Insulin Resistance versus Insulin Deficiency in Non

Transcription

0163-769X/98/$03.00/0
Endocrine Reviews 19(4): 477– 490
Copyright © 1998 by The Endocrine Society
Printed in U.S.A.
Insulin Resistance versus Insulin Deficiency in NonInsulin-Dependent Diabetes Mellitus: Problems and
Prospects
ELE FERRANNINI
C.N.R. Institute of Clinical Physiology and Department of Internal Medicine, University of Pisa, 56126
Pisa, Italy
I. Introduction
II. Preliminary Issues
A. Prevalence of insulin resistance in NIDDM
B. Methodology
C. Source data
III. Insulin Resistance vs. Insulin Deficiency
A. High-risk conditions and predictors
B. The problem by mechanism
C. The genetic approach
IV. Summary
V. Insulin Resistance: The Cluster Concept
VI. Prospects
with high frequency. As a consequence, many NIDDM patients may be presumed to have some degree of insulin
resistance and, by definition, every diabetic patient secretes
less insulin than necessary for his/her level of insulin sensitivity. Moreover, the mix of insulin resistance and insulin
deficiency is likely to be different in each patient and, in any
patient, may vary during the course of the disease. Clinically,
to regard insulin deficiency or insulin resistance as the central
element of the individual patient with NIDDM is rather a
matter of preference than an evidence-based judgment.
2. Pathophysiologically, both insulin resistance alone and
insulin deficiency alone can alter plasma glucose levels. Rare
inherited forms of extreme insulin resistance (7) and secondary diabetes (8) are clear examples for each case. Common forms of hyperglycemia, however, are mostly mixed
cases. In fact, insulin secretion and action govern glucose
homeostasis in a dual regulatory cycle: a rise in glucose
concentration stimulates insulin secretion, which in turn
lowers plasma glucose in a time- and concentration-dependent manner (outer circle in Fig. 1). In addition, sustained
hyperinsulinemia inhibits both insulin secretion (9) and action (10). In turn, chronic hyperglycemia can impair both the
insulin-secretory response to glucose (11) and cellular insulin
sensitivity [i.e., glucose toxicity (12)] (inner circle in Fig. 1).
Due to the operation of these loops, it is a priori unlikely that
chronic hyperglycemia is due exclusively to a deficit in insulin secretion or to an isolated defect in insulin action.
Instead, the prevalent scenario must be one in which chronic
hyperglycemia is associated with a compound of insulin
deficiency and resistance, in proportions that are also influenced by the relative gains and time constants of the feedback
effects.
Since the above points are essentially undisputed, it can be
safely stated that insulin deficiency and insulin resistance are
salient elements in the pathogenesis of typical NIDDM and
that both contribute to the hyperglycemia of this disease.
Whether insulin deficiency or insulin resistance is the primary determinant in the etiology of NIDDM is altogether a
different question. In general, the etiology of NIDDM could
follow three models: genetic (e.g., Down’s syndrome), environmental (e.g., trauma), or mixed. Once again, the unanimous opinion is that both genetic and environmental influences confer risk for NIDDM. Given that both insulin
secretion and insulin action are under genetic control, mutations in either set of genes (those encoding insulin secretion
I. Introduction
I
nsulin resistance has attracted much attention in recent
years (1, 2). From the field of obesity and diabetes, insulin
resistance has spread to adjacent areas [e.g., hypertension,
dyslipidemia, ischemic heart disease (3)], from which new
angles have emerged. Nevertheless, the role of insulin resistance in human non-insulin-dependent diabetes mellitus
(NIDDM) is still subject to controversy, particularly when
contrasted with the role of insulin deficiency.
As often is the case, semantics provide some fuel to the
controversy. Thus, the question of whether insulin deficiency
or insulin resistance is the central element (or equivalent term)
of NIDDM is intrinsically confusing. In fact,
1. Clinically, NIDDM is diagnosed on the basis of raised
plasma glucose levels (no other substrate or hormone being
considered) that can be controlled without exogenous insulin
(although proneness to ketosis may eventually appear) (4).
Such a broad definition pigeonholes a heterogeneous group
of conditions, ranging from maturity-onset diabetes of the
young (MODY) to the common form of NIDDM to latent
autoimmune diabetes of the adult [or LADA, as evidenced
by the finding of autoantibodies against a variety of islet cell
and other antigens (5)]. Obesity, estimated to accompany
NIDDM in 80% of the patients in the westernized world, is
featured in some subclassifications of diabetes (6) but is not
required for its diagnosis. As obesity is often associated with
insulin resistance, the latter feature is passed on to NIDDM
Address reprint requests to: Ele Ferrannini, M.D., CNR Institute of
Clinical Physiology, Via Savi, 8, 56126 Pisa, Italy. E-mail: ferranni@
nsifc.ifc.pi.cnr.it
477
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FERRANNINI
FIG. 1. Dual feedback connection between plasma glucose ([glucose])
and insulin ([insulin]) concentrations in vivo. (1), Stimulation; (2),
inhibition.
and those responsible for insulin action) could theoretically
be the primary event in NIDDM. When either mutation is
functionally expressed in the phenotype as b-cell failure or
insulin resistance, however, the companion abnormality is
likely to follow suit, according to the interactions in Fig. 1.
Equally admissible is a paradigm in which an environmental
factor initially strikes either insulin secretion or insulin sensitivity, thereby starting the cycle. The general question, then,
is which event—mutation or environmental insult, affecting
insulin secretion or insulin resistance—is the cause of typical
NIDDM? The solution theoretically admits numerous combinations, especially if there is linkage dysequilibrium
and/or interactions between genes as well as between environment and genes. Thus, solving the etiology of NIDDM
is a highly complex undertaking. It may be useful to review
the approaches and identify the critical issues as they apply
to in vivo investigation.
