Monocyte-to-HDL ratio (MHR) as a novel biomarker: reference ranges and associations with inflammatory diseases and disease-specific mortality

Abstract

Background

Monocyte-to-HDL Ratio (MHR) biomarker reflects monocyte-driven inflammation and HDL’s anti-inflammatory properties. MHR’s reference ranges and prognostic utility remain undefined. We establish normal MHR reference ranges and examine its association with inflammatory diseases and mortality.

Methods

Using NHANES data (1999–2018, 2021–2023), two sets of sex-specific MHR reference ranges were generated using two healthy adult populations (monocyte count: 6,757; monocyte percentage: 6,817). Further analyses utilized MHR by monocyte count for more straightforward interpretation. Adjusted associations between MHR and inflammatory diseases were assessed in 49,929 adults, and disease-specific mortality in 35,781.

Results

The 2.5th–97.5th percentiles for MHR by monocyte count were 0.175 (90% CI: 0.167–0.184) to 0.709 (90% CI: 0.690–0.727) in males and 0.135 (90% CI: 0.130–0.140) to 0.511 (90% CI: 0.503–0.520) in females, with similar trends for MHR by monocyte percentage. High MHR was most strongly associated with diabetes (aOR = 1.76, p < 0.001) and cardiovascular disease (aOR = 1.69, p < 0.001), while mortality risk was highest for kidney disease (aHR = 3.13, p < 0.001) and diabetes (aHR = 2.26, p < 0.001).

Conclusion

MHR is a feasible and accessible biomarker of inflammation and lipid dysregulation that can be derived from routine laboratory tests and shows strong associations with cardiometabolic diseases and disease-related mortality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-025-02755-8.

Introduction

Inflammation constitutes the immune system’s response to harmful stimuli, which may include pathogens, damaged cellular structures, toxic substances, or irradiation [1]. It acts as a double-edged sword, exerting both protective and deleterious effects. On the one hand, inflammation helps in eliminating the underlying cause of injury, clear cellular debris, and promote tissue healing and repair, ultimately restoring tissue homeostasis [2]. On the other hand, growing evidence suggests that inflammation contributes to the onset, progression, and severity of a wide spectrum of diseases, including cardiovascular [3], respiratory [4], and kidney diseases [5], alongside cancers [6], and metabolic diseases such as diabetes [7]. Notably, the prevalence of inflammatory diseases in the United States (U.S.) has risen over the past few decades, with cardiovascular diseases, cancers, and diabetes raking among the top ten causes of mortality [8–10]. This growing burden underscores the urgent need for advanced diagnostic methodologies and inflammation monitoring techniques to improve early detection and management.

Traditional inflammatory biomarkers, such as high-sensitivity C-reactive protein (hsCRP) and erythrocyte sedimentation rate (ESR), remain the most widely accepted laboratory markers for detecting and monitoring inflammation [11–13]. However, while these biomarkers effectively detect systemic inflammation, they often fall short of providing a comprehensive understanding of the complex interplay between inflammation and other homeostatic processes, particularly lipid metabolism, which is closely interconnected with inflammation in a bidirectional manner. Inflammation can disrupt lipid homeostasis, leading to altered lipid profiles, impaired cholesterol transport, and increased oxidative stress [14–16]. Concurrently, dysregulated lipid profiles, including cholesterol and fatty acids, can activate inflammatory pathways, further driving disease progression [17]. Moreover, preclinical models have demonstrated that circulating lipoproteins regulate leukocyte activity, while proinflammatory cytokines alter lipid metabolism [18]. This dynamic crosstalk underscores the limitations of conventional inflammatory markers, which provide only a partial view of the underlying pathophysiological mechanisms in inflammatory diseases. Therefore, the need for more comprehensive biomarkers has intensified, driving research toward alternatives that not only detect subclinical inflammation but also capture its intricate relationship with metabolic dysregulation.

Recently, the Monocyte-to-High-Density Lipoprotein Cholesterol Ratio (MHR) has emerged as a novel biomarker reflecting both inflammatory status and lipid. MHR integrates the pro-inflammatory role of monocytes with the anti-inflammatory effects of HDL. Monocytes are key immune cells that differentiate into cytokine-secreting macrophages, essential for the initiation and resolution of inflammation or cellular perturbations. Therefore, an elevated monocyte count often indicates an underlying inflammation [13, 19]. In contrast, HDL counteracts the pro-inflammatory effects of monocytes through various suppressive mechanisms, including inhibiting the proliferation and differentiation of monocyte progenitor cells into cytokine-secreting pro-inflammatory cells or macrophages [19]. Moreover, HDL is of particular relevance because HDL-associated proteins, such as Apolipoprotein A-I (ApoA-I) and apolipoprotein M (ApoM), exert potent anti-inflammatory and immunomodulatory effects, including scavenging of lipopolysaccharides (LPS), inhibition of monocyte activation, and promotion of cholesterol efflux. Recent evidence shows that vitamin D enhances these HDL-mediated anti-inflammatory properties, further underscoring HDL’s role as a regulator of immunity and inflammation. Therefore, HDL was selected as the lipid denominator in the Monocyte-to-HDL Ratio (MHR) to reflect the balance between pro-inflammatory monocyte burden and anti-inflammatory lipid capacity [20]. Hence, elevated monocyte levels and reduced HDL concentration drive an increase in MHR, serving as an indicator of disruption of cellular homeostasis and underlying inflammation.

