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Record W4414361910 · doi:10.1007/s13300-025-01796-7

Pillar Risk-Based Treatment for Chronic Kidney Disease in People With Type 2 Diabetes: A Narrative Review

2025· article· en· W4414361910 on OpenAlexaff
Alice Cheng, Amy K. Mottl, Melissa Magwire

Bibliographic record

VenueDiabetes Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsPublic Health OntarioTrillium Health CentreUniversity of Toronto
FundersBayer
KeywordsKidney diseaseTolerabilityContext (archaeology)DiseaseNarrative reviewDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

Chronic kidney disease continues to be a significant burden for people living with type 2 diabetes, despite the available guideline-directed treatment options. Traditionally, a stepwise approach has been implemented for the management of chronic kidney disease and type 2 diabetes, which involves the linear sequential initiation of one therapy after the other on the basis of an individual’s treatment outcomes. However, this approach is not beneficial for all individuals, as it can lead to treatment inertia and subsequent disease progression. Therefore, primary care practitioners should consider implementing a more proactive treatment strategy to optimize care. The pillar risk-based approach is an emerging concept with goals of glucose control and blood pressure control as well as comprising simultaneous or rapid sequential initiation of multiple therapies, such as renin–angiotensin system inhibitors (RASi), sodium–glucose cotransporter 2 inhibitors, a nonsteroidal mineralocorticoid receptor antagonist (finerenone), and glucagon-like peptide-1 receptor agonists, which target the different hemodynamic, metabolic, and fibrotic/inflammatory pathways involved in chronic kidney disease and type 2 diabetes. This approach enables earlier chronic kidney disease risk reduction, and the recently published CONFIDENCE trial reported tolerability and efficacy of simultaneous initiation of two of these therapies (finerenone and empagliflozin) in those already receiving RASi. This review article provides primary care practitioners with practical considerations, discussing current guideline-directed treatment options for chronic kidney disease in people with type 2 diabetes in the context of a historical stepwise approach versus the new patient-centric pillar risk-based approach. Chronic kidney disease is a major complication for people with type 2 diabetes. Treatments for people with type 2 diabetes and chronic kidney disease has traditionally followed a stepwise approach, wherein therapies are introduced sequentially, one by one, on the basis of how the person responds to treatment. This stepwise approach to treatment is slow and may take months or years for a person with chronic kidney disease to be on maximal therapy, thus losing crucial time and nephrons. We discuss a potentially different approach to treatment (the pillar risk-based approach) that offers a more proactive strategy and involves starting multiple therapies early and simultaneously or in a rapid stepwise fashion. This pillar risk-based approach integrates blood glucose control and blood pressure control alongside medications that slow progression of chronic kidney disease. Key treatments include renin–angiotensin system inhibitors, sodium–glucose cotransporter 2 inhibitors, a nonsteroidal mineralocorticoid receptor antagonist, and glucagon-like peptide-1 receptor agonists. By addressing multiple disease mechanisms at the same time, the pillar risk-based approach to treatment aims to reduce the risk for kidney failure requiring dialysis or kidney transplantation, as well as cardiovascular events and death. This review article discusses the current guideline-directed treatment options for chronic kidney disease in people with type 2 diabetes in the context of a historical stepwise approach versus the patient-focused pillar risk-based approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.282
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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