糖尿病及糖尿病前期中系统性炎症指标的机器学习与人群归因分数分析用于死亡风险预测

数据集获取（Figshare）

• 源站标题：Integrated machine learning and population attributable fraction analysis of systemic inflammatory indices for mortality risk prediction in diabetes and prediabetes
• DOI：10.6084/m9.figshare.29648741.v1
• 发布日期：2025-07-26
• 文件数：7 个
• 文件总体积：6.99 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Integrated_machine_learning_and_population_attributable_fraction_analysis_of_systemic_inflammatory_indices_for_mortality_risk_prediction_in_diabetes_and_prediabetes/29648741

命令行下载

curl -L -o "Supplementary Figure 1 (6).tif" "https://ndownloader.figshare.com/files/56563829"
curl -L -o "Supplementary Figure 2.tif" "https://ndownloader.figshare.com/files/56563832"
curl -L -o "Supplementary Figure 3.tif" "https://ndownloader.figshare.com/files/56563835"
curl -L -o "Supplementary Figure 4.tif" "https://ndownloader.figshare.com/files/56563838"
curl -L -o "Supplementary Figure 5.tif" "https://ndownloader.figshare.com/files/56563841"
curl -L -o "Supplementary Figure 6.tif" "https://ndownloader.figshare.com/files/56563844"
curl -L -o "iann_a_2536204_sm6277.docx" "https://ndownloader.figshare.com/files/56563847"

文件清单

• Supplementary Figure 1 (6).tif（759.81 KB）
• Supplementary Figure 2.tif（1.14 MB）
• Supplementary Figure 3.tif（942.78 KB）
• Supplementary Figure 4.tif（1.85 MB）
• Supplementary Figure 5.tif（1.04 MB）
• Supplementary Figure 6.tif（1.10 MB）
• iann_a_2536204_sm6277.docx（148.89 KB）

文件总体积 6.99 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Chronic systemic inflammation is a key contributor to cardiometabolic complications in diabetes mellitus (DM) and prediabetes (PreDM). Composite inflammatory indices—including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), platelet-to-hemoglobin ratio (PHR), and aggregate inflammation systemic index (AISI)—have shown prognostic value for mortality. However, their integrated assessment using machine learning and quantification at the population level remain limited.
In this retrospective cohort study, 11,304 adults with DM or PreDM from the National Health and Nutrition Examination Survey (NHANES, 2005–2018) were analyzed. The primary outcomes were all-cause and cardiovascular mortality. Associations between inflammatory indices and mortality were evaluated using Cox proportional hazards models. Predictive performance was assessed via Extreme Gradient Boosting (XGBoost), and population attributable fractions (PAFs) estimated the mortality burden related to systemic inflammation.
NLR, MLR, SIRI, SII, and AISI were independently associated with all-cause and cardiovascular mortality. MLR showed the strongest association (HR: 2.948 and 3.717 for all-cause and CVD mortality, respectively). XGBoost identified SIRI, SII, AISI, MLR, and NLR as key predictors, with SIRI ranked highest for cardiovascular mortality. Inclusion of inflammatory indices improved model discrimination and calibration. PAF analysis suggested that 10–20% of mortality reduction could be attributed to improved inflammatory profiles.
Systemic inflammatory indices are independent predictors of mortality in individuals with DM or PreDM. Their integration into machine learning models enhances risk prediction and may inform population-level strategies for cardiometabolic risk stratification.
What is currently known about this topic?
Chronic inflammation contributes to the progression of DM or PreDM.
Traditional inflammatory markers are costly and less accessible for widespread screenings.
Systemic inflammatory indices provide an easily accessible alternative for diagnosis and prognosis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.29648741.v1（source: https://tandf.figshare.com/articles/dataset/Integrated_machine_learning_and_population_attributable_fraction_analysis_of_systemic_inflammatory_indices_for_mortality_risk_prediction_in_diabetes_and_prediabetes/29648741）。