Over the last few years, MHR has garnered growing interest as a composite biomarker that reflects the delicate balance between the pro-inflammatory effects of monocytes and the anti-inflammatory effects of HDL [19, 21, 22]. Several studies have linked MHR to chronic inflammatory diseases, such as cardiovascular disease [23], chronic obstructive pulmonary disease [24], diabetic nephropathy [25], metabolic syndrome [26], and vitamin D deficiency [22]. Furthermore, emerging evidence suggests that MHR may offer greater prognostic potential for clinical outcomes compared to monocyte count or HDL concentration independently, offering a more comprehensive assessment of systemic inflammation [21]. This underscores the potential of MHR in improving diagnostic and prognostic assessments in inflammation-related diseases.

Despite MHR’s growing recognition, proposed as a marker of inflammation, standardized reference ranges for it have yet to be established. To address this gap, our study aims to define the normal upper and lower reference values for MHR using a large, nationally representative cohort of adults enrolled in the National Health and Nutrition Examination Survey (NHANES). We also investigate the association between MHR and five prevalent inflammatory diseases, including cardiovascular, respiratory, renal disorders, cancers and diabetes. Moreover, MHR’s associations with disease-related mortality of these inflammatory diseases were evaluated to explore its potential prognostic utility.

Methods

Population and study design

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) database, a publicly available cross-sectional survey, overseen by the Centers for Disease Control and Prevention (CDC). NHANES evaluates the health and nutritional status of the United States’ (U.S.) population, by employing a multistage probability sampling design to obtain a nationally representative cohort that reflects the demographic diversity of the U.S. populace. Data was collected through face-to-face or telephonic interviews, detailed questionnaires, laboratory tests, and physical examinations [27]. Detailed information regarding survey data and methodologies is available at https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm. Ethical approval has been obtained from the National Center for Health Statistics Institutional Ethics Review Board, and all participants included have signed the relevant consent forms.

A total of 113,249 participants were initially included in this study, drawn from eleven NHANES cycles spanning from 1999 to 2018, alongside the 2021–2023 cycle. The first two cycles spanning 1999–2002 were appropriately weighted using the WTMEC4YR survey sample weight, while the remaining nine cycles (2003–2018 and 2021–2023) were weighted using the WTMEC2YR survey sample weight. After applying these weights, our sample was found to represent an estimate of 301,486,924 individuals in the U.S. population. Afterwards, three different analyses were conducted on this sample after applying appropriate inclusion and exclusion criteria (Figure S1).

Firstly, the initial sample was carefully curated to establish reference ranges for MHR, using only apparently healthy adult participants. Exclusions were made based on the following criteria: (1) participants younger than 18 years, (2) those with missing data needed for MHR calculation, (3) individuals with inflammatory diseases (including dyslipidemia, hypertension, diabetes, gout, gastrointestinal diseases, respiratory diseases, autoimmune diseases, malignancies, cardiovascular diseases, kidney diseases, infections, or hepatic diseases, including chronic liver diseases such as metabolic-associated fatty liver disease (MAFLD) and cirrhosis), (4) participants taking medications known to alter monocyte or HDL values, such as steroids, anti-infective agents, immunological agents, niacin, or fibrates, and (5) those with MHR values classified as statistical outliers (Table S1). Outliers were identified by implementing the interquartile ranges (IQR) method, whereby values exceeding 1.5 times the IQR beyond the highest or lowest tertile were classified as extreme observations and excluded from analysis [28]. After applying these criteria, two different samples were included for the determination of MHR reference intervals. A sample of 6,757 apparently healthy adults was used to calculate MHR reference intervals based on monocyte count (#), corresponding to an estimated 27,984,636 apparently healthy U.S. adults. Meanwhile, a separate sample of 6,817 apparently healthy adults, representing approximately 28,225,782 apparently healthy U.S. adults, was utilized for MHR reference intervals calculation based on monocyte percentage (%).

Secondly, a cross-sectional analysis was conducted to evaluate the potential of MHR as an inflammatory marker by examining its relationship with various inflammatory diseases. Participants under the age of 18 years, as well as those with incomplete data on any of the exposure, outcome, or covariate variables, were excluded from the analysis. This ultimately provided a final sample of 49,929 adult participants, representing an estimated 171,242,153 adults in the U.S. population.

Lastly, MHR’s associations with disease-specific mortality due to inflammatory conditions was analyzed retrospectively, using the same exclusion criteria as the cross-sectional analysis, with the additional exclusion of those with missing follow-up data. This yielded a final sample of 35,781 adult participants, equivalent to around 130,792,435 adults in the U.S. population.

