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.

Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects).

The strongest associations in the MHC class II region (rs3957146, β [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (β [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, β [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA.

In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.

Least squares mean difference (LSMD) in estimated glomerular filtration rate (eGFR) from baseline between the EQW and placebo groups was calculated for 13,844 participants. Cox regression models were used to estimate effects by group on incident macroalbuminuria, retinopathy, and major adverse CV events (MACE). Interval-censored time-to-event models estimated effects on renal composite 1 (40% eGFR decline, renal replacement, or renal death) and renal composite 2 (composite 1 variables plus macroalbuminuria).

EQW did not change eGFR significantly (LSMD 0.21 mL/min/1.73 m² [95% CI –0.27 to 0.70]). Macroalbuminuria occurred in 2.2% of patients in the EQW group and in 2.5% of those in the placebo group (hazard ratio [HR] 0.87 [95% CI 0.70–1.07]). Neither renal composite was reduced with EQW in unadjusted analyses, but renal composite 2 was reduced after adjustment (HR 0.85 [95% CI 0.74–0.98]). Retinopathy rates did not differ by treatment group or in the HbA_1c-lowering or prior retinopathy subgroups. CV outcomes in those with eGFR <60 mL/min/1.73 m² did not differ by group. Those with eGFR ≥60 mL/min/1.73 m² had nominal risk reductions for MACE, all-cause mortality, and CV death, but interactions by renal function group were significant for only stroke (HR 0.74 [95% CI 0.58–0.93]; P for interaction = 0.035) and CV death (HR 1.08 [95% CI 0.85–1.38]; P for interaction = 0.031).

EQW had no impact on unadjusted retinopathy or renal outcomes. CV risk was modestly reduced only in those with eGFR ≥60 mL/min/1.73 m² in analyses unadjusted for multiplicity.

In a post hoc analysis of 14,752 Exenatide Study of Cardiovascular Event Lowering (EXSCEL) participants, we examined time-dependent associations between SHEs and subsequent major adverse cardiac events (CV death, nonfatal myocardial infarction [MI] or stroke), fatal/nonfatal MI, fatal/nonfatal stroke, hospitalization for acute coronary syndrome (hACS), hospitalization for heart failure (hHF), and all-cause mortality (ACM), as well as time-dependent associations between nonfatal CV events and subsequent SHEs.

SHEs were uncommon and not associated with once-weekly exenatide therapy (hazard ratio 1.13 [95% CI 0.94–1.36], P = 0.179). In fully adjusted models, SHEs were associated with an increased risk of subsequent ACM (1.83 [1.38–2.42], P < 0.001), CV death (1.60 [1.11–2.30], P = 0.012), and hHF (2.09 [1.37–3.17], P = 0.001), while nonfatal MI (2.02 [1.35–3.01], P = 0.001), nonfatal stroke (2.30 [1.25–4.23], P = 0.007), hACS (2.00 [1.39–2.90], P < 0.001), and hHF (3.24 [1.98–5.30], P < 0.001) were all associated with a subsequent increased risk of SHEs. The elevated bidirectional time-dependent hazards linking SHEs and a composite of all CV events were approximately constant over time, with those individuals at dual risk showing higher comorbidity scores compared with those without.

These findings, showing greater risk of SHEs after CV events as well as greater risk of CV events after SHEs, validate a bidirectional relationship between CV events and SHEs in patients with high comorbidity scores.

In this large-scale retrospective cohort study in Japan, new users of antidepressants (exposure group) and nonusers (nonexposure group), aged 20–79 years, were included between 1 April 2006 and 31 May 2015. Patients with a history of diabetes or receipt of antidiabetes treatment were excluded. Covariates were adjusted by using propensity score matching; the associations were analyzed between risk of new-onset type 2 diabetes and the duration of antidepressant use/dose of antidepressant in the exposure and nonexposure groups by using Cox proportional hazards models. Changes in glycated hemoglobin (HbA_1c) level were examined in groups with continuous use, discontinuation, or a reduction in the dose of antidepressants.

Of 90,530 subjects, 45,265 were in both the exposure and the nonexposure group after propensity score matching; 5,225 patients (5.8%) developed diabetes. Antidepressant use was associated with the risk of diabetes onset in a time- and dose-dependent manner. The adjusted hazard ratio was 1.27 (95% CI 1.16–1.39) for short-term low-dose and 3.95 (95% CI 3.31–4.72) for long-term high-dose antidepressant use. HbA_1c levels were lower in patients who discontinued or reduced the dose of antidepressants (F[2,49] = 8.17; P < 0.001).

Long-term antidepressant use increased the risk of type 2 diabetes onset in a time- and dose-dependent manner. Glucose tolerance improved when antidepressants were discontinued or the dose was reduced after diabetes onset.

Five cohort studies’ participants (n = 8,270) were classified as statin users when they were on statin therapy at the time of DNAm assessment with Illumina 450K or EPIC array or noncurrent users otherwise. Associations of DNAm with various outcomes like incident type 2 diabetes, plasma glucose, insulin, and insulin resistance (HOMA of insulin resistance [HOMA-IR]) as well as with gene expression were investigated.

Discovery (n = 6,820) and replication (n = 1,450) phases associated five DNAm sites with statin use: cg17901584 (1.12 x 10^–25 [DHCR24]), cg10177197 (3.94 x 10^–08 [DHCR24]), cg06500161 (2.67 x 10^–23 [ABCG1]), cg27243685 (6.01 x 10^–09 [ABCG1]), and cg05119988 (7.26 x 10^–12 [SC4MOL]). Two sites were associated with at least one glycemic trait or type 2 diabetes. Higher cg06500161 methylation was associated with higher fasting glucose, insulin, HOMA-IR, and type 2 diabetes (odds ratio 1.34 [95% CI 1.22, 1.47]). Mediation analyses suggested that ABCG1 methylation partially mediates the effect of statins on high insulin and HOMA-IR. Gene expression analyses showed that statin exposure and ABCG1 methylation were associated with ABCG1 downregulation, suggesting epigenetic regulation of ABCG1 expression. Further, outcomes insulin and HOMA-IR were significantly associated with ABCG1 expression.

This study sheds light on potential mechanisms linking statins with type 2 diabetes risk, providing evidence on DNAm partially mediating statins’ effects on insulin traits. Further efforts shall disentangle the molecular mechanisms through which statins may induce DNAm changes, potentially leading to ABCG1 epigenetic regulation.