Immunometabolic Signatures of Circulating Monocytes in Humans With Obesity and Insulin Resistance Free

Obesity is strongly associated with a dysregulated immune system and promotes various complications, including type 2 diabetes mellitus and cardiovascular complications (1). Furthermore, the development of a chronic, low-grade inflammatory state is frequently reported and regarded as a substantial contributor to the progression of metabolic complications of obesity (2).

Insulin resistance (IR) is a common hallmark of obesity that often precedes the development of type 2 diabetes (2). IR primarily occurs in insulin-sensitive tissues, such as muscle, liver, and adipose tissue, although the development rate and primary location of IR vary among individuals (3). Therefore, methods to thoroughly phenotype IR among individuals have been developed and have led to the classification of muscle-specific IR (MIR) and liver-specific IR (LIR) (4,5). Chronic inflammation associated with obesity has been implicated in the progression of IR because inflammatory signals interfere with insulin signaling (6). Furthermore, varying degrees of systemic and tissue-specific IR have been linked to differences in circulating immune cell populations and inflammatory markers (7,8).

Several alterations in the immune system characterize chronic inflammation associated with obesity and IR. First, a classic example is the elevated levels of the circulating inflammatory mediators IL-6 and TNF-α in individuals with obesity compared with lean individuals (9,10). Second, obesity and IR are associated with shifts in immune populations, including shifts in lymphocyte subtypes leading to impaired pathogenic-specific responses and increased risk for infection (11,12). Additionally, an increased number of monocytes may contribute to chronic inflammation (13,14). Also, alterations in immune effector functions have been reported. For example, circulating monocytes from individuals with obesity adapt to a proinflammatory phenotype, as illustrated by increased surface expression of Toll-like receptors (TLRs) and chemokine receptors, and increased TLR4-mediated IL-1β secretion, compared with individuals with a healthy body weight (14,15).

Immune cells, including monocytes, modulate effector functions by rewiring intracellular metabolism. This is illustrated by the observation that resting or anti-inflammatory immune cells use glucose through aerobic metabolism, whereas a switch toward anaerobic metabolism characterizes immune activation (16). The increase in glycolytic rate upon monocyte activation not only facilitates ATP production; glycolytic enzymes and tricarboxylic acid cycle intermediates have been implicated in the secretion of cytokines (e.g., IL-6, IL-1β) and chemokines like CXCL8 (17,18). Therefore, perturbations in metabolic pathways of immune cells can significantly affect immune cell function. Although previous studies have shown alterations in the immunometabolic signatures of peripheral blood mononuclear cells (PBMCs) among 42 individuals with obesity-related cardiovascular disease (19–21) and type 2 diabetes (22), insights into naturally occurring variations in immunometabolic responses, their relation to immune effector functions, and overall health status in humans are currently incomplete.

Little is known about immunometabolic responses in humans and the impact of obesity-associated IR on the metabolism and functionality of circulating immune cells, including monocytes. To increase insight into the interindividual variation of immunometabolic responses of monocytes and to better understand the role of monocyte metabolism and function in the development of IR in overweight and obesity, we aimed to explore monocyte immunometabolic signatures and their association with circulating inflammatory markers and clinical characteristics, including the degree of obesity and IR. Therefore, we characterized monocyte metabolic signatures and cytokine secretion, as well as measured circulating inflammatory markers and the degree of systemic and tissue-specific IR in 194 individuals with a BMI ≥25 kg/m² without established type 2 diabetes.

The study population included 194 individuals (n = 110 women and 84 men) with overweight and obesity (BMI ≥25 kg/m²) who were screened for eligibility for the Personalized Glucose Optimization Through Nutritional Intervention (PERSON) study (23) at Wageningen University between September 2018 and February 2020. This study was approved by the Medical Ethics Committee of Maastricht University Medical Center+ (approval no. NL63768.068.1) and registered with ClinicalTrials.gov under the identifier NCT03708419. An elaborate description of the study design can be found in the corresponding article on the study methods (23). In short, participants arrived in the morning after an overnight fast for body weight and height measurements, and IR was assessed using a 7-point oral glucose tolerance test (OGTT). Additional fasting blood samples were collected for monocyte isolation.