II. Preliminary Issues
A. Prevalence of insulin resistance in NIDDM
What fraction of NIDDM is insulin resistant? As mentioned, abundant data document the presence of resistance
to insulin action in the majority of NIDDM patients (1–3, 7,
13). For example, a recent study measuring insulin sensitivity
in a large, tri-ethnic (non-Hispanic whites, Hispanics, and
African-Americans) cohort of subjects (the Insulin Resistance
Atherosclerosis Study) (14) found that the proportion of insulin-sensitive diabetics was quite low (4 –17%) in lean as
well as obese patients, regardless of ethnicity. Against this
background, however, significant exceptions have been reported. In one study in Scandinavian men (15), lean NIDDM
patients over 65 yr of age were found to be as insulin sensitive
as their age-matched nondiabetic controls. In a study of African-American men with NIDDM, insulin resistance was
present in only 60% of the patients with a body mass index
(BMI) ,30 kgzm22, and was strongly associated with an
increase in intraabdominal adipose tissue (16). In another
case-control study, lean NIDDM patients were shown to
have similar insulin sensitivity to nondiabetic controls if they
were free from both microalbuminuria and hypertension,
suggesting that the insulin resistance was carried by these
Vol. 19, No. 4
abnormalities rather than by diabetes itself (17). The coexistence of familial dyslipidemia also may make a separate
contribution to the prevalence of insulin resistance in
NIDDM, a possibility that has not been tested in large cohorts. It is also presumable that the frequency of lean, insulinsensitive NIDDM may vary depending on the ethnic composition of the population (18) as well as on the definitions
of obesity and insulin resistance. Although the latter issue
has not been formally evaluated, it is safe to state that, insofar
as they have neither MODY nor LADA, lean NIDDM patients are frequently, but by no means invariably, insulin
resistant.
The lean, insulin-sensitive NIDDM phenotype is an interesting model. It attests to the possibility that an unknown
insult to the b-cell can be such as to produce a slowly evolving diabetic syndrome without marked insulin resistance. In
these patients, it is possible that insulin sensitivity was supernormal in the prediabetic phase but decreased to ’normal’
levels once hyperglycemia ensued. The nature of their b-cell
dysfunction, genetic defect or environmental (nonautoimmune) damage, remains undetermined. To the best of available evidence, the prevalence of this type of NIDDM should
be low, because filtering out MODY, LADA, and overweight
patients leaves only a thin segment of the entire NIDDM
population.
B. Methodology
The definitions of insulin deficiency and insulin resistance
are dependent on methodology. Insulin action in vivo can be
measured with the use of the euglycemic hyperinsulinemic
clamp technique, which has become the gold standard
against which other methods [insulin suppression test, minimal model analysis of the intravenous glucose tolerance test
(ivGTT), Constant Infusion of Glucose with Model Assessment (CIGMA), Homeostatic Model Assessment (HOMA),
etc. (19)] are validated. Merits of the insulin clamp are that
its estimates are relatively free of assumptions, are derived
under conditions approximating a steady state, and are well
reproducible [;10% intraindividual coefficient of variation
(19)]. One limitation is that the clamp, in its simplest version,
offers a point estimate of insulin action: one substrate (glucose), one stimulus (insulin concentration 3 time), both at
one level only. Full dose-response curves for in vivo insulinmediated glucose uptake have been constructed by performing multiple euglycemic (20) or hyperglycemic (21) clamps
sequentially or on different days, but this laborious approach
is feasible only in small numbers of subjects. An additional
difficulty is that insulin sensitivity estimated during insulin
administration may not bear a close relation to the insulin
sensitivity of the fasting state, when hepatic glucose output
and non-insulin-dependent glucose utilization dominate
glucose homeostasis. Despite its limitations, the simple version of the euglycemic insulin clamp has become prevalent
enough to generate databases in Pima Indians (22) and, more
recently, in healthy white Europeans [the European Group
for the Study of Insulin Resistance (EGIR) study] (23). The
minimal model analysis of insulin sensitivity—probably the
closest relative of the insulin clamp— has also been applied
to a population-based sample of young healthy subjects (24)
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INSULIN RESISTANCE VS. INSULIN DEFICIENCY IN NIDDM
and to the tri-ethnic cohort in the IRAS study (25). Thus,
insulin resistance can now be defined by statistical criteria,
and modulation of insulin action by such common factors as
age, obesity, fat distribution, and life-style has been given a
quantitative description (22–25).
There is no equivalent standard for insulin secretion.
Acute insulin release (AIR), measured as the sum of the
peripheral hormone concentrations during the initial 10 min
of an ivGTT, has been interpreted as the unloading of preformed insulin in response to maximal impulsive stimulation
(26). The plasma insulin concentrations measured at later
times during the ivGTT represent augmented hormone synthesis in response to sustained glucose stimulation (26). Indeed, when in vivo insulin secretion is accurately reconstructed from peripheral insulin concentrations by means of
deconvolution analysis, the b-cell response to intravenous
glucose is frequently multiphasic in healthy subjects, with
progressively damped peaks of release in phase with plasma
glucose oscillations (27). Continuous glucose infusion evokes
ultradian insulin secretory oscillations (28), which are superimposed on the more rapid, spontaneous pulsatility of
fasting b-cell activity (29). A mixed meal also elicits a roughly
biphasic pattern of insulin release, with a first peak of peripheral insulinemia at 30 – 45 min followed by a sustained
response occurring in the face of steadily declining plasma
glucose levels (30, 31). In all of these tests, in which the
glucose stimulus is not standardized, the insulin secretory
response is commonly assessed as the plasma insulin increment divided by the plasma glucose increment. The hyperglycemic version of the glucose clamp, on the other hand, can
maintain a square wave of hyperglycemia. The insulin response to this format of glucose challenge is again biphasic,
a typical AIR being followed by a gradually increasing secretory activity. The hyperglycemic clamp is optimal to compare responses to a single equipotent stimulus and to construct dose-response functions by creating ramp-like
hyperglycemic plateaus (19). Finally, intravenous arginine
pulses can be superimposed on hyperglycemia to explore
glucose potentiation (26).
While much has been learned from the application of these
techniques in physiological studies, the distribution and
physiological correlates of specific insulin-secretory responses have not been well characterized. Only recently (24),
modulation of AIR by gender, body fatness, physical fitness,
and other life-style factors has been described at the population level in a sample of young healthy volunteers (24). In
contrast, many small case-control studies have examined the
pathogenetic significance of impaired insulin secretion.
