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Determining the relationship between dietary carbohydrate intake and insulin resistance

  • Atlantic Technological University

Abstract and Figures

Insulin resistance underlies type 2 diabetes, CVD and the metabolic syndrome, driven by changes in diet, lifestyle, energy over-consumption and obesity. Nutritional recommendations for insulin resistance remain an area of controversy, particularly the quantity and types of dietary carbohydrate. The present review gives an overview of insulin resistance, its relationship to impaired insulin secretion and the metabolic syndrome, research methodologies used to measure insulin action and the epidemiological and intervention studies on the relationship between dietary carbohydrate and insulin resistance. Epidemiological studies provide little evidence to suggest that total dietary carbohydrate predicts risk of type 2 diabetes, and high-carbohydrate, high-fibre diets with low-glycaemic index (GI) may even contribute to diabetes prevention. Despite inherent limitations associated with techniques used to measure insulin resistance and dietary assessment, most intervention studies reveal an increase in glucose tolerance or insulin sensitivity with high-carbohydrate, low-fat diets in non-diabetic and diabetic individuals. When energy is restricted the source or reduced content of carbohydrate does not appear to be as important as fat for body weight. Thus, low energy intake is key to weight loss and augmentation of insulin sensitivity. Given this, widespread adoption of popular low-carbohydrate high-fat diets highlights the necessity to evaluate dietary interventions regarding safety and metabolic effects. While current evidence supports FAO/WHO recommendations to maintain a high-carbohydrate diet with low-GI foods, the relationships between carbohydrate and insulin sensitivity remains an important research area. Emerging technologies should further enhance understanding of gene-diet interactions in insulin resistance, providing useful information for future nutrition policy decisions.
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Determining the relationship between dietary carbohydrate intake
and insulin resistance
Neville H. McClenaghan
School of Biomedical Sciences, University of Ulster, Cromore Road, Coleraine BT52 1SA, UK
Insulin resistance underlies type 2 diabetes, CVD and the metabolic syndrome, driven by
changes in diet, lifestyle, energy over-consumption and obesity. Nutritional recommendations
for insulin resistance remain an area of controversy, particularly the quantity and types of
dietary carbohydrate. The present review gives an overview of insulin resistance, its
relationship to impaired insulin secretion and the metabolic syndrome, research
methodologies used to measure insulin action and the epidemiological and intervention
studies on the relationship between dietary carbohydrate and insulin resistance.
Epidemiological studies provide little evidence to suggest that total dietary carbohydrate
predicts risk of type 2 diabetes, and high-carbohydrate, high-fibre diets with low-glycaemic
index (GI) may even contribute to diabetes prevention. Despite inherent limitations
associated with techniques used to measure insulin resistance and dietary assessment, most
intervention studies reveal an increase in glucose tolerance or insulin sensitivity with high-
carbohydrate, low-fat diets in non-diabetic and diabetic individuals. When energy is
restricted the source or reduced content of carbohydrate does not appear to be as important
as fat for body weight. Thus, low energy intake is key to weight loss and augmentation of
insulin sensitivity. Given this, widespread adoption of popular low-carbohydrate high-fat
diets highlights the necessity to evaluate dietary interventions regarding safety and metabolic
effects. While current evidence supports FAO/WHO recommendations to maintain a high-
carbohydrate diet with low-GI foods, the relationships between carbohydrate and insulin
sensitivity remains an important research area. Emerging technologies should further enhance
understanding of genediet interactions in insulin resistance, providing useful information
for future nutrition policy decisions.
Diet: Carbohydrate: Human studies: Insulin resistance
Diet and lifestyle modifications are widely regarded as the
cornerstones of treatment of insulin resistance and
management of other diseases of the metabolic (insulin
resistance) syndrome. The major aims of this approach are
to reduce body weight, improve glycaemic control and
reduce the risk of cardiovascular and other complications,
which may account for up to 80 % of deaths of indi-
viduals with diabetes (National Institutes of Health, 1995).
Modern dietary strategies to prevent or overcome the
hyperglycaemia of insulin resistance often advise complex
carbohydrates or starches and avoidance of simple
carbohydrates or sugars. However, this approach assumes
that simple sugars are digested and absorbed more
quickly, thus inducing a more rapid postprandial glucose
response and does not take into account metabolic
evidence that many starchy foods (including baked
potatoes and white bread) can produce even higher
glycaemic responses than simple sugars (Kalergis et al.
1998). Conceptual confusion regarding dietary carbo-
hydrate and the increasingly widespread use of low-
carbohydrate diets for weight loss has led to generalis-
ations regarding carbohydrate intake and the development
or progression of insulin resistance, obesity and diabetes.
The following sections aim to provide a comprehensive
and balanced overview of the nature of insulin resistance,
its relationship to impaired insulin secretion and diseases
of the metabolic syndrome, the research methodologies
used to measure insulin action and, given the above, a
review of the literature on epidemiological and interven-
tion studies investigating the relationship between dietary
carbohydrate and insulin resistance.
Abbreviations: EHC, euglycaemichyperinsulinaemic clamp; GI, glycaemic index; HOMA, homeostasis model assessment; IRS, insulin
receptor substrate.
Corresponding author: Dr Neville H. McClenaghan, fax þ44 28 7032 4965, email
Nutrition Research Reviews (2005), 18, 222–240
qThe Author 2005
DOI: 10.1079/NRR2005109
Insulin in health and disease
Insulin plays a pivotal role in whole-body metabolism, and
insufficient insulin production, secretion or action signifi-
cantly alters the homeostatic regulation of numerous
metabolic processes. Insulin resistance was first described
in diabetes mellitus, which has been defined by the
American Diabetes Association Expert Committee rec-
ommendations (Anonymous, 1997) as ‘a group of metabolic
disorders characterised by hyperglycaemia resulting from
defects in insulin secretion, insulin action or both’, covering
a range of heterogeneous diseases of the metabolic
syndrome (Haffner & Cassells, 2003). While the mechan-
isms regulating insulin production, secretion and in
particular action are fundamental to understanding the role
of insulin in health and disease, in-depth consideration of
this complex area is beyond the scope of the present review.
The following provides a brief overview of insulin secretion
and insulin resistance in health and disease.
Regulation of insulin secretion
Insulin is exclusively produced by pancreatic b-cells of the
islets of Langerhans, small clusters of exocrine tissue
scattered throughout the pancreas, by enzymic cleavage of
its precursor molecule, proinsulin. Much insight into the
mechanisms controlling insulin production and secretion
has been derived from studies of freshly isolated pancreatic
islets and constituent b-cells or bioengineered insulin-
secreting cell lines (McClenaghan & Flatt, 1999a). The
pancreatic b-cell acts a nutrient fuel sensor, monitoring
minute-to-minute circulating levels of glucose (Fig. 1) and
other important classes of nutrient (including amino, keto
and fatty acids) and responding with modulation of insulin
secretion and b-cell function (Flatt, 1992; McClenaghan &
Flatt, 1999b). Glucose is the principal regulator of insulin
secretion and b-cell function which is internalised by
GLUT1 (in man, GLUT2 in rodents), after which is it is
rapidly metabolised by glucokinase (Fig. 1). Metabolically
derived ATP and other metabolites mediate a number of so-
called K
channel-dependent and -independent actions of
glucose, raising intracellular Ca concentrations and
modulating other important events regulating insulin
exocytosis (McClenaghan & Flatt, 1999a,b). There has
been much debate as to the relative contribution of impaired
insulin secretion or action to the onset and pathogenesis of
diabetes, but it is clear that there is interplay between these
two fundamental features in the onset and progression of the
Fig. 1. Overview of the key mechanisms underlying insulin secretion, insulin action and insulin resistance in man. IGT, impaired glucose
tolerance; T2DM, type 2 diabetes mellitus; IRS, insulin receptor substrate proteins; K
channel, ATP-sensitive K
channel; VDCC, voltage-
dependent Ca
Carbohydrate intake and insulin resistance 223
condition (Fig. 1). Indeed, insulin secretion can be regarded
as defective at the time of diagnosis and declines with
progression of the type 2 diabetes, often paralleled with a
worsening of insulin resistance (Yki-Jarvinen, 1992; Kitano
et al. 2004). Furthermore, various peptides (including
bradykinin and oxytocin) that stimulate insulin release and
hormones or mediators (including growth hormone and
prolactin) may exert long-term effects on b-cell growth and
function. This in turn may influence insulin secretion, and,
together with other factors (such as excess cortisol and
parathyroid hormone) and physiological and pathophysio-
logical states (such as pregnancy or obesity), may induce
insulin resistance (Pickup & Williams, 2003).
Regulation of insulin action
The overarching action of insulin is to control the storage
and release of energy during feeding and fasting. Insulin
plays a key metabolic role through suppressing gluconeo-
genesis and promoting lipogenesis (hepatocytes), promoting
storage of glucose as glycogen (liver and muscle) and
storage of amino acids (muscle) and triacylglycerols
(adipose tissue) (Fig. 1). The principal actions of insulin
are mediated through its binding to insulin receptors on
target tissues, regulating membrane expression of GLUT4
and thus cellular glucose uptake (Fig. 1). Insulin binding
activates receptor tyrosine kinase activity and promotes
autophosphorylation essential for receptor and post-receptor
insulin signalling events mediated by tyrosine phosphoryl-
ation of intracellular proteins associated with the insulin
receptor including insulin receptor substrate (IRS)-1 and -2.
As illustrated in Fig. 1, IRS and insulin signalling pathways
represent a complex cascade of events, which regulate a
number of processes including ion transport, protein
synthesis, lipid metabolism and gene transcription (see
White, 2003). However, the physiological insulin signalling
system is linked with other receptors and pathways that
regulate gene expression in numerous tissues (Pessin &
Saltiel, 2000). Thus, defective insulin signalling and
uncompensated insulin resistance is closely associated
with the diverse disorders comprising the metabolic
syndrome. The lack of evidence for a common mechanism
explaining the occurrence of acute and chronic insulin
resistance highlights the complexity of the insulin signalling
system, although prime molecular targets include the IRS
proteins, pro-inflammatory cytokines (including TNF-a)
and phosphorylation (serine and tyrosine) pathways (White,
2003). Post-receptor defects include decreased activation of
IRS-1 and phosphatidylinositol 3-kinase or impaired
translocation of GLUT4 (muscle).
Insulin and the metabolic syndrome
Although impaired insulin action and defective glucose
regulation and disposal are fundamental features of insulin
resistance and diseases of the metabolic syndrome, insulin
resistance reflects changes in the biological actions of insulin
on carbohydrate, lipid and protein metabolism and other
actions of insulin on tissues (including vascular and
endothelial cells) and mitogenic processes (including
growth, differentiation and gene transcription) (see Pickup
& Williams, 2003). As such, insulin resistance is character-
ised by impaired ability of insulin to (i) inhibit hepatic
glucose production, (ii) stimulate glucose uptake by muscle,
(iii) inhibit assembly and production of VLDL (despite
suppressing NEFA concentrations), and (iv) suppress
lipolysis in adipose tissue. As illustrated in Fig. 1, the latter
results in an increase in circulating NEFA concentrations,
which stimulates gluconeogenesis, triacylglycerol synthesis
and glucose production in the liver and further impedes
glucose uptake by skeletal muscle. Notably, NEFA,
deposition of triacylglycerol in insulin-sensitive tissues,
and humoral factors produced by adipose tissue may act
directly or indirectly to impair insulin action (Fig. 1). Obesity
and diabetes are associated with increased circulating NEFA,
at least partly attributable to altered sympathetic nervous
system activity, and NEFA may exert a detrimental influence
in insulin-sensitive target tissues mediated by a mechanism
involving enhanced IRS-1 serine phosphorylation coupled
with decreased IRS-1 tyrosine phosphorylation and
decreased activity of IRS-1-associated phosphatidylinositol
3-kinase activity (Yu et al. 2002). This may contribute to
hepatic and peripheral tissue insulin resistance and appears to
be further compounded by other factors related to the
metabolic (insulin resistance) syndrome, such as excess
cortisol and altered activity of the hypothalmo pituitary
adrenal axis (Bjorntorp, 1997; Pickup & Williams, 2003).
