Short-term high-fructose intake was associated with increased DNL and liver fat in healthy men fed weight-maintaining diets.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Although an association between the metabolic syndrome and hyperandrogenism has been suggested in women with polycystic ovarian syndrome, few studies have investigated this relationship in postmenopausal women. We measured estradiol, testosterone, and sex hormone-binding globulin (SHBG) and calculated the free androgen index (FAI) in 212 postmenopausal women not using hormone therapy in the Women's Health Study. A modified definition of the metabolic syndrome (3 or more of the following: abdominal obesity, hypertriglyceridemia, low high-density lipoprotein, elevated blood pressure, and abnormal glucose metabolism) from the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults was used. Women with the metabolic syndrome had higher mean levels of estradiol, testosterone, and FAI values and lower SHBG levels. Higher FAI and lower SHBG were associated with all components of the metabolic syndrome. After adjustment for BMI and other factors, women in the highest tertile of FAI had an odds ratio of 12.6 (95% confidence interval, 3.8-41.6) for the metabolic syndrome, whereas those in the lowest SHBG tertile had an odds ratio of 7.3 (95% confidence interval, 2.7-19.8). When stratified by body mass index, the associations with high FAI and low SHBG remained significant even in women with body mass index less than 26.7 kg/m2. An androgenic hormone profile is associated with both the individual components of the metabolic syndrome and clustering of metabolic abnormalities in postmenopausal women.