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Insulin Resistance versus Insulin Deficiency in Non-Insulin-Dependent Diabetes Mellitus: Problems and Prospects

C.N.R. Institute of Clinical Physiology and Department of Internal Medicine, University of Pisa, 56126 Pisa, Italy

	Abstract

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

 

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

	I. Introduction

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

 
Insulin 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 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.

View larger version (15K): [in this window] [in a new window]   Figure 1. Dual feedback connection between plasma glucose ([glucose]) and insulin ([insulin]) concentrations in vivo. (+), Stimulation; (-), inhibition.

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 and those responsible for insulin action) could theoretically be the primary event in NIDDM. When either mutation is functionally expressed in the phenotype as ß-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

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

 
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, 2, 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 kg·m^-2, 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 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, insulin-sensitive 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 ß-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 ß-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 x time), both at one level only. Full dose-response curves for in vivo insulin-mediated 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) 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, 23, 24, 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 ß-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 ß-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 ß-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, 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 ß-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 ß-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, 37, 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): 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.

View larger version (27K): [in this window] [in a new window]   Figure 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 ± 1 SD of mean values; the solid line is the linear fit of the data (recalculated from Ref. 77).

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 ß-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 case-control 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

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

 
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 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, 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 = 235) were significantly more insulin resistant (by 13% on average) than subjects without a family history of NIDDM (n = 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 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 ß-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 hyperinsulinemia 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 glucose-induced 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 ß-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.

View larger version (32K): [in this window] [in a new window]   Figure 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 ± 2 SD of the mean value of the population to which the subject belongs. See text for explanation.

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 ß-cell (75) are all phenomena that have been characterized in isolation. However, they have not been integrated into a comprehensive model of ß-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 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.

View larger version (21K): [in this window] [in a new window]   Figure 4. Plot of insulin sensitivity (as measured by a euglycemic insulin clamp) against insulin delivery rate (= 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 = 0.44, P < 0.0001, recalculated from Ref. 77).

View larger version (19K): [in this window] [in a new window]   Figure 5. The data in Fig. 5 are plotted here as a simultaneous function of BMI (multiple r = 0.59; P < 0.0001).

A further way in which insulin resistance can impair ß-cell function is glucose toxicity (11, 12). Insulin resistance is compensated for by hyperinsulinemia through small increases in plasma glucose, which signal to the ß-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 x time) and ß-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 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 ß-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 ß-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 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, 98, 99, 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

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

 
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 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, ß-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: ß-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 ß-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 ß-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 ß-cell dysfunction: amyloid deposits in the pancreas (126), changes in insulin secretagogues (amylin, GLP-1, GIP, galanin) (127, 128, 129, 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 ß-cell dysfunction is the precipitating factor in the 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.

View larger version (54K): [in this window] [in a new window]   Figure 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 ).

	V. Insulin Resistance: The Cluster Concept

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

	VI. Prospects

Top Abstract I. Introduction II. Preliminary Issues III. Insulin Resistance vs.... IV. Summary V. Insulin Resistance: The... VI. Prospects References

 
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 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 ß-cell exhaustion both evolve as long-term 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.

	Footnotes

 
Address reprint requests to: Ele Ferrannini, M.D., CNR Institute of Clinical Physiology, Via Savi, 8, 56126 Pisa, Italy.

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