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1.
Sci Rep ; 14(1): 7451, 2024 03 28.
Article in English | MEDLINE | ID: mdl-38548796

ABSTRACT

For 100 years, the Intravenous glucose tolerance test (IVGTT) has been used extensively in researching the pathophysiology of diabetes mellitus and AIRg-the IVGTT-induced acute insulin response to the rapid rise in circulating glucose-is a key measure of insulin secretory capacity. For an effective evaluation of AIRg, IVGTT glucose loading should be adjusted for glucose distribution volume (gVOL) to provide an invariant, trend-free immediate rise in circulating glucose (ΔG0). Body weight-based glucose loads have been widely used but whether these achieve a trend-free ΔG0 does not appear to have been investigated. By analysing variation in AIRg, ΔG0 and gVOL with a range of IVGTT loads, both observed and simulated, we explored the hypothesis that there would be an optimum anthropometry-based IVGTT load calculation that, by achieving a trend-free ΔG0, would not compromise evaluation of AIRg as an index of beta cell function. Data derived from patient and research volunteer records for 3806 IVGTT glucose and insulin profiles. Among the non-obese, as gVOL rose, weight increased disproportionately rapidly. Consequently, the IVGTT glucose load needed for an invariant ΔG0 was progressively overestimated, accounting for 47% of variation in AIRg. Among the obese, ΔG0 was trend-free yet AIRg increased by 11.6% per unit body mass index, consistent with a more proportionate increase in weight with gVOL and a hyperinsulinaemic adaptation to adiposity-associated insulin resistance. Simulations further confirmed our hypothesis by demonstrating that a body surface area-based IVGTT load calculation could provide for a more generally invariant IVGTT ΔG0.


Subject(s)
Blood Glucose , Insulin Resistance , Humans , Glucose Tolerance Test , Insulin Secretion , Blood Glucose/metabolism , Insulin/metabolism , Glucose , Obesity
2.
Int J Epidemiol ; 33(4): 769-76, 2004 Aug.
Article in English | MEDLINE | ID: mdl-15166209

ABSTRACT

BACKGROUND: This study aimed to assess the association between lifetime exposure to urban environment (EU) and obesity, diabetes, and hypertension in an adult population of Sub-Saharan Africa. METHODS: We studied 999 women and 727 men aged > or =25 years. They represent all the adults aged > or =25 years living in households randomly selected from a rural and an urban community of Cameroon with a 98% and 96% participation rate respectively. Height, weight, blood pressure, and fasting blood glucose were measured in all subjects. Current levels of physical activity (in metabolic equivalents [MET]) were evaluated through the Sub-Saharan African Activity Questionnaire. Chronological data on lifetime migration were collected retrospectively and expressed as the total (EUt) or percentage (EU%) of lifetime exposure to urban environment. RESULTS: Lifetime EUt was associated with body mass index (BMI) (r = 0.42; P < 0.0001), fasting glycaemia (r = 0.23; P < 0.0001), and blood pressure (r = 0.17; P < 0.0001) but not with age. The subjects who recently settled in a city (< or =2 years) had higher BMI (+2.9 kg/m(2); P < 0.001), fasting glycaemia (+0.8 mmol/l; P < 0.001), systolic (+23 mmHg; P < 0.001) and diastolic (+9 mmHg; P = 0.001) blood pressure than rural dwellers with a history of 2 years EU. EU during the first 5 years of life was not, on its own, associated with glycaemia or BMI. However, both lifetime EUt and current residence were independently associated with obesity and diabetes. The association between lifetime EUt and hypertension was not independent of current residence and current level of physical activity. CONCLUSIONS: This study suggests that for the study of obesity and diabetes, in addition to current residence, both lifetime exposure to an urban environment and recent migration history should be investigated.


Subject(s)
Developing Countries , Diabetes Mellitus/epidemiology , Hypertension/epidemiology , Obesity/epidemiology , Urbanization , Adult , Cameroon/epidemiology , Emigration and Immigration , Female , Humans , Male , Multivariate Analysis , Rural Population , Urban Population
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