Diabetes is a leading cause of disability and mortality.^1,2 In the US, its total economic cost was $327 billion in 2017 and care for people with diabetes accounted for 24% of all health care dollars.³ The estimated prevalence of diabetes among US adults increased from 5.3% in 1976-1980 to 11.5% in 2011-2014, increasing at a rate faster than the global increase during the same period.^2,4,5 The prevalence of diabetes varies substantially by sociodemographic variables and weight status.^6-8 Also, it has been associated with increased risk of cardiovascular disease.⁹ Quiz Ref ID Cardiovascular disease prevention in diabetes requires appropriate management of well-established risk factors such as hemoglobin A_1c (HbA_1c) level, blood pressure (BP), and serum cholesterol level.^10,11 An improvement in the control of the 3 risk factors (HbA_1c <7.0% or individualized HbA_1c targets, BP <130/80 mm Hg, and low-density lipoprotein cholesterol [LDL-C] level <100 mg/dL) was observed between 1988 and 2010, but only an estimated 18.8% of US adults achieved all 3 goals in 2007-2010.^12,13 It is unclear whether trends in control of risk factors in diabetes have changed during the past decade. This information is critical to forecasting population-level complications and guiding prevention efforts.

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.