Home › Research › How common it is
A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations
Mao Y, Lin J, Zhou A, Zeng S, Yang D, Lin W, Wen J, Yang W et al.. medRxiv, 2026-08-12
Topic: Genetics and APOE
Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a novel deep neural network–derived IR index (DNN-IR) using a Mixture-of-Experts (MoE) framework and evaluated its predictive performance for incident cardiovascular disease (CVD) and mortality in general populations. Methods We utilized data from three cohorts: the cross-sectional REACTION study (Fujian subcohort, 2011-2012) for DNN-IR derivation and internal validation; and two prospective cohorts, NHANES (1999-2018, linked to the National Death Index) and CHARLS (2011-2018), for external validation. The DNN-IR was developed using a deep learning model based on a Mixture-of-Experts (MoE) architecture, trained on the REACTION dataset. We evaluated the DNN-IR’s utility in predicting incident CVD, cardiovascular mortality, and non-cardiovascular mortality among 13,889 NHANES and 7,047 CHARLS participants. Predictive performance was assessed via the area under the receiver operating characteristic curve (AUC). Multivariable logistic regression, restricted cubic splines, and Kaplan-Meier analyses characterized the associations between DNN-IR and clinical outcomes. Results In the REACTION cohort, DNN-IR demonstrated superior predictive performance for atherosclerotic outcomes, achieving AUROCs of 0.89 (training) and 0.84 (internal validation). In the external CHARLS cohort (median follow-up: 7 years; 1,135 incident CVD cases [16.1%]), DNN-IR yielded AUROCs of 0.72 for incident CVD and 0.77 for all-cause mortality. Fully adjusted models showed that each 1-SD increment in DNN-IR was associated with a 23% higher CVD risk (OR=1.23, 95% CI: 1.14-1.32), exhibiting a predominantly linear dose-response relationship ( P -nonlinearity=0.453). In NHANES, DNN-IR robustly predicted cardiovascular (AUROC=0.77) and all-cause mortality (AUROC=0.72), alongside specific mortalities like diabetes (0.91), Alzheimer’s disease (0.88), and kidney disease (0.96). Higher DNN-IR levels correlated with stepwise increases in cumulative mortality (log-rank P <0.001). Conclusions The MoE-derived DNN-IR index demonstrated robust and stable performance in predicting atherosclerosis, incident CVD, cardiovascular mortality, and all-cause mortality in the general population. Further validation in larger, more diverse cohorts is warranted to support its broad clinical applicability.
Key findings
- Results In the REACTION cohort, DNN-IR demonstrated superior predictive performance for atherosclerotic outcomes, achieving AUROCs of 0.89 (training) and 0.84 (internal validation).
- Fully adjusted models showed that each 1-SD increment in DNN-IR was associated with a 23% higher CVD risk (OR=1.23, 95% CI: 1.14-1.32), exhibiting a predominantly linear dose-response relationship ( P -nonlinearity=0.453).
- Conclusions The MoE-derived DNN-IR index demonstrated robust and stable performance in predicting atherosclerosis, incident CVD, cardiovascular mortality, and all-cause mortality in the general population.
New to this subject? Start with our plain-English guide: Alzheimer's in numbers →
Study details
- Study designcross-sectional
- Effect size16.1%; OR=1.23
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 35 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for sample size, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 22 of the 62 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 22 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 22 of the 62 points we could judge it on is 35 out of 100. |
| Total | 35 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Mao, Y., Lin, J., Zhou, A., Zeng, S., Yang, D., Lin, W., et al. (2026). A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations. medRxiv. https://doi.org/10.64898/2026.08.10.26360145
Vancouver
Mao Y, Lin J, Zhou A, Zeng S, Yang D, Lin W, et al. A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations. medRxiv. 2026. doi:10.64898/2026.08.10.26360145.
BibTeX
@article{mao2026-044d1e6b,
author = {Mao, Y. and Lin, J. and Zhou, A. and Zeng, S. and Yang, D. and Lin, W. and Wen, J. and Yang, W. and others},
title = {{A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.08.10.26360145},
url = {https://doi.org/10.64898/2026.08.10.26360145}
}
Related studies in How common it is
Also tagged “convolutional”
205 studies in the library carry the topic tag “convolutional”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 205 tagged studies.