Geographical variation in the progression of type 2 diabetes in Peru: The CRONICAS Cohort Study

Abstract

BackgroundThe study aims were to estimate the incidence and risk factors for T2D in four settings with different degree of urbanization and altitude in Peru.MethodsProspective cohort study conducted in urban, semi-urban, and rural areas in Peru. An age- and sex-stratified random sample of participants was taken from the most updated census. T2D was defined as fasting blood glucose ⩾7.0 mmol/L or taking anti-diabetes medication. Exposures were divided into two groups: geographical variables (urbanization and altitude), and modifiable risk factors. Incidence, relative risks (RR), 95% confidence intervals (95%CI), and population attributable fractions (PAF) were estimated.ResultsData from 3135 participants, 48.8% males, mean age 55.6 years, was analyzed. Overall baseline prevalence of T2D was 7.1% (95%CI 6.2–8.0%). At follow-up, including 6207 person-years of follow-up, a total of 121 new T2D cases were accrued, equating to an incidence of 1.95 (95%CI 1.63–2.33) per 100 person-years. There was no urban to rural gradient in the T2D incidence; however, compared to sea level sites, participants living in high altitude had a higher incidence of diabetes (RR = 1.58; 95%CI 1.01–2.48). Obesity had the highest attributable risk for developing T2D, although results varied by setting, ranging from 14% to 80% depending on urbanization and altitude.ConclusionsOur results suggest that the incidence of T2D was greater in high altitude sites. New cases of diabetes were largely attributed to obesity, but with substantial variation in the contribution of obesity depending on the environment. These findings can inform appropriate context-specific strategies to reduce the incidence of diabetes.

Elevated incidence rates of diabetes in Peru: report from PERUDIAB, a national urban population-based longitudinal study

“…Our estimate of incidence (19.5 cases per 1000 person-years) is consistent with population-based estimates of incidence published in 2016 in four areas of three administrative regions of Peru (two on the coast and two in the highlands), using a bigger probability sample than ours (n=3126), that placed the combined incidence of the four communities in 19.5 (16.3–23.3) per 1000 person-years 9. The sample analyzed in this report is also a probabilistic one, and includes clusters of all administrative regions (24 regions), distributed in the three natural regions (coast, highlands and jungle), which has a broader representation.…”

The Andean Latin-American burden of diabetes attributable to high body mass index: A comparative risk assessment

“…We used the same relative risks across genders, thus these differences could be due to higher mean BMI and diabetes prevalence estimates in women than in men; robust evidence supports this argument [2,12]. Similarly, regarding the region level analysis, higher BMI and diabetes incidence have been observed in women than in men [13,64]. This female disadvantage has also been reported when looking at hard endpoints such as mortality, where higher proportions [55,57] and more attributable deaths [58,59] have been reported in women than in men.…”

“…We integrated global population-based prevalence estimates of diabetes (regardless of type 1 or 2) and BMI (available through NCD-RisC) [2,12], along with relative risks (RR) estimating the association between high BMI and diabetes derived from population-based cohorts in Peru [13,14], to estimate the fraction and absolute number of diabetes cases attributable to high BMI in Bolivia, Ecuador and Peru (Andean Latin-America). We also used nationally representative data on BMI and diabetes prevalence from Peru to make estimates for all twenty-five regions in Peru.…”

“…Regional expert knowledge was also used to retrieve additional data sources. To the best of our knowledge, three cohort studies have assessed the association between BMI and diabetes in Andean Latin-America [13][14][15], and two of these were accessed and re-analysed for this study [13,14].…”

“…The relative risks quantifying the association between a five unit increase in BMI and diabetes risk were re-analysed in each of the two accessed cohorts independently [13,14]. These risk estimates were pooled following a randomeffects meta-analysis.…”