Evaluation of the insulin resistance syndrome in 5- to 10-year-old overweight/obese African-American children

OBJECTIVE

To characterize the insulin sensitivity of overweight and obese 5- to 10-year-old (Tanner stage 1-3) African-American children screened for participation in a diabetes prevention study and to identify the association of insulin sensitivity with obesity, hyperlipidemia, and hypertension.

RESEARCH DESIGN AND METHODS

Measures of insulin resistance (homeostasis model assessment) and insulin sensitivity (Matsuda and DeFronzo's whole-body insulin sensitivity) were calculated from a 2-h oral glucose tolerance test in 137 African-American children recruited into a diabetes prevention study. Measures of lipids (LDL, HDL, total cholesterol, and triglycerides), blood pressure, and body composition were obtained for a subset of the children.

RESULTS

In response to a glucose challenge, girls and older and heavier children produced significantly more insulin. As BMI increased, there was a statistically significant decrease in insulin sensitivity, particularly in girls. Insulin sensitivity was inversely correlated with increases in blood pressure, triglycerides, subcutaneous fat, the percentage of total body fat, and Tanner stage, but it was not correlated with LDL and HDL.

CONCLUSIONS

Reduced insulin sensitivity and the cluster of risk factors known as the insulin resistance syndrome (IRS) are already apparent in these overweight African-American children. Young African-American girls, in particular, already show evidence of hyperinsulinemia in response to a glucose load, suggesting that the early stages of metabolic decompensation that lead to type 2 diabetes are already occurring. Monitoring of those risk factors known to be part of IRS should become part of routine medical care for overweight or obese African-American children.

Care giver perception of children's obesity-related health risk: a study of African American families

OBJECTIVE

To examine care giver perception of children's weight-related health risk in African American families.

RESEARCH METHODS AND PROCEDURES

One-hundred and eleven families (representing 48 boys and 63 girls) screened for participation in a diabetes prevention study participated. Care givers completed a health awareness questionnaire that assessed their perception of the child's weight, eating habits, appearance, exercise habits, and health risk. The care givers also reported each subject's family history of obesity, diabetes, and other chronic diseases. After a physical examination, height and weight were used to compute an age- and sex-adjusted body mass index for each child.

RESULTS

Despite the fact that a substantial number of children were obese (57%) and super-obese (12%), only 44% of the care givers perceived the child's weight to be a potential health problem. Regression analysis showed that 21% of the variance in parental perception of obesity-related health risk could be predicted by child age, body mass index, perception of frame size, and perception of exercise habits.

DISCUSSION

A number of reasons for the apparent minimization of child health risk are discussed, including cultural differences in the acceptance of a large body habitus, lack of knowledge about the connection between childhood obesity and future health risk, and an optimistic bias in the perception of personal health risk.

Provider impact in diabetes education. What we know, what we would like to know, paradigms for asking

PURPOSE

A literature search from 1985 to the present was performed using Web-based search engines to identify evidence-based studies of diabetes education.

METHODS

Twelve studies were identified in which a provider characteristic was defined as a discriminate variable associated with impact or efficacy of the education intervention. Provider was defined as all those participating in diabetes care and education (e.g., patient, education and care teams, funding and policy agencies). The Certified Diabetes Educator credential also was described.

RESULTS

Specialty or discipline of the provider/care team, technology when used to assist providers, and physician practice patterns have been assessed. There is a paucity of research that assesses provider impact or the specific impact of provider characteristics on diabetes outcomes in a controlled and scientifically rigorous fashion.

CONCLUSIONS

Suggestions for evaluating provider impact based on literature concerning psychotherapy and healthcare education outcomes are: identify provider characteristics/attitudes/skills and link them to outcomes, define therapeutic alliance in diabetes care/education and assess its contribution to outcomes; and use performance measures as provider characteristics.

Biopsychobehavioral model of risk of severe hypoglycemia. Self-management behaviors

OBJECTIVE

To identify self-management antecedents of low blood glucose (BG) (< 3.9 mmol/l) that might be easily recognized, treated, or avoided altogether.

RESEARCH DESIGN AND METHODS

Ninety-three adults with type 1 diabetes (age, 35.8 +/- 8 years [mean +/- SD]; duration of diabetes, 17.0 +/- 11 years; daily insulin dose, 0.58 +/- 0.18 U/kg; and HbAlc, 8.6 +/- 1.8%) were recruited to participate in the study. Of the 93 subjects, 42 had a history of severe hypoglycemia (SH), defined as two or more hypoglycemic episodes in the preceding 12 months, and 51 subjects had no history of SH (No-SH) in the same time period. Before each of 70 BG measurements obtained over a 3-week period, subjects used a handheld computer to record whether their most recent insulin, food, and exercise was more than, less than, or the same as usual. Associations among self-management behaviors preceding BG readings < 3.9 mmol/l versus those preceding BG readings of 5.6-7.8 mmol/l were determined using chi 2 tests, analyses of variance, and logistic regression analyses.

RESULTS

Analysis of 6,425 self-management/self-monitoring of BG events revealed that the usual amounts of insulin, food, and exercise preceded the events 58.3% of the time. No significant differences were observed for changes in insulin before readings of BG < 3.9 mmol/l versus 7.8 < BG > 5.6 mmol/l, but significantly less food (P < 0.01) was eaten and more exercise (P < 0.001) was performed before the low BG measurement. No interactions between SH and No-SH groups and management behaviors were observed. However, each of the three management variables entered significantly in a logistic model that predicted 61% of all readings of BG < 3.9 mmol/l.

CONCLUSIONS

Subjects with a history of SH did not report managing their diabetes differently from those with no such history. Specifically, when low BG occurred, the preceding management behaviors, although predictive of low BG, were not different in SH and No-SH subjects. Overall, self-management behaviors did not distinguish SH from No-SH subjects. Thus, even though it might be beneficial for all patients to review their food and exercise management decisions to reduce their frequency of low BG, an educational intervention whose content stresses insulin, food, and exercise would be unlikely by itself to be sufficient to reduce the frequency of SH.

Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index

OBJECTIVE

To evaluate the clinical/research utility of the low blood glucose index (LBGI), a measure of the risk of severe hypoglycemia (SH), based on self-monitoring of blood glucose (SMBG).

RESEARCH DESIGN AND METHODS

There were 96 adults with IDDM (mean age 35+/-8 years, duration of diabetes 16+/-10 years, HbA1 8.6+/-1.8%), 43 of whom had a recent history of SH (53 did not), who used memory meters for 135+/-53 SMBG readings over a month, and then for the next 6 months recorded occurrence of SH. The SMBG data were mathematically transformed, and an LBGI was computed for each patient.

RESULTS

The two patient groups did not differ with respect to HbA1, insulin units per day, average blood glucose (BG) and BG variability. Patients with history of SH demonstrated a higher LBGI (P < 0.0005) and a trend to be older with longer diabetes duration. Analysis of odds for future SH classified patients into low- (LBGI <2.5), moderate- (LBGI 2.5-5), and high- (LBGI >5) risk groups. Over the following 6 months low-, moderate-, and high-risk patients reported 0.4, 2.3, and 5.2 SH episodes, respectively (P = 0.001). The frequency of future SH was predicted by the LBGI and history of SH (R2 = 40%), while HbA1, age, duration of diabetes, and BG variability were not significant predictors.

CONCLUSIONS

LBGI provides an accurate assessment of risk of SH. In the traditional relationship history of SH-to-future SH, LBGI may be the missing link that reflects present risk. Because it is based on SMBG records automatically stored by many reflectance meters, the LBGI is an effective and clinically useful on-line indicator for SH risk.