The metabolic syndrome is described by a cluster of
cardiovascular risk factors linked to insulin resistance (Beck-
Nielsen, 1999). Characterisation of data from the European
Group for the Study of Insulin Resistance and Danish Twin
Register databases revealed that insulin resistance correlates
closely with various components of the metabolic syndrome
and estimated prevalence in 1999 at about 16 % of Caucasians
(Beck-Nielsen, 1999). Furthermore, genetically determined
and environmentally induced insulin resistance may
precipitate onset of the metabolic syndrome which is
probably driven by inappropriate diet and lifestyle leading
to energy over-consumption and truncal obesity.
Genes, environment and the mechanisms of insulin
Insulin resistance and the prevalence of diseases of the
metabolic syndrome, including type 2 diabetes, vary
throughout continents and across the globe (see Pickup &
Williams, 2003). There are many hypotheses that attempt to
explain these differences, but among the most intriguing lies
in the impact of the environment (acquired factors including a
diabetogenic Westernised lifestyle) superimposed on a
genotype susceptible to energy storage (so-called ‘thrifty’
genes). Genetic predisposition to the polygenic disorder type
2 diabetes including thrifty genes may count for up to 80 % of
susceptibility. According to the ‘thrifty genotype’ hypoth-
esis, certain ‘thrifty’ genes promote energy (particularly fat)
storage and insulin resistance (see Chakravarthy & Booth,
2004). Malnutrition in utero or during early life, resulting in
low birth weight and/or inappropriate development, may also
predispose to type 2 diabetes in adulthood (see Hales &
Barker, 2001) by inherent ‘programming’ of insulin
resistance and/or reduced b-cell mass (so-called ‘thrifty
N. H. McClenaghan224
It is widely regarded that a genetic background that
determines a reduction in insulin sensitivity would promote
progressive insulin resistance in the liver and the peripheral
tissues, skeletal muscle and fat. In an evolutionary context,
thrifty genes determining insulin resistance may be
favourable under conditions of starvation, providing a
metabolic advantage through ensuring that a ready supply
of glucose is available and averting the immediate danger of
hypoglycaemia (DeFronzo, 1997). However, exposure of
individuals with this genetic background to a Westernised
lifestyle of high energy intake and reduced physical activity
may result in development of diseases of the metabolic
syndrome including obesity-related type 2 diabetes
(Chakravarthy & Booth, 2004). The metabolic conse-
quences of insulin in normal and insulin-resistant states in
muscle, liver and other tissues are illustrated in Fig. 1.
As outlined earlier, the hyperglycaemia characteristic of
type 2 diabetes results from insulin resistance superimposed
over compromised insulin secretion and b-cell function. The
hyperinsulinaemia observed early in diabetes is believed to be
an attempt to maintain glucose homeostasis under conditions
of insulin resistance (Polonsky et al. 1996). However, the
massive challenge to the b-cells cannot ultimately be
sustained and initial hyperinsulinaemia is replaced by
hypoinsulinaemia and hyperglycaemia (Polonsky et al.
1996). The dynamic relationship between insulin resistance
and b-cell function can be seriously impaired by chronic
hyperglycaemia through ‘glucotoxicity’ (Yki-Jarvinen, 1992)
which can exacerbate both fundamental defects, aggregated
by environmental factors superimposed on genetic predis-
position to insulin resistance and type 2 diabetes. Under
conditions of glucotoxicity, insulin is glycated in the
pancreatic b-cell and may be released into the circulation
in this form (Abdel-Wahab et al. 1996; Hunter et al. 2003).
Glycated proteins including HbA
and insulin do not have
the same properties as parental molecules; in the case of
glycated insulin, this glycated form is likely to contribute to
the progressive deterioration of glucose tolerance in diabetes
(Hunter et al. 2003; Kaiser et al. 2003). The demise of the b-
cell in diabetes may arise as a result of glucotoxicity,
‘lipotoxicity’, overexertion or exhaustion, or dysregulated
growth that accompany the insulin-resistant state.
Clinical determination of insulin resistance
As described earlier, the mechanisms underlying insulin
resistance are complex. The term ‘insulin resistance’ is used
in a variety of contexts to refer to a generalised impairment
of the physiological actions of insulin. Given the diverse
metabolic roles of insulin, coupled with different tissue
sensitivities to the actions of this hormone in an individual,
population or between populations, it is perhaps not
surprising that determination of insulin resistance is
remarkably difficult. Insulin resistance is generally
measured in terms of the glucose-lowering effect of insulin,
thus reflecting only the hypoglycaemic effect of insulin in an
individual. This is important as impaired glucose-lowering
action indicates resistance to insulin’s hypoglycaemic action
but does not necessarily indicate insensitivity to other
actions of insulin. Since the pioneering research by
Himsworth (1936), which distinguished insulin-resistant
and insulin-deficient forms of diabetes, various clinical
methods have been devised to measure insulin resistance. An
overview of the modern methodologies used to determine in
vivo insulin resistance is given in Table 1; methodologies are
briefly described under the three overarching categorisations
‘steady-state’, ‘dynamic’ or ‘basal-state’ techniques.
Steady-state techniques
Steady-state techniques are based on the principle that to
maintain euglycaemia the rate of glucose infusion under
hypoglycaemic conditions should match insulin-stimulated
glucose disposal in skeletal muscle. The ‘euglycaemic
hyperinsulinaemic clamp’ (EHC) (DeFronzo et al. 1979)
and modifications to include measurement of hepatic
glucose output (Steele 1959; Chiasson et al. 1977) is
arguably the best available standard technique (Ferrannini &
Mari, 1998). Alternatives are the ‘insulin sensitivity (or
suppression) test’ and ‘continuous infusion of glucose with
model assessment’ both of which correlate well with data
from the EHC. The ‘insulin sensitivity (or suppression) test’
refers to several methods, which rely on infusion of fixed
doses of glucose and insulin until a ‘steady state’ is reached
(Shen et al. 1970; Harano et al. 1977).
Dynamic techniques
Dynamic techniques involve regular plasma sampling for
determination of glucose and/or insulin concentrations in
response to glucose challenge. Modifications of the most
popular diagnostic tool of diabetes, the ‘oral glucose
tolerance test’, can be used to assess insulin sensitivity,
although validity depends on normal insulin secretion
(Home, 1988). The ‘frequently sampled intravenous glucose
tolerance test’ (Bergman et al. 1979) can be modified with a
bolus injection of intravenous insulin (Beard et al. 1986;
Yang et al. 1987) or the insulinotropic hypoglycaemic
sulfonylurea, tolbutamide (Finegood et al. 1990), and so be
improved in its sensitivity and reproducibility (Beard et al.
1986; Bergman et al. 1989; Saad et al. 1994). An alternative
approach is the ‘insulin tolerance test’ that determines insulin
sensitivity from the glucose-lowering rate of intravenous
insulin administration (Bonora et al. 1989; Hirst et al. 1993).
Basal-state techniques
The widely used ‘homeostasis model assessment’ (HOMA)
devised by Matthews et al. (1985) calculates insulin
sensitivity (HOMA-R) and b-cell function from fasting
measures of plasma glucose and insulin. HOMA measure-
ments correlate closely with EHC measurement of insulin
resistance (Hermans et al. 1999). Another measure is the
‘quantitative insulin sensitivity check index’, which is
derived from the relationship between fasting plasma insulin
and glucose and closely agrees with data derived from
HOMA (Katz et al. 2000).
Choice of technique to determine insulin resistance
The main factors determining choice of in vivo technique
relate to precision, nature of information required, and
Carbohydrate intake and insulin resistance 225
practical considerations including time, complexity, number
and experience of investigators, classification and number of
subjects, and cost (Ferrannini & Mari, 1998; Del Prato,
1999). Notably, ‘continuous infusion of glucose with model
assessment’, ‘frequently sampled intravenous glucose
tolerance test’ and HOMA can all give indices of b-cell
function, thus generating additional useful research data.
While data arising from most of these methods are somewhat
comparable, the EHC, despite being laborious and
complicated, is still regarded as the ‘gold-standard’ method
of choice in small-scale studies (Ferrannini & Mari, 1998).
However, in epidemiological studies with large sample size,
HOMA, despite analytical and biological variability, is
convenient and thus arguably more appropriate (Wallace &
Matthews, 2002). Other methods used to assess
insulin resistance are ‘insulin-stimulated glucose uptake in
adipocytes’ (Kashiwagi et al. 1985; Garvey et al. 1988) or the
‘organ perfusion/catheterisation technique’ (Zierler, 1961),
but these will not be considered further as they are difficult
and invasive and thus not widely adopted.
The wide variety of techniques used to assess insulin
resistance (Table 1) clearly serves to indicate that there is no
ideal or universal method to measure this important
parameter. As a further complexity, insulin resistance is not
the same across all tissues (see Bessesen, 2001) and the
various actions of insulin may be implicated to different
degrees in the development of insulin resistance. Unfortu-
nately, tissue-specific insulin sensitivities have rarely been
assessed in studies with dietary manipulations; thus some
caution is required when interpreting studies of the influence
of dietary change on insulin resistance. Despite these various
shortcomings, coupled with various criticisms regarding
Table 1. Overview of main methodologies used to assess insulin resistance
Description Comments
Steady state
EHC Studies insulin kinetics in fasted state. Insulin
infused intravenously at constant rate to
achieve stable hyperinsulinaemia and
maximum peripheral glucose uptake. Glucose
infused intravenously to maintain blood
glucose at 5 mmol/l. Steady state is when
glucose infusion equals rate of glucose disposal
Gold standard for determining insulin
sensitivity. Reproducible. Can be modified
to examine other aspects including
contribution of hepatic v. extrahepatic
insulin action (i.e. hepatic glucose output)
Insulin sensitivity or
suppression test
Co-infusion of fixed doses of insulin and
glucose coupled with suppression of
endogenous insulin secretion by
use of somatostatin. Steady-state
plasma glucose is the measured
index (i.e. glucose reflects level of
insulin resistance)
Easier to perform than EHC. Poor reproducibility.
Makes assumptions likely to lead to
underestimation of insulin resistance
Continuous infusion of
glucose with model
Multivariable computer model evaluating
physiological insulin response to infused
glucose, with insulin sensitivity and
b-cell function calculated from the model
Easy to perform and gives index of insulin
secretion. Model makes assumptions
but correlates well with EHC data
OGTT Plasma glucose and insulin are measured
following glucose load. Insulin sensitivity is
assessed from endogenous insulin
concentrations in response to glucose load.
