Evidence map›Paper›PMID 36922829›Full record

ArticleReproductive biology and endocrinology : RB&E2023

Machine learning-based clustering to identify the combined effect of the DNA fragmentation index and conventional semen parameters on in vitro fertilization outcomes.

Tianwen Peng, Chen Liao, Xin Ye, Zhicong Chen, Xiaomin Li, Yu Lan, Xin Fu, Geng An

Open access · goldFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in Reproductive biology and endocrinology : RB&E, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 8% 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

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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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Tianwen Peng *Department of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Chen Liao *Department of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Xin Ye *Department of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Zhicong ChenDepartment of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Xiaomin LiDepartment of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Yu LanDepartment of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Xin FuDepartment of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China.
Geng AnDepartment of Obstetrics and Gynecology, Center of Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China. angeng0505@outlook.com.
Third Affiliated Hospital of Guangzhou Medical University · CNGuangzhou Medical University · CN

Funding

National Key Research and Development Program of China 2019YFE0109500the Guangdong Natural Science Foundation 2019A1515011439the National Key R&D Plan 2019YFE0109500the National Natural Science Foundation of China 82171589the National Natural Science Foundation of China 82203709the Science and Technology Program of Guangzhou, China 202102010075
6 · The paper itself

Abstract

backgroundPrevious studies have demonstrated an association between male sperm quality and assisted reproduction outcomes, focusing on the effects of individual parameters and reaching controversial conclusions. The WHO 6th edition manual highlights a new semen assay, the sperm DNA fragmentation index, for use after routine semen examination. However, the combined effect of the sperm DNA fragmentation index (DFI) and routine semen parameters remains largely unknown.

methodsWe assessed the combined effect of the sperm DFI and conventional semen parameters on single fresh conventional IVF outcomes for infertile couples from January 1, 2017, to December 31, 2020. IVF outcomes were obtained from the cohort database follow-up records of the Clinical Reproductive Medicine Management System of the Third Affiliated Hospital of Guangzhou Medical University. An unsupervised K-means clustering method was applied to classify participants into several coexposure pattern groups. A multivariate logistic regression model was used for statistical analysis.

resultsA total of 549 live births among 1258 couples occurred during the follow-up period. A linear exposure-response relationship was observed among the sperm DFI, sperm motility, and IVF outcomes. In multivariable adjustment, increased sperm DFI values and decreased sperm motility and semen concentration levels were associated with reduced odds of favourable IVF outcomes. Four coexposure patterns were generated based on the sperm DFI and the studied semen parameters, as follows: Cluster 1 (low sperm DFI values and high sperm motility and semen concentration levels), Cluster 2 (low sperm DFI values and moderate sperm motility and semen concentration levels), Cluster 3 (low sperm DFI values and low sperm motility and semen concentration levels) and Cluster 4 (high sperm DFI values and low sperm motility and semen concentration levels). Compared with those in Cluster 1, participants in Cluster 3 and Cluster 4 had lower odds of a live birth outcome, with odds ratios (95% confidence intervals [CIs]) of 0.733 (0.537, 0.998) and 0.620 (0.394, 0.967), respectively.

conclusionsWhen combined with low sperm DFI values, there was no significant difference between high or moderate sperm concentration and motility levels, and both were associated with favourable IVF outcomes. Low sperm parameter levels, even when DFI values remain low, may still lead to poor IVF outcomes. Participants with high sperm DFI values and low sperm motility and semen concentration levels had the worst outcomes. Our findings offer a novel perspective for exploring the joint effects of sperm DFI and routine semen parameter values.

Indexed as

Infertility, MaleSemenCluster AnalysisDNA FragmentationFertilization in VitroHumansMaleSpermatozoaSperm MotilityCoexposureIVF outcomesK-means clusteringRoutine semen parametersSperm DNA fragmentation index

Identifiers

PMID36922829
PMCPMC10015711
OpenAlexW4324325888

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LicenceCC BY
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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.