Can weight gain in healthy, nonobese volunteers be predicted by differences in baseline plasma insulin concentration?

The Metabolomic Signatures of Weight Change

Obesity represents a major health concern, not just in the West but increasingly in low and middle income countries. In order to develop successful strategies for losing weight, it is essential to understand the molecular pathogenesis of weight change. A number of pathways, implicating oxidative stress but also the fundamental regulatory of insulin, have been implicated in weight gain and in the regulation of energy expenditure. In addition, a considerable body of work has highlighted the role of metabolites generated by the gut microbiome, in particular short chain fatty acids, in both processes. The current review provides a brief understanding of the mechanisms underlying the associations of weight change with changes in lipid and amino acid metabolism, energy metabolism, dietary composition and insulin dynamics, as well as the influence of the gut microbiome. The changes in metabolomic profiles and the models outlined can be used as an accurate predictor for obesity and obesity related disorders.

Myths about Insulin Resistance: Tribute to Gerald Reaven

Gerald Reaven was often called the "father of insulin resistance." On the 1-year anniversary of his death in 2018, we challenge three myths associated with insulin resistance: metformin improves insulin resistance; measurement of waist circumference predicts insulin resistance better than body mass index; and insulin resistance causes weight gain. In this review, we highlight Reaven's relevant research that helped to dispel these myths associated with insulin resistance.

Hyperinsulinemia: a Cause of Obesity?

PURPOSE OF REVIEW

This perspective is motivated by the need to question dogma that does not work: that the problem is insulin resistance (IR). We highlight the need to investigate potential environmental obesogens and toxins.

RECENT FINDINGS

The prequel to severe metabolic disease includes three interacting components that are abnormal: (a) IR, (b) elevated lipids and (c) elevated basal insulin (HI). HI is more common than IR and is a significant independent predictor of diabetes. We hypothesize that (1) the initiating defect is HI that increases nutrient consumption and hyperlipidemia (HL); (2) the cause of HI may include food additives, environmental obesogens or toxins that have entered our food supply since 1980; and (3) HI is sustained by HL derived from increased adipose mass and leads to IR. We suggest that HI and HL are early indicators of metabolic dysfunction and treating and reversing these abnormalities may prevent the development of more serious metabolic disease.

A longitudinal study of serum insulin and insulin resistance as predictors of weight and body fat gain in African American and Caucasian children

BACKGROUND

The influence of insulin and insulin resistance (IR) on children's weight and fat gain is unclear.

OBJECTIVE

To evaluate insulin and IR as predictors of weight and body fat gain in children at high risk for adult obesity. We hypothesized that baseline IR would be positively associated with follow-up body mass index (BMI) and fat mass.

SUBJECTS/METHODS

Two hundred and forty-nine healthy African American and Caucasian children aged 6-12 years at high risk for adult obesity because of early-onset childhood overweight and/or parental overweight were followed for up to 15 years with repeated BMI and fat mass measurements. We examined baseline serum insulin and homeostasis model of assessment-IR (HOMA-IR) as predictors of follow-up BMI Z-score and fat mass by dual-energy X-ray absorptiometry in mixed model longitudinal analyses accounting for baseline body composition, pubertal stage, sociodemographic factors and follow-up interval.

RESULTS

At baseline, 39% were obese (BMI⩾95th percentile for age/sex). Data from 1335 annual visits were examined. Children were followed for an average of 7.2±4.3 years, with a maximum follow-up of 15 years. After accounting for covariates, neither baseline insulin nor HOMA-IR was significantly associated with follow-up BMI (Ps>0.26), BMIz score (Ps>0.22), fat mass (Ps>0.78) or fat mass percentage (Ps>0.71). In all models, baseline BMI (P<0.0001), body fat mass (P<0.0001) and percentage of fat (P<0.001) were strong positive predictors for change in BMI and fat mass. In models restricted to children without obesity at baseline, some but not all models had significant interaction terms between body adiposity and insulinemia/HOMA-IR that suggested less gain in mass among those with greater insulin or IR. The opposite was found in some models restricted to children with obesity at baseline.

CONCLUSIONS

In middle childhood, BMI and fat mass, but not insulin or IR, are strong predictors of children's gains in BMI and fat mass during adolescence.

Fasting hyperglycemia predicts lower rates of weight gain by increased energy expenditure and fat oxidation rate

CONTEXT

Increased adiposity and insulin resistance are associated with hyperglycemia and previous studies have reported that higher glucoses are associated with lower rates of weight gain. One possible mechanism is via increased energy expenditure (EE).

OBJECTIVE

To assess the relationships between changes in EE during spontaneous weight gain and concomitant changes in glucose levels.

