Evidence map›Paper›PMID 41058237›Full record

ArticleAnnals of medicine2025

Temporal trends and machine learning prediction for chronic kidney disease due to hypertension in young adults from 1990 to 2021: an analysis of the global burden of disease study 2021.

Yiqun Miao, Yuan Luo, Yuhan Zhao, Huiying Wang, Ying Wu

Abstract read
In one paragraph

Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

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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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

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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.

Yiqun MiaoSchool of Nursing, Capital Medical University, Beijing, China.ORCID 0000-0002-6084-3662
Yuan LuoSchool of Nursing, Capital Medical University, Beijing, China.
Yuhan ZhaoSchool of Nursing, Capital Medical University, Beijing, China.
Huiying WangSchool of Nursing, Capital Medical University, Beijing, China.
Ying WuSchool of Nursing, Capital Medical University, Beijing, China.ORCID 0000-0002-8633-5404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundYoung adults aged 25-49, at the peak of work and family responsibilities, face serious health and productivity challenges when affected by chronic kidney disease (CKD) due to hypertension.

methodsUsing Global Burden of Disease (GBD) 1990-2021 data, we assessed global, regional, and national CKD burden due to hypertension in young adults, focusing on mortality and disability-adjusted life years (DALYs). Trends analyzed by Joinpoint regression and forecasted to 2040 using eight machine learning models.

resultsIn 2021, global mortality from CKD due to hypertension reached 18,675.24 cases, a 157.55% increase since 1990. The age-standardized mortality rate (ASMR) rose from 0.31 to 0.45 per 100,000 persons, with an average annual percentage change (AAPC) of 1.24% (

conclusionsCKD due to hypertension is a growing global challenge in young adults. Substantial heterogeneity exists in the disease burden and its temporal trends across countries and regions, highlighting the strong distributive inequities of CKD worldwide.

Indexed as

HypertensionMachine LearningRenal Insufficiency, ChronicAdultDisability-Adjusted Life YearsFemaleGlobal Burden of DiseaseGlobal HealthHumansMaleMiddle AgedYoung AdultChronic kidney diseaseglobal disease burdenhypertensionmachine learningtemporal trend

Identifiers

PMID41058237
PMCPMC12509297

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