The higher the glucose:insulin ratio,
the higher the insulin resistance
Intrinsically poor precision and reproducibility.
Glucose:insulin ratio dependent on insulin
clearance as well as secretion. Not valid
in diabetic subjects
Frequently sampled
intravenous glucose
tolerance test
Estimation of insulin sensitivity by
multi-compartment modelling of changes in
plasma insulin and glucose after intravenous
glucose bolus. Insulin sensitivity and glucose
effectiveness are derived. Accuracy and sensitivity
improved by modifications (including tolbutamide
bolus or short insulin infusion)
Overcomes some drawbacks of OGTT caused by
intestinal glucose absorption. Reproducible
and valid for non-diabetic and diabetic subjects
Insulin tolerance test Determines insulin sensitivity from rate at which blood
glucose falls in response to intravenous insulin in
fasted subjects. Sampling every 2 min and oral
glucose given afterwards to prevent hypoglycaemia
Simple and inexpensive. Reproducible. Occasional
risk of hypoglycaemia in more insulin-sensitive
subjects (unusual)
Basal state
HOMA Fasting plasma glucose and insulin measured for
calculation of insulin sensitivity (HOMA-R) and
b-cell function using model. Assumes normal-weight
subjects .35 years have insulin resistance of 1 and
100 % b-cell function. Error reduced by average of
three fasting readings. Increasing HOMA-R values
indicate increasing insulin resistance
Simple, inexpensive and widely
used. Validated across wide
range of insulin sensitivity.
Poor reproducibility. Confounded by
exogenous insulin and oral
glycaemic drugs. Useful for
epidemiological research
Quantitative insulin
sensitivity check index
Insulin sensitivity derived from relationship between
fasting insulin and glucose concentrations.
Alternative to HOMA method
Simple and inexpensive. Measures
closely compare to similar
HOMA method
EHC, euglycaemic –hyperinsulinaemic clamp; OGTT, oral glucose tolerance test; HOMA, homeostasis model assessment.
N. H. McClenaghan226
study design and rigour or selection and application of
technique, collectively there is a sufficient body of data from
these techniques to draw certain useful conclusions.
Dietary factors, energy intake and the development
of insulin resistance
The epidemic rise in incidence of type 2 diabetes has fuelled
research into the complex interplay between genes and
environmental factors in the pathogenesis of the hypergly-
caemic diabetic state. Key among the environmental factors
is the Western lifestyle typified by high energy intake and low
physical activity, driving individuals towards energy storage,
overweight and obesity. As outlined earlier, it is generally
accepted that energy over-consumption is fundamental to the
development of insulin resistance and diseases of the
metabolic syndrome, including type 2 diabetes (DeFronzo &
Ferrannini, 1991; Haffner & Cassells, 2003; Kitano et al.
2004). Indeed, energy over-consumption driving the
deposition of metabolically active abdominal fat has been
proposed as a major cause of insulin resistance (Ruderman
et al. 1998) and may explain the increasing prevalence of
insulin resistance and type 2 diabetes in children (Ehtisham
et al. 2000; Bundred et al. 2001). Non-splanchnic adipose
tissue from the upper body, from head, neck trunk, and upper
extremities also makes a significant contribution to NEFA
flux (Jensen & Johnson, 1996). Thus, excess deposition of
subcutaneous truncal fat may also cause insulin resistance in
non-diabetic subjects and in type 2 diabetes (see Garg, 2004),
prompting future research into the relationship between body
fat distribution and insulin resistance.
Hyperglycaemia resulting from insulin resistance is a
defining feature of type 2 diabetes; however, there has been
much debate over the relationship between diet and in
particular dietary carbohydrate intake and insulin resistance.
The historic link between diet and diabetes hails back to
antiquity when Charak and Sushrut (400– 500 BC)
described sweet-tasting diabetic urine in individuals who
were slothful, overweight and gluttonous and gorged on
sweet and fatty foods (Pickup & Williams, 2003). While an
important role of diet in the pathogenesis of insulin
resistance is generally accepted, the relative contribution of
individual macronutrients is relatively poorly understood.
Disturbances in macronutrient metabolism, particularly
carbohydrate and fat metabolism through raised plasma
glucose (glucotoxicity) and fatty acids (lipotoxicity), are
characteristic of the insulin-resistant state and it is
understood that type 2 diabetes is as much defined by
defective fat as glucose metabolism (Pickup & Williams,
Overweight is an extremely important risk factor for
insulin resistance and diseases of the metabolic syndrome
(see Parillo & Riccardi, 2004). Energy over-consumption
and the increase in metabolically active abdominal adipose
tissue tends to a greater flux of NEFA to the liver which can
impact on glucose oxidation and hepatic extraction of
insulin and impair insulin secretion (Fig. 1). Intervention
studies have also demonstrated that weight loss can reduce
fasting hyperglycaemia in type 2 diabetes, improves insulin
sensitivity and increases the capacity of non-oxidative
glucose metabolism (Parillo & Riccardi, 2004). Excessive
body-fat deposition results from an imbalance between
energy intake and expenditure. With the increase in
sedentary lifestyles, energy intake is the main driver of
overweight precipitated by high consumption of fat-rich
foods (Astrup, 2001).
A large number of clinical studies have shown that high-
fat (and thus energy-dense) diets may impact on insulin
action, impair glucose tolerance and promote obesity, and
cardiovascular and other disorders of the metabolic
syndrome (Choudhary, 2004). However, the type of fat
also appears important, and while saturated fat intake can
worsen metabolic abnormalities, monounsaturated fat may
have beneficial effects on lipid profiles. This conclusion
comes from epidemiological data that also suggest a
particularly adverse affect of trans fatty acids on the risk of
insulin resistance and type 2 diabetes (Hu et al. 2001).
Indeed, while saturated and certain monounsaturated fats
are implicated in causing insulin resistance, in general
polyunsaturated and n-3 fatty acids do not appear to have
adverse effects on insulin action (Hu et al. 2001; Lovejoy,
2002). Although dietary fat is linked with type 2 diabetes
through increased body weight, the mechanism by which
dietary fat intake influences development of diabetes
appears to be largely mediated through its effects on insulin
sensitivity, and dietary fat can influence insulin sensitivity
independently of body weight (Parillo & Riccardi, 2004).
Animal and human studies consistently show an association
between dietary fat quality and impaired insulin sensitivity
and mechanistically this may be mediated through
alterations in fatty acid composition of cell membranes
(Vessby, 2000).
The total amount of fat intake appears to influence insulin
sensitivity only when it exceeds a threshold level of 35 – 40 %
total energy consumption (Parillo & Riccardi, 2004), adding
further complexity, and highlighting the requirement for
further study. It is also interesting to note that dietary
carbohydrate intake primarily affects short-term glycaemic
control while long-term glycaemic control is regulated by the
total energy consumption. As outlined earlier, although over-
consumption of fat-rich energy-dense foods is key to the
development of overweight and obesity, dietary carbo-
hydrates can also be converted into fats for storage. While the
latter is an energy-requiring process which usually only takes
place when carbohydrate intake greatly exceeds daily energy
requirements (Franz, 2001), fructose is a better substrate for
hepatic fatty-acid synthesis than glucose, and thus excess
fructose may be converted into fat in the liver (Pickup &
Williams, 2003). Understanding of the differences in
satiating effects of macronutrients is also important with
regard to energy over-consumption. As high-fat foods have
disproportionately weak (joule-for-joule comparing with
protein or carbohydrate) satiating effects, this can lead to
passive over-consumption driven, in part, by the high
palatability of high-fat foods (Blundell & MacDiarmid,
1997). Although both dietary macronutrient and energy
intake play a role in obesity and insulin resistance, the quality
and quantity of macronutrients, together with diet compo-
sition, remain important questions for dietitians and health
practitioners, with serious implications in devising new
strategies to curb the growing incidence of insulin resistance
diseases of the metabolic syndrome.
Carbohydrate intake and insulin resistance 227
Dietary carbohydrate, glycaemic index
and glycaemic load
The most common forms of dietary carbohydrate are starch,
sugars and fibre and current guidelines suggest that 80 % of
energy intake for diabetes should be a combination of
carbohydrate and monounsaturated fat (for a review, see
Choudhary, 2004). The simple sugars include the mono-
saccharides (glucose, fructose and galactose) and disacchar-
ides (sucrose, maltose and lactose). Complex carbohydrates
on the other hand take the form of polymers of
monosaccharides. Polymers of glucose can occur in linear
(amylose) or branched (amylopectin) forms. Amylopectin is
more rapidly digested and absorbed than amylose and the
latter may also interact with dietary fat to slow absorption
further, which might be beneficial to insulin sensitivity.
Starches that are not directly absorbed, but rather fermented
in the gut to produce SCFA, are known as ‘resistant starch’.
Carbohydrate polymers comprising non-glucose monomers
are indigestible and are the constituents of soluble or
insoluble fibre, including b-glucan, guar gum and hemi-
celluloses. Fibre is a nominal energy source but can interact
with other nutrients in the gastrointestinal tract.
Traditional classification of carbohydrates purely on the
basis of chemical structure does not provide many useful
insights into their relative importance to health and disease.
Rather, the ability of a carbohydrate to contribute directly or
indirectly to the carbohydrate pool (glycaemic carbo-
hydrates) coupled with postprandial glycaemic response
provides more useful information (Cummings & Englyst,
1987). As different carbohydrate-containing foods generate
different glycaemic responses, this led Jenkins et al. (1981)
to develop a glycaemic index (GI) to rank foods. Using this
convention, carbohydrate foods that are rapidly absorbed are
high GI, while those that are more slowly absorbed are low
GI. The GI is based on the post-ingestion glucose area under
the curve in response to ingestion of 50 g carbohydrate in
test food compared with 50 g reference carbohydrate
(glucose or white bread) and depends on the rate of
digestion and rapidity of absorption of carbohydrate (Brand-
Miller et al. 1999) (Table 2).
However, recommendations associated with low-GI diets
are the subject of much debate as they are based on various
generalisations, for example the application of exact GI to
foods, and the fact that some low-GI foods are high in fat
(including ice cream). As such, when evaluating the health
effects of individual foods, GI values cannot be used in
isolation and nutrient composition of the foods and overall
dietary pattern are also important. As there is no exact value
of a low-GI or high-GI diet, this can add to consumer
confusion, although it appears that the lower the GI the
better the effects, and the differences between a low- (,70)
and a high- (.80) GI diet should be at least 10 % in order to
achieve measurable metabolic effects (Willett et al. 2002).
The fact that the plasma glucose response to feeding is not
only determined by the GI value of a given food but also the
amount of carbohydrate in the food led to the concept of the
glycaemic load. Glycaemic load is a function of GI and
carbohydrate content, thus representing the quality and
quantity of carbohydrates consumed (Table 2). Thus,
glycaemic load can be altered by changing carbohydrate
intake or changing dietary GI and is arguably less restrictive
in application than the more simplistic measure of GI.
