Evidence map›Paper›PMID 39107586›Full record

ArticleScientific reports2024

A novel nomogram for predicting optimal weight loss response following diet and exercise intervention in patients with obesity.

Lei Yu, Jing Wang, Zhendong Hu, Tiancheng Xu, Weihong Zhou

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Lei Yu *Department of Health Management Centre, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, China.
Jing Wang *Department of Health Management Centre, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, China.
Zhendong HuDepartment of Esophageal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, China.
Tiancheng XuDepartment of Esophageal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, China. drxutiancheng@foxmail.com.
Weihong ZhouDepartment of Health Management Centre, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, China. njzhouwh@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify factors associated with optimal weight loss response by analyzing pre-weight loss data from a cohort of 2577 patients with obesity who visited weight management clinics between 2013 and 2022. Out of these, 1276 patients had follow-up data available. Following dietary and exercise interventions, 580 participants achieved optimal weight loss outcomes. Participants were subsequently divided into two groups based on their weight loss outcomes: those who achieved optimal weight loss response and those who did not. Statistical analysis, conducted using RStudio, identified thirteen predictor variables through LASSO and logistic regression, with age emerging as the most influential predictor. A nomogram was developed to predict optimal weight loss response, showing good predictive performance (AUC = 0.807) and clinical applicability, validated by internal validation methods. Decision curve analysis (DCA) further illustrated the nomogram's clinical utility. The developed nomogram prediction model for optimal weight loss response is user-friendly, highly accurate, and demonstrates excellent discriminative and calibration capabilities.

Indexed as

ExerciseNomogramsObesityWeight LossAdultDiet, ReducingExercise TherapyFemaleHumansMaleMiddle AgedTreatment OutcomeNomogramObesityPrediction modelWeight loss

Identifiers

PMID39107586
PMCPMC11303791

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.