Evidence map›Paper›PMID 38893112›Full record

ReviewCancers2024

Metabolic Response to Androgen Deprivation Therapy of Prostate Cancer.

Yubin Chen, Pao-Hwa Lin, Stephen J Freedland, Jen-Tsan Chi

Abstract readReview
In one paragraph

Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

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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

4 authors.

Yubin ChenDepartment of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, USA.ORCID 0009-0001-7317-818X
Pao-Hwa LinDepartment of Medicine, Duke University, Durham, NC 27708, USA.
Stephen J FreedlandCenter for Integrated Research in Cancer and Lifestyle, Cedars-Sinai, Los Angeles, CA 90048, USA.
Jen-Tsan ChiDepartment of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, USA.ORCID 0000-0003-3433-903X

Funding

National Institute of Health 1K24CA160653-01A1
6 · The paper itself

Abstract

Prostate cancer (PC) stands as the most frequently diagnosed non-skin cancer and ranks as the second highest cause of cancer-related deaths among men in the United States. For those facing non-metastatic PC necessitating intervention, solely local treatments may not suffice, leading to a possible transition toward systemic therapies, including androgen deprivation therapy (ADT), chemotherapy, and therapies targeting androgen. Yet, these systemic treatments often bring about considerable adverse effects. Additionally, it is observed that overweight men are at a higher risk of developing aggressive forms of PC, advancing to metastatic stages, and succumbing to the disease. Consequently, there is a pressing demand for new treatment options that carry fewer side effects and enhance the current standard treatments, particularly for the majority of American men who are overweight or obese. In this article, we will review the metabolic response to ADT and how lifestyle modulation can mitigate these ADT-associated metabolic responses with a particular focus on the two clinical trials, Carbohydrate and Prostate Study 1 (CAPS1) and Carbohydrate and Prostate Study 2 (CAPS2), which tested the effects of low-carbohydrate diets on the metabolic side effects of ADT and PC progression, respectively. Furthermore, we will summarize the findings of serum metabolomic studies to elucidate the potential mechanisms by which ADT and low-carbohydrate diets can affect the metabolic response to mitigate the metabolic side effects while maximizing therapeutic efficacy.

Indexed as

ADTdietmetabolic responseprostate cancer

Identifiers

PMID38893112
PMCPMC11171316

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.