Metabolic differences among newborns born to mothers with a history of leukemia or lymphoma

Association of Maternal Age and Blood Markers for Metabolic Disease in Newborns

Pregnancy at an advanced maternal age is considered a risk factor for adverse maternal, fetal, and neonatal outcomes. Here we investigated whether maternal age could be associated with differences in the blood levels of newborn screening (NBS) markers for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). Population-level NBS data from screen-negative singleton infants were examined, which included blood metabolic markers and covariates such as age at blood collection, birth weight, gestational age, infant sex, parent-reported ethnicity, and maternal age at delivery. Marker levels were compared between maternal age groups (age range: 1544 years) using effect size analyses, which controlled for differences in group sizes and potential confounding from other covariates. We found that 13% of the markers had maternal age-related differences, including newborn metabolites with either increased (Tetradecanoylcarnitine [C14], Palmitoylcarnitine [C16], Stearoylcarnitine [C18], Oleoylcarnitine [C18:1], Malonylcarnitine [C3DC]) or decreased (3-Hydroxyisovalerylcarnitine [C5OH]) levels at an advanced maternal age (≥35 years, absolute Cohen's d > 0.2). The increased C3DC levels in this group correlated with a higher false-positive rate in newborn screening for malonic acidemia (p-value < 0.001), while no significant difference in screening performance was seen for the other markers. Maternal age is associated with inborn metabolic differences and should be considered together with other clinical variables in genetic disease screening.

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

BACKGROUND

Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches.

RESULTS

In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer.

CONCLUSIONS

This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.