Exposure measure: monocyte-to-HDL ratio (MHR)

Two distinct approaches were used to calculate MHR. The first method involved dividing the monocyte count (#) by HDL concentration (mmol/L) [29], while the second involved dividing the monocyte percentage (%) by HDL concentration (mmol/L) [30]. To determine the reference ranges for MHR, participants were first categorized by sex, as males generally tend to have higher monocyte levels, while females tend to have higher HDL levels [31, 32]. Two separate reference ranges were established, one for each calculation method. However, for the remainder of the analysis, only the calculation based on monocyte count was used, as it provides absolute values, thereby facilitating a more straightforward interpretation. Reference ranges were determined following the guidelines outlined in CLSI EP28: Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory, 3rd Edition [33]. Once reference ranges were established, MHR levels were further classified into low, normal, and high categories.

Outcome measures

Our primary outcome was to determine reference ranges for MHR using the two different MHR calculation methods. For the secondary outcomes, MHR was evaluated as a potential biomarker of inflammation through examining its association with five different inflammatory diseases, including cardiovascular diseases (CVD), respiratory diseases (RD), kidney diseases (KD), cancers, and diabetes mellitus (DM). Inflammatory diseases were identified through self-reported questionnaires detailing physician-confirmed diagnoses, the use of medications for the respective inflammatory conditions within the past 30 days, and relevant laboratory investigations. The inflammatory diseases were classified as follows: (1) CVD, including congestive heart failure, coronary artery disease, angina pectoris, myocardial infarction, or stroke; (2) RD, encompassing asthma, chronic bronchitis, emphysema, chronic obstructive pulmonary disease, influenza, pneumonia, or common cold; (3) KD, including kidney failure; (4) cancers, defined as any self-reported malignancy, and (5) DM, which included individuals who reported taking glucose-lowering medications, had a self-reported confirmed diagnosis, or met the American Diabetes Association (ADA) criteria for diabetes. According to the ADA, diabetes is defined as having laboratory values of HbA1c ≥ 6.5%, Fasting Plasma Glucose (FPG) ≥ 126 mg/dL (7 mmol/L), or an Oral Glucose Tolerance Test (OGTT) ≥ 200 mg/dL (11.1 mmol/L) [34].

Our third outcome was MHR’s association with disease-specific mortality, determined through probabilistic linkage to death certificate records from the National Death Index, with data available up to December 31, 2019. The recorded cause of death was classified according to the International Classification of Diseases, Tenth Revision (ICD-10). Disease-specific mortality was then classified as follows: (1) CVD-related Mortality, when death was attributed to diseases of the heart or cerebrovascular disease (ICD-10 codes I00-I09, I11, I13, I20-I51, I60-I69); (2) RD-related Mortality, when death was caused by chronic lower respiratory diseases, influenza, or pneumonia (ICD-10 codes J09-J18, J40-J47); (3) KD-related Mortality, when nephritis, nephrotic syndrome, or nephrosis were reported as causes of death (ICD-10 codes N00-N07, N17-N19, N25-N27); (4) Cancer-related Mortality, when death reported due to neoplasms (ICD-10 codes C00-C97); and (5) DM-related Mortality, when death reported due to diabetes mellitus (ICD-10 codes E10-E14) [35, 36].

Covariates

Covariates included in the analyses for the latter two outcomes were age, race, physical activity, and obesity. These covariates were identified using Directed Acyclic Graphs (DAG), establishing the minimal adjustment set as seen in Supplementary Figure S2. Age and race were extracted from self-reported demographic variables. Physical activity was assessed according to the Global Physical Activity Questionnaire (GPAQ) across five categories: vigorous-intensity work-related activity, moderate-intensity work-related activity, walking or bicycling for transportation, vigorous-intensity leisure-time activity, and moderate-intensity leisure-time activity. Each activity was assigned a corresponding Metabolic Equivalent of Task (MET) score, and the cumulative MET hours per week were calculated to quantify overall physical activity levels, wherein higher values signified greater activity. Obesity was identified using anthropometric measurements of waist circumference (≥ 102 cm in males and ≥ 88 cm in females) [37].

Statistical analysis

Frequencies (N) and percentages (%) were used to summarize categorical variables. Continuous variables were first assessed for normality using histograms and Shapiro-wilk test (Table S2), which revealed a non-normal distribution for all variables. Thus, they were summarized using medians and IQRs. Group differences were tested using Pearson’s chi-squared test for categorical variables and Kruskal-Wallis test for continuous variables. To ensure the representativeness of both descriptive and inferential analyses, appropriate survey weights were applied. In accordance with established guidelines, reference ranges for MHR were determined using an apparently healthy adult sample stratified by sex. Weighted means, standard deviations, medians and IQRs were computed. A nonparametric statistical approach was then employed to compute the 2.5th and 97.5th percentiles, along with their respective 90% CIs, to encompass 95% of the reference population [33]. The correlation between MHR calculated using absolute monocyte count and monocyte percentage was evaluated using Spearman’s rank correlation coefficient (Table S3). Multivariable logistic regression models were employed to generate adjusted odds ratios (aOR) to evaluate the relationship between MHR and each of the five inflammatory diseases of interest. Models were adjusted for potential confounders. Goodness of fit was determined using Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC), whereas goodness of link was determined using linktest in Stata. Moreover, a sensitivity analysis restricting RD to chronic conditions only (asthma, chronic bronchitis, emphysema, COPD) was conducted.

