Background Chronically higher inflammation, which may partly result from diet and lifestyle, is implicated in risk for multiple chronic diseases. The dietary inflammatory index (DII) and empirical dietary inflammatory pattern (EDIP), developed to characterize dietary contributions to systemic inflammation, have several limitations. There are no scores to characterize contributions of lifestyle to inflammation. Objectives To reflect dietary/lifestyle contributions to inflammation, we developed novel, inflammation biomarker panel-weighted, dietary (DIS) and lifestyle (LIS) inflammation scores in a subset (n = 639) of the Reasons for Geographic and Racial Differences in Stroke Study (REGARDS) cohort. Methods We selected a priori 19 food groups and 4 lifestyle characteristics to comprise the DIS and LIS, respectively. We calculated the components’ weights based on their strengths of association with an inflammation biomarker score [comprising high-sensitivity C-reactive protein (hsCRP), IL-6, IL-8, and IL-10] using multivariable linear regression. The sums of the weighted components constitute the scores, such that higher scores reflect, on balance, more proinflammatory exposures. We calculated the DIS, LIS, DII, and EDIP with cross-sectional data from the remaining REGARDS cohort (  n = 14,210 with hsCRP measurements) and 2 other study populations with hsCRP and/or an 8-component inflammation biomarker panel, and investigated their associations with circulating inflammation biomarker concentrations using multivariable logistic regression. Results In REGARDS, those in the highest relative to the lowest DIS, LIS, DII, and EDIP quintiles had statistically significant 1.66-, 4.29-, 1.56-, and 1.32-fold higher odds of a high hsCRP concentration (>3 mg/dL), respectively (all P-trend < 0.001). Those in the highest relative to the lowest joint DIS/LIS quintile had a statistically significant 7.26-fold higher odds of a high hsCRP concentration. Similar findings were noted in the other 2 validation populations. Conclusion Our results support that dietary and lifestyle exposures collectively contribute substantially to systemic inflammation, and support the use of our novel DIS and LIS.

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and nonmalignant breast disease in a case‐control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 nonmalignant breast disease cases, N = 414 population‐based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray‐Curtis and unweighted/weighted UniFrac distance), and the presence and relative abundance of select taxa with breast cancer and nonmalignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13‐0.36; Ptrend < .001). Alpha diversity associations were similar for nonmalignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen‐conjugating and immune‐related functions were strongly associated with breast cancer (all Ps < .001). There were no statistically significant differences between breast cancer and nonmalignant breast disease cases in any microbiota metric. In conclusion, fecal bacterial characteristics were strongly and similarly associated with breast cancer and nonmalignant breast disease. Our findings provide novel insight into potential microbially‐mediated mechanisms of breast disease.

Few previous studies have assessed stability and “gold-standard” concordance of fecal sample collection methods for whole-genome shotgun metagenomic sequencing (WGSS), an increasingly popular method for studying the gut microbiome. We used WGSS data to investigate ambient temperature stability and putative gold-standard concordance of microbial profiles in fecal samples collected and stored using fecal occult blood test (FOBT) cards, fecal immunochemical test (FIT) tubes, 95% ethanol, or RNAlater. Among 15 Mayo Clinic employees, for each collection method, we calculated intraclass correlation coefficients (ICCs) to estimate stability of fecal microbial profiles after storage for 4 days at ambient temperature and concordance with immediately frozen, no-solution samples (i.e., the putative gold standard). ICCs were estimated for multiple metrics, including relative abundances of select phyla, species, KEGG k-genes (representing any coding sequence that had >70% identity and >70% query coverage with respect to a known KEGG ortholog), KEGG modules, and KEGG pathways; species and k-gene alpha diversity; and Bray-Curtis and Jaccard species beta diversity. ICCs for microbial profile stability were excellent (≥90%) for fecal samples collected via most of the collection methods, except those preserved in 95% ethanol. Concordance with the immediately frozen, no-solution samples varied for all collection methods, but the number of observed species and the beta diversity metrics tended to have higher concordance than other metrics. Our findings, taken together with previous studies and feasibility considerations, indicated that FOBT cards, FIT tubes, and RNAlater are acceptable choices for fecal sample collection methods in future WGSS studies. IMPORTANCE A major direction for future microbiome research is implementation of fecal sample collections in large-scale, prospective epidemiologic studies. Studying microbiome-disease associations likely requires microbial data to be pooled from multiple studies. Our findings suggest collection methods that are most optimal to be used standardly across future WGSS microbiome studies.

The gut microbiome likely plays a role in the etiology of multiple health conditions, especially those affecting the gastrointestinal tract. Little consensus exists as to the best, standard methods to collect fecal samples for future microbiome analysis. We evaluated three distinct populations (N = 132 participants) using 16S rRNA gene amplicon sequencing data to investigate the reproducibility, stability, and accuracy of microbial profiles in fecal samples collected and stored via fecal occult blood test (FOBT) or Flinders Technology Associates (FTA) cards, fecal immunochemical tests (FIT) tubes, 70% and 95% ethanol, RNAlater, or with no solution. For each collection method, based on relative abundance of select phyla and genera, two alpha diversity metrics, and four beta diversity metrics, we calculated intraclass correlation coefficients (ICCs) to estimate reproducibility and stability, and Spearman correlation coefficients (SCCs) to estimate accuracy of the fecal microbial profile. Comparing duplicate samples, reproducibility ICCs for all collection methods were excellent (ICCs ≥75%). After 4–7 days at ambient temperature, ICCs for microbial profile stability were excellent (≥75%) for most collection methods, except those collected via no-solution and 70% ethanol. SCCs comparing each collection method to immediately-frozen no-solution samples ranged from fair to excellent for most methods; however, accuracy of genus-level relative abundances differed by collection method. Our findings, taken together with previous studies and feasibility considerations, indicated that FOBT/FTA cards, FIT tubes, 95% ethanol, and RNAlater are excellent choices for fecal sample collection methods in future microbiome studies. Furthermore, establishing standard collection methods across studies is highly desirable.