Dietary carbohydrate and insulin sensitivity
When manipulating diets, a change in any one dietary
component is accompanied by reciprocal change in other
dietary components. So when the effect of dietary
carbohydrates on insulin action is examined, alterations
are being made to dietary macronutrients as a consequence
of the relative change in carbohydrate. The earliest
experiments on human subjects examined the effect of
altering relative amounts of carbohydrate and fat on insulin
action and Himsworth (1936) reported that the blood
glucose-lowering action of insulin was improved as dietary
carbohydrate increased. This much-cited study was the first
indication of an association between low-fat, high-
carbohydrate diets and improvement in insulin’s ability to
stimulate glucose disposal. Positive effects of high-
carbohydrate diets on human insulin sensitivity have
Table 2. Examples of glycaemic index (GI) and glycaemic load (GL) of common foods (Hu et al. 2001)
Food GI* Serving size†
per serving (g) GL per serving (g)
White rice, low-amylose 125 1 cup (225 ml) 53 67
Baked potato 121 1 51 61
Cornflakes (breakfast cereal) 119 1 cup (225 ml) 24 29
Honey 104 1 tablebspoon (15 ml) 17 18
Sucrose 92 1 teaspoon (5 ml) 4 4
Ice cream 87 0·5 cups (112·5 ml) 16 14
White rice, high-amylose 84 1 cup (225 ml) 45 37
Orange juice 81 6 ounces (170·1 g) 20 16
Brown rice 78 1 cup (225 ml) 45 35
Parboiled rice 67 1 cup (225 ml) 43 29
All-bran (breakfast cereal) 60 0·5 cup (112.5 ml) 23 14
Apple juice 58 6 ounces (170·1 g) 22 13
Spaghetti 58 1 cup (225 ml) 40 23
Whole milk 38 1 cup (225 ml) 12 5
Fructose 33 2 tablespoons (30 ml) 31 10
Groundnuts 20 1 ounce (28·35 g) 5 1
* White bread ¼100.
1oz ¼28·35 g; 1 cup ¼225 ml; 1 tablespoon ¼15 ml; 1 teaspoon ¼5ml.
N. H. McClenaghan228
remained an area of debate, largely because of the stark
contrast between the outcomes of human studies and those
in rodents. A large number of studies conducted in rodents
suggest that high intakes of fructose (up to 60 % of energy)
and sucrose (up to 70 % of energy) result in a decline in
insulin sensitivity in the liver and peripheral tissues (for a
review, see Storlien et al. 2000). These rodent studies
demonstrated adverse effects as a function of the dose of
sucrose or fructose and duration of exposure, such that to
observe an effect the duration of exposure must be longer
with lower doses. However, other animal studies have
shown that fructose may preserve b-cell mass and prevent
diabetes (Orban et al. 2001), suggesting that there may be
particular effects in different animal strains or species.
Studies in human adults with or without type 2 diabetes have
consistently shown no effect on insulin sensitivity of
isoenergetic substitution of sucrose or fructose for
starch (Wolever, 2000). If anything, both fructose and
sucrose have been associated with lower glucose excursions
after ingestion, and palatability alone would suggest that it is
unlikely that humans would ever ingest sugar in the
quantities consumed in the aforementioned animal studies.
Epidemiological studies considering dietary
carbohydrate intake and insulin resistance
Prospective studies
An overview of outcomes of major prospective studies
dating back to the early 1970s on dietary carbohydrate, fibre
and incidence of type 2 diabetes is given in Table 3. It is
evident that, apart from very early epidemiological studies
(Kahn et al. 1971; Medalie et al. 1974), collectively there is
little evidence to suggest that the intake of total
carbohydrate predicts the risk of type 2 diabetes.
Interestingly, some of these studies support the view that
high-carbohydrate, high-fibre diets, with low GI, may even
contribute to diabetes prevention (Table 3). Prospective
studies that include an evaluation of dietary fibre, or fruit or
vegetable consumption consistently reveal an inverse
association between these components and incidence of
type 2 diabetes (Feskens et al. 1995; Ford & Mokdad, 2001;
Stevens et al. 2002; Montonen et al. 2003). In a study of
Swedish adult females, with 12-year follow-up, Lungren
et al. (1989) reported no significant differences in
carbohydrate intake in women who subsequently developed
diabetes compared with those who did not. This lack of
association between total carbohydrate and diabetes
incidence was also observed in the large Nurses’ Health
Study (Colditz et al. 1992) and Health Professionals’ Study
(Salmeron et al. 1997b). The former study was conducted in
females initially aged 4065 years, while the latter
considered males initially aged between 40 and 75 years,
indicating at least in adults that there is no association
between total carbohydrate and diabetes incidence
regardless of sex. As illustrated in Table 3, data derived
from both the Nurses’ Health Study (Salmeron et al. 1997a)
and Health Professionals’ Study (Salmeron et al. 1997b)
indicated an association between the GI or glycaemic load
and risk of developing type 2 diabetes, while the Iowa
Women’s Health Study (with older participants) did not
reveal any association (Meyer et al. 2000). When
considering the large-population Harvard Group studies
(Salmeron et al. 1997a,b) it is important to note that the
reported outcomes have been questioned on the basis of the
application of validated food-frequency questionnaires with
repeated measures with inherent multiplication of errors
(Pi-Sunyer, 2002). On balance it is clear that there are
inherent errors with every dietary assessment method, and
while food-frequency questionnaires may have poor
accuracy, food diaries also provide unreliable estimates of
actual nutrient consumption and under-reporting adds
further confusion when relating metabolic variables with
dietary intake.
Cross-sectional, longitudinal and casecontrol studies
Table 4 considers the literature on dietary carbohydrate
type and incidence of impaired glucose tolerance or type 2
diabetes. In the San Luis Valley Diabetes Study (Table 4),
which tracked the development of hyperinsulinaemia over
Table 3. Prospective studies on dietary carbohydrate, fibre and incidence of type 2 diabetes
Study Reference Subjects (n) Dietary method Outcome
Israel Ischemic Heart Study Kahn et al. (1971);
Medalie et al. (1974)
373 Dietary history Inverse association with sucrose
Study on Swedish adults Lungren et al. (1989) 1462 24 h recall No association
Zutphen Study Feskens & Kromhout (1989) 841 Dietary history No association
Nurses’ Health Study Colditz et al. (1992) 84 360 FFQ No association
Seven Countries Study Feskens et al. (1995) 338 Dietary history Inverse association with vegetables
and legumes
Nurses’ Health Study Salmeron et al. (1997b) 65173 FFQ Inverse association with fibre;
positive association with GL
Health Professionals’ Study Salmeron et al. (1997a) 42 759 FFQ Inverse association with fibre;
positive association with GL
Iowa Women’s Health Study Meyer et al. (2000) 35 988 FFQ Inverse association with fibre; no
association with GI or GL
Study on US adults Ford & Mokdad (2001) 9665 24 h recall Inverse association with vegetables
ARIC Study Stevens et al. (2002) 12 251 FFQ Inverse association with cereal fibre
Finnish Mobile Clinic Study Montonen et al. (2003) 4316 Dietary history Inverse association with cereal fibre
Women’s Health Study USA Janket et al. (2003) 39 345 FFQ No association
FFQ, food-frequency questionnaire; GL, glycaemic load; GI, glycaemic index; ARIC, Atherosclerosis Risk in Communities.
Carbohydrate intake and insulin resistance 229
13 years in individuals with pre-existing impaired
glucose tolerance, no relationship between dietary
carbohydrate and either hyperinsulinaemia or onset of
diabetes was described (Marshall et al. 1994); rather, the
study reported a trend for an inverse relationship. This
strong, well-conducted prospective study, however,
revealed a significant relationship between dietary fat
and newly diagnosed cases of diabetes (Marshall et al.
1994). Earlier cross-sectional studies suggest a decreased
prevalence of diabetes with high carbohydrate intake.
Indeed, Tsunehara et al. (1990) found lower intakes of
refined carbohydrates in diabetics compared with non-
diabetics, although it is unclear whether these subjects
were aware of their diagnosis at the time of dietary
assessment. So, consistent with the prospective studies
described earlier (Table 3), the studies described in Table 4
collectively support the view that high carbohydrate intake
does not affect incidence of impaired glucose tolerance or
type 2 diabetes.
Epidemiological studies of dietary sugars intake
As illustrated in Table 5, there is less consistency in the
patterns arising from epidemiological studies on non-
diabetic and diabetic subjects when assessing dietary sugars
intake and insulin resistance. While inverse associations
between intake of simple carbohydrates and measures of
glycaemia and insulin resistance were observed in four of the
larger cross-sectional studies (Keen et al. 1979; Feskens &
Kromhout, 1990; Buyken et al. 2000; Williams et al. 2000),
five other cross-sectional (Mooy et al. 1995; Boeing et al.
2000; Rosell et al. 2003; Yang et al. 2003) and longitudinal
(Marshall et al. 1997) studies revealed no association. A weak
positive association between simple carbohydrate intake and
fasting insulin levels was reported in the ‘Coronary Artery
Risk Development in Young Adults’ study of young black and
white Americans (Manolio et al. 1991; Archer et al. 1998).
Similarly, in a study by Sevak et al. (1994), which had the
advantage of a 7 d weighed intake, total carbohydrate and
sucrose intake was positively correlated with insulin
resistance but only for postprandial and not fasting insulin
measures (Sevak et al. 1994). Interestingly, these latter
studies that demonstrated a positive relationship between
dietary sugars and insulin resistance involved South Asian
adults in London and black American children (Manolio et al.
1991; Archer et al. 1998). Thus, when considering these
studies, the possible influence of genetic factors, which may
predispose to insulin resistance, should not be overlooked as
illustrated by the impact of ‘thrifty’ insulin resistance genes
in South Asian (Lindquist et al. 2000) and Westernised
Native American Pima Indian populations (Weyer et al.
Intervention studies considering dietary carbohydrate
intake and insulin resistance
Intervention studies comparing effects of high and low
carbohydrate intake
Table 6 gives an overview of dietary intervention studies
comparing high with low carbohydrate intake in non-
diabetic and diabetic individuals. In these intervention
studies, various solid or liquid diets were combined with
different techniques to yield measures of glucose
tolerance or insulin sensitivity. As shown, in non-diabetic
subjects most studies revealed an increase of glucose
tolerance (Anderson et al. 1973; Swinburn et al. 1991) or
insulin sensitivity (Chen et al. 1988; Fukagawa et al.
1990; Vidon et al. 2001; Sunehag et al. 2002).
Exceptionally, in one study by Jeppesen et al. (1997)
that indicated a lowering of insulin sensitivity with a
high-carbohydrate diet, a number of women, although
Table 4. Cross-sectional, longitudinal and case– control studies on dietary carbohydrate type and incidence of
impaired glucose tolerance or type 2 diabetes
Study Reference Subjects (n) Methods Outcome
Study on Japanese-
American adults
Tsunehara et al. (1990) 229 OGTT; diet history Inverse association with carbohydrate,
energy and sucrose intake
San Luis Valley
Diabetes Study
Marshall et al. (1991) 1317 OGTT; 24 h recall Inverse association with total
carbohydrate intake; no association
between carbohydrate subtype and
glucose tolerance
Study of Native
Canadian adults
Wolever et al. (1997);
Gittelsohn et al. (1998)
728 OGGT; 24 h recall;
No association with simple sugar intake;
positive association with high-fat
‘junk food’ intake
Study on Dutch adults Feskens et al. (1991) 175 OGTT; dietary history No association between monosaccharide
or disaccharide intake and development
of IGT
San Luis Valley
Diabetes Study
Marshall et al. (1994) 123 OGTT; 24 h recall No association between sucrose, total
carbohydrate or starch intake and
development of diabetes
Study of adults in Papua
New Guinea
Hodge et al. (1996) 285 OGTT; FFQ No association, no differences between
cases and controls for any carbohydrate
OGTT, oral glucose tolerance test; FFQ, food-frequency questionnaire; IGT, impaired glucose tolerance.