Adjusted hazard ratios (aHR) were generated using survival analysis to explore MHR’s potential prognostic utility by evaluating MHR’s associations with mortality related to the five inflammatory diseases. This was performed using Cox proportional hazards regression models, ensuring adherence to the model’s assumptions, examined using estat phtest on Stata [38]. P-Values and 95% confidence intervals (CIs) were reported where applicable. Data visualization was performed using histograms as appropriate. All statistical analysis was conducted using Stata version SE18.5 (Stata Corp., College Station, TX, USA).

Results

MHR distribution and reference intervals

MHR by monocyte count distribution and reference intervals in the apparently healthy US adult population

After applying the exclusion criteria, a total of 6,757 apparently healthy adults, comprising 3,465 males and 3,292 females, remained eligible for the determination of reference intervals for MHR by monocyte count. This cohort is estimated to represent 27,984,636 apparently healthy adults in the United States. The distribution of MHR values within the male and female subpopulations is illustrated in Fig. 1a and b, respectively. In both sexes, the distribution of MHR exhibited a positively skewed pattern. As illustrated in Table 1, males demonstrated a higher median MHR value of 0.388 (95% CI: 0.380–0.395), whereas females exhibited a lower median MHR of 0.282 (95% CI: 0.276–0.287).

Fig. 1.

Histograms illustrating the distribution of MHR by monocyte count in the apparently healthy adult US population, excluding outliers

Table 1.

Summary statistics of MHR by monocyte count in the apparently healthy adult US male and female populations, excluding outliers

Sex	Mean (95% CI)	Standard Deviation	Median (95% CI)	IQR (Q3-Q1)	Lower Whisker	Upper Whisker	2.5th percentile (90% CI)	97.5th percentile (90% CI)	Study Participants	Corresponding US population
Males	0.403 (0.397–0.410)	0.125	0.388 (0.380–0.395)	0.194 (0.495 − 0.301)	0.010	0.786	0.175 (0.167–0.184)	0.709 (0.690–0.727)	3,465	14,375,524
Females	0.292 (0.287–0.297)	0.088	0.282 (0.276–0.287)	0.141 (0.361 − 0.220)	0.009	0.573	0.135 (0.130–0.140)	0.511 (0.503–0.520)	3,292	13,609,112

Sex-specific intervals for MHR, along with their respective 90% CI, were established. Both the lower and upper reference limits were higher in males than females, with the 2.5th and 97.5th percentile MHR values determined to be 0.175 (90% CI: 0.167–0.184) and 0.709 (90% CI: 0.690–0.727), respectively, compared to 0.135 (90% CI: 0.130–0.140) and 0.511 (90% CI: 0.503–0.520) in females. Based on these sex-specific reference intervals, MHR values were subsequently categorized as low, normal, or high.

MHR by monocyte percentage distribution and reference intervals in the apparently healthy US adult population

Following exclusion criteria application, 6,817 apparently healthy adults, 3,494 males and 3,323 females, remained eligible for the determination of MHR by monocyte percentage reference intervals, representing an estimated 28,225,782 healthy adults in the United States. The MHR distribution, shown in Figures S3 and S3b, was positively skewed in both sexes. As presented in Table S4, the median MHR for males was 6.082 (95% CI: 5.978–6.185), while for females, it was lower at 4.323 (95% CI: 4.253–4.392). Sex-specific reference intervals, along with their respective 90% CI, were established, with males exhibiting higher reference limits than females. The 2.5th and 97.5th percentile MHR values for males were 3.116 (90% CI: 3.010–3.221) and 10.097 (90% CI: 9.937–10.258), respectively, whereas for females, the corresponding values were 2.331 (90% CI: 2.238–2.425) and 7.000 (90% CI: 6.871–7.129). These findings reflect the same trends observed for MHR when calculated using monocyte count, as mentioned in Sect. 3.1.1.

Analytical sample baseline characteristics

Table 2 presents the baseline characteristics of the final analytical cohort, stratified according to MHR by monocyte count status. The study included a total of 49,929 participants, which, after applying survey weights, corresponded to an estimated 171,242,153 adults in the U.S. population. Of these participants, 36,886 (73.9%) exhibited MHR values within the normal range, whereas 8,248 (16.5%) and 1,015 (2.03%) individuals were classified as having high and low MHR, respectively. Age distribution varied across MHR categories, with participants in the low MHR group demonstrating the highest median age of 52 years. Racial composition also differed among the groups, with white individuals predominating in both the normal and high MHR categories, whereas Black individuals constituted the majority within the low MHR group. Furthermore, individuals classified as having low MHR exhibited the lowest prevalence of obesity, which was consistent with their comparatively higher levels of physical activity. Conversely, participants in the high MHR category demonstrated an increased prevalence of MHR-altering medications use, as well as a greater burden of all five inflammatory conditions.

Table 2.

