A Blood Test That Tracks Inflammation May Also Signal Metabolic Syndrome, U.S. Study Finds
A routine blood count could contain a surprisingly broad warning signal for metabolic syndrome, according to a large analysis of U.S. health data. Researchers examining 15,846 adults who participated in the National Health and Nutrition Examination Survey, or NHANES, from 2013 through 2020 found that people with higher pan-immune-inflammation values were more likely to have metabolic syndrome. The association persisted after the investigators adjusted for a wide range of demographic, lifestyle and clinical factors, suggesting that the relationship was not explained simply by age, sex or body weight. Metabolic syndrome is not a single disease but a cluster of abnormalities—including abdominal obesity, elevated blood pressure, high blood sugar, high triglycerides and reduced levels of protective HDL cholesterol—that together raise the risk of cardiovascular disease, stroke and type 2 diabetes. Of the participants included in the analysis, 3,845 met the study’s definition of metabolic syndrome.
The biomarker at the center of the study, known as the pan-immune-inflammation value, or PIV, is designed to combine information from several types of blood cells into one numerical estimate of systemic inflammatory activity. It is generally calculated using platelet, neutrophil, monocyte and lymphocyte counts: platelet count multiplied by neutrophil count and monocyte count, divided by lymphocyte count. Each component reflects a different aspect of the body’s immune and inflammatory state. Neutrophils and monocytes are innate immune cells that can rise during inflammation, while lymphocytes represent an important arm of adaptive immunity. Platelets participate in clotting but also interact with immune cells and blood-vessel walls. By integrating these measurements, PIV may capture a more complex biological pattern than any one cell count or a simpler ratio such as the neutrophil-to-lymphocyte ratio.
The new analysis does not show that inflammation causes metabolic syndrome, nor does it establish that PIV can diagnose the condition. Instead, it identifies a statistical association in a nationally representative, cross-sectional dataset. The researchers divided participants into four groups, or quartiles, according to their PIV values and compared the prevalence of metabolic syndrome across those groups. They then used weighted statistical models designed to account for the complex sampling structure of NHANES, which combines interviews, physical examinations and laboratory measurements to represent the civilian U.S. population. The investigators applied multivariable logistic regression, sensitivity analyses, subgroup comparisons and restricted cubic spline modeling to explore whether the relationship remained after accounting for potential confounding factors and whether it followed a straight-line pattern.
Across four increasingly adjusted statistical models, higher PIV was consistently linked to greater odds of metabolic syndrome. In the least adjusted model, each increase in the analyzed PIV measure was associated with an odds ratio of 1.19, with a 95 percent confidence interval from 1.13 to 1.26. After additional variables were introduced, the association remained statistically significant: the odds ratios were 1.17, 1.19 and finally 1.12 in the most fully adjusted model. The last estimate had a 95 percent confidence interval of 1.04 to 1.19 and a P value of 0.002. An odds ratio above one indicates higher odds of the outcome, although it should not be interpreted as a direct increase in an individual’s absolute risk. The confidence intervals also indicate uncertainty around each estimate; because they did not cross one, the researchers considered the associations statistically significant.
The pattern was not perfectly linear. Restricted cubic spline analysis, a flexible statistical technique that allows the data to curve rather than forcing them into a straight line, detected a nonlinear relationship between PIV and metabolic syndrome, with a P value of 0.044 for nonlinearity. This suggests that the change in metabolic-syndrome odds may not be identical at every point on the PIV scale. In biological terms, inflammation could have different implications at relatively low, intermediate or very high levels, or the association could reflect interactions with obesity, insulin resistance, liver dysfunction, kidney disease or medication use. The analysis found a positive relationship across the PIV range, but the detailed shape of that relationship would need to be tested in prospective studies before it could guide clinical thresholds.