DESIGN AND PARTICIPANTS

Body composition, metabolic, and glycemic data were available from nondiabetic Native Americans who underwent two measurements of 24-h EE during eucaloric feeding in a metabolic chamber (N = 144; time between measurements: 5.0 ± 3.3 years) or resting EE by ventilated hood system during the euglycemic-hyperinsulinemic clamp (N = 261; 4.5 ± 3.2 years). Long-term follow-up data (8.3 ± 4.3 years) for weight and body composition were available in 131 and 122 subjects, respectively.

MAIN OUTCOME MEASURES

Twenty four hour EE and respiratory quotient (RQ), resting (RMR), and sleeping (SMR) metabolic rates, glucose, and insulin levels, basal glucose output (BGO).

RESULTS

Weight gain-associated increase in fasting plasma glucose (FPG) levels was accompanied with decreased 24-h RQ (partial R = -0.24, P = .002) and increased 24-h EE, RMR, SMR, and fat oxidation after accounting for changes in body composition (partial R: 0.12 to 0.19, all P ≤ .05). Upon weight gain, BGO tended to increase (P = .07), while insulin infusion induced a decrease in EE (P = .04). Higher baseline FPG predicted lower rates of future weight gain (partial R = -0.18, P = .04).

CONCLUSIONS

Higher FPG after weight gain was associated with greater-than-expected increase in EE. The rise in BGO and the insulin-induced EE suppression at follow-up indicate that increased hepatic gluconeogenesis may be an important mediator of EE changes associated with weight gain.

Does insulin resistance, visceral adiposity, or a sex hormone alteration underlie the metabolic syndrome? Studies in women

Insulin resistance, obesity, and a sex hormone alteration have each been suggested as the underlying link for the constellation of risk factors for myocardial infarction (MI) commonly referred to as the metabolic syndrome or the insulin resistance syndrome. In an attempt to identify in women which of these variables is the most likely link, insulin, adiposity variables, sex hormones, and risk factors for MI were measured and their relationships analyzed statistically in 58 premenopausal and 20 postmenopausal healthy women. On controlling for age, visceral adipose tissue (VAT) correlated more strongly with risk factors for MI, insulin, and free testosterone (FT) than did total adipose tissue or subcutaneous adipose tissue. VAT, therefore, was used as the adiposity variable for further data analysis. Waist circumference was a better surrogate of VAT than was waist-hip ratio, which was a poor surrogate of VAT. VAT correlated positively with insulin, FT, triglyceride, and glucose, and negatively with high-density lipoprotein and sex hormone-binding globulin. On controlling for age, FT and insulin correlated with risk factors for MI and with each other, but on controlling for age and VAT, all of their correlations lost statistical significance except for FT-triglyceride and FT-insulin in the postmenopausal women. In conclusion, VAT accumulation in women, independently of other measures of adiposity, may largely explain the correlations of insulin, obesity, and sex hormones with risk factors for MI and may be the immediate underlying factor that links risk factors for MI to form the metabolic syndrome. Insulin resistance, which has been generally accepted to be the underlying factor, may be a component of the syndrome rather than its underlying link. We hypothesize that in women FT may effect preferential VAT accumulation and induce insulin resistance directly, as well as via VAT accumulation, so that a sex hormone alteration may underlie VAT accumulation and thus ultimately underlie the metabolic syndrome (with insulin resistance as a component).

Pathophysiologic mechanisms of obesity and related metabolic disorders: an epidemiologic study using questionnaire and serologic biomarkers

BACKGROUND

It is still unclear whether individuals with the same degree of obesity but different weight histories since young adulthood have different insulin concentration, prevalence of metabolic syndrome components and their clustering.

METHODS

A cross-sectional study was conducted on 3,399 (for weight difference analysis) and 1,879 (for weight fluctuation analysis) Japanese men aged 40-59 years. Weight difference was calculated by subtracting the recalled weight at about 25 years old from the current weight. The root mean square error around the slope of weight on age (weight - RMSE) was calculated by a simple linear regression model, in which the subject's actual weights at ages 20, 25, 30, 40 years and 5 years prior to the study, as well as current weight, were dependent variables against the subject's age as the independent variable. Each metabolic syndrome component was defined as follows: serum triglycerides ≥150 mg/dL; HDL-cholesterol <40 mg/dL; fasting glucose ≥110 mg/dL; and blood pressure ≥140/90 mm Hg.

RESULTS

Those who gained <10%, <20%, or 20% or more in weight had a significantly higher than unity odds ratio of having two or more metabolic syndrome components in relation to those whose weight remained stable: 1.28 (95% confidence interval: 0.95-1.73), 2.49 (1.91-3.24), and 5.30 (3.97-7.07), respectively. Weight-RMSE was significantly and positively associated with fasting insulin concentration independent of current weight, weight-slope or other lifestyle-related factors.

CONCLUSIONS

Metabolic syndrome components would likely tend to cluster more in individuals with large weight gain on a physiologic basis characterized by high fasting insulin concentration. Furthermore, weight fluctuation was suggested to increase the risk of fasting hyperinsulinemia.