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CARDIOVASCULAR JOURNAL OF AFRICA • Volume 28, No 2, March/April 2017
AFRICA 125
Review Article
The integrated effect of moderate exercise on coronary
heart disease
Marc J Mathews, Edward H Mathews, George E Mathews
Abstract
Background: Moderate exercise is associated with a lower
risk for coronary heart disease (CHD). A suitable integrated
model of the CHD pathogenetic pathways relevant to moder-
ate exercise may help to elucidate this association. Such a
model is currently not available in the literature.
Methods: An integrated model of CHD was developed and
used to investigate pathogenetic pathways of importance
between exercise and CHD. Using biomarker relative-risk
data, the pathogenetic effects are representable as measurable
effects based on changes in biomarkers.
Results: The integrated model provides insight into higher-
order interactions underlying the associations between CHD
and moderate exercise. A novel ‘connection graph’ was devel-
oped, which simplifies these interactions. It quantitatively
illustrates the relationship between moderate exercise and
various serological biomarkers of CHD. The connection
graph of moderate exercise elucidates all the possible inte-
grated actions through which risk reduction may occur.
Conclusion: An integrated model of CHD provides a summa-
ry of the effects of moderate exercise on CHD. It also shows
the importance of each CHD pathway that moderate exercise
influences. The CHD risk-reducing effects of exercise appear
to be primarily driven by decreased inflammation and altered
metabolism.
Keywords: moderate exercise, biomarkers, integrated model
Submitted 11/9/15, accepted 5/5/16
Published online 12/12/16
Cardiovasc J Afr 2017; 28: 125–133 www.cvja.co.za
DOI: 10.5830/CVJA-2016-058
Coronary heart disease (CHD) is known to be the major
cause of death globally.1 However, it is well documented that
regular moderate physical exercise is associated with fewer
CHD events in symptomatic2 and asymptomatic3,4 subjects.
The precise mechanisms underlying this inverse association
are unclear. However, it is apparent that CHD risk may be
substantially mediated, through moderate exercise, by changes
in blood pressure, insulin resistance and glucose intolerance,
systemic inflammation, triglyceride concentrations, low high-
density lipoprotein (HDL) levels and obesity.4,5
It may therefore prove beneficial to quantify and elucidate
the underlying pathogenetic effect of moderate exercise on the
pathogenesis of CHD. Using a previously described integrated
model of CHD,6,7 we investigated the interconnectivity of
moderate exercise and the pathogenesis and pathophysiological
attributed to CHD.
Methods
An integrated model was developed as part of a larger research
project.6 This project has partially been described in previous
articles dealing with certain subsets of the research.7-9 Briefly,
a systematic review of the literature post-1998 and including
highly cited articles was conducted for CHD pathogenesis,
health factors, biomarkers and pharmacotherapeutics. This
research was combined to develop the integrated model of CHD.
During the systematic literature review, PubMed, Science
Direct, Ebsco Host and Google Scholar were searched for
publications with ‘coronary heart disease’ or ‘coronary artery
disease’ or ‘cardiovascular disease’ or ‘CHD’ as a keyword and
combinations with ‘lifestyle effects’, ‘relative risk prediction’,
‘network analysis’, ‘pathway analysis’, ‘interconnections’,
‘systems biology’, ‘pathogenesis’, ‘biomarkers’, ‘conventional
biomarkers’, ‘drugs’, ‘therapeutics’, ‘pharmacotherapeutics’,
‘hypercoagulability’, ‘hypercholesterolaemia’, ‘hyperglycaemia’,
‘hyperinsulinaemia’, ‘inflammation’ and ‘hypertension’ in the
title of the study.
Also searched were all major relevant speciality journals
in the areas of cardiology, alcohol consumption, nutrition,
cigarette smoking, physical exercise, oral health, psychological
stress, depression, sleep disorders, endocrinology, psychoneuro-
endocrinology, systems biology, physiology, periodontology,
CHD, the metabolic syndrome and diabetes.