Plasma glucose and insulin levels from OGTT samples were used to calculate estimates for IR (namely, HOMA-IR; Matsuda Index for insulin sensitivity; MIR using the inverse of the muscle IR index; and LIR using the liver insulin sensitivity index, as described previously by Gijbels et al. (23)).

Inflammatory markers were measured in fasting plasma samples from a subpopulation of 79 participants by the proximity extension assay using the Olink target 96 inflammatory panel (Olink, Uppsala, Sweden). Data quality checks were performed using Olink internal quality check methods and excluded eight participants and 17 proteins on the basis of a deviation of a ≥0.3 normalized protein expression level from the median of control samples (pooled plasma samples). A total of 75 inflammatory markers were expressed as arbitrary standardized units for 71 participants.

PBMCs were isolated from cell preparation tubes (CPT, catalog 362782; BD) using density-gradient centrifugation and monocytes were purified by magnetic-activated cell sorting using CD14⁺ microbeads (Miltenyi Biotec, catalog 130–050–201). Freshly isolated monocytes were directly used in Seahorse assays and stimulation experiments.

Monocytes were resuspended in standard growth medium (RPMI1640, Dutch modification; Gibco, catalog 22409031) supplemented with 2 mmol/L Glutamax (Gibco, catalog 35050061), 1 mmol/L sodium pyruvate (Gibco, catalog 11360070), and 50 µg/mL gentamycin to 1 × 10⁻⁶ cells/mL and seeded at 200 µL/well in 96-well Seahorse plates (Agilent, catalog 101085-004) in five replicates. Upon adherence, the standard growth medium was replaced by glucose-free nonbuffered Seahorse medium (8.3 g/L DMEM powder [Sigma, catalog D5040]; 8 mg/L phenol red [Sigma, catalog P5530]; and 925 mg/L NaCl, with pH set to 7.4 and filter sterilized using 0.2 μmol/L filters, supplemented with 2 mmol/L l-glutamine [Sigma, catalog G7513]) and incubated for 60 min in a CO₂-free environment at 37°C before the assay. Oxygen consumption rates (OCRs) and extracellular acidification rates (ECARs) were measured in a Seahorse XF96 Extracellular Flux Analyzer (Seahorse Bioscience, Agilent). After four baseline measurements, glucose (11 mmol/L; Sigma, catalog G8644) and Pam3Cys (10 µg/mL; EMC Microcollections, catalog L2000) were sequentially injected with three measurement cycles in between. ECARs and OCRs were measured during 18 measurement cycles at approximately 6-min intervals.

We designed a customized Python pipeline for data compilation and preprocessing of multiple Seahorse data files. Data preprocessing started with the removal of negative values. If this resulted in <40% of the remaining data of time points within one sample well, the well was removed from the analysis. If this resulted in fewer than two replicates for a study participant, that participant was removed from the analysis. ECARs and OCRs were normalized to the average of the three baseline measurements followed by log2 transformation. Subsequently, the ECAR or OCR response to glucose was calculated as the maximal ECAR or the average OCR from three measurement cycles. To calculate the response to Pam3Cys, the response to glucose was subtracted from the maximal ECAR or OCR from 11 measurement cycles after Pam3Cys injection. The metabolic potential was calculated as the maximal minus the lowest ECAR or OCR obtained during the assay. To calculate the ECAR-to-OCR (ECAR/OCR) ratio, measurements for ECAR were divided by OCR, followed by log2 transformation.