Thus, short (60-min) glucose infusions at fixed rates have
been used to characterize low and high insulin responders
(32). Loss of AIR has long been known to be a good predictor
of subsequent development of NIDDM (33). A blunted early
insulin response to a mixed meal has been shown to be in part
responsible for the excessive prandial hyperglycemia of
NIDDM patients (34). Even the ability of exogenous glucose
infusions to entrain cyclic insulin secretory pulses has been
shown to have discriminating power for b-cell function in
humans (28). Nevertheless, a number of important questions
remain unanswered. For example, we do not know whether
AIR is related to the oscillatory patterns of insulin release,
479
whether it is influenced by habitual diet or antecedent physical activity, or whether it aggregates in the family [as does
insulin resistance (35)]. Likewise, the relationship between
AIR and the early insulin response to a mixed meal is poorly
understood. In everyday life there is no AIR; the early prandial insulin response is much slower than AIR and is strongly
influenced by the gastrointestinal release of insulinotrophic
hormones. Yet, an impairment of the ’initial’ response of the
b-cell to either intravenous glucose or a meal is so consistently found in hyperglycemic states to constitute a hallmark
of diabetes. The cellular basis for the predictivity of AIR for
the global failure of insulin release that ultimately leads to
hyperglycemia is obscure. Less specifically, we do not know
whether and which other parameters of the complex insulin
secretory dynamics relate or anticipate the b-cell defect of
NIDDM.
In summary, insulin action can be assessed in vivo with
good accuracy, specificity, and reproducibility, and its measurement at the population level continues to produce valuable data. For insulin secretion, no single test appears to
convey information as densely as the insulin clamp does for
insulin sensitivity, and none has gained sufficient diffusion.
C. Source data
Information relevant to the etiology of NIDDM can be
derived from epidemiological surveys and case-control studies. Both sources have inherent limitations. In epidemiological studies, in which only simple measurements can usually
be obtained, insulin sensitivity and insulin secretion have
both been inferred from plasma insulin concentrations, either
fasting or postglucose (36 –38). The only published studies in
which insulin resistance has been measured by a direct
method (the insulin clamp) at the population level are those
performed in the Pima Indians of the Southwestern United
States (22) and the EGIR study (23). Although peripheral
insulin levels do reflect insulin sensitivity, the extent of this
relationship can be appreciated from the EGIR data (Fig. 2):
FIG. 2. Inverse relationship between fasting plasma insulin concentrations (in logarithmic scale) and insulin sensitivity (as measured by
a euglycemic insulin clamp) in 1,146 nondiabetic subjects. The shaded
area includes 6 1 SD of mean values; the solid line is the linear fit of
the data (recalculated from Ref. 77).
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FERRANNINI
lower insulin sensitivity is associated with higher fasting
insulin concentrations, but the scatter around the relationship is wide. Similar data relating insulin secretion [independently measured from C-peptide kinetics (31)] and
plasma insulin concentrations are simply lacking. Thus, epidemiological findings based on plasma insulin measurements, even when they are consistent, are intrinsically insufficient to discriminate insulin resistance from insulin
deficiency.
A further difficulty is the interpretation of epidemiological
data. In dealing with multiple measurements, associations
are tested while simultaneously accounting for other variables (multivariate analysis). However, when statistical adjustment is applied, linear relations between the variables are
usually assumed, the sample size must be large enough for
the number of variables considered, interactions among variables are often ignored (inevitably, when the number of
independent variables is large), and the strength of associations is influenced by the precision with which variables are
measured (e.g., body weight is more precise than insulin
measurements). As a result, in a complex statistical model
true cause-effect relationships may be lost to imprecision or
overadjustment.
The validity of case-control studies critically depends on
the selection of subjects as well as on the matching of cases
and controls on all factors known to affect the test variable.
Here the disparity between current information on insulin
resistance and insulin secretion becomes critical. Many factors that affect insulin sensitivity have been identified: age
and gender (23), body fat (39) and its distribution (40), physical fitness (41), arterial blood pressure (42), family history of
diabetes (43) or hypertension (44), smoking (45), and presence of ischemic heart disease (46, 47), to name only those
that would be recorded in a clinical chart. In contrast, less is
known about the physiological correlates of the insulin response to glucose (gender, ethnicity, antecedent diet, time of
day, and duration of fast, etc.). Thus, even when cases and
controls are matched on the main confounders of insulin
sensitivity, comparison of b-cell function [as done by Pimenta et al. (48) in first-degree relatives of NIDDM patients]
may still be biased by some unmeasured confounder of insulin secretion that has no detectable effect on insulin action.
In summary, sources such as epidemiological and casecontrol studies are useful tools to identify patterns and disease-related changes but generally have limited power to
establish the etiological primacy of events.
III. Insulin Resistance vs. Insulin Deficiency
A. High-risk conditions and predictors
Bearing in mind the general limitations outlined above, we
can examine the results of some common strategies that have
been adopted to gain insight into the etiology of NIDDM.
One approach is to analyze the known predecessors of the
disease. Impaired glucose tolerance (IGT) is an established
precursor of NIDDM, the conversion rate of one into the
other (estimated at 2–12% per year in different populations)
being roughly 10-fold higher than the incidence of NIDDM
in nondiabetic individuals (49). Progression of IGT to
Vol. 19, No. 4
NIDDM with age is independently predicted by higher fasting plasma glucose and BMI values and by a positive family
history of diabetes. Although the predictive pattern varies
among different populations, progression is generally heralded by further deterioration of glucose-induced insulin
release (49). In case-control studies, IGT subjects have been
found to be insulin resistant like patients with overt NIDDM
(50), independent of obesity (51). Although often hyperinsulinemic in the fasting state or in response to oral glucose,
in some studies IGT subjects have been shown to secrete less
insulin than subjects with normal glucose tolerance when
studied at matched plasma glucose concentrations (52). Likewise, control by glucose of ultradian oscillations of insulin
secretion has been reported to be lost in IGT as is the case in
NIDDM patients (28). Thus, IGT appears to present with the
same mix of insulin resistance and deficiency as overt
NIDDM. More importantly, in prospective studies insulin
resistance and insulin deficiency both predict the development of IGT (53) just as they predict NIDDM (22). Therefore,
by all available evidence, IGT is an early stage of typical
NIDDM, and, like the latter, cannot decide the etiology issue.
