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  ["property_scientific_abstract"]=>
  string(2697) "Background

Type 2 diabetes mellitus (T2DM) is a complex and highly heterogeneous disease characterized by insulin resistance or insulin deficiency. Based on the age at diagnosis, body mass index (BMI), HbA1c, glutamic acid decarboxylase antibodies (GADA), homeostasis model-assessed beta-cell function (HOMA2-?) and insulin resistance (HOMA2-IR),  diabetes has been reclassified into the following subgroups by the K-means cluster method:  mild obesity-related diabetes (MOD), mild age-related diabetes (MARD), severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), and severe insulin-resistant diabetes (SIRD). The five phenotypes differ in terms of the risk of diabetic complications, drug responses and metabolic surgery. However, whether the effects of SGLT2i on glucose lowering and cardiovascular outcomes are different among clusters is unknown.

Objective

To incorporate new clusters in several cohorts of type 2 diabetes (T2DM) patients and compare the anti-glycemic effects of SGLT-2 inhibitors across different clusters.

Study Design

This is a retrospective study. We used data from five randomised, double-blinded, multicentre clinical trials of SGLT-2 inhibitors

Participants

Diabetic patients with the treatment of SGLT-2 inhibitors were enrolled in this study.

Primary and Secondary Outcome Measure(s);

The primary outcomes were the changes in glucose metabolism and cardiovascular outcomes from baseline to the end in different clusters. Secondary endpoints included BMI, lipid profiles, urinary albumin. Lipid profiles consisted of triglyceride (TG), low-density lipoprotein cholesterol, (LDL-C) and high-density lipoprotein cholesterol (HDL-C).

Statistical Analysis.

Clusters were associated with the baseline characteristics and post-treatment outcomes of the corresponding participants. The characteristics of patients with T2DM were presented as the mean  standard deviation or standard error for quantitative parameters and percentage for categorical variables. ANOVA tests and chi-squared tests were used for normally distributed continuous variables and categorical variables for inter-group, separately. The efficacy endpoints (change of HbA1c, fasting glucose, 2hPG and BMI) were analyzed using a Linear regression model adjusted by age, gender, weight, baseline HbA1c, baseline fasting glucose and baseline P2BG. In subgroup analysis, the p value for interaction (pinteraction) across clusters was measured by the likelihood ratio test (LRT).

R software version 4.1.0 was used for all statistical analyses. Python software version 3.5 was used for clustering. A 2-tailed with P"
  ["project_brief_bg"]=>
  string(611) "Currently, evidence is accumulating to address whether the clinical response of oral glucose-lowering drugs varies across different diabetic subtypes. However, whether the effects of SGLT2i on glucose lowering and clinical outcomes are different among data-driven clusters is largely unknown. In the realm of precision medicine, unsupervised learning is frequently used in predicting clinical outcomes and the clinical response to the OHD. This study aimed to investigate whether stratification of individuals by data-driven clustering  can distinguish those deriving greater benefit from treatment with SGLT2i."
  ["project_specific_aims"]=>
  string(171) "This study aimed to investigate whether stratification of individuals by data-driven clustering  can distinguish those deriving greater benefit from treatment with SGLT2i."
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      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
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      ["label"]=>
      string(49) "New research question to examine treatment safety"
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  string(196) "Among them, patients aged ?18 years at the time of first diagnosis of diabetes, and for whom, complete clinical data were available were included in this study. Patients were excluded if they aged"
  ["project_main_outcome_measure"]=>
  string(431) "The primary endpoint of this study was the glucose metabolism and cardiovascular outcomes in response to SGLT-2 inhibitors in four subgroups. Secondary endpoints included HbA1c, BMI, lipid profiles, urinary albumin. Lipid profiles consisted of triglyceride (TG), low-density lipoprotein cholesterol, (LDL-C) and high-density lipoprotein cholesterol (HDL-C). All variables met quality-control standards with less than 25% variation."
  ["project_main_predictor_indep"]=>
  string(901) "We applied the clustering method provided by Ahlqvist on MARCH cohort with newly diagnosed diabetes. Clinical indicators for clustering included age at diagnosis, body mass index (BMI), HbA1c, HOMA2-?, and HOMA2-IR. The HOMA2-? and HOMA2-IR were calculated based on the concentration of C-peptide (C-P) as described before9. Patients were classified into four subgroups: SIDD, SIRD, MARD and MOD at baseline, week 24 and week 48. The K-means clustering algorithm was used to derive four centroids from all available patients at each time point, corresponding to the four subgroups. Euclidean distances between each patient and the four centroids were calculated to measure the patients? similarity to each subgroup, following which the patients were classified into the most similar subgroups. The cluster analysis was performed with a built-in k-means algorithm in the scikit-learn library of Python."
  ["project_other_variables_interest"]=>
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  ["project_stat_analysis_plan"]=>
  string(925) "Clusters were associated with the baseline characteristics and post-treatment outcomes of the corresponding participants. The characteristics of patients with T2DM were presented as the mean  standard deviation or standard error for quantitative parameters and percentage for categorical variables. ANOVA tests and chi-squared tests were used for normally distributed continuous variables and categorical variables for inter-group, separately. The efficacy endpoints (change of HbA1c, fasting glucose, 2hPG and BMI) were analyzed using a Linear regression model adjusted by age, gender, weight, baseline HbA1c, baseline fasting glucose and baseline P2BG. In subgroup analysis, the p value for interaction (pinteraction) across clusters was measured by the likelihood ratio test (LRT).

R software version 4.1.0 was used for all statistical analyses. Python software version 3.5 was used for clustering. A 2-tailed with P"
  ["project_timeline"]=>
  string(221) "Project start date: 2022.12.01

Analysis completion date: 2023.06.01

Manuscript drafted: 2023.12.01

First submitted for publication: 2024. 02.01

Results reported back to the YODA Project: 2024.03.01"
  ["project_dissemination_plan"]=>
  string(72) "Potentially suitable journals: diabetologia, diabetes care, ebiomedicine"
  ["project_bibliography"]=>
  string(1391) "
1. Philipson, L. H. Harnessing heterogeneity in type 2 diabetes mellitus. Nat. Rev. Endocrinol. 16, 79?80 (2020).

2. Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361?369 (2018).

3. Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7, 442?451 (2019).

4. Raverdy, V. et al. Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study. Lancet Diabetes Endocrinol. 10, 167?176 (2022).

5. Zou, X., Zhou, X., Zhu, Z. & Ji, L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol. 7, 9?11

6. Li, X. et al. Validation of the Swedish Diabetes Re-Grouping Scheme in Adult-Onset Diabetes in China. J. Clin. Endocrinol. Metab. 105, dgaa524 (2020).

7. Wang, W. et al. Application of new international classification of adult-onset diabetes in Chinese inpatients with diabetes mellitus. Diabetes Metab. Res. Rev. (2020) doi:10.1002/dmrr.3427.
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    string(36) "yoda_project_review_-_2022-5076_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(6164)
    ["date"]=>
    string(19) "2023-08-09 16:58:23"
    ["modified"]=>
    string(19) "2023-08-09 19:10:55"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["search_order"]=>
  string(5) "-8460"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(1) {
  [0]=>
  string(5) "China"
}


pi affil
array(1) {
  [0]=>
  string(5) "Other"
}


products
array(1) {
  [0]=>
  string(8) "invokana"
}


num of trials
array(1) {
  [0]=>
  string(1) "5"
}


res
array(1) {
  [0]=>
  string(1) "3"
}