Primary Language Spoken at Home and Speech Outcomes Among Children With Cleft Palate.

The gut microbiota is crucial in sustaining host health [, ], and its stability is influenced by genetic predisposition and environmental exposures [, , , ]. In recent years, there has been growing evidence that gut microbiota dysbiosis may be involved in the pathogenesis of various diseases [, , ], especially major psychiatric disorders [, , ] such as major depressive disorder (MDD), schizophrenia (SZ), and bipolar disorder (BD). Although these disorders affect millions worldwide and impose a growing public health burden, their diagnosis remains primarily symptom‐based and lacks objective biomarkers [, ]. This gap underscores the necessity for approaches that can link gut microbial alterations to clinical phenotypes. With growing interest in microbiota‐disease associations, numerous studies have reported significant alterations in gut microbial diversity and disruptions in the core gut microbiota in a variety of disease states [, , , , , , ]. However, considerable heterogeneity has been observed across studies [], likely reflecting differences in cohort composition, sample size, data preprocessing, and analytical methods []. This inconsistency reflects the lack of large‐scale, globally representative microbiome landscapes processed with standardized pipelines, as well as the absence of approaches capable of capturing the ecological continuum and dynamic perturbations associated with disease. At the methodological level, traditional case‐control analyses can effectively explain group‐level differences in microbial features, but new frameworks are still needed to characterize the coordinated dynamics of the gut microbiota as an integrated ecosystem []. Moreover, given the substantial variability in individual health status, robust measures to characterize the dynamic changes in individual gut microbial composition are still lacking [, ]. Together, these limitations underscore the need for novel analytical frameworks that reconcile individual‐level variability with group‐level coordination in the gut microbiome. The classic enterotype model has provided important insights into population heterogeneity and established a foundational framework for classifying gut microbial community structures []. Studies based on partitioning around the medoid [] (PAM) and Dirichlet multinomial mixture models [] (DMM) have identified three to four major enterotypes and demonstrated strong robustness in bacterial and fungal community analysis []. Nevertheless, these discrete classification approaches still involve a certain degree of simplification in capturing the complexity of microbiota variation, as reflected by their limited ability to represent the continuity and dynamic nature of microbial community states []. Drawing on economic physics and complex systems theory, we aim to construct a set of microbial ecological factors (MEFs) that capture the continuous variation of microbial communities and collectively define the latent ecological signature underlying individual health phenotypes. This idea is similar to the application of factor analytic methods, such as latent Dirichlet allocation to reveal structural and functional heterogeneity of the brain in MRI studies []. Although the data modalities are different, both face the need to extract low‐dimensional latent factors from high‐dimensional complex patterns. In addition, non‐negative matrix factorization (NMF) has been applied to microbiome data, supporting its utility for capturing latent ecological structures []. NMF‐derived latent factors can be viewed as continuous and partially overlapping ecological axes capturing coordinated variation in microbial community structure, forming a biologically interpretable low‐dimensional representation. However, current approaches still require improved high‐resolution characterization of microbial community structures []. Moreover, growing attention has been directed toward microbial structural similarity, given its importance for systematically delineating healthy microbiome states []. Improving species‐level resolution and jointly considering inter‐individual heterogeneity and intra‐individual similarity may help to more comprehensively characterize the biological features and potential functional roles of the gut microbiota.