, smoking, impaired glucose tolerance, or diabetes.
When measuring fat mass to predict CVD risk factors, such as hypertension,
hyperlipidemia or diabetes, WC is recommended over WHR because 1) WC
is more strongly correlated to VAT than WHR [194]; 2) some studies found a
56
Regional obesity and cardiovascular disease
stronger association between WC and CVD risk factors than WHR [82-84];
and 3) the use of a ratio may compound measurement errors [195].
Additionally, changes in WC correlate better than WHR to changes in VAT.
While WC is associated with the absolute amount of VAT, the WHR signifies
the proportion of VAT. This reasoning is shown in Figure 11.
Figure 11. Accumulation of visceral and peripheral fat mass over several
decades. Although there has been an absolute increase in VAT, reflected as
an increased WC, WHR is unchanged.
In contrast, large studies investigating the association between body
composition and incident CVD have found that WHR tends to be better than
WC at predicting incident CVD risk [85,86,196-198].
The results of study I and II are partly in line with these results. In study I,
abdominal fat mass showed the highest association to risk factor levels in
men, while in women no measure tended to be clearly better. In study II, the
57
Discussion
abdominal to gynoid fat mass ratio was the strongest predictor of MI risk in
women, while in men no measure of fat mass was associated with increased
risk. In women, the results of study I and II showed that the proportion of
gynoid fat mass was inversely related to CVD risk factors, and was a negative
predictor of MI risk. A higher proportion of gynoid fat is associated with
decreased risk factor levels and thus hypothetically lower MI risk. However,
our results from study II suggest that gynoid fat mass influence the risk of
MI, and this is only partly mediated by association to traditional risk factors.
Adjusting for hypertension, diabetes, hypertriglyceridemia and smoking did
not substantially change the results.
Data on the association of MI to gynoid fat mass is scarce. Previous studies
investigating the relationship of MI risk and gynoid fat mass almost
exclusively used anthropometric measures such as WHR or hip
circumference. Inherently, these methods are in ways measuring not only fat
mass but also skeletal structure and muscle mass. The advantage of using
DEXA instead of anthropometric methods is that DEXA accurately measures
both total fat mass and regional fat mass, circumventing the confounding
effects of skeletal structure and fat free mass.
Several hypotheses explain why gynoid fat mass is beneficial. First, higher
leg fat mass is inversely associated with CVD risk factors, adjusting for trunk
fat [199,200]. In part, this could be because gynoid adipocytes are different
than visceral adipocytes in FFA metabolism [201]. Consequently, gynoid
adipocytes might be able to act as a metabolic sink, buffering excess
postprandial triglycerides, preventing ectopic lipid deposition and protecting
against lipotoxicity in other organs [36]. Another hypothesis is that gynoid
adipocytes have a different adipokine profile than visceral adipocytes. For
example, higher leg fat mass is associated with adiponectin, an antiinflammatory and insulin-sensitizing adipokine [34], after adjustment for
trunk fat [202,203]. Expression of IL-6 and TNF-α also differs. These
adipokines are thought to be involved in insulin resistance, since they are
secreted in smaller quantities from SAT than from VAT [29-32].
58
Osteoporosis and cardiovascular disease
Osteoporosis and cardiovascular disease
Osteoporosis is an insidious disease that progresses silently for decades until
it manifests as a fragility fracture. The lifetime risk of an osteoporotic
fracture is 50% for women and 25% for men [90].
The prevalence of osteoporosis and CVD overlap, and a shared etiology has
been proposed. The risk of fractures after a CVD event has been studied, and
seems to show that prevalent CVD increases the risk of a fracture
[204,205,140]. Whether the association between CVD and fractures is
because of decreased BMD is not fully understood. Falls are the most
important risk factor for fractures, and risk of falling might be influenced by
prevalent CVD. As stated above, several CVD risk factors, including
hypertension, hyperlipidemia, smoking, and diabetes, not only increase the
risk of incident CVD, but can also negatively affect BMD.
In study III we investigated whether low BMD increased MI risk, and
whether traditional CVD risk factors explained any association. We
investigated BMD in the femoral neck and total hip in a large group of men
and women. We followed this cohort for a mean of 5.7 years. The most
important finding was that an approximately 15% decrease in BMD of the
femoral neck in women, and BMD of the total hip in men, increased the risk
of MI by 33% in women and 76% in men. BMD was also associated with
hypertension in both men and women, and with diabetes and smoking in
women. In men, BMD tended to be associated with diabetes. Adjusting for
these traditional CVD risk factors only marginally decreased the relationship
between MI risk and low BMD. While low BMD is associated with a more
adverse risk profile, the increase in MI risk because of low BMD may largely
be mediated through other mechanisms.
