Do we know what homeostasis model assessment measures? If not, does it matter?

The point-counterpoint articles in the September issue of Diabetes Care (1,2) raise several interesting issues on our understanding of insulin resistance, homeostasis model assessment (HOMA), and the future of measurement shortcuts for insulin resistance and secretion. McAuley et al. (1) provide an overview of methods to assess insulin sensitivity and secretion. The conditional nature of the title of their article implies that there is room for improvement and that something better will come along to assess insulin resistance and secretion with the convenience of HOMA but with better accuracy. For the time being, however, they support its use. Hockaday et al. (2) bring up many points that undermine confidence in HOMA as a measure of insulin resistance. Input into HOMA consists of fasting insulin and glucose concentration and thus will reflect conditions present in the basal state, with the liver as the main target for insulin action as manifested by the suppression of gluconeogenesis. A shortcoming of HOMA is the lack of complete capture of brain glucose uptake, of which 50% is non–insulin mediated. Although HOMA has been compared against the euglycemic hyperinsulinemic clamp, the current gold standard for assessment of insulin sensitivity, the latter method assesses insulin resistance in the stimulated state, which is a function to a large extent of muscle glucose disposal. Thus, an implicit assumption of HOMA is that steadystate and stimulated insulin resistance are highly correlated. Other concerns include test variability over time and the assumption that insulin resistance, if present, is common to major sites of insulin action (liver, muscle, and adipose tissue). Similarly, a number of concerns are raised about the use of HOMA to assess insulin secretion. Of particular concern is whether -cell glycemic sensitivity can be assumed constant. In addition, other factors bear on insulin secretion that are not directly related to -cell mass, glycemia, or glycemic sensitivity, for example, some amino acids, nonesterified fatty acids, cortisol, and growth hormone. Despite these concerns, Hockaday et al. accept some of the conclusions from research using HOMA and therefore at least indirectly seem to be supportive of its use for certain applications. Both articles appear accepting of HOMA use in epidemiologic research. Though Hockaday et al. do not state this directly, we nevertheless infer it from their acceptance of its use in research on “discovering the pathogenesis of type 2 diabetes.” An important issue to consider is whether it matters if we have a comprehensive understanding of what HOMA measures. Research shows that HOMA and other shortcut measures of insulin resistance and secretion can provide useful information on risk of developing diabetes and related conditions. A recent report from the Womens’ Health Initiative (WHI) observational cohort—a nested, case-control study conducted within the larger cohort of 82,069 WHI women without diabetes at baseline, followed for an average of 5 years—found that the relative risks per increment SD increase in HOMA of insulin resistance (HOMA-IR) and HOMA of insulin secretion (HOMA%B) were 3.40 and 0.57, respectively (3). Assuming for the moment that HOMA-IR and HOMA-%B really do measure insulin resistance and insulin secretion, this finding confirms that greater insulin resistance predisposes to type 2 diabetes, while better insulin secretion is protective for the development of this condition among WHI women aged 50–79 years at baseline. Similar results were reported by another study that used HOMA to predict diabetes risk (4). These same findings were also reported in a prospective study among Pima Indians that used gold standard measurements of insulin resistance (the euglycemic hyperinsulinemic clamp) and insulin secretion (acute insulin response to glucose) (5). In fact, simple measures of insulin sensitivity and secretion were later shown to be highly predictive of diabetes occurrence in the Pima population (6). HOMA has also been used effectively in nonepidemiologic studies. For example, Meyer et al. (7) used HOMA to identify differences in the mechanisms for the development of impaired fasting glucose and impaired glucose tolerance, while the UK Prospective Diabetes Study reported changes over time in HOMA measures of insulin sensitivity and secretion with different treatments (8). If HOMA represented random noise, it would not be consistently associated with disease states in a manner that to some extent can be predicted with other biologic information. Although admittedly noisy (having some degree of error), the signal (insulin sensitivity or secretion) is still detectable in some research studies. Holding HOMA to an extremely high standard of accuracy would not be consistent with other widely used surrogate measures in diabetes research and clinical care, such as BMI for overall adiposity, waist circumference as a measure of visceral fat, or race/ethnicity as a marker for ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●