To examine whether the result was being driven by specific types of participants, the researchers performed stratified analyses across subgroups. The positive association between PIV and metabolic syndrome remained broadly consistent, rather than disappearing in one particular demographic or clinical category. The investigators also repeated the analysis after excluding people taking fibrates or omega-3 products, which can affect blood lipids, as well as medications used to lower blood glucose or blood pressure. In that restricted sample, the association became stronger, with an odds ratio of 1.73 and a 95 percent confidence interval from 1.26 to 2.38. This finding may indicate that treatment-related changes in metabolic measurements or blood-cell profiles had partly obscured the relationship in the full dataset, although it could also reflect differences between people who do and do not receive those medications.
The researchers tested additional definitions and methods to assess the robustness of their findings. PIV remained significantly and positively associated with metabolic syndrome when the condition was defined using the Harmonized criteria, an internationally developed approach that brings together several commonly used diagnostic thresholds. Missing data were also addressed using random forest imputation, a machine-learning method that estimates absent values from patterns in the observed data. With that approach, the association remained stable in the first three models but weakened in the most fully adjusted model. Such attenuation is important: it shows that the strength of the association can depend on how missing information and potential confounders are handled, even when the overall signal remains suggestive.
Metabolic syndrome has long been linked to chronic, low-grade inflammation. Excess visceral fat—the metabolically active fat stored around internal organs—can release inflammatory signaling molecules and attract immune cells. These signals may interfere with insulin action, promote abnormal lipid metabolism and impair the function of the vascular endothelium, the cell layer lining blood vessels. Insulin resistance can lead the pancreas to produce more insulin to maintain normal blood glucose, while the liver may continue releasing glucose and producing triglyceride-rich particles. At the same time, inflammation and oxidative stress can alter platelet activity and leukocyte behavior. A composite measure such as PIV could therefore reflect several biological processes that overlap with the development or expression of metabolic syndrome, although it cannot reveal which process comes first.
The potential appeal of PIV is practical as much as biological. Platelet and white-cell counts are routinely included in complete blood counts, making the components relatively inexpensive and widely available compared with specialized inflammatory assays. If future research confirms that PIV adds meaningful information beyond waist circumference, blood pressure, glucose and lipid measurements, it could become a supplementary risk marker for identifying people who warrant closer metabolic evaluation. But the current study is not sufficient to support that use. NHANES provides a powerful population snapshot, yet its cross-sectional design measures exposure and outcome at roughly the same time. The data cannot establish whether elevated PIV precedes metabolic syndrome, results from it, or is influenced by an unmeasured factor such as infection, smoking, diet, medication, chronic disease or socioeconomic conditions.
The authors, led by Qian Dai and colleagues at Shanghai Fifth People’s Hospital affiliated with Fudan University and Fudan University’s Center for Community-Based Health Research, conclude that higher PIV is positively associated with the presence of metabolic syndrome among U.S. adults. They emphasize that prospective cohort studies in diverse populations are needed to determine whether the biomarker can predict future metabolic syndrome and whether it offers advantages over established measures of inflammation and insulin resistance. Clinical trials would also be needed to learn whether changing PIV through lifestyle or medical treatment changes metabolic outcomes, rather than merely accompanying them. For now, the study adds PIV to a growing list of inflammation-related indicators connected with cardiometabolic health. Its most important message is not that a single blood index can replace standard screening, but that the immune system, blood cells and metabolism may be more tightly intertwined than conventional checkups reveal.
Subject of Research: Association between pan-immune-inflammation value and metabolic syndrome in U.S. adults
Subject of Research: Medicine
Article Title: Association between pan-immune-inflammation value and metabolic syndrome in US adults: findings from NHANES 2013–2020
Article References: Dai, Q., Zeng, M., Chen, H., Yang, X., Xie, D., & Zhang, D. (2026). Association between pan-immune-inflammation value and metabolic syndrome in US adults: findings from NHANES 2013–2020. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02513-6
Image Credits: AI Generated
DOI: 10.1186/s12902-026-02513-6
Keywords: pan-immune-inflammation value, metabolic syndrome, NHANES, systemic inflammation, insulin resistance, cardiometabolic health, blood biomarkers, cross-sectional study
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