Our results support the hypothesis that low BMD is a risk factor for CVD,
and MI in particular. Several previous studies have investigated the
association between BMD and CVD but their results were inconclusive.
Farhat et al. [206] used a biracial population of black and white men and
women and found that decreased BMD in black women and white men was
associated with increased risk of CVD, while no associations were found for
black men and white women. Samelson et al. [207] studied a cohort from the
Framingham Study and found that being in the bottom quartile for BMD
compared to the top quartile was associated with a 37% increased risk of any
form of coronary heart disease in women. No associations were found in
men. Szulc et al. [182] studied a cohort of men older than 50 years and found
that the lowest quartiles of BMD of the spine, total body and radius were
59
discussion
associated with a doubled risk of a CVD event compared to the other three
quartiles.
The disparate results of previous studies may be explained by the use of
different measuring sites, and different composite CVD outcomes. The
collective result of these studies suggest that BMD predicts risk of CVD, but
the impact of CVD risk factors on the association of low BMD and risk of
CVD is not fully elucidated.
The skeleton in energy metabolism
Several lines of evidence point not only to a link between osteoporosis and
CVD, but between the skeleton and adipose tissue [129]. Total fat mass and
VAT can have opposite associations with BMD [131,138]. This suggests a
more intricate association than simply increased mechanical load on the
skeleton. The answer might lie in adipokines secreted by the adipocytes.
Leptin, a hormone responsible for regulating appetite, can influence bone
formation by acting on the hypothalamus [128]. Adipose tissue influences
the skeleton, and the skeleton may have have similar capabilities. One of the
molecules released into systemic circulation by bone is OC. OC is exclusively
produced by osteoblasts, and correlates with increases in bone gain. It is
presumed to be a good marker of osteoblastic activity, and has been used as a
bone formation marker. Recently, evidence has shown that it could also be
an important player in glucose homeostasis by influencing the adipose tissue
[129]. This suggests that bone formation and regulation of energy
homeostasis might be coupled, and mediated through OC signalling.
In study IV we tested the hypothesis that high impact loading on the skeleton
would lead to changes in glucose and lipid metabolism. Another objective
was to investigate whether these putative changes could be explained by
changes in OC levels.
Few studies have used high impact loading on the skeleton as an
intervention, and also investigated metabolic variables. Kemmler et al.
showed in menopausal women that two years of intense high impact exercise
led to increased BMD, and decreased total cholesterol and triglycerides
[208]. Similar results were found by Vainionpää et al., who found that in
middle-aged, pre-menopausal women high impact exercise led to decreases
in total cholesterol and LDL cholesterol [209].
Their interventions
contained a large aerobic component, confounding the putative role of the
high impact exercises in metabolic regulation. In our study we minimized the
aerobic component to see whether high impact loading per se was associated
with changes in glucose and lipid metabolism.
60
Adipose tissue, the skeleton and cardiovascular disease
The intervention led to decreased glucose levels and decreased adrenalin,
seemingly independent of aerobic exercise. All metabolic parameters favored
the intervention group. Group-specific analyses showed that the intervention
group had increased fasting lipolysis and decreased total cholesterol,
although these changes were not significantly different from the control
group. These results seem to support the hypothesis that the skeleton can
influence glucose and lipid metabolism, but the proposed link, OC, was
unchanged in the intervention group. Instead, adrenalin and noradrenalin
significantly decreased in the intervention group, suggesting that the
sympathetic nervous system (SNS) could be involved in skeletal metabolic
signaling. Although no changes in OC were seen in the intervention group
differences were still observed between the control and intervention groups.
Speculatively, the intervention might have blunted a normal cyclical increase
in OC or increased OC removal from the blood.
We did not find any significant associations between OC and body
composition, baseline glucose, or lipid parameters. This is in contrast to
previous studies that demonstrated an association between OC, body
composition, and glucose levels [174,179,180]. The study populations in
these studies were fundamentally different from our study population.
