2009
DOI: 10.1007/s00125-009-1519-0
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Seasonality of birth in adult type 2 diabetic patients in three Ukrainian regions

Abstract: To the Editor: Epidemiological studies have repeatedly shown a link between poor fetal growth and increased risk of developing type 2 diabetes [1]. Month of birth is a good instrument for the assessment of effects of early growth on adult health independent of life-course factors. This is true because in decades past there were important seasonal differences in nutrition, especially in developing countries such as Ukraine. These differences in access to high-quality food supply can potentially influence intrau… Show more

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Cited by 32 publications
(31 citation statements)
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“…Findings from a Dutch hospital-based series study of 282 patients aged 30-90 years with type 2 diabetes showed an excess of diabetes births in the first quarter of the year and a deficiency in the final quarter when comparing the month of birth with the standard birth curve [9]. In another study conducted in three regions of Ukraine, a variation in season of birth was observed in 52,214 individuals with type 2 diabetes who were born before 1960, with a peak in April and a nadir between November and December, compared with the month-of-birth patterns in the general population [10]. The only prospective study conducted in a Danish population-based cohort of 223,099 adults born between 1930 and 1989 showed no association between birth seasonality and risk of type 2 diabetes [11].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Findings from a Dutch hospital-based series study of 282 patients aged 30-90 years with type 2 diabetes showed an excess of diabetes births in the first quarter of the year and a deficiency in the final quarter when comparing the month of birth with the standard birth curve [9]. In another study conducted in three regions of Ukraine, a variation in season of birth was observed in 52,214 individuals with type 2 diabetes who were born before 1960, with a peak in April and a nadir between November and December, compared with the month-of-birth patterns in the general population [10]. The only prospective study conducted in a Danish population-based cohort of 223,099 adults born between 1930 and 1989 showed no association between birth seasonality and risk of type 2 diabetes [11].…”
Section: Discussionmentioning
confidence: 99%
“…Factors exhibiting seasonal variation include, but are not limited to, exposure to sunlight, food availability and eating habits, and outdoor physical activity [4][5][6][7][8]. Only a few studies conducted in white populations living in relatively high-latitude regions have examined the association between the season or month of birth and type 2 diabetes in adulthood, with mixed results [9][10][11].…”
Section: Introductionmentioning
confidence: 99%
“…Birthweight has been found to vary according to the season of birth, with peaks in birthweight in both autumn and summer [6,7], and it has been suggested that risk of type 2 diabetes displays a similar seasonal variation depending on the month of birth [8][9][10]. Indeed, studies in American children [10] and in adult populations in the Netherlands [8] and Ukraine [9] have reported seasonal variations in type 2 diabetes development according to month of birth.…”
Section: Introductionmentioning
confidence: 99%
“…Indeed, studies in American children [10] and in adult populations in the Netherlands [8] and Ukraine [9] have reported seasonal variations in type 2 diabetes development according to month of birth. The lowest type 2 diabetes prevalence in these studies was observed in people born in October to December, and the highest prevalence was in people born in January to April.…”
Section: Introductionmentioning
confidence: 99%
“…In particular our studies of T2D patients (Khalangot et al, 2009 b ) indicate that Hazard Ratios (HRs) of cardiovascular disease (CVD) mortality among extremely obese patients [body mass index (BMI) ≥ 35 kg/m 2 ] adjusted for age, smoking and alcohol consumption were higher than for overweight patients [BMI 25-29 kg/m 2 ]: HR=1.54 (95% CI 1.16-2.05) and 1.35 (95% CI 1.15-1.59) among men an women respectively, p<0.01. Furthermore, the graph that shows risks of general and CVD mortality for T2D patients depending on BMI has the shape of an asymmetric parabola: HRs associated with low and normal BMI were significantly higher comparing to those, related to overweight or moderate obesity.…”
Section: Register-based T2d Epidemiology Studiesmentioning
confidence: 97%