Baseline characteristics of the study’s final analytical sample categorized by MHR by monocyte count status (N = 171,242,153; n = 49,929)

Variable	Normal MHR n = 36,886	High MHR n = 8,248	Low MHR n = 1,015	p-Value
Age (Years), median (IQR)	48.00 (32.00, 63.00)	47.00 (31.00, 64.00)	52.00 (37.00, 63.00)	< 0.001
Gender, N (%)
Males	18,353 (49.8%)	3569 (43.3%)	464 (45.7%)	< 0.001
Females	18,533 (50.2%)	4679 (56.7%)	551 (54.3%)
Race, N (%)
Hispanic	9,185 (24.9%)	2,251 (27.3%)	135 (13.3%)	< 0.001
White	16,172 (43.8%)	4,116 (49.9%)	301 (29.7%)
Black	7,582 (20.6%)	1,142 (13.8%)	399 (39.3%)
Others/Multi-Racial	3,947 (10.7%)	739 (9.0%)	180 (17.7%)
Obesity Status, N (%)
No Obesity	17,459 (47.3%)	2,206 (26.7%)	698 (68.8%)	< 0.001
Obesity	19,427 (52.7%)	6,042 (73.3%)	317 (31.2%)
Physical Activity Status, median (IQR)	2.00 (0.00, 40.00)	0.00 (0.00, 34.00)	2.67 (0.00, 40.00)	0.014
Monocyte (#) to HDL ratio, median (IQR)
Males	0.43 (0.33, 0.54)	0.84 (0.77, 1.00)	0.15 (0.14, 0.17)	< 0.001
Females	0.32 (0.25, 0.39)	0.61 (0.55, 0.72)	0.12 (0.11, 0.13)
Monocyte Count (#), median (IQR)
Males	0.50 (0.40, 0.60)	0.80 (0.60, 0.90)	0.30 (0.20, 0.30)	< 0.001
Females	0.50 (0.40, 0.60)	0.70 (0.60, 0.80)	0.20 (0.20, 0.30)
Monocyte (%) to HDL ratio, median (IQR)
Males	6.39 (5.17, 7.80)	10.00 (8.52, 11.94)	3.21 (2.63, 3.95)	< 0.001
Females	4.68 (3.80, 5.67)	7.25 (6.12, 8.71)	2.63 (2.06, 3.19)
Monocyte Percentage (%), median (IQR)
Males	8.00 (6.80, 9.40)	9.20 (7.90, 10.80)	6.20 (4.80, 7.70)	< 0.001
Females	7.10 (5.90, 8.40)	8.10 (6.80, 9.60)	5.70 (4.40, 7.20)
Direct HDL-Cholesterol (mmol/L), median (IQR)
Males	1.24 (1.06, 1.47)	0.93 (0.80, 1.06)	1.86 (1.60, 2.30)	< 0.001
Females	1.53 (1.29, 1.78)	1.11 (0.96, 1.29)	2.17 (1.78, 2.46)
Inflammatory Disease/MHR-Altering Medication Status, N (%)
Apparently Healthy	6,137 (16.6%)	330 (4.0%)	220 (21.7%)	< 0.001
Inflammatory Disease/MHR-Altering Medication	30,749 (83.4%)	7,918 (96.0%)	795 (78.3%)
Cardiovascular Disease Status, N (%)
No CVDs	26,518 (71.9%)	5,192 (62.9%)	777 (76.6%)	< 0.001
CVDs	10,368 (28.1%)	3,056 (37.1%)	238 (23.4%)
Diabetes Status, N (%)
No Diabetes	31,150 (84.4%)	6,313 (76.5%)	899 (88.6%)	< 0.001
Diabetes	5,736 (15.6%)	1,935 (23.5%)	116 (11.4%)
Respiratory Disease Status, N (%)
No Respiratory Disease	26,647 (72.2%)	5,338 (64.7%)	757 (74.6%)	< 0.001
Respiratory Disease	10,239 (27.8%)	2,910 (35.3%)	258 (25.4%)
Cancers Status, N (%)
No Cancers	33,307 (90.3%)	7,368 (89.3%)	914 (90.0%)	0.029
Cancers	3,579 (9.7%)	880 (10.7%)	101 (10.0%)
Kidney Disease Status, N (%)
No Kidney Disease	33,720 (97.3%)	7,405 (95.8%)	941 (97.2%)	< 0.001
Kidney Disease	950 (2.7%)	328 (4.2%)	27 (2.8%)

Associations between MHR by monocyte count status and inflammatory diseases

Table 3 and S5 display the adjusted associations between MHR by monocyte count status and a range of inflammatory diseases, as assessed through multivariable logistic regression models. The results indicate that high MHR is positively associated with all examined diseases, with the exception of cancers, for which no meaningful association was observed. Specifically, high MHR was associated with a 76% (95% CI: 1.63–1.90, p < 0.001) increase in the odds of diabetes, compared to normal MHR, marking it as the most robust association across all inflammatory diseases assessed. This association was closely followed by CVD, with an OR of 1.69 (95% CI: 1.56–1.84, p < 0.001). In contrast, weaker, yet significant, associations between high MHR and both kidney diseases and respiratory diseases were noted, as illustrated in Table 3 and S5. Conversely, low MHR was linked to a reduction in the odds of diabetes, respiratory diseases, and CVD relative to normal MHR, with CVD showing the most pronounced protective effect, reflecting a 29% (95% CI: 0.54–0.93, p = 0.015) reduction in the odds compared to normal MHR. ROC curves and their corresponding AUC values are presented in Figure S4.

Table 3.

