Glycated haemoglobin (HbA1c ) and fasting plasma glucose relationships in sea-level and high-altitude settings.

AIM: Higher haemoglobin levels and differences in glucose metabolism have been reported among high-altitude residents, which may influence the diagnostic performance of HbA1c . This study explores the relationship between HbA1c and fasting plasma glucose (FPG) in populations living at sea level and at an altitude of > 3000 m. METHODS: Data from 3613 Peruvian adults without a known diagnosis of diabetes from sea-level and high-altitude settings were evaluated. Linear, quadratic and cubic regression models were performed adjusting for potential confounders. Receiver operating characteristic (ROC) curves were constructed and concordance between HbA1c and FPG was assessed using a Kappa index. RESULTS: At sea level and high altitude, means were 13.5 and 16.7 g/dl (P > 0.05) for haemoglobin level; 41 and 40 mmol/mol (5.9% and 5.8%; P < 0.01) for HbA1c ; and 5.8 and 5.1 mmol/l (105 and 91.3 mg/dl; P < 0.001) for FPG, respectively. The adjusted relationship between HbA1c and FPG was quadratic at sea level and linear at high altitude. Adjusted models showed that, to predict an HbA1c value of 48 mmol/mol (6.5%), the corresponding mean FPG values at sea level and high altitude were 6.6 and 14.8 mmol/l (120 and 266 mg/dl), respectively. An HbA1c cut-off of 48 mmol/mol (6.5%) had a sensitivity for high FPG of 87.3% (95% confidence interval (95% CI) 76.5 to 94.4) at sea level and 40.9% (95% CI 20.7 to 63.6) at high altitude. CONCLUSION: The relationship between HbA1c and FPG is less clear at high altitude than at sea level. Caution is warranted when using HbA1c to diagnose diabetes mellitus in this setting.

Obesity risk in rural, urban and rural-to-urban migrants: prospective results of the PERU MIGRANT study.

BACKGROUND: Although migration and urbanization have been linked with higher obesity rates, especially in low-resource settings, prospective information about the magnitude of these effects is lacking. We estimated the risk of obesity and central obesity among rural subjects, rural-to-urban migrants and urban subjects. METHODS: Prospective data from the PERU MIGRANT Study were analyzed. Baseline data were collected in 2007-2008 and participants re-contacted in 2012-2013. At follow-up, outcomes were obesity and central obesity measured by body mass index and waist circumference. At baseline, the primary exposure was demographic group: rural, rural-to-urban migrant and urban. Other exposures included an assets index and educational attainment. Cumulative incidence, incidence ratio (IR) and 95% confidence intervals (95% CI) for obesity and central obesity were estimated with Poisson regression models. RESULTS: At baseline, mean age (±s.d.) was 47.9 (±12.0) years, and 53.0% were females. Rural subjects comprised 20.2% of the total sample, whereas 59.7% were rural-to-urban migrants and 20.1% were urban dwellers. A total of 3598 and 2174 person-years were analyzed for obesity and central obesity outcomes, respectively. At baseline, the prevalence of obesity and central obesity was 20.0 and 52.5%. In multivariable models, migrant and urban groups had an 8- to 9.5-fold higher IR of obesity compared with the rural group (IR migrants=8.19, 95% CI=2.72-24.67; IR urban=9.51, 95% CI=2.74-33.01). For central obesity, there was a higher IR only among the migrant group (IR=1.95; 95% CI=1.22-3.13). Assets index was associated with a higher IR of central obesity (IR top versus bottom tertile 1.45, 95% CI=1.03-2.06). CONCLUSIONS: Peruvian urban individuals and rural-to-urban migrants show a higher incidence of obesity compared with their rural counterparts. Given the ongoing urbanization occurring in middle-income countries, the rapid development of increased obesity risk by rural-to-urban migrants suggests that measures to reduce obesity should be a priority for this group.