Abstract
Aims
The association of body weight and weight change with mortality and cardiovascular (CV) outcome in patients with diabetes mellitus (DM) is not clearly established. We assessed the ...relationship between weight, weight change, and outcomes in patients with established CV risk factors and type 2 DM or pre-diabetes.
Methods and results
A total of 12 521 participants from the ORIGIN trial were grouped in BMI categories of low body weight body mass index (BMI) < 22 kg/m2 normal (22–24.9), overweight (25–29.9), obesity Grades 1–3 (30–34.9, 35–39.9, ≥40 kg/m2, respectively). Outcome variables included total and CV mortality and composite outcomes of CV death, non-fatal stroke, or myocardial infarction plus revascularization or heart failure hospitalization. Follow-up was 6.2 years (interquartile range 5.8–6.7 years). After multivariable adjustment, lowest risks were seen in patients with overweight and mild obesity for total mortality overweight: hazard ratio (HR) 0.80 (95% confidence interval (CI) 0.69–0.91); obesity Grade 1: HR 0.82 (0.71–0.95), both P < 0.01) and CV mortality overweight: HR 0.79 (0.66–0.94); obesity Grade 1: 0.79 (0.65–0.95), all compared to patients with normal BMI, P < 0.05. Obesity of any severity was not associated with higher mortality. Low body weight was related to higher mortality HR 1.28 (1.02–1.61); CV mortality: HR 1.34 (1.01–1.79), P < 0.05. A continued 2-year weight loss was associated with higher risk of mortality HR 1.32 (1.18–1.46), P < 0.0001 and CV mortality HR 1.18 (1.02–1.35), compared to patients without weight loss, P < 0.05. In turn, weight gain was not related to any adverse outcome.
Conclusion
Obesity in patients with DM or pre-diabetes and CV risk profile was not associated with higher mortality or adverse CV outcome. The lowest mortality risk was seen in patients with overweight and moderate obesity (BMI 25–35 kg/m2). Weight loss was an independent risk factor for higher mortality compared to no weight loss.
...rapid increases in the availability of registries, electronic health records, and insurance claims, and the ability to access, process, link, and analyse data from these sources at fairly low cost ...lend support for calls to replace randomised controlled trials (RCTs) with so-called real-world studies to establish the efficacy of a therapy,1,2 particularly for common serious diseases with abundant, easily collected data such as diabetes.3 This push is driven partly by the need to show payers that therapies are working and are therefore of value when used in the real world.4 Other driving factors include the industry's wish to reduce costs and time to get results, a mistaken belief that real-world data are somehow more relevant than RCT data for establishing efficacy, and the ease and speed with which registry data can be accessed and publications generated. When the effects of insulin on mortality in the observational study described above were evaluated in a large RCT, the effect of insulin on the risk of death was neutral.9 Thus, apparently clear findings based on observational data from the real world were not corroborated when the possibility of confounding was removed by randomisation. ...they are robust for the assessment of the efficacy of a therapy and to produce the clear, unbiased, and least confounded evidence needed to inform clinical care.
Intensive glucose control is understood to prevent complications in adults with type 2 diabetes. We aimed to more precisely estimate the effects of more intensive glucose control, compared with less ...intensive glucose control, on the risk of microvascular events.
In this meta-analysis, we obtained de-identified individual participant data from large-scale randomised controlled trials assessing the effects of more intensive glucose control versus less intensive glucose control in adults with type 2 diabetes, with at least 1000 patient-years of follow-up in each treatment group and a minimum of 2 years average follow-up on randomised treatment. The prespecified and standardised primary outcomes were kidney events (a composite of end-stage kidney disease, renal death, development of an estimated glomerular filtration rate <30 mL/min per 1·73m
, or development of overt diabetic nephropathy), eye events (a composite of requirement for retinal photocoagulation therapy or vitrectomy, development of proliferative retinopathy, or progression of diabetic retinopathy), and nerve events (a composite of new loss of vibratory sensation, ankle reflexes, or light touch). We used a random-effects model to calculate overall estimates of effect.
We included four trials (ACCORD, ADVANCE, UKPDS, and VADT) with 27 049 participants. 1626 kidney events, 795 eye events, and 7598 nerve events were recorded during the follow-up period (median 5·0 years, IQR 4·5-5·0). Compared with less intensive glucose control, more intensive glucose control resulted in an absolute difference of -0·90% (95% CI -1·22 to -0·58) in mean HbA
at completion of follow-up. The relative risk was reduced by 20% for kidney events (hazard ratio 0·80, 95% CI 0·72 to 0·88; p<0·0001) and by 13% for eye events (0·87, 0·76 to 1·00; p=0·04), but was not reduced for nerve events (0·98, 0·87 to 1·09; p=0·68).
More intensive glucose control over 5 years reduced both kidney and eye events. Glucose lowering remains important for the prevention of long-term microvascular complications in adults with type 2 diabetes.
None.
