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Role of Inactivity in Chronic Diseases: Evolutionary Insight and Pathophysiological Mechanisms

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Abstract

This review proposes that physical inactivity could be considered a behavior selected by evolution for resting, and also selected to be reinforcing in life-threatening situations in which exercise would be dangerous. Underlying the notion are human twin studies and animal selective breeding studies, both of which provide indirect evidence for the existence of genes for physical inactivity. Approximately 86% of the 325 million in the United States (U.S.) population achieve less than the U.S. Government and World Health Organization guidelines for daily physical activity for health. Although underappreciated, physical inactivity is an actual contributing cause to at least 35 unhealthy conditions, including the majority of the 10 leading causes of death in the U.S. First, we introduce nine physical inactivity-related themes. Next, characteristics and models of physical inactivity are presented. Following next are individual examples of phenotypes, organ systems, and diseases that are impacted by physical inactivity, including behavior, central nervous system, cardiorespiratory fitness, metabolism, adipose tissue, skeletal muscle, bone, immunity, digestion, and cancer. Importantly, physical inactivity, itself, often plays an independent role as a direct cause of speeding the losses of cardiovascular and strength fitness, shortening of healthspan, and lowering of the age for the onset of the first chronic disease, which in turn decreases quality of life, increases health care costs, and accelerates mortality risk.
ROLE OF INACTIVITY IN CHRONIC DISEASES:
EVOLUTIONARY INSIGHT AND
PATHOPHYSIOLOGICAL MECHANISMS
XFrank W. Booth, Christian K. Roberts, John P. Thyfault, Gregory N. Ruegsegger,
and Ryan G. Toedebusch
Department of Biomedical Sciences, University of Missouri, Columbia, Missouri; Department of Medical
Pharmacology and Physiology, University of Missouri, Columbia, Missouri; Department of Nutrition and Exercise
Physiology, University of Missouri, Columbia, Missouri; Dalton Cardiovascular Research Center, University of
Missouri, Columbia, Missouri; Geriatrics, Research, Education and Clinical Center (GRECC), VA Greater Los
Angeles Healthcare System, Los Angeles, California; Department of Molecular and Integrative Physiology,
University of Kansas Medical Center, Kansas City, Kansas; and Cardiovascular Division, Department of
Medicine, University of Missouri, Columbia, Missouri
LBooth FW, Roberts CK, Thyfault JP, Ruegsegger GN, Toedebusch RG. Role of
Inactivity in Chronic Diseases: Evolutionary Insight and Pathophysiological Mechanisms.
Physiol Rev 97: 1351–1402, 2017. Published August 16, 2017; doi:10.1152/
physrev.00019.2016.—This review proposes that physical inactivity could be consid-
ered a behavior selected by evolution for resting, and also selected to be reinforcing in
life-threatening situations in which exercise would be dangerous. Underlying the notion are human
twin studies and animal selective breeding studies, both of which provide indirect evidence for the
existence of genes for physical inactivity. Approximately 86% of the 325 million in the United States
(U.S.) population achieve less than the U.S. Government and World Health Organization guidelines
for daily physical activity for health. Although underappreciated, physical inactivity is an actual
contributing cause to at least 35 unhealthy conditions, including the majority of the 10 leading
causes of death in the U.S. First, we introduce nine physical inactivity-related themes. Next,
characteristics and models of physical inactivity are presented. Following next are individual exam-
ples of phenotypes, organ systems, and diseases that are impacted by physical inactivity, including
behavior, central nervous system, cardiorespiratory fitness, metabolism, adipose tissue, skeletal
muscle, bone, immunity, digestion, and cancer. Importantly, physical inactivity, itself, often plays an
independent role as a direct cause of speeding the losses of cardiovascular and strength fitness,
shortening of healthspan, and lowering of the age for the onset of the first chronic disease, which
in turn decreases quality of life, increases health care costs, and accelerates mortality risk.
I. GENERAL INTRODUCTION: THEMES 1351
II. CHARACTERISTICS OF PHYSICAL... 1354
III. PHYSICAL INACTIVITY MODELS 1362
IV. BEHAVIORAL INFLUENCE ON... 1364
V. CENTRAL NERVOUS SYSTEM... 1365
VI. CARDIORESPIRATORY FITNESS 1369
VII. METABOLISM 1374
VIII. ADIPOSE TISSUE 1377
IX. SKELETAL MUSCLE 1380
X. BONE 1383
XI. IMMUNITY 1385
XII. DIGESTION 1386
XIII. CANCER 1386
XIV. SUMMARY 1386
I. GENERAL INTRODUCTION: THEMES
Multiple definitions of physical inactivity exist. For the pur-
poses of the current review, physical inactivity is defined as
the spectrum of any decrease in bodily movement that pro-
duces decreased energy expenditure toward basal level (FIG-
URE 1).
Our definition of physical inactivity is converse to the
United States (U.S.) government’s definition of physical ac-
tivity, which is “any bodily movement produced by the
contraction of skeletal muscle that increases energy expen-
diture above a basal level” (82a). First, we will provide an
overview of nine important themes and concepts about
physical inactivity, followed by more in-depth consider-
ation later in the review.
Theme 1: While the definitions of physical inactivity and phys-
ical activity are in essence the converse of each other, many of
the underlying biochemical and molecular mechanisms of
physical inactivity are not simply the converse of physical ac-
tivity. Instead, mechanisms of physical inactivity in some cases
employ totally different pathways than physical activity uses.
Physiol Rev 97: 1351–1402, 2017
Published August 16, 2017; doi:10.1152/physrev.00019.2016
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One explanation is that unidirectional steps often occur in
biochemical pathways for anabolic and catabolic pathways
(see sect. IXE). Importantly, potential consequences of
some differing biochemical pathways between physical in-
activity and physical activity suggest that 100% fidelity can-
not be made for physical inactivity mechanisms merely by
reversing directionality of known mechanisms for physical
activity.
Theme 2: Epidemiological evidence exists that physical in-
activity actually causes risk factors that, in turn, increase
morbidity and mortality.
The U.S. Centers for Disease Control (CDC) published a
series of papers in JAMA over the past quarter of a century
on physical inactivity. In 1990, Hahn et al. (196) concluded
that the risk factor of sedentary lifestyle contributed 23% to
excess deaths from nine of the major chronic diseases. Mok-
dad et al. (337) titled their article and their descriptor of
poor diet and physical inactivity as an “actual cause” of
15.2% of deaths in the U.S. In 2015, Carlson et al. (75)
noted that 11.1% of all health care costs were associated
with “inadequate” physical activity. Thus we contend that
physical inactivity is an important component of the non-
communicable disease epidemic in the U.S., as well as
worldwide (240, 305) (see sect. IIE).
Theme 3: Gene and environmental evidence exists for phys-
ical inactivity actually causes risk factors that, in turn, in-
crease morbidity and mortality.
Our artificial breeding experiment determined if we could
develop rats with the phenotype of low voluntary running
distance in wheels, which provided indirect evidence for the
existence of genes with functions for physical inactivity
(422). Additional evidence is from twin studies [1,654
twins, 420 monozygotic and 352 dizygotic same-sex twin
pairs, whose average age was 56 yr old, and body mass
index (BMI) was 26.1 kg/m
2
] (113), that noted unique or
nonshared environmental factors accounted for 55% of the
variability for sedentary behavior, while additive genetic
factors accounted for 31%. The remaining 15% was ac-
counted for by common or shared environmental factors
(see sect. IIG). Furthermore, Keller (242) has suggested re-
placement of the concept of the genome as a “static” col-
lection of active genes with the “reactive genome.” Keller
(242) contends that genome appears to function as “an
exquisitely sensitive reaction (or response) mechanism–a
device for regulating the production of specific proteins in
response to the constantly changing signals it receives from
its environment....”Herconcept would describe the ge-
nome as sensitive to physical inactivity. For example, in a
1968 study (443), maximal cardiac output, maximal stroke
volume, and maximal oxygen consumption decreased 26,
29, and 28%, respectively, secondary to bed rest for 20 days
in healthy young men. Additionally, rat gastrocnemius and
soleus muscles atrophied 23 and 27%, respectively, within
1 wk when immobilized in a shortened position, the losses
occurring as a first-order rate constant in a 1977 report
(51). Taken together, the physical inactivity losses, if con-
tinued, without recovery, would increase the risk of chronic
diseases and early mortality in later life (54, 55).
Theme 4: The incubation period for physical inactivity-
developing pathologies to reach overt clinical symptoms is
often long in duration and yet preclinically silent.
Goodman et al. in a CDC statement (183) offer 10 “Se-
lected Definitions for Chronic Disease and Other Chronic
Conditions” in their Table 1. Our selected first definition
from the 10 is from the World Health Organization (WHO)
(541), “Chronic diseases are diseases of long duration and
generally slow progression.” Our selected second definition
from the 10 is based upon McKenna and Collins in Good-
man et al. (183); it provides greater specificity. “They are
generally characterized by uncertain etiology, multiple risk
factors, a long latency period, a prolonged course of illness,
noncontagious origin, functional impairment or disability,
and incurability.” A critical concept is that the roadmap
from physical inactivity to overt type 2 diabetes (T2D), or
Spinal cord
Paralysis/weakness
Both arms and
legs (cervical)
• Legs (thoracic)
Legs and lower
abdomen (lumbar)
Legs and hips
(sacral)
Complete
bed rest
Recovery
from medical
condition
Physical
frailty
Wheelchair,
walker,
and/or
bed rest
Orthopedic
leg surgery
Immobilization
and/or
wheelchair
Sub-orbital
space flight
Near zero
gravity
Sitting
Job, school,
and home
Aging
Less physical
activity
Motorized
transportaion
Cars vs. bikes
Physical Inactivity Spectrum
FIGURE 1. Spectrum of the types of physical inactivity. Following the arrow from right (low intensity of physical
inactivity) to left (high intensity of physical inactivity) shows our estimate of the intensity of physical inactivity per
unit of time. Not shown is the volume (intensity duration) of physical inactivity. For example, spinal cord
severance is high intensity and health decrements appear within days. In opposite manner, sitting is low
intensity, with long-term health effects not clinically apparent within days, but nonetheless unhealthy when first
appearing after many years.
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most other chronic conditions, is that the process is “gen-
erally slow in progression” and a “long latency period.”
Often the slow, natural progressions of chronic diseases
require studies of aspects of the progression rather than the
entire continuance (from physical inactivity ¡physiologi-
cal dysfunction ¡concealed pathobiology ¡overt symp-
toms ¡diagnosis) (FIGURE 2). In summary, progression to
more severe pathobiology during continuous physical inac-
tivity is slow and long in duration.
Theme 5: Continuous physical inactivity accelerates the
lifelong decline in cardiovascular (maximal ability of the
entire body to deliver and consume oxygen with all skeletal
muscles in maximal rhythmic contraction) and strength
(maximal force produced by a single contraction by a group
of skeletal muscles) fitness.
Premature drops in either of the two aforementioned fitness
levels accelerates the decline rate and the onset and preva-
lence of 1) morbidity and mortality and 2) endurance and
strength frailty (see sects. VI and IX).
Theme 6: Selective breeding for the characteristic of low
voluntary running distance provides evidence for the poten-
tial existence of genes having functions to produce physical
inactivity.
For the first four generations of selective breeding, no de-
cline in mean voluntary running distance was observed in
offspring. However, the fifth generation produced offspring
with low voluntary running distances less than in the found-
ing population (422), suggesting that using artificial breed-
ing that physical inactivity genes exist (see sect. IIH2).
Theme 7: Chronic disease genes and physical inactivity are
both polygenic. Single gene variants correlated to any
chronic disease prevalence offer insufficient predictability
to be clinically relevant.
Bouchard et al. (59) were the initial pioneers of exercise
genomic research with the Heritage Family Study. They
studied in genetic variation in the adaptation to regular
physical activity in terms of cardiorespiratory endurance
and changes in cardiovascular disease and T2D risk factors.
He provided a summary of the lack of progress in exercise
genomics in a comprehensive review (58), where he noted
that exercise genomics 1) has potential to make substantial
contributions to an understanding of exercise biology; 2)
has yet to deliver high-quality data; 3) “would benefit from
a greater reliance on experimental studies and unbiased
technologies to identify genomics, epigenomics and tran-
scriptomics targets”; and 4) while worthy, translation is
“highly premature” to advise fitness or athletic goals.
Joyner (229) concurred, suggesting “many common dis-
eases might have subtle genetic or DNA sequence variant
based components but perhaps the best way to categorize
most of them is ‘just barely’” (see sect. VB).
Theme 8: Adaptations to physical inactivity selected by
physical inactivity during evolution enhanced survival by
allowing for rapid transitions between endurance and
strength phenotypes.
We speculate that genes for physical inactivity could have
been advantageous for survival during natural selection, for
example, for the intrinsic characteristic of rapid protein
turnover. Most rate-limiting steps in biochemical pathways
have short protein half-lives for rapid turnover (180), per-
mitting protein concentrations to be able to change rapidly
from one level to another, relative to longer protein turn-
over (222, 448). This notion may be explained by Darwin-
ian (or evolutionary) medicine, which we define as the ap-
plication of modern evolutionary theory to an understand-
ing of health and disease (see sect. IIH).
Theme 9: The phenotype of physical inactivity behavior
begins to become overt at, or near, puberty.
Supportive evidence to this concept for the existence of
physical inactivity genes include decreases in voluntary
wheel running, a subcategory of locomotor activity (174).
Marck et al. (318) report that five species (Caenorhabditis
elegans, Mus domesticus, Canis familiaris, Equus caballus,
Environment
Risk factors
Genes
Pathobiology
Physical
inactivity
Parents
Mortality
Public health
Chronic
disease
FIGURE 2. Parents provide their offspring with genes and environ-
ment, which both produce physical inactivity. Physical inactivity inter-
acts with inherited gene predisposition of offspring to produce patho-
physiology, which, in turn, interacts with risk factors to establish
probability for chronic disease and mortality.
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and Homo sapiens) have locomotion that has “an asymmet-
rical pattern throughout life” with its peak intersecting a
rising developmental and declining phase (318). Indeed,
maximal lifetime distance peaks early in life when rats vol-
untary run in wheels, thereafter falling with increasing age,
at 8–9 wks of age in female rats (498) and at 6 wks of age in
domestic mice (318). Gilbert defines aging as “the time-
related deterioration of the physiological functions neces-
sary for survival and fertility” (178). A conclusion from the
above could be that biological aging of voluntary running is
apparent around the age of puberty.
II. CHARACTERISTICS OF PHYSICAL
INACTIVITY
A. History of Recognition of Physical
Inactivity
Physical inactivity was recognized at least 2,500 yr ago.
Physical inactivity has been based on health for millennia.
In 600 BC, Susruta believed that regular moderate exer-
cise offered resistance to disease(s) and “against physical
decay” (497). In 400 BC, Hippocrates wrote “eating
alone will not keep a man well; he must also take exercise,
for food and exercise work together...toproduce health”
(35). Tipton (496) and Myers et al. (348) discuss a more
complete history from Hippocrates to 50 yr ago. Paffen-
barger, Blair, and Lee (376) recounted how Morris et al.
(342) published in 1953 that London bus drivers, whose
occupation was continuous sitting, had greater incidence of
coronary heart disease twice that of physically active con-
ductors in London double-decker buses. Taken together,
physical inactivity has been historically defined based on its
effect on health. To define “inactivity” in this review, we
will base the definition on its impact on health.
Using the first U.S. Physical Activity Guidelines published
in 2008 (511), we have set arbitrary time durations for
physical activity based on public health criteria. The defini-
tion is 60 min/day of physical activity for ages of 17 yr old
and under and 150 min of weekly physical activity for ages
of 18 yr and older; these are minimum requirements for health
(TABLE 1).
The U.S. definition of physical inactivity is similar to the
WHO’s (541) definition. WHO divides physical inactivity
into two classifications. Level 1 of physical inactivity (inac-
tive) is defined as “doing no or very little physical activity at
work, at home, for transport or in discretionary time.”
Level 2 of physical inactivity (insufficiently active) is defined
by WHO as “doing some physical activity, but less than 150
minutes of moderate-intensity physical activity or 60 min-
utes of vigorous-intensity physical activity a week accumu-
lated across work, home, transport or discretionary do-
mains (141). The current review considers the two WHO
categories of physical inactivity together as a part of a con-
tinuum, as illustrated in FIGURE 2, providing a continuum
of theme 4.