The health factors in the integrated model were considered
as lifestyle effects or co-morbid health disorders that have been
associated with statistically significant increases or decreases in
CHD risk. This resulted in nine health factors being considered
in the model, namely alcohol, food, exercise, smoking, oral
health, stress, depression, insomnia and sleep apnoea.
The biomarkers considered for the integrated model were
mainly those whose measurement has been associated with
statistically significant increases or decreases in CHD risk.
Centre for Research and Continued Engineering Development,
North-West University, Potchefstroom, South Africa
Marc J Mathews, PhD
Edward H Mathews, PhD
George E Mathews, 20270046@nwu.ac.za
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126 AFRICA
This resulted in 23 biomarkers being considered in the model,
namely triglycerides, low-density lipoprotein (LDL), HDL,
apolipoprotein-B (Apo B), leptin, high-sensitivity C-reactive
protein (hsCRP), interleukin-6 (IL-6), tumour necrosis
factor-α (TNF-α), growth-differentiation factor-15 (GDF-
15), osteoprotegerin (OPG), myeloperoxidase (MPO), B-type
natriuretic peptide (BNP), homocysteine, fibrinogen, troponins,
urinary albumin-to-creatinine ratio (ACR), glycosylated
haemoglobin (HbA1c), insulin-like growth factor-1 (IGF-1),
adiponectin, cortisol, brain-derived neurotrophic factor (BDNF)
and insulin resistance.
In brief, the systematic review of the literature revealed the
pathological effects of various health factors on the pathogenesis
of CHD. This information was combined to form a visual
representation of the pathogenesis of CHD as it is affected by
these health factors. The biomarkers were included in the visual
representation to show functionally measurable aspects of the
pathogenesis.6,7 This visual representation presents an integrated
model of CHD.
This integrated model of CHD schematically illustrates the
complexity of CHD and shows all theoretical pathogenetic
pathways between health factors and CHD. The model has been
previously used to describe the effects of high-carbohydrate
diets on CHD,7 and the possible mechanisms through which
antidepressants9 and moderate alcohol consumption8 may reduce
CHD risk.
In this study the integrated model was used to describe the
integrated effects of exercise on the pathogenesis of CHD.
Furthermore, the effect of exercise on CHD was investigated
by analysing the effect that exercise has been shown to have on
measurable and quantifiable biomarkers.
Statistical analysis
It must be noted that some of the relative risk (RR) values in
this article differ from convention. The need for this comes as a
result of the visual scaling of the traditional RR. Traditionally,
if one plots an RR = 3 and RR = 0.33, respectively, one does not
‘look’ three times worse and the other three times better than
the normal RR = 1. The reason is that the scales for the positive
and negative effects are not numerically similar. A graph of
‘good’ and ‘bad’ RR can therefore be deceptive for the untrained
person, for example a patient.
This article rather uses the method that the conventional
RR = 3 is three times worse than the normal RR = 1, while the
conventional RR = 0.33 means that the patient’s position is three
times better than the normal RR = 1. Therefore, in summary, a
conventional RR = 3 is presented as per normal, as a three-fold
increase in risk and a conventional RR = 0.33 is presented as a
three-fold decrease in risk (1/0.33 = 3).
Results
Integrated model of coronary heart disease
The integrated model of CHD that was developed in previous
studies is presented in Fig. 1. The pathways (pathogenesis of
CHD) within the integrated model can be tracked from where a
chosen health factor influences the relevant tissue, to the end state
of CHD. The pathways are therefore a visual representation of
previously published knowledge. Salient serological biomarkers
(shown in Fig. 1 as ) and pharmacotherapeutics (shown in Fig.
1 as ) that act on the pathways are further indicated in Fig. 1.
The focus of this review is on using the integrated model
to describe the interconnections of moderate exercise on the
pathogenesis of CHD. Therefore a more detailed discussion
of Fig. 1, relevant to exercise, is given in the next section.
