Standards of medical care for type 2 diabetes in China 2019

Jia, Weiping; Weng, Jianping; Zhu, Dalong; Ji, Linong; Lu, Juming; Zhang, Zhou; Zou, Da-jin; Guo, Lixin; Ji, Qiuhe; Chen, Li; Chen, Liming; Dou, Jingtao; X, Guo; Kuang, Hongyu; Li, Ling; Li, Qifu; Li, Xiaoying; Liu, Jing; Ran, Xingwu; Shi, Lixin; Song, Guangyao; Xiao, Xinhua; Yang, Liyong; Zhao, Zhigang

doi:10.1002/dmrr.3158

Cited by 452 publications

(460 citation statements)

References 117 publications

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“…Our results indicated that individuals 139 with age >66.7 years or waist circumference >84.6cm were considered at high risk of 140 T2D (Fig.S3). This is consistent with the standards of medical care for T2D in China 141 (14,15), which suggests that individuals >65 years old or with waist 142 circumference >85cm (male) or 80cm (female) are at high risk of T2D. These results 143 further demonstrated the validity of our novel machine learning-based analytic 144 framework.…”

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confidence: 69%

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Gou

Ling

et al. 2020

Preprint

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New interpretable machine-learning analytic framework identifies a combination 55 of microbes consistently associated with type 2 diabetes risk across three 56 independent cohorts involving 9111 participants 57 • Faecal microbiota transplantation from humans to germ-free mice demonstrates a 58 causal role of the identified combination of microbes in the type 2 diabetes 59 development 60 • Body shape could modify the gut microbiome-diabetes relationship 61 4 Abstract 62Gut microbiome targets for type 2 diabetes (T2D) prevention among human cohorts 63 have been controversial. Using an interpretable machine learning-based analytic 64 framework, we identified robust human gut microbiome features, with their optimal 65 threshold, in predicting T2D. Based on the results, we constructed a microbiome risk 66 score (MRS), which was consistently associated with T2D across 3 independent 67 Chinese cohorts involving 9111 participants (926 T2D cases). The MRS could also 68 predict future glucose increment, and was correlated with a variety of gut microbiota-69 derived blood metabolites. Faecal microbiota transplantation from humans to germ-70 free mice demonstrated a causal role of the identified combination of microbes in the 71 T2D development. We further identified adiposity and dietary factors which could 72 prospectively modulate the MRS, and found that body fat distribution may be the key 73 factor modulating the gut microbiome-T2D relationship. Taken together, we proposed 74 a new analytical framework for the investigation of microbiome-disease relationship. 75 The identified microbiota may serve as potential drug targets for T2D in future. 77Type 2 diabetes (T2D) is a complex disorder influenced by both host genetic and 78 environmental factors (1), and its prevalence is rising rapidly in both developed and 79 developing countries (2). Gut microbiome is considered as a modifiable 80 environmental factor, which plays an important role in the development of T2D (3-7). 81The research interest to identify gut microbiome-related treatment/prevention target is 82 emerging recently (8). Although there are a few human studies investigating the 83 association of gut microbiome with T2D in the past few years, the results are 84 inconsistent, and the causality is lacking (9). So far, there are sparse human evidence 85 robustly linking specific gut microbiome features to T2D. 87Machine learning has been widely used in biomedical fields in recent years (10). 88However, its application in the clinical setting is still limited as their predictions are 89 usually difficult to interpret. Of note, with the methodology development in the past 90 few years, interpretable algorithms could unlock the traditional "black box" of 91 machine learning results (11). The integration of the new algorithms with large-scale 92 gut microbiome data have the potential to radically unveil the relationship between 93 gut microbiome and T2D. Yet, no such investigation has been done. 95Therefore, in the present study, we aimed to identify robust h...

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supporting

confidence: 69%

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Gou

Ling

et al. 2020

Preprint

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“…Guidelines of the Chinese Diabetes Society and the Chinese Taipei Diabetes Association recommend a target HbA1c of <7.0% and the Korean Diabetes Association recommend an HbA1c target of <6.5%. [3][4][5] Rates of glycaemic control are low in these Asia Pacific countries, with around one in three patients in China and Taiwan achieving an HbA1c of <7.0% and around one in four patients in Korea achieving an HbA1c of <6.5%. [6][7][8][9] Many people with type 2 diabetes (T2D) may require basal insulin therapy to achieve glycaemic control as their disease progresses.…”

Section: Based On the 2017 International Diabetes Federation Global Dmentioning

confidence: 99%

Efficacy and safety of insulin glargine 300 U/mL versus insulin glargine 100 U/mL in Asia Pacific insulin‐naïve people with type 2 diabetes: The EDITION AP randomized controlled trial