Another model of antecedence is provided by subjects
who carry a genetic risk of developing the disease later in life.
In 12 pairs of identical twins discordant for NIDDM, Vaag et
al. (54) detected both insulin resistance and delayed or reduced insulin response to oral or intravenous glucose in the
nondiabetic twins whether they had normal or impaired
glucose tolerance. A number of case-control studies have
reported the presence of insulin resistance in nondiabetic
first-degree relatives of diabetic patients, at a time when their
glucose tolerance is still within normal lipits (e.g., Refs. 55 and
56). Detailed metabolic investigations have traced the defect
in insulin action to the skeletal muscle tissue (56), where
stimulation of both the tyrosine kinase activity of the insulin
receptor and glycogen synthase was found to be reduced (57,
58). In other studies in this high-risk group, however, insulin
resistance was not a prominent feature (59), and impaired
early-phase insulin release on a hyperglycemic clamp (48) or
loss of the normal oscillatory pattern of insulin release (60)
have been described. The available literature is interpreted
by Gerich (61) as offering preponderant evidence in favor of
normal insulin sensitivity in NIDDM relatives. More recently, however, an analysis of the largest published series
of clamp studies (the EGIR study) showed that the subjects
with a positive family history for NIDDM (n 5 235) were
significantly more insulin resistant (by 13% on average) than
subjects without a family history of NIDDM (n 5 564) after
adjusting for gender, age, and body mass (62). Incidentally,
given the high between-subject variability of insulin sensitivity in nondiabetic individuals [coefficient of variation of
30% in the lean and 40% in the obese (23)], a difference such
as that found in the EGIR study could only be detected in a
very large sample.
These discrepancies cannot be decided by simply weighing the negative against the positive findings (61). On the one
hand, the disparity of results confirms that case-control studies can be misleading because of the potential for multiple
errors due to small sample size, selection bias, and variable
accuracy and precision of measurements. On the other hand,
if NIDDM is multifactorial, there is no good reason to expect
August, 1998
that the phenotype of individuals at risk of NIDDM should
be uniform. With regard to this, a recent study (63) reported
that, in lean, nondiabetic offspring of NIDDM parents, insulin sensitivity showed a trimodal distribution, but a clear
deficit in AIR (on the ivGTT) was only detectable in the very
insulin-resistant subgroup. Thus, the population of NIDDM
offspring sampled in that study apparently was a mixture of
subjects who had normal sensitivity and secretion, insulin
resistance but adequate b-cell function, or insulin resistance
and insulin deficiency. Even more interestingly, another
study (64) has shown that the distribution of defects, insulin
deficiency or insulin resistance, in 38 nondiabetic offspring
of one diabetic proband fell into line with that of the proband
cohort: the offspring of diabetics with a low fasting C peptide
level were insulin deficient but had normal insulin sensitivity, whereas the offspring of diabetics with a high fasting C
peptide showed the reverse pattern. Significantly, the prevalence of IGT among this randomly selected sample was the
same irrespective of the prevalent defect, suggesting that
insulin resistance and insulin deficiency can be inherited
separately and can produce equivalent amounts of glucose
intolerance in the progeny.
Follow-up of cohorts at high risk would generate more
compelling data. Thus, if a large number of at-risk subjects
[with familial NIDDM or gestational diabetes (65), or homozygous twins discordant for NIDDM (54)] were accurately phenotyped for insulin action (by a clamp technique)
as well as secretion (by a range of tests), and then followed
up for a long enough time to accumulate incident NIDDM,
comparison of insulin secretion and sensitivity between confirmed prediabetics and matched controls would suggest
which abnormality is the earlier factor precipitating the disease in individuals with a high a priori risk. This approach
was followed by Warram et al. (66) in a large group of nondiabetic offspring of two diabetic parents. On the ivGTT,
both slower glucose removal rates and higher acute and late
insulin responses characterized the offspring of diabetics,
who then went on to develop NIDDM at a rate that was 8
times that of the general population.
Longitudinal observations of random samples of the general population are commonly used to identify predictors of
NIDDM. In prospective studies, the presence of hyperinsu-
481
linemia in nondiabetic individuals has predicted NIDDM
consistently (37, 67). So has, however, the plasma glucose
concentration itself, independently of insulin (67). In other
words, NIDDM has been more frequent among individuals
who at baseline were hyperinsulinemic and had relatively
higher plasma glucose levels than among subjects with the
opposite characteristics. What exactly do such findings tell
about etiology? An example may help clarify this point (Fig.
3). Fasting plasma glucose is a tracking variable: in an individual in whom its value falls into the upper half of the
distribution of the population, fasting glucose tends to remain above the population mean over time despite swinging
in response to environmental stimuli. Plasma insulin concentrations will also tend to be on the high side of the distribution, as tracking has also been described for insulin
levels in both children of school age (68) and adults (69).
Assuming the subject represents a subgroup of nondiabetic
individuals who are found to be hyperglycemic at follow-up,
bivariate analysis of development of diabetes in the population will establish that diabetes was independently predicted by both higher glucose and higher insulin levels. This
statistical association does not tell how higher baseline
insulin levels led to incident diabetes. In fact, insulin levels
may have been elevated because of a high gain for glucoseinduced insulin secretion (primary hypersecretion) or because of underlying insulin resistance (compensatory hypersecretion). Indeed, insulin hypersecretion might have been
the initial abnormality, rapidly followed by down-regulation
of insulin action (69) and exhaustion of a predisposed b-cell.