This review therefore attempts to quantify the CHD effect of
moderate exercise by the connection of these to an array of
biomarkers that represent increasing or decreasing CHD risk.
Pathogenetic effects of physical exercise
In order to appraise the CHD effects of moderate exercise, the
relevant pathogenetic pathways need to be considered. While
Fig. 1 also indicates other health factors, only the pathways
activated by moderate exercise are summarised in Table 1. It
is however important to note that not all the pathways will be
relevant to every patient and that all the pathways may not be
active simultaneously, or occur in the same patient.
Fig. 1 (pathway: 3a-53-55-hyperglycaemia) shows the
pathways involved in a lack of physical exercise (and decreased
daily energy expenditure) and how this affects carbohydrate
metabolism through changes in muscle glucose transporter
Table 1. Putative effects of moderate exercise and salient CHD
pathogenetic pathways
Pathways, and pathway numbers corresponding to those in
Fig. 1 References
a. 3a-53-↓ blood glucose-55-↓ hyperglycaemia 38, 39
b. 3a-53-↓ blood glucose-54-↓ PI3K:MAPK-69-↓ insulin
resistance-72-↓ platelet factors-73-↓ hypercoagulability
40–47
c. 3a-53-↓ blood glucose-54-↓ PI3K:MAPK-69-↓ insulin
resistance-72-↓ ROS
38, 40,
45–48
d. 3a-53-↓ blood glucose-54-28-101-↓ insulin resistance-72-
↑ vasodilation
49
e. 3b-27-↓ cortisol-47-↓ insulin resistance-70-↓ angiotensin
II-89-↓ hypertension-100-↓ ROS-85-↓ COX1/2-85-↓
inflammatory state
29, 30, 38,
45, 48
f. 3b-27-↓ cortisol-47-↓ insulin resistance-70-↓ angiotensin
II-89-↓ SMC proliferation
50
g. 3b-27-↓ cortisol-47-↓ insulin resistance-70-↓ angiotensin
II-89-↑ IGF1-84-↓ SMC proliferation
51–54
h. 3b-27-↓ cortisol-47-↓ insulin resistance-70-↓ angiotensin
II-89-↓ VCAM1/MCP1-73-↓ hypercoagulation
29
i. 3c-↓ visceral adipose tissue-↓ ectopic fat 38, 55, 56
j. 3c-19-↑ adiponectin-38-↓ TNFα/IL6-56-Liver-12-↓
LDL-33-↓ oxLDL-51-↓ hypercholesterolaemia
38, 56, 57
k. 3c-19-↑ adiponectin-39-↓ insulin resistance 58
l. 3c-19-↑ adiponectin-39-↓ SMC proliferation 55
m. 3c-21-↓ TNFα/IL6-56-Liver-12-↓ LDL-33-↓
oxLDL-51-↓ hypercholesterolaemia
5, 32,
59–62
n. 3c-21-↓ TNFα/IL6-41-↓ P. gingivalis-43-↓ periodonti-
tis-64-↓ platelet factors-73-↓ hypercoagulability
5, 32,
59–62
o. 3c-18-↓ FFA-37-↓ plasma lipids-34-Liver-12-↓ LDL-33-
↓ oxLDL-51-↓ hypercholesterolaemia
5, 32, 38,
56, 59–62
↑, up regulation/increase; ↓, down regulation/decrease; x-y-z indicates
pathway connecting x to y to z. FFA, free fatty acids; IGF 1, insulin-
like growth factor-1; IL6, interleukin-6; LDL, low-density lipoprotein;
MAPK, mitogen-activated protein (MAP) kinase; MCP 1, monocyte
chemo-attractant protein-1; NO, nitric oxide; oxLDL, oxidised LDL;
P gingivalis, Porphyromonas gingivalis; PI3K, phosphatidylinositol
3-kinase; PI3K:MAPK, ratio of PI3K to MAPK; ROS, reactive oxygen
species; SMC, smooth muscle cell; TNFα, tumour necrosis factor-α;
VCAM 1, vascular cell adhesion molecule-1.
