Insulin Resistance Precedes Weight Loss in Adults without Diabetes

The Rancho Bernardo Study

Nicole M. Wedick1, Elizabeth J. Mayer-Davis2, Deborah L. Wingard1, Cheryl L. Addy2 and Elizabeth Barrett-Connor1

1 Department of Family and Preventive Medicine, School of Medicine, University of California, San Diego, La Jolla, CA.
2 Department of Epidemiology and Biostatistics, School of Public Health, University of South Carolina, Columbia, SC.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Insulin resistance is closely associated with both aging and overweight; yet in old age, weight loss is common, although insulin resistance increases. To study this paradox, the authors evaluated the role of insulin resistance in weight change among older adults from the Rancho Bernardo Study cohort. Participants were 725 nondiabetic men and women who were aged 50–89 years when weight and insulin were measured at baseline (1984–1987). The participants were evaluated again in 1992–1996, at which time weight was remeasured. Fasting insulin and homeostasis model assessment (HOMA) measurements were evaluated in separate but parallel statistical models as surrogates for insulin resistance. Insulin resistance, when defined as the top quartile of fasting insulin level or HOMA value, was significantly associated with weight loss before and after adjustment for baseline weight and age (fasting insulin: ß = -1.30 kg, p = 0.01; HOMA: ß = -1.18 kg, p = 0.01). Results were the same for men versus women, for the overweight (body mass index (weight (kg)/height (m)2) <=26.6) versus the normal weight (body mass index >26.6), and for younger persons (age <70 years) versus older persons (age >=70 years). Insulin-resistant individuals had a threefold increased likelihood of losing 10 or more kg compared with those without insulin resistance. The authors conclude that hyperinsulinemia, independently of age and baseline weight, may have a catabolic effect in the elderly.

aging; body weight changes; cardiovascular diseases; diabetes mellitus; insulin resistance; obesity

Abbreviations: ARIC, Atherosclerosis Risk In Communities; HOMA, homeostasis model assessment


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Cardiovascular disease remains the leading cause of death in the United States and is associated with significant morbidity, currently affecting more than 58 million people (1Go). Risk factors for cardiovascular disease are continually being elucidated, among them the role of insulin resistance. Studies of the association between insulin levels and cardiovascular disease have yielded conflicting results (2Go, 3Go), and the high correlation between insulin resistance and diabetes mellitus, dyslipidemia, hypertension, and obesity makes it difficult to disentangle the independent effect of insulin concentration or insulin resistance (4Go, 5Go). At least 60 percent of people with type II diabetes are obese (6Go). Data from the United States show that individuals with a body mass index (weight (kg)/height (m)2) >=27 have at least a 70 percent chance of having a comorbid condition related to obesity (7Go).

The question of the role of insulin resistance in the development of obesity is unresolved. An association between insulin resistance and weight loss has been found in some populations (8GoGoGoGo–12Go), while insulin resistance appears to predict increased weight gain in other populations (8Go, 9Go, 13Go, 14Go). Most studies have examined this relation in young or middle-aged individuals, which may partly explain the inconsistency in findings. The primary focus of this study was to evaluate the association of insulin resistance with weight change in older individuals.

In this paper, we report findings from a prospective investigation of the association between insulin resistance and subsequent weight change among older, middle-class Whites residing in Rancho Bernardo, California. Because the likelihood of being in positive (weight gain) or negative (weight loss) energy balance may have important implications for understanding the role of insulin resistance, in our analyses we considered weight change as a complex phenomenon with categories reflecting positive, negative, or stable energy balance.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Overview of study design
Persons who were eligible for this study were older adult participants in the Rancho Bernardo Heart and Chronic Disease Study, a population-based cohort study begun in 1972 (15Go). From January 1984 to February 1987, surviving participants from the original cohort were recruited into the Epidemiology of Diabetes Study (hereafter called Diabetes I); 85 percent of men and 78 percent of women participated (16Go). A follow-up study took place approximately 8 years later between May 1992 and December 1996 (hereafter called Diabetes II); approximately 63 percent of Diabetes I participants who were alive, ambulatory, and living locally participated in Diabetes II.

Measurements
At Diabetes I and II, weight and height were measured with participants wearing light clothing and no shoes. Waist circumference was measured in centimeters at the participant's bending point, and hip measurements were taken at the iliac crest (17Go). Body mass index was calculated as weight (kg) divided by height squared (m2). Weight change was determined by subtracting the participant's weight (kg) at baseline from weight at the follow-up visit.