Adjusted associations between different MHR by monocyte count categories and various inflammatory diseases among the study’s final analytical sample (N = 171,242,153; n = 49,929)¹

Exposure	CVD aOR (p-Value, 95%CI)	RD aOR (p-Value, 95%CI)	KD aOR (p-Value, 95%CI)	Cancers aOR (p-Value, 95%CI)	DM aOR (p-Value, 95%CI)
Normal MHR	1	1	1	1	1
Low MHR	0.71 (0.015, 0.54–0.93)	0.86 (0.143, 0.70–1.05)	1.17 (0.598, 0.67–2.07)	1.20 (0.342, 0.82–1.76)	0. 75 (0.053, 0.56-1.00)
High MHR	1.69 (< 0.001, 1.56–1.84)	1.36 (< 0.001, 1.27–1.45)	1.45 (< 0.001, 1.23–1.72)	1.02 (0.760, 0.91–1.13)	1.76 (< 0.001, 1.63–1.90)

¹ Model adjusted for age, race, obesity, and physical activity 

CVD cardiovascular diseases, RD respiratory diseases, KD kidney diseases, DM Diabetes mellitus

Association between MHR by monocyte count and diseases-related mortality of inflammatory diseases

Table 4 and S5 present the adjusted association between MHR by monocyte count status and the disease-related mortality of various inflammatory diseases, investigated via multivariable Cox proportional hazards regression models. Interestingly, our findings indicate a strong positive association between high MHR status and mortality of all assessed inflammatory diseases. The strongest associations observed were with kidney diseases and diabetes mortalities with a 3.13-fold (95% CI: 1.91–514, p < 0.001) and 2.26-fold (95% CI: 1.56–3.27, p < 0.001) increase in the risk of mortality during the follow-up period compared to participants with normal MHR, respectively. The weakest association of high MHR was with cancers-related mortality, as illustrated in Table 4. Similarly, low MHR status exhibited positive associations with the mortality of all examined diseases, with diabetes having the strongest association with a HR of 2.08 (95% CI: 0.32–13.65, p = 0.442). One exception is in the case of CVDs, in which a protective effect of low MHR was observed, with a HR of 0.91 (95% CI: 0.52–1.60, p = 0.741). A summary of disease-specific mortality rates among the adult U.S. population in both weighted and unweighted samples can be found in Table S6.

Table 4.

Adjusted associations between different MHR by monocyte count categories and disease-related mortality of various inflammatory diseases among the study’s final analytical sample (N = 130,792,435; n = 35,781)¹

Exposure	CVD-related Mortality aHR (p-Value, 95%CI)	RD-related Mortality aHR (p-Value, 95%CI)	KD-related Mortality aHR (p-Value, 95%CI)	Cancer-related Mortality aHR (p-Value, 95%CI)	DM-related Mortality aHR (p-Value, 95%CI)
Normal MHR	1	1	1	1	1
Low MHR	0.91 (0.741, 0.52–1.60)	1.20 (0.674, 0.50–2.87)	1.87 (0.284, 0.59–5.92)	1.17 (0.602, 0.65–2.11)	2.08 (0.442, 0.32–13.65)
High MHR	1.49 (< 0.001, 1.28–1.73)	1.83 (< 0.001, 1.34–2.50)	3.13 (< 0.001, 1.91–5.14)	1.44 (0.001, 1.16–1.79)	2.26 (< 0.001, 1.56–3.27)

¹ Model adjusted for age, race, obesity, and physical activity 

CVD cardiovascular diseases, RD respiratory diseases, KD kidney diseases, DM Diabetes mellitus

Discussion

In this study, we utilized a large representative NHANES sample, reflecting a broader sample of apparently healthy U.S. adults. We established two separate sex-specific reference intervals for MHR as a biomarker for subclinical and systemic inflammation, each based on the calculation method employed, and further explored its associations with five prevalent inflammation-based diseases and their corresponding disease-specific mortality. MHR exhibited a positively skewed distribution, with males demonstrating higher median values and broader reference limits than females. Among the inflammatory diseases analyzed, diabetes and cardiovascular diseases emerged as the most strongly associated with high MHR, displaying the highest ORs of 1.76 and 1.69, respectively. Mortality analyses further reinforced these associations, with high MHR being associated with higher risk of death from all inflammatory diseases, most notably kidney diseases and diabetes, exhibiting HRs of 3.13 and 2.26, respectively. All ORs and HRs were adjusted for age, race, obesity, and physical activity to eliminate their confounding effects.