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(GLUT) protein content. Denervation of skeletal muscle results
in rapid decreases in both muscle GLUT-4 contents and insulin-
stimulated glucose uptake, therefore resulting in hyperglycaemia
and concomitant hyperinsulinaemia (both CHD hallmarks) in
non-diabetic patients.10
Lack of physical exercise may also contribute to the
accumulation of visceral fat, reduced lipoprotein lipase activity
and reduced clearance of triglycerides, leading to increased
LDL levels, decreased HDL levels, and increased LDL-to-HDL
ratios, and eventually to hypercholesterolaemia.11 This state
Fig. 1. Conceptual model of general health factors, salient CHD pathogenetic pathways and CHD hallmarks. (From: M Mathews,
L Liebenberg, E Mathews. How do high glycemic load diets influence coronary heart disease? Nutr Metab 2015; 12(1): 6.7)
The affective pathway of pharmacotherapeutics (blue boxes) is shown in Fig. 1, and salient serological biomarkers are indi-
cated by the tags ( ). The blunted arrows denote antagonise or inhibit, and pointed arrows denote up-regulate or facilitate.
ACE, angiotensin converting enzyme; BDNF, brain-derived neurotrophic factor; β-blocker, beta-adrenergic antagonists; BNP,
B-type natriuretic peptide; COX, cyclooxygenase; CRP, C-reactive protein; D-dimer, fibrin degradation product D; FFA, free
fatty acids; GCF, gingival crevicular fluid; HbA1c, glycosylated haemoglobin A1c; HDL, high-density lipoprotein; Hs, homocyst-
eine; ICAM, intracellular adhesion molecule; IGF-1, insulin-like growth factor-1; IL, interleukin; LDL, low-density lipoprotein;
MAPK, mitogen-activated protein (MAP) kinase; MCP, monocyte chemo-attractant protein; MIF, macrophage migration
inhibitory factor; MMP, matrix metalloproteinase; MPO, myeloperoxidase; NFκβ, nuclear factor-κβ; NLRP3, Inflammasome
responsible for activation of inflammatory processes as well as epithelial cell regeneration and microflora; NO, nitric oxide;
NO-NSAIDs, combinational NO-non-steroidal anti-inflammatory drug; OPG, osteoprotegerin; oxLDL, oxidised LDL; PAI,
plasminogen activator inhibitor; PDGF, platelet-derived growth factor; P gingivalis, Porphyromonas gingivalis; PI3K, phosphati-
dylinositol 3-kinase; RANKL, receptor activator of nuclear factor kappa-beta ligand; ROS, reactive oxygen species; SCD-40,
recombinant human sCD40 ligand; SMC, smooth muscle cell; SSRI, serotonin reuptake inhibitors; TF, tissue factor; TMAO,
an oxidation product of trimethylamine (TMA); TNF-α , tumour necrosis factor-α; VCAM, vascular cell adhesion molecule;
vWF, von Willebrand factor.
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subsequently activates the oxidative stress/inflammation cascade.
This in turn underlies insulin resistance and the evolution of
micro- and macrovascular complications (Fig. 1, pathways:
3a-53-blood glucose-54-PI3K:MAPK-69-insulin resistance-72-
ROS). Hyperinsulinaemia, by itself, contributes significantly to
atherogenecity, leading to CHD.12
An increase in plasma free fatty acid (FFA) concentrations
plays a key role in the pathogenesis of insulin resistance through
actions that block insulin signal transduction. An increase in FFA
levels results in induction of oxidative stress, low-grade systemic
inflammation, and subnormal vascular reactivity, in addition
to causing insulin resistance.5 As insulin resistance also results
in the relative non-suppression of adipocyte hormone-sensitive
lipase,13 there is further enhancement in lipolysis, increased FFA
and insulin resistance. As insulin suppresses pro-inflammatory
transcription factors, such as nuclear factor-κβ (NF-κβ), and
also suppresses reactive oxygen species (ROS) generation, insulin
resistance therefore also has a comprehensive pro-inflammatory
effect (Fig. 1, pathways: 3c-18-FFA-37-plasma lipids-34-12-
LDL-33-oxLDL-51-hypercholesterolaemia).