At Diabetes I, participants completed standardized questionnaires on current medication use, cigarette smoking, alcohol consumption, physical activity, and medical history (18Go). All prescriptions and/or pills taken were brought to the clinic for confirmation of medication use. A 75-g oral glucose tolerance test was administered in the morning after an 8-hour minimum fast (mean = 13.5 hours). Fasting and 2-hour postchallenge glucose levels were measured by the glucose oxidase method. Fasting and 2-hour insulin levels were measured by double-antibody radioimmunoassay (19Go).

Insulin resistance was estimated in two ways. Fasting insulin level is a surrogate measure for insulin resistance, with increasing fasting insulin levels indicating increased insulin resistance; fasting insulin levels have been shown to be highly correlated with hyperinsulinemic euglycemic clamp results (r = -0.65, p < 0.01) in persons without diabetes (20Go). Homeostasis model assessment (HOMA) is also a good estimate of insulin resistance (R) (14Go, 21GoGo–23Go). The formula for HOMA (21Go) is R = insulin (mU/liter)/22.5e-ln glucose (mmol/liter) and is simplified as R = (insulin x glucose)/22.5. HOMA has been shown to be correlated with other measures of insulin resistance (euglycemic clamp: r = 0.88, p < 0.0001; fasting insulin concentration: r = 0.81, p < 0.0001) (21Go). HOMA and fasting insulin measurements were highly correlated in the present study (r = 0.98), similar to the correlation between HOMA and fasting insulin in other studies (e.g., see Haffner et al. (24Go) (r = 0.98)).

All participants who had type I or type II diabetes at baseline according to the criteria of the American Diabetes Association (25Go) were excluded (n = 95) from this analysis, because fasting insulin is a less valid marker for insulin resistance in persons who have diabetes (20Go). Standardized insulin assays were not available for the first year of the study. This left 308 men and 417 women aged 50 years or older without diabetes who had insulin assay results and follow-up weights for the present analyses.

Analysis
The Statistical Analysis System was used for all analyses (26Go). Mean values were calculated for continuous variables, and frequency distributions were calculated for categorical variables. Quartiles of fasting insulin level were used to determine whether the relation between exposure and outcome was monotonic. The overall F statistic from general linear model procedures was used to assess whether mean values for baseline variables differed significantly from each other across insulin quartiles. Tukey multiple comparison procedures and Student-Newman-Keuls contrasts were performed to test a priori hypotheses. The chi-squared statistic was used to assess whether values for categorical variables differed significantly across insulin quartiles. Statistical significance was defined as p < 0.05.

Multiple regression analyses were used to determine the association between baseline fasting insulin level and weight change between Diabetes I and Diabetes II. All analyses were repeated using the HOMA variable as the primary exposure. Consistent with the literature, the models were adjusted for baseline weight and age at Diabetes I. Physical activity, smoking, alcohol consumption, and use of antihypertensive medication were evaluated as potential confounders by analysis of covariance. Variables identified as confounders were those associated with both the exposure, fasting insulin, and the outcome, weight change. In addition, overweight status (defined as being in the upper quartile of body mass index (>26.6 for this population)) and older age (dichotomized a priori as >=70 years) were evaluated for potential effect modification, using interaction terms in the regression model ({alpha} = 0.10).

All analyses were initially stratified by gender to evaluate potential effect modification. Sex-specific associations were similar, and the result of a statistical test for interaction between gender and fasting insulin level was not significant (p = 0.41). Therefore, all data are presented for men and women combined.

Finally, polytomous logistic regression was used to evaluate the risk for mutually exclusive categories of weight change ("gained 2.5 kg or more," "stable," "lost 2.5 kg or more," "lost between 2.5 kg and 5.0 kg," "lost between 5.0 kg and 10.0 kg," and "lost 10.0 kg or more") according to insulin resistance status. In this analysis, insulin resistance was defined as the top quartile of fasting insulin level or HOMA. Statistical significance was determined by the 95 percent confidence intervals around the risk estimate.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Baseline values for metabolic variables according to quartile of fasting insulin level for the 725 nondiabetic men and women are shown in table 1. Study-specific quartile cutpoints for fasting insulin were <=48.0, 48.1–66.0, 66.1–90.0, and >=90.1 pmol/liter. HOMA, 2-hour insulin, fasting glucose, and 2-hour glucose were each significantly higher with increasing insulin resistance. The mean baseline fasting insulin level was 72 pmol/liter, and the mean 2-hour insulin level was 497 pmol/liter. The mean value for HOMA (R) was 2.9. The average fasting plasma glucose level was 5.4 mmol/liter, while the average 2-hour glucose level was 6.6 mmol/liter.