Freshly isolated monocytes were resuspended to 1 × 10⁻⁶ cells/mL in standard growth medium and seeded at 200 μL/well in flat-bottom, 96-well plates. Upon adherence, the supernatant was replaced by fresh medium containing the vehicle (MiliQ in standard growth medium), Pam3Cys (10 µg/mL), or lipopolysaccharide (LPS; 10 ng/mL; Sigma, catalog L6529) for 24 h at 37°C 5% CO₂. After 24 h, the medium was collected, and released IL-6, IL-1β, and CXCL8 were measured using ELISAs (BioTechne - R&D Biosystems, catalog DY206 [IL-6]; DY201 [IL-1β]; DY208 [CXCL8]).

Data were analyzed using R software and GraphPad Prism 9. Total group comparisons of monocyte metabolism and cytokine secretion were performed using unpaired one-way ANOVA with Bonferroni correction. Correlations within metabolic responses or cytokine secretion of monocytes were analyzed using the Pearson correlation coefficient.

Simple linear regression models on the OGTT-derived estimates for IR (i.e., fasting glucose, fasting insulin, HOMA-IR, Matsuda Index, MIR, and LIR), monocyte metabolism, and cytokine secretion were constructed using the lm() function on auto-scaled variables in R. OGTT-derived estimates for IR were log transformed before analysis to achieve normality and used as independent variables. Monocyte metabolism and cytokine secretion were used as dependent variables. In the linear models between monocyte metabolism and cytokine secretion, the monocyte metabolic variables were used as independent variables and cytokine secretion as the dependent variable. Age, sex, and BMI were added as covariates in all models. False discovery rates (FDRs) were generated by Benjamini-Hochberg methods using the p.adjust() function. Figures were created with BioRender.com.

The data sets generated and analyzed during this study are available from the corresponding author upon reasonable request.

We measured metabolic signatures of freshly isolated circulating monocytes from 194 individuals (n = 110 women and 84 men) with a BMI ≥25 kg/m² without established type 2 diabetes. Participants’ characteristics are listed in Table 1. To measure metabolic signatures of monocytes, the Seahorse assay included stimulation with glucose to assess metabolic responses to acute changes in glucose availability (metabolic challenge). Subsequently, we activated monocytes with the TLR2 ligand Pam3Cys, known for simultaneously triggering glycolytic and oxidative metabolic response, unlike other TLR ligands that primarily activate glycolysis. This allowed us to assess glycolytic and oxidative metabolic responses to pathogenic cues (pathogenic challenge) (Supplementary Fig. 1A).

To deal with the challenging amount of extracellular flux data, we created a pipeline to compile and preprocess data from multiple Seahorse experimental files into a ready-to-use data set (Supplementary Fig. 1A). Data preprocessing resulted in 185 complete observations (Supplementary Fig. 1A). Subsequent steps included normalization to baseline (Supplementary Fig. 1B) and the calculation of variables, including the glucose- and Pam3Cys-induced ECARs and OCRs, as well as the metabolic potential—indicative of the range between the highest and lowest values—and the ECAR/OCR ratio (Fig. 1A–E).

Measurements of glycolysis (i.e., ECAR) and oxidative metabolism (i.e., OCR) upon exposure to glucose and Pam3Cys revealed high interindividual variation in monocyte metabolic responses within our study population. Exposure to glucose increased ECAR (P < 0.01) without change in OCR, whereas Pam3Cys increased both ECAR and OCR (P < 0.01) (Fig. 1B and C). Hence, these data suggest that glucose exposure enhances glycolysis without altering oxidative metabolism, and pathogenic stimulation with Pam3Cys augments both glycolysis and oxidative metabolism.

We set out to gain further insight into monocyte metabolism using ECAR/OCR ratios, representing the relative utilization of the glycolytic or oxidative metabolic pathway (Fig. 1D and E). Acute exposure to glucose increased the ECAR/OCR ratio (P < 0.01), whereas the subsequent exposure to Pam3Cys reduced the ECAR/OCR ratio (P = 0.01), reflecting slightly higher increases in OCR compared with ECAR upon Pam3Cys exposure (Fig. 1E).