Fig. 1 therefore shows why an insulin-resistant state may be
pro-inflammatory. The origin of the insulin resistance may be
traced back to the pro-inflammatory cytokine TNF-α, which
is expressed by adipose tissue.14 Adipose tissue has been shown
to express not only TNF-α, but also other pro-inflammatory
mediators, including CRP. Macrophages residing in the adipose
tissue may also be a source of pro-inflammatory factors and they
can also modulate the secretory activities of adipocytes15 (Fig. 1,
pathway: 3c-21-TNFα/IL6).
During regular moderate exercise, IL-6 is produced by skeletal
muscle fibres via a TNF-independent pathway. IL-6 stimulates
the appearance in the circulation of anti-inflammatory cytokines,
which inhibit the production of pro-inflammatory TNF-α.16
Additionally, IL-6 enhances lipid turnover, stimulating lipolysis
as well as fat oxidation. Regular physical exercise therefore
induces suppression of TNF-α and thereby offers protection
against TNF-α-induced insulin resistance.16 Low-grade systemic
inflammation therefore appears to be aetiologically linked to
the pathogenesis of CHD,17 countered by moderate exercise
with its anti-inflammatory effects5 (Fig. 1, pathway: 3a-53-blood
glucose-54-69-insulin resistance-71).
The adipokine adiponectin is anti-inflammatory and
potentially anti-atherogenic.5 Low adiponectin levels act as a
marker for CHD and are associated with overweight subjects.18
Regular physical exercise (and an energy-controlled diet) reduces
visceral fat mass, with a subsequent increased release of anti-
inflammatory adiponectin, therefore resulting in reduced risk of
CHD19 (Fig. 1, pathway: 3c-19-39-insulin resistance).
Lack of physical exercise may lead to hypertension, another
CHD hallmark, through increased vascular and sympathetic
tone created by reduced bioavailability of nitrous oxide (NO) and
activation of the renin–angiotensin system20, 21 (Fig. 1, pathway:
3a-53-blood glucose-54-60-72-vasodilation). Hypertension is
directly correlated with visceral fat mass, which may be decreased
by moderate exercise.22
The lower blood glucose levels that result from moderate
exercise lead to a reduction in the phosphatidylinositol 3-kinase
(PI3K) to mitogen-activated protein kinase (MAPK) ratio,
which in turn decreases insulin resistance23 (Fig. 1, pathway:
3a-53-blood glucose-54-69-72-73-hypercoagulabilty). Increased
insulin sensitivity decreases serum levels of platelet factors and
thus reduces the potential for hypercoagulability.24,25
Moderate exercise also increases coronary blood flow,26 which
increases the release of prostaglandins.27 This is important in
heart microvasculature, in which prostaglandins are substantially
involved in flow-mediated vasodilation.27
Moderate exercise acts on the central nervous system by
decreasing serum cortisol levels.28 This in turn reduces insulin
resistance, which decreases angiotensin II levels and results
in reduced hypertension. Reactive oxygen species (ROS) and
cyclooxygenase (COX) 1/2 levels reduce concomitantly, which
lead to a lower inflammatory state20 (Fig. 1, pathway: insulin
resistance-85-inflammatory state).
It is apparent that moderate exercise directly and indirectly
affects a plethora of interconnected pathogenetic mechanisms.
