Came across this in my news feed:
Biological BMI measures metabolic health more accurately (news-medical.net)
A Better Measure of Metabolic Health: What Is Your Biological BMI? (scitechdaily.com)
"The current study involved the recruitment of people who participated in a wellness program by a commercial company between 2015 and 2019. Individuals were included in the current study if they were over 18 years of age, residents of any U.S. state except New York, and not pregnant.
Participants were included if their datasets contained all main omic measurements, genetic information, and a BMI measurement within 1.5 months from the first blood draw. The external cohort was obtained from participants who participated in the TwinsUK Registry and underwent two or more clinical visits for biological sampling between 1992 and 2022. Only participants whose datasets contained all measurements for metabolomics, obesity-related standard clinical measures and BMI were included in the current study.
Peripheral blood, saliva, and stool samples were collected from participants for analysis of genetic ancestry, measurement of blood omics, and generation of gut microbiome data. Information on height, weight, blood pressure, waist circumference, and daily physical activity was also collected.
The analysis of blood metabolomics, BMI, gut microbiome data, and BMI of baseline visits took place for the TwinsUK participants. Machine learning models were trained to predict baseline BMI for each of the omics platforms including proteomics, metabolomics, and clinical lab, or in combination with clinical labs (chemistries)-based BMI (ChemBMI), proteomics-based BMI (ProtBMI), metabolomics-based BMI (MetBMI), and combined omics-based BMI (CombiBMI) models. Another ten fitted sparse models were generated using the least absolute shrinkage and selection operator (LASSO) algorithm for each omics category.
This was followed by the health classification of each participant based on the World Health Organization (WHO) international standards for BMI cutoffs. Gut microbiome models were also generated for the classification of obesity. Assessments of longitudinal changes took place in the measured and omics-inferred BMIs. Finally, an analysis of the plasma analyte correlation network was performed.
Study findings
A total of 1,277 adults participated in the study, most of whom were White, female, and middle-aged. The BMI prevalence at baseline was similar among the normal, overweight, and obese classes.
The models retained 30 proteins, 62 metabolites, 20 clinical laboratory tests, as well as 132 analytes. The CombiBMI model was found to be the best in BMI prediction.
Investigation of the TwinsUK cohort indicated that blood metabolomics better capcaptures BMI as compared to standard clinical measures. Notably, omics-inferred BMI maintained the characteristics of classical BMI.
Proteins were the strongest predictors in the CombiBMI model. More specifically, fatty acid-binding protein 4 (FABP4), adrenomedullin (ADM), and leptin (LEP) were positive regulators, while advanced glycosylation end-product-specific receptor (AGER) and insulin-like growth factor-binding protein 1 (IGFBP1) were negative regulators.
The misclassification rate of omics-inferred BMI was about 30% across all BMI classes and omics categories. The mismatched groups of the normal BMI class showed higher values of the markers positively associated with BMI and lower values of the markers negatively associated with BMI, while the opposite was observed for the mismatched group of the obese BMI class. The omics-based BMI model also captured obesity characteristics, including abdominal obesity.
The MetBMI class reflected ​​bacterial diversity better than the standard BMI class and had stronger associations with gut microbiome features. Lifestyle interventions decreased the overall BMI estimate of the entire cohort, where a decrease of MetBMI was the highest and ProtBMI was the least.
A total of 100 analyte–analyte correlation pairs were significantly modified by the baseline MetBMI. Among them, 27 analyte-analyte correlation pairs were significantly modified by days in the program and were mainly derived from metabolites.
One such time-varying pair was homoarginine and phenyllactate (PLA). A positive association between homoarginine and PLA was observed in the obese MetBMI class at baseline, which became weaker during the intervention.
Conclusions
The current study demonstrates the importance of blood multi-omic profiling for preventive and predictive medicine. Furthermore, these findings demonstrate that multi-omic characterization of obesity can be useful for the characterization of metabolic health, as well as identifying targets for health management."
No idea how the average Joe would go about getting a biological BMI reading though!