Evidence map›Paper›PMID 35757194›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2022

The Association Between Hypertriglyceridemic-Waist Phenotype and Chronic Kidney Disease in Patients with Type 2 Diabetes: A Cross-Sectional METAL Study.

Yan Xuan, Wen Zhang, Yuying Wang, Bin Wang, Yi Chen, Fangzhen Xia, Kun Zhang, Qing Li, Ningjian Wang, Yingli Lu

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

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

10 authors at 2 institutions in 1 country.

Yan Xuan *Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Wen Zhang *Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yuying WangInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Bin WangInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yi ChenInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Fangzhen XiaInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Kun ZhangInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Qing LiInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Ningjian WangInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yingli LuInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Shanghai Ninth People's Hospital · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The aim of this study was measuring the association between the hypertriglyceridemic-waist (HTGW) phenotype and chronic kidney disease in a large type 2 diabetes population. Methods: A total of 4254 diabetic patients from the cross-sectional Environmental Pollutant Exposure and Metabolic Diseases in Shanghai (METAL) study were enrolled. The hypertriglyceridemic-waist (HTGW) phenotype was defined as the presence of an elevated waist circumference (WC) and elevated triglyceride (TG) concentration. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m Results: The prevalence of CKD was 29% and 35.8% in total participants and participants with HTGW phenotype, respectively. Subjects in the HTGW phenotype group were more likely to have CKD (OR 1.47, 95% CI: 1.11, 1.95) compared with subjects in the normal waist circumference and normal triglycerides (NTNW) group. HTGW phenotype was both associated with the increasing risk of decreased eGFR (OR 1.31, 95% CI: 1.02, 1.75) and elevated uACR (OR 1.57, 95% CI: 1.18, 2.11). Furthermore, the stratified analysis showed that the strongest positive association between HTGW phenotype and CKD presence was found in the subgroup of presence of hypertension. The associations were all fully adjusted for age, sex, BMI, current smoking, current drinking and other confounding factors. Conclusion: Our study suggested a positive association between the HTGW phenotype and CKD in Chinese type 2 diabetes patients. Further prospective studies are needed to confirm our findings and to investigate the underlying biological mechanisms.

Indexed as

central obesitychronic kidney diseasehypertriglyceridemia waist phenotypetype 2 diabetes mellitus

Identifiers

PMID35757194
PMCPMC9231417
OpenAlexW4283164785

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.