Each CHD hallmark and pathogenetic trait can amplify the
Table 2. Association between biomarkers and
prediction of CHD relative risk
Biomarker
(class and salient examples)
Prediction of
CHD relative
risk (95% CI)
Size of studies
(N = number of trials,
n = number of patients)
Refer-
ences
Lipid-related markers
Triglycerides 0.99 (0.94–1.05) (N = 68, n = 302 430) 63
LDL 1.25 (1.18–1.33) (N = 15, n = 233 455) 64
HDL 0.78 (0.74–0.82) (N = 68, n = 302 430) 63
Apo B 1.43 (1.35–1.51) (N = 15, n = 233 455) 64
Leptin 1.04 (0.92–1.17) (n = 1 832) 65
Inflammatory markers
hsCRP 1.20 (1.18–1.22) (N = 38, n = 166 596) 66
IL-6 1.25 (1.19–1.32) (N = 25, n = 42 123) 67
TNF-α1.17 (1.09–1.25) (N = 7, n = 6 107) 67
GDF-15 1.40 (1.10–1.80) (n = 1 740) 68
OPG 1.41 (1.33–1.57) (n = 5 863) 69
Marker of oxidative stress
MPO 1.17 (1.06–1.30) (n = 2 861) 70
Marker of vascular function and neurohormonal activity
BNP 1.42 (1.24–1.63) (N = 40, n = 87 474) 71
Homocysteine 1.15 (1.09–1.22) (N = 20, n = 22 652) 72, 73
Coagulation marker
Fibrinogen 1.15 (1.13–1.17) (N = 40, n = 185 892) 66
Necrosis marker
Troponins 1.15 (1.04–1.27) (n = 3265) 58
Renal function marker
Urinary ACR 1.57 (1.26–1.95) (n = 626) 74
Metabolic markers
HbA1c 1.42 (1.16–1.74) (N = 2, n = 2 442) 75
IGF-1 0.76 (0.56–1.04) (n = 3 967) 76
Adiponectin 0.97 (0.86–1.09) (N = 14, n = 21 272) 77
Cortisol 1.10 (0.97–1.25) (n = 2 512) 78, 79
BDNF ? ? 80–82
Insulin resistance
(HOMA)
1.46 (1.26–1.69) (N = 17, n = 51 161) 83
From: M Mathews, L Liebenberg, E Mathews. How do high glycemic load
diets influence coronary heart disease? Nutr Metab 2015; 12(1): 6.7
n , number of participants; N, number of trials; CI, confidence interval; ACR,
albumin-to-creatinine ratio; Apo B, apolipoprotein-B; BDNF, brain-derived
neurotrophic factor; BNP, B-type natriuretic peptide; GDF-15, growth-
differentiation factor-15; HbA1c, glycated haemoglobin A1c; HDL, high-density
lipoprotein; HOMA, homeostasis model assessment; hsCRP, high-sensitivity
C-reactive protein; IGF-1, insulin-like growth factor-1; IL-6, interleukin-6;
LDL, low-density lipoprotein; MPO, myeloperoxidase; OPG, osteoprotegerin;
TNF-α, tumour necrosis factor-α.
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patient’s risk of CHD, therefore necessitating an integrated,
multi-faceted therapeutic approach.
In this section, the pathogenetic pathways activated by
moderate exercise are described, but the effects of these pathways
have not been quantified. The next interrogation was therefore
whether biomarkers could quantify the CHD effect of moderate
exercise. This was accomplished by using connection graphs,
which link the relative effect of a health or pathogenic factor to
the individual biomarkers through the pathways that are shown
in Fig. 1.
Biomarkers of coronary heart disease
The integrated model that was developed is a high-level
conceptual model, from which the interconnectedness of CHD is
immediately apparent (Fig. 1). The model is however complicated.
Biomarkers can be used as indicators of an underlying disorder
and the measurement of specific biomarkers enables prediction
of the RR for CHD associated with the biomarker.29-31 The
relevant biomarkers and their association with CHD risk per one
standard deviation increase in said biomarker are given in Table
2. This can allow for the quantification of the effects of moderate
exercise on the pathogenesis of CHD.
