A cost-effectiveness analysis was conducted to estimate lifetime costs and health gains using a stochastic microsimulation model of oral health conditions, T2D, T2D-related microvascular diseases, and CVD of the U.S. population. Model parameters were obtained from the nationally representative National Health and Nutrition Examination Survey (NHANES) (2009–2014) and randomized trials of periodontal treatment among patients with T2D.

Expanding periodontal treatment coverage among patients with T2D and periodontitis would be expected to avert tooth loss by 34.1% (95% CI –39.9, –26.5) and microvascular diseases by 20.5% (95% CI –31.2, –9.1), 17.7% (95% CI –32.7, –4.7), and 18.4% (95% CI –34.5, –3.5) for nephropathy, neuropathy, and retinopathy, respectively. Providing periodontal treatment to the target population would be cost saving from a health care perspective at a total net savings of $5,904 (95% CI –6,039, –5,769) with an estimated gain of 0.6 quality-adjusted life years per capita (95% CI 0.5, 0.6).

Providing nonsurgical periodontal treatment to patients with T2D and periodontitis would be expected to significantly reduce tooth loss and T2D-related microvascular diseases via improved glycemic control. Encouraging patients with T2D and poor oral health conditions to receive periodontal treatment would improve health outcomes and still be cost saving or cost-effective.

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed 7,522 genetically high-risk children in Sweden, Finland, Germany, and the U.S. from birth for a median of 9.0 years (interquartile range 5.7–10.6) with available growth data. Of these, 761 (10.1%) children developed IA and 290 (3.9%) children were diagnosed with T1D. Bayesian two-phase piecewise linear mixed models with a random change point were used to estimate children’s individual growth trajectories. Cox proportional hazards models were used to assess the effects of associated growth parameters on the risks of IA and progression to T1D.

A higher rate of weight gain in infancy was associated with increased IA risk (hazard ratio [HR] 1.09 [95% CI 1.02, 1.17] per 1 kg/year). A height growth pattern with a lower rate in infancy (HR 0.79 [95% CI 0.70, 0.90] per 1 cm/year), higher rate in early childhood (HR 1.48 [95% CI 1.22, 1.79] per 1 cm/year), and younger age at the phase transition (HR 0.76 [95% CI 0.58, 0.99] per 1 month) was associated with increased risk of progression from IA to T1D. A higher rate of weight gain in early childhood was associated with increased risk of progression from IA to T1D (HR 2.57 [95% CI 1.34, 4.91] per 1 kg/year) in children with first-appearing GAD autoantibody only.

Growth patterns in early life better clarify how specific growth phases are associated with the development of T1D.

We examined the association between SH and cognitive function in 718 older adults with T1D from the Study of Longevity in Diabetes (SOLID). Subjects self-reported recent SH (previous 12 months) and lifetime history of SH resulting in inpatient/emergency department utilization. Global and domain-specific cognition (language, executive function, episodic memory, and simple attention) were assessed. The associations of SH with cognitive function and impaired cognition were evaluated via linear and logistic regression models, respectively.

Thirty-two percent of participants (mean age 67.2 years) reported recent SH and 50% reported lifetime SH. Compared with those with no SH, subjects with a recent SH history had significantly lower global cognition scores. Domain-specific analyses revealed significantly lower scores on language, executive function, and episodic memory with recent SH exposure and significantly lower executive function with lifetime SH exposure. Recent SH was associated with impaired global cognition (odds ratio [OR] 3.22, 95% CI 1.30, 7.94) and cognitive impairment on the language domain (OR 3.15, 95% CI 1.19, 8.29).

Among older adults with T1D, recent SH and lifetime SH were associated with worse cognition. Recent SH was associated with impaired global cognition. These findings suggest a deleterious role of SH on the brain health of older patients with T1D and highlight the importance of SH prevention.

We included 17,026 adults (8,346 men and 8,680 women) who were part of the China Health and Nutrition Survey (1991–2015) prospective cohort. Dietary intake was measured by three consecutive 24-h dietary recalls combined with a household food inventory. Diabetes cases were identified through a questionnaire. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs.

A total of 547 men and 577 women developed diabetes during 202,138 person-years of follow-up. For men, the adjusted HRs (95% CIs) for quintiles of nonheme iron intake were 1.00, 0.77 (0.58–1.02), 0.72 (0.54–0.97), 0.63 (0.46–0.85), and 0.87 (0.64–1.19) (P-nonlinearity = 0.0015). The corresponding HRs (95% CIs) for women were 1.00, 0.63 (0.48–0.84), 0.57 (0.43–0.76), 0.58 (0.43–0.77), and 0.67 (0.49–0.91) (P-nonlinearity < 0.0001). The dose-response curves for the association between nonheme iron and total iron intake and diabetes followed a reverse J shape in men and an L shape in women. No significant associations were observed between heme iron intake and diabetes risk.

Total iron and nonheme iron intake was associated with diabetes risk, following a reverse J-shaped curve in men and an L-shaped curve in women. Sufficient intake of nonheme or total iron might be protective against diabetes, while excessive iron intake might increase the risk of diabetes among men.

Data on health care resource utilization from 100,391 people with type 2 diabetes were extracted from the electronic database used at the Catalan Health Institute. Multivariate regression models were carried out to test the impact of glycemic control (HbA_1c) on total health care, hospital admission, and medication costs; model 1 adjusted for a variety of covariates, and model 2 also included micro- and macrovascular complications. Glycemic control was classified as good for HbA_1c <7%, fair for ≥7% to <8%, poor for ≥8% to <10%, and very poor for ≥10%.

Mean per patient annual direct medical costs were 3,039 ± SD 6,581. Worse glycemic control was associated with higher total health care costs: compared with good glycemic control, health care costs increased by 18% (509.82) and 23% (661.35) in patients with very poor and poor glycemic control, respectively, when unadjusted and by 428.3 and 395.1, respectively, in model 2. Medication costs increased by 12% in patients with fair control and by 28% in those with very poor control (model 2). Patients with poor control had a higher probability of hospitalization than those with good control (5% in model 2) and a greater average cost when hospitalization occurred (811).

Poor glycemic control was directly related to higher total health care, hospitalization, and medication costs. Preventive strategies and good glycemic control in people with type 2 diabetes could reduce the economic impact associated with this disease.