N. H. McClenaghan230
non-diabetic, had high degrees of insulin resistance.
However, it would be difficult to conceive that subjects
with the greatest degrees of initial insulin resistance
would show positive responses to short-term intervention
with a high-carbohydrate diet.
The trend of improved glucose tolerance or increased
insulin sensitivity after high-carbohydrate, low-fat diets was
also recorded with diabetic subjects (Table 6) (Brunzell et al.
1971, 1974; Anderson, 1977; Hjollund et al. 1983; Hughes
et al. 1995). Other studies reported no difference between
diabetics consuming high- and low-carbohydrate diets
(Coulston et al. 1987; Garg et al. 1992). The latter studies,
reporting a lack of effect of a high-carbohydrate diet on
insulin resistance in diabetes, may reflect differences in the
degree of initial insulin sensitivity (and thus lack of effect of
short-term high-carbohydrate intervention in severe insulin
resistance) in population or age groups and perhaps also
different genetic backgrounds. As noted above, there are
also differences in initial insulin sensitivity in studies on
non-diabetic subjects. In this regard, it is also interesting
that lower levels of insulin sensitivity have been reported in
adolescent compared with pre-pubertal children (Sunehag
et al. 2002). This is consistent with the view that there may
be recordable differences in insulin sensitivity between
adults and children, thus emphasising the importance of
population, ethnic background and age when interpreting
insulin resistance data.
Intervention studies comparing effects of different
carbohydrates on insulin sensitivity
Dietary intervention studies on fructose, sucrose, and simple
carbohydrates v. complex carbohydrates are summarised in
Table 7. While some studies revealed an increase in insulin
sensitivity during fructose diets (Crapo & Kolterman, 1984;
Crapo et al. 1986; Koivisto & Yki-Jarvinen, 1993), others
revealed no association (Turner et al. 1979; Sunehag et al.
2002), or even a decrease in insulin sensitivity (Beck-
Nielsen et al. 1980; Hallfrisch et al. 1983). The study by
Beck-Nielsen et al. (1980) represented an over-feeding or
supplementation study in which healthy subjects were given
an extra 4180 kJ (1000 kcal)/d of fructose in water and
cannot really be compared with other studies due to lack of
an effective control. Furthermore, this study, when related to
others in non-diabetic subjects, suggests that much higher
intakes of fructose are required to significantly reduce
insulin sensitivity, and is arguably too short to be of
metabolic significance (Bantle et al. 1986). The Beltsville
study (Hallfrisch et al. 1983) recruited men with abnormally
high insulin responses to sucrose load, and noted very high
insulin and glucose responses to consumption of a 15 %
fructose diet (although fasting insulin levels were not
affected by the fructose diets). This was particularly the case
in the hyperinsulinaemic men, who may or may not be
insulin resistant, which is confusing particularly as the other
studies (Table 7) would suggest that fructose increases
insulin sensitivity in type 2 diabetes (Crapo et al. 1986;
Koivisto & Yki-Jarvinen, 1993).
When considering sucrose a different pattern emerges,
with either no association (Bossetti et al. 1984; Jellish et al.
1984; Colagiuri et al. 1989; Thorburn et al. 1990; Raben
et al. 2001; Brynes et al. 2003) or a decrease in insulin
sensitivity (Reiser et al. 1981a,b; Coulston et al. 1985). In
the studies by Reiser et al. (1981a,b) on non-diabetics, the
‘gorging’ meal pattern may at least partly have been
responsible for the outcome, as it is known that this eating
style is in itself associated with higher fasting serum insulin
and lipid values (Jenkins et al. 1989). The study by Coulston
et al. (1985) assessed the metabolic effects of sucrose in
subjects with type 2 diabetes, only demonstrating a decrease
in insulin sensitivity-related postprandial parameters, and
did not undertake direct measures of insulin sensitivity.
Indeed, the studies denoted in Table 7 emphasise the
diversity in techniques employed to determine insulin
Table 5. Epidemiological studies on dietary sugars intake and insulin resistance
Study Reference Subjects (n) Methods Outcome
Study of English
professional adults
Keen et al. (1979) 3554 OGTT; diet record Inverse association
Zutphen Study Feskens & Kromhout (1990) 418 OGTT; diet history Inverse association
CARDIA Study Manolio et al. (1991);
Archer et al. (1998)
4734 Fasting insulin; diet history Weak positive
Hoorn Study Mooy et al. (1995) 2484 OGTT; FFQ No association
Study of UK South Asian
and white adults
Sevak et al. (1994) 173 OGTT; weighed record Positive association
EPIC Study (Potsdam cohort) Boeing et al. (2000) 1773 HbA
; FFQ No association
EURODIAB Study Buyken et al. (2000) 2079 HbA
; diet record Inverse association
Study of African-American
and white children
Lindquist et al. (2000) 95 IVGTT; 24 h recall ( £3) Inverse association
Ely Study Williams et al. (2000) 802 OGTT and insulin; FFQ Inverse association
Study of Swedish adults Rosell et al. (2003) 301 Fasting insulin; diet record No association
NHANES III Study Yang et al. (2003) 11 855 HbA
, glucose, insulin;
24 h recall
No association
San Luis Valley
Diabetes Study
Marshall et al. (1997) 1069 Fasting insulin; 24 h recall No association
OGTT, oral glucose tolerance test; CARDIA, Coronary Artery Risk Development in Young Adults; FFQ, food-frequency questionnaire; EPIC, European Prospective
Investigation into Cancer; HbA
, glycated Hb; IVGTT, intravenous glucose tolerance test; NHANES, National Health and Nutrition Examination Survey.
Carbohydrate intake and insulin resistance 231
Table 6. Dietary intervention studies comparing a high with low carbohydrate (CHO) intake
Reference Subjects (n) Methods Outcome
Liquid (20–80 % glucose) Anderson et al. (1973) 13 OGTT Glucose tolerance improved with increasing glucose
Normal v. 85 % CHO v. 30 % CHO Chen et al. (1988) 10 IVGTT High CHO increased insulin sensitivity
68 % CHO–high fibre v. 43 % CHO low fibre Fukagawa et al. (1990) 12 GC High CHO increased insulin sensitivity
.50 % CHO v. ,40 % CHO (high fat) Borkman et al. (1991) 8 GC No difference
Normal v. 70 % CHO v. 30 % CHO Swinburn et al. (1991) 24 OGTT Glucose tolerance improved with high CHO
51 % CHO–low fat v. 8 % CHO high fat Cutler et al. (1995) 10 EHC No difference
60 % CHO v. 40 % CHO Jeppesen et al. (1997) 10 IST High-CHO diet decreased insulin sensitivity
55 % CHO v. 45 % CHO–high MUFA Thomsen et al. (1999) 16 IVGTT No difference
Liquid (2–85 % CHO plus 0 83 % fat) Bisschop et al. (2001) 6 EHC No dose– response differences
40 % CHO–45 % fat v. 55 % CHO 30 % fat Vidon et al. (2001) 7 OGTT High CHO increased insulin sensitivity
30 % CHO–55 % fat v. 60 % CHO 25 % fat Sunehag et al. (2002) 24 IVGTT High CHO increased insulin sensitivity
Liquid (45 % CHO v. 85 % CHO) Brunzell et al. (1971) 22 GTT; fasting insulin High CHO increased insulin sensitivity
Liquid (45 % CHO v. 85 % CHO) Brunzell et al. (1974) 15 GTT; fasting insulin High CHO increased insulin sensitivity
Liquid (44 % CHO v. 75 % CHO) Anderson (1977) 11 GTT Glucose tolerance improved with high CHO
High fibre, high starch, low fat Hjollund et al. (1983) 18 ITT Test diet increased insulin sensitivity
60 % CHO–20 % fat v. 40 % CHO 40 % fat Coulston et al. (1987) 9 Fasting, day-long glucose, insulin No difference
60 % CHO v. 30 % CHO Garg et al. (1992) 8 GC No difference
60 % CHO–low fat v. 40 % CHO high MUFA Parillo et al. (1992) 10 GC Low CHO high MUFA improved insulin sensitivity
60 % CHO (with or without exercise) Hughes et al. (1995) 10 GC Small improvement in insulin sensitivity
OGTT, oral glucose tolerance test; IVGTT, intravenous glucose tolerance test; GC, glucose clamp; EHC, euglycaemic– hyperinsulinaemic clamp; IST, insulin sensitivity and suppression test; GTT, glucose tolerance test;
ITT, insulin tolerance test.
N. H. McClenaghan232
Table 7. Dietary intervention studies of effects of different carbohydrates (CHO) on insulin sensitivity
Reference Subjects (n) Methods Outcome
Simple sugars – fructose
20 % CHO energy fructose Turner et al. (1979) 6 HTG Fasting/post-load glucose
and insulin
No association
Normal diet plus 4180 kJ
(1000 kcal) fructose
Beck-Nielsen et al. (1980) 15 ND ITT Decrease in insulin sensitivity
0–15 % CHO energy fructose Hallfrisch et al. (1983) 12 ND; 12 HI Fasting, post-load glucose
and insulin
Decrease in insulin sensitivity
(particularly in HI)
24 % CHO energy fructose Crapo & Kolterman (1984) 11 ND OGTT Increase in insulin sensitivity
24 % CHO energy fructose Crapo et al. (1986) 7 T2DM OGTT Increase in insulin sensitivity
20 % energy fructose Koivisto & Yki-Jarvinen (1993) 10 T2DM EHC; GTT, HbA
Increase in insulin sensitivity
12 or 24 % total energy fructose Sunehag et al. (2002) 12 ND IVGTT No association
Simple sugars – sucrose
5–33 % energy sucrose Reiser et al. (1981a,b) 24 ND Fasting insulin Decrease in insulin sensitivity
33 % CHO energy sucrose Bossetti et al. (1984) 8 ND Fasting glucose and insulin No association
Isoenergetic diet with 3 –220 g sucrose Jellish et al. (1984) 24 T2DM Fasting/post-load glucose No association
1 or 16 % energy sucrose Coulston et al. (1985) 11 T2DM Fasting, day-long glucose
and insulin
Decrease in insulin
sensitivity (day-long only)
Diet with 3 or 220 g sucrose Abraira & Derler (1988) 18 T2DM Fasting, post-load glucose,
insulin, HbA
No association
Diet with 45 g sucrose or 16 mg asp Colagiuri et al. (1989) 9 T2DM EHC; HbA
, plasma glucose No association
Fructose replaced sucrose Thorburn et al. (1990) 6 T2DM EHC No association
23 % sucrose v. 2 % sucrose Raben et al. (2001) 18 ND HOMA No association
(HOMA-R unchanged)
Diet with 90 g sucrose Brynes et al. (2003) 17 ND HOMA No association
Simple sugars v. complex CHO
20 % energy as sucrose, fructose
or starch
Bantle et al. (1986) 12 T1DM; 12 T2DM Plasma and urinary glucose Increase in insulin sensitivity
(fructose only)
Sucrose replaced 45 g complex CHO Peterson et al. (1986) 23 DM HbA
; fasting glucose No association
CHO as 35 % complex v. 35 % simple Reiser et al. (1986) 19 ND GTT; Fasting insulin Decrease in insulin sensitivity
(35 % simple only)
20 % fructose or 20 % starch energy Reiser et al. (1989) 10 ND; 10 HI Fasting, day-long insulin Increase in glucose tolerance
20 % fructose plus/minus starch Bantle et al. (1992, 1993) 6 T1DM; 12 T2DM Plasma glucose Increase in insulin sensitivity
(fructose minus starch)
HTG, hypertriacylglycerolaemia; ND, non-diabetic; ITT, insulin tolerance test; HI, hyperinsulinaemic; OGTT, oral glucose tolerance test; T2DM, type 2 diabetes; EHC, euglycaemic –hyperinsulinaemic clamp; GTT, glucose
tolerance test; HbA
, glycated Hb; IVGTT, intravenous glucose tolerance test; asp, aspartame; HOMA, homeostasis model assessment; T1DM, type 1 diabetes mellitus; DM, diabetes mellitus.