On the other hand, fasting glucose may have been set at a
higher level by a mechanism separate from insulin (e.g.,
glucagon excess). These (and other) interpretations are all
compatible with the primary data. Thus, even in prospective,
population-based studies, hyperinsulinemia does suggest
potential mechanisms for the development of NIDDM but
falls short of proving primacy in the etiology of the condition.
The study in nondiabetic Pima Indians (22) is the only one
in which insulin sensitivity was directly measured by the
gold standard technique, the insulin clamp. In this population sample, insulin resistance was a strong independent
predictor of NIDDM [confirming previous data on the IGT
segment of this population obtained with the use of fasting
FIG. 3. The phenomenon of cotracking
illustrated as the hypothetical time
course of plasma glucose (top) and insulin concentrations (bottom) of a subject with normal glucose tolerance at
screening, who develops diabetes at follow-up. Shaded areas include 6 2 SD of
the mean value of the population to
which the subject belongs. See text for
explanation.
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FERRANNINI
plasma insulin concentrations (70)]. In the Pima studies, a
lower insulin secretory response to oral (70) or intravenous
glucose (22) was also a precursor of NIDDM. When insulin
action was directly measured, secretory dysfunction predicted half as many incident cases as did insulin resistance
(22). The Pima paradigm, established in a very obese population at high risk for NIDDM, awaits to be confirmed by
clamp studies in large samples of populations with a different ethnic background.
In synthesis, available evidence from high-risk groups and
prospective population studies demonstrates that both insulin resistance and its surrogate, fasting hyperinsulinemia,
are associated with the development of NIDDM. The corresponding data on insulin secretory dysfunction are both less
consistent and of inferior quality than those on insulin resistance. Nevertheless, the evidence is not fully sufficient to
defend the primacy of either insulin resistance or insulin
deficiency in the sense of 1) coming first in time, 2) having
stronger genetic determination, and 3) being quantitatively
more important.
B. The problem by mechanism
Ultimately, it may prove impossible to dissociate insulin
resistance from insulin deficiency precisely because changes
in insulin secretion and action are coordinated by strong
physiological interactions (71). How likely is this possibility?
Information about the relationship between insulin secretion and insulin action is surprisingly meager. Insulin output
in humans increases as a sigmoidal function of circulating
glucose concentrations. This relationship has been interpreted as a dependence on the actual level of plasma glucose
as well as its rate of change (27). Thus, a quick rise in glycemia
stimulates, and a sudden drop inhibits, insulin secretion
independently of the initial and final levels of glucose. Furthermore, glucose potentiation and inhibition (72), neural
influences (73), gastrointestinal potentiation (74), spontaneous oscillatory cycles (29), and glucose toxicity to the b-cell
(75) are all phenomena that have been characterized in isolation. However, they have not been integrated into a comprehensive model of b-cell response in vivo. On the other side
of the feedback, the in vivo dose-response relationship between insulin concentrations and glucose fluxes also is curvilinear (76), compatible with saturation kinetics. It is therefore expected that the relationship between insulin secretion
and insulin action on glucose levels be a highly nonlinear
one. In fact, in a large group of lean and obese subjects with
normal glucose tolerance (77), declining insulin sensitivity
(as assessed by the clamp technique) was associated with a
hyperbolic increase in basal posthepatic insulin delivery rate
(Fig. 4). Assuming that fractional hepatic insulin extraction
is 50%, and that 24-h insulin release is twice the fasting rate
of insulin output (31), it can be calculated (from the extremes
of the distribution shown in Fig. 4) that normal glucose
tolerance can be achieved with as little as 10, but may require
as much as 400, U of insulin per day. This huge range reflects
the variability of individual setpoints (i.e., the product of
insulin secretion and insulin sensitivity) as well as the impact
of obesity. Indeed, when body mass (BMI) is factored in, the
relationship between insulin sensitivity and secretion can be
Vol. 19, No. 4
FIG. 4. Plot of insulin sensitivity (as measured by a euglycemic insulin clamp) against insulin delivery rate (5 posthepatic insulin delivery rate, estimated as the product of fasting plasma insulin concentration by plasma insulin clearance rate calculated during
euglycemic insulin infusion). The solid line represents the best linear
fit of the data in a log-log plot (or hyperbolic function, r 5 0.44, P ,
0.0001, recalculated from Ref. 77).
FIG. 5. The data in Fig. 5 are plotted here as a simultaneous function
of BMI (multiple r 5 0.59; P , 0.0001).
plotted as a simultaneous function of degree of obesity. It can
be seen (Fig. 5) that at each level of insulin sensitivity, obesity
strongly potentiates insulin release, and that the same decline
in insulin sensitivity is associated with a larger increase in
insulin secretion in obese than in lean subjects. Thus, insulin
resistance and obesity each augment the functional demand
on the pancreas in a nonlinear manner.
How long can the b-cell resist stress before decompensation? This is tantamount to asking how long an obese, insulin-resistant individual can remain free of diabetic hyperglycemia. Although hard data are not available, the
prevailing view holds that the great majority of obese subjects do not get NIDDM. However, calculations based on
published data can be used to challenge this view. In a large
prospective study of risk factors for NIDDM in middle-aged
British men (78), obesity was the single most powerful predictor, with a 12-fold increase in relative risk between the
lowest and highest BMI fifth (,22.9 vs. $27.9 kgzm22). These
August, 1998
findings are typical of studies investigating the impact of
obesity on the development of NIDDM (e.g., Ref. 79). However, in a very obese population like the Pima Indians, obesity was not an independent precursor of NIDDM when
plasma glucose and plasma insulin levels (5 insulin resistance) were used to predict the disease (70). Thus, it can be
argued that only the insulin-resistant segment of an obese
population is at increased risk for NIDDM. In the data used
for Fig. 5, only 52% of the obese subjects (defined as those
with a BMI $ 28 kgzm22) were insulin resistant (i.e., they had
an insulin sensitivity value below the 10th percentile of the
lean group). Since the incidence of NIDDM in the British
cohort was 0.47% per year among the obese (BMI $ 28
kgzm22) as compared with 0.14% per year in the lean, it can
be calculated that 40 yr of obesity would convert 32% of the
insulin-resistant obese phenotype into NIDDM. In line with
this prediction, in a cross-sectional, hospital-based analysis
of the impact of obesity duration on NIDDM, the majority of
the subjects who had been obese for longer than 40 yr were
found to be diabetic (80). Thus, exhaustion of the b-cell secretory capacity under the joint pressure of obesity and insulin resistance can be presumed from the quantitative relationships in Fig. 5 and is compatible with the epidemiology
of NIDDM and obesity. Stress failure of insulin secretion
must be a more frequent event in the natural history of
glucose intolerance than previously thought.
