Anabolic hormones are indispensable regulators of tissue growth, repair, and metabolic homeostasis [1]. Among the most prominent are testosterone and insulin, both conventionally classified within this group [2,3]. Testosterone, the principal male sex hormone, is widely recognized for its strong anabolic effects on lean body mass, particularly skeletal muscle accretion [1,4]. Insulin, however, has key physiological functions centered on energy conservation and storage [5,6]. It orchestrates a wide array of actions to achieve this goal, including facilitating cellular uptake of glucose and lipids, promoting glycogenesis and lipogenesis, retaining uric acid, and restricting access to stored energy by inhibiting lipolysis, glycogenolysis, proteolysis, and autophagy [3,5–11].

Although these processes involve synthesis and anabolism, their overarching purpose is to maximize energy reserves, prevent deficits, and maintain substrate storage. To better reflect this primary physiological function, we propose adopting the term ‘reponic’ (from the Latin repono, ‘to store’) to more accurately capture insulin’s role—a designation previously suggested in the literature [7]. In contrast, testosterone exemplifies a classic anabolic hormone whose actions target an energy-intensive process: the development of metabolically active lean tissue through stimulation of protein synthesis [1,4].

The rationale for a reponic classification is supported by a hierarchy of evidence. At the primary level, well-established physiological principles define insulin’s core function as energy storage and conservation. At the secondary level, our analysis of National Health and Nutrition Examination Survey (NHANES) data provides large-scale empirical evidence demonstrating insulin’s universal association with adiposity across sexes and a broad age spectrum. At the tertiary and illustrative level, testosterone serves as a classical anabolic contrast, highlighting the divergent metabolic outcomes of two hormones that are currently grouped under the same classification.

We conducted a cross-sectional analysis of 2,910 non-pregnant adults (≥18 years, no upper age limit) from the NHANES 2011–2018 cycles. The final sample was derived from an initial pool of 39,156 participants after excluding individuals with incomplete data for dual-energy X-ray absorptiometry (DXA), anthropometric measures, and relevant fasting blood biomarkers (insulin, testosterone, lipid profile, glycohemoglobin, liver enzymes, and uric acid).

Participants were not excluded on the basis of diabetes status, as our objective was to evaluate these relationships across a metabolically diverse population. Secondary analysis of these data was approved as exempt (category 4, IRB# 18000) by the Institutional Review Board of the University of Oklahoma–Tulsa. The primary predictors were log-transformed serum insulin and testosterone. Outcomes included DXA-derived body composition (fat mass index [FMI], fat-free mass index), anthropometrics (waist circumference, body mass index), and metabolic biomarkers. We used survey-weighted multiple linear regression to assess associations between hormone levels and outcomes, adjusting for age, sex, race/ethnicity, education, poverty status, smoking, and alcohol use. These covariates were selected because they are well-established demographic and lifestyle factors influencing hormone levels and metabolic outcomes.

To facilitate interpretation, we calculated predicted changes in each outcome associated with a shift from the 25th to 75th percentile of hormone distributions. For log-transformed outcomes, changes were expressed as percentage differences using the formula (exp[β×log(p75/p25)]−1)×100, where β is the regression coefficient. For non-log-transformed outcomes, absolute changes were calculated as β×log(p75/p25). These calculations were performed for the overall sample and stratified by sex. Statistical significance was set at P<0.05.

Our analytical sample included 2,910 participants (1,469 men and 1,441 women), representing a weighted United States adult population of approximately 138.3 million from NHANES 2011–2018. The mean age was 39.2±0.4 years, with participants spanning diverse racial and ethnic backgrounds: 49.6% non-Hispanic White, 11.8% Mexican American, 7.5% other Hispanic, 11.6% non-Hispanic Black, 5.6% non-Hispanic Asian, and 4.0% other race/multi-racial. Laboratory results revealed significant sex differences in testosterone (men: 466.8±6.7 ng/dL; women: 26.5±1.1 ng/dL), whereas insulin levels were comparable between men (11.5±0.4 μU/mL) and women (11.0±0.3 μU/mL). All hormone measurements were obtained from fasting blood samples.

