Diet and Physical Activity as Determinants of Continuously Measured Glucose Levels in Persons at High Risk of Type 2 Diabetes.

We examined how dietary and physical activity behaviors influence fluctuations in blood glucose levels over a seven-day period in people at high risk for diabetes. Twenty-eight participants underwent a mixed meal tolerance test to assess glucose homeostasis at baseline. Subsequently, they wore an accelerometer to assess movement behaviors, recorded their dietary intakes through a mobile phone application, and wore a flash glucose monitoring device that measured glucose levels every 15 min for seven days. Generalized estimating equation models were used to assess the associations of metabolic and lifestyle risk factors with glycemic variability. Higher BMI, amount of body fat, and selected markers of hyperglycemia and insulin resistance from the meal tolerance test were associated with higher mean glucose levels during the seven days. Moderate- to vigorous-intensity physical activity and polyunsaturated fat intake were independently associated with less variation in glucose levels (CV%). Higher protein and polyunsaturated fatty acid intakes were associated with more time-in-range. In contrast, higher carbohydrate intake was associated with less time-in-range. Our findings suggest that dietary composition (a higher intake of polyunsaturated fat and protein and lower intake of carbohydrates) and moderate-to-vigorous physical activity may reduce fluctuations in glucose levels in persons at high risk of diabetes.

Beyond HbA1c: Comparing Glycemic Variability and Glycemic Indices in Predicting Hypoglycemia in Type 1 and Type 2 Diabetes.

BACKGROUND: Hypoglycemia is the major impediment to therapy intensification in diabetes. Although higher individualized HbA1c targets are perceived to reduce the risk of hypoglycemia in those at risk of hypoglycemia, HbA1c itself is a poor predictor of hypoglycemia. We assessed the use of glycemic variability (GV) and glycemic indices as independent predictors of hypoglycemia. METHODS: A retrospective observational study of 60 type 1 and 100 type 2 diabetes subjects. All underwent professional continuous glucose monitoring (CGM) for 3-6 days and recorded self-monitored blood glucose (SMBG). Indices were calculated from both CGM and SMBG. Statistical analyses included regression and area under receiver operator curve (AUC) analyses. RESULTS: Hypoglycemia frequency (53.3% vs. 24%, P < 0.05) and %CV (40.1% ± 10% vs. 29.4% ± 7.8%, P < 0.001) were significantly higher in type 1 diabetes compared with type 2 diabetes. HbA1c was, at best, a weak predictor of hypoglycemia. %CVCGM, Low Blood Glucose Index (LBGI)CGM, Glycemic Risk Assessment Diabetes Equation (GRADE)HypoglycemiaCGM, and Hypoglycemia IndexCGM predicted hypoglycemia well. %CVCGM and %CVSMBG consistently remained a robust discriminator of hypoglycemia in type 1 diabetes (AUC 0.88). In type 2 diabetes, a combination of HbA1c and %CVSMBG or LBGISMBG could help discriminate hypoglycemia. CONCLUSION: Assessment of glycemia should go beyond HbA1c and incorporate measures of GV and glycemic indices. %CVSMBG in type 1 diabetes and LBGISMBG or a combination of HbA1c and %CVSMBG in type 2 diabetes discriminated hypoglycemia well. In defining hypoglycemia risk using GV and glycemic indices, diabetes subtypes and data source (CGM vs. SMBG) must be considered.

Contribution of early weight gain to childhood overweight and metabolic health: a longitudinal study (EarlyBird 36).