A further way in which insulin resistance can impair b-cell
function is glucose toxicity (11, 12). Insulin resistance is compensated for by hyperinsulinemia through small increases in
plasma glucose, which signal to the b-cell the increased cellular need for the hormone. The rise in plasma glucose may
be small (e.g., in the case of weight gain), but in the long run
it may nevertheless exert a negative effect on insulin-secretory function. Thus, although we ignore the exact relationship between exposure (hyperglycemia 3 time) and b-cell
damage in humans, it is conceivable that obesity strains the
endocrine pancreas at the same time as it slowly intoxicates
it over many years.
There is another mode of cross-talk between insulin secretion and its action, for which the term lipotoxicity has been
coined. By inhibiting triglyceride hydrolysis, insulin restrains delivery of oxidizable fatty substrates (FFA) to target
tissues, thereby favoring carbohydrate oxidation in a substrate competition cycle (Randle’s cycle). Resistance of lipolysis to insulin inhibition impedes glucose metabolism by
curtailing both glucose oxidation and glucose storage in glycogen as well as glucose transport and phosphorylation in
skeletal muscle (81, 82). FFA levels have long been known to
be elevated in both obesity and NIDDM (83, 84), and raised
fasting and postglucose FFA concentrations have recently
been associated specifically with the presence of insulin resistance irrespective of body adiposity (85). In addition to the
circulating FFA pool, triglyceride content in skeletal muscle
(86, 87) and the fatty acid composition of skeletal muscle
phospholipids (88) have been found to correlate with insulin
sensitivity in vivo. Intramuscular triglyceride stores are increased in muscle tissue of patients with IGT (89). Circulating
FFAs sustain hepatic glucose production through stimulation of gluconeogenesis (90); under certain circumstances,
high FFA levels can exacerbate glucose overproduction in
483
NIDDM (91). The epidemiological counterpart of these physiological connections is the evidence from prospective studies that FFAs predict NIDDM independently of insulin sensitivity (92, 93).
On the other side of the loop, FFA can inhibit insulin
secretion. Thus, raising FFA for 48 h (through the infusion of
a triglyceride emulsion) impairs the b-cell response to glucose in rats (94), and long-term incubation of rat islets with
fatty acids depresses their ability to release and synthesize
insulin in response to glucose stimulation (95). Recent studies
in healthy volunteers have shown that, while experimental
increases in FFA enhance the acute insulin response to intravenous glucose in the short term (6 h), longer (24-h) exposure to raised FFA reduces AIR by almost 50% (96).
In sum, analysis of the etiology problem by mechanism
does not solve the ambivalence. Rather, it suggests that insulin resistance and deficiency are so tightly connected
through multiple mechanisms that the a priori likelihood that
they may segregate in distinct phenotypes even in the early
stages of NIDDM (i.e., IGT, high-risk groups) should not be
high. Obesity, a prevalent and powerful factor, further reduces insulin action and strains the b-cell, thereby confounding the relative contribution of insulin resistance and insulin
deficiency to the genesis of hyperglycemia.
C. The genetic approach
Genetic analysis and genetic epidemiology are increasingly popular strategies to establish etiology. Segregation
analysis can link NIDDM with anonymous polymorphic loci
in the genome (97); positional cloning can then be applied to
map out relevant genes (98). With the availability of a large
number of polymorphic markers and an improved sampling
design [based on affected sibling pairs (99)], genome-wide
search of disease loci has become possible (e.g., Ref. 100).
Quantitative traits rather than clinical outcome can be used
for genetic analysis (101). For example, in more than 200
family members of patients with NIDDM (102), fasting insulin levels segregated as an autosomal recessive allele, with
a frequency of 0.25. In Mexican-American families, segregation of postglucose insulin levels was best described by a
single dominant locus, with residual polygenic and/or environmental effects (103). Clearly, these estimates depend on
the number and quality of pedigrees suitable for analysis and
ultimately represent only the best statistical fit of the data
from a range of compatible models. It should also be noted
that, when used as a surrogate for insulin sensitivity, the
fasting insulin concentration behaves better (in terms of
strength of association with other variables) than stimulated
insulin levels. However, in the RIA, fasting insulin is measured with less precision than stimulated insulin. Furthermore, proinsulin and split proinsulins make up a greater
percentage of fasting than stimulated insulin concentrations
(104). These propeptides may carry their own message as
markers for NIDDM, as recently suggested (105). Despite
these limitations, it appears indisputable that the fasting
insulin concentration, although subject to modulation by
many external factors, is a moderately inheritable trait. Insulin sensitivity, as measured by direct methods, shows a
tri-modal distribution in Pima Indians (106) and a skewed
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FERRANNINI
distribution in nondiabetic Europeans (77); it clusters in the
family in Pima Indians (107) and Caucasians (35). Comparatively less is known on the distribution, familial aggregation, and heritability of indices of insulin secretion (e.g., Ref.
108).
Direct genetic analysis can identify mutations in genes
encoding the various functions involved in insulin secretion
[insulin, glucagon, glucokinase, glucose transporter 2
(GLUT2), sulfonylurea receptor, etc.] (e.g., Ref. 109) or action
[insulin receptor, glucose transporter 4 (GLUT4), insulin receptor substrate-1, hexokinase II, fatty acid-binding protein,
etc.] (e.g., Ref. 110). Overall, genetic analysis has been successful in the monogenic forms of diabetes, including MODY
(111), isolated mutations in the insulin gene (112), the insulin
receptor gene (110), and in the mitochondrial genome (113).
