Modeling conditional reference regions: Application to glycemic markers

Abstract OBJECTIVES Previous studies conducted in mostly homogeneous sociodemographic samples have reported a relationship between weakened and/or disrupted rest-activity patterns and metabolic dysfunction. This study aims to examine rest-activity rhythm characteristics in relation to glycemic markers in a large nationally-representative and diverse sample of American adults. METHODS This study used data from the National Health and Nutrition Examination Survey 2011-2014. Rest-activity characteristics were derived from extended cosine models using 24-hour actigraphy. We used multinomial logistic regression and multiple linear regression models to assess the associations with multiple glycemic markers (i.e., glycated hemoglobin, fasting glucose and insulin, homeostatic model assessment of insulin resistance, and results from the oral glucose tolerance test), and compared the results across different categories of age, gender, race/ethnicity and body-mass index. RESULTS We found that compared to those in the highest quintile of F statistic , a model-fitness measure with higher values indicating a stronger cosine-like pattern of daily activity, participants in the lowest quintile (i.e, those with the weakest rhythmicity) were 2.37 times more likely to be diabetic (OR Q1 vs. Q5 2.37 (95% CI 1.72, 3.26), p-trend <.0001). Similar patterns were observed for other rest-activity characteristics, including lower amplitude (2.44 (1.60, 3.72)), mesor (1.39 (1.01, 1.91)), and amplitude:mesor ratio (2.09 (1.46, 2.99)), and delayed acrophase (1.46 (1.07, 2.00)). Results were consistent for multiple glycemic biomarkers, and across different sociodemographic and BMI groups. CONCLUSIONS Our findings support an association between weakened and/or disrupted rest-activity rhythms and impaired glycemic control among a diverse US population.

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Blood Viscosity, Glycemic Markers and Blood Pressure: A Study in Middle-Aged Normotensive and Hypertensive Type 2 Diabetics

Indian Journal of Clinical Biochemistry ◽

10.1007/s12291-018-0798-y ◽

2018 ◽

Vol 35 (1) ◽

pp. 102-108 ◽

Cited By ~ 1

Author(s):

Yogita Dhas ◽

Joyita Banerjee ◽

Neetu Mishra

Keyword(s):

Blood Pressure ◽

Blood Viscosity ◽

Middle Aged ◽

Type 2 Diabetics ◽

Glycemic Markers

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Flash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2019-001052 ◽

2020 ◽

Vol 8 (1) ◽

pp. e001052 ◽

Cited By ~ 4

Author(s):

Fernando Gomez-Peralta ◽

Timothy Dunn ◽

Katherine Landuyt ◽

Yongjin Xu ◽

Juan Francisco Merino-Torres

Keyword(s):

Real World ◽

Glucose Monitoring ◽

Glycemic Variability ◽

Real World Data ◽

Flash Glucose Monitoring ◽

Testing Frequency ◽

Scan Rate ◽

Time In Range ◽

Glycemic Markers ◽

Design And Methods

ObjectiveObservations in real-world settings support and extend findings demonstrated in randomized controlled trials that show flash glucose monitoring improves glycemic control. In this study, Spain-specific relationships between testing frequency and glycemic parameters were investigated under real-world settings.Research design and methodsDeidentified glucose and user scanning data were analyzed and readers were rank ordered into 20 equal sized groups by daily scan frequency. Glucose parameters were calculated for each group: estimated HbA1c, time below range (<70 and ≤54 mg/dL), within range (70–180 mg/dL), and above range (>180 mg/dL). Glycemic variability (GV) metrics were described and data obtained from sensors in Spain and worldwide were compared.ResultsSpanish users (n=22 949) collected 37.1 million glucose scans, 250 million automatically recorded glucose readings, and checked glucose values via a mean of 13 scans/day. Estimated HbA1c, time below 70 mg/dL, at or below 54 mg/dL, above 180 mg/dL, and GV metrics were significantly lower in the highest compared with lowest scan rate group (39.6 to 3.9 scans/day). Time-in-range was higher for the highest versus lowest scan rate group at 15.6 vs 11.5 hours/day, respectively. GV metrics correlated positively with time below 70 mg/dL, at or below 54 mg/dL, above 180 mg/dL, and negatively with time-in-range. The relationship between glucose metrics and scan rate was similar in Spain and worldwide. However, time in hypoglycemia in Spain was higher in the groups with lower scan rates.ConclusionsAs seen in clinical trials, flash glucose monitoring in real-world settings allows frequent glucose checks. High scan rates are associated with the favorable glycemic markers of increased time-in-range and reduced time in hyperglycemia and hypoglycemia, and GV. The same trends, with unique nuances, are observed in both Spanish and global data.

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