Earlier studies used middle-aged men and women [179], the elderly [174] or
individuals with overweight or obesity [180]. Bone metabolism is agedependent, with altered levels of bone turnover markers in both men and
women [210,211]. Additionally, acute aerobic and resistance exercise studies
including only young, healthy individuals often fail to produce a change in
OC [212-214]. Insulin resistance is associated with decreased levels of OC
[215], and in studies including obese or diabetic individuals OC rises
significantly after exercise [216,217]. This might mean that in an individual
with tightly regulated metabolic parameters and normal bone turnover, the
metabolic aspects of the skeleton plateaus and does not respond unless
vigorously stressed. In contrast, an intervention in individuals with poor
glycemic control or other adverse metabolic attributes could have a more
profound effect than in healthy individuals. This could be because OC levels
are depressed, suggesting impaired skeletal metabolic signaling.
Adipose tissue, the skeleton and cardiovascular disease
Obesity is a heterogenous disease. Although increased body weight is
consistently associated with increased CVD mortality and incident CVD, a
subset of obese individuals is metabolically healthy while a subset of normal
weight individuals displays metabolic abnormalities. Studies on obese
individuals show that about 1 in 5 does not have insulin resistance,
dyslipidemia, or hypertension [23], a discrepancy that has been suggested to
61
discussion
be caused by a lower proportion of VAT relative to total fat mass. Similar
explanations may explain the associations between obesity and BMD. While
total fat mass is positively correlated with BMD, VAT is associated with
increased bone loss, and inversely correlated with BMD [138,139]. VAT is
thus a probable cause of both metabolic disturbances and lower BMD,
perhaps partly explaining the association between low BMD and CVD.
As shown in study III, low BMD is associated with an increased risk of MI.
The common pathophysiological link between CVD and lower BMD is
suggested to be atherosclerosis [147,148]. The progression of atherosclerosis
is influenced by several factors, e.g. hypertension, diabetes, smoking and
dyslipidemia, which are also associated with visceral obesity as described
above. Thus, excess VAT could influence BMD through metabolic
disturbances. Lower BMD might, to some degree, be a marker of the
metabolic burden caused by increased VAT. However, in this hypothesis, the
skeleton is only a passive bystander, responding to changes in adipose tissue
metabolism through one-way communication. This may not be the case, as
illustrated by the proposed actions of OC [129]. Additionally, the results of
study IV suggest that stressing the skeleton is associated with changes in
glucose metabolism irrespective of aerobic exercise.
Physical activity could be a key factor in the association of VAT, lower BMD,
and CVD. Physical activity is associated with VAT [218-220] and BMD in a
dose-dependent fashion, and cessation of physical activity causes a decrease
in BMD [221,159]. Physical activity also improves hypertension, insulin
sensitivity and lipid profile [222,223], partly through a decrease in VAT and
partly through improved metabolic and physical fitness [224-226]. The
current literature is treating the beneficial effects of physical activity for
metabolic health and for increased BMD as separate functions. The
cardiorespiratory component is considered to be responsible for increased
metabolic health, and the mechanical loading component of physical
exercise is considered to be responsible for skeletal adaptment. However,
parts of the beneficial metabolic effects of physical exercise may be mediated
through efferent signaling pathways of the skeleton, as suggested by study IV
(Figure 12). Bone turnover markers are used to assess the effect of physical
activity on bone metabolism. Speculatively, skeletal metabolic signaling is
coupled to bone turnover, as illustrated by OC and its role as a bone
formation marker, and its newly proposed role in glucose and lipid
metabolism [129]. Physical inactivity could therefore be associated with
impaired skeletal metabolic signaling because of a decrease in mechanical
loading, and subsequent alterations in bone turnover. Additionally, these
changes might be more pronounced in older men and women as bone
turnover is altered by age [210,211].
62
Adipose tissue, the skeleton and cardiovascular disease
Speculatively, while VAT could increase bone loss, impaired skeletal
metabolic signaling, perhaps presenting as increased bone loss, might induce
VAT accumulation through decreased metabolic health.
Figure 12. Schematic model of how physical activity might affect the link
between adipose tissue, the skeleton and CVD. Different activities confer
different strains on the skeleton. Speculatively high-impact loading on the
skeleton, independently of cardiorespiratory effects, might upregulate
efferent skeletal metabolic signaling, increasing metabolic health and
decreasing the risk of T2DM and CVD.
63
discussion
Future research
Low BMD increases the risk of CVD, an association that perhaps may be
explained by the effects of atherosclerosis on both conditions. To investigate
whether the association between BMD and CVD is dependent on
atherosclerosis in longitudinal studies is of interest.