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TABLE 1. Baseline values for metabolic variables by quartile of fasting insulin level among 725 nondiabetic men and women, Rancho Bernardo, California, 1984–1987

 
Baseline characteristics by insulin quartile for the 725 nondiabetic men and women are shown in table 2. The mean baseline age was 65 years, ranging from 50 years to 89 years; the oldest individuals were in the lowest insulin quartile. Average baseline weight was 69.9 kg, and average body mass index was 24.7. All baseline anthropometric measurements (i.e., weight, height, body mass index, waist circumference, hip circumference, and waist:hip ratio) were significantly higher for participants in the top insulin quartile. This insulin-resistant group had a mean baseline body mass index of 26.8, which is considered overweight according to the National Heart, Lung, and Blood Institute's clinical guidelines for defining overweight and obesity in adults (body mass index >=25 and body mass index >=30, respectively) (27Go). Weight change between visits varied by insulin quartile; only persons in the fourth quartile experienced weight loss.


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TABLE 2. Baseline values for anthropometric and lifestyle characteristics and weight change by quartile of fasting insulin level among 725 nondiabetic men and women, Rancho Bernardo, California, 1984–1987

 
Values for categorical variables by insulin quartile are also shown in table 2. Overall, only 6 percent of the population was obese at baseline; 15.4 percent of persons in quartile 4 were obese as compared with 3.4 percent of the least insulin-resistant individuals (quartile 1). The prevalence of anti-hypertensive medication use was almost twice as high for insulin-resistant individuals as for the least insulin-resistant individuals (29 percent vs. 17 percent). Approximately 75 percent of the cohort consumed alcohol at least 1–2 times per week, with the most insulin-resistant individuals consuming the least amount of alcohol. Only 13 percent of the population currently smoked at the baseline visit; there were fewer current smokers among insulin-resistant individuals. There were no significant differences across insulin quartiles for exercise status in this cohort; 85 percent reported exercising three or more times per week.

Because a linear, monotonic relation did not exist between fasting insulin and weight change (table 2), we evaluated fasting insulin level stratified into quartiles as the main exposure measure, using analysis of covariance. Table 3 shows results from both unadjusted and adjusted models. Baseline weight and age were the only covariates determined to confound the association, and they were therefore entered into all final models. There were no significant interactions for gender (p = 0.41), overweight status (p = 0.51), or age (p = 0.75). After data were controlled for baseline weight and age, the association between the greatest insulin resistance and subsequent weight change was significant (p = 0.03). The direction of weight change for the most insulin-resistant persons was negative (ß = -1.259): an average weight loss of 1.3 kg between Diabetes I and Diabetes II. The same inverse association (p = 0.03) was observed when the analysis was repeated using the HOMA variable (ß = -1.268).


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TABLE 3. Regression coefficients (ß) from analysis of covariance using quartile of fasting insulin level or HOMA* as the primary independent variable and weight change as the dependent variable, Rancho Bernardo, California, 1984–1996

 
On the basis of the results shown in table 3, we conducted a post hoc analysis of covariance, dichotomizing fasting insulin level as the top quartile versus the other three quartiles. Regression coefficients for unadjusted and adjusted models using fasting insulin as the insulin resistance surrogate are shown in table 4. Insulin resistance was significantly associated with weight loss before and after adjustment for baseline weight and age. The ß coefficient for insulin resistance in the adjusted model was -1.303 kg based on fasting insulin (p = 0.01) and -1.180 kg based on HOMA (p = 0.01).


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TABLE 4. Regression coefficients (ß) from analysis of covariance using insulin resistance status (top quartile of fasting insulin level or HOMA* versus the other quartiles) as the primary independent variable and weight change as the dependent variable, Rancho Bernardo, California, 1984–1996

 
Table 5 presents odds ratios and corresponding 95 percent confidence intervals associated with categories of weight change, using persons who gained >=2.5 kg (those most likely to be experiencing a true positive weight change) as the referent group. The highest risk was observed for insulin-resistant individuals with the most extreme weight loss (i.e., those who lost 10 kg or more) (fasting insulin: odds ratio = 3.3, 95 percent confidence interval: 1.4, 7.9; HOMA: odds ratio = 2.8, 95 percent confidence interval: 1.2, 6.5). This association was also independent of baseline weight and age.