To simplify the integrated model, serological biomarkers
(which can easily be measured) are used to link the effect of
exercise to the corresponding RR of CHD. Fig. 2 presents a
comparison of the RR associated with an array of serological
biomarkers per one standard deviation increase in the biomarker.7
Effects of moderate exercise
Using the integrated model in Fig. 1, it is possible to account
for the impact that moderate exercise would have on the
serological biomarkers of CHD. This enables a simplification of
the integrated model into a connection graph, which shows all
the connections between moderate exercise and the measurable
serological biomarkers.
The connection graph presented in Fig. 3 does not neglect
any of the underlying complexity of CHD. To more clearly
determine the effect of exercise on different biomarkers in Fig.
3, the biomarkers previously shown in Fig. 2 were divided into
eight classes, namely vascular function and neurohormonal
activity, renal function, necrosis, coagulation, oxidative stress,
lipids, and metabolic and inflammatory markers.
The pathogenetic pathways (from Fig. 1) are superimposed
on the connecting lines in Fig. 3. Increasing line thickness
indicates a connection with possible greater pathogenetic effect
(as quantified by biomarker relative-risk prediction of CHD).
For example, the risk of CHD is relatively low when considering
leptin, therefore the connection line between exercise and leptin
is thinner than for others (e.g. Apo B).
It is intriguing to see that moderate exercise has a connection to
all the serological biomarkers. This further highlights the inverse
correlation between CHD risk and moderate exercise. From the
connection graph in Fig. 3, it can be noted that the potential risk
reduction effect of moderate exercise may be greatly influenced
by changes in inflammatory, metabolic and lipid markers, which
provide a considerable increased risk for CHD.2-4
Fig. 2. Normalised relative risks (fold-change) of salient current biomarkers or of potential serological biomarkers for CHD. (From:
M Mathews, L Liebenberg, E Mathews. How do high glycemic load diets influence coronary heart disease? Nutr Metab
2015; 12(1): 6.7) Increased IGF-1 and HDL levels are associated with a moderately decreased CHD risk. (IGF-1 and HDL
levels are significantly inversely correlated to relative risk for CHD.) N indicates number of trials; I, 95% confidence inter-
val; ACR, albumin-to-creatinine ratio; Adipo, adiponectin; ApoB, apolipoprotein-B; BDNF, brain-derived neurotrophic factor;
BNP, B-type natriuretic peptide; Cort, cortisol; CRP, C-reactive protein; cysteine, homocysteine; fibrin, fibrinogen; GDF-15,
growth-differentiation factor-15; HbA1c, glycosylated haemoglobin A1c; HDL, high-density lipoprotein; IGF-1, insulin-like growth
factor-1; IL-6, interleukin-6; LDL, low-density lipoprotein; MPO, myeloperoxidase; OPG, osteoprotegerin; TNF-α, tumour
necrosis factor-α; Trigl, triglycerides; Trop, troponins.
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Mora and co-workers determined the mechanisms of the
reduced risk of CHD associated with exercise in women.2 They
found that a reduction in inflammatory biomarkers were the
largest contributors to lowered risk. These were followed, in
order, by blood pressure, lipid levels, body mass index (BMI)
and haemoglobin level. In the study, the combination of different
individual risk factors quantified only 35.5% of the total risk
reduction due to physical exercise on CHD.2
It is therefore clear that the risk factors used by Mora and
co-workers, in terms of serological biomarkers, did not fully
quantify the risks associated with CHD. In their study, LDL,
HDL and Apo B serum levels were recorded to monitor lipid
levels, but only hsCRP serum levels were used for deducing
inflammatory levels.2 It may therefore be possible that with the
addition of the other biomarkers indicated in Fig. 3, the effect of
moderate exercise may be better quantified.
