Evidence map›Paper›PMID 39100221›Full record

ReviewDrug design, development and therapy2024

Impact of Lipids on Insulin Resistance: Insights from Human and Animal Studies.

Rani Yulifah Elkanawati, Sri Adi Sumiwi, Jutti Levita

Abstract readReview
In one paragraph

Review in Drug design, development and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.

0numbers the graph read from it
0cells of the map it votes in
63citing 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

63 citing papers in PubMed.

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  15. [Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
    Article
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3 more citing papers are in PubMed but not listed here.

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

3 authors.

Rani Yulifah Elkanawati *Master Program in Pharmacy, Faculty of Pharmacy, Padjadjaran University, Jawa Barat, West Java, 45363, Indonesia.
Sri Adi SumiwiDepartment of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Padjadjaran University, Sumedang, West Java, 45363, Indonesia.
Jutti Levita *Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Padjadjaran University, Sumedang, West Java, 45363, Indonesia.ORCID 0000-0002-4578-4174

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insulin resistance (IR) is a complex pathological condition central to metabolic diseases such as type 2 diabetes mellitus (T2DM), cardiovascular disease, non-alcoholic fatty liver disease, and polycystic ovary syndrome (PCOS). This review evaluates the impact of lipids on insulin resistance (IR) by analyzing findings from human and animal studies. The articles were searched on the PubMed database using two keywords: (1) "Role of Lipids AND Insulin Resistance AND Humans" and (2) "Role of Lipids AND Insulin Resistance AND Animal Models". Studies in humans revealed that elevated levels of free fatty acids (FFAs) and triglycerides (TGs) are closely associated with reduced insulin sensitivity, and interventions like metformin and omega-3 fatty acids show potential benefits. In animal models, high-fat diets disrupt insulin signaling and increase inflammation, with lipid mediators such as diacylglycerol (DAG) and ceramides playing significant roles. DAG activates protein kinase C, which eventually impairs insulin signaling, while ceramides inhibit Akt/PKB, further contributing to IR. Understanding these mechanisms is crucial for developing effective prevention and treatment strategies for IR-related diseases.

Indexed as

Insulin ResistanceAnimalsDiabetes Mellitus, Type 2Disease Models, AnimalHumansLipidsSignal TransductionLipidshigh-fat dietinsulin resistancelipid profiletype 2 diabetes mellitus

Identifiers

PMID39100221
PMCPMC11298177

What OpenQuestion holds

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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 OpenQuestion graph.