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TABLE 5. Odds ratios from polytomous logistic regression analysis relating baseline insulin resistance to various categories of weight change, Rancho Bernardo, California, 1984–1996

 
In additional analyses, we examined other associations with covariates that we did not want to treat as typical confounders (on the basis of potential overadjustment, collinearity between variables, or suspicion of variables' being in the causal pathway). Adding baseline waist circumference to the final model slightly attenuated the magnitude of the association between insulin resistance and weight loss (ß = -1.20), but the direction of the association remained. Other analyses excluding persons who were taking antihypertensive medication (n = 152) showed that insulin-resistant individuals still lost an average of 1.5 kg between visits (p < 0.05). Finally, in analyses excluding the 27 individuals who died within 2 years of the follow-up visit (those most likely to have weight loss due to serious illness), results were not substantially changed.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In this cohort of older White adults without diabetes, individuals with insulin resistance (i.e., persons in the highest quartile of fasting insulin level or HOMA) were significantly more likely to have subsequent weight loss. Insulin resistance at baseline was associated with a threefold increased risk for losing 10 or more kg over an 8-year interval. These associations were independent of age, baseline weight, fat distribution, use of antihypertensive medication, and early mortality.

These results are consistent with those of five other studies that found a significant association between insulin resistance and weight loss (8GoGoGoGo–12Go) but are inconsistent with the results of four studies reporting that insulin resistance was predictive of weight gain (8Go, 9Go, 13Go, 14Go). These incongruous results could be due to age and ethnic differences between study populations. For example, Folsom et al. (9Go) found evidence for higher fasting insulin levels and lower rates of weight gain among older adults (mean age = 54 years) in the Atherosclerosis Risk in Communities (ARIC) Study but not among younger adults (mean age = 25 years) in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. In addition, the associations found in the ARIC Study were significant for Black women but not for Black men. In studies observing Pima Indian children (13Go) and Chinese Mauritians (14Go), researchers found that insulin resistance predicted weight gain, whereas Swinburn et al. (12Go) found an inverse association among young Pima adults (average age = 25 years). Valdez et al. (11Go) found that increased fasting insulin levels were associated with a lower likelihood of weight gain among Mexican Americans and non-Hispanic Whites aged 25–64 years. Thus, age, gender, and ethnicity may modify the association between insulin resistance and weight change.

Several hypotheses have been proposed to explain these inconsistent associations. Neel proposed the "thrifty genotype" hypothesis (28Go, 29Go), which posited that populations genetically predisposed to diabetes have higher levels of circulating insulin to increase their efficiency of fat storage—a capacity that may have evolved to ensure survival during times of famine. This would be compatible with the associations that Odeleye et al. (13Go) and Hodge et al. (14Go) observed in Pima children and Chinese Mauritians. However, the Normative Aging Study (8Go), the ARIC Study (9Go), the San Luis Valley Diabetes Study (10Go), the San Antonio Heart Study (11Go), and the study of Pima adults (12Go) all found higher insulin levels to be associated with lower rates of weight gain. In three of these four studies (8GoGo–10Go), participants were more than 50 years of age, on average.

High fasting insulin levels could be a more effective surrogate for insulin resistance in older adults (9Go). It has been hypothesized that reduced tissue sensitivity to insulin, with resultant hyperinsulinemia and insulin resistance, is part of the aging process (30Go). The opposite was observed in the present study, wherein the oldest individuals were the least insulin-resistant, based on the proportion in the lowest quartile of fasting insulin (p < 0.001). This could reflect survivorship in this elderly cohort if insulin resistance predicts mortality. In this population, however, insulin resistance (marked by fasting insulin level) was unrelated to cardiovascular disease death in women or men (18Go).

Increasing insulin resistance with aging could be caused by the increase in visceral adiposity commonly observed with aging. Cefalu et al. (31Go) reported that intraabdominal fat measured by magnetic resonance imaging accounted for 51 percent of the variance in insulin sensitivity, with less than 1 percent being explained by age. To examine the effect of fat distribution in the present study, we repeated the analyses while adjusting for baseline waist circumference, a better measure of visceral adiposity than waist:hip ratio in older adults (17Go, 32Go). The association between insulin resistance and weight loss was attenuated, but it remained statistically significant. A more precise measure of visceral fat (e.g., computed tomography) might have reduced misclassification bias.