Another interesting hypothesis is that the skeleton may be involved in energy
metabolism. If skeletal metabolic signaling affects metabolic health, these
signaling pathways could perhaps partly explain the association between low
BMD and CVD. Whether these pathways can be influenced by impact loading
on the skeleton to improve metabolic health is inconclusive. Physical activity
can be stratified according to skeletal strain, and investigating whether
higher skeletal strain gives extra metabolic benefits, can expand our
knowledge of the skeleton in energy metabolism. Moreover, if skeletal
metabolic response is dependent on age and health status is unknown, and
future studies should take this into consideration. Further research into the
efferent signaling pathways of the skeleton is warranted.
The links between adipose tissue, the skeleton and CVD are of great interest,
since identifying common mechanisms might improve risk stratification, and
identify individuals at high risk for osteoporosis and CVD. Additionally,
preventive and therapeutical interventions targeted at these common
mechanisms might reduce the morbidity and mortality associated with these
conditions.
64
summary and conclusions
Summary and conclusions
Different depots of regional fat mass seem to be associated with different risk
profiles for CVD in both men and women. Abdominal fat mass is associated
with higher CVD risk factor levels, while the proportion of gynoid fat mass is
associated with lower CVD risk factor levels. In women, the same
associations could also be found for the risk of MI, while in men, gynoid fat
mass per se was associated with a decreased risk of MI.
A 15% decrease in BMD of the femoral neck for women, and 15% decrease of
BMD of the total hip for men, increased the relative risk of MI with 33% in
women and 74% in men. Lower BMD was associated with hypertension in
both men and women, and lower BMD was in women also associated with
diabetes and smoking. However, the association between BMD and MI was
seemingly independent of these risk factors as adjusting for these conditions
only slightly attenuated the association in both men and women. The MI risk
associated with lower BMD was consistently higher in men than in women in
all tested models.
Finally, a high-impact loading intervention consisting of a series of jumps
was, after adjusting for physical activity, associated with a decrease in
glucose levels compared to a control group. Favorable trends were found in
other metabolic parameters as total cholesterol decreased and fasting
lipolysis increased.
The results of this thesis corroborate the findings that abdominal obesity is
associated with increases in CVD risk, and suggest that the gynoid fat mass
depot is associated with a decreased CVD risk, partly mediated through
associations with traditional CVD risk factors. Lower BMD is associated with
an increased risk of MI, seemingly independently of traditional CVD risk
factors. The finding that a high-impact loading intervention on the skeleton
is associated with decreased glucose levels suggests that the role of the
skeleton in energy metabolism can be stimulated through mechanical
loading.
65
acknowledgements
Acknowledgements
This research project would not have been possible without the help of
several people. I would like to extend my deepest gratitude to all of you.
To Peter and Anna Nordström, my supervisors, who have guided me through
the work of this thesis and who have taught me that the single most
important thing in research and life is to truly enjoy what you do. You have
sparked my interest in research, and I couldn’t have asked for better role
models.
To my PhD student colleagues, for always being willing to discuss matters of
research and non-research alike. I would especially like to thank Fredrik
Toss, Fredrik Eklund, Taru Tervo and Gabriel Högström who have with
warmth, compassion and friendship enriched my scientific journey.
To all personnel and co-workers at the department of Sports Medicine, thank
you for an enjoyable working environment. I have always felt welcome, and I
have greatly appreciated the various discussions in and out of the lunch
room. Without you, completing this work would have been much more
difficult.
To all co-authors who have provided valuable input and different
perspectives into the process of writing the research papers in this thesis. I
have greatly appreciated the opportunity to learn from your experience.
This thesis would not have been the same without the amazing coverart,
figures and flowcharts. I would like to express my profound gratitude to
Frida Persson, Nils Persson and Gustav Andersson for their amazing
contributions. You have greatly surpassed all of my expectations, and I
expected a lot!
Finally I would like to express my deepest gratitude to my loving family; to
my parents Ove and Eva, and siblings Mattias and Henrik for your abundant
love and support throughout my life; to Urban and Birgitta for your loving
care. A big heartfelt thank you to Lisa, for supporting and encouraging me
every step of the way. I cannot count all the times you patiently have listened
to me talk about my work, always interested and always eager to help. You
are the love of my life.
66
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