B. Physical Inactivity Has Increased in the
Last Century
Societies today that do not employ power-driven machines
and motorized transportation can provide estimates of
what daily step count might have been centuries ago, allow-
ing an educated guess as to the increase in physical inactiv-
ity seen today. Bassett et al. (24) provided one such estimate
in The Old Order Amish in Canada who today refrain from
using automobiles, electrical appliances, and other modern
conveniences, and their occupation is labor-intensive farm-
ing. Men and women averaged ~18,000 and ~14,000 steps/
day, reported 10 and 3 h/wk of vigorous physical activity,
43 and 40 h/wk of moderate physical activity, and 12 and 6
h/wk of walking, respectively. Many modern cultures have
approximately one-third the number of daily steps as taken
by Amish (23). On average, non-Amish adults report an
average of 5,117 steps per day, and were separated into four
groups: “very active” (6,805 steps/day); “somewhat active”
(5,306 steps/day); “somewhat inactive” (4,140 steps/day);
and “very inactive” (3,093 steps/day). Additionally,
Church et al. (88) estimated that occupational energy ex-
penditure decreased by 100 calories in both genders over
the four decades. They concluded that a 100-kcal reduction
in occupational energy expenditure would account for
much of U.S. weight gain over the past half century. Myers,
McAuley, Lavie, Despres, Arena, and Kokkinos (348), all
experts in the field, support the high levels of physical inac-
tivity in their quotation: “Current physical activity patterns
are undeniably the lowest they have been in human history,
with particularly marked declines in recent generations and
future projections indicate further declines around the
globe...Non-communicable health problems that afflict
Table 1. Definitions of physical inactivity for various age ranges
Age Range
Frequency &
Duration Type of Physical Activity
5–17 yr 60 min/day Including both moderate- or vigorous-intensity aerobic, including some muscle strengthening
18 yr 150 min/wk 150 min/wk of either moderate- or vigorous-intensity aerobic, or 75 min/week of mixed moderate
and high-intensity aerobic, preferably spread throughout the week; plus 2 days/wk of resistance
training involving moderate to high intensities for all major muscle groups
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current societies are undeniably attributable to the fact that
PA patterns are markedly different than those for which
humans were genetically adapted” (8, 127, 356, 366).
C. Approximately 86% in the U.S. Do Not
Meet Physical Activity Guidelines:
Physical Inactivity Is Now Pandemic
Accelerometers to measure movement have superseded re-
call self-reporting and pedometers for validity recording of
physical activity (509). We used accelerometer data in
Troiano et al. (505) as a basis for estimating the prevalence
of physical inactivity in the U.S. at ~86%, making it one of
the highest, if not the highest, unhealthy condition in the
U.S. With the U.S. population at ~325 million people and
using the Troiano et al. (505) percentages of inactive hu-
mans, we estimate that 280 million in the U.S. are not
meeting the U.S. physical activity guidelines for minimal
physical activity to improve health.
It is unquestionable that physical inactivity has become a
global health issue. Kohl et al. (254) concluded, “Physical
inactivity is pandemic, a leading cause of death in the world,
and clearly one of the top four pillars of a noncommunica-
ble disease strategy. However, the role of physical activity
continues to be undervalued despite evidence of its protec-
tive effects and the cost burden posed by present levels of
physical inactivity globally.” Worldwide, percentages are
similar to the U.S., as only 6 and 4% of English men and
women, respectively, met requirements for 30 min of mod-
erate or vigorous on at least 5 days/week with accumulated
bouts of at least 10 min (86). Limited-available nonacceler-
ometer data suggest that 30% of the world’s population
does not meet the minimum U.S. recommendations for
physical activity (541). Therefore, 2.5 billion would be
considered inactive by U.S. physical activity guideline stan-
dards. Lee et al. (287) estimate that 6–10% of worldwide
deaths from noncommunicable diseases are due to physical
inactivity. The incidence of physical inactivity is high, and
unfortunately, current trends do not suggest a reversal is on
the horizon. Taken together, the underappreciation of
physical inactivity as a health threat could be described as
“stealth” pandemic.
In the above context, the presentation of physical inactivity
to alter behavior could use one of two generalized ap-
proaches. One proposal by Rose (431) is that structure
(pertaining to social institutions and norms that shape the
actions of individuals) may be more effective than from
agency (pertaining to an individual’s capacity to make the
choice to act). Readers are directed to papers favoring the
structure option (331). For example, Adams et al. (1) men-
tion the example that if packaged foods had reduced salt
content, then individuals would not have to “consciously
engage with any information or actively change their behav-
ior.” We speculate that an inactivity analogy could be pro-
viding safe bike paths (structure) instead of governmental
policy recommendations to exercise 30 min most days of
the week.
D. Annual Costs of Physical Inactivity in the
U.S. Are Estimated Between $131 and
$333 Billion and Are Rising
11.1% of aggregate health care expenditures in the U.S.
during 2006–2011 were associated with physical inactivity
according to Carlson et al. (75) at the CDC. They conser-
vatively estimated the inactivity cost to be $131 billion.
Carlson et al. (75) state that all previous cost estimates to
U.S. health care were fivefold underestimates, implying the
newest estimate is also an underestimate, and state that
“this study did not estimate indirect costs, which include
lost productivity from premature death and disability asso-
ciated with illness, nor does it address the costs in the insti-
tutionalized population that may be associated with inade-
quate levels of physical activity. Other studies indicate that
their estimates of physical inactivity are due to conservative
methodologies (119). On the other hand, we estimated
2014 U.S. health care costs to be ~$333 billion (11.1%
$3 trillion of the total U.S. health care costs in 2014) (82b).
Future studies that consider these additional costs may im-
prove estimates of the economic burden of inadequate phys-
ical activity. Nevertheless, this study found that inadequate
physical activity is associated with a significant percentage
of health care expenditures in the U.S.” They also found
health care expenditures were very similar for inactive
adults in three independent studies. The costs were 26.6%
for 51,000 U.S. adults who were 21 yr of age or older (75),
26.3% for 7,004 Australian women aged 50–55 yr old
(68), and 23.5% for 5,689 individuals, 75% of whom were
40 yr or older in a Minnesota health plan (407). On the
other hand, costs of physical inactivity in a total of 142
countries were “conservatively estimated” to be direct and
indirect costs of $67.5 billion (international dollars) world-
wide in 2013 (119), which is about one-half the estimate
given above for the U.S. alone by Carlson et al. (75).
E. Evidence Exists That Physical Inactivity
Actually Causes Risk Factors, Leading to
Increased Morbidity and Mortality
This section continues theme 2 by providing detailed evi-
dence of the link between physical inactivity, epidemiolog-
ical evidence, and risk of morbidity and mortality. As men-
tioned, Morris et al. in 1953 (342) reported the novel ob-
servation that bus conductors in London double-deck
buses, who had to continually climb stair to collect fares,
had ~30% less coronary heart disease, were older when the
disease was diagnosed, and had a lower death rate than bus
drivers who sat on the same buses. In 2010, Blair et al. (42)
wrote “the research field of exercise epidemiology that was
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initiated by Morris nearly 60 years earlier had grown to an
impressive body of physical inactivity and low cardiorespi-
ratory fitness (CRF) are major causes for increased physio-
logical dysfunction, morbidity, and mortality. Blair et al.
(41) noted in 40,000 subjects from the Aerobics Center
Longitudinal Study that low CRF is a stronger predictor of
mortality than any other risk factor.
Physical exercise is not an actual causal mechanism of
chronic diseases, but rather physical activity “protects” or
is a therapy for diseases/conditions caused by physical inac-
tivity (52). A 72-page review on prescribing exercise as a
therapy for chronic diseases is available from Pedersen and
Saltin (390). Physical inactivity, on the other hand, is one of
numerous actual causes of 35 chronic diseases/conditions
(55) (FIGURE 3).
Many chronic diseases are polygenic, so it is not unexpected
that more than a single mechanistic pathway may cause a
polygenic disease. For some diseases, including six of the
more prevalent chronic diseases discussed below, percent
increases associated with physical inactivity range between
20 and 45%.
1. Cardiovascular diseases
Individuals performing no physical activity had 45% more
cardiovascular diseases than those performing 41 MET·
hr/wk (where 1 MET is the value of resting oxygen con-
sumption).
2. T2D
Low activity groups had a 35 and 26% greater risk of T2D
than in high activity groups in meta-analyses when total
activity was determined in 14 cohort studies and leisure
time activity was reported in a different 55 cohort studies,
respectively (10).
3. Breast cancer
A 25% average increase in breast risk was present in the low
physical activity groups compared with the high activity
groups in the 51 studies that showed an increased risk.
Case-control studies had a stronger effect (an average 30%
increase) than cohort studies (a 20% increase) (310).
4. Colon cancer
The risk of proximal and distal colon cancers were in-
creased by 27 and 26%, respectively, among the least active
individuals in 21 meta-analyzed studies, as compared with
the most physically active people (63).
5. Dementia
Beyboun et al. (37) noted that decreased physical activity
was a strong predictor of incident Alzheimer’s disease based
on an average of 27 studies that found an estimated popu-
lation attributable risk percentage in dementia by physical
activity to be 31.9%.
E
n
d
o
c
r
i
n
e
S
k
e
l
e
t
a
l
m
u
s
c
l
e
• Heart disease
• Myocardial infarction
• Hypertension
• Stroke
• Hemostasis
Congestive heart failure
• Endothelial dysfunction
• Atherosclerosis
Peripheral artery disease
Deep vein thrombosis
B
o
n
e
I
m
m
u
n
e
D
i
g
e
s
t
i
v
e
R
e
p
o
d
u
c
t
i
o
n
N
e
r
v
o
u
s
Physical
inactivity
• Breast cancer
• Endometrial cancer
Polycystic ovary syndrome
• Gestational diabetes
• Pre-eclampsia
• Erectile dysfunction
Nonalcoholic fatty liver
• Colorectal cancer
• Diverticulitis
• Constipation
• Osteoporosis
• Osteoarthritis
• Balance
• Fracture/falls
• Insulin resistance
• Metabolic syndrome
Type 2 diabetes
• Obesity
• Sarcopenia
• Disuse atrophy
• Rheumatoid arthritis
• Pain
• Cognitive dysfunction
• Depression
• Anxiety
FIGURE 3. Physical inactivity increases
35 chronic diseases. See Booth et al. (55)
for more details on how physical inactivity is
a major cause of chronic diseases.
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6. Depression
Meta-analysis of 25 studies (453) showed a large signif-
icant improvement of depression by exercise and that
1,000 studies with negative results would be required
to reject the positive effects of exercise on depression,
including larger effects for interventions in major depres-
sive disorders. The effect size of exercise on depression is
at a moderate level of 0.56 (533). Furthermore, it has
been shown that exercise improves depressive symptoms
to a comparable extent as pharmacotherapy and psycho-
therapy (48).
The CDC began categorizing physical inactivity as an actual
cause of most chronic diseases only two decades ago. CDC
evaluation of U.S. mortality from physical inactivity has
provided varying results. Initially in 1993, McGinnis and
Foege (328) published in JAMA that diet/activity pattern
was an actual cause of ~300,000 deaths (or 13% of total
deaths) in 1990. The CDC authors concluded “...the
public health burden they [the major external (nongenetic)
factors that contribute to death in the U.S.] impose is
considerable and offers guidance for shaping health pol-
icy priorities.” Mokdad et al. (337) followed a decade
later with estimates of 365,000 “actual” deaths (or
15.2%) from poor diet and physical inactivity. Another
decade later, Murray et al. (345) estimated that ~230,000
(or 8.9%) of deaths were from physical inactivity and
~660,000 (or 25.4%) deaths from dietary risks occurred
in the U.S. Interestingly, of the 2,596,993 deaths that
occurred in the U.S. in 2013 (552), ~85% (2,000,000)
of those dying were directly or indirectly physically inac-
tive during the majority of their lives, according to U.S.
guidelines of 150 min/wk of moderate physical inactivity,
or 75 min/wk of intense physical activity (505), yet phys-
ical inactivity was estimated by the CDC as responsible
for only ~230,000 deaths. Thus we propose that the cor-
rect interpretation could be that physical inactivity
makes at least some contribution to 2 million (or 86%)
of U.S. deaths per year based on failure to reach 150
min/wk of moderate physical inactivity, or 75 min/wk of
intense physical activity in ages 5 yr old.
Associations exist between physical inactivity and increased
chronic disease and mortality. For example, inactive ado-
lescents and young adults express a less healthy coronary
risk profile, as compared with constantly active subjects
(410). Pedersen (385) wrote, “Physical inactivity is an inde-
pendent risk factor for abdominal obesity.” Manson et al.
(219) noted, “both increased adiposity and reduced physi-
cal activity are significant and independent predictors of
death.” Weinstein et al. (535) found, “BMI and physical
inactivity are independent predictors of incident diabetes.”
Blair’s group (534) stated, “Low cardiorespiratory fitness
and physical inactivity are independent predictors of all-
cause mortality in men with type 2 diabetes.” Later Blair’s
group (286) further showed that the influence of physical
inactivity on mortality is directed largely by CRF. However,
physical inactivity, itself, decreases CRF (443).
Myers et al. (347) in 2004 reported that for each 1,000-
kcal/wk loss decrease in physical activity, cardiovascular
fitness fell one MET, and importantly, both were associated
with a 20% increase in death rate. Myers et al. (347) also
noted that age-adjusted mortality fell per each quartile in-
crease in exercise capacity: hazard ratios of 1.0, 0.59, 0.46,
and 0.28 for very low exercise capacity, low, moderate, and
high quartiles, respectively. The same pattern existed for
physical activity, but with less dramatic reductions com-
pared with fitness: hazard ratios of 1.0, 0.63, 0.42, and 0.38
for very low physical activity, low, moderate and high quar-
tiles, respectively. Myers et al. wrote, “. . . these two vari-
ables (aerobic fitness and physical activity quantity) were
stronger predictors than established risk factors such as
smoking, hypertension, obesity, and diabetes.” Warburton
et al. (531) identified 254 articles with eligibility criteria for
premature all-cause mortality. Women and men had ~45%
average risk reductions for comparisons between high and
low aerobic fitness categorizations. Furthermore, they
found that high aerobic fitness also decreased mortality for
seven clinical conditions: breast cancer, cardiovascular dis-
ease, colon cancer, hypertension, osteoporosis, stroke, and
T2D.
Physical inactivity and poor diet are the second leading
actual causes of death in the U.S. (337). The WHO report
ranks physical inactivity as the fourth leading cause for
global mortality, with responsibility for 6% deaths
worldwide (287, 549a). Vita et al. (520) produced a met-
ric to approximate delays with chronological aging pro-
ducing chronic disease, described by Vita et al. (520) as
the percentage of remaining lifespan after the onset of
“cumulative disability.” A low percentage of remaining
lifetime before cumulative health disabilities divided by
the total lifespan would be “compression of morbidity.”
Fries (167) has tested his concept with two longitudinal
studies (29 and 31 yr in duration), comparing two
groups: “ever runners” versus “never runners.” “Never
runners” had initial cumulative disability from chronic
diseases 16 yr earlier and died 3–4 yr younger, thus ex-
hibiting low compression of morbidity, i.e., a longer per-
centage of life having at least one chronic disease. Fries
(167) stated, “. . . the greatest effectiveness (on postpone-
ment of biological aging) may come from physical exer-
cise, begun early, practiced hard, and continued for a
lifetime.” Later, others presented an alternative termi-
nology “healthspan,” which can be described as the per-
centage of life free before any chronic diseases. In sum-
mary, while physical inactivity causes hundreds of thou-
sands of deaths, it is likely these estimates are
underestimates of inactivity’s true contribution to death;
however, knowing the relative contribution of individual
factors is not easy to ascertain.
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F. Why Extend Epidemiology to Inactivity-
Induced Pathophysiology?
One simple answer is that despite all the outstanding
epidemiological studies, they have not stopped the phys-
ical inactivity pandemic. The strong dogma that physical
activity prevents chronic disease has not reversed the
public health challenge of the physical inactivity pan-
demic. For example, 50 million out of 325 million in
the U.S. population meet 2008 U.S. guidelines for mini-
mal public health.
Pathophysiology is defined here as the structural and
functional manifestations of a disease. One clinically sig-
nificant factor is that structural changes are often irre-
versible. Thus, to prevent a chronic disease in the first
place, i.e., primary prevention, structural changes must
be prevented. Prevention of chronic physical inactivity is
the primary preventer of chronic diseases. FIGURE 4 illus-
trates this concept.
As mentioned, the strong cycle of physical inactivity induces
dysfunction and pathophysiology, causing at least 35
chronic diseases/conditions, which in turn results in greater
levels of physical inactivity. The rapidity and severity of the
response to physical inactivity is startling and is exemplified
in the 1968 Dallas Bed Rest Study (443). Five healthy males
underwent 20 days of continuous bed rest. The percentage
declines in mean maximal physiological values for the sub-
jects during maximal running on a treadmill were very sig-
nificant for a 20-day period. V
˙O
2max
(aerobic fitness) fell
27%. Underlying the decrease were decreases of 11% de-
crease in heart size, 26% decrease in maximal cardiac out-
put, and 29% decrease in maximal stroke volume, but no
significant changes in maximal heart rate or in mean arte-
rial-venous O
2
difference were noted. Taken together, the
percentage size of the decrements in mass and function of
the cardiopulmonary system in 20 days emphasizes the con-
cept that the human body was built to rapidly adapt to a
dysfunctional state with very short periods of physical in-
activity. Remarkably, the transition from normal function
for aerobic fitness in 20-yr-old, healthy men (physiological
state) to low aerobic fitness for that age (physiological dys-
function) was equivalent to 30 yr of normal aging from 20
to 50 yr old. Twenty days of continuous bed rest places
these individuals in a pathophysiological state leading
closer to disease. These bed rest studies have been extended
to present everyday living. For example, Pedersen et al.
(263) had young Danish men reduce their daily step counts
from 10,501 to 1,344 for a 2-wk period. A startling 6.6%
decrease in V
˙O
2max
suggests physical inactivity is a major
environmental component. Unfortunately, the gene mecha-
nisms underlying the decline in V
˙O
2max
with aging and/or
inactivity are virtually unknown. To further complicate this
area of research is the fact that V
˙O
2max
is regulated by mul-
tiple organ systems, each with unique contributions to
V
˙O
2max
.
Mechanisms of disease can be defined as defects in processes
that trigger specific pathologies. Joyner and Green (230)
comment that approximately half of the protective effects
of physical activity are accounted for by traditional risk
factors such as reductions in blood pressure and blood lip-
ids. They suggest the missing one-half is due to the lack of
knowledge to understand how the protective effects of
physical activity are linked to health benefits, knowledge
that is still lacking. We suggest that the missing link may be
related to the concept that different molecular adaptations
produce health-beneficial consequences of physical training
and physical inactivity. In 2000, Booth et al. (52) proposed
“the biochemical, molecular, and cellular mechanisms of
physical inactivity will provide the scientific foundation for
appropriate individual prescription of physical activity for
health.” The topic is discussed in greater detail in Booth et
al. (55).
G. General Disappointment in Gene Variants
Becoming Predictive as Medicine
Therapies to Prevent Chronic Diseases
Caused by Physical Inactivity
This section provides the background for theme 3 regarding
gene-environment evidence. Joyner and Pedersen’s review
(231) notes disappointment that the promise that simple
gene variances have not emerged for common diseases by
suggesting “a second key example was the sequencing of
the entire human genome announced in 2001 and the
idea that a limited number of genetic variants would
emerge and explain common diseases like cancer, hyper-
tension, atherosclerosis, diabetes, etc.” For example, no
significant genetic risk score to the incidence of total
cardiovascular disease was observed for 101 single nu-
cleotide polymorphisms in a prospective study of 19,000
Physical inactivity
(Actual cause;
Risk factor)
Voluntary
physical
activity
Primary prevention Pathophysiology
Prescribed
physical
activity
Secondary prevention
(Therapy)
Most chronic
conditions/diseases
Premature mortality
FIGURE 4. Chronic physical inactivity initiates a cascade of events.
Physical inactivity is an actual cause of the numerous abnormal
physiological values (physiological dysfunctions) that, in turn, cause
usually permanent pathological changes (pathophysiology), which
over time lead to overt diagnosed chronic diseases, that culminate
as contributors to premature mortality. Two categories of physical
activity are presented: voluntary physical activity, which commonly
serves in primary prevention of pathophysiology, and prescribed
physical activity, which is shown for common usage of secondary
prevention of existing chronic disease.
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initially healthy white women (383). Pedersen (392)
commented that “findings such as those reported by Se-
shadri et al. (459) reinforce the futility of using individual
genetic risk profiling for AD [Alzheimer’s disease] be-
yond collecting information on age, sex, family history,
and APOE status.” A 2016 update by Talwar et al. (483)
is, “therefore, these identified genetic markers individu-
ally or in combination have little or no clinical (predictive
or diagnostic) utility in predicting AD [Alzheimer’s dis-
ease] risk.” The odds ratio for developing dementia in
APOE ε4 non-carriers were twice as high in non-exercis-
ers than in exercisers. However, APOE ε4 carriers found
no difference in the odds ratio for dementia development
was present between non-exercisers and exercisers (143).
One conclusion in a 2013 issue of Diabetes Care for the
status of genetic screening for T2D risk is summed by its
statements, “however, available data to date do not yet
provide convincing evidence to support use of genetic
screening for the prediction of T2D . . . Genetic testing
for the prediction of T2D in high risk individuals is cur-
rently of little value in clinical practice” (311). In addi-
tion, some outcomes of the Functional Single Nucleotide
Polymorphisms Associated with Human Muscle Size and
Strength study or FAMuSS were as follows: 1) “individ-
ual genetic variants explain a small portion of the vari-
ability in (511) muscle strength and size response to re-
sistance training (393). 2) Genetic variants that were ex-
amined in the resistance training study only 1-12% of
body composition and cardiometabolic markers in habit-
ual physical activity levels, “suggesting these traits are
highly polygenic with many loci contributing a very small
proportion of the variation, and these phenotype-geno-
type associations were often sex specific” (393). Further-
more, in ~6,400 individuals of European descent and
over 65 yr of age, no significant gene-variant associations
were observed with lower body strength (325). The out-
come contrasted with handgrip strength, which revealed
an association with molecular targets in ~27,000 individ-
uals 65 yr old, whose genes were of European descent
(325). Taken together, the above verifies the quotation
“simple genetic answers have not emerged for common
diseases” (231).
Only one human gene variant has been identified that is
related to physical inactivity. A gene variant in the FTO (fat
mass and obesity-associated protein) gene only expresses its
negative health effect of increased probability of obesity in
the presence of physical inactivity. One FTO risk allele that
is associated with obesity is 27% higher in physically inac-
tive adults (243). Demerath et al. (111) stated that the FTO
variant is the strongest common genetic susceptibility locus
for obesity yet discovered (118, 161, 456). Thus we inter-
pret that physical inactivity is a strong environmental stim-
ulant of one gene variant in the FTO gene variant for obe-
sity.
H. Polygenic Heritable Factors Regulate
Sedentary Behavior
The existence of genes for sedentary behavior has indirect,
strong support from 1) twin studies which identified hered-
ity as a source of inactivity between pairs if twins (113), 2)
selective breeding for the phenotype of physical inactivity in
rats (423), and 3) comparisons between naturally low and
high levels of voluntary running. FIGURE 5 provides an
overview that evolution can be used as a foundation to
speculate as to how physical inactivity could explain a gen-
esis for observed interactions among genes, environment,
and chronic diseases.
1. Humans predisposed to physical inactivity
The median heritability of exercise participation was 62%
in seven countries (Scandinavia, United Kingdom, and Aus-
tralia) (475). Comparisons were made between 13,676 mo-
nozygotic twin pairs and 23,375 dizygotic twin pairs. An-
other later study of 1,654 twins (same-sex twins comprised
420 monozygotic and 352 dizygotic same-sex twin pairs)
monitored by heart rate and accelerometers to time spent in
moderate-to-vigorous intensity physical activity and seden-
tary behavior. Roos and co-workers (113) reported that
sedentary behavior is moderately heritable in adults. Addi-
tive genetic factors (i.e., heritability) explained 31% of the
time spent in sedentary behavior, with environmental and
EVOLUTION
Physical
inactivity
Genes
Chronic
disease
Environment
Environment
FIGURE 5. Overview of physical inactivity’s interactions. The three
terms inside the triangle (chronic disease, genes, and environment)
all interact directly with physical inactivity, and physical inactivity can
directly influence them. The green circle indicates that evolution has
and continues to play a role in shaping the interactions of all the
terms inside the triangle.
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other factors explaining most of the remaining two-thirds.
Moore-Harrison and Lightfoot (338) in their review of
genomic locations associated with physical activity cite the
Quebec Family Study as reporting different chromosomal
linkages between human physical activity and physical in-
activity.
2. Selective breeding
The question considered in the current section is as follows:
Is there an evolutionary foundation for physical inactivity,
and if so how much of physical inactivity was selected by
evolution? One opinion to the question is from the anthro-
pologist Lieberman (297). His contention was that limita-
tions of the daily, caloric intake in hunter-gatherer popula-
tions drove a behavior of rest bouts during a part of the day
to save calories to match calorie intake. Lieberman also
contends, “selection never operated to cope with the long-
term effects of chronic inactivity” (297). Thus he contends
evolution never had an opportunity to develop protections
against physical inactivity to producing chronic diseases.
Selective breeding can be defined as selecting one phenotype
each generation to enrich genes underlying the phenotype.
Our approach was modeled after two identical strategies
that have provided significant insights into gene function:
one by Garland (480) selectively bred mice for high dis-
tances of voluntary running distance, as compared with
control mice, and another by Britton and Koch (251) from
rats selectively bred for either high or low exercise capacity
by forced running on motor-driven treadmills. Their publi-
cations led the Booth laboratory to selectively breed for the
trait of low voluntary running by rats. The strategy was to
determine if genes could be enriched to produce low behav-
ior to voluntarily run. The selective breeding protocol
uniquely produced rats with voluntary running distance
behavior approaching zero for some rats (422). The found-
ing population of females and males voluntarily ran 10.7
and 6.9 km/day, respectively. However, after nine genera-
tions of selective breeding for low voluntary running dis-
tance, females and males were running 1.4 and 1.1 km/day
(422), translating to 87 and 84%, respectively, less com-
pared with the founder population. To selectively breed for
the phenotype of low voluntary running distance indicates
the existence of genes to favor low voluntary running dis-
tance. While the genes, or gene variants, producing inherent
physical inactivity are yet to be identified, complex tran-
scriptomic responses direct physical inactivity. Taken to-
gether, the details above provide evidence for the role of
selective breeding studies (theme 6).
Taken together, both studies provide inferential evidence
that genes for physical inactivity exist and thus physical
inactivity has an evolutionary basis. An enormous volume
of literature exists on the artificial selection for the pheno-
type of high levels of voluntary running (173, 252, 298,
472). Since evidence indicates that physical activity has an
evolutionary basis (31), one might ask why evolution would
select the opposite, the need to be physically inactive?
3. Comparisons between mouse strains exhibiting
naturally low and high levels of voluntary running
In contrast to studies of artificial breeding for voluntary
running phenotypes that reveal gene identities, another type
of model compares different mouse strains, which we des-
ignate as a “natural” model of voluntary running. Lightfoot
and co-workers reported separate proteome signatures in
the nucleus accumbens between naturally high- and low-
physically active mice (145). Ferguson et al. (145) com-
pared two strains of mice having an 8.9-fold difference in
voluntary running distance. In sedentary mice of the higher
voluntary running strain that were never allowed voluntary
running, three proteins with metabolic functions were
higher in the nucleus accumbens (creatine kinase B, succi-
nyl-CoA ligase, and endophilin), as compared with the low-
est natural running strain. The higher mouse strain volun-
tarily ran 10.7 km/day, as compared with the second mouse
strain with a low voluntary running distance (1.2 km/day),
which exhibited four different proteins (stress 70 mitochon-
drial protein, V-type proton ATPase catalytic subunit A,
dihydropryimidinase, and transcription elongation factor
A) in the nucleus accumbens of mice never exposed to run-
ning wheels. In skeletal muscle, Ferguson et al. (144) re-
ported that transient knockdown of annexin A6 or calse-
questrin 1 protein within hindlimb skeletal muscles of high-
er-active mouse strain was associated with reductions in
voluntary running distance. They concluded that their data
support a hypothesis that factors from skeletal muscle con-
tribute to regulation of voluntary running. The finding re-
flects an earlier study using mice with sevenfold and three-
fold higher GLUT4 mRNA in hindlimb muscle and in heart,
respectively (507); the GLUT4 overexpressing had a four-
fold greater voluntary running distance than wild-type mice
(508). The GLUT4 overexpressing mice are an early dem-
onstration of muscles putatively “communicating” with
brain regions regulating voluntary running distances.
Animal behavior is evidence for the existence of an evolu-
tionary selection of “inactivity genes” in lower animals.
Predators employ various foraging modes in nature. Two
are ambush/sit-and-wait and active predation. They are
considered to be the two extremes of the foraging mode
spectrum (446). For example, copepods sit motionlessly in
the water column to prevent detection by the prey (245),
and sidewinder rattlesnakes sit and wait to ambush their
prey (90). Thus some physical inactivity behaviors can be
considered as inherent behavior.
In addition, transduction of physical inactivity is polygenic.
An example of polygenic response to increasing physical
activity is allowing pre-pubertal rats to perform natural
voluntarily running in wheels, which mitigates growth of
perirenal adipose tissue as body size increases, relative to
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peers without running wheels (268, 281). Running cessa-
tion (termed “wheel lock”) is associated with a rapid
catch-up growth of abdominal adipose tissue to match the
size of rats that never performed voluntary running. In a
transcriptomic experiment, perirenal adipose tissue mass of
1-wk, wheel-locked rats were 78% greater than rats contin-
ued running; 646 known transcripts were differentially ex-
pressed between wheel-lock and continued wheel access
groups in a pathway analysis of RNA-seq data (439). In
wheel-locked rats, pathway analysis revealed increased
transcripts for the functions of extracellular matrix, immu-
nity, inflammation, and macrophage infiltration. These
findings were interpreted to suggest polygenic responses in
perirenal adipose tissue when the pre-pubertal rat became
physically inactive following voluntary running. Together
with the discussion on the polygenicity of chronic diseases
in section IIG, these findings provide examples for theme 7.
I. Darwinian Medicine Application to Fitness
In our view, Darwinian medicine could suggest that survival
of the species depends on rapid responses to new changes in
the environment to increase the probability to survive,
which was introduced earlier as theme 8. From this, we
could suggest that a rapid transition between endurance
and strength fitness might have been a survival advantage,
as the two fitness phenotypes differ in function. We define
endurance exercise as continuous submaximal contractions
of large muscle groups, while strength exercise is defined as
producing near-maximal forces for a short period (seconds)
in any skeletal muscle group. Holloszy and Booth (217)
noted that hypertrophied muscles from strength training
have minimal, or no, increases in skeletal muscle mitochon-
drial concentration versus endurance-trained skeletal mus-
cle having increased mitochondrial concentrations without
hypertrophy. If one approximates the time for each contrac-
tion (repetition) against a near-maximal load to be ~3 s,
then as an example, training a muscle for 3 sets with 8
repetitions per set would be ~72 s, and if trained 2 times/
week, weekly duration of strength training would be ~2
min/wk. This duration is ~1% of the duration of 150
min/wk for endurance training in the U.S. physical activity
guidelines. Taken together, the actual duration of muscular
contraction may differ by 100-fold between two major mo-
dalities of exercise training for health adaptations to phys-
ical activity.
Along these lines, Coffey and Hawley (92) discuss the phe-
notype differences between endurance versus strength
training, reporting that differing signaling pathways are not
only producing the two phenotypes, but moreover that “it is
likely that multiple integrated, rather than isolated, effec-
tors or processes are required to generate the interference
effect,” whereby maximal strength development is impaired
in individuals who train using both strength and endurance
workouts, as compared with strength training alone. Dar-
winian medicine could suggest that survival might have de-
pended, in part, on the rapidity to transform from endur-
ance to strength optimization, or vice versa. Such specula-
tion could contribute to why physical inactivity is
associated with rapid skeletal muscle atrophy (493) (for
strength training) and rapid decline in mitochondrial con-
centrations (343) (for endurance training). Both genetically
optimal skeletal muscle endurance and size/strength require
differing molecular signals to produce different phenotypes.
While Darwinian Medicine concepts are applied to the gain
in either endurance or strength fitness, the gain of one type
of fitness is often associated with a decline in the other type
of fitness because signaling pathways inducing both pro-
duce conflicting phenotypes. For example, endurance run-
ning requires small fiber diameters of skeletal muscle fibers
to limit diffusion distance for optimal oxygen transport,
while muscle strength is associated with large-diameter fi-
bers to increase force per fiber. A speculative hypothesis
would be that the rapid time required to increase endurance
type of fitness for survival purposes in a new environment
requiring endurance could be dependent on the rapid loss
(increased degradation rate) of skeletal muscle diameter to
obtain the short oxygen diffusion distance (see sect. IXBfor
detailed discussion).
J. Environmental Manipulation of Genes by
Physical Inactivity and Possible Link to
Epigenetics
Pima Indians provide an example of a human population
highly predisposed to obesity and T2D. Although they
share a common genetic background, they have come to
reside in two geographical locations upon separation
~1,000 yr ago, with those residing in Arizona adopting a
Western lifestyle of physical inactivity and diet (454). Ari-
zona Pima Indians have developed one of the world’s high-
est prevalence of T2D (248). In contrast, the Mexican Pima
Indian population maintained their historical, relatively
low T2D prevalence (413), related to a physically active
lifestyle that included wood milling, nonmechanized farm-
ing, livestock breeding, security guarding, construction,
mining, and homemaking (136). Arizona Pima Indians
were estimated to to expend ~500– 600 kcal/day fewer than
their Mexican counterparts (136). Although DNA se-
quences most likely did not change in the 1,000-yr separa-
tion, epigenetic changes likely occurred in the Arizona Pima
population due to their lifestyle changes to less physical
activity and a Western diet. Such a supposition could be
based on what Noble’s reference (363) to Waddington who
“demonstrated the inheritance of a characteristic acquired
in a population in response to an environmental stimulus.”
It is a reasonable notion that physical inactivity could in-
duce changes in gene expression by epigenetic mechanisms.
More recently, epigenetics was described as a molecular
event that involves heritable changes in gene expression.
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Epigenetics encompasses alterations in gene expression
without nucleotide alterations in the DNA coding sequence
that are heritable through cell division. These modifications
include histone modifications and DNA methylation, but
new mechanisms suggest that other molecular events, such
as noncoding RNAs, are implicated in several epigenetic
mechanisms.” One example of this is Alibegovic et al. (6),
who noted a trend toward greater DNA methylation of
PPARGC1A in the vastus lateralis muscle after 10 days of
bed rest, which could contribute to the impaired expression
of PPARGC1A.
III. PHYSICAL INACTIVITY MODELS
As noted in FIGURE 1, the human physical inactivity contin-
uum ranges from extreme (spinal cord injury) to limited
inactivity (reduced stepping and sitting). Additionally, we
present rodent preclinical models of physical inactivity.
A. Bed Rest
Bed rest represents an extreme level of physical inactivity.
Typically, subjects participating in bed rest studies only
move the upper limbs, while removing all weight bearing
against gravity from the legs. In the early 1950s, the stan-
dard of care for a myocardial infarction was bed rest. How-
ever, President Eisenhower’s personal cardiologist, Paul
Dudley White, argued against bed rest after President Eisen-
hower’s heart attack (277). He prescribed early ambula-
tion, which later was credited for saving, or at least pro-
longing, the Presidents’ life. Interestingly, cardiac rehabili-
tation was developed around the same time after acute
myocardial infarction, where before this, the standard of
care was bedrest and inactivity. Interestingly, bed rest was
used in early experiments to better understand the deleteri-
ous effects that occur during spaceflight, while on land. The
strength of bed rest as physical inactivity model is that it
permits a more homogeneous experimental treatment, i.e.,
the experimenter can control for subject variability and can
limit amount of movement. The first human bed rest study
with major impact was the Dallas bed rest study by Saltin et
al. (443), described above. After only 20 days of continuous
bed rest by healthy young men, remarkable decreases oc-
curred in V
˙O
2max
, maximal total heart volume, maximal
stroke volume, and maximal cardiac output. One weakness
of conducting bed rest studies is the expense of conducting
these types of studies and the deleterious health outcomes
common to the subjects involved. Furthermore, bed rest
studies are clinically relevant to diseases/accidents that re-
quire bed rest to heal the primary disorder.
B. Spinal Cord Injury
Spinal cord injury approaches the most absolute form of
physical inactivity. Within spinal cord injury, there are var-
ious forms and severities of spinal cord injury, such as para-
plegia or quadriplegia (246). The location that the lesion or
damage occurs within the spinal cord will determine the loss
of function. If anything can be said positive about the tragic
condition of quadriplegia, it is that this condition offers
insights into the effects of inactivity in the absence of inner-
vations. One experimental weakness of the condition is the
heterogeneity in human subjects due to high variability in
the severity of spinal cord injury. Several reviews on the
model exist, including non-human primates for transla-
tional research to the human condition (365) and using the
rat as the preclinical model (130).
Other types of skeletal muscle denervation are present. Ag-
ing is associated with loss of neuromuscular junctions (223,
438) in sarcopenia. Loss of motor unit numbers is slowed
by life-long high-intensity physical activity (402), implying
a role for inactivity in motor unit loss. A part of a recent
review (93) considers molecular mechanisms during sar-
copenia.
C. Spaceflight
Spaceflight is deleterious to many organ systems due to the
lack of gravity (319). The uniqueness of the near-zero grav-
ity form of physical inactivity in near-orbital flight is that it
enlightens the role of lack of gravity in physical inactivity
adaptation on Earth. Even though astronauts can perform
physical activity in near-orbit in space under near zero grav-
ity conditions, many of the positive physiological adapta-
tions from exercise are not conferred due to the lack of
Earth’s gravity. For example, running on a treadmill in
near-zero gravity does not prevent bone loss by itself, as
there is no mechanical stimulus developed from a weightless
body on lower extremity bones on a treadmill belt in
weightlessness. The skeletal system does not experience
weight bearing and therefore undergoes bone decalcifica-
tion (283, 518). The strength of spaceflight as a model of
physical inactivity is due to being able to separate the factor
of gravity from other exercise responses. Some weaknesses
are that due to the limited numbers of individuals who go
into space and the high demands placed on the astronauts in
space on short missions, limited data are available on short-
duration space flights for studying this form of inactivity.
Additional, Morey-Horton and Globus (341) reviewed
ground-based animal models of space flight.
D. Limb Immobilization
1. Humans
In 1948, Deitrick et al. (110) published a report on human
immobilization that was imposed from the umbilicus to the
toes in which nitrogen and calcium excretion increased.
Some of the main concerns with human limb immobiliza-
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tion now are failure to recover lost bone strength and mus-
cle mass post-limb immobilization in elderly. Recently, 2
wk of hindlimb immobilization on leg strength and work
capacity of 23- and 68-yr-old men provides evidence of the
impact of immobilization and retraining (519).
2. Animals
Several models of limb immobilizations have been applied
to rodents in the elucidation of disuse atrophy and sarcope-
nia (50, 539). The strengths of limb immobilization are that
it approximates a real-world model. A weakness is that it is
not a model of whole body physical inactivity. An earlier
review on rat hindlimb that provides basic information
(50), and a recent article provides some mechanistic insight
into effects of one-limb immobilization upon skeletal mus-
cle atrophy (316).
E. Sitting
1. Human
In 1953, Morris et al. (342, 376) performed the aforemen-
tioned classic London double-level bus study, where drivers
sat while conductors had to walk up and down stairs on
double-level buses. Conductors, compared with drivers,
had a 30% lower incidence rate of coronary heart disease.
Furthermore, conductors were older when they developed
the disease, which was less severe with lower fatality rates
than the drivers (376). Follow-up research to the 1953
Morris study was relatively untested in humans until a 2007
study by Hamilton et al. (200). Sitting as a form of physical
inactivity has had a recent surge of publications (going from
309 papers in 1995 to 513 papers in 2005 to 1,109 papers
in 2015). Readers can refer to recent reviews for more in-
depth analyses showing some evidence for a likely causal
relationship between sedentary behavior and all-cause mor-
tality (38, 132, 543). For example, van der Berg and co-
workers (512, 543) noted using accelerometers on the
thigh, that for each additional hour daily of sitting or prone
position during waking hours, odds increased 22 and 39%
for T2D and metabolic syndrome, respectively, and T2D
outcomes were not related to sedentary breaks per day.
2. Animal
Hindlimb suspension mimics human sitting in regard to
removal of weight-bearing on the legs. Several reviews on
hindlimb suspension (unloading) exist (15, 87, 371), in-
cluding Baldwin et al. (15) who reviewed molecular mech-
anisms underlying myosin heavy chain isoform switching
during unloading of skeletal muscle by tail suspension.
F. Intermittent Breaks in Physical Inactivity
Edgerton’s laboratory hindlimb suspended rats for 1 wk
such that soleus and gastrocnemius muscles became non-
weightbearing (210). One group underwent the counter-
measure of intermittent breaks from non-weightbearing,
and the authors noted that high-load exercise at four inter-
vals spaced over 12 h daily prevented about half of soleus
muscle atrophy and hypertrophied the gastrocnemius mus-
cle. In a subsequent study, the Edgerton laboratory (205)
noted that non-weightbearing rats walking 40 min/day (10
min every 6 h) halved the amount of atrophy in the soleus
muscle and mitigated atrophy in the gastrocnemius (185).
In a separate study, the Booth laboratory (109) provided a
non-weightbearing group 2 h daily centrifugation for 1 wk
at one of three gravity levels. Soleus muscle masses were 48,
56, and 65% of control masses at 1, 1.5, or 2.6 G force,
respectively, of the atrophy produced with continuous non-
weightbearing. They followed up this noting that rats un-
dergoing four 15-min periods of centrifugation at 1.2 G,
spaced over a 12-h interval in the sleep period of the day,
prevented 67% of soleus muscle atrophy (108).
G. Decrease in Daily Step Numbers
1. Human
Many humans obtained sufficient steps during earlier times
historically simply by requirements for daily function and
work productivity (farmer, construction, mining, black-
smith, etc.). Today, one of the simplest forms of physical
activity, walking, has been largely engineered out of society.
Indeed, motorized forms of transportation (automobiles,
trains, boats, planes, public transportation, elevators, esca-
lators, moving walkways, and modern walking machines)
have replaced walking, carrying loads, and many occupa-
tional tasks such as production and farming use machinery.
To provide a human model of physical inactivity mimicking
the reduction in daily step numbers that has occurred in the
last few decades, many investigators have use a reduced step
model to investigate changes in real-world physical activity
levels. Another approach is the 12-yr walkability study in
Southern Ontario, where only the top quintile of walkable
neighborhoods had associations with decreases in incidence
of T2D prevalence (103).
2. Animal
The Booth laboratory has generated a model of rats that
were selectively bred for low voluntary running. This is a
unique rodent model that allows for the investigation of
behavioral and physiological drivers of physical inactivity.
Currently, this model is being used to understand central
nervous system-related contributors to voluntary running
(422, 424, 441). In addition, the Booth laboratory has used
the previously described wheel-lock model of physical inac-
tivity (95, 375, 414), in which access to voluntary running is
permitted for several weeks followed by locking of the
wheels, thereby ceasing normal activity and promoting in-
activity. The acute effects of this inactivity can then be as-
sessed in various tissues and organ systems.
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IV. BEHAVIORAL INFLUENCE ON
VOLUNTARY PHYSICAL INACTIVITY
It is well understood that an increase in physical inactivity
increases chronic disease (287); however, the increase in
physical inactivity with age is often nonlinear. Data support
the notion that lifetime physical activity peaks in the pre-
pubertal to pubertal ages, followed by a nonlinear decline in
physical activity occurring thereafter. An innovation oc-
curred when objective measurements of 24-h physical ac-
tivity and sedentary times became available with the advent
of accelerometer use (3). Accelerometers are purported to
be superior in validity to recall questionnaires (289).
A. Accelerometer Data on U.S. Children and
Adolescents
The data below from several studies indicate physical activ-
ity falling in childhood, implying the inverse that physical
inactivity is increasing at the same time. Trost et al. (506)
concluded that physical activity drops rapidly during child-
hood and adolescence, as evidenced by a 40% decline in
moderate-vigorous physical activity going from grades 1–3
to 4–6 (FIGURE 6).
The U.S. Physical Activity Guidelines designate 60 min of
daily moderate- or greater-intensity activity on 5 of 7 days/
week is required for health. Both sexes also fell below the
U.S. Guidelines for the chronological ages between the
groups for grades 7–9 and 10 –12. In addition, participation
in 20-min continuous bouts of physical activity was low to
nonexistent.
Troiano et al. (505) noted the majority of U.S. children and
adolescents did not meet the U.S. Guidelines for physical
activity. In 6–11 yr olds, 65 and 51% of females and males,
respectively, were physically inactive by the U.S. Physical
Activity Guidelines, while 12–19 yr olds exhibited an
alarming 96 and 92% physically inactivity rates of females
and males, respectively. More importantly, these percenta-
ges are high compared with other risk factors for cardiovas-
cular disease. For comparison, 21% of children and adoles-
cents exhibit at least one abnormal cholesterol measure
[low high-density lipoprotein (HDL) cholesterol, high total
cholesterol, or high non-HDL cholesterol] (357). The above
comparison suggests that physical inactivity is an unappre-
ciated risk factor for chronic disease.
Wolff-Hughes et al. (547) uniquely analyzed data from the
US 2003–2006 National Health and Nutrition Examina-
tion Survey (NHANES). They employed wrist accelerome-
ters on 3,700 U.S. youth (FIGURE 6). Moderate to vigorous
physical activity decreased ~67 and ~60% in U.S. girls and
boys, respectively, from the age of 6 to 19 yr old. After 8.7
and 10 yr of age, 50% of girls and of boys, respectively,
engaged in 60 min of daily, intermittent moderate-vigor-
810121416186
50
250
0
200
150
100
Male
Female
B
200
50
250
Age (years)
0
C
Boys
Girls
0
Obesity (%)
15
5
2 - 5 12 - 19
Age (years)
10
A
8.9
17.5
20.5
20
6 - 11
Age (years)
150
100
Moderate-to-Vigorous
Physical Activity (min/day)
Moderate-to-Vigorous
Physical Activity (min/day)
6 - 8 12 - 149 - 11 15 - 17
6 - 11 12 - 18
FIGURE 6. Increasing obesity and decreasing voluntary physical
activity as a function of age in youth. A: percentage of overweight or
obese (BMI for age grouping 85th percentile of the Centers for
Disease Control Growth Charts) in the three age ranges increases
from 2 to 5 yr (infants) to 6–11 yr (children) and 12–19 yr (adoles-
cents) as originally presented in JAMA by Ogden et al. (369). B:
best-fit lines for ages ascending from 6 to 19 yr old are descending
curves that represent the 50th percentile of females and males.
Accelerometer-determined moderate-to-vigorous physical activity
decreases during 6–11 yr old ages and then plateaued during
12–18 yr old age range. [Modified from Wolff-Hughes et al. (547).]
C: second confirming study to Bthat accelerometer-based moder-
ate-to-vigorous physical activity decreased during 6–11 yr old age
range and then began to asymptope during 12–18 yr old age range.
[Redrawn from Trost et al. (506), with permission from Medicine
and Science in Sports and Exercise.]
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ous physical activity. Rääsk et al. (409) also noted using
accelerometry that pubertal boys started to be less active in
their pubertal period. Taken together, the above four re-
ports showed a significant decline accelerometer-detected
physical activity in youth over time. Furthermore, more
than half of U.S. youth did not perform sufficient daily
physical activity, according to U.S. Physical Activity Guide-
lines. This has been extended to other countries, as Hallal et
al. (197) reported that 80% of 13–15 yr olds in 105 coun-
tries averaged 60 min of moderate to vigorous, daily phys-
ical activity, with girls being less physically active than
boys.
Despite the epidemic levels of physical inactivity, little is
known about the underlying genetic and biological mecha-
nisms that may contribute to the regulation of physical
inactivity behavior. Some of what we know has emerged
from physical activity studies. Garland and Carter (172)
reviewed that physiologists have historically recognized
that animals living in extreme environments show “clear
examples of evolutionary adaption because of the presum-
ably intense past selective pressures.” Swallow et al. (480)
reported that an ~75% increase in distance run in voluntary
running in wheels after 10 generations for high voluntary
running. While cultural and social pressures definitely influ-
ence physical activity in humans, they do not regulate these
behaviors 100%, and the estimated genetic component for
physical inactivity has been estimated at between 20 and
80% in animal and human studies (247). We tested whether
selective breeding would reveal the characteristic of low
voluntary running. After nine generations, female and male
rats selected for lowest distance of running in wheels exhib-
ited an 87 and 84%, respectively, decrease in wheel running
distance, as compared with wild-type rats (422). Our find-
ings provide supporting evidence for a genetic component
influencing sedentary behavior, that we have seen continue
in future generations of rats selectively bred for the primary
characteristic of low voluntary running distance (67, 422,
423, 425, 440, 441).
The above studies may also have evolutionary significance.
The studies described above (409, 505, 506, 547) exhibit a
similar age for declines in moderate to vigorous physical
activity. Voluntary physical activity also reaches its lifetime
highest value around the age of puberty in Wistar female
rats bred to be high voluntary runners (498). Next, we will
extend information from theme 9, introducing the topic of
voluntary running behavior peaking early in the lifecourse.
We speculate that evolution may have concurrently
matched the ages, puberty, and maximum voluntary phys-
ical activity to increase the probability of successful mating
to the next generation. Inherent gene regulation may trigger
the initial decline from peak lifetime voluntary physical ac-
tivity. As the clinical diagnosis of most inactivity-produced
human chronic diseases occurs post-puberty, even in cases
where chronic disease onset is pre-pubertal, reproductive
viability is usually maintained into later adulthood. Thus
reproduction will be successful in transferring genes to the
next generation independent of the inactivity status of the
youth. Consequently, it is unlikely that natural selection
would extinguish inherited genes for physical inactivity.
B. Childhood Activity Sets the Stage for
Adult Health
Some evidence exists to support the assertion that physical
inactivity during youth is associated with a higher proba-
bility of lower CRF, bone strength, skeletal muscle mass/
strength, and other cardiometabolic factors throughout the
remainder of life (146). Furthermore, the likelihood of these
factors being retained later in life increases through child-
hood to adolescence (158, 160).
For example, a 26-yr follow-up study of 1.1 million Swed-
ish men who had mandatory military conscription and
physicals noted that 18-yr-old recruits that has a combina-
tion of low exercise capacity and low muscle strength had
23% higher hazard ratios for vascular events 26 yr later (7).
Forrest and Riley (158) contend that the health of the pop-
ulation at later ages is affected by modifiable precursors of
many common chronic disorders that arise during child-
hood. Some mechanistic evidence supports this contention.
For example, voluntary wheel running after weaning re-
duced diet-induced obesity (381). Six-week exposure to
early voluntary running exercise prevented diet-induced
obesity in susceptible rats while they continued to consume
an obesogenic diet, but not engaging in voluntary running.
For hypothalamic peptides, the 6-wk voluntary running,
7-wk sedentary rats had a 55% greater mRNA expression
of arcuate nucleus proopiomelanocortin, as compared with
sedentary rats without voluntary running, suggesting a hy-
pothalamic contribution to their sustained obesity resis-
tance.
V. CENTRAL NERVOUS SYSTEM
(COGNITION, MEMORY, AND
MENTAL HEALTH)
Although some behavioral effects of physical inactivity are
linked to the nervous system, it is also well established that
participation in physical activity can enhance other nervous
system functions, such as cognition and memory, as well as
alleviate psychological conditions such as depression and
anxiety (221, 326, 404, 514). While many studies have
noted positive relationships between higher physical activ-
ity levels and brain health, based on our evolutionary con-
tention that “normal” human behavior is highly dependent
on physical activity, these data, by inference, argue that
physical inactivity may be a factor causing declines in men-
tal health. However, few studies have directly addressed
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how reductions in physical activity level influence mental
health. We highlight findings linking decreases in physical
activity with impaired brain function, as well as describe
mechanisms by which physical activity could prevent de-
clines in cognitive and mental health.
A. Physical Inactivity Increases Cognitive
Dysfunction Throughout the Lifespan
Cognitive function is defined as the intellectual processes by
which an individual becomes cognizant, perceptive, or un-
derstanding of ideas. The process engages all aspects of
perception, reasoning, remembering, and thinking. Cogni-
tive decline is associated with aging, including Alzheimer’s
disease and other forms of dementia. Thus cognitive func-
tion is arbitrarily divided into two time frames of develop-
ing and then declining cognition. Cotman and co-workers
(102, 221), Kramer et al. (404), van Pragg et al. (524), and
others have noted that increased physical activity drives
cognitive development of specific brain areas. On the other
hand, physical inactivity is associated with reduced cogni-
tive abilities. Low physical activity in older women in-
creased risk of cognitive impairment, Alzheimer’s disease,
and any type of dementia by 72, 100, and 59%, respectively
(279). Laurin et al. (279) concluded: “Regular physical ac-
tivity could represent an important and potent protective
factor for cognitive decline and dementia in elderly per-
sons.”
Similar findings show physical activity improvements on
cognitive health is prevalent across lifespan (214). Physi-
cally active Dutch men between 15 and 25 yr of age had a
lower age-related decline in informational processing abil-
ity compared with individuals physically inactive over the
same age range (115). Likewise, women had a lower likeli-
hood of cognitive dysfunction later in life if they were phys-
ically active either early in life or became active after being
teenagers (335). Importantly, physical activity during the
teenage years appeared to strongly relate to improved cog-
nitive function and to decreased cognitive impairment later
in life (335). Increased physical activity level in children (age
4–18 yr) is strongly associated with increased achievement,
developmental level/academic readiness, intelligence quo-
tient, perceptual skills, and verbal and math test scores
(137). Children and adolescents with low physical activity
levels have lower cognitive performance as compared with
physically active children (70, 81, 83a, 84, 122, 399), and
physical activity may enhance children’s executive function,
such as making decisions and prioritizing tasks, managing
time efficiently, and organizing thoughts and activities
(500).
Comparable relationships between cognitive function and
physical inactivity have also been found in older adults. In
~18,000 women aged 71–80 yr, lower levels of long-term,
regular exercise were related to decreased cognitive func-
tion and a 20% increased risk of cognitive impairment
(538). Likewise, the incidence of dementia rose from 13.0
per 1,000 person-years to 19.7 per 1,000 person-years with
greater physical inactivity (3 bouts of exercise/week) in a
65 yr-old group (276). Physical activity levels were in-
versely related to dementia in men and women who were 65
yr old and older (398). Lower V
˙O
2peak
was related nega-
tively with preserved cognitive function during a 6-yr pe-
riod in 349 subjects greater than 55 yr old (20). Similarly,
24 wk of resistance training positively affected multiple
measures of cognitive function in 65–75 yr old males (80).
However, whether the improvements in cognitive function
may be dictated by physical activity or fitness is unclear. In
a ~1,300 subject meta-analysis, Etnier et al. (137) con-
cluded that cognitive performance is positively associated
with physical activity, but that empirical evidence to sup-
port a relationship between cognitive performance and aer-
obic fitness was lacking. Other meta-analytic reviews have
observed similar findings (94, 142). Conversely, Voss et al.
(525) concluded “CRF is an important factor in moderating
the adverse effects of aging on cognitively and clinically
relevant functional brain networks.” They also caution that
it is still necessary to measure both physical activity and
CRF fitness because results for physical activity could be
due to higher fitness among high responders to regular
physical activity.
Minimal information is available for mechanisms by which
physical inactivity initiates mechanisms causing cognitive
dysfunction. However, physical activity could be used to
build hypotheses for mechanisms by which exercise rescues
cognitive dysfunction. A limitation of such a suggested ap-
proach would be that for some of the few known inactivity
mechanisms, these signaling pathways are not always the
reversal of exercise signaling pathways (470). A further lim-
itation is “the underlying mechanisms for the positive ef-
fects of exercise on wellbeing remain poorly understood”
(199). Thus only a limited number of exercise mechanisms
are available to use in a strategy already limited by lack of
inactivity mechanisms being a simple reversal of exercise
mechanisms. For example, physical activity increases den-
tate gyrus neurogenesis, which reviews interpret as associ-
ating with cognitive preservation (312, 517). Other reviews
(102, 313, 524) highlight that growth factors [brain-de-
rived neurotrophic factor (BDNF), vascular endothelial
growth factor (VEGF), insulin-like growth factor I (IGF-I)]
transmit downstream exercise signaling to enhance hip-
pocampal plasticity and related memory benefits (91, 135,
313, 521, 523).
We could hypothesize that physical inactivity with aging
may lower V
˙O
2max
to a level that may limit exercise intensity
and reversibility of low neurogenesis, plasticity, cognition,
BDNF, VEGF, and IGF-I in the dentate gyrus in old hu-
mans. For example, in older (60–77 yr), sedentary, healthy
males and females, after 12 wk of progressive interval train-
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ing, regional cerebral blood flow tended to increase in those
near 60 yr old, but, in contrast, decreased in those in the
70–77 yr old age range (314). In addition, several correla-
tions were reported, including three determinations (per-
cent increase in hippocampal regional cerebral blood flow,
percent increase in hippocampal volume, and percent in-
crease in the Complex Figure Test, a measure of long-term
memory), that all correlated with the increase in oxygen
consumption obtained at anaerobic threshold. In addition,
increase in hippocampal volume and Complex Figure Test
were both correlated with the increase in hippocampal re-
gional cerebral blood flow, and the increase in hippocampal
regional cerebral blood flow was correlated with the in-
crease in hippocampal volume (314). We speculate that as
humans age from 60 to 77 yr old, age-associated declines in
cardiovascular fitness will increase the relative work load
intensity needed for the absolute oxygen consumption value
that is required for the beneficial exercise effects on the
hippocampal functions/structure, potentially decreasing the
ability to work at the higher relative work intensities. In a
second report (380) of older subjects (male and female sub-
jects, age 60–72) with inadequate physical activity levels
(2–3 exercise events/month) and high levels of the pro-
inflammatory biomarker IL-12p40 (one of two subunits of
IL-12), at the end of a 6-yr period, the subjects had smaller
volumes of hippocampus and lateral prefrontal cortex as-
sociated with greater declines in Mini-Mental State Exam-
ination test than two physically inactivity individuals with
low values of IL-12p40 (380). The authors concluded that
“these patterns of data suggested that inflammation was
particularly detrimental in inactive older adults and may
exacerbate the negative effects of physical inactivity on
brain and cognition in old age.”
Others have hypothesized additional downstream mecha-
nisms by which physical inactivity could produce cognitive
decline. For example, sedentary rats had higher oxidative
stress, as determined by protein carbonyls, and decreases in
superoxide dismutase-1, glutathione peroxidase, as well as
decreases in p-AMPK and PGC-1
proteins in hippocam-
pus than endurance-trained rats by treadmill running (320).
In humans, greater amyloid deposition in brain was found
in sedentary, cognitively normal individuals (aged 55 to 88
yr old), as compared with those who exercised regularly
(206), implying a negative role for physical inactivity in
Alzheimer’s disease. The same investigators also tested sub-
jects who were both cognitively normal and Apolipopro-
tein_E (APOE
4)-positive individuals (aged 45 to 88 yr).
Physically inactive subjects with APOE
4 genotype also
had greater amyloid than the exercised subjects (206). Fur-
thermore, in a large epidemiological study (364), physical
inactivity, as a risk factor for Alzheimer’s disease, exceeded
each of six other modifiable risk factors, including depres-
sion, diabetes, low education, hypertension, obesity, and
smoking.
B. A Lack of True Physical Inactivity Studies
in Healthy Adults
While most reports associate cognitive benefits with in-
creases in physical activity from a sedentary/baseline mea-
sure, very few reports have shown direct relationships be-
tween reductions in physical activity and cognitive func-
tion. One physical inactivity model previously discussed is
spaceflight, and according to Strangeman (474), available
evidence is inclusive of supporting or denying the existence
of specific cognitive deficits during long-duration space-
flight. Cognitive effects of bed rest are also not conclusive
and remain to be established (260, 302, 303). Furthermore,
relatively few animal studies have analyzed how reductions
in physical activity influence cognitive abilities. This paucity
in research directly studying the mechanisms by which
physical inactivity may promote decreases in cognitive
function is an important research gap to fill, given the mag-
nitude of its clinical consequences and how reductions in
physical activity affect cognition.
C. Mechanistic Links Between Physical
Inactivity and Cognitive Impairments
Seminal work by van Praag et al. (515) reported that vol-
untary wheel running (VWR) increased survival of nascent
cells in dentate gyrus, a hippocampal region important for
spatial recognition in 3-mo-old mice. Similar findings show
that following 10 wk of voluntary wheel running, spatial
pattern separation was strongly correlated with increased
vasculature and neurogenesis in the dentate gyrus of 3-mo-
old mice (104). Improvements in brain blood flow are also
associated with improved cognitive performance. Underly-
ing greater blood flow with higher brain angiogenesis that
was associated with enhanced improvement in water maze
time and retention of spatial-reference memory (516).
VWR increases densification of blood vessel density, capil-
lary perfusion, and blood flow in the motor cortex in rats
(40, 479), potentially through increases in angiopoietin 1,
endothelial proliferation, density of microvessels, and
VEGF protein (120).
Many of the physical inactivity-related decreases in cogni-
tive function have been associated with local and systemic
expression of growth factors. For example, BDNF, partic-
ularly in the hippocampus, has been associated with many
of the positive effects on cognitive enhancement (101, 353).
Rodent studies demonstrate that BDNF protein levels in-
crease progressively with regular VWR (32). Conversely,
stopping VWR decreases hippocampal BDNF and BDNF/
NT-3 growth factor receptor (TrkB) mRNAs (542). Addi-
tionally, BDNF promotes long-term potentiation by im-
proving synaptic plasticity in the hippocampus of BDNF
knockout mice (382), an analog of learning and memory.
Improvements in long-term potentiation and synaptic plas-
ticity (304, 513), as well as enhancements in dendritic ar-
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borization and synaptic plasticity in the hippocampus (114,
473), occur in response to physical activity.
Similarly, IGF-I is another critical growth factor for neuro-
protection and brain health. Like BDNF, IGF-I levels are
decreased in the circulation of sedentary compared with
physically active animals (504). Both treadmill running and
systemic infusion of IGF-I enhance the number and surviv-
ability of hippocampal BrdUcells (306, 504). Intracarotid
infusion of IGF-I mimicked increases in neuronal c-fos and
BDNF in the hippocampus observed with treadmill run-
ning, which was reversed by infusion of an anti-IGF-I anti-
body (76). Additionally, anti-IGF-I antibody treatment ab-
rogated the protective effects of treadmill running on spatial
memory in mice with hippocampal injury (77).
In the aforementioned studies on rats selectively bred for
low voluntary wheel running (67, 422, 423, 425, 440, 441),
after eight generations, selected low voluntary runners ex-
hibited a 10-fold decrease in wheel running distance, as
compared with rats simultaneously bred for high voluntary
running, and roughly 4-fold less running distance than the
outbred founding population (422). Genetically engineered
rats for the phenotype of low voluntary running were linked
to depressed function in the mesolimbic dopamine system, a
system central to functions such as motivation, reward, and
learning, and co-segregate with the selection for low volun-
tary running behavior (423, 425). Additionally, transcrip-
tomic analysis of the nucleus accumbens, an important re-
gion of the brain containing the mesolimbic dopamine sys-
tem, has identified inherent decrements in neuronal
maturation in rats selected for low running behavior (425).
Similar results were found in mice bred for high voluntary
running and also implicate dopamine and certain midbrain
structures as being important in the evolutionary regulation
of physical activity (323, 332, 419). Although strong evi-
dence exists to support a genetic contribution to physical
activity regulation, other biological (nongenetic) and envi-
ronmental factors must be investigated to completely un-
derstand the precise mechanisms regulating this complex
and essential behavior.
Interestingly, mechanisms of activity may be linked to
evolutionary changes. Ruben and Bennett (437) postu-
lated in 1980 that the selection of burst-speed physical
activity by animals might have contributed to the co-
selection of “cephalization in protovertebrates and the
appearance of vertebrates themselves.” They mention
that adult invertebrate chordates have both a low degree
of cephalization and are “relatively sedentary.” They
then postulate that vertebrate cephalization might have
developed during selection to fulfill the need for in-
creased sensory and locomotor control as their more ac-
tive lifestyle evolved. Overall, while epidemiological and
mechanistic insight suggest that physical inactivity has-
tens the decline in cognitive function, this decline can be
lessened, or even potentially reversed, by exercise. How-
ever, many questions remain unanswered concerning the
best strategies to minimize these deficits.
In addition, it should be noted that physical inactivity in
children in school can have a negative impact on cognitive
ability and academic performance. Hillman and others have
published numerous timely reviews in this emerging area of
research (123, 213, 234).
D. Physical Inactivity Increases Risk of
Depression and Anxiety
Depression is a leading cause of disability within developed
nations (307), and by 2020 depression is predicted to be the
second leading cause of human disability, next to cardio-
vascular disease (346). Depression has a lifetime prevalence
of 16%. Furthermore, depression’s cost yearly in the U.S. is
$210 billion and is growing (187). Similarly, the prevalence
of anxiety is 10% in the general population, and it has many
parallel symptoms and treatments as does depression. Both
anxiety and depression are linked with many other disease
risks. Recent research suggests that physical inactivity may
be an actual cause of depression (385). Additionally, signif-
icant attention has been focused on the potential role of
exercise in preventing and/or managing depression and de-
pressive symptoms (378).
In general, data from observational and intervention
studies hint that physical inactivity has similarities to
depression and depressive systems (488). More than 100
population-based, observational experiments have been
published since 1995. In analyzing these studies, the Na-
tional Physical Activity Guidelines Report (511) con-
cluded that inactive individuals were ~45% more likely
to exhibit depressive symptoms than active individuals.
Similarly, 28 prospective cohorts were examined to de-
termine physical activity levels before the appearance of
depression symptoms. Physical inactivity for 4 yr aug-
mented the risk of depression by 49%, before any risk
factor adjustments. After adjusting for the risk factors of
age, alcohol use, chronic health conditions, education,
income, race, sex, and smoking, 22% of depression was
due to physical inactivity. Furthermore, in eight cohort
studies containing the clinical diagnoses of depression
symptoms, physically inactive individuals had 40% in-
creased risk of depression diagnosis. Similar trends have
been found in children. In children under the age of 15 in
the United Kingdom, every hour of exercise reduced de-
pressive symptoms by 8% reduction in depression symp-
toms (434).
The high association between physical inactivity and de-
pression has made exercise a viable treatment of depression.
As early as 1979, Greist et al. (188) found that running
reduced depression symptoms similarly to time-limited/un-
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limited psychotherapy. The need for medication and per-
centage of relapses were reduced by exercise in depressed
patients (11). Strikingly, the depressed patients had greater
adherence to physical activity (66%) than to drug medica-
tion (40%). An exercise dose of walking roughly 12
miles/wk of walking for 12 wk, consistent with public
health guidelines, lowered depressive symptoms by 47%
(126). The antidepressive effects of physical activity can be
seen as early as after only walking for 30 min/day for 10
days (117).
However, the precise mechanisms by which physical inac-
tivity may cause and/or physical activity may prevent or
treat depression remain largely unknown. Decreases in
brain neurotransmitters and neurotrophic factors (e.g., do-
pamine, glutamate, serotonin, norepinephrine, BDNF, endor-
phins, and endocannabinoids) (209) accompany chronic phys-
ical inactivity and provide key hypotheses. Furthermore, many
of these relationships have been determined in urine rather
than the brain (128), and the precise relationship of physical
inactivity with these neurotransmitters has not been stud-
ied. These factors are influenced by peripheral factors,
which provide more potential explanations by which inac-
tivity causes depression. Agudelo et al. (2) demonstrated
that exercise training induces the activation of skeletal mus-
cle PGC-1
1 and kynurenine aminotransferase, an enzyme
whose activity is protected from stress-induced increases in
depression.
Similarly, after examining cross-sectional studies of greater
than 120,000 Americans, the National Physical Activity
Guidelines Report (511) concluded that physical inactivity
increases the odds for the development of an anxiety disor-
der. In particular, the National Comorbidity Survey noted
that physical inactivity enhanced anxiety disorders by 1.75-
fold using raw odds and by 1.38-fold after adjusting for
sociodemographic and illness (184). Based on these popu-
lation-based studies, the National Physical Activity Guide-
lines Report (511) concluded that moderate (25 min/day)
amounts of exercise (both aerobic and resistance types) less-
ened anxiety symptoms. Like depression, changes in mono-
amines and other circulating biomarkers are related to in-
activity-induced augmentation in anxiety. Specifically, sig-
naling and production of norepinephrine in the brain stem
is reduced with physical inactivity, which is the origination
of the only norepinephrine producing neurons serving the
cerebellum, hippocampus, frontal cortex, and thalamus
(121). Furthermore, chronic wheel running for as little as 30
min/day in rats lessened rises in norepinephrine levels in
response to repeated stress (121). However, associations
have not been studied among physical activity dosage, set-
ting, and the likelihood of depression. Future studies must
examine these relationships to provide additional evidence
to support public health recommendations regarding the
specific prescription of physical activity required to reduce
the risk of depression.
VI. CARDIORESPIRATORY FITNESS
CRF has multiple synonymous and/or related terms, includ-
ing maximal oxygen uptake/consumption (V
˙O
2max
), peak
oxygen consumption (V
˙O
2peak
), aerobic fitness, aerobic ca-
pacity, and others; however, these subtle differences in the
terminology will not be discussed here. The section gives
evidence to support theme 5 on the impact of physical in-
activity on fitness.
A. The Decline in V
˙O
2max
With Aging Begins
in Early Adulthood in Sedentary Humans:
Impact of Aerobic Activity
CRF generally increases until late adolescence or early
adulthood and then declines the remainder of life in seden-
tary humans (9, 116, 321, 430) The lifetime decline in
V
˙O
2max
is not trivial, as Schneider (450) found ~40% de-
cline in healthy males and females (11 independent studies
for both sexes) that spanned 20–70 yr of age. However, the
percentage declines would have been greater if frailty levels
were reached at 16–18 ml O
2
/kg body wt. Such a drop to
physical frailty values would be 61 and 67% in females and
males, respectively, from lifetime highest V
˙O
2max
values
(450). Two important factors contribute to decreases in
CRF, biological aging beginning in the third decade of
life, and physical inactivity which speeds the decline in
V
˙O
2max
at a given age. Furthermore, cross-sectional stud-
ies show the most physically active group has the same
V
˙O
2max
as a three-decade younger, less-physically active
group (FIGURE 7).
Aerobic training delays CRF’s decline with aging by two to
four decades (FIGURE 7 shows 3 independent data sets). The
physiological importance is that this important determinant
of healthspan and mortality is not fixed by genes, but is
modifiable by the level of lifelong physical activity. Healths-
pan and life expectancy are lengthened and shortened by
aerobic training and physical inactivity, respectively.
In an attempt to better understand and elucidate molecular
mechanisms governing the concept of lifetime-apex V
˙O
2peak
and its initial decline, the Booth laboratory (498) studied
rats that had access to voluntary running wheels or were
sedentary. The initial hypothesis was that the active rats
would experience the decline in CRF later in life compared
with the sedentary rats; however, the only benefit that was
conferred with activity was the ~20% increase V
˙O
2peak
noted in the voluntary running group until 6 mo of age.
Thus, when rats are sedentary during adolescence, their
genetically highest peak CRF is not attenuated. Translating
this to humans, if children and adolescents were physically
inactive, they would have a lower CRF than their potential
maximum.
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B. Low CRF Is Associated With Increased
Chronic Diseases and Increases Mortality
Rate Three- to Fourfold
High levels of CRF are associated with reduced prevalence
of several cardiometabolic risk factors including hyperten-
sion, hyperlipidemia, inflammation, and insulin resistance
and lower incident rates of metabolic syndrome and T2D.
Indeed, physical inactivity leads to a decrease in CRF, in-
creasing the risk of numerous chronic diseases/conditions
(25, 280, 530). In the Aerobics Center Longitudinal Study
(19), the cumulative incidence rate of hypertension was
highest in women with low CRF. Each successive 1-MET
loss in CRF level was associated with increased hyperten-
sion risks of 19, 16, and 32% risk in all subjects, men, and
women, respectively.
Mortality begins to increase when CRF falls below ~10
METs (FIGURE 8). Remarkably, when CRF falls from 10 to
4 METs, death rate increases ~4.5 times (4, 44, 256). With
regard to the metabolic syndrome, Finnish men in the upper
fourth of loadings on the metabolic syndrome factor were
2.3, 3.2, and 3.6 times more likely to die of any cause,
cardiovascular disease, and coronary heart disease, respec-
tively (272). In another study of 15,400 healthy men, and
3,700 men with the metabolic syndrome, the middle and
lower tertiles for CRF had 2.08 and 3.48 times, respectively,
the risk of death from cardiovascular disease than men in
the upper tertile (241). The lowest third in V
˙O
2max
from 676
Finnish women and 671 Finnish men, between 57 and 79 yr
old, exhibited 10.8- and 10.2-fold higher risks, respectively,
while those in the middle third demonstrated 4.7- and 2.9-
fold higher risks, respectively, had the metabolic syndrome
as compared with the highest V
˙O
2max
after performing mul-
tivariable adjustments (204). In addition, men with high
CRF display significantly lower levels of abdominal adipose
tissue compared with those with low CRF (549). Overall,
these examples clearly establish that CRF, which is de-
creased in part through increased physical inactivity, leads
to increases in hallmark risk factors for metabolic diseases.
Regarding the impact of CRF on mortality, in the Aerobics
Center Longitudinal Study, cardiovascular disease mortal-
ity increased 19% for every 1-MET loss in CRF in 14,345
men who were 44 yr old after an average 11.4-yr period
(285). Low CRF had a 27% greater risk of cardiovascular
disease mortality. Those who exhibited an increase CRF
over the 11.4-yr period decreased risk of cardiovascular
mortality by 39%.
In addition, a direct dose-response relationship exists be-
tween exercise volume (duration intensity) and CRF
70
60
50
40
30
20
10
20 30 40 50 60 70 80 20 30 40 50 60 70 80 20 30 40 50 60 70 80
Age (years)
VO2max (ml/kg/min)
Masters Athletes
Young Athletes
Lean Untrained
Endurance-trained
Sedentary
Healthy men
normative values
- Octogenarian lifelong
endurance athletes
ABC
FIGURE 7. The age span shown for equivalent maximal oxygen consumption (V
˙O
2max
) values is many decades
later in life in a comparison of lifelong masters athletes (A), endurance-trained (B), or octogenarian endurance
athletes (C)toV
˙O
2max
values in younger, sedentary subjects. Data set 1: ~80-yr-old masters athletes’ V
˙O
2max
was equivalent to lean untrained men, aged ~30 yr old, which is a 5 decades difference between trained and
untrained humans (A). Similar decades’ difference for V
˙O
2max
between lifelong trained and sedentary groups
were published in two later publications. Data set 2: a 3 decades earlier in life equivalent V
˙O
2max
was reported
in younger sedentary as compared with the endurance-trained athletes (B). Data set 3: a 2–3 decades earlier
in life for V
˙O
2max
value was found in normative octogenarians who were lifelong, octogenarian athletes (C). Data
in the panels were obtained by copying curvilinear lines from the original figure. Each line begins as early as the
age of 20 yr old and ends at the oldest age group reported in each original figure. Superimposed upon each
curvilinear line are dashed lines with arrows so to form a 3-sided-rectangle above each solid curved line. The
vertical dashed line furthest to the right has an upward pointed arrow extending from the oldest age at which
V
˙O
2max
was determined, intercepting at the endurance-training V
˙O
2max
curvilinear line. The second dashed line
is horizontal and extends left to intercept the lower curvilinear line for the lesser V
˙O
2max
. The final line in series
of three dashed lines is a vertical drop-down from its interception point upon descending V
˙O
2max
line. [Afrom
Heath et al. (208). Bfrom Tanaka and Seals (484). Cfrom Trappe et al. (501).]
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(464, 481). Along the same lines, two landmark studies
report a dose-response relationship between low fitness and
increased mortality. Blair et al. (44) reported two fitness
assessments performed on average 8 yr apart on 10,224
men and 3,124 women. From lowest to highest fitness quin-
tile, age-adjusted all-cause mortality decreased 3.4-fold,
falling from 64.0/10,000 yr in the least fit quintile to 18.6/
10,000 yr in the highest fit quintile. A second report by
Myers et al. (349) noted that for every one MET drop in
maximal fitness, mortality increased 12%. Two cardio-
vascular fitness assessments performed on average 6 yr
apart on 6,213 men averaging in their 7th decade of life.
From lowest to highest fit quintile of cardiovascular fit-
ness, age-adjusted all-cause mortality decreased 4.5-fold
among normal subjects and 4.1-fold among patients with
cardiovascular or pulmonary disease. Taken together,
physical inactivity is a very powerful predictor of mor-
tality once maximal cardiovascular fitness falls to less
than ~10 METs (54).
C. CRF Is Not Fixed in Humans: Modifiability
Throughout the Lifespan
Trappe et al. (503) mention that two men who had nearly
the same CRF (V
˙O
2max
) at the end of the 22 yr (no aging
effect) when they continued the same high volume of endur-
ance training volume (endurance volume is defined as du-
ration volume). They comment in their discussion (503),
“thus it may be possible to augment the decline in aerobic
capacity if running training is maintained at a very high
level. Although running volume and intensity may drop off
slightly, it appears that the reduction in aerobic capacity is
significantly less compared with individuals who run less or
not at all.” This indicates two points. First, the decline with
normal aging between 30 and 50 yr of age could likely be
almost 100% due to physical inactivity. An extreme exten-
sion of the notion that voluntary physical activity is one
driver of the decline in CRF is to observe humans in an
old-age facility. Most human lives at the end of a long life
are spent sitting and lying down. The behavior of physical
inactivity in old age is likely a driver of a negative cycle of
inactivity begetting lower CRF (18, 436), which makes vol-
untary physical activity more fatiguing so less is performed,
and so forth in a negative cycle.
In addition, low V
˙O
2max
can be rescued by adding physical
activity to inactive humans. Improvements in risks from
co-morbidities and co-mortality are possible with the addi-
tion of physical training by inactive humans. A meta-anal-
ysis of 160 randomized clinical trials containing 7,487
women and men found that the inactive group did not im-
prove in comparison to the healthy improvements by the
exercise-trained group in CRF and in metabolic syndrome
biomarkers for lipid and lipoprotein metabolism, glucose
intolerance and insulin resistance, systemic inflammation,
and hemostasis during the trial (300).
Regarding mortality, in a Cooper Clinic study, 9,777 men
aged 20 82 yr old, who changed from unfit to fit over a
4.9-yr period, lowered their mortality risk by 44% (43).
Mortality risk was lowered 7.9% for each minute increase
in maximal treadmill time between measurements. In a Vet-
erans Administration study of 5,314 male veterans aged
65–92 yr, Kokkinos et al. (255) noted that males who went
from a low CRF to a high CRF decreased their risk of death
by ~50% over an 8-yr period. The opposite occurred for fit
men who became unfit in the two aforementioned studies
(255), increasing mortality risk by ~50%. Taken together,
physical inactivity can produce the loss of most cardiovas-
cular fitness associated with physical activity.
D. Cardiovascular Fitness Is a Multi-organ
Determined Phenotype
Nearly 100 yr ago, Nobel-Prize winner A.V. Hill discovered
the concept of maximal oxygen (211, 212). To supply skel-
etal muscle with the oxygen, atmospheric air must first pass
through the lung airways, where oxygen transport into pul-
monary capillaries with hemoglobin that will carry 99% of
oxygen. While the above may be taken for granted as they
are not rate-limiting steps in oxygen flux from air to skeletal
muscle mitochondria at sea level, this can become rate-
limiting in specific disorders. Maximal cardiac output is
thought to be a rate-limiting step (469), while capillariza-
tion and diffusion of oxygen to muscle mitochondria are
10.0
8.0
6.0
4.0
2.0
0.0
Mortality Rate (per 1000 person-years)
<2
2.1-4
4.1-6
6.1-8
8.1-10
10.1-12
12.1-14
16.1-18
>18
Maximal METs
14.1-16
1.0
0.8
0.6
0.4
0.2
0.0
Relative Risk of Death
FIGURE 8. Relative risk of death for all MET values (x-axis) for 10
and greater are all similar during maximal aerobic-type exercise.
When METs fall from ~10 to 4 with aging, risk of death increases
4-fold. Three studies are shown, with each from a different decade.
Study 1: Blair et al.’s 1989 study (44) of relative risk of death (left
y-axis) includes both male and female data from original Figure 4 in
JAMA (shown within ovals). Study 2: Kokkinos’ 2008 study in Circu-
lation (256) is relative risk of death in males (shown in rectangles).
Study 3: Al-Mallah et al.’s 2016 publication in Mayo Clinic Proceed-
ings (4) shows mortality rate (right y-axis) females (blue line and
black circles) and males (red line and black diamonds) with the outer
lines showing the 95% confidence intervals.
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generally not envisioned as rate-limiting in healthy humans.
However, the concentration of muscle mitochondria is con-
sidered to be a second potential rate-limiting step, to limit
V
˙O
2max
. Even today, we are still trying to understand what
limits V
˙O
2max
(469). For example, Richardson’s group
(177) has provided new data on whether maximal cardiac
output or skeletal muscle mitochondria are rate-limiting for
V
˙O
2max
. In untrained subjects, V
˙O
2max
is limited by working
muscle’s demand for mitochondrial O
2
, with indication of
adequate O
2
supply, whereas in trained subjects, the exer-
cise training-induced enlargement in mitochondrial concen-
tration in skeletal muscle causes skeletal muscle V
˙O
2max
to
become limited by O
2
supply. In addition, 7 days of bed rest
reduced erythrocyte volume by 9% in healthy men who
were 36 40 yr old (97).
E. Peripheral Circulation
One organ that epitomizes physical inactivity’s negative ef-
fects is the peripheral vasculature. The deleterious effects of
inactivity on the vasculature depend on the type of inactiv-
ity and also on the specific region/location of the vascula-
ture (491). For example, regional differences and severity of
consequences between resistance and conduit vessels are
with physical inactivity and the pathophysiological mecha-
nisms underlying changes that occur are unique to the type
of vessel (490). During physical activity and/or exercise,
sheer stress and hemodynamic stimuli induce effects on the
vasculature, promoting remodeling and an anti-atherogenic
phenotype. In contrast, with inactivity and the absence of
these physical stresses, endothelial dysfunction and arterial
remodeling (186, 491, 528) have been postulated to initiate
some of the negative phenotypic manifestations of inactiv-
ity on the vasculature.
Vascular responses to physical inactivity depend on the type
of inactivity. We will begin with data collected from studies
that are less extreme and more physiologically relevant,
moving to vasculature consequences resulting from more
extreme models of physical inactivity. Boyle et al. (62) used
a simple model of reduced step count in recreationally ac-
tive humans to determine whether reduced physical activity
(5,000 steps/day for 1, 3, and 5 days) altered flow-medi-
ated dilation in the popliteal and brachial arteries. After 5
days, flow-mediated dilation was impaired in the popliteal
artery (lower limb), but was not impaired in the brachial
artery (upper limb) and increased circulating endothelial
microparticles. These findings highlight the vulnerability of
reduced physical activity on localized vasculature in a short
time period. Along the same lines, a second study from the
above research group looked at the impact of prolonged
sitting on limb dilator function (417). A 6-h protocol of
sitting was performed, and flow-mediated dilation was
measured pre-sit, post-sit, and post-walk. The authors
found that the impaired dilator function with sitting can be
fully reversed after a short 10-min walk in the lower limbs,
but not the upper-arm vasculature. In addition, several
hindlimb unloading studies have been carried out in rodents
examining the effects on the peripheral vasculature. For
example, it has been shown that hindlimb unweighting re-
duces endothelium-dependent vasodilation and expression
of endothelial nitric oxide synthase in isolated rat soleus
skeletal muscle feed arteries (228, 452). This suggests that
with 14 days of disuse, the vasculature’s ability to vasodi-
late via endothelium-dependent mechanisms is compro-
mised.
Many studies to date examining vascular function in re-
sponse to inactivity have chosen more extreme models of
physical inactivity (39, 45, 46, 112, 198, 352). Bed rest
typically does not restrict upper extremity movement;
therefore, studying the effects on the vasculature in the
lower limbs versus the upper limbs may yield different out-
comes. Furthermore, unilateral lower limb immobilization
only allows study of one leg and can increase the risk of
deep vein thrombosis (33, 47, 170). For example, spinal
cord injury patients exhibited increased femoral and carotid
artery wall thicknesses after a 6-wk latent period (489).
Distinct mechanisms regulate conduit artery wall thickness
and diameter both above and below the spinal cord injury.
Intriguingly, only the femoral artery diameter (below the
spinal cord lesion) decreased over the 24-wk period post-
spinal cord injury (489). Thus localized effects occur for
arterial diameter (435). Spinal cord injury and 3 wk of
unilateral limb immobilization patients (274) both exhib-
ited reduced hyperemic flow, but spinal cord injury was
diminished to a greater magnitude. In addition, intima-me-
dia thickness-to-lumen ratio was increased with both short-
and long-term deconditioning (274).
Transcriptional adaptation to physical inactivity is one
mechanistic factor that has been investigated. In the above
study, Lammers et al. (274) noted downregulation of tran-
scripts including HIF-2
, which binds the VEGFA and
FLT1 promoter regions, VEGF co-receptor NRP, VEGF B,
VEGF C, caveolin-1 (CAV1), nitric oxide synthase traf-
ficker, soluble guanyyl cyclase, and the nitric oxide syn-
thase. Several transcripts were upregulated including
TGFB1, inhibiting angiogenesis and inducing arterial stiff-
ening, and the authors concluded “thus, the VEGF signaling
pathway, regulation through TGFB1 and involvement of
extracellular matrix-related proteins seem to be important
mechanisms after deconditioning, which may lie at the base
of the associated vascular adaptations.”
F. Aerobic Capacities in Hunter-Gatherer
Societies
Modern humans age 20–29 and 40 – 49 yr old have V
˙O
2max
values of 40 and 35 ml·kg
-1
·min
-1
, respectively (100).
V
˙O
2max
values of various hunter-gatherer societies exceed
those of modern human societies. For example, hunter-
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gatherer cultures, such as Eskimos living in the Canadian
Arctic community of Igloolik, had V
˙O
2max
values of 49–54
ml·kg
-1
·min
-1
(428) and 62 and 42 ml·kg
-1
·min
-1
for male
and female, respectively, in 20–29 yr old Ache of Eastern
Paraguay (526). V
˙O
2max
values of 60–70 ml·kg
-1
·min
-1
are
noted in New Guinea Lufas, Mexican Indians, and Tanza-
nian Masai, and 50 60 ml·kg
-1
·min
-1
in Venezuelan Indi-
ans and Finnish Laps (100). Cordain et al. (100) interpreted
the V
˙O
2max
values in modern humans as follows: “it should
not be surprising that the limited physical activity typical of
modern affluent humans generates mediocre aerobic fitness,
nor that the aerobic fitness levels of recently-studied forag-
ers are superior to those of Northern Americans.” The
above taken together could support the notion that physical
inactivity produced V
˙O
2max
values that are 30% lower than
their likely genetic potential.
G. Primary Artificial Selection for Low
Endurance Capacity Co-selects for Low
Aerobic Capacity
Britton, Koch, and Wisloff developed their working hy-
pothesis: “variation in capacity for oxygen metabolism is
the central mechanistic determinant between disease and
health (aerobic hypothesis)” (252). As an unbiased test for
their aerobic hypothesis, Wisloff et al. (545) employed se-
lective breeding of rats, with the primary selection factor
being the distance completed during forced running on the
motor-driven treadmill. Two separate lines were artificially
bred from the original founder line, with the primary selec-
tive factor for generation 1 being either low or high intrinsic
endurance running distance (termed “endurance exercise
capacity”). Compared with the distance run by the founder
population, which was used to breed generation 1, rats
selected for the low distance line ran 46% less distance and
for the high distance line ran 140% further the distance that
the founder runners had run 11 generations earlier. At the
11th generation, the primary selected phenotype (distance
during a forced run) had co-selected the two phenotypes
(aerobic capacity and risk factors for chronic diseases).
V
˙O
2max
was 58% lower in the low capacity runner (LCR)
line and cardiovascular risk factors were worse for LCR,
including 13% higher blood pressure, 7.8-fold more acetyl-
choline infusion required for one-half maximal vascular
relaxation, fasting blood insulin and glucose 131% and
20% higher, respectively, lower mitochondrial protein con-
centrations in skeletal muscle, visceral adiposity/body
weight ratio 63% higher, and blood triglycerides and free
fatty acids 168% and 94% higher, respectively (545). A
follow-up study reported that LCR rats had 28% shorter
mean lifespan, with a calculated hazard ratio of 5.7 for
death (253). The co-selections of aerobic capacity and
chronic disease risk factors in artificial breeding imply some
evidence for their co-selection could have occurred in nat-
ural selection during evolution and specific genetic differ-
ences based on endurance running capacity were developed
between LCR and HCR lines. Koch and Britton (250) ex-
pressed their view by stating “the strong linkage of disease
with low aerobic capacity is consistent with a pivotal role of
oxygen in our evolutionary history. Nevertheless, even if
these clues about the critical role of oxygen are correct,
recognizing the mechanistic footprint of oxygen in our evo-
lutionary path remains a challenge.”
H. Mechanisms
The cardiovascular disease risk factors produced by
physical inactivity are mediated via multiple mecha-
nisms. Mora et al. (340) categorized 60% of the risk
factors for cardiovascular diseases that are found in
physically inactive subjects. In rank order from highest to
lowest, percentage contributions were inflammatory/he-
mostatic biomarkers, blood pressure, traditional blood lip-
ids, and novel blood lipids. Remaining physical inactivity-
induced risk factors for cardiovascular diseases are not
known. A recent review (354) updates the risk factor “gap,”
indicating “in fact, epidemiological evidence suggests that
the protective effects of physical activity on cardiovascular
disease are nearly double that which would be predicted
based on changes in traditional risk factors,” suggesting
that ~50% of the protection afforded by physical activity
remains unexplained. Furthermore, the role of physical in-
activity in the “gap” also remains unknown. Joyner and
Green (230) suggest that the cardiovascular risk-factor gap
producing cardiovascular disease is the attenuation of three
positive physiological responses: 1) lower vagal tone so to
increase heart rate variability via lessened peripheral baro-
reflex function and CNS cardiovascular regulation; 2)
lower endothelial function so to decrease vascular compli-
ance that diminishes vasodilatation and attenuates periph-
eral baroreflexes; and 3) heightened baseline sympathetic
outflow on blood pressure by diminishing interactions be-
tween endothelial function and sympathetic outflow.
An example of different regulatory pathways for cardiovas-
cular adaptations to exercise and deconditioning comes
from Thijssen et al. (490), who reported that the vascular
responses to deconditioning and exercise did not employ
the same pathway in opposite directions. Specifically, they
found that the cardioprotective effects of exercise were due
to vasodilation by the nitric oxide pathway; however, de-
conditioning activated vasoconstrictor pathways. Taken to-
gether, the above findings provide evidence to support a
viewpoint that exercise mechanisms sometimes cannot be
simply reversed to explain physical inactivity mechanisms.
One outcome from some separate regulatory mechanisms
for exercise and inactivity could be that current emphasis on
molecular transducers of physical activity as a treatment for
modern sedentary chronic diseases/disorders will not reveal
with 100% fidelity actual molecular causes of chronic dis-
eases/disorders caused by physical inactivity.
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I. “Phenotypic Knock-down” of
Cardiovascular Fitness
We define “phenotypic ‘knock-down’” models of physical
activity (53) as reductions in physical activity independent
of transgenic methodologies. Physiological knockdowns
produce physical inactivity relative to the baseline of initial
physical activity. Thus “knocking down” physical activity
is a primary act of environmental manipulation that causes
secondary alterations in gene expression.
Two human models of endurance physical activity knock-
down are presented. First, Saltin et al.’s (443) 1968 bed rest
found that 20 days of complete bed rest by healthy 20-yr-
old men had a 28% decrease in V
˙O
2max
and a 26% decrease
in maximal cardiac output (the latter being produced by a
29% decrease in maximal stroke volume with no change in
maximal heart rate) (330). Of further interest is the five
subjects exhibited a twofold range in their percentage de-
crease in V
˙O
2max
, ranging from 46 to 20% to produce the
mean decrease of 28%. After bed rest, the subjects under-
went an approximate 8-wk period of endurance training for
the aim of returning the subjects to pre-bed rest values, and
V
˙O
2max
increased from 2 to 52% in the retrained subjects.
When the same subjects underwent V
˙O
2max
testing 30 yr
later at the age of 50, the percentage decline in V
˙O
2max
had
not decreased as much as it did after 20 days of bed rest
when the subjects were 20 yr old. Second, Pedersen’s group
(372) had 24-yr-old men reduce their daily step count from
10,501 to 1,344 steps/day for 2 wk. At the end of reduced
stepping, plasma insulin increased 57 and 50% during oral-
glucose tolerance test and oral-fat tolerance testing (OFTT),
respectively, and plasma triglycerides increased 21% more
during the OFTT. In addition, intra-abdominal fat mass
increased 7% after 2 wk of reduced stepping. Both the
Saltin et al. (443) and Olsen et al. (372) studies reported 26
and 7% percentage declines in human V
˙O
2max
after only 3
and 2 wk of continuous bed rest and reduced daily stepping,
respectively. The genes underlying the large decline in
V
˙O
2max
phenotype are currently unknown. In summary, the
above two human experiments provide support that large
increases in physical inactivity produce remarkably large
percentage declines in physiological functions in very short
periods.
As sitting is the most common form of inactivity and com-
monplace in today’s society, it is often reported that this has
a significant impact per se, independent of fitness, on
chronic disease risk. However, a shortcoming with this re-
search is the lack of control for fitness. Nauman et al. (351)
performed a cross-sectional study on 26,400 Norwegian
adults and found that high levels of CRF protect against
cardiovascular risk factors in men and women sitting 7
h/day, independent of whether or not these subjects were
meeting U.S. weekly physical activity recommendations.
Remarkably, high CRF (43.3 ml·kg
-1
·min
-1
) abolished the
odds for increased cardiovascular risk factor clustering (de-
termined on the basis of the definition of the metabolic
syndrome) in those subjects with high prolonged sitting
times, independent of subjects not meeting current recom-
mendations for physical activity (FIGURE 9). Furthermore,
Myers et al. (347) reported that “no deaths were observed
among subjects who were both fit (10 METs) and active
(1,500 kcal/wk)” in their study of 842 males aged 59 yr
old,” after subjects had a 5.5-yr follow-up period during
which 89 deaths occurred.
VII. METABOLISM
A. Overview of Inactivity and Altered
Metabolism
Whole body energy metabolism, substrate utilization, and
trafficking are altered by numerous factors including nutri-
tion, gender, age, and adiposity. But a largely underappre-
ciated factor altering energy metabolism and substrate uti-
lization is physical inactivity (85, 240). If Darwinian medi-
cine is to be used as a guide, then metabolic pathways
evolved under conditions in which physical activity and
energy expenditure in most of human existence were per-
formed for basic functions such as obtaining food through
hunting and gathering or agriculture work. Such a defense
mechanism has been noted in obese states. Rosenbaum and
Leibel (432) suggest that physiological mechanisms exist
for defense of body fat by acting to override maintenance of
the reduced body weight in obese humans (149). To protect
Low
Cardiorespiratory Fitness
High Moderate
25.2
25.4
27.9
6.2
7.8
9.9
1.0 0.9 0.9
Odds Ratio for Cardiorespiratory
Risk Factor Clustering
35
30
25
20
15
10
5
0
FIGURE 9. Adjusted odds ratio of clustering of cardiorespiratory
risk factors in combined categories of fitness and sedentary time in
men with decreasing cardiovascular fitness levels. Low, moderate,
and high CRF levels were defined as the least fit 20%, the next fit
40%, and the most fit 40%, respectively, corresponding to 35.7
(low), 35.7–43.3 (moderate), and 43.3 ml·kg
-1
·min
-1
in men.
Sedentary time is reported as 4 h (black bars), 5–7 h (dark gray
bars), and 7 h (light bars) *Significant difference (P0.05) from
reference category. [Data from Nauman et al. (351), with permis-
sion from Medicine and Science in Sports and Exercise.]
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against weight loss, such as would occur in a weight loss
program, an apparent evolutionary program of persistent
hypometabolic and hyperphagic state emerges making it
difficult to maintain a new, lower body mass. Their data
suggest, “the hypometabolic state is characterized by de-
clines in resting energy expenditure and activity energy ex-
penditure (due to increased contractile efficiency of skeletal
muscle), and is augmented by decreased activity of the hy-
pothalamic-pituitary-thyroid axis, decreased sympathetic
nervous system tone, and increased parasympathetic tone”
(149).
As a part of efficient storage, glucose, which is limited in
quantity (532), is utilized in tissues as dictated only by en-
ergy demands so as to protect limited circulating reserves of
glucose (only4gofglucose in circulation and ~300 –500 g
of glycogen in muscle and liver) (532). Maintenance of glu-
cose levels is essential for brain function and consciousness.
The nervous system generally does not oxidize fatty acids
except in long-term fasts (73) or in the absence of glucose
intake. Fatty acids, which are a quite abundant energy
source, are also preferentially shuttled towards energy
stores or maintained in stores (adipose depots) if their use is
reduced due to lower energy expenditure or constant feed-
ing. However, as physical activity and energy demands in-
crease, these depots of glucose (stored in muscle and liver
glycogen) and fatty acids (stored in adipose tissue or within
muscle and liver) can be rapidly mobilized to fuel muscle
contraction and other essential processes until the work is
done and food is procured. Again, Darwinian medicine
would indicate that much like a car engine fills up with gas,
the motor runs, and the tank must be filled up again, the
human body was designed for cyclical periods of energy
expenditure and energy storage. However, in the current
physically inactive environment this cyclical pattern has
theoretically been broken due to physical inactivity and a
lack of contractile activity to drive energy demand (85),
changes that putatively lay a foundation for subsequent
metabolic dysfunction (495). Indeed, accumulating evi-
dence strongly links inactivity to the development of obesity
and insulin resistance (240). Although complex, a chronic
positive energy balance results in weight gain, expanding
adiposity, and obesity. Given that energy balance is dictated
by energy intake and energy expenditure, chronic inactivity
and associated reduced energy expenditure often leads to
weight gain if energy intake levels are not lowered ade-
quately. This concept is illustrated in FIGURE 10.
Adult hunter-gatherers, for example, Tanzanian Hadza, do
not hunt, or gather, all day. Rather when not hunting and
gathering, energy intake was reduced sufficiently, when in-
active to make daily, total energy usage unchanged. Pontzer
et al. (401) suggested that their reduced energy observations
were an apparent compensation to keep total daily energy
usage unchanged. Pontzer et al. (400) recently modified
their interpretation of their above data with the constrained
total energy expenditure model. The theory suggests that
total energy expenditure is maintained at a chronically tight
homeostasis even if physical activity levels are changed over
a chronic period of time. This is a controversial idea, be-
cause it suggests that physical activity or inactivity would
not adjust total energy expenditure chronically. Even if
true, there is no doubt that the percentage of total energy
expenditure comprised of activity energy expenditure
would be higher in an active state, and lower in an inactive
state. Moreover, energy requirements in tissues and thus
cyclical substrate utilization and storage patterns would be
quite different between individuals who had the same daily
total energy expenditure, but did so with robustly different
activity levels within the same day (highly active vs. inac-
tive). Therefore, the flux or lack of flux through these path-
ways could play a primary mechanistic role in metabolic
dysfunction induced by inactivity.
B. Physical Inactivity Produces Rapid
Skeletal Muscle Insulin Insensitivity
Although, largely underappreciated, the regulation of skel-
etal muscle insulin sensitivity (insulin-stimulated glucose
Changes that occur with
transition from normal physical
activity to physical inactivity
Normal
activity
Glucose
Fatty acids
FFA from lipolysis
Glucose
Fatty acids
FFA from lipolysis
Inactivity
Skeletal muscle:
Reduced fatty acid oxidation
Reduced glucose uptake
Reduced insulin signaling
Reduced muscle mass
Reduced turnover of musle
triglycerides and glycogen?
Adipose tissue:
Increased adipose mass
Increased cell volume
Increased free fatty acid
trafficking to triglyceride storage
Reduced turnover of adipose
through lipolysis?
Increased glucose uptake?
FIGURE 10. Schematic of metabolic dys-
functions produced by physical inactivity in
white adipose tissue and skeletal muscle.
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transport) is driven by similar concepts as energy balance.
Skeletal muscle is a critical glucose disposal site during post-
prandial conditions, especially if glycogen stores are not full
and there is some level of energy demand due to contractile
activity. If skeletal muscle is being regularly contracted,
ATP demand is elevated, then there is a need for increased
glucose to be actively transported into muscle cells (494).
This can occur at even higher rates if glycogen is depleted
(79, 171). Therefore, higher levels of activity and depletion
of glycogen stores promote increased insulin sensitivity.
However, if skeletal muscles are inactive, ATP demand is
low, and glycogen is elevated, then the demand for glucose
is low and thus insulin sensitivity decreases. Indeed, adipos-
ity and physical inactivity contribute more to insulin insen-
sitivity with aging than aging itself (275). Glucose is tightly
regulated in this manner putatively because it is a substrate
in limited supply [only ~4 g in circulation (532)] and is
critical for brain metabolism.
C. Rat Wheel-lock Studies Link Physical
Inactivity and Metabolic Changes
As discussed above, Booth and colleagues (267–269) con-
ducted a series of experiments analyzing the effects of tran-
sitioning rats from daily wheeling running to cage only
(sedentary conditions). Rats were provided with running
wheel in their cages for weeks before the wheels being
locked, effectively transitioning the rats to physical inactiv-
ity. Important aspects of VWR are that rodents run inter-
mittently through the night and display mild improvements
in hindlimb muscle mitochondrial adaptations (218, 415),
compared with exercise training done on treadmills (154).
Therefore, VWR is a model that can arguably emulate bouts
of low to moderate intensity ambulatory activity of humans
throughout the day. The model allows study of physical
inactivity mimicking at least two human conditions, includ-
ing 1) having an occurrence that temporarily stops daily
physical activity, such as an illness, injury, etc.; and 2)a
“fast-tracking” the aging portion of life.
Rats typically ran for 3–6 wk followed by being studied
acutely after wheel-lock (from a few hours to 7 days post).
Daily VWR increased insulin sensitivity in isolated skeletal
muscle (267); however, wheel-lock lowered insulin-stimu-
lated glucose transport to the level found in sedentary rats
condition within 2 days. In a previous animal study, after a
single day of hindlimb immobilization, mouse muscle ex-
hibited decreased insulin responsiveness (457). Addition-
ally, a prior human study observed that only 3 days of
absolute bed rest in young healthy men caused significant
reductions in peripheral glucose uptake that were similar to
14 days of bed rest, which was secondary to peripheral
insulin resistance, rather than insulin deficiency (301). Fur-
thermore, when endurance athletes stopped daily exercise,
insulin sensitivity decreased to what was observed in seden-
tary, age-matched controls in only 2 days (71). Follow-up
analysis revealed that reductions in insulin-stimulated glu-
cose uptake into skeletal muscle tracked with reduced insu-
lin-stimulated activation of the insulin-signaling pathway at
both the levels of the insulin receptor and Akt, and also
tracked reduced insulin binding to the insulin receptor
(267). In addition, there was also a reduction in GLUT4
protein content, which tracks with insulin sensitivity levels.
In addition, these changes were likely mediated in part by
increases in the association of protein tyrosine phosphatase
1B (PTP1B) and protein kinase C (
) with insulin receptors.
PTP1B dephosphorylates the insulin receptor, while protein
kinase C is a serine kinase, which putatively blocks tyrosine
phosphorylation of the insulin receptor, and insulin recep-
tor substrate, leading to downstream inactivation of the
signaling pathway to translocate GLUT4 (444). Certainly,
the induction of reduced insulin sensitivity in skeletal mus-
cle after a transition to inactivity would not be considered
“pathological.” Thus these changes were physiological ad-
aptations to a condition of reduced energy expenditure
within the skeletal muscle, and it appears that molecular
mediators that have been implicated in the insulin resistance
field played a role in feedback inhibition. However, it can be
contended without the aforementioned physiological dec-
rements that the likelihood of progression to the pathology
of T2D is lessened. In other words, it would be rare for
insulin resistance to develop in skeletal muscle if physical
inactivity were eliminated on a daily basis (55).
D. Human Reduced Stepping Studies to
Examine Links Between Physical
Inactivity and Altered Metabolism
In the aforementioned study, Pedersen and co-workers
(263, 372) took young men who were physically active with
~10,500 steps/day and had them change their lifestyle so
that they approached 1,400 steps/day for 2 wk. After 2 wk,
in response to an oral-glucose tolerance test (OGTT), the
plasma insulin in the area under the curve increased 57%,
coupled with a 17% decreased in glucose infusion rate dur-
ing euglycemic clamp, while the area under the curve during
an OFTT increased plasma insulin 50% and plasma triglyc-
erides 21%. Additionally, intra-abdominal fat mass in-
creased 7% and total fat-free mass decreased 2%. These
studies were validated by studies in the Thyfault laboratory
that demonstrated inducing a transition from 10,000
steps a day to 5,000 steps/day after only 3–5 days pro-
vided similar results in terms of elevated glucose, and insu-
lin responses to an OGTT and elevated free-living postpran-
dial glucose levels measured by continuous glucose moni-
tors (336, 418). Thus, even with a shorter and smaller
change in daily activity levels, insulin sensitivity was low-
ered. These studies also documented that there were greater
swings in the peaks and nadirs of glucose levels measured by
continuous glucose monitors worn during free living con-
ditions (336, 418).
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Knudsen et al. (249) went on to perform an additional
inactivity study using reduced daily steps (from 10,000 to
1,500 steps/day), but also added the effects of hypercaloric
burden through subjects eating an extra 1,500 kcal/day dur-
ing the 2 wk of reducing stepping and found that inactivity,
when paired with a 50% elevated energy intake, induced a
50% decrease in insulin sensitivity. A subsequent paper
from Pedersen et al. (262) also examined the deleterious
effects that a high caloric-intake diet would have over 2 wk
on individuals who maintained activity greater than 10,000
steps/day or who reduced activity below 1,500 steps/day.
Although both groups gained body mass, the inactive
group gained more visceral fat, displayed worse glycemic
control, and had greater increases in hepatic glucose pro-
duction than the 10,000-step group. Stephens et al. (471)
examined if 1 day of increased sedentary time would also
lower insulin sensitivity and if this depended on energy
balance. Subjects either performed a day of increased
sitting with their normal caloric intake or a day of sitting
in which caloric intake was lowered to appropriately
match their lowered energy expenditure, and the authors
noted that both conditions lowered insulin sensitivity,
but the greatest effect occurred when energy intake was
not lowered during the sedentary condition. Healy et al.
(207) found interruptions in sedentary time were associ-
ated with healthier levels in metabolic risk variables. In-
creased numbers of breaks in sedentary time were related
to smaller waist circumference, lower fasting serum trig-
lycerides, and 2-h plasma glucose in OGTT. The differ-
ences were independent of both total sedentary time and
moderate-to-vigorous intensity activity time, suggesting
that the manner in which total daily sedentary time is
accumulated may be important, rather than simply the
total accumulated duration of sedentary time.
In addition, a recent study of 2,497 subjects age 40–75 yr
old with either T2D, impaired glucose metabolism, or
normal glucose metabolism noted that for each addi-
tional hour of physical inactivity, the odds of acquiring
T2D and metabolic syndrome increased by 22 and 39%,
respectively (512). The increased risk of T2D and the
metabolic syndrome were independent of high-intensity
physical activity. Furthermore, only weak associations
with increased metabolic syndrome risks were observed
with number of physical activity breaks, number of pro-
longed physical inactivity bouts, and average physical
inactivity duration.
In summary, physical inactivity exerts powerful effects to
alter substrate metabolism, including lowered insulin sensi-
tivity, and alterations utilization of fatty acids. These mech-
anisms provide strong evidence that chronic inactivity plays
a fundamental role in metabolic dysfunction and T2D.
Without initial, chronic physiological dysfunction, depen-
dent pathological and clinical maladies are less likely to
occur (55).
VIII. ADIPOSE TISSUE
At one time, adipose tissue was said to be an “inert” tissue.
Freidman’s studies contributed to ending that belief when
he found adipose tissue was an endocrine organ that re-
leased an adipocyte hormone, leptin (166, 554), which sig-
nals adipocyte size to regulating neurons in the arcuate
nucleus of the hypothalamus (308).
A. Physical Inactivity Contributes to Obesity
As discussed earlier, the Old Order Amish in Canada per-
form high levels of physical activity, averaging 18,000 and
14,000 steps/day (24). As mentioned, 2,252 individuals
aged 13 yr and older had an average of 5,117 steps/day (23),
while 325 women (average age 57 yr old) reported 4,944
steps/day (482). Additionally, data from NHANES (2005–
2006) noted that 3,725 subjects had an average of 6,549
steps/day from self-reported accelerometers (455), and out
of 3,744 adults, 84% were below 10,000 steps/day (36.1%
had 5000 steps/day) as determined by accelerometers
(463). Taken together, daily step numbers are less than half
in the general population as compared with Amish adult
men and women, equivalent to at least 300 kcal/day on
average (7,500 step difference 2,500 steps/100 kcal/
mile). As body fat is largely a balance of fat calories ex-
pended and fat/sugar intake, it is also of note that Amish
males report a greater energy intake (2,780 kcal) compared
with non-Amish males (2,298 kcal) (107). In another study,
average daily caloric intake for non-Amish men and women
was 2,504 and 1,771 kcal, respectively (550). Together, the
data suggest caloric intakes of Amish are greater than non-
Amish. Some might suggest that Amish should then have
higher percentages of body fat. However, Amish have very
low levels of obesity, with 0% of the men and 9% of the
women having a BMI 30 (24), compared with 35% in
men and 40% in women adults (155), and 1.4% of the
Amish youth being obese (22), compared with 17% of U.S.
children (369). Taken together, both physical inactivity and
caloric intake play shared roles in obesity.
Four other historical studies are of interest. One report
contains BMI values of U.S. adults from 1882 to 1986
(257). The rise in U.S. BMI values peaked in the early
1920s, declining to a trough in 1945, and beginning to
increase again by 1950. The declining period of BMI in-
cludes the years of the great depression (1929–1941) and
World War II (1941–1945). Another comparison statistic is
the number of automobiles. U.S. automobile number (in
millions) were 8, 17, 23, 23, 27, 26, and 40 in the years
1920, 1925, 1930, 1935, 1940, 1945, and 1950 (368).
Automobile numbers were relatively flat from 1930 to
1945. An association thus exists between percentage de-
cline in BMI and an essential plateau in automobile number.
A second study by the pioneer Jean Mayer concluded that
within the sedentary activity range, decreasing physical ac-
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tivity was not associated with a decrease in food intake, but
paradoxically was associated with an increase in food and
body weight in mill workers in West Bengal (327). A third
study covered the years following the 1991–1995 Cuban
economic crisis. From 1995 to 2010, bicycling decreased
from 80 to 55% of the total population concurrent with
caloric intake increasing from 2,400 to 3,200 kcal (159).
During this time, obesity rose 58%, from 33.5% in 1995 to
52.9% in 2010. A fourth paper reporting on 155,000
United Kingdom women and men reported higher BMI in
car-only individuals when compared with mixed public and
active transport commuters (156). Therefore, we caution as
to whether physical inactivity or caloric intake is more im-
portant relative to the other in human obesity. We suggest
that both have a participating role in weight gain, with
each’s relative percentage dependent on the situation.
B. Overview for Current Physiological
Regulation of Adiposity
Basal metabolic rate (BMR) is defined here as the minimal rate
of energy expenditure needed to keep the human body alive
with all of its vital functions at rest. While various BMR values
occur for differences in age, gender, lean body mass, and body
fat, among other factors. The storage of glucose is limited with
the amount of glucose in blood and stored as glycogen on the
same order of magnitude (532) as the daily BMR. Thus, to
conserve glucose as a source of calories, fatty acids are impor-
tant alternative sources of ATP. Importantly, neurons and
erythrocytes normally oxidize glucose, although neurons can
adapt to be able to oxidize fatty acids. Fat is very efficient per
unit of its mass in storage of calories, as exemplified by a
200-pound human with 20% body fat approximating
140,000 stored fat calories, and storage of triglycerides is fa-
vored in adipose tissue energy to be available to substitute
when food was not available. In the past, evolutionary pres-
sures often limited fat stores, and Reed et al. (416) noted,
“based on a dataset of 1,977 knockout strains, we found that
that 31% of viable knockout mouse strains weighed less and
an additional 3% weighed more than did controls.” Thus ~10
times greater molecules contribute to store triglycerides than
to oxidize them.
C. Both Childhood Obesity and Inactivity
Have Increased Dramatically in the Past
Half Century: Links to Adult Obesity
The likely causes of childhood obesity include environmen-
tal changes by diet, physical inactivity, and/or epigenetic
changes that induce changes in gene expression, versus new
DNA nucleotide sequences. Obesity is defined as a BMI
value at or above the 95th percentile for children. From the
period of 1971–74 to 2013–14, the percentage of U.S. chil-
dren (2–19 yr of age) who are “obese” increased from 5.1 to
17.0% (369, 370). Interestingly, from 2003-04 to 2013–14,
obesity in youth did not change. These changes are associ-
ated with a large increase in physical inactivity. The mean
minutes of moderate to vigorous physical activity per day
(FIGURE 6) showed continuous yearly drops of ~67 and
~60% when the activity was plotted from 6 to 11 yr old,
and then essentially leveling off each year from 12 to 19 yr
of age (547). It seems reasonable to propose the notion that
the 60% drop in physical activity from 6 to 11 yr old would
increase body adiposity as well as numerous other cardio-
metabolic risk factors. The data beg the question of what
the percentage of obese children have been if moderate to
vigorous physical activity were to have been maintained at
the 6-yr-old level until 11 yr of age? Also, what is the bio-
logical basis for the 60 67% drop in moderate to vigorous
physical activity per day? The Wolff-Hughes et al. (547)
data suggest that although other factors definitely contrib-
ute, this decrease in activity likely contributes to the in-
crease in childhood obesity. Of course, a limitation is the
lack of direct measures of calorie expenditure; however,
while decreased physical activity does not provide absolute
caloric values, the magnitude (60 and 67%) in the percent-
age decline in daily duration of moderate physical activity
implies decreases in caloric expenditures in children.
A recent study (270) used accelerometers to estimate moderate
to vigorous physical activity levels and DXA to determine
body composition between 1998 and 2014. Only 9.5% of the
“consistent active” subjects had entered the “becoming obese”
group at age 19 yr old, while 23.8% of participants in the
“decreasing active” group entered the “becoming obese
group” and 88.3% in the “consistently inactive” or “de-
creasing activity” groups were in the “consistently
obese” group. The authors (270) summarized that “adi-
posity development at age 13 or younger could be critical
to determining obesity development in young adulthood.
This finding sheds light on the importance of adiposity
development during prepuberty and puberty, and sup-
ports current public health efforts to prevent obesity by
focusing on early childhood.” Intriguingly, the lifetime
peak of voluntary running in wheels by female rats dis-
cussed earlier occurs around the age of puberty (498).
A recent meta-analysis by Simmonds et al. (462) of the clinical
importance of childhood obesity in increasing the odds of
adult obesity was performed that included large prospec-
tive cohort studies. Overall, obese children and adoles-
cents had 5.2 times the probability of becoming obese
adults as compared with nonobese children and adoles-
cents, ~55% of obese children retain being obese in ado-
lescence, ~80% of obese adolescents maintain having
obesity when in their 20s, and ~70% will be still be obese
over age 30. The authors cautioned that ~70% of obese
adults were not obese as children or adolescents, so what
contributes to obesity in childhood should also be tar-
geted in adults.
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D. Physical Inactivity Causes Obesity
Importantly, the medical community largely believes that obe-
sity, and particularly central obesity (abdominal or visceral)
(129, 162), is a primary cause of insulin resistance; thus the
role of physical inactivity can be largely ignored. However, an
adipocentric hypothesis is not correct. The putative adipocen-
tric mechanism is that obesity and in particular expansion of
visceral adipose leads to increases in circulating fatty acids and
inflammatory cytokines, which promote insulin resistance
(460, 447). Insulin resistance, the reduced ability of insulin to
promote glucose transport, is functionally simply “reduced
insulin sensitivity” and is often used in terms of describing a
pathological condition, although there is no clearly established
clinical definition of insulin resistance. Indeed, experimental
studies in humans, rodents, and muscle cells all support the
hypothesis linking visceral adiposity and increased circulating
factors that promote insulin resistance in skeletal muscle (34,
309). Free fatty acids and inflammatory cytokines such as tu-
mor necrosis factor (TNF)-
reduce insulin sensitivity in all of
these models (309, 444). However, the common feature in
these studies may be inactivity or reduced energy expenditure
that must likely be present for cytokines or free fatty acids to
decrease insulin sensitivity. In other words, this only occurs
when skeletal muscle energy demand is low, such as is found in
physically inactive humans living in a modern environment or
rodents in cages without access to running wheels.
Importantly, one opposition to the adipocentric hypothesis
is that humans who are overfed calories or rodents that are
hyperphagic or fed high-fat diets do not develop insulin
resistance if they are active or exercising (190, 261, 249,
415, 536). For example, Haskell-Luevano et al. (203) found
that voluntary running of melanocortin-4-receptor null
mice prevented obesity and diabetic metabolic syndrome
from developing, as had been reported in sedentary, mela-
nocortin-4-receptor null mice.
The removal of wheel-running allows a rapid initial growth
in adipose tissue mass following its retarded growth by
wheel running in rats just post-weaning when they are
growing (267, 281, 282, 439) or examining adipose tissue
regrowth after a dietary restriction (477). Rats that had
been exposed to VWR had reduced adiposity, body fat, and
body weight compared with sedentary rats that did not have
access to wheels. However, after only 2–7 days of wheel-
lock, when rats were no longer undergoing voluntary run-
ning, total mass of various abdominal fat pad depots (epi-
didymal, retroperitoneal, perirenal) and total body fat
(measured by DXA) increased dramatically and matched
levels found in sedentary animals (268, 282). Thus a tran-
sition to inactivity for only a few days caused an accrual of
abdominal fat that occurred over multiple weeks in seden-
tary rats. Further analysis revealed that both the cell volume
and lipid content of adipocytes within epididymal fat de-
pots also increased following wheel-lock. In addition,
Kump et al. (268) found that palmitate incorporation into
triacylglycerol of adipocyte homogenates, an in vitro assess-
ment of triacylglycerol synthesis, increased by ~3.5-fold
from5hofwheel-lock compared with only 10 h of wheel-
lock. Furthermore, palmitate incorporation into adipocyte
triacylglycerol at 10 h after wheel-lock no longer cycled, but
progressively continued to increase for 24 h/day at 1 and 2
days following wheel-lock, an effect described as an over-
shoot due to wheel lock-induced inactivity (53). These ani-
mal findings resemble a body of human evidence that phys-
iological mechanisms exist to defend adiposity after weight
loss programs (149). Furthermore, VWR rats consumed
more food on a daily basis than age-matched, sedentary
rats, and they continued to consume more food at a decreas-
ing daily amount for 3 days following wheel-lock before
matching food intake in age-matched, sedentary rats never
exposed to VWR (282). Therefore, logic would indicate
that the increased food consumption and associated posi-
tive energy balance might be driving the rapid increases in
adiposity found during wheel-lock. However, Booth and
colleagues (282) performed followup studies in which
wheel-locked rats had food intake controlled by pair feed-
ing to adjust food intake to what it should be in their new
physically inactive state, and a rapid increase in abdominal
adiposity and body fat after 7 days of wheel lock still oc-
curred. The group went on to use transcriptomics analysis
in the perirenal adipose tissue and detected an enrichment
of transcripts having functions for proinflammation, extra-
cellular matrix, macrophage infiltration, and immunity
(439). Unloading of the rat hindlimbs had a delay of 4 h
before a rapid fall in heparin-releasable lipoprotein lipase
activity from its soleus muscle so that by the 10th hour of
unloading, 95% of heparin-releasable lipoprotein lipase
was no longer present (36).
Furthermore, in humans, daily physical activity is highly
correlated to insulin sensitivity, and this is only modestly
attenuated by adiposity (16). Therefore, inactivity generally
must be present for insulin resistance to occur. Indeed, in-
activity is a key etiological factor in the development of
insulin resistance through two mechanisms: 1) inactivity,
itself, lowers insulin sensitivity, and 2) inactivity provides a
permissive environment whereby signaling molecules can
impair insulin signaling processes and further reduce insulin
sensitivity. Interestingly, the transition of rodents and hu-
mans from high to low levels of daily activity as a model to
study inactivity leads to dampened insulin sensitivity and
increased central adiposity that occur at the same time
frame. Thus not only does inactivity promote insulin resis-
tance through reduced utilization of glucose in muscle, but
it may also promote the increased storage of glucose and
free fatty acids into adipose tissue. Inactivity-induced re-
ductions in fuel utilization of both glucose and fatty acids
may divert these fuels to storage in adipose tissue. As such,
adiposity and insulin resistance may develop at the same
time through these processes. In addition, impaired insulin
signaling was not found in muscle cells treated with TNF-
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