Insulin use increases risk of asthma but metformin use reduces the risk in patients with diabetes in a Taiwanese population cohort

Chen, Chiung-Zuei; Hsu, Chih Hui; Li, Chung‐Yi; Hsiue, Tzuen Ren

doi:10.1080/02770903.2017.1283698

Cited by 34 publications

(49 citation statements)

References 29 publications

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Section: Discussionsupporting

confidence: 92%

“…Our findings relating to insulin are consistent with those of Chen et al who observed an increased risk of asthma in patients with diabetes who are regularly prescribed insulin compared with matched controls (OR 2.23, 1.52‐3.58). Previous work by this group has yielded similar results . The lung is known to have insulin receptors, and high levels of insulin binding may promote airway smooth muscle contraction and hyperresponsiveness …”

Section: Discussionsupporting

confidence: 92%

“…Chen et al also reported an increased risk of asthma in those with diabetes ( n = 58 284) over a mean 8.6 years of follow‐up in a Taiwanese cohort. Notably, only obesity (defined by a physician diagnostic code for obesity) and not BMI levels were controlled for.…”

Section: Discussionmentioning

confidence: 91%

“…Prevalence studies involving hospitalised patients have shown T2DM and asthma to coexist more frequently than would be expected by chance . Additionally, an American retrospective cohort study of 1.8 million patients demonstrated a modest increase in asthma incidence (HR 1.08, 1.03‐1.12) in those with T2DM compared to healthy controls, and a recent Taiwanese retrospective cohort study yielded similar results (HR 1.30, 1.24‐1.38) . These studies were limited by the use of asthma‐related hospitalisation as the primary end point and potentially confounded by body mass index (BMI).…”

Section: Introductionmentioning

confidence: 99%

“…Insulin receptors are present in the lung and when activated, may lead to smooth muscle contraction . Conversely, metformin has antiinflammatory properties, which may reduce airways hyperresponsiveness …”

Section: Introductionmentioning

confidence: 99%

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The impact of therapy on the risk of asthma in type 2 diabetes

Rayner

McGovern

Sherlock

et al. 2019

Clinical Respiratory J

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Background and Objectives There are limited data about the risk of asthma in people with diabetes. We examined the incidence of asthma in subjects with type 2 diabetes (T2DM) compared to controls, and the association with metformin, sulphonylureas and insulin therapy. Materials and Methods We conducted a retrospective cohort study using a representative UK primary care database (N = 894 646 adults). We used 1:1 propensity score matching (age, gender, socio‐economic deprivation, body mass index and smoking status) to match 29 217 pairs of T2DM cases and controls. We used Cox proportional hazard regression to compare the incidence of asthma in both groups over 8 years of follow‐up. In those with T2DM, we used Cox proportional hazard regression to assess for any impact of antidiabetic medications on asthma incidence. Results Individuals with T2DM were less likely to develop asthma than matched controls (hazard ratio [HR] 0.85, 95% CI 0.77‐0.93). Insulin increased the risk of incident asthma (HR 1.25, 95% CI 1.01‐1.56), whilst metformin and sulphonylureas were associated with reduced risk (HR 0.80, 95% CI 0.69‐0.93 and HR 0.76, 95% CI 0.60‐0.97, respectively). There was no association with diabetes duration, complications or glycaemic control. Conclusions T2DM may have a protective effect against asthma development. Insulin use was associated with an increased risk of asthma, while metformin and sulphonylureas reduced the risk in those with T2DM.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of therapy on the risk of asthma in type 2 diabetes

Rayner

McGovern

Sherlock

et al. 2019

Clinical Respiratory J

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Self‐matched learning to construct treatment decision rules from electronic health records

Chen

Zeng

et al. 2022

Statistics in Medicine

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Electronic health records (EHRs) collected from large-scale health systems provide rich subject-specific information on a broad patient population at a lower cost compared to randomized controlled trials. Thus, EHRs may serve as a complementary resource to provide real-world data to construct individualized treatment rules (ITRs) and achieve precision medicine. However, in the absence of randomization, inferring treatment rules from EHR data may suffer from unmeasured confounding. In this article, we propose a self-matched learning method inspired by the self-controlled case series (SCCS) design to mitigate this challenge. We alleviate unmeasured time-invariant confounding between patients by matching different periods of treatments within the same patient (self-controlled matching) to infer the optimal ITRs. The proposed method constructs a within-subject matched value function for optimizing ITRs and bears similarity to the SCCS design. We examine assumptions that ensure Fisher consistency, and show that our method requires weaker assumptions on unmeasured confounding than alternative methods. Through extensive simulation studies, we demonstrate that self-matched learning has comparable performance to other existing methods when there are no unmeasured confounders, but performs markedly better when unobserved time-invariant confounders are present, which is often the case for EHRs. Sensitivity analyses show that the proposed method is robust under different scenarios. Finally, we apply self-matched learning to estimate the optimal ITRs from type 2 diabetes patient EHRs, which shows our estimated decision rules lead to greater advantages in reducing patients' diabetes-related complications.

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The Association Between Insulin Use and Asthma: An Epidemiological Observational Analysis and Mendelian Randomization Study

Lin

Huang

Xie

et al. 2023

Lung

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BackgroundAsthma is a common respiratory disease caused by genetic and environmental factors, but the contribution of insulin use to the risk of asthma remains unclear. This study aimed to investigate the association between insulin use and asthma in a large population-based cohort, and further explore their causal relationship by Mendelian randomization (MR) analysis. MethodsAn epidemiological study including 85,887 participants from the National Health and Nutrition Examination Survey (NHANES) 2001-2018 was performed to evaluate the association between insulin use and asthma. Based on the inverse-variance weighted approach, the MR analysis involving multiple cohorts was conducted to estimate the causal effect of insulin use on asthma. ResultsIn the NHANES cohort, we found that insulin use was associated with an increased risk of asthma (odd ratio [OR] = 1.38; 95%CI: 1.16-1.64; p < 0.001). For the MR analysis, we found a causal relationship between insulin use and a higher risk of asthma in both Finn (OR = 1.10; p < 0.001) and UK Biobank cohorts (OR = 1.18; p < 0.001). Meanwhile, there was no causal association between diabetes and asthma. After multivariable adjustment for diabetes, the insulin use remained signi cantly associated with an increased risk of asthma (OR = 1.17, p < 0.001). ConclusionsAn association between insulin use and an increased risk of asthma was found via the real-world data from the NHANES. In addition, the current study identi ed a casual effect and provided a genetic evidence of insulin use and asthma. More studies are needed to elucidate the mechanisms underlying the association between insulin use and asthma.

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Insulin use increases risk of asthma but metformin use reduces the risk in patients with diabetes in a Taiwanese population cohort

Abstract: Individuals with diabetes are at an increased risk of asthma. Insulin may further increase the risk of asthma, but the risk could possibly be reduced by using metformin.

Cited by 34 publications

References 29 publications

The impact of therapy on the risk of asthma in type 2 diabetes

The impact of therapy on the risk of asthma in type 2 diabetes

Self‐matched learning to construct treatment decision rules from electronic health records

The Association Between Insulin Use and Asthma: An Epidemiological Observational Analysis and Mendelian Randomization Study

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