In Fig. 3, it is clear from the risk associated with inflammation
that reduction in inflammation would prove beneficial to CHD
risk. The full extent of the relationship between exercise and
inflammation has not been determined but it has been proven
that chronic moderate exercise has a systemic anti-inflammatory
effect.5,16,32 It has further been shown that the anti-inflammatory
effect of exercise provides the largest individual risk-reduction
component of moderate exercise in women.2
Naturally there is a strong link to the metabolic process that
is manifested in the connection to the metabolic biomarkers,
specifically insulin resistance and glycated haemoglobin level.33,34
This connection may be largely mediated by the increased
expenditure of energy, which produces favourable effects on
CHD pathogenesis.10, 23 Moderate exercise is also related to
changes in lipid factors such as increases in HDL cholesterol and
decreases in LDL cholesterol and Apo B levels.33,34
Discussion
It is clear that there are a wide variety of effects of exercise on
the pathogenesis of CHD, which can be described by the changes
in biomarkers. However, from the connection graph in Fig. 3,
it is not immediately clear what the overall effect of moderate
exercise is on CHD. This effect has been quantified in the RR
reduction for CHD, which is observed in those who engage in
moderate exercise.
Moderate-intensity physical exercise of 1 100 kcal/week is
associated with an average RR of 0.75 (0.71–0.79), based on a
large meta-analysis.35 The RR of 0.75 would correlate to a RR
reduction of 1.33-fold using the method previously described in
the Methods section.
The data from Fig. 3 show that inflammation and metabolic
Fig. 3. Interconnection of relative risk effects of moderate exercise and serological biomarkers for CHD. ACR, albumin-to-creatinine
ratio; Adipo, adiponectin; Apo B, apolipoprotein-B; BDNF, brain-derived neurotrophic factor; BNP, B-type natriuretic peptide; Cort,
cortisol; CRP, C-reactive protein; cysteine, homocysteine; fibrin, fibrinogen; GDF-15, growth-differentiation factor-15; HbA1c, glyco-
sylated haemoglobin A1c; HDL, high-density lipoprotein; IGF-1, insulin-like growth factor-1; IL-6, interleukin-6; LDL, low-density
lipoprotein; MPO, myeloperoxidase; OPG, osteoprotegerin; TNF-α, tumour necrosis factor-α; Trigl, triglycerides; Trop, troponins.
CARDIOVASCULAR JOURNAL OF AFRICA • Volume 28, No 2, March/April 2017
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dysregulation may be key aspects in the pathogenesis of
CHD.5,10,16,23,32-34 These aspects decrease during exercise and may
therefore play a part in the 1.33-fold decreased risk for CHD.
Based on the evidence, it is believed that the CHD benefit
associated with exercise is substantial and should garner a similar
level of public interest as do other risk factors such as smoking,
high cholesterol levels and treatments such as statin therapy.
However, while exercise is frequently advised for healthy living,36
it is unfortunate that only 48.9% of Americans meet the physical
activity guidelines. It follows from this that 51.1% of Americans
do not meet the minimum physical activity guidelines, which
results in 162.8 million Americans at a greater risk of CHD due
to physical inactivity.37
The individual studies selected unfortunately represent only
the risk associated with the cohort studied and cannot accurately
be extrapolated to other populations without further research.
Conclusion
Although it is well known that moderate exercise is associated
with a lower risk of CHD, all the positive effects on CHD
pathogenesis were not available in a detailed integrated model.
Such a model would help provide further insight. A high-level
conceptual model was therefore developed, which links moderate
exercise with the pathogenesis, hallmarks and biomarkers of
CHD.
The novel connection graph developed from this model
shows, at a glance, the positive effect of moderate exercise on
certain important aspects of the pathogenesis of CHD. It helps
to graphically explain why moderate exercise is associated with
lower CHD risk. From this it is apparent that exercise has a wide-
ranging impact on the pathogenesis of CHD, with these effects
notable in changes in CHD biomarkers.
The integrated high-level CHD model and simplified
connection graph provide a summary of evidence for a causal
relationship between CHD risk and moderate exercise. We
acknowledge the fact that the integrated view is relevant to
other lifestyle issues and for full comprehension will have to be
replicated in other articles describing these factors.
The angel investor was Dr Arnold van Dyk and the research was later self-
funded. Prof Leon Liebenberg was involved in the initial research.
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