Carbohydrate intake and insulin resistance 233
sensitivity and prompt further long-term intervention
studies using the EHC or other well-validated techniques
to measure insulin resistance. This is highlighted by the
studies comparing simple sugars and complex carbohydrate,
which tended to show that fructose increases insulin
sensitivity (Bantle et al. 1986, 1992, 1993) and glucose
tolerance (Reiser et al. 1989), without actual measurements
of insulin sensitivity before and after dietary intervention.
Such studies, not using the ‘gold-standard’ EHC, do not add
greatly to our understanding of the effects of simple sugars
compared with complex carbohydrate in the development or
progression of insulin resistance.
Intervention studies comparing effects of glycaemic index
on metabolic control
As is the case with the studies reported in Table 7, those
given in Table 8, which study the effects of GI, largely rely
on indirect measures of insulin resistance and thus only
really provide an estimate of overall metabolic control.
However, despite this limitation, these data largely support
an association between low-GI diets and improved
metabolic control (Table 8). When interpreting this overall
outcome it is important to emphasise again that low GI does
not mean low carbohydrate, rather reflecting the features or
quality of dietary carbohydrate in particular foods (Table 2).
Although an increasing body of evidence would suggest
merit in adopting high-carbohydrate, low-GI diets, the
charge that high-GI diets result in insulin resistance is
unproven on the basis of current experimental data arising
from the use of well-validated measures of insulin resistance
(see Pi-Sunyer, 2002).
While intake of dietary fat, particularly saturated fat, shows
clear associations with insulin resistance in animals
(Storlien et al. 1996) and human subjects (Marshall et al.
1997) and could predispose to development of diabetes
(Marshall et al. 1994), the effects of carbohydrate on insulin
sensitivity are not so clear. While simple sugars have been
shown to cause insulin resistance in some rodent strains,
most literature on human subjects would not support this
view. Indeed, sugars are both normal and important dietary
components, sourced from cane or beet sugar (sucrose),
‘high-fructose’ syrups (found in soft drinks in the USA), and
most fruits, where they can comprise up to 8 % of their
weight or more. However, in a recent article, Mann (2004)
draws attention to the fact that the source of dietary sugars
(such as fructose and sucrose) may be important when
considering the impact of intake on human health. This may
be particularly important when considering the apparent
increase in daily intake of drinks rich in non-milk extrinsic
sugars in the UK and USA (Henderson et al. 2003; Mann,
2004). In this regard, while fructose has hyperlipidaemic
properties, these are short-lived in humans (Grigoresco et al.
1988), thus averting any fears over the current recommen-
dations for increased fruit consumption in healthy eating
programmes. It is also important to note that many products
with high saturated fat content (such as pre-prepared meals,
confectionery and chocolate) also contain dietary sugars and
excessive consumption of such products is associated with
weight gain and may thus contribute significantly to obesity.
Indeed, when considering the dietary patterns and current
obesitytype 2 diabetes epidemic, it is imperative that diets
are viewed in terms of ability to promote regulated weight
loss and ameliorate insulin resistance. Important in this
regard was the observation that a low-fat, high-carbohydrate
diet may lead to gradual and sustained weight loss while
preventing fat gain in individuals who are not overweight
(Kirk et al. 2000). In addition, there is evidence that eating
frequently and often (so-called ‘grazing’) on low-fat, low-
energy foods such as fruits and cereal bars may be the best
way of achieving a healthy body weight (Green et al. 2000).
When energy is restricted the source of carbohydrate does
not appear to be important for body weight (Vermunt et al.
2003), nor indeed does the reduced carbohydrate content
(Bravata et al. 2003). Interestingly, in a recent intervention
study of subjects with impaired glucose tolerance, signifi-
cantly more weight was lost with a high-GI diet than with
low-GI or MUFA diet which does not support the view that
reducing glycaemic load, either by reducing GI or
carbohydrate intake, necessarily results in weight loss
(Wolever & Mehling, 2003). Indeed, a very recent systematic
review of low-carbohydrate diets found that weight loss is
associated with the duration of the diet and restriction of
energy intake, but not the restriction of carbohydrates (Astrup
et al. 2004). Also, when considering the literature on dietary
intervention studies it is important to consider that
Table 8. Dietary intervention studies of effects of glycaemic
index (GI) on metabolic control
Subjects (n)
Studies in which low-GI diet was
associated with improved
metabolic control
Fontvieille et al. (1988) 8 T1DM
Brand et al. (1991) 16 T1DM
Fontvieille et al. (1992) 18 DM
Wolever et al. (1992) 6 T2DM
Frost et al. (1994) 51 T2DM
Behall & Howe (1995) 10 ND and
14 HI
Howe et al. (1996) 9 ND and
13 HI
Frost et al. (1996) 28 HD
Frost et al. (1998) 28 ND
Jarvi et al. (1999) 20 T2DM
Wolever & Mehling (2002, 2003) 34 IGT
Brynes et al. (2003) 17 ND
Goff et al. (2003) 21 ND
Studies in which low-GI diet was
associated with worsening of
metabolic control
Kiens & Richter (1996) 7 ND
Studies in which low-GI diet
had no association with
metabolic control
Calle-Pascual et al. (1988) 35 DM
Luscombe et al. (1999) 21 T2DM
Tsihlias et al. (2000) 91 DM
Herrmann et al. (2001) 9 ND
T1DM, type 1 diabetes mellitus; DM, diabetes mellitus; T2DM, type 2
diabetes mellitus; ND, non-diabetic; HI, hyperinsulinaemic; HD,
heart disease; IGT, impaired glucose tolerance.
N. H. McClenaghan234
manipulation of carbohydrate content in a given diet cannot
conclusively demonstrate the effects of carbohydrate
independent of other dietary factors. As an example, studies
comparing the effects of a high-fat, high-carbohydrate diet
with a low-fat, high-fibre diet do not allow for separation of
effects of a high-carbohydrate diet from the effects of a high-
fat diet. This adds to the complexity, as does the consequences
of long-term adherence to radical low-carbohydrate diets,
which has been associated with an increased prevalence of
halitosis, muscle cramps, constipation and headache and,
perhaps more importantly, may increase risk of CVD and
cancer (Astrup et al. 2004).
Collectively, the data described in the present paper
support the view that high-carbohydrate diets do not
adversely affect insulin sensitivity, and may in fact offer
some beneficial effects. While epidemiological studies play
an essential role in helping devise prevention strategies for
diseases of the metabolic syndrome, in order to get a more
convincing picture, future intervention studies need to
research the effects of sugars, complex carbohydrate or fibre
in different populations including those presumed to be most
susceptible to their actions. As emphasised earlier, these
studies should cover both short- and long-term adherence
to these diets and employ direct measures of insulin
resistance, preferably using the EHC. Standardisation of
approach and global multi-centre studies would certainly
provide more convincing data. This is also true for the
measures of insulin resistance, which should expand on the
rather simplistic approach of measuring insulin and glucose
levels, perhaps to cover other important genetic markers and
metabolic variables. Indeed, research application of emer-
ging genomic, proteomic and metabolomic approaches
should increase understanding of the complex mechanisms
underlying insulin resistance. Adopting these new tools also
offers considerable opportunities to unravel the complex
relationships between carbohydrate and other dietary
components and risk or pathogenesis of insulin resistance
and the metabolic syndrome.
Although genetic predisposition and advancing age cannot
be directly modified, clearly other risk factors driving the
diabetes obesity epidemic including physical inactivity and
energy over-consumption can potentially be changed
through targeted lifestyle modification programmes. While
current evidence supports the FAO/WHO recommendations
to maintain a high-carbohydrate diet with low-GI starchy
foods (Wolever & Mehling, 2002), the relationships between
dietary carbohydrate and insulin sensitivity remain an
important area of research focus, in order to provide the
most useful information for future nutrition policy decisions.
This review was supported, in part, by a grant from The
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... Glucose is the main diet component that regulates insulin release and interferes with the function of pancreatic β cells and pre-surgery BMI; 58 participants were followed up for 3 months, and eight participants dropped out of the study. The remaining 50 patients were followed up for 12 months [21]. However, there is no consistent evidence regarding the effect of carbohydrate restriction on insulin resistance [22]. ...
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Objective The role of carbohydrates in weight loss in patients undergoing bariatric surgery (BS) remains poorly understood. Therefore, this study aimed to verify the relationship of the carbohydrate quality index (CQI) with weight loss and cardiometabolic risk markers up to 1 year after BS. Material and Methods This study included 50 patients with obesity undergoing Roux-en-Y gastric bypass. Data collection was performed preoperatively and 3 and 12 months after surgery. The foods consumed were documented using a 24-h food recall in 3 days. The CQI was calculated considering the following parameters: dietary fiber intake, sugar level; whole grains: proportion of total grains; solid carbohydrate: total carbohydrate ratio. Results From the total study sample, 58 participants were followed up for 3 months, and eight participants dropped out of the study. The remaining 50 patients were followed up for 12 months. Subjects were classified into tertiles according to the index score. A 1-unit increase in CQI was associated with a −1.02 decrease in insulin concentrations at 12 months and a −1.04 decrease in HOMA-IR. Concerning the total sample, the median of the CQI was 8 points and did not change at 3 and 12 months after surgery, but there was an improvement in some components of the index. Conclusion The data suggest that the quality of carbohydrates can interfere with markers of insulin resistance after BS and the quality of carbohydrates is a point to be guided in patients undergoing BS. Graphical abstract
... Part of the problem is that despite the rapid expansion of the literature, there is still no clear definition of what level of carbohydrate intake constitutes a significant restriction, as multiple dietary regimens with different carbohydrate percentages have shown various effects in the amount of weight loss in subjects with obesity and the degree of glycemic control in individuals with T2D. Some reports consider a carbohydrate-restricted diet a dietary plan where carbohydrates constitute anywhere between 25-40% of total energy intake, while other investigators consider it when the percentage is as low as those found in ketogenic diets where carbohydrates account for 10-15% of total energy intake, amounting to approximately 50 g/day or less (McClenaghan 2005;Wood and Fernandez 2009). Due to this variety, a focus of intensive research is the study of the metabolic effects related to the quality, such as form, digestibility, absorption rate and glycemic index (GI), rather than the total amount of dietary carbohydrates (Shafaeizadeh et al. 2018;Wood and Fernandez 2009). ...
