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Protein-energy malnutrition still impacts children’s growth and development. We investigated the prolonged effects of egg supplementation on growth and microbiota in primary school children. For this study, 8–14-year-old students (51.5% F) in six rural schools in Thailand were randomly assigned into three groups: (1) whole egg (WE), consuming 10 additional eggs/week (n = 238) (n = 238); (2) protein substitute (PS), consuming yolk-free egg substitutes equivalent to 10 eggs/week (n = 200); and (3) control group (C, (n = 197)). The outcomes were measured at week 0, 14, and 35. At the baseline, 17% of the students were underweight, 18% were stunted, and 13% were wasted. At week 35, compared to the C group the weight and height difference increased significantly in the WE group (3.6 ± 23.5 kg, p < 0.001; 5.1 ± 23.2 cm, p < 0.001). No significant differences in weight or height were observed between the PS and C groups. Significant decreases in atherogenic lipoproteins were observed in the WE, but not in PS group. HDL-cholesterol tended to increase in the WE group (0.02 ± 0.59 mmol/L, ns). The bacterial diversity was similar among the groups. The relative abundance of Bifidobacterium increased by 1.28-fold in the WE group compared to the baseline and differential abundance analysis which indicated that Lachnospira increased and Varibaculum decreased significantly. In conclusion, prolonged whole egg supplementation is an effective intervention to improve growth, nutritional biomarkers, and gut microbiota with unaltered adverse effects on blood lipoproteins.

Summary The current gold standard for classifying lupus nephritis (LN) progression is a renal biopsy, which is an invasive procedure. Undergoing a series of biopsies for monitoring disease progression and treatments is unlikely suitable for patients with LN. Thus, there is an urgent need for non-invasive alternative biomarkers that can facilitate LN class diagnosis. Such biomarkers will be very useful in guiding intervention strategies to mitigate or treat patients with LN. Urine samples were collected from two independent cohorts. Patients with LN were classified into proliferative (class III/IV) and membranous (class V) by kidney histopathology. Metabolomics was performed to identify potential metabolites, which could be specific for the classification of membranous LN. The ratio of picolinic acid (Pic) to tryptophan (Trp) ([Pic/Trp] ratio) was found to be a promising candidate for LN diagnostic and membranous classification. It has high potential as an alternative biomarker for the non-invasive diagnosis of LN., Graphical abstract, Highlights • The urinary [Fum], [Ket], [Pyr], and [Mal] are significantly higher in patients with LN • The urinary [Pic/Trp] ratio is significantly lower in patients with LN • The [Pic/Trp] is very specific for classifying proliferative LN from membranous LN • Combining [Pic/Trp] with urine protein–creatinine ratio (UPCR) and estimated glomerular filtration rate (eGFR) further improves membranous LN prediction, Biopsy sample; Body substance sample; Metabolomics

Sleep-disordered breathing (SDB) is common in type 2 diabetes (T2D). This study aimed to develop a screening tool to detect SDB in T2D. Method: SDB was screened in 95 patients by an overnight monitor and diagnosed if apnea-hypopnea index ≥ 5. Anthropometric and HbA1c data, and SDB risk by questionnaire were obtained. Quantitative NMR experiment was performed on a Bruker AVANCE 400 Ascend NMR spectrometer. Carr−Purcell−Meiboom−Gill (CPMG) pulse sequence at 310 K was applied to serum samples to enhance signal-to-noise ratio and hence detect low-molecular weight metabolites. Models based on six machine learning methods (e.g., learned vector quantization, stochastic gradient boosting model, support vector machines, decision tree, partial least squares, generalized linear model, and logistic regression) were constructed to predict SDB, tuned with the CARET R package, and evaluated using three repeats of 10-fold cross validation. Results: Seventy-six patients (80.0%) had SDB. Compared with those without, those with SDB were more likely to be male, have SDB risk, higher BMI and neck circumferences, longer diabetes duration, and higher HbA1c. NMR-based metabolic profiling identified significantly lower levels of branched-chain amino acids (BCAAs) (e.g., L-leucine and L-isoleucine), lactate and threonine metabolites in those with SDB, while level of D-glucose-6 phosphatase was higher than those without SDB. Accuracies for predicting SBD using clinical data alone were ∼80% for each machine learning model. L-leucine and D-glucose-6-phosphatase were identified as being independently associated with SDB using a logistic regression, in addition to clinical factors, with an improved accuracy of 86.3%, and a Cohen’s kappa of 0.429. Conclusions: Due to its high prevalence, clinical factors alone were not sufficient in predicting SDB in T2D. The discovery of metabolic profiles, including BCAAs and those involved in glucose metabolism, could facilitate a more precise and specific SDB prediction. Disclosure K. Lertdetkajorn: None. P. Poungsombat: None. S. Khoomrung: None. J. Phetcharaburanin: None. S. Hongthong: None. C. Kuhakarn: None. N. Siwasaranond: None. A. Manodpitipong: None. H. Ninitphong: Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Takeda Pharmaceutical Company Limited. S. Saetung: None. B. Ongphiphadhanakul: None. V. Reutrakul: None. S. Reutrakul: None. Funding Endocrine Society of Thailand; Ramathibodi Hospital; Mahidol University; Thailand Center of Excellence for Innovation in Chemistry