Serum Metal Levels and Dyslipidemia Risk in a Rural Chinese Cohort

A 2026 nested case-control study of 782 adults from a cohort in Ningxia, China, analyzed associations between 15 serum trace metals and dyslipidemia. The research, authored by Jiangwei Qiu, used conditional logistic regression, Bayesian kernel machine regression, and weighted quantile sum regression to evaluate joint metal exposures. The dataset includes results for 391 cases and 391 matched controls.

Use Cases

• Assessing the association between lithium and tin levels and dyslipidemia risk based on the reported odds ratios.
• Modeling the joint effect of metal mixtures on health outcomes using the Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS) methods described.
• Investigating potential interactions between metals like lithium-nickel and tin-manganese mentioned in the results.
• Studying biomarker distributions for 15 trace metals in a rural adult population.

Strengths

• Includes data for 782 subjects with a 1:1 case-control ratio, providing a balanced study design.
• Analyzes 15 distinct trace metals, allowing for multi-exposure assessment.
• Employs multiple statistical methods (conditional logistic regression, BKMR, WQS) for robust analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying dataset is unknown, which may limit suitability assessment.
• Data reflects a specific geographic and demographic bias inherent to the rural Ningxia cohort.

Provenance

Source

figshare, author Jiangwei Qiu.

Collection Method

Nested case-control analysis from a cohort study; serum metal levels determined by inductively coupled plasma atomic emission spectrometry.

Freshness

Last updated 2026-05-21 04:19:24; freshness should be verified.

Geography

Ningxia, China (rural cohort).