Incidence of Type 1 and Type 2 Diabetes in Adults Aged 30–49 Years

Bruno, Graziella; Runzo, Cristina; Cavallo‐Perin, P.; Merletti, Franco; Rivetti, M; Pinach, Silvia; Novelli, Giulia; Trovati, Mariella; Cerutti, Fránco; Pagano, Gianfranco

doi:10.2337/diacare.28.11.2613

Cited by 163 publications

(98 citation statements)

References 32 publications

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“…The diagnosis of T1D was precise and clinically verified, which facilitated the inclusion of patients diagnosed after 30 years of age. Almost 1 in 3 patients in our T1D population was diagnosed after 30 years of age, which is in accordance with the distribution found in other studies 12,52 ; information on this group of patients is particularly scarce in the literature.…”

Section: Discussionsupporting

confidence: 77%

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

et al. 2016

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Background— Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus. Methods and Results— From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9–10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A 1c , albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807–0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767–0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration ( P >0.05) in both cohorts. Conclusions— This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting.

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Section: Discussionsupporting

confidence: 77%

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

et al. 2016

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“…In the same study these authors identified two cohorts (children born in 1985 and 1995) with sharp increases of incidence. Examinations in Italy (8,9) showed a general drift in the period and birth cohort, and in a Finish study (21), a period effect was shown, but a birth cohort effect was not found to be statistically significant. Rewers et al (10) reported that the incidence of type 1 diabetes in a Polish county showed a significant period effect, but no similar period effect was found for a U.S. county.…”

Section: Strengths and Limitationsmentioning

confidence: 94%

Spatiotemporal Trends and Age-Period-Cohort Modeling of the Incidence of Type 1 Diabetes Among Children Aged <15 Years in Norway 1973–1982 and 1989–2003

Aamodt

Stene²,

Njølstad³

et al. 2007

Diabetes Care

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OBJECTIVE -We have investigated age-period-cohort effects and spatial and temporal trends for the incidence of type 1 diabetes among 0-to 14-year-old children in Norway.RESEARCH DESIGN AND METHODS -We included children with the diagnosis of type 1 diabetes in Norway during 1973-1982 and 1989 -2003. We studied age, calendar period, and birth cohort effects using Poisson regression, including Holford's method of parameterization, to model the dependencies between age, period, and cohort effects. To study spatiotemporal clustering of cases, we used spatial scan statistics.RESULTS -The overall incidence rate for the study population Ͻ15 years of age was 22.7 cases per 100,000 (95% CI 22.1-23.4), showing an average annual increase of 1.2% (95% CI 0.7-1.5%) during the study period. One specific area with 30% increased incidence rates was identified in the southern part of Norway during 1976 -1980 (P ϭ 0.001). Also, children born during 1964 -1966 in a specific region in the southern part of Norway as well as children born during 1987-1989 in a region in northern Norway showed 2.0 and 2.6 times, respectively, higher incidence rates compared with the rest of the country (both P ϭ 0.001).CONCLUSIONS -The incidence of type 1 diabetes among children increased during the study period. Birth cohort effects were identified using the spatiotemporal scan statistic but not using age, period, and birth cohort modeling. Such effects, within the relatively homogenous Norwegian population, suggest the influence of nongenetic etiological factors. Diabetes Care 30:884 -889, 2007

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“…The annual T2DM incidence increases from < 0.1% below the age of 30 to 1% around the age of 70 years (e.g. [8][9][10]). Currently, in Europe and the USA the prevalence of diabetes peaks around an age of 70 years, at 10 to 20% [11,12].…”

Section: A Brief Update On T2dmmentioning

confidence: 99%

Caffeine, Diabetes, Cognition, and Dementia

Biessels

2010

JAD

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Abstract. People with diabetes mellitus are at increased risk of cognitive dysfunction. This review explores the relation between caffeine intake, diabetes, cognition and dementia, focusing on type 2 diabetes (T2DM). Epidemiological studies on caffeine/coffee intake and T2DM risk are reviewed. Next, the impact of T2DM on cognition is addressed. Finally, the potential for caffeine to modulate the risk of cognitive decline in the context of diabetes is explored. The conclusion is that, although epidemiological studies indicate that coffee/caffeine consumption is associated with a decreased risk of T2DM and possibly also with a decreased dementia risk, we can at present not be certain that these associations are causal. For now, recommendations for coffee consumption in individuals with T2DM or pre-diabetic stages are therefore difficult to establish, but it should be acknowledged that caffeine does appear to have several properties that warrant further investigations in this field.

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Incidence of Type 1 and Type 2 Diabetes in Adults Aged 30–49 Years

Cited by 163 publications

References 32 publications

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

Spatiotemporal Trends and Age-Period-Cohort Modeling of the Incidence of Type 1 Diabetes Among Children Aged <15 Years in Norway 1973–1982 and 1989–2003

Caffeine, Diabetes, Cognition, and Dementia

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Incidence of Type 1 and Type 2 Diabetes in Adults Aged 30–49 Years

Cited by 163 publications

References 32 publications

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

Spatiotemporal Trends and Age-Period-Cohort Modeling of the Incidence of Type 1 Diabetes Among Children Aged &lt;15 Years in Norway 1973–1982 and 1989–2003

Caffeine, Diabetes, Cognition, and Dementia

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Spatiotemporal Trends and Age-Period-Cohort Modeling of the Incidence of Type 1 Diabetes Among Children Aged <15 Years in Norway 1973–1982 and 1989–2003