Evidence map›Paper›PMID 35923113›Full record

ArticleAnnals of clinical and translational neurology2022

Serum lipidomic determinants of human diabetic neuropathy in type 2 diabetes.

Farsad Afshinnia, Evan L Reynolds, Thekkelnaycke M Rajendiran, Tanu Soni, Jaeman Byun, Masha G Savelieff, Helen C Looker, Robert G Nelson, George Michailidis, Brian C Callaghan and 2 more

Open access · goldAbstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
3.2field-weighted citation impact, top 7% 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

25 citing papers in PubMed, 36 citations in OpenAlex.

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  12. Type 2 diabetes mellitus in adults: pathogenesis, prevention and therapy.Signal transduction and targeted therapy · 2024 · on this map
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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

12 authors at 3 institutions in 2 countries.

Farsad AfshinniaDepartment of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-3051-3141
Evan L ReynoldsNeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-0138-8436
Thekkelnaycke M RajendiranUniversity of Michigan, Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, Michigan, USA.
Tanu SoniUniversity of Michigan, Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, Michigan, USA.
Jaeman ByunDepartment of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
Masha G SavelieffNeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-5575-2494
Helen C LookerChronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA.
Robert G NelsonChronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA.
George MichailidisDepartment of Statistics and the Informatics Institute, University of Florida, Gainesville, Florida, USA.
Brian C CallaghanNeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-8885-6748
Subramaniam PennathurDepartment of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-3628-6883
Eva L FeldmanNeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-9162-2694
University of Michigan · USNational Institute of Diabetes and Digestive and Kidney Diseases · USUniversity of Florida · US

Funding

Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Scott Soleimanpour · 2013 to 2026
$24.3M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Joyce Lee · 2010 to 2026
$20.3M
Integrated Systems Biology Approach to Diabetic Microvascular ComplicationsR24DK082841 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABCOUWER, STEVEN F, BROSIUS, FRANK C · 2008 to 2020
$13.1M
University of Michigan O'Brien Kidney Translational Core CenterP30DK081943 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PENNATHUR, SUBRAMANIAM · 2008 to 2022
$12.9M
Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
TRAINING IN CLINICAL AND BASIC NEUROSCIENCET32NS007222 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Henry L Paulson · 1985 to 2026
$8.0M
Prognostic Biomarkers in Chronic Kidney DiseaseK08DK106523 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AFSHINNIA, FARSAD · 2015 to 2019
$789k
Predicting complications of diabetes with longitudinal metabolic trajectoriesR00DK129785 · NIDDK · HENRY FORD HEALTH + MICHIGAN STATE UNIVERSITY HEALTH SCIENCES · PI Evan L Reynolds · 2024 to 2026
$745k
Predicting complications of diabetes with longitudinal metabolic trajectoriesK99DK129785 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI REYNOLDS, EVAN L · 2022 to 2023
$182k
Lipidomic Predictors of Diabetic Kidney Disease Progression in Patients with Type-1 DiabetesR03DK121941 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AFSHINNIA, FARSAD · 2019 to 2020
$169k
NIDDK NIH HHS K08 DK106523NIDDK NIH HHS K99 DK129785NIDDK NIH HHS P30 DK020572NIDDK NIH HHS P30 DK081943NIDDK NIH HHS P30 DK089503NIDDK NIH HHS P30 DK092926NIDDK NIH HHS R00 DK129785NIDDK NIH HHS R03 DK121941NIDDK NIH HHS R24 DK082841NINDS NIH HHS T32 NS007222
6 · The paper itself

Abstract

objectiveThe serum lipidomic profile associated with neuropathy in type 2 diabetes is not well understood. Obesity and dyslipidemia are known neuropathy risk factors, suggesting lipid profiles early during type 2 diabetes may identify individuals who develop neuropathy later in the disease course. This retrospective cohort study examined lipidomic profiles 10 years prior to type 2 diabetic neuropathy assessment.

methodsParticipants comprised members of the Gila River Indian community with type 2 diabetes (n = 69) with available stored serum samples and neuropathy assessment 10 years later using the combined Michigan Neuropathy Screening Instrument (MNSI) examination and questionnaire scores. A combined MNSI index was calculated from examination and questionnaire scores. Serum lipids (435 species from 18 classes) were quantified by mass spectrometry.

resultsThe cohort included 17 males and 52 females with a mean age of 45 years (SD = 9 years). Participants were stratified as with (high MNSI index score > 2.5407) versus without neuropathy (low MNSI index score ≤ 2.5407). Significantly decreased medium-chain acylcarnitines and increased total free fatty acids, independent of chain length and saturation, in serum at baseline associated with incident peripheral neuropathy at follow-up, that is, participants had high MNSI index scores, independent of covariates. Participants with neuropathy also had decreased phosphatidylcholines and increased lysophosphatidylcholines at baseline, independent of chain length and saturation. The abundance of other lipid classes did not differ significantly by neuropathy status.

interpretationAbundance differences in circulating acylcarnitines, free fatty acids, phosphatidylcholines, and lysophosphatidylcholines 10 years prior to neuropathy assessment are associated with neuropathy status in type 2 diabetes.

Indexed as

Diabetes Mellitus, Type 2Diabetic NeuropathiesFatty Acids, NonesterifiedFemaleHumansLipidomicsLysophosphatidylcholinesMaleMiddle AgedPhosphatidylcholinesRetrospective StudiesFatty Acids, NonesterifiedLysophosphatidylcholinesPhosphatidylcholines

Identifiers

PMID35923113
PMCPMC9463947
OpenAlexW4289731719

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.