Hidden hunger (HH) refers to the deficiency of certain micronutrients. Current research suggests that approximately 70% of chronic diseases are linked to HH, which significantly affects public health. Consequently, there is an urgent need for an effective method to assess the risk of HH. This study aims to develop risk prediction models for HH using machine learning (ML).

We conducted a questionnaire survey among 9336 high school students in 11 cities within Anhui Province and assessed their HH risk using a scale. After quality control, we designated 632 students from Xuancheng City as the external test cohort and used the remaining 6477 students as the training cohort to develop predictive models. We used six ML algorithms (i.e., deep‐learning neural network [DNN], random forest, support vector machine, extreme gradient boosting, gradient boosting decision tree, and k‐nearest neighbor) to fit the training set using five‐fold cross‐validation, with hyperparameter tuning performed via Bayesian optimization. We used the “Streamlit” library to construct an online application and the “shapley additive explanations” library for model interpretability analysis.

We observed that the DNN model performed best. In the external test cohort, the area under the curve reached 0.813, accuracy was 0.739, and sensitivity and specificity were 0.720 and 0.760, respectively. Furthermore, the precision‐recall curve, calibration curve, and decision curve analysis also indicated that our model had high predictive accuracy. To aid practical use, we developed an online application (http://sec.mitusml.com:9000/). Through model interpretability analysis, we discovered that the frequent consumption of fruits and coarse grains was likely to reduce the risk of HH, whereas frequently eating snacks and fried foods increased the risk of HH.

We developed an effective prediction model for HH and analyzed the factors that influence its risk.

Clear cell renal cell carcinoma (ccRCC) is one of the most common urological diseases and the most common subtype of renal cell carcinoma. Nuclear mitochondria‐related genes (MTRGs) play an essential role in cancer, but their effect on ccRCC has not been clarified. This work aimed to investigate the role of nuclear MTRGs in ccRCC.

We collected nuclear MTRGs from the MITOMAP database and obtained the ccRCC profile from the TCGA database. Gene expression validation came from the GEO database. The ccRCC subtypes were determined by unsupervised clustering analysis based on the nuclear MTRGs. Immune scores were computed using the EPIC algorithm. The drug sensitivity scores were calculated using GDSC resources. A nomogram explored the diagnostic value of the nuclear MTRGs.

In total, 11 nuclear MTRGs were identified as both related to prognosis and differentially expressed in ccRCC, showing a significant positive correlation with CD274 expression. We determined two subtypes of ccRCC based on these genes and found remarkable differences in survival status, immune infiltration, mutation landscape, and drug sensitivity between the two subtypes. The high‐MTRG group had a better prognosis and a lower tumor stage than the low‐MTRG group. Immune checkpoint blockade therapy was more effective for the high‐MTRG group. A nomogram based on the nuclear MTRGs concluded that patients with a higher score had poorer survival. SUCLA2 (succinate‐CoA ligase ADP‐forming subunit beta) was identified as the hub gene linked to ccRCC. High SUCLA2 expression showed a correlation with better survival and a negative correlation with the tumor mutation burden in ccRCC. Pan‐cancer analysis revealed wide‐ranging roles for SUCLA2 across human tumors.

Nuclear MTRGs play vital roles in determining the molecular features, immune infiltration, and drug sensitivity of ccRCC. High levels of nuclear MTRGs may indicate a better prognosis for patients with ccRCC. SUCLA2 is a representative nuclear MTRG and may serve as a protective biomarker in ccRCC. Our study provides therapeutic guidance and potential biomarkers for ccRCC patients, and contributes to the advancement of precision medicine.