A Simple and Improved Predictor of Insulin Resistance Extracted From the Oral Glucose Tolerance Test: The I0*G60

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Study Highlights

The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Lipoprotein Insulin Resistance Index: A Simple, Accurate Method for Assessing Insulin Resistance in South Asians

Context

Identification of insulin resistance (IR) in South Asians, who are at a higher risk for type 2 diabetes, is important. Lack of standardization of insulin assays limits the clinical use of insulin-based surrogate indices. The lipoprotein insulin resistance index (LP-IR), a metabolomic marker, reflects the lipoprotein abnormalities observed in IR. The reliability of the LP-IR index in South Asians is unknown.

Objective

We evaluated the predictive accuracy of LP-IR compared with other IR surrogate indices in South Asians.

Methods

In a cross-sectional study (n = 55), we used calibration model analysis to assess the ability of the LP-IR score and other simple surrogate indices (Homeostatic Model Assessment of Insulin Resistance, Quantitative insulin sensitivity check index, Adipose insulin resistance index, and Matsuda Index) to predict insulin sensitivity (S_I) derived from the reference frequently sampled intravenous glucose tolerance test. LP-IR index was derived from lipoprotein particle concentrations and sizes measured by nuclear magnetic resonance spectroscopy. Predictive accuracy was determined by root mean squared error (RMSE) of prediction and leave-one-out cross-validation type RMSE of prediction (CVPE). The optimal cut-off of the LP-IR index was determined by the area under the receiver operating characteristic curve (AUROC) and the Youden index.

Results

The simple surrogate indices showed moderate correlations with S_I (r = 0.53-0.69, P < .0001). CVPE and RMSE were not different in any of the surrogate indices when compared with LP-IR. The AUROC was 0.77 (95% CI 0.64-0.89). The optimal cut-off for IR in South Asians was LP-IR >48 (sensitivity: 75%, specificity: 70%).

Conclusion

The LP-IR index is a simple, accurate, and clinically useful test to assess IR in South Asians.

Link between Insulin Resistance and Obesity—From Diagnosis to Treatment

Insulin resistance (IR) has become a common health issue in medical practice. There are no detailed data on IR prevalence, but it is an increasing problem due to its close association with obesity. However, IR is not considered as a separate nosological entity and the diagnostic criteria are not well defined, which leads to overdiagnosis of IR and an inappropriate approach. This review aims to summarize the available literature on IR pathophysiology, its relationship with obesity, as well as diagnostic methods, clinical presentation and treatment. Excessive energy intake results in cell overload that triggers mechanisms to protect cells from further energy accumulation by reducing insulin sensitivity. Additionally, hypertrophied adipocytes and macrophage infiltration causes local inflammation that may result in general inflammation that induces IR. The clinical picture varies from skin lesions (e.g., acanthosis nigricans) to metabolic disorders such as diabetes mellitus or metabolic-associated fatty liver disease. There are numerous IR laboratory markers with varying sensitivities and specificities. Nutrition changes and regular physical activity are crucial for IR management because a reduction in adipose tissue may reverse the inflammatory state and consequently reduce the severity of insulin resistance. In cases of obesity, anti-obesity medications can be used.

Extreme Variations in Muscle Fiber Composition Enable Detection of Insulin Resistance and Excessive Insulin Secretion

CONTEXT

Muscle fiber composition is associated with peripheral insulin action.

OBJECTIVE

We investigated whether extreme differences in muscle fiber composition are associated with alterations in peripheral insulin action and secretion in young, healthy subjects who exhibit normal fasting glycemia and insulinemia.

METHODS

Relaxation time following a tetanic contraction was used to identify subjects with a high or low expression of type I muscle fibers: group 1 (n = 11), area occupied by type I muscle fibers = 61.0 ± 11.8%, and group 2 (n = 8), type I area = 36.0 ± 4.9% (P < 0.001). Biopsies were obtained from the vastus lateralis muscle and analyzed for mitochondrial respiration on permeabilized fibers, muscle fiber composition, and capillary density. An intravenous glucose tolerance test was performed and indices of glucose tolerance, insulin sensitivity, and secretion were determined.

RESULTS

Glucose tolerance was similar between groups, whereas whole-body insulin sensitivity was decreased by ~50% in group 2 vs group 1 (P = 0.019). First-phase insulin release (area under the insulin curve during 10 minutes after glucose infusion) was increased by almost 4-fold in group 2 vs group 1 (P = 0.01). Whole-body insulin sensitivity was correlated with percentage area occupied by type I fibers (r = 0.54; P = 0.018) and capillary density in muscle (r = 0.61; P = 0.005) but not with mitochondrial respiration. Insulin release was strongly related to percentage area occupied by type II fibers (r = 0.93; P < 0.001).

CONCLUSIONS

Assessment of muscle contractile function in young healthy subjects may prove useful in identifying individuals with insulin resistance and enhanced glucose-stimulated insulin secretion prior to onset of clinical manifestations.