We next explored correlations between glucose and Pam3Cys-induced metabolic responses and between glycolytic and oxidative metabolic rates. First, we found a negative correlation between glucose-induced ECARs and Pam3Cys-induced ECARs (r = −0.15; P = 0.04) (Fig. 1F). Likewise, the OCR in response to glucose and Pam3Cys was significantly negatively correlated (r = −0.26; P < 0.01) (Fig. 1G). Hence, mild glycolytic and oxidative metabolic responses to glucose were correlated to higher metabolic responses to Pam3Cys. Second, the ECAR and OCR upon glucose and the ECAR and OCR upon Pam3Cys were not correlated, indicating specific reprogramming of glycolysis and oxidative metabolism depending on the external stimulus (Fig. 1H and I).

The ECAR/OCR ratio induced by glucose positively correlated with the ECAR/OCR ratio upon Pam3Cys exposure (r = 0.68; P < 0.01), suggesting metabolic stability in the relative utilization of glycolysis and oxidative metabolism upon different stimuli (Fig. 1J).

To learn more about the functional implications of metabolic responses of monocytes to Pam3Cys, we determined the association between the Pam3Cys-induced metabolic responses and cytokine secretion of the monocytes after ex vivo stimulation with Pam3Cys. Stimulation with Pam3Cys increased the secretion of several proinflammatory cytokines: IL-6, IL-1β, and CXCL8 (P < 0.01) (Fig. 1K), and cytokine secretion levels were positively associated with ECAR (scaled β > 0.3; FDR < 0.04 (Fig. 1L). The levels of IL-6 and IL-1β were also associated with OCR (scaled β > 0.2; FDR < 0.04).

In addition to Pam3Cys-mediated TLR1/2 stimulation, we stimulated monocytes with LPS to assess TLR4-induced cytokine secretion. As expected, LPS stimulation increased IL-6, IL-1β, and CXCL8 cytokine secretion in monocytes (Supplementary Fig. 1C), and the LPS-induced secretion levels of these cytokines correlated with the Pam3Cys-induced secretion levels (r > 0.5; P < 0.01) (Supplementary Fig. 1D). Furthermore, the LPS-induced cytokine secretion levels were positively associated with Pam3Cys-induced metabolic rates, in particular with ECAR (ECAR: scaled β > 0.2, FDR < 0.04; OCR: scaled β > 0.2, FDR < 0.02) (Supplementary Fig. 1E). These data suggest that multiple inflammatory signaling routes similarly rely on glycolytic metabolism to secrete cytokines.

Next, we studied the relationship between monocyte metabolic signatures and functional responses with participant characteristics using simple linear regression models. We observed significant associations between sex- and glucose-induced ECAR and ECAR metabolic potential, indicating higher ECARs in men than in women (Fig. 2A). We observed a trend toward lower ECARs and higher OCRs with age and did not find significant associations between monocyte metabolism and BMI (Fig. 2A). Additionally, no association between ECAR/OCR ratio and age, sex, or BMI was observed (Fig. 2A). Likewise, cytokine secretion was not significantly associated with age, sex, or BMI in our cohort (Supplementary Fig. 2A)

We characterized the level of systemic and tissue-specific IR (Supplementary Table 1) and explored associations with monocyte metabolic and functional responses. Fasting insulin levels, but not glucose levels, were negatively associated with the ECAR/OCR ratio after exposure to glucose (scaled β = −0.14, P = 0.01; FDR = 0.06) (Fig. 2B, Supplementary Fig. 2B). Likewise, the HOMA-IR, an estimate for IR, was negatively associated with the ECAR/OCR ratio after glucose exposure (scaled β = −0.12, P < 0.02; FDR < 0.16) (Fig. 2B, Supplementary Fig. 2C). Furthermore, the Matsuda Index, an insulin sensitivity parameter, was positively associated with the ECAR/OCR ratio (scaled β = 0.13, P = 0.01; FDR = 0.09) (Fig. 2C, Supplementary Fig. 2D). Additionally, we observed a negative association between LIR and the glucose-induced ECAR/OCR ratio (scaled β = −0.1, P = 0.03; FDR = 0.25) (Fig. 2C, Supplementary Fig. 2E). Estimates for IR were not significantly associated with cytokine secretion by monocytes, except from MIR, which was positively associated with LPS-induced IL-1β secretion (scaled β = 0.25, P = 0.02; FDR = 0.12) (Supplementary Fig. 2F).

To explore the relationships among monocyte metabolism, the degree of IR, and circulating inflammatory markers, we measured the abundance of circulating immune markers in fasted plasma samples using the Olink platform. The ECARs and OCRs in monocytes were not associated with plasma immune markers (Supplementary Fig. 3A). Still, the ECAR/OCR ratio was positively associated with multiple inflammatory plasma markers, including CXCL11 (scaled β = 0.32; FDR = 0.02) and SIRT2 (scaled β = 0.29; FDR = 0.02) (Fig. 3A, Supplementary Fig. 3B).

Although we did not observe significant associations between BMI or sex with circulating immune markers, age was positively associated with plasma CXCL9 (scaled β = 0.36, FDR < 0.01), CDCP1 (scaled β = 0.29, FDR < 0.01), and CCL25 (scaled β = 0.23, FDR = 0.03) (Supplementary Fig. 3C). Furthermore, estimates for IR were negatively associated with the level of circulating CXCL11 (fasting insulin: scaled β = −0.84, FDR = 0.02; HOMA-IR: scaled β = −0.78, FDR = 0.05) (Fig. 3B, Supplementary Fig. 3D and E). Similarly, the Matsuda Index showed a trend toward a positive association with CXCL11 (scaled β = 0.66, P < 0.01; FDR = 0.09) (Fig. 3B). Additionally, fasting insulin levels were negatively associated with SIRT2 (scaled β = −0.77, FDR = 0.02) and CASP-8 (scaled β = −0.63, FDR = 0.02) (Fig. 3B).

Obesity is a major determinant of metabolic complications and is associated with impaired immune responses. Immune cells drive inflammation through tight regulation of intracellular metabolism, but little is known about the metabolic signatures of immune cells in humans. Our study describes metabolically and pathogenically induced metabolic signatures in circulating monocytes (from 185 adults with a BMI ≥25 kg/m² without established type 2 diabetes) in response to ex vivo stimulation with glucose and Pam3Cys. Our results show that both glycolytic and oxidative metabolic rates and monocyte cytokine secretion are highly variable between individuals in both the magnitude and direction of responses but are strongly associated with each other. Furthermore, the ECAR/OCR ratio, representing the balance between glycolysis and oxidative metabolism, was negatively associated with the degree of IR, indicating that monocytes from individuals with higher IR were relatively more dependent on oxidative metabolism. Additionally, a relatively high metabolic dependency on oxidative metabolism in monocytes and high IR were both associated with reduced circulating levels of CXCL11.

The intricate connection between immune metabolism and functionality has been widely acknowledged (16,17). However, the existence of this interplay has mainly been based on results from in vitro or animal studies. Our results using monocytes from 185 individuals revealed strong positive associations between Pam3Cys-induced glycolytic and oxidative metabolic rates and cytokine secretion levels by monocytes, providing evidence from a large human study for the existence of a tight relationship between immune cell metabolic rewiring and functionality upon activation. Interestingly, LPS-induced cytokine levels were also associated with Pam3Cys-induced metabolic rates. Hence, these results suggest that the potential of monocytes to produce cytokines may predominantly rely on the capacity to rewire and upregulate the metabolism instead of the type of activated TLR.

In our study, the observed interindividual variability in glucose- and Pam3Cys-induced glycolytic and oxidative metabolic rates and cytokine secretion of monocytes could not be explained by age or BMI. Still, sex affected glycolytic rates, because we observed higher glucose-induced glycolysis and glycolytic metabolic potential in men than in women. To our knowledge, sex-specific differences in immunometabolism have not been previously described, although it has long been recognized that innate immune responses can vary between men and women (24). For example, innate immune cells from men are known to secrete higher levels of TNF-α in response to LPS compared with those from women (24,25). However, a previous study showed, among 489 healthy adults, that IL-6 and IL-1β secretion by PBMCs and macrophages did not differ according to sex (25). Likewise, the cytokines we measured in our study (IL-6, IL-1β, and CXCL8) were not different between men and women. Because we observed sex differences in the glycolytic response of monocytes, our results may imply that the sex effects might be more pronounced on the level of immunometabolism than the capacity of immune cells to secrete cytokines. However, these results need to be verified in different cohorts, including healthy individuals with a BMI <25 kg/m², to make firm conclusions on the sex differences in immune cell metabolism.

Although BMI is often considered an important determinant of immune function, we did not observe associations between BMI and monocyte metabolic rates or cytokine secretion within our study population, with BMIs ranging from 25 to 40 kg/m². Previous work using a BMI range between 15 and 35 kg/m² (average, 22.7 kg/m²) also did not find associations between BMI and ex vivo cytokine secretion of PBMCs and macrophages (25). Another study, including individuals with a BMI >27 kg/m², showed that cytokine secretion by ex vivo–stimulated PBMCs was not associated with key characteristics of obesity, such as adipocyte size and adipose tissue macrophage content (26). However, studies comparing lean individuals with those with overweight or obesity showed higher proinflammatory gene expression in PBMCs and monocytes, including IL-6, IFN-γ, and chemokine receptors among individuals with obesity (14,27,28). Shirakawa et al. (29) observed higher oxidative metabolic rates of PBMCs among individuals with obesity compared with lean individuals. Although these studies indicate that immune cell metabolism and function respond to profound differences in body weight, it is possible that beyond a certain BMI threshold, other factors become more influential in determining metabolic health and shaping immune cell function.

It is well established that obesity-associated complications are linked to perturbations in the immune system (28,30). Hartman et al. (22) showed higher mitochondrial metabolic rates in PBMCs from individuals with overweight and type 2 diabetes compared with glucose-tolerant control study participants, whereas DeConne et al. (21) found that oxidative metabolic rates of PBMCs were negatively associated with blood pressure and LDL concentration among individuals with overweight and obesity. However, in our study, neither glycolytic and oxidative metabolic rates nor cytokine secretion were associated with IR. However, our study population is unique in being overweight or obese but without established type 2 diabetes. Additionally, we used monocytes in our study and previous studies used PBMCs, which may vary greatly in composition between individuals, and this may account for the observed disparities in results (12).

Although glycolytic and oxidative metabolic rates were not associated with IR, we found that the monocyte ECAR/OCR ratio upon glucose exposure was negatively associated with IR, suggesting that monocytes from individuals with IR were more dependent on oxidative rather than glycolytic metabolism compared with monocytes from more insulin-sensitive individuals. Hence, in addition to the single glycolytic and oxidative metabolic rates representing the metabolic capacity of the cell, the ECAR/OCR ratio provides additional information on metabolic dependency. Interestingly, this metabolic dependency appears to be a clinically relevant feature of monocyte metabolic signatures in overweight and obese individuals.

The ECAR/OCR ratio in monocytes upon Pam3Cys exposure showed no significant association with estimates for IR but followed similar directions as the significant negative association between IR and glucose-induced ECAR/OCR ratio. This suggests stable monocyte metabolic dependency across different stimuli and potentially a higher prevalence of oxidative metabolism dependency in monocytes among individuals with IR than insulin-sensitive individuals. However, further investigation with different inflammatory stimuli is warranted to confirm the stability of monocyte metabolic dependency.

Our data may indicate a shift in monocyte metabolic dependency from glycolysis to oxidative metabolism when IR progresses. Whether this shift in monocyte metabolic dependency with IR is a cause or consequence of IR remains to be explored. Variations in monocyte subtype composition may have influenced our findings, given our sample primarily comprised classical and some intermediate monocytes, with nonclassical monocytes excluded by CD14⁺ selection. However, we anticipate minimal impact because prior research demonstrated no association between monocyte subset composition and HOMA-IR in individuals with obesity (31). On the cellular level, it may be postulated that the higher dependency on oxidative metabolism could result from higher oxygen consumption for reactive oxygen species secretion, which is increased in monocytes of patients with type 2 diabetes (32). Furthermore, it could be speculated that glycolysis may be impaired because of impaired insulin signaling in monocytes in IR (33,34). In addition, extracellular factors, such as circulating nutrients or inflammatory metabolites, may drive differences in the metabolic and functional profiles of monocytes. For instance, macrophages persistently exposed to lipids highly rely on oxidative metabolism (35). Hence, circulating lipids could potentially drive monocytes toward an oxidative metabolic dependency. Furthermore, our data showed a potential role for the circulating inflammatory protein CXCL11 in driving differences in monocyte metabolic signatures, because both the monocyte metabolic dependency on oxidative metabolism and systemic IR were associated with lower levels of circulating CXCL11 in our study. As a ligand for ACKR3, a receptor expressed on circulating monocytes, CXCL11 may stimulate anaerobic glycolysis by activating PKM2 (36–38). Reduced CXCL11 levels may result in aerobic glycolysis because of reduced activation of PKM2, leading to increased dependence on oxidative metabolism, as observed in our study. However, future research should elucidate a potential role for CXCL11 and ACKR3, and other drivers such as circulating nutrients, in driving differences in monocyte metabolic signatures. Furthermore, the potential reversibility of changes in monocyte metabolic dependency and its functional consequences remain to be explored.

In conclusion, we observed high interindividual variation together with positive associations between monocyte glycolytic and oxidative metabolic rates and functional responses of monocytes, as measured by cytokine secretion in response to both Pam3Cys and LPS. Furthermore, we explored the association between obesity-associated IR and the metabolism and cytokine secretion of circulating monocytes in individuals with overweight or obesity. Although we did not observe associations between IR and cytokine secretion, we observed that monocytes from individuals with IR were relatively more reliant on oxidative metabolism. In contrast, monocytes from more insulin-sensitive individuals used glycolysis relatively more. This monocyte metabolic dependency on oxidative rather than glycolytic metabolism and IR was associated with reduced circulating CXCL11 levels. Our results contribute to understanding the potential role of monocyte metabolism in developing obesity-associated IR. Future studies are needed to understand the functional consequences of differences in monocyte metabolic balance during overweight and obesity.

Acknowledgments. We thank the participants of the PERSON study and the following employees of the Human Research Unit and laboratory at Wageningen University: H.J. Fick, K.C.M. Manusama, M.M. Grootte Bromhaar, and H.J. Jansen.

While preparing this work, we used ChatGPT 3.5 (OpenAI) for the purpose of writing. After using this tool, we formally reviewed the content for its accuracy and edited it as necessary. The authors take full responsibility for all the content of this publication.

Funding. This study is conducted as part of the PERSON study, organized and executed by Top Institute Food and Nutrition and funded by DSM Nutritional Products, FrieslandCampina, Danone Nutricia Research, AMRA Medical AB, and the Top-Sector Agri&Food. This work was additionally supported by a Diabetes Fonds grant to R.S.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. A.G., I.T., K.M.J., and G.B.H. organized and executed the PERSON study. L.S. and J.P.B.-N. performed subsequent experiments and data collection. L.S. and F.V. performed the statistical analysis, and M.B.B. designed the software for extracellular flux data preprocessing. L.S., L.A.A., and R.S. wrote the manuscript, and all other authors read and approved the manuscript. E.J.M.F., E.E.B., G.H.G., and L.A.A. designed the PERSON study. R.S. designed the present work. L.A.A. and R.S. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the Annual Dutch Diabetes Research Meeting, Wageningen, the Netherlands, 3–4 November 2022 and 2–3 November 2023, and at the NuGOweek, Tarragona, Spain, 29 August to 1 September 2022.