This is a large and comprehensively phenotyped cohort with fasting glycaemia where the predictive value of body composition and anthropometric measures of total and central fat distribution for postprandial carbohydrate intolerance are studied.
Subjects aged 18-70 years, who attended the Department of Endocrinology and Nutrition of the Clínica Universidad de Navarra from 2009-2014 for a check-up were offered to participate in the DICAMANO study. 853 subjects agreed to take part. Only those individuals with a normal fasting glucose level (≤5.5 mmol l-1) were analysed. Subjects with T2DM or severe renal, liver or thyroid dysfunction were excluded. Participants were instructed to temporarily discontinue for 48 hours any medication known to affect glucose or lipid metabolism. On the day of the study visit, each subject had a complete routine clinical assessment to evaluate the presence of cardiovascular, respiratory, renal or endocrine disorders. All patients underwent a 75-g OGTT with a concomitant anthropometric study, blood pressure monitoring and lipid profile analyses. They were classified by glucose tolerance on the basis of blood glucose levels according to ADA diagnostic criteria for T2DM (2017). Carbohydrate intolerance was defined as a 2-hOGTT glucose level ≥7.8 mmol l-1 (mg dl-1). Body composition, visceral adipose tissue, anthropometry study, OGTT-based parameters and cardiovascular risk factors are measured.
Study Type
OBSERVATIONAL
Enrollment
853
Body fat percentage and carbohydrate intolerance
Investigate whether body fat percentage estimated by air-displacement plethysmography (Bod-Pod®, Life Measurements, Concord, CA, USA) predicts postprandial carbohydrate intolerance early on in the metabolic dysregulation process. Body fat percentage (BF%) is calculated from body density by means of the Siri equation.
Time frame: Baseline
Neck circumference as screening tool
Examine the predictive value of neck circumference as screening tool for the selection of patients who are most likely to benefit from an oral glucose tolerance test (OGTT)
Time frame: Baseline
Waist-to-hip ratio as screening tool
Examine the predictive value of waist-to-hip ratio as screening tool for the selection of patients who are most likely to benefit from an oral glucose tolerance test (OGTT). Waist-to-hip ratio was calculated as waist circumference divided by hip circumference. Waist circumference was measured at the midpoint between the iliac crest and the rib cage on the mid-axillary line, and hip circumference at the level of the greater trochanters was measured to the nearest millimetre using a flexible tape.
Time frame: Baseline
Waist-to-height ratio as screening tool
Examine the predictive value of waist-to-height ratio as screening tool for the selection of patients who are most likely to benefit from an oral glucose tolerance test (OGTT). Waist-to-height ratio was calculated as waist circumference divided by height.
Time frame: Baseline
BMI as screening tool
Examine the predictive value of body adiposity index (BMI) as screening tool for the selection of patients who are most likely to benefit from an oral glucose tolerance test (OGTT). BMI was calculated as weight in kilograms divided by height in meters squared.
Time frame: Baseline
Body adiposity index as screening tool
Examine the predictive value of body adiposity index (BAI) (\[hip circumference/height1.5\]-18) as screening tool for the selection of patients who are most likely to benefit from an oral glucose tolerance test (OGTT).
Time frame: Baseline
Central fat depot and carbohydrate intolerance
Investigate whether central fat depot predicts postprandial carbohydrate intolerance early on in the metabolic dysregulation process. Visceral and abdominal adiposity was quantified by the use of the abdominal bioelectrical impedance analysis device ViScan (Tanita AB-140, Tanita Corp., Tokyo, Japan).
Time frame: Baseline
Central fat depot and cardiometabolic risk
Investigate whether a higher central fat depot is able to identify those individuals with higher inflammatory parameters (c-reactive protein, homocysteine and uric acid) and cardiovascular risk (higher rate of hypercholesterolemia, hypertension and/or obstructive sleep apnea). Body fat percentage (BF%) is calculated from body density by means of the Siri equation.
Time frame: Baseline
Body fat percentage and cardiometabolic risk
Investigate whether a higher body fat percentage is able to identify those individuals with higher inflammatory parameters (c-reactive protein, homocysteine and uric acid) and cardiovascular risk (higher rate of hypercholesterolemia, hypertension and/or obstructive sleep apnea). Body fat percentage (BF%) is calculated from body density by means of the Siri equation.
Time frame: Baseline
Prevalence of postprandial carbohydrate intolerance
Assess the prevalence of postprandial carbohydrate intolerance in individuals with normal fasting glycaemia
Time frame: Baseline
Oral glucose tolerance test parameters and cardiometabolic profile
Verification of the utility of the two-hour OGTT glucose value to select those individuals with higher cardiometabolic risk (higher rate of hypercholesterolemia, hypertension and/or obstructive sleep apnea).
Time frame: Baseline
Non-alcoholic fatty liver disease (NAFLD) and glucose dysregulation
Analyse the association between NAFLD and OGTT-based ß-cell function and insulin resistance in non-diabetic subjects.
Time frame: Baseline
OGTT-based indices as screening tool of NAFLD
Examine whether OGTT-based ß-cell function and insulin resistance indices could be used as screening tools for the selection of patients who are most likely to benefit from a NAFLD-study.
Time frame: Baseline
OGTT-derived glucose curve as screening tool of NAFLD
Examine whether the glucose response curve could be used as screening tool for the selection of patients who are most likely to benefit from a NAFLD-study.
Time frame: Baseline
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