ArticleClinical and experimental nephrology2017
Genetic and environmental influences on the associations between change in kidney function and changes in cardiometabolic factors in Koreans.
Article in Clinical and experimental nephrology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- AHA Life's Essential 8 and new-onset CKD: a prospective cohort study from the UK Biobank.Clinical and experimental nephrology · 2024Article
- Article
- Glomerular Hyperfiltration as Predictor of Cardiometabolic Risk Factors among Children and Adolescents: The Childhood and Adolescence Surveillance and Prevention of Adult-V Study.International journal of preventive medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to evaluate genetic and environmental relations between change in estimated glomerular filtration rate (eGFR) and changes in cardiometabolic factors.
methodsIn 1772 Korean adults without diabetes and chronic kidney disease at baseline, changes in eGFR using the Chronic Kidney Disease Epidemiology Collaboration equation, blood pressure (BP), fasting serum glucose (FSG), insulin, homeostasis model assessment of insulin resistance index (HOMA-IR), hemoglobin A1c, triglycerides, high and low density lipoprotein cholesterol (HDL and LDL), uric acid, white blood cell (WBC) count, and body mass index (BMI) were calculated as follows: (value at follow-up - value at baseline) × 100/[value at baseline × follow-up interval (years)].
resultseGFR change was associated with 10 % changes in FSG (Odds ratio, OR = 1.36), uric acid (OR = 2.49), HDL (OR = 0.69), LDL (OR = 1.26), and WBC (OR = 1.15) after adjusting for age, sex, intra-familial and twin correlations, smoking, alcohol use, and physical activity at baseline, and BMI change using a generalized estimating equation. In bivariate variance-component analysis, eGFR change had additive genetic correlations ([Formula: see text]) with changes in insulin (-0.26), HOMA-IR (-0.24), diastolic BP (-0.15), uric acid (-0.45), triglycerides (-0.30), WBC (-0.46), and HDL (0.41), and environmental correlations ([Formula: see text]) with changes in FSG (-0.11), uric acid (-0.32), LDL (-0.14), and WBC (0.10). In co-twin control analyses in 319 monozygotic twin pairs, the ORs for having a greater eGFR decline with a 1 % increase in diastolic BP, uric acid, and LDL were 1.04, 1.09, and 1.03, respectively after adjusting for change in BMI and health behaviors at baseline.
conclusionsIn these Korean twins and families, additive genetic influences and environmental effects play significant roles in the associations between eGFR change and changes in cardiometabolic factors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.