Evidence map›Paper›PMID 38258774›Full record

ArticleEndocrine, metabolic & immune disorders drug targets2024

Insight into the Predictive Power of Surrogate Diagnostic Indices for Identifying Individuals with Metabolic Syndrome.

Shaghayegh Hosseinkhani, Katayoon Forouzanfar, Nastaran Hadizadeh, Farideh Razi, Somayeh Darzi, Fatemeh Bandarian

Abstract read
PubMed Publisher
In one paragraph

Article in Endocrine, metabolic & immune disorders drug targets, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

6 authors.

Shaghayegh HosseinkhaniDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Katayoon ForouzanfarMetabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular- Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Nastaran HadizadehMetabolic Disorders Research Center, Endocrinology and Metabolism Molecular -Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Farideh RaziEndocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Somayeh DarziEndocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Fatemeh BandarianMetabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular- Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to assess the diagnostic capability of insulin surrogate measurements in identifying individuals with metabolic syndrome (MetS) and propose applicable indices derived from fasting values, particularly in large study populations.

methodsData were collected from the datasets of the Surveillance of Risk Factors of NCDs in Iran Study (STEPS). MetS was defined based on the National Cholesterol Education Program (NCEP) criteria. Various insulin surrogate indices, including Homeostasis Model Assessment (HOMA), Quantitative Insulin Sensitivity Check Index (QUICKI), Fasting glucose to insulin ratio (FGIR), Reynaud, Reciprocal insulin, McAuley, Metabolic Score for Insulin Resistance (METS-IR), Triglyceride-glucose index (TyG), TG/ HDL-C, TG/ BMI, and TG/ WC ratio were assessed. Receiver Operating Characteristic (ROC) curves were used to assess pathologic conditions and determine the optimal cut-off through the highest score of the Youden index. Also, Area Under the Curve (AUC) values were established for each index totally and according to sex, age, and BMI differences.

resultsThe study population consisted of 373 individuals (49.9% women; 75.1% middle age, 39.1% obese, and 27.3% overweight), of whom 117 (31.4%) had MetS. The METS-IR (AUC: 0.856; 95% CI: 0.817-0.895), TG/ HDL-C (AUC: 0.820; 95% CI: 0.775-0.886), TyG (AUC: 0.808; 95% CI: 0.759-0.857), and McAuley (AUC: 0.804; 95% CI: 0.757-0.852) indices provided the greatest AUC respectively for detection of MetS. The values of AUC for all the indices were higher in men than women. This trend was consistent after data stratification based on BMI categories, middle age, and senile individuals.

conclusionThe present study indicated that indices of insulin, including METS-IR, TG/HDLC, TyG, and McAuley, have an equal or better capacity in determining the risk of MetS than HOMA-IR, are capable of identifying individuals with MetS and may provide a simple approach for identifying populations at risk of insulin resistance.

Indexed as

BiomarkersBlood GlucoseInsulin ResistanceMetabolic SyndromePredictive Value of TestsAdultAgedBody Mass IndexCross-Sectional StudiesFemaleHumansInsulinIranMaleMiddle AgedRisk FactorsBiomarkersBlood GlucoseInsulinTriglyceridesBMI.insulin indicesinsulin resistanceMetabolic syndromeMetSreceiver operating characteristic

Identifiers

PMID38258774

What Socratic holds

Textmetadata
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.