Typical late-onset NIDDM, however, is heterogeneous both
genetically as well as phenotypically. As a result, genetic
analysis has made limited progress: so far, responsible genes
have been identified in only a very small fraction (;5%) of
adult diabetes (114).
Whether NIDDM will eventually be shown to be truly
polygenic (numerous genes with small effects) or whether a
small number of major genes (diabetogenes) will account for
the majority of cases is unknown at present. Several problems
limit success in genetic analysis (97–100). Among them, diabetogenes and susceptibility genes may vary among different ethnic groups just as much as does the prevalence of
NIDDM [e.g., ;50% in Pima Indians vs. ,2% in mainland
China (115)]. Thus, though ethnic differences may be useful
to map genes for their relevance to the disease, results obtained in any one population may not be generalizable.
Moreover, the search for genes may be confounded by factors
acting during early intrauterine life, as implied by the work
of Hales and Barker (116). Such transmissible influences,
marked by low birth weight, need to be distinguished from
true heredity. Third, environmental factors (e.g., hyperglycemia, diet) may induce susceptibility genes and do so differentially according to ethnicity and life-style. Finally, even
when a major NIDDM gene has been associated with the
disease, its pathogenic role must be proved by identifying the
gene product, measuring it, and quantitatively relating its
malfunction to the insulin resistance or insulin deficiency
observed in vivo.
In sum, the genetic approach has the best a priori chances
of solving the etiology problem. The answer, however, may
be a long way off and may eventually turn out to be complex
and particular rather than simple and general.
IV. Summary
A definitive assessment of the relative roles of insulin
resistance and insulin deficiency in the etiology of NIDDM
is hampered by several problems. 1) Due to better methodology, data on insulin resistance are generally more accurate
and consistent than data on insulin deficiency. 2) In source
data, case-control studies are prone to selection bias, while
epidemiological associations, whether cross-sectional or longitudinal, are liable to misinterpretation. 3) Insulin secretion
and action are physiologically interconnected at multiple
Vol. 19, No. 4
levels, so that an initial defect in either is likely to lead with
time to a deficit in the companion function. The fact that both
insulin resistance and impaired insulin release have been
found to precede and predict NIDDM in prospective studies
may be in part a reflection of just such relatedness. 4) Direct
genetic analysis is effective in rarer forms of glucose intolerance (MODY, mitochondrial mutations, etc.) but encounters serious difficulties with typical late-onset NIDDM.
Despite these uncertainties, the weight of current evidence
supports the view that insulin resistance is very important in
the etiology of typical NIDDM for the following reasons: 1)
it is found in the majority of patients with the manifest
disease; 2) it is only partially reversible by any form of treatment (117); 3) it can be traced back through earlier stages of
IGT and high-risk conditions; and 4) it predicts subsequent
development of the disease with remarkable consistency in
both prediabetic and normoglycemic states. Of conceptual
importance is also the fact that the key cellular mechanisms
of skeletal muscle insulin resistance (defective stimulation of
glucose transport, phosphorylation, and storage into glycogen) have been confirmed in NIDDM subjects by a variety of
in vivo techniques [ranging from catheter balance (118) to
multiple tracer kinetics (119) to 13C nuclear magnetic resonance spectroscopy (120)], and have been detected also in
normoglycemic NIDDM offspring (121).
If insulin resistance is a characteristic finding in many
cases of NIDDM, insulin-sensitive NIDDM does exist. On the
other hand, given the tight homeostatic control of plasma
glucose levels in humans, b-cell dysfunction, relative or absolute, is a sine qua non for the development of diabetes. If
insulin deficiency must be present whereas insulin resistance
may be present, is this proof that the former is etiologically
primary to the latter? If so, do we have convincing evidence
that the primacy of insulin deficiency is genetic in nature?
The answer to both questions is negative on several accounts.
The defect in insulin secretion in overt NIDDM is functionally severe but anatomically modest: b-cell mass is reduced
by 20 – 40% in patients with long-standing NIDDM (122).
Moreover, the insulin secretory deficit is progressively worse
with more severe hyperglycemia (123) and recovers considerably upon improving glycemic control (124). These observations indicate that part of the insulin deficiency is acquired
(through glucose toxicity, lipotoxicity, or both). In addition,
although insulin deficiency is necessary for diabetes, it may
not always be sufficient to cause NIDDM. In fact, subtle
defects in the b-cell response to glucose may be widespread
in the population (108, 125) and only cause frank hyperglycemia when obesity/insulin resistance stress the secretory
machinery. Conceivably, there could be b-cell dysfunction
without NIDDM just as there is insulin resistance without
diabetes. Incidentally, any defect in insulin secretion,
whether in normoglycemic or hyperglycemic persons, could
be due to other factors than primary b-cell dysfunction: amyloid deposits in the pancreas (126), changes in insulin secretagogues (amylin, GLP-1, GIP, galanin) (127–130), early intrauterine malnutrition (131). Finally, the predictive power of
early changes in insulin secretion for the development of
typical NIDDM is generally lower than that of insulin resistance. Thus, the balance of current evidence indicates that,
while b-cell dysfunction is the precipitating factor in the
August, 1998
emergence of hyperglycemia, its molecular mechanisms
(132) and genetic basis remain more elusive than those of
insulin resistance.
It should be emphasized that, even if the NIDDM disease
loci in the genome should eventually prove to be numerous
and, possibly, disparate, NIDDM can always be reduced to
a bifactorial problem, i.e., one of insulin resistance and insulin deficiency (Fig. 6). As long-term human studies indicate
that insulin resistance and insulin deficiency can be traced far
back in the natural history of the disease, NIDDM can be
thought to evolve under the joint drive of these two etiological factors. Because of the multiple physiological connections, in general, insulin deficiency and resistance will
co-vary. However, variability of the two functions at each
stage and overlap between successive stages will be large on
account of differences in genetic pressure and environmental
influences. MODY and LADA, falling off the line to the left,
may exemplify monogenic (or oligogenic) hyperglycemia;
obesity will curve the relationship to the right.