It has been proposed that insulin resistance may develop as a protective mechanism to attenuate further weight gain (12Go, 33Go). The insulin-resistant individuals in Rancho Bernardo who had lost weight by the time of the follow-up visit (8 years later) tended to have been overweight at baseline (mean body mass index = 26.8). These overweight hyperinsulinemic individuals may have increased fat oxidation relative to carbohydrate oxidation, protecting against further accumulation of adipose tissue (12Go, 34Go) and compatible with a direct effect of insulin resistance on weight change, redirected through a feedback loop of interrelated biologic processes.

In a recent review article, Porte et al. (35Go) proposed that insulin acts as a central nervous system catabolic hormone promoting a state of negative energy balance (i.e., that it has central nervous system effects different from its anabolic effects in the peripheral tissues). The authors hypothesized that insulin levels increase in an environment of increasing body mass, which signals satiety, thereby limiting food intake over time to reduce further weight gain. This would be compatible with our observations in the present study, where overweight individuals in quartile 4 had lost weight by the time of the follow-up visit 8 years later. Less insulin-resistant individuals in quartiles 1–3, who had relatively "normal" body mass indices, gained weight during the follow-up period. These results are compatible with the theory that insulin levels decrease under circumstances of decreasing body mass, thereby reducing satiety and promoting weight gain.

Alternatively, insulin resistance may be associated with weight loss by means of a common causal pathway with inflammatory diseases, which are marked by proinflammatory cytokines (36Go, 37Go). Levels of C-reactive protein, a marker for inflammation, have been found to be higher among persons with increased insulin resistance (38Go). Interactions between insulin and inflammatory disorders could synergistically promote weight loss. Proinflammatory cytokines were not measured in this study.

The association between insulin resistance and weight loss in this study was not explained by several potential biases. For example, during the baseline measurement years (1984–1987), most blood pressure medications prescribed were diuretics, which could cause insulin resistance or weight loss. However, parallel analyses excluding individuals who were taking blood pressure medications (n = 152) yielded significant associations of similar magnitude, demonstrating that the association of insulin resistance with subsequent weight loss was independent of antihypertensive medication use.

Because weight loss can be a marker for severe illness, especially in older individuals, we repeated the analyses after excluding the 27 individuals who died within 2 years of the follow-up visit. Although approximately 30 percent of these individuals were insulin-resistant, the results remained the same. Alternatively, unintentional weight loss with insulin resistance in older adults may be a marker for deteriorating metabolic status, possibly indicating progression towards diabetes. In this cohort, 10 percent of participants with insulin resistance at baseline had progressed to diabetes after 8 years. An analysis that excluded these 15 individuals still showed significant associations.

Nonresponse bias is common in studies of elderly persons. Approximately 38 percent of the people who participated in the baseline examination had died before the follow-up visit. Decedents were significantly older than persons who attended both examinations, but their fasting insulin concentrations did not differ significantly. Individuals who were alive and eligible for follow-up but did not participate in Diabetes II were significantly older; however, their fasting insulin concentrations did not differ significantly from those of Diabetes II participants.

In the present study, fasting insulin levels and HOMA values were closely correlated (r = 0.98) indirect measures of insulin resistance. Their validity as surrogates for insulin resistance in epidemiologic investigations has been repeatedly reported (20Go, 21Go, 23Go). Although a more precise measure of insulin resistance might add to the validity of these findings, it would be unlikely to change the direction of the association.

In summary, insulin resistance preceded weight loss among these older, community-dwelling adults without diabetes, independently of baseline age, weight, fat distribution, use of antihypertensive medication, or early mortality. Other studies are necessary to determine the frequency and mechanisms whereby insulin resistance may be related to weight loss.


    ACKNOWLEDGMENTS
 
This research was supported by grant DK31801 from the National Institute of Diabetes and Digestive and Kidney Diseases.


    NOTES
 
Reprint requests to Dr. Elizabeth Barrett-Connor, School of Medicine, University of California, San Diego, 9500 Gilman Drive, 349 Stein Clinical Research Building, La Jolla, CA 92093-0607 (e-mail: ebarrettconnor{at}ucsd.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication April 21, 2000. Accepted for publication November 7, 2000.