BACKGROUND: Early weight gain (0-5 years) is thought to be an important contributor to childhood obesity and consequently metabolic risk. There is a scarcity of longitudinal studies in contemporary children reporting the impact of early weight gain on metabolic health. OBJECTIVE: We aimed to assess the impact of early weight gain on metabolic health at 9 years of age. METHOD: Two hundred thirty-three children (134 boys, 99 girls) with a gestational age of >37 weeks were assessed at birth, 5 years of age, and 9 years of age. Measures included weight SD scores at each time point and excess weight gained (Delta weight SD score) between them. The outcome measure included composite metabolic score (sum of internally derived z scores of insulin resistance, mean blood pressure, triglyceride level, and total cholesterol/high-density lipoprotein cholesterol ratio). RESULTS: Weight SD score increased by 0.29 SD score in girls and 0.26 SD score in boys from 0 to 5 years of age and by 0.03 SD score in girls and 0.11 SD score in boys from 5 to 9 years of age. Weight SD score correlated poorly to moderately before 5 years of age but strongly after 5 years of age. Birth weight SD score predicted (girls/boys) 2.4%/0% of the variability in composite metabolic score at 9 years of age. Adding Delta weight SD score (0-5 years old) contributed (girls/boys) 11.2%/7.0% to the score, and adding Delta weight SD score (5-9 years old) additionally contributed (girls/boys) 26.4%/16.5%. Importantly, once weight SD score at 9 years of age was known, predictive strength was changed little by adding Delta weight SD score. CONCLUSIONS: Most excess weight before puberty is gained before 5 years of age. Weight at 5 years of age bears little relation to birth weight but closely predicts weight at 9 years of age. Single measures of current weight are predictive of metabolic health, whereas weight gain within a specific period adds little. A single measure of weight at 5 years of age provides a pointer to future health for the individual. If metabolic status at 9 years of age means future risk, diabetes/cardiovascular prevention strategies might better focus on preschool-aged children, because the die seems to be largely cast by 5 years of age, and a healthy weight early in childhood may be maintained at least into puberty.

Trends, associations and predictions of insulin resistance in prepubertal children (EarlyBird 29).

BACKGROUND: Rising obesity has been observed in all age groups. Anthropometric cut-points have been used to predict metabolic risk in children, although they are not based on known outcomes. AIM: We examined the trends, associations and predictions of metabolic health from anthropometry in prepubertal children. METHOD: Three hundred and seven healthy children were examined annually between 5 and 8 yr. MEASURES: height, weight, body mass index (BMI), sum of skinfold thickness at five sites (SSF) and waist circumference (WC). OUTCOME MEASURES: homeostasis model assessment of insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). RESULTS: Two hundred and thirty-one [131 boys (B) and 100 girls (G)] children had complete data sets at all four time points. (i) All measures of adiposity rose from 5 to 8 yr (BMI - B: +3.4%, G: +5.7%; WC - B: +10.4%, G: +11.8%; SSF - B: +23.3%, G: +30.7%, all p < 0.001). HOMA-IR unexpectedly fell (B: -16.6%, p = 0.01; G: -32.5%, p < 0.001). This fall was significant between 5 and 6 yr in both genders (5-6 yr - B: -17.8%, p < 0.001; G: -20.0%, p = 0.002) and between 6 and 7 yr in girls only (6-7 yr - B: -10.8%, p = 0.12; G: -19.2%, p = 0.001). HDL-C rose (B: +17.8%, G: +17.1%, both p < 0.001) and TG fell (B: -4.8%, p = 0.16; G: -11.6%, p = 0.006). (ii) Correlations between insulin resistance (IR) and anthropometry were poor at 5 yr but strengthened by 8 yr (BMI - B: r = 0.20/0.38, G: r = 0.28/0.49; WC - B: r = 0.25/0.40, G: r = 0.32/0.58; SSF - B: r = 0.11/0.36, G: r = 0.18/0.53). (iii) In girls, but not boys, adiposity at 5 yr predicted IR better at 8 yr (BMI - r(2 )= 0.17; WC - r(2 )= 0.28; SSF - r(2 )= 0.17, all p < 0.001) than it did at 5 yr (BMI - r(2 )= 0.08, p < 0.01; WC - r(2 )= 0.10, p < 0.01; SSF - r(2 )= 0.03, p = 0.07). CONCLUSIONS: Cross-sectional association cannot indicate direction of trend or predict the future. Predicting metabolic health from anthropometric measures in prepubertal children requires longitudinal data, tracking variables from childhood into adulthood. Until the data set reaches adulthood, it is probably not safe to make recommendations on which children to 'target' or whether early intervention would be of benefit.