Evidence mapPaperPMID 38297986Full record

ArticleDiabetes, obesity & metabolism2024

Some patients with type 2 diabetes may benefit from intensive glycaemic and blood pressure control: A post-hoc machine learning analysis of ACCORD trial data.

Tianze Jiao, Hamed Kianmehr, Yilu Lin, Piaopiao Li, Naykky Singh Ospina, Hans K Ghayee, Mohammed Ruzieh, Vivian Fonseca, Lizheng Shi, Ping Zhang and 1 more

Open access · greenAbstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 1 country.

Tianze JiaoDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Hamed KianmehrDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Yilu LinDepartment of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0001-7040-4287
Piaopiao LiDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.ORCID 0000-0003-0941-5563
Naykky Singh OspinaDivision of Endocrinology, Diabetes, and Metabolism, University of Florida College of Medicine, Gainesville, Florida, USA.
Hans K GhayeeDepartment of Medicine, Division of Endocrinology, Diabetes, and Metabolism, University of Florida College of Medicine, Malcom Randall VA Medical Center, Gainesville, Florida, USA.
Mohammed RuziehDepartment of Medicine, Division of Cardiovascular Medicine, University of Florida College of Medicine, Gainesville, Florida, USA.
Vivian FonsecaDepartment of Medicine and Pharmacology, School of Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0002-3381-7151
Lizheng ShiDepartment of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0002-7827-6766
Ping ZhangDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Hui ShaoDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.ORCID 0000-0002-4088-546X
University of Florida · USTulane University · USCenter for Drug Evaluation and Research · USCenters for Disease Control and Prevention · USEmory University · US

Funding

Technologies Advancing Translation - Regional CoreP30DK111024 · EMORY UNIVERSITY · 2025 to 2025
$775k
Building Equity Improvement into Quality Improvement in the use of New Glucose-lowering Drugs (GLDs) through Individualized Drug Value Assessment in People with DiabetesR01DK133465 · UNIVERSITY OF FLORIDA · 2025 to 2025
$593k
NCI NIH HHS K08 CA248972NIDDK NIH HHS P30 DK111024NIDDK NIH HHS R01 DK133465NIH HHS K08CA248972
6 · The paper itself

Abstract

aimThe action to control cardiovascular risk in diabetes (ACCORD) trial showed a neutral average treatment effect of intensive blood glucose and blood pressure (BP) controls in preventing major adverse cardiovascular events (MACE) in individuals with type 2 diabetes. Yet, treatment effects across patient subgroups have not been well understood. We aimed to identify patient subgroups that might benefit from intensive glucose or BP controls for preventing MACE. MATERIALS AND

methodsAs a post-hoc analysis of the ACCORD trial, we included 10 251 individuals with type 2 diabetes. We applied causal forest and causal tree models to identify participant characteristics that modify the efficacy of intensive glucose or BP controls from 68 candidate variables (demographics, comorbidities, medications and biomarkers) at the baseline. The exposure was (a) intensive versus standard glucose control [glycated haemoglobin (HbA1c) <6.0% vs. 7.0%-7.9%], and (b) intensive versus standard BP control (systolic BP <120 vs. <140 mmHg). The primary outcome was MACE.

resultsCompared with standard glucose control, intensive one reduced MACE in those with baseline HbA1c <8.5% [relative risk (RR): 0.79, 95% confidence interval (CI): 0.67-0.93] and those with estimated glomerular filtration rate ≥106 ml/min/1.73 m

conclusionsOur findings suggest heterogeneous treatment effects of intensive glucose and BP control and could provide biomarkers for future clinical trials to identify more precise HbA1c and BP treatment goals for individualized medicine.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2BiomarkersBlood GlucoseBlood PressureFemaleGlycated HemoglobinHeart Disease Risk FactorsHumansMaleBiomarkersBlood GlucoseGlycated Hemoglobincardiovascular diseasecomparative effectivenessdiabetes complicationsglycaemic controlmachine learning

Identifiers

PMID38297986
PMCPMC10987080
OpenAlexW4391444133

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.