Kang

Dong

et al. 2019

Diabetes Obesity Metabolism

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Aim To compare the efficacy and safety of Gla‐300 versus Gla‐100 in insulin‐naïve people with type 2 diabetes in Asia Pacific. Materials and Methods In this open‐label, randomized, active‐controlled, 26‐week study, insulin‐naïve participants with type 2 diabetes inadequately controlled with non‐insulin antihyperglycaemic drugs were randomized (2:1) to Gla‐300 or Gla‐100. The initial daily dose of basal insulin was 0.2 U/kg and was adjusted at least weekly for 8–12 weeks to a target fasting self‐monitored plasma glucose (SMPG) of 4.4–5.6 mmol/L. Results Of the 604 participants randomized, 570 (Gla‐300, n = 375; Gla‐100, n = 195) completed the study. Non‐inferiority of Gla‐300 versus Gla‐100 in HbA1c reduction from baseline to week 26 was confirmed. In the Gla‐300 and Gla‐100 groups, 51.1% and 52.2% of participants achieved the HbA1c target of <7.0% (rate ratio [95% CI]: 0.98 [0.84 to 1.14]) and 19.1% and 21.9% achieved the target without hypoglycaemia during the last 12 weeks of treatment (rate ratio [95% CI]: 0.87 [0.63 to 1.20]). Changes in fasting plasma glucose and 24‐hour average eight‐point SMPG were comparable between groups. Incidence of hypoglycaemia at any time of day was similar between treatment groups at week 26, but incidence of any nocturnal hypoglycaemia was numerically lower with Gla‐300 than Gla‐100 over the initial 12‐week titration period and 26‐week on‐treatment period. Rates of adverse events were similar between groups and low for serious adverse events. Conclusions Glycaemic control of Gla‐300 is non‐inferior to Gla‐100 with a similar or lower incidence and proportion of hypoglycaemia in people with type 2 diabetes in Asia Pacific, reinforcing the results in the global EDITION programme.

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“…However, a report by the South Asian Federation of Endocrine Societies (SAFES) stated that, despite their well-established efficacy, safety and proven benefits, the clinical utility of SUs and their place in therapy are being inappropriately overshadowed by newer therapies [10]; however, this is not the case in China. Chinese T2DM guidelines are updated every 3 years, with the latest version published in a Chineselanguage journal in 2017 and an English-language journal in 2019 [11]. These guidelines show that SUs form the backbone of T2DM treatment in Chinese clinical practice, recommending SUs as an alternative choice of monotherapy when metformin is not tolerated and combined with other agents as dual and triple therapies [11].…”

Section: The Role Of Sulfonylurea-based Combination Therapy In This Pmentioning

confidence: 99%

“…Chinese T2DM guidelines are updated every 3 years, with the latest version published in a Chineselanguage journal in 2017 and an English-language journal in 2019 [11]. These guidelines show that SUs form the backbone of T2DM treatment in Chinese clinical practice, recommending SUs as an alternative choice of monotherapy when metformin is not tolerated and combined with other agents as dual and triple therapies [11]. This central place in the treatment algorithm is underpinned by the relatively superior glucose-lowing efficacy of SUs with reported mean HbA1c reductions of 1.0-1.5%, similar to metformin and GLP-1RAs, and better than newer agents such as SGLT2 inhibitors (mean reductions of 0.5-1.0) and dipeptidyl peptidase-4 (DPP-4) inhibitors (mean reductions of 0.4-0.9) ( Fig.…”

Section: The Role Of Sulfonylurea-based Combination Therapy In This Pmentioning

confidence: 99%

“…This central place in the treatment algorithm is underpinned by the relatively superior glucose-lowing efficacy of SUs with reported mean HbA1c reductions of 1.0-1.5%, similar to metformin and GLP-1RAs, and better than newer agents such as SGLT2 inhibitors (mean reductions of 0.5-1.0) and dipeptidyl peptidase-4 (DPP-4) inhibitors (mean reductions of 0.4-0.9) ( Fig. 1) [11][12][13].…”

Section: The Role Of Sulfonylurea-based Combination Therapy In This Pmentioning

confidence: 99%

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The Place of Sulfonylureas in the Evolving Landscape of Combination Therapy

2020

Diabetes Ther

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This article summarizes a presentation from a recent symposium entitled ''SUs in the treatment of T2DM: a fresh look and new insights'' held on 18 September 2019 during the 55th Annual Meeting of the European Association for the Study of Diabetes (EASD) in Barcelona, Spain, and discusses whether sulfonylureas (SUs) are a good 'team player.' It examines the likely impact of using SUs early in the course of type 2 diabetes mellitus (T2DM), either alone or in combination with other agents, on glycemic outcomes and net side effects. The management of patients with T2DM and cardiovascular disease or chronic kidney disease is discussed, highlighting how glycemic control and cardiorenal effects are equally important in these patients and chronic exposure to hyperglycemia should be minimized. The role of SU-based combination therapy in this patient group is explored, demonstrating how later-generation SUs, either as monotherapy or combined with other antidiabetic drugs, help to ensure maximum benefits with minimal side effects. Evidence regarding the combination of SUs with a sodium-glucose transport protein 2 inhibitor shows that this might prove to be a good clinical option, especially in patients with renal impairment.

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Standards of medical care for type 2 diabetes in China 2019

Cited by 452 publications

References 117 publications

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Efficacy and safety of insulin glargine 300 U/mL versus insulin glargine 100 U/mL in Asia Pacific insulin‐naïve people with type 2 diabetes: The EDITION AP randomized controlled trial

The Place of Sulfonylureas in the Evolving Landscape of Combination Therapy

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