Obesity and its related co-morbidities, namely type 2 diabetes (T2D), pose a significant global public health problem. Insulin resistance (IR) in muscle and liver is the core pathophysiologic defect that underlies obesity preceding and predicting the onset of T2D in susceptible humans. There is a broad population with IR that has no indication for prescription of medications, who still need medical consultation and specific advice in this respect. This prevalent need can be achieved by appropriate diet, exercise, and other behavioral therapies for lifestyle interventions. Despite a well-recognized role of IR in the progression to metabolic diseases, no specific nutritional recommendations exist to manage this condition, to the best of our knowledge. An international panel of experts reviewed and critically appraised the updated literature published about this topic. This review primarily examines the evidence for areas of consensus and ongoing uncertainty or controversy about diet and exercise approaches for IR. The aim of this article is to present the most common IR states, namely obesity and Polycystic Ovary Syndrome (PCOS), and provide nutritional advice to manage IR, hyperinsulinemia, and reactive hypoglycemia. These nutritional guidelines could prevent progression or worsening of IR with resultant beta-cell failure and, as a result, T2D.
... How and why the body fails to respond to insulin is not fully understood but overnutrition and macronutrient composition likely play a role in the regulation of the underlying mechanisms (Parillo & Riccardi 2004). There is much controversy around the optimal diet for maintenance of insulin action and there is lack of a clear consensus on macronutrient subtype recommendations to maintain metabolic health (Daly et al. 1997, McClenaghan 2005, Feinman et al. 2015. Elucidation of mechanisms linking nutrients to regulation of insulin action is critical for improving current treatments of T2DM as diet modification is almost always prescribed and is easily manipulated. ...
Poor nutrition plays a fundamental role in the development of insulin resistance, an underlying characteristic of type 2 diabetes. We have previously shown that high-fat diet-induced insulin resistance in rats can be ameliorated by a single glucose meal, but the mechanisms for this observation remain unresolved. To determine if this phenomenon is mediated by gut or hepatoportal factors, male Wistar rats were fed a high-fat diet for three weeks before receiving one of five interventions: high-fat meal, glucose gavage, high-glucose meal, systemic glucose infusion or portal glucose infusion. Insulin sensitivity was assessed the following day in conscious animals by a hyperinsulinemic-euglycemic clamp. An oral glucose load consistently improved insulin sensitivity in high-fat fed rats, establishing the reproducibility of this model. A systemic infusion of a glucose load did not affect insulin sensitivity, indicating that the physiological response to oral glucose was not due solely to increased glucose turnover or withdrawal of dietary lipid. A portal infusion of glucose produced the largest improvement in insulin sensitivity, implicating a role for the hepatoportal region rather than the gastrointestinal tract in mediating the effect of glucose to improve lipid-induced insulin resistance. These results further deepen our understanding of the mechanism of glucose mediated regulation of insulin sensitivity and provide new insight into the role of nutrition in whole body metabolism.
... Consumption of a healthy diet, particularly one with significant amounts of dietary fiber, appears to be a good strategy for minimizing a multitude of disorders, including insulin resistance, according to reviews by Anderson et al. [19] and McAuley et al. [20]. A number of studies show that the risk of insulin resistance is reduced and/or insulin sensitivity is increased when fiber is consumed abundantly [19][20][21][22][23][24][25]. However, a comparison of these investigations is difficult, since a broad array of designs and measurement methods have been employed to evaluate the fiber and insulin resistance relationship. ...
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A cross-sectional design was used to evaluate the relationship between fiber intake and insulin resistance, indexed using HOMA (homeostatic model assessment), in a National Health and Nutrition Examination Study (NHANES) sample of 6374 U.S. adults. Another purpose was to test the influence of covariates on the association. A third aim was to compare HOMA levels between two groups based on the recommended intake of 14 grams of fiber per 1000 kilocalories (kcal). Fiber intake was measured using a 24-hour recall. With demographic variables controlled, results showed that HOMA differed across High, Moderate, and Low fiber categories (F = 5.4, p = 0.0072). Adjusting for the demographic variables, the possible misreporting of energy intake, smoking, and physical activity strengthened the relationship (F = 8.0, p = 0.0009), which remained significant after adjusting for body fat (F = 7.0, p = 0.0019) and body mass index (BMI) (F = 4.9, p = 0.0108), with the other covariates. However, the fiber–HOMA relationship was eliminated after adjusting for waist circumference (F = 2.3, p = 0.1050). Dividing participants based on the recommended 14-gram standard resulted in meaningful HOMA differences (F = 16.4, p = 0.0002), and the association was not eliminated after controlling for waist circumference. Apparently, adults with high fiber consumption have less insulin resistance than their counterparts. However, much of the association is due to differences in waist circumference, unless the recommended intake of fiber is attained.
... At this moment, the mechanisms involved in utilizing glucose are: (a) breakdown of glucose in the process of glycolysis, (b) glucose is converted to glycogen in the liver and muscles, and (c) insulin acts on adipose tissue to promote fatty acids synthesis and inhibit release of available fatty acids [25]. Prolonged [26][27][28]. Information on the metabolic impact of carbohydrate on animal models of MetS is absent. Most of the diet regimens were designed with the combination of high-carbohydrate [22]. ...
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Metabolic syndrome (MetS) consists of several medical conditions that collectively predict the risk for cardiovascular disease better than the sum of individual conditions. The risk of developing MetS in human depends on synergy of both genetic and environmental factors. Being a multifactorial condition with alarming rate of prevalence nowadays, establishment of appropriate experimental animal models mimicking the disease state in humans is crucial in order to solve the difficulties in evaluating the pathophysiology of MetS in human. This review aims to summarize the underlying mechanisms involved in the pathophysiology of dietary, genetic, and pharmacological models of MetS. Furthermore, we will discuss the usefulness, suitability, pros and cons of these animal models. Even though numerous animal models of MetS have been established, further investigations on the invention of new animal model and clarification of plausible mechanisms are still necessary to confer a better understanding to researchers on the selection of animal models for their studies.
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Background: Metabolic syndrome (MetS) increases the risk of diabetes. Olea europaea fruit exerts protective effects on metabolic disorders. Therefore, the aim of the present study was to investigate the effect of O. europaea fruit extracts on sucrose-induced MetS in rats. Methods: Male adult Wistar rats (200±50 g, n=32) were randomly divided into four groups (n=8) consisting of control group, untreated sucrose group (sucrose 50% in drinking water for 10 weeks), sucrose plus aqueous extract of O. europaea fruit treated group (200 mg/kg) and sucrose plus hydroalcoholic extract of O. europaea fruit treated group (200 mg/kg) by gavage for 2 weeks. Body weight, serum glucose, insulin, leptin, lipid profile, homeostatic model assessment of insulin resistance (HOMA-IR) and hepatic enzymes were measured. Data were analyzed by one-way analysis of variance (ANOVA, SPSS, 16.0). P<0.05 was regarded as significance level. Results: The aqueous extract exhibited higher protective effects on serum glucose, insulin and HOMA-IR than hydroalcoholic extract (P<0.05). Body weight, serum glucose, leptin (P<0.01), insulin, triglyceride, very-low-density lipoprotein cholesterol (VLDL-C), HOMA-IR, alkaline phosphatase (ALP), (P<0.001) and aspartate aminotransferase (AST) (P<0.05) significantly elevated but high-density lipoprotein cholesterol (HDL-C) (P<0.05) decreased in the sucrose group. Aqueous extract of O. europaea fruit significantly improved blood glucose, triglyceride, VLDL-C (P<0.01), insulin, HOMA-IR, ALP (P<0.001), body weight, AST and leptin (P<0.05) levels. Hydroalcoholic extract of O. europaea fruit significantly restored insulin, HOMA-IR (P<0.01), ALP (P<0.001), body weight, leptin, VLDL-C, triglyceride, blood glucose and AST (P<0.05). Conclusion: Our results indicated O. europaea fruit extracts could improve metabolic disorders induced by MetS in the rats.
This chapter examines the evidence for sex-specific dietary recommendations for the prevention and treatment of the metabolic syndrome (MetS) and type 2 diabetes mellitus (T2DM) in women. Women with the MetS are at an increased risk for developing T2DM [1,2], so rather than discussing all forms of diabetes, we have chosen to focus in particular on T2DM. A central feature of the MetS, which has also been linked to increased incidence of T2DM, is abdominal obesity. While this is briefly discussed in the section on the etiology of the MetS and T2DM, it was not the purpose of this chapter to review the literature on women, nutrition, and abdominal obesity. The reader is referred to Chapter 7 for this discussion. This chapter discusses present nutrition knowledge pertaining to the MetS and T2DM, with an emphasis on findings relevant to women.
Worldwide, the number of persons with diabetes and those who are at risk for diabetes is increasing at an alarming rate, largely driven by the rising prevalence of obesity and inactivity. Of concern in the United States are the approximately 86 million people who have prediabetes and one in three people with metabolic syndrome (Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006, Hyattsville, 2009). These individuals are at high risk for conversion to type 2 diabetes and for cardiovascular disease if lifestyle prevention strategies are not implemented. After years of rises in the incidence of diabetes, one positive finding has been the reporting of a leveling off in the prevalence from 2009 to 2014 in the 45–64 years age group, although an upward trend is still evident in older age groups (
Objective: To assess insulin sensitivity in patients with systemic lupus erythematosus (SLE) in response to a meal tolerance test (MTT). Methods: In this cross-sectional study, 33 adult females mild/inactive SLE and 16 age- and body mass index (BMI)-matched female healthy controls (CTRL) underwent a MTT and were assessed for insulin sensitivity and beta-cell function. Skeletal muscle protein expressions of total and membrane GLUT-4 were also evaluated (SLE, n=10; CTRL, n=5); muscle biopsies were performed after MTT. Further measurements included inflammatory cytokines, adipocytokines, physical activity level, body composition, and food intake. Results: SLE and CTRL showed similar fasting glucose, glucose response, and skeletal muscle GLUT-4 translocation after MTT. However, SLE demonstrated higher fasting insulin levels (p=0.01; ES=1.2), HOMA IR (p=0.03; ES=1.1), insulin-to-glucose ratio response to MTT (p=0.02; ES=1.2), fasting glucagon levels (p=0.002; ES=2.7), glucagon response to MTT (p=0.0001; ES=2.6), and a tendency towards lower Matsuda index of whole-body insulin sensitivity (p=0.06; ES=-0.5) when compared with CTRL. Fasting proinsulin-to-insulin ratio and proinsulin-to-insulin ratio response to MTT were similar between groups (p>0.05), while SLE showed a higher insulinogenic index when compared with CTRL (p=0.02; ES=0.9). Conclusion: We have identified that SLE patients had a bi-hormone metabolic abnormality characterized by increased insulin resistance and hyperglucagonemia despite normal glucose tolerance and preserved beta-cell function and skeletal muscle GLUT-4 translocation. Strategies capable of ameliorating insulin sensitivity to reduce the risk of T2DM and CVD in SLE may require more than targeting insulin resistance alone. This article is protected by copyright. All rights reserved.
