ArticleNutrients2022
Dietary Pattern Associated with the Risk of Poor Glycemic Control in Chinese Diabetic Adults: Results from the China Nutrition and Health Surveillance 2015-2017 Survey.
Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 5 citations in OpenAlex.
- Machine Learning Analysis of Blood Glucose Regulation in Korean Male Workers with Type 2 Diabetes.Iranian journal of public health · 2025Article
- Factors influencing health-related quality of life among people with uncontrolled type II diabetes mellitus in Wenzhou, China: A cross-sectional study.Belitung nursing journal · 2025Article
- Dietary Patterns and New-Onset Diabetes Mellitus in Southwest China: A Prospective Cohort Study in the China Multi-Ethnic Cohort (CMEC).Nutrients · 2024Article
- The association between alcohol drinking and glycemic management among people with type 2 diabetes in China.Journal of diabetes investigation · 2024Article
- Socio-Demographic Factors Associated with Rural Residents' Dietary Diversity and Dietary Pattern: A Cross-Sectional Study in Pingnan, China.Nutrients · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study was conducted to investigate the nutrition status of dietary patterns and the association between dietary patterns and the risk of poor glycemic control in Chinese diabetics. The relevant data was collected from the China Nutrition and Health Surveillance 2015-2017 survey, which is a national cross-sectional surveillance program. A total of 2031 participants were included in the present statistical analysis. Food consumption was assessed through a validated and standard food frequency questionnaire. Dietary patterns were derived with reduced rank regression using hemoglobin A1c. Diabetes was diagnosed by medical institutions, glycemic control was defined as hemoglobin A1c less than 7%, poor glycemic control was defined as hemoglobin A1c greater than 7%. A multiple-variable-adjusted logistic regression, including age, living area, income level, educational attainment, body mass index, occupational physical activity, energy intake, current smoking status, current drinking status, diabetic medication use, insulin use, following diabetic diets, increased exercise, and glucose monitoring, was adjusted to explore the association between dietary patterns and the risk of poor glycemic control in diabetes. Two gender-specific dietary patterns have an increased risk of poor glycemic control and are characterized by a low intake of freshwater fish, poultry, and fruits. For male participants, the dietary pattern was characterized by a high intake of wheat and its products, a low intake of vegetables, corn and its products, shrimp and crab, and beans (Q4 vs. Q1,
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.