Metformin is a commonly used glucose-lowering drug. However, apart from glycemic measures, no biomarker for its presence or dose has been identified.
A total of 237 biomarkers were assayed in ...baseline serum from 8,401 participants (2,317 receiving metformin) in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial. Regression models were used to identify biomarkers for metformin use.
Growth differentiation factor 15 (GDF15) was strongly linked to metformin, such that the odds of metformin use per SD increase in level varied from 3.73 (95% CI 3.40, 4.09) to 3.94 (95% CI 3.59, 4.33) depending on the other included variables. For the remaining 25 linked biomarkers, the odds ranged from 0.71 to 1.24. A 1.64 ng/mL higher GDF15 level predicted a 188-mg higher metformin dose (P < 0.0001).
GDF15 levels are a biomarker for the use of metformin in people with dysglycemia, and its concentration reflects the dose of metformin.
Hypoglycaemia caused by glucose-lowering therapy has been linked to cardiovascular (CV) events. The ORIGIN trial provides an opportunity to further assess this relationship.
A total of 12 537 ...participants with dysglycaemia and high CV-risk were randomized to basal insulin glargine titrated to a fasting glucose of ≤ 5.3 mmol/L (95 mg/dL) or standard glycaemic care. Non-severe hypoglycaemia was defined as symptoms confirmed by glucose ≤ 54 mg/dL and severe hypoglycaemia as a requirement for assistance or glucose ≤ 36 mg/dL. Outcomes were: (i) the composite of CV death, non-fatal myocardial infarction or stroke; (ii) mortality; (iii) CV mortality; and (iv) arrhythmic death. Hazards were estimated before and after adjustment for a hypoglycaemia propensity score. During a median of 6.2 years (IQR: 5.8-6.7), non-severe hypoglycaemic episodes occurred in 41.7 and 14.4% glargine and standard group participants, respectively, while severe episodes occurred in 5.7 and 1.8%, respectively. Non-severe hypoglycaemia was not associated with any outcome following adjustment. Conversely, severe hypoglycaemia was associated with a greater risk for the primary outcome (HR: 1.58; 95% CI: 1.24-2.02, P < 0.001), mortality (HR: 1.74; 95% CI: 1.39-2.19, P < 0.001), CV death (HR: 1.71; 95% CI: 1.27-2.30, P < 0.001) and arrhythmic death (HR: 1.77; 95% CI: 1.17-2.67, P = 0.007). Similar findings were noted for severe nocturnal hypoglycaemia for the primary outcome and mortality. The severe hypoglycaemia hazard for all four outcomes was higher with standard care than with insulin glargine.
Severe hypoglycaemia is associated with an increased risk for CV outcomes in people at high CV risk and dysglycaemia. Although allocation to insulin glargine vs. standard care was associated with an increased risk of severe and non-severe hypoglycaemia, the relative risk of CV outcomes with hypoglycaemia was lower with insulin glargine-based glucose-lowering therapy than with the standard glycaemic control. Trial Registration (ORIGIN ClinicalTrials.gov number NCT00069784).
To evaluate the association of a multicomponent advanced glycation end product (AGE) panel with decline in kidney function and its utility in predicting renal function loss (RFL) when added to ...routine clinical measures in type 2 diabetes.
Carboxymethyl and carboxyethyl lysine and methylglyoxal, 3-deoxyglucosone, and glyoxal hydroimidazolones were measured in baseline serum and plasma samples, respectively, from Action to Control Cardiovascular Risk in Diabetes (ACCORD) (n = 1,150) and Veterans Affairs Diabetes Trial (VADT) (n = 447) participants. A composite AGE score was calculated from individual AGE z scores. The primary outcome was a sustained 30% decline in estimated glomerular filtration rate (eGFR) (30% RFL in both cohorts). Secondary outcomes (in ACCORD) were 40% RFL, macroalbuminuria, and high-risk chronic kidney disease (hrCKD).
After adjustment for baseline and follow-up HbA1c and other risk factors in ACCORD, the AGE score was associated with reduction in eGFR (β-estimate -0.66 mL/min ⋅ 1.73 m2 per year; P = 0.001), 30% RFL (hazard ratio 1.42 95% CI 1.13-1.78; P = 0.003), 40% RFL (1.40 1.13-1.74; P = 0.003), macroalbuminuria (1.53 1.13-2.06; P = 0.006), and hrCKD (1.88 1.37-2.57; P < 0.0001). AGE score improved net reclassification (NRI) and relative integrated discrimination (IDI) for 30% RFL (NRI 23%; P = 0.02) (relative IDI 7%; P = 0.009). In VADT, the AGE score calculated by the ACCORD-derived coefficients was associated with 30% RFL (1.37 1.03-1.82); P = 0.03) and improved NRI (24%; P = 0.03) but not IDI (P = 0.18).
These data provide further support for a causal role of AGEs in diabetic nephropathy independently of glycemic control and suggest utility of the composite AGE panel in predicting long-term decline in renal function.
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