Study links microbial-rich diets to better insulin and cholesterol markers

New Australian research suggests that foods naturally rich in live microbes may be associated with better metabolic health, offering new insight into diet, microbiome interactions and chronic disease risk.

Study: Association between dietary intake of foods estimated to contain live microbes and health markers in Australian adults: An exploratory analysis. Image credit: UliAb / Shutterstock

In a recent study published in the journal Nutrition Researchresearchers investigated associations between intake of foods containing live microbes (LMs) and health indicators in Australians, with the primary aim of developing a database to estimate LM content in Australian food and drink and a secondary exploratory aim of examining health correlates.

LMs are naturally present in many common foods, including raw vegetables and fruits, fermented foods, and probiotics. Interest in the intake of LM-containing foods has increased in recent years, given their association with health and disease risk. High consumption of beneficial LMs has been associated with lower risk of mortality in previous observational studies, particularly in analyzes of US population cohorts rather than Australian samples. However, most research on dietary LM intake has focused on US populations or specific foods rather than the whole diet.

Development of an Australian Live Microbial Food Database and Study Design

In the present study, researchers assessed associations between dietary LM intake and health markers in Australians. First, they developed a database of LM content for common foods and beverages from Australian Food and Nutrient (AUSNUT) database, related to the Australian Eating Survey (AES). Foods and beverages were then divided into low, medium, or high LM categories based on the expected prevalence of viable microbes, using previously published methods. Microbial levels were assessed indirectly rather than directly in individual food samples.

The low class had an estimated microbial count < 104 colony forming units per gram (CFU/g) the medium and high classes had readings of 104–107 CFU/g and > 107 CFU/g, respectively. These data were then used in an exploratory cross-sectional analysis to investigate the relationship between estimated dietary LM intake and health indicators. Data were analyzed from adults recruited in 2019-20 from the Newcastle region of Australia.

Participants were aged 18 years or older and had a stable weight over the past two months. Individuals trying to conceive, pregnant or lactating, those taking medications affecting weight, fluid balance, or metabolic rate, and individuals with food allergies, chronic medical conditions, certain implanted medical devices, claustrophobia, or other protocol-specified exceptions were excluded. Participants reported demographic data and dietary intake using the AES Food Frequency Questionnaire, a validated tool that may however overestimate some dietary intakes due to self-report.

The following markers of cardiometabolic health were measured: body mass index (BMI), blood pressure (BP), waist circumference, fasting plasma glucose, total cholesterol, triglycerides, fasting insulin, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Assessed inflammatory markers included interleukin-6 (IL-6), tumor necrosis factor-α (TNF-αand C-reactive protein (CRP).

Differences in the consumption of LM-containing foods by gender, smoking status, and ethnicity were assessed using the Kruskal-Wallis test and the Mann-Whitney U test. To investigate the relationships between the estimated LM content categories and health indicators, Spearman’s rank correlation was first used to assess the direction of association. Then, weighted least squares (WLS) regression was used, adjusting for relevant covariates, including sex, smoking status and energy intake, to account for potential confounding. However, residual confounding cannot be excluded in the observational analyses.

Associations between LM food categories and health indicators

The team categorized more than 200 foods from the AUSNUT database to create the LM database. About 229 items were classified as low in LM, including vegetables, grain-based products and meat, poultry and game products. In addition, 21 items, including fruits, vegetables and dairy products, had moderate LM content.

Of the five fermented foods, two were classified as high in LM and three as moderate. Given the limited number of high LM foods, medium and high LM food groups were pooled (Med/Hi) to improve statistical power, with yogurt remaining the only food with clearly high LM after grouping. The study included 58 adults, mostly Caucasian (86%) and female (69%), with a mean age of 38.16 years and a BMI of 26.18 kg/m.². Participants reported a relatively higher intake of fruit and vegetables than is typically seen in the general Australian population.

Participants mainly consumed the low LM food group (mean daily intake, 1,902 g), followed by the medium LM group (253.6 g/day). Men consumed significantly more low LM foods than women, and nonsmokers had significantly higher intakes of Med/Hi LM foods than smokers. Consumption of the low LM food group was positively associated with blood pressure.

In contrast, intakes of the Medium and Med/Hi-LM food groups were positively associated with HDL-C and negatively associated with BMI, fasting insulin, body weight, waist circumference, CRP, and IL-6, although associations with inflammatory markers did not remain statistically significant after adjustment for covariates, and adjusted HDL-C analyzes remained significantly adjusted for HDL-C analyses. WLS regression showed that consumption of the Med/Hi-LM food group was significantly inversely associated with BMI, insulin and waist circumference and positively associated with HDL-C. No significant adjusted associations were observed with fasting glucose, triglycerides, LDL cholesterol, total cholesterol, or TNF-α.

Interpretation, Limitations, and Future Research Needs

In summary, more frequent intake of foods with high or medium LM content was positively associated with HDL-C and inversely associated with insulin levels, BMI, waist circumference and body weight in this Australian sample.

Further research is needed to confirm these findings in larger, diverse populations and to determine whether dietary LM intake is associated with changes in gut microbiota, particularly due to the exploratory cross-sectional design, relatively small sample size, potential dietary reporting bias, and the inability of observational studies to establish causal relationships.