MHR emerges as a compelling biomarker reflecting inflammation and lipid dysregulation, with strong associations observed for cardiometabolic disturbances. These findings suggest potential prognostic utility, particularly in cardiovascular diseases and diabetes. Cardiometabolic disturbances refer to a cluster of risk factors, such as hypertension and impaired lipid profile, that collectively increase the likelihood of cardiovascular diseases and type 2 diabetes mellitus [39]. Consistent with existing literature, our findings suggest that elevated MHR is not only highly associated with these conditions but might also serve as a potential prognostic factor of their mortality. Indeed, high MHR has been linked to several cardiovascular diseases, including acute coronary syndrome [40], post-ablation recurrence of atrial fibrillation [41], in-stent atherosclerosis [42], and coronary artery disease [43]. Although the direct association of diabetes with MHR has not been previously explored, Ruan et al. (2024) reported a non-linear positive association between MHR and prediabetes, with each unit increase in MHR correlating to a 64% higher odds of prediabetes (95% CI: 1.48–1.82, p < 0.001) [44]. Furthermore, elevated MHR has demonstrated significant associations with various diabetic complications, including diabetic retinopathy [45], diabetic nephropathy [46], and increased carotid intima-media thickness [47]. On the other hand, the prognostic role of MHR in predicting mortality remains less explored. Nonetheless, Açıkgöz et al. (2016) concluded that higher MHR predicted mortality in ST-segment elevation myocardial infarction (STEMI), with increased risk of in-hospital mortality (HR = 3.75, 95% CI: 1.31–5.95) and five-year mortality (HR = 2.05, 95% CI: 1.23–4.09) [48].

From a broader perspective, our study, which compared MHR associations across various inflammatory diseases, revealed that MHR demonstrates the strongest associations with cardiometabolic disturbances. These findings not only reinforce previous evidence but, more importantly, highlight MHR’s strong associations with cardiometabolic conditions. The strong link between high MHR and cardiometabolic disorders stems from the intricate interplay between chronic inflammation, oxidative stress, and lipid metabolism dysregulation. Monocytes, key mediators of systemic inflammation, produce pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1 beta (IL-1β), which drive insulin resistance, endothelial dysfunction, and atherogenesis. Conversely, HDL, known for its role in reverse cholesterol transfer, as well as its anti-inflammatory and antioxidant properties, counteracts monocyte-induced damage by inhibiting monocyte adhesion and migration [42, 49]. Therefore, high MHR, characterized by increased monocyte and decreased HDL, reflects a pro-inflammatory state and metabolic dysregulation, accelerating the progression of metabolic disorders [13].

For instance, this inflammatory burden plays a crucial role in atherosclerosis, where oxidized LDL accumulates in macrophages, forming foam cells. In an inflammatory setting, foam cell formation is accelerated due to increased monocyte levels, the precursors of macrophages, and reduced HDL, which normally prevents LDL oxidation and facilitates cholesterol efflux from foam cells [50, 51]. Similarly, inflammation is known to drive insulin resistance, ultimately causing type 2 diabetes. Particularly, elevated monocytes contribute to diabetes development by promoting chronic low-grade inflammation through pro-inflammatory cytokines and chemokines release, exacerbating insulin resistance and pancreatic beta-cell dysfunction​. Additionally, they differentiate into macrophages within adipose tissue, further amplifying inflammatory responses [13, 52]. Interestingly, HDL normally protects pancreatic beta-cells from oxidative stress and inflammation, and its dysfunction in diabetes exacerbates beta-cell damage, contributing to insulin resistance and worsening glycemic control​. Low HDL levels also drive diabetes by impairing cholesterol efflux from beta-cells, leading to cholesterol accumulation and disruption of insulin secretion [53]. Taken together, MHR holds promise to be considered a potentially robust prognostic factor of cardiometabolic disturbances by capturing the underlying inflammation and lipid dysregulation.

Besides cardiovascular diseases and diabetes, our findings revealed that high MHR exhibited strong associations for kidney diseases-related mortality with a HR of 3.31, the highest observed in this study. This striking association underscores the intricate interplay between kidney disease and cardiometabolic disturbances, whether as a cause or a consequence. Notably, the term “Cardiovascular-kidney-metabolic (CKM) syndrome” has been recently introduced to address a complex cluster of maladaptive cardiovascular, renal, metabolic, prothrombotic, and inflammatory abnormalities [54]. While data on MHR’s association with kidney disease remains somewhat limited, recent evidence by Xu et al. (2024) indicated that higher MHR values were associated with an increased risk of chronic kidney disease (CKD). Specifically, higher MHR was linked to approximately 30% and 15% increase in odds of low estimated glomerular filtration rate (eGFR) and proteinuria, respectively, with both ORs being significant​ [55]. These findings are largely attributed to underlying inflammation and oxidative stress, partly driven by elevated monocytes, leading to glomerulosclerosis, tubulointerstitial fibrosis, and endothelial dysfunction. Concurrently, reduced HDL impairs its protective functions against oxidation, atherosclerosis, and renal injury. This imbalance fosters pro-inflammatory, pro-atherogenic environment, accelerating kidney damage and disease progression [56, 57].

Therefore, we postulate that MHR’s superior associations with mortality in kidney diseases, rather than with their initial occurrence, stems from the progressive nature of glomerulosclerosis and tubulointerstitial fibrosis. These pathological changes develop insidiously, often remaining subclinical in early stages, and hence exerting a less pronounced effect on MHR. However, as kidney disease advances, systemic inflammation, oxidative stress, and lipid dysregulation intensify, all of which is reflected by elevated MHR. This escalation contributes directly to disease exacerbation, complications, and ultimately, mortality. As renal function deteriorates, the inflammatory burden amplifies, oxidative stress worsens, and dyslipidemia further accelerates vascular and renal damage, making MHR a stronger marker of disease progression, complications, and ultimately, mortality rather than just the presence of CKD itself.