Adjusted regression analyses revealed distinct and often opposing metabolic signatures for insulin and testosterone (Table 1). Higher log-insulin levels were significantly associated with greater adiposity in both men and women, including higher FMI, trunk fat mass, and waist circumference (all P<0.001). Insulin also correlated with an adverse metabolic profile in both sexes, demonstrating positive associations with triglycerides and glycohemoglobin and a negative association with high-density lipoprotein cholesterol (HDL-C; all P<0.001). In addition, insulin was positively associated with markers of hepatic stress (alanine aminotransferase, gamma-glutamyl transferase) and with higher uric acid levels (all P<0.001).

In contrast, the effects of log-testosterone were strongly sex-specific, with favorable associations observed almost exclusively in men. Among men, higher testosterone was robustly associated with lower FMI (β=−0.377, P<0.001), reduced waist circumference, lower triglycerides, reduced glycohemoglobin, and higher HDL-C (all P<0.001). These beneficial associations were minimal or absent in women (Table 1), which likely reflects their physiologically lower testosterone concentrations.

Fig. 1 visually illustrates these opposing metabolic associations in men. For example, an interquartile range increase in insulin was linked to a 40.7% increase in trunk fat mass, whereas an equivalent increase in testosterone was associated with a 19.6% reduction. This consistent pattern of opposite effects across multiple adiposity and metabolic health parameters underscores the fundamentally divergent physiological roles of these two hormones in energy metabolism.

This analysis provides additional empirical support for the proposed reponic framework [7], emphasizing established physiology that insulin and testosterone exhibit fundamentally distinct and largely opposing metabolic signatures at the population level. Insulin’s metabolic profile aligns closely with its primary role in energy storage. Its consistent positive associations with adiposity, triglycerides, liver enzymes, and uric acid, combined with negative associations with HDL-C, clearly delineate a systemic energy-conserving role. By contrast, testosterone—particularly in men—was associated with reduced adiposity and improved metabolic health, directly opposing insulin on several key markers.

Although testosterone demonstrated stronger associations in men than in women, insulin’s storage-promoting effects were robust and consistent across both sexes. This universal pattern reflects insulin’s fundamental evolutionary role in maximizing energy reserves during periods of abundance. Adopting the reponic classification therefore provides a clearer interpretive framework for clinical practice. Hyperinsulinemia is highly prevalent, strongly associated with cardiometabolic disease [12–14], and predictive of future diabetes development [13,15–17]. Viewing clinical presentations of excessive energy storage, such as central adiposity, hypertriglyceridemia, and reduced HDL-C, as signs of a dominant storage hormone reframes treatment priorities. This perspective moves beyond traditional counseling focused narrowly on caloric deficits irrespective of insulin dynamics [18] and beyond interventions that address only downstream markers like glucose. Instead, it highlights the importance of strategies aimed at reducing supraphysiologic insulin levels, which are central to obesity and type 2 diabetes, thereby targeting the underlying pathology of energy storage dysregulation [19,20].

This report has several limitations. First, the cross-sectional design of NHANES restricts causal inference, and findings are presented as supportive evidence for established physiological concepts. Second, reliance on single fasting hormone measurements may not account for diurnal variations. Third, unmeasured confounding factors, such as diet, medication use, and physical activity, could influence the results. Finally, the attenuated associations for testosterone in women are an expected consequence of their much lower physiological hormone levels. Importantly, though, insulin’s storage-promoting role was consistent across both sexes.

In conclusion, the metabolic signature of insulin is one of energy storage, in direct contrast to the lean tissue–building profile of testosterone in men. While testosterone’s effects are sex-specific, insulin’s consistent association with increased adiposity and markers of energy storage across both sexes reinforces its universal reponic role. This physiological distinction carries important clinical implications.

Recognizing insulin as primarily a reponic hormone provides a more precise physiological framework that better explains its role in metabolic health and disease. The term ‘anabolic’ fails to distinguish between building functional tissue (testosterone’s primary role) and storing energy as fat (insulin’s primary role). This conceptual clarity may inform more effective strategies for addressing the twin epidemics of hyperinsulinemia and metabolic disease, particularly by prioritizing interventions that reduce unnecessary insulin secretion rather than merely managing its downstream effects. By aligning terminology with insulin’s core function, we can enhance both scientific communication and clinical decision-making in the treatment of metabolic disorders.