V. Insulin Resistance: The Cluster Concept
Insulin resistance clusters with a number of abnormalities
not directly related to hyperglycemia. Thus, raised serum
triglycerides and lower high-density lipoprotein cholesterol
(but not low-density lipoprotein cholesterol) concentrations,
higher uric acid and plasminogen activator inhibitor (PAI)-1
levels, high blood pressure, and microalbuminuria are significantly more represented among insulin-resistant, hyperinsulinemic individuals than among insulin-sensitive subjects (133) irrespective of obesity (134). The cluster has been
termed syndrome X (3). The nature of the associations in the
cluster has not been fully elucidated. For some of them (e.g.,
serum lipids and uric acid), the underlying physiology is
sufficiently clear; for others (e.g., raised blood pressure or
microalbuminuria), identification of mechanisms is still tentative (3, 133). For some, insulin resistance appears to be the
mechanism (e.g., intracellular calcium metabolism); for oth-
FIG. 6. Scheme illustrating the progressive involvement of insulin
resistance and insulin deficiency in the natural history of typical
NIDDM. The square for each stage roughly encompasses the range of
severity of each defect. Note the substantial overlap among stages
along both axes and the outlying positions of MODY and LADA.
During near-complete acute insulin deficiency, i.e., ketosis, insulin
resistance is extreme (160).
485
ers, it is the compensatory hyperinsulinemia that appears to
be responsible (e.g., renal sodium retention) (133). Regardless
of their origin, these correlates of insulin resistance have been
described not only in NIDDM but also in IGT, IDDM, essential hypertension, and certain dyslipidemias (3). In the
general population, these abnormalities have been shown to
co-track with plasma insulin concentrations, so that plasma
insulin behaves as a multiple predictor of NIDDM, hypertension, and dyslipidemia (135). A primacy of insulin (levels
or resistance) within the cluster (3) may be difficult to prove
by epidemiological techniques alone. Nevertheless, the negative added value of this cluster lies in the observation that
each of its components is a risk factor for atherosclerotic
cardiovascular disease (ACVD), the main cause of death in
NIDDM (136). Hypertriglyceridemia, low HDL-cholesterol
levels, hypertension, hyperuricemia, microalbuminuria, and
reduced fibrinolysis have each been associated with an increased prevalence and incidence of ACVD in both diabetic
and nondiabetic populations (136). In some studies, plasma
insulin itself (137–140) or insulin resistance (141) have been
shown to be an independent risk factor for ACVD, although
this concept still raises controversy (142–144). Whether insulin resistance actually causes, or simply marks, the presence of these abnormalities, it adds a major tax to the overall
burden of NIDDM.
VI. Prospects
At the same time as the human genome is searched for loci
associated with diabetes, clinical investigation can improve
our understanding of the etiology of NIDDM in several
ways.
It is increasingly clear that accurate phenotyping of patients and at-risk groups is key to the genetic analysis of
heterogeneous disorders. Different forms of diabetes can
cluster in the same family, thereby confounding the pattern
of transmission of hyperglycemia. Extensive testing for a
panel of autoantibodies and HLA typing will 1) elucidate the
basis for the cosegregation of IDDM and NIDDM (145), 2)
distinguish autoimmune (LADA or IDDM) from metabolic
diabetes, and 3) improve the prediction of the clinical course
of the disease (146).
In vivo techniques can be refined so as to obtain a reliable
estimate of both insulin sensitivity and secretion from a single test. The minimal model analysis of the ivGTT and the
hyperglycemic clamp (19) are attempts in this direction, but
recent developments in non-steady-state modeling (147),
coupled with the use of stable isotopes (148), are likely to lead
to improved tests to measure insulin release and action simultaneously (i.e., the slope of the relationship between secretion and biological effects of insulin as an integrated index
of insulin control of the glucose system). The application of
refined measurements in field studies will increase information on the distribution and modulation of both traits, insulin
sensitivity and secretion. The natural history of both functions in normal subjects progressing to hyperglycemia will be
described in more detail.
Obesity and fat distribution are likely to be investigated in
more depth. Although the role of obesity in amplifying the
486
FERRANNINI
risk of NIDDM has been known for a long time, intriguing
circumstances have recently emerged. Thus, the close association of NIDDM with obesity may be due not only to
insulin resistance but also to linkage/interactions between
the respective genetic background of the two conditions
(149). The insulin hypersecretion of obesity (77), particularly
in some ethnic groups (150), may turn out to be the primary
etiological factor in a subset of NIDDM patients in whom
insulin resistance and b-cell exhaustion both evolve as longterm consequences of paroxysmal stimulation of insulin release (Fig. 1). In the central nervous system, both feeding and
stress signals originate: this is also a candidate site for the link
between insulin hypersecretion and fat distribution (77, 151),
eventually leading to excess cardiovascular disease in subjects with android obesity (152). With regard to this, it is
interesting that in Japanese-American men, impaired insulin
release precedes visceral adiposity in the natural course of
NIDDM (153), a sequence that reverses the paradigm of body
fat distribution as a determinant of insulin action and secretion (154).
Finally, and perhaps most importantly, more information
will be forthcoming on the relative role of insulin deficiency
and insulin resistance in the appearance of diabetic complications. Here, the bifactorial version of the NIDDM etiology
problem becomes drastically insufficient, as it is clear that
additional genetic and environmental influences drive the
natural history of complications. By way of example, ACVD
mortality among diabetic patients varies greatly between
countries (155); microvascular and macrovascular complication cross-predict one another (156); microalbuminuria segregates with insulin resistance (17) and predicts mortality in
NIDDM (157); in Chinese-Americans both the insulin receptor and apolipoprotein gene contribute to the emergence of
NIDDM (158); familial hypertension and glycemic control
are major predictors of diabetic nephropathy (159). In this
area, insulin resistance is likely to play a greater part than
insulin deficiency due to its many links and its putative role
as a direct antecedent of ACVD (3, 133). After all, insulin
resistance is a larger problem than hyperglycemia in the
population; for complexity and implications, it will therefore
remain a threat to the patient, a concern to the clinician, and
a challenge to the investigator.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
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Insulin Resistance versus Insulin Deficiency in Non

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