To the best of our knowledge, our study is the first to explore and propose the upper and lower reference ranges values for MHR, utilizing a large nationally representative cohort of apparently healthy U.S. adults, enabling categorization of MHR levels and easier interpretability. Additionally, a key merit of this study beyond establishing reference ranges, is that analyses were conducted using MHR values calculated based on monocyte count rather than percentage. This decision was primarily based on the fact that monocyte count provides absolute value, enhancing its clinical utility by directly reflecting the total number of monocytes. Nevertheless, using monocyte percentage to calculate MHR remains valuable in subclinical inflammation research context, as it captures the intricate interactions between different leukocytes subtypes, offering a more comprehensive view of the inflammatory status. In addition, our correlation analysis supports this approach, with Spearman correlation between count-based and percent-based MHR demonstrating strong positive correlation (ρ = 0.765, p < 0.001; Table S3), indicating that percent-based MHR can serve as a reasonable proxy. Therefore, we encourage researchers to utilize monocytes percentage to calculate MHR when studying immune system dynamics, while clinicians are encouraged to use MHR calculated with monocyte count for clearer interpretation and greater clinical relevance. Moreover, this is the first study to simultaneously explore the association between MHR and multiple inflammatory diseases, along with their corresponding mortality. This comprehensive approach allows for a holistic evaluation of MHR’s role across various conditions while enabling an unbiased comparison of odds ratios and hazard ratios. An important strength of this study is the use of a large, nationally representative sample from the NHANES database. The impact of this dataset is further enhanced by the application of sample weights, ensuring that our findings are generalizable to a broader U.S. population.

The inherent limitation of the NHANES database is its cross-sectional design, which prevents inferences about causality and directionality. However, the probabilistic linkage of NHANES data with death certificates from the National Death Index facilitated the implementation of a retrospective cohort study design for the assessment of mortality risk. This methodological approach enabled the establishment of both directionality and causal inferences specifically within the context of mortality-related analyses. Moreover, some variables relied on self-reported data rather than objective clinical measurements. Despite these constraints, the study provides valuable insights into MHR’s associations with a spectrum of inflammatory diseases and their related mortality.

Taken together, our findings establish MHR as a promising inflammatory marker with significant prognostic potential, particularly in the context of cardiometabolic disturbances. A major avenue for future research lies in validating the proposed MHR cut-off values and tailoring them to diverse populations, accounting for factors such as ethnicity, age, and baseline health status. Future studies with sufficient sample sizes in each racial group are warranted to explore potential population-specific associations between MHR, inflammatory diseases, and mortality, which could help clarify underlying biological, environmental, or socioeconomic factors. Additionally, longitudinal studies are essential to elucidate the causal relationship between MHR and various inflammatory diseases. Longitudinal studies could also provide valuable insights into the clinical utility of MHR in monitoring disease trajectories. Furthermore, although our findings indicate that MHR is strongly associated with the outcomes of the inflammatory diseases studied, it is important to note that formal evaluation of its incremental predictive value, including integrated discrimination improvement (IDI), net reclassification improvement (NRI), decision curve analysis, and calibration, was beyond the scope of the present study. Future prospective studies should explore and validate these predictive aspects. If validated, MHR could be integrated into clinical practice guidelines, offering a feasible tool for risk stratification and disease management in routine healthcare settings.

Conclusion

Our findings underscore MHR’s potential as a robust biomarker, particularly for cardiometabolic diseases, namely, diabetes, cardiovascular diseases, and kidney diseases, while also highlighting its strong associations with disease-related mortality. If validated as a biomarker, MHR, which is derived from routinely available laboratory tests (CBC and lipid panel) without the need for specialized assays, could be incorporated into routine clinical practice, offering a feasible and accessible means of improving risk prediction and patient outcomes in cardiometabolic conditions. Moreover, its ability to capture both inflammation and metabolic disturbances provides a comprehensive reflection of underlying pathophysiology, reinforcing its clinical utility. Hence, future research should focus on refining MHR thresholds for different populations and integrating MHR into risk assessment models.

Authors’ contributions

A.A. (Ahmed Arabi) contributed to writing—review and editing, writing—original draft, conceptualization, data curation, formal analysis, methodology, and visualization.A.Ah. (Alaa Abdelhamid) contributed to writing—original draft, conceptualization, data curation, formal analysis, and methodology.D.N. (Dima Nasrallah) contributed to writing—review and editing, writing—original draft, conceptualization, data curation, formal analysis, methodology, and visualization.Y.A. (Yaman Al-Haneedi) contributed to writing—original draft, conceptualization, and methodology.D.As. (Deemah Assami) contributed to writing—original draft, conceptualization, and methodology.R.A. (Raneem Alsheikh) contributed to writing—original draft, conceptualization, and methodology.S.M.Z. (Susu M. Zughaier) contributed to writing—review and editing, validation, supervision, conceptualization, and methodology.

Funding

None.

Data Availability

Publicly available datasets (NHANES) were analyzed in this study. The dataset presented in this study is available at https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm.

Declarations

Ethics approval and consent to participate

Informed consent was obtained from all subjects involved in NHANES cohort used in the study. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Review Board of the National Center for Health Statistics.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.