Lifestyle medicine for prediabetes and diabetes includes nutrition therapy, physical activity, education/counseling and support, and monitoring. Nutrition therapy, physical activity, and education/counseling and support for persons with prediabetes are essential interventions for the prevention of progression of prediabetes to diabetes. In the management of type 1 and type 2 diabetes (T1D and T2D), research has documented the effectiveness of nutrition therapy for achieving lifestyle and medical goals. For persons with type 2 diabetes, individualized nutrition therapy interventions with a focus on reduced energy intake are effective in achieving management goals. For persons with type 1 diabetes, the use of carbohydrate counting for determining prandial insulin doses is a proven nutrition therapy intervention for achieving glucose goals. For all, monitoring carbohydrate intake, whether by carbohydrate counting or experience-based estimation remains a key nutrition therapy strategy. For good health, carbohydrate intake from vegetables, fruits, whole grains, legumes, and dairy products is encouraged over intake from other carbohydrate sources, especially those that contain added fats, sugars, or sodium. Collaboration between health care professionals and persons with diabetes in determining lifestyle strategies and goals is necessary. For all physical activity is encouraged, education/counseling and support are essential, monitoring must be done to determine if lifestyle and medical goals are being met, and the integration of nutrition therapy interventions and medications is important.
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Objective To determine trends in weight, height, and body mass index in children between 1989 and 1998. Design Retrospective series of cross sectional studies of routinely collected data. Setting Primary care in the Wirral Health Authority. Participants 35 662 infants aged 1-3 months (representing 88% of live births) and 28 768 children aged 2.9-4.0 years. 21 582 infants and children (25.1%) were excluded because of missing or inaccurate data. Main outcome measures Weight, height, sex, and age routinely recorded by health visitors. Height, weight, and body mass index standardised for age and sex. SD score > 1.04 for body mass index (> 85th centile) was defined as overweight and > 1.64 (> 95th centile) as obese. Body mass index was not calculated in infants as it is difficult to interpret. Results From 1989 to 1998 there was a highly significant increasing trend in the proportion of overweight children (14.7% to 23.6%; P < 0.001) and obese children (5.4% to 9.2%; P < 0.001). There was also a highly significant increasing trend in the mean SD score for weight (0.05 to 0.29; P < 0.001) and body mass index (− 0.15 to 0.31; P < 0.001) but not height. Infants showed a small but significantly increasing trend in mean SD score for weight (− 0.17 to − 0.05; P = 0.005). Conclusions From 1989 to 1998 there was a highly significant increase in weight and body mass index in children under 4 years of age. Routinely collected data are valuable in identifying anthropometric trends in populations.
We examined the relationship between usual patterns of food intake, fattiness of food preparation and consumption, and diabetes and obesity status in a Native Canadian reserve in northwestern Ontario. Patterns of intake were estimated using a 34-item food frequency instrument. Scales and scores were developed using factor analysis procedures and were tested for reliability using coefficient alpha. Impaired glucose tolerance (IGT) and diabetes status was determined by administering a 75-g glucose tolerance test. A number of the food groups appear to have a protective effect in regard to IGT and diabetes, including vegetables [odds ratio (OR) = 0.41, confidence interval (CI) = 0.18–0.91], breakfast foods (OR = 0.41, CI = 0.18–0.93) and hot meal foods (OR = 0.29, CI = 0.11–0.78). Most of these foods are relatively high in fiber and low in fat. High consumption of junk foods and the bread and butter group was associated with substantial increases in risk for diabetes (OR = 2.40, CI = 1.13–5.10; OR = 2.22, CI = 1.22–4.41, respectively). These foods tend to be high in simple sugars, low in fiber and high in fat. More fatty methods of food preparation are also associated with increased risk for diabetes in this population (OR = 2.58, CI = 1.11–6.02). This information has been incorporated into an ongoing community-based diabetes prevention program in the community.
It has been suggested that foods with a high glycemic index are detrimental to health and that healthy people should be told to avoid these foods. This paper takes the position that not enough valid scientific data are available to launch a public health campaign to disseminate such a recommendation. This paper explores the glycemic index and its validity and discusses the effect of postprandial glucose and insulin responses on food intake, obesity, type 1 diabetes, and cardiovascular disease. Presented herein are the reasons why it is premature to recommend that the general population avoid foods with a high glycemic index.
Background: Underreporting is a common problem in dietary surveys. Few studies have shown the implication of this when investigating diet-disease relations Objective: We investigated how underreporting affects the associations between dietary factors and the metabolic syndrome. Design: Dietary intake measured with a 7-d food record, fasting insulin concentrations, and other variables of the metabolic syndrome were assessed in a cross-sectional study of 301 healthy men aged 63 y. Biological markers for intakes of protein, sodium, and potassium were measured in 24-h urine samples. Underreporters (URs, n = 88) were identified by Goldberg's equation, which compares energy intake with energy expenditure, both expressed as multiples of the basal metabolic rate. Physical activity level was estimated, and individual cutoffs were calculated. Results: The URs had higher nutrient and food densities in their diet than did the non-URs, which suggested that they followed a healthier diet. The URs had a higher prevalence of the metabolic syndrome than did the non-URs (18% and 9%, respectively; P = 0.029). The biological markers confirmed a low validity of the dietary data in the URs. The correlations between fasting insulin concentrations, a central component of the metabolic syndrome, and the intakes of polyunsaturated fats, n-6 fats, and fat from milk products were stronger in the URs than in the non-URs, which indicates that inaccurate data can introduce spurious associations. Conclusion: The association between diet and fasting insulin differed between URs and non-URs in this study of 301 healthy men aged 63 y. If URs are not identified and excluded or treated separately in studies in nutritional epidemiology, spurious diet-disease relations may be reported.
Glycaemic indexes are calculated using test-meals in acute conditions. Their use has not yet proved to be effective in routine diabetic diet counselling. We studied 8 insulin-dependent diabetic patients (4 M, 4 F); mean age: 43.5 ± 3.5 yr; body mass index (BMI): 24.1 ± 1.9; diabetes duration: 14.6 ± 2.4 yr (mean ± SEM). Each patient received in random order and with a cross-over design two diet prescriptions lasting 3 wk each, one (L) with a relatively low mean glycaemic index (mGI) and the other (H) with a higher mGI, equivalent in term of calories, carbohydrate (CHO), fat, protein and fibre intake. A more liberal use of pasta and rice was recommended in diet L, when bread and potato were more recommended in diet H. Diet L contained 2152 ± 79 kcal/d; protein = 17.4 ± 0.5%; fat = 35.0 ± 1.0%; CHO = 46.1 ± 1.6%; mGI = 46.5 ± 0.9%; diet H: 2118 ± 96 kcal/d; protein = 16.9 ± 0.6%; fat = 36.0 ± 1.0%; CHO = 45.4 ± 1.6%; mGI = 60.1 ± 1.8%; (mGI L vs H: p<0.001). At the end of the L period the following parameters were improved: fructosamine (% of decrease: -18 ± 7% from 2.77 ± 0.21 to 2.17 ± 0.24 mmol; p<0.05), triglycerides (-16 ± 7% from 1.40 ± 0.21 to 1.18 ± 0.20 mmol; p<0.05), phospholipids (-8 ± 1% from 3.36 ± 0.16 to 3.07 ± 0.13 mmol; p<0.01), daily insulin needs (-6 ± 2% from 50.2 ± 5.45 to 47.4 ± 5.9 IU; p<0.05). Mean fasting plasma glucose levels were lower at the end of the L period (9.0 ± 1.6 VS 12.7 ± 3.3) but not significantly (p<0.1). No significant variations were observed concerning body weight and other circulating blood lipid levels. Increasing the proportion of low glycaemic index foods ingested lead to an improvement of the metabolic control and is thus recommendable in Type I diabetic patients on a chronic basis.
Methods for the quantification of beta-cell sensitivity to glucose (hyperglycemic clamp technique) and of tissue sensitivity to insulin (euglycemic insulin clamp technique) are described. Hyperglycemic clamp technique. The plasma glucose concentration is acutely raised to 125 mg/dl above basal levels by a priming infusion of glucose. The desired hyperglycemic plateau is subsequently maintained by adjustment of a variable glucose infusion, based on the negative feedback principle. Because the plasma glucose concentration is held constant, the glucose infusion rate is an index of glucose metabolism. Under these conditions of constant hyperglycemia, the plasma insulin response is biphasic with an early burst of insulin release during the first 6 min followed by a gradually progressive increase in plasma insulin concentration. Euglycemic insulin clamp technique. The plasma insulin concentration is acutely raised and maintained at approximately 100 muU/ml by a prime-continuous infusion of insulin. The plasma glucose concentration is held constant at basal levels by a variable glucose infusion using the negative feedback principle. Under these steady-state conditions of euglycemia, the glucose infusion rate equals glucose uptake by all the tissues in the body and is therefore a measure of tissue sensitivity to exogenous insulin.
To learn more about the metabolic effects of dietary fructose and sucrose, 12 type I and 12 type II diabetic subjects were fed three isocaloric (or isoenergic) diets for eight days each according to a randomized, crossover design. The three diets provided, respectively, 21% of the energy as fructose, 23% of the energy as sucrose, and almost all carbohydrate energy as starch. The fructose diet resulted in significantly lower one- and two-hour postprandial plasma glucose levels, overall mean plasma glucose levels, and urinary glucose excretion in both type I and type II subjects than did the starch diet. There were no significant differences between the sucrose and starch diets in any of the measures of glycemic control in either subject group. The fructose and sucrose diets did not significantly increase serum triglyceride values when compared with the starch diet, but both increased postprandial serum lactate levels. We conclude that short-term replacement of other carbohydrate sources in the diabetic diet with fructose will improve glycemic control, whereas replacement with sucrose will not aggravate glycemic control.(JAMA 1986;256:3241-3246)
Objective. —To examine prospectively the relationship between glycemic diets, low fiber intake, and risk of non—insulin-dependent diabetes mellitus.Desing. —Cohort study.Setting. —In 1986, a total of 65173 US women 40 to 65 years of age and free from diagnosed cardiovascular disease, cancer, and diabetes completed a detailed dietary questionnaire from which we calculated usual intake of total and specific sources of dietary fiber, dietary glycemic index, and glycemic load.Main Outcome Measure. —Non—insulin-dependent diabetes mellitus.Results. —During 6 years of follow-up, 915 incident cases of diabetes were documented. The dietary glycemic index was positively associated with risk of diabetes after adjustment for age, body mass index, smoking, physical activity, family history of diabetes, alcohol and cereal fiber intake, and total energy intake. Comparing the highest with the lowest quintile, the relative risk (RR) of diabetes was 1.37 (95% confidence interval [CI], 1.09-1.71, Ptrend=.005). The glycemic load (an indicator of a global dietary insulin demand) was also positively associated with diabetes (RR=1.47; 95% CI, 1.16-1.86, Ptrend=.003). Cereal fiber intake was inversely associated with risk of diabetes when comparing the extreme quintiles (RR=0.72,95% CI, 0.58-0.90, Ptrend=.001). The combination of a high glycemic load and a low cereal fiber intake further increased the risk of diabetes (RR=2.50, 95% CI, 1.14-5.51) when compared with a low glycemic load and high cereal fiber intake.Conclusions. —Our results support the hypothesis that diets with a high glycemic load and a low cereal fiber content increase risk of diabetes in women. Further, they suggest that grains should be